Miracle-dz/UniFAR-FTUs huggingface dataset:
Futurescapes: Imagination and temporal reorganization in the design of strategic narratives

Recent work in organizational and strategy research increasingly accords centrality to narratives in developing and legitimating future-oriented strategies. Despite this growing interest in the use of strategic narratives for prospective sensemaking and sensegiving, the design of such narratives as persuasive vehicles that shape beliefs about possible futures and firms’ potential roles in them remains undertheorized. We build on Paul Ricœur’s narrative theory to develop a theoretical framework of how firms can employ imaginative thought, in the form of productive and creative imagination, to depict plausible and desirable “possible worlds. ” We term these worlds “futurescapes” and theorize how they synthesize heterogenous information through factual and fictive means to project different types of futures that have differential persuasive qualities. We argue that continuous or near futures are likely to be perceived as more plausible, whereas discontinuous or distant futures are likely to be perceived as more desirable by stakeholder audiences. We further draw on Ricœur’s ideas about narrative time to theorize how narratives are used by firms for temporal reorganization that can alter perceptions of plausibility and desirability, thereby increasing the overall persuasiveness of their futurescapes. We illustrate our arguments using narratives created by auto manufacturers in response to the changing landscape of their industry. Our ideas have implications for research on the strategic use of narratives, future-focused strategy making as temporal work, and shaping strategies under uncertainty.
Social Implications of the Social Media Use by Students with Disabilities at the University of Jordan: Facebook as a Model

This study aimed to measure the benefits of using the social media website as perceived by students with disabilities at the University of Jordan. The sample consisted of 150 students with disabilities and used a 5-point Likert scale to rank the perceived benefits on a 12-item survey. On average, the students indicated that using social media websites provided a moderate level of social benefits. In contrast, the students indicated that social media websites provided a high level of social benefit for increasing the number of new friends, deepening their relationship with others who have a disability or who are concerned with disabilities, and identifying societal attitudes toward disability and disability support services.
Semiconducting Metal Oxides: SrTiO3, BaTiO3 and BaSrTiO3 in Gas-Sensing Applications: A Review

In this work, a broad overview in the field of strontium titanate (ST, SrTiO3)-, barium titanate (BT, BaTiO3)- and barium strontium titanate (BST, BaSrTiO3)-based gas sensors is presented and discussed. The above-mentioned materials are characterized by a perovskite structure with long-term stability and therefore are very promising materials for commercial gas-sensing applications. Within the last 20 years, the number of papers where ST, BT and BST materials were tested as gas-sensitive materials has ten times increased and therefore an actual review about them in this field has been expected by readers, who are researchers involved in gas-sensing applications and novel materials investigations, as well as industry research and development center members, who are constantly searching for gas-sensing materials exhibiting high 3S parameters (sensitivity, selectivity and stability) that can be adapted for commercial realizations. Finally, the NO2-sensing characteristics of the BST-based gas sensors deposited by the authors with the utilization of magnetron sputtering technology are presented.
An Adaptive Method for Nonlinear Sea Level Trend Estimation by Combining EMD and SSA

Adaptive and accurate trend estimation of the sea level record is critically important for characterizing its nonlinear variations and its study as a consequence of anthropogenic climate change. Sea level change is a nonstationary or nonlinear process. The present modeling methods, such as least squares fitting, are unable to accommodate nonlinear changes, including the choice of a priori information to help constrain the modeling. All these problems affect the accuracy and adaptability of nonlinear trend estimation. Here, we propose a method called EMD‐SSA, that effectively combines adaptive empirical mode decomposition (EMD) and singular spectrum analysis (SSA). First, the sea level change time series is decomposed by EMD to estimate the intrinsic mode functions. Second, the periodic or quasiperiodic signals in the intrinsic mode functions can be determined using Lomb‐Scargle spectral analysis. Third, the numbers of the identified periodicities/quasiperiodicities are used as embedding dimensions of SSA to identify possible nonlinear trends. Then, the optimal nonlinear trend with the largest absolute Mann‐Kendall rank is selected as the final trend for the sea level change. Based on a comprehensive experiment using simulated sea level change time series, we concluded that the EMD‐SSA method can adaptively provide better estimate of the nonlinear trend in a realistic sea level change time series with consistency or high accuracy. We suggest that EMD‐SSA can be used not only to robustly extract nonlinear trends in sea level data, but also for trends in other geodetic or climatic records, including gravity, GNSS observed displacements, and altimetry observations.
Voltage Control Strategy for Energy Storage System in Sustainable Distribution System Operation

Due to the increasing penetration of distributed energy resources (DERs) required for the sustainable distribution system, new voltage control strategy is needed by utilities. Traditional voltage control strategy can not support the increasing number of DERs in a coordinated and scalable manner to meet the operational voltage regulation requirement. Supported by the power electronics converter, the energy storage system can provide fast, smooth, and flexible voltage control services. In this paper, an effective and easy to implement sensitivity-based voltage control strategy is developed for the energy storage system. The developed control strategy is validated using an industrial feeder data in Northwest Washington. The proposed strategy can mitigate the voltage unbalance issue, improve the voltage profile, and correct power factors while supporting sustainable distribution system operation.
Low-Rank Tensor Graph Learning for Multi-View Subspace Clustering

Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph clustering methods learn graphs directly from the raw data, when the raw data is distorted by noise and outliers, their performance may seriously decrease; (2) subspace clustering methods use a “two-step” strategy to learn the representation and affinity matrix independently, and thus may fail to explore their high correlation. To address these issues, we propose a novel multi-view clustering method via learning a <underline>L</underline>ow-<underline>R</underline>ank <underline>T</underline>ensor <underline>G</underline>raph (LRTG). Different from subspace clustering methods, LRTG simultaneously learns the representation and affinity matrix in a single step to preserve their correlation. We apply Tucker decomposition and <inline-formula> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula>-norm to the LRTG model to alleviate noise and outliers for learning a “clean” representation. LRTG then learns the affinity matrix from this “clean” representation. Additionally, an adaptive neighbor scheme is proposed to find the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> largest entries of the affinity matrix to form a flexible graph for clustering. An effective optimization algorithm is designed to solve the LRTG model based on the alternating direction method of multipliers. Extensive experiments on different clustering tasks demonstrate the effectiveness and superiority of LRTG over seventeen state-of-the-art clustering methods.
Slow scholarship for social work: A praxis of resistance and creativity

Expediency, efficiency, and rapid production within compressed time frames represent markers for research and scholarship within the neoliberal academe. Scholars who wish to resist these practices of knowledge production have articulated the need for Slow scholarship—a slower pace to make room for thinking, creativity, and useful knowledge. While these calls are important for drawing attention to the costs and problems of the neoliberal academy, many scholars have moved beyond “slow” as being uniquely referencing pace and duration, by calling for the different conceptualizations of time, space, and knowing. Guided by post-structural feminisms, we engaged in a research project that moved at the pace of trust in the integrity of our ideas and relationships. Our case study aimed to better understand the ways macro forces such as neoliberalism, criminalization and professionalization shape domestic violence work. This article discusses our praxis of Slow scholarship by showcasing four specific key markers of Slow scholarship in our research; time reimagined, a relational ontology, moving inside and towards complexity, and embodiment. We discuss how Slow scholarship complicates how we understand constructs of productivity and knowledge production, as well as map the ways Slow scholarship offers a praxis of resistance for generating power from the epistemic margins within social work and the neoliberal academy.
Backtesting expected shortfall and beyond

We conduct a comprehensive study of the performance of leading backtesting procedures for expected shortfall. The tests differ in their analytical complexity, stability over different models, sensitivity to the sample sizes (both estimation and backtesting), and computational burden. The best performing scenario depends on the interaction between estimation error and backtesting error. We document that the speed of convergence to the nominal size also varies across tests. Traditional tests may fail to validate the candidate model, in which case we show that a scoring function test based on the joint elicitability of VaR-ES may have merit from a model comparison perspective.
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. Nevertheless, from a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. To address this problem, we first make use of the concept of local differential privacy (LDP), and propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers. According to our analysis, the UDP framework can realize <inline-formula><tex-math notation="LaTeX">$(\epsilon _{i}, \delta _{i})$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>ε</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>δ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq1-3056991.gif"/></alternatives></inline-formula>-LDP for the <inline-formula><tex-math notation="LaTeX">$i$</tex-math><alternatives><mml:math><mml:mi>i</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3056991.gif"/></alternatives></inline-formula>th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes. We then derive a theoretical convergence upper-bound for the UDP algorithm. It reveals that there exists an optimal number of communication rounds to achieve the best learning performance. More importantly, we propose a communication rounds discounting (CRD) method. Compared with the heuristic search method, the proposed CRD method can achieve a much better trade-off between the computational complexity of searching and the convergence performance. Extensive experiments indicate that our UDP algorithm using the proposed CRD method can effectively improve both the training efficiency and model quality for the given privacy protection levels.
Assessing the Effect of Diazepam on Methamphetamine-Induced Seeking Behavior in Rats

Background: Relapse is a common problem in substance-use disorders. Accumulating evidence has shown that diazepam can reduce the abuse potential of some drugs. Objectives: We investigated the effects of diazepam during and after the withdrawal period on the reinstatement of conditioned place preference (CPP) induced by a single dose of methamphetamine in male Wistar rats. Methods: Three groups of rats were trained to acquire methamphetamine CPP. The rats in the control group received, respectively, saline and methamphetamine (1 mg/kg, daily, ip) in the extinction and priming phases. Two other groups (one and two) had a similar process, but the rats in group one received diazepam (3 mg/kg, daily, ip) in the extinction phase and those in group 2 received a single dose of diazepam just before the priming phase. The CPP was checked daily during all processes. Results: We showed that a 3-day period injection of methamphetamine (1 mg/kg) resulted in a positive conditioning score on the first day after conditioning. Also, 3 mg/kg of diazepam daily in the extinction period shortened the withdrawal period for one day and extinction happened on the 9th and 10th days. Diazepam also reduced the conditioning score at the reinstatement phase (P < 0.01). Furthermore, diazepam injection (3 mg/kg) before the methamphetamine priming dose did not have any significant effect on the reinstatement of drug-seeking. Conclusions: This study demonstrated that administration of diazepam during the extinction period was associated with reduced maintenance and reinstatement of methamphetamine-induced CPP, while the single dose of diazepam could not prevent the reinstatement of methamphetamine.
How Do Founder-CEOs Sell Their Remaining Ownership Shares? Theory and Evidence

PurposeIn this paper, the author attempts to answer an important question upon founder-CEOs' exiting: How do they sell their remaining ownership shares? The literature has largely been silent on this question, and therefore is missing an important piece of the puzzle on the final stage of the founding entrepreneurs' involvement in their companies.Design/methodology/approachThe author uses both theoretical models and empirical methods to examine how founder-CEOs sell their remaining ownership shares.FindingsThe author finds that founder-CEOs of high-growth firms and those with high managerial ability are more likely to sell remaining ownership shares gradually rather than suddenly. Moreover, if either the growth or the managerial ability is high, founder-CEOs managing firms with high volatility tend to sell gradually.Originality/valueThis paper provides insights into the final stage of founding entrepreneurs' involvement in companies. The methodology of pattern recognition also helps investors and regulators in tracking and monitoring stock trading of founders and other company insiders.
Willingness to Pay for Improving Household Solid Waste Management in Vietnam

Management of solid waste is a major challenge in urban areas of most parts of the world, especially in developing countries. The study aimed to determine and estimate the willingness to pay (WTP) of the selected households for the improvement of solid waste management in Vietnam. The research evaluated WTP of household solid waste in case studies in Bien Hoa and My Tho cities using contingent valuation method (CVM). The data were collected through a closed-ended questionnaire survey and interviews with 200 households. The results showed that the most willingness to pay was equal to 10,000 and 15,000 VND per month. The respondents were willing to pay in Bien Hoa and My Tho with an average of 14,450 VND (0.63 USD) and 13,000 VND (0.56 USD) per month, respectively. Regression model identified influential factors on WTP of households in the two cities of Vietnam. The factors that significantly influence households’ WTP were monthly household income, education of respondents, age, occupation and solid waste volume (p<0.05). The results of this study can be used for the urban planning and implemented the sustainable development process in Bien Hoa and My Tho cities.
Effects of brain-based learning strategies on secondary school students’ motivation to learn in Federal Capital Territory, Abuja, Nigeria

The study was carried out to find out whether Brain-Based Learning Strategies has any effect on Senior Secondary School Students Motivation towards Learning in the Federal Capital Territory, Abuja. The pre-test post-test Quasi Experimental Research design was used with a sample size of 142 Students drawn from two Senior Secondary Schools. Two intact classes were used; each assigned to the Control and Experimental group. Data for the study was sourced using Students’ Motivation Scale (SMS). Hypotheses were tested at a significant level of 0.05 using ANCOVA. Findings revealed significant difference in the Motivation towards Learning in favor of the Experimental Group. No significant difference was observed in Motivation towards Learning of students based on gender. It was recommended that teachers should adopt the Brain-based learning strategies in teaching Economics in Senior Secondary Schools in order to improve on motivation. Gender biases in implementation of Brain-Based learning strategy should be discouraged.
Impact of Management Regime and Regime Change on Gravel Barrier Response to a Major Storm Surge

Gravel barriers represent physiographic, hydrographic, sedimentary, and ecological boundaries between inshore and open marine offshore environments, where they provide numerous important functions. The morphosedimentary features of gravel barriers (e.g., steep, energy reflective form) have led to their characterization as effective coastal defense features during extreme hydrodynamic conditions. Consequently, gravel barriers have often been intensively managed to enhance coastal defense functions. The Blakeney Point Barrier System (BPBS), U.K., is one such example, which offers the opportunity to investigate the impact of alternative management regimes under extreme hydrodynamic conditions. The BPBS was actively re-profiled along its eastern section from the 1950s to the winter of 2005, whilst undergoing no active intervention along its western section. Combining an analysis of remotely sensed elevation datasets with numerical storm surge modeling, this paper finds that interventionist management introduces systemic differences in barrier morphological characteristics. Overly steepened barrier sections experience greater wave run-up extents during storm surge conditions, leading to more extreme morphological changes and landward barrier retreat. Furthermore, while high, steep barriers can be highly effective at preventing landward flooding, in cases where overwashing does occur, the resultant landward overtopping volume is typically higher than would be the case for a relatively lower crested barrier with a lower angled seaward slope. There is a growing preference within coastal risk management for less interventionist management regimes, incorporating natural processes. However, restoring natural processes does not immediately or inevitably result in a reduction in coastal risk. This paper contributes practical insights regarding the time taken for a previously managed barrier to relax to a more natural state, intermediary morphological states, and associated landward water flows during extreme events, all of which should be considered if gravel barriers are to be usefully integrated into broader risk management strategies.
Incorporating both Zoom and YouTube in Micro Teaching Class during the Covid-19 Pandemic: An Effectiveness Investigation

The article aims at seeking the effectiveness of incorporating both Zoom and YouTube in Micro teaching class by webinar. This class at the first was conducted traditionally by face-to-face interaction in the classroom. After that the Covid-19 pandemic came, all systems were shocked and changed. Students and lecturers had to keep distance socially and physically by learning and working at home. So, all Micro teaching classes were carried out on line by using internet aid using applications such as Zoom and YouTube. The research used quantitative method by distributing questionnaire to students and lecturers after they used the applications during the teaching learning process. Students’ scores in teaching performance were also considered to seek the effectiveness. The sample was selected purposively since not all lecturers applied webinar in their classes. The finding reveals that incorporating both zoom as a synchronous learning model and YouTube as an asynchronous one is effective in teaching micro teaching based on the lecturers’ responses in the questionnaires and viewed from students’ score in final teaching performance which was shared via YouTube in the middle of pandemic era.
A SURVEY TO ASSESS KNOWLEDGE, ATTITUDE AND PRACTICES TOWARDS
COVID-19 AMONGST NHS HEALTHCARE WORKERS AT A TERTIARY HEALTH
BOARD IN THE UNITED KINGDOM

Background and aim: The world is currently facing a public health emergency in the form of COVID-19 pandemic. United Kingdom conrmed it's initial cases in February 2020 and since then the NHS (National Health Services) and its healthcare workers have been working diligently to ght this pandemic facing unprecedented challenges. Materials and methods: A cross-sectional knowledge, attitude and practices (KAP) survey was conducted using a questionnaire among healthcare professionals belonging to an NHS University Health Board in Wales, UK. The responses were assessed and relevant statistical analyses were performed using SPSS software. Results: Atotal of 208 frontline NHS Workers participated in the study. Mean knowledge score in the study was 87.83%. Majority (67.79%) of the participants had a high knowledge score. Knowledge scores were found to have a positive association with age. The general attitude among the participants was found to be optimistic with a mean attitude score of 69.87%. Practices varied amongst the participants with 77.5% reporting regular use of the mask at work. Conclusion: This study reects that majority of healthcare professionals had a sound knowledge and understanding about the pandemic with an optimistic attitude towards its control. Majority of them have been implementing safe practices at workplace.