Miracle-dz/UniFAR-FTUs huggingface dataset:
An Enhanced Random Vibration and Fatigue Model for Printed Circuit Boards

AEROSPACE VEHICLES ARE MOSTLY EXPOSED TO RANDOM VIBRATION LOADS DURING ITS OPERATIONAL LIFETIME. THESE HARSH CONDITIONS EXCITES VIBRATION RESPONSES IN THE VEHICLES PRINTED CIRCUIT BOARDS, WHAT CAN CAUSE FAILURE ON MISSION FUNCTIONALITY DUE TO FATIGUE DAMAGE OF ELECTRONIC COMPONENTS. A NOVEL ANALYTICAL MODEL TO EVALUATE THE USEFUL LIFE OF EMBEDDED ELECTRONIC COMPONENTS (CAPACITORS, CHIPS, OSCILLATORS ETC.) MOUNTED ON PRINTED CIRCUIT BOARDS (PCB) IS PRESENTED. THE FATIGUE DAMAGE PREDICTIONS ARE CALCULATED BY THE RELATIVE DISPLACEMENT BETWEEN THE PCB AND THE COMPONENT, THE LEAD STIFFNESS, AS WELL THE NATURAL VIBRATION MODES OF THE PCB AND THE COMPONENT ITSELF. STATISTICAL METHODS ARE USED FOR FATIGUE CYCLE COUNTING. THE MODEL IS APPLIED TO EXPERIMENTAL FATIGUE TESTS OF PCBS AVAILABLE ON LITERATURE. THE ANALYTICAL RESULTS ARE OF THE SAME MAGNITUDE ORDER OF THE EXPERIMENTAL FINDINGS.
Immune-Mediated Hepatitis During Immune Checkpoint Inhibitor cancer Immunotherapy: Lessons From Autoimmune Hepatitis and Liver Immunology

Immune checkpoint inhibitors (ICI) are being increasingly used to successfully treat several types of cancer. However, due to their mode of action, these treatments are associated with several immune-related adverse events (irAEs), including immune-mediated autoimmune-like hepatitis in 5 to 10% of cases. The specific immune mechanism responsible for the development of immune-mediated liver injury caused by immune checkpoint inhibitors (ILICI) is currently unknown. This review summarizes the current knowledge on hepatic irAEs during cancer immunotherapy. It also addresses the clinical management of ILICI and how it is becoming an increasingly important clinical issue. Clinical, histological, and laboratory features of autoimmune hepatitis (AIH) and ILICI are compared, and their shared and distinctive traits are discussed in an effort to better understand the development of hepatic irAEs. Finally, based on the current knowledge of liver immunology and AIH pathogenesis, we propose a series of events that could trigger the observed liver injury in ICI-treated patients. This model could be useful in the design of future studies aiming to identify the specific immune mechanism(s) at play in ILICI and improve immune checkpoint inhibitor cancer immunotherapy.
Relationship between University Organizational Management Climate, Teacher Self-Efficacy and Job Satisfaction in Chinese University

Taking university teachers as the survey object and sample survey the current situation of university Teachers' organizational management climate, teachers' self-efficacy and teachers' job satisfaction, discusses the correlation and direction of action among the three, and constructs a simple structural equation model among the three. The results of structural equation model show that there is a significant positive correlation among organizational management climate, teachers' self-efficacy and teachers' job satisfaction; The organizational management climate in colleges and universities affects teachers' job satisfaction through the mediating effect of teachers' self-efficacy How to create a good organizational and management atmosphere in colleges and universities, so as to stimulate teachers' sense of self-efficacy and improve teachers' job satisfaction is undoubtedly an important work that cannot be ignored in Chinese colleges and universities
Introduction to supervised machine learning in clinical epidemiology

ABSTRACT Machine learning refers to a series of processes in which a computer finds rules from a vast amount of data. With recent advances in computer technology and the availability of a wide variety of health data, machine learning has rapidly developed and been applied in medical research. Currently, there are three types of machine learning: supervised, unsupervised, and reinforcement learning. In medical research, supervised learning is commonly used for diagnoses and prognoses, while unsupervised learning is used for phenotyping a disease, and reinforcement learning for maximizing favorable results, such as optimization of total patients’ waiting time in the emergency department. The present article focuses on the concept and application of supervised learning in medicine, the most commonly used machine learning approach in medicine, and provides a brief explanation of four algorithms widely used for prediction (random forests, gradient-boosted decision tree, support vector machine, and neural network). Among these algorithms, the neural network has further developed into deep learning algorithms to solve more complex tasks. Along with simple classification problems, deep learning is commonly used to process medical imaging, such as retinal fundus photographs for diabetic retinopathy diagnosis. Although machine learning can bring new insights into medicine by processing a vast amount of data that are often beyond human capacity, algorithms can also fail when domain knowledge is neglected. The combination of algorithms and human cognitive ability is a key to the successful application of machine learning in medicine.
Classification of junior Ethiopian football players using anthropometric and physical fitness attributes: Developing a predictive model

Objetivo: El objetivo del presente estudio fue desarrollar un modelo predictivo que identifique la condición física y antropométrica que prediga mejor el estado y las posiciones de juego (N=400; 15–17 años) en el programa de desarrollo del fútbol juvenil masculino etíope. Métodos. La medición antropométrica en términos de masa, altura, grasa corporal relativa y composición corporal derivada se realizó utilizando las pautas de la Sociedad Internacional para el Avance de la Cineantropometría (ISAK). El estado físico se midió mediante pruebas de velocidad (10 metros, 20 metros y 40 metros planos), la prueba de agilidad de Illinois, la prueba de salto vertical y el VO2 máximo estimado. Se utilizó ANOVA factorial para probar las relaciones entre la residencia de los jugadores, la posición y los respectivos términos de interacción. Además, se utilizó el análisis discriminante para identificar las variables que contribuyeron a las selecciones. Resultados. Los jugadores de residencias de campamentos deportivos fueron significativamente diferentes en todos los atributos antropométricos, así como en los atributos de condición física en p<0.05 excepto en el VO2 máx. El porcentaje de jugadores correctamente clasificados en los grupos originales fue ligeramente inferior al porcentaje calculado tras el análisis realizado para la muestra total (86%) y tras el cálculo por posiciones (86-90%). Conclusión. El estudio concluyó que la velocidad evaluada en 10 m, 40 m, salto vertical y altura fueron los atributos discriminados de los jugadores por unidad residencial en los jugadores de fútbol juveniles etíopes. Se recomienda que los atributos antropométricos y de aptitud física sean importantes en la evaluación y selección de jugadores de fútbol jóvenes con potencial para una selección de rendimiento de alto nivel.
Effects of Diabetic Neuropathy on the Postural Stability of Adults with Type 2 Diabetes Mellitus

Diabetic peripheral neuropathy (DPN) is one of the most common complications, which occurs in 50–70% of people with Type 2 diabetes mellitus (T2DM). The disease usually affects the peripheral nerves and can cause loss of sensation in the feet, leading to impaired body equilibrium during quiet stance and gait, and elevated risk of falls. A total of 38 non-insulin-dependent T2DM adults (mean age 53.21 ± 7.74 years) – 18 with DPN and 20 without DPN, and 20 age- and sex-matched controls took part in the study. The DPN group was selected with standard clinical criteria and a Bulgarian version of the Michigan neuropathy screening instrument. Postural stability was evaluated with static posturography under two visual conditions (eyes open and eyes closed) on firm and soft support. Both diabetic groups exhibited significantly higher postural instability compared with the healthy controls, as well as stronger visual dependence. While standing on firm support, subjects with DPN exhibited significantly higher postural instability than those without DPN in the closed eyes condition only, whereas when standing on soft support, their postural stability was significantly more impaired compared with the diabetic subjects without DPN in both experimental conditions (open and closed eyes). A better understanding of the changes in postural stability in persons with T2DM would contribute to early diagnostics, rehabilitation and prevention of DPN.
Structural Analysis of Induction Machine and Switched Reluctance Machine

Abstract Here we present a comprehensive analysis of structural performance of Induction Machine (IM) and Switched Reluctance Machine (SRM) rotors under different operational conditions. Electric machines are susceptible to forces of diverse origins. In addition to traditionally considered magnetic and centrifugal forces, this article discusses independent and inclusive effects of thermal expansion and shaft attachment techniques on structural integrity of the machines. A comparative analysis of structural performance of IMs and SRMs has also been performed, under a range of scenarios, to facilitate application-specific machine selection from a structural perspective.
Prevalence and predictors of appropriate complementary feeding practice among mothers with children 6–23 months in Northern Ghana

IntroductionAdequate assessment of appropriate complementary feeding practices requires an indicator that can measure its key components concurrently, but past studies often described the practice using single indicators. This study in northern Ghana therefore assessed the prevalence and predictors of appropriate complementary feeding practices as measured using a composite indicator.MethodsThe source of data for this analysis is from baseline studies of a community-based quasi-experimental study conducted prior to implementing a nutrition behaviour change communication intervention on radio. The study population comprised mothers and their children selected using a two-stage cluster sampling procedure. Multivariable logistic regression was used to identify factors associated with appropriate complementary feeding practices.ResultsThe overall prevalence of appropriate complementary feeding practices combining three key indicators (timely introduction of complementary food, adequacy of meal frequency, and meeting minimum dietary diversity) was 29.8% (95% CI: 27.4 - 32.3). Children aged 12-23 months [AOR = 2.26 (95% CI:1.41 - 3.61)], higher nutrition related knowledge ofcaretakers [AOR 1.51; 95% CI (1.15 - 1.98)], higher educational level of mothers [AOR1.95; 95% CI (1.17 - 3.25)], and positive nutrition related attitudes towards appropriatecomplementary feeding practices [AOR 1.59; 95% CI (1.21 - 2.09)] were significantlyassociated with appropriate complementary feeding practice.ConclusionsThe prevalence of appropriate complementary feeding practices was quite low among children aged 6–23 months. Sustainable nutrition education to mothers/caretakers during prenatal, delivery, postnatal, and child welfare clinic on appropriate complementary feeding should be strengthened to increase nutrition related knowledge and attitude towards appropriate complementary feeding practices.
Effect of thermosonication in a laboratory-scale continuous system on the survival of thermophilic bacteria and indigenous microbiota in milk

Fluid foods are typically heat-treated to eliminate pathogens and reduce microbial counts. Coupling thermosonication with heat may reduce the microbial load in fluid foods and enhance the product quality during storage. This study evaluated the effect of thermosonication along with heat in a laboratory-scale continuous system, on the survival of Geobacillus stearothermophilus in milk, at two different settings (setting 1: 27.7 s total heating time with or without 11.9 s of sonication; setting 2: 20.3 s total heating time with or without 7.1 s of sonication). This study also investigated the effect of thermosonication along with heat on indigenous microbiota in raw milk; and milk quality was assessed by pH, free fatty acid (FFA) content, and casein/total protein (CN/TP) content during storage at the two different settings. Overall, thermosonication with heat resulted in higher log reductions for G. stearothermophilus; but, the reduction was not significant overall. Thermosonication with heat significantly decreased the indigenous microbiota in milk as compared to heat alone at both settings. Longer residence times (setting 1) had significantly higher log reductions at week 0, and treatment samples had significantly higher reductions than control during storage time at both the settings. Treatment samples at setting 1 had significantly higher pH and CN/TP, and lower FFA content at week 4, as compared to the control. Thermosonication using practical residence times along with heat may reduce milk microbiota. Results from this study need to be verified in a scale-up study employing pasteurization conditions.
Clinical Article Value of the quantity and distribution of subarachnoid haemorrhage on CT in the localization of a ruptured cerebral aneurysm

Summary Background. Computed tomography (CT) is the ‘‘gold standard’’ for detecting subarachnoid haemorrhage (SAH) and digital subtraction angiography (DSA) for visualising the vascular pathology. We studied retrospectively 180 patients with subarachnoid haemorrhage (SAH) who underwent first non-enhanced computed tomography (CT), then digital subtraction angiography (DSA) and finally operative aneurysm clipping. Our aim was to assess if the location of the ruptured aneurysm could be predicted on the basis of the quantity and distribution of haemorrhage on the initial CT scan. Methods. 180 patients with SAH were retrospectively studied. All the CT and DSA examinations were performed at the same hospital. CT was performed within 24 hours after the initial haemorrhage. DSA was performed after the CT, within 48 hours after the initial haemorrhage. Two neuroradiologists, blind to the DSA results, analysed and scored independently the quantity and distribution of the haemorrhage and predicted the site of the ruptured aneurysm on the basis of the nonenhanced CT. DSA provided the location of the ruptured aneurysm. All the patients were operated upon, and the location of the ruptured aneurysm was determined. Findings. The overall reliability value (� -value) between the two neuroradiologists for locating all ruptured aneurysms was 0.780. The corresponding value for the right MCA was 0.911, that for the left MCA 0.877 and that for the AcoA 0.736. Not all of the � -values were calculated, either because the location of the rupture was constant or because the number of ruptures in the vessel was too small. Subarachnoid haemorrhage with a parenchymal hematoma is an excellent predictor of the site of the ruptured aneurysm with a statistical significance of p ¼ 0.003. Interpretation. The quantity and pattern of the blood clot on CT within the day of onset of SAH is a reliable and quick tool for locating a ruptured MCA or AcoA aneurysm. It is not, however, reliable for locating other ruptured aneurysms. Subarachnoid haemorrhage with a parenchymal hematoma is an excellent predictor of the site of a ruptured aneurysm.
Three Days Compared to One Day Per Week of Self-Monitoring of Blood Glucose in Mild Gestational Diabetes: A Randomized Trial

Background: The International Diabetes Federation estimates that 16.2% of livebirths in 2017 were affected by hyperglycemia in pregnancy, with 85.1% due to gestational diabetes mellitus (GDM). Daily blood glucose monitoring compared with alternate day testing in mild GDM is associated with similar pregnancy outcomes. Data are sparse on the ideal frequency for self-monitoring of blood glucose (SMBG) in mild GDM for glycemic control. A higher HbA1c at late pregnancy is associated with adverse pregnancy outcomes. We sought to evaluate three days compared to one day per week of four-point self-monitoring of blood glucose (SMBG) in gestational diabetes mellitus (GDM) controlled by lifestyle changes for glycemic control. Methods: This randomized trial was conducted from February–December 2018. A total of 106 women with lifestyle-controlled GDM were randomized to three days (SMBG3) or one day (SMBG1) per week of four-point (fasting and two-hours post-meal) SMBG. The primary outcome was the change in the HbA1c level at recruitment and 36-weeks gestation within and across trial arms. The student t-test was used for between-arm analyses and a paired t-test for within-arm analyses. Results: The HbA1c level through pregnancy increased significantly in both trial arms: mean increase of 0.21% ± 0.26%, p < 0.001 (SMBG3), and 0.19% ± 0.24%, p < 0.001 (SMBG1), but the 0.02% difference across trial arms was not significant (p = 0.79). Maternal weight gain (3.1 ± 2.1 kg vs. 3.3 ± 3.0 kg, p = 0.72), cesarean delivery (24/52 (48%) vs. 23/53 (43%), RR 1.06, 95% CI: 0.69–1.62, p = 0.77), neonatal birthweight (3.1 ± 0.4 kg vs. 3.0 ± 0.4 kg, p = 0.53) and neonatal intensive care unit admission (4/52 (8%) vs. 3/53 (6%), RR 1.36, 95% CI: 0.32–5.78, p = 0.68) were not significantly different for SMBG3 vs. SMBG1, respectively. Other maternal and neonatal secondary outcomes were not significantly different. Conclusion: In mild GDM, three days compared to one day per week showed a similar HbA1c levels change at 36-weeks gestation. Maternal and neonatal outcomes were also not significantly different. Less frequent monitoring of SMBG as a standard of care in mild GDM deserves further study and consideration.
Spore detection algorithm of wheat powdery mildew based on weight adaptive feature fusion

Aiming at the characteristics of small targets, many interferents and inconspicuous features of spore images of wheat powdery mildew, a weight adaptive feature fusion model is proposed based on SSD network structure to improve the accuracy of spore detection. Firstly, a feature fusion path is constructed to recursively fuse features of various scales from deep to shallow, and at the same time, a layer of feature matrix is added to enhance the utilization of deep and shallow features by the network; Secondly, a hybrid attention module is proposed, which redistributes the weights of features adaptively to enhance the ability of extracting network context information. Finally, the k-means algorithm is used to set the shape of the prior box, which effectively improves the problem that it is difficult to manually adjust the hyperparameter of the neural network. The AP of powdery mildew spores was 91.17%, Compared with the classical SSD detection method, it has been greatly improved.
Building Students’ Creativity during The Pandemic Covid-19 Using Project-Based Learning

The world changes massively, so it is necessary to prepare adequate human resources with competent skills so that they are ready to adapt and compete on a global scale. Empowerment and improvement of human resources through education are keys to following the development of the Industrial Revolution 4.0. All graduates should be equipped with some skills needed to face the industrial revolution. One of the skills is creativity. However, the Covid-19 pandemic influenced the implementation of teaching and learning that must be carried on virtually. The condition forced the teachers to find the solution to facilitate the students by selecting the right teaching model. This research was conducted to answer the question: "How can Project-Based Learning build the students' creativity in Profession English Subjects? " This research employed a qualitative approach. The data was collected using observation in the online class, interviewing the representative students, and collecting the documents. The researcher used an observation checklist proposed by Torrance to find out their creativity. The results show that the students created a project at the end of the lecture. They were assigned to make a book chapter for teaching English to elementary school students and a video teaching English through songs. The students' creativity appeared when they created the projects. It can be seen from the indicators of their curiosity, flexibility, and originality in creating the works.
The Influence of Material Position towards the Bending Strength of the 4 Pass AA1050/AA6082 and AA6082/AA1050 FSPed Joints

This paper reports on the influence of material position towards the bending strength of the 4 pass AA1050/AA6082 and AA6082/AA1050 FSPed joints. FSW approach was utilized to create dissimilar joints using two dissimilar plates. After that, the created dissimilar joints were put through a multi-pass friction stir processing. The microstructural analysis results revealed that the 4P 6082/1050 joint had substantially finer grains than the 4P 1050/6082 joint. The 4P 1050/6082 joint yielded the highest ultimate tensile strength when compared to that of the 4P 6082/1050 joint. The Vickers microhardness of 1050/6082 FSPed joints was found to be increased towards the AA6082, while 6082/1050 FSPed joints decreased towards AA1050. The bending strength analysis showed that there was no obvious trend in flexural strength.
Institutional Knowledge at Singapore Management University Institutional Knowledge at Singapore Management University W-Air: Enabling personal air pollution monitoring on wearables W-Air: Enabling personal air pollution monitoring on wearables

Accurate, portable and personal air pollution sensing devices enable quantification of individual exposure to air pollution, personalized health advice and assistance applications. Wearables are promising ( e.g. , on wristbands, attached to belts or backpacks) to integrate commercial off-the-shelf gas sensors for personal air pollution sensing. Yet previous research lacks comprehensive investigations on the accuracies of air pollution sensing on wearables. In response, we proposed W-Air, an accurate personal multi-pollutant monitoring platform for wearables. We discovered that human emissions introduce non-linear interference when low-cost gas sensors are integrated into wearables, which is overlooked in existing studies. W-Air adopts a sensor-fusion calibration scheme to recover high-fidelity ambient pollutant concentrations from the human interference. It also leverages a neural network with shared hidden layers to boost calibration parameter training with fewer measurements and utilizes semi-supervised regression for calibration parameter updating with little user intervention. We prototyped W-Air on a wristband with low-cost gas sensors. Evaluations demonstrated that W-Air reports accurate measurements both with and without human interference and is able to automatically learn and adapt to new environments.