Miracle-dz/UniFAR-FTUs huggingface dataset:
SMART IMPLEMENTATION IN DETERMINING EXAMPLE EMPLOYEES

: PDAM Tirta Silaupiasa is currently the only Regional Owned Enterprise in Asahan Regency; this requires PDAM Tirta Silaupiasa to continue to make improvements to realize optimal service to customers. The services provided to customers are closely related to human re-sources and employee performance. Employee performance has a very important role in the company's success so that we can see the strengths, weaknesses, and potential managerial gaps in the organization. The problem faced by PDAM Tirta Silaupiasa is the lack of motivation ex-perienced by employees. One of the efforts to increase employee motivation is to select role models/exemplary employees who will later become examples for other employees. Based on this, it is necessary to use technology, namely the design of a decision support system in determining exemplary employees by applying the Simple Multi-Attribute Rating Technique (SMART) method. The SMART method has advantages, namely in deciding with many alternatives and criteria. It is more complex to make a decision, and the decision results are better. The results of this study get Alfiansyah's alternative with a value of 74.85202777 to be a role model for PDAM Tirta Silaupiasa. Badan Usaha Milik Daerah yang ada di kabupaten Asahan, hal ini menuntut PDAM Tirta Silaupiasa untuk terus melakukan pembenahan guna mewujudkan pelayanan yang optimal kepada pelanggan. Pelayanan yang diberikan kepada pelanggan tentunya erat kaitannya dengan sumber daya manusia dan kinerja karyawan. Kinerja karyawan memiliki peran yang sangat penting bagi keberhasilan perusahaan sehingga dapat dilihat kekuatan, kelemahan, dan potensi kesenjangan manajerial dalam organisasi. Permasalahan yang dihadapi PDAM Tirta Silaupiasa adalah kurangnya motivasi yang dialami oleh pegawai. Salah satu upaya untuk meningkatkan motivasi pegawai adalah dengan memilih role model/pegawai teladan yang nantinya akan menjadi contoh bagi pegawai lainnya. Berdasarkan hal tersebut maka perlu digunakan teknologi yaitu perancangan sistem pendukung keputusan dalam menentukan pegawai teladan dengan menerapkan metode Simple Multi Attribute Rating Technique (SMART). Metode SMART memiliki kelebihan yaitu dalam keadaan pengambilan keputusan dengan banyak alternatif dan kriteria sehingga lebih kompleks dalam pengambilan keputusan akurasi hasil keputusan yang lebih baik. Hasil penelitian ini mendapatkan alternatif Alfiansyah dengan nilai 74,85202777 untuk menjadi role model bagi PDAM Tirta Silaupiasa.
What determines takeover likelihood? A review and propositions for future research

Prior takeover prediction research has advanced eight hypotheses to explain why specific firms are targeted through takeovers (Palepu, 1986; Powell, 2001; Tunyi, 2021a). However, takeover targets remain difficult to empirically predict ex-ante, perhaps because these established sets of hypotheses do not substantially explain takeover likelihood (Danbolt, Siganos, & Tunyi, 2016). This paper reviews the literature on takeover prediction, particularly focusing on theory, propositions and testable hypotheses on the factors that drive firms’ takeover likelihood. Drawing from prior research outside this literature, the paper then develops conceptual arguments underlying six new predictors of firms’ takeover likelihood including; information asymmetry, mergers and acquisitions (M&A) rumours, financial distress, payroll synergies, share repurchases and industry competition. Specifically, we predict that a firm’s likelihood of receiving future takeover bids increases with merger rumours and industry competition and declines with information asymmetry and share repurchases. Additionally, takeover likelihood plausibly has an inverse U-shaped relationship with payroll excesses and the level of financial distress.
Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning

Hybrid maize seed production is a relatively complex task due to the coexistence of three distinct types of maize plants in the field: female, male, and contaminant/off-type plants. Female and contaminant/off-type plants’ tassels should be removed immediately following flowering initiation, while male tassels should be retained to allow cross-pollination between male and female plants. Therefore, development of an intelligent tassel classification system is deemed critical for hybrid purity decision-making. The research’s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. Tenfold cross-validation was used to evaluate the model performance. Cloud computing was also investigated using EfficientNet to compare the predictive performance of the models. The models’ performance was assessed using four metrics: accuracy, AUC, precision, and recall. The results depicted an appropriate developed model that properly distinguished male, female, and contaminant plants. The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. Among all the evaluated CNN architecture and stacking models, Inception V3-embedded images with logistic regression metaclassifier outperformed other models with accuracy of about 98%. SqueezeNet and EfficientNet provided comparable results for consistent tassel classification with slightly lower performance measures. The model was also subjected to a multidimensional scaling (MDS) analysis to investigate and comprehend misclassification. Male and female plants are clearly distinguished by MDS, but female and off-type/contamination plants are ambiguous. This indicates that the prediction errors were caused by highly similar data features among female and off-type images. The developed modern plant phenotyping model can be used to assist breeders/technicians in maintaining the quality of large-scale hybrid maize seed production activities in Indonesia.
SIMULATION OF 9 LEVEL MLI USING POD-SPWM

: Multilevel inverter is usually utilized in high voltage and high-power applications because of their lower filtering requirement smaller dv/dt changes medium voltage. But, as the output voltage level increase, the number of using the semiconductor device is increased. The demerit of conventional MLIs is that it requires a greater number of components which in turn increase the complexity of gate pulse generation. Therefore, the cost of MLI will increase. Conventional MLI topologies are classified into DCMLI (Diode Clamped Multilevel Inverter), Flying Capacitor, Cascaded h-bridge MLI (CHBMLI). In this paper we have used hybrid cascaded h-bridge MLI. It has more advantages over other topologies. It is proposed with a minimum number of switching devices like diodes, dc source etc. cost and space required is considerably reduced with increase in no of steps in output voltage. This paper includes the design and simulation of hybrid Cascaded h-bridge multi-level inverter using POD SPWM technique with R and RL load as shown in the paper “Genetic algorithm based 15 level multilevel inverter with Selective Harmonic Elimination technique of PWM”. Int. J. Eng. Tech no l, 7, 893-897. This paper proposes a nine-level balanced inverter with decreased number of switches and components. This proposed technique actualizes by utilizing MATLAB/Simulink. We know that MLI based topologies are expected to do a significant work for both high power and high or medium voltage applications. The proposed staggered inverter topology here accomplishes a high yield voltage waveform. The firm full scale complete consonant mutilation (THD) estimator is used to survey the possibility of the inverter yield voltage, which is the intensity of the improvement. Thus, the proposed framework here accomplishes the reasonableness of the nonappearance of channel with the lesser THD of diversion and exploratory outcomes. H-Bridge Multi the given Induction Motor Drive Reduced Switches”.
Cytotoxic Evaluation, Molecular Docking, and 2D-QSAR Studies of Dihydropyrimidinone Derivatives as Potential Anticancer Agents

The diverse pharmacological role of dihydropyrimidinone scaffold has made it to be an interesting drug target. Because of the high incidence and mortality rate of breast cancer, there is a dire need of discovering new pharmacotherapeutic agents in managing this disease. A series of twenty-two derivatives of 6-(chloromethyl)-4-(4-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (3a-3k) and ethyl 6-(chloromethyl)-4-(2-hydroxyphenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (4a-4k) synthesized in a previous study were evaluated for their anticancer potential against breast cancer cell line. Molecular docking studies were performed to analyze the binding mode and interaction pattern of these compounds against nine breast cancer target proteins. The in vitro cell proliferation assay was performed against the breast cancer cell line MCF-7. The structure activity relationship of these compounds was further studied using QSARINS. Among nine proteins, the docking analysis revealed efficient binding of compounds 4f, 4e, 3e, 4g, and 4h against all target proteins. The in vitro cytotoxic assay revealed significant anticancer activity of compound 4f having IC50 of 2.15 μM. The compounds 4e, 3e, 4g, and 4h also showed anticancer activities with IC50 of 2.401, 2.41, 2.47 and 2.33 μM, respectively. The standard tamoxifen showed IC50 1.88 μM. The 2D qualitative structure-activity relationship (QSAR) analysis was also carried out to identify potential breast cancer targets through QSARINS. The final QSAR equation revealed good predictivity and statistical validation R2 and Q2 values for the model obtained from QSARINS was 0.98 and 0.97, respectively. The active compounds showed very good anticancer activities, and the binding analysis has revealed stable hydrogen bonding of these compounds with the target proteins. Moreover, the QSAR analysis has predicted useful information on the structural requirement of these compounds as anticancer agents with the importance of topological and autocorrelated descriptors in effecting the cancer activities.
A novel scheme for control by active and reactive power utilized in gearless variable speed wind turbine system with PMSG connected to the grid

Introduction. As a result of increasing fossil fuel price and state-of-the-art technology, more and more residential and commercial consumers of electricity have been installing wind turbines. The motivation being to cut energy bills and carbon dioxide emissions. Purpose. The main goal of this work is developing a control scheme for a variable speed wind turbine generator in order to produce utmost power from varying wind types, and variable wind speed. Novelty. This research paper presents an IGBT power converter control scheme for active power in relation to wind speed and reactive power by adjusting Q-reference (Qref) value in a gearless variable speed wind turbine with permanent magnet synchronous generator. Methods. An effective modelling and control of the wind turbine with the suggested power converter is executed by utilizing MATLAB/Simulink software. The control scheme consists of both the wind turbine control and the power converter control. Simulation results are utilized in the analysis and deliberation of the ability of the control scheme, which reveals that the wind turbine generator has the capability to actively sustain an electric power grid network, owing to its ability to independently control active and reactive power according to applied reference values at variable wind speed. Practical value. This research can be utilized for assessing the control methodology, the dynamic capabilities and influence of a gearless variable-speed wind energy conversion system on electric power grids. A case study has been presented with a (3×10 MW = 30 MW) wind farm scheme.
Does sectoral loan portfolio composition matter for the monetary policy transmission?

Purpose ─ The paper empirically explores the conditioning role of loan portfolio diversification in the monetary policy pass-through via the bank lending and risk-taking channels. Methods ─ Data of Vietnamese commercial banks during 2007–2019 is employed to perform regression using the two-step system generalized method of moments in dynamic panel models. For robustness, we approach different choices of monetary policy indicators, ranging from interest-based tools to quantitative-based policy, and consider a rich set of sectoral exposure measures to proxy loan portfolio diversification. Findings ─ Lower interest rates or greater liquidity injection during monetary expansion may increase bank lending and bank risk, thus confirming the working of the bank lending and risk-taking channels of monetary policy transmission. Notably, the potency of these banking channels may be weakened for banks diversifying loan portfolios more into various economic sectors. Implication ─ The findings call for monetary authorities to concentrate on certain types of banks, depending on their loan portfolios when setting monetary policy. When managing banking supervision, banking supervisors should also acknowledge the tradeoff between bank lending and bank risk in response to monetary shocks. Originality ─ For the first time, this paper explores the conditional role of loan portfolio composition and thus further supports the recent upsurge in empirical studies highlighting the role of business models in monetary policy pass-through.
Functional Analysis of Bronchopulmonary Dysplasia-Related Neuropeptides in Preterm Infants and miRNA-Based Diagnostic Model Construction

Background Bronchopulmonary dysplasia (BPD) has a high mortality rate. This study was aimed at identifying and analysing the risk factors associated with BPD using bioinformatic and mechanical analyses and establishing a predictive model to assess the risk of BPD in preterm infants. Methods We identified differentially expressed RNAs via the intersection of miRNAs between datasets. Online analysis tools were used to predict genes targeted by differentially expressed miRNAs (DEmiRNAs) and to generate and visualise competing endogenous RNA (ceRNA) coexpression networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed on the DEmiRNAs. In addition, an intersection analysis was performed on mRNA and neuropeptide-related genes in the ceRNA network. DEmiRNAs associated with BPD and those involved in ceRNA networks were used to establish a diagnostic prediction model. The GSE108604 dataset was used as a validation set to verify the model. Results A total of 26 DEmiRNAs were identified from the tracheal aspirates (TAs) of patients with BPD and healthy controls. In addition, a total of 1076 DEmRNAs were obtained from the GSE8586 dataset. Functional enrichment analysis of DEmRNAs revealed an abnormal reduction in mitochondrial-related activity and cellular responses to oxidative stress in patients with BPD. The neuropeptide-related genes OPRL1 and NPPA were found to be upregulated in BPD samples. Eventually, hsa-miR-1258, hsa-miR-298, hsa-miR-483-3p, and hsa-miR-769-5p were screened out and used to establish the prediction model. Calibration curves and detrended correspondence analysis (DCA) revealed that the model had good clinical applicability. Conclusions The prediction model provided a simple method for individualised assessment, early diagnosis, and prevention of BPD risk in preterm infants.
An Alternative Therapeutic Strategy for Root Canal Disinfection

Aim of the study : This study was performed to evaluate the antibacterial effectiveness of Boswellic acids (BA) as root canal irrigation solution with Sodium hypochlorite, Chlorhexidine. Materials and Methods: forty five patients having single rooted teeth with single canal diagnosed as necrotic pulps with chronic apical periodontitis were included in the study. Bacterial samples were taken from the root canal before preparation (S1) , Post instrumentation sample S2 after using the tested irrigants. All samples collected were transferred directly for microbiological analysis, and cultured on blood agar plates in aerobic and anaerobic conditions, and the bacterial growth was counted as colony forming units (CFUs) using manual counting technique. The antibacterial effectiveness of the tested materials was evaluated by the decrease in the CFUs from S1 to S2. Results: NaOCl, CHX, and BA solutions showed significant reduction in the bacterial count from S1 to S2 (P < 0.05) with no significant difference between them P=0.136. Cleaning and shaping resulted in > 99.5% decrease in the count of bacteria from S1 to S2 samples. Conclusions: BA could be considered as a promising root canal irrigant owing to its comparable antibacterial effect with the most commonly used root canal irrigants (NaOCl, CHX). BA gel reduced the possibility of post-operative pain compared to the other medicaments.
TwitterBulletin: An Intelligent and Real-Time Automated News Categorization Tool for Twitter

Social media platforms have become popular news sources thanks to their immense popularity and high speed of information dissemination. Using these platforms is essential for news organizations and journalists to track and discover news in digital journalism age. However, the abundance of meaningless data and the lack of organization on these platforms make it difficult to reach valuable news for journalists. In this paper, we create the first public dataset containing large number of real-world Turkish news tweets belonging to different news categories, to the best of our knowledge. We propose an artificial intelligence-based two-step approach to assist journalists for accessing the news shared by various sources on social media under the relevant categories like politics (elections, riots, etc.), health (pandemic, covid-19, etc.), etc. via a single platform by reducing the possibility of overlooking needed information. In the first step, we propose a machine learning based novel model for collecting and categorizing news posts on social media. We implement several traditional machine learning and deep learning based algorithms and evaluate their classification performance in terms of accuracy, precision, recall, and F1 score. In the second step, we develop a software tool, named TwitterBulletin, which automatically retrieves Turkish news tweets and groups them under news categories in real time by using the CNN classifier which achieves the best performance in the first step. The results show that the overall accuracy rate of TwitterBulletin is reasonably high and satisfactory despite the challenge of classifying short tweets.
Patient-Nurse Partnerships to Prevent Medication Errors: A Concept Development Using the Hybrid Method

Medication safety is the most patient-centered aspect of nursing, and the medication process needs patients’ active participation to effectively prevent medication errors. The aim of this study was to develop the concept of a patient–nurse partnership for medication safety activities. The study design used the three-phase hybrid model for concept analysis: the theoretical phase, fieldwork phase, and final phase for integration. The results of a study define the concept of patient–nurse partnership for medication safety as “a fair cooperative relationship of mutual responsibility in which patients and nurses share information and communicate with each other through mutual trust. ” Seven attributes were derived: mutual trust, mutual respect, mutual sharing, mutual communication, mutual responsibility, fair relationship, and mutual cooperation. The conclusion of the study of patient–nurse partnerships for medication safety was that it is necessary to ensure a balance in power between patient and nurse. This balance can be established through patient-centered nursing by implementing the active transfer of authority from nurses as professionals to patients.
The Effect of Endurance and Endurance-Strength Training on Bone Health and Body Composition in Centrally Obese Women—A Randomised Pilot Trial

There is no consensus exercise programme to reduce body weight and improve body composition simultaneously preventing bone loss or stimulating osteogenesis. This pilot study compared the effect of endurance and endurance-strength training on body composition and bone metabolism in centrally obese women. Recruited subjects were randomly assigned to three-month endurance (n = 22) or endurance-strength training (n = 22). Body composition, bone mineral density (BMD) and content (BMC) were assessed before and after the intervention and markers of bone formation and resorption were measured. Both training significantly decreased fat mass; however, endurance-strength training had a more favourable effect on lean mass for the gynoid area (p = 0.0211) and legs (p = 0.0381). Endurance training significantly decreased total body BMC and BMD (p = 0.0440 and p = 0.0300), whereas endurance-strength training only reduced BMD (p = 0.0063). Changes in densitometric parameters did not differ between the groups but endurance training increased osteocalcin levels (p = 0.04845), while endurance-strength training increased tartrate-resistant acid phosphatase 5b concentrations (p = 0.00145). In conclusion, both training programmes were effective in the reduction of fat mass simultaneously negatively affecting bone health. However, endurance-strength training seemed to be more effective in increasing lean mass. The study protocol was registered in the ClinicalTrials.gov database under the number NCT03444207, date of registration: 23 February 2018 (retrospective registration).
What Breeds Conspiracy Theories in COVID-19? The Role of Risk Perception in the Belief in COVID-19 Conspiracy

Conspiracy theories often emerge during public health crises, and can provide some explanation for the causes behind the crises. However, the prevalence of conspiracy theories also poses a serious threat to public health order and hinders the implementation of disease prevention and control measures. No studies have examined the role of multiple risk perceptions in the formation of beliefs in conspiracy theories from a cognitive perspective in the context of the epidemic. In this cross-sectional study, participants filled in an online survey in order to investigate the relationship between epidemic severity and beliefs in conspiracy theories and the mediating role of risk perception in this relationship. The results showed that COVID-19 epidemic severity positively predicted beliefs in both in- and out-group conspiracy theories. Risk perception mediated the positive relationship between COVID-19 epidemic severity and belief in in-group conspiracy theories. These results suggest that in a major public health crisis event: (1) residents at the epicenter may be more prone to believing in both in- and out-group conspiracy theories; and (2) beliefs in in- and out-group conspiracy theories may have different psychological mechanisms. Therefore, conspiracy theories about public health incidents, such as the COVID-19 pandemic, should be classified and treated by policy stakeholders.