bluuebunny/crossref_metadata_2025 huggingface dataset:
A Wi-Fi-Based Passive Indoor Positioning System via Entropy-Enhanced Deployment of Wi-Fi Sniffers
Authors: Chan Poh Yuen , Chao Ju-Chin , Wu Ruey-Beei
Published: January 2023

This study presents a Wi-Fi-based passive indoor positioning system (IPS) that does not require active collaboration from the user or additional interfaces on the device-under-test (DUT). To maximise the accuracy of the IPS, the optimal deployment of Wi-Fi Sniffers in the area of interest is crucial. A modified Genetic Algorithm (GA) with an entropy-enhanced objective function is proposed to optimize the deployment. These Wi-Fi Sniffers are used to scan and collect the DUT’s Wi-Fi received signal strength indicators (RSSIs) as Wi-Fi fingerprints, which are then mapped to reference points (RPs) in the physical world. The positioning algorithm utilises a weighted k-nearest neighbourhood (WKNN) method. Automated data collection of RSSI on each RP is achieved using a surveying robot for the Wi-Fi 2.4 GHz and 5 GHz bands. The preliminary results show that using only 20 Wi-Fi Sniffers as features for model training, the offline positioning accuracy is 2.2 m in terms of root mean squared error (RMSE). A proof-of-concept real-time online passive IPS is implemented to show that it is possible to detect the online presence of DUTs and obtain their RSSIs as online fingerprints to estimate their position.
Reciprocating Arc Silicon Strain Gauges
Authors: Han Ji-Hoon , Min Sung Joon , Kim Joon Hyub , Min Nam Ki
Published: January 2023

Currently, silicon-strain-gauge-based diaphragm pressure sensors use four single-gauge chips for high-output sensitivity. However, the four-single-gauge configuration increases the number of glass frit bonds and the number of aluminum wire bonds, reducing the long-term stability, reliability, and yield of the diaphragm pressure sensor. In this study, a new design of general-purpose silicon strain gauges was developed to improve the sensor output voltage while reducing the number of bonds. The new gauges consist grid patterns with a reciprocating arc of silicon piezoresistors on a thin glass backing. The gauges make handling easier in the bonding process due to the use of thin glass for the gauge backing. The pressure sensors were tested under pressure ranging from 0 to 50 bar at five different temperatures, with a linear output with a typical sensitivity of approximately 16 mV/V/bar and an offset shift of –6 mV to 2 mV. The new approach also opens the possibility to extend arc strain gauges to half-bridge and full-bridge configurations to further reduce the number of glass frit and Al wire bonds in the diaphragm pressure sensor.
An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization
Authors: Mangalampalli Sudheer , Karri Ganesh Reddy , Elngar Ahmed A.
Published: January 2023

Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency.
Personalized LSTM Models for ECG Lead Transformations Led to Fewer Diagnostic Errors Than Generalized Models: Deriving 12-Lead ECG from Lead II, V2, and V6
Authors: Shyam Kumar Prashanth , Ramasamy Mouli , Kallur Kamala Ramya , Rai Pratyush , Varadan Vijay K.
Published: January 2023

Background and Objective: The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations. Methods: Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R2, and Pearson correlation (ρ). We compare qualitatively by matching ECG interpretations of board-certified cardiologists. Results: ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were (RMSE-74.31 µV, R2-72.05, ρ-0.88) and (RMSE-26.27 µV, R2-96.38, ρ-0.98), respectively. Conclusions: Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs.
Human Activity Recognition with an HMM-Based Generative Model
Authors: Manouchehri Narges , Bouguila Nizar
Published: January 2023

Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded–scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.
A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm
Authors: Menshawi Alaa , Hassan Mohammad Mehedi , Allheeib Nasser , Fortino Giancarlo
Published: January 2023

The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline’s ability to be retrained and used for other datasets collected using different measurements and with different distributions.
A Simple Approach to Estimate the Drag Coefficients of a Submerged Floater
Authors: Hoffman Yuval , Nagar Liav , Shachar Ilan , Diamant Roee
Published: January 2023

The calculation of the drag force is a fundamental requirement in the design of any submerged system intended for marine exploration. The calculation can be performed by analytic analysis, numerical modeling, or by a direct calculation performed in a designated testing facility. However, for complex structures and especially those with a non-rigid design, the analytic and numerical analyses are not sufficiently accurate, while the direct calculation is a costly operation. In this paper, we propose a simple approach for how to calculate the drag coefficient in-situ. Aimed specifically at the complex case of elastic objects whose modeling via Computer-Aided Design (CAD) is challenging, our approach evaluates the relation between the object’s speed at steady-state and its mass to extract the drag coefficient in any desired direction, the hydro-static force, and, when relevant, also the thruster’s force. We demonstrate our approach for the special case of a highly complex elastic-shaped floater that profiles the water column. The analysis of two such floaters in two different sea environments shows accurate evaluation results and supports our claim for robustness. In particular, the simplicity of the approach makes it appealing for any arbitrary shaped object.
BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN
Authors: Ramirez Heilym , Velastin Sergio A. , Cuellar Sara , Fabregas Ernesto , Farias Gonzalo
Published: January 2023

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.
Structural Stability Monitoring of Model Test on Highway Tunnel with Lining Backside Voids Using Dynamic and Static Strain Testing Sensors
Authors: Du Chaofei , Zhou Chuanbo , Jiang Nan , Huang Yiwen
Published: January 2023

Voids behind a lining may develop due to insufficient backfilling, poor workmanship, water erosion or gravity. They affect the interaction between the surrounding rock and lining and even cause instability of the lining structure. To ensure the safe operation of tunnels, it is very important to study the influence of voids behind the lining of the lining structure. In this paper, a laboratory model of a tunnel lining was established by taking the voids behind the lining of the Wushan Tunnel as an example. By changing the position and size of the voids, the corresponding stress variation law of the lining was obtained, and the influence of the voids behind the lining on the structural stability of the highway tunnel was analyzed. The experimental results showed that the voids behind the lining led to an increase in the stress near the voids, especially the voids at the vault. The circumferential stress and axial stress increased with increasing void depth and length, and the increase was greater with increasing void depth than increasing length; that is, the void depth had a greater effect on the lining stress. When the vault void depth was 30 mm, the axial tensile stress of the vault was 0.281 MPa, and the maximum increase was 178.2% compared with that without voids. The safety factors at different lining positions, from large to small, are: arch foot > spinner > arch top > arch waist. In the processes of lining operation and maintenance, special attention should be given to the treatment of voids behind the lining, especially deep voids.
A Predictive Model of a Driver’s Target Trajectory Based on Estimated Driving Behaviors
Authors: Yan Zhanhong , Yang Bo , Wang Zheng , Nakano Kimihiko
Published: January 2023

With the development of automated driving, inferring a driver’s behavior can be a key element for designing an Advanced Driver Assistance System (ADAS). Current research is focused on describing and predicting a driver’s behaviors as labels, e.g., lane shifting, lane keeping, etc., during driving. In our work, we consider that predicting a driver’s behavior can be described as predicting a trajectory the driver may follow in the near future. The target trajectory can be calculated through certain polynomial functions. Via the data set collected by a Driving Simulator experiment covering nine volunteers, we proposed a model based on a deep learning network which is capable of predicting the corresponding coefficients of polynomial functions and then generating the trajectories in the next few seconds. The results also discussed and analyzed some possible factors affecting the prediction error. In conclusion, the model proved to be effective in predicting the target trajectory of a driver.
Dynamic Zero Current Method to Reduce Measurement Error in Low Value Resistive Sensor Array for Wearable Electronics
Authors: Zhang Huanqian , Teoh Jee Chin , Wu Jianfeng , Yu Longteng , Lim Chwee Teck
Published: January 2023

One advantage of a resistive sensor array (RSA) with shared rows (M) and shared columns (N) is the reduced number of wires from M × N + 1 to M + N which can greatly lessen the complexity and burden on wearable electronic systems. However, the drawback is the crosstalk current effect between adjacent elements, which will lead to high measurement error. Although several solutions have been reported, they mainly focus on RSAs with high resistance (≥100 Ω). There is a lack of research that addresses RSAs with resistor values below 100 Ω. Here, we introduce a new circuit design named the dynamic zero current method (DZCM) to further decrease the measurement error. From the low value RSA test with ideal resistors, the DZCM exhibits lower error than the zero potential method (ZPM). In the case of the error variation ratio of amplifier offset voltage, the DZCM has a 4%/mV (row) to 7%/mV (column) ratio, while the ZPM has an almost 25%/mV (row) to 45%/mV (column) ratio and it increases with array size.
Effect of the Position of Overflow Pipe with Mixed Spiral Structures on the Separation Performance of Hydrocyclones
Authors: Liu Peikun , Fu Wenxiu , Jiang Lanyue , Yang Xinghua , Zhang Yuekan , Li Xiaoyu , Zhang Yulong
Published: January 2023

Overflow pipes are important components of hydrocyclones. The overflow products can carry huge amounts of residual energy when being discharged. In order to take full advantage of the residual energy and enhance hydrocyclone separation performance, this research designed a novel hydrocyclone by adding static mixing units with spiral elements in the overflow pipe. This study performed numerical simulations to investigate the effects of the install position of the spiral structure on the separation performance and inner flow field of the hydrocyclone. It can be concluded that both tangential velocity and pressure are first enhanced and then reduced by the elevation of the spiral structure. When the spiral structure is installed 30 mm away from the overflow pipe bottom, because of the hindrance of spiral elements, the discharge of coarse particles with the overflow are fully decreased and the quality of overflow products are enhanced.
Multi-Objective Intercity Carpooling Route Optimization Considering Carbon Emission
Authors: Lu Xiaojuan , Wang Jianjun , Yuen Choon Wah , Liu Qian
Published: January 2023

In recent years, intercity carpooling has been vigorously developed in China. Considering the differences between intercity carpooling and intracity carpooling, this paper first defines the intercity carpooling path optimization problem with time window. Based on the balance of interests among passengers, platform, and government, a multi-objective function is constructed to minimize passenger cost, maximize platform revenue, and minimize carbon emission cost, with vehicle capacity, boarding and alighting points, vehicle service, and other constraints. Secondly, in order to further improve the coordination ability and search speed of the operator, this paper uses the particle swarm optimization algorithm to help the operator remember the previous search position and iterative information, and designs the PSO (Particle Swarm Optimization) improved NSGA-II (Non-dominated Sorting Genetic Algorithm) algorithm to solve the multi-objective model. Finally, the feasibility of the model is verified by numerical analysis of Xi’an–Xianyang intercity carpool. The results show that the path of vehicle 1 is 5-8-O-D-16-13, the path of vehicle 2 is 7-3-6-O-D-15-11-14, and the path of vehicle 3 is 2-1-4-O-D-12-10-9. Compared with NSGA-II algorithm, the PSO-NSGA-II algorithm designed in this paper has significant advantages in global search ability and convergence speed.
Development of Sustainable Creative Three-Dimensional Virtual Woven Textiles Using Clothing Waste
Authors: Lee Hye Won
Published: January 2023

The purpose of this study was to design weaving methods for the aesthetic and practical recycling of various types of clothing waste, making creative woven textiles and three-dimensional virtual textiles based on them. This study is a follow-up study on the production of upcycling fabric using clothing waste and was conducted to overcome the limitations of the preceding study. Before conducting this study, a preliminary survey was conducted on the perception of clothing waste recycling among weaving participants. The investigation found that the necessity of recycling clothing waste was recognized, but methods for doing so were not known. The demand for easy and diverse recycling methods that can aesthetically improve clothing waste has been identified. In this study, seven weaving methods based on plain weaving were designed. The weaving method was based on the plain weaving method, and warp, weft, and matt weaving were divided into regular or irregular weaving. Warp yarn was used to improve the durability of the textile, and weft yarn was utilized to increase the use of clothing waste and maintain the aesthetic effects of the original materials. The twenty people who participated in the preliminary survey performed creative textile production using clothing waste and evaluated materials and weaving methods. Creative textiles using clothing waste were created as 3D virtual textiles by the author. A group of experts evaluated the novelty and appropriateness of the creative textiles and 3D virtual textiles and participated in a focus group interview. As a result of this study, 140 creative textiles and 3D virtual textiles were produced based on the developed methods. According to the evaluation of the materials and design methods, the preparation of the material was easy, the suitability of the material was high, and the difficulty of the weaving method was low. The difficulty of each weaving type was the lowest for the plain and matt regular method, and the highest for the weft irregular method. The irregular type was highly evaluated in the novelty category, and the regular type was highly evaluated in the appropriateness category. In the focus group interviews, experts positively evaluated the usefulness of the material, the appropriateness of the design methods, the novelty of the woven textile, and the appropriateness of the material for 3D virtual clothing.
A Model of E-Learning through Achievement Motivation and Academic Achievement among University Students in Saudi Arabia
Authors: Alamri Mahdi Mohammed
Published: January 2023

In relation to E-learning, achievement motivation is the persistent drive that students have to succeed to a particular degree of quality in a competitive environment. Goals, task values, ability self-concepts, and achievement motives are only a few of the many diverse constructions that make up achievement motivation, which is not one single construct. According to the few studies that have looked at different motivation constructs as predictor variables of university students’ academic achievement well beyond cognitive abilities and achievement motivation, most motivational constructs predicted educational success beyond intelligence, and students’ ability self-concepts as well as task morals are more potent in predicting their achievement than goals and achievement motives. However, an effort was made in this study to examine the impact of academic achievement motivation on university students inside the Kingdom of Saudi Arabia. The purpose of the current study was to investigate the variables that influence motivation for achievement. It was predicted that these variables include ability, effort, perseverance, responsibility, the viewpoint of the teacher, and tasks. The major data collection strategy used by the researchers to accomplish their research goal involved distributing a questionnaire to 248 students. Structural equation modeling (SEM), a quantitative research technique, was used to produce the results. Because all of the criteria were significantly correlated in this study, it can be concluded that the tasks assigned to students and the perspective of the teacher both contribute to students’ motivation for achievement. The significance of the findings for studying motivational constructs with various theoretical underpinnings and structural models is highlighted. The associations among all hypotheses were investigated using the following variables based on the suggested model: aptitude, efforts and persistence, duties, the teacher’s viewpoint, and tasks.
Born Not Made: The Impact of Six Entrepreneurial Personality Dimensions on Entrepreneurial Intention: Evidence from Healthcare Higher Education Students
Authors: Mohamed Maged E. , Elshaer Ibrahim Abdelhamid , Azazz Alaa M. S. , Younis Nancy S.
Published: January 2023

Background: The KSA government has envisioned a national transformation plan via Vision 2030. The purpose of this plan is liberalization which provides an opportunity for the private sector to increase its share in the healthcare system. Evaluating the entrepreneurial potential of healthcare students is essential due to their future role in the healthcare environment. Thus, the goal of the current study is to measure entrepreneurial intentions and identify the characteristics and personality dimensions among junior healthcare students and fresh graduates in Saudi Arabia. Materials and methods: A cross-sectional study design was utilized to survey healthcare students in their final years of study and fresh graduates at King Faisal University (KFU), Saudi Arabia. The research team identified six as the most common entrepreneurial intention traits. Entrepreneurial traits included internal locus of control, innovativeness, risk-taking, proactiveness, autonomy, and problem-solving. A previously recognized and validated questionnaire was distributed through social media platforms and formal university emails. PLS-SEM was employed for the examination of both the measurement and structural models. Results: Internal locus of control was found to have a positive impact on entrepreneurial intention, so H1 was supported. Likewise, innovativeness showed a positive impact on entrepreneurial intention among healthcare students, which confirmed H2. The results also showed that autonomy substantially impacted entrepreneurial intention, so H3 was supported. Similarly, proactiveness has a direct influence on entrepreneurial intention; thus, H5 was supported. Furthermore, problem-solving personality dimensions also had a positive impact on entrepreneurial intention, so H5 and H6 were supported. Conversely, risk-taking propensity exhibited a negative insignificant effect on entrepreneurial intention. Thus, H4 was not supported. Conclusions: Saudi healthcare students revealed their intention towards entrepreneurship and were quite positive about becoming entrepreneurs. Furthermore, students’ entrepreneurial intention was affected by their quality of proactiveness, innovativeness, internal locus of control, problem-solving, and autonomy but not risk-taking propensity.
Motivational Facets of Sustainable Entrepreneurship: A Systematic Review and Future Research Agenda
Authors: Reuther Kevin , Dahle Yngve , Schmidt Carolin , Schösser Franziska
Published: January 2023

Entrepreneurial activity and especially sustainable entrepreneurship (SE) significantly impact the achievement of the sustainability transition. While we have a decent understanding of the drivers of entrepreneurial activity, there is limited knowledge and no systematic consolidation of existing work examining what motivates entrepreneurs to engage in SE. To address this issue, we conducted a systematic literature review analyzing 50 papers derived from the Scopus database since 2017. We found that the specific construct discussed in this literature is the Motivation for Sustainable Entrepreneurship (MSE) and argue that it is a combined construct comprising the motivation to have an impact on sustainability and the motivation to become an entrepreneur. We conclude by suggesting three areas for future research: First, a clear definition of MSE is needed, distinguishing what constitutes motivation for impacting sustainable development and motivation for engaging in entrepreneurship. Second, despite having a good understanding of the factors influencing MSE, these are often based on specific case-studies and could benefit from larger and more diverse samples as well as research methods. Third, we have a very limited understanding of the effect of MSE on new venture creation, entrepreneurial practices, performance, and success.
Children’s Training and Competition in Football: The Coach’s View on Family Participation and Healthy Development
Authors: Bettega Otávio Baggiotto , Marques Filho Cesar Vieira , Leonardo Lucas , Machado João Cláudio Braga Pereira , Scaglia Alcides José , Galatti Larissa Rafaela
Published: January 2023

The different deployments that involve youth soccer players’ development must be discussed and organized by adults, thinking about creating safe, challenging, and stimulating environments for them. Thus, our aim with this study is to investigate variables about family participation in the players’ development processes in the context of children’s competitions in Brazil. This qualitative–descriptive research was conducted in the under-10 category of the Nova Liga Gaúcha de Futebol Infantil (NOLIGAFI) through interviews with twelve coaches and in loco observations. The results showed that all coaches highlighted the importance and role of the family in the healthy development of young players. However, fact-based evidence reveals negative family participation. From this complex scenario, the coaches become fundamental figures for mediating and guiding this process, organizing proposals aimed at educating parents in the sports scenarios to promote an environment of competitive practice in football that is healthier for children.
The Possibilities of Capturing Rainwater and Reducing the Impact of Floods: A Proposal for the City of Beira, Mozambique
Authors: Santos Michael M. , Ferreira Ana Vaz , Lanzinha João C. G.
Published: January 2023

Modern societies face various challenges, including climate change, rapid urbanization, and sometimes inadequate urban planning policies. In recent years, extreme weather events have received increasing attention for their impacts on cities, humans, and ecosystems worldwide, particularly on coastal towns in Mozambique, such as cyclones, floods, water pollution, and water scarcity, demonstrating their vulnerability to climate change. Cities must adapt to cope with the pressure on their water resources, and it is essential to ensure that communities have access to safe, reliable, and affordable water. A viable way to promote this resilience and simultaneously reduce costs in domestic budgets is to use rainwater to meet daily needs where water quality parameters are not required for consumption. According to the results of this study, it is possible to significantly reduce potable water use from the municipal water supply network by harvesting rainwater, up to 40% when the use does not require potable water at all, proactively protecting this vital resource. In addition to these direct benefits, the large-scale deployment of rainwater harvesting (RWH) systems in densely urbanized areas can also provide indirect benefits, such as reducing peak flow volumes in stormwater drainage systems and potentially reducing the frequency of urban floods. These benefits result from the reduction in the volume and duration of water sent to the drainage network, which can help to improve the overall resilience of communities in the face of climate change and other challenges.
Technoeconomic Assessment of Biopolymer Production from Crustacean Waste with the UK as a Case Study
Authors: Okoro Oseweuba Valentine , Nie Lei , Gunduz Oguzhan , Ulag Songul , Hamidi Masoud , Shavandi Amin
Published: January 2023

Marine pollution has increased in recent decades, largely due to the proliferation of seafood processing plants and the improper disposal of their associated waste streams. The waste streams consist mainly of shells that are composed of chitin, which is the most abundant aminopolysaccharide biopolymer in nature. Recognizing the value of chitin, the potential for the valorization of crustacean waste for chitin production was explored. In this regard, biogenic crab waste was subjected to chemical-only, enzymatic–chemical, and microbial treatments for chitin production. The results were employed as inputs for process simulation as a precursor to undertaking performance assessments. This study subsequently showed that the net present values (NPVs) of the chemical-only, enzyme–chemical, and microbial chitin production pathways were GBP 118.63 million, GBP 115.67 million, and GBP 132.34 million, respectively, indicating that the microbial chitin production pathway constituted the most appropriate technology for future investment. Employing a cost–benefit (CB) analysis, the CB ratios for the chemical-only, enzymatic–chemical, and microbial approaches were determined to be 7.31, 0.45, and 0.23, respectively. These results reinforced the dominant status of the microbial approach for chitin production from crab waste as the preferred valorization strategy. This study was able to provide information regarding the implications of executing alternative scenarios for crustacean waste.
Asbestos Stocks and Flows Legacy in Australia
Authors: Brown Belinda , Hollins Ian , Pickin Joe , Donovan Sally
Published: January 2023

Information about asbestos stocks and flows is paramount for effective legacy management, both for understanding potential asbestos exposure risks from the different product types remaining in the built environment and proactive resource planning for their safe decommissioning, removal and disposal. This paper provides an overview of the Australian Stocks and Flows Model for Asbestos, a national model that provides best estimates to examine asbestos legacy stocks remaining in the built environment and flows to waste, now and into the future in Australia. The model was updated in 2021 to reflect new information from literature and input from industry experts and includes a Monte Carlo analysis to better reflect the range in the value estimates, as well as allowing for input of data from asbestos removal programs. Australia’s total asbestos stocks peaked at approximately 11 million tonnes in the 1980s. Over 95% of stocks comprise asbestos cement products, such as wall sheeting and water pipes. Australia’s current remaining asbestos stocks in the built environment are estimated at 6.2 million tonnes, with just under half of total consumption estimated to have gone to landfill as waste. The model can continue to be used with updated information to help track how much of Australia’s hazardous asbestos legacy is remaining and by how much it is reducing. The model can also be used to test scenarios and implications for predicted development trends and waste infrastructure needs. It is a valuable resource to assist with sustainable planning across a range of government departments that are responsible for managing asbestos waste in Australia.
Sustainable Urban Last-Mile Logistics: A Systematic Literature Review
Authors: Silva Vasco , Amaral António , Fontes Tânia
Published: January 2023

Globalisation, urbanisation and the recent COVID-19 pandemic has been raising the demand for logistic activities. This change is affecting the entire supply chain, especially the last-mile step. This step is considered the most expensive and ineffective part of the supply chain and a source of negative economic, environmental and social externalities. This article aims to characterise the sustainable urban last-mile logistics research field through a systematic literature review (N = 102). This wide and holistic review was organised into six thematic clusters that identified the main concepts addressed in the different areas of the last-mile research and the existence of 14 solutions, grouped into three types (vehicular, operational, and organisational solutions). The major findings are that there are no ideal last-mile solutions as their limitations should be further explored by considering the so-called “triple bottom line of sustainability”; the integration and combination of multiple last-mile alternative concepts; or by establishing collaboration schemes that minimise the stakeholders’ conflicting interests.
An Extensive Questionnaire about Metacognition during Emergency Remote Teaching Involving More Than 3000 Engineering Students
Authors: Mazzola Roberto , Bozzi Matteo , Testa Italo , Sancassani Susanna , Zani Maurizio
Published: January 2023

By 11 March 2020, the phrase “COVID-19” had officially entered everyday life across most of the word. Each level of education suddenly faced new changes and new challenges. Emergency remote teaching became widespread, and new methodologies to deliver classes and courses were adopted by educational institutions. In this paper, we focus on the impact of the remote learning experience of engineering students enrolled at the Politecnico di Milano. The subjects were recruited from all engineering courses from the first to the fifth year and were asked to complete a multidimensional survey. The survey featured 66 items regarding the participants’ perceptions of the challenges of emergency remote teaching compared with pre-COVID-19 in-person teaching. The questionnaire addressed six dimensions: the organization of emergency remote teaching, subjective well-being, metacognition, self-efficacy, identity, and socio-demographic information. In this paper, we describe the entire survey and discuss a preliminary analysis. Using Cronbach’s alpha test, a confirmatory factor analysis, and the t-test, we performed a more in-depth analysis concerning the outcomes of metacognition and self-efficacy. The data analysis suggested a small, unexpected change in the metacognition strategies. The students, in some regards, improved their learning strategies. Some other answers underlined their appreciation of the courses’ organization and the lack of relationships with their peers and teachers.
A Low-Cost Gamified Urban Planning Methodology Enhanced with Co-Creation and Participatory Approaches
Authors: Kavouras Ioannis , Sardis Emmanuel , Protopapadakis Eftychios , Rallis Ioannis , Doulamis Anastasios , Doulamis Nikolaos
Published: January 2023

Targeted nature-based small-scale interventions is an approach commonly adopted by urban developers. The public acceptance of their implementation could be improved by participation, emphasizing residents or shopkeepers located close to the areas of interest. In this work, we propose a methodology that combines 3D technology, based on open data sources, user-generated content, 3D software and game engines for both minimizing the time and cost of the whole planning process and enhancing citizen participation. The proposed schemes are demonstrated in Piraeus (Greece) and Gladsaxe (Denmark). The core findings can be summarized as follows: (a) the time and cost are minimized by using online databases, (b) the gamification of the planning process enhances the decision making process and (c) the interactivity provided by the game engine inspired the participation of non-experts in the planning process (co-creation and co-evaluation), which decentralizes and democratizes the final planning solution.
Potentials and Limits of Photovoltaic Systems Integration in Historic Urban Structures: The Case Study of Monument Reserve in Bratislava, Slovakia
Authors: Hubinský Tomáš , Hajtmanek Roman , Šeligová Andrea , Legény Ján , Špaček Robert
Published: January 2023

In the context of the current energy crisis and climate change, the importance of discussions on how to incorporate monument protection into sustainable strategies that mitigate the human impact on the environment and implement renewable sources while preserving cultural values is raised. Through the case study of the Monument Reserve in Bratislava, Slovakia, this article presents the potentials and limits of the integration of photovoltaic systems in historic urban structures that directly affect their feasible participation in smart city and positive energy district concepts by means of energy cooperativeness. This study highlights the most current recommendations and basic principles on how to assess their visual impact and select the most appropriate solutions. Using the datafication process, it analyzes the irradiance of pitched and flat roof polygons of the set area based on their characteristics such as the normal vector azimuth and slope of the rooftops. For this purpose, a 3D morphological model in LOD3 detail and the open-source solar irradiation model r.sun implemented in GRASS GIS / QGIS were used. The data obtained provided an estimate of the output potential to endow the city’s power grid and were compared to the electricity consumption of the particular city district. Furthermore, these data are suitable for designing a customized technical and aesthetic solution for the integration of photovoltaics with respect to cultural sustainability, as well as for decision- and policy makers.
Existence of Global and Local Mild Solution for the Fractional Navier–Stokes Equations
Authors: Awadalla Muath , Hussain Azhar , Hafeez Farva , Abuasbeh Kinda
Published: January 2023

Navier–Stokes equations (NS-equations) are applied extensively for the study of various waves phenomena where the symmetries are involved. In this paper, we discuss the NS-equations with the time-fractional derivative of order β∈(0,1). In fractional media, these equations can be utilized to recreate anomalous diffusion equations which can be used to construct symmetries. We examine the initial value problem involving the symmetric Stokes operator and gravitational force utilizing the Caputo fractional derivative. Additionally, we demonstrate the global and local mild solutions in Hα,p. We also demonstrate the regularity of classical solutions in such circumstances. An example is presented to demonstrate the reliability of our findings.
Poisoning Regulation, Research, Health, and the Environment: The Glyphosate-Based Herbicides Case in Canada
Authors: Bacon Marie-Hélène , Vandelac Louise , Gagnon Marc-André , Parent Lise
Published: January 2023

Despite discourse advocating pesticide reduction, there has been an exponential increase in pesticide use worldwide in the agricultural sector over the last 30 years. Glyphosate-Based Herbicides (GBHs) are the most widely used pesticides on the planet as well as in Canada, where a total of almost 470 million kilograms of declared “active” ingredient glyphosate was sold between 2007 and 2018. GBHs accounted for 58% of pesticides used in the agriculture sector in Canada in 2017. While the independent scientific literature on the harmful health and environmental impacts of pesticides such as GBHs is overwhelming, Canada has only banned 32 “active” pesticide ingredients out of 531 banned in 168 countries, and reapproved GBHs in 2017 until 2032. This article, based on interdisciplinary and intersectoral research, will analyze how as a result of the scientific and regulatory captures of relevant Canadian agencies by the pesticide industry, the Canadian regulation and scientific assessment of pesticides are deficient and lagging behind other countries, using the GBH case as a basis for analysis. It will show how, by embracing industry narratives and biased evidence, by being receptive to industry demands, and by opaque decision making and lack of transparency, Health Canada’s Pest Management Regulatory Agency (PMRA) promotes commercial interests over the imperatives of public health and environmental protection.
Effect of Pesticide Exposure over DNA Damage in Farmers from Los Reyes, Michoacan in Mexico
Authors: Valencia-Quintana Rafael , Milić Mirta , Bonassi Stefano , Ochoa-Ocaña Maria Antonieta , Campos-Peña Victoria , Tenorio-Arvide Maria Guadalupe , Pérez-Flores Guillermo Alejandro , Sánchez-Alarcón Juana
Published: January 2023

In the municipality of Los Reyes, Michoacán, in Mexico, several economic activities coexist; however, the most relevant is agriculture. It stands out as an agro-industrial center and commercial enclave in the region, suitable for the cultivation of sugar cane; however, currently fruit growing takes first place with blackberry, raspberry and blueberry, followed by avocado, peach, strawberry and other crops. A large quantity and variety of pesticides are applied to crops, consequently the population is at constant risk. This study aimed to evaluate whether pesticides are a factor in genetic damage to agricultural workers from Los Reyes, Michoacán, using alkaline comet assay. Fifty-nine residents participated (41 workers and 18 controls). Results included confounding factors (alcohol consumption, smoking habit, gender, age, BMI, etc.) indicated a non-significant statistical difference between two groups, with higher DNA damage values in workers that was higher than the values expected in a normal healthy unexposed population. It seems that the control measures, safe handling of pesticides and quality standards, required by the producers so that their products can be exported, have resulted in less damage, despite workers’ activity, but higher damage than the reference values still requires regular surveillance of those exposed. The use of protective equipment or measures can reduce the risk of damage, so it is also necessary to promote their service and comply with labor regulations for agricultural workers.
A Practical Approach in Refining Binary Outcome for Treatment Effect of COVID-19 According to Geographical Diversity
Authors: Tzeng I-Shiang
Published: January 2023

The recent COVID-19 pandemic has drawn attention to health and economics worldwide. Initially, diseases only ravage local populations, while a pandemic could aggravate global economic burdens. Lopinavir/Ritonavir is an anti-HIV drug that was used on small scale patients during SARS, but its effectiveness for COVID-19 treatment is still unclear. Previous studies or meta-analysis have retrieved clinical data of subgroup analysis to evaluate the efficacy and safety of Lopinavir/Ritonavir for the treatment of COVID-19 in a few affected regions. However, geographical diversity and small number of studies bias correction were not achieved in such subgroup analysis of published meta-analysis. The present study demonstrates a practical approach in refining the binary outcome for COVID-19 treatment of Lopinavir/Ritonavir according to geographical location diversity and small number of studies (less than or equal to five) for subgroup analysis. After performing practical approach, the risk of adverse event with LPV/RTV for treatment of COVID-19 becomes nonsignificant compared to previous meta-analysis. Furthermore, we also notice heterogeneity of random effect of meta-analysis may be declined after proposed adjustment. In conclusion, proposed practical approach is recommend for performing a subgroup analysis to avoid concentration in a single geographical location and small number of studies bias.
Marburg and Ebola Virus Infections Elicit a Complex, Muted Inflammatory State in Bats
Authors: Jayaprakash Anitha D. , Ronk Adam J. , Prasad Abhishek N. , Covington Michael F. , Stein Kathryn R. , Schwarz Toni M. , Hekmaty Saboor , Fenton Karla A. , Geisbert Thomas W. , Basler Christopher F. , Bukreyev Alexander , Sachidanandam Ravi
Published: January 2023

The Marburg and Ebola filoviruses cause a severe, often fatal, disease in humans and nonhuman primates but have only subclinical effects in bats, including Egyptian rousettes, which are a natural reservoir of Marburg virus. A fundamental question is why these viruses are highly pathogenic in humans but fail to cause disease in bats. To address this question, we infected one cohort of Egyptian rousette bats with Marburg virus and another cohort with Ebola virus and harvested multiple tissues for mRNA expression analysis. While virus transcripts were found primarily in the liver, principal component analysis (PCA) revealed coordinated changes across multiple tissues. Gene signatures in kidney and liver pointed at induction of vasodilation, reduction in coagulation, and changes in the regulation of iron metabolism. Signatures of immune response detected in spleen and liver indicated a robust anti-inflammatory state signified by macrophages in the M2 state and an active T cell response. The evolutionary divergence between bats and humans of many responsive genes might provide a framework for understanding the differing outcomes upon infection by filoviruses. In this study, we outline multiple interconnected pathways that respond to infection by MARV and EBOV, providing insights into the complexity of the mechanisms that enable bats to resist the disease caused by filoviral infections. The results have the potential to aid in the development of new strategies to effectively mitigate and treat the disease caused by these viruses in humans.