Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Demographic Status of Ksheena Shukra(Oligozoospermia) in Male Patients.

Oligozoospermia contributes as the major factor of male infertility. Day by day drastic changes in activities like life style, sexual life, food habits, industrial and occupational hazards, environmental pollution have contributed to the increased incidence of Oligozoospermia. According to Ayurvedic classics, Oligozoospermia can be correlated with KsheenaShukra.It was reported that 40 % of infertility cases related to men, 40 % related to women and 20 % related to both of sex. A male factor is present in the one half of infertile couples. The study was conducted on 60 diagnosed & confirmed patients of Oligozoospermiafrom OPD & IPD of NIA, Hospital,Jaipur. Maximum 56.67 % patients belonged to the age group of 21-35 years, 88.33 % patients were Hindu. 80 %of patients were married. 100 % of the patients had masturbation habit. 76.67% of the patients had masturbation habit before marriage. Maximum40 % of the patient were having psychological factor like stress.65 % of the patients reported to have moderateoligozoospermia i.e 6-10 million/ml.85 % of the patients had primary infertility and85 % of the patients had Shrama Maithuna (Exertion during intercourse).
Links Between Cyberbullying, Depression And Self-Esteem In A Sample Of Brazilian Adolescents

Cyberbullying is considered a serious public health issue, causing concern to students, teachers, parents, and policy makers. Although research on cyberbullying is rapidly growing, most studies to date have come from economically developed countries. This study explores the links between cyberbullying, depression and self-esteem in a sample of Brazilian adolescents (N = 454). Two aspects of cyberbullying are explored: cyber victimization (i.e. being a victim) and cyber aggression (i.e. being a perpetrator). Both cyber aggression and cyber victimisation were positively linked to depression. In addition, cyber aggression was negatively linked to self-esteem. The links between cyberbullying with self-esteem were mediated by depression. The models yielded full mediation effects, in which 97% of the effects of cyber victimization and 86% of the effects of cyber aggression on self-esteem operated via depression. The indirect paths – particularly for cyber aggression – indicate a possible route in which adolescents might suffer the consequences of cyber aggression.
Lithology, Petrography, and Mineralogy of Basalts from DSDP Sites 482, 483, 484, and 485 at the Mouth of the Gulf of California

The majority of the basalts drilled on Leg 65 in the Gulf of California are aphyric to sparsely phyric massive flows ranging in average thickness between 5 meters in the upper part of the sections in Holes 483 and 483B, where they are interlayered with sediment, and 14 meters in Hole 485A, where interlayered sediments constitute more than half of the section. Massive flows interlayered with pillows are generally less than 4 meters thick. The pillow lavas recovered are more phyric (up to 15 modal%) and contain two to three generations of plagioclase and olivine ± clinopyroxene. Plagioclase generally exceeds 60% of any given phenocryst assemblage. Resorbed olivine, clinopyroxene, and plagioclase megacrysts may reflect a high-pressure stage, the phenocrysts crystallizing in the main magma chamber and the skeletal microphenocrysts in dikes. Precise measurements of length/width ratios of different phenocryst types and compositions show low aspect ratios and large crystal volumes for early crystals and high ratios and low volumes for late crystals grown under strong undercooling conditions. The minerals examined show wide ranges in composition: in particular, plagioclase ranges from An92 to An36; clinopyroxene ranges from Ca41Mg51Feg in the cores of phenocrysts to Ca^ó Mg^Fef< in the groundmass; and olivine ranges from Fo86 to Fo8,. The wide range in mineral compositions, together with evidence of disequilibrium based on textures and comparisons of glass and mineral compositions, indicate complex crystallization histories involving both polybaric crystal fractionation and magma mixing.
Reliability-based design in spatially variable soils using deep learning: an illustration using shallow foundation

ABSTRACT This paper presents the reliability-based design of a strip footing against bearing capacity failure in spatially variable soils using a deep learning approach. In this method, Convolutional Neural Networks (CNNs) is modified to construct, in the presence of soil spatial variabilities and uncertain loads, a single global response surface to emulate the behaviour of the geotechnical system over the entire design space. To improve the efficiency and accuracy of the deep learning approach, an adaptive training strategy is adopted. With sufficient training samples, the CNN-based global response surface can be used in lieu of the time-demanding finite-element model for efficient reliability-based design of the geotechnical system. The integration of the deep learning approach into the conventional double-loop framework is also demonstrated. Some key limitations of the double-loop framework are addressed using the deep learning approach. The results highlight that (i) the CNN-based global response surface successfully captures the behaviour of the strip footing over the entire design space, (ii) it can be seamlessly integrated into the conventional double-loop framework, (iii) it can effectively accommodate changes in target failure probability without excessive computational costs, and (iv) it successfully achieves a significant reduction in computational costs compared to other techniques.
Entrepreneurship, Leadership, and the Value of Feminist Approaches to Understanding Them

Entrepreneurship research principally focuses on business growth. This focus valorizes the masculine and marginalizes other interpretations. Consequently, entrepreneurship is restricted to a phenomenon that is rare in the diverse business world. The leadership literature proposes that entrepreneurship may not be as masculine as we assume anyway. Our understanding of entrepreneurship needs development at the conceptual level. We argue that performativity, as described in feminist theory, can contribute to how we interpret entrepreneurship and that this might inform both the entrepreneurship and leadership literatures to afford us better understanding of what we might mean by “entrepreneurial leadership. ”
Assembling of Topological Defects at Neck‐Shaped Membrane Parts

The impact of intrinsic and extrinsic curvature on the distribution of topological defects (TDs) in neck‐like regions of biological membranes is studied quantitatively. Biological membranes are modeled effectively at the mesoscopic level as two‐dimensional films described in terms of the tensor nematic order parameter field and curvature fields. It is demonstrated that antidefects robustly form at the neck area and can promote a membrane fission. The assembling of antidefects near the catenoid's equatorial ring, where catenoids roughly mimic neck shapes are analyzed in more detail. It is demonstrated that for sufficiently strong curvatures, the effective topological charge Δmeff within a strongly curved region equals zero, and the resulting structures are topologically neutral. Consequently, the total charge of antidefects within the region equals Δm = −ΔmV − ΔmK. In most cases, the positions of antidefects are strongly influenced by the extrinsic curvature.
Novel Techniques in Chemistry and Physics and Current Applications for Discovering the Physical and Chemical Compounds

Physical and chemical techniques have gained special interest for discovery of novel materials, nanoparticles characterization, crystals and metallic based characterization and semiconductor based materials. Nanotechnology has made tremendous advances and infinitely divisible into smaller pieces, or composed of small, indivisible and indestructible particles in the form atoms. Different approaches have used for designing of nanoparticles. Physical and chemical methods have been used for the synthesis of nanoparticles, although, some of them needed more advances for high quality and biodegradable materials to meet the industrial demands. Carbon and silica based nanoparticles can be designed in order to improve the rubber properties , while on the other hand, carbon based nanoparticles can be used for improving the polymers to increase their strength and impact resistance. Semiconductor quantum dots also made revolutionary advances for replacing the toxic materials employed. Nuclear magnetic resonance spectroscopy is the most important type of spectroscopy that helpful for industrial promotions of magnetic nanoparticles. High-performance liquid chromatography is the most important technique used in physics and chemistry in components in the given sample can be identifying easily within short period of time. Different nanoparticles can be easily accessed through Powder X-ray diffraction. One of the examples is silver nanoparticles. Crystallization is one of the most important process for solidification of atoms or molecules in such a way that they get shape the crystal.
Entrepreneurial intentions amongundergraduate students in university of Jaffna

This research aimed at studying the entrepreneur intention among undergraduates in university of Jaffna. The study was built on psychological model based on Ajzen’s theory of plannedbehavior to identify the factors influencing the entrepreneurialintention of these students. The study analyzed the factorscontributing towards entrepreneurial intention among University of Jaffna students. Attitudinal, educational and behavioral factors have been taken as characteristic of entrepreneurs. Data were collected throughquestionnaires obtained from 534 students of four differentfaculties in University of Jaffna. The studyutilized correlation and regression statistics to analyze the data.The findings showed a significant positive relationship between attitudinalfactor(r=0.535), behavioral factor (r=0.624) and educationalsupport(r=0.52.4) towards entrepreneurial intention. From the linear regression analysis, can be concluded that entrepreneurial characteristics contributed 46.5% towards entrepreneurial intention among undergraduates at University of Jaffna.The result of the study hasvaluable implications for policy makers in Higher Education Division,university administrators, educators and Government.
IMPACTS OF GATT/URUGUAY ROUND TRADE NEGOTIATIONS ON U.S. BEEF AND CATTLE PRICES

The GATT/Uruguay Round trade negotiations have resulted in a multilateral relaxation of beef trade restrictions. A linear elasticity model of the U.S. beef industry is developed using log differentials equations. Beef consumption, production, and trade are disaggregated into appropriate ground and table cut components. The model predicts that GATT/Uruguay Round will cause asymmetric effects on ground and table cut beef consumers. In general, fed cattle and cow/calf producers will benefit from trade liberalization because of increases in fed and feeder cattle prices. However, nonfed cattle price will decrease.
Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery

The extraction of texture features from high‐resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey‐level co‐occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non‐forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro‐texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral‐only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21×21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT‐texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM‐mean texture (micro‐textures) than by WT‐derived texture (macro‐textures), the results show that the micro–macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer‐assisted classification of high‐spatial‐resolution remotely sensed imagery has a good potential to augment the present ground‐based forest inventory methods.
A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images

Diabetes mellitus (DM) is one of the most prevalent diseases in the world, and is correlated to a high index of mortality. One of its major complications is diabetic foot, leading to plantar ulcers, amputation, and death. Several studies report that a thermogram helps to detect changes in the plantar temperature of the foot, which may lead to a higher risk of ulceration. However, in diabetic patients, the distribution of plantar temperature does not follow a standard pattern, thereby making it difficult to quantify the changes. The abnormal temperature distribution in infrared (IR) foot thermogram images can be used for the early detection of diabetic foot before ulceration to avoid complications. There is no machine learning-based technique reported in the literature to classify these thermograms based on the severity of diabetic foot complications. This paper uses an available labeled diabetic thermogram dataset and uses the k-mean clustering technique to cluster the severity risk of diabetic foot ulcers using an unsupervised approach. Using the plantar foot temperature, the new clustered dataset is verified by expert medical doctors in terms of risk for the development of foot ulcers. The newly labeled dataset is then investigated in terms of robustness to be classified by any machine learning network. Classical machine learning algorithms with feature engineering and a convolutional neural network (CNN) with image-enhancement techniques are investigated to provide the best-performing network in classifying thermograms based on severity. It is found that the popular VGG 19 CNN model shows an accuracy, precision, sensitivity, F1-score, and specificity of 95.08%, 95.08%, 95.09%, 95.08%, and 97.2%, respectively, in the stratification of severity. A stacking classifier is proposed using extracted features of the thermogram, which is created using the trained gradient boost classifier, XGBoost classifier, and random forest classifier. This provides a comparable performance of 94.47%, 94.45%, 94.47%, 94.43%, and 93.25% for accuracy, precision, sensitivity, F1-score, and specificity, respectively.
eview nhance , delete , incept : Manipulating hippocampus-dependent emories

Here we provide a brief overview of recent research on memory manipulation. We focus primarily on memories for which the hippocampus is thought to be required due to its central importance in the study of memory. The repertoire of methods employed is expanding and includes optogenetics, transcranial stimulation, deep brain stimulation, cued reactivation during sleep and the use of pharmacological agents. In addition, the possible mechanisms underlying these memory changes have been investigated using techniques such as single unit recording and functional magnetic resonance imaging (fMRI).
Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets

Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classifier trained with a limited number of high-quality fundus images in detecting glaucoma and methods to improve its performance across different datasets. A CNN classifier was constructed using EfficientNet B3 and 944 images collected from one medical center (core model) and externally validated using three datasets. The performance of the core model was compared with (1) the integrated model constructed by using all training images from the four datasets and (2) the dataset-specific model built by fine-tuning the core model with training images from the external datasets. The diagnostic accuracy of the core model was 95.62% but dropped to ranges of 52.5–80.0% on the external datasets. Dataset-specific models exhibited superior diagnostic performance on the external datasets compared to other models, with a diagnostic accuracy of 87.50–92.5%. The findings suggest that dataset-specific tuning of the core CNN classifier effectively improves its applicability across different datasets when increasing training images fails to achieve generalization.
An analysis of the relationship between foreign trade and economic growth in Turkey over the period 1980-2009

This study analyzes the relationship between foreign trade and economic growth in Turkey during the last three decades while particularly observing distinctive feature of the last decade in terms of economic growth-foreign trade relationship. The study adopts two main methodological approaches. The first approach includes two econometric analyses based on VAR and VEC models regarding periods 1987:1-2007:3 and 2000:1-2007:3 for investigating the dynamic relationship between GDP, exports and imports. The second approach includes the analysis of composition change in foreign trade of Turkey in the period 1980-2009 based on descriptive statistics. The results of analyses suggest that imports was one of the significant determinants of the economic growth in Turkey during the periods 1987:1-2007:3 and 2000:1-2007:3 whereas exports did not have important impacts on it. It is found that the export growth was significantly affected by the GDP growth, which indicates growth-led export pattern in Turkey during these periods. Also there is found no significant relationship between exports and imports. Even though the distinctive characteristic of foreign trade pattern of the last decade is observed, the results do not support that the last decade distinct from the previous decades in terms of foreign trade and economic growth relationship.
Disparities in Elective Spine Surgery for Medicaid Beneficiaries: A Systematic Review

Study Design Systematic review Objectives We sought to synthesize the literature investigating the disparities that Medicaid patients sustain with regards to 2 types of elective spine surgery, lumbar fusion (LF) and anterior cervical discectomy and fusion (ACDF). Methods Our review was constructed in accordance with Preferred Reporting Items and Meta-analyses (PRISMA) guidelines and protocol. We systematically searched PubMed, Embase, Scopus, CINAHL, and Web of Science databases. We included studies comparing Medicaid beneficiaries to other payer categories with regards to rates of LF and ACDF, costs/reimbursement, and health outcomes. Results A total of 573 articles were assessed. Twenty-five articles were included in the analysis. We found that the literature is consistent with regards to Medicaid disparities. Medicaid was strongly associated with decreased access to LF and ACDF, lower reimbursement rates, and worse health outcomes (such as higher rates of readmission and emergency department utilization) compared to other insurance categories. Conclusions In adult patients undergoing elective spine surgery, Medicaid insurance is associated with wide disparities with regards to access to care and health outcomes. Efforts should focus on identifying causes and interventions for such disparities in this vulnerable population.
Importance of local culture in advertising: Case of Serbia

The paper explores the importance of local culture in advertising in Southeast Europe, in particular in Serbia. The aim of the research was to examine whether there is any difference in perception among people in Serbia toward brands using local advertising campaign and brands using global advertising campaigns. The research included 520 respondents and the analysis included different statistical methods. The study examined the extent to which the influence of culture on the communication strategy is felt, and whether this impact is the same for all target audiences and for all product categories. The research reveals whether customers from Serbia prefer local or global campaigns, and whether the choice of the local versus the global has a more positive impact on brand perception. The findings showed some interesting and contradictory results: consumers prefer local advertisements rather than global campaign, though other finding indicate strong influence of globalization Serbia as represent of Southeast Europe countries.
Wind direction sensor based on thermal anemometer for olfactory mobile robot

A wind direction sensor has been implemented for many applications, such as navigation, weather, and air pollution monitoring. In an odor tracking system, the wind plays the important role to carry gas from its source. Therefore, the precise, low-cost, and effective wind direction sensor is required to trace the gas source. In this study, a new design of wind direction sensor has been developed using thermal anemometer principle with the main component of the positive temperature coefficient thermistor. Three anemometers each of which has different directions are used as inputs for the neural network to determine the direction of the wind automatically.The experimental results show that the wind sensor system is able to detect twelve wind directions. A mobile robot equipped with this sensor system can navigate to a wind source in the open air with a success rate of 80%. This system is expected to increase the success rate of the mobile robot in searching for dangerous leaking gas in the open air.
Non-volatile magnonic logic circuits engineering

We propose a concept of magnetic logic circuits engineering, which takes an advantage of magnetization as a computational state variable and exploits spin waves for information transmission. The circuits consist of magneto-electric cells connected via spin wave buses. We present the result of numerical modeling showing the magneto-electric cell switching as a function of the amplitude as well as the phase of the spin wave. The phase-dependent switching makes it possible to engineer logic gates by exploiting spin wave buses as passive logic elements providing a certain phase-shift to the propagating spin waves. We present a library of logic gates consisting of magneto-electric cells and spin wave buses providing 0 or π phase shifts. The utilization of phases in addition to amplitudes is a powerful tool which let us construct logic circuits with a fewer number of elements than required for CMOS technology. As an example, we present the design of the magnonic Full Adder circuit comprising only 5 magneto-elec.