Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of The Project Approach in Engineering Higher Education

The purpose of this article is the methodological aspects of the development of project-based higher education at a machine -building university, the object of the article – higher education in mechanical engineering, the purpose of the article is to increase the efficiency of the processes of development of project higher education at the University of mechanical Engineering, to achieve this goal, the following tasks are solved: research of the essence of project education in mechanical engineering, clarification of the concept of "paradigm of development of project higher education in mechanical engineering", description of the structure and content of the elements of the structure of the paradigm of development of the named project education at the University of mechanical engineering, structure discussion, the essence and content of the directions of development of such project education; research methods are historical, logical, structural analysis, qualitative analysis, predictive analysis, synthesis, search and heuristic forecasting, heuristic assessments; scientific novelty of the work is associated with the study of the essence of project education in mechanical engineering and the formation of methodological foundations for the development of the concept and policy of development of project higher education in mechanical engineering universities in the interests of improving the quality of higher education.
Lacking P2X7-receptors protects substantia nigra dopaminergic neurons and hippocampal-related cognitive performance from the deleterious effects of high-fat diet exposure in adult male mice

Background Dietary fat consumption, involved in the pathogenesis of insulin resistance and impaired glucose metabolism, is linked with decline in cognitive functions, dementia, and development of Parkinson’s disease and Alzheimer’s disease. Mature IL-1β, requiring the activation of the P2X7 receptor (P2X7R)-inflammasome complex, is an important mediator of neuroinflammation. The aim of the study was to test whether P2X7R activation might interfere with systemic and cerebral metabolic homeostasis. Methods We treated WT and P2X7R KO mice with a high-fat diet (HFD) for 16 weeks, evaluating the effects on the Substantia Nigra and Hippocampus, target areas of damage in several forms of cognitive impairment. Results HFD-treated WT and P2X7R KO mice showed a different brain mRNA profile of Insulin and Igf-1, with these genes and relative receptors, more expressed in KO mice. Unlike P2X7R KO mice, WT mice treated with HFD displayed a diameter reduction in dopaminergic neurons in the Substantia Nigra, accompanied by an increased IBA1 expression in this area; they also showed poor performances during Y-Maze and Morris Water Maze, tasks involving Hippocampus activity. Conversely, Parkin, whose reduction might promote neuronal cell death, was increased in the brain of P2X7R KO animals. Conclusion We report for the first time that HFD induces damage in dopaminergic neurons of the Substantia Nigra and a Hippocampus-related worse cognitive performance, both attenuated in the absence of P2X7R. The involved mechanisms might differ in the two brain areas, with a predominant role of inflammation in the Substantia Nigra and a metabolic derangement in the Hippocampus.
Detection of Cross Language Clones of C and Java Language using Levenshtein Distance Algorithm

— : In code engineering the study on software forking presents that 10-15% of the code in large codebase are clones. gcc-8.7%, JDK-29%, Linux-22% There are state of art tools for detecting clones like CCFinderX, EqMiner, Dup, Simjava, Nicad but cannot work with IDE’s hence To solve the software maintenance efforts in development process it is important to propose efficient techniques to identify clones(especially type-III and type-IV clones) . we propose dictionary based Approach to detect cross clones of C and Java to provide proper inputs to the developers who engage in software forking or porting activities by detecting and correcting porting and copying errors that arise during porting process for IDE’s like NetBeans, Eclipse.
Thick Cloud Removal in Multitemporal Remote Sensing Images via Low-Rank Regularized Self-Supervised Network

The existence of thick clouds covers the comprehensive Earth observation of optical remote sensing images (RSIs). Cloud removal is an effective and economical preprocessing step to improve the subsequent applications of RSIs. Deep learning (DL)-based methods have attracted much attention and achieved state-of-the-art results. However, most of these methods suffer from the following issues: 1) ignore the physical characteristics of RSIs; 2) require paired images with/without cloud or extra auxiliary images; and 3) demand the cloud mask. These issues might have limited the flexibility of existing networks. In this article, we propose a novel low-rank regularized self-supervised network (LRRSSN) that couples model-driven and data-driven methods to remove the thick cloud from multitemporal RSIs (MRSIs). First, motivated by the equal importance of image and cloud components as well as their intrinsic characteristics, we decompose the observed image into low-rank image and structural sparse cloud components. In this way, we obtain a model-driven thick cloud removal method where the spectral–temporal low-rank correlation of the image component and the spectral structural sparsity of the cloud component are effectively exploited. Second, to capture the complex nonlinear features of different scenarios, the data-driven self-supervised network that does not require external training datasets is designed to explore the deep prior of the image component. Third, the coupled model-driven and data-driven LRRSSN is optimized by an efficient half quadratic splitting (HQS) algorithm. Finally, without knowing the exact cloud mask, we estimate the cloud mask to preserve information in cloud-free areas as much as possible. Experiments conducted in synthetic and real-world scenarios demonstrate the effectiveness of the proposed approach.
[Coronavirus Pandemic - #STAYHOME: How Are You Holding Up? Questions And Tips For 11-18-Year-Olds To Make It Better].

PURPOSE Adolescents have to cope with several challenges and restrictions due to the COVID-19 pandemic, with many of those incongruent with the typical developmental tasks of adolescent age. Some adolescents might be particularly vulnerable in this situation, including those who are deprived of psychological, social or health care services and/or are exposed to abuse or neglect in their home environment. The aims of the current international multicentre follow-up study are to: 1. collect data on the mental health and quality of life of adolescents during and after the pandemic; 2. improve their mental health by providing an online prevention program that addresses their actual needs; 3. accelerate the development of culturally adapted prevention programs by involving an international team, and 4. to contribute to adequate preparation for any potentially occurring, similar situationin the future. METHODS Participants aged 11-18 years and their parents/caregivers from diff erent parts of Europe and non-European countries are recruited online. Data are collected regularly in a follow-up study by means of structured self-administered online questionnaires on adolescents' mental health, quality of life and current attitudes and needs. The baseline data collection was in March 2020 at first restrictions of the COVID pandemic in Europe. It is followed up several times (at the beginning weekly, later monthly, bi-monthly, three-monthly) to study changes in mental health, quality of life and attitudes of children and adolescents during the coronavirus disease pandemic. Data were collected by means of structured questionnaires (see below). The time frame of the study is set to one year from study start, March 2021. The last data collection was done in December 2020. The prevention program is developed and provided based on continuously analysed incoming data. CONCLUSIONS Prevention based on the results of the study is expected to contribute to maintaining adolescents' mental health, improve their quality of life, increase their and their environment's cooperation with the necessary restrictions during the pandemic, and to make reintegration easier once the restrictions are over. Furthermore, the study has the potential to inform on the wellbeing of children and adolescents in extreme situations in general, thus contribute to future preventive measures and policymaking. Implications and Contribution: The proposed international online follow-up study is expected to provide scientifi c evidence for 1. possible changes in the mental health and quality of life of adolescents during and after a pandemic situation, 2. the eff ectiveness of a culturally adapted prevention program developed to address challenges associated with these changes.
Experimental and clinical evidence in favour of an effective immune stimulation in ER-positive, endocrine-dependent metastatic breast cancer

In ER+ breast cancer, usually seen as the low immunogenic type, the main mechanisms favouring the immune response or tumour growth and immune evasion in the tumour microenvironment (TME) have been examined. The principal implications of targeting the oestrogen-mediated pathways were also considered. Recent experimental findings point out that anti-oestrogens contribute to the reversion of the immunosuppressive TME. Moreover, some preliminary clinical data with the hormone-immunotherapy association in a metastatic setting support the notion that the reversion of immune suppression in TME is likely favoured by the G0-G1 state induced by anti-oestrogens. Following immune stimulation, the reverted immune suppression allows the boosting of the effector cells of the innate and adaptive immune response. This suggests that ER+ breast cancer is a molecular subtype where a successful active immune manipulation can be attained. If this is confirmed by a prospective multicentre trial, which is expected in light of the provided evidence, the proposed hormone immunotherapy can also be tested in the adjuvant setting. Furthermore, the different rationale suggests a synergistic activity of our proposed immunotherapy with the currently recommended regimen consisting of antioestrogens combined with cyclin kinase inhibitors. Overall, this lays the foundation for a shift in clinical practice within this most prevalent molecular subtype of breast cancer.
Prediction skill of tropical synoptic scale transients from ECMWF and NCEP Ensemble Prediction Systems

Abstract The prediction skill of tropical synoptic scale transients (SSTR) such as monsoon low and depression during the boreal summer of 2007–2009 are assessed using high resolution ECMWF and NCEP TIGGE forecasts data. By analyzing 246 forecasts for lead times up to 10 days, it is found that the models have good skills in forecasting the planetary scale means but the skills of SSTR remain poor, with the latter showing no skill beyond 2 days for the global tropics and Indian region. Consistent forecast skills among precipitation, velocity potential, and vorticity provide evidence that convection is the primary process responsible for precipitation. The poor skills of SSTR can be attributed to the larger random error in the models as they fail to predict the locations and timings of SSTR. Strong correlation between the random error and synoptic precipitation suggests that the former starts to develop from regions of convection. As the NCEP model has larger biases of synoptic scale precipitation, it has a tendency to generate more random error that ultimately reduces the prediction skill of synoptic systems in that model. The larger biases in NCEP may be attributed to the model moist physics and/or coarser horizontal resolution compared to ECMWF.
Fractional-Order Sliding-Mode Control and Radial Basis Function Neural Network Adaptive Damping Passivity-Based Control with Application to Modular Multilevel Converters

This paper proposes a hybrid control scheme that combines fractional-order sliding-mode control (FOSMC) with radial basis function neural network adaptive damping passivity-based control (RBFPBC) for modular multilevel converters (MMC) under non-ideal operating conditions. According to the passive control theory, we establish the Euler–Lagrange (EL) models of positive and negative sequences based on the unbalanced grid. A passivity-based controller that satisfies the energy dissipation law is designed. To enable rapid convergence of the system energy storage function, a radial basis function neural network (RBFNN) is introduced to adjust the injection damping adaptively. Additionally, a fractional-order sliding-mode controller (FOSMC) is designed. The fractional-order sliding mode surface used can improve tracking performance, and effectively suppressed the undesirable chattering phenomenon compared to the traditional sliding-mode control (SMC). Finally, combining the two control methods can effectively solve the issue of passivity-based control (PBC) being too dependent on parameters. The proposed hybrid control scheme enhances the ability of the system to resist disturbances, and improves its overall robustness. Simulation results demonstrate the feasibility and effectiveness of this control method.
Dyschromatosis universalis hereditaria: A rare case report from Northeast India

DUH is a rare genodermatoses characterised by hyperpigmented and hypopigmented macules which are variable in size and shape [1]. It is a spectrum of diseases which includes dyschromatosis universalis hereditaria (generalized form), dyschromatosis symmetrica hereditaria (localized form) and unilateral dermatomal pigmentary dermatosis (segmental form) [2]. Dyschromatosis universalis hereditaria was first described by Ichikawa and Hiraga in 1933 [3].
Ultrasound Assisted Green Synthesis of Silver and Iron Oxide Nanoparticles Using Fenugreek Seed Extract and Their Enhanced Antibacterial and Antioxidant Activities

This study reports a facile and ecofriendly approach for the ultrasound assisted synthesis of silver and iron oxide nanoparticles and their enhanced antibacterial and antioxidant activities. The fenugreek seed extract was used as reducing, capping, and stabilizing agent in the synthesis of nanoparticles. The transmission electron microscopy results showed that nanoparticles synthesized by ultrasonication have a smaller size (~20 nm) as compared to the nanoparticles fabricated by magnetic stirring (~40 nm). The color change of the solution from milky white to brown suggested the formation of silver nanoparticles which was confirmed by the presence of an absorbance peak at 396 nm. The results of powder X-ray diffraction and energy dispersive X-ray spectroscopy confirmed the crystallinity and elements present in nanoparticles synthesized using fenugreek seed extract. Fourier transform infrared analysis showed that the fenugreek seed phytochemicals were coated on the nanoparticle surface. Thermal gravimetric analysis showed the thermal degradation and stability of nanoparticles. Magnetization study of iron oxide nanoparticles confirmed the superparamagnetic nature. The silver nanoparticles showed antibacterial activities against both gram-negative (Escherichia coli) and gram-positive (Staphylococcus aureus) bacteria, while no antibacterial activities were observed for iron oxide nanoparticles. The ultrasound assisted nanoparticles showed higher stability and antibacterial and antioxidant activity compared with the nanoparticles fabricated by magnetic stirring.
Not All Levels of Social Re-Inclusion Allow for Recovery from Negative Outcomes of Social Exclusion: The Moderating Role of Self-Esteem

Previous studies on social exclusion have focused on its adverse effects, rarely exploring how social re-inclusion can aid recovery from exclusion-induced distress. The level of social re-inclusion that can help individuals recover from social exclusion, and whether the recovery effect is influenced by individual characteristics are unclear. The present experimental study extends the Cyberball paradigm, adding a re-inclusion stage to explore the recovery effects of four levels of social re-inclusion on affect; furthermore, it tests the moderating role of self-esteem in the recovery effect. A total of 154 Chinese college students participated in the experiment. Results showed that (1) recovery was effective when the level of re-inclusion was equal to (replica re-inclusion) or greater than (moderate and high over-re-inclusions) the pre-exclusion level of inclusion, but ineffective when it was below this level (token re-inclusion); (2) the re-inclusion level positively predicted recovery, and this was moderated by self-esteem—the prediction was effective for participants with middle and high self-esteem, but not for participants with low self-esteem. These results are discussed from a group process and self-psychology perspective.
Z American English: A Mobile Language Learning App

Z American English is an application that works as a personal instructor for L1 speakers of Arabic who wish to learn English as it takes them on a fun learning trip that starts from the absolute beginner's level until they gain proficiency in English and acquire other abilities. It offers a comprehensive approach to language acquisition, with lessons created by an experienced teacher, Ibrahim Adel. The app is highly accessible, free, and compatible with both iOS and Android devices. One of its notable strengths is its clear learning path, suitable for learners of all levels. The addition of video content and interactive quizzes makes learning enjoyable and effective. Additionally, the premium "Activities Icon" subscription offers extra features. The inclusion of chat rooms fosters learner interaction, enhancing the educational experience. Overall, Z American English is a valuable resource for learners of English, providing a structured and engaging path to language mastery.
Statistical Analysis of the Relationship between Gene Expression and Location

DNA microarray data provide us with a large amount of information on gene expressions in the living cell. One of the important goals of such expression analysis is to estimate the relationships among genes in entire genome [4, 1]. The completion of bacterial genomes provided us the tendencies of genes with related functions to remain together across several genomes. Furthermore, the case of genes, whose protein products physically interact, conserved the gene order [3]. The observation implies the possibility of correlation between the gene expression level and the gene location on the genome. In this study, we present a statistical approach to investigate the relationships between gene expression levels and their location on the genome.
Analysis Of Nike Brand Operation and Marketing Strategy in Different Business Periods Based On 4P Marketing Theory

This article presents a comprehensive overview of Nike's evolution, tracing its origins in the 1960s to the contemporary marketing challenges it faces. It delves into Nike's formative years, characterized by the successful introduction of groundbreaking sneakers and the formulation of distinct marketing tactics. The article underscores Nike's foray into the running market, emphasizing the promotion of a healthy and active lifestyle. Furthermore, it dissects Nike's pricing strategies and the establishment of retail stores as pivotal distribution channels. Transitioning into the illustrious era spanning 1990 to 2010, this article scrutinizes Nike's pinnacle marketing strategies. These include celebrity endorsements, poignant storytelling, and premium pricing aimed at elevating brand perception and desirability. However, it also candidly addresses Nike's recent challenges, marked by difficulties in sustaining market leadership, stemming from a perceived lack of innovation and intensifying competition from rival brands. This analysis underscores the imperative for Nike to reinvigorate its marketing approach and rekindle its innovative spirit. In conclusion, the article underscores the significance of differentiation as a core strategy for Nike's resurgence. It advocates for embracing the Direct-to-Consumer model, enhancing the online shopping experience, and prioritizing social responsibility to enhance brand image and regain a dominant position in the fiercely competitive sporting goods market.
A comparison of unsupervised segmentation parameter optimization approaches using moderate- and high-resolution imagery

Unsupervised segmentation optimization methods have been proposed to aid in selecting an “optimal” set of scale parameters quickly and objectively for object-based image analysis. The goal of this study was to qualitatively assess three unsupervised approaches using both moderate-resolution Landsat and high-resolution Ikonos imagery from two study sites with different landscape characteristics to demonstrate the continued need for analyst intervention during the segmentation process. The results demonstrate that these methods selected parameters that were optimal for the scene which varied with method, image type, and site complexity. Several takeaways from this exercise are as follows: (1) some methods do not work as intended, (2) single-scale unsupervised optimization procedures cannot be expected to properly segment all the features of interest in the image every time, and (3) many multi-scale approaches require subjectively chosen weights or thresholds or additional testing to determine those values that meet the objective. Visual inspection of segmentation results is still required in order to assess over and under-segmentation as no method can be expected to select the best parameters for land cover classifications every time. These approaches should instead be used to narrow down parameter values in order to save time.
NARMAX model identification using a randomised approach

Structure selection is one of the most critical steps in nonlinear system identification. A large family of methods, based on model prediction error, use concepts and tools from linear algebra. Other methods, based on model simulation error, have to deal with non-convex optimisation problems. More recently a family of methods have been put forward that has probabilistic setting. The randomised algorithm for model structure selection (RaMSS) belongs to this family and it has been shown to be effective to select regressors for NARX models. In the present paper, such a method is extended to cope with NARMAX models. The performance of the proposed method is illustrated using simulated and experimental data. It is shown that the proposed method is capable of correctly selecting model structures from simulation data. The method was also applied to experimental data with successful results.
Deposit Market Competition during the Great Financial Crisis

This paper investigates competition on the deposit market across banks during the Great Financial Crisis (GFC) of 2007-09. Using branch-level data on U.S. banks, we analyze banks’ pricing behavior, in particular, how banks react to changes in competitors’ deposit rates. Our fndings show that banks reacted more strongly to changes in competitor rates during the crisis, suggesting heightened competition compared to normal times. The intense competition persisted even after the banking sector experienced a substantial deposit infow starting in the fall of 2008. The aggressive pricing behavior was not only limited to poorly capitalized banks but was widespread across the banking sector. Finally, we fnd evidence that increases in deposit spreads contributed to higher deposit growth during the crisis, but exclusively for well-capitalized banks after the Lehman collapse, suggesting uneven benefts from the infow of deposits