Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining organizational politics as the cause of the crisis in energy generation in developing countries

The majority of state-owned power companies (SoEs) have experienced organizational cancer for nearly three decades without showing any signs of ceasing operations. Academicians generally agree that organizational politics are to blame for the ongoing energy-generating dilemma that has plagued rising nations. Worldwide, economies rely heavily on electricity as an energy source, to the point where they would not function or even survive without it. In order to reduce unfavorable political behavior, this study examines organizational politics at the Zimbabwe Supply Authority, a SoE in charge of energy production. A sample of 1400 individuals who were chosen at random from a population of 2210 staff members participated in the study using a quantitative research approach to collect data. By the deadline, 358 completed questionnaires had been returned; however, 11 of them were rejected because they had been wrongly filled out, with a response rate of 24.78%. The data were analyzed using the statistical tool SPSS 21.0 for Windows. To ensure that the samples were adequate for factor analysis, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's Test of sphericity were applied to the data before exploratory factor analysis could be conducted on them (Field, 2007:619). The results of the study showed that bad political behavior and ineffective managerial leadership techniques had a detrimental impact on the power generation crisis. According to the report, managers should receive training on modern managerial leadership behavior, managerial methods for behavior modification, and improved communication.
Testing the Target Congruence Approach: Do Vulnerability, Gratifiability, and Antagonism Explain Cyberstalking Victimization Among Young Adults?

To extend theoretical understanding of cyberspace victimization beyond lifestyle-routine activity theory, we tested the target congruence approach as a plausible explanation for cyberstalking victimization. Of secondary interest, we assessed whether the relationship between target congruence and cyberstalking victimization differs by pursued–pursuer relationship. Survey data from a sample of 1,500 young adults (18–25 years old) revealed 32% had experienced cyberstalking victimization within the previous year. Logistic regression results moderately support the target congruence approach. Measures capturing target vulnerability (e.g., age, disability), target gratifiability (e.g., gender identity, types of photos posted), and target antagonism (e.g., relationship status, cyberstalking perpetration) significantly predicted cyberstalking victimization. Moreover, it appears that the target congruence elements better explain cyberstalking victimization conducted by someone known to the target (compared with a stranger). Implications for the applicability of the target congruence approach are discussed, along with suggestions for future research.
The comparison of umbilical cord artery pH in newborns with and without thick meconium stained amniotic fluid

Meconium-stained amniotic fluid (MSAF) affects 15-20% of term pregnancies. Recent studies have shown that MSAF has adverse effects on neonatal outcomes. There is no scientific consensus on the incidence of fetal distress in MSAF neonates, and most cesarean sections due to MSAF are unnecessary. The present study was conducted to assess umbilical artery blood pH in neonates with MSAF and to examine whether there is a relationship between MSAF and fetal distress. A clinical survey case-control was conducted on the neonates of 200 pregnant women admitted to a delivery unit of the obstetrics and Gynecology center in Amir al-Momenin Hospital, Zabol in 2014. Neonates born with MSAF made up the case group, and the control group consisted of neonates born with clear amniotic fluid. Umbilical cord arterial pH, gestational age, gender, mode of delivery, and one and five-minute Apgar scores were considered in both groups. The mean pH of the umbilical cord artery blood in the infants of the case group was 7.25, and the mean pH of the umbilical cord artery in the infants of the control group was 7.29 (P = 0.93). The mean gestational age in the case and control groups was 40.08 weeks and 38.32 weeks, respectively (P= 0.03). In this study, a cesarean delivery (P=0.001) and female gender (P= 0.016) were higher in the case group than in the control group. MSAF does not necessarily imply fetal distress, so urgent cesarean sections are unnecessary. This study showed that based on the acidity variables of the umbilical cord artery, there is no statistically significant correlation between the Apgar score at the 1st and 5th minute. While a significant difference has been observed between the type of delivery, gestational age and baby's gender.
LDPC codes from matrix equations

Different constructions of LDPC codes based on matrix equations are investigated. The parameters such as the dimension, rate and distance are computed. The classical Tanner graph representation known for LDPC codes are described. The main difference between standard LDPC codes and the LDPC codes based on matrix equations lies in the structure of their codewords. Whereas in the classical situation codewords are simply vectors, the codewords in this new setting will be two-dimensional vectors or matrices. This implies that the parity-check constraints must be satisfied in both perpendicular directions of the codeword. Therefore, a codeword may be interpreted as a two-dimensional array which is suitable for recording on two-dimensional pattern-oriented storage media
Methodology of uncertainty analysis prediction based on multi-well data fusion

Abstract During drilling activities, geological parameters of a well to be drilled (target well) can be predicted within a limited interval based on multi-well data fusion which aims at ensuring a drilling safety, enhancement of drilling efficiency, reduction of drilling cost as well as acquiring accurate measurements in respect to Oil and Gas protection layers. This work presents a method of uncertainty analysis prediction of pressures using fusion data (formation pressures) from adjacent multi-well. The Eaton method, effective stress theory, and mathematical confidence interval were the various methods used to establish the formation pressure matrix of the target well. The results revealed that due to the complexity and variability of the formations, data interpretation errors of the geological parameters were inevitable. Therefore, the probability density distribution function was established through stratigraphy, probability statistics, and information diffusion. Moreover, the real value of the wells’ formation pressure (target well) was within the distribution interval of multi-well data fusion. Hence, the developed method cannot only effectively reduce the interval of geological parameter of the target well but also enhance the accuracy of parameters prediction.
Non-contact arrhythmia assessment in natural settings: a step toward preventive cardiac care

Cardiovascular disease is a major contributor to US morbidity. Taking preventive action can greatly reduce or eliminate the impact on quality of life. However, many issues often go undetected until the patient presents a physical symptom. Non-intrusive continuous cardiovascular monitoring systems may make detecting and monitoring abnormalities earlier feasible. One candidate system is photoplethysmographic imaging (PPGI), which is able to assess arterial blood pulse characteristics in one or multiple individuals remotely from a distance. In this case study, we showed that PPGI can be used to detect cardiac arrhythmia that would otherwise require contact-based monitoring techniques. Using a novel system, coded hemodynamic imaging (CHI), strong temporal blood pulse waveform signals were extracted at a distance of 1.5 m from the participant using 850-1000 nm diffuse illumination for deep tissue penetration. Data were recorded at a sampling rate of 60 Hz, providing a temporal resolution of 17 ms. The strong fidelity of the signal allowed for both temporal and spectral assessment of abnormal blood pulse waveforms, ultimately to detect the onset of abnormal cardiac events. Data from a participant with arrhythmia was analyzed and compared against normal blood pulse waveform data to validate CHI’s ability to assess cardiac arrhythmia. Results indicate that CHI can be used as a non-intrusive continuous cardiac monitoring system.
Research on Computer Vision Image Classification

: Computer vision image classification is an important research direction in the field of computer vision, which aims to classify images into different categories for more effective processing. With the continuous increase of image data, how to effectively classify and organize this data has become a persistent issue. This article introduces the research process, methods, applications, and future development trends of computer vision image classification in detail.
The Practice of Positive Psychological Interventions on Students' Well-Being in the Model of Academy System—the Case of Xi'an Eurasian National University

: The academy system model is effective in the context of education in the era of all-round education, and has positive significance for the cultivation of students' practical ability, innovation ability and social adaptability. But the problems of identity deprivation, time fragmentation, complexity of interpersonal relationships and other problems that appear under the model also bring challenges to student growth, if there is no timely guidance students are prone to low belonging, weak sense of happiness, confused anxiety and other mental health problems, which put forward higher requirements for student care education.The academy system model is effective in the context of all-round education, and has positive significance for the cultivation of students' practical ability, innovation ability and social adaptability.We first investigated the basic information, positive psychological qualities and the current state of well-being of 4374 students in Xi'an Eurasian National University, and then conducted a positive psychological intervention for 480 students using the method of Stochastic intervention experiments.The results of the experiment showed that positive psychological levels and well-being indices were improved through positive psychological interventions.The experimental results showed that students' positive psychological level and happiness index
Penentuan Relevansi Artikel Ilmiah dengan Metode Word2Vec

ArXiv is a place to store electronic preprints including scientific articles and can be accessed by all internet users online. Over time, the number of scientific article documents has increased. This can reduce the level of effectiveness of grouping scientific articles based on the user's area of interest. A recommendation system is needed to determine the relevance of scientific articles that can provide users with recommendations for scientific articles that are in accordance with the user's areas of interest, so that users will find it easier to access scientific articles on the system. The author will do word embedding by applying the Word2Vec method. Word2Vec is a machine learning method for obtaining high quality vector. Based on the test results, it can be concluded that the Word2Vec method can be used to produce recommendations for scientific articles according to the user's areas of interest and produce the best parameter values n=150, epoch=100, window size=3 and learning rate=0.01.
Estimating Soil Erosion to Highlight Potential Areas for Conservation Priority in Rukarara Catchment, South-western Rwanda

Soil erosion is one of the major environmental problems in tropical ecosystems; however, the lack of information on the amount of eroded soils in Rwandan mountainous watersheds hinders effective decision-making toward sustainable soil management. This study aimed at predicting soil erosion in the Rukarara River watershed, one of the mountainous watersheds in Rwanda, and identifying potential areas of high erosion risk using the revised universal soil loss equation (RUSLE) implemented in a GIS environment. The annual soil loss was estimated by computing and performing a spatial overlay analysis of relevant layers including rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C), and conservation practice (P) factors. The results indicate that the annual soil erosion varies from 54 to 134 t ha−1 year−1 (95% confidence interval) with a mean of 39.96 t ha−1 year−1. The study area is generally characterized by very low and low soil erosion classes and the mean erosion correlated with literature results in the tropics. Agricultural lands are the hotspots of soil erosion with a mean of soil loss of 61.29 t ha−1 year−1. The produced maps and soil loss estimates can facilitate informed decision-making toward sustainable soil management in mountainous areas of Rwanda, especially in the Rukarara River watershed.
Evaluation of Predictive and Prognostic Importance of Lung Immune Prognostic Index in Locally Advanced Rectal Cancer Patients Treated With Neoadjuvant Chemoradiotherapy

Objective: The systemic inflammatory response (SIR) is known as an important factor associated with tumorigenesis and tumor progression, and can be reflected by inflammatory markers. One of the markers that reflect this is the lung immune prognostic index (LIPI). It is based on a derived neutrophil-to-lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH) level. We aimed to investigate the significance of LIPI in locally advanced rectal cancer (LARC) patients treated with neoadjuvant chemoradiotherapy (NACRT). Methods: In this retrospective study, we stratified the patients according to LIPI score as good LIPI and intermediate (int)/poor LIPI. According to pathological response to NACRT, we divided the patients into two groups as those with complete response (CR) or near-CR, and those with partial response (PR) or poor/no response. We classified CR and near-CR as good response. We evaluated the predictive and prognostic significance of LIPI for NACRT response, disease-free survival (DFS), and overall survival (OS) by univariate and multivariate analyses. Results: We included 137 patients in the results, with 72 (52.6%) having good LIPI and 65 (47.4%) having int/poor LIPI. The median follow-up period was 44.7 months (range: 10-105 months). Thirteen patients (18.0%) in the good LIPI group and 22 patients (34.0%) in the int/poor LIPI group achieved good response. In multivariate analysis, we found only the LIPI score as an independent risk factor (hazard ratio (HR): 2.4, p = 0.04) for NACRT response. Median DFS was 89.2 months (95% CI: 11.4-167.0) in the int/poor LIPI group; however, the DFS of all study populations and patients in the good LIPI group did not reach the median value. In multivariate analysis for DFS, we identified abdominoperineal resection (APR) (HR: 2.21, p = 0.02), presence of tumor deposit (HR: 2.96, p = 0.003), and int/poor LIPI score (HR: 2.07, p = 0.02) as separate risk variables. OS of all study populations and the patients in the LIPI groups did not reach the median value. In multivariate analysis for OS, we identified APR (HR: 2.74, p = 0.02), surgical margin positivity (HR: 12.94, p < 0.001), and adjuvant CT (HR: 0.20, p = 0.002) as separate risk variables for OS. Conclusion: This is the first study investigating the predictive and prognostic significance of LIPI in LARC patients treated with NACRT. The results revealed that int/poor LIPI was associated with a higher rate of good response but shorter DFS compared to good LIPI. The baseline LIPI score serves as an easily accessible and useful prognostic index, and it has significant potential for making appropriate treatment decisions in LARC.
Proof of concept: Machine learning based filling level estimation for bulk solid silos

On construction work, the constant availability of the necessary bulk materials like concrete, plaster and screed is essential but today not given for the continuous work. Especially the use of mobile bulk solids silos seems to have significant potential to avoid downtimes on construction work and thus to increase the efficiency of the project. The basis for this is the robust and continuous knowledge about the filling level of the bulk solid silo. In this paper, an innovative acoustic sensor concept for fill level estimation in bulk solid silos using knocking signals will be introduced. To prove feasibility, initial trials will be performed atfilled and emptied test silos, recording the resulting sound in order to show a correlation between the filling level and the acoustic impulse response. Since the correlation between some data can also be spurious, the causality will be shown with a mathematical toy model, whereby the mathematical analysis is based on the shell theory. Furthermore a suitable Machine Learning method shall be used to use the different data pattern order to classify different filling levels. Finally, further actions of the ongoing research project, regarding the transfer learning capability of Machine Learning will be discussed.On construction work, the constant availability of the necessary bulk materials like concrete, plaster and screed is essential but today not given for the continuous work. Especially the use of mobile bulk solids silos seems to have significant potential to avoid downtimes on construction work and thus to increase the efficiency of the project. The basis for this is the robust and continuous knowledge about the filling level of the bulk solid silo. In this paper, an innovative acoustic sensor concept for fill level estimation in bulk solid silos using knocking signals will be introduced. To prove feasibility, initial trials will be performed atfilled and emptied test silos, recording the resulting sound in order to show a correlation between the filling level and the acoustic impulse response. Since the correlation between some data can also be spurious, the causality will be shown with a mathematical toy model, whereby the mathematical analysis is based on the shell theory. Furthermore a suitable Machine.
ConverGenT: Automated Topology Generation and Analysis of Hybrid DC–DC Converters

Owing to their high efficiency and high power density, hybrid dc–dc converters are gaining attention in academia and industry. However, no systematic procedure is currently available for creating new converter topologies where the number of inductors and capacitors is different. This articles proposes ConverGenT, an automated topological synthesis and analysis framework for two-phase hybrid dc–dc converters with one inductor and two capacitors consisting of one flying and one output capacitor. Consequently, all the converter topologies of this type are discovered. In contrast to the conventional approach that relies on intuition, this method requires less time and generates a complete set of converter topologies in a systematic manner. These two key advantages ensure that no possible converter topology is missed, making this method superior to the conventional approach. After generating a topology, all information about the converter topologies can be analyzed and verified manually or automatically, and the most suitable topology for a given application can be selected. ConverGenT has identified 62 converter families, each with a maximum of six topologies per family, that outperform conventional converters in certain aspects, with at least eight of these topologies having been published within the last five years. Details on topologies and performance of these converters are given in the Appendix.
Inovação de Serviços em Cidades Inteligentes: Interação de Pessoas Não-Especialistas com Knowledge Graphs

Permitir aos cidadãos a utilização dos dados gerados pela cidade é essencial para o desenvolvimento de cidades inteligentes e a utilização de grafos de conhecimento abre novas possibilidades em vários domínios nas cidades inteligentes. O problema é que a construção e o consumo de grafos de conhecimento têm sido restrito a uma comunidade técnica, o que pode limitar uma ampla adoção deste tipo de tecnologia. Existem perfis de utilizadores em cidades inteligentes, que infelizmente não podem utilizar este tipo de dados porque normalmente não são tecnicamente capazes de consumir grafos de conhecimento. Assim, o objetivo deste trabalho é desenvolver uma forma de interação visual para tomadores de decisão com grafos de conhecimento que conectam semanticamente dados de diferentes domínios da cidade, assim endereçando problemas citados pelos Grandes Desafios de Pesquisa em Sistemas de Informação no Brasil 2016-2026.
Nonparallel Dictionary-Based Voice Conversion Using Variational Autoencoder with Modulation-Spectrum-Constrained Training

In this paper, we present a nonparallel voice conversion (VC) approach that does not require parallel data or linguistic labeling for the training process. Dictionary-based voice conversion is a class of methods aiming to decompose speech into separate factors for manipulation. Non-negative matrix factorization (NMF) is the most common method to decompose an input spectrum into a weighted linear combination of a set comprising a dictionary (basis) and weights. However, the requirement for parallel training data in this method causes several problems: 1) limited practical usability when parallel data are not available, 2) the additional error from the alignment process degrades the output speech quality. To alleviate these problems, we present a dictionary-based VC approach by incorporating a variational autoencoder (VAE) to decompose an input speech spectrum into a speaker dictionary and weights without parallel training data. According to evaluation results, the proposed method achieves better speech nat-uralness while retaining the same speaker similarity as NMF-based VC even though unaligned data is used.
Vitamin D Level Among Patients with Different Thyroid Diseases

: To investigate the differences in vitamin D levels in patients with different types of thyroid disease and to investigate the factors influencing vitamin D levels in patients with thyroid disease, this study collected clinical data from 1603 patients with thyroid disease attending the Department of Endocrinology and Metabolism of the First People's Hospital of Yunnan Province and conducted a retrospective cross-sectional study. The clinical data of 1603 patients with thyroid diseases including Graves' disease (GD), subacute thyroiditis (SAT), autoimmune thyroid disease (AITD) and hypothyroidism (HP) were collected from the Department of Endocrinology and Metabolism of yunnan First People's Hospital. Gender, age, BMI, blood lipids, thyroid hormones, thyroid autoantibodies, and serum 25-hydroxyvitamin D(25(OH)D) levels were measured. Results: (1) The level of 25(OH)D in patients with different thyroid diseases was from high to low, in the following order: GD group (29.54±7.54 ng/ml), SAT group (28.01±7.14 ng/ml), AITD group (26.97±6.58 ng/ml), HP group (25.88±6.75 ng/ml), the differences were statistically significant (P<0.05). The 25(OH)D level in GD group was significantly higher than that in other groups (P<0.05), while the 25(OH)D level in HP group was significantly lower than that in SAT group (P<0.05). (2) The changing trend of thyroid hormone levels (T3, FT3 and FT4) was consistent with that of 25(OH)D (P <0.005). (3) 25(OH)D was established as the dependent variable with multiple linear regression equation, Y = 3.80 gender + 0.058FT3-0.036 age + 28.122(female as control group). Conclusions: There were significant differences in vitamin D levels among patients with thyroid diseases, with the highest vitamin D level in GD patients and the lowest vitamin D level in HP patients. The higher the thyroid hormone level, the higher the 25(OH)D, which may be related to the strength of the immune response or the effect of thyroid hormone on vitamin D synthase. Male and high levels of FT3 were associated with increased 25(OH)D levels in thyroid patients.