Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of adoption of multiple sustainable agriculture practices among mandarin producing farmers in Salyan District of Karnali Province, Nepal

Sustainable agriculture practices (SAPs) are eco-friendly farming techniques, which ensure the agricultural production and environmental protection simultaneously. The aim of this study was to identify the potential SAPs being adopted by farmers, their adoptability, and to assess the determinant factors for multiple SAPs adoption. For this, face-to-face interview with 120 mandarin producing farmers, six focus groups, and 11 key informants was conducted in Salyan District of Karnali Province, Nepal. Farms were categorized into four categories on the basis of sustainability score and required inferences on quantitative and qualitative data were made by descriptive statistics and probit regression. Results revealed that majority of the mandarin farms were categorized as fairly low and fairly high categories. Factors such as age, gender, and schooling of household head, access to the Internet, distance of market for farm inputs, and availability of human labor had significantly increased the probability of multiple SAPs adoption at varied level of significance. Farm level policies regarding dissemination of integrated nutrient and pest management tactics, subsidization on eco-friendly farm inputs via cooperatives, trainings, demonstration, and field workshops should be formulated and implemented effectively.
The mental health and well-being of students and teachers during the COVID-19 pandemic: combining classical statistics and machine learning approaches

Abstract The aims of this study were to (1) to explore the state of students’ and teachers’ well-being and (2) examine the factors that predict their well-being during the pandemic-related school closures in the Philippines. Our sample comprised 733 students and 1168 teachers. During the height of the pandemic, 22.10% of the students and 13.44% of teachers met the cut-off for depression; 13.91% of the students and 15.92% of the teachers met the cut-off for anxiety. Both classical statistics and machine learning approaches were used to identify the roles of demographic, psychological, and socio-contextual factors that statistically predicted well-being outcomes. Results highlighted that family support was the strongest predictor of students’ and teachers’ positive well-being. For mental health outcomes, the strongest predictors of depression were anxiety and stress, while the strongest predictors of anxiety were depression, stress, and fear of COVID. Implications for students’ and teachers’ well-being amidst COVID are discussed.
Modeling Multiple Aesthetic Views for Series Photo Selection

Numerous photos are taken in daily life, and sorting them is laborious and time consuming. The large number of similar images exacerbates the difficulty of album management, under this scenario, serial photo selection (SPS) emerges. As an important branch of image aesthetic quality assessment, it focuses on identifying the best image among a series of almost identical photos. Currently, most existing SPS methods focus only on extracting features from the original image, while neglecting the fact that multiple views of the image can provide much more detailed aesthetic information. In this article, we propose a Siamese network structure called SPSNet to enhance the representation learning of multi-view features by acquiring the depth, generic, and handcrafted features of images. In specific, we implement a parallel structure to extract deep and shallow features, fusing local and global representations at different resolutions interactively. The aggregation of multiple views of image via a self-attentive module with adaptive weights enables the model to discriminate the importance of each view. Moreover, we employ a graph neural network to construct the relationships among the multi-view features. Our proposed method, which is trained by a Siamese network, can effectively distinguish the nuances of similar images, and thus, select the best one from a series of almost identical photos. Extensive experiments conducted on the aesthetic dataset demonstrate that our method outperforms other state-of-the-art SPS methods, which achieves the 75.36% accuracy on the Phototriage dataset. Besides, our model is up to 3.04% better than the baseline methods in terms of the average accuracy.
Genetic Characterization of Echinococcus granulosus sensu stricto Isolated from Human Cysts from Sardinia, Italy

This study involved 20 patients affected by cystic echinococcosis (CE) who were referred to different hospitals of Sardinia (Italy) from 2017 to 2022. By means of a multidisciplinary approach, diagnosis was confirmed for CE in 18 patients and for different aetiologies in two subjects. Moreover, serology was positive for 15 subjects. Since multiple CE cysts were found in five patients, a total of 27 lesions were collected; however, only one for each patient was investigated for genetic characterization of E. granulosus s.s. DNA isolates. Our results included 15 fertile cysts that underwent DNA extraction and amplification by three different PCRs targeting nuclear (calreticulin) and mitochondrial genes (cox1 and nad5). DNA was sequenced, and by neighbour-joining phylogenetic trees we determined 10 G1 and five G3 genotypes previously reported in Sardinia. These sequences were used to construct a network, along with those circulating in Mediterranean areas. The haplotype network calculated on cox1 evidenced seven different haplotypes of the 15 isolates, with SAR2 the most represented, carried by seven cysts, and SAR17 never described in the Mediterranean area. Meanwhile, the nad5 sequences showed the most common haplotype as nd5SAR7, as well as two new haplotypes not previously described, nd5SAR13, isolated from a Sardinian patient, and nd5SAR14, isolated from a Romanian patient.
Comparative analysis of entrepreneurial intention models: Self-efficacy versus entrepreneurial characteristics

Abstract Purpose: The purpose of this research is to analyze whether self-efficacy and entrepreneurial characteristics (e.g., risk-taking, planning, opportunity recognition, persistence, sociability, innovation, and leadership) differ in the explanation of entrepreneurial intention models, having entrepreneurial education as a moderator of relationships. Originality/value: This paper offers an in-depth perspective on which behavioral characteristics fit best into entrepreneurial intention models and helps to fill a theoretical and practical gap on the need for university education to increase its positive impact on human capital, particularly on students’ professional skills, demonstrating which characteristics are most impacted by entrepreneurial education. Design/methodology/approach: Quantitative methodology was employed, and data were analyzed with structural equation modeling. The sample consisted of 1,004 Brazilian university students from public and private universities. The research was conducted in a non-probabilistic way and with a convenience sample. In order to address the research hypothesis and objectives, all constructs were adapted from relevant literature on entrepreneurship. Findings: The results indicate it seems more appropriate to use a set of entrepreneurial characteristics when investigating an entrepreneurial intention model if you are looking for a higher explanatory factor and a more robust model; also, entrepreneurial characteristics represent a more sensitive model, which brings accurate assessments on factors that affect entrepreneurial intention. With these findings, entrepreneurial education can be planned to shape certain characteristics through actions in the university environment, making it possible to measure the impact of education on entrepreneurial intention.
Distribution, Trends, and Antimicrobial Susceptibility of Bacteroides, Clostridium, Fusobacterium, and Prevotella Species Causing Bacteremia in Japan During 2011–2020: A Retrospective Observational Study Based on National Surveillance Data

Abstract Background The increasing prevalence of anaerobic bacteremia is a major concern worldwide and requires longitudinal monitoring. Methods We present one of the largest and longest longitudinal studies on the prevalence and antimicrobial resistance of Bacteroides, Clostridium, Fusobacterium, and Prevotella spp. isolated from blood culture samples using national comprehensive surveillance data in Japan during 2011–2020 as part of the Japan Nosocomial Infections Surveillance. Results Data for 41 949 Bacteroides spp. , 40 603 Clostridium spp. , 7013 Fusobacterium spp. , and 5428 Prevotella spp. isolates were obtained. The incidences of bacteremia caused by Bacteroides fragilis, Clostridium perfringens, and Fusobacterium nucleatum significantly increased during the period (P < .0001). Among the 20 species analyzed, 18 showed no significant changes in susceptibility over time, including B. fragilis, C perfringens, and F. nucleatum. However, resistance to clindamycin increased in B. thetaiotaomicron (P = .0312), and resistance to ampicillin increased in B. ovatus (P = .0008). Conclusions Our comprehensive national surveillance data analysis demonstrated a continuous increase in the incidence of anaerobic bacteremia, particularly in B. fragilis, C. perfringens, and F. nucleatum. This may be linked to the increasing number of colorectal cancer cases or advancing methods for species identification and susceptibility testing, requiring cautious interpretation. The discovery of an upsurge in anaerobic bacteremia and potential alterations in susceptibility highlights the necessity for more extensive studies in this field.
Ophthalmic talbot moire wavefront sensor

A wavefront sensor for the eye measurement is described for transmitting a narrow, parallel alignment beam 12 includes a light source 10 for reflecting from the beam splitter 14. The beam 12 is focused to a point 20 on the retina (18) where enters the eye 16 through the pupil 17. The beam 12 is reflected from the retina (18) passes through a series of relay lenses 22,24. Subsequently, the light passes through one or more of the reticle (26, 28). Solid-state image pickup element camera (30) records the shadow pattern formed by the reticle (26, 28). The shadow pattern is digitized and input into a computer, that algorithms are created to calculate the distortions. As a result of the procedures are performed in the above, the refractive power of not wearing corrective lenses or eye wear can be measured simultaneously at several points. Power, eye, retina, pupil, reticle, a relay lens, and achromatism, beam splitter, distortion
Screen addiction among children and adolescents and the self-efficacy of mothers in screen use management during the COVID-19 lockdown in Sri Lanka

Background: The prevalence of problematic screen use (PSU) or “screen addiction” among children and adolescents may have escalated during the COVID-19 pandemic. The self-efficacy of the mothers in managing screen use in their children may play an important preventive role in children developing PSU. Aims: This study aimed to assess PSU among children aged 4-18 years during the lockdown period imposed during the COVID-19 pandemic and to explore the association between PSU and self-efficacy of mothers in managing screen use. Methods: An online survey was conducted using social media groups among mothers of children aged 4-18 years. The PSU in their children was measured using the Problematic Media Use Measure (PMUM), and maternal self-efficacy in managing screen use was assessed using the Parental Screen Use Management Scale (PSUMS). The two questionnaires were translated and validated into Sinhala prior to this study. Results: A total of 320 mothers responded to the survey. Based on the PMUM cut-off score, 25.3% of the children were found to have PSU. PSU was inversely correlated with maternal self-efficacy in managing screen use (r=-0.63, p<0.001). All three subscales of the PSUMS; reactive management (r=-0.56, p<0.001), proactive management (r=-52, p<0.001), and monitoring (r =-45, p<0.001), were significantly correlated with PSU. Younger age at screen use onset showed a marginal correlation (rho=-0.11, p=0.05) with PSU. The child’s age, gender and the educational level of the mother were not associated with PSU. Conclusion: Maternal self-efficacy in managing screen use among children was associated with lower PSU in children.
Simulating Gibberellic Acid Effect on Pasture Yield on Naturally Deposited and Fixed Area Urine

Nitrate (NO3−-N) leaching from urine patches is a serious environmental concern in dairy pastoral systems. In our previous research, we established that application of a plant growth hormone, gibberellic acid (GA), can potentially reduce NO3−-N leaching in urine patches; however, this was investigated in two locations in New Zealand. The performance of GA in influencing pasture nitrogen (N) uptake and NO3−-N leaching needs to be undertaken in multi-locations to draw conclusions. However, multi-location studies are a challenge due to a lack of funding and time constraints, so models such as the agricultural production systems simulator (APSIM) have been used. Therefore, field studies were conducted to determine whether APSIM can be used to quantify and simulate the effect of GA on NO3−-N leaching and pasture yield in three experimental sites known as Ashburton, Stratford, and Rotorua in New Zealand. Treatments examined were control (no urine applied), urine at 600 kg N ha−1, urine + GA at 8 g ha−1. The observed data was used to calibrate and validate the model. APSIM simulated that application of GA reduced NO3−-N leaching (relative to urine treatment) by 4.6, 5.1, and 8.8 kg NO3−-N ha−1 in Ashburton, Stratford, and Rotorua, respectively. APSIM reliably simulated pasture dry matter yield, and this was confirmed by the coefficient of determination ranging from R2 = 0.8562 to 0.995 in all treatments and experimental sites. This study demonstrated that APSIM can effectively be used to simulate the effect of GA application on NO3−-N leaching and pasture yield. Therefore, APSIM can be applied in other areas to simulate NO3−-N leaching and pasture yield.
Asynchronous Self-Triggered Stochastic Distributed MPC for Cooperative Vehicle Platooning Over Vehicular Ad-Hoc Networks

In this article, an asynchronous stochastic self-triggered distributed MPC (DMPC) control scheme is proposed for vehicular platoon systems under coupled state constraints and additive stochastic disturbance. In considered platoon systems, each vehicle broadcasts its predicted state as beacon information to its neighbouring vehicles through the vehicular ad-hoc network (VANET). To reduce the communication burden in the VANET, each vehicle proactively determines the next sampling time instant by solving the stochastic self-triggered DMPC problem at the sampling time instant. The self-triggered problem is formulated by utilizing local vehicle states and asynchronous beacon information from its neighbours. Consequently, the proposed scheme reduces the communication load dramatically in the VANET while maintaining a satisfactory control performance compared to periodic time-triggered stochastic DMPC. To handle the state coupling between vehicles, e.g., for collision avoidance or communication connectivity purpose, probabilistic coupled state constraints are incorporated into the DMPC problem. Based on the information on stochastic disturbance, the probabilistic coupled state constraints are transformed into deterministic forms using the stochastic tube-based method. Theoretical analysis has shown that closed-loop platooning is quadratically stable at triggering time instants. Numerical examples illustrate the efficacy of the proposed control method in terms of data transmission reduction.
Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts

Over the past decades, Colombia has suffered complex social problems related to illicit crops, including forced displacement, violence, and environmental damage, among other consequences for vulnerable populations. Considerable effort has been made in the regulation of illicit crops, predominantly Cannabis sativa, leading to advances such as the legalization of medical cannabis and its derivatives, the improvement of crops, and leaving an open window to the development of scientific knowledge to explore alternative uses. It is estimated that C. sativa can produce approximately 750 specialized secondary metabolites. Some of the most relevant due to their anticancer properties, besides cannabinoids, are monoterpenes, sesquiterpenoids, triterpenoids, essential oils, flavonoids, and phenolic compounds. However, despite the increase in scientific research on the subject, it is necessary to study the primary and secondary metabolism of the plant and to identify key pathways that explore its great metabolic potential. For this purpose, a genome-scale metabolic reconstruction of C. sativa is described and contextualized using LC-QTOF-MS metabolic data obtained from the leaf extract from plants grown in the region of Pesca-Boyaca, Colombia under greenhouse conditions at the Clever Leaves facility. A compartmentalized model with 2101 reactions and 1314 metabolites highlights pathways associated with fatty acid biosynthesis, steroids, and amino acids, along with the metabolism of purine, pyrimidine, glucose, starch, and sucrose. Key metabolites were identified through metabolomic data, such as neurine, cannabisativine, cannflavin A, palmitoleic acid, cannabinoids, geranylhydroquinone, and steroids. They were analyzed and integrated into the reconstruction, and their potential applications are discussed. Cytotoxicity assays revealed high anticancer activity against gastric adenocarcinoma (AGS), melanoma cells (A375), and lung carcinoma cells (A549), combined with negligible impact against healthy human skin cells.
Wellbeing Amid Digital Risks: Implications of Digital Risks, Threats, and Scams on Users’ Wellbeing

This study investigates how users perceive their wellbeing amid the risks associated with digital media use in Norway. According to the literature, some of these risks include digital dependence, online privacy, scams, thefts, information misuse, and harassment. To expand knowledge on how these and other digital risks are construed by users, this study addresses the following research questions: What implications do digital risks have on users’ perceived sense of wellbeing? What are the solutions proposed by users to manage these risks? Methodologically, the inquiry is led through a qualitative approach comprising 17 semi-structured in-depth interviews of university students in Norway. The investigation centers on an interpretative phenomenological analysis. This study contributes to the existing literature by empirically evaluating the notion of digital wellbeing in the everyday choices of university students, thereby comprehending their safety concerns and how they manage online risks while exploring solutions to combat the risks of digital usage. The study adds value to the present literature on digital wellbeing by juxtaposing digital risks with the construct of wellbeing in digitalized societies.
THE EFFECTIVENESS DIFFERENCES OF CANANGA AROMATHERAPY AND JAVA LANGGAM MUSIC ON BLOOD PRESSURE OF THE ELDERLY WITH HYPERTENSION IN BPSTW YOGYAKARTA

Elderly encounter decreased body functions, including cardiovascular function. The cardiovascular issue in elderly is hypertension. One of the non-pharmacological managements for hypertension is through the intervention of cananga aromatherapy and java langgam music. Quasi experiment with a control group design approach is the research method. The researcher divided two groups, namely cananga aromatherapy and java langgam music intervention groups. Interventions were given 4 times a week for two weeks. The researcher conducted Repeated Measure ANOVA and Friedman statistic tests for 3 measurement periods, while also performed independent t-test and Mann Whitney test to compare the both groups. The acquired results show that cananga aromatherapy can lower systolic blood pressure significantly (p-value: 0.007), while the java langgam music can lower systolic and diastolic blood pressures significantly (p-value: 0.000). There were disparities between systolic and diastolic blood pressures on both groups during the first week of the intervention (p-value: <0.05). The java langgam music intervention was more effective in lowering the blood pressure in elderly compared to cananga aromatherapy intervention. Keywords: Cananga aromatherapy; java langgam music; elderly with hypertension
Disparities in Youth Arrest Across Racial and Ethnic Subgroups

An extensive body of research suggests that youth of color are more likely to experience an arrest than their White counterparts. Theoretically, these findings have been understood, at least in part, as the result of the differential deployment of law enforcement to areas with higher Black and Hispanic concentrations as well as stereotyped attributions of dangerousness and threat implicitly assigned to these suspects by police before and during encounters. However, previous studies typically have employed conventional racial/ethnic categorizations, which might obscure potential nuances in arrest disparities across subgroups. Using data on a statewide representative sample of adolescents from the 2018 Florida Youth Substance Abuse Survey (N = 54,611), these analyses reveal that the likelihood of a self-reported arrest is greatest among Haitian, West Indian/Caribbean, Dominican, and non-Hispanic Black youth. Further, Mexican and Puerto Rican adolescents have a higher risk of experiencing an arrest than members of other Hispanic subgroups.
The Impacts of Animal-Based Diets in Cardiovascular Disease Development: A Cellular and Physiological Overview

Cardiovascular disease (CVD) is the leading cause of death in the United States, and diet plays an instrumental role in CVD development. Plant-based diets have been strongly tied to a reduction in CVD incidence. In contrast, animal food consumption may increase CVD risk. While increased serum low-density lipoprotein (LDL) cholesterol concentrations are an established risk factor which may partially explain the positive association with animal foods and CVD, numerous other biochemical factors are also at play. Thus, the aim of this review is to summarize the major cellular and molecular effects of animal food consumption in relation to CVD development. Animal-food-centered diets may (1) increase cardiovascular toll-like receptor (TLR) signaling, due to increased serum endotoxins and oxidized LDL cholesterol, (2) increase cardiovascular lipotoxicity, (3) increase renin-angiotensin system components and subsequent angiotensin II type-1 receptor (AT1R) signaling and (4) increase serum trimethylamine-N-oxide concentrations. These nutritionally mediated factors independently increase cardiovascular oxidative stress and inflammation and are all independently tied to CVD development. Public policy efforts should continue to advocate for the consumption of a mostly plant-based diet, with the minimization of animal-based foods.