Miracle-dz/UniFAR-FTUs huggingface dataset:
Profiling gendered multidimensional poverty and inequality in post-apartheid South Africa

Reducing the gender gap in poverty remains a priority for South African policymakers. Using the 2018 General Households Survey (GHS), this paper examines gendered multidimensional poverty in post-apartheid South Africa. The analyses draw on Alkire-Foster multidimensional poverty methodology to present multidimensional poverty measures for men and women. The dimensions and indicators used in this paper are slightly adapted to reflect the socioeconomic realities and circumstances that are unique to South African households and individuals. The results show a persistence in multidimensional poverty among women, as the magnitudes of multidimensional poverty measures remain higher for women than for men. Further findings show that the economic activity and health dimensions contribute substantially to poverty among men and women. Notably, contributions of unemployment and the presence of chronic disease(s) indicators for gendered multidimensional poverty are more considerable for women than for men. In order to further reduce gendered poverty, this paper recommends strengthening a social policy review that strongly promotes more job creation, health-enhancing interventions and potent ‘gender sensitive’ ideologies.
Intellectual and Digital Industrial Enterprise as a Way for Becoming the Industry’s Leader

The economy of any modern state should ensure the intensification of intellectual processes in production, especially in industries. If the economic policy of the country is almost impossible to influence by economic entities, the management of intellectual development within the enterprise and at the micro level will allow to acquire significant competitive advantages. The stepped-up the use of intellectual capital at the level of economic entities will allow to launch innovative processes not only at the enterprise itself, but also will have an impact on the whole industry and even on territorial associations. The article discusses the relationship between the level of intellectual development of an enterprise and its situation in the industry. The purpose of the work is to determine the efficiency of production activities relative to industry average indicators through the use of intellectual capital. To achieve the goal, it is proposed to consider the relationship between intellectual capital value added and economic value added, to identify their dependence and to justify the possibility of comparison with industry average indicators of economic value added. The practical significance of the proposed approach will allow to assess the position of the enterprise in the industry by the volume of economic value added, taking into account the level of intellectual development of the company, and to develop recommendations on intellectual capital management in order to maximize value.
The Effectiveness of an Online Positive Psychology Intervention among Healthcare Professionals with Depression, Anxiety or Stress Symptoms and Burnout

This study aimed to test the effectiveness of an online positive psychology intervention among Greek healthcare professionals with depression, anxiety and/or stress symptoms and increased levels of burnout. The sample was consisted of 38 participants with mild, severe, or extremely severe symptoms of depression, anxiety and stress, and moderate or high levels of burnout, who were randomly assigned either to the intervention (N = 19) or the active control group (N = 19). Participants in the intervention group were introduced weekly to three positive psychology activities, whilst participants in the control group were given three placebo activities. All participants completed self-report questionnaires (Depression, Anxiety and Stress Scale, Maslach Burnout Inventory, Satisfaction with Life Scale, Positive and Negative Affect Scale) at the beginning and at the end of the three-week online intervention. The results indicated that the intervention group experienced statistically significant decreases in depression, anxiety, stress, and emotional exhaustion scores, as well as increases in satisfaction with life, compared to the control group which reported no changes. These findings suggest that such a positive psychology intervention could help healthcare professionals to manage their difficulties more effectively. Other clinical implications and recommendations for future research are also considered.
When Brokerages Restrict Retail Investors, Does the Game Stop?

In this paper, we study a recent, high-profile 2021 event, often known as the GameStop episode, where Robinhood and several other retail-oriented brokerage firms restrict equity purchases and/or options positions in GameStop equity and around 30 other stocks. While these restrictions are short-lived, lasting from a day to just over a week, and have at most modest effects on trading volume and equity bid-ask spreads, they have large effects on stock returns and options markets. We find that when equity restrictions are imposed, affected stocks fall in price, with cumulative abnormal returns of -18.53% in the first two hours and -95.34% in the first five trading days of equity restrictions. When restrictions are later lifted, share prices do not reverse their earlier declines. Traders also move from equities to the options markets, with large increases in options trading volumes and open interest. Options prices rise sharply in implied volatility terms, and implied volatilities rise by much more than realized volatilities. Options purchasers are thus wildly overpaying to buy puts and calls, which create large transfers from options purchasers to options market-makers and other options sellers.
Modeling the characteristics of collaborative science and technology policy network

ABSTRACT Despite the growing trend of networking approaches in policymaking, the concept of collaborative policy networks (CPNs) and, in particular, the identification of their characteristics, remain a serious research vacuum. Thus, the main purpose of this study is to provide a model for identifying the characteristics of CPNs. For this purpose, the science and technology policy network in Iran was studied on a case-by-case basis. In this study, the theoretical framework of research was formulated as a sequential exploratory mixed design by first using literature review, policy documents analysis and in-depth interviews with 20 experts. Then, a survey was conducted by distributing a questionnaire among 100 science and technology policy experts and scholars in Iran. Data analysis was performed through confirmatory factor analysis using structural equation analysis method and, subsequently, the final research model was developed. The final model of CPNs consists of 17 characteristics in four dimensions of ‘actors’, ‘relationships among actors’, ‘structure’ and ‘the rules of conduct between actors’. Thus, the presence of such characteristics in the policy networks can lead to increased participation of actors and, of course, the effectiveness of policies.
Diverging in vitro inflammatory responses toward Streptococcus uberis in mouse macrophages either preconditioned or continuously treated with β-hydroxybutyrate

Graphical Abstract Summary: Elevated ketone concentrations have been associated with mastitis in postpartum dairy cows. Therefore, we sought to determine the effect of a ketone on macrophage inflammatory responses during a Streptococcus uberis challenge. When cells were pretreated with a ketone before S. uberis challenge, mRNA abundance of a membrane receptor for a bacterial cell wall component was reduced. However, when cells were treated during the challenge, the opposite was observed. These responses could help explain the association of ketosis with mastitis.
Design of intrusion detection systems on the internet of things infrastructure using machine learning algorithms

Network intrusion detection systems (NIDS) for Internet-of-Things (IoT) infrastructure are among the most critical tools to ensure the protection and security of networks against malicious cyberattacks. This paper employs four machine learning algorithms and evaluates their performance in NIDS considering the accuracy, precision, recall, and F-score. The comparative analysis conducted using the CICIDS2017 dataset reveals that the Boosted machine learning techniques perform better than the other algorithms reaching the predicted accuracy of above 99% in detecting cyberattacks. Such ML-based attack detectors also have the largest weighted metrics of F1-score, precision, and recall. The results assist the network engineers in choosing the most effective machine learning-based NIDS to ensure network security for today’s growing IoT network traffic.
Quantification of Microplastics in North-Western Mediterranean Harbors: Seasonality and Biofilm-Related Metallic Contaminants

The Mediterranean Sea is one of the most impacted basins in terms of microplastics pollution. Land-based activities are the major sources of plastic litter to the ocean, with harbors probably representing significant hotspots. In the framework of the SPlasH! project (Stop alle Plastiche in H2O, Interreg Marittimo project), microplastics were sampled in three north-western Mediterranean harbors during summer and winter. In this study, the areal concentrations of microplastics ranged from 5576 to 379,965 items·km−2. A decreasing gradient was observed from the inner to the outer zones of the studied harbors, pointing out these enclosed systems as hotspots regarding microplastic pollution. During summer, the areal concentrations of microplastics were higher than in winter, which could be explained by an enhancement of port activities leading to MPs production. The investigation of microplastics size classes distribution in the surface waters revealed that microplastics within the size range between 300 µm and 500 µm were the least represented. In this study, we assessed trace metal (Pb, Fe, Cu, V, Cd and As) bioaccumulation by the biofilm which developed on the surface of microplastics. The results highlighted that concentrations within the biofilm were higher than those in the surrounding waters. This result strongly suggested trace metal bioaccumulation on microplastics through biofilm formation. When trace metal concentrations were normalized over the corresponding surface of microplastics and macroplastics, higher values were obtained for microplastics, evidencing their enhanced capacities to bioaccumulate contaminants when compared to macroplastics.
From resilience to wellbeing: Identity‐building as an alternative framework for schools’ role in promoting children’s mental health

This paper explores schools’ new role in promoting children’s mental health, as a key focus for policy makers across the global north. An education policy analysis is conducted for England and Australia, two nations advocating a ‘bottom-up’ approach to mental health promotion, granting flexibility to schools and municipal authorities. Here it is argued that a common policy lexicon is evident where key concepts — wellbeing, resilience, character — are constructed on taken-for-granted assumptions. These are argued to be limited by an emphasis upon the individual constituents of mental health, which is contrasted against a broader conception of wellbeing, evident in recent non-governmental international policy. Empirical data is then presented from two separate studies in England and Australia, where young peoples’ perspectives are used to arbitrate the efficacy of the policy construction of wellbeing, in canvassing a relational and social identity approach as a viable alternative. Both studies privilege the voices of young people on wellbeing and identity, with findings from England highlighting that the dominant schooling narrative of resilience perceived by students, misses the ontological and social dimensions of their wellbeing. Research with Indigenous young people in Australia is then evaluated for the affordances generated by identity building and affirming programmes in schools. Notwithstanding cultural differences, common aspects were evident across both datasets, revealing the centrality to young people of self-authenticity, relatedness, and connectedness to nature, as key to their wellbeing. The paper concludes in advancing a set of principles to underpin a relational and social identity approach to schools’ wellbeing promotion strategy.
Applications of deep learning in big data analytics for aircraft complex system anomaly detection

Big data analytics with deep learning approach have attracted increasing attention in transportation engineering, involving operations, maintenance, and safety. In commercial aviation sectors, operational, and maintenance data produced on modern aircraft is increasing exponentially, and predictive analysis of these data is an exciting and promising field in aviation maintenance, which has a potential to revolutionize aerospace maintenance industry. This study illustrates the state-of-the-art applications of deep learning in big data analytics for predictive maintenance and a real-world case study for commercial aircraft. A Long Short-Term Memory network based Auto-Encoders (LSTM-AE) is proposed for complex aircraft system fault detection and classification, which makes use of the raw time-series data from heterogeneous sensors. The proposed method uses nominal time-series samples corresponding to healthy behavior of the system to learn a reconstruction model based on LSTM-AE framework. Then the system health index (HI) and fault feature vectors are derived from the reconstruction error matrix for fault detection and classification. The proposed method is demonstrated on a real-world data set from a commercial aircraft fleet. The typical PCV faults as well as the 390 F sensor and 450 F sensor faults due to sense line air leakage are successfully detected and distinguished based on the extracted features. The case study results show that the computed HI can effectively characterize the health state of the aircraft system and different fault types can be identified with high confidence, which is helpful for line fault troubleshooting.
A scoping review of global vaccine certificate solutions for COVID-19

ABSTRACT Globally, measures, such as lockdown, quarantining, and physical distancing, have been implemented to curb the spread of COVID-19. As the vaccines are now available and reintegration into society is beginning, measures such as vaccine certificates are being implemented around the world. We conducted a scoping review to identify the initial digital solutions for COVID-19 vaccine certificates and evaluate them on the basis of purpose and use case, technological architecture, and ethical and legal implications. Articles identified from a Google search and a search of MEDLINE, Ovid and preprint servers were reviewed in duplicate, and data were extracted using a data extraction form. Data were extracted for date, location, type of article, source, companies identified for creating vaccine certificates, technology used, type of evidence provided (article quoting research study or an expert opinion), digital architecture, security and privacy measures, and use cases. Technology emerged as the most dominant theme followed by ethics, travel, legal concerns, public policy, and scientific concerns. Our review identified eight solutions that are working toward COVID-19 vaccine certificates world-wide, all optimizing blockchain technology. COVID-19 vaccine certificates are being considered in 11 countries and are in place in 5 others. Many issues concerning the themes we identified remain to be addressed to facilitate successful implementation.
Sensitivity of Solid-Scintillator Detectors to Dark Matter

This paper shortly reviews the sensitivities that can be achieved to unambiguously point out the presence of a signal of Galactic origin in dark matter experiments with solid-scintillator detectors. Examples of the experimental sensitivities obtained by exploiting the annual and diurnal modulation signatures are reported with particular regard to the investigations performed in the framework of the DAMA Collaboration. The directionality approach in solid scintillators is also presented and, in particular, the perspectives of the ADAMO project are discussed.
Digital Technologies—And Teacher Wellbeing?

The concept of teacher wellbeing, the importance of considering teacher wellbeing, concerns for developing digital wellbeing and concerns for using digital technologies to support teaching practices have all been previously studied. The idea that uses of digital technologies can support teacher wellbeing (or not) and ways that uses might do this have not been studied to the same extent. Indeed, it can be argued that this topic requires a complete and focused area of study in its own right. This methodologically focused paper takes an initial step in this direction, exploring existing research and backgrounds to wellbeing, teacher wellbeing, digital wellbeing and uses of digital technologies to support teachers’ practices. The paper reviews conceptions of digital technologies supporting teacher wellbeing and offers a newly developed outline conceptual model and framework for this research field. The framework is tested, identifying influencing factors from evidence presented in a number of existing relevant case studies where digital technologies have been used to support teacher practices. The efficacy of the proposed framework is assessed, and the paper concludes by offering a proposed research instrument and strategy to advance knowledge in this area.
Understanding the Role of Brand Salience in Brand Choice Decisions in the Charity Sector

Despite the importance of branding in customer acquisition, little is known about the extent to which brand salience influences brand choice intention of new customers. Drawing upon associative network memory theory, we propose that brand salience is composed of brand prominence and brand distinctiveness, which are linked to brand choice intention of new customers. Our theoretical contention was empirically examined in the context of monetary donation to international aid-related charities by new donors. A mixed-method approach was utilized with semi-structured interviews with practitioners and donors, and two cross-sectional surveys. The study offers a holistic view for understanding brand salience and, as such, advances recent work focusing on the breadth and depth of brand associations in the customer’s mind.
Himalaya Air Quality Impacts From the COVID‐19 Lockdown Across the Indo‐Gangetic Plain

Starting in January 2020, the novel coronavirus, now known as acute respiratory syndrome coronavirus (SARS‐CoV‐2) and the disease that it causes (COVID‐19) has had significant impacts on human health, the environment, and the economy globally. The rapid lockdown that occurred as well as its well documented timing allows for an unprecedented opportunity to examine the impact of air pollution from densely populated regions has on adjacent and pristine environments. Here, we use in situ and satellite observations to show that there was a step function decrease in two key indicators of air quality, nitrogen dioxide and airborne particulates, in locations within the Indo‐Gangetic Plan (IGP) as a result of the Spring 2020 lockdown. Based on anomaly patterns, we find a dipole response with a statistically significant reduction in air pollution along the western IGP and Himalaya and an increase in air pollution in the eastern IGP and Himalaya. We show that spatial variability in the reductions in economic activity across northern India and the adjoining countries of Nepal, Pakistan, and Bangladesh contributed to this dipole as did a persistent atmospheric circulation anomaly across the region during the lockdown.
Memristor-based Chaotic System with Upper-Lower Chaotic Attractors and Abundant Dynamical Behaviors

Memristor, a controllable nonlinear element, is able to produce the chaotic signal easily. Most of the current researchers concentrate on the nonlinear properties of memristor, whereas its ability to control and adjust chaotic systems is often neglected. Thus a memristor-based chaotic system is designed to generate double-scroll chaotic attractors in this paper. The key features of the system are as follows: (a) when the polarity of the strength of memristor is adjusted, the upper-lower double-scroll chaotic attractors will be presented in the system. Chaotic motion of the system will be weakened and suppressed by properly selecting the strength of memristor, which enables the system to generate chaotic signals or suppress chaotic interference; (b) The system has very abundant dynamical behaviors, including sustained chaos, bistability, coexisting attractors, transient chaos, transient period and intermittency. To further explain the complex dynamics of the system, several basic dynamical behaviors, such as dissipation, symmetry, the stability of the equilibria, Poincare-maps, offset boosting control, recurrence analysis, 0-1 test analysis, and instantaneous phase analysis are displayed, either analytically or numerically. Moreover, the analogy circuit of the system is constructed. Experiment results prove that the PSPICE simulation and numerical analysis are consistent, which verifies the chaotic system's capability that produces chaos. Additionally, the proposed chaotic system has a strong immunity to any Gaussian noise with the zero mean. Therefore, a convenient method is employed to detect a weak multi-frequency signal embedded in the Gaussian noise based on the proposed chaotic system and the recursive back-stepping controller. The method based on the memristor-based chaos system provides a new train of thought for detecting weak signals, which is of great significance to promote the application of memristor.
A Multi-Layered LSTM for Predicting Physician Stress During an ED Shift

Emergency Department (ED) physicians are faced with complex care settings, including a high level of uncertainty and intensity. Burnout among physicians is increasing every year, and ED physicians are one of the groups most prone to burnouts and work-related stress in the US. This research focused on developing a supervised Long Short-Term Memory (LSTM) artificial neural network model to predict a physician’s stress level based on their physiological data. Twelve attending physicians working a 3:00 pm - 11:00 pm shift at Greenville Memorial Hospital (GMH) in Greenville, SC, participated in the study. Stress levels were estimated using physiological measures, including heart rate and electrodermal activity. Over 100 hours of physiological data were collected from 12 eight-hour shifts. Initially, an 80:20 split was used on the 12 individual datasets for training and testing the model. Further, to develop a generalizable model, the data were merged, and a 60:20:20 split was used for training, validating, and testing the model. On the test set, the model achieved values of 0.98, 0.17, and 0.005 as the R-squared, RMSE, and loss for EDA data and 0.99, 0.41, and 0.002 for HR data.
The Role of Price Promotion and Product Quality in Influencing the Intention to Repurchase Cok-Kis Cookies

Repurchase intention is crucial to the continuity of businesses especially snack businesses. Customer satisfaction is one of the dominant variables influencing repurchase intention. This purpose of this study was to determine the role of price promotion and product quality in affecting customer satisfaction, which in turn influences the repurchase intention of snack products. The sample consisted of 117 people (a minimum sample of 91 was calculated using the Yamane and Isaac and Michael formulas). A questionnaire was given to the participants and data were analyzed quantitatively using SmartPLS version 3.0 (Partial Least Square). The results showed that customer satisfaction had a significant influence on the repurchase interest of Cok-Kis customers. Price promotion and product quality had a significant effect on Cok-Kis customer satisfaction. Customer satisfaction could mediate the effect of price promotion and product quality on the repurchase interest of Cok-Kis costumers,