Miracle-dz/UniFAR-FTUs huggingface dataset:
Economic crisis in the Norwegian offshore industry : How may it affect safety conditions in offshore operations?

The purpose of this paper is to discuss the safety consequences regarding the ongoing crisis in the Norwegian offshore industry. We will discuss the opinions among different actors in this industry; shipping companies, unions, authority and broker about how they experience this situation and what they do to deal with it. We discuss changes in trust relations and alliances that have developed during and before the crisis, and how a tougher climate and business may affect safety conditions offshore. The discussion in the paper is based on qualitative data from an ongoing research project, conducted in 2016. We conclude that tripartite collaboration and trust between the parties is crucial to maintain, especially in a crisis. An unpredictable market situation with fewer assignments, downsizing, and tougher contractual agreements with short-term contracts may have negative impact on safety conditions offshore in the future. concern and organizations need to constantly encourage processes developing trust. (Giddens, 1994). Whether or not the changes will lead to more (or less) accidents is an empirical question that will become evident – but as of now we can see that the economic situation and the imagined future (Bottos, 2008) of the industry has caused fundamental changes in alliances, and the relations between core stakeholders. Conflicts due to a battle for scarce resources will not only change relationships and alliances, but also allow for underlying structures to be visible (Gluckman, 1965). Fundamental changes within Norwegian capitalism, driven by neoliberalist principles, had not been visible prior to these conflicts of interest and have made structural changes become apparent. In hindsight, those might be coined as going from the Nordic model (tripartite collaboration), through welfare capitalism (individuals rewarded and recourses abundant), and now real capitalism of competition between companies and rationalization
A New Method to Determine the Optimal Location and Amount of Shed Load in Multi-Stage Under Frequency Load Shedding Using DIgSILENT

Abstract Under-frequency load shedding (UFLS) appears to be an effective approach to preventing cascading outages that includes various methods. In recent years, much research has been done in the field of dynamic schemes. However, these schemes are less commonly used due to their disadvantages such as the inability to calculate the optimal shed load amount, the need to additional relays or communication infrastructures, etc. In contrast, static UFLS schemes are more common due to their simplicity, efficiency, fast implementation, as well as the unnecessity of additional equipment. However, how to determine the load shedding (LS) location and amount in static methods seems to be still challenging. Furthermore, conventional methods have a major drawback because of over shedding. In order to solve this problem, in this paper, a new static method is proposed to determine the location and amount of optimal LS based on co-simulation of DIgSILENT and MATLAB software. The system dynamics is simulated in DIgSILENT and optimization using imperialist competitive algorithm (ICA) runs in MATLAB. This co-simulation makes it possible to take advantage of both pieces of software. The results of the simulation on the IEEE 39-Bustest system in several scenarios clearly demonstrate the efficiency of the proposed method.
Methodical bases of formulating the strategy for large potash companies

The importance of strategic behavior has increased dramatically in the recent decades. In the article the basic principles of formulating the strategic alternatives for large potash companies, such as the ones on the oligopolistic market, specific features of the product and influence of the state, are set and justified. The strategic alternatives for the potash company “Uralkali” are formulated and the main directions for these alternatives are presented. The group and individual criteria for the strategic choice for the potash companies are provided. The necessity of strategic choice by using the multiple criteria decisionmaking methods is justified. The article contains 3 tables and 1 figure.
Influence of Trust in the Adoption of Digital Banking Platforms: A Case Study of a Public Ghanaian Bank

Digital banking has existed within the Ghanaian banking sector over the years and has evolved to incorporate more technological developments and are playing a more significant role in the development of the Ghanaian economy. The banking sector has been pursuing various policies to ensure that more customers adopt digital banking for their transactions. Despite the rapid rollout of numerous banking services in the country, adoption of digital banking has been generally slow. This present study seeks to examine the influence of trust on the adoption of digital banking services. The aim of the study was specifically to examine the influence of knowledge based trust on digital banking service adoption, to examine the influence of institution based trust on digital banking service adoption and to examine the influence of propensity to trust on digital banking service adoption. The positivism paradigm was the philosophical framework that guided the study and a quantitative approach. Data was obtained through the use of a survey and was analysed using simple linear regression. The population of the study consisted of customers of various banks within the Accra sub-metro. Using convenient sampling technique, 150 participant took part in the study. From the analysis of the data, the results showed propensity to trust had a positive relationship with adoption of digital banking platforms. Institution based trust had no significant relationship to adoption of digital banking platforms. Knowledge based trust had a positive relationship with adoption of digital banking platforms. The findings of the study can be applied to improve upon the adoption and use of digital banking platforms by banks. Particularly, banks can focus on providing quality banking experiences across its digital channels and physical banking channels. In addition, digital banking service providers need to highlight improving on their level of integrity, competence and benevolence in order to facilitate users’ adoption and usage of mobile banking.
Development of drowsiness detection system

The development of technologies for preventing drowsiness at the wheel is a major challenge in the field of accident avoidance systems. Preventing drowsiness during driving requires a method for accurately detecting a decline in driver alertness and a method for alerting and refreshing the driver. As a detection method, the authors have developed a system that uses image processing technology to analyze images of the driver's face taken with a video camera. Diminished alertness is detected on the basis of the degree to which the driver's eyes are open or closed. This detection system provides a noncontact technique for judging various levels of driver alertness and facilitates early detection of a decline in alertness during driving. <<ETX>>
Concurrent Skill Composition Using Ensemble of Primitive Skills

One of the key characteristics of an open-ended cumulative learning agent is that it should use the knowledge gained from prior learning to solve future tasks. That characteristic is especially essential in robotics, as learning every perception-action skill from scratch is not only time consuming but may not always be feasible. In the case of reinforcement learning, this learned knowledge is called a policy. The lifelong learning agent should treat the policies of learned tasks as building blocks to solve those future tasks. One of the categorizations of tasks is based on its composition, ranging from primitive tasks to compound tasks that are either a sequential or concurrent combination of primitive tasks. Thus, the agent needs to be able to combine the policies of the primitive tasks to solve compound tasks, which are then added to its knowledge base. Inspired by modular neural networks, we propose an approach to compose policies for compound tasks that are concurrent combinations of disjoint tasks. Furthermore, we hypothesize that learning in a specialized environment leads to more efficient learning; hence, we create scaffolded environments for the robot to learn primitive skills for our mobile robot-based experiments. We then show how the agent can combine those primitive skills to learn solutions for compound tasks. That reduces the overall training time of multiple skills and creates a versatile agent that can mix and match the skills.
Sensor virtual para classificação de emissões de so2 baseado em k-NN (k-Nearest Neighbors)

As indústrias em geral buscam cada vez mais, além do aumento de produção com qualidade, redução de custos e operações mais seguras, uma produção mais limpa. De outro lado, o uso de modelos obtidos diretamente a partir de dados históricos sobre as operações se fortaleceu a partir da geração massiva de dados pelos processos industriais. Outro fator que contribuiu para esse avanço foi a aumento da complexidade das operações com o tempo. O objetivo deste trabalho foi construir um sensor virtual para a classificação de seis categorias de emissões de SO2 em uma caldeira de recuperação química de uma fábrica de celulose kraft no Brasil. Baseou-se esse sensor no algoritmo k-vizinhos mais próximos (k-NN; k-Nearest Neighbors), que é uma técnica de aprendizado supervisionado. Testaram-se três abordagens para a obtenção dos modelos k-NN: univariável (usada como referência), a partir de subconjuntos pré-selecionados de preditores, e a partir de comitês de modelos k-NN. O melhor desempenho ocorreu para um comitê de vinte e cinco modelos k-NN, com oito preditores, k (número de vizinhos mais próximos) igual a 2 e distância de Manhattan. Foi possível aumentar a acurácia de classificação das classes minoritárias (4 a 6), sem perda de desempenho sobre as classes majoritárias (1 a 3), como usualmente ocorre em problemas de classificação com dados desbalanceados. Para a classe 6 por exemplo, com o menor número de observações, a precisão passou de 0.794 para 0.906, o recall, de 0.640 para 0.715, a F1-score, de 0.693 para 0.791, e a g-mean de 0.797 para 0.843, sem perda de generalização para as demais classes, em comparação com a abordagem de subconjuntos de preditores. Um sensor virtual pode ser útil como uma fonte de informação redundante sobre variáveis-chave de processo, ou para antecipar resultados de análises de laboratório, o que demanda um tempo razoável em geral, entre outras aplicações.
Model-agnostic Counterfactual Explanations in Credit Scoring

The past decade has shown a surge in the use and application of machine learning and deep learning models across different domains. One such domain is the credit scoring domain, where applicants are scored to assess their credit-worthiness for loan applications. During the scoring process, it is key to assure that there are no biases or discriminations that are incurred. Despite the proliferation of machine learning and deep learning models (referred to as black-box models in the literature) in credit scoring, there is still a need to explain how each prediction is made by the black-box models. Most of the machine learning and deep learning models are likely to be prone to unintended bias and discrimination that may occur in the datasets. To avoid the element of model bias and discrimination, it is imperative to explain each prediction during the scoring process. Our study proposes a novel optimisation formulation that generates a sparse counterfactual via a custom genetic algorithm to explain a black-box model’s prediction. This study uses publicly available credit scoring datasets. Furthermore, we validated the generated counterfactual explanations by comparing them to the counterfactual explanations from credit scoring experts. The proposed explanation technique does not only explains rejected applications, it can also be used to explain approved loans.
Linear-nonlinear control applied to switching power supplies to get fast transient response

This paper shows the utilization of the new linear-nonlinear control (LnL) to get a better fast transient response in switching power supplies. The linear-nonlinear control allows reducing the recovery time of the converters working under a load current step. This type of control helps designers to solve the problems presented in system such us DSPs and microprocessors. Four different prototypes have been built to show the actual operation and to compare the experimental results: a conventional buck converter with linear control, a buck converter with LnL control, a hybrid source with threshold band and a hybrid source with LnL control. The results prove that using LnL control the output voltage variation is limited, the efficiency is improved, and a better transient response is obtained.
Performance Evaluation of Cooperative OMA and NOMA Systems in 6G Deployment Scenarios

Optimization of the energy efficiency, fairness, and rates of the system is a vital part of communication systems. Multiple access techniques have a huge potential to enhance such performance parameters. This paper studies the performance of NOMA and OMA systems in a singular cell environment, where the cellular users are distributed randomly, and cooperative relays are considered for better system reliability. The relay nodes forward the signals to the cell-edge users. This paper considers a practical scenario where all the relay equipment is distributed with non-uniform battery power levels. The performance of OMA and NOMA schemes is compared based on the key performance indicators: sum rate, fairness, and energy efficiency. The fairness factor determines fairness in the allocation of resources to all the system’s users. The performance of the two schemes is assessed in three deployment scenarios: urban, suburban, and rural scenarios. Through numerical results, it is proved that the performance of the NOMA dominates the OMA scheme.
Efficiency optimising strategy for dual-coupled transmitters based WPT systems

The wireless power transfer (WPT) system based on dual-coupled transmitters has been proved to be a good alternative for high-power applications. However, the efficiency of the WPT system is various with different loads. An active rectifier (H-bridge) is equipped in the secondary side to replace the passive rectifier (diode rectifier). By controlling the conduction angle and the output voltage of the active rectifier, the equivalent optimal load of the secondary side can be achieved. Owing to the equivalent optimal load, the WPT system with dual-coupled transmitters can achieve optimising power efficiency. The experimental results agree well with the theoretical analysis. The results show the proposed efficiency optimising strategy is a good candidate for a WPT system with dual-coupled transmitters.
Evaluating Signs of Pulmonary Hypertension on Computed Tomography and Correlating With Echocardiography: A Study at a Tertiary Care Hospital

Introduction: Pulmonary hypertension (PH) is a threatening condition, and it is far more common than previously assumed, especially after the COVID pandemic. Its outcome is not good; if detected late, and can lead to right ventricular failure, which can be fatal. Our goal was to evaluate CT signs of PH, correlate them with echocardiography, and identify the cut-off values of these signs in our population. Method: In this study, 160 patients having both CT and echocardiography with a maximum gap of one month were assessed from June to November 2021. The association between CT signs and echocardiography to diagnose PH was investigated. The Pearson and Spearman correlation and area under receiver operating curve (AUROC) tests were performed in the analysis. Receiver operating characteristic curve analysis was also used to assess CT’s diagnostic capability and cut-off values. Result: The correlation between main pulmonary artery (MPA) diameter and main pulmonary artery to aorta ratio (MPA/AO) with mean pulmonary artery pressure (mPAP) was weak but statistically significant (r = 0.316 and r = 0.321, p<0.001). However, there was a very weak correlation between the right and left pulmonary artery and mPAP with correlation coefficients (r) of 0.155 and 0.138, respectively. For the first time in our population, we measured the cut-off values of MPA and MPA/AO ratios for PH which were 26 and 0.88 mm, respectively. Conclusions: The CT signs of PH correlate with echocardiography; however, should not be used solely; the cut-off values should be used according to race and population.
Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry.
A 3D multi-modal intelligent intervention system using electromagnetic navigation for real-time positioning and ultrasound images: a prospective randomized controlled trial

Background Anesthesia, nerve block, therapeutic injections, and biopsies all require an acupuncture intervention. However, traditional two-dimensional (2D) ultrasound-guided needle puncture is often challenging and therefore requires the use of three-dimensional (3D) ultrasound images to accurately identify and evaluate the patient’s anatomical structure. Methods In this study, a 3D multi-modal intelligent intervention system using electromagnetic navigation for real-time positioning and ultrasound images was described. A total of 190 cases requiring puncture were randomly divided into control (conventional 2D ultrasound instrument) and experimental (novel 3D ultrasound imedis9000) groups. The advantages and disadvantages of the two puncture methods were prospectively analyzed in the 190 cases, and the feasibility of electromagnetic navigation real-time positioning was compared to ultrasound imaging. Results This study included 190 cases from two centers that required puncture treatment and were randomly assigned to the control (conventional 2D ultrasound instrument; n=95) or the experimental (novel 3D ultrasound imedis9000; n=95) groups. Percutaneous vascular puncture, percutaneous biopsy, percutaneous bile duct puncture, thoracic paravertebral nerve block, and sciatic nerve block operations were performed separately. The results indicated that the puncture time and number of trials in the experimental group were significantly lower than those in the control group. No significant difference was identified in the basic vital signs between the two groups before and after surgery. The success rate of the novel 3D ultrasound imedis9000 was 100%, and the success rate of the conventional 2D ultrasound instrument was 95.7%. Furthermore, the results also showed that the novel 3D ultrasound imedis9000 and the matching coaxial positioning channel puncture needle had low pain, good toughness and strength, and great convenience. Conclusions The new 3D multi-modal intelligent intervention system using electromagnetic navigation real-time positioning and ultrasound images has significant advantages compared with conventional 2D ultrasound in terms of puncture time, number of trials, operation difficulty, and convenience, and is worthy of further promotion and use in clinics. Trial Registration Beijing Municipal Drug Administration, 20190015.
Funktionalisierte Faserkunststoffbewehrung zur Wärmespeicherung in Betonbauteilen

Durch die Vorgaben des Erneuerbare‐Energien‐Gesetzes wird Strom aus Erneuerbaren Energien vorrangig in das elektrische Netz eingespeist. Zur Vermeidung von Überlastungen im Stromnetz muss diese Einspeisung temporär reduziert werden. Die elektrische Energie, die in diesen Zeiträumen technisch erzeugt werden kann, aber nicht wird, wird als Ausfallarbeit bezeichnet. Diese fällt zum größten Teil in der Heizperiode an und kann zur Beheizung von Gebäuden genutzt werden. Ein Ansatz für die effiziente Einbindung von Erneuerbaren Energien eröffnet die Nutzung der Gebäudetragstruktur zur Wärmespeicherung. Im Rahmen dieses Beitrags wird eine Lösung vorgestellt, mit der elektrische Energie in Wärme umgewandelt, in Betonbauteilen gespeichert und zur Beheizung von Gebäuden genutzt wird. Die Einspeicherung von Wärme erfolgt über eine neuartige, funktionalisierte Faserkunststoffbewehrung mit einem Heizdraht in Querschnittsmitte. Um das Ziel der Reduktion von Ausfallarbeit zu erreichen, wird ein Modell benötigt, welches abhängig von der Beladedauer und der ‐spannung die Temperatur im Bauteil vorhersagt. Das mithilfe von Messungen entwickelte Modell erlaubt dies für Beladedauern bis zu 12 h. Zudem werden die im Bauteil gespeicherte Wärme sowie die resultierende Heizleistung prognostiziert. Im untersuchten Beispiel werden Leistungen von bis zu 174 W/m2 und Temperaturänderungsraten von rd. 1 K/h erreicht.
Symptoms of Burnout Syndrome among Physicians during the Outbreak of COVID-19 Pandemic—A Systematic Literature Review

Background: Studies in the recent decades show that the medical profession has a high risk to develop burnout due to constant exposure to mental and physical suffering or death. The pandemic period induced additional stress for healthcare professionals due to the likelihood of a high rate of infection, long working shifts, using protective equipment, staying away from family, implementing new medical procedures. The present study is focusing on assessing the prevalence of burnout among physicians working in the healthcare system during the COVID-19 pandemic, and discovering the main factors associated with burnout syndrome among the population of physicians. Material and methods: A systematic review was conducted by searching PubMed, Wiley, and Google Scholar in November 2021. A total of 35 studies were eligible for the evaluation. Results: The samples ranged from 39 to 3071 physicians, and the overall burnout ranged from 14.7% to 90.4%. Sociodemographic characteristics associated with a high prevalence of burnout were the female gender, less experienced, not having children, and single marital status, associated with high levels of anxiety, depression, and stress in the female gender. The highest level of burnout among all the studies was 90.4% on a sample of physicians from the Republic of Korea, 80.2% among psychiatrists in Saudi Arabia, followed by a study in Ireland with a 77% level of burnout among senior and specialist physicians, and 74.7% prevalence of burnout for emergency physicians in USA. Conclusions: During the pandemic, the factors that contribute to burnout are the lack of personal protective equipment and the violence of issues related to organizational health; the high prevalence of burnout symptoms is associated with anxiety, depression, and stress.