Miracle-dz/UniFAR-FTUs huggingface dataset:
Featuring Layers of Abstraction as a Modeling Resource in an Educational Game-Based Learning Platform

This paper presents a feature supporting the modeling, management and visualization of layers of abstraction in an educational game engine. Computer science education has conquered significant recognition supported by the concept of Computational Thinking, an essential skill set in a world where computing is pervasive. One of its pillars, abstraction, has many facets on its own, and some of them still struggle to find support of educational tools. Attempting to fill this gap, this work proposes a resource that allows grouping elements while modeling games. Abstract hierarchical graph grammars are used as theoretical foundation and practical examples of how it fosters abstraction are given. The processes found to be possible to grasp with it are generalization, refining, modularization, nesting and navigation through layers of abstraction.
ENHANCING WEBSITE USABILITY TESTING: CORRELATING EYE-TRACKING, GSR, AND SUS DATA WITH RESPECT TO GENDER PREFERENCES

Having a professional team of web developers can produce a professional website, but cannot guarantee an expected usable website. This study presents a comprehensive multilayer approach for examining the correlations between different layers of user consciousness in website usability testing. It utilizes visual attention data from eye-tracking, emotional engagement data from galvanic skin response, and self-reporting data from the system usability scale. Testing AUK and UoZ university websites with 18 users using the Gazepoint GP3 system revealed insightful correlations among different layers of user consciousness, such as high emotional engagement is associated with higher fixation counts and shorter time-to-complete and thus lower SUS scores. Whereas low emotional engagement is associated with lower fixation counts, longer time-to-complete, and thus higher SUS scores. Gender preferences verifies the results from the literature on female users generally experiencing higher emotional arousal thus having lower time-to-complete and lower SUS scores. Design problems are presented in the form of improvement recommendations. The findings of the study highlight the importance of considering different layers of user consciousness in website usability testing, as well as the importance of gender preferences. Finally, current limitations and future works are presented.
Skull Vibration-Induced Nystagmus in Superior Semicircular Canal Dehiscence: A New Insight into Vestibular Exploration—A Review

The third window syndrome, often associated with the Tullio phenomenon, is currently most often observed in patients with a superior semicircular-canal dehiscence (SCD) but is not specific to this pathology. Clinical and vestibular tests suggestive of this pathology are not always concomitantly observed and have been recently complemented by the skull-vibration-induced nystagmus test, which constitutes a bone-conducted Tullio phenomenon (BCTP). The aim of this work was to collect from the literature the insights given by this bedside test performed with bone-conducted stimulations in SCD. The PRISMA guidelines were used, and 10 publications were included and analyzed. Skull vibration-induced nystagmus (SVIN), as observed in 55 to 100% of SCD patients, usually signals SCD with greater sensitivity than the air-conducted Tullio phenomenon (ACTP) or the Hennebert sign. The SVIN direction when the test is performed on the vertex location at 100 Hz is most often ipsilaterally beating in 82% of cases for the horizontal and torsional components and down-beating for the vertical component. Vertex stimulations are more efficient than mastoid stimulations at 100 Hz but are equivalent at higher frequencies. SVIN efficiency may depend on stimulus location, order, and duration. In SCD, SVIN frequency sensitivity is extended toward high frequencies, with around 400 Hz being optimal. SVIN direction may depend in 25% on stimulus frequency and in 50% on stimulus location. Mastoid stimulations show frequently diverging results following the side of stimulation. An after-nystagmus observed in 25% of cases can be interpreted in light of recent physiological data showing two modes of activation: (1) cycle-by-cycle phase-locked activation of action potentials in SCC afferents with irregular resting discharge; (2) cupula deflection by fluid streaming caused by the travelling waves of fluid displacement initiated by sound or vibration at the point of the dehiscence. The SVIN direction and intensity may result from these two mechanisms’ competition. This instability explains the SVIN variability following stimulus location and frequency observed in some patients but also discrepancies between investigators. SVIN is a recent useful insight among other bedside examination tests for the diagnosis of SCD in clinical practice.
Spatio-Temporal Contextual Conditions Causality and Spread Delay-Aware Modeling for Traffic Flow Prediction

Mobility is essential for all of us, and the daily routine of the majority is impacted by vehicular transportation. Thus, the ability to predict traffic flow is a challenging task in the field of intelligent transportation systems. However, achieving precise predictions of the state of traffic is a complex undertaking, there are two challenges: 1) Existing studies do not explicitly account for the causal influence of the “trigger effect” from contextual conditions on spatial dependencies. 2) Prior methods ignore the fact that there is a time delay in the spread of information in large-scale regions. To address these limitations, we present a novel Graph Structural Causality Spread Delay-aware Model (i.e., GSCSDM) for accurate traffic flow prediction. First, we develop a contextual causality graph that learns the spatial graph structure under the “triggering effect”. Second, we present a spread time-delay module that captures the information spread delaying triggered by contextual conditions in global regions. Furthermore, we construct a multi-graph fusion matrix to extract spatial correlation from diverse perspectives, which enhances the understanding of regions’ state interaction. Experiments on two real datasets demonstrate that GSCSDM significantly outperforms the state-of-the-art methods. Since the “trigger effect” widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications.
Agroecological Aspects of Chickpea Growing in the Dry Steppe Zone of Akmola Region, Northern Kazakhstan

The aim of this study was to develop and offer technology for growing chickpea which comprises the application of stimulants for biological growth and mineral fertilizers adapted to conditions of dry steppe zone of Akmola region in Northern Kazakhstan. To achieve the goal, the study was focused on determining the combined effect of an integrated system of optimal agro-ecological conditions, soil cultivation technology (i.e. zero tillage versus traditional) and addition of nutrients (fertilizers, stimulants of legumes) on the growth and productivity of chickpea. This research shows that chickpea is well developed in growing season under traditional technology as compared to the zero-tillage technology, i.e. traditional soil cultivation improves germination and vitality of seeds and makes effective use of soil aeration and soil porosity in the soil layer of 0-20 cm. The results show that the combinations of mineral fertilizer CaSO4 + 2H2O5 + Rizotorfin (root inoculants) and the combination of Izagry Phosphorus + Rizotorfin can be recommended to farmers. The application of Izagry Phosphorus reduces the phenophases’ elapsing time during chickpea growth and the total length (in days) of the chickpea growth. It gives farmers an opportunity to apply late sowing, for example in early June, and make use of higher temperatures during the main growing season that could have a positive impact on biological productivity of chickpea. It also improves chickpea morphological parameters (i.e. plant height) which produces a significant increase in biological productivity, i.e. yield, which was significantly higher under the traditional technology than under the zero-tillage. A remarkable increase of grain yield by 88% to 95% compared to the control variant, upon treatment with Izagry Phosphorus and Izagry Phosphorus + Rizotorfin, respectively, was observed. It provides new economic incentives for farmers that practice environmentally-friendly farming in Kazakhstan.
Impact of soil seed bank dynamics on weedy rice distribution and farmer involvement in management: Case study in Marata district, Sri Lanka

Weedy rice (Oryza sativa f. spontanea) reduces rice yield qualitatively and quantitatively. This case study was carried out to assess the impact of weedy rice seed soil bank dynamics to the temporal density variation in consecutive two years and to assess the farmers’ involvement on the management of weedy rice in the area. For soil seed bank dynamics, soil samples were collected from 12”x12” 3” space with ten random samples per each location while number of weedy rice plants in 1m2 quadrate was counted in five different places per each location with at least 5m distance between populations in following two consecutive seasons. Our results revealed significantly wide range of variability of weedy rice seed persistency (153.9 - 42.2) in soil seed bank showing their high adaptability and direct correlation to temporal density in rice ecosystems. The 77% of weedy rice infested paddy lands were direct seeded and collective approaches were used by farmers to control weedy rice in the area. The best preventive measure identified was practice of high-quality paddy seeds. The understanding of the soil seed bank dynamics, diversity, density and distribution is vital to implement successful and efficient management practices.
Developing a Novel Read-Across Concept for Ecotoxicological Risk Assessment of Phosphate Chemicals: A Case Study

This study introduces a novel concept approach for a read-across assessment, considering species sensitivity differences among phosphate chemicals within structurally similar compound groups. Twenty-five organic chemicals, with a log Kow of 5 or less, were categorized into three functional groups based on acetylcholinesterase (AChE) inhibition as a specific mode of action (MOA). The short-term aquatic toxicity data (LC50) for fish, crustaceans, and insects were collected from the U.S. EPA Ecotoxicology (ECOTOX) Knowledgebase. A geometric mean calculation method was applied for multiple toxic endpoints. Performance metrics for the new read-across concept, including correlation coefficient, bias, precision, and accuracy, were calculated. Overall, a slightly higher overestimation (49.2%) than underestimation (48.4%) in toxicity predictions was observed in two case studies. In Case study I, a strong positive correlation (r = 0.93) between the predicted and known toxicity values of target chemicals was observed, while in Case study II, with limited information on species and their ecotoxicity, showed a moderate correlation (r = 0.75). Overall, the bias and precision for Case study I were 0.32 ± 0.01, while Case study II showed 0.65 ± 0.06; however, the relative bias (%) increased from 37.65% (Case study I) to 91.94% (Case study II). Bland–Altman plots highlight the mean differences of 1.33 (Case study I) and 1.24 (Case study II), respectively. The new read-across concept, focusing on AChE inhibition and structural similarity, demonstrated good reliability, applicability, and accuracy with minimal bias. Future studies are needed to evaluate various types of chemical substances, diverse modes of action, functional groups, toxic endpoints, and test species to ensure overall comprehensiveness and robustness in toxicity predictions.
Sum Throughput Maximization in a Cognitive Multiple Access Channel With Cooperative Spectrum Sensing and Energy Harvesting

This paper focuses on the problem of sensing throughput optimization in a fading multiple access cognitive radio (CR) network, where the secondary user (SU) transmitters participate in cooperative spectrum sensing and are capable of harvesting energy and sharing energy with each other. We formulate the optimization problem as a maximization of the expected achievable sum-rate over a finite horizon, subject to an average interference constraint at the primary receiver, peak power constraints, and energy causality constraints at the SU transmitters. The optimization problem is a non-convex, mixed integer non-linear program (MINLP) involving the binary action to sense the spectrum or not, and the continuous variables, such as the transmission power, shared energy, and sensing time. The problem is analyzed under two different assumptions on the available information pattern: 1) non-causal channel state information (CSI), energy state information (ESI), and infinite battery capacity and 2) the more realistic scenario of the causal CSI/ESI and finite battery. In the non-casual case, this problem can be solved by an exhaustive search over the decision variable or an MINLP solver for smaller problem dimensions, and a novel heuristic policy for larger problems, combined with an iterative alternative optimization method for the continuous variables. The causal case with finite battery is optimally solved using a dynamic programming (DP) methodology, whereas a number of sub-optimal algorithms are proposed to reduce the computational complexity of DP. Extensive numerical simulations are carried out to illustrate the performance of the proposed algorithms. One of the main findings indicates that the energy sharing is more beneficial when there is a significant asymmetry between average harvested energy levels/channel gains of different SUs.
Improved Coyote Optimization Algorithm and Deep Learning Driven Activity Recognition in Healthcare

Healthcare is an area of concern where the application of human-centred design practices and principles can enormously affect well-being and patient care. The provision of high-quality healthcare services requires a deep understanding of patients’ needs, experiences, and preferences. Human activity recognition (HAR) is paramount in healthcare monitoring by using machine learning (ML), sensor data, and artificial intelligence (AI) to track and discern individuals’ behaviours and physical movements. This technology allows healthcare professionals to remotely monitor patients, thereby ensuring they adhere to prescribed rehabilitation or exercise routines, and identify falls or anomalies, improving overall care and safety of the patient. HAR for healthcare monitoring, driven by deep learning (DL) algorithms, leverages neural networks and large quantities of sensor information to autonomously and accurately detect and track patients’ behaviors and physical activities. DL-based HAR provides a cutting-edge solution for healthcare professionals to provide precise and more proactive interventions, reducing the burden on healthcare systems and improving patient well-being while increasing the overall quality of care. Therefore, the study presents an improved coyote optimization algorithm with a deep learning-assisted HAR (ICOADL-HAR) approach for healthcare monitoring. The purpose of the ICOADL-HAR technique is to analyze the sensor information of the patients to determine the different kinds of activities. In the primary stage, the ICOADL-HAR model allows a data normalization process using the Z-score approach. For activity recognition, the ICOADL-HAR technique employs an attention-based long short-term memory (ALSTM) model. Finally, the hyperparameter tuning of the ALSTM model can be performed by using ICOA. The stimulation validation of the ICOADL-HAR model takes place using benchmark HAR datasets. The wide-ranging comparison analysis highlighted the improved recognition rate of the ICOADL-HAR method compared to other existing HAR approaches in terms of various measures.
Elucidation of Molecular Interactions Between Drug–Polymer in Amorphous Solid Dispersion by a Computational Approach Using Molecular Dynamics Simulations

Introduction Amorphous drug dispersion is frequently used to enhance the solubility and dissolution of poorly water-soluble drugs, thereby improving their oral bioavailability. The dispersion of these drugs into polymer matrix can inhibit their recrystallization. The inter-molecular interactions between drug and polymer plays a role in the improvement of the dissolution rate, solubility, and physical stability of drug. Aim This study aims to investigate the formation and interactions of ritonavir (RTV)/poloxamer (PLX) amorphous formulation using a computational approach via molecular dynamics (MD) simulations, which mimicked solvent evaporation and melt-quenching method. Methods TheRoot Mean Square Deviation (RMSD) value, Root Mean Square Fluctuation (RMSF), Radial Distribution Function (RDF), Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA), and hydrogen bond interactions were analyzed to determine interaction mechanisms between RTV and PLX in amorphous solid dispersion. Results The pi-alkyl bonds between RTV and PLX were formed after simulations of solvent evaporation, while the hydrogen bond interactions of RTV-PLX was observed during melt method simulations. These results indicate the successful formulation of amorphous solid dispersion (ASD) from RTV and PLX. The RMSD values obtained from the solvent evaporation, melt-cooling-A, melt-cooling-B, and melt-cooling-C methods were 3.33 Å, 1.97 Å, 1.30 Å, and 1.29 Å, respectively, while the average RMSF values were 2.65 Å, 1.04 Å, 1.05 Å, and 1.07 Å, respectively. This indicates that the suppression of translational motion of RTV from the melt method can be stronger than solvent evaporation caused by the intermolecular interactions of RTV-PLX. Conclusion MD simulations helped in understanding the formation and interaction mechanisms of ASD formulations that were difficult to detect by experimental approaches.
Comparison Between Atmospheric Turbidity Coefficients of Desert and Temperate Climates

Knowledge of the solar radiation available on the earth’s surface is essential for the development of solar energy devices and for estimating of their performance efficiencies. For this purpose it is helpful to study the attenuation of direct normal irradiance by the atmosphere, in terms of fundamental quantities, including optical thickness, relative optical air mass, water vapor content, and aerosol amount. In the present article, we will not deal with cloudy atmospheres because of their great variability in space and time, but will focus our attention on atmospheres characterized by the complete absence of condensed water. The objectives of this article are to report data on aerosol optical depth and atmospheric turbidity coefficients for a desert climate, and to compare them with those of a temperate climate. Aerosol optical depth, the Linke turbidity factor, TL, and ngstrom turbidity coefficients, _, are calculated from measurements of broadband filters at Helwan, Egypt, which has a desert climate. A linear regression model is to be determined between the Linke factor and the ngstrom turbidity coefficient. This relation is compared with similar relations reported for a temperate climate [Prague, Czech Republic]. This comparison is made to determine whether a universal relation exists between these two important coefficients, or whether the relation is location dependent.
THE CORRELATION AMONG LEVEL OF KNOWLEDGE ABOUT ERGONOMIC POSITION AND LOW BACK PAIN COMPLAINTS IN INPATIENT NURSES

Low Back Pain (LBP) is a collection of several clinical symptoms that are marked by pain or discomfort in the lower back, which may or may not extend to the legs. Incorrect ergonomics is the primary factor contributing to lower back pain (LBP) among nurses. The objective of this study was to establish a correlation between the extent of ergonomics knowledge and the occurrence of lower back pain (LBP) complaints among nurses who work with hospitalised patients. This study employed a correlational quantitative design, utilising a cross-sectional technique. The sample size for this study consisted of 158 nurses working in the inpatient unit of a private hospital in Indonesia. The sampling technique employed was a total sampling, resulting in a sample size of 158 nurses. The research employed questionnaires that were both valid and trustworthy. The questionnaires assessing knowledge of ergonomics were devised by Bunga et al. (2019), whilst the questionnaire pertaining to low back pain was formulated by Deria (2021). The findings of the Pearson chi-square statistical test indicated a p-value of 0.105, suggesting that there was no statistically significant association between the level of knowledge of ergonomics postures and complaints of low back pain among inpatient nurses. Inpatient nurses have the ability to discover and implement ergonomic postures in their regular tasks. Further research could be undertaken on the elements that can induce low back discomfort.
Low-Profile Artificial Grid Dielectric Resonator Antenna Arrays for mm-Wave Applications

Wideband artificial grid dielectric resonator antenna (GDRA) arrays at 32 GHz for mm-wave applications are presented. The antenna array comprised a GDRA layer and a substrate-integrated waveguide feeding layer. The GDRA array layer is built by embedding small rectangular metal grid structures in low-permittivity dielectric polymethyl methacrylate (PMMA) using deep X-ray lithography (DXRL) and electroforming. The rectangular metallic inclusions significantly increase the effective permittivity of the base material up to 17 by creating high electric flux density regions inside. Low-loss substrate-integrated waveguide (SIW) feeding with longitudinal slots is utilized to excite the GDRA array layer. A <inline-formula> <tex-math notation="LaTeX">$200~\mu \text{m}$ </tex-math></inline-formula>-thin perforated layer of PMMA is applied between the rectangular grid structures and the SIW feedlines to avoid shorting the metal inclusions to the excitation slots while improving broadband energy coupling to the GDRA layer. The size of the single GDRA array element is only 2.7 mm <inline-formula> <tex-math notation="LaTeX">$\times2.7$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times0.5$ </tex-math></inline-formula> mm (<inline-formula> <tex-math notation="LaTeX">$0.29\lambda _{\mathrm {o}}\times 0.29\lambda _{\mathrm {o}}\times 0.05\lambda _{\mathrm {o}}$ </tex-math></inline-formula>). Four-element (<inline-formula> <tex-math notation="LaTeX">$1 \times 4$ </tex-math></inline-formula>) and eight-element (<inline-formula> <tex-math notation="LaTeX">$1 \times 8$ </tex-math></inline-formula>) GDRA arrays have been fabricated and measured. A measured impedance bandwidth of 6 GHz with a broadside peak gain of 12 dBi and 76% measured radiation efficiency is obtained at 32 GHz for the <inline-formula> <tex-math notation="LaTeX">$1 \times 8$ </tex-math></inline-formula> GDRA array.