Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensile Force Transmission from the Upper Trunk to the Contralateral Lower Leg throughout the Posterior Oblique Sling System

Although recent studies have confirmed that muscles of the upper body are obliquely linked with the muscles of the contralateral lower extremity through posterior oblique sling (POS) system, the upper portion of the POS connected to the lower leg below the hamstring is yet to be determined. In the supine position, the active maximum dorsiflexion (DF) angles of the right and left legs were randomly measured. During passive trunk rotation performed by the therapist, the active maximum DF angles of the right leg (contralateral side) and left leg (ipsilateral side) were measured. In a long sitting position with and without trunk rotation, the active maximum DF angles in both legs were measured. The left upper body was made to rotate rightward in trunk rotation. In the contralateral lower extremity, the DF ROM in the sitting position with trunk rotation was significantly different compared with those in the sitting position without trunk rotation, and in the supine position with trunk rotation. In the comparison between the ipsilateral and contralateral lower extremity, significant differences were found in the DF ROM in the sitting position with trunk rotation. This study indicates that the tensile force generated by trunk rotation is transmitted to the contralateral terminal end by examining changes in DF ROM. Since the upper portion of the POS is connected all the way down to the contralateral calf, the POS must be considered during the clinical treatment of patients with restricted DF mobility.
Distributed Deep Reinforcement Learning Computations for Routing in a Software-Defined Mobile Ad Hoc Network

The need for reliable and flexible wireless networks has significantly increased in recent years, according to the growing reliance of an enormous number of devices on these networks to establish communications and access service. Mobile Ad-hoc Networks (MANETs) allow the wireless network to establish communications without the need for infrastructure by allowing the nodes to deliver each other’s packets to their destination. Such networks increased flexibility but require more-complex routing methods. In this study, we proposed a new routing method, based on Deep Reinforcement Learning (DRL), that distributes the computations in a Software Defined Network (SDN) controller and the nodes, so that, no redundant computations are executed in the nodes to save the limited resources available on these nodes. The proposed method has been able to significantly increase the lifetime of the network, while maintaining a high Packet Delivery Rate (PDR) and throughput. The results also show that the End-to-End delay of the proposed method is slightly larger than existing routing methods, according to the need for longer alternative routes to balance the loading among the nodes of the MANET.
Radial Variation of Wood Anatomical Properties Determines the Demarcation of Juvenile-Mature Wood in Schima superba

Schima superba is a precious broadleaf tree species that produces excellent timber. Analyzing the radial variation of wood anatomical properties within stems and determining the boundary between the juvenile and mature wood can provide a theoretical basis for the breeding and cultivation of S. superba. Herein, 30 54-year-old trees were used to analyze the radial variation of wood anatomical properties qualitatively. The correlation among wood properties was analyzed. On this basis, four types of model were used to predict the wood properties quantitatively, and the optimal segmentation model was used to determine the boundary between juvenile and mature wood. The results showed that the variation of the early stage (before the 20th year) was larger than that of the later stage (after the 20th year); moreover, the variation of the vessel features (9.56%) was greater than that of the fiber features (7.42%), the vessel lumen diameter (11.94%) and the fiber length (8.00%) had the most variation. There were three radial variation patterns in the wood properties. In the early stage, there was a significant positive correlation between the ring width and wood air-dry density (0.78) and wood basic density (0.89), and a significant negative correlation between the ring width and the fiber characters (−0.79~−0.93) and vessel characters (−0.64~−0.90). The growth models of Logistic and Richards were more suitable (R2 = 84.2%~95.2%) for the radial variation of wood anatomical properties than the nonlinear least square model (R2 = 67.7%~90.9%). The transition wood between juvenile and mature wood of S. superba was from the 11th to the 16th year based on aggregative indicators from the pith to bark at breast height. Finally, when taken together, the wood anatomical properties of S. superba had regular radial variation. Breeding programs need to consider both growth and wood anatomical properties in the early stage, but they can be neglected in the mature stage. The formation of mature wood of S. superba occurred after the 16th year.
Towards an End-to-End Visual-to-Raw-Audio Generation With GAN

Automatically synthesizing sounds for different visual contents poses a challenge and there is a strong need to facilitate the direct creation of realistic sounds. Different from previous works, in this paper, we propose a novel deep learning based approach, which formulates sound simulation as a regression problem. This allows us to circumvent the complexity of the acoustic theory by a novel, general-purpose neural sound synthesis (V2RA) network. Moreover, the end-to-end architecture of V2RA ensures full training without any extra inputs, which thereby greatly improves the scalability and reusability over previous works. In contrast to conventional visual-to-audio generation methods, the V2RA problem is established and solved by generative adversarial networks (GANs). Furthermore, our network architecture can directly predict synchronized raw audio signals (unlike most existing approaches that handle the audio through spectrograms) and generate sound in real time. To evaluate the performance of the neural network generator, we specifically introduce two quantitative scores. Various experiments demonstrate that our V2RA network can produce compelling sound results, which thus provides a viable solution for applications such as sound design and dubbing.
MIGRATION IN STATISTICS INDEXES’ TERMS. ROMANIA STUDY CASE

Complex political, economic, natural and social events shaping the migration phenomenon in all Europe and abroad. Besides, globalisation and Europeanization have resulted in a significant increase in migration flows. Thus, migration become a complex and multi-criteria phenomena with different meanings and estimation methods according to different research objectives. These particularities create an additional pressure on policymakers’ shoulders who need a better understanding of the variation of time-series database. Although, have been developed several databases and indices, at the time being there are difficulties regarding the measuring and conceptualization of migration, particularly the return migration within and cross-national framework. This paper discusses these challenges and the methodological limits on migration policy indexes for comparative analysis and learning lessons.
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Vega-Redondo (1997) showed that imitation leads to the Walrasian outcome in Cournot Oligopoly. We generalize his result to aggregative quasi-submodular games. Examples are the Cournot Oligopoly, Bertrand games with differentiated complementary products, CommonPool Resource games, Rent-Seeking games and generalized Nash-Demand games. JEL-Classifications: C72, D21, D43, L13.
Catalytic Applications of Pyridine‐Containing Macrocyclic Complexes

The introduction of a pyridine moiety into the skeleton of a polyazamacrocyclic ligand affects both the thermodynamic properties and the coordination kinetics of the resulting metal complexes. These features have attracted great interest from the scientific community in recent years. The field of application of pyridine-containing macrocyclic ligands ranges from biology to supramolecular chemistry, encompassing MRI, molecular recognition, materials and catalysis. In this microreview we provide a perspective of the catalytic applications of metal complexes of pyridine-containing macrocycles, including an account of investigations from the authors' laboratories dealing with stereoselective C–C and C–O bond-forming reactions. The increased conformational rigidity imposed by the pyridine ring allowed for the isolation and characterisation of metal complexes in high oxidation states and the study of their relevance in oxidation reactions. On the other hand, the very different conformations accessible upon the metal coordination and the easily tuneable synthesis of the macrocyclic ligands have been exploited in stereoselective synthesis.
A Systematic Mapping Study on Analysis of Code Repositories

Code repositories contain valuable information, which can be extracted, processed and synthesized into valuable information. It enabled developers to improve maintenance, increase code quality and understand software evolution, among other insights. Certain research has been made during the last years in this field. This paper presents a systematic mapping study to find, evaluate and investigate the mechanisms, methods and techniques used for the analysis of information from code repositories that allow the understanding of the evolution of software. Through this mapping study, we have identified the main information used as input for the analysis of code repositories (commit data and source code), as well as the most common methods and techniques of analysis (empirical/experimental and automatic). We believe the conducted research is useful for developers working on software development projects and seeking to improve maintenance and understand the evolution of software through the use and analysis of code repositories.
Automatic monitoring of the growth of plants using deep learning-based leaf segmentation

ABSTRACT Plants are a source of food, medicines, fiber, fuel, etc. and are therefore crucial for our survival. Due to this, intensive care of plants should be done and it requires monitoring of their growth, size, yield, etc. However, manually monitoring such factors is often time-consuming and necessitates one to have in-depth knowledge of agriculture and plants. Thus, automatic systems for plant image analysis would be beneficial for practical and productive agriculture. Therefore, an automatic method is proposed for monitoring the growth of plants by first performing the segmentation of leaves in plant images and then calculating the segmented area. A deep learning-based architecture “U-Net” was used for the segmentation task. A benchmark dataset of 810 images was used to train and test the proposed deep learning network. The proposed model was trained within 3 hours and achieved a dice accuracy of 94.91% on the training set, 94.93% on the validation set, and 95.05% on the testing set. The proposed architecture was found very lightweight with fewer computations but achieved promising results as compared to other methods in the literature.
Learnings from the Impact of Online Learning on Elementary Students' Mental and Social-emotional Well-being Amid the COVID-19 Pandemic

This research examined practices that foster students’ mental and emotional well-being, quality relationships among students and staff, and safe and inclusive climates through online platforms, especially during times of crisis such as during the COVID-19 pandemic. To achieve that goal, this study sought to identify the kinds of protective and risk factors that help or hinder students’ ability to cope and thrive, through a scholarly framework of Critical Race Theory (Yosso, 2005), Online Learning pedagogy (Hughes, 2004), and Social and emotional learning (Durlak et al., 2011). The researcher conducted qualitative interviews with a variety of educators at largely low-income public primary schools and with bilingual and non-native Englishspeaking parents in Willow Creek County. The findings highlighted that students do not equate to academic selves, that the level of expected responsiveness was surprisingly high while online, and that new connections and social and emotional support systems emerged. These findings have important implications for understanding how teachers and educational professionals iterate their practices of online learning going forward.
Environmental protection and social development: an optimization perspective from two polarities

The development of human society is currently in a dilemma, where the further development of society needs to be considered, simultaneously, the environmental problems caused by development are becoming increasingly prominent. Therefore, how to weigh the relationship between environmental protection and social development is still a hot research topic. In consideration of such a bipolar conflict problem, a novel optimization model was proposed to study the relationship between environmental protection and social development. First, this problem is modeled as a single objective optimization problem. Secondly, a series of representative characteristics are selected from social development and environmental protection. Then, these features are preprocessed, decomposed and analyzed. Finally, the objective function is obtained through regression analysis. In order to verify the effectiveness of the proposed approach, the realistic case of China from 2000 to 2018 are employed and analyzed. The analysis result confirms that proposed model is able to achieve state-of-the-art performances.
Candida albicans Activation of Human Monocytes Toward M2 Profile is Reversed by Amphotericin B and Fluconazole

Phagocytes, including monocytes/macrophages, play an important role in the host defense during Candida albicans infections. In the L-arginine metabolism, the balance between the activation of two enzymes, inducible Nitric Oxide Synthase (iNOS) and arginase, promotes in the macrophages two alternative metabolic states, while M1 profile is related with host protection, M2 favored the fungal growth and evasion. Our aim was to evaluate the effect of Amphotericin B (AMB) and Fluconazole (FLC) on polarization of human monocytes to M2 profile induced by C. albicans. The human monocytic (Mo) cell line U937 was co-cultured with viable yeast of C. albicans, or Lipopolysaccharides (LPS) or Phorbol-12-myristate-13-acetate (PMA). Nitric Oxide (NO), cytokines production and arginase activity were evaluated. The effect of AMB or FLC on these metabolic pathways in immune cells and on fungus intrinsic arginase activity was studied. C. albicans inhibits NO production in human-monocyte and induces strong host arginase activity (p<0.0001). AMB and FLC inhibited C. albicansinduced arginase activity in immune cells (p<0.001), reaching a percentage of inhibition of 90% for AMB and 78% for FLC. Arginase intrinsic activity of the fungus was blocked by nor-NOHA (arginase inhibitor) and AMB (p<0.05). These results show that C. albicans drives human Mo toward M2 profile and that both antifungal drugs evaluated have the ability to revert C. albicans-induced M2 profile. In a relevant manner, it also provides data about additional effect of AMB as inhibitor of C. albicans endogenous arginase activity. Here in we provide new evidence for the effect of these drugs over the immune cells and the yeast.
Investigation of DNA damage and protein damage caused by oxidative stress in canine visceral leishmaniasis

Leishmaniasis, considered by the World Health Organization as one of the most important zoonotic diseases, causes death when it is not treated in its self-healing skin form. The aim of this study was to investigate DNA damage and oxidative protein damage that occurs in dogs infected with Leishmania infantum. The study group consisted of 25 dogs, including 10 clinically healthy dogs aged 2 to 6 years and 15 dogs infected with Leishmania infantum diagnosed by means of the rk39 dipstick test and the immunofluorescence antibody test (IFAT). The effects of oxidative stress on protein were evaluated in the dogs infected with Leishmania infantum by determining plasma malondialdehyde, protein carbonyl groups, nitrotyrosine, and total antioxidant capacity in blood. DNA damage, on the other hand, was determined by the COMET method. Plasma protein carbonyl content (PCO) and nitrotyrosine (NT) levels, which are considered as indicators of protein damage, were found to be higher in the dogs infected with Leishmania infantum compared to the control group, but the difference in both values was not statistically significant (p > 0.05). Plasma malondialdehyde (MDA), an indicator of lipid peroxidation, was significantly higher in the dogs infected with Leishmania infantum than in the control group. TAC levels, however, were lower in the leishmanial dogs (p < 0.05). According to the results of the COMET assay, lymphocyte cells were damaged in the leishmanial dogs, and both tail intensity (TI) and tail moment (TM) values were higher in those dogs than they were in the control group (p < 0.05, p < 0.01). The parasites caused oxidative stress through protein and DNA damage in the host and decreased the antioxidant capacity concentration that prevents destructive effects.
Revealing of biodiversity and antimicrobial effects of Artemisia asiatica endophytes

Endophytic fungi produce a plethora of secondary metabolites, which may open new avenues to study their applicability in pharmaceuticals. Therefore, the present study focuses on the fungal endophytic community of Artemisia asiatica. During our work, fungal endophytes were isolated from a medicinal plant, A. asiatica. The culturable endophytic fungi were identified using molecular techniques and biodiversity, richness and tissue specificity were examined. As these microorganisms have been generally identified as an abundant reservoir of novel antimicrobial compounds, the antimicrobial (i.e. antibacterial and antifungal) activities of the metabolites produced by the isolated fungi were studied. Numerous extracts containing the endophytic metabolites proved to be active against the applied test microorganisms including Gram-positive and Gram-negative bacteria, as well as yeasts and filamentous fungi, which can be examined in detail in the future and, based on the the chemical nature of these active metabolites, allow to discover novel bioactive metabolites.
Robust Design Optimization of Switched Reluctance Motor Drive Systems Based on System-Level Sequential Taguchi Method

In this paper, a new system-level sequential Taguchi method (SLSTM) is proposed to achieve the optimal solution with high robustness for switched reluctance motor (SRM) drive systems. An SRM drive system consisting of a segmented-rotor SRM and the angle position controller is investigated as a case study. In the implementation, the optimization function contains torque, loss, and torque ripple. The control factors of the system are selected according to the sensitivity analysis results. Manufacturing tolerances are considered to guarantee that the optimal solution features low sensitiveness to uncertainties. The process of defining the design levels of all the control factors and noise factor is illustrated and the orthogonal array is established. The optimization of the SLSTM is carried out sequentially until the certain convergence condition is satisfied. Finally, the component-level sequential Taguchi method (CLSTM) is carried out for comparison. It appears that the proposed SLSTM is efficient in searching for the robust optimal solution for the SRM drive system. Besides, it can achieve better output performance, such as higher average torque and lower torque ripple, and a higher level of robustness compared with the initial design and CLSTM.
Efficient Behavior Prediction Based on User Events

—In 2020 we have witnessed the dawn of machine learning enabled user experience. Now we can predict how users will use an application. Research progressed beyond recommendations, and we are ready to predict user events. Whenever a human interacts with a system, user events are dispatched. They can be as simple as a mouse click on a menu item or more complex, such as buying a product from an eCommerce site. Collaborative ﬁltering (CF) has proven to be an excellent approach to predict events. Because each user can generate many events, this inevitably leads to a vast number of events in a dataset. Unfortunately, the operation time of CF increases exponentially with the increase of data-points. This paper presents a generalized approach to reduce the dataset’s size without compromising prediction accuracy. Our solution transformed a dataset containing over 20 million user events (20,692,840 rows) into a sparse matrix in about 7 minutes (434.08 s). We have used this matrix to train a neural network to accurately predict user events.
Designing an Entrepreneurial Supply Chain Model during Disasters in Iran

INTRODUCTION: Relief organizations, especially the Red Crescent, lack any specific entrepreneurial strategy and program for production, identification, and distribution of relief supplies. These organizations mainly focus on the preparation and distribution of supplies in times of crisis. In this regard, the present study aimed to design an entrepreneurial supply chain model with an emphasis on technology in 2020 in Iran. METHODS: The present study was conducted based on a qualitative and quantitative design. In the first phase, some indicators were obtained by observing the current situation and interviewing 30 experts. Following that, the final model was achieved by considering all indicators and categorizing the topics. In the Delphi process, experts' interviews and theoretical consensus suggested some hypotheses. In other words, in the second phase, structural equation modeling was used to finalize the model. In the next stage, the final questionnaire was provided to 186 Red Crescent employees. FINDINGS: After the analysis and extraction of the criteria from the interviews, components of the model were retrieved, and two questionnaires were designed. The first questionnaire was about supply chain management encompassing four main components of customer integrity, supplier integrity, internal integrity, and innovative orientation. The second questionnaire was related to technology, including seven components: personal characteristics, attitudinal factors, educational factors, technical factors, economic factors, environmental factors, as well as human and managerial factors. Considering the KMO value (˃0.7) and the significant value of the Bartlett Sphericity test, it can be concluded that the data are suitable for factor analysis. The model fit values all exceeded 0.9, indicating that the model has a “good fit. The path coefficients were significant for seven relationships at the level of 0.05. CONCLUSION: As evidenced by the obtained results, the supply chain in disasters requires experts' comprehensive approach and innovative perspectives. The tendency of countries to take innovative measures in disasters requires macro-policies at the national and regional levels. Therefore, all dimensions and aspects of the entrepreneurial supply chain in disasters must be considered in order to attain the final goal which is effective and efficient disaster management.