Miracle-dz/UniFAR-FTUs huggingface dataset:
Local-scale impacts of water hyacinth on water quality in a hypereutrophic lake

Increased nutrient concentrations have been linked to water quality impairments across the world. In Lake Victoria, the second largest freshwater lake in the world, decades of eutrophication have challenged water quality management and led to conditions with compromising levels of cyanobacteria and cyanotoxins. Our study focuses on Winam Gulf, where a resurgence of water hyacinth has led to renewed challenges with navigation and anoxia, but also may present a local-scale opportunity for water quality improvements. Over the course of 1.5 years, we conducted three, in situ experiments to vary levels of water hyacinth density and determined its effects on lake water nutrient composition, biochemical parameters (chlorophyll a, cyanotoxins), and fecal indicators (fecal coliforms and Escherchia coli). While our study did not show overwhelming improvements in water quality with increasing water hyacinth density, we did find that small-scale removal of water hyacinth led to decreases in coliforms (F1, 11 = 11.14, p < 0.01), E. coli (F1, 11 = 22.95, p < 0.001), and total microcystins (F1, 9 = 3.47, p = 0.095). Throughout the experiment, the majority of samples collected had both E. coli and microcystin concentrations, notably exceeding World Health Organization guidelines for drinking water. Our data suggest that magnitude of scale, as well as season considerations such as rainfall and background level of water hyacinth, will strongly influence the phytoremediation potential of water hyacinth. The data encourages further exploration of remediation potential of native and introduced macrophytes.
Individual species and site dynamics are the main drivers of spatial scaling of stability in aquatic communities

Introduction Any measure of ecological stability scales with the spatial and temporal extent of the data on which it is based. The magnitude of stabilization effects at increasing spatial scale is determined by the degree of synchrony between local and regional species populations. Methods We applied two recently developed approaches to quantify these stabilizing effects to time series records from three aquatic monitoring data sets differing in environmental context and organism type. Results and Discussion We found that the amount and general patterns of stabilization with increasing spatial scale only varied slightly across the investigated species groups and systems. In all three data sets, the relative contribution of stabilizing effects via asynchronous dynamics across space was higher than compensatory dynamics due to differences in biomass fluctuations across species and populations. When relating the stabilizing effects of individual species and sites to species and site-specific characteristics as well as community composition and aspects of spatial biomass distribution patterns, however, we found that the effects of single species and sites showed large differences and were highly context dependent, i.e., dominant species can but did not necessarily have highly stabilizing or destabilizing effects on overall community biomass. The sign and magnitude of individual contributions depended on community structure and the spatial distribution of biomass and species in space. Our study therefore provides new insights into the mechanistic understanding of ecological stability patterns across scales in natural species communities.
Pro-environmental behavior, green HRM practices, and green psychological climate: Examining the underlying mechanism in Pakistan

The success of an organization’s environmental sustainability objectives is contingent on the environmental behavior of its personnel. The present study was conducted to observe how the green human resource management (GHRM) practice improves environmental performance (EP) through psychological green climate (PGC) and pro-environmental behavior (PEB). It also evaluates the moderating role of the individual green value (IGV). Data were collected from HR professionals and health officers directly engaged in human resource practices in private hospitals in Sialkot, Pakistan. To gather the responses, questionnaires were distributed and PLS analysis was used to analyze the data. The findings showed that GHRM explains that the PGC stimulates employees to perform pro-environmental behaviors for better environmental performance. Furthermore, the individual green value moderates the employee’s behavior for better environmental performance. This research paper gives vital practical implications to the top management and regulators in assuring employee engagement in applying green human resource management practices.
Crossover condition in the coherent rotation model and the Preisach-type models

The crossover of the hysteresis branches in the classical coherent rotation model is analyzed. The analysis is extended when the truncated Stoner-Wohlfarth astroid is used. The physical explanation of the crossover occurrence is given. More accurate expressions of the anisotropy free energy density are required to better describe the behavior of the system when the angle between the easy axis and the magnetic moment of the particle is near 90/spl deg/. A simple approximate formula for the hysteresis loop branches, which are in good agreement with those obtained when the discussed corrections are made, is given. The effect of the use of the Stoner-Wohlfarth astroid in the calculus of the equilibrium state in some vector Preisach-type models is analyzed.
Pilot fatigue survey: A study of the mutual influence among fatigue factors in the “work” dimension

Background Fatigue risk management for pilots has received increasing attention. The existing fatigue management systems have detailed descriptions of the factors and the mutual influences among the factors that affect the dimension of “sleep”, which is one of the most important causes of fatigue. However, the analysis of the influencing factors of the “work” dimension of fatigue causes has not been very detailed or accurate, especially the exploration of the mutual influence among many fatigue-influencing factors in the “work” dimension. Objective The purpose of this study was to explore the mutual influence among fatigue-influencing factors related to the “work” dimension in the analysis of pilot fatigue causes. Methods This study designed a questionnaire on the dimension of “work” in the causes of pilot fatigue and collected a total of 270 feedback data points from international flight pilots. Based on the questionnaires and data, descriptive statistical analysis, exploratory factor analysis and confirmatory factor analysis were performed to explore the influencing factors and their mutual influences on the “work” dimension of pilot fatigue. Results There is a strong, mutual influence relationship among the fatigue causes of long-haul flight pilots – working status, working conditions and working schedules – in the dimension of “work”. The workload only has a strong correlation with the working schedule, and the interaction relationships with the working status or working conditions are weak. Conclusion This study analyses the mutual influence among the influencing factors of the “work” dimension of pilot fatigue, and we expect to provide empirical data for pilot fatigue risk management and to help improve fatigue risk management systems.
Methodological investigation on photo-stimulated discharge to obtain accurate trap information in polymer dielectrics

Based on photo-stimulated discharge (PSD) theory about release of charges from the mono- and multi-levels of traps, two methods on the energy distribution of trapped charges (charge distribution)—step scanning method and combined calibration–scanning method are proposed. The typical polymeric dielectric, low-density polyethylene (LDPE), is selected to be investigated in this paper. The linear relation between PSD current and time at selected wavelengths in LDPE samples indicates that the effective interaction cross sections between the photons and trapped charges depend on the photon energy. It is indicated that both of the largest ratios in charge distributions based on the two methods are around the energy level of 4.97 eV for LDPE. Compared with the step scanning method, the experimental time is extremely shortened and thermal release of trapped charges is decreased in the combined calibration–scanning method, and charge distribution based on combined calibration–scanning method is more reliable.
Cardiac copper content and its relationship with heart physiology: Insights based on quantitative genetic and functional analyses using BXD family mice

Background Copper (Cu) is essential for the functioning of various enzymes involved in important cellular and physiological processes. Although critical for normal cardiac function, excessive accumulation, or deficiency of Cu in the myocardium is detrimental to the heart. Fluctuations in cardiac Cu content have been shown to cause cardiac pathologies and imbalance in systemic Cu metabolism. However, the genetic basis underlying cardiac Cu levels and their effects on heart traits remain to be understood. Representing the largest murine genetic reference population, BXD strains have been widely used to explore genotype-phenotype associations and identify quantitative trait loci (QTL) and candidate genes. Methods Cardiac Cu concentration and heart function in BXD strains were measured, followed by QTL mapping. The candidate genes modulating Cu homeostasis in mice hearts were identified using a multi-criteria scoring/filtering approach. Results Significant correlations were identified between cardiac Cu concentration and left ventricular (LV) internal diameter and volumes at end-diastole and end-systole, demonstrating that the BXDs with higher cardiac Cu levels have larger LV chamber. Conversely, cardiac Cu levels negatively correlated with LV posterior wall thickness, suggesting that lower Cu concentration in the heart is associated with LV hypertrophy. Genetic mapping identified six QTLs containing a total of 217 genes, which were further narrowed down to 21 genes that showed a significant association with cardiac Cu content in mice. Among those, Prex1 and Irx3 are the strongest candidates involved in cardiac Cu modulation. Conclusion Cardiac Cu level is significantly correlated with heart chamber size and hypertrophy phenotypes in BXD mice, while being regulated by multiple genes in several QTLs. Prex1 and Irx3 may be involved in modulating Cu metabolism and its downstream effects and warrant further experimental and functional validations.
Sense of agency predicts severity of moral judgments

Sense of Agency (SoA) refers to the awareness of being the agent of our own actions. A key feature of SoA relies on the perceived temporal compression between our own actions and their sensory consequences, a phenomenon known as “Intentional Binding. ” Prior studies have linked SoA to the sense of responsibility for our own actions. However, it is unclear whether SoA predicts the way we judge the actions of others – including judgments of morally wrong actions like harming others. To address this issue, we ran an on-line pilot experiment where participants underwent two different tasks designed to tap into SoA and moral cognition. SoA was measured using the Intentional Binding task which allowed us to obtain both implicit (Intentional Binding) and explicit (Agency Rating) measures of SoA. Moral cognition was assessed by asking the same participants to evaluate videoclips where an agent could deliberately or inadvertently cause suffering to a victim (Intentional vs. Accidental Harm) compared with Neutral scenarios. Results showed a significant relation between both implicit and explicit measures of SoA and moral evaluation of the Accidental Harm scenarios, with stronger SoA predicting stricter moral judgments. These findings suggest that our capacity to feel in control of our actions predicts the way we judge others’ actions, with stronger feelings of responsibility over our own actions predicting the severity of our moral evaluations of other actions. This was particularly true in ambiguous scenarios characterized by an incongruency between an apparently innocent intention and a negative action outcome.
Wound healing properties of a new formulated flavonoid-rich fraction from Dodonaea viscosa Jacq. leaves extract

Background: Dodonaea viscosa Jacq. (D. viscosa) belongs to the family of Sapindaceae, commonly known as “Sinatha,” and is used as a traditional medicine for treating wounds due to its high flavonoids content. However, to date there is no experimental evidence on its flavonoid-rich fraction of D. viscosa formulation as an agent for healing wounds. Objective: The present study aimed to evaluate the wound healing effect of ethyl acetate fraction of D. viscosa leaves on dermal wounds. Methods: The ethyl acetate fraction was produced from a water-ethanol extract of D. viscosa leaves and was quantitatively evaluated using the HPLC technique. The in-vivo wound healing ability of the ethyl acetate fraction of D. viscosa ointment (DVFO, 2.5%w/w and 5%w/w) was investigated in Sprague-Dawley rats utilizing an incision and excision paradigm with povidone-iodine ointment (5% w/w) as a control. The percentage of wound closure, hydroxyproline and hexosamine concentrations, tensile strength and epithelialization duration were measured. Subsequently, histopathology analysis of skin samples as well as western blots were performed for collagen type 3 (COL3A1), basic fibroblast growth factor (bFGF) and vascular endothelial growth factor (VEGF). Results: The ethyl acetate fraction of D. viscosa revealed flavonoids with high concentrations of quercetin (6.46% w/w) and kaempferol (0.132% w/w). Compared to the control group, the DVFO (2.5% and 5.0% w/w) significantly accelerated wound healing in both models, as demonstrated by quicker wound contraction, epithelialization, elevated hydroxyproline levels and increased tensile strength. Histopathological investigations also revealed that DVFO treatment improved wound healing by re-epithelialization, collagen formation and vascularization of damaged skin samples. Western blot analysis further demonstrated an up-regulation of COL3A, vascular endothelial growth factor and bFGF protein in wound granulation tissue of the DVFO-treated group (p < 0.01). Conclusion: It is concluded that flavonoid-rich D. viscosa ethyl acetate fraction promotes wound healing by up-regulating the expressions of COL3A, VEGF and bFGF protein in wound granulation tissue. However, extensive clinical and pre-clinical research on the flavonoid-rich fraction of D. viscosa is needed to determine its significant impact in the healing of human wounds. Graphical Abstract
A hybrid deep learning model for predicting the Kuroshio path south of Japan

At present, many prediction models based on deep learning methods have been widely used in ocean prediction with satisfactory results. However, few deep learning models are used to predict the Kuroshio path south of Japan. In this study, a hybrid deep learning prediction model is constructed based on the long short-term memory (LSTM) neural network, combined with the complex empirical orthogonal function (CEOF) and bivariate empirical mode decomposition (BEMD), called CEOF-BEMD-LSTM. We train the model by using a 50-year (1958-2007) long time series of daily mean positions of the Kuroshio path south of Japan extracted from a regional ocean reanalysis dataset. During the test period of 15 years (2008-2022) by using daily altimetry dataset, our model shows a good performance for the Kuroshio path prediction with the lead time of 120 days, with 0.44° root-mean-square error (RMSE) and 0.75 anomaly correlation coefficient (ACC). This model also has good prediction skill score (SS). Moreover, the CEOF-BEMD-LSTM model successfully hindcasts the formation of the latest Kuroshio large meander since the summer of 2017. Predictions of the Kuroshio path for the coming 120 days (from January1 to April 30, 2023) indicate that the Kuroshio will continue to remain in the state of the large meander. Besides, predictor(s) of the Kuroshio path south of Japan need to be sought and added in future research.
Novel maternal autoantibodies in autism spectrum disorder: Implications for screening and diagnosis

Introduction Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which early recognition is a major challenge. Autoantibodies against fetal brain antigens have been found in the blood of mothers of children with ASD (m-ASD) and can be transferred to the fetus where they can impact neurodevelopment by binding to fetal brain proteins. This study aims to identify novel maternal autoantibodies reactive against human fetal brain antigens, and explore their use as biomarkers for ASD screening and diagnosis. Methods A custom-made human fetal brain cDNA phage display library was constructed, and screened for antibody reactivity in m-ASD samples from the Simons Simplex Collection (SSC) of the Simons Foundation Autism Research Initiative (SFARI). Antibody reactivity against 6 identified antigens was determined in plasma samples of 238 m-ASD and 90 mothers with typically developing children (m-TD). Results We identified antibodies to 6 novel University Hasselt (UH)-ASD antigens, including three novel m-ASD autoantigens, i.e., ribosomal protein L23 (RPL23), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and calmodulin-regulated spectrin-associated protein 3 (CAMSAP3). Antibody reactivity against a panel of four of these targets was found in 16% of m-ASD samples, compared to 4% in m-TD samples (p = 0.0049). Discussion Maternal antibodies against 4 UH-ASD antigens could therefore provide a novel tool to support the diagnosis of ASD in a subset of individuals.
q-Rung orthopair fuzzy hypersoft ordered aggregation operators and their application towards green supplier

Green Supply Chain Management (GSCM) is essential to ensure environmental compliance and commercial growth in the current climate. Businesses constantly look for fresh concepts and techniques for ensuring environmental sustainability. To keep up with the new trends in environmental concerns related to company management and procedures, Green Supplier Selection (GSS) criteria are added to the traditional supplier selection processes. This study aims to identify general and environmental supplier selection criteria to provide a framework that can assist decision-makers in choosing and prioritizing appropriate green supplier selection. The development and implementation of decision support systems aimed to solve these difficulties at a rapid rate. In order to manage inaccurate data and simulate decision-making problems. Fuzzy sets introduced by Zadeh, are a useful technique to handle the imperfectness and uncertainty in different problems. Although fuzzy sets can handle incomplete information in different real worlds problems, but its cannot handle all type of uncertainty such as incomplete and indeterminate data. Therefore different extensions of fuzzy sets such as intuitionistic fuzzy, pythagorean fuzzy and q-rung orthopair fuzzy sets introduced to address the problems of uncertainty by considering the membership and non-membership grade. However, these concepts have some shortcomings in the handling uncertainty with sub-attributes. To overcome this difficulties Khan et al. developed the structure of q-rung orthopair fuzzy hypersoft sets by combining q-rung orthopair fuzzy sets with hypersoft sets. A remarkable and beneficial research work is done in the field of q-rung orthopair fuzzy hypersoft sets, and then we think about the application. In this paper, we use the structure of q-rung orthopair fuzzy hypersoft in multi-criteria supplier selection problems. For this, we present aggregation operator to solve multi-criteria decision-making (MCDM) problems with q-rung orthopair fuzzy hypersoft (q-ROFH) information, known as ordered weighted geometric aggregation operator. Since the uncertainty and vagueness is an unavoidable feature of multi-criteria decision-making problems, the proposed structure can be a useful tool for decision making in an uncertain environment. Further, the expert opinions were investigated using the multi-criteria decision-making (MCDM) technique, which helped identify interrelationship and causal preference of green supplier evaluation aspects that used aggregation operators. Finally, a numerical example of the proposed method for the task of Green Supplier Selection is presented.
Developing machine learning methods for automatic recognition of fishing vessel behaviour in the Scomber japonicus fisheries

Introduction With a higher degree of automation, fishing vessels have gradually begun adopting a fishing monitoring method that combines human and electronic observers. However, the objective data of electronic monitoring systems (EMS) has not yet been fully applied in various fishing boat scenarios such as ship behavior recognition. Methods In order to make full use of EMS data and improve the accuracy of behaviors recognition of fishing vessels, the present study proposes applying popular deep learning technologies such as convolutional neural network, long short-term memory, and attention mechanism to Chub mackerel (Scomber japonicus) fishing vessel behaviors recognition. The operation process of Chub mackerel fishing vessels was divided into nine kinds of behaviors, such as “pulling nets”, “putting nets”, “fish pick”, “reprint”, etc. According to the characteristics of their fishing work, four networks with different convolutional layers were designed in the pre-experiment. And the feasibility of each network in behavior recognition of the fishing vessels was observed. The pre-experiment is optimized from the perspective of the data set and the network. From the standpoint of the data set, the size of the optimized data set is significantly reduced, and the original data characteristics are preserved as much as possible. From the perspective of the network, different combinations of pooling, long short-term memory(LSTM) network, and attention(including CBAM and SE) are added to the network, and their effects on training time and recognition effect are compared. Results The experimental results reveal that the deep learning methods have outstanding performance in behaviors recognition of fishing vessels. The LSTM and SE module combination produced the most apparent optimization effect on the network, and the optimized model can achieve an F1 score of 97.12% in the test set, surpassing the classic ResNet, VGGNet, and AlexNet. Discussion This research is of great significance to the management of intelligent fishery vessels and can promote the development of electronic monitoring systems for ships.