Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimal Time-Based Resource Allocation for Biomedical Workflow Applications in Cloud

ABSTRACT Biomedical workflow applications’ necessities have increased with the progress of the biotechnology industry and it involves large volumes of data which require large-scale computation. Due to the upcoming data deluge of biomedical data, processing and scheduling of the same in computing resources have presented many challenges. A biomedical workflow application requires heterogeneous resources for execution. Thus, it utilizes the advantage of cloud computing, as a cloud provides scalable high-performance computing resources. Scheduling of these workflows in the cloud is a crucial problem for which the proposed solution contributes greatly towards execution improvement. The proposed algorithm provides optimal and effective utilization of computational resources by novel scheduling of algorithms. Least Execution Time Cloud Workflow Scheduling (LETCWS) and Revised Heterogeneous Earliest Finish Time (RHEFT) algorithms are proposed and implemented in two distinct levels to schedule the biomedical workflow applications in such a way that it reduces the execution time while reducing the cost. The proposed algorithms are evaluated using the Workflowsim tool with real-world biomedical workflow applications. Experimental results illustrate the performance of scheduling workflow applications over the other existing approaches. The result shows that the proposed algorithm achieves better performance in terms of execution time and cost.
A Two-stage Multilevel Randomized Response Technique With Proportional Odds Models and Missing Covariates

Surveys of income are complicated by the sensitive nature of the topic. The problem researchers face is how to encourage participants to respond and to provide truthful responses in surveys. To correct biases induced by nonresponse or underreporting, we propose a two-stage multilevel randomized response (MRR) technique to investigate the true level of income and to protect personal privacy. For a wide range of applications, we present a proportional odds model for two-stage MRR data and apply inverse probability weighting and multiple imputation methods to deal with covariates on some subjects that are missing at random. A simulation study is conducted to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. The practicality of the proposed methods is illustrated with the regular monthly income data collected in the Taiwan Social Change Survey. Furthermore, we provide an estimate of personal regular monthly mean income.
High-Frequency Noise Characterization and Modeling of Graphene Field-Effect Transistors

This article presents the small-signal and noise characterization of different technologies based on chemical vapor deposition (CVD) and silicon-carbide (SiC) graphene field-effect transistors (GFETs). The noise model, built on noise figure measurements under <inline-formula> <tex-math notation="LaTeX">$50~\Omega $ </tex-math></inline-formula> using the F<sub>50</sub> method, was verified by additional source-pull measurements, with special care for the GFET stability. The four noise parameters were then extracted by using the validated F<sub>50</sub> model up to 18 GHz, and the correlations between noise and small-signal parameters were shown for two different configurations: top-gated and back-gated GFETs.
Methods And Solvers Used For Solving Mixed Integer Linear Programming And Mixed Nonlinear Programming Problems: A Review

: This paper presents a complete review of the significance of deterministic mixed - integer linear program (MILP) and mixed - integer nonlinear program (MINLP) solution methods for problems involving linear, nonlinear, convex and nonconvex functions. The mathematical description of methodologies, algorithms, software, and solvers to solve this problem are presented. Mixed - integer program (MIP) problem is one where some of the decision variables are constrained to be integer values. MILP involves problems in which only some variables are constrained to be integers, while other variables can be non - integers. It deals with linear objective function subjected to linear constraints. Minimisation and maximisation of a nonlinear objective function subject to nonlinear constraints with continuous and discrete variables are performed by MINLP solution methods. In this paper, the recent MILP and MINLP solution methods, algorithms along with solvers and software, introduced by various researchers has been elaborated profoundly.
The Effect of a Dam Construction on Subtidal Nematode Communities in the Ba Lai Estuary, Vietnam

Nematode communities and relevant environmental variables were investigated to assess how the presence of a dam affects the Ba Lai estuary benthic ecosystem, in comparison to the adjacent dam-free estuary Ham Luong. Both estuaries are part of the Mekong delta system in Vietnam. This study has shown that the dam’s construction had an effect on the biochemical components of the Ba Lai estuary, as observed by the local increase in total suspended solids and heavy metal concentrations (Hg and Pb) and by a significant oxygen depletion compared to the natural river of Ham Luong. The nematode communities were also different between the two estuaries in terms of density, genus richness, Shannon–Wiener diversity, and dominant genera. The Ba Lai estuary exhibited lower nematode densities but a higher diversity, while the genus composition only slightly differed between estuaries. The results indicate that the present nematode communities may be well adapted to the natural organic load, to the heavy metal accumulation and to the oxygen stress in both estuaries, but the dam presence may potentially continue to drive the Ba Lai’s ecosystem to its tipping point.
How a Participatory Budget Can Support Sustainable Rural Development—Lessons From Poland

Since the announcement of the Sustainable Development Goals (SDGs), the need for localization of SDGs has been emphasized. In this context, sustainable rural development is still a relatively undescribed area in the context of using the participatory budget as a tool to implement SDGs. Few countries have introduced legal regulations in practice, enabling the creation of participatory budgets (especially in rural areas), so a multifaceted analysis of a decade of Poland's experience may provide important guidelines for countries considering introducing such solutions, which we consider to be the main purpose of this study. This is the first study covering all communes where participatory budgets (Solecki Fund—FS) were created in Poland during the 2010–2018 period (up to 60% of all), covering both the analysis of the process of creating FSs, the directions of spending and the scale of spending (including regional differentiation), as well as legal regulations and the consequences of including central government support in this mechanism. On the basis of the research, it can be observed that, despite the small scale of FS spending, the number of municipalities using this form of citizen participation is increasing. At the same time, there is significant variation between regions, which indicates the flexibility of the FSs in adapting to the needs reported by residents. The analysed directions of expenditure indicate that the FSs are in line with the SDG objectives related to the improvement of residents' quality of life. It can be concluded that, despite the existing legal regulations, the introduction of the Solecki Fund undoubtedly depends on the political will of the local government's legislative authorities and the willingness of residents to participate in decisions on spending directions.
Metapsychology of the creative process

The theory of creativity on offer in Metapsychology of the Creative Process is embedded in a broader theory of the cognitive architecture of the mind. Given that the latter is the main focus of the book, this review will primarily serve to introduce the basic outline of the theory and provide a critical response. The developed view of creativity in Brown’s work is reliant on the success or failure of the surrounding theory of the mind. It is hoped that this review will point potential readers of Brown’s work toward areas of merit of this theory, and areas in need of further development.
Microfabricated Nano-Gap Tunneling Current Zika Virus Sensors With Single Virus Detection Capabilities

Electron tunneling through thin (~1–50 nm), insulating materials can provide information regarding their band structure, density of states, surface and bulk defect densities and their energy levels and distributions. While electron tunneling has been used in thin inorganic insulating materials for nearly 7 decades, its applications in organic and biological materials, with exceptions of deoxyribonucleic acid and a few other materials, are not extensively explored. Here we report room temperature electron tunneling studies to differentiate between aptamers designed to attach to the capsid proteins on Zika viruses and the aptamer-Zika complexes. We show that the electron tunneling induced by the current versus voltage (I-V) measurements through these materials contain information that enable identification of the Zika virus without any need for DNA sequencing or additional molecular labels. The electron tunneling measurements were carried out using a conducting atomic force microscope, a home-made vertical tunneling junction and an electromigration-induced nanogap in a microfabricated $1~\mu \text{m}$ -wide gold line on silicon nitride bridges. The current through aptamers and aptamer-Zika complexes all have a threshold voltage just before the exponential increase in the junction current. Below this voltage, the leakage currents are different for the aptamer and aptamer-Zika samples. The tunnel nanogap device is suitable for detecting viruses that are nanometer-scale biomaterials.
Investment potential forecast and strategies for its expansion: case of Ukraine

In the current conditions of capital market liberalization, developing countries achieve a faster economic growth rate by actively attracting various types of foreign investment. The steady rise in the volume of foreign investment into the country could be achieved only due to its high investment potential.Therefore, this study aims to develop the methodology for determining the dynamic changes in the country’s investment potential, and its relevant medium-term indicators identify the degree of informational technology influence on Ukraine’s investment potential. It is essential to define the position of Ukraine in the global context in terms of the level of information technologies as the catalyst for investment attractiveness.The relevant indicators defining Ukraine’s investment potential were forecasted using the Brown-Meyer exponential smoothing model. To calculate the integral indicator of the investment potential, the Hurst exponent was applied. Kohonen self-organizing maps were used to group the countries according to their informational technology parameters.Ukraine’s investment potential was found to decrease since 2019 and is equal to 0.6493 units in 2020 and 0.6407 units in 2021 due to the decline of the indicators describing the human capital, infrastructure, technological development, and socio-economic conditions. Technology has a significant influence on Ukraine’s investment potential. Its impact is rising each year from 1.70% to 5.17% and 13.04% between 2019 and 2021, respectively. According to the level of technology, Ukraine is in the group with Spain, Romania, and Poland since 2017.The decreasing investment potential forecast and the positive influence of technology level on it bring the opportunity to form the priority areas for expansion of investment potential based on the adaptation of world instruments to implement the investment policy within national economic conditions.
The effect of uniform magnetic field on spatial-temporal evolution of thermocapillary convection with the silicon oil based ferrofluid

A uniform axial or transverse magnetic field is applied on the silicon oil based ferrofluid of high Prandtl number fluid (Pr ? 111.67), and the effect of magnetic field on the thermocapillary convection is investigated. It is shown that the location of vortex core of thermocapillary convection is mainly near the free surface of liquid bridge due to the inhibition of the axial magnetic field. A velocity stagnation region is formed inside the liquid bridge under the axial magnetic field (B = 0.3-0.5 T). The disturbance of bulk reflux and surface flow is suppressed by the increasing axial magnetic field. There is a dynamic response of free surface deformation to the axial magnetic field, and then the contact angle variation of the free surface at the hot corner is as following, ? hot, B = 0.5 T = 83.34? > ? hot, B = 0.3 T = 72.16? > > ? hot,B = 0.1 T = 54.21? > ? hot, B = 0 T = 43.33? The results show that temperature distribution near the free surface is less and less affected by thermocapillary convection with the increasing magnetic field, and it presents a characteristic of heat-conduction. In addition, the transverse magnetic field does not realize the fundamental inhibition for thermocapillary convection, but it transfers the influence of thermocapillary convection to the free surface.
Bayesian Analysis for Assessing Risks of Rotating Equipment and Its Financial Loss

The petrochemical industry is equipped with major and rotating equipment, such as storage tanks and pumps, to produce the required outputs. The equipment could pose a high level of risks should the organization failed to provide control and mitigation measures. Risk analysis is a conventional and practical technique in identifying hazards and assessing risks associated with equipment. The risk analysis can be integrated with the Bayes’s theorem to assess risks of major and rotating equipment. Therefore, this paper presents the use of Bayesian analysis for assessing risks of rotating equipment and its financial loss, specifically in the petrochemical industry. The study begins with developing a cause-and-effect relationship using a Bayesian network. The probability of failure of equipment and its financial loss can be analyzed based on the Bayesian network. The cause-and-effect relationship could also reveal human factors contributing to the probability of failure of equipment. This paper focuses on an accident involving pumps during maintenance activity in the petrochemical plant. The human factors issues contributing to the accident and the financial loss are inadequate procedures and poor work processes.
Influence of Principals’ Conflict Management Techniques on Teacher Job Satisfaction in Selected Secondary Schools in Uriri and Nyatike Sub Counties, Kenya

According to a survey by the American Management Association, managers spend 24% of their time managing conflicts. This is a waste of time as a resource, reducing this time wastage would mean improving the quality of management. Conflict is inevitable and a natural phenomenon, the secondary school principals in Uriri and Nyatike sub-counties have not been spared of the challenges of conflict management on teacher job satisfaction. Therefore, the study aimed to determine the principals’ conflict management techniques on teacher job satisfaction in public secondary schools in Uriri and Nyatike sub-counties, Kenya. It was guided by Engle& Kane, M. J. (2004 Herzberg's two-factor theory and a conceptual framework used to show the interplay between the principals’ conflict management techniques and teacher job satisfaction. The research employed a descriptive survey design to obtain information. The target study population consisted of 1960 teachers, 87 principals in 87 secondary schools in Uriri and Nyatike sub-counties. Stratified simple random sampling was used to obtain a sample of 29 principals and 319 teachers. Data was collected using questionnaires. Face and content validity of the instruments was determined by experts in educational management and policy studies. In order to enhance the reliability of the instrument, a pilot study was conducted in 6 secondary schools in the sub-counties, which were excluded from the main study. The reliability was tested using test-retest method and a Pearson’s r coefficient of 0.79 for principals’ and 0.83 for teachers’ questionnaires obtained. Quantitative data was analysed using frequency count, percentages and mean. The study established that integrating obliging had a strong and positive correlation with teacher job satisfaction, dominating and avoiding techniques had p>0.05 which is not statistically significant. The findings add to the existing body of knowledge and may be useful in developing guidelines for principals and other stakeholders to enable them to manage schools effectively. Also, help the Ministry of Education to formulate training materials for school managers to enable them to manage conflicts effectively and to enable teachers and educators to improve and manage conflicts in schools more effectively and efficiently.
Can metal organic frameworks outperform adsorptive removal of harmful phenolic compound 2-chlorophenol by activated carbon?

Abstract: Removal of persistent organic compounds from aqueous solutions is generally achieved using adsorbent like activated carbon (AC) but it suffers from limited adsorption capacity due to low surface area. This paper describes a pioneering work on the adsorption of an organic pollutant, 2-chlorophenol (2-CP) by two MOFs with high surface area and water stability; MIL-101 and its amino-derivative, MIL-101-NH2. Although MOFs have higher surface area than AC, the latter was proven better having the highest equilibrium 2-CP uptake (345 mg.g-1), followed by MIL-101 (121 mg.g-1) and MIL-101-NH2 (84 mg.g-1). Used MIL-101 could be easily regenerated multiple times by washing with ethanol and even showed improved adsorption capacity after each washing cycle. These results can open the doors to meticulous adsorbent selection for treating 2-CP-contaminated water
The complete chloroplast genome sequence of Sauropus spatulifolius Beille

Abstract Sauropus spatulifolius Beille is one kind of Chinese herbal medicine with anti-inflammatory and analgesic activities. In this study, we reported the complete chloroplast genome of S. spatulifolius Beille, and assembled and annotated with high-throughput sequencing data, which would provide help for its taxonomy research. The chloroplast sequence was 154,707 bp , with two of 87,438 bp and 19,427 bp single-copy regions, which were separated by two inverted repeat regions with 23,921 bp . A total of 129 genes were predicted, with GC content of 36.61%. Phylogenetic analysis showed that S. spatulifolius Beille closest to Glochidion chodoense in Malpighiales.
An extended-2D CNN for multiclass Alzheimer's Disease diagnosis through structural MRI

Current techniques trying to predict Alzheimer's disease at an early-stage explore the structural information of T1-weighted MR Images. Among these techniques, deep convolutional neural network (CNN) is the most promising since it has been successfully used in a variety of medical imaging problems. However, the majority of works on Alzheimer's Disease tackle the binary classification problem only, i.e., to distinguish Normal Controls from Alzheimer's Disease patients. Only a few works deal with the multiclass problem, namely, patient classification into one of the three groups: Normal Control (NC), Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI). In this paper, our primary goal is to tackle the 3-class AD classification problem using T1-weighted MRI and a 2D CNN approach. We used the first two layers of ResNet34 as feature extractor and then trained a classifier using 64 × 64 sized patches from coronal 2D MRI slices. Our extended-2D CNN proposal explores the MRI volumetric information, by using non-consecutive 2D slices as input channels of the CNN, while maintaining the low computational costs associated with a 2D approach. The proposed model, trained and tested on images from ADNI dataset, achieved an accuracy of 68.6% for the multiclass problem, presenting the best performance when compared to state-of-the-art AD classification methods, even the 3D-CNN based ones.
Nonlinear parameter of heart rate variability can diagnose early cardiac autonomic neuropathy

This is a case of a 63‐year‐old female presenting with diabetes mellitus, hypertension, hypothyroidism, and postmenopausal osteoporosis with findings showing abnormal AFT with reduced heart rate variability (HRV) and multiple lobed Poincaré plot, pointing to premature atrial contractions.
List all Maximal Cliques of an Undirected Graph: A Parallable Algorithm

Listing all the maximal cliques of an undirected graph is considered as a NP-complete problem, even if the existing fastest algorithm for listing all the maximal cliques also needs an exponential time complexity, and these algorithms are not able to be reconstructed into parallel processing algorithms, therefore, which are also unsuited to solve all maximal cliques of a complex undirected graph with a large number of maximal cliques or vertices. This paper aims to develop a parallable algorithm for listing all maximal cliques of the large size complex undirected graph. Firstly we gave some definitions, including adjacent sub-graph, suspended sub-graph and so on, then adopted a partition and recursive strategy to partition equivalently a parent-graph into smaller sub-graphs, so we can get all maximal cliques of parent-graph by recursively listing all maximal cliques of sub-graphs. we have developed a single-thread and a multi-thread program of the algorithm by using Java language. Lastly, we compared the performance of our algorithm in a benchmark data set DIMACS with the existing main algorithms. The results show that our single-thread program is basically equivalent performance with existing algorithms, but the multi-thread program has better performance than the existing algorithms. Because our method is to divide the complex problem into some smaller sub-problems and achieve the solution of the original problem by solving these sub-problems, our algorithm is easy to be implemented by using multi-thread, and also easy to be expanded to the parallel algorithm, which has better feasibility and practicability in solving the large scale complex graph.
Design and Evaluation of a Mobile Application for Relapse Prevention to Methamphetamine Use Disorder

Background: As a cheap and affordable tool, smartphones technology has provided an opportunity for effective treatment of addiction and relapse prevention. Objectives: This study aimed to design and evaluate a mobile application for relapse prevention to methamphetamine use disorder. Patients and Methods: This study was conducted in two stages in 2018. Initially, the features of Marlatt’s cognitive-behavioral model were studied. The conceptual model of the application was then developed according to identifying the users’ needs and features of the Marlatt’s model and based on object-oriented analysis. The coding was performed using Java Script, CSS, and HTML. After developing the application and its installation in an addiction recovery center, the application was quantitatively evaluated with the participation of five experts in psychology and addiction studies and five patients. Results: The main requirements of the software were identified in the form of a daily plan for recovery (learning and training materials, relaxation), motivation (motivational messages and encouragement for continuous use of the software), and evaluation of the recovery process. The results of the evaluation showed that 89% of the participants were satisfied with the software usability. Conclusions: The prevention of relapse to substance use disorders, such as methamphetamine, requires motivation and education. Design and deployment of mobile-based application could be a cost-effective approach for recovery of a variety of addictive behaviors and the follow up of recovery.