Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrical Power Quality and the Challenges Faced by Power Assemblies Applications in Petrochemical Industry

The use of power switchgear and controlgear assemblies in typical electrical power distribution systems of petrochemical industries, both in medium voltage and low voltage levels, has faced different challenges related to their specification and operation in order to deliver solutions that ensure the quality of electricity to be distributed. Many of these problems are related to the equipment used for both switching and protection. Components such as circuit breakers, protective relays, current transformers, and voltage transformers require careful analysis of the electrical system in question. The philosophies adopted for electrical protection, type of neutral grounding, and topographic conditions (arrangement) of the circuits can create conditions, which are unacceptable from the point of view of electrical power quality. Currently, in many of the analyses related to power quality in electrical installations, basic care in the definition and use of the internal components of the power assemblies is often not considered. This paper discusses actual case studies of different power assemblies installations in the industry, aiming in this way to introduce basic guidelines that can help in the specification and installation of new equipment or modernization of existing equipment for industrial facilities.
Agronomic Performance of Grain Sorghum (Sorghum bicolor (L.) Moench) Cultivars under Intensive Fish Farm Effluent Irrigation

The growing global water shortage is an increasing challenge for the agricultural sector, which aims to produce sufficient quantity and quality of food and animal feed. In our study, effluent water from an intensive African catfish farm was irrigated on grain sorghum plants in four consecutive years. In our study the effects of the effluent on the N, P, K, Na content of the seeds, on the phenological parameters (plant height, relative chlorophyll content), the green mass, and on the grain yield of three varieties (‘Alföldi 1’, ‘Farmsugro 180’ and ‘GK Emese’) were investigated. Five treatments (Körös River (K) water and effluent (E) water: 30 and 45 mm weekly irrigation water dose; non-irrigated control) were applied with micro-spray irrigation. Compared to non-irrigated plants, effluent water did not negatively affect the N, P, K and Na contents of the grain crop. In terms of phenological parameters, the quality of the irrigation water (150–230 cm) had no negative effect on any of the measured parameters compared to the control (133–187 cm) values. In terms of biomass in 2020, grain yields were 89–109 g/plant with variety Alföldi 1, 64–91 g/plant with variety Farmsugro 180, and 86–110 g/plant with GK Emese.
A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster

The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of methods for automatic data-driven variable construction and machine learning techniques (MLT) show promising results. In this framework, varicella-zoster virus (VZV) infection was chosen to perform an automatic case identification with MLT. Pedianet, an Italian pediatric primary care database, was used to train a series of models to identify whether a child was diagnosed with VZV infection between 2004 and 2014 in the Veneto region, starting from free text fields. Given the nature of the task, a recurrent neural network (RNN) with bidirectional gated recurrent units (GRUs) was chosen; the same models were then used to predict the children’s status for the following years. A gold standard produced by manual extraction for the same interval was available for comparison. RNN-GRU improved its performance over time, reaching the maximum value of area under the ROC curve (AUC-ROC) of 95.30% at the end of the period. The absolute bias in estimates of VZV infection was below 1.5% in the last five years analyzed. The findings in this study could assist the large-scale use of EHRs for clinical outcome predictive modeling and help establish high-performance systems in other medical domains.
Non-Intrusive Information Sources for Activity Analysis in Ambient Assisted Living Scenarios

As people grow older, they are often faced with some degree of decreasing cognitive abilities or physical strength. Isolation from social life, poor quality of life, and increased risk or injuries are the consequence. Ambient Assisted Living (AAL) is a vision for the way people live their life in their own home, as they grow older: disabilities or limitations are compensated for by technology, where care-giving personnel is scarce or relatives are unable to help. Affected people are assisted by technology. The term "Ambient" in AAL expresses, what this technology needs to be, beyond assistive. It needs to integrate into the living environment in such a way that it is not recognized as such any more. Interaction with residents needs to be intuitive and natural. Technical equipment should be unobtrusive and well integrated. The areas of application targeted in this thesis are activity monitoring and activity pattern discovery in apartments or small houses. The acquisition of information regarding the residents' activity is vital for the success of any assistive technology. In many areas of daily life, this is routine already. State-of-the-art sensing technology includes cameras, light barriers, RFID sensors, radio signal localization using transponders, and pressure sensitive Floors. Due to their operating principles, they have a big impact on home and living environments. Therefore, this thesis is dedicated to research for non-intrusive activity information acquisition technology, that has minimal impact on daily life. Two base technologies are taken into account in this thesis.
Soyasapogenol C from Fermented Soybean (Glycine Max) Acting as a Novel AMPK/PPARα Dual Activator Ameliorates Hepatic Steatosis: A Novel SANDA Methodology

(1) Background: Soyasapogenol C (SSC), a derivative of soyasapogenol B (SSB), is specifically found high in many fermented soybean (Glycine max) products, including Cheonggukjang (in Korean). However, the biological activities for preventing and treating hepatic steatosis, and the precise underlying mechanisms of SSC, remain to be explored. (2) Methods: A novel SANDA (structural screening, ADMET prediction, network pharmacology, docking validation, and activity evaluation) methodology was used to examine whether SSC exerts hepatoprotective effects in silico and in vitro. (3) Results: SSC had better ADMET characteristics and a higher binding affinity with predicted targets chosen from network pathway analysis than SSB. SSC induced the phosphorylation of AMP-activated protein kinase (AMPK) and stimulated the nuclear translocation of peroxisome proliferator-activated receptor alpha (PPARα), further enhancing PPAR response element (PPRE) binding activity in HepG2 cells. Concurrently, SSC significantly inhibited triglyceride accumulation, which was associated with the suppression of lipogenesis genes and the enhancement of fatty acid oxidation gene expression in HepG2 cells. (4) Conclusions: Soyasapogenol C, discovered using a novel SANDA methodology from fermented soybean, is a novel AMPK/PPARα dual activator that is effective against hepatic steatosis. Dietary supplementation with soyasapogenol C may prevent the development of hepatic steatosis and other diseases associated with fat accumulation in the liver.
Maintenance Strategies Definition Based on Systemic Resilience Assessment: A Fuzzy Approach

The present paper aims to provide production systems with a decision-making tool that allows the assessment of the resilience of their equipment, processes and/or sub systems and, certainly, the system itself. The ultimate goal is to detect the critical points of a production system in order to gain strength, efficiency and resilience to face new and existing threats and challenges. In this regard, this paper presents a model that aims to calculate the resilience score of a process from various parameters and to rate it on the resilience scale using linguistic terms. In order to manage uncertainty, a fuzzy model has been put forward with the objective of being an integral tool of maintenance strategy surveillance and definition. Finally, and towards putting into practice the proposed model, a real case study, in the context of mining operations, is presented. This will help the process identify the areas/equipment for improvement and afterwards overcome weaknesses that may affect the entire system’s resilience. The aforementioned model provides great support to the decision-making process in the field of plant maintenance strategies and physical asset management. We believe that the main impact of this proposal is that through the use of this type of decision tool, the correct definition of maintenance strategies and the appropriate selection of system topologies could be made regarding the systemic resilience as an overall objective.
Unified M-estimation of matrix exponential spatial dynamic panel specification

Abstract In this paper, a unified M-estimation method in Yang (2018) is extended to the matrix exponential spatial dynamic panel specification (MESDPS) with fixed effects in short panels. Similar to the STLE model which includes the spatial lag effect, the space-time effect and the spatial error effect in Yang (2018), the quasi-maximum likelihood (QML) estimation for MESDPS also has the initial condition specification problem. The initial-condition free M-estimator in this paper solves this problem and is proved to be consistent and asymptotically normal. An outer product of martingale difference (OPMD) estimator for the variance-covariance (VC) matrix of the M-estimator is also derived and proved to be consistent. The finite sample property of the M-estimator is studied through an extensive Monte Carlo study. The method is applied to US outward FDI data to show its validity.
Ex Ante Moral Hazard in the Health Insurance Market in Indonesia

This paper will empirically examine the existence of ex ante moral hazard in health insurance market in Indonesia with respect to lifestyle choices such as smoking and obesity. A key challenge in understanding the moral hazard problem is endogeneity. Decisions regarding having insurance and lifestyle choices are arguably sequential, not simultaneous. Then, it is difficult to decide which one precedes the other. To address this problem, this paper uses a multivariate probit analysis. This paper uses three waves from the IFLS (Indonesian Family Life Survey) over 2000, 2007, and 2014, as these have information about insurance status, smoking behavior, and obesity condition. This paper finds evidence of the existence of ex ante moral hazard in health insurance for obesity of the head of household, but not for smoking. The multivariate probit regression shows that having health insurance increases the propensity of being obese. Using a wider sample, including all members of households, there is not sufficient evidence of the existence of ex ante moral hazard for either smoking or obesity. Surprisingly, the decisions between smoking and obesity are substitutes. This means that there are non-observable elements that increase the tendency to smoke, but reduce the propensity of being obese, and vice versa.
SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans

Accurate lung tumor identification is crucial for radiation treatment planning. Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. This paper effectively integrates the U-Net with the channel attention module (CAM) to segment the malignant lung area from the surrounding chest region. The SegChaNet method encodes CT slices of the input lung into feature maps utilizing the trail of encoders. Finally, we explicitly developed a multiscale, dense-feature extraction module to extract multiscale features from the collection of encoded feature maps. We have identified the segmentation map of the lungs by employing the decoders and compared SegChaNet with the state-of-the-art. The model has learned the dense-feature extraction in lung abnormalities, while iterative downsampling followed by iterative upsampling causes the network to remain invariant to the size of the dense abnormality. Experimental results show that the proposed method is accurate and efficient and directly provides explicit lung regions in complex circumstances without postprocessing.
A Multi-Secret Reputation Adjustment Method in the Secret Sharing for Internet of Vehicles

Secure data sharing for the Internet of Vehicles (IoV) has drawn much attention in developing smart cities and smart transportation. One way to achieve that is based on the reputation of the participant vehicles and the interaction between them. Most of the reputation adjustment schemes are aimed at single-secret situations, and there are cases where shared information leaks after reconstruction; and the shared information of all participants need to be updated again. This paper uses binary asymmetric polynomials to realize the asynchronous reconstruction of secrets, that is, multiple secrets are distributed at once, and each secret is independent of each other when it is restored. Each participant holds the shared divided information part of a one-variable polynomial. When the secret is reconstructed, the shared information part is not directly shown to ensure that the shared information is not leaked. Furthermore, we provide another way to keep the secret unchanged and modify the secret threshold.
Vulnerability Assessment of Asphalt Plant through Machine Learning Techniques

Many businesses throughout the globe have recently realized the value of Supervisory Control and Data Acquisition (SCADA) systems. Many critical infrastructures, such as electricity grids, asphalt plants, and wastewater disposals, are controlled by these systems. With the introduction of Fourth Industrial Revolution, 4IR or Industry 4.0, today’s SCADA systems cannot be separated from the outside world, making them more susceptible to hostile assaults. Conventional security systems including different antivirus software and firewalls are unable to safeguard SCADA systems as they are of distinct requirements. For this, different machine learning algorithms, i.e., SVM, KNN, and random forest, are tested to cover the anomaly detection along with security protection for SCADA systems. The dataset used in this research study was made locally in an asphalt plant by using different sensor data grouped in two classes: one is natural signal values, and the other one is attack class in which different sensor values are found out of range while in operation. Amongst the above-mentioned algorithms, KNN outperformed with an accuracy rate of 89% for anomaly detection and any kind of external attack can be detected and notified to the control room for on-time actions.
Exploring Learners’ Motivation Through The Pygmalion Effect

This study discusses the motivation of students when studying at home. This situation is due to the learning sessions taking place virtually at home. It is different when students follow the learning session with the lecturer face to face. Formerly, the objective of this study is also to try to see how to do beliefs, expectations, behavior, and results in influence learners’ motivation to learn. This study is a quantitative study in which a questionnaire will be distributed to respondents. A total of 550 respondents were involved in this study. Through the questionnaire, there are three main sections along with questions related to the study conducted. In general, the results of the study found that the process of student excellence starts from the motivation of a lecturer to his students, in addition to self -confidence with independence in understanding each lesson also makes a person more resilient and resilient. This situation will make the student more mature and make the student more excellent in the future. Thus, the impact of this study is that the researcher will know how to ensure that the student is always motivated and will ensure that the student is always enthusiastic in seeking knowledge after each student's needs are known through such studies.
Factors affecting sexuality of elderly people and sexual dysfunctions

Introduction: Sexuality of elderly people is often an omitted topic. There isn’t a compact definition of sexuality of seniors. The available data shows that there are many factors influencing sexual activity in later life. There are also specific changes which contribute sexual dysfunctions. Purpose: The aim of present is to investigate what factors are related with decreased sexual activity of seniors, biological basis of sexual dysfunctions and how many elderly people it affects. Materials and methods: There were analyzed publications from 2007 to 2022 from PubMed and Google Scholar using phrases: older adults, elderly people, sexuality of elderly people, factors influencing sexuality, sexual dysfunctions. The state of knowledge: Almost half of people above 60 years old are sexually active. There are factors like low availability of partner, stereotypes, chronic diseases and treatments that contribute worse quality of sexual life. There are reports that communication between doctor and patient on the topic of sexuality is not sufficient, even more positive attitudes towards sexuality of seniors. Biological changes like lower hormone levels in women as well as in men contribute sexual dysfunctions. There are almost one in five women above 57 years old who feel pain during intercourse and there are about 43% of men above 65 with erectile problems. The awareness of sexual dysfunctions could make it easier to treat them. Conclusions: It is essential to pay attention to psychological, social, cultural and attitudes of health professionals towards sexuality of elderly people to diagnose sexual dysfunctions. Understanding biological basis of sexual dysfunction can facilitate matching appropriate treatment. Education can improve seniors’s quality of life and sexual functions.
CAPTURE of the Human U2 snRNA Genes Expands the Repertoire of Associated Factors

In order to identify factors involved in transcription of human snRNA genes and 3′ end processing of the transcripts, we have carried out CRISPR affinity purification in situ of regulatory elements (CAPTURE), which is deadCas9-mediated pull-down, of the tandemly repeated U2 snRNA genes in human cells. CAPTURE enriched many factors expected to be associated with these human snRNA genes including RNA polymerase II (pol II), Cyclin-Dependent Kinase 7 (CDK7), Negative Elongation Factor (NELF), Suppressor of Ty 5 (SPT5), Mediator 23 (MED23) and several subunits of the Integrator Complex. Suppressor of Ty 6 (SPT6); Cyclin K, the partner of Cyclin-Dependent Kinase 12 (CDK12) and Cyclin-Dependent Kinase 13 (CDK13); and SWI/SNF chromatin remodelling complex-associated SWI/SNF-related, Matrix-associated, Regulator of Chromatin (SMRC) factors were also enriched. Several polyadenylation factors, including Cleavage and Polyadenylation Specificity Factor 1 (CPSF1), Cleavage Stimulation Factors 1 and 2 (CSTF1,and CSTF2) were enriched by U2 gene CAPTURE. We have already shown by chromatin immunoprecipitation (ChIP) that CSTF2—and Pcf11 and Ssu72, which are also polyadenylation factors—are associated with the human U1 and U2 genes. ChIP-seq and ChIP-qPCR confirm the association of SPT6, Cyclin K, and CDK12 with the U2 genes. In addition, knockdown of SPT6 causes loss of subunit 3 of the Integrator Complex (INTS3) from the U2 genes, indicating a functional role in snRNA gene expression. CAPTURE has therefore expanded the repertoire of transcription and RNA processing factors associated with these genes and helped to identify a functional role for SPT6.
Remote sensing image multi-feature fusion based on NSCT transform and compressive sensing

For the remote sensing image fusion algorithm based on non-subsampled Contourlet transform (NSCT) calculation complexity and cannot extract details from source images effectively. So a new remote sensing image multi-feature fusion algorithm based on NSCT transform and compressive sensing is proposed. Firstly, luminance component V of multi-spectral image is extracted via HSV transform, and decompose the luminance component V and PAN image by NSCT; Then, Apply PCNN-based fusion rules to fusing low frequency sub-band, and the equivalent number is utilized as the linking strength; For the high frequency sub-band, a fusion rule based on the multi-feature in compressive sensing domain is presented; Finally, the fused images are obtained by inverse NSCT transform and inverse HSV transform, respectively. The experimental results show that the proposed fusion algorithm substantially outperforms the best-known remote sensing image fusion algorithms in terms of calculation complexity and the quality of the fused image, and has the better performance in visual effect and objective evaluation metrics
Stato dell’arte nella sintesi di texture sonore

The synthesis of sound textures, such as rain, wind, or crowds, is an important application for cinema, multimedia creation, games and installations. However, despite the clearly defined requirements of naturalness and flexibility, no automatic method has yet found widespread use. After clarifying the definition, terminology, and usages of sound texture synthesis, we will give an overview of the many existing methods and approaches, and the few available software implementations, and classify them by the synthesis model they are based on, such as subtractive or additive synthesis, granular synthesis, corpus-based concatenative synthesis, wavelets, or physical modeling. Additionally, an overview is given over analysis methods used for sound texture synthesis, such as segmentation, statistical modeling, timbral analysis, and modeling of transitions.
Evaluation of health service outcomes for an audiology first point of contact retrocochlear clinic: a 6-year retrospective cohort study

Abstract Objective Although existing studies of audiology first point of contact clinics which screen for retrocochlear pathology have demonstrated positive clinical outcomes, they have provided limited information regarding service impacts. Thus, this study aimed to evaluate both the clinical and health service outcomes of an audiology first point of contact (FPOC) clinic for adults referred to ENT services with suspected retrocochlear pathology. Design Retrospective cohort study. Study sample All 1123 patients referred to the clinic over a 6-year period (2013–2019). Results Most (73.7%) of the 1123 patients referred to the Retrocochlear Clinic were managed and discharged by the audiologist and did not require ENT appointment. Almost half (43.1%) were directly referred for MRI imaging, of which 4 (1.1%) were diagnosed with Vestibular Schwannoma. Waiting times for first appointments significantly (p < 0.001) reduced from a median of 748 days to 63.5 days over the 6-year period. Attendance rates also significantly (p < 0.001) improved over this time (from 52.2% to 90%). There were no adverse events reported. Of those discharged without attending an ENT appointment, 1.8% were rereferred to ENT within 12 months of discharge. Conclusions This audiology FPOC Retrocochlear Clinic was shown to be a safe and effective alternative service model.