Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling Weirs in Two-Dimensional Shallow Water Models

2D models based on the shallow water equations are widely used in river hydraulics. However, these models can present deficiencies in those cases in which their intrinsic hypotheses are not fulfilled. One of these cases is in the presence of weirs. In this work we present an experimental dataset including 194 experiments in nine different weirs. The experimental data are compared to the numerical results obtained with a 2D shallow water model in order to quantify the discrepancies that exist due to the non-fulfillment of the hydrostatic pressure hypotheses. The experimental dataset presented can be used for the validation of other modelling approaches.
Gambaran Penggunaan Garam Beryodium di Desa Telemung Kecamatan Kalipuro Kabupaten Banyuwangi

Masalah Gangguan akibat kekurangan yodium (GAKY) merupakan salah satu masalah gizi utama di Indonesia. Pemetaan GAKY pada anak sekolah yang dilakukan melalui survey nasional GAKY oleh Departemen Kesehatan menunjukan angka TGR di Indonesia mencapai 11,1 persen. GAKY diketahui mempunyai kaitan yang erat dengan gangguan perkembangan mental dan kecerdasan sehingga dapat mempengaruhi kelangsungan hidup dan kualitas manusia. Penelitian ini bertujuan untuk mengetahui bagaimana gambaran praktek penggunaan garam beryodium di Desa Telemung Kecamatan Kalipuro Kabupaten Banyuwangi. Jenis penelitian adalah deskriptif dengan desain studi cross sectional. Sampel penelitian adalah ibu rumah tangga yang ada di Desa Telemung sebanyak 80 orang dipilih dengan menggunakan metode accident sampling. Data tentang pengetahuan penggunaan garam beryodium dikumpulkan dengan menggunakan wawancara dengan dipandu instrumen kuesioner. Hasil penelitian ini menunjukan bahwa terdapat 93,8 persen dari 80 ibu rumah tangga yang telah menggunakan garam beryodium. 33,8 persen dari 80 ibu rumah tangga yang menambahkan garam pada makanan setelah matang. 95 persen dari 80 ibu rumah tangga yang mennyimpan garam di wadah yang tertutup. Dan 3,8 persen dari 80 ibu rumah tangga yang meletakkan garam jauh dari kompor yakni lebih dari satu meter. Kesimpulan penelitian ini adalah ketersediaan dan penggunaan garam, wadah dan lokasi penyimpanan garam sudah sangat baik, akan tetapi praktek penggunaan garam masih kurang.
Service Priority Climate and Service Performance Among Hospitality Employees: The Role of Emotional Labor and Workload Pressure

Frontline hotel employees face a complex organizational environment that constantly makes multiple demands, creating a persistent trade-off between service as a key element of the organization’s strategy and other competing or even conflicting goals. This study proposes an integrated and unique way of discerning the relationship between service climate and service performance through the prism of surface and deep acting emotional labor and suggests a new dimension of the service climate—the service priority climate. Specifically, we examined employees’ use of emotional labor strategies as a mechanism that explains the relationship between service priority climate and service performance. We also investigated whether workload pressure influences this relationship. Using a multilevel, multisource study, we surveyed a sample of 245 hotel employees working in 39 departments and their direct managers. The results demonstrated that when employees regarded service as a priority compared with other competing goals, they used more deep acting emotional labor strategies, resulting in better service performance. However, this was apparent only when workload pressure was low. Implications for hospitality organizations are discussed.
Exploiting Spatial Information to Enhance DTI Segmentations via Spatial Fuzzy c-Means with Covariance Matrix Data and Non-Euclidean Metrics

A diffusion tensor models the covariance of the Brownian motion of water at a voxel and is required to be symmetric and positive semi-definite. Therefore, image processing approaches, designed for linear entities, are not effective for diffusion tensor data manipulation, and the existence of artefacts in diffusion tensor imaging acquisition makes diffusion tensor data segmentation even more challenging. In this study, we develop a spatial fuzzy c-means clustering method for diffusion tensor data that effectively segments diffusion tensor images by accounting for the noise, partial voluming, magnetic field inhomogeneity, and other imaging artefacts. To retain the symmetry and positive semi-definiteness of diffusion tensors, the log and root Euclidean metrics are used to estimate the mean diffusion tensor for each cluster. The method exploits spatial contextual information and provides uncertainty information in segmentation decisions by calculating the membership values for assigning a diffusion tensor at one voxel to different clusters. A regularisation model that allows the user to integrate their prior knowledge into the segmentation scheme or to highlight and segment local structures is also proposed. Experiments on simulated images and real brain datasets from healthy and Spinocerebellar ataxia 2 subjects showed that the new method was more effective than conventional segmentation methods.
Cross-Platform Item Recommendation for Online Social E-Commerce

Social e-commerce, as a new concept of e-commerce, uses social media as a new prevalent platform for online shopping. Users are now able to view, add to cart, and buy products within a single social media app. In this paper, we address the problem of cross-platform recommendation for social e-commerce, i.e., recommending products to users when they are shopping through social media. To the best of our knowledge, this is a new and important problem for all e-commerce companies (e.g., Amazon, Alibaba), but it has never been studied before. Existing cross-platform and social-related recommendation methods cannot be applied directly to this problem since they do not co-consider the social information and the cross-platform characteristics together. To study this problem, we collect two real-world datasets from social e-commerce services. We first investigate the heterogeneous shopping behaviors between traditional e-commerce app and social media. Based on these observations from data, we propose CROSS (Cross-platform Recommendation for Online Shopping in Social Media), a recommendation framework utilizing not only user-item interaction data on both platforms, but also social relation data on social media. The framework is general, and we propose two variants, CROSS-MF and CROSS-NCF. Extensive experiments on two real-world social e-commerce datasets demonstrate that our proposed CROSS significantly outperforms existing state-of-the-art methods.
A CpG Methylation Signature as a Potential Marker for Early Diagnosis of Hepatocellular Carcinoma From HBV-Related Liver Disease Using Multiplex Bisulfite Sequencing

Background Aberrant methylation of CpG sites served as an epigenetic marker for building diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC). Methods Using Illumina 450K and EPIC Beadchip, we identified 34 CpG sites in peripheral blood mononuclear cell (PBMC) DNA that were differentially methylated in early HCC versus HBV-related liver diseases (HBVLD). We employed multiplex bisulfite sequencing (MBS) based on next-generation sequencing (NGS) to measure methylation of 34 CpG sites in PBMC DNA from 654 patients that were divided into a training set (n = 442) and a test set (n = 212). Using the training set, we selected and built a six-CpG-scorer (namely, cg14171514, cg07721852, cg05166871, cg18087306, cg05213896, and cg18772205), applying least absolute shrinkage and selection operator (LASSO) regression. We performed multivariable analyses of four candidate risk predictors (namely, six-CpG-scorer, age, sex, and AFP level), using 20 times imputation of missing data, non-linearly transformed, and backwards feature selection with logistic regression. The final model’s regression coefficients were calculated according to “Rubin’s Rules”. The diagnostic accuracy of the model was internally validated with a 10,000 bootstrap validation dataset and then applied to the test set for validation. Results The area under the receiver operating characteristic curve (AUROC) of the model was 0.81 (95% CI, 0.77–0.85) and it showed good calibration and decision curve analysis. Using enhanced bootstrap validation, adjusted C-statistics and adjusted Brier score were 0.809 and 0.199, respectively. The model also showed an AUROC value of 0.84 (95% CI 0.79–0.88) of diagnosis for early HCC in the test set. Conclusions Our model based on the six-CpG-scorer was a reliable diagnosis tool for early HCC from HBVLD. The usage of the MBS method can realize large-scale detection of CpG sites in clinical diagnosis of early HCC and benefit the majority of patients.
An improved nonlinear model considering relative velocity for the friction behavior between untwisted glass-fiber tow and roller

Friction between the tow and tool surface normally happens during the tow production, fabric weaving, and application process and has an important influence on the quality of the woven fabric. Based on this fact, this paper studied the influence of tension and relative velocity on the three kinds of untwisted-glass-fiber tow-on-roller friction with a Capstan-based test setup. Furthermore, an improved nonlinear friction model taking both tension and velocity into account was proposed. According to statistical test results, firstly, the friction coefficient was found to be positively correlated with tension and relative velocity. Secondly, tension and velocity were complementary on the tow-on-roller friction behavior, with neither being superior to the other. Thirdly, an improved model was found to present well the nonlinear characteristics between friction coefficient and tension and velocity, and predicational results of the model were found to agree well with the observations from Capstan tests.
Adaptation and genomic erosion in fragmented Pseudomonas aeruginosa populations in the sinuses of people with cystic fibrosis.

Pseudomonas aeruginosa notoriously adapts to the airways of people with cystic fibrosis (CF), yet how infection-site biogeography and associated evolutionary processes vary as lifelong infections progress remains unclear. Here we test the hypothesis that early adaptations promoting aggregation influence evolutionary-genetic trajectories by examining longitudinal P. aeruginosa from the sinuses of six adults with CF. Highly host-adapted lineages harbored mutator genotypes displaying signatures of early genome degradation associated with recent host restriction. Using an advanced imaging technique (MiPACT-HCR [microbial identification after passive clarity technique]), we find population structure tracks with genome degradation, with the most host-adapted, genome-degraded P. aeruginosa (the mutators) residing in small, sparse aggregates. We propose that following initial adaptive evolution in larger populations under strong selection for aggregation, P. aeruginosa persists in small, fragmented populations that experience stronger effects of genetic drift. These conditions enrich for mutators and promote degenerative genome evolution. Our findings underscore the importance of infection-site biogeography to pathogen evolution.
Trust in Public Policy Algorithms

Algorithms are playing an increasingly important role in many areas of public policy. Yet, we know surprisingly little about the degree of trust individuals are willing to place in these algorithms. This article reports on a series of experiments on trust in algorithms for forecasting political events and criminal recidivism. Contrary to previous literature on algorithm aversion, we find that people show high levels of trust in algorithms relative to other sources of advice, even with minimal information about the algorithm. We also explore evaluation of combined human and algorithm advice. We find that, when experts make decisions in light of algorithm advice, the relative weight given to their judgments increases, but the algorithm’s guidance is not disregarded. Finally, using a conjoint experiment, we evaluate the factors that influence people’s preferences for algorithms, finding that risk aversion, data size, human in the loop, and developer reputation play an important role in engendering trust.
A deep learning-based workflow for high-throughput and high-quality widefield fluorescent imaging of 3D samples

3D wideﬁeld ﬂuorescent microscopic (wFLM) imaging is a widespread technology used to study living three-dimensional samples, such as tumor spheroids during drug development. However, 3D wFLM imaging suﬀers from a severe trade-oﬀ between image quality and throughput limiting its applicability. In this project, we present a novel workﬂow that enables high-throughput 3D wFLM imaging, which has previously been impossible, and apply it to ﬂuorescent indicators of cell health within 3D tumor spheroids. The workﬂow combines deep learning with state-of-the-art live-cell imaging techniques to speed up the acquisition of a ﬂuorescent image of a three-dimensional sample by a factor of a hundred.
Establishment of Entrepreneurial Character Santri Based on Human Resources Management

Objective: This research analysis about management human resources for santri Pondok Pesantren Darul Falah Amtsilati to manage academic and business personality. Research Design & Methods: This research uses qualitative research methods with case study approach. The data source in this study is primary data by conducting in-depth interviews to the manager (academic & business) and santri Pondok Pesantren Darul Falah Amtsilati. And observations were made to observe directly about the state of Darul Falah Amtsilati boarding school. Data analysis techniques use source triangulation. Findings: The results of this study showed that in the formation of the character of boarding schools Darul Falah Amtsilati using methods of precision, planting discipline, habituation, creating a conducive atmosphere, as well as integration and internalization. From the formation of the entrepreneurial character has been done based on the character of Islamic human resource. Where shiddiq is taught with honesty, trust by emphasizing responsibility, tabligh conveys correctly, and fathonah is realized by the selection of santri based on criteria. Implications & Recommendations: The implication of the results of this study is that the pesantren has used Islamic resource principles in the recruitment and learning process but needs to be improved or perfected in the implementation of learning by creating special classes for business character building. The aim is to help develop the quality of productive pesantren business managers. Contribution & Value Added: The contribution of the results of this study is to provide input to pesantren in improving the quality of prospective Islamic resources who will become managers of productive business activities of pesantren.
Mental Health Status of Pregnant Women and Breastfeeding Mothers During COVID-19 Outbreak in Bangladesh

In March 2020, Coronavirus disease (COVID-19) was declared a public health emergency of international concern. Studies have suggested a higher prevalence of mental health problems during this pandemic among the general population in Bangladesh. However, there was no information regarding the mental health problems of pregnant women and breastfeeding mothers as this pandemic poses a threat to these specific population's mental health. Therefore, the present study aimed to assess pregnant and breastfeeding women's mental health status during the COVID 19 pandemic in Bangladesh. In the present study, data were collected from a sample of 201 pregnant women and 279 breastfeeding mothers via an online survey. The Depression Anxiety Stress Scale -21 (DASS-21) and the Coronavirus Anxiety Scale (CAS) were utilized to assess the mental problems in this study. Results suggested 39.31% had moderate to extremely severe depression symptoms, 43.29% had moderate to extremely severe anxiety symptoms, 28.86% moderate to extremely severe stress symptoms, and 11.42% COVID-19 anxiety symptoms among pregnant women. For the breastfeeding mothers, these ratios were 39.78%, 36.98%, 26.16%, and 12.90%, respectively. Breastfeeding mothers who were students had higher depression, anxiety, and stress symptoms compared to housewives and job holder mothers. Mothers who gave birth for the first time had a higher depression ratio than mothers who had previously delivered babies. These findings suggested the strong need for heightened assessment and treatment of mental health for pregnant women and breastfeeding mothers during the current pandemic. Funding: None to declare. Declaration of Interest: None to declare. Ethics Approval Statement: The Ethical Review Committee of the Noakhali University of Science and Technology, Bangladesh, has approved this study (ERB No: 66/2021, dated: 10/02/2021). This study was carried out in accordance with the Declaration of Helsinki and its later amendments or comparable ethical standards.
Computational simulation of a single Taylor bubble rising in a vertical column with stagnant liquid

This work presents a computational simulation of a single Taylor bubble rising in a vertical column of stagnant liquid. The computational simulation was based on the Navier-Stokes equations for isothermal, incompressible, and laminar flow, solved by using the open source software OpenFOAM. The two fluids were assumed immiscible. The governing equations were discretized by the volume-of-fluid (VOF) method and solved using the Gauss iteration method. Parametric mesh was used in order to improve the modeling of curvilinear geometry. Numerical solutions were obtained for the rise velocities and shapes of the bubbles which are in excellent agreement with experimental data and correlations from literature.
PERCEPTIONS, ATTITUDE AND PRACTICES TOWARD ELDERLY DEPRESSION AMONG PRIMARY HEALTH CARE PHYSICIANS, RIYADH, SAUDI ARABIA 2021

Background: Elderly people need care services in particular to maintain a high quality of life and health status. Managing the health needs of geriatric patients is part of the continuity of care family physicians provide to their patients. Aims: to assess physicians attitude, perception and practice toward depression in elderly in primary care sitting. Methodology: A cross- sectional study to assess primary health care physicians attitude and perceptions and practices toward depression in elderly patients in primary health care centers of King Saud medical city in Riyadh, kingdom of Saudi Arabia using self-administrated questionnaire Results: We received 210 responses to our questionnaire with response rate of 100 % where 51 % of them were females. Furthermore, 37 % of them have experience in PHC less than 5 years while 32 % have experience for more than 10 years. PHC physicians routinely screen for sleep disturbance (79 %), loss of interest or pleasure (79 %), sad mood (72 %), and decreased energy (63 %) in order to diagnosis of depression. Moreover, we found that 56 % of physicians would use clinical guidelines for diagnosis and treatment of geriatric depression where PHQ-9 was the most reported used tool (28 %) followed by geriatric depression scales (24%). Moreover, 71 % of physicians would refer patients with depression to psychiatry and 65 % to CBT instead to prescribe medications including SNRI (29 %), TCA (17 %) and SSRI (2 %). Moreover, the main barriers to adequate diagnosis and treatment of elderly depressed patients were rejection of patients to treatment (22 % of them indicated it as major problem) and difficulty for access to mental health care in our community (19 % of them indicated it as major problem). Conclusion: we found that most of physicians in Riyadh show high positive attitude toward depression of elderly however, there is some limitations in knowledge about symptoms of depression and restriction to guideline. Main barriers to adequate diagnosis and treatment of elderly depressed patients were rejection of patients to treatment and difficulty for access to mental health care in our community.
AI and machine learning for the analysis of data flow characteristics in industrial network communication security

AI and machine learning are revolutionary technologies being explored by the communication industry to integrate them into communication networks, provide modern services, improve network efficiency and user experience. The intrusion detection system is important for ensuring security of the industrial control system. Hence, in this paper, a machine learning assisted intrusion detection system (MLAIDS) has been proposed to analyse data flow characteristics in industrial network communication security. The progressive use of proposed ML algorithms will improve IDS functionality, especially in industrial control systems. Analysis of data flow characteristics given in this article involves the method of ensuring an adequate degree of security for a dispersed industrial network concerning some main elements, including system features, the present state of requirements, the implementation of suitable countermeasures that may lead to reducing the security risk under a predefined, acceptable threshold. The numerical results show that proposed MLAIDS method achieves high detection accuracy of 98.2%, a performance ratio of 97.5%, a prediction ratio of 96.7%, F1-score of 95.8%, and less root mean square error of 10.5% than other existing methods.
Relationship between Serum Vitamin D in Male, Sperm Function and Clinical Outcomes in Infertile Men Candidate for ICSI: A Cohort Study

Background: Today, vitamin D deficiency (VDD) is one of the major health issues around the world and VDD is associated with several diseases. This study was conducted to find the relationship between vitamin D status in male’s serum with sperm function and clinical outcomes in infertile men candidate for intracytoplasmic sperm injection (ICSI). Materials and Methods: In this cohort study, different parameters of male fertility such as sperm parameters, oxidative stress, and sperm chromatin status were evaluated in sperm samples of 30 infertile couples candidate for ICSI. Clinical outcomes like fertilization, embryo quality, and implantation were also assessed. Data were analyzed using SPSS Statistics 25.0 software. Besides, assessment of the correlation between aforementioned parameters with the level of serum vitamin D, in this study, ICSI candidates were divided into three groups [individuals with sufficient vitamin D levels (>30 ng/ml), insufficient vitamin D levels (between 20-29 ng/ml), and VDD (<20 ng/ml)]. The aforementioned parametesr were also compared between these study groups. Results: Analysis of all the data revealed a significant correlation between the level of vitamin D with sperm concentration (P=0.000, r=0.5), sperm count (P=0.03, r=0.31) and sperm reactive oxygen species (ROS) level (P=0.000, r=-0.77). Moreover, comparing clinical outcomes within study groups showed a significant difference in implantation rate between sufficient and other groups (insufficient and deficient) (P=0.02). Conclusion: Considering the association between sperm concentration and level of ROS with vitamin D and, higher implantation rate in individuals with vitamin D sufficient group compared to other two groups, our data call for vitamin D supplementation as part of male infertility treatment. But considering our sample size, further research is needed to verify these findings.