Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Quality Analysis of an Open-Pit Mine Electrical Network: Case Study Benguerir MinEx

In the context of open-pit mining operations, the utilization of electrical power to drive equipment such as drills, conveyors, and crushers is crucial. However, the quality of electrical power in these environments may be compromised by various factors, including voltage sags, surges, and harmonic distortion. These issues can lead to equipment damage and operational disruption. This study presents the results of a comprehensive power quality analysis of an open-pit mine's electrical network utilizing the Electrical Transient Analyzer Program (ETAP). The analysis included evaluations of load flow, harmonics, and short-circuit of the power grid. The findings indicated the presence of various power quality issues, including harmonic distortion, resulting from a combination of factors such as high load on the electrical system and the harsh mining environment. Based on these results, a power quality improvement plan was proposed, including solutions such as the implementation of power factor correction, power filters, dynamic voltage restorers, and uninterruptible power supplies to enhance power quality and energy-efficient equipment. Furthermore, the integration of advanced Flexible AC Transmission Systems (FACTS) devices to control power flow and improve the stability of the electrical system was recommended.
Effects of integrated reporting in the stajeholder communication process

Financial and non-financial reporting has been the primary means of communication between the firm and its stakeholders from its inception. Over the years, businesses and organizations have evolved to meet the everchanging demands of the commercial and social environment. The integration and reciprocal connection of both financial and non-financial data is now the final link in the evolution of reporting. Integrated reporting is undoubtedly a significant step forward in the evolution of corporate reporting; nonetheless, it is critical to examine if stakeholders' information demands are sufficiently met and whether they will function as a universal communication tool for businesses and their stakeholders. The purpose of this study is to identify and assess the benefits and drawbacks of using integrated reporting as a tool for communicating with stakeholders. The examination of the literature, comparative analysis, as well as induction and deduction are the research methods utilized to attain the specified aim.
Forecasting of Taiwan's weighted stock Price index based on machine learning

This study proposes a stack framework of light gradient boosting machine (LGBM) for Taiwan stock market index prediction. Stock market predictions have been regarded as a challenging task, as the market is affected by several factors such as political events, general economic conditions, institutional investors' choices, movement of the global market, psychology of investors. We construct a rich feature set to capture the impacts of global markets, institutional investors' choices, and the psychology of investors. A feature selection algorithm is proposed to choose important feature subset and enhance the training performance. To further improve the prediction accuracy, we employ stacking strategy to combine multiple classifiers together. A 10‐year period of the Taiwan stock exchange capitalization weighted stock index (TAIEX) is used to verify the performance of the proposed model. The experimental results suggest that our prediction model as well as the feature selection method can achieve good prediction performance.
Power Efficient Long Range IoT Communication of Wireless PPM System in ISM Band

This paper addresses the wireless PPM (pulse position modulation) system for the advanced energy-efficient Internet-of-Things (IoT) communication. For the wireless IoT communication applications, a new long-range energy efficient IoT communication technology is critically necessary for the minimum power loss because of the battery problem. Therefore, an advanced energy-efficient wireless PPM system has been proposed to minimize power loss. In this paper, the reduction of inter-symbol interference (ISI) and data collisions within the limited narrow bandwidth, information, and header terms are presented in the form of pulses. The distinction between the middle and start pulse trains is the header and the gap among the end and middle pulse trains are the data/information. The narrow pulses of the system will transmit a reasonable amount of data by which it will achieve the optimum amount of energy savings. As a result, the probability of data collision within the network will be lower due to the reduction of air-on time. Basically, the communication will be performed in such transmission of information signals agreement, and all the zero time duration between the pulses is the core power-saving effect. The calculated total cycle time of the proposed system is 0.1067 ms during 915 MHz ISM band frequency. In this way, the proposed system will be more energy efficient than the previous systems.
The influence of coastal upwelling on the biodiversity of sandy beaches in South Africa

x Chapter 1: General Introduction.1 Chapter 2: Study sites.20 Chapter 3: The influence of upwelling on macrofaunal assemblages across multiple scales.30 Chapter 4: Testing the design small scale variability.141 Chapter 5: Influence of temperature on the filtration rate and oxygen consumption of the sandy beach bivalve Donax serra (Röding). 211 Chapter 6: General Discussion.236 References.250 Appendix 1: Taxonomic descriptions and catalogue.CD Appendix 2: Statistical outputs from Chapter 3.CD Appendix 3: Statistical outputs from Chapter 4.CD
Diagnosis of Liver Diseases Using Machine Learning Algorithms and their Prediction Using Logistic Regression and ANN

Globally, liver diseases affect a large number of population and they are the 11th leading cause of death. It is tedious and time-consuming for trained experts to identify patients with liver disease through liver biopsy and MRI checks. Liver disease can be difficult to identify in the early stages due to mild symptoms. As a result, symptoms emerge when it is too late. A timely diagnosis can potentially save a person's life. Machine Learning can detect early signs of these diseases, even when they are not readily apparent to even experienced doctors. Therefore, the objective of this work is to assess how well various machine-learning algorithms perform. Support Vector Machine, Logistic Regression, Naïve Bayes, Conventional Neural Network, and Artificial Neural Network were the five algorithms we employed in this study. The performance of these techniques was evaluated using a variety of measuring metrics, such as accuracy, f-1 score, precision, and recall. The accuracy was 65.26%, 73.65%, 70.05%, 97.5%, and 93.75% for SVM, LR, NB, CNN, and ANN. The analysis's findings indicate that CNN achieved the greatest accuracy. Furthermore, the current study focused on the use of clinical data and imaging scans to predict liver disease.
The Influence of Trade Facilitation on Export Diversification in Tanzania

This paper examines the relationship between trade facilitation and export diversification in Tanzania. Specifically, it attempts to ascertain the role of infrastructure in facilitating export diversification. The study uses trade data for Tanzania’s export with its trading partners for the period from 2008 to 2017. The gravity model is used for data analysis. Findings reveal that the quality of airport infrastructures positively influences export diversification in Tanzania while other infrastructures had no influence on export diversification. The paper concludes that reliable infrastructure specifically airport infrastructures reduces trade costs, improves competitiveness, and increases export diversification in Tanzania and; thus recommends continual investment towards airport infrastructure development and maintenance.
I Refuse if You Let Me: Studying User Behavior with Privacy Banners at Scale

Privacy Banners are a common experience while surfing the Web. Mandated by privacy regulations, they are the way for users to express their consent to the usage of cookies and data collection. They take various forms, carry different wordings and offer different interaction mechanisms. While several works have qualitatively evaluated the effectiveness of privacy banners, it is still unclear how users take advantage of the options offered and if and how the design of the banner could influence their choice. This work presents a large-scale analysis of how the Privacy Banner options impact on users' interaction with it. We use data from a global Consent Management Platform serving more than 400 websites with visitors from all countries. With this, we observe more than 4M interactions collected over three months. We find that only 1–4% of visitors opt out of cookies when more than one click is required. Conversely, when offered a Reject All button to deny consent with a single click, the percentage of users who deny consent increases to about 21%. We further investigate other properties, such as the visitor's country, device type, banner position, etc. While the results confirm some common beliefs, to the best of our knowledge, this is the first work to accurately quantify how people interact with Privacy Banners and observe the effect of offering a single-click refusal option. We believe our work improves the understanding of user behaviour and perception of privacy, as well as the implications and effectiveness of privacy regulations.
Vascular Aphasias: Main Characteristics of Patients Hospitalized in Acute Stroke Units

Background and Purpose— Aphasia is frequent in stroke patients and is associated with poor prognosis. However, characteristics and determinants of vascular aphasias remain controversial. The aim of this study was to evaluate aphasia characteristics at the acute stage in patients admitted to a stroke unit. Methods— The study was performed in 308 patients consecutively assessed with a standardized aphasia battery. Results— Aphasia was observed in 207 patients; global and nonclassified aphasias accounted for 50% of aphasic syndromes at the acute stage, whereas classic aphasias (Wernicke’s, Broca’s, transcortical, and subcortical aphasias) were less frequent. Age differed across aphasic syndromes in ischemic stroke patients only; patients with conduction aphasia were younger, and patients with subcortical aphasia were older. Sex did not significantly differ across aphasic syndromes. The presence of a previous stroke was more frequent in nonclassified aphasia. Conclusions— This study shows (1) that vascular aphasias are frequently severe or nonclassic at the acute stage, a finding explained in part by the presence of a previous stroke; (2) that the age effect is due mainly to its influence on infarct location; and (3) that the main determinant of aphasia characteristics is lesion location.
Impact of the COVID-19 Pandemic on Training Sessions of Young Japanese Handball Players: A Questionnaire-Based Retrospective Cohort Study

Background: The training environment for handball, particularly for young athletes, was affected by the COVID-19 pandemic in 2019 and 2020. Objectives: This study aimed to investigate the training sessions, injury incidence, and injury prevention program participation of youth handball teams during the COVID-19 pandemic and assess the barriers and factors influencing the motivation to perform such programs. Methods: This retrospective questionnaire-based cohort study included participants in the national handball championship in Japan in March 2021. The respondents included 48 coaches and 745 players from 66 teams. The coaches were asked questions about changes in the training time and intensity during the pandemic. The players were asked questions about barriers and motivational factors for performing the prevention exercises. Results: We found that 66.7% of the teams reduced their training time during the pandemic, while 45.8% reduced their training intensity. Owing to the COVID-19 pandemic, 91.7% of all teams experienced game cancellations, and player contact decreased in 33.3% of the teams. The main reason for not performing these exercises was a lack of knowledge on how to perform them correctly, as reported by 52.1% of the respondents. The main motivational factors were handball movement-related exercises (51.7%) and improved physical fitness (32.7%). Conclusions: The COVID-19 pandemic influenced the training sessions of Japanese youth handball teams in many ways. Education on correctly performing injury prevention program exercises is a key factor in maximizing the adoption of such programs. In addition, injury prevention exercises must be handball-specific to ensure that players are motivated to keep performing such exercises.
High-Swing, Power-efficient, Current-Mode Hybrid Circuit Topologies for Simultaneous Bidirectional Communication

In this work, authors propose high-output voltage swing, power-efficient, echo cancellation hybrid circuit topologies deploying modified current-mode logic (MCML) and dynamic current-mode logic (DCML) circuit topologies for bidirectional simultaneous signaling. The conventional static current-mode logic hybrid (CMLH) circuit topology suffers from high static power consumption due to a fixed tail current source. Moreover, it produces low output differential eye-opening at high data rates. To address the above issues, this work proposes a modified current-mode logic hybrid (MCMLH) and dynamic current-mode logic hybrid (DCMLH) circuit topologies to separate the near-end received signal from the superimposed signal on the interconnect. The proposed hybrid circuit topologies have high output differential eye-opening with moderate power consumption. The proposed hybrid topologies are implemented in 1.2 V, and 65 nm CMOS over a 20 cm FR4-PCB interconnect. The performance results of the proposed MCMLH and DCMLH circuit topologies have energy efficiencies of 0.44 pJ/b and 0.8 pJ/b, respectively, at a 10-Gb/s simultaneous bidirectional (SBD) data rate. The differential eye-opening of the MCMLH and DCMLH hybrid circuit topologies are 1.1 V and 0.8 V, respectively, at 10-Gb/s SBD data rate.
USO DE SUBSTÂNCIAS PSICOATIVAS E RENDIMENTO ACADÊMICO DE UNIVERSITÁRIOS DA ÁREA DE SAÚDE

RESUMO Objetivo: verificar as substâncias psicoativas mais prevalentes entre alunos de cursos de graduação da área de saúde e a interferência destas no desempenho acadêmico. Métodos: 115 acadêmicos de Enfermagem e Educação Física de Universidade pública do interior do Ceará, Brasil, responderam ao questionário Drug Use Screening Inventory, em 2021. Para análise dos resultados, utilizaram-se dos testes estatísticos de qui-quadrado de Pearson e da razão de verossimilhança. Resultados: 65 (56%) alunos usavam analgésicos; 54 (47%) o álcool e 14 (12,2%) a maconha. Não houve associação estatística entre o uso de substâncias psicoativas e o desempenho acadêmico. Todavia, dentre aqueles que utilizavam analgésicos ou álcool, mais de 1/3 analisaram o desempenho como ruim/péssimo ou regular e metade dos que usavam maconha analisaram da mesma maneira o desempenho acadêmico. Conclusão: o estudo contribuiu para o entendimento sobre as substâncias psicoativas prevalentes no contexto universitário e os aspectos associados, enfatizando a necessidade de intervenções.
Apple Leaves Disease Detection Using Multilayer Convolutional Neural Network

Agriculture is the process of growing crops and raising livestock and cultivating other forms of food. It has been a fundamental activity for human societies throughout history, providing food and other resources necessary for survival. Hence plant diseases can have a significant impact on the economy, particularly in agricultural-dependent countries. Crop yield loss, trade restrictions, increased production costs and reduced agricultural productivity are some of the ways plant diseases can affect the economy. Farmers visually inspect their crops for symptoms of diseases, such as discoloration, spots, wilting, and deformities. Farmers can also use their sense of touch and smell to detect diseases, such as the sticky or slimy feel of plant leaves infected with fungal diseases and the foul smell of rotting or decaying plant material. However, traditional methods of plant disease detection do have limitations. Visual inspection and other traditional methods may not always detect diseases at an early stage, and there is a risk of misdiagnosis. Furthermore, conventional techniques may fail to identify illnesses that are not perceptible to the unaided observation. The other alternatives are the use of Artificial Intelligence(AI) which includes training computers to detect plant diseases using image recognition technology. These AI methods are increasingly being used for plant disease detection because they offer a fast, accurate, and cost-effective way to diagnose plant diseases, which can help prevent crop losses and increase yields. In this article, a Multilayer CNN model, inspired by InceptionNet, is put forward. This model is trained on the "Plant Pathology 2021: FGVC8 dataset". This proposed model is compared with three existing CNN models using RMSprop with Nesterov Momentum Optimizer. The suggested model outperforms other models that are already trained and achieves an accuracy of 92.56% in diagnosing the disease.
Preoperative risk evaluation and perioperative management of patients with obstructive sleep apnea: a narrative review

Obstructive sleep apnea (OSA) is a common sleep-breathing disorder associated with significant comorbidities and perioperative complications. This narrative review is aimed at comprehensively overviewing preoperative risk evaluation and perioperative management strategies for patients with OSA. OSA is characterized by recurrent episodes of upper airway obstruction during sleep leading to hypoxemia and arousal. Anatomical features, such as upper airway narrowing and obesity, contribute to the development of OSA. OSA can be diagnosed based on polysomnography findings, and positive airway pressure therapy is the mainstay of treatment. However, alternative therapies, such as oral appliances or upper airway surgery, can be considered for patients with intolerance. Patients with OSA face perioperative challenges due to difficult airway management, comorbidities, and effects of sedatives and analgesics. Anatomical changes, reduced upper airway muscle tone, and obesity increase the risks of airway obstruction, and difficulties in intubation and mask ventilation. OSA-related comorbidities, such as cardiovascular and respiratory disorders, further increase perioperative risks. Sedatives and opioids can exacerbate respiratory depression and compromise airway patency. Therefore, careful consideration of alternative pain management options is necessary. Although the association between OSA and postoperative mortality remains controversial, concerns exist regarding adverse outcomes in patients with OSA. Understanding the pathophysiology of OSA, implementing appropriate preoperative evaluations, and tailoring perioperative management strategies are vital to ensure patient safety and optimize surgical outcomes.
Identity formation and transformation of Chinese university translator trainers

ABSTRACT While there has been increased scholarly interest in the translator trainer’s competence, the translator trainer’s professional identity, that is, how they conceptualise their work and roles in daily teaching practice, remains an unexplored area. This multiple-case study aims to fill this gap by examining four university translator trainers’ constructions of professional identities in the Chinese context. A purposeful sample of four translator trainers was recruited from three Chinese universities. Two rounds of in-depth semi-structured interviews were conducted along with the collection of lecture slides, syllabi and observed teaching sessions. Subsequently, an inductive thematic analysis was applied to data, and three dimensions embedded in teachers’ accounts – temporality, sociality and place – were scrutinised. The analysis of the data revealed five key identity themes among the translator trainers: the curriculum maker, the praxis initiator, the learning facilitator, the advocate for translation technologies, and the broker between the translation market and training programmes. The study also identified changes in pedagogical roles and relationships with translation technologies within their identity discourse. This study is concluded with implications for translator trainers’ professional development.
Untapped potential of commercial drivers in providing post-crash care to road traffic accident victims: A cross-sectional study from fast urbanizing city of Jodhpur, India

Objectives: Most road traffic accident (RTA) deaths occur before victim reaches a definitive trauma care facility. The aim of the study was to determine the role of commercial drivers toward providing post-crash care to RTA victims in Jodhpur, Rajasthan. Materials and Methods: This cross-sectional study assessed the role of commercial auto-rickshaw and cab drivers for providing post-crash care in urban Jodhpur during 2019–2020. Eligible participants volunteering for the study were included from taxi and auto stands in urban Jodhpur. A pre-tested questionnaire was administered by a trained interviewer. Data analysis was done using SPSS v23.0. Summary measures in terms of frequencies, means, and range are reported. Chi-square test, Fisher’s Exact test, and Multivariate Logistic Regression analysis were done for statistical associations. Results: Two hundred male participants completed the study with a mean age of 37.74 (8.96) years having an average work experience as commercial drivers of more than 10 years. Over 70% of participants witnessed RTA in the past year and 52% provided care to victims. The knowledge of post-crash care was most affected by their education level (adjusted odds ratio [aOR]: 1.778, 95% confidence interval [CI]: 0.958, 3.301), whereas the intended post-crash care practices were significantly better among participants with previous training (aOR: 15.376, 95% CI: 2.149, 110.017). Conclusion: The current study establishes the role of commercial drivers as first responders at accident sites in the fast urbanizing city of Jodhpur, Rajasthan. Systematic capacity building initiatives of commercial drivers to salvage RTA victims have potential to strengthen pre-hospital trauma care continuum in non-metro districts of India experiencing high burden of accidents.
Edge Computing Resource Management Based on Genetic Algorithm

Resource management in edge computing has always been a hot research topic. In the scenario of multiple edge servers and multiple tasks, this paper proposes a task offloading model that optimizes the total cost by weighting energy consumption and latency to reduce energy consumption and latency. Based on the proposed task offloading model, an improved genetic algorithm is proposed to solve the optimal task offloading strategy. Simulation experiments have shown that applying improved genetic algorithms to task offloading models can accelerate convergence speed, find the optimal computing offloading strategy, and improve system performance.