Miracle-dz/UniFAR-FTUs huggingface dataset:
Teachers’ Emotions: Validation of the Teacher Emotions Scales in Albanian

The topic of teacher emotions has gained increasing research attention over the past years. Initial predominantly qualitative inquiry methods have been complemented by quantitative ones, and different instruments to measure teacher emotions have been developed. These instruments mainly stem from Western, educated, industrialized, rich, and democratic (WEIRD) countries, and yet it is still unknown if these instruments are of universal cultural functionality. The current study aimed to validate the Teacher Emotions Scale (TES) in the low-to-middle income, Southeastern European country of Kosovo. Findings from N = 258 teachers in Kosovo provide evidence that TES-Albanian operates equivalently in terms of factor structure as the original German version of TES and the English version. Supporting the external validity of the scale, we found consistent low-to-medium relationships between the three emotions measured with the TES (enjoyment, anger, and anxiety) with other teachers’ experiential and behavioral constructs such as positive and negative affect, job satisfaction, burnout, self-efficacy, and the teacher–student relationship. Overall, we conclude that TES can effectively be translated into different languages to measure teacher emotions also in non-WEIRD cultural contexts.
SOCIAL HOUSING ALLOCATION IN SCOTLAND: THE ADVERSE SELECTION ISSUES

Since the social housing in Scotland is delivered through the public purse, ideally, the social landlords should allocate their stock efficiently to the right recipients. However, a social landlord may occasionally be subjected to adverse selection situations when applicants withhold certain information during the social housing application. Within the information theory spectrum, adverse selection stems from the belief that humans tend to act opportunistically by putting their self-interest first above others’ interest. While there is ample evidence of vast adaptation of the information theory in different fields and sectors, there is still a remaining gap in the information theory application in understanding the actors’ interaction in the social housing sector. By utilising semi- structured interviews with the housing officers from one case study, ten (10) housing officers from the case study in Aberdeen participated to share their views and experience during the pre-allocation stage. Three (3) broad themes were discovered which consist of withholding information, exploiting the loophole in the system and finally, lack of ways in verifying the information. Data has been analysed through a combination of thematic and content analysis. The thematic analysis primarily aims to establish common themes across settings. The content analysis is employed to support the thematic analysis by conducting a textual investigation and subsequently establishing the frequencies of the event shared by the housing officers. The findings yielded some noteworthy results that signified the matters commonly manipulated by the applicants, leading to adverse selection situations during the pre-allocation stage. It produces critical insights on the issues that the social landlord should thoroughly assess during the pre-allocation stage in improving their allocation practice. Predominantly, helping them to meet the right target recipients in the social housing allocation.
Probing masses of SZ-detected clusters using clustering signatures

Abstract We investigate the clustering of clusters detected by Planck via the Sunyaev-Zeldovich (SZ) effect. The bias (the offset of the clustering relative to what is expected for dark matter) is then used to estimate the average mass of clusters in the sample, using mass-bias relations found in simulations. We also compare the clustering of Planck clusters with those detected in the Sloan Digital Sky Survey (SDSS) using the GMBCG method. Our results indicate that Planck clusters have a higher average mass than that inferred from their SZ signatures.
A theory of coupled strain-gradient plasticity with species transport at small deformations

This work presents a mechanical isotropic rate-independent theory for plastically deformed materials coupled with species transport. The mass and virtual power balances are natural ingredients used in this work to obtain appropriate local balance laws for species transport, macroscopic, and microscopic forces. The second law of thermodynamics is another key tool used to obtain thermodynamically consistent constitutive relations for the species flux and microscopic stresses. The free energy is approximated as a quadratic form and used to obtain the energetic microscopic stresses as linear combinations of their respective energy conjugates and the species densities. Rate-independent Mises flow rule is deduced in terms of accumulated plastic strain and species density. Furthermore, variational formulation for the coupled theory is obtained as a variational inequality.
Effects of shear-thinning nature and opposing buoyancy from a square geometry in a confined framework

ABSTRACT Numerical computations were brought up to study the effect of opposing buoyancy mixed convection (Ri = 0 to − 1) flow of power law shear-thinning fluids past a confined cooled square bluff body at Prandtl numbers (Pr) = 1, 50 and Reynolds numbers (Re) = 1–40. Irrespective of the n, Ri, Pr, and Re, the flow separation is delayed with increasing confinement (β). The vortex shedding and flow separation start earlier for shear-thinning fluids than Newtonian fluid. For opposing buoyancy (Ri < 0), the vortex shedding starts earlier on increasing Pr (except for n = 0.2, Ri = −1). Also, the periodic unsteady transition appears at some higher value of Re on increasing Ri for fixed Pr. The drag coefficient (CD) value reduces with the decrease in n, whereas the maximum CD is noted for Newtonian fluids. The maximum augmentation in the heat transfer was reached about 9 and 36% on comparing with Newtonian fluids and forced convection case, respectively, and also the corresponding maximum compression in heat transfer was found about 15 and 5%, respectively. The numerical results have also been correlated for CD and the Colburn jh factor values for various Re, Pr, Ri, and n. In surplus, the effects of wall confinement ranging from β = 25 to 50% on flow separation and engineering output parameters were studied in a steady regime.
Solution of stochastic optimal control problems and ﬁnancial applications

In this paper, the stochastic optimal control problems, which frequently occur in economic and ﬁnance are investigated. First, using Bellman’s dynamic programming method the stochastic optimal control problems are converted to Hamilton-Jacobi-Bellman (HJB) equation. Then, obtained HJB equation is solved through the method of separation of variables by guessing a solution via its terminal condition. Also, the non-linear optimal feedback control law is constructed. Finally, the solution procedure is illustrated for solving some examples that two of them are ﬁnancial models. In fact, to highlight the applications of stochastic optimal control problems in ﬁnancial mathematics, some ﬁnancial models are presented.
Effectiveness of Digital Physiotherapy Practice Compared to Usual Care in Long COVID Patients: A Systematic Review

Long COVID syndrome has been recognized as a public health problem. Digital physiotherapy practice is an alternative that can better meet the needs of patients. The aim of this review was to synthesize the evidence of digital physiotherapy practice in Long COVID patients. A systematic review was carried out until December 2022. The review was complemented by an assessment of the risk of bias and methodological quality. A narrative synthesis of results was conducted, including subgroup analyses by intervention and clinical outcomes. Six articles, including 540 participants, were selected. Five articles were considered of high enough methodological quality. Parallel-group, single-blind, randomized controlled trials were the most commonly used research design. Tele-supervised home-based exercise training was the most commonly used intervention. Great heterogeneity in clinical outcomes and measurement tools was found. A subgroup analysis showed that digital physiotherapy is effective in improving clinical outcomes. Significant differences in favor of digital interventions over usual care were reported. Nevertheless, discrepancies regarding effectiveness were found. Improvements in clinical outcomes with digital physiotherapy were found to be at least non-inferior to usual care. This review provides new evidence that digital physiotherapy practice is an appropriate intervention for Long COVID patients, despite the inherent limitations of the review. Registration: CRD42022379004.
Dynamics of Quantum Information of the Central Spin Problem

Environmental effects on the evolution of a spin system in the context of the central spin problem, have been studied for more than 60 years. With the growing complexity of quantum information processors there is a new need to better understand and control the interactions of qubits with their environment. Decoherence is an apparent loss of quantum coherence of the central spin, which is the result of the coherent evolution of the central spin and its spin environment. This evolution may be understood as the consequence of local field fluctuations induced by heteronuclear dipolar interaction between the central spin and the environment spins and homonuclear dipolar interaction of spins in the environment. A complete theoretical description for the evolution of the central spin does not exist and numerical solutions are restricted to small spin environments. Another way of looking at the central spin problem is to study the correlations between the central spin and the environment spins. In this method the evolution of the central spin is described with the dynamics of multi-spin correlations resulting from interactions of the central spin and the environment spins. Using Multiple Quantum Nuclear Magnetic Resonance (MQ NMR) techniques, we have designed experiments for the direct detection of multi-spin correlations between the central spin and environment spins. These experiments are used to observe the progress in production of correlations between the central spin and the environment. They reveal the multi-spin dynamics that underlies the decoherence process. The central spin is initially uncorrelated with the environment and quantum information resides exclusively on it. After the interaction with the environment spins quantum information is shared in the form of correlated operators between the central spin and the environment. Using our experiments this flow of quantum information from the central spin to the environment and the quantum information content of the environment, can be quantified. Further, these experiments are used to gauge the sensitivity of correlation to perturbation in the environment, by observing the mixing dynamics of the multi-spin correlations. The Out-of-Time Correlation metric is used for the sensitivity measurements. We find that the dynamics of correlations in our system is better explained by the extent of information flow to the environment rather than the evolution time.
A Study on Mechanical Properties of Low-Cost Thermoplastic-Based Materials for Material Extrusion Additive Manufacturing

The present research focused on studying the mechanical properties of three commercially available thermoplastic-based materials used for the additive manufacturing (AM) fused filament deposition (FFD) method. The scientific motivation for the study was the limited information available in the literature regarding the materials’ properties, the inconsistencies that were recorded by other scientists between the materials’ properties and the technical datasheets and the anisotropic behavior of additively manufactured materials. Thereby, it was considered of great importance to perform an extensive study on several materials’ mechanical properties, such as tensile properties and flexural properties. Three materials were tested, Tough PLA, nGen CF10 and UltraFuse PAHT CF15. The tests consisted of monotonic tensile tests, open-hole tensile tests and three-point bending tests. The tests were assisted also with the use of microscopical investigations. Framed specimens’ configurations with two different raster orientations (90°/0° and −45°/+45°) were manufactured using an in-house-developed 3D printing equipment. The best mechanical performances were recorded for UltraFuse PAHT CF15. The 90°/0° raster orientations ensured the highest tensile, open-hole tensile and flexural strength, regardless of the material type, while the −45°/+45° raster orientations ensured the highest elongation values. The analysis showed the importance of the experimental validation of materials for AM.
Trafformer: Unify Time and Space in Traffic Prediction

Traffic prediction is an important component of the intelligent transportation system. Existing deep learning methods encode temporal information and spatial information separately or iteratively. However, the spatial and temporal information is highly correlated in a traffic network, so existing methods may not learn the complex spatial-temporal dependencies hidden in the traffic network due to the decomposed model design. To overcome this limitation, we propose a new model named Trafformer, which unifies spatial and temporal information in one transformer-style model. Trafformer enables every node at every timestamp interact with every other node in every other timestamp in just one step in the spatial-temporal correlation matrix. This design enables Trafformer to catch complex spatial-temporal dependencies. Following the same design principle, we use the generative style decoder to predict multiple timestamps in only one forward operation instead of the iterative style decoder in Transformer. Furthermore, to reduce the complexity brought about by the huge spatial-temporal self-attention matrix, we also propose two variants of Trafformer to further improve the training and inference speed without losing much effectivity. Extensive experiments on two traffic datasets demonstrate that Trafformer outperforms existing methods and provides a promising future direction for the spatial-temporal traffic prediction problem.
Selective HIF2A Inhibitors in the Management of Clear Cell Renal Cancer and Von Hippel–Lindau-Disease-Associated Tumors

Von Hippel–Lindau (VHL) loss is the hallmark event characterizing the clear cell renal cancer subtype (ccRCC). Carriers of germinal VHL mutations have an increased prevalence of kidney cysts and ccRCC as well as hemangioblastoma, pheochromocytoma and pancreatic neuroendocrine tumors. In both sporadic and inherited ccRCC, the primary mechanism of VHL-mediated carcinogenesis is the abnormal stabilization of hypoxia-inducible factors (HIF1A and HIF2A). While HIF1A acts as a tumor suppressor and is frequently lost through inactivating mutations/14q chromosome deletions, HIF2A acts as an oncogene promoting the expression of its target genes (VEGF, PDGF, CAIX Oct4, among others). Selective HIF2a inhibitors block the heterodimerization between HIF2A and ARNT, stopping HIF2A-induced transcription. Several HIF2A inhibitors have entered clinical trials, where they have shown a favorable toxicity profile, characterized by anemia, fatigue and edema and promising activity in heavily pretreated ccRCC patients. Belzutifan, a second-generation HIF2a inhibitor, was the first to receive FDA approval for the treatment of unresectable ccRCC in VHL syndrome. In this review, we recapitulate the rationale for HIF2a blockade in ccRCC, summarize the development of HIF2a inhibitors from preclinical models up to its introduction to the clinic with emphasis on Belzutifan, and discuss their role in VHL disease management.
Evaluation of the Effects of Different Polishing Protocols on the Surface Characterizations of 3D-Printed Acrylic Denture Base Resins: An In Vitro Study

Chairside polishing kits are an alternative to laboratory polishing techniques. The effects of using a chairside polishing kit on a three-dimensional (3D)-printed acrylic denture base (ADB) have not been reported previously. Thus, this study aimed to evaluate the effects of different chairside polishing techniques on the surface characterizations of ABD, including surface roughness average (Ra), average maximum profile height (Rz), and scanning electron microscopy (SEM) representations. One hundred and twenty disc-shaped specimens were fabricated from one conventional heat-polymerized (HP) ADB resin and two 3D-printed (Asiga (AS) and NextDent (ND)) ADB resins (n = 40 per material). Each group was further divided based on the polishing protocol (n = 10) as follows: conventional polishing protocol (C), microdont chairside polishing kit (M), shofu chairside polishing kit (S), and an unpolished group (U). The Ra and Rz values were measured using an optical profilometer. Two-way ANOVA and post hoc tests were used for data analysis (α = 0.05) at significant levels. In unpolished groups, there was a statistically significant difference between HP-U vs. AS-U and ND-U groups (p < 0.0001). For Ra, the lowest values were observed in HP-C, AS-S, and ND-C. While the highest values were shown in all unpolished groups. Within the material, there were statistically significant differences between the three polishing protocols (C, M, and S) vs. unpolished (p < 0.0001), while there was no significant between C, M, and S groups (p = 0.05). The Rz values had the same pattern as the Ra values. The two chairside polishing kits were comparable to conventional polishing techniques, and they can be recommended for clinical application.
Vehicle detection in remote sensing images using denoizing-based convolutional neural networks

ABSTRACT Vehicle detection in remote sensing images is a tough task and of great significance due to the fast increasing number of vehicles occurring in big cities. Recently, convolutional neural network (CNN)-based methods have achieved excellent performance in classification task due to their powerful abilities in high-level feature extraction. However, overfitting is a serious problem in CNN when applying complicated fully-connected layers, especially when the quantity of training samples is limited. In order to tackle this problem, a denoizing-based CNN called DCNN is proposed in this letter. More specially, a CNN with one fully-connected layer is pre-trained first for feature extraction. After that, features of this fully-connected layer are corrupted and used to pre-train a stacked denoizing autoencoder (SDAE) in an unsupervised way. Then, the pre-trained SDAE is added into the CNN as the fully-connected layer. After fine-tuning, DCNN can make the extracted features more robust and the detecting rate higher. With the help of our proposed locating method, vehicles can be detected effectively even when they are parked in a residential area. Comparative experiments demonstrate that our method has achieved state-of-the-art performance.
Privacy Enhancing for Fog Computing based - IoT

: With the massive inflation of newly developed technologies, recourse to data has become a necessity in light of the current inflation and excessive need dominating the world and developed societies. According to the control of millions of smart devices and sensors connected to an interconnected and controlled automated system within installed scales due to the services provided by IOT devices through the created fog layer that connects the cloud centers and those devices, in addition, very large amounts of that data including public and private are passed through the connection of Internet of Things devices to each other. Smart and advanced networks as one of the fog computing applications play a prominent and accurate role in the infrastructure for reliable and sound data transmission. Accordingly, the process of data aggregation is an important and common matter in the world of fog-enhancing Internet of Things, so preserving the privacy of that data is a matter of concern, and based on this principle, we propose in this paper a model for data aggregation that maintains privacy using a foggy computing environment called PPFDA (privacy preserving based-fog computing data aggregation). We use in our scheme DF homomorphic cryptosystem as it consider one of the aggregation models that ensures the privacy purpose. The theoretical results and analyzes show that our design is ensuring the privacy of data during collection using an algorithm of DF. The results confirm that the proposed scheme achieves security and privacy purposes in modern network systems for the Internet of things based in fog computing. In addition, it contributes significantly to the efficient performance of storage operations.
FiberGrowth Pipeline: A Framework Toward Predicting Fiber-Specific Growth From Human Gut Bacteroidetes Genomes

Dietary fibers impact gut colonic health, through the production of short-chain fatty acids. A low-fiber diet has been linked to lower bacterial diversity, obesity, type 2 diabetes, and promotion of mucosal pathogens. Glycoside hydrolases (GHs) are important enzymes involved in the bacterial catabolism of fiber into short-chain fatty acids. However, the GH involved in glycan breakdown (adhesion, hydrolysis, and fermentation) are organized in polysaccharide utilization loci (PUL) with complex modularity. Our goal was to explore how the capacity of strains, from the Bacteroidetes phylum, to grow on fiber could be predicted from their genome sequences. We designed an in silico pipeline called FiberGrowth and independently validated it for seven different fibers, on 28 genomes from Bacteroidetes-type strains. To do so, we compared the existing GH annotation tools and built PUL models by using published growth and gene expression data. FiberGrowth’s prediction performance in terms of true positive rate (TPR) and false positive rate (FPR) strongly depended on available data and fiber: arabinoxylan (TPR: 0.89 and FPR: 0), inulin (0.95 and 0.33), heparin (0.8 and 0.22) laminarin (0.38 and 0.17), levan (0.3 and 0.06), mucus (0.13 and 0.38), and starch (0.73 and 0.41). Being able to better predict fiber breakdown by bacterial strains would help to understand their impact on human nutrition and health. Assuming further gene expression experiment along with discoveries on structural analysis, we hope computational tools like FiberGrowth will help researchers prioritize and design in vitro experiments.
The Limitations of Hashtag Feminist Activism on South African Twitter: A Case Study of #MenAreTrash and #WomenAreTrash

Social media has become a central arena for debates around feminism and gender in South Africa. One recent example can be found in the hashtag #MenAreTrash, which has been a trending topic multiple times on South African Twitter. However, despite the prevalence of gender-based violence in the country, these conversations have also come under strong criticism, especially from men. Existing literature on social media expressions of misogyny and anti-feminism has primarily focused on North American and European case studies. This article expands existing literature by analyzing men’s reactions to the #MenAreTrash hashtag and highlighting the role and backlash against feminism in South Africa. Based on a thematic analysis of 600 tweets, this article explores the discursive strategies employed by South African cisgender men on Twitter in response to the #MenAreTrash hashtag. Beyond some marginal yet relevant examples of reflexibility and allyship, the overwhelmingly negative response from men online, as found by the study, reflected a lack of self-reflection about their masculinity. The article provides additional insights into the role of digital feminist activism in shaping networked masculinities in the South African context.