Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-Based Culture Independent Pervasive M-Learning Prototype Using UI Plasticity Design

This paper explains the development of a culturally inclusive ubiquitousM-Learning platform (“Mobile Academy”) with an AI-based adaptive user interface. The rationale and need for this research and development are justified by the continuing widespread adoption of the Internet and Internet enabled devices, especially smartphones. The M-learning platform was designed from the onset for the global traveller. The characteristics and limitations of the application are also discussed. The Mobile Academy, proof of concept prototype, was created to facilitate teaching and learning on the move or in environments where the use of a desktop computer is inconvenient or simply impossible. The platform’s primary objective was cross-cultural usability through the use of a combination of AI and plasticity of user interface design techniques. The usability evaluation plan was comprehensive and the results obtained were studied in detail. This also included consideration of the results of the SVM (Support Vector Machine) classifiers’ performance and cross-device evaluation. The AI-based adaptive interface prototype has been tested and evaluated to show itsmerits and capabilities in terms of its usability, inclusivity and effectiveness of the interface. From the results, it was concluded that such a culture independent application was also affordable.
Instabilités de plissement lors du séchage de films polymères plans

La cinetique de sechage de solutions polymere/solvant depend a la fois des conditions d'echange avec le milieu exterieur et des caracteristiques physico-chimiques du couple polymere/solvant. Nous nous interessons dans cette etude au sechage de films plans de Polyisobutylene-Toluene et donnons quelques premiers resultats sur l'analyse de l'instabilite de plissement observee pour certaines conditions operatoires. Seuls les mecanismes d'instabilites de type hydrodynamique (thermique ou solutale), induits par des differences de masse volumique et/ou de tension superficielle entre solvant et polymere, sont consideres dans cette etude.
Assessment of the Restoration of the Remolar Dune System (Viladecans, Barcelona): The Resilience of a Coastal Dune System

The Remolar beach-dune system (700 m long and more than 100 m wide, 070N direction) borders a campground that was closed (2003), due to the Barcelona airport expansion. In order to recover and restore the dune ecosystem, a series of soft measures were performed. After 10 years, a study of the morphology, sedimentology, and vegetation of the ecosystem was carried out to evaluate the results of these measures. For this purpose, a series of topographic and ground-penetrating radar (GPR) profiles, grain-size analysis, and an analysis of plant communities found along the profiles were carried out. The data obtained were compared with data from a former 2004 study. The results show that the morphology of the dunes recovered, and a new primary dune has arisen. The system now has a greater process of aggradation than of progradation. The vegetation has recovered the global composition of dune systems, with a typical community of embryo dunes and others of primary dunes that are set in strips parallel to the coast. Despite this improvement, the opportunistic and ruderal component in the primary dune vegetation evidences a strong anthropic inheritance in the system.
A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

Estimating the target position of low-frequency sound sources in a shallow sea environment is difficult due to the high cost of hydrophone placement and the complexity of the propagation model. We propose a compressed recurrent neural network (C-RNN) model that compresses the signal received by a vector hydrophone into a dynamic sound intensity signal and compresses the target position of the sound source into a GeoHash code. Two types of data are used to carry out prior training on the recurrent neural network, and the trained network is subsequently used to estimate the target position of the sound source. Compared with traditional mathematical models, the C-RNN model functions independently under the complex sound field environment and terrain conditions, and allows for real-time positioning of the sound source under low-parameter operating conditions. Experimental results show that the average error of the model is 56 m for estimating the target position of a low-frequency sound source in a shallow sea environment.
Telomerase Positive Totipotent Stem Cells and Pluripotent Stem Cells in the Adult Brain I. Cerebral Cortex

Previous isolation studies noted the presence of native undifferentiated telomerase positive stem cells within the brains of adult rats. Further segregation of the cells from the brain isolates demonstrated the presence of telomerase positive totipotent stem cells and pluripotent stem cells within the cell isolates. Characterization studies of their differentiation potential noted that both the telomerase positive totipotent stem cells and pluripotent stem cells would form neurons, ganglion cells and glial cells, as well as cells from surface ectoderm, mesodermal and endodermal embryonic germ layer lineages. Since reports from other groups noted the presence of neural stem cells within the subventricular zone of the lateral ventricles and the dentate gyrus of the hippocampus, we wanted to ascertain the locations of these native telomerase positive totipotent stem cells and pluripotent stem cells within various regions of the adult brain. The brains from adult rats were examined. Adult rats were euthanized following the guidelines of Mercer University School of Medicine’s IACUC. The rats were perfused with ELICA fixative, the brains harvested, frozen, cryosectioned and stained with antibodies diagnostic for the endogenous totipotent stem cells, i.e., carcinoembryonic antigen-cell adhesion molecule-1 (CEA-CAM-1) and pluripotent stem cells, i.e., stage-specific embryonic antigen-4 (SSEA-4).
Sustainable Universities as Brand Marketing for Universities: A Case of Universiti Sains Malaysia

Numerous universities have promoted a sustainable university’s brand by offering sustainability curricula, conducting sustainability studies, enforcing sustainable policies, etc. As such, sustainable universities take advantage of the opportunity to market these activities to attract potential students. This research explores international students’ perception towards the sustainable brand of Universiti Sains Malaysia (USM) and choosing a sustainable university. The study further examines the influence of factors in choosing USM regarding being USM a sustainable university. A survey was conducted involving 391 international students, using a self-assessment questionnaire followed by collecting and analysing the data using PLS-SEM. The results discovered positive relationships between brand image and brand meaning of USM as a sustainable university regarding students’ intention in choosing USM. Aside from that, USM’s credibility, informativeness, entertainment, and irritation of information affected students’ intention to study at USM. In contrast, the study found that both the perception of USM as a sustainable university and the brand identity of USM as a sustainable university had an antagonistic relationship towards students’ intention to study at USM.
A Deep Belief Network and Dempster-Shafer Theory Multiclassifier for Reliability of Wind Turbine System

Object. Reliability of wind turbine system could be made by predicting the status of power system in case it could remind engineers to fix in advance. There are multiple variables associate with system. An approach for classification according to these variables needed. Method. I proposed a deep belief network and Dempster-Shafer Theory (DBN-DS) by multiclassifier for whether wind turbine system needed to fix. DS would make a correct decision delete from DBN when model outputted incorrect result. And diagnose the wind turbine system to make a correct decision. Result. The new method was validated on wind turbine system data in 42-day intervals. The accuracy of SVM, KNN, MLP, DBN and DBN-DS were 95.1%, 96.1%, 91.2%, 97.0% and 97.5% respectively. Conclusion. This DBN-DS was more efficient that KNN, MLP, SVM and DBN. The performance was high because of binary Restricted Boltzmann Machines. The proposed method may be effective in decision support for diagnosing the wind turbine system.
Acute Stress State Classification Based on Electrodermal Activity Modeling

Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single-subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62 percent. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00 percent. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.
Automated Alignment With Respect to a Moving Inductive Wireless Charger

Wireless charging is an attractive technology that often promises increased mobility for electrical devices. However, commonly available wireless charging systems are generally intolerant to misalignment between the charger and the receiving device due to the need for inductive coupling between the two, defeating the promise of mobility. Many techniques have been studied to address this issue, including improved electrical circuits, novel charger coil designs, and the use of sensors for detecting misalignment. In this article, a new arrangement of sensing coils is used to detect lateral misalignment between a moving wireless charger and a mobile robot. Data from the sensing coils are used in a dual-loop control of the robot. The inner loop controls the robot velocities and consists of a linear quadratic regulator with integral action. The outer loop provides reference velocities to the inner loop based on the readings of the sensing coils. Using this method, an experimental mobile robot maintains alignment within 2 cm with respect to a 5-W wireless charger that moves up to 0.145 m/s. This automated alignment method is a low-cost solution that enables mobile systems, such as autonomous vehicles, to wirelessly charge while the charger is moving.
Near-Real-Time Estimation of 1-km All-Weather Land Surface Temperature by Integrating Satellite Passive Microwave and Thermal Infrared Observations

A widely used approach for all-weather land surface temperature (LST) estimation is the integration of satellite passive microwave (MW) and thermal infrared (TIR) remote sensing observations. However, there are still few methods for estimating near-real time (NRT) all-weather (AW) (NRT-AW) LST. Besides, estimation of the LST within the swath gap of the satellite MW images is still greatly limited. This letter proposes a so-called NRT-AW method for the estimation of NRT-AW LST. NRT-AW firstly fills up the brightness temperatures (BT) inside the AMSR2 swath gap. Then, the NRT AW LST is estimated by learning the mapping between the time series of AMSR2 BT and MODIS LST on the annual scales. The results of the application of NRT-AW in the Heihe River Basin (HRB) show that the NRT-AW LST is spatially continuous and highly consistent with the original MODIS LST with a standard deviation (STD) of 1.27–1.77 K. Validation based on in situ LST indicates that the NRT-AW LST estimate has a root mean square error (RMSE) of 2.46–4.62 K. This method is beneficial for rapid mapping of all-weather LST over large areas and, thus, can satisfy associated applications.
Surface Electromyography Image-Driven Torque Estimation of Multi-DoF Wrist Movements

Simultaneous and proportional control (SPC) using surface electromyography (sEMG) signals has become a prevailing solution for the intuitive control of prosthesis and human–robot interaction. However, only time and frequency domain features are generally involved in conventional SPC algorithms, ignoring the globally spatial information across channels. In this article, we presented an instantaneous sEMG-image-based model for continuous estimation on multiple degrees of freedom wrist torques. Specifically, a convolutional neural network was used to extract features from sEMG-images, and then to estimate wrist torques. The results demonstrated that the sEMG-image-based model outperformed three conventional regression methods and a deep-learning-based method with superior performance on estimation accuracy and smoothness. Furthermore, we found a strong linear correlation between the first two principal components of extracted features and the recorded wrist torques, validating the effectiveness of the proposed approach. The outcomes open up a new perspective for SPC and potentially enhance the performance of myoelectric control in practical application.
Supplemental irrigation using brackish water on maize in tropical semi-arid regions of Brazil: yield and economic analysis

: Supplemental irrigation during dry spells could reduce maize yield losses in tropical semi-arid regions, notably in small farms. However, good quality water reserves are not sufficient to meet the demands of farming communities. Consequently, our objective was to evaluate the productivity and profitability of maize under different simulated rainfall scenarios, with and without supplemental irrigation (SI) with brackish water. The field experiments were carried out during the cropping seasons of 2018 and 2019 on Ultisol. The experiment followed a randomized block design in the split-plot scheme with four replicates. The plots were formed by simulating the water supply in the soil corresponding to four water scenarios (rainy, normal, drought and severe drought), and the sub-plots with or without SI with brackish water (Electrical conductivity of 4.5 dS m –1 ). Mean yields of green maize ears were 13,083, 11,532, 10,358 and 9,609 kg ha –1 for rainy, normal, drought and severe drought scenarios, as supplemented with brackish water. For the same treatments without SI the values were 11,394, 7,896, 3,913 and 1,374 kg ha –1 , respectively. Data on farmer’s income showed that under normal, drought and severe drought supplementation with brackish water becomes economically advantageous from 0.9, 1.0 and 2.4 ha, respectively, while maize cultivation without SI is not advantageous under the last two scenarios. For severe drought conditions, however, SI using brackish water reduced commercial yield by 27 % with negative effects on the farmers’ profitability. In this case, it is necessary to use water with either lower salinity or select salt tolerant crops.
Effect of acetate metabolism modulation on 2'-fucosyllactose production in engineered Escherichia coli

Abstract 2'-Fucosyllactose (2'-FL) is one of the most important oligosaccharides in human milk. In this study, a glucose–glycerol mixed carbon source and the deletion of the genes arcA and iclR were attempted for the first time in engineered Escherichia coli to improve 2'-FL production. The highest 2'-FL titre of 5.04 g/L, almost 1.4 times higher than that in the control strain, was obtained in the arcA mutant strain using shake-flask culture. In addition, the arcA mutant showed a significantly reduced accumulation of acetate. However, the deletion of iclR and the double deletion of arcA/iclR provided no benefit in terms of 2'-FL biosynthesis. Overall, these results suggest that deletion of arcA could efficiently increase the 2'-FL yield in E. coli and could lay a foundation for more efficient and larger-scale production. Moreover, in shake-flask culture under optimal conditions, the 2'-FL titer reached a maximum value of 7.14 g/L.
THE VALENCE ELECTRON STRUCTURE OF AUSTENITE IN LOW ALLOY ULTRAHIGH-STRENGTH STEELS AND ITS INFLUENCES ON KINETICS OF PHASE TRANSFORMATION

Based on S. H. Yu's empirical electron theory of solids and molecules, the valence electron structure of austenite in low alloy ultrahigh-strength steels, 30CrMnSiNi 2 A and 40 CrMnSiMoVA is established. The behavior of various main-added elements in the kinetics of phase transformation is discussed on the basis of C-Me segregation caused by the valence electron structure, The influence of alloying elements on the structure and morphology of transformed products is discussed from the viewpoint of interaction between the driving force of phase transformation and segregating force. Then on the basis of the valence electron structure of the alloy, the composition design of ultrahlgh-strength steels is discussed.
Geoinformation Technologies in Land Use Monitoring of Fast-Growing Cities for Sustainable Urban Development

To understand the territory of fast-growing cities, where there are multiple stakeholders involved, the observation of such dynamics seems indispensable to formulate and implement policies and actions based on a better understanding of these territorial systems. This chapter offers a perspective on how urban territories should be observed through geoinformation technologies that can provide a means for creating monitoring indicators concerning land use of fast-growing cities. The city of Maputo was used as an experimental laboratory for the use of geoinformation technologies in the observation, discussion, and reflection on methods for sustainable urbanism. The discussion includes the implications of the case study and possible developments to take a step forward in land use planning processes to achieve the desired socio-spatial equality.
Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning

The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.
18F-FDG PET/CT for Therapy Monitoring in Subcutaneous Panniculitis-Like T Cell Lymphoma

1. Abstract 1.1. Purpose: The aim of this study was to describe the role of 18 F-FDG PET/CT imaging in the staging and monitoring therapy response for subcutaneous panniculitis-like T cell lymphoma (SPTCL) and to analyze the maximum standardized uptake value (SUVmax) of FDG uptake with regard to clinical factors. 1.2. Methods: Seven patients with pathologically proven SPTCL who had undergone PET/CT imaging at initial diagnosis and/or remission were included. All seven patients were treated with different protocols of chemotherapy or haematopoietic stem cell transplantation. Characteristic findings of PET/CT imaging in patients
Executive Functions in an Egyptian Sample of Adults with Generalized Epilepsy

Epilepsy affects 65 million patients worldwide, Furthermore, Impacts of epilepsy are experienced across many aspects of an individual’s life, including mental and physical wellness, cognitive abilities, instructional performance, occupational roles, and social networks, Cognitive function is critical to the quality of life of patients with epilepsy, so this study considered executive functions in epilepsy patients. Epilepsy syndromes are divided into focal and generalized types, which is dependent on whether seizures emerge from a localized brain area or display a widespread involvement of both hemispheres from the outset. Objective: The present study aimed to investigate the executive functions (EFs) (planning, cognitive flexibility, and working memory) in adults with generalized epilepsy. Materials and methods: A total of 25 adults aged 18 to 44 years with generalized epilepsy were recruited from the Epilepsy outpatient clinic of the Department of Neurology, Kasr Al-Ainy Hospital in Cairo. A similar amount of age- and sex-matched healthy volunteer’s controls (numbered 32) were included as a control group. The participants completed subtests from the Arabic version of the Wechsler Intelligence Scale for Adults (Fourth Edition) including Digit Span, Arithmetic, Letter-Number Sequencing, and Block Design, as well as the Stroop Color-Word Test as a neuropsychological test. Results: Compared to the Control group Adults with generalized epilepsy exhibited poor performance in EFs - planning, cognitive flexibility, and working memory. Conclusion: Adults with generalized epilepsy had impairment in planning, cognitive flexibility, and working memory which may affect their Quality of life.
A Novel Hierarchical Demand Response Strategy for Residential Microgrid

Incentive-based demand response (IBDR) was considered as a feasible option to solve the sudden shortage of distributed energy resource in the microgrid. However, the current IBDR policies and strategies are not directly applicable to residents due to many defects. To this end, a versatile hierarchical IBDR strategy is proposed in this article. To ensure the effectiveness of IBDR in load reduction on critical period, clustering with demand response (DR) levels decision-making algorithm is proposed to obtain the reliable estimation of DR quantity. To enhance system reliability, a reliability constraint-based DR contingency management scheme is also proposed. Moreover, to avoid the additional costs cause by DR rebound effect, a systematic costs minimization algorithm is developed. To evaluate the performance of the proposed method, the actual case-based series testing was conducted. The proposed strategy is proved that is able to effectively reduce the overall cost of microgrid while the deficiency of DER occurs.