Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.
A simple model of smart learning environment

ABSTRACT In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand and to use as guidance, both for implementation and maturity measurement. This research proposes a simple model of SLE as a guideline that is easy to understand and to be used for developing learning environments that support effective, efficient, and engaging learning processes. The proposed model adopted the intelligent tutoring system architecture as a foundation. The components of the model were generated by mapping of 12 existing SLE models, frameworks, and best practices. Model formation and validation followed the system dynamics (SD) modeling process. In addition, indirect model validation process was also performed by interviewing educational technology practitioners and experts, based on assessment results of two newly developed SLE instances. For measuring maturity of existing SLE instances, this research also derived SLE maturity model. It is concluded that the simple model is usable for easy understanding, for establishing new SLE instances, and also for assessing the maturity level of existing SLE instances.
Path planning of servo mechanical arm based on improved ant colony algorithm

According to the limitation of basic ant colony algorithm when the working platform is smaller and the barriers is almost the same size with target,the improved ant colony algorithm is presented to the path planning of servo mechanical arm.The effect of the compartmentalize grid size to path planning speed and accuracy has been discussed,the methods of local information updating have been improved,and the range of ants’visual field have been enlarged.The experiment results show that the improved ant colony algorithm could find better path in shorter time,achieve the function of avoidance obstacle and the global searching performance is better than the basic one.
Photocatalytic dye degradation and photoexcited anti-microbial activities of green zinc oxide nanoparticles synthesized via Sargassum muticum extracts

Drug-resistant superbugs (DRS) were isolated from hospital sewage waste and confirmed by a 16S rDNA molecular technique as B. filamentosus, B. flexus, P. stutzeri, and A. baumannii. Green nanotechnologies provide a new promising alternative pathway that was found to be much safer, eco-friendly, and has economic benefits over physical/chemical methods. Sargassum muticum (SM) mediated zinc oxide nanoparticles (ZnO-NPs) were proved to be photocatalytic and anti-microbial agents. Anti-microbial action was demonstrated by a maximal growth inhibition activity of 18 mm against A. baumannii and a minimal of 12 mm against B. flexus at 80 μg mL−1 concentrations. The anti-microbial mechanism of SMZnO-NPs employed a biphasic phenomenon persuaded by an osmotic shock that can attack the DRS bacterial cells directly and lead to death. In addition, photocatalytic activity was investigated by SMZnO-NPs for the degradation of methylene blue (MB) dye under different light conditions. Natural sunlight irradiation shows effective enhancement with the highest efficiencies of 96% being achieved within 60 min compared to UV-light and visible-light. The reusability of SMZnO-NPs provides up to 6 consecutive cycles towards MB decolorization for environmental water cleansing.
The Role of Social Work in Residential Aged Care Facilities: Evaluation of a Pilot Program in Australia

In some international settings, social workers are employed within aged care settings. However, in Australia, social workers rarely work in residential aged care facilities. In an innovative program, an Australian health network employed a social worker in an aged residential care facility from 2010 to 2011. In this research we examine and evaluate this program. Qualitative semi-structured interviews with nine key stakeholders and data extraction from medical records were conducted. Data from medical records and interview transcripts were coded and themes extracted using thematic analysis. Thematic analysis identified five key themes reflecting the roles performed by the social worker. These were: (1) The importance of having an independent third party, (2) The provision of emotional support to residents, carers and families during the transition period, (3) The importance of role clarity, (4) The provision of family-centered care, and (5) Social work responses to potential difficulties which were preventative rather than reactive. The move into residential aged care can be an overwhelming, and in some cases, traumatic transition for residents and families. Results identified that timely and expert social work intervention can improve the transition process through the provision of counselling to effectively manage grief, loss, and psychosocial issues.
Research on Investment Portfolio Mechanism in the Context of COVID-19

Portfolio theory provides an important conceptual foundation and a comprehensive set of analysis systems for portfolio creation and analysis, and it has a significant impact on modern investment management practice. The rationality of portfolio theory on decentralized investing is a critical theoretical foundation for the fund management industry's existence. Furthermore, Markowitz's notion of an "effective portfolio" redirected managers' focus away from individual stocks and toward constructing successful portfolios. Markowitz's portfolio theory has shown to be successful in reality regarding the optimal allocation of main asset types in a portfolio. This paper takes the investment portfolio as the theoretical entry point. In-depth research from concept, domestic and foreign research review, and application model (CAPM, Fama-French 5 factor model, and q-factor model). And this paper tried to explore the predictive power of asset pricing under the impact of the COVID-19 outbreak. On the one hand, the thorough analysis and summary of portfolio theory. On the other hand, it provides a theoretical basis for relevant portfolio practice.
Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach

Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented.
Achieving State Space Reduction in Generated Ajax Web Application State Machine

The testing of Ajax (Asynchronous JavaScript and XML) web applications poses novel challenges for testers because Ajax constructs dynamic web applications by using Asynchronous communication and run time Document Object Model (DOM) manipulation. Ajax involves extreme dynamism, which induces novel kind of issues like state explosion, triggering state changes and unreachable states etc. that require more demanding web-testing methods. Model based testing is amongst the effective approaches to detect faults in web applications. However, the state model generated for an Ajax application can be enormous and may be hit by state explosion problem for large number of user action based changes and for immense dynamism. Recent research has not addressed this issue comprehensively because existing techniques either apply partial reduction or compromise the effectiveness of testing. This research uses soft computing based Fuzzy C Means (FCM) clustering algorithm to generate state machine model of an Ajax web application. The focus is on devising a framework to avoid the state explosion problem. The framework prioritizes the requirements and use cases based on requirements weightage, stakeholder weightage and user session based use case frequency. FCM uses this data to reduce the state space by identifying the most pivotal usage areas. The resultant DOM mutations for only these usage areas are considered to induce the finite state machine thus avoiding the state explosion. Experimental results show that the framework controls the size of generated state machine without compromising the effectiveness of testing.
Herstein’s theorem for prime ideals in rings with involution involving pair of derivations

Abstract Herstein proved that a prime ring R of is commutative if there is a nonzero derivation d of R such that for all The aim of this paper is to prove the -version of Herstein’s result with a pair of derivations on prime ideals of a ring with involution. Precisely, we prove the following result: let R be a ring with involution of the second kind, P a prime ideal of R such that and If d 1 and d 2 are derivations of R satisfying the condition for all then one of the following holds: (a) (b) (c) R/P is a commutative integral domain. Moreover, some related results are also discussed. As consequences of our main theorems, many known results can be either generalized or deduced.
Beyond the prey: male spiders highly invest in silk when producing worthless gifts

In the spider Paratrechalea ornata, males have two gift-giving mating tactics, offering either a nutritive (prey) or a worthless (prey leftovers) silk wrapped gift to females. Both gift types confer similar mating success and duration and afford males a higher success rate than when they offer no gift. If this lack of difference in the reproductive benefits is true, we would expect all males to offer a gift but some males to offer a worthless gift even if prey are available. To test this, we allowed 18 males to court multiple females over five consecutive trials. In each trial, a male was able to produce a nutritive gift (a live housefly) or a worthless gift (mealworm exuviae). We found that, in line with our predictions, 20% of the males produced worthless gifts even when they had the opportunity to produce a nutritive one. However, rather than worthless gifts being a cheap tactic, they were related to a higher investment in silk wrapping. This latter result was replicated for worthless gifts produced in both the presence and absence of a live prey item. We propose that variation in gift-giving tactics likely evolved initially as a conditional strategy related to prey availability and male condition in P. ornata. Selection may then have favoured silk wrapping as a trait involved in female attraction, leading worthless gift-giving to invade.
Mediating role of trust in the relationship between public governance quality and tax compliance

This paper examined the mediating role of trust in government on the influence of public governance quality indicators (accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption) on tax compliance in Africa. Cross-country data obtained from 38 African countries for 2015 was used and analyzed using Ordinary Least Squares (OLS) regression analysis. The study found that accountability, political stability, control of corruption, and trust have a significant influence on tax compliance among the sampled African countries, but government effectiveness, regulatory quality, and the rule of law and have insignificant influence on tax compliance. The result of the mediating effects revealed that trust mediates the influence of accountability and political stability on tax compliance in Africa. However, it failed to mediate the influence of government effectiveness, regulatory quality, rule of law, and control of corruption on tax compliance among sample African countries. The study offers theoretical insights on the role of trust as a mediator on social exchange relationships from the context of public governance quality on tax compliance. It also implies to the policymakers that building trust is an important mechanism through which the impact of public governance on tax compliance would be more pronounced. The study further calls for replication of its findings in other continents such as the Americas, Asia, and Europe.
Data-Driven Modelling and Control for Robot Needle Insertion in Deep Anterior Lamellar Keratoplasty

Deep anterior lamellar keratoplasty (DALK) is a technique for cornea transplantation which is associated with reduced patient morbidity. DALK has been explored as a potential application of robot microsurgery because the small scales, fine control requirements, and difficulty of visualization make it very challenging for human surgeons to perform. We address the problem of modelling the small scale interactions between the surgical tool and the cornea tissue to improve the accuracy of needle insertion, since accurate placement within 5% of target depth has been associated with more reliable clinical outcomes. We develop a data-driven autoregressive dynamic model of the tool-tissue interaction and a model predictive controller to guide robot needle insertion. In an ex vivo model, our controller significantly improves the accuracy of needle positioning by more than 40% compared to prior methods.
Analysis of traffic data based on ARMA-TS-GARCH finite mixture model

Based on the fact that in order to study time-series data,their auto-regression and periodic and the variance of the sequence and long-term random fluctuations must be analyzed,ARMA-TS-GARCH model based on ARMA model and APARCH model was proposed,and the construction of the model and the parameter estimation with the Los Angeles Long Beach area traffic data and diagnostic tests were analyzed.The results of this model were compared with those of the GARCH and ARMA-GARCH model.The results show that the ARMA-TS-GARCH model fits the data better than ARMA model and APARCH model.Using ARMA-TS-GARCH model to predict the traffic data set,the results are well.
Study of yarn properties and displacement deviation of acceleration points based on the novel drafting system

Abstract Drafting process plays an important role in spinning systems as it is closely linked with yarn qualities. Therefore, a novel drafting system (NDS) is applied in short staple spinning, which shows innovations in the friction field and the floating zone. This article compared the traditional drafting system (TDS) and novel drafting device by spinning four kinds of yarns (in partI). The experimental results demonstrate that NDS produces yarns with better properties with regard to the yarn evenness, imperfections and strength compared to TDS, indicating the varietal adaptability of the novel drafting device. Besides, a comparative analysis of the displacement deviations of acceleration points produced by NDS was performed in terms of the different fibers and varied tracing fiber lengths (in partII). The differences in displacement distributions between polyester and cotton fibers were investigated, showing that even under the same normal pressure distribution, different internal friction fields of fibers lead to different displacement distributions for acceleration points. The displacement distributions of different tracing fiber lengths embedded in the roving reveal that the longer the fibers, the more concentrated the acceleration points.
Impact of Dynamic Voltage Scaling on SEU Sensitivity of COTS Bulk SRAMs and A-LPSRAMs Against Proton Radiation

In the aerospace industry, commercial-off-the-shelf (COTS) static random access memories (SRAMs) are a cost-effective solution for obtaining high performance at the system level, which is difficult to obtain using space-qualified components. In addition, the usage of dynamic voltage scaling (DVS) is commonly used in space environments, where low power consumption is a critical issue. This article presents an analysis of the sensitivity against single-event upsets (SEUs) of various COTS bulk SRAMs and advanced low-power SRAMs (A-LPSRAMs) against proton radiation when using DVS to save power. Experimental results will show clear evidence that the sensitivity to SEUs increases when the power is lowered. Two sets of successive technologies (130-, 90-, and 65-nm bulk SRAMs, and 150- and 110-nm A-LPSRAMs) are evaluated against 15-MeV protons and compared with results of 14-MeV neutrons presented in a previous work. Experimental data are finally compared with analytical simulations obtained by using the multiscales single-event phenomena predictive platform (MUSCA-SEP3) Monte Carlo tool to predict the effect of DVS on the SEE sensitivity on more modern technologies in the ITRS/IRDS roadmap.
Phonological Variation in Verb in Pashto Spoken in District Swat

1. M.Phil. Scholar, English Department, Hazara University, Mansehra, KP, Pakistan 2. Assistant Professor, English Department, Hazara University, Mansehra, KP, Pakistan 3. Assistant Professor, English Department, University of Swat, Swat, KP, Pakistan DOI http://doi.org/10.47205/plhr.2021(5-II)2.33 PAPER INFO ABSTRACT Received: August 23, 2021 Accepted: December 12, 2021 Online: December 15, 2021 The present study seeks to find out the phonological variations
Exploratory Study on Digital transformation: Capabilities and Expected Performances

Nowadays many firms encounter the macro-level changes called digital transformation and research on it has drastically increased since 2014. In response to this emerging phenomenon, this study explores how firms prepare for digital transformation and what do they expect from it. More specially, based on the survey from 439 Korean scaleup firm (high-growth firm), we suggest the concepts to necessary capabilities for and expected performance improvement through digital transformation. Our study illustrates that how a firm’s perceptions on those concepts varies upon firm size and industry type. Additionally, our study offers a clue for ‘digital divide’ that can potentially threaten the survival of numerous firms.
Organizational learning, resilience and psychological empowerment as antecedents of work engagement during COVID-19

PurposeThis paper aims to explore the relationship between organizational learning (OL) and work engagement (WE) in the Middle East region amid the COVID-19 pandemic and to test the mediating role of employee resilience (ER) and psychological empowerment (PE) on this relationship.Design/methodology/approachThe sample size was 208 respondents working in the Middle East area during COVID-19. Hypotheses were tested using regression analysis with bootstrapping.FindingsThe findings indicate a significant positive impact of OL on WE. Moreover, both constructs – ER and WE – were identified as mediators for this relationship.Practical implicationsPractical implications within this study call for organizations to focus on promoting a learning culture in order to adapt and respond effectively to unprecedented external challenges.Originality/valueThe current study extends previous research and strengthens the antecedents of WE, namely, OL, ER and PE in the Middle East region while controlling for COVID-19 perceptions.
The Image of Men in Advertising as Perceived by Polish Students of English Philology

Images presented in advertisements are very powerful and have huge impact on our perception of the world. The roles of women and men have changed within the last decade and therefore it is worth investigating the way they are perceived in advertising. The purpose of our study was twofold. First, we aimed to find out how Polish students from the English Department at a tertiary level institution in Poland, who are mainly females, perceive the image of a man in advertising. Our second purpose was to analyse their reception of slogans used in the advertisements. In order to find out the answers to our research questions concerning the image of males in advertising as well as language of advertising, we have decided to design a special questionnaire in which the students were asked to comment on advertisements associated with particular image concerning men. The advertisements had been chosen in relation to the categories established by Frith and Müeller (2010). While analysing the data we decided to use these categories, which are to be described in the theoretical part of the article. The obtained data has allowed us to find out what the image of a man is and what kind of language is used to make the advertisements effective.
MONITORING OF ENVIRONMENT PARAMETERS BASED ON WIRELESS SENSOR NETWORK

The main goal of the article is to design an automated wireless sensor system to monitor environment parameters from different locations. If we want to control our environment, we have to know the measurable parameters of it. These parameters are temperature, humidity, light intensity, etc. All of these measurements can help us to improve the efficiency of home automation systems or to save money. The theoretical part contains an overview of the technologies used for the design of a system.The next part contains an explanation of the software part of the work and the electrical interconnection of the equipment used. The last part is devoted to the cloud environment where the data was collected and visualized. The article presents the results of scientific-methodological research (description of the problem, task).