Miracle-dz/UniFAR-FTUs huggingface dataset:
A Self-Tuning Adaptive Control Scheme for a Grid-Connected Three-Phase PV System

This article presents a new self-tuning sinusoidal recursive controller (STSRC) for handling the uncertainties and disturbances in a three-phase grid-connected photovoltaic system (GCPVS). The performance of a GCPVS is greatly influenced by the uncertainties, such as intermittency in solar irradiation, change in capacitance and equivalent series resistance (ESR) of dc-link capacitor, and variation of impedance between the inverter and the grid. This STSRC comprises improved linear sinusoidal tracer-based recursive least-square estimation (IRLS) to achieve robust performances, despite the parametric uncertainties through real-time estimation of its parameters, and updation of its gains. The efficacy of the proposed STSRC is verified through simulation using MATLAB/Simulink and experimental studies pursued on a 2.5-kW prototype. It is observed that STSRC exhibits superior tracking of PV voltage with reduced ripple and improved grid current with reduced total harmonic distortion (THD) of 1.66%. The estimation accuracy is found above 92%.
Understanding Depth of Reflective Writing in Workplace Learning Assessments Using Machine Learning Classification

Self-reflection and reflective writing have been pivotal for developing a deep understanding of concepts and fostering professional competency in learners. The confluence of the importance of reflective practices within the educational curriculum and the increased proliferation of technology have resulted in numerous studies of how to use automated approaches to analyze broad themes of reflection exhibited by learners in higher educational settings. However, there is a dearth of research in the context of automated analysis of reflective writing demonstrated by professionals within workplace learning. Building on a four-level reflective content analysis model, this article evaluates the depth of reflection exhibited by learners within a professional development MOOC using an automated assessment classifier. Our results identify the varying association of linguistic features across these different levels of reflection. The proposed approach can effectively support the deployment at scale of the labor-intensive evaluation of reflective writing by highly trained professionals.
Benchmarking global pulp and paper companies using self-organizing maps

Performing financial benchmarks in today's information-rich society can be a daunting task. With the evolution of the Internet, access to massive amounts of financial data, typically in the form of financial statements, is widespread. Managers and stakeholders are in need of a tool allowing them to quickly and accurately analyze this data. An emerging technique that may be suited for this application is the self-organizing map. The purpose of this study was to evaluate the performance of self-organizing maps for the purpose of financial bench-marking of international pulp and paper companies. For the study, financial data, in the form of seven financial ratios, was collected, using the Internet as the primary source of information. A total of 76 companies, and six regional averages, were included in the study. The time frame of the study was the period 1995-99. A number of benchmarks were performed, and the results analyzed based on information contained in the annual reports. The results of the study indicate that self-organizing maps can be feasible tools for the financial benchmarking of large amounts of financial data.
Face Recognition Technology Based on Neural Network: A Review

An artificial neural network (ANN) is an information processing system established by simulating the structure and logical thinking of the human brain. It uses the interconnection between a large number of neurons to form a network system that can perform complex calculations, and is widely used. For various complex problems, by choosing different model structures and transfer functions, various neural networks can be formed and different expressions of the relationship between output and input can be obtained. Face recognition is one of the important research directions of ANN. It mainly refers to the automatic inference of identity, expression, age, gender and other attribute information through the analysis of facial images, videos or pictures and video collections of people. Widely used in mobile payment, safe city, criminal investigation and other fields. The origin, types and research progress of ANN are introduced, and the face recognition technology based on neural network are investigated.
A Thematic Review on Prophetic Leadership: Analysis of Publication Patterns from 2010 Till 2021

Prophetic leadership is a model of leadership which is primarily based on the Holy Quran and the exemplary of the Prophets’ characteristics. Prophetic leadership has been theoretically and practically proven to improve the quality of leadership in organizations. However, despite its importance, there have been relatively small amount of research discussing the patterns in prophetic leadership publications. Therefore, this thematic review aims to explore the pattern of the publications from 2010 till 2021 that discussed on prophetic leadership in Muslimowned or Islamic organizations. Three databases including Dimensions, Microsoft Academics, and Google Scholar were searched under the keywords prophetic leadership and Islam. The findings from the code-to-document analysis using Atlas.ti8 found the patterns of publications which were grouped into six main themes namely 1) impact; 2) application; 3) internalization; 4) supporting or inhibiting factor; 5) model; and 6) role of leader. This study offered several significant contributions for practical purposes, for the body of knowledge, and for recommendations of future studies on prophetic leadership.
Abhinav International Monthly Refereed Journal of Research in Management & Technology EFFECTIVENESS OF 5S IMPLEMENTATION ON ORGANIZATIONS PERFORMANCE

S is a TQM technique used by organizations comes from five Japanese words; Seiri (sort), Seiton (set in order), Seiso (shine), Seiketsu (standardize), and Shitsuke (sustain). This system helps to organize a workplace for efficiency and decrease wasting and optimize quality and productivity via monitoring an organized environment. It also provides useful visual evidences to obtain more firm results. There is a real need for empirical studies in field of new management systems and their impact on company's performance. As importance role of continuous improvement in today's organizations, and lack of sufficient evidence to show the positive impact of 5S on organizational performance, this paper aims to identifying the effectiveness of 5S implementation on organizational as well employees performance and their attitude towards 5s. Surveying method is used and data collection is done by distributing questionnaire among general employees of the organizations which have implemented 5S techniques. The target organizations are chosen from different industries and diverse field of work. The results show that 5S is an effective tool for improvement of organizational performance, regardless of organization type, size, its production or its service. Consequently, 5S techniques would strongly support the objectives of organization to achieve continuous improvement and higher performance.
Implementation of Online Behavior Modification Techniques in the Management of Chronic Musculoskeletal Pain: A Systematic Review and Meta-Analysis

Purpose: The main aim of this systematic review and meta-analysis (MA) was to assess the effectiveness of online behavior modification techniques (e-BMT) in the management of chronic musculoskeletal pain. Methods: We conducted a search of Medline (PubMed), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, APA PsychInfo, and Psychological and Behavioral Collections, from inception to the 30 August 2021. The main outcome measures were pain intensity, pain interference, kinesiophobia, pain catastrophizing and self-efficacy. The statistical analysis was conducted using RStudio software. To compare the outcomes reported by the studies, we calculated the standardized mean difference (SMD) over time and the corresponding 95% confidence interval (CI) for the continuous variables. Results: Regarding pain intensity (vs. usual care/waiting list), we found a statistically significant trivial effect size in favor of e-BMT (n = 5337; SMD = −0.17; 95% CI −0.26, −0.09). With regard to pain intensity (vs. in-person BMT) we found a statistically significant small effect size in favor of in-person BMT (n = 486; SMD = 0.21; 95%CI 0.15, 0.27). With respect to pain interference (vs. usual care/waiting list) a statistically significant small effect size of e-BMT was found (n = 1642; SMD = −0.24; 95%CI −0.44, −0.05). Finally, the same results were found in kinesiophobia, catastrophizing, and self-efficacy (vs. usual care/waiting list) where we found a statistically significant small effect size in favor of e-BMT. Conclusions: e-BMT seems to be an effective option for the management of patients with musculoskeletal conditions although it does not appear superior to in-person BMT in terms of improving pain intensity.
Co-creation in youth sport development: examining (mis)alignment between coaches and parents

ABSTRACT The authors explore the challenges and opportunities for a youth sport system in an entrepreneurial marketplace by identifying areas of (mis)alignment between parents and coaches, a key partnership in youth sport development. The context of tennis was used to recruit a sample of 130 parents and 113 coaches based in the United States. Choice-based conjoint analysis was used to compare the relative importance parents and coaches place on key youth sport program attributes and their preference for specific attribute levels. Information sources were also examined. Significant differences were found between parents’ and coaches’ preferences for specific program implementations, and the importance they placed on each attribute. Univariate analyses revealed significant differences for parents’ and coaches’ preferences for levels within each attribute, although the order of preference for each level was the same. Parents and coaches also differed in the importance placed on information sources. Overall findings indicate misalignment, which may affect parent/coach collaboration and ultimately inhibit effective athlete development. However, the findings do suggest the potential for collaboration, as parents and coaches value shared responsibility. Recommendations for facilitating collaboration for more effective athlete development include reducing consumer confusion via shared language and interpretation, and stimulating co-creation via structural change. HIGHLIGHTS Youth sport parents considered coach certification the most important element of a youth sport program. Coaches placed the most importance on who had overall responsibility for athlete development. Parents and coaches saw shared responsibility for athlete development as preferrable. Relatively low preference for interaction acts as a barrier to effective co-creation.
Biodiversity and Distribution of <i>Aspergillus</i> and Their Toxins in Maize from Western and Eastern Regions of South Africa

Aspergillus species and aflatoxins production are more prevalent during times of high heat and drought. In South Africa, there is frequent occurrence of drought as a result of climate change. The aim of this study was to investigate the biodiversity and distribution of Aspergillus species with their corresponding toxins in maize from main maize producing regions of South Africa; [Western Regions (WR) and Eastern Regions (ER)]. One hundred and twen-ty-three (64 from WR and 59 from ER) maize samples from the two agro-climatic regions in South Africa were analyzed using cultural, molecular and analytical methods. Across agro-climatic regions, Aspergillus species contaminated about 62% of the maize samples, while Aspergillus flavus was the most prevalent (47.15%) followed by Aspergillus fumigatus (4.69%) while the least was Aspergillus parasiticus (0.81%). The Western Regions showed a higher distribution of varieties of Aspergillus species compared to the Eastern Regions. Aflatoxins contaminated only 27.64% of the maize samples with a mean total aflatoxin concentration of 2.40 μg/kg which is below the South Africa’s set standard for total aflatoxin in food (5 μg/kg). About 10.57% of the samples produce aflatoxins above the 5 μg/kg permissible limit for total aflatoxin in foods. The ratio of toxigenic to atoxigenic strains of Aspergillus flavus was generally low in all the regions of South Africa. This study could aid policy makers to make informed decisions in developing remediation strategies for Aspergillus mycotoxins.
Social Capital Resources in Coping with Distance Education during the COVID-19 Pandemic: A Content Analysis of the Statements of Teachers Working in Poland at Different Educational Stages

The article aims to show social capital resources in coping with distance education during the COVID-19 pandemic of Polish teachers working at different educational stages. The sample consisted of 1104 women (91.2%) and 107 men (8.8%) who described their remote professional experiences as valued positively during the pandemic. The collected verbal material was analyzed with quantitative content analysis based on theory-driven categories of social capital: Relationships, trust, commitment, and fulfilling obligations. Then, the frequency of words belonging to the categories in each participant’s utterance was assessed. The results indicate that when describing positive experiences (situations and events) during distance education, teachers referred mainly to social capital resources in terms of relationships and fulfilling obligations. The results indicate that teachers working in secondary schools in Poland put less emphasis on building social capital during distance education, especially in terms of relationships, than teachers working with younger children.
Analysis of Once-Through Boiler Concepts in Parabolic Troughs

Solar thermal power plants use collectors to concentrate the sunlight and heat up a fluid to high temperatures. This heat can be used to run a steam turbine and generate electricity. Such plants work similar to conventional steam power plants, with the only difference in replacing the fossil steam Generator by a solar one. The steam can directly be generated within the solar field. This direct steam generation (DSG) process is analyzed in detail for parabolic trough collectors. As in conventional power plants, there exist two main concepts, one with forced circulation during the evaporation phase and one in a once-through configuration. This work covers the complete range of solar once-through steam generators for the first time. Design criteria are derived, transient models are validated and robust control strategies for large-scale commercial plants are developed. Those are validated by experiments at a Commercial scale test facility at Plataforma Solar de Almeria in Spain. Furthermore, a methodology for component life time estimation is proposed for such plants and the overall economic potential of the once-through configuration is compared to the state-of-the-art recirculation type. It is demonstrated for the first time that solar once-through steam generators are technically feasible and economically very promising.
Progress on deep learning in digital pathology of breast cancer: a narrative review.

Background and Objective Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology. Methods A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied. Key Content and Findings DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches. Conclusions Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.
Moral Distress in Healthcare Providers Who Take Care of Critical Pediatric Patients throughout Italy—Cultural Adaptation and Validation of the Italian Pediatric Instrument

Background: Although Moral Distress (MD) is a matter of concern within the Pediatric Intensive Care Unit (PICU), there is no validated Italian instrument for measuring the phenomenon in nurses and physicians who care for pediatric patients in Intensive Care. The authors of the Italian Moral Distress Scale-Revised (Italian MDS-R), validated for the adult setting, in 2017, invited further research to evaluate the generalizability of the scale to clinicians working in other fields. Our study aims to reduce this knowledge gap by developing and validating the pediatric version of the Italian MDS-R. Methods: We evaluated the new instrument for construct validity, then we administered it in a multicenter, web-based survey that involved healthcare providers of three PICUs and three adult ICUs admitting children in northern, central, and southern Italy. Finally, we tested it for internal consistency, confirmatory factorial validity, convergent validity, and differences between groups analysis. Results: The 14-item, three-factor model best fit the data. The scale showed good reliability (a = 0.87). Still, it did not correlate with the Emotional Exhaustion and Depersonalization sub-scales of the Maslach Burnout Inventory (MBI) or with the 2-item Connor-Davidson Resilience Scale (CD-RISC 2) or the Satisfaction with Life Scale (SWLS). A mild correlation was found between the Italian Pediatric MDS-R score and intention to resign from the job. No correlation was found between MD and years of experience. Females, nurses, and clinicians who cared for COVID-19 patients had a higher MD score. Conclusions: The Italian Pediatric MDS-R is a valid and reliable instrument for measuring MD among Italian health workers who care for critically ill children. Further research would be helpful in better investigating its applicability to the heterogeneous scenario of Italian Pediatric Critical Care Medicine.
Computational engine performance and emission analysis using Ceiba pentandra biodiesel

ABSTRACT This paper investigates Ceiba pentandra methyl ester (CPME) as a renewable and alternative fuel source. It presents the results of performance and emissions characteristics of CPME blended with conventional diesel fuels in a diesel engine under full load conditions. The physicochemical properties of Ceiba pentandra oil were experimentally evaluated after transesterification and found to be within acceptable limits of relevant standards. It was clear that, when B25 tested in engine the BSFC increased by 6.2% and brake thermal efficiency reduced by 5.44% compared to diesel fuel. On the other hand carbon monoxide (CO) decreased by 8.33%, hydrocarbon (HC) reduced by 7.4% and oxides of nitrogen (NOx) also decreased by 3.72% compared to diesel. Smoke emissions of the biodiesel blend were 4.5% higher than diesel. Thus, B25 biodiesel blend can be used without any modification of the engine as an alternative and environmental friendly fuel.
A 4-Channel Ultra-Low Power Front-End Electronics in 65 nm CMOS for ATLAS MDT Detectors

A 4-channel front-end electronics (4 × FEE) system for the muon drift tube in the ATLAS detector in the High-Luminosity LHC is presented. The overall channel architecture is optimized to reduce the power and area of the design. Each channel comprises a charge-sensitive preamplifier (CSP), shaper, discriminator and differential low-voltage signaling drivers. The proposed channel operates with a 5–100 fC input charge and exhibits a linear sensitivity of 8 mV/fC for the entire input charge range. The peaking time delay of the analog channel is 14.6 ns. At the output, the time representation of the input signal is provided in terms of the CMOS level and in scalable low-voltage signal (SLVS). The FEE consumes a current of 10.6 mA per channel from a single 1.2 V supply voltage. The full 4 × FEE design is realized in TSMC 65 nm CMOS technology and its die-area is 2 mm × 2 mm.
Personalized Neural Network for Patient-Specific Health Monitoring in IoT: A Metalearning Approach

The Internet of Things (IoT) has been widely applied in personal health monitoring on biosignals. Conventional detection methods in the field count on a variety of heuristic criteria by utilizing extracted features, which are carefully selected through extensive clinical trials and experts’ experiences. Recently, deep learning (DL) gains rapidly growing attention in health monitoring. The most significant advantage of DL-based methods is that DL could execute feature engineering automatically with only labeled data, which results in a great reduction in the expertise involved and manual works in the detection method’s design. However, individual differences among various patients (subjects) can lead to accuracy degradation of the pretrained deep model. Simply fine-tuning the deep model with the patient-specific data cannot alleviate the problem since the pretrained model may not generalize well to new data. To address the problem, we propose a metalearning-based personalization method to generate the personalized neural network for each patient to conduct patient-specific detection. Specifically, the proposed metalearning method leverages a novel patientwise training tasks formatting strategy to train the neural network that ends up with a well-generalized model initialization containing across-patient knowledge. The well-generalized model initialization would then be utilized to perform a quick adaptation to the specific patient’s data domain. In this way, a new patient could be immediately assigned with a personalized neural network using limited labeled data. Experimental results show that the proposed metalearning-based personalization method achieves 8.2%, 2.5%, and 6.4% higher accuracy when compared with the existing DL detection methods in VF detection, AF detection, and human activity recognition, respectively.