Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between Mental Well-being and Academic Stress with the Ability to Solve Social Problems in High School Students

: The main purpose of this study was to investigate the relationship between mental well-being and academic stress with the ability to solve social problems in first and third grade students of the theoretical branches of the second-high school in Ivan Gharb in the academic year 1397-98. The research method was descriptive-correlational and in terms of purpose was considered an applied research. The statistical population of the study consisted of all first grade female students (210 students) and third grade students (190 students). Using cluster random sampling method, the sample size was 130 people for the first grade and 120 people for the third grade were selected and tested according to Morgan and Krejcie table. To collect data from the Questionnaire, Mental Well-Being (Dinner, Emmons, Larsen, & Griffin), Life Satisfaction Scale of Watson, Clark, & Telgen (1988) Positive and Negative Emotion Scale (1988), Academic Expectations of Ang and Huan, the revised short form of Social Problem Solving Ability (SPSI-R) of Dezruila, Nezu and Meido-Olivares, were used. The reliability of the research tools was also confirmed using Cronbach's alpha. Descriptive statistics (mean and standard deviation) and inferential statistics (multivariate analysis of variance, Pearson correlation coefficient and regression) were used to analyze the data. Findings showed that mental well-being and academic stress predicted the ability to solve social problems. The results also showed that in some aspects of mental well-being and academic stress, there was a significant relationship with the ability to solve social problems. Also, there was no significant difference between educational level and research variables. well-being with the ability to solve social problems showed that a significant effect of positive and negative emotion variables in regression and life satisfaction did not have a significant effect on regression. Also, the results of regression between the dimensions of academic stress with the ability to solve social problems showed that a significant effect on the expectation of teachers-parents in regression and self-expectation did not represent a significant effect on regression. Multiple regression tests between social problem-solving ability and dimensions of mental well-being and academic stress showed that there is a significant relationship between variables. The effect of regression obtained at the relevant significance level is significant and shows that the dimensions of mental well-being and academic stress predict the ability to solve social problems.
Trypsin inhibitor in Enterolobium contortisiliquum calli grown in the presence of plant growth regulators

Abstract The objective of this work was to investigate the effect of plant growth regulators (PGR) on the induction of calli from cotyledons of Enterolobium contortisiliquum grown in vitro, as well as to evaluate the inhibitory activity of trypsin in these calli. Cotyledons of E. contortisiliquum were cultivated in culture medium supplemented with dichlorophenoxyacetic acid (2,4-D), picloram (PIC), kinetin (KIN), and 6-benzylaminopurine (BAP). Callus mass, inhibitory activity of trypsin, morphogenetic and cytochemical observations, and biological activity were measured after 60 days. The Pareto diagram showed that only 0.5 mg L-1 PIC influenced the increase of fresh mass, whereas 0.5 mg L-1 BAP influenced dry mass. The principal component analysis plot showed the formation of two clear clusters. The inhibitory activity of trypsin is achieved at a higher intensity in the treatment with 0.5 mg L-1 2,4-D + 2.0 mg L-1 PIC + 0.5 mg L-1 KIN + 2.0 mg L-1 BAP. The combination of PGR at different rates causes an intense callus formation. Low concentrations of PGR result in a higher fresh mass, dry mass, and biological activity. The use of PGR is efficient in obtaining E. contortisiliquum calli with a satisfactory inhibitory activity of trypsin.
A Surface Exposed, Two-Domain Lipoprotein Cargo of a Type XI Secretion System Promotes Colonization of Host Intestinal Epithelia Expressing Glycans

The only known required component of the newly described Type XI secretion system (TXISS) is an outer membrane protein (OMP) of the DUF560 family. TXISSOMPs are broadly distributed across proteobacteria, but properties of the cargo proteins they secrete are largely unexplored. We report biophysical, histochemical, and phenotypic evidence that Xenorhabdus nematophila NilC is surface exposed. Biophysical data and structure predictions indicate that NilC is a two-domain protein with a C-terminal, 8-stranded β-barrel. This structure has been noted as a common feature of TXISS effectors and may be important for interactions with the TXISSOMP. The NilC N-terminal domain is more enigmatic, but our results indicate it is ordered and forms a β-sheet structure, and bioinformatics suggest structural similarities to carbohydrate-binding proteins. X. nematophila NilC and its presumptive TXISSOMP partner NilB are required for colonizing the anterior intestine of Steinernema carpocapsae nematodes: the receptacle of free-living, infective juveniles and the anterior intestinal cecum (AIC) in juveniles and adults. We show that, in adult nematodes, the AIC expresses a Wheat Germ Agglutinin (WGA)-reactive material, indicating the presence of N-acetylglucosamine or N-acetylneuraminic acid sugars on the AIC surface. A role for this material in colonization is supported by the fact that exogenous addition of WGA can inhibit AIC colonization by X. nematophila. Conversely, the addition of exogenous purified NilC increases the frequency with which X. nematophila is observed at the AIC, demonstrating that abundant extracellular NilC can enhance colonization. NilC may facilitate X. nematophila adherence to the nematode intestinal surface by binding to host glycans, it might support X. nematophila nutrition by cleaving sugars from the host surface, or it might help protect X. nematophila from nematode host immunity. Proteomic and metabolomic analyses of wild type X. nematophila compared to those lacking nilB and nilC revealed differences in cell wall and secreted polysaccharide metabolic pathways. Additionally, purified NilC is capable of binding peptidoglycan, suggesting that periplasmic NilC may interact with the bacterial cell wall. Overall, these findings support a model that NilB-regulated surface exposure of NilC mediates interactions between X. nematophila and host surface glycans during colonization. This is a previously unknown function for a TXISS.
Autonomous Vehicle State Estimation and Mapping Using Takagi–Sugeno Modeling Approach

This paper proposes an optimal approach for state estimation based on the Takagi–Sugeno (TS) Kalman filter using measurement sensors and rough pose obtained from LIDAR scan end-points matching. To obtain stable and optimal TS Kalman gain for estimator design, a linear matrix inequality (LMI) is optimized which is constructed from Lyapunov stability criteria and dual linear quadratic regulator (LQR). The technique utilizes a Takagi–Sugeno (TS) representation of the system, which allows modeling the complex nonlinear dynamics in such a way that linearization is not required for the estimator or controller design. In addition, the TS fuzzy representation is exploited to obtain a real-time Kalman gain, avoiding the expensive optimization of LMIs at every step. The estimation schema is integrated with a nonlinear model-predictive control (NMPC) that is in charge of controlling the vehicle. For the demonstration, the approach is tested in the simulation, and for practical validity, a small-scale autonomous car is used.
Predictive Torque Control of Induction Motor Based on a Robust Integral Sliding Mode Observer

The parameter estimators and the disturbance observers are two widely used methods for the robustness improvement of the model predictive control schemes. This article presents a hybrid solution to improve the robustness of predictive torque control (PTC) for induction motor (IM) drive. A novel integral sliding mode observer (ISMO)-based ultralocal model and an adaptive observer are combined in the proposed method to establish a robust prediction model for the PTC. The stator current prediction model of the conventional PTC contains different parameters and variables of the IM that increase the sensitivity of the method. The proposed method solves this problem by replacing the conventional stator current prediction model with the ISMO-based ultralocal model, which does not require the IM’s parameters. On the other hand, the stator flux prediction model of the PTC just depends on the stator resistance. So, an adaptive Luenberger observer is utilized to cancel the effect of resistance variation from the stator flux prediction model. The proposed ISMO and the Luenberger observer are constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. The experimental validation of the proposed method is performed. Also, the robustness of this method has been validated experimentally.
Energy‐efficient cooperative localization in mobile WSN

Design and implementation of an energy‐efficient protocol is one of the main challenges in wireless sensor networks (WSNs). In terms of localization, both energy efficiency and accuracy must be addressed to achieve the final goals of localization. In mobile sensor nodes, where battery power is the most hardware resource limitation, accurate localization needs to be extremely energy efficient. In this work, a virtual multiple‐input multiple‐output (VMIMO) technique is deployed to tackle the problems of getting more energy efficiency and higher accuracy simultaneously.In this case, the optimum selection of the number of transceiver nodes can be obtained with the lowest possible total energy consumption, localization error, and speed of nodes. In addition, VMIMO decreases the power of transmitters, and therefore will lead to the reduction of destructive effects of electromagnetic sensitivity (EMS) on the body. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Floral resource continuity boosts bumble bee colony performance relative to variable floral resources

Centuries of landscape changes associated with agriculture have dramatically reduced the amount and increased the temporal variability of the floral resources that support key pollinating insects such as bumble bees. Adapting to these novel resource conditions is important to ensure the persistence of bumble bee species. While several species appear to be in decline in modern agricultural landscapes, others have thrived, suggesting adaptation to exploit highly variable floral resources. Bombus impatiens, the common eastern bumble bee, is a prime example of such a species. We designed an experiment to compare how free‐foraging colonies of B. impatiens performed adjacent to areas with either temporally continuous or variable (pulsed) patches of purple tansy (Phacelia tanacetifolia) plantings. We found that colonies in Phacelia landscapes grew faster, had gained more mass, and produced more gynes than did colonies in reference landscapes with no Phacelia. Comparing colony responses between pulsed and continuous flowering resources showed that total mass gain at the end of the experiment was greater with continuous flowering resources. In contrast, colony growth rate and total gyne production were comparable for colonies adjacent to Phacelia plantings that were continuous versus pulsed. While low in statistical replication, given the scale of the experimental manipulation, our experiment shows that although B. impatiens colonies can exploit periods of resource discontinuity and gain mass, these continuously available floral resources appear important for colony growth and benefit gyne production.
IoT based Substation Monitoring and Control

Abstract: The Smart Voltage and Current Monitoring System (SVCMS) approach is utilized in this undertaking. It shows a solitary stage electrical design that uses voltage and flow from the sensor by means of a microcontroller and afterward remotely conveys the expected information to the outcomes screen through another PC. It is vital to utilize the Internet of Things (IoT) to upgrade the idea of the power with substation observing and control discretionary change. Accordingly, it is basic to have a perception framework that can distinguish and channel. The Arduino Uno is utilized as a microcontroller in the coordinated Smart Voltage and Current Monitoring System (SVCMS) set up to ascertain the voltage and current sensor results. It then sends this data, after assessment, to the end client's PC contraption utilizing the IoT module. The Arduino Uno controller and IoT modules are a microcontroller and far off devices, separately. In light of the heap got by the electrical machines in the event that the heap is high, the power conveyed is to be limited or the power must be closed down. In the olden days there should be a one person at the substation to look for a faults if anything occurred and now due to the increase in the tremendous technology there are many new technologies introduced into the life cycle of the of the human beings. In that one of the technology that helps to reduce the efforts of the human beings is Internet of Things(IoT) that uses the Wi-Fi module that helps to connect the hardware and the software. The data collected will be shared to the ThingSpeak platform and it will displayed by using the graphs for each parameter different bar graphs are used. Keywords: Arduino Uno, LCD display, Servo Motor, wi-fi module, Thing view App, Internet of Things, Substation Monitoring and Control
Dual‐constrained and primal‐constrained principal component analysis

Uniform Manifold Approximation and Projection (UMAP) and related manifold embedding techniques generate nonlinear projections of high‐dimensional data onto low‐dimensional subspaces. However, due to the nonlinearity of these methods, there is no analogous transformation matrix (i.e., the loadings or eigenvectors) that allows one to see the corresponding relationship between each sample location and each feature (e.g., wavelength). We propose a mechanism that embeds the pairwise distance structure of UMAP‐type embeddings into the machinery of principal component analysis such that one can recover an approximate set of loading vectors associated with the intrinsic topology of high‐dimensional data.
An Integration of Linear Model and 'Random Forest' Techniques for Prediction of Norway Spruce Vitality: A Case Study of the Hemiboreal Forest, Latvia

The increasing extreme weather and climate events have a significant impact on the resistance and resilience of Norway spruce trees. The responses and adaptation of individual trees to certain factors can be assessed through the tree breeding programmes. Tree breeding programmes combined with multispectral unmanned aircraft vehicle (UAV) platforms may assist in acquiring regular information of individual traits from large areas of progeny trials. Therefore, the aim of this study was to investigate the vegetation indices (VI) to detect the early stages of tree stress in Norway spruce stands under prolonged drought and summer heatwave. Eight plots within four stands throughout the vegetation season of 2021 were monitored by assessing spectral differences of tree health classes (Healthy, Crown damage, New crown damage, Dead trees, Stem damage, Root rot). From all tested VI, our models showed a moderate marginal R2 and total explanatory power—for Normalized Difference Red-edge Index (NDRE), marginal R2 was 0.26, and conditional R2 was 0.49 (p < 0.001); for Normalized Difference Vegetation Index (NDVI), marginal R2 was 0.34, and conditional R2 was 0.60 (p < 0.001); for Red Green Index (RGI), marginal R2 was 0.36, and conditional R2 was 0.55 (p < 0.001); while for Chlorophyll Index (CI), marginal R2 was 0.27, and conditional R2 was 0.49 (p < 0.001). The reliability of the identification of tree health classes for selected VI was weak to fair (overall classification accuracy ranged from 34.4% to 56.8%, kappa coefficients ranged from 0.09 to 0.34) if six classes were assessed, and moderate to substantial (overall classification accuracy ranged from 71.1% to 89.6% and kappa coefficient from 0.39 to 0.71) if two classes (Crown damage and Healthy trees) were tested.
Changes in Inflammatory Markers in Patients with Chronic Thromboembolic Pulmonary Hypertension Treated with Balloon Pulmonary Angioplasty

Background: Inflammatory response and endothelial dysfunction contribute to the progression of chronic thromboembolic pulmonary hypertension (CTEPH). We aimed to assess changes in biomarkers involved in those processes in inoperable CTEPH patients treated with balloon pulmonary angioplasty (BPA). Methods: We enrolled 20 patients with inoperable CTEPH qualified for BPA and a control group. Interleukin 6, 8, 10 (IL-6, IL-8, IL-10), monocyte chemoattractant protein-1 (MCP-1), and C-reactive protein (hsCRP) constituted the markers of systemic inflammation. Endothelin 1 (ET-1) served as a marker of endothelial dysfunction. Selected markers were assessed before the BPA treatment, 24 h after the first BPA, and six months after completion of the BPA treatment. Results: At baseline, the CTEPH patients had increased serum concentrations of IL-6, IL-8 and ET-1. Twenty-four hours after a BPA session, we observed an increase in concentrations of IL-6 (∆ = 3.67 (1.41; 7.16); p < 0.001), of IL-10 (∆ = 0.25 (0; 0.47); p = 0.003), of MCP-1 (∆ = 111 (60.1; 202.8); p = 0.002), and of hsCRP (∆ = 4.81 (3.46; 8.47); p < 0.001). Six months after completion of the BPA treatment, there was a decrease in concentrations of IL-6 (∆ = −1.61 (−3.11; −0.20); p = 0.03), of IL8 (∆ = −3.24 (−7.72; 0.82); p = 0.01), and of ET-1 (∆ = −0.47 (−0.96; 0.05); p = 0.005). Conclusions: Patients with inoperable CTEPH exhibit increased systemic inflammation and endothelial dysfunction, which improves after completion of the BPA treatment. A single BPA session evokes an acute inflammatory response.
Automatic Cancer Cell Taxonomy Using an Ensemble of Deep Neural Networks

Simple Summary As of recently, cancer is considered a major cause of death in developed and developing countries. Therefore, there is an urgent need for improvements in existing diagnostic methods for effective early diagnosis. However, cross-contamination of cancer cell lines results in the development of inappropriate treatments that cannot be administered to patients. To address this issue, we propose an automatic cancer cell taxonomy with high accuracy using optical images of cells obtained through low-scale benchtop optical microscopy. Specifically, we built a deep-learning-based framework to classify cervical, hepatocellular, breast, and lung cancer cells. The experimental results demonstrated that the proposed deep-learning-based approach facilitates the automatic identification of cancer cells. Moreover, our findings provide important insights into the design of convolutional neural networks for various clinical tasks that utilize microscopic images. Abstract Microscopic image-based analysis has been intensively performed for pathological studies and diagnosis of diseases. However, mis-authentication of cell lines due to misjudgments by pathologists has been recognized as a serious problem. To address this problem, we propose a deep-learning-based approach for the automatic taxonomy of cancer cell types. A total of 889 bright-field microscopic images of four cancer cell lines were acquired using a benchtop microscope. Individual cells were further segmented and augmented to increase the image dataset. Afterward, deep transfer learning was adopted to accelerate the classification of cancer types. Experiments revealed that the deep-learning-based methods outperformed traditional machine-learning-based methods. Moreover, the Wilcoxon signed-rank test showed that deep ensemble approaches outperformed individual deep-learning-based models (p < 0.001) and were in effect to achieve the classification accuracy up to 97.735%. Additional investigation with the Wilcoxon signed-rank test was conducted to consider various network design choices, such as the type of optimizer, type of learning rate scheduler, degree of fine-tuning, and use of data augmentation. Finally, it was found that the using data augmentation and updating all the weights of a network during fine-tuning improve the overall performance of individual convolutional neural network models.
Design of a Decoupled Six-axis Force Sensor for Powered Lower Limb Exoskeleton

In this paper, we introduce the six-axis force sensor used for detecting the human-machine interaction and predicting the wearer' s movement intention of the lower limbs powered exoskeleton robot, including the mechanical design, parameters determination and finite element simulation. With the help of the sensor, the exoskeleton can conclude the current motion state and forecast the human movement intention, and achieve human-machine following movement and supply power. K eywords: Six-axis force sensor; Mechanical design; Finite element simulation; Exoskeleton.
Factors Regulating or Regulated by Myogenic Regulatory Factors in Skeletal Muscle Stem Cells

MyoD, Myf5, myogenin, and MRF4 (also known as Myf6 or herculin) are myogenic regulatory factors (MRFs). MRFs are regarded as master transcription factors that are upregulated during myogenesis and influence stem cells to differentiate into myogenic lineage cells. In this review, we summarize MRFs, their regulatory factors, such as TLE3, NF-κB, and MRF target genes, including non-myogenic genes such as taste receptors. Understanding the function of MRFs and the physiology or pathology of satellite cells will contribute to the development of cell therapy and drug discovery for muscle-related diseases.
Determination of Primary Surface Water Pollution Indicators by Multivariate Statistical Techniques in an Industrialized Basin

- This study aimed to investigate the point and diffuse pollution sources of high total phosphorus (TP) pollution detected in the dry and wet seasons of the industrialized and urbanized Saz-Cayirova Basin through field observation and multivariate statistical techniques. In this context, nineteen water quality parameters were analyzed in surface water samples collected monthly between June 2020 and July 2021 from nineteen different sites along the Saz-Cayirova stream. Firstly, two reference sites representing a better surface water status were determined and assessed the water quality on the stream tributaries affecting the wastewater treatment plant in the two Organized Industrial Zones (OIZ). Secondly, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were performed to evaluate the complex water quality dataset and reveal the latent sources of TP pollution. The results showed that the tributaries in the pressure of OIZ discharges were highly enriched in COD, TOC, NO 3 -N, NH 3 -N, and TP concentrations compared to the reference level. Compared with reference sites, the concentrations of the four heavy metals examined were at a plausible level. Besides, the pollution sources of TP were industrial processes wastewater such as dyeing, washing, phosphating, domestic wastewater from OIZ, urban diffuse waters with organic character, and continuous discharges of undefined sources. The seasonal variation of TP values is observed relatively low, indicating that the stream network is greatly affected by point source pollution. Our observation and analysis imply that the treatment technologies adopted by the OIZ wastewaters plant is sufficient to treat heavy metals. However, measures need to be formulated to remove TP and organic pollution from the secondary production process.