title: Modelling the Economic Impacts of Epidemics in Developing Countries Under Alternative Intervention Strategies

text: Modern levels of global travel have intensified the risk of new infectious diseases becoming pandemics. Particularly at risk are developing countries whose health systems may be less well equipped to detect quickly and respond effectively to the importation of new infectious diseases. This chapter examines what might have been the economic consequences if the 2014 West African Ebola epidemic had been imported to a small Asia-Pacific country. Hypothetical outbreaks in two countries were modelled. The post-importation estimations were carried out with two linked models: a stochastic disease transmission (SEIR) model and a quarterly version of the multi-country GTAP model, GTAP-Q. The SEIR model provided daily estimates of the number of persons in various disease states. For each intervention strategy the stochastic disease model was run 500 times to obtain the probability distribution of disease outcomes. Typical daily country outcomes for both controlled and uncontrolled outbreaks under five alternative intervention strategies were converted to quarterly disease-state results, which in turn were used in the estimation of GTAP-Q shocks to country-specific health costs and labour productivity during the outbreak, and permanent reductions in each country’s population and labour force due to mortality. Estimated behavioural consequences (severe reductions in international tourism and crowd avoidance) formed further shocks. The GTAP-Q simulations revealed very large economic costs for each country if they experienced an uncontrolled Ebola outbreak, and considerable economic costs for controlled outbreaks in Fiji due to the importance of the tourism sector to its economy. A major finding was that purely reactive strategies had virtually no effect on preventing uncontrolled outbreaks, but pre-emptive strategies substantially reduced the proportion of uncontrolled outbreaks, and in turn the economic and social costs.


title: [Image: see text]: Exploring the Bias of Web Domains Through the Eyes of Users

text: Ethical issues, along with transparency, disinformation and bias are in the focus of our information society. In this demo, we will present the [Image: see text] system, that based on the web graph computes the bias characteristics of web domains to user-defined concepts. Our approach uses adaptations of propagation models and a variation of the pagerank algorithm named Biased-PR, that models various behaviours of biased surfers. Currently, the system runs over a subset of the greek web graph. We have developed cross-browser plugins that let users explore the bias characteristics of domains, define their own biased concepts and monitor the consumption of biased information through some analytics.


title: The Pathogenesis of Coronavirus Disease 2019 (COVID-19): Evaluation and Prevention

text: Coronavirus Disease 2019 (COVID-19) has become a major health problem causing severe acute respiratory illness in humans. It has spread rapidly around the globe since its first identification in Wuhan, China, in December 2019. The causative virus is called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the World Health Organization (WHO) named the new epidemic disease Coronavirus Disease (COVID-19). The incidence of COVID-19 continues to increase with more than three million confirmed cases and over 244,000 deaths worldwide. There is currently no specific treatment or vaccine against COVID-19. Therefore, in the absence of pharmaceutical interventions, the implementation of precautions and hygienic measures will be essential to control and to minimize human transmission of the virus. In this review, we highlight the epidemiology, transmission, symptoms, and treatment of this disease, as well as future strategies to manage the spread of this fatal coronavirus.


Hypofractionated Accelerated Concurrent Chemoradiotherapy in Inoperable Stage III Non-small Cell Lung Cancer: SOCCAR. A Large Single-Centre Experience


Commentary: Splitting over lumping in mechanical support for ARDS


Dismantling racism in education: In 2020, the year of the nurse & midwife, “it's time.”


How to maintain a vital surgical activity in the event of a serious health crisis? The example of transplant in adults


How to manage, after containment, patients with chronic conditions at risk?


title: Strategies for Managing the Impacts of Disruptions During COVID-19: an Example of Toilet Paper

text: Due to the recent pandemic of coronavirus, known as the COVID-19 outbreak, the supply chains have been impacted most significantly. Manufacturers of certain items have experienced a substantial increase in demand, and on the other hand, raw materials supply, to produce those items, has reduced because of supply failure. To overcome these challenges, this paper proposes some strategies to improve service level during an extraordinary pandemic outbreak, such as COVID-19, for the most wanted products such as toilet paper. This study considers meeting the increased demand of the customers for an essential product of daily life like toilet paper during a pandemic is beyond the traditional economic objective, i.e., increase profit, of the manufacturers. Instead, this should be more about the social responsibility of all the manufactures to ensure that they can serve more customers. Motivated by this and taking toilet paper as an example of the product, we first analyzed the current scenario of the manufacturing and the demand for the product and then proposed some strategies to deal with this unprecedented risk and analyzed the results. We have compared the results, using hypothetical data, between the current scenario and proposed strategies. The result shows that sharing information and resources from all manufacturers to produce under a single brand, emergency sourcing, producing basic quality items, and packing in the smallest sizes have a significant positive impact on the service level. This paper first investigates the strategies for a high-demand and essential item during a pandemic situation and proposes strategies to deal with this unique, extraordinary disruption.


docid: hokuferf

title: A minimum dataset for a Level 1 echocardiogram: a guideline protocol from the British Society of Echocardiography

text: The British Society of Echocardiography has previously outlined a minimum dataset for a standard transthoracic echocardiogram, and this remains the basis on which an echocardiographic study should be performed. The importance of ultrasound in excluding critical conditions that may require urgent treatment is well known. Several point-of-care echo protocols have been developed for use by non-echocardiography specialists. However, these protocols are often only used in specific circumstances and are usually limited to 2D echocardiography. Furthermore, although the uptake in training for these protocols has been reasonable, there is little in the way of structured support available from accredited sonographers in the ongoing training and re-accreditation of those undertaking these point-of-care scans. In addition, it is well recognised that the provision of echocardiography on a 24/7 basis is extremely challenging, particularly outside of tertiary cardiac centres. Consequently, following discussions with NHS England, the British Society of Echocardiography has developed the Level 1 echocardiogram in order to support the rapid identification of critical cardiac pathology that may require emergency treatment. It is intended that these scans will be performed by non-specialists in echocardiography and crucially are not designed to replace a full standard transthoracic echocardiogram. Indeed, it is expected that a significant number of patients, in whom a Level 1 echocardiogram is required, will need to have a full echocardiogram performed as soon as is practically possible. This document outlines the minimum dataset for a Level 1 echocardiogram. The accreditation process for Level 1 echo is described separately.


Participatory digital health research: A new paradigm for mHealth tool development


title: Archive Update Strategy Influences Differential Evolution Performance

text: In this paper the effects of archive set update strategies on differential evolution algorithm performance are studied. The archive set is generated from inferior solutions, removed from the main population, as the search process proceeds. Next, the archived solutions participate in the search during mutation step, allowing better exploration properties to be achieved. The LSHADE-RSP algorithm is taken as baseline, and 4 new update rules are proposed, including replacing the worst solution, the first found worse solution, the tournament-selected solution and individually stored solution for every solution in the population. The experiments are performed on CEC 2020 single objective optimization benchmark functions. The results are compared using statistical tests. The comparison shows that changing the update strategy significantly improves the performance of LSHADE-RSP on high-dimensional problems. The deeper analysis of the reasons of efficiency improvement reveals that new archive update strategies lead to more successful usage of the archive set. The proposed algorithms and obtained results open new possibilities of archive usage in differential evolution.


title: Inferring Candidate CircRNA-Disease Associations by Bi-random Walk Based on CircRNA Regulatory Similarity

text: Identification of associations between circular RNAs (circRNA) and diseases has become a hot topic, which is beneficial for researchers to understand the disease mechanism. However, traditional biological experiments are expensive and time-consuming. In this study, we proposed a novel method named BWHCDA, which applied bi-random walk algorithm on the heterogeneous network for predicting circRNA-disease associations. First, circRNA regulatory similarity is measured based on circRNA-miRNA interactions, and circRNA similarity is calculated by the average of circRNA regulatory similarity and Gaussian interaction profiles (GIP) kernel similarity for circRNAs. Similarly, disease similarity is the mean of disease semantic similarity and GIP kernel similarity for diseases. Then, the heterogeneous network is constructed by integrating circRNA network, disease network via circRNA-disease associations. Subsequently, the bi-random walk algorithm is implemented on the heterogeneous network to predict circRNA-disease associations. Finally, we utilize leave-one-out cross validation and 10-fold cross validation frameworks to evaluate the prediction performance of BWHCDA method and obtain AUC of 0.9334 and 0.8764 ± 0.0038, respectively. Moreover, the predicted hsa_circ_0000519-gastric cancer association is analyzed. Results show that BWHCDA could be an effective resource for clinical experimental guidance.


title: A Tool for Supporting the Evaluation of Active Learning Activities

text: Active learning becomes a strategical approach for an educational principle. The student engagement become a wider concern. Many researches have been proposed to support the approach. However, one of issues is how to effectively evaluate the performance and progress of students’ learning. Although, having student engagement in a classroom is vital, the evaluation of students’ performance is more important. However, keeping up the details or records of students’ progress is a difficult task. We therefore propose a support for instructors to evaluate the performance of their students. In particular, a prototype tool is designed and developed in order to facilitate the evaluation of activities based on an active learning class. The tool also encompasses the web service for a function of face feature recognition. Two scenarios of active learning classrooms are created in order to evaluate the prototype tool. We also plan to create a larger number of scenarios which involve different class objectives. The results show that the tool can detect and determine students with high precision values. However, the prototype tool takes a long time to be processed depending on the size and number of photos.


title: Parasite-Guest Infection Modeling: Social Science Applications

text: In this study we argue that parasite-host infections are a major research topic because of their implications for human health, agriculture and wildlife. The evolution of infection mechanisms is a research topic in areas such as virology and ecology. Mathematical modelling has been an essential tool to obtain a better systematic and quantitative understanding of the processes of parasitic infection that are difficult to discern through strictly experimental approaches. In this article we review recent attempts using mathematical models to discriminate and quantify these infection mechanisms. We also emphasize the challenges that these models could bring to new fields of study such as social sciences and economics.


title: Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

text: The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.


title: An Ant Colony Optimization Algorithm Based Automated Generation of Software Test Cases

text: Software testing is an important process of detecting bugs in the software product thereby a quality software product is developed. Verification and Validation (V & V) activities are the effective methods employed to achieve quality. Static and dynamic testing activities are performed during V & V. During static testing, the program code is not executed while in dynamic testing (Black Box and White Box), the execution of the program code is performed. Effective test cases are designed by both these methods. Tables are employed to represent test case documentation. The most beneficial representation - State table based testing, for generating test inputs is explained with the help of state graphs and state tables. This technique is mainly employed in embedded system software testing, real time applications and web application based software product testing where time constraints are a major criteria. Automatic generation of test cases will help to reduce time overhead in testing activities. Our study is to develop optimum test cases by a modified Ant Colony Optimization (ACO) technique in an automated method and it ensures maximum coverage. The prediction model used in this paper ensures better accuracy of the design of test inputs. A comparison of the similar optimization techniques was also discussed that is used in automated test case generation. A case study of the various states during the execution of a task in an operating system has been presented to illustrate our approach.


title: An Adapting Chemotaxis Bacterial Foraging Optimization Algorithm for Feature Selection in Classification

text: Efficient classification methods can improve the data quality or relevance to better optimize some Internet applications such as fast searching engine and accurate identification. However, in the big data era, difficulties and volumes of data processing increase drastically. To decrease the huge computational cost, heuristic algorithms have been used. In this paper, an Adapting Chemotaxis Bacterial Foraging Optimization (ACBFO) algorithm is proposed based on basic Bacterial Foraging Optimization (BFO) algorithm. The aim of this work is to design a modified algorithm which is more suitable for data classification. The proposed algorithm has two updating strategies and one structural changing. First, the adapting chemotaxis step updating strategy is responsible to increase the flexibility of searching. Second, the feature subsets updating strategy better combines the proposed heuristic algorithm with the KNN classifier. Third, the nesting structure of BFO has been simplified to reduce the computation complexity. The ACBFO has been compared with BFO, BFOLIW and BPSO by testing on 12 widely used benchmark datasets. The result shows that ACBFO has a good ability of solving classification problems and gets higher accuracy than the other comparation algorithm.


title: Modified Bacterial Foraging Optimization for Fuzzy Mean-Semivariance-Skewness Portfolio Selection

text: In this paper, a novel bacterial foraging optimization with decreasing chemotaxis step combined with sine function is employed to solve a fuzzy portfolio optimization with a modified mean-semivariance-skewness model which includes the transaction fee and no short sales. First of all, a decreasing chemotaxis step combined with sine function (BFO-SDC) takes the place of constant chemotaxis step size. It is a nonlinear decreasing strategy at every iteration of the algorithm. And then, the variance is replaced by semivariance and skewness is taken into account in order to generate asymmetry of return distributions to overcome the inadequacy of the standard mean-variance model. Finally, fuzzy variables are used to express the uncertain and imprecise elements in the decision-making process. The results of the simulation show that the model can be solved more reasonably and effectively by BFO-SDC than the original bacterial foraging optimization.


title: Computational Analysis of Third-Grade Liquid Flow with Cross Diffusion Effects: Application to Entropy Modeling

text: The key goal of this current study is to analyze the entropy generation with cross diffusion effects. The third-grade type non-Newtonian fluid model is used in this study. The current flow problem is modelled with stretching plate. Modified Fourier heat flux is replaced the classical heat flux. The appropriate transformation is availed to convert the basic boundary layers equations into ODEs and then verified by homotopy algorithm. The consequences of various physical quantities on temperature, velocity, entropy and concentration profile are illustrated graphically.


title: Imbalanced Ensemble Learning for Enhanced Pulsar Identification

text: Pulsars can be detected based on their emitted radio waves. Machine learning methods can be employed to support automated screening of a large number of radio signals for pulsars. This is however a challenging task since training these methods is affected by an inherent imbalance in the acquired data with signals relating to actual pulsars being in the minority. In this paper, we demonstrate that ensemble classification methods that are dedicated to imbalanced classification problems can be successfully employed for pulsar identification. Classifier ensembles combine several individual classifiers to yield more robust and improved classification, while class imbalance can be addressed through careful sampling or through cost-sensitive classification. Experimental results, based on HTRU2 data, show that the investigated ensembles outperform methods that do not consider class balance, and suggest their use for other applications in astrophysics.


title: Optimizing Hydrography Ontology Alignment Through Compact Particle Swarm Optimization Algorithm

text: With the explosive growth in generating data in the hydrographical domain, many hydrography ontologies have been developed and maintained to describe hydrographical features and the relationships between them. However, the existing hydrography ontologies are developed with varying project perspectives and objectives, which inevitably results in the differences in terms of knowledge representation. Determining various relationships between two entities in different ontologies offers the opportunity to link hydrographical data for multiple purposes, though the research on this topic is in its infancy. Different from the traditional ontology alignment whose cardinality is 1:1, i.e. one source ontology entity is mapping with one target ontology entity and vice versa, and the relationship is the equivalence, matching hydrography ontologies is a more complex task, whose cardinality could be 1:1, 1:n or m:n and the relationships could be equivalence or subsumption. To efficiently optimize the ontology alignment, in this paper, a discrete optimal model is first constructed for the ontology matching problem, and then a Compact Particle Swarm Optimization algorithm (CPSO) based matching technique is proposed to efficiently solve it. CPSO utilizes the compact real-value encoding and decoding mechanism and the objective-decomposing strategy to approximate the PSO’s evolving process, which can dramatically reduce PSO’s memory consumption and runtime while at the same time ensure the solution’s quality. The experiment exploits the Hydrography dataset in Complex track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CPSO-based approach can effectively reduce PSO’s runtime and memory consumption, and determine high-quality hydrography ontology alignments.


title: Newtonian Heating Effects of Oldroyd-B Liquid Flow with Cross-Diffusion and Second Order Slip

text: The current study highlights the Newtonian heating and second-order slip velocity with cross-diffusion effects on Oldroyd-B liquid flow. The modified Fourier heat flux is included in the energy equation system. The present problem is modeled with the physical governing system. The complexity of the governing system was reduced to a nonlinear ordinary system with the help of suitable transformations. A homotopy algorithm was used to validate the nonlinear system. This algorithm was solved via MATHEMATICA software. Their substantial aspects are further studied and reported in detail. We noticed that the influence of slip velocity order two is lower than the slip velocity order one.


title: Prediction of Photovoltaic Power Using Nature-Inspired Computing

text: Prediction of photovoltaic (PV) energy is an important task. It allows grid operators to plan production of energy in order to secure stability of electrical grid. In this work we focus on improving prediction of PV energy using nature-inspired algorithms for optimization of Support Vector Regression (SVR) models. We propose method, which uses different models optimized for various types of weather in order to achieve higher overall accuracy compared to single optimized model. Each sample is classified by Multi-Layer Perceptron (MLP) into some weather class and then model is trained for each weather class. Our method achieved slightly better results compared to single optimized model.


title: Adaptive Bacterial Foraging Optimization Based on Roulette Strategy

text: Bacterial foraging optimization has drawn great attention and has been applied widely in various fields. However, BFO performs poorly in convergence when coping with more complex optimization problems, especially multimodal and high dimensional tasks. Aiming to address these issues, we therefore seek to propose a hybrid strategy to improve the BFO algorithm in each stage of the bacteria’s’ foraging behavior. Firstly, a non-linear descending strategy of step size is adopted in the process of flipping, where a larger step size is given to the particle at the very beginning of the iteration, promoting the rapid convergence of the algorithm while later on a smaller step size is given, helping enhance the particles’ global search ability. Secondly, an adaptive adjustment strategy of particle aggregation is introduced when calculating step size of the bacteria’s swimming behavior. In this way, the particles will adjust the step size according to the degree of crowding to achieve efficient swimming. Thirdly, a roulette strategy is applied to enable the excellent particles to enjoy higher replication probability in the replication step. A linear descent elimination strategy is adopted finally in the elimination process. The experimental results demonstrate that the improved algorithm performs well in both single-peak function and multi-peak function, having strong convergence ability and search ability.


title: Use of the Industrial Property System in Colombia (2018): A Supervised Learning Application

text: The purpose of this paper is to establish ways to predict the spatial distribution of the use of the intellectual property system from information on industrial property applications and grants (distinctive signs and new creations) and copyright registrations in 2018. This will be done using supervised learning algorithms applied to information on industrial property applications and grants (trademarks and new creations) and copyright registrations in 2018. Within the findings, 4 algorithms were identified with a level of explanation higher than 80%: (i) Linear Regression, with an elastic network regularization; (ii) Stochastic Gradient Descent, with Hinge loss function, Ringe regularization (L2) and a constant learning rate; (iii) Neural Networks, with 1,000 layers, with Adam’s solution algorithm and 2,000 iterations; (iv) Random Forest, with 10 trees.


title: A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System

text: A modified artificial bee colony algorithm is proposed for solving the scheduling optimization problem of multi-aisle automatic storage/retrieval system. The optimization model of the problem is analyzed and founded, in which the sequence constraint of tasks and calculation of the number of aisles are more realistic. According to the features of the problem, the encoding and decoding strategies for solutions to MABC algorithm are redesigned. Probability selection-based updating method is also introduced to enhance the neighborhood search and preserve the good fragments. The experimental results show that MABC can obtain better results than PSO and GA algorithm, and is a competitive approach for AS/RS scheduling optimization.


title: Analytical Study of Radiative Casson Nanoliquid Flow with Heat Absorption

text: The divergence of thermally radiative MHD flow of a Casson nanofluid over a stretching paper alongside heat absorption. The governing non linear equations are remodeled into a nonlinear ODE’s. The HAM is adopted to find the series solution. The changes of pertinent parameters are analyzed with diagrams and tables. The fluid velocity is controlled by suction and it develops with injection. The local Nusselt number rapidly suppresses with increasing the magnetic field parameter in heat generation case.


title: A Two-Step Approach to the Search of Minimum Energy Designs via Swarm Intelligence

text: Recently, Swarm Intelligence Based (SIB) method, a nature-inspired metaheuristic optimization method, has been widely used in many problems that their solutions fall in discrete and continuous domains. SIB 1.0 is efficient to converge to optimal solution but its particle size is fixed and pre-defined, while SIB 2.0 allows particle size changes during the procedure but it takes longer time to converge. This paper introduces a two-step SIB method that combines the advantages of two SIB methods. The first step via SIB 2.0 serves as a preliminary study to determine the optimal particle size and the second step via SIB 1.0 serves as a follow-up study to obtain the optimal solution. This method is applied to the search of optimal minimum energy design and the result outperforms the results from both SIB 1.0 and SIB 2.0.


title: Success-History Based Parameter Adaptation in MOEA/D Algorithm

text: In this paper two parameter self-adaptation schemes are proposed for the MOEA/D-DE algorithm. These schemes use the fitness improvement ration to change four parameter values for every individual separately, as long as in the MOEA/D framework every individual solves its own scalar optimization problem. The first proposed scheme samples new values and replaces old values with new ones if there is an improvement, while the second one keeps a set of memory cells and updates the parameter values using the weighted sum. The proposed methods are testes on two sets of benchmark problems, namely MOEADDE functions and WFG functions, IGD and HV metrics are calculated. The results comparison is performed with statistical tests. The comparison shows that the proposed parameter adaptation schemes are capable of delivering significant improvements to the performance of the MOEA/D-DE algorithm. Also, it is shown that parameter tuning is better than random sampling of parameter values. The proposed parameter self-adaptation techniques could be used for other multi-objective algorithms, which use MOEA/D framework.


title: Research on Sliding Mode Control of Underwater Vehicle-Manipulator System Based on an Exponential Approach Law

text: To improve the performance of underwater vehicle-manipulator system (UVMS), which is subject to system uncertainties and time-varying external disturbances in trajectory tracking control, a sliding mode controller is proposed in this paper. Firstly, in order to reduce a influence of system uncertainties and external disturbances, a sliding mode controller is designed based on an exponential approach law. Then the error asymptotic convergence of the trajectory tracking control is proven by the Lyapunov-like function. Finally, the effectiveness of the sliding mode controller is verified by rich simulation. Results show that the designed controller can not only realize the coordination control of UVMS accurately, but also can eliminate the chattering of control signal.


title: Bacterial Foraging Optimization Based on Levy Flight for Fuzzy Portfolio Optimization

text: In this paper, a new kind of bacterial foraging optimization that combines with levy flight (LBFO) is employed to solve a novel portfolio optimization (PO) problem with fuzzy variables and modified mean-semivariance model which includes the transaction fee (including the purchase fee and sell fee), no short sales and the original proportion of the different assets. First of all, a chemotaxis step size using levy distribution takes the place of fixed chemotaxis step size, which makes a good balance between local search and global search through frequent short-distance search and occasional long-distance search. Moreover, fuzzy variables are used to signify the uncertainty of future risks and returns on assets and some constrained conditions are taken into consideration. The results of the simulation show that the model can be solved more reasonably and effectively by LBFO algorithm than the original bacterial foraging optimization (BFO).


title: An Ant-Inspired Track-to-Track Recovery Approach for Construction of Cell Lineage Trees

text: Correct track-to-track association is crucial to the construction of cell lineage trees as well as the discovery of novel biological phenomenon that occur at rare frequencies. In this paper, an ant colony optimization based heuristic approach is proposed to link potential tracks through minimizing the cost function that mainly occurs on the fragmented intervals with the constraint of maximum inter-frame displacement. Specifically, both cell motion and morphology are emphasized in the defined cost function, and two decisions are made respectively to recover the mitotic and non-mitotic cases. Our method has proven to be feasible that can repair the broken tracklets caused by large migration, occlusion and mitosis missing, as well as false positives and missed detections, and can effectively help the construction of reliable cell lineage trees.


title: Research on Crowd-Sensing Task Assignment Based on Fuzzy Inference PSO Algorithm

text: To solve the problem of load unbalance in the case of few users and multi-task, a fuzzy inference PSO algorithm (FPSO) crowd sensing single objective task assignment method is proposed. With task completion time, user load balancing and perceived cost as the optimization goals, the fuzzy learning algorithm dynamically adjusts the learning factor in the PSO algorithm, so that the PSO algorithm can perform global search in the scope of the task space, thus obtaining the optimal task assignment solution set. Finally, the FPSO algorithm is compared with the PSO, GA and ABC algorithms on the optimization objectives, such as the algorithm convergence, task completion time, perceived cost and load balance. The experimental results show that the FPSO algorithm not only has faster convergence rate than the other algorithms, and shorten the task completion time, reduce the platform’s perceived cost, improve the user’s load balance, and have a good application effect in the crowd sensing task assignment.


docid: xosgprae

title: On Assessing the Temporal Characteristics of Reaching the Milestone by a Swarm

text: The physical swarm operation is investigated. Swarm units are considered as three-wheeled mobile robots, moving through rough terrain. For the longitudinal movement of such type vehicle the dynamic model is obtained. The article discusses the issue of forecasting the time a physical swarm reaches a milestone and proposes a hypothesis on the form of the law of the distribution of time during which a swarm unit reaches a milestone. Obtaining this time distribution is carried out with use the Petri-Markov net fundamental apparatus. With use Petri-Markov nets time densities of reaching the milestone both one unit and swarm as a whole are obtained. More common formula of distribution of time of milestone reaching by l units of K is obtained too. To confirm the hypothesis about the type of theoretical time distribution a computer experiment was carried out using the Monte Carlo method.


title: Improved Bacterial Foraging Optimization Algorithm with Comprehensive Swarm Learning Strategies

text: Bacterial foraging optimization (BFO), a novel bio-inspired heuristic optimization algorithm, has been attracted widespread attention and widely applied to various practical optimization problems. However, the standard BFO algorithm exists some potential deficiencies, such as the weakness of convergence accuracy and a lack of swarm communication. Owing to the improvement of these issues, an improved BFO algorithm with comprehensive swarm learning strategies (LPCBFO) is proposed. As for the LPCBFO algorithm, each bacterium keeps on moving with stochastic run lengths based on linear-decreasing Lévy flight strategy. Moreover, illuminated by the social learning mechanism of PSO and CSO algorithm, the paper incorporates cooperative communication with the current global best individual and competitive learning into the original BFO algorithm. To examine the optimization capability of the proposed algorithm, six benchmark functions with 30 dimensions are chosen. Finally, experimental results demonstrate that the performance of the LPCBFO algorithm is superior to the other five algorithms.


title: Aula Touch Game: Digital Tablets and Their Incidence in the Development of Citizen Competences of Middle Education Students in the District of Barranquilla-Colombia

text: Citizen competences are considered as a fundamental aspect in the social development of man with his environment, which allows him to carry out actions that are articulated with the different guidelines established by law, which leads the citizen to live in a coherent and peaceful way in a nation that tends for freedom of thought framed in a democratic society. That is why it is considered of high importance that in educational establishments there are spaces that tend for training in peaceful coexistence framed in the law of the educated. This article resulted from the research project: “Social Appropriation of citizen and mathematical competences making use of MIDTablets”, in which the mediation of Information and Communication Technologies is proposed to support the training of citizens with competences citizens who ensure adequate behavior in society. This project was developed in 31 educational institutions in the district of Barranquilla-Colombia, with support from resources of both the Ministry of Information Technology and Communications (MinTic), and the Secretariat of District Education of Barranquilla, in compliance with national goals, departmental and district regarding the quality of education of the national population


title: A Parallel Evolutionary Algorithm with Value Decomposition for Multi-agent Problems

text: Many real-world problems involve cooperation and/or competition among multiple agents. These problems often can be formulated as multi-agent problems. Recently, Reinforcement Learning (RL) has made significant progress on single-agent problems. However, multi-agent problems still cannot be easily solved by traditional RL algorithms. First, the multi-agent environment is considered as a non-stationary system. Second, most multi-agent environments only provide a shared team reward as feedback. As a result, agents may not be able to learn proper cooperative or competitive behaviors by traditional RL. Our algorithm adopts Evolution Strategies (ES) for optimizing policy which is used to control agents and a value decomposition method for estimating proper fitness for each policy. Evolutionary Algorithm is considered as a promising alternative for signal-agent problems. Owing to its simplicity, scalability, and efficiency on zeroth-order optimization, EAs can even outperform RLs on some tasks. In order to solve multi-agent problems by EA, a value decomposition method is used to decompose the team reward. Our method is parallel on multiple cores, which can speed up our algorithm significantly. We test our algorithm on two benchmarking environments, and the experiment results show that our algorithm is better than traditional RL and other representative gradient-free methods.


title: Binary Pigeon-Inspired Optimization for Quadrotor Swarm Formation Control

text: This paper proposes a binary pigeon-inspired optimization (BPIO) algorithm, for the quadrotor swarm formation control problem. The expected position is provided by the BPIO. Quadrotor moves to the position with control strategy, and the strategy is based on the proportional integral derivative (PID) control method. The BPIO algorithm which is based on pigeon-inspired optimization (PIO) algorithm can effectively solve the combination problem in the binary solution space. The BPIO keeps the fast convergence of the PIO, and can explore the space effectively at the same time. The parameters to be optimized are encoded with binary bits. A special fitness function is designed to avoid the happening of crash. The simulation experiment shows how the BPIO works. The results of simulation verify the feasibility and effectiveness of the BPIO to solve the swarm formation problem.


title: Econometric Algorithms Applied to the Incidence of Income on Satisfaction with Quality of Life in Latin American Capitals

text: The purpose of this research is to determine the incidence of income on the satisfaction of quality of life in Latin American capitals. The data was taken from the CAF Survey in 2017 (ECAF), made by Development Bank of Latin America. To do this, an econometric model was constructed which represents the relationship between the studied variables. Among the main findings, it was identified that, except for Mexico City, in all the analyzed cities a deteriorated state of health reduces the probability that the individual feels satisfied with his or her life. Therefore, health is a crucial element to increase the citizens’ perception of the quality of life in Latin American cities.


title: A New EDA with Dimension Reduction Technique for Large Scale Many-Objective Optimization

text: The performance of many-objective evolutionary algorithms deteriorates appreciably in solving large-scale many-objective optimization problems (MaOPs) which encompass more than hundreds variables. One of the known rationales is the curse of dimensionality. Estimation of distribution algorithms sample new solutions with a probabilistic model built from the statistics extracting over the existing solutions so as to mitigate the adverse impact of genetic operators. In this paper, an Gaussian Bayesian network-based estimation of distribution algorithm (GBNEDA-DR) is proposed to effectively tackle continued large-scale MaOPs. In the proposed algorithm, dimension reduction technique (i.e. LPP) is employed in the decision space to speed up the estimation search of the proposed algorithm. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different decision space dimensions, and achieves comparable results on some compared with many existing algorithms.


title: Site Selection of the Colombian Antarctic Research Station Based on Fuzzy-Topsis Algorithm

text: By 2025 the Republic of Colombia aims to be an advisory member of the Antarctic Treaty System (ATS) and the installation of a scientific station is necessary to upscale the scientific capabilities. The aim of this paper is showing the results of the implementation of a Fuzzy TOPSIS algorithm for site selection of the Colombian Antarctic Scientific Station. A three-phase methodology was proposed, and the obtained results allowed to identify the optimum location for the station, considering key success factors and regulatory constraints.


title: Methods of Machine Learning in System Abnormal Behavior Detection

text: The aim of the research is to develop mathematical and program support for detecting abnormal behavior of users. It will be based on analysis of their behavioral biometric characteristics. One of the major problems in UEBA/DSS intelligent systems is obtaining useful information from a large amount of unstructured, inconsistent data. Management decision-making should be based on real data collected from the analysed feature. However, based on the information received, it is rather difficult to make any management decision, as the data are heterogeneous and their volumes are extremely large. Application of machine learning methods in implementation of mobile UEBA/DSS system is proposed. This will make it possible to achieve a data analysis high quality and find complex dependencies in it. A list of the most significant factors submitted to the input of the analysing methods was formed during the research.


title: BSO-CLS: Brain Storm Optimization Algorithm with Cooperative Learning Strategy

text: Brain storm optimization algorithms (BSO) have shown great potential in many global black-box optimization problems. However, the existing BSO variants can suffer from three problems: (1) large-scale optimization problem; (2) hyperparameter optimization problem; (3) high computational cost of the clustering operations. To address these problems, in this paper, we propose a simple yet effective BSO variant named Brain Storm Optimization Algorithm with Cooperative Learning Strategy (BSO-CLS). It is inspired by the new ideas generating process of brain storm in which the participators propose their own ideas by cooperatively learning other participators’ ideas. Thus, BSO-CLS iteratively updates the candidate solutions by linearly combining other solutions with the weights deriving from the fitness values of other solutions. To validate the effectiveness of the proposed method, we test it on 6 benchmark functions with the 1000 dimensions. The experimental results show that BSO-CLS can outperform the vanilla BSO and the other BSO variant with the learning strategy.


title: Synchronized Swarm Operation

text: Physical swarm system, including number of units, operated in physical time according to corporative algorithm, is considered. It is shown, that for proper corporative algorithm interpretation it is necessary to synchronize computational processes in units. Structural-parametric model of synchronized swarm operation, based on Petri-Markov nets apparatus, is worked out. In the Petri-Markov net transitions are abstract analogues of synchronization procedure, while places simulate corporative algorithm parts interpretation by swarm units. Primary Petri-Markov model is transformed into complex semi-Markov process. Formulae for calculation of stochastic and time characteristics of the process are obtained. It is shown, that after transformation all methods of ordinary semi-Markov processes investigation may be used for synchronized systems. With use the concept of distributed forfeit effectiveness of synchronization is evaluated.


title: A Performance Class-Based Particle Swarm Optimizer

text: One of the main concerns with Particle Swarm Optimization (PSO) is to increase or maintain diversity during search in order to avoid premature convergence. In this study, a Performance Class-Based learning PSO (PCB-PSO) algorithm is proposed, that not only increases and maintains swarm diversity but also improves exploration and exploitation while speeding up convergence simultaneously. In the PCB-PSO algorithm, each particle belongs to a class based on its fitness value and particles might change classes at evolutionary stages or search step based on their updated position. The particles are divided into an upper, middle and lower. In the upper class are particles with top fitness values, the middle are those with average while particles in the bottom class are the worst performing in the swarm. The number of particles in each group is predetermined. Each class has a unique learning strategy designed specifically for a given task. The upper class is designed to converge towards the best solution found, Middle class particles exploit the search space while lower class particles explore. The algorithm’s strength is its flexibility and robustness as the population of each class allows us to prioritize a desired swarm behavior. The Algorithm is tested on a set of 8 benchmark functions which have generally proven to be difficult to optimize. The algorithm is able to be on par with some cutting edge PSO variants and outperforms other swarm and evolutionary algorithms on a number of functions. On complex multimodal functions, it is able to outperform other PSO variants showing its ability to escape local optima solutions.


title: Learning Automata-Based Fireworks Algorithm on Adaptive Assigning Sparks

text: Fireworks algorithm (FWA) is an emerging swarm intelligence inspired by the phenomenon of fireworks explosion. The numbers of sparks generated by fireworks have a great impact on the algorithm performance. It is widely accepted that promising fireworks should generate more sparks. However, in many researches, the quality of a firework is judged only on its current fitness value. This work proposes a Learning Automata-based Fireworks Algorithm (LA-FWA) introduced Learning automata (LA) to assign sparks for a better algorithm performance. Sparks are assigned to fireworks according to a state probability vector, which is updated constantly based on feedbacks from an environment so that it accumulates historical information. The probability vector converges as the search proceeds so that the local search ability of the LAFWA turns strong in the late search stage. Experimental results performed on CEC2013 benchmark functions show that the LAFWA outperforms several pioneering FWA variants.


title: A Multi-agent Ant Colony Optimization Algorithm for Effective Vehicular Traffic Management

text: An intelligent agent refers to an autonomous entity directing its activity towards achieving goals, acting upon an environment using data obtained with the help of a sensory mechanism. Intelligent agent software is a software system that performs tasks independently on behalf of a user in a networking environment based on user interface and past experiences. By the design of an intelligent sensing software program we can regulate the flow of traffic in a transportation infrastructure network. The problems leading to inefficiencies like loss of time, decrease in safety of vehicles and pedestrians, massive pollution, high wastage of fuel energy, degradation in the quality of life can be achieved by the optimized design. Ant Colony Optimization (ACO) has proven to be a very powerful optimization model for combinatorial optimization problems. The algorithm has the objective of regulating high real time traffic enabling every vehicle in the network with increased efficiency to minimize factors like time delay and traffic congestion.


docid: ekwfpshq

title: Determinative Brain Storm Optimization

text: Brain Storm Optimization (BSO) is a swarm intelligence optimization algorithm, based on the human brainstorming process. The ideas of a brainstorming process comprise the solutions of the algorithm, which iteratively applies solution grouping, generation and selection operators. Several modifications of BSO have been proposed to enhance its performance. In this paper, we propose a novel modification enabling faster convergence of BSO to optimal solutions, without requiring setting an upper bound of algorithm iterations. It considers a brainstorming scenario where participating groups with similar ideas recognize that their ideas are similar, and together, collaborate for the determination of a better solution. The proposed modification, called Determinative BSO (DBSO), implements this scenario by applying a cluster merging strategy for merging groups of similar solutions, while following elitist selection. Experimental results using eleven benchmark functions show that the proposed modified BSO performs better than both the original and a state-of-the-art algorithm.


title: Deep Learning Strategies for Survival Prediction in Prophylactic Resection Patients

text: Human race is looking forward to an era where science and technology can wipeout the threats laid by lethal diseases. Major statistics shows that about 10 million people die from various forms of cancer annually. Every sixth death in the world is caused by cancer. Treatment to cancer always depend on its type and spread. Treatment includes single or combination of surgery, chemotherapy and radiation therapy. In this paper, survival prediction in prophylactic resection patients are carried out using various deep learning methods. Prophylactic resection has been found to be very effective in colon cancer, breast cancer and ovarian cancer. In this paper, we try to validate the results in a test environment using multi layered deep neural network. Classical Navie Bayer’s algorithm has been used to classify the dataset and convolution neural network (CNN) has been used to create the survival prediction model. Results affirm better survival results in prophylactic resection patients.


title: O-Flocking: Optimized Flocking Model on Autonomous Navigation for Robotic Swarm

text: Flocking model has been widely used in robotic swarm control. However, the traditional model still has some problems such as manually adjusted parameters, poor stability and low adaptability when dealing with autonomous navigation tasks in large-scale groups and complex environments. Therefore, it is an important and meaningful research problem to automatically generate Optimized Flocking model (O-flocking) with better performance and portability. To solve this problem, we design Comprehensive Flocking (C-flocking) model which can meet the requirements of formation keeping, collision avoidance of convex and non-convex obstacles and directional movement. At the same time, Genetic Optimization Framework for Flocking Model (GF) is proposed. The important parameters of C-flocking model are extracted as seeds to initialize the population, and the offspring are generated through operations such as crossover and mutation. The offspring model is input into the experimental scene of autonomous navigation for robotic swarms, and the comprehensive fitness function value is obtained. The model with smallest value is selected as the new seed to continue evolution repeatedly, which finally generates the O-flocking model. The extended simulation experiments are carried out in more complex scenes, and the O-flocking and C-flocking are compared. Simulation results show that the O-flocking model can be migrated and applied to large-scale and complex scenes, and its performance is better than that of C-flocking model in most aspects.


title: A Structural Testing Model Using SDA Algorithm

text: Path testing is the most needed and useful coverage criterion in structural testing. Tracing and obtaining the resultant paths is the main problem in path coverage testing. Evolutionary techniques are adopted in many software product evaluation methods such as generating and selection of input test data. The priority of the feasible paths is also to be determined. In this paper, we proposes an optimization algorithm for identifying the effective test data execution paths in control flow graph for the program module under test and finding the most efficient test paths using modified smell detection agent based optimization algorithm. New innovations are being conducted for bio-motivated algorithmic techniques from the characteristics of animal behavior. Smell detection agent based algorithm helps to identify most feasible paths and it uses sequential search to obtain all paths in a graph. The tester achieves the paths to be tested through a number of smell spot values from the source node to the target node. We will use control flow graph to produce perfect test paths and cyclomatic complexity number for obtaining the number of feasible test paths. The best feasible paths are prioritized using smell detection agent algorithm such that all the paths are thoroughly tested which ensures structural testing. This algorithm generates paths equal to the cyclomatic complexity. It can be illustrated that the proposed approach guarantees full path coverage.


title: Image Clustering by Generative Adversarial Optimization and Advanced Clustering Criteria

text: Clustering is the task that has been used in numerous applications including digital image analysis and processing. Image clustering refers to the problem of segmenting image for different purposes which leads to various clustering criteria. Finding the optimal clusters represented by their centers is a hard optimization problem and it is one of the main research focuses on clustering methods. In this paper we proposed a novel generative adversarial optimization algorithm for finding the optimal cluster centers while using standard and advance clustering criteria. The proposed method was tested on seven benchmark images and results were compared with the artificial bee colony, particle swarm optimization and genetic algorithm. Based on the obtained results, the generative adversarial optimization algorithm founded better cluster centers for image clustering compared to named methods from the literature.


title: The Research of Flexible Scheduling of Workshop Based on Artificial Fish Swarm Algorithm and Knowledge Mining

text: The Job Shop Scheduling problem is critical in the manufacturing industry. At present, the decision tree reasoning technique and data mining are often used in multi-objective optimization research to solve flexible job shop scheduling issues. Unfortunately, when job shop scheduling problems involve complex logic, it becomes difficult to implement data-driven automatic scheduling without human intervention. Based on the analysis of mass data and specialized knowledge in the scheduling domain, an ontology-based scheduling knowledge model and a method of knowledge representation can be established. Considering the relationship between data mining and knowledge, this paper illustrates the acquisition process of scheduling rules. These scheduling rules were applied to improve the initialization process of the artificial fish algorithm. Then, a scheduling experiment was designed, the results of which show that the efficiency and accuracy of the algorithm has been improved. The desired uncertain information analysis, decision-making support for production planning and scheduling on the shop floor are provided and an adaptive scheduling algorithm for complex manufacturing systems is established by building a knowledge-based system.


title: Method Based on Data Mining Techniques for Breast Cancer Recurrence Analysis

text: Cancer is a constantly evolving disease, which affects a large number of people worldwide. Great efforts have been made at the research level for the development of tools based on data mining techniques that allow to detect or prevent breast cancer. The large volumes of data play a fundamental role according to the literature consulted, a great variety of dataset oriented to the analysis of the disease has been generated, in this research the Breast Cancer dataset was used, the purpose of the proposed research is to submit comparison of the J48 and randomforest, NaiveBayes and NaiveBayes Simple, SMO Poli-kernel and SMO RBF-Kernel classification algorithms, integrated with the Simple K-Means cluster algorithm for the generation of a model that allows the successful classification of patients who are or Non-recurring breast cancer after having previously undergone surgery for the treatment of said disease, finally the methods that obtained the best levels were SMO Poly-Kernel + Simple K-Means 98.5% of Precision, 98.5% recall, 98.5% TPRATE and 0.2% FPRATE. The results obtained suggest the possibility of using intelligent computational tools based on data mining methods for the detection of breast cancer recurrence in patients who had previously undergone surgery.


title: Colour Quantisation by Human Mental Search

text: Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image which are then represented by a colour palette. The selection of appropriate entries in this palette is a challenging issue while the quality of the quantised image is directly related to the colour palette. In this paper, we propose a novel colour quantisation algorithm based on the human mental search (HMS) algorithm. HMS is a recent population-based metaheuristic algorithm with three main operators: mental search to explore the vicinity of candidate solutions based on Levy flight, grouping to determine a promising region based on a clustering algorithm, and movement towards the best strategy. The performance of our proposed algorithm is evaluated on a set of benchmark images and in comparison to four conventional algorithms and seven soft computing-based colour quantisation algorithms. The obtained experimental results convincingly show that our proposed algorithm is capable of outperforming these approaches.


title: Multi-objective Particle Swarm Optimisation for Cargo Packaging in Large Containers

text: Cargo management in all mode of transports like airlines, ships and trucks is a challenging task. The way in which an optimal allocation of packages in different containers are done using a software controlled method. An agent based software module is enabled as a service for the optimum allocation of cargo packages in the container terminals. There are multiple factors that will affect this allocation - size, shape, weight of the cargo packets and the container. When we design an optimal allocation module in a software these components need to be addressed along with capacity of the container. Hence, a multi-objective optimization algorithm will improve the performance of cargo management software. In this paper we suggest a Mixed Species Particle Swarm Optimisation (MSPSO) procedure for optimal allocation of cargo packages in containers of different size and capacity. The redesigned version of cargo management software performs well with search space on normal time complexity. The simulated results gives an improved optimised allocation than normalised allocation of cargo packets. The improved implementation performed better in terms of efficient cargo package allocation.


docid: dlooiipf

title: An Improved Bacterial Foraging Optimization with Differential and Poisson Distribution Strategy and its Application to Nurse Scheduling Problem

text: Bacterial Foraging Optimization (BFO) has been predominately applied to some real-world problems, but this method has poor convergence speed over complex optimization problems. In this paper, an improved Bacterial Foraging Optimization with Differential and Poisson Distribution strategies (PDBFO) is proposed to promote the insufficiency of BFO. In PDBFO, the step size of bacteria is segmented and adjusted in accordance with fitness value to accelerate convergence and enhance the search capability. Moreover, the differential operator and the Poisson Distribution strategy are incorporated to enrich individual diversity, which prevents algorithm from being trapped in the local optimum. Experimental simulations on eleven benchmark functions demonstrate that the proposed PDBFO has better convergence behavior in comparison to other six algorithms. Additionally, to verify the effectiveness of the method in solving the real-world complex problems, the PDBFO is also applied to the Nurse Scheduling Problem (NSP). Results indicate that the proposed PDBFO is more effective in obtaining the optimal solutions by comparing with other algorithms.


title: An Improved CMA-ES for Solving Large Scale Optimization Problem

text: In solving large scale optimization problems, CMA-ES has the disadvantages of high complexity and premature stagnation. To solve this problem, this paper proposes an improved CMA-ES, called GI-ES, for large-scale optimization problems. GI-ES uses all the historical information of the previous generation of individuals to evaluate the parameters of the distribution of the next generation. These estimates can be considered as approximate gradient information, which complete covariance information is not required. Thus GI-ES is friendly to large scale optimization problems. Comparative experiments have been done on state-of-the-art algorithms. The results proved the effectiveness and efficiency of GI-ES for large scale optimization problems.


docid: lbjdfosf

title: Multi-objective Dynamic Scheduling Model of Flexible Job Shop Based on NSGAII Algorithm and Scroll Window Technology

text: The production process is often accompanied by a lot of disturbances, which make it difficult for flexible job shop to execute production according to the original job plan. It is necessary to dynamically adjust the production plan according to real-time conditions. To this end, this paper proposes a multi-objective dynamic scheduling model. In this model, scroll window technology and NSGAII algorithm is adopted to adapt the dynamic production evironment. A specific chromosome retention strategy and a variable objective selection mechanism are designed to ensure that the proposed model can select different objectives according to different disturbance events to solve the optimal solution. Finally, a case test is used to verify the feasibility and effectiveness of the model.


title: A New Local Search Adaptive Genetic Algorithm for the Pseudo-Coloring Problem

text: Several applications result in a gray level image partitioned into different regions of interest. However, the human brain has difficulty in recognizing many levels of gray. In some cases, this problem is alleviated with the attribution of artificial colors to these regions, thus configuring an application in the area of visualization and graphic processing responsible for categorizing samples using colors. However, the task of making a set of distinct colors for these regions stand out is a problem of the NP-hard class, known as the pseudo-coloring problem (PsCP). In this work, it is proposed to use the well-known meta-heuristic Genetic Algorithm together with operators specialized in the local search for solutions as well as self-adjusting operators responsible for guiding the parameterization of the technique during the resolution of PsCPs. The proposed methodology was evaluated in two different scenarios of color assignment, having obtained the best results in comparison to the techniques that configure the state of the art.


title: A Genetic Algorithm-Based Solver for Small-Scale Jigsaw Puzzles

text: In this paper, we present a genetic algorithm-based puzzle solver, which is mainly used to solve small-scale puzzle problems. We introduce a new measurement function that improves its accuracy by normalizing the Mahalanobis distance and the Euclidean distance between two puzzle pieces. By calculating the difference between edges of two puzzle pieces and using the genetic algorithm to assemble pieces correctly, two “parent” solutions are merged into one improved “child” solution. Using the idea of local search, it avoids the problem of local optimum solutions brought by the genetic algorithm, which greatly improves the accuracy of the puzzle.


title: A Novel Biogeography-Based Optimization Algorithm with Momentum Migration and Taxonomic Mutation

text: Biogeography-based optimization (BBO) algorithm is not good at dealing with regions where function values change dramatically or barely. A novel biogeography-based optimization algorithm is proposed in this paper based on Momentum migration and taxonomic mutation. The momentum item is added to the original migration operation of BBO. It makes the algorithm more advantageous in dealing with regions where function values change dramatically or barely. At the same time, taxonomic mutation strategy divides the solutions into three categories: promising class, middle class and inferior class. Promising solutions do not take part in this mutation operation. Solutions of middle class use balanced differential mutation, and inferior solutions adopt exploration-biased random mutation. This strategy further increases the diversity of population. The simulation experiments are carried out with different types of CEC2014 benchmark functions. The proposed algorithm is compared with other algorithms and shows stronger global search ability, faster convergence speed and higher convergence accuracy.


title: A Novel Image Segmentation Based on Clustering and Population-Based Optimisation

text: Image segmentation is an essential step in image processing and computer vision with many image segmentation algorithms having been proposed in the literature. Among these, clustering is one of the prominent approaches to achieve segmentation. Traditional clustering algorithms have been used extensively for this purpose, although they have disadvantages such as dependence on initialisation conditions and a tendency to find only local optima. To overcome these disadvantages, population-based metaheuristic algorithms can be applied. In this paper, we propose a novel clustering algorithm based on human mental search (HMS) for image segmentation. HMS is a relatively new population-based metaheuristic inspired from the manner of searching in online auctions. HMS comprises three operators: mental search, which explores the neighbourhood of candidate solutions using Levy flight; grouping, which clusters candidate solutions; and moving candidate solutions towards a promising area. To verify the efficacy of the proposed algorithm, we conduct several experiments based on different criteria including mean cost function value, statistical analysis and image segmentation criteria. The obtained results confirm superior performance of our proposed algorithm compared to competitors.


title: Map Generation and Balance in the Terra Mystica Board Game Using Particle Swarm and Local Search

text: Modern board games offer an interesting opportunity for automatically generating content and models for ensuring balance among players. This paper tackles the problem of generating balanced maps for a popular and sophisticated board game called Terra Mystica. The complexity of the involved requirements coupled with a large search space makes of this a complex combinatorial optimisation problem which has not been investigated in the literature, to the best of the authors’ knowledge. This paper investigates the use of particle swarm optimisation and steepest ascent hill climbing with a random restart for generating maps in accordance with a designed subset of requirements. The results of applying these methods are very encouraging, fully showcasing the potential of search-based metaheuristics in procedural content generation.


title: Multi-objective Combinatorial Generative Adversarial Optimization and Its Application in Crowdsensing

text: With the increasing of the decision variables in multi-objective combinatorial optimization problems, the traditional evolutionary algorithms perform worse due to the low efficiency for generating the offspring by a stochastic mechanism. To address the issue, a multi-objective combinatorial generative adversarial optimization method is proposed to make the algorithm capable of learning the implicit information embodied in the evolution process. After classifying the optimal non-dominated solutions in the current generation as real data, the generative adversarial network (GAN) is trained by them, with the purpose of learning their distribution information. The Adam algorithm that employs the adaptively learning rate for each parameter is introduced to update the main parameters of GAN. Following that, an offspring reproduction strategy is designed to form a new feasible solution from the decimal output of the generator. To further verify the rationality of the proposed method, it is applied to solve the participant selection problem of the crowdsensing and the detailed offspring reproduction strategy is given. The experimental results for the crowdsensing systems with various tasks and participants show that the proposed algorithm outperforms the others in both convergence and distribution.


title: Case Classification Processing and Analysis Method for Respiratory Belt Data

text: Human respiratory signal is the important physiological indicator to reflect the physical condition. The respiratory belt, compared with the other human respiratory data measurement methods, has the advantages of being portable, cheap, non-invasive, etc. However, it is unclear which features of the breathing data can effectively classify the normal/abnormal state of breathing state. To solve the problem, we proposed a novel approach based on long-short-term-memory (LSTM) and breathing features of respiratory data. First, LSTM structure were used, then compared the result with the traditional method which extract the feature to experiment (in our paper which is RIE (ratio of inspiratory time to expiratory time)). In the end, a novel methodology proposed which combined the RIE feature with the LSTM structure. Experiment the three methods above using 342 normal and abnormal 24-h breathing data, the results show that the third method has higher classification accuracy.


title: Canine Algorithm for Node Disjoint Paths

text: Node Disjoint Paths (NDP) is one of the extensively studied Graph Theory problem. In this problem, the input is a directed n vertex graph and the set of source destination pair of vertices. The goal is to find the maximum number of paths connecting each such pair, so that such discovered paths are node-disjoint. In this paper, a novel Canine Inspired Algorithm is proposed which is a bio-inspired one, based on the olfactory capabilities of canines in tracing and reaching a destination. Currently many of the existing algorithms try to identify disjoint paths in a linear manner, whereas the Canine algorithm can be executed in a concurrent manner, depending on the number of canines deployed to find the disjoint paths. The time complexity of the algorithm is estimated to be [Formula: see text] . We hope that this algorithm finds many applications in problems related to various fields such as communication networks, scheduling and transportation and provides better results.


title: Target Tracking Algorithm Based on Density Clustering

text: The traditional Siamese network-based target tracking algorithm needs to use the convolution feature of the target to scan around the target location when predicting the location of the target in the next frame image, and perform similarity calculation to obtain the similarity score matrix with the highest score. It is the next frame target position. The highest similarity score often does not represent the precise target position of the target, which is often affected by the sliding step size during scanning. Aiming at this problem, this paper proposes a target tracking method based on density clustering. By combining the Siamese network to predict the next frame target position, and adding the target’s motion trajectory information, the direction of the target motion is given more weight, the other directions are given a smaller weight, and finally the target position is predicted by the density clustering method. The results show that the proposed algorithm effectively improves the accuracy of the target location prediction of the Siamese network when tracking targets.


title: A Hybrid Brain Storm Optimization Algorithm for Dynamic Vehicle Routing Problem

text: The Dynamic Vehicle Routing Problem (DVRP) has many real-world applications and practical values. The objective of DVRP is to find the optimal routes for a fleet of vehicles to service the given customer requests, without violating the vehicle capacity constraint. In this paper, a hybrid algorithm is proposed for solving the DVRP with the objective to minimize the total distance of the vehicles. The Brain Storm Optimization in objective space (BSO-OS) is applied to guide the choice of different strategies for the periodic reoptimization of routes. In the BSO-OS procedure, Adaptive Large Neighborhood Search (ALNS) and Ant Colony System (ACS) are used to generate new solutions. The experiments on the DVRP benchmark and comparative studies are conducted, from which 12 out of 21 new best solutions are obtained by the proposed algorithm, and the other nine solutions are also very competitive. The experimental results show that the proposed algorithm is very effective and competitive.


title: The Improvement of V-Shaped Transfer Function of Binary Particle Swarm Optimization

text: Binary Particle Swarm Optimization (BPSO) is a swarm intelligence to optimize discrete space problems by extending the Particle Swarm Optimization. Its transfer function is the key element of BPSO. In this paper, a new V-shaped transfer function with a parameter k has been proposed. The parameter k was used to control the opening size of the transfer function. At first, the setting of the parameter k has been obtained by the experiments, and then the new V-shaped transfer with the optimal k value is compared with the other kinds of the V-shaped transfer functions by the experiment of feature selection. The results have indicated that the new V-shaped transfer function improved the performance of Binary Particle Swarm Optimization.


title: Optimal Reservoir Optimization Using Multiobjective Genetic Algorithm

text: Scarcity of fresh water resources has thrown various challenges to hydrologist. Optimum usage of resource is the only way out to handle this situation. Among the various water resources the most controllable one is the dam reservoirs. This paper deals with optimal reservoir optimization using multi objective genetic algorithm (MOGA). Various parameters like reservoir storage capacity, spill loss, evaporation rate, water used for irrigation, water used for electricity production, rate of inflow, outflow all need to be managed in an optimal way so that water levels are managed and resource specifications are met. This is normally managed using a software, but sudden change in scenarios and change in requirements cannot be handled by such softwares. Hence we are incorporating an optimised software layer to handle such situation. Multi objective genetic algorithm was able to optimise the water usage within the usage constrains. The results were assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. The simulated result shows that MOGA derived rules are promising and competitive and can be effectively used for reservoir optimization operations.


title: Hybrid Ant Colony Optimization-Based Method for Focal of a Disease Segmentation in Lung CT Images

text: The detection of chest CT scan images of the lung play a key role in clinical decision making for some lung disease, such as tumors, pulmonary tuberculosis, solitary pulmonary nodule, lung masses and so on. In this paper, a novel automated CT scan image segmentation algorithm based on hybrid Ant Colony algorithm and snake algorithm is proposed. Firstly, traditional snake algorithm is used to detect the possible edge points of focal of a disease. Then Ant Colony Optimization (ACO) algorithm is applied to search the possible edge points of focal of a disease repeatedly. Finally, real edges can be extracted according to the intensity of pheromones. Simulation experiment results demonstrate that the proposed algorithm is more efficient and effective than the methods we compared it to.


title: A Method for Localization and Classification of Breast Ultrasound Tumors

text: Ultrasound instruments are suitable for large-scale examination of breast tumors, especially for women from Asian whose glands are dense. However, ultrasound images have the low contrast and resolution, blurred boundary and artifacts, which bring great difficulties to the interpretation of the junior doctor. However, traditional methods of breast ultrasound tumor recognition often use manually extracted features to gradually realize ROI region location and tumor classification with low accuracy, poor robustness and weak universality. Deep learning is limited to the location of tumor ROI region or the classification of a given tumor ROI region. In this paper, YOLOV3 algorithm is used for breast ultrasound tumor recognition, which could realize ROI localization and tumor classification at the same time. In addition, K-Means is optimized by K-Means++ and K-Mediods algorithm to generate anchor boxes of YOLOV3, and based on the Darknet-53 network structure of YOLOV3, ResNet and DenseNet are combined to design ResNet-DenseNet_Darknet-53. The proposed method is tested on the breast ultrasound tumor data set. Experiments show that the improved YOLOV3 algorithm shows better detection results on multiple evaluation indicators.


Looking for Brighter Days


title: Die Ausgangslage

text: Die psychotherapeutische Praxis in den deutschsprachigen Ländern entwickelte sich bisher in einem gesellschaftlichen Klima von Frieden und Sicherheit und konnte sich unter diesen Bedingungen als fester Bestandteil des Gesundheitssystems und der Gesellschaft etablieren. Im Gegensatz etwa zu den Arbeitsumgebungen von Kolleg*innen in Ländern des Nahen Ostens oder auch vielen afrikanischen Staaten wurde Psychotherapie bei uns auf dem Boden eines generellen Gefühls sozialer Stabilität und in Abwesenheit grundsätzlicher Gefahrenszenarien praktiziert. Durch globale Krisen wie zuletzt der Corona-Pandemie schwindet diese bisher selbstverständliche Basis und eine allgemeine, auch den/die Therapeut*in erfassende Verunsicherung zieht in die Praxen ein.


title: Verunsicherung und der Verlust an Eindeutigkeiten

text: Am Beispiel der Corona-Krise wird deutlich: Kollektive Gefahrenszenarien konfrontieren mit mangifach Unbekanntem und Ungewissen. Schon in „normalen“ Zeiten hat unsere Gesellschaft mit dem schmerzhaft erlebten Verlust von vermeidlichen Eindeutigkeiten zu tun, was z. B. in den teils hoch emotional geführten Zuwanderungs- oder Transgenderdebatten deutlich wird. Die reflexhaften unmittelbaren Reaktionen sind Verunsicherung und Unsicherheit als kollektive und individuelle Gefühlslagen.


title: Tiefenpsychologische Überlegungen

text: Persönliche Katastrophen der Unsicherheit, Verlassenheit und (Lebens-) Bedrohung, Erfahrungen von Verlust, Gewalt und Ohnmacht und deren Bewältigung bilden die zum großen Teil unbewusste Grundlage unserer Verarbeitung aktueller individueller oder gesellschaftlicher Krisen. Gleichzeitig treffen aktuelle Notszenarien aber auch auf gesellschaftlich-unbewusste Schichten, in denen die gesellschaftlichen Erfahrungen mit Kriegen, Umweltkatastrophen oder im Falle von Corona von Pest, Cholera und Spanischer Grippe abgelagert sind


title: Citrullinated histone H3, a marker of extracellular trap formation, is increased in blood of stable asthma patients

text: BACKGROUND: Emerging data indicates that extracellular traps (ETs), structures formed by various immune cell types, may contribute to the pathology of noninfectious inflammatory diseases. Histone hypercitrullination is an important step in ETs formation and citrullinated histone H3 (H3cit) is considered a novel and specific biomarker of that process. In the present study we have evaluated circulating H3cit in stable asthmatics and investigated its relationship with asthma severity, pulmonary function and selected blood and bronchoalveolar lavage (BAL) biomarkers. METHODS: In 60 white adult stable asthmatics and 50 well-matched controls we measured serum levels of H3cit. In asthmatics we also performed bronchoscopy with BAL. We analyzed blood and BAL biomarkers, including interleukin (IL)-4, IL-5, IL-6, IL-10, IL-12p70, IL-17A and interferon γ. For statistical analysis, Mann–Whitney U-test, χ(2) test, one-way ANCOVA, ROC curve analysis and univariate linear regression were applied. Independent determinants of H3cit were established in a multiple linear regression model. RESULTS: Asthma was characterized by elevated circulating H3cit (17.49 [11.25–22.58] vs. 13.66 [8.66–18.87] ng/ml, p = 0.03). In asthmatics positive associations were demonstrated between serum H3cit and lung function variables, including total lung capacity (TLC) (β = 0.37 [95% CI 0.24–0.50]) and residual volume (β = 0.38 [95% CI 0.25–0.51]). H3cit was increased in asthma patients receiving systemic steroids (p = 0.02), as well as in subjects with BAL eosinophilia above 144 cells/ml (p = 0.02). In asthmatics, but not in controls, circulating H3cit correlated well with number of neutrophils (β = 0.31 [95% CI 0.19–0.44]) and monocytes (β = 0.42 [95% CI 0.29–0.55]) in peripheral blood. Furthermore, BAL macrophages, BAL neutrophils, TLC, high-sensitivity C-reactive protein, Il-12p70 and bronchial obstruction degree were independent determinants of H3cit in a multivariate linear regression model. CONCLUSIONS: Asthma is characterized by increased circulating H3cit likely related to the enhanced lung ETs formation. Inhibition of ETs might be a therapeutic option in selected asthma phenotypes, such as neutrophilic asthma.


title: Acute suppurative thyroiditis with transient hyperthyroidism in thalassemia child: an uncommon association in a common disease—a rare case report

text: BACKGROUND: Thyroid infection is rare in paediatric age group in anatomically normal thyroid gland. However, acute suppurative thyroiditis (AST) is common in high-risk cases like thyroglossal duct remnant and thyroglossal pyriform sinus fistula. This may be associated with variable thyroid dysfunctions. Though transient hyperthyroidism is reported in some cases, its association was not documented in thalassemia child. CASE PRESENTATION: We are reporting an 8-year-old male child with a history of fever, pain, and left neck swelling for 5 days. He was evaluated and empirically started intravenous antibiotics (ceftriaxone, cloxacillin, and amikacin) along with other supportive treatment for localized signs of thyroid infection. Ultrasonography of the neck revealed intercommunicating pockets of collections with internal echoes in the left thyroid lobe. CT neck was done and suggested that the left lobe of the thyroid gland was replaced by hypodense lesions of fluid attenuation without any congenital abnormality of the thyroid gland. His thyroid function tests were suggestive of biochemical hyperthyroidism. He was treated with intravenous antibiotics and ultrasonography-guided fine-needle aspiration of pus. His pus culture showed growth of methicillin resistant Staphylococcus aureus. He also had microcytic hypochromic anaemia which was evaluated and found to have thalassemia trait. His biochemical hyperthyroidism normalized after completion of 2 weeks of treatment and required no intervention. CONCLUSION: Acute suppurative thyroiditis is an uncommon clinical condition in an anatomically normal thyroid gland which could be associated with thyroid dysfunction. Its association in a thalassemia child is also not documented in literature. Clinical evaluation and proper history taking and non-invasive thyroid imaging are the cornerstone for diagnosis. Antibiotics and pus drainage are the mainstay management. Associated biochemical hyperthyroidism is transient and resolves without any specific medication in asymptomatic cases.


title: Half a century and more of PhD theses by published papers: Comment on: “Bringing the doctoral thesis by published papers to the Social Sciences and the Humanities: A quantitative easing? A small study of doctoral thesis submission rules and practice in two disciplines in the UK” by John Rigby and Barbara Jones in Scientometrics published online 15-May-2020

text: The recent article by Rigby and Jones in Scientometrics (15-May-2020) again draws attention to basing PhD-theses on published works, in their case introducing the system into the Social Sciences and Humanities. In this short communication we endeavour to provide additional information that is essential for this debate.


A crisis of accountability for women's, children's, and adolescents' health


title: Einleitung

text: Durch immer komplexer werdende Baumaßnahmen, bei gleichzeitig wachsendem Termin- und Kostendruck, sowie steigenden Ansprüchen an die Qualität, kommt es in der baugewerblichen Praxis immer häufiger zu gestörten Abläufen. Zurückzuführen ist dies auf fehlende Pufferzeiten, die teilweise bereits in der Planungsphase gestrichen werden, um den Ansprüchen des Auftrages gerecht werden zu können. Eine Bauablaufstörung liegt immer dann vor, wenn von der eigentlich vertraglich festgelegten Soll-Leistung abgewichen werden muss, unabhängig davon, wer diese Abweichung zu vertreten hat. Um den wirtschaftlichen Erfolg der Maßnahme zu sichern und eine verantwortungsgerechte Kostenallokation herzustellen, kann die Seite, welche die Störung nicht zu verantworten hat, berechtigte Mehrkosten zurückfordern oder die Erstattung des entstandenen Schadens von der anderen Partei verlangen.


title: Baubetriebliche und baurechtliche Grundlagen

text: Im zweiten Kapitel wird zunächst die Bedeutung der Bauzeit für die einzelnen am Bau beteiligten Parteien dargelegt, bevor die verschiedenen Bauablaufstörungen klassifiziert werden. Zudem erfolgt eine Erläuterung zu den möglichen Anspruchsgrundlagen des Auftragnehmers für eine Erstattung von Mehrkosten aus einem gestörten Bauablauf. Dieser kann in dem Verantwortungsbereich des Auftraggebers liegen oder aus höherer Gewalt resultieren. In einem weiteren Schritt wird auf die aktuelle Rechtsprechung des Bundesgerichtshofes eingegangen und die Mindestanforderungen dargelegt, die dieser an die Nachweis- bzw. Beweisführung stellt.


title: Concluding Considerations and Directions for Future Research

text: luxury fashion firms compete in an environment in which two different sets of competitive variables, traditional and new, coexist. However, these two sets are coherent and complementary within their own boundaries and less so with each other and challenge firms willing to design a new competitive landscape; luxury fashion firms are tightropes in between zeroing the brand and respecting the heritage; in between craftmanship, increased number of collections and sustainability; in between street style, increased number of collections and perception of uniqueness and rarity; and in between speed and creativity; additional studies are needed to understand how Maisons will cope with the above trade-offs, which variables will lead to superior competitive advantage, how new technologies and their usage will shape competitive advantaged.


title: An Overview of the Luxury Fashion Industry

text: provides a definition of luxury and a detailed discussion of how it has changed in time till arriving at the concepts of new luxury and zeroing; offers an overview of the industry, its performance and its major regional trends; concludes with a description of the industry’s concentration process driven by merger and acquisitions.


title: Conclusion

text: This conclusion of the compendium of essays by Anthony E. Clark summarizes the content and significance of his research on the history of Catholicism in China. It was written as the COVID-19 virus was sweeping across the globe, and Clark reflects upon the comparisons between the virus’ outbreak at Wuhan in 2019 and two French Catholic missionaries, François-Régis Clet, and Jean-Gabriel Perboyre, who were martyred in the Wuchang district of Wuhan in the mid-nineteenth century. Also considered in this conclusion is the trend among scholars to depict China’s relationship with the West and Christianity as one of “conflict” or “cooperation,” highlighting the two extremes of either irreconcilable difference or congruous sameness. Clark concludes this collection of essays with the suggestion that the historical exchange between China and the West has been rather an admixture of conflict and cooperation, but defined mostly as a relationship of friendship.


title: Pathophysiology and therapy of systemic vasculitides

text: Systemic vasculitides represent uncommon conditions characterized by the inflammation of blood vessels that can lead to different complex disorders limited to one organ or potentially involving multiple organs and systems. Systemic vasculitides are classified according to the diameter of the vessel that they mainly affect (small, medium, large, or variable). The pathogenetic mechanisms of systemic vasculitides are still partly unknown, as well as their genetic basis. For most of the primary systemic vasculitides, a single gold standard test is not available, and diagnosis is often made after having ruled out other mimicking conditions. Current research has focused on new management protocol and therapeutic strategies aimed at improving long-term patient outcomes and avoiding progression to multiorgan failure with irreversible damage. In this narrative review, authors describe different forms of systemic vasculitides through a review of the literature, with the aim of highlighting the current knowledge and recent findings on etiopathogenesis, diagnosis and therapy.


Commentary: Mentoring Trainees When the Going Gets Tough


Assisted reproduction and COVID-19: A joint statement of ASRM, ESHRE and IFFS


Commentary: Let’s Just Celebrate This Win


Commentary: Cardiac Surgery in North America: Facing an inevitable “new normal”?


Original Article Highlights From This Issue


title: From Small Association to Global Pundit: 2009–2020

text: When Jean Todt became Fédération Internationale de l’Automobile (FIA) president in 2009, he made good use of the Mosley legacy. At the same time, he engineered a series of new reforms that would mark the greatest difference between the old and the new FIA. This chapter explores a selection of organisational innovations during Todt’s three presidential periods (2009–2021). This selection includes two presidential elections in 2009 and 2013 which had a hitherto unrivalled emphasis on good governance, a political debacle (the F1 Bahrain Grand Prix), a structural revamp (the establishing the FIA Statutes Review), the introduction of an external audit (aided by consulting firm, Deloitte), and a proactive media strategy to comprehend a media logic (by publishing AUTO magazine from 2012 onwards). Together, this combination of substantive and symbolic actions represents the FIA’s desire to renew its legitimacy among its members, motorists, and other stakeholders.


Special issue in honor of John C. Rothwell


Commissaris krijgt de regie


José Geertsema


title: The Sars-Cov-2 Pandemic and the Brave New Digital World of Environmental Enrichment to Prevent Brain Aging and Cognitive Decline

text: Individuals experiencing brain aging, cognitive decline, and dementia are currently confronted with several more complex challenges due to the current Sars-Cov-2 pandemic as compared to younger and cognitively healthy people. During the first six months of the pandemic, we are experiencing critical issues related to the management of mild cognitive impairment (MCI) and dementia. The evolving, highly contagious global viral spread has created a pressure test of unprecedented proportions for the existing brain health care infrastructure and related services for management, diagnosis, treatment, and prevention. Social distancing and lock-down measures are catalyzing and accelerating a technological paradigm shift, away from a traditional model of brain healthcare focused on late symptomatic disease stages and towards optimized preventive strategies to slow brain aging and increase resilience at preclinical asymptomatic stages. Digital technologies transform global healthcare for accessible equality of opportunities in order to generate better outcomes for brain aging aligned with the paradigm of preventive medicine.


Maurice van den Boch


title: Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market

text: The Covid-19 pandemic and the social distancing that followed have affected all walks of society, also education. In order to keep education running, educational institutions have had to quickly adapt to the situation. This has resulted in an unprecedented push to online learning. Many, including commercial digital learning platform providers, have rushed to provide their support and ‘solutions’, sometimes for free. The Covid-19 pandemic has therefore also created a sellers’ market in ed-tech. This paper employs a critical lens to reflect on the possible problems arising from hasty adoption of commercial digital learning solutions whose design might not always be driven by best pedagogical practices but their business model that leverages user data for profit-making. Moreover, already before Covid-19, there has been increasing critique of how ed-tech is redefining and reducing concepts of teaching and learning. The paper also challenges the narrative that claims, ‘education is broken, and it should and can be fixed with technology’. Such technologization, often seen as neutral, is closely related to educationalization, i.e. imposing growing societal problems for education to resolve. Therefore, this is a critical moment to reflect how the current choices educational institutions are making might affect with Covid-19 education and online learning: Will they reinforce capitalist instrumental view of education or promote holistic human growth? This paper urges educational leaders to think carefully about the decisions they are currently making and if they indeed pave the way to a desirable future of education.