Miracle-dz/UniFAR-FTUs huggingface dataset:
Academic stress as a predictor of mental health in university students

Abstract Identifying the relationship between academic stress and mental health of undergraduate university students is crucial for reducing and understanding its negative effects, enhancing students’ ability to cope with stressful situations, and thereby reducing the harm it causes on academic performance and overall well-being. This study aims to examine the correlation and predictive value of academic stress on mental health in undergraduate university students. A representative sample of 1,265 undergraduate university students from a private university in Bolivia was assessed using Pearson’s correlation analysis to determine the predictive value of academic stress on mental health. To validate the measurements obtained, a stepwise Hierarchical Multiple Linear Regression analysis was applied. A probability model was estimated to identify academic stressors that contribute to the probability of students experiencing Languishing Mental Health. The study revealed that Self-inflicted Stress was the most significant stressor among undergraduate students. This indicates that students’ self-demands and self-efficacy perceptions are essential factors in the development of high academic stress levels. There is a clear correlation between high levels of academic stress and the probability of experiencing Languishing Mental Health.
Master‐slave synchronization for coupled neural networks with Markovian switching topologies and stochastic perturbation

In this paper, the master‐slave synchronization for coupled neural networks with Markovian jumping topology and stochastic perturbation is discussed. Based on a graph theory, the ergodic property of the Markovian chain, and the strong law of the large numbers for local martingales, several sufficient conditions are established to ensure the almost sure exponential synchronization or asymptotic synchronization in mean square for coupled neural networks with Markovian jumping topology. By the pinning control method, the chaotic synchronization between the master system and the slave systems with stochastic disturbance is achieved. The effectiveness of the results is finally illustrated by a numerical example.
Critical Thinking in the Kurdistan Region of Iraq Schools: A Case Study of Two Prominent Schools in Erbil

Critical thinking goes beyond memorization, promotes independent thinking and reasoned judgment. It is a crucial element in connecting ideas, solving problems, and creating knowledge. Schools are considered to be among the initial steps in developing critical thinking where pedagogical methods would guarantee the formation of it. Critical thinking in schools develops the intellectual capability instead of using it as a memory bank of information where mind does not have any role to play. This work is an attempt to demonstrate role and effect of practicing critical thinking in primary school and analyse reflection and ability of practicing critical thinking in the Kurdistan Region of Iraq. The study relies on deep interview and observation of eighth-grade students and teachers in two prominent schools in Erbil, focusing on four different subjects. Type of questions raised by teachers and students during the lectures have been examined, and a detailed analysis of the reflection and ability to practice critical thinking is presented.
SIMD Operations in GSW-FHE

: We construct the ﬁrst single-instruction-multiple-data (SIMD) variant of GSW-FHE that is a fully homomorphic encryption (FHE) scheme provided by Gentry, Sahai, and Waters (CRYPTO 2013). Our scheme can encrypt a message vector into one ciphertext and homomorphically compute SIMD operations. All of the previous SIMD FHE schemes have complex and expensive homomorphic multiplication algorithms involving the dimension or modulus reduction. In our scheme, homomorphic SIMD addition and multiplication are just matrix addition and multiplication.
Deep reinforcement learning for continuous-time self-triggered control with experimental evaluation

This paper develops a deterministic policy gradient (DPG) method for self-triggered control (STC) on continuous-time systems. To this end, we propose a reinforcement learning (RL) algorithm for STC. In general, control systems have high sampling rate (communication cost) to guarantee control performance. However, STC policy allows reducing the sampling rate while maintaining reasonable control performance. Moreover, it compresses the size of the historical data set, slowing down the data explosion. The STC-based RL demonstrates high data efficiency and the potential for industrial applications. A numerical simulation of a rotary inverted pendulum and the corresponding experiments are employed to show the effectiveness of the algorithm. GRAPHICAL ABSTRACT
Wearable Smart Fabric Based on Hybrid E-Fiber Sensor for Real-Time Finger Motion Detection

Wearable electronic sensors have attracted considerable interest in hand motion monitoring because of their small size, flexibility, and biocompatibility. However, the range of motion and sensitivity of many sensors are inadequate for complex and precise finger motion capture. Here, organic and inorganic materials were incorporated to fabricate a hybrid electronic sensor and optimized and woven into fabric for hand motion detection. The sensor was made from flexible porous polydimethylsiloxane (PDMS) filled with multiwalled carbon nanotubes (MWCNTs). The weight ratios of MWCNTs and geometric characteristics were optimized to improve the hybrid electronic sensor, which showed a high elongation at the breaking point (i.e., more than 100%) and a good sensitivity of 1.44. The strain-related deformation of the PDMS/MWCNT composite network resulted in a variation in the sensor resistance; thus, the strain level that corresponds to different finger motions is be calculated. Finally, the fabricated and optimized electronic sensor in filiform structure with a 6% MWCNT ratio was integrated with smart fabric to create a finger sleeve for real-time motion capture. In conclusion, a novel hybrid E-fiber sensor based on PDMS and MWCNTs was successfully fabricated in the current study with an optimal M/P ratio and structure, and textile techniques were adopted as new packaging approaches for such soft electronic sensors to create smart fabric for wearable and precise detection with highly enhanced sensing performance. The successful results in the current study demonstrate the great potential of such hybrid soft sensors in smart wearable healthcare management, including motion detection.
Direct Imaging of Local pH Reveals Bubble-Induced Mixing in a CO2 Electrolyzer

Electrochemical CO2 reduction poses a promising pathway to produce hydrocarbon chemicals and fuels without relying on fossil fuels. Gas diffusion electrodes allow high selectivity for desired carbon products at high current density by ensuring a sufficient CO2 mass transfer rate to the catalyst layer. In addition to CO2 mass transfer, the product selectivity also strongly depends on the local pH at the catalyst surface. In this work, we directly visualize for the first time the two-dimensional (2D) pH profile in the catholyte channel of a gas-fed CO2 electrolyzer equipped with a bipolar membrane. The pH profile is imaged with operando fluorescence lifetime imaging microscopy (FLIM) using a pH-sensitive quinolinium-based dye. We demonstrate that bubble-induced mixing plays an important role in the Faradaic efficiency. Our concentration measurements show that the pH at the catalyst remains lower at −100 mA cm–2 than at −10 mA cm–2, implying that bubble-induced advection outweighs the additional OH– flux at these current densities. We also prove that the pH buffering effect of CO2 from the gas feed and dissolved CO2 in the catholyte prevents the gas diffusion electrode from becoming strongly alkaline. Our findings suggest that gas-fed CO2 electrolyzers with a bipolar membrane and a flowing catholyte are promising designs for scale-up and high-current-density operation because they are able to avoid extreme pH values in the catalyst layer.
Measurement of the large scale anisotropy in cosmic ray electrons, positrons and protons with the AMS-02 detector on the ISS

The field of astroparticle physics started with the discovery of cosmic rays in 1912 by Victor Hess. Cosmic rays, which initially led to important advances in the field of Particle Physics, provide precious information about the violent phenomena in the Universe producing such high-energy particles and the mechanisms of their propagation in the interstellar medium. In the last years, Cosmic Ray Physics has profited from the rise in space research,and space-based experiments are currently providing direct measurements with unprecedent precision. In this context, recent measurements of the spectra of several cosmic ray species cannot be fully explained within the current paradigm of cosmic ray origin and propagation, and constitute an open window into new phenomena. In particular, AMS-02 has provided the most precise measurement of the positron fraction, and the positron and electron fluxes. These results allowed an accurate description of the rise in the positron fraction and led to a plethora of models proposed to explain the so-called positron excess non consistent with the standard secondary origin of antimatter in cosmic rays. For most cases, the observation requires the inclusion of primary sources, which are typically classified into two scenarios: dark matter or astrophysical origin.
Involvement of the H3.3 Histone Variant in the Epigenetic Regulation of Gene Expression in the Nervous System, in Both Physiological and Pathological Conditions

All the cells of an organism contain the same genome. However, each cell expresses only a minor fraction of its potential and, in particular, the genes encoding the proteins necessary for basal metabolism and the proteins responsible for its specific phenotype. The ability to use only the right and necessary genes involved in specific functions depends on the structural organization of the nuclear chromatin, which in turn depends on the epigenetic history of each cell, which is stored in the form of a collection of DNA and protein modifications. Among these modifications, DNA methylation and many kinds of post-translational modifications of histones play a key role in organizing the complex indexing of usable genes. In addition, non-canonical histone proteins (also known as histone variants), the synthesis of which is not directly linked with DNA replication, are used to mark specific regions of the genome. Here, we will discuss the role of the H3.3 histone variant, with particular attention to its loading into chromatin in the mammalian nervous system, both in physiological and pathological conditions. Indeed, chromatin modifications that mark cell memory seem to be of special importance for the cells involved in the complex processes of learning and memory.
Orbital and rotational dynamics of solar power satellites in geosynchronous orbits

Designs for geostationary (GEO) solar power satellites (SPS) are extremely large in scale, more than one order of magnitude larger than the International Space Station. In this thesis a detailed study of the orbit dynamics of SPS is performed. Analytical equations, derived by the process of averaging of the SPS equations of motion, are used to determine the long-term orbital evolution. Previous SPS studies have simply assumed a GEO as the operational orbit, and then designed control systems for maintaining the orbit within acceptable nominal values. It is found that an alternative SPS orbital location known as the geosynchronous Laplace plane orbit (GLPO) is superior to GEO in many aspects. An SPS in GLPO requires virtually no fuel to maintain its orbit, minimises the risk of debris creation at geosynchronous altitude, and is extremely robust operationally, i.e. loss of control is inconsequential. The GLPO SPS requires approximately 10^5 kg less fuel per year compared to a GEO SPS while providing near equivalent power delivery. Although savings in orbit control are achieved, depending on the mass distribution of the SPS, attitude control costs may be incurred by placing an SPS in GLPO. Consideration of the attitude dynamics of SPS has motivated the development of a model for the rotational dynamics of a body which includes energy dissipation and the effects of external torques. Multiple spring-damper masses are used to provide a mechanism for energy dissipation. This rotational dynamics model is used to assess the naturally stable attitude configurations of a SPS design in geosynchronous orbit subject to gravity gradient torque. It is found that for a large planar array, a dynamically stable configuration requiring nominal orbit-attitude control is possible. This involves rotating around the maximum axis of inertia at the orbit rate, with the minimal axis aligned in the radial direction. It will be shown that a SPS in this configuration while in GLPO requires virtually no orbit or attitude control. The most significant result of the research in this thesis is proving that a SPS can operate in GLPO with nominal orbit control and yet still deliver almost equivalent power to the Earth’s surface as the same SPS would in a controlled GEO.
Spatially weighted PCA for monitoring video image data with application to additive manufacturing

Abstract Machine vision systems for in-line process monitoring in advanced manufacturing applications have attracted an increasing interest in recent years. One major goal is to quickly detect and localize the onset of defects during the process. This implies the use of image-based statistical process monitoring approaches to detect both when and where a defect originated within the part. This study presents a spatiotemporal method based on principal component analysis (PCA) to characterize and synthetize the information content of image streams for statistical process monitoring. A spatially weighted version of the PCA, called ST-PCA, is proposed to characterize the temporal auto-correlation of pixel intensities over sequential frames of a video-sequence while including the spatial information related to the pixel location within the image. The method is applied to the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras. A k-means clustering-based alarm rule is proposed to provide an identification of defects in both time and space. A comparison analysis based on simulated and real data shows that the proposed approach is faster than competitor methods in detecting the defects. A real case study in selective laser melting (SLM) of complex geometries is presented to demonstrate the performances of the approach and its practical use.
Human Identification Based on SIFT Features of Hand Image

: The use of hand features for human identification is a reliable and convenient method that has a greater influence than other biometric techniques because of its simplicity and dependability. In this study, we suggested a technique for identifying individuals using all the features of a hand image which di ff ers from the previous methods of identifying individuals depending on either the features of the palmprint only or the features of the fingerprint only. Therefore, the paper aims to build an e ffi cient system to identify individuals depending on the features of the hand image using Scale Invariant Feature Transform (SIFT) features. Our proposed system comprises three main stages. The first stage includes removing the background and unimportant objects and extracting the hand area from the captured image. The SIFT feature is then applied to extract the robust features from the hand area. Finally, the matching stage depends on the maximum matching points of the SIFT points between the test and training image. The outcomes demonstrate the correct recognition rate (CRR), accuracy, and recall are 99.65%, 99.32%, and 98.62%, respectively. Thus, the e ff ectiveness and e ffi ciency of the suggested approach have been proven.
Optimization of the extraction of bioactive compounds from Arabica coffee husk using ultrasound-assisted and their potential as a source of antioxidant, and aromatic substances

This work aimed to optimize the process of polyphenolic compounds extraction from Arabica coffee industrial residue (pulp, skin, mucilage, and parchment) using the ultrasound-assisted technique, evaluate the antioxidant potential of the extracts and determine the volatile profile of the extract with the highest antioxidant activity. In the experiments, the ethanol concentration, solvent/solid ratio and extraction time were varied through a central composite rotational design. Mathematical models were obtained for the extraction of total phenolic, total flavonoid and antioxidant activity were obtained, where the ethanol concentration was the parameter with the greatest influence on the extraction. In the extract with the highest antioxidant potential by the DPPH and FRAP methods, 43 compounds were detected, with higher concentrations of diethyl acetal, isoamyl acetate, ethyl hexadecanoate, and limonene, compounds known as fragrance aromas. Arabica coffee industrial residue showed the potential to be used in future applications as a source of bioactive and aroma compounds in food, pharmaceutical or cosmetic products.
Assessment of the Phytochemical Profile, Antioxidant Capacity, and Hepatoprotective Effect of Andrographis paniculata against CCl4-Induced Liver Dysfunction in Wistar Albino Rats

Recent studies have highlighted the necessity to thoroughly evaluate medicinal plants due to their therapeutic potential. The current study delves into the phytochemical profile, antioxidant capacity, and hepatoprotective effect of Andrographis paniculata. The investigation specifically targets its effectiveness in mitigating liver dysfunction induced by carbon tetrachloride (CCl4) in Wistar albino rats, aiming to uncover its promising role as a natural remedy for liver-related ailments. A. paniculata leaf extract was screened for phytoconstituents and antioxidant and hepatoprotective effects in Wistar albino rats against CCl4-induced liver dysfunction. Phytochemical analysis revealed the presence of flavonoids, alkaloids, and phenolic compounds in all extracts. The phenolic concentration ranged from 10.23 to 19.52 mg gallic acid per gram of the sample, while the highest flavonoid concentration was found in the ethanol fraction (8.27 mg rutin equivalents per gram). The antioxidant activity varied from 10.23 to 62.23. GC-MS analysis identified several phytochemicals including octadecanoic acid, stigmasterol, phenanthrenecarboxylic acid, and others. Effects of the ethanol extract of A. paniculata were evaluated in four groups of animals. Biochemical estimations of serum glutamine oxaloacetate transaminase, serum glutamine pyruvate transaminase, and serum bilirubin were significantly higher (p < 0.05) in the CCl4-treated group. Treatment with 300 mg/kg b.w. of the ethanol extract of A. paniculata significantly (p < 0.05) decreased these serum enzymes. Lipid peroxidation levels in carbon tetrachloride-treated animals showed a substantial (p < 0.05) rise when compared to untreated animals, while the lipid peroxidation levels were considerably (p < 0.05) reduced after treatment with ethanol extract at 300 mg/kg b.w. Liver biochemical catalase activities were significantly reduced in the carbon tetrachloride-treated animals. The results of this study conclusively demonstrate that A. paniculata extracts are a rich source of phytochemicals and possess significant antioxidant, free radical scavenging, and hepatoprotective properties.
MultiFDF: Multi-Community Clustering for Fairness-Aware Recommendation

The fairness consideration has received increasing attention in artificial intelligence (AI), especially in the significant application, recommender system. One kind of fairness issue is to balance the exposure of popular items and less popular items. Existing works mainly compensate the less popular items when predicting their ratings. However, the compensation for the items may result in a loss of recommendation accuracy, as the compensated items may not match user’s preference. For this problem, we propose a multi-community clustering recommendation with fair decision fusion (MultiFDF) framework to compensate less popular items locally, which can reduce the negative impact on user’s preferred popular item. We first design an experiment and deliver a causal graph-based mathematical proof to demonstrate the feasibility of local compensation. It proves that there is a discrete tendency of ratings predicted by a recommendation algorithm and a recommendation algorithm gives a higher rating to the popular item than the less popular item. The MultiFDF consists of three parts, community exploration module, local recommendation module, and fair decision fusion module. The community exploration module outputs several communities for local recommendation module to generate local recommendation lists, respectively. The fair decision fusion module then computes the discrete ratings of items based on local recommendation lists and designs an edge reranking strategy based on their discrete ratings to obtain the final fair top- $N$ recommendation list. To verify the superiority of our proposed MultiFDF, we conduct experiments on three real world datasets and the results demonstrate that MultiFDF can improve fairness at the cost of lower accuracy than the state-of the-art algorithms.
Fabrication, Characterization and Biomedical Evaluation of a Statistically Optimized Gelatin Scaffold Enriched with Co-Drugs Loaded into Controlled-Release Silica Nanoparticles

The current study focused on the fabrication of a well-designed, biocompatible, physically stable, non-irritating and highly porous gelatin scaffold loaded with controlled-release triamcinolone acetonide (TA) and econazole nitrate (EN) co-loaded into mesoporous silica nanoparticles (EN-TA-loaded MSNs) to provide a better long-lasting antifungal therapeutic effect with minimal unfavorable effects. Optimization of the MSNs-loaded scaffold was performed using central composite rotatable design (CCRD), where the effect of gelatin concentration (X1), plasticizer (X2) and freezing time (X3) on the entrapment of EN (Y1) and TA (Y2) and on the release of EN (Y3) and TA (Y4) from the scaffold were studied. The significant compatibility of all formulation ingredients with both drugs was established from XRD, DSC and FT-IR spectra analyses while SEM and zeta studies represented a very precise unvarying distribution of the loaded MSNs in the porous structure of the scaffold. The stability of the optimized scaffold was confirmed from zeta potential analysis (−16.20 mV), and it exhibited higher entrapment efficiency (94%) and the slower (34%) release of both drugs. During in vitro and in vivo antifungal studies against Candida albicans, the MSNs-loaded scaffold was comparatively superior in the eradication of fungal infections as a greater zone of inhibition was observed for the optimized scaffold (16.91 mm) as compared to the pure drugs suspension (14.10 mm). Similarly, the MSNs-loaded scaffold showed a decreased cytotoxicity because the cell survival rate in the scaffold presence was 89% while the cell survival rate was 85% in the case of the pure drugs, and the MSNs-loaded scaffold did not indicate any grade of erythema on the skin in comparison to the pure medicinal agents. Conclusively, the scaffold-loaded nanoparticles containing the combined therapy appear to possess a strong prospective for enhancing patients’ adherence and therapy tolerance by yielding improved synergistic antifungal efficacy at a low dose with abridged toxicity and augmented wound-healing impact.