Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilayer perceptron modeling for social dysfunction prediction based on general health factors in an Iranian women sample

In the year 2022, this research conducted an in-person study involving 780 single or widowed women, aged between 20 and 70, falling within the bottom three economic deciles and possessing varying levels of education. All participants held educational qualifications below a high school diploma and were beneficiaries of charitable financial support in Khorasan province, Iran. The study aimed to investigate the predictive factors of social dysfunction in this specific demographic. Data collection spanned a 12-month period throughout 2022, with participants completing the GHQ-28 questionnaire during their visits to the charity office. Clinical in-person interviews were also conducted to gather comprehensive data. Data analysis was carried out using IBM SPSS version 27. The research employed a Multilayer Perceptron (MLP) neural network model, considering an extensive set of input factors and covariates. These factors included cognitive functioning, anxiety, depression, age, and education levels. The MLP model exhibited robust performance, achieving high overall accuracy and sensitivity in identifying cases of high social dysfunction. The findings emphasized the significance of cognitive functioning, anxiety, and depression as pivotal predictors of social dysfunction within this specific demographic, while education and age displayed relatively lower importance. The normalized importance scores provided a relative measure of each covariate’s impact on the model’s predictions. These results furnish valuable insights for the development of targeted interventions and evidence-based policies aimed at addressing social dysfunction and promoting societal well-being among economically disadvantaged, single or widowed women. Notably, the research underscores the potential of MLP modeling in social science research and suggests avenues for further research and refinement to enhance the model’s predictive accuracy, particularly for cases of low social dysfunction.
Evaluating evidence from the Torridonian Supergroup (Scotland, UK) for eukaryotic life on land in the Proterozoic

Abstract The Stoer, Sleat and Torridon groups lie unconformably on Palaeoproterozoic Lewisian metamorphic rocks. They contain organic carbon microfossils claimed to be non-marine and to include eukaryotes. We consider the evidence for terrestrial interpretations from each formation of the Torridonian Supergroup. The range of sedimentary structures and the boron content of illite led us to the overall conclusion that, based on the currently available evidence, the Torridonian Supergroup was probably entirely non-marine. Evidence for terrestrial life in these rocks comes from microbially induced sedimentary structures, including wrinkle structures with reticulate and elephant skin fabrics. Organic remains and microscopic carbonaceous compressions mostly reported from phosphates in the grey shales of the Stoer, Aultbea and Applecross formations are dominated by sphaeromorph acritarchs. The Diabaig phosphatic lagerstätte includes three-dimensional preservation of eukaryotic and prokaryotic organisms, providing remarkable insights into non-marine life around 1 billion years ago. Supplementary material: Taxonomy of Torridon Group microfossils from thin sections of phosphatic material (adapted from Battison 2012) is available at https://doi.org/10.6084/m9.figshare. c.3522753
Development Model of Brick Business Competitiveness

This research departs from the fact that MSMEs are the pillars of economic growth to date, but their business growth is hampered by problems of weak control of strategic resources and limited market access. The general aim of this research is to examine the development of a brick business competitiveness model. The research method uses a quantitative approach with an emphasis on explanatory research. The sample units are brick businesses located in Limbung Village, Bajeng District, Gowa Regency, totaling 65 businesses. Data collection used a survey with a questionnaire instrument using a Likert scale measurement. Respondents are asked to provide answers to closed statements. The respondents are brick business owners. The data analysis method in this research uses descriptive statistics to describe research variables and inferential statistics through PLS (Partial Least Square) analysis version of the WarpPLS 4.0 program. The results of the research analysis will be supported and deepened using a qualitative approach through in-depth interviews with several key informants and stakeholders.
Using Multiplex Polymerase Chain Reaction (m-PCR) Methods to detect six Pathogenic Bacteria in Human Oral Cavity

According to estimates, the human oral cavity can harbour 700–1000 different species of bacteria, which are associated with periodontal and dental problems such as dental caries. Therefore, bacterial infections in the oral cavity may lead to significant health consequences, therefore early and correct identification is essential for effective treatment. This study has tried to create a multiplex PCR (m-PCR) procedure to detect six pathogenic bacteria frequently found in oral cavity infections: Streptococcus oralis, Streptococcus mutans , Porphyromonas gingivalis , Fusobacterium nucleatum , and Veillonella parvula . To confirm the sensitivity of the m-PCR assay, the assay was able to detect as little as 250 pg/μL of genomic DNA from each target bacterium, demonstrating high sensitivity. In conclusion, we have developed a rapid and reliable m-PCR assay for the simultaneous detection of six pathogenic bacteria frequently found in oral cavity infections. This assay has the potential to be a valuable tool for the early and accurate diagnosis of oral cavity bacterial infections, leading to more effective treatment and improved patient outcomes.
Wetland Remote Sensing Classification Using Support Vector Machine Optimized With Genetic Algorithm: A Case Study in Honghe Nature National Reserve

Wetland remote sensing classification,as an important means of wetland management,monitoring and assessment,has been widely concerned.Genetic Algorithm(GA) does heuristic search optimization which references the law of biological evolution,while Support Vector Machine(SVM) is a new kind of spatial data mining method.Combination of both can develop their own advantages to do wetland remote sensing classification exactly,by searching the global optimal parameters of Support Vector Machine.Taking Honghe Nature Reserve as a case study,wetland remote sensing classification using Support Vector Machine optimized with Genetic Algorithm(GA-SVM) was explored in this paper.In comparison with wetland classification using support vector machine with parameters searched by Grid and the Maximum Likelihood Classification.The experimental results show that,the overall accuracy of Genetic Algorithm optimization has increased 7.29% compared to Grid Search method,and has increased 12.06% compared to the Maximum Likelihood Classification,by improving the discrimination among marsh,meadow and bare land.Therefore,GA-SVM is an effective tool in wetland remote sensing classification.
Empirical Models of Pigweed (Amaranthus spp.) Interference in Soybean (Glycine max)

Three empirical crop yield loss models were used to describe the interference of redroot pigweed and Powell amaranth populations with soybean. Data were obtained from field experiments conducted in 1992 and 1993. Pigweed densities of 0 to eight plants m−1 were established within the soybean row. Pigweed sowing dates were selected so that weed seedling emergence coincided with VE, VC, and V2 soybean growth stages within the time frame of the critical weed-free period. The model incorporating pigweed density and time of emergence gave the best description of soybean yield loss in comparison to the two relative leaf area models. This model was fit to a combined data set of percent yield loss because parameter estimates did not differ among locations and years. Estimated soybean yield losses decreased from 16.4 to 0.5% with delayed pigweed emergence from 0 to 20 degree days. Leaf area of pigweed relative to soybean encompassed pigweed density and time of emergence. Relationship between relative leaf area and soybean yield loss was best described by the one-parameter model estimating a relative damage coefficient ‘q’ than the two-parameter model that also estimated maximum expected yield loss. The relative damage coefficient ‘q’ decreased with later times of leaf area assessment but could be predicted with one leaf area observation. Empirical models that incorporate time of weed emergence represent a step toward improving predictions of yield loss. This is important for the selection of cost-effective weed control strategies.
Coronary Microvascular Dysfunction and Hypertension: A Bond More Important than We Think

Coronary microvascular dysfunction (CMD) is a clinical entity linked with various risk factors that significantly affect cardiac morbidity and mortality. Hypertension, one of the most important, causes both functional and structural alterations in the microvasculature, promoting the occurrence and progression of microvascular angina. Endothelial dysfunction and capillary rarefaction play the most significant role in the development of CMD among patients with hypertension. CMD is also related to several hypertension-induced morphological and functional changes in the myocardium in the subclinical and early clinical stages, including left ventricular hypertrophy, interstitial myocardial fibrosis, and diastolic dysfunction. This indicates the fact that CMD, especially if associated with hypertension, is a subclinical marker of end-organ damage and heart failure, particularly that with preserved ejection fraction. This is why it is important to search for microvascular angina in every patient with hypertension and chest pain not associated with obstructive coronary artery disease. Several highly sensitive and specific non-invasive and invasive diagnostic modalities have been developed to evaluate the presence and severity of CMD and also to investigate and guide the treatment of additional complications that can affect further prognosis. This comprehensive review provides insight into the main pathophysiological mechanisms of CMD in hypertensive patients, offering an integrated diagnostic approach as well as an overview of currently available therapeutical modalities.
The Role of Emotional Intelligence in the Rehabilitation of the Former Prisoners of War

The emotional state of military personnel engaged in the extensive operations taking place in Ukraine is undoubtedly impacted. This especially applies to former prisoners of war (POWs) who have experienced mental and physical trauma. Optimizing their subjective well-being and life satisfaction can contribute to the development of emotional intelligence during the rehabilitation process. The purpose of the study is to identify the importance of emotional intelligence in enhancing the subjective well-being of former POWs. Methods. The following psychometric tests were used for diagnosis: EQ-Test, the Scale of subjective well-being, and Satisfaction with life scale (SWLS). During the statistical analysis, descriptive statistics and correlation analysis were utilized. Results. The study found that the subjects have low emotional intelligence (M=39.31, SD=12.85), low subjective well-being (M=50.19, SD=11.06), and an average level of life satisfaction (M= 17.05, SD=8.96). Correlation analysis established a direct relationship between emotional intelligence and subjective well-being (r=0.483, p≤0.01) and life satisfaction (r=0.723, p≤0.01). Conclusions. The study statistically confirmed that emotional intelligence is an essential factor in the process of rehabilitation of former prisoners of war, as it contributes to their attainment of subjective well-being and life satisfaction. This aids in reinstating the individual's psychological balance after captivity, enhances the medical and physical recuperation process of the body, and ensures full reintegration. Prospects. The obtained results contribute to the rehabilitation system of rehabilitation for former prisoners of war, as they elaborate a comprehensive emotional intelligence approach to ensure their subjective well-being and life satisfaction.
The new system of Kakioka automatic standard magnetometer

The part oC variation measurement oC Kakioka Automatic Standard Magnetometer (KASMMER) was replaced as a Cour year program Crom 1989 to 1992. The new instrument is a set of a high sensitivity fluxgate magnetometer and four Overhauser magnetometers with three Fanselau・BraunbekCoils. The Cormer is Cor getting high resolution one second values and the latter Cor stable base lines. After test observations Cor about one year. the sensors oC optical pumping magnetometers which have been used these 20 years were replaced with those of Overhauser magnetometers and the routine observation by the new instrument was started at April. 1993. The computer system which had been two sets oC stand-alone mini computers was also replaced with the new system composed oC seven UNIX work stations connected by ethernet cables. In this article. we introduce the design of the Dew system of KASMMER and report the brieC observational results.
Supervised Sentiment Analysis of Indirect Qualitative Student Feedback for Unbiased Opinion Mining

: In the education domain, the signiﬁcance of student feedback and other stakeholders for raising educational standards has received more attention in recent years. As a result, numerous instruments and strategies for obtaining student input and assessing faculty performance, as well as other facets of education, have been developed. There are two main methods to collect feedback from students, as follows: the direct and indirect methods. In the direct method, feedback is collected by distributing a questionnaire and taking their responses. The limitation of this method is that the true experience of students is not revealed, and there is room for bias in the collection and assessment of such a questionnaire. To overcome this limitation, the indirect method can be followed where social media posts can be used to collect feedback from students as they are active on social media and use it to express their opinions as posts. To address the problem of the manual annotation of large volumes of data, this paper proposes a machine learning method that uses the sentiment 140 dataset as the training set to automate the process of annotations of tweets. The same method can be used to label any qualitative data. In total, 5000 tweets were scraped and considered for this study. Various pre-processing methods, including byte-order-mark removal, hashtag removal, stop word removal, and tokenization, were applied to the data. The term frequency-inverse document frequency (TF-IDF) trigrams technique was then used to process the cleaned data. The TF-IDF technique using trigrams captures negation for sentiment analysis. The vectorized data are then processed using various machine learning algorithms to classify the polarity of tweets. Performance parameters such as the F1-score, recall, accuracy, and precision are compared. With a 94.16% F1-score, 94% precision, 94% recall, and 95.16% accuracy, the Ridge Classiﬁer performed better than the others.
Glycosyl Thiourea: Synthesis, Cyclization, Reaction, Molecular Docking, and Evaluation as Potential Acetylcholinesterase Inhibitors

In this research, we successfully synthesized a distinctive group of iminothiazolidinone derivatives, using glucose isothiocyanate as the starting material. The structural elucidation of these newly created compounds was achieved through a combination of analytical techniques, including IR, 1 H NMR, and 13 C NMR. We then evaluated the inhibitory activity of these compounds against acetylcholinesterase (AChE) using the Ellman's method spectrophotometer, comparing their performance to standard drugs like donepezil, rivastigmine, and tacrine. Impressively, the majority of the tested compounds demonstrated inhibitory activity against AChE, with iminothiazolidinone derivative (5a) standing out as the most potent (IC 50 = 0.209 μ g/mL). It even surpassed the effectiveness of rivastigmine and tacrine, coming close to the potency of donepezil. Further investigation into the potential of these compounds as AChE inhibitors for Alzheimer's disease drug development involved docking simulations using Molecular Operating Environment (MOE). Derivatives 3,5-disubstituted-(2,3,4,6-tetra-o-acetyl-β -D- glucopyranosyl) imino) thiazolidin-4-ones (5a), (5f), (6c) and 3,5-disubstituted-( β -D-glucopyranosyl)imino)thiazolidin-4-one (6d) displayed promising docking scores in MOE simulations. In Silico ADMET experiments assessed their pharmacokinetic and toxicity studies, demonstrating strong binding affinity and favorable interactions with the target protein. Pharmacophore models confirmed their potential as selective enzyme inhibitors through 3D virtual screening.
Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis

Background and aims It is important to diagnose cerebral infarction at an early stage and select an appropriate treatment method. The number of stroke-trained physicians is unevenly distributed; thus, a shortage of specialists is a major problem in some regions. In this retrospective design study, we tested whether an artificial intelligence (AI) we built using computer-aided detection/diagnosis may help medical physicians to classify stroke for the appropriate treatment. Methods To build the Stroke Classification and Treatment Support System AI, the clinical data of 231 hospitalized patients with ischemic stroke from January 2016 to December 2017 were used for training the AI. To verify the diagnostic accuracy, 151 patients who were admitted for stroke between January 2018 and December 2018 were also enrolled. Results By utilizing multimodal data, such as DWI and ADC map images, as well as patient examination data, we were able to construct an AI that can explain the analysis results with a small amount of training data. Furthermore, the AI was able to classify with high accuracy (Cohort 1, evaluation data 88.7%; Cohort 2, validation data 86.1%). Conclusion In recent years, the treatment options for cerebral infarction have increased in number and complexity, making it even more important to provide appropriate treatment according to the initial diagnosis. This system could be used for initial treatment to automatically diagnose and classify strokes in hospitals where stroke-trained physicians are not available and improve the prognosis of cerebral infarction.
Development of watershed hydrologic research at Santee Experimental Forest, coastal South Carolina

Managing forested wetland landscapes for water quality improvement and productivity requires a detailed understanding of functional linkages between ecohydrological processes and management practices. Watershed studies are being conducted at USDA Forest Service Santee Experimental Forest, South Carolina, to understand the fundamental hydrologic and biogeochemical processes and their linkages with soils, vegetation, topography, climate, and management practices in the low gradient forested landscapes of the South Carolina Coastal Plain. This study presents an overview of and builds on the long-term watershed hydrologic research begun in 1964 by our predecessors at this experimental forest. Monitoring and modeling studies using a paired watershed approach are being conducted to describe the effects of management practices on two first-order, forested watersheds. Long-term flow data from two 160-ha first-order, one 500-ha second-order, and one 4,500-ha third-order watersheds, provide an opportunity to evaluate the flow dynamics and hydrologic effects of scale, land use distribution, and climate on these coastal watersheds. DRAINMOD-based models are being tested with these data for their applicability as a water management tool on these poorly drained natural forested watersheds. The long-term information on hydro-meteorology, water quality and water table levels from these watersheds has also provided baseline data on the ecohydrologic processes that are useful to researchers, planners, land owners and industries for the assessment of land management and climatic impacts. Such information for poorly drained low gradient coastal watersheds is becoming increasingly important for sustainable development as population pressure and timber demand continue to rise in the southeastern United States.
Millimeter-Wave Massive MU-MIMO Performance Analysis for Private Underground Mine Communications

In this article, a performance analysis of millimeter wave (mmWave) massive multiuser multiple-input and multiple-output (MU-MIMO) channel within an underground mine is performed. The analysis is based on channel measurements conducted at 28 GHz using a base station of 64 virtual antenna elements serving multiple users. Channel characteristics such as large-scale path loss, time dispersion, coherence bandwidth and sum-rate capacity are reported and evaluated. The results indicate that multislope path loss model is better suited for precise prediction of path loss across various propagation segments within the mining gallery. The time dispersion analysis reveals that the underground mine channel does not cause significant time dispersion, as 90% of the root-mean-square (rms) delay spreads are below 4 ns. In addition, it was found that the rms delay spread is not dependent on the propagation distance. The study on sum-rate capacity highlights the potential of employing massive MIMO technology to improve the channel’s spectral efficiency. The analysis reveals that the capacity, with eight active users, can reach up to 33.54 bit/s/Hz. The outcomes of this article offer valuable insights into the propagation properties of underground mine environment, which is characterized by rich-scattering and irregular topology.
Synthesis, Antimalarial, Antileishmanial, and Cytotoxicity Activities and Preliminary In Silico ADMET Studies of 2-(7-Chloroquinolin-4-ylamino)ethyl Benzoate Derivatives

A series of heterocyclic chloroquine hybrids, containing a chain of two carbon atoms at position four of the quinolinic chain and acting as a link between quinoline and several benzoyl groups, is synthesized and screened in vitro as an inhibitor of β-hematin formation and in vivo for its antimalarial activity against chloroquine-sensitive strains of Plasmodium berghei ANKA in this study. The compounds significantly reduced haeme crystallization, with IC50 values < 10 µM. The values were comparable to chloroquine’s, with an IC50 of 1.50 ± 0.01 µM. The compounds 4c and 4e prolonged the average survival time of the infected mice to 16.7 ± 2.16 and 14.4 ± 1.20 days, respectively. We also studied the effect of the compounds 4b, 4c, and 4e on another important human parasite, Leishmania mexicana, which is responsible for cutaneous leishmaniasis, demonstrating a potential leishmanicidal effect against promasigotes, with an IC50 < 10 µM. Concerning the possible mechanism of action of these compounds on Lesihmania mexicana, we performed experiments demonstrating that these three compounds could induce the collapse of the parasite mitochondrial electrochemical membrane potential (Δφ). The in vitro cytotoxicity assays against mammalian cancerous and noncancerous human cell lines showed that the studied compounds exhibit low cytotoxic effects. The ADME/Tox analysis predicted moderate lipophilicity values, low unbound fraction values, and a poor distribution for these compounds. Therefore, moderate bioavailability was expected. We calculated other molecular descriptors, such as the topological polar surface area, according to Veber’s rules, and except for 2 and 4i, the rest of the compounds violated this descriptor, demonstrating the low antimalarial activity of our compounds in vivo.