Miracle-dz/UniFAR-FTUs huggingface dataset:
DISCOUNT RATE AS A MEASURE OF MONETARY POLICY FOR MARKET STABILIZATION - EXAMPLE OF THE NATIONAL BANK OF SERBIA

The increase in interest rate is related to the efforts of monetary authorities to neutralize inflationary pressures; while the rate reduction can be interpreted as an invitation to business sector to access more favorable lending conditions. The effectiveness of these changes largely depends on the development level of financial system, the role of the central bank as a creditor and current economic situation. In this research an econometric regression double-logarithmic model was applied, with the main goal of examining the relationship between discount rate and its influence on interest rates and money supply on the example of the National Bank of Serbia. The results show the correlation between the NBS discount rate and interest rates of commercial banks and the amount of money in circulation, indicating their influence on market stability.
Optimizing Count Responses in Surveys: A Machine-learning Approach

Count responses with grouping and right censoring have long been used in surveys to study a variety of behaviors, status, and attitudes. Yet grouping or right-censoring decisions of count responses still rely on arbitrary choices made by researchers. We develop a new method for evaluating grouping and right-censoring decisions of count responses from a (semisupervised) machine-learning perspective. This article uses Poisson multinomial mixture models to conceptualize the data-generating process of count responses with grouping and right censoring and demonstrates the link between grouping-scheme choices and asymptotic distributions of the Poisson mixture. To search for the optimal grouping scheme maximizing objective functions of the Fisher information (matrix), an innovative three-step M algorithm is then proposed to process infinitely many grouping schemes based on Bayesian A-, D-, and E-optimalities. A new R package is developed to implement this algorithm and evaluate grouping schemes of count responses. Results show that an optimal grouping scheme not only leads to a more efficient sampling design but also outperforms a nonoptimal one even if the latter has more groups.
Improved secondary control for optimal total harmonic distortion compensation of parallel connected DGs in islanded microgrids

This study proposes a two-layer hierarchical control to actualize optimal total harmonic distortion (THD) compensation in different buses of parallel-connected inverters in islanded microgrids which had not been studied so far. The proposed secondary layer is used to realize THD compensation of sensitive load bus (SLB) and make distributed generators (DGs) distribute the compensating efforts between them according to their rated capacity. It is noteworthy that improving THD at the SLB can lead to an increase in THD at local buses and/or DG terminals. Although the THD limitations of these buses are not as strict as the THD limitation of SLB, it is necessary to control them within their allowed range. This important problem is not well studied in the literature. A novel complementary part is designed and added to the secondary control to tune the compensation portion of each DG while the THD limitations in DG terminals and local buses are considered. The proposed method actualizes a multi-level voltage quality control in multi-bus islanded microgrids with parallel DGs through a simple yet effective solution. Furthermore, considering the DGs peak current limitation is added to the controller and a method for calculating this peak value is proposed.
Flow Physics of Diffused-Exit Film Cooling Holes Fed by Internal Crossflow

Internal crossflow, or internal flow that is perpendicular to the overflowing mainstream, reduces film cooling effectiveness by disrupting the diffusion of coolant at the exit of axial shaped holes. Previous experimental investigations have shown that internal crossflow causes the coolant to bias toward one side of the diffuser and that the severity of the biasing scales with the inlet velocity ratio, VRi, or the ratio of crossflow velocity to the jet velocity in the metering section of the hole. It has been hypothesized and computationally predicted that internal crossflow produces an asymmetric swirling flow within the hole that causes the coolant to bias in the diffuser and that biasing contributes to ingestion of hot mainstream gas into the hole, which is undesirable. However, there are no experimental measurements as of yet to confirm these predictions. In the present study, in- and near-hole flow field and thermal field measurements were performed to investigate the flow structures and mainstream ingestion for a standard axial shaped hole fed by internal crossflow. Three different inlet velocity ratios of VRi = 0.24, 0.36, and 0.71 were tested at varying injection rates. Measurements were made in planes normal to the nominal direction of coolant flow at the outlet plane of the hole and at two downstream locations—x/d = 0 and 5. The predicted swirling structure was observed for the highest inlet velocity ratio and flow within the hole was shown to scale with VRi. Ingestion within the diffuser was significant and also scaled with VRi. Downstream flow and thermal fields showed that increased biasing contributed to more severe jet detachment and coolant dispersion away from the surface.
X-Ray Micro-Tomography Applied to Nasa's Materials Research: Heat Shields, Parachutes and Asteroids

X-ray micro-tomography is used to support the research on materials carried out at NASA Ames Research Center. The technique is applied to a variety of applications, including the ability to characterize heat shield materials for planetary entry, to study the Earthimpacting asteroids, and to improve broadcloths of spacecraft parachutes. From micro-tomography images, relevant morphological and transport properties are determined and validated against experimental data.
On Sequential Monte Carlo Sampling of Discretely Observed Stochastic Differential Equations

This article considers the application of sequential importance resampling to optimal continuous-discrete filtering problems, where the dynamic model is a stochastic differential equation and the measurements are obtained at discrete instances of time. In this article it is shown how the Girsanov theorem from mathematical probability theory can be used for numerically evaluating the likelihood ratios needed by the sequential importance resampling. Rao-Blackwellization of continuous-discrete filtering models is also considered. The practical applicability of the proposed methods is demonstrated with a numerical simulation.
INTELLIGENT IRRIGATION SYSTEM USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING: A COMPREHENSIVE REVIEW

Agriculture automation is the main concern and emerging subject for every country. Where many types of research have been carried out like the implementation of fuzzy logic and Neuro- Fuzzy logic,automation using Expert systems and Artificial intelligence which led to great benefits.First and foremost, this paper broaches the subject of Artificial intelligence, machine learning, and embedded system. It further discusses the blending of AI and embedded technology in the agriculture sector. There are still some areas of problems which are causing the problems to agriculture field like Crop diseases infestations, lack of storage management, pesticide control, weed management, lack of irrigation and water management. In the Country like India where the water is one of the major problems for the people in agriculture sectors,and the government is trying to provide more and more support to implement automation in irrigation and agriculture. As the review suggests, The automation can be achieved along with smart embedded system by using the Arduino and Raspberry pi3 with the temperature and moisture sensor by loop holes in this system and the alarming need to protect the agricultural land leads to the development of agriculture automation. This paper represents an idea to make a system with the use of sensors, IOT and machine learning to automate the traditional practices in agriculture. The idea is to implement ANN and KNN method both in the same system. Raspberry Pi module has algorithms of the KNN methodwhich helps the machine to learn from the previous data set of crop selection, crop growth and crop yield to give the data to the farmer. Whilethe ANN method continually measures the soil health and sends the data to the cloud. Use of more layers gives better results and more segregated data. The data is stored on the web server which then can be visible to the farmer on the web browser and on the mobile phone too. Real-time monitoring of all the data given by the sensors and machines is possible with the use of IOT. The HTML code is dumped into the Wi-Fi module and as a result, farmer can view the data on the local host of the web browser. This paper discusses various ideas where advanced technologies can be used to reduce the efforts in agriculture. In future, if the proposed idea is carriedforward, then it would lead to a great reduction in labour, easy agriculture practices, effective use of water resources and less power consumption.
Predicting Sumatriptan Responsiveness Based on Structural Connectivity in Patients Newly Diagnosed With Migraine

Background and Purpose We aimed to determine whether structural brain connectivity is significantly associated with the response to sumatriptan in patients with migraine. Methods We retrospectively enrolled patients with newly diagnosed migraine who underwent brain diffusion-tensor imaging (DTI) at the time of diagnosis, with regular follow-up for at least 6 months after the initial diagnosis. Patients were classified into good- and poor-responder groups according to their response to sumatriptan. We analyzed the structural connectivity using DTI by applying graph theory using DSI Studio software. Results We enrolled 59 patients (35 good responders and 24 poor responders) and 30 healthy controls. Global structural connectivity differed significantly between patients with migraine and healthy controls, while local structural connectivity differed significantly between good and poor responders. The betweenness centrality was lower in good responders than in poor responders in the left lateral geniculate thalamic nucleus (26.078 vs. 41.371, p=0.039) and right medial mediodorsal magnocellular thalamic nucleus (60.856 vs. 90.378, p=0.021), whereas was higher in good responders in the left lateral pulvinar thalamic nucleus (98.365 vs. 50.347, p=0.003) and right medial pulvinar thalamic nucleus (216.047 vs. 156.651, p=0.036). Conclusions We found that structural connectivity in patients with migraine differed from that in healthy controls. Moreover, the local structural connectivity varied with the response to sumatriptan, which suggests that structural connectivity is a useful factor for predicting how a patient will respond to sumatriptan.
THE IMPACT OF ENTREPRENEURIAL ASPECTS, FAMILY FACTOR & UNIVERSITY ENVIRONMENT ON ENTREPRENEURIAL INTENTION

Purpose – The research aimed to explore the influence of university environment, family factor and entrepreneurial aspects on the entrepreneurial intention of universitiesstudents. Design/methodology/approach – This research is based upon a questionnaire conducted to the students of Alexandria Universities. Findings – The findings from this research revealed that, universities students perceived their entrepreneurial passion, university environment and family factor impacting their entrepreneurial intention, with a higher priority if compared to the variables of risk-taking and emotional intelligence, which they considered with no impact on their mentioned entrepreneurial intention; additionally, they thought that their self-efficacy served as a mediator, allowing their entrepreneurial passion to positively influence their entrepreneurial intention, however this latter mediator wasn’t perceived to be affected neither by their planning nor innovation characteristics.
Estimation of External Force-Torque Vector Based on Double Encoders of Industrial Robots Using a Hybrid Gaussian Process Regression and Joint Stiffness Model

Industrial robots are increasingly used in industry for contact-based manufacturing processes such as milling and forming. In order to meet part tolerances, it is mandatory to compensate tool deflections caused by the external force-torque vector. However, using a third-party measuring device for sensing the external force-torque vector lowers the cost efficiency. Novel industrial robots are increasingly equipped with double encoders, in order to compensate deviations caused by the gearboxes. This paper proposes a method for the usage of such double encoders to estimate the external force-torque vector acting at the tool centre point of an industrial robot. Therefore, the joint elasticities of a six revolute joint industrial robot are identified in terms of piecewise linear functions based on the angular deviations at the double encoders when an external force-torque vector is applied. Further, initial deviations between the encoder values caused by gravitational forces and friction are modelled with a Gaussian process regression. Combining both methods to a hybrid model enables the estimation of external force-torque vectors solely based on measurements of the joint angles of secondary encoders. Based on the proposed method, additional measurement equipment can be saved, which reduces investment costs and improves robot dynamics.
Automated Cancer Prognostic Modeling With Pathological Imaging Features

Objectives. To assess the reproducibility and overlapping of information in the automated pathological imaging features pipeline for lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and skin cutaneous melanoma (SKCM); and to construct an integrated prognostic model using these pathological imaging features for LUAD, LUSC and SKCM. Methods. 470 LUAD patients, 477 LUSC patients and 428 SKCM patients are included in the study. Whole-slide histopathology images as well as demographical and clinical data are obtained from the TCGA website. Histopathological images are processed using the Openslide Python library and chopped into twenty small 500*500or 700*700-pixel subimages. CellProfiler software was used to extract imaging features and provide its statistical values. RV coefficients are computed to assess robustness and reproducibility of the pipeline. Cox Proportional Hazards Model with Principal Component Analysis, and Cox Proportional Hazards Model with elastic net regularization are used to construct the survival model. Concordance indexes are calculated to measure the predictivity of the models using random splitting-based evaluation. Results. The high RV coefficients indicate the robustness and reliability of the imaging feature pipeline in the context of extracting feature statistical values. By using the random splittingbased evaluation, we observe that pathological imaging data has moderate/weak predictive performance for LUAD, LUSC and SKCM, respectively. Conclusion. This study suggests that histopathological imaging features extracted from CellProfiler software may not be predictive for LUAD, LUSC and SKCM prognosis, but provide some broadly insight into cancer prognosis modeling and analysis. Future large-scale studies that incorporate other cell properties analysis software are needed.
The disappearance of the pfotzer‐regener maximum in dose equivalent measurements in the stratosphere

The NASA Radiation Dosimetry Experiment (RaD‐X) successfully deployed four radiation detectors on a high‐altitude balloon for a period of approximately 20 h. One of these detectors was the RaySure in‐flight monitor, which is a solid‐state instrument designed to measure ionizing dose rates to aircrew and passengers. Data from RaySure on RaD‐X show absorbed dose rates rising steadily as a function of altitude up to a peak at approximately 60,000 feet, known as the Pfotzer‐Regener maximum. Above this altitude absorbed dose rates level off before showing a small decline as the RaD‐X balloon approaches its maximum altitude of around 125,000 feet. The picture for biological dose equivalent, however, is very different. At high altitudes the fraction of dose from highly ionizing particles increases significantly. Dose from these particles causes a disproportionate amount of biological damage compared to dose from more lightly ionizing particles, and this is reflected in the quality factors used to calculate the dose equivalent quantity. By calculating dose equivalent from RaySure data, using coefficients derived from previous calibrations, we show that there is no peak in the dose equivalent rate at the Pfotzer‐Regener maximum. Instead, the dose equivalent rate keeps increasing with altitude as the influence of dose from primary cosmic rays becomes increasingly important. This result has implications for high altitude aviation, space tourism and, due to its thinner atmosphere, the surface radiation environment on Mars.
Fault Diagnosis Method for Rotating Machinery Based on Improved CBR

A novel method to detect the faults of rotating machinery using improved case-based reasoning is proposed in this article, which can fully utilize the experience and intelligence of previous cases to realize an intelligent diagnosis. Especially, the identification process of a new fault state is designed to address the problem that the state not included in the case base cannot be automatically recognized. The presented approach is performed to test the gear and bearing datasets. And good diagnosis results are achieved, which verifies the effectiveness of the presented approach.
STUDY ON THE ROLE OF TDRD1 VARIANTS IN MALE INFERTILITY AMONG 310 VIETNAMESE INDIVIDUALS

Infertility is a global reproductive health burden affected by various genetic factors, including spermatogenic defects. The instability of the germ cells’ genome caused by the unregulated replication of transposable elements is one of the causes of spermatogenic impairment. Tudor domain-containing 1 ( TDRD1 ) expressed only in germ cells plays a significant role in the piRNA (PIWI-interacting RNA) pathway to maintain genome integrity via suppressing transposon elements during spermatogenesis. Despite the protein’s role in male germline development, TDRD1 has not been studied intensively. In this study, we established the relationship between male infertility and single nucleotide polymorphisms (SNPs) of TDRD1 (rs541192490, rs77559927) among 310 Vietnamese men (160 infertile patients and 150 healthy controls). Genotypes of single nucleotide polymorphisms of the TDRD1 gene (SNPs) were identified using the PCR-RFLP method. The results showed that TDRD1 SNPs were not associated with male infertility in all three test models (additive, dominant, and recessive) ( p -value > 0.05). Haplotype analysis of the two SNPs also showed similar findings. This study would contribute to the knowledge of TDRD1 ’s association with male infertility in the Vietnamese population.
Generation of Regular Expression from Aligned Sequences of Text Snippets

Introduction: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Vitae sapien pellentesque habitant morbi tristique senectus et netus. Dignissim cras tincidunt lobortis feugiat vivamus at augue eget arcu. At risus viverra adipiscing at in. Cras semper auctor neque vitae tempus quam. Sed cras ornare arcu dui. Turpis massa sed elementum tempus. Risus commodo viverra maecenas accumsan lacus vel facilisis volutpat est. Objectives: Effective data management has become an absolute necessity in today's world as a direct result of the widespread adoption of electronic medical records for patients. For medicine and healthcare domains, particularly effective web healthcare providers, computer-assisted categorization of such information into functional classifications such as medical issues or illnesses may save time and efforts. However, a substantial volume of data in the healthcare domain is still unstructured. Methods: The utilization of unstructured text datasets is a challenge in comparison with structured text datasets. Many researchers in various domains have proposed to convert unstructured text into structured text. The text needs to be built in a fixed and aligned pattern for easy comparison, matching, and classification. In the text classification process, accuracy is one of the challenges when text is unstructured. The text with a fixed pattern is easy to classify. The regular expressions are sequences of characters with fixed patterns of text. This strength of regular expression is used in the proposed text classification. Finding patterns in unstructured text is possible with the use of sequence alignment. Basically, sequence alignment is a concept of biomedical research, but the same concept is used to align text snippets that are not perfectly similar to each other. Results: The sequence alignment technique detects the similarity score assigned to each potential alignment in order to choose among the numerous local alignments of sequences. The proposed local pairwise alignment method is used to get sequence alignments, which are useful to generate regular expressions. Conclusions: A regular expression is a string of characters used to describe a text pattern. The regular expressions are typically created manually by the domain experts. The proposed work is the automatic generation of regular expressions from aligned sequences with a bottom up approach. The generated regular expressions are used as a dataset on which various machine learning algorithms are applied for text classification.
SÉRIE “ Psychologie”

The mental health of communities is an extremely important aspect in times of war, as it is a time when people face stress, anxiety and trauma. Military conflicts have a negative impact on the psychological resilience and well-being of the population. In this research paper, we will look at the psychological terms used to analyze and influence the mental health of communities in war. In order to ensure the mental health of communities in war, it is also necessary to pay attention to the factors that contribute to post-traumatic growth. Post-traumatic growth is a process when a person, despite traumatic events, finds the potential for development, self-improvement and transformation of his or her life. Stimulating factors of post-traumatic growth may include the support of the social environment, the ability to rebuild one's life story, and the search for meaning and significance in traumatic events. It is also important to remember the impact of war on children and youth. They can be particularly vulnerable to the psychological consequences of military conflict. To ensure their mental health, it is necessary to develop and implement special programs that provide support to children and youth, teach them strategies for coping with stress and developing resilience, and promote their social inclusion and normal development. Finally, the importance of post-conflict rehabilitation and recovery from war must be taken into account. This means providing access to psychological assistance, reintegration programs, and resources for the psychological recovery of community members. Post-conflict rehabilitation is aimed at facilitating the transition from martial law to normal life, restoring social functioning, and strengthening communities.