Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Numerical Integration to Stochastic Estimation of the Gini Coefficient

Over the years, measuring inequality based on the distribution of income has been a major concern to economist. Inequality has had a broader concept than poverty in that it is defined over the entire population not just for the portion of the population below a certain poverty line. The Gini coefficient satisfy many desirable properties of a good measure of inequality such as mean independence, population size independence, symmetry, and Pigou-Dalton Transfer sensitivity. The empirical observation (income) distribution exhibit excess kurtosis and heavy tails. This research first described the probability distribution of income. The study presented a proposed numerical integration method to stochastic estimation of the Gini coefficient. The Proposed Numerical Integration Method showed a better estimate of functions as compared to the Newton’s cotes methods such as the Trapezium rule, Simpson’s 1/3 rule, Simpson’s 3/8 rule, Boole’s rule and Weddle’s rule. Diagnostic tests such as Q-Q plots and Kolmogorov-Smirnov test were graphically and quantitatively used to assess the fitness to the income data respectively. The study therefore concludes that the proposed method is superior to the Newton-Cotes methods of integration. Also, the Gini coefficient estimate using the proposed numerical integration method with k=3 was 0.48 which shows that there is disparity in income in Ghana and recommend to statisticians or mathematicians to use the proposed numerical integration method when computing functions that can’t be easily integrated. v University of Ghana http://ugspace.ug. edu.gh
Enhancing Contact Tracing for Serratia marcescens Biofilm on High-Usage Body Towels in Rivers State Bathrooms

The placement of body towels before and after use in bathrooms where Serratia marcescens proliferates calls for concern, as Serratia marcescens, an airborne opportunistic pathogen has been reported keratinolytic. Serratia marcescens, a bacterium commonly noticed with a pink or red slimy appearance in toilet sinks, bowls, and tiles presents an aesthetically, unappealing and disgusting appearance in the toilet surroundings. The study aimed to trace the presence of Serratia marcescens on frequently used body towels hung in the bathroom doors. Swabs from forty (40) differently used towels were collected from twenty (20) volunteered homes and analyzed using standard microbiological procedures. Microbiological procedures involved inoculating the swab sample on a prepared peptone broth and plating on MacConkey agar media, followed by identification and streak of the recovered isolate onto Congo red agar media for biofilm formation. Results showed the recovery of Serratia marcescens isolates. The three homes showed a Serratia marcescens count of 3 X 10, 1 X 102, and 7 X 10 CFU per swab for house units C, I, and P respectively. Serratia marcescens could form a biofilm, a basic feature that allowed it to strive on a body towel. The results derived strongly identified the presence of Serratia marcescens biofilm on body towels hung in the bathrooms. This could have health implications for towel users due to the bacteria's keratolytic properties. Hence, the need for constant surveillance to support effective measures of hygiene, aimed at preventing the spread of Serratia marcescens is recommended.
Experimental study on the performance of different photovoltaic thermal collectors with nano-technology

ABSTRACT The process of cooling the photovoltaic cell effectively leads to improving the electrical efficiency of the cell. Herein, the current experimental study employed three different nanofluids (CuO, ZnO, and TiO2) to cool the PV panel to produce a hybrid collector, also known as a photovoltaic thermal solar collector (PVT). The nanoparticle volume fraction was 0.1, 0.2, and 0.3 vol%. The process of cooling the PV panel was carried out using a copper tube on the back of the PV, which was placed to cover the largest possible area on the back of the PV. Three different flow cross sections – rectangular, square, and circular – were used to compare them. This experimental study was carried out under solar radiation conditions ranging from 450 W/m2 to 750 W/m2, and the flow rates of the nanofluid were 0.5, 1, 1.5, and 2 L/min. The results show the electrical and thermal efficiency of the PVT system at different conditions. The electrical efficiency increased as a result of adding nanofluid compared to normal water, where the cell with CuO/nanofluids gave the highest value of electrical efficiency at 450 W/m2, equaling 11.8%, while it was equal to 11.6%, 11.5%, and 10.8% for ZnO/nanofluids, TiO2/nanofluids, and water, respectively. An increase in mass flow rate leads to increased thermal, electrical, and combined PVT efficiencies. As well. The high mass flow rate increases the heat transfer coefficients between the tube wall and flowing fluid, which in turn decreases the PV module’s temperature. Finally, the rectangular section and the CuO/nanofluids gave the best value for the electrical power, which reached 83.17 W, and the highest electrical efficiency, which reached 11.5%.
Optimized Deep Neural Networks Audio Tagging Framework for Virtual Business Assistant

— A virtual assistant has a huge impact on business and an organizations development. It can be used to manage customer relations and deal with received queries, automatically reply to e-mails and phone calls.Audio signal processing has become increasingly popular since the development of virtual assistants. Deep learning and audio signal processing advancements have dramatically enhanced audio tagging. Audio Tagging (AT) is a challenge that requires eliciting descriptive labels from audio clips. This study proposes an Optimized Deep Neural Networks Audio Tagging Framework for Virtual Business Assistant to categorize and analyze audio tagging. Each input signal is used to extract the various audio tagging features. The extracted features are input into a neural network to carry out a multi-label classification for the predicted tags. Optimization techniques are used to improve the quality of the model fit for neural networks. To test the efficiency of the framework, four comparison experiments have been conducted between it and some of the others. From these results, it was concluded that this framework is better than the others in terms of efficiency. When the neural network was trained, Mel-Frequency Cepstral Coefficient (MFCC) features with Adamax achieved the best results with 93% accuracy and a 0.17% loss. When evaluating the performance of the model for seven labels, it achieved an average of precision 0.952, recall 0.952, F-score 0.951, accuracy 0.983, and an equal error rate of 0.015 in the evaluation set compared to the provided Detection and Classification of Acoustic Scenes and Events (DSCASE) baseline where he achieved and accuracy of 72.5% and
Opportunistic CT Screening—Machine Learning Algorithm Identifies Majority of Vertebral Compression Fractures: A Cohort Study

Vertebral compression fractures (VCF) are common in patients older than 50 years but are often undiagnosed. Zebra Medical Imaging developed a VCF detection algorithm, with machine learning, to detect VCFs from CT images of the chest and/or abdomen/pelvis. In this study, we evaluated the diagnostic performance of the algorithm in identifying VCF. We conducted a blinded validation study to estimate the operating characteristics of the algorithm in identifying VCFs using previously completed CT scans from 1200 women and men aged 50 years and older at a tertiary‐care center. Each scan was independently evaluated by two of three neuroradiologists to identify and grade VCF. Disagreements were resolved by a senior neuroradiologist. The algorithm evaluated the CT scans in a separate workstream. The VCF algorithm was not able to evaluate CT scans for 113 participants. Of the remaining 1087 study participants, 588 (54%) were women. Median age was 73 years (range 51–102 years; interquartile range 66–81). For the 1087 algorithm‐evaluated participants, the sensitivity and specificity of the VCF algorithm in diagnosing any VCF were 0.66 (95% confidence interval [CI] 0.59–0.72) and 0.90 (95% CI 0.88–0.92), respectively, and for diagnosing moderate/severe VCF were 0.78 (95% CI 0.70–0.85) and 0.87 (95% CI 0.85–0.89), respectively. Implementing this VCF algorithm within radiology systems may help to identify patients at increased fracture risk and could support the diagnosis of osteoporosis and facilitate appropriate therapy. © 2023 Amgen, Inc. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Numerical Analysis, Optimization, and Multi-Criteria Design of Vacuum Insulated Glass Composite Panels

The subject of this study is Vacuum Insulated Glass (VIG) panels, which consist of two glass panes with an evacuated space and evenly distributed micro-support pillars between them. The deflection of panes towards the centre of the structure caused by atmospheric pressure is a mechanical problem that occurs in this type of structure. The aim of this study was to extend previous research on the optimal arrangement of support pillars in terms of eigenfrequencies and dynamics to include aesthetic aspects. Using Abaqus/CAE v2017 software, a large number of numerical models were created and subjected to a comprehensive multi-criteria analysis. Fractal analysis was employed to automatically assess the aesthetics of the proposed solutions. The study presents theoretical solutions that could be implemented in industrial production. The presented study shows that it is possible to effectively extend the criteria for optimizing the arrangement of pillars with new design criteria. Most studies focus on pillar placement, amount, or shape in terms of panes thermal or mechanical properties. Due to the increasing number of VIG panels applications in places exposed to external vibrations, other design criteria for VIG panels are also required and are provided by the following study.
Exogenous Salicylic Acid Induced Drought Stress Tolerance in Immature Tea (Camellia sinensis L.) Plants

Salicylic acid (SA) has been known to induce drought tolerance in many plant species. In this study, we investigated the potential of exogenous application of SA to enhance drought tolerance in immature tea plants under glasshouse conditions at the Tea Research Institute in Talawakelle, Sri Lanka. One-year-old potted tea cultivars known for drought tolerance were used in the study. The plants were subjected to a drying cycle while being foliar sprayed with different concentrations of SA along with well-watered (WW), water-spray (WS) and no-spray (NS) treatments. Data were collected at 18 hours, 14 days after spraying (DAS), 21 DAS, and during the recovery after re-watering at 21 DAS. Based on the results obtained from the glasshouse study, the effective concentration of 150 mg L-1 SA was selected for further testing under field conditions in Talawakelle using three-year-old tea plants. The field experiment followed a randomized complete block design (RCBD) with three blocks. When the plants reached a moderate moisture stress level, they were foliar-sprayed with 150 mg L-1 SA, WS and NS treatments were included as controls. Data were collected at 7 DAS, 14 DAS, 21 DAS, and during the recovery phase after rain. The results showed that drought stress led to a decline in gas exchange parameters, relative water content, and an increase in the accumulation of osmolytes. However, the exogenous application of 150 mg L-1 SA significantly improved physiological processes such as gas exchange, osmolyte accumulation, and antioxidant activity, thus effectively enhancing drought tolerance in immature tea plants.
Assessing the failure of Open Government Data initiatives in Brazil

While assessing the potential of a particular digital innovation initiative, especially when it has implications for a range of societal stakeholders, it becomes pertinent to understand the possible bottlenecks in its acceptability as well. In this regard, the present study seeks to understand how the Open Government Data (OGD) initiatives in Brazil are being confronted with bottlenecks in terms of their execution and acceptability. This exploratory study adopts a qualitative cross‐sectional research approach wherein interviews are being conducted with 11 managers working in public organizations and are directly associated with the OGD initiatives. Findings from the interview responses delineate internal and external factors, resource availability, data maintenance, and lack of knowledge as the key determinants for the bottlenecks associated with the execution and acceptability of OGD initiatives by the societal stakeholders. The study's originality lies in its theoretical contribution towards an understanding of how a novel digital innovation‐OGD, in the present case‐ is fraught with impediments in terms of its execution and acceptability. The study concludes with directions for further research and practitioner implications.
Character Education through Islamic Education: An Implementation to High School Muslim Students in North Minahasa

This study aims to investigate the implementation of the Character Education method through Islamic Education learning for Muslim students at a senior high school in North Minahasa. The method used in this research is the descriptive qualitative method using a case study design. The researchers collected the data through observation and interviews and analyzed the obtained data through content analysis. The results of the study reported that the implementation of character education methods through Islamic Education learning for Muslim students referred to the National education goals listed in the 2013 curriculum and the Decree of the Minister of National Education Number; 54 of 2013 concerning Competency Standards for graduating elementary and secondary education. Character education can be integrated into all subjects. The implementation of character education for Muslim students at the high school in North Minahasa Regency consisted of parents’ involvement, extracurricular activities, habituation methods, exemplary, giving advice, and giving rewards and punishments.
EDITORIAL: RECENT ADVANCES IN SPARSE STATISTICAL MODELING

The first term L(β) is a loss function and the second term λ ∑p j=1 |βj | is a penalty term. Here λ (λ > 0) is a tuning parameter which controls the sparsity and the model fitting. Because the penalty term consists of the sum of absolute values of the parameter, we can carry out the sparse estimation, that is, some of the elements of β are estimated by exactly zeros. It is well-known that we cannot often obtain the analytical solutions of the minimization problem (1), because the penalty term λ ∑p j=1 |βj | is indifferentiable when βj = 0 (j = 1, . . . , p). Therefore, it is important to develop efficient computational algorithms. This special issue includes six interesting papers related to sparse estimation. These papers cover a wide variety of topics, such as statistical modeling, computation, theoretical analysis, and applications. In particular, all of the papers deal with the issue of statistical computation. Kawasaki and Ueki (the first paper of this issue) apply smooth-threshold estimating equations (STEE, Ueki, 2009) to telemarketing success data collected from a Portuguese retail bank. In STEE, the penalty term consists of a quadratic form ∑p j=1 wjβ 2 j instead of ∑p j=1 |βj |, where wj (j = 1, . . . , p) are positive values allowed to be ∞, so that we do not need to implement a computational algorithm that is used in the L1 regularization. Kawano, Hoshina, Shimamura and Konishi (the second paper) propose a model selection criterion for choosing tuning parameters in the Bayesian lasso (Park and Casella, 2008). They use an efficient sparse estimation algorithm in the Bayesian lasso, referred to as the sparse algorithm. Matsui (the third paper) considers the problem of bi-level selection, which allows the selection of groups of variables and individuals simultaneously. The parameter estimation procedure is based on the coordinate descent algorithm, which is known as a remarkably fast algorithm (Friedman et al., 2010). Suzuki (the fourth paper) focuses attention on the alternating direction method of multipliers algorithm (ADMM algorithm, Boyd et al., 2011), which is applicable to various complex penalties such as the overlapping group lasso (Jacob et al., 2009). He reviews a stochastic version of the ADMM algorithm that allows the online learning. Hino and Fujiki (the fifth paper) propose a penalized linear discriminant analysis that adheres to the normal discriminant model. They apply the Majorize-Minimization algorithm (MM algorithm, Hunter and Lange 2004), which is often used to replace a non-convex optimization problem with a reweighted convex optimization
Junction trees constructions in Bayesian networks

Junction trees are used as graphical structures over which propagation will be carried out through a very important property called the ruining intersection property. This paper examines an alternative method for constructing junction trees that are essential for the efficient computations of probabilities in Bayesian networks. The new proposed method converts a sequence of subsets of a Bayesian network into a junction tree, in other words, into a set of cliques that has the running intersection property. The obtained set of cliques and separators coincide with the junction trees obtained by the moralization and triangulation process, but it has the advantage of adapting to any computational task by adding links to the Bayesian network graph.
On Fuzzy BF-Algebras

In this paper the notion of fuzzy BF-algebra and fuzzy topological BF-algebras are introduced. We stated and proved some theorem in fuzzy BF-algebras and level subalgebras. Finally the Foster’s results on homomorphic images and inverse images in fuzzy topological BFalgebras are studied. Mathematics Subject Classification: 06F35, 03G25, 94D05
Computing Simulation of the Generalized Duffing Oscillator Basedon EBM and MHPM

This paper is concerned with analytical approximate solutions, to the generalized Duffing oscillation. Modified Homotopy Perturbation Method (MHPM) and Energy Balance Method (EBM) are applied to solve nonlinear equation and consequently the relationship between the natural frequency and the initial amplitude is obtained in an analytical form. The general solution can be used to yield the relationship between amplitude and frequency in different strengths of nonlinearity. To verify the accuracy of the present approach, illustrative examples are provided and compared with exact solutions. The procedure yields rapid convergence with respect to the exact solution obtained by numerical integration.
Fast ion acceleration in a foil plasma heated by a multi-picosecond high intensity laser

We study the one-dimensional expansion of a thin foil plasma irradiated by a high intensity laser with multi-picosecond (ps) pulse durations by using particle-in-cell simulation. Electrons are found to recirculate around the expanding plasma for many times, which results in stochastic heating leading to increase of the electron temperature in the multi-ps time scale beyond the ponderomotive scaling. The conventional isothermal model cannot describe such an expansion of plasmas in the long time scale. We here developed a non-isothermal plasma expansion theory that takes the time dependence of electron temperature into account for describing the multi-ps interactions in one-dimensional geometry. By assuming that the time scale of electron temperature evolution is slow compared with the plasma expansion time scale, we derived a non-self-similar solution. The time evolution of ion maximum energy obtained by the non-isothermal theory explains the details of that observed in the simulation.
Enhancing Hydrophobicity and Oxygen Barrier of Xylan/PVOH Composite Film by 1,2,3,4-Butane Tetracarboxylic Acid Crosslinking

Hemicellulose has potential advantages in food packaging because of its abundant reserves, degradability and regeneration. However, compared with fossil-derived plastic films, hemicellulose-based films show inferior hydrophobicity and barrier properties because of their low degree of polymerization and strong hydrophilicity. Focusing on such issues, this work covers the modification of a xylan/polyvinyl alcohol (PVOH) film using 1,2,3,4-butane tetracarboxylic acid (BTCA) as esterifying agent. The thus prepared composite film was more compact owing to the esterification reaction with xylan and PVOH forming a crosslinked network structure and reducing the distance between molecular chains. The results showed that BTCA had a positive effect on the oxygen barrier, hydrophobicity and mechanical properties of the composite film. The tensile strength of the xylan/PVOH composite film with 10% BTCA content increased from 11.19 MPa to 13.99 MPa. A 20% BTCA loading resulted in an increase in the contact angle of the composite film from 87.1° to 108.2°, and a decrease in the oxygen permeability from 2.11 to 0.43 (cm3·µm)/(m2·d·kPa), corresponding to increase in the contact angle by 24% and a decrease in oxygen permeability by 80%. The overall performance enhancement indicates the potential application of such composites as food packaging.
The rise of the mosque as a reflection of the religious metamorphosis of residential residents

A mosque is an efficient tool for inhabitants to engage in diverse religious activities, contributing to its prosperity, filling it with diverse religious activities, and fostering Islamic brotherhood ties between locals. This study explores the religious transformation of urban populations in the East Pamulang neighborhood of South Tangerang. The purpose of this study is to determine the tendency of solid religiosity of residents in four mosques in housing complexes, including Bukit Pamulang Indah complex (Al-Muhajirin mosque), Pamulang Indah complex (Darussalam mosque), Pamulang Estate complex (AtTaqwa mosque), and Vila Inti Persada complex (Inti Iman mosque). This research uses a qualitative method with a phenomenological perspective, showing an exciting interaction between the Mosque Prosperity Council and the community, which have diverse understandings of religion, ethnicity, culture, and regional origin. The excellent relationship between the Mosque Prosperity Council and the locals has increased the religiosity of Muslims in East Pamulang. For locals, a mosque is a place of worship and a space to create unity and improve family ties, encouraging East Pamulang residents to become dependent on the mosque, emotionally attached to it, and devoted to its growth
CGAN-MBL for Reliability Assessment With Imbalanced Transmission Gear Data

In term of data-driven techniques for reliability assessment of transmission gears, it is necessary to collect sufficient data of each class in practice. However, the fact that highest and lowest reliability events rarely occur would result in a class imbalance problem. To resolve such an issue, we propose conditional generative adversarial network-mean-covariance balancing labeling (CGAN-MBL) to improve reliability assessment of transmission gears with insufficient and imbalanced data. First, we establish a CGAN-based model to generate credible instances with a similar distribution of original data. In the model, it is proven that by introducing a network weight initialization scheme, the ability to escape from local optimum is enhanced and global search ability of CGAN is strengthened; minibatch discriminator is designed to decrease mode collapse by distinguishing a batch of original and generated data iteratively. Second, in order to overcome the drawback of CGAN with unlabeled generated data, MBL is introduced to tag the generated data through exploring the nearest distance between generated instances and original class centers. Finally, synthetic data combined generated data with original ones train classifiers. Simulations show the effectiveness of the proposed approach for imbalance learning in real-world transmission gear data.