Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the influence of cultural and health beliefs on intentions to adopt COVID-19 prevention measures

ABSTRACT This study explores how individuals react to COVID-19 prevention measures in relation to their national cultural values and health belief factors. Specifically, guided by the most relevant Hofstede’s cultural dimensions and the health belief model (HBM), this study tested a conceptual framework through conducting a survey of the U.S. adult population. The study results did not support Hofstede’s classification of the U.S. to be among the most individualistic culture with the lowest level of uncertainty avoidance in the world. Demographic factors such as ethnicity, gender, age, and education were found to be significantly related to collectivistic tendency and/or uncertainty avoidance. Findings also demonstrated that the tendency for collectivism and uncertainty avoidance were both relevant to people’s intention to adopt COVID-19 prevention measures. Results of testing the HBM variables revealed that perceived barriers, perceived self-efficacy, and cues to action were significantly related to preventive behavioral intentions. This study highlights the importance of considering subculture factors when promoting COVID-19 preventive measures in a multicultural society.
Various scalographic representation of electrocardiograms through wavelet transform with pseudo-differential operator like operators

This paper presents various experimental scalograms of electrocardiograms (ECG) from which the accuracy of discrimination between shockable and non-shockable arrhythmia is improved. To derive the scalograms, for the ECG signals the Gabor wavelet transform, having the various pseudo-di ff erential operator like operators, is applied. Also, for the transformed signals, several nonlinear transforms by means of nonlinear functions are performed. These scalograms are analyzed by the normalized spectrum index (NSI) to find the statistical characteristics, and then the qualitative evaluation is performed to select the best pair of pseudo-di ff erential operator and nonlinear f unction. Through the best pair selected, a good discrimination performance in the decision algorithm is guaranteed. The histogram is used in the decision stage to distinguish the shockable and non-shockable arrhythmia.
Glomerulus-on-a-Chip: Current Insights and Future Potential Towards Recapitulating Selectively Permeable Filtration Systems

Abstract Glomerulopathy, characterized by a dysfunctional glomerular capillary wall, results in proteinuria, leading to end-stage renal failure and poor clinical outcomes, including renal death and increased overall mortality. Conventional glomerulopathy research, including drug discovery, has mostly relied on animal experiments because in-vitro glomerulus models, capable of evaluating functional selective permeability, was unavailable in conventional in-vitro cell culture systems. However, animal experiments have limitations, including time- and cost-consuming, multi-organ effects, unstable reproducibility, inter-species reliability, and the social situation in the EU and US, where animal experiments have been discouraged. Glomerulus-on-a-chip, a new in-vitro organ model, has recently been developed in the field of organ-on-a-chip research based on microfluidic device technology. In the glomerulus-on-a-chip, the podocytes and endothelial cells are co-cultured in a microfluidic device with physical stimuli that mimic the physiological environment to enhance cell function to construct a functional filtration barrier, which can be assessed by permeability assays using fluorescently labeled molecules including inulin and albumin. A combination of this glomerulus-on-a chip technology with the culture technology to induce podocytes and endothelial cells from the human pluripotent stem cells could provide an alternative organ model and solve the issue of animal experiments. Additionally, previous experiments have verified the difference in the leakage of albumin using differentiated podocytes derived from patients with Alport syndrome, such that it could be applied to intractable hereditary glomerulopathy models. In this review, we provide an overview of the features of the existing glomerulus-on-a-chip systems, focusing on how they can address selective permeability verification tests, and the challenges they involved. We finally discuss the future approaches that should be developed for solving those challenges and allow further improvement of glomerulus-on-a-chip technologies.
Influence of the European Union (EU) on International Maritime Organization (IMO) about the Market-based Measures on Emissions

Market-based Measures (MBM), a catalyst for new and effective technologies, can be advantageous for the shipping industry on its way to reaching 50% decarbonization by 2050. MBMs are the focal point at the decision-taking level for the International Maritime Organization (IMO) and European Commission (EC) to regulate emissions and improve energy efficiency in the maritime transportation industry. EC included shipping into European Union’s (EU) existing Emission Trading Scheme (ETS). However, IMO’s scheme will be functional after 2023, and the decision of an MBM is not finalized yet. The motivation of this paper is to analyze viable MBMs under the Initial IMO Greenhouse Gases (GHG) Strategy based on their superiority and evaluate the influence of EU policy implementation on the global shipping industry. The issues such as economic implications, feasibility, social and environmental responsibilities, and harmony of the policy are included in the paper to assess the success of the MBMs. Main concerns and thoughts revolving around EU ETS; such as economic implications of taxation, the existence of an administrative body for shipping in EU ETS, carbon leakage caused by high tax, voyage evasion of emissions by not delivering goods to distant areas, the emergence of a fraud-proof and more secure system with an administrative body, success factors of EU ETS after 16 years, full certainty of reducing emissions rather than incentives to reduce emissions, monitoring, and reporting schemes; are discussed to bridge the issue with EU’s reasonings.
Improved Intuitionistic Fuzzy Entropy and Its Application in the Evaluation of Regional Collaborative Innovation Capability

Intuitionistic fuzzy entropy is an important concept to describe the uncertainty of intuitionistic fuzzy sets (IFSs). To fully measure the fuzziness of IFSs, this paper comprehensively considers the deviation between membership and non-membership and the influence of hesitation, constructs the general expression of intuitionistic fuzzy entropy based on special functions, and proves some of its major properties. Then, it is verified that some existing intuitionistic fuzzy entropies can be constructed by specific functions. Finally, based on a specific parametric intuitionistic fuzzy entropy, this paper applies it to evaluate the regional collaborative innovation capability, to verify the feasibility and practicability of the entropy. In addition, the effectiveness and practicability of this entropy in decision making are further illustrated by comparing it with other entropy measures.
The Future of Tipping in Hospitality: An Exploratory Study on Consumer Attitude in Canada

This exploratory study examines the perception of gratuity behaviours in Canada during the COVID-19 pandemic. Using a nationwide survey, results were analyzed using statistical and cross tabulation analysis and interpreted through a prosocial spending framework to understand consumer behaviour in which food service patrons perceive emotional benefits from the experience. We find tipping is still strongly supported and most do not anticipate this to change. Social expectations of tipping amounts have not changed and remain at 15% (restaurant) and 10% (deliveries). Though most respondents perceive an increase in social expectations during the pandemic, there seems to be a slight decrease in support for tipping with the youngest age range and more supportive of tipping alternatives. Financial impacts of income loss may have contributed to less use of tipped services but does not seem to affect tipping behaviours. With people feeling in control of their own decisions, and perceive it as benefiting others and enabling connection, there is still strong support for tipping as a social norm.
A 5 Gb/s Time-Interleaved Voltage-Mode Duobinary Encoding Scheme for 3-D-Stacked IC

A time-interleaved duobinary encoding scheme for the low-power high-bandwidth memory (HBM) I/O interface is proposed with a 65-nm CMOS process. To reduce power consumption in HBM I/O using multiple through-silicon via (TSV) I/Os, a transmitter (TX) that performs duobinary signaling with a voltage-mode driver is proposed. A small area encoder is implemented to generate duobinary output and an edge-boosted pre-driver is proposed to improve the slew rate and the robustness against the process, voltage, and temperature (PVT) variations of the duobinary voltage-mode driver. To convert the duobinary signal into a non-return-to-zero (NRZ) signal, a one-tap decision feedback equalizer (DFE) is used at the receiver (RX). NRZ signal conversion is proposed using one reference voltage and one PMOS switch to reduce the hardware complexity caused by additional reference voltages. An eight-stacked TSV is emulated in the 65-nm CMOS process, and the emulated capacitance of each stack is 100 fF. The energy efficiency of the proposed transceiver chip is 0.373 pJ/b/pF with a 27 – 1 pseudorandom binary sequence at 5 Gb/s.
Immunogenicity mechanism of mRNA vaccines and their limitations in promoting adaptive protection against SARS-CoV-2

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) in late 2019, hundreds of millions of people have been infected worldwide. There have been unprecedented efforts in acquiring effective vaccines to confer protection against the disease. mRNA vaccines have emerged as promising alternatives to conventional vaccines due to their high potency with the capacity for rapid development and low manufacturing costs. In this review, we summarize the currently available vaccines against SARS-CoV-2 in development, with the focus on the concepts of mRNA vaccines, their antigen selection, delivery and optimization to increase the immunostimulatory capability of mRNA as well as its stability and translatability. We also discuss the host immune responses to the SARS-CoV-2 infection and expound in detail, the adaptive immune response upon immunization with mRNA vaccines, in which high levels of spike-specific IgG and neutralizing antibodies were detected after two-dose vaccination. mRNA vaccines have been shown to induce a robust CD8+T cell response, with a balanced CD4+ TH1/TH2 response. We further discuss the challenges and limitations of COVID-19 mRNA vaccines, where newly emerging variants of SARS-CoV-2 may render currently deployed vaccines less effective. Imbalanced and inappropriate inflammatory responses, resulting from hyper-activation of pro-inflammatory cytokines, which may lead to vaccine-associated enhanced respiratory disease (VAERD) and rare cases of myocarditis and pericarditis also are discussed.
Virtual Screening of Natural Chemical Databases to Search for Potential ACE2 Inhibitors

The angiotensin-converting enzyme II (ACE2) is a multifunctional protein in both health and disease conditions, which serves as a counterregulatory component of RAS function in a cardioprotective role. ACE2 modulation may also have relevance to ovarian cancer, diabetes, acute lung injury, fibrotic diseases, etc. Furthermore, since the outbreak of the coronavirus disease in 2019 (COVID-19), ACE2 has been recognized as the host receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The receptor binding domain of the SARS-CoV-2 S-protein has a strong interaction with ACE2, so ACE2 may be a potent drug target to prevent the virus from invading host cells for anti-COVID-19 drug discovery. In this study, structure- and property-based virtual screening methods were combined to filter natural product databases from ChemDiv, TargetMol, and InterBioScreen to find potential ACE2 inhibitors. The binding affinity between protein and ligands was predicted using both Glide SP and XP scoring functions and the MM-GBSA method. ADME properties were also calculated to evaluate chemical drug-likeness. Then, molecular dynamics (MD) simulations were performed to further explore the binding modes between the highest-potential compounds and ACE2. Results showed that the compounds 154-23-4 and STOCK1N-07141 possess potential ACE2 inhibition activities and deserve further study.
The rise of obfuscated Android malware and impacts on detection methods

The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious code. Therefore, Android applications developers tend to use state-of-the-art obfuscation techniques to mitigate the risk of application plagiarism. The malware authors adopt the obfuscation and transformation techniques to defeat the anti-malware detections, which this paper refers to as evasions. Malware authors use obfuscation techniques to generate new malware variants from the same malicious code. The concern of encountering difficulties in malware reverse engineering motivates researchers to secure the source code of benign Android applications using evasion techniques. This study reviews the state-of-the-art evasion tools and techniques. The study criticizes the existing research gap of detection in the latest Android malware detection frameworks and challenges the classification performance against various evasion techniques. The study concludes the research gaps in evaluating the current Android malware detection framework robustness against state-of-the-art evasion techniques. The study concludes the recent Android malware detection-related issues and lessons learned which require researchers’ attention in the future.
Predicting recidivism: psychosocial collateral consequences among registered offenders

ABSTRACT The relationship between collateral consequences and recidivism among individuals who sexually offend continues to be proffered in the literature, yet empirical evidence of these links has yet to be established. This exploratory study investigated the correlational and predictive relationships between social and psychological collateral consequences and overall recidivism, sexual recidivism specifically, and probation/parole/registry violations among a sample of 180 registrants. Results revealed significant relationships between demographic indicators (age, years registered, education), social and psychological collateral consequences, overall recidivism, and sexual recidivism and violations. Results of the hierarchical logistic regressions indicated that neither social nor psychological collateral consequences significantly improved model fit for overall recidivism or sexual recidivism. Social collateral consequences, however, predicted an individual’s likelihood to accrue probation, parole, or registry violations post-offence. Practical implications are discussed within the context of building an empirical basis for the potential contributory effects of the registry on recidivism, via collateral consequences.
On Optimization of Automation Systems: Integrating Modular Learning and Optimization

Compositional Optimization (CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses a modular learning that takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization. Note to Practitioners—Automation systems are becoming increasingly large and complex and the automation of more and more advanced tasks often requires the coordination of multiple subsystems. Optimization can have a great impact on the efficiency of these systems in terms of cost and operation speed. Finding optimal solutions is, however, a difficult task. As the number of tasks and subsystems of the automation system increases, the search space of the optimization problems tends to grow exponentially. Compositional Optimization (CompOpt) is a method specifically designed for the optimization of large-scale automation systems. A challenge with the application of CompOpt is that it takes as input a specific type of optimization model that divides the system into subsystems; like machines, vehicles, etc. Formulating these models requires a high level of expertise and system knowledge. This paper addresses this challenge with an algorithm that learns these models from a simulation of the system. The simulation can be implemented in any software as long as a suitable interface exists or can be constructed. To divide the learning into subsystems, the algorithm uses an initial plant structure hypothesis (PSH). This can be viewed as a meta-model that includes known structural information, such as the number of subsystems and which actions that affect each subsystem. The more structural information that is added to PSH, the more efficient the learning and subsequent optimization will be. The purpose of this is to reduce the level of expertise and system knowledge needed in the application of optimization, to simplify the transition to Industry 4.0.
Power Losses Models for Magnetic Cores: A Review

In power electronics, magnetic components are fundamental, and, unfortunately, represent one of the greatest challenges for designers because they are some of the components that lead the opposition to miniaturization and the main source of losses (both electrical and thermal). The use of ferromagnetic materials as substitutes for ferrite, in the core of magnetic components, has been proposed as a solution to this problem, and with them, a new perspective and methodology in the calculation of power losses open the way to new design proposals and challenges to overcome. Achieving a core losses model that combines all the parameters (electric, magnetic, thermal) needed in power electronic applications is a challenge. The main objective of this work is to position the reader in state-of-the-art for core losses models. This last provides, in one source, tools and techniques to develop magnetic solutions towards miniaturization applications. Details about new proposals, materials used, design steps, software tools, and miniaturization examples are provided.
A study of license plate character recognition based on wavelet moment

License plate character recognition is a main step in license plate recognition system, but its emphasis is to choose efficient features. Wavelet motion is a new visual invariant, it is a integration of moment and wavelet multi-scale analysis. Wavelet moment feature of image can reflect the image's part and whole characters and has strong anti-jamming ability. But different method of discretization of wavelet moment shows different performance. On the basis of analysis of wavelet moment, the paper introduces the algorithm of discretization of wavelet moment, and applies it to license plate character recognition system. With the wavelet moment features and improved BP neural network, the recognition of license plate character is realized and good performance gained.
Weighted Network Model Based Recognition of Dangerous Lines Under Power Flow Transferring

In allusion to the incompleteness of existing method to search the path by which power flow is transferred,a weighted network model based method to recognize dangerous line under power flow transferring is proposed.A comprehensive analysis on topological structure of power grid and the imbalanced power flow distribution is performed,on this basis two kinds of weighted power grid models,in which the line reactances and the reciprocals of loading rates of transmission lines are taken as the edge weights respectively,are built;then the shortest transmission path of the two models are searched by Floyd algorithm,and the transmission lines included in the search result are the dangerous lines.In the proposed method the influence of heterogeneity of connective nodes on power flow transferring is synthetically considered,thus the lines containing transformers that are directly connected with generators can be effectively recognized and the dangerous lines obtained by the recognition under power flow transferring are more exact.The correctness and effectiveness of the proposed method are verified by simulation analysis based on IEEE 30-bus system.
k‐means clustering based energy and trust management routing algorithm for mobile ad‐hoc networks

MANETs (Mobile Ad‐Hoc Networks) have limited energy and are unable to energize themselves. This work aims to create a power management strategy for the MANET that saves energy. While routing based on the cluster, the cluster heads may fail or work improperly by the cause of power issues. As a result, the cluster heads experience instability when gathering information and interacting. The main focus of this research is identifying the unstable cluster heads and replacing them with other nodes that implement the self‐configurable cluster method. For defining the cluster heads successfully, we presented a k‐means protocol approach. The proposed k‐means technique identifies the abnormal or unnecessary variations of the cluster heads and the change in cluster count. A node is designated as a cluster head (CH) in MANET after the cluster value remains lesser than the existing routing threshold value. Information gathering, computing trust levels, and trust‐based configuration are the three key elements of the proposed trust management system, which communicates with each node to establish secure network connections. Finally, the testing results show that the proposed approach uses less energy and effectively manages trust in MANET.