Miracle-dz/UniFAR-FTUs huggingface dataset:
Food Security and Social Inequalities from the Perspective of Women's Rights: Case Study of District Torghar

Purpose: The research study was designed with the sole aim to explore the food security status in the purview of women’s rights in the District Tor Ghar, KP, Pakistan Approach: A sample size of 291 out of 760 household heads was selected randomly from 3 villages of Khander Tehsil namely Bilyani, Bimbal, and Dharo. The conceptual framework comprised of an independent variable (availability of food) and one dependent variable (food insecurity). Data on study variables were collected using the Likert scale. The Chi-square test was applied to test the strength of association among study variables. Findings: The study found that the limited availability of vegetables, fruits, bread, and meat required for a daily meal for women alongside sufficient milk/ dairy products and dry fruit to feed women at home, were found significant (p <0.05) in association with food insecurity. Thus, lack of availability of food is the root cause of food insecurity among women of the study inhabitants. Implications: A policy drive led by an awareness-raising campaign at an institutional, cultural and societal level that ensures the availability of a sufficient amount of quality food to all social segments to overcome the problem of food insecurity, especially with respect to women folk was the major study recommendations.
A Stability Analysis for the Arbitrary Lagrangian Eulerian Formulation with Finite Elements

In this paper we present some theoretical results on the Arbitrary Lagrangian Eulerian (ALE) formulation. This formulation may be used when dealing with moving domains and consists in recasting the governing differential equation and the related weak formulation in a frame of reference moving with the domain. The ALE technique is first presented in the whole generality for conservative equations and a result on the regularity of the underlying mapping is proven. In a second part of the work, the stability property of two types of finite element ALE schemes for parabolic evolution problems are analyzed and its relation with the so-called Geometric Conservation Laws is addressed.
Functional and Immunological Studies Revealed a Second Superantigen Toxin in Staphylococcal Enterotoxin C Producing Staphylococcus aureus Strains

Staphylococcus aureus is a human and animal pathogen as well as a commensal bacterium. It can be a causative agent of severe, life-threatening infections with high mortality, e.g., toxic shock syndrome, septic shock, and multi-organ failure. S. aureus strains secrete a number of toxins. Exotoxins/enterotoxins are considered important in the pathogenesis of the above-mentioned conditions. Exotoxins, e.g., superantigen toxins, cause uncontrolled and polyclonal T cell activation and unregulated activation of inflammatory cytokines. Here we show the importance of genomic analysis of infectious strains in order to identify disease-causing exotoxins. Further, we show through functional analysis of superantigenic properties of staphylococcal exotoxins that even very small amounts of a putative superantigenic contaminant can have a significant mitogenic effect. The results show expression and production of two distinct staphylococcal exotoxins, SEC and SEL, in several strains from clinical isolates. Antibodies against both toxins are required to neutralise the superantigenic activity of staphylococcal supernatants and purified staphylococcal toxins.
Exploration of Emotion Dynamics Sensing Using Trapezius EMG and Fingertip Temperature

Exploration of the physiological signals associated with subjective emotional dynamics has practical significance. Previous studies have reported that the dynamics of subjective emotional valence and arousal can be assessed using facial electromyography (EMG) and electrodermal activity (EDA), respectively. However, it remains unknown whether other methods can assess emotion dynamics. To investigate this, EMG of the trapezius muscle and fingertip temperature were tested. These measures, as well as facial EMG of the corrugator supercilii and zygomatic major muscles, EDA (skin conductance level) of the palm, and continuous ratings of subjective emotional valence and arousal, were recorded while participants (n = 30) viewed emotional film clips. Intra-individual subjective–physiological associations were assessed using correlation analysis and linear and polynomial regression models. Valence ratings were linearly associated with corrugator and zygomatic EMG; however, trapezius EMG was not related, linearly or curvilinearly. Arousal ratings were linearly associated with EDA and fingertip temperature but were not linearly or curvilinearly related with trapezius EMG. These data suggest that fingertip temperature can be used to assess the dynamics of subjective emotional arousal.
Nanomechanics of Retained Austenite in Medium-Carbon Low-Temperature Bainitic Steel: A Critical Analysis of a One-Step versus a Two-Step Treatment

A two-step bainitic treatment with a final isothermal temperature below MS was adopted to obtain bainitic steel with abundant retained austenite (RA). Nanoindentation testing was used to investigate the stability of RA in bainite steel and clarify the effect of RA on the deformation of medium-carbon steel. The results showed that, in contrast to the traditional one-step approach, a greater amount of nanoscale RA film was obtained using the two-step treatment. This was due to a lower final bainitic transformation temperature, which induced a higher carbon concentration in the untransformed austenite in the stasis stage; this resulted in untransformed austenite with a higher carbon content existing as RA rather than forming martensite in the subsequent cooling process. In addition, it was determined that the increased stability of RA during the two-step transformation delayed the pop-in point.
Fund-level Investor Sentiment and Mean-variance Relation: Evidence From Singapore-listed ETFs

This paper tests the influences of fund-level sentiment on the mean-variance relation in ETF market. We find that in low (high)-sentiment periods, the expected excess return is positively (negatively) related to the conditional variances. Sentiment traders undermine the otherwise positive risk-return tradeoff and even twist it into a negative one in high-sentiment periods. The impact of sentiment is stronger during the global pandemic.
3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics

Abstract Brain network analyses have exploded in recent years and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to phenotypic traits has lagged behind. Our previous work developed a novel analytic framework to assess the relationship between brain network architecture and phenotypic differences while controlling for confounding variables. More specifically, this innovative regression framework related distances (or similarities) between brain network features from a single task to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. Here we extend that work to the multitask and multisession context to allow for multiple brain networks per individual. We explore several similarity metrics for comparing distances between connection matrices and adapt several standard methods for estimation and inference within our framework: standard F test, F test with scan-level effects (SLE), and our proposed mixed model for multitask (and multisession) BrAin NeTwOrk Regression (3M_BANTOR). A novel strategy is implemented to simulate symmetric positive-definite (SPD) connection matrices, allowing for the testing of metrics on the Riemannian manifold. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.
Modelling the Kinetics, Thermodynamic and Physical Properties of Coconut (Cocos nucifera L.) during Convective Drying

The drying kinetics, thermodynamic properties, and energy consumption of five potential coconut cultivars identified by Ghana's CSIR-Oil Palm Research Institute were studied. Drying was carried out in a convectional dryer using four temperatures (70, 80, 90 and 100oC) and 2.0 m/s air velocity. The asymptotic model was adjudged the best fit model in predicting moisture content based on the highest coefficient of determination (0.9589-0.9998) and lowest residual sum of squares (8.427-252.61), chi-square (0.52671-16.8409) and root mean square error (2.8744-3.4421). Temperature caused between 66.8-96.5% variations in moisture diffusivity. Thermodynamic study revealed endothermic and non-spontaneous reactions in the drying system resulting from enthalpy change and Gibbs free energy change. Meanwhile, a direct relation was established among higher spontaneity and higher temperature. Despite the high drying temperatures used for the experiment, internal cellular composition was not affected as a result of excellent rehydration capacity. In effect, the Vanuatu TThe drying kinetics, thermodynamic properties, and energy consumption of five potential coconut cultivars identified by Ghana's CSIR-Oil Palm Research Institute were studied. Drying was carried out in a convectional dryer using four temperatures (70, 80, 90 and 100oC) and 2.0 m/s air velocity. The asymptotic model was adjudged the best fit model in predicting moisture content based on the highest coefficient of determination (0.9589-0.9998) and lowest residual sum of squares (8.427-252.61), chi-square (0.52671-16.8409) and root mean square error (2.8744-3.4421). Temperature caused between 66.8-96.5% variations in moisture diffusivity. Thermodynamic study revealed endothermic and non-spontaneous reaction in the drying system resulting from enthalpy change and Gibbs free energy change. Meanwhile, a direct relation was established among higher spontaneity and higher temperature. Despite the high drying temperatures used for the experiment, internal cellular composition was not affected as a result of excellent rehydration capacity. In effect, the Vanuatu Tall was adjudged as the best coconut variety based on its lower energy consumption and activation energy, shorter drying time and higher moisture diffusivity. All was adjudged as the best coconut variety based on its lower energy consumption and activation energy, shorter drying time and higher moisture diffusivity.
Data-Driven Cyber-Vulnerability Maintenance Policies

The frequencies of cyber attacks and known cyber vulnerabilities continue to increase and there is a need for models to focus limited administrator attention and build cases for additional resources. A related challenge is the scarcity of available data partly because of security concerns. In this paper, we propose a method based on Markov decision processes (MDP) for the generation and graphical evaluation of relevant maintenance policies for cases with limited data availability. The proposed method also provides an estimate of the cost benefit of collecting additional data. Both Bayesian and non-Bayesian formulations of the transition probabilities and cost models are considered. We apply the proposed method to a real-world cyber-vulnerability dataset and generate specific guidance and cost predictions. We also illustrate the relevance of the proposed method to general MDP modeling using a numerical example involving three levels of data scarcity.
D = 3 superconformal symmetry of the superstring

Invariance of the superstring under D = 3 superconformal symmetry is discussed in the sector described by the OSp(4|6)/(SO(1, 3) × U(3)) supercoset sigma-model action presented in the conformal basis for the osp(4|6)/(so(1, 3) × u(3)) Cartan forms. Transformation rules under D = 3 superconformal symmetry for the (10|24)-dimensional ‘reduced’ superspace coordinates are obtained and used to derive corresponding world-sheet currents.
Detection of Subarachnoid Hemorrhage in Computed Tomography Using Association Rules Mining

Subarachnoid hemorrhage (SAH) is one of the serious strokes of cerebrovascular accidents. There is an approx. 15% probability of spontaneous subarachnoid hemorrhage in all acute cerebrovascular accidents (CVAs). Most spontaneous subarachnoid hemorrhages are caused by ruptures of intracranial aneurysms, accounting for about 85% of all occurrences. About 15% of acute cerebrovascular disorders are caused by spontaneous subarachnoid hemorrhage. This illness is mostly caused by brain/spinal arteriovenous malformations, extracranial aneurysms, and hypertension. Computed tomography (CT) scan is the common diagnostic modality to evaluate SAH, but it is very difficult to identify the abnormality. Thus, automatic detection of SAH is required to recognize the early signs and symptoms of SAH and to provide appropriate therapeutic intervention and treatment. In this article, the gray-level cooccurrence matrix (GLCM) is used to extract useful features from CT images. Then, the New Association Classification Frequent Pattern (NCFP-growth) algorithm is applied, which is based on association rules. Then, it is compared with FP-growth methods with association rules and FP-growth methods without association rules. The experimental results indicate that the suggested approach outperforms in terms of classification accuracy. The proposed approach equates to a 95.2% accuracy rate compared to the conventional data mining algorithm.
Investigation of the turning speed for immersive environments using freehand‐based technique

Designing a natural, usable, and efficient gesture‐based virtual locomotion interface in immersive environments has been challenging. Freehand‐based virtual locomotion technique (VLT) has the advantage of eliminating the need to see the handhold devices and has been implemented in many applications. Translation control and direction control are two essential components of freehand‐based VLT; however, the transfer function of turning speed in direction control has not been well explored. In this article, we systematically investigate the effects of turning speed on freehand‐based virtual locomotion for the first time. A transfer function that maps the position of the hand to the turning speed is defined, and three parameters related to the turning speed are investigated. In the experiment, participants are asked to travel in the virtual environment from a first‐person perspective with different parameters. Statistical analysis of the experimental results reveals the relationship between the turning speed and the traveling performance as well as the traveling experience.
Establishment of Recombinant Trisegmented Mopeia Virus Expressing Two Reporter Genes for Screening of Mammarenavirus Inhibitors

Highly pathogenic Arenaviruses, like the Lassa Virus (LASV), pose a serious public health threat in affected countries. Research and development of vaccines and therapeutics are urgently needed but hampered by the necessity to handle these pathogens under biosafety level 4 conditions. These containment restrictions make large-scale screens of antiviral compounds difficult. Therefore, the Mopeia virus (MOPV), closely related to LASV, is often used as an apathogenic surrogate virus. We established for the first time trisegmented MOPVs (r3MOPV) with duplicated S segments, in which one of the viral genes was replaced by the reporter genes ZsGreen (ZsG) or Renilla Luciferase (Rluc), respectively. In vitro characterization of the two trisegmented viruses (r3MOPV ZsG/Rluc and r3MOPV Rluc/ZsG), showed comparable growth behavior to the wild type virus and the expression of the reporter genes correlated well with viral titer. We used the reporter viruses in a proof-of-principle in vitro study to evaluate the antiviral activity of two well characterized drugs. IC50 values obtained by Rluc measurement were similar to those obtained by virus titers. ZsG expression was also suitable to evaluate antiviral effects. The trisegmented MOPVs described here provide a versatile and valuable basis for rapid high throughput screening of broadly reactive antiviral compounds against arenaviruses under BSL-2 conditions.
A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products

Progressively monitoring water quality is crucial, as aquatic contaminants can pose risks to human health and other organisms. Machine learning can support the development of new effective tools for water monitoring, including the detection of algal blooms from remotely sensed image series. Therefore, in this paper, we introduce the Algal Bloom Forecast (ABF) framework, a fully automated framework for algal bloom prediction in inland water bodies. Our approach combines machine learning, time series of remotely sensed products (i.e., Moderate-Resolution Imaging Spectroradiometer (MODIS) images), environmental data and spectral indices to build anomaly detection models that can predict the occurrence of algal bloom events in the posterior period. Our assessments focused on the application of the ABF framework equipped with the support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) methods, the outcomes of which were compared through different evaluation metrics such as global accuracy, the kappa coefficient, F1-Score and R2-Score. Case studies covering the Erie (USA), Chilika (India) and Taihu (China) lakes are presented to demonstrate the effectiveness and flexibility of our learning approach. Based on comprehensive experimental tests, we found that the best algal bloom predictions were achieved by bringing together the ABF design with the RF model.
Using a Socio-Ecological Framework to Understand Romantic Relationship Satisfaction Among Emerging Adults During the COVID-19 Pandemic

The formation and maintenance of satisfying romantic relationships, a developmental milestone for many emerging adults, has been challenged by the negative impact of the COVID-19 pandemic. Yet, the impact of COVID-19 stress on relationship satisfaction has not been explored within the context of individual and relationship factors. Guided by a socio-ecological framework, this study used a longitudinal design to investigate the impact of individual-level (i.e., growth beliefs, destiny beliefs), couple-level (i.e., daily criticism), and societal-level (i.e., COVID-19 stress) factors on relationship satisfaction during Fall 2021. We also explored the moderating effects of destiny beliefs and growth beliefs. Results revealed relationship satisfaction was negatively associated with daily criticism, but not directly associated with destiny beliefs, growth beliefs, or COVID-19 stress. However, growth beliefs buffered against the negative impact of criticism on relationship satisfaction. These findings are consistent with the notion that growth beliefs may play a protective role in relationship processes.
Investigating Extreme Dependences: Concepts and Tools

We investigate the relative information content of six measures of dependence between two random variables $X$ and $Y$ for large or extreme events for several models of interest for financial time series. The six measures of dependence are respectively the linear correlation $\rho^+_v$ and Spearman's rho $\rho_s(v)$ conditioned on signed exceedance of one variable above the threshold $v$, or on both variables ($\rho_u$), the linear correlation $\rho^s_v$ conditioned on absolute value exceedance (or large volatility) of one variable, the so-called asymptotic tail-dependence $\lambda$ and a probability-weighted tail dependence coefficient ${\bar \lambda}$. The models are the bivariate Gaussian distribution, the bivariate Student's distribution, and the factor model for various distributions of the factor. We offer explicit analytical formulas as well as numerical estimations for these six measures of dependence in the limit where $v$ and $u$ go to infinity. This provides a quantitative proof that conditioning on exceedance leads to conditional correlation coefficients that may be very different from the unconditional correlation and gives a straightforward mechanism for fluctuations or changes of correlations, based on fluctuations of volatility or changes of trends. Moreover, these various measures of dependence exhibit different and sometimes opposite behaviors, suggesting that, somewhat similarly to risks whose adequate characterization requires an extension beyond the restricted one-dimensional measure in terms of the variance (volatility) to include all higher order cumulants or more generally the knowledge of the full distribution, tail-dependence has also a multidimensional character.
Theoretical Analysis of a Multi-Tier Supply Chain Management Process Integration: A Case Study Method

Purpose: - Case studies of 12 automotive supply chains are used to build a theory of multi-tier supply chain management (SCM) processes integration which links patterns of multi-tier process integration with cost and differentiation strategies of the focal firms. Design/Methodology/Approach: The cases build on previous researches by examining the rationales and extents in which a focal firm integrates the key supply chain management processes across first, second and third-tier suppliers, logistics service providers and authorized dealers. Data was collected in two rounds, followed by codification and analysis. Findings: The analyses suggest that firms with lower scope and span of process integration prioritized cost efficiency and firms with larger scope and span of process integration prioritized both cost efficiency and customer service differentiation. As a result, propositions for explaining strategic priorities driving multi-tier SCM process integration are developed. Implications/Originality/Value: This study provides managerial accounts and theoretical foundation to explain the needs for different levels, scopes and span of SCM process integration and the extent to which the span of integration (tiers) should be extended.
Multi-view Re-weighted Sparse Subspace Clustering with Intact Low-rank Space Learning

In this paper, we propose a new Multi-view Re-weighted Sparse Subspace Clustering with Intact Low-rank Space Learning (ILrS-MRSSC) method, trying to find a sparse representation of the complete space of information. Specifically, this method integrates the complementary information inherent in multiple angles of the data, learns a complete space of potential low-rank representation, and constructs a sparse information matrix to reconstruct the data. The correlation between multi-view learning and subspace clustering is strengthened to the greatest extent, so that the subspace representation is more intuitive and accurate. The optimal solution of the model is solved by the augmented lagrangian multiplier (ALM) method of alternating direction minimal. Experiments on multiple benchmark data sets verify the effec-tiveness of this method.