Miracle-dz/UniFAR-FTUs huggingface dataset:
Antecedents of Behavioral Intentions for Purchasing Hybrid Cars Using Sustainability Theory of Planned Behavior Integrated with UTAUT2

Hybrid cars were developed and are widely utilized in developed countries due to their sustainability advantages. However, developing countries were seen to underutilize this product where research showed that the economic, societal, and environmental concerns were considered by consumers. The current study aimed to assess the behavioral intentions for purchasing hybrid cars with the use of an established new framework called sustainability theory of planned behavior (STPB) and integrated with the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. A total of 1048 valid respondents were purposely gathered to completely assess the behavioral, technological, and sustainable domains of purchasing intentions of hybrid cars through structural equation modeling. From the results, the perceived economic concerns and perceived authority support showed the most significant factor indirectly affecting behavioral intention to purchase hybrid cars, followed by perceived environmental concern. All domains under the theory of planned behavior were significant. However, only performance expectancy, hedonic motivation, and price value were deemed significant on the technological aspect. With the underutilization of hybrid cars in the country, habit, effort expectancy, and facilitating conditions were insignificant. The theoretical framework can be used independently (STPB), or as a whole, for the evaluation of purchasing intention of eco-friendly or smart technologies products. This study was also able to provide implications on the behavioral, managerial, and sustainability aspects of hybrid cars. Lastly, the theoretical implications suggested that the sustainability theory of planned behavior may be extended or applied in holistically assessing different product evaluation, industries, and related studies—even in different countries.
Rescuing reconciliation: finding its role in peace research and practice

Abstract A rich and complex literature on reconciliation has emerged in response to political transitions since the 1990s, yet reconciliation’s value as a concept within peace studies is unclear. Definitions are contested, impressionistic or overlap with other concepts, while ‘reconciliation’ remains politically contested in many conflict-affected societies. This article considers the four leading understandings of reconciliation: reconciliation as peacebuilding, reconciliation as transitional justice, reconciliation as forgiveness, and reconciliation as identity change. Each is assessed according to whether it is (1) conceptually coherent, and (2) likely to be credible to people in conflict. The article argues that by restricting reconciliation’s meaning to a modified version of the fourth understanding – reconciliation as transformed social identity – the term can hold a distinct meaning in the peace studies field and direct a clear research agenda, as well as attract much less political criticism and misunderstanding.
Effect of Dietary Organic Selenium on Growth Performance, Gut Health, and Coccidiosis Response in Broiler Chickens

Simple Summary Selenium is an essential trace mineral for better performance, meat quality, and health benefits in farm animals. In broiler diets, selenium has been added in inorganic or organic form to maintain the selenium requirement of the chickens. Researchers have studied the effects and applications of both inorganic and organic selenium in broiler nutrition. However, there is a paucity of information pertaining to organic selenium application under coccidiosis conditions. In this study, we evaluated the effect of dietary organic selenium on growth performance, gut health, and tissue selenium concentrations in broiler chickens under coccidiosis conditions. Results indicated that the addition of organic selenized yeast improved the growth performance and enhanced the selenium concentrations in the tissue, regardless of the Eimeria challenge. Therefore, organic selenized yeast supplementation can be recommended to produce selenium-enriched organic broiler chickens. Abstract A total of 252 one-day-old Ross broilers were randomly allocated to one of six treatments in a 2 × 3 factorial arrangement with respective Eimeria challenges (non-infection and infection) and three different selenium (Se) diets. Dietary treatments were as follows: (1) Se un-supplemented control (CON), (2) inorganic Se treatment (SS; 0.3 mg/kg as sodium selenite), and (3) organic Se treatment (SY; 0.3 mg/kg as selenized yeast). Six replicate cages were allocated per treatment. Chickens in the respective Eimeria infection groups were infected with an E. acervulina, E. tenella, and E. maxima oocyst mixture (15,000 oocysts/chicken) on day 16. Growth performance was measured on days 16, 22, and 24. On day 22, intestinal samples were collected from randomly selected chickens to evaluate gut lesion scores, antioxidant enzymes, and tight junction gene expression. Blood, breast, and liver samples were collected to analyze the Se concentrations on day 24. Dietary SY supplementation improved (p < 0.05) the growth performance of the chickens regardless of the Eimeria challenge. Moreover, independent of Eimeria infection, Se supplementation elevated (p < 0.05) the heme oxygenase 1 (HMOX-1) expression in jejunal mucosa at 6 days post-infection (dpi). Duodenal junctional adhesion molecule 2 (JAM-2) expression and jejunal occludin (OCLN) were elevated (p < 0.05) with dietary SY supplementation at 6 dpi. Among Se sources, broiler chickens fed with the SY diet showed higher (p < 0.05) Se concentrations in breast muscle and serum on 8 dpi. These results confirmed the beneficial effects of dietary Se and the efficiency of organic Se compared with inorganic Se for growth improvement and muscle Se enrichment in broiler chickens regardless of coccidiosis infection.
Interventions to Improve Antibiotic Use in Hospitals with Different Levels of Complexity in Colombia: Findings from a Before-and-After Study and Suggestions for the Future

Background: In the collaborative efforts to control bacterial antimicrobial resistance (AMR), the challenge for many low- and middle-income countries currently lies in the adequate design and successful implementation and operation of different strategies aimed at improving antibiotic use during hospital care. This study aims to provide data on these different strategies in three hospitals with different levels of complexity and geographic locations in Colombia. Methods: This before-and-after study describes and analyzes the development and implementation of clinical practice guidelines (CPGs), continuing education courses, quick consultation tools, and antimicrobial stewardship programs (ASPs) with the use of telemedicine. This includes measuring indicators in the ASP framework such as adherence to CPGs and antibiotic consumption. Results: We used five CPGs developed in the Colombian context. We designed and developed a Massive Open Online Course (MOOC) and a mobile application (app) as strategies for dissemination and implementation. The ASP was designed and implemented according to each institution’s level of complexity. In the three hospitals, a progressive increase in adherence to the antibiotic recommendations proposed in the CPGs was observed, and there was a lower use of antibiotics with the ASPs, both in the general wards and ICUs. Conclusions: We concluded that in medium-complexity hospitals located in small rural cities, successful development of ASPs is possible when they are well-planned, implemented, and supported by the organization. It is necessary that Colombia and other Latin American countries continue activities that reduce AMR by designing, implementing, and improving these interventions throughout the national territory.
Evaluation of Air Permeability of Knitted Fabrics at Various Washing Intervals

The air permeability of fabrics is one of the most important factors to be considered in making comfortable clothing for consumers. It helps to transport moisture in the form of vapours from the inner skin to the outer environment. This study aims at determining the rate of airflow through the prepared knitted fabrics after various washing intervals. Circular knitted fabrics with 100% cotton and a blend of cotton and polyester having a ratio of 70% / 30% were made by setting different construction parameters. Prepared fabrics were evaluated for their air permeability by following the ASTM D737-1996 test procedure. Then these fabrics were laundered with standard procedure and evaluated after various intervals. It was concluded from the obtained results that construction parameters such as kind of polymer, loop length, hairiness, yarn count, number and size of pores and their distribution play a major role in determining the air permeability of knitted fabrics. Moreover, an increase in washing cycles also reduces the airflow through the fabric.
Explainable Deep Convolutional Neural Network for Valvular Heart Diseases Classification Using PCG Signals

The heart sounds recorded through a phonocardiogram (PCG) can assist in the early detection of valvular heart diseases (VHD), which is important for minimizing the chances of cardiac complications like heart failure and sudden cardiac death. Despite a large number of previous studies with highly accurate classifications, PCG-based deep learning (DL) methods for VHD detection are not suitable for clinical practice as they lack transparency and interpretability. The proposed work utilizes analytic continuous wavelet transform (CWT) scalograms as the time–frequency representations (TFRs) of the PCG signals. A 2-D convolutional neural network (CNN) is designed for the multiclass classification (aortic stenosis, mitral regurgitation, mitral stenosis, mitral valve prolapse, and normal) of PCG signal’s TFR. Besides introducing a VHD classification method in this article, we also carry out the interpretation of the proposed CNN architecture by using occlusion maps and deep dream images for local and global explanations, respectively. The highest accuracy achieved during fivefold cross validation is 99.6%, and the overall accuracy is 98.32% for a publicly available PCG database. The accuracy of the proposed method for binary classification (abnormal and normal) tested on the PhysioNet database is 93.07%. DL visualization methods assisted in determining what features or regions of TFR of PCGs the proposed network was “looking at” for making a class-specific correct prediction. Class-specific morphological and time–frequency features were observed. DL visualization makes the network decision more reliable. The novel classification framework along with its interpretations would enable successful clinical translation of PCG-based VHD detection.
Identifying Protein-Protein Interaction using Tree-Transformers and Heterogeneous Graph Neural Network

For a better understanding of the underlying biological mechanisms, it is crucial to identify the reciprocity between proteins. Often, extracting such interactions between proteins from biomedical articles faces challenges due to the complex sentence structure of the textual information sources. Most of the prominent previous works have applied additional hand-crafted features for the protein-protein interaction task. In this work, we have utilized two tree-structured attention-based neural network models along with a heterogeneous graph approach to perform this task. We suggest that the proposed model preserves the syntactic as well as the semantic information of the text. The experimental results demonstrate that even without using any additional feature extraction techniques, this model achieves significant performance boosts when applied on the five standard benchmark corpora compared to the previous works.
Numerical Analysis of Dynamic Resistance and Total Loss in REBCO-Coated Conductors at Low Temperature Under High Perpendicular AC Magnetic Fields

In several applications of high-temperature superconductors, a REBCO-coated conductor (CC) carries a dc current under varying magnetic fields. This generates ac loss, which can be an important factor to consider in the thermal design of the application. In this work, the dynamic resistance (<italic>R</italic><sub>dyn</sub>) and total loss in a 4-mm wide Superpower REBCO CC are numerically investigated for ac perpendicular magnetic fields (<italic>B</italic><sub>m</sub>) up to 1 T and temperatures ranging from 30 to 50 K. For <italic>I</italic><sub>dc</sub>/<italic>I</italic><sub>c0</sub> > 0.1 (where <italic>I</italic><sub>dc</sub> is the dc current level and <italic>I</italic><sub>c0</sub> is the self-field critical current of the conductor), either of the previously developed equations, the nonlinear or the simple linear expression for determining <italic>B</italic><sub>th</sub> (threshold magnetic field), can be used to predict <italic>R</italic><sub>dyn</sub>. Finite dynamic loss is observed even below the <italic>B</italic><sub>th</sub> points due to the emergence of resistance. The resistance arises from the superposition of subcritical shielding and transport currents, which introduces a finite electric field in the CC. The total loss at higher fields decreases as the temperature increases from 30 to 50 K for all current values, except for <italic>I</italic><sub>dc</sub>/<italic>I</italic><sub>c0</sub> = 0.9. A lift-off in the total loss is evident for fields around 1 T for all temperatures investigated.
Identification of Piperlongumine as Potent Inhibitor of Necroptosis

Purpose Excessive necroptosis contributes to the pathogenesis of several inflammatory and neurodegenerative diseases. Here, using a high-throughput screening approach, we investigated the anti-necroptosis effects of piperlongumine, an alkaloid isolated from the long pepper plant, in vitro and in a mouse model of systemic inflammatory response syndrome (SIRS). Methods A natural compound library was screened for anti-necroptosis effects in cellular. The underlying mechanism of action of the top candidate piperlongumine was explored by quantifying the necroptosis marker phosphorylated receptor-interacting protein kinase 1 (p-RIPK1) by Western blotting. The anti-inflammatory effect of piperlongumine was assessed in a tumor necrosis factor α (TNFα)-induced SIRS model in mice. Results Among the compounds investigated, piperlongumine significantly rescued cell viability. The half maximal effective concentration (EC50) of piperlongumine for inhibiting necroptosis was 0.47 μM in HT-29 cells, 6.41 μM in FADD-deficient Jurkat cells, and 2.33 µM in CCRF-CEM cells, while the half maximal inhibitory concentration (IC50) was 95.4 µM in HT-29 cells, 93.02 µM in FADD-deficient Jurkat cells, and 161.1 µM in CCRF-CEM cells. Piperlongumine also significantly inhibited TNFα-induced intracellular RIPK1 Ser166 phosphorylation in cell lines and significantly prevented decreases in body temperature and improved survival in SIRS mice. Conclusion As a potent necroptosis inhibitor, piperlongumine prevents phosphorylation of RIPK1 at its activation residue Ser166. Piperlongumine thus potently inhibits necroptosis at concentrations safe enough for human cells in vitro and inhibits TNFα-induced SIRS in mice. Piperlongumine has potential clinical translational value for the treatment of the spectrum of diseases associated with necroptosis, including SIRS.
On the optimal multiple stopping problem

This report presents the optimal multiple stopping problem for geometric Brownian motion. The study is primarily based on the classical optimal stopping theory and an appropriate choice of a sequence of Snell envelopes. The classical theory of Snell envelope admits an explicit solution to the perpetual American put option in the case of the geometric Brownian motion. By introducing suitable Snell envelope processes and solving the single American exercise problem sequentially, the value of American contingent claims with multiple exercise rights is identiﬁed. Additionally, the analysis of these options with ﬁnitely many American exercises allows the results to be extended when the number of exercise rights is unbounded.
On the Solution of Moser's Problem in Four Dimensions

Abstract The problem of finding the largest set of nodes in a d-cube of side 3 such that no three nodes are collinear was proposed by Moser. Small values of d (viz., d ≤,3) resulted in elegant symmetric solutions. It is shown that this does not remain the case in 4 dimensions where at most 43 nodes can be chosen, and these must not include the center node.
Electrode Design Theory Using Highly Accurate Equivalent Circuits in Biological Capacitive WPT

This letter presents an electrode design theory for capacitive biological wireless power transmission (WPT) using highly accurate equivalent circuits. First, we construct a lumped constant circuit that considers the permittivity and conductivity of the biological tissue and parasitic elements of the electrodes. Next, the sizes of the optimal electrodes that can achieve high efficiency within an area of $30 \times 30\,\,\text {mm}^{2}$ are determined based on the relationship between electrode size and equivalent circuit elements. The experimental results show that the equivalent circuit and simulation can be accurately modeled. Furthermore, the horizontal misalignment is simulated to evaluate and prove the design’s resistance to misalignment. Finally, as a safety evaluation, the power that satisfies the specific absorption rate (SAR) requirements specified in IEEE-C95.1 is obtained, and it is determined that 71 mW of power can be received.
AFFD-Net: A Dual-Decoder Network Based on Attention-Enhancing and Feature Fusion for Retinal Vessel Segmentation

The morphological characteristics of retinal vessels in the fundus serve as the primary basis for diagnosing and assessing the risk of ophthalmic diseases. An effective segmentation scheme for retinal vessels can aid in the early diagnosis and treatment of these diseases, as well as help prevent their progression. However, accurate vessel segmentation is challenging due to the low contrast of fundus images and the complexity of the vessels’ morphological structure. To address the low sensitivity and poor generalization ability of the existing methods in vascular extraction, a Dual-decoder Network based on Attention-enhancing and multi-scale Feature Fusion (AFFD-Net) is proposed. AFFD-Net inherits the codec concept of U-Net. To improve the performance of our U-Net model, we made two modifications. Firstly, we reduced the number of convolution kernel filters in each layer, thereby significantly reducing the number of training parameters. This helps to avoid overfitting and improves the model’s ability to generalize. Secondly, we added a Multi-scale Feature Extraction (MFE) module and an M/A intermediate decoder to enhance the model’s sensitivity. MFE is designed as the first encoding unit of AFFD-Net to obtain rich vascular features in the complex anatomical background and adapt to the large-scale variations of vessels. The M/A intermediate decoder is composed of the Multi-scale Feature Fusion (MFF) module and the Attention-enhancing Hybrid Feature Fusion (AHFF) module. The MFF module integrates deep semantic information and shallow spatial information to ensure that the features at each scale in the middle layer are fully utilized. The AHFF module adaptively fuses the hybrid features at different scales to generate two feature descriptors with different focuses which can improve the expressiveness of the model. AFFD-Net is evaluated on three public databases including DRIVE, STARE, and CHASE_DB1, and the sensitivity values obtained are 84.19%, 84.58%, and 82.62%, respectively. It has higher sensitivity and better generalization ability than other state-of-the-art methods. Compared with classical networks including U-Net, U-Net++, and U-Net3+, AFFD-Net has fewer parameters and higher segmentation accuracy. Our proposed segmentation model exhibits superior performance across a range of metrics, indicating its promising potential for practical applications.
Inversion and Validation of FY-4A Official Land Surface Temperature Product

The thermal infrared data of Fengyun 4A (FY-4A) geostationary meteorological satellite can be used to retrieve hourly land surface temperature (LST). In this paper, seven candidate algorithms are compared and evaluated. The Ulivieri (1985) algorithm is determined to be optimal for the algorithm of FY-4A LST official products. The refined algorithm coefficients for distinguishing dry and moist atmosphere were established for daytime and nighttime, respectively. Then, FY-4A LST official products under clear-sky conditions are produced. The validation results show that: (1) Compared with in-situ measured LST data at the HeBi crop measurement network, the root mean square errors (RMSE) were 2.139 and 2.447 K. Compared with in-situ measured LST data at Naqu alpine meadow site of Tibet plateau, the RMSE was 2.86 K. (2) When compared with the MODIS LST product, the RMSE was 1.64, 2.17, 2.6, and 1.73 K in March, July, October, and December, respectively. By the bias long-time change at a single site, RMSE of the XLHT (city) and GZH (desert) sites were 2.735 and 2.97 K, respectively. Overall, the preferred algorithm exhibits good accuracy and meets the required accuracy of the FY-4A mission.
The Effect of Probiotic Supplementation on the Gut–Brain Axis in Psychiatric Patients

The pathophysiology of several psychiatric diseases may entail disturbances in the hypothalamic–pituitary–adrenal (HPA) axis and metabolic pathways. Variations in how these effects present themselves may be connected to individual variances in clinical symptoms and treatment responses, such as the observation that a significant fraction of participants do not respond to current antipsychotic drugs. A bidirectional signaling pathway between the central nervous system and the gastrointestinal tract is known as the microbiota–gut–brain axis. The large and small intestines contain more than 100 trillion microbial cells, contributing to the intestinal ecosystem’s incredible complexity. Interactions between the microbiota and intestinal epithelium can alter brain physiology and affect mood and behavior. There has recently been a focus on how these relationships impact mental health. According to evidence, intestinal microbiota may play a role in neurological and mental illnesses. Intestinal metabolites of microbial origin, such as short-chain fatty acids, tryptophan metabolites, and bacterial components that might stimulate the host’s immune system, are mentioned in this review. We aim to shed some on the growing role of gut microbiota in inducing/manipulating several psychiatric disorders, which may pave the way for novel microbiota-based therapies.