Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal circuit model parameters identification of oil-immersed transformer based on PSO algorithm

The thermal circuit model of oil-immersed transformer needs to improve its accuracy in predicting the winding hottest-spot temperature, especially in the case of overload and different cooling modes. In the case of overload condition, the estimate values obtained from most existing models are smaller than the actual measurement values, which increase the potential of transformer overheating fault because of the estimate shortage. These limitations are mainly due to the thermal circuit parameters which are deviating when overloaded or having different cooling modes. In order to overcome these limitations, a parameter identification and correction approach based on PSO algorithm is proposed. The approach works on daily load pattern, permits better accuracy and improves the estimation security margin of thermal circuit in the presence of overload condition and different cooling modes.
Analysis of three-phase short circuit current of doubly fed induction generator

In order to research the low voltage ride through(LVRT) control of doubly fed induction generator(DFIG),the principle of short circuit current of DFIG when three-phase short circuit fault occurred in stator is analyzed.The short circuit current approximate equations of DFIG in free load and synchronous speed condition were deduced.The influences caused by pre-fault state such as speed,active power and reactive power were analyzed based on the electromagnetic relationship of DFIG.A 3MW DFIG model was built,and the three-phase fault was simulated in stator side of the DFIG.The simulation results showed that the short circuit current of DFIG can be analyzed through the short circuit current approximate equations and generator pre-fault condition.It can be taken as theoretical base for the research on LVRT control of DFIG.
The prediction of diesel engine NOx emissions using artificial neural network / Mohd. Mahadzir Mohammud and Khairil Faizi Mustafa

This paper describes an experimental and computer simulation studies used to develop a suitable algorithm to predict and control the oxides of nitrogen (NO ) emitted from the Yanmar L60AE-D single cylinder direct injection diesel engine, fitted in a Cusson's Engine Test Bed Model P8160. NOx contained in the exhaust gases of diesel engines have been identified as elements responsible for polluting our atmosphere. In order to reduce or to control diesel engine polluting emissions, the formation mechanism of NOx can be predicted. A neural network model is developed to obtain the NOx emission concentration under various operating condition. The neural network, well suited for non-linear phenomena modelization, is able to deal with high uncertainly input level and able to operate outside of their range of training experience. A feedforward neural network structure has been selected with a backpropagation training procedure. Four operating parameters (engine speed, engine load, exhaust temperature and air fuel ratio) have been used as an input data in the modelling process. The modelling algorithm implemented, takes a large set of measurements to learn how to predict the NOx emission from four operating parameters. The predicted values obtained using neural network model are compared with the experimental values. The studies show that the predicted results are in good agreement with experimental values, within less 9 % relative error.
A Policy Agnostic Framework for Post Hoc Analysis of Organ Allocation Policies

The amount of patients that need a donor organ far surpasses the number of donors. This shortage results in many patients dying or being removed from the transplant waitlist before ever receiving a transplant, leading us to question whether allocation policies are assigning organs in an equitable, effective, and efficient manner. Using historical data, researchers simulate how a novel allocation policy would offer organs across the patient-population before it is actually implemented in practice. However, to our knowledge, no framework exists to provide a post hoc analysis of a policy after it has been implemented. Post hoc analysis is necessary because the implemented policy may differ wildly from its original proposal. Such variation can be the result of (among others) clinical preference across transplant centers, patients rejecting organ offers, or changing clinical practice. Further complicating matters are conflicting definitions of optimal allocation. For example, the UK takes an altruistic perspective on allocation by maximising a population-wide survival, whereas the US and central EU take an individualistic perspective by selecting the patient that is most in need. To account for all these sources of variation, an accurate system for post hoc analysis should learn and model the implemented policy from data. In addition, to support clinicians in assessing any past or future implemented policy in any jurisdiction a policy agnostic framework is required. We propose a policy agnostic framework based on priority queues, able to learn any implemented policy from data to effectively model patient priority, health deterioration, and variations in allocation practices. Our framework is tested using the United States’ liver transplantation waitlist data, provided by the United Network for Organ Sharing (UNOS). With a case study on the MELD-Na policy for liver transplantation, we show our framework can accurately model an implemented policy, with which we can conduct meaningful analyses. Specifically, we confirm that MELD-Na places emphasis on patients with hyponatremia when allocating organs. Moreover, health professionals have clinically verified the applicability and correctness of the results of our framework. The work in this dissertation is the first step towards analysing any implemented policy. It can be used to verify whether policies meet their intended effect when implemented, or to conduct further analyses to refine current policies in the aim of guiding strategic policy improvements.
The Blue Ocean Strategy And Its Impact On Achieving A Sustainable Competitive Advantage For The Organization Is A Survey Study In A Number Of Government And Private Banks In Basra Province

Many Iraqi banks are used to their work in relation to the customer and fulfilling their wishes and maintaining their presence as a permanent customer because the basis of their work depends on him in the movement of funds, so competition between government and private banks intensifies to gain the largest number of customers, in a way that prompts the need to look for a strategy to keep it working. The study attempts to test the relationship of the Blue Ocean Strategy to the sustainable competitive advantage over three branches of government banks in Basra province.
Social Distancing and Face Mask Detection Using Deep Learning

:- The COVID-19 Pandemic caused by the new Coronavirus is the cause of this 21st-century global health crisis. It has forced the government to impose a lockdown to prevent the transmission of the virus. This led to the unprecedented shutdown of economic activities. The health care system is in crisis. Many different types of safety measures are being taken in order to reduce the risk of the spread of this disease at unprecedented times. Verified reports from renowned scientists and medial health practitioner indicated that wearing a face mask and maintaining social distance reduces the risk of transmitting the virus. Hence, we decided on an approach that is effective and economic by using deep learning techniques to create a safe environment in setups such as manufacturing plants, markets, malls, and other such places. To demonstrate our approach, the training dataset is composed of people, the images with and without the masks, which are collected from a variety of sources and use it in order to build a robust algorithm in order to measure the social distance with the help of the classic geometry methods. Our goal is to determine if a person is wearing a mask, or whether or not they maintain social distance as per protocols and guidelines which are given by leading scientists and governments in this pandemic. We hope that this study will serve as a useful tool for reducing the spread of this dangerous infectious disease in the world.
The role of pharmacists’ interventions in improving drug-related problems, blood pressure, and quality of life of patients with stage 5 chronic kidney disease

This six-month cohort study analyzed the impact of pharmacy interventions (PI) on drug-related problems (DRPs), blood pressure (BP), and quality of life (QoL) among patients with stage 5 chronic kidney disease (CKD) (n = 83) admitted to Pirngadi Hospital, Medan, Indonesia period February to July 2021. DRPs, BP, and QoL of the patients were analyzed before and after PI. DRPs were analyzed applying PCNE version 9, BP scores were extracted from the patients’ medical records. Their QoL was assessed using a WHOQoL questionnaire. The impacts of PI on the incidence of DRPs, BP, and the patients’ QoL were analyzed applying Wilcoxon Signed Rank and Friedman test in the SPSS program version 23.0. The number of DRPs was significantly reduced from 470 before PI to 162 following PI (p = 0.000). The patients’ QoL improved significantly from 40 ± 9.87 before PI to 69 ± 12. 45 after PI, p = 0.000. PI is crucial to improve the outcomes of CKD patients.
Probabilistic deep learning based fast running model of thermal-hydraulic code

Seunghyoung Ryu , Hyeonmin Kim , Seung Geun Kim , Kyungho Jin , Jaehyun Cho , Jinkyun Park b Applied Artificial Intelligence Lab, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989beon-gil, Yuseong-gu, Daejeon 34057, South Korea Risk and Environmental Safety Research Division, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989beon-gil, Yuseong-gu, Daejeon 34057, South Korea Corresponding author: kshpjk@kaeri.re. kr
Impact of Organizational Value System, Perceived Organizational Support, and Job Satisfaction on Organizational Commitment

This paper aims to investigate the effects of perceived organizational support, organizational values system, and job satisfaction on organizational commitment. The study was conducted in the educational sector of Pakistan. Building on the previous literature, the authors propose a model which links an organization's internal procedures/practices, employees' perceptions, views, and his/her level of contentment with his/her job, loyalty and emotions. Through the assumed connections between these aspects, the relationship among them was inspected and analyzed in the given study. To test the proposed model, the data was collected online from 210 faculty members and teachers from different universities in Pakistan. It was accomplished through convenient sampling. Moreover, a cross-sectional method was adopted for data gathering. As per the author's assumptions, the results generated through regression analysis provide shreds of evidence that there exists a significant positive relationship between organizational commitment, job satisfaction, organizational values, and perceived organizational support. The overall study displays the importance of providing support, improving job satisfaction, and clearly defining organizational values to the employees to enhance their organizational commitment. The study validates the significance of organizational practices over employees' attachment towards the organization and deepens the understanding of the determinants of organizational commitment. Furthermore, suggestions about theoretical and managerial practices of the given study have been discussed in the end.
GAN-Based Image Colorization for Self-Supervised Visual Feature Learning

Large-scale labeled datasets are generally necessary for successfully training a deep neural network in the computer vision domain. In order to avoid the costly and tedious work of manually annotating image datasets, self-supervised learning methods have been proposed to learn general visual features automatically. In this paper, we first focus on image colorization with generative adversarial networks (GANs) because of their ability to generate the most realistic colorization results. Then, via transfer learning, we use this as a proxy task for visual understanding. Particularly, we propose to use conditional GANs (cGANs) for image colorization and transfer the gained knowledge to two other downstream tasks, namely, multilabel image classification and semantic segmentation. This is the first time that GANs have been used for self-supervised feature learning through image colorization. Through extensive experiments with the COCO and Pascal datasets, we show an increase of 5% for the classification task and 2.5% for the segmentation task. This demonstrates that image colorization with conditional GANs can boost other downstream tasks’ performance without the need for manual annotation.
HFIR SiC-SiC Composite Clad Tube Bowing Test: Pre-Irradiation Characterization

This report describes the pre-irradiation characterization of the SiC-SiC composite clad tube bowing experiment that will be irradiated in the High Flux Isotope Reactor (HFIR). There are concerns that SiC-SiC fuel cladding in light water reactors could undergo bowing because of the non-uniform fast neutron flux profiles. This experiment is combined with the SiC-SiC channel box experiment, which has similar concerns regarding control blade movements in boiling water reactors. The HFIR experiment aims to validate the predicted deformation and stresses in the composite clad specimen after the exposure of fast neutron flux gradients. Significant radial fast neutron flux gradients that exist in the permanent reflector of HFIR were thoroughly characterized by using detailed 3D neutronic calculations. The 3D displacement damage dose rate profile and the resulting volumetric swelling in SiC were used as inputs to structural analyses to determine the predicted deformation that will affect the specimens. The tube specimens were characterized by using traditional dimensional
Importance of Epidemiology and R0 in Wildlife Rehabilitation

Wildlife rehabilitators are at the intersection between individual and population–level wildlife health. Epidemiology is the study of diseases at population levels and mounting evidence suggests that disease outbreaks can impact wildlife populations significantly. The basic reproduction number (R0) is used as a threshold value to predict whether a disease will result in an outbreak or die out. It is defined as the expected number of secondary cases caused by one infectious individual (the index or primary case) during this individual’s entire infectious period in a fully susceptible population. Rehabilitators should be familiar with the concept of R0 and the important effect on and contribution to wildlife disease epidemiology that they can have.
New Insights of Scaffolds Based on Hydrogels in Tissue Engineering

In recent years, biomaterials development and characterization for new applications in regenerative medicine or controlled release represent one of the biggest challenges. Tissue engineering is one of the most intensively studied domain where hydrogels are considered optimum applications in the biomedical field. The delicate nature of hydrogels and their low mechanical strength limit their exploitation in tissue engineering. Hence, developing new, stronger, and more stable hydrogels with increased biocompatibility, is essential. However, both natural and synthetic polymers possess many limitations. Hydrogels based on natural polymers offer particularly high biocompatibility and biodegradability, low immunogenicity, excellent cytocompatibility, variable, and controllable solubility. At the same time, they have poor mechanical properties, high production costs, and low reproducibility. Synthetic polymers come to their aid through superior mechanical strength, high reproducibility, reduced costs, and the ability to regulate their composition to improve processes such as hydrolysis or biodegradation over variable periods. The development of hydrogels based on mixtures of synthetic and natural polymers can lead to the optimization of their properties to obtain ideal scaffolds. Also, incorporating different nanoparticles can improve the hydrogel’s stability and obtain several biological effects. In this regard, essential oils and drug molecules facilitate the desired biological effect or even produce a synergistic effect. This study’s main purpose is to establish the main properties needed to develop sustainable polymeric scaffolds. These scaffolds can be applied in tissue engineering to improve the tissue regeneration process without producing other side effects to the environment.
An Architecture for Service Integration to Fully Support Novel Personalized Smart Tourism Offerings

The continuous evolution of IT (information technology) technologies is radically transforming many technical areas and social aspects, also reshaping the way we behave and looking for entertainment and leisure services. In that context, tourism experiences request to enhance the level of user involvement and integration and to create an ever more personalized and connected experience, by leveraging on the differentiated tourist services and information locally present in the territory, by pushing active participation of customers, and by taking advantage of the ever-increasing presence of sensors and IoT (Internet of Things) devices deployed in many realities. However, the deep fragmentation of services and technologies adopted in tourism context characterizes the whole information provided also by customer sensing and IoTs (Internet of Things) heterogeneity and deep clashes with an effective organization of smart tourism. This article presents APERTO5.0 (an Architecture for Personalization and Elaboration of services and data to Reshape Tourism Offers 5.0), an innovative architecture aiming at a whole integration and deep facilitation of tourism service and information organization and blending, to enable the re-provisioning of novel services as advanced aggregates or re-elaborated ones. The proposed solution will demonstrate its effectiveness in the context of Smart Tourism by choosing the real use case of the “Francigena way” (a pilgrim historical path), the Italian part.
In Vitro–In Silico Modeling of Caffeine and Diclofenac Permeation in Static and Fluidic Systems with a 16HBE Lung Cell Barrier

Static in vitro permeation experiments are commonly used to gain insights into the permeation properties of drug substances but exhibit limitations due to missing physiologic cell stimuli. Thus, fluidic systems integrating stimuli, such as physicochemical fluxes, have been developed. However, as fluidic in vitro studies display higher complexity compared to static systems, analysis of experimental readouts is challenging. Here, the integration of in silico tools holds the potential to evaluate fluidic experiments and to investigate specific simulation scenarios. This study aimed to develop in silico models that describe and predict the permeation and disposition of two model substances in a static and fluidic in vitro system. For this, in vitro permeation studies with a 16HBE cellular barrier under both static and fluidic conditions were performed over 72 h. In silico models were implemented and employed to describe and predict concentration–time profiles of caffeine and diclofenac in various experimental setups. For both substances, in silico modeling identified reduced apparent permeabilities in the fluidic compared to the static cellular setting. The developed in vitro–in silico modeling framework can be expanded further, integrating additional cell tissues in the fluidic system, and can be employed in future studies to model pharmacokinetic and pharmacodynamic drug behavior.
Simulation Study of a Commercial Transport Airplane During Stall and Post-Stall Flight

As part of NASA's Aviation Safety and Security Program, a simulation study of a twin-jet transport aircraft crew training simulation was conducted to address fidelity for upset or loss-of-control flight conditions. Piloted simulation studies were conducted to compare the baseline crew training simulation model with an enhanced aerodynamic model that was developed for high-angle-of-attack conditions. These studies were conducted in a flaps-up configuration and covered the approach-to-stall, stall and post-stall flight regimes. Qualitative pilot comments and preliminary comparison with flight test data indicate that the enhanced model is a significant improvement over the baseline. Some of the significant unrepresentative characteristics that are predicted by the baseline crew training simulation for flight in the post-stall regime have been identified. This paper presents preliminary results of this simulation study and discusses key issues regarding predicted flight dynamics characteristics during loss-of-control flight conditions.
Identification of the Potential Genes Regulating Seed Germination Speed in Maize

Seed germination is the crucial stage in plant life cycle. Rapid and uniform germination plays an essential role in plant development and grain yield improvement. However, the molecular mechanism underlying seed germination speed is largely unknown due to the complexity of the dynamic process and the difficulty in phenotyping. Here, we conducted a time-series comparative transcriptome study of two elite maize inbred lines, 72-3 and F9721, with striking difference in seed germination speed, and identified a major locus underlying maize germination speed through genome-wide association analysis (GWAS) of an F2 segregation population. Comparative transcriptome study identified 12 h after imbibition (HAI) as the critical stage responsible for the variation in germination speed. The differentially expressed genes (DEGs) between 72-3 and F9721 were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, oxidoreductase activity pathways, hormone signal transduction, and amino acid transporter activity pathways. GWAS revealed that germination speed was controlled by a major locus on chromosome 1 with the leading SNP as AX-91332814, explaining 10.63% of phenotypic variation. A total of 87 proposed protein-coding genes surrounding the locus were integrated with DEGs. Combined with evidence from the gene expression database and gene synteny with other model species, we finally anchored three genes as the likely candidates regulating germination speed in maize. This study provides clues for the further exploration of genes controlling the maize seed germination speed, thus facilitating breeding of rapid germinated elite lines through marker assistant selection.