bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging Google Earth Engine and Machine Learning to Estimate Evapotranspiration in a Commercial Forest Plantation
Authors: Gokool Shaeden , Kunz Richard , Clulow Alistair , Toucher Michele
Published: July 2024

Estimation of actual evapotranspiration (ETa) based on reference evapotranspiration (ETo) and the crop coefficient (Kc) remains one of the most widely used ETa estimation approaches. However, its application in non-agricultural and natural environments has been limited, largely due to the lack of well-established Kc coefficients in these environments. Alternate Kc estimation approaches have thus been proposed in such instances, with techniques based on the use of leaf area index (LAI) estimates being quite popular. In this study, we utilised satellite-derived estimates of LAI acquired through the Google Earth Engine geospatial cloud computing platform and machine learning to quantify the water use of a commercial forest plantation situated within the eastern region of South Africa. Various machine learning-based models were trained and evaluated to predict Kc as a function of LAI, with the Kc estimates derived from the best-performing model then being used in conjunction with in situ measurements of ETo to estimate ETa. The ET estimates were then evaluated through comparisons against in situ measurements. An ensemble machine learning model showed the best performance, yielding RMSE and R2 values of 0.05 and 0.68, respectively, when compared against measured Kc. Comparisons between estimated and measured ETa yielded RMSE and R2 values of 0.51 mm d−1 and 0.90, respectively. These results were quite promising and further demonstrate the potential of geospatial cloud computing and machine learning-based approaches to provide a robust and efficient means of handling large volumes of data so that they can be optimally utilised to assist planning and management decisions.
Proximity Sensor for Measuring Social Interaction in a School Environment
Authors: Hernández-Heredia Tania Karina , Reyes-Manzano Cesar Fabián , Flores-Hernández Diego Alonso , Ramos-Fernández Gabriel , Guzmán-Vargas Lev
Published: July 2024

Social interactions are characterized by being very diverse and changing over time. Understanding this diversity and dynamics, as well as their emerging patterns, is of great interest from social, health, and educational perspectives. The development of new devices has been made possible in recent years by advances in applied technology. This paper presents the design and development of a novel device composed of several sensors. Specifically, we propose a proximity sensor integrated by three devices: a Bluetooth sensor, a global positioning system (GPS) unit and an accelerometer. By means of this sensor it is possible to detect the presence of neighboring sensors in various configurations and operating conditions. Profiles based on the Received Signal Strength Indicator (RSSI) exhibit behavior consistent with that reported by empirical relationships. The present sensor is functional in detecting the proximity of other sensors and is thus useful for the identification of interactions between people in relevant contexts such as schools.
Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
Authors: Zhang Xiaoxi , Chen Yang , Hassan Modar , Suzuki Kenji
Published: July 2024

We propose a hands-free control system for a human-guided smart stroller. The proposed method uses real-time peer-to-peer localization technology of the human and stroller to realize an intuitive hands-free control system based on the relative position between the human and the stroller. The control method is also based on functional and mechanical safety to ensure the safety of the stroller’s occupant (child) and the pilot (parent) during locomotion. In this paper, first, we present a preliminary investigation of the humans’ preference for the relative position in the context of hands-free guided strollers. Then, we present the control method and a prototype implemented with an electric wheelchair and UWB sensors for localization. We present an experimental evaluation of the proposed method with 14 persons walking with the developed prototype to investigate the usability and soundness of the proposed method compared to a remote joystick and manual operation. The evaluation experiments were conducted in an indoor environment and revealed that the proposed method matches the performance of joystick control but does not perform as well as manual operation. Notably, for female participants, the proposed method significantly surpasses joystick performance and achieves parity with manual operation, which shows its efficacy and potential for a smart stroller. Also, the results revealed that the proposed method significantly decreased the user’s physical load compared to the manual operation. We present discussions on the controllability, usability, task load, and safety features of the proposed method, and conclude this work with a summary assessment.
Global Path Planning for Articulated Steering Tractor Based on Multi-Objective Hybrid Algorithm
Authors: Xu Ning , Li Zhihe , Guo Na , Wang Te , Li Aijuan , Song Yumin
Published: July 2024

With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search. Second, the genetic algorithm fitness function and search strategy are improved; the fitness function is set as the path tree row center offset factor; the smoothing factor and safety coefficient are set; and the search strategy adopts differential evolution for cross mutation. Finally, the shortest path obtained by the improved bidirectional search A* algorithm is used as the initial population of an improved differential evolution genetic algorithm, optimized iteratively, and the optimal path is obtained by adding kinematic constraints through a cubic B-spline curve smoothing path. The convergence of the AGADE hybrid algorithm and GA algorithm on four different maps, path length, and trajectory curve are compared and analyzed through simulation tests. The convergence speed of the AGADE hybrid algorithm on four different complexity maps is improved by 92.8%, 64.5%, 50.0%, and 71.2% respectively. The path length is slightly increased compared with the GA algorithm, but the path trajectory curve is located in the center of the tree row, with fewer turns, and it meets the articulated steering tractor operation needs in the orchard environment, proving that the improved hybrid algorithm is effective.
Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection
Authors: Hu Dikun , Gao Weidong , Ang Kai Keng , Hu Mengjiao , Chuai Gang , Huang Rong
Published: July 2024

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.
CATM: A Multi-Feature-Based Cross-Scale Attentional Convolutional EEG Emotion Recognition Model
Authors: Yu Hongde , Xiong Xin , Zhou Jianhua , Qian Ren , Sha Kaiwen
Published: July 2024

Aiming at the problem that existing emotion recognition methods fail to make full use of the information in the time, frequency, and spatial domains in the EEG signals, which leads to the low accuracy of EEG emotion classification, this paper proposes a multi-feature, multi-frequency band-based cross-scale attention convolutional model (CATM). The model is mainly composed of a cross-scale attention module, a frequency–space attention module, a feature transition module, a temporal feature extraction module, and a depth classification module. First, the cross-scale attentional convolution module extracts spatial features at different scales for the preprocessed EEG signals; then, the frequency–space attention module assigns higher weights to important channels and spatial locations; next, the temporal feature extraction module extracts temporal features of the EEG signals; and, finally, the depth classification module categorizes the EEG signals into emotions. We evaluated the proposed method on the DEAP dataset with accuracies of 99.70% and 99.74% in the valence and arousal binary classification experiments, respectively; the accuracy in the valence–arousal four-classification experiment was 97.27%. In addition, considering the application of fewer channels, we also conducted 5-channel experiments, and the binary classification accuracies of valence and arousal were 97.96% and 98.11%, respectively. The valence–arousal four-classification accuracy was 92.86%. The experimental results show that the method proposed in this paper exhibits better results compared to other recent methods, and also achieves better results in few-channel experiments.
Unsupervised Transfer Learning Method via Cycle-Flow Adversarial Networks for Transient Fault Detection under Various Operation Conditions
Authors: Yang Xiaoyue , Chen Long , Feng Qidong , Yang Yucheng , Xie Sen
Published: July 2024

The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked by background noise, particularly during dynamic operating conditions. Moreover, acquiring a sufficient number of samples across the entire scenario presents a challenging task, resulting in imbalanced data for FD. To address these limitations, an unsupervised transfer learning (TL) method via federated Cycle-Flow adversarial networks (CFANs) is proposed to effectively detect TFs under various operating conditions. Firstly, a CFAN is specifically designed for extracting latent features and reconstructing data in the source domain. Subsequently, a transfer learning framework employing federated CFANs collectively adjusts the modified knowledge resulting from domain alterations. Finally, the designed federated CFANs execute transient FD by constructing residuals in the target domain. The efficacy of the proposed methodology is demonstrated through comparative experiments.
Unravelling Influence Factors in Pattern Recognition Myoelectric Control Systems: The Impact of Limb Positions and Electrode Shifts
Authors: Wang Bingbin , Li Jinglin , Hargrove Levi , Kamavuako Ernest Nlandu
Published: July 2024

Pattern recognition (PR)-based myoelectric control systems can naturally provide multifunctional and intuitive control of upper limb prostheses and restore lost limb function, but understanding their robustness remains an open scientific question. This study investigates how limb positions and electrode shifts—two factors that have been suggested to cause classification deterioration—affect classifiers’ performance by quantifying changes in the class distribution using each factor as a class and computing the repeatability and modified separability indices. Ten intact-limb participants took part in the study. Linear discriminant analysis (LDA) was used as the classifier. The results confirmed previous studies that limb positions and electrode shifts deteriorate classification performance (14–21% decrease) with no difference between factors (p > 0.05). When considering limb positions and electrode shifts as classes, we could classify them with an accuracy of 96.13 ± 1.44% and 65.40 ± 8.23% for single and all motions, respectively. Testing on five amputees corroborated the above findings. We have demonstrated that each factor introduces changes in the feature space that are statistically new class instances. Thus, the feature space contains two statistically classifiable clusters when the same motion is collected in two different limb positions or electrode shifts. Our results are a step forward in understanding PR schemes’ challenges for myoelectric control of prostheses and further validation needs be conducted on more amputee-related datasets.
Finite-Time Disturbance Observer-Based Adaptive Course Control for Surface Ships
Authors: Xu Ming , Gong Chenglong
Published: July 2024

In this paper, a finite-time disturbance observer-based adaptive control strategy is proposed for the ship course control system subject to input saturation and external disturbances. Based on the Gaussian error function, a smooth saturation model is designed to avoid the input saturation of the system and reduce steering engine vibrations, and an auxiliary dynamic system is introduced to compensate for the effect of the rudder angle input inconsistency on the system. By constructing an auxiliary dynamic, a finite-time disturbance observer is designed to approximate the external disturbance of the system; an adaptive updating law is also constructed to estimate the upper bound of the derivative of the external disturbance. Combining the finite-time disturbance observer with the auxiliary dynamic system, a novel adaptive ship course control law is proposed by using the hyperbolic tangent function. Moreover, according to LaSalle’s Invariance Principle, a system stability analysis method with loose stability conditions and easy realizations is designed, while the stability of the closed-loop system and the ultimately uniformly boundedness of all its signals are proven. Finally, the course control simulation analysis of a surface ship is carried out. The results show that the proposed control law has a strong resistance to external disturbances and a strong non-fragility to system parameter perturbations, which ensure that the course control system has great control performance.
Image-Based Hidden Damage Detection Method: Combining Stereo Digital Image Correlation and Finite Element Model Updating
Authors: Cheng Wei-Han , Huang Hsin-Haou
Published: July 2024

Maintenance and damage detection of structures are crucial for ensuring their safe usage and longevity. However, damage hidden beneath the surface can easily go unnoticed during inspection and assessment processes. This study proposes a detection method based on image techniques to detect and assess internal structural damage, breaking the limitation of traditional image methods that only analyze the structure’s surface. The proposed method combines full-field response on the structure’s surface with finite element model updating to reconstruct the structural model, using the reconstructed model to detect and assess hidden structural damage. Initially, numerical experiments are conducted to generate known damaged areas and parameter distributions. Data from these experiments are used to update the finite element model, establish and validate the proposed model updating method, and assess its accuracy in evaluating hidden damage, achieving an accuracy rate of 90%. Furthermore, discussions on more complex damage scenarios are carried out through numerical experiments to demonstrate the feasibility and applicability of the proposed method in reconstructing different forms of damage. Ultimately, this study utilizes stereoscopic digital imaging techniques to acquire full-field information on surfaces, and applies the proposed method to reconstruct the structure, enabling the detection and assessment of hidden damage with an accuracy rate of 86%.
Strain Gauge Calibration for High Speed Weight-in-Motion Station
Authors: Socha Agnieszka , Izydorczyk Jacek
Published: July 2024

The development of systems for weighing vehicles in motion aims to introduce systems allowing automatic enforcement of regulations. HSWIM (high speed weight-in-motion) systems enable measurement of a mass of vehicles passing through a measurement station without disturbing the traffic flow. This article focuses on the calibration of a weighing station for moving vehicles, where strain gauge sensors are used to measure pressures. A solution was proposed to replace the calibration coefficients with calibration functions. The analysis was performed for two methods of determining wheel loads: based on the maximum of the signal from strain gauge sensors and on a method using the field under the signal and the vehicle’s speed. Calibration functions were determined jointly for all test vehicles and separately for each of them. The use of a calibration function for a specific vehicle type made it possible to determine wheel pressure and gross weight with a level of accuracy that allowed the weigh-in-motion station to be classified as a direct enforcement system. The achieved improvement in the accuracy of weighing in motion did not require any interference with the measurement station. The proposed change in the method of calibration and, ultimately, determination of wheel loads required only a change in the algorithm for determining wheel loads.
Inertial Sensor-Based Quantification of Movement Symmetry in Trotting Warmblood Show-Jumping Horses after “Limb-by-Limb” Re-Shoeing of Forelimbs with Rolled Rocker Shoes
Authors: Bark Craig , Reilly Patrick , Weller Renate , Pfau Thilo
Published: July 2024

Hoof care providers are pivotal for implementing biomechanical optimizations of the musculoskeletal system in the horse. Regular visits allow for the collection of longitudinal, quantitative information (“normal ranges”). Changes in movement symmetry, e.g., after shoeing, are indicative of alterations in weight-bearing and push-off force production. Ten Warmblood show jumping horses (7–13 years; 7 geldings, 3 mares) underwent forelimb re-shoeing with rolled rocker shoes, one limb at a time (“limb-by-limb”). Movement symmetry was measured with inertial sensors attached to the head, withers, and pelvis during straight-line trot and lunging. Normalized differences pre/post re-shoeing were compared to published test–retest repeatability values. Mixed-model analysis with random factors horse and limb within horse and fixed factors surface and exercise direction evaluated movement symmetry changes (p < 0.05, Bonferroni correction). Withers movement indicated increased forelimb push-off with the re-shod limb on the inside of the circle and reduced weight-bearing with the re-shod limb and the ipsilateral hind limb on hard ground compared to soft ground. Movement symmetry measurements indicate that a rolled rocker shoe allows for increased push-off on soft ground in trot in a circle. Similar studies should study different types of shoes for improved practically relevant knowledge about shoeing mechanics, working towards evidence-based preventative shoeing.
Multi-Type Structural Damage Image Segmentation via Dual-Stage Optimization-Based Few-Shot Learning
Authors: Zhong Jiwei , Fan Yunlei , Zhao Xungang , Zhou Qiang , Xu Yang
Published: July 2024

The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent maintenance of smart cities. Deep learning and computer vision have made profound impacts on automatic structural damage recognition using nondestructive test techniques, especially non-contact vision-based algorithms. However, the recognition accuracy highly depends on the training data volume and damage completeness in the conventional supervised learning pipeline, which significantly limits the model performance under actual application scenarios; the model performance and stability for multi-type structural damage categories are still challenging. To address the above issues, this study proposes a dual-stage optimization-based few-shot learning segmentation method using only a few images with supervised information for multi-type structural damage recognition. A dual-stage optimization paradigm is established encompassing an internal network optimization based on meta-task and an external meta-learning machine optimization based on meta-batch. The underlying image features pertinent to various structural damage types are learned as prior knowledge to expedite adaptability across diverse damage categories via only a few samples. Furthermore, a mathematical framework of optimization-based few-shot learning is formulated to intuitively express the perception mechanism. Comparative experiments are conducted to verify the effectiveness and necessity of the proposed method on a small-scale multi-type structural damage image set. The results show that the proposed method could achieve higher segmentation accuracies for various types of structural damage than directly training the original image segmentation network. In addition, the generalization ability for the unseen structural damage category is also validated. The proposed method provides an effective solution to achieve image-based structural damage recognition with high accuracy and robustness for bridges and buildings, which assists the unmanned intelligent inspection of civil infrastructure using drones and robotics in smart cities.
Parametric Estimation in Fractional Stochastic Differential Equation
Authors: Pramanik Paramahansa , Boone Edward L. , Ghanam Ryad A.
Published: July 2024

Fractional Stochastic Differential Equations are becoming more popular in the literature as they can model phenomena in financial data that typical Stochastic Differential Equations models cannot. In the formulation considered here, the Hurst parameter, H, controls the Fraction of Differentiation, which needs to be estimated from the data. Fortunately, the covariance structure among observations in time is easily expressed in terms of the Hurst parameter which means that a likelihood is easily defined. This work derives the Maximum Likelihood Estimator for H, which shows that it is biased and is not a consistent estimator. Simulation data used to understand the bias of the estimator is used to create an empirical bias correction function and a bias-corrected estimator is proposed and studied. Via simulation, the bias-corrected estimator is shown to be minimally biased and its simulation-based standard error is created, which is then used to create a 95% confidence interval for H. A simulation study shows that the 95% confidence intervals have decent coverage probabilities for large n. This method is then applied to the S&P500 and VIX data before and after the 2008 financial crisis.
Strategic Advancements in Rice Cultivation: Combating Heat Stress through Genetic Innovation and Sustainable Practices—A Review
Authors: Mthiyane Pretty , Aycan Murat , Mitsui Toshiaki
Published: July 2024

Rice is one of the most important staple foods globally, sustaining over half of the world’s population. However, the sustainability of grain production is increasingly threatened by heat stress, which is intensified by global climate change. Heat stress, characterized by temperatures exceeding crop-specific optimal growth thresholds, significantly impacts the rice yield and quality, particularly during critical reproductive stages. This review synthesizes current research on strategies to mitigate heat stress in rice through genetic and agronomic approaches. It highlights the implementation of advanced genetic tools such as marker-assisted selection (MAS) and genomic selection (GS) to accelerate the breeding of heat-tolerant rice varieties. Additionally, it discusses sustainable agronomic practices, including adjusting planting dates, optimizing water management, and crop rotation, which enhance resilience to heat stress. The objective of this review is to bridge the gap between research findings and practical agricultural applications, providing a comprehensive resource that guides future research directions and informs policy interventions. This review emphasizes the importance of integrating genetic innovations with traditional and modern farming practices to develop rice varieties that can withstand the adverse effects of heat stress, ensuring food security and agricultural sustainability in the face of climatic challenges.
Effects of Wind Farm Construction on Soil Nutrients and Vegetation: A Case Study of Linxiang Wind Farm in Hunan Province
Authors: Li Lin , Ma Wenjing , Duan Xiangyi , Wang Shuo , Wang Qiong , Gu Huangling , Wang Jingsong
Published: July 2024

Amidst escalating global energy demands, the advancement and utilization of renewable energy sources have emerged as critical strategies for addressing environmental concerns and alleviating energy crises. Among them, wind power, as a renewable and clean energy source, has been widely applied and developed in China. However, the construction of wind farms may have some impact on vegetation cover and soil properties. This study aims to assess the impact of wind farm construction on vegetation cover and soil characteristics, thereby offering a scientific foundation for the sustainable management of wind farm development sites. The present study was carried out in the area of Jingzhushan wind farm in Linxiang City, Hunan Province, to examine the trends of the normalized difference vegetation index (NDVI), the fractional vegetation cover (FVC), and the indexes expressing the physicochemical properties of the soil in this area. The results showed the following: (1) The NDVI of the wind farm for the three periods was 0.742 in 2013, 0.770 in 2016, and 0.758 in 2023, respectively. According to the analysis of the index of FVC, it can be seen that the trend of the FVC of the study area for the three periods was basically the same as that of the NDVI. The average value of FVC was 0.754 in 2013, 0.791 in 2016, and 0.769 in 2023. This indicated that the vegetation cover in the early stage of wind farm construction (2013) was lower than that in the late stage of operation (2016, 2023), and it also suggested that the vegetation cover gradually recovered over time. (2) Compared with natural ecosystems, both altitude and wind farm construction significantly affected the organic carbon, the total nitrogen, the effective phosphorus, and the rapidly available potassium in the soil. At the same altitude, these four soil indicators in the area where the wind turbines were constructed had significantly lower levels compared with the control (CK), which indicated a decrease in soil fertility—the closer to the turbine construction area, the lower the levels of each indicator. In addition, soil pH did not change significantly during the construction of the wind farm. The analysis and comparison of various data showed that the construction and operation of wind farms can have an impact on local vegetation cover, and it had a significant negative impact on soil properties. Reasonable measures are needed to protect vegetation and soil to achieve the sustainable development of the ecological environment.
Entrepreneurial Pathways to Sustainability: A Theoretical Paper on Green Human Resource Management, Green Supply Chain Management, and Entrepreneurial Orientation
Authors: Ahmed Taimoor , Yousaf Amna , Clavijo Roberto Chavez , Sanders Karin
Published: July 2024

The textile industry of Pakistan, a major contributor to its economy, is encountering serious sustainability challenges, akin to many developing nations. The literature is replete with assertions on the importance of sustainability; however, cross-functional research is sporadic. This paper theoretically links Green HRM (GHRM) practices, Green SCM (GSCM) practices, and Triple Bottom Line (TBL) performance within this context. In scaffolding through Resource Orchestration (ROT) and Dynamic Capabilities (DCT) theories, a conceptual framework is proposed. We posit that GSCM transforms GHRM practices into tangible, eco-friendly supply chain activities resulting in enhanced sustainable performance, thereby playing a mediating role. Additionally, a unique interplay of Entrepreneurial Orientation (EO) as a moderator between GHRM and GSCM is introduced. Firms with higher levels of EO employ innovative approaches to implement GHRM practices into their supply chain strategies, leading to robust GSCM initiatives. The literature review indicates that research on the synergistic effects of GHRM and GSCM on TBL performance in developing nations is scarce. The study warrants carrying out empirical validation and provides a clear plan for future research. The exclusive emphasis on Pakistan’s textile industry introduces contextual intricacies, facilitating a profound comprehension for implementing sustainable practices within the constraints and opportunities of an emerging economy.
A Systematic Review of Postgraduate Programmes Concerning Ethical Imperatives of Data Privacy in Sustainable Educational Data Analytics
Authors: Ncube Mthokozisi Masumbika , Ngulube Patrick
Published: July 2024

This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, investigated the intersection of data privacy, postgraduate educational data analytics (EDA), and sustainability. Existing literature focuses on general privacy concerns in EDA, neglecting the specific data collected and related risks in postgraduate programmes. This review addresses this gap by identifying data types used by higher education institutions in postgraduate initiatives and evaluating the adequacy of current ethical frameworks, particularly for sustainability goals. Recognising the lack of established best practices for balancing data utility and privacy, the review analyses privacy-preserving techniques. Through identifying key data types collected in postgraduate initiatives, evaluating existing ethical frameworks, and exploring privacy-preserving educational data analytics techniques, this study provided practical guidance for higher education institutions to navigate the challenges of balancing data utility and student privacy. The results suggest that higher education institutions can achieve sustainable data use by adopting a comprehensive approach that incorporates best practices, emerging technologies, and continuous monitoring to safeguard student privacy while leveraging the benefits of educational data analytics for achieving the Sustainable Development Goals.
The Problem of Nurturing Sustainable Inclusion within Team Sports in Physical Education
Authors: Graça Amandio , Estriga Luisa , Batista Paula
Published: July 2024

Team sport games, as a deep-rooted facet of physical education, have been upheld throughout the history of this school subject area. The confining of teaching games instruction and assessment to isolated skills, as well as the selective pressure of competition, generate serious problems for dealing with the diversity and inclusion of all students in physical education classes. Framed using the Salamanca Declaration for inclusive education, the purpose of this paper is to contribute to the discussion on renewing and bettering the opportunities for accessing, participating, and learning team sports gameplay in physical education. DeLuca’s four conceptions of inclusion (normative, integrative, dialogical, and transgressive) constitute the analytical framework adopted to support the discussion about the game-based models’ learning goals, strategies, and the ways to contribute to dealing with students’ diversity and inclusion. The discussion breaks new ground by emphasizing dialogical and transgressive inclusion in teaching games in physical education advocating for co-regulation and team regulation through active co-construction by students and teachers. These efforts ultimately aim to cultivate more inclusive and equitable physical education learning environments, fostering a sustainable and socially just society.
Research Geographical Distribution, Strategies, and Environmental and Socioeconomic Factors Influencing the Success of Land-Based Restoration: A Systematic Review
Authors: Hernandez Jonathan O. , Cortes Damcelle T. , Park Byung Bae
Published: July 2024

The effectiveness of restoration programs may differ in terms of environmental and socioeconomic metrics, depending on the strategies employed and ecological settings. Thus, we synthesized different restoration strategies and discussed the environmental and socioeconomic factors influencing restoration success. In the present systematic review, we examined peer-reviewed articles published between 1990 and 2024 that discussed strategies and factors impacting land-based restoration. Only 227 of 55,229 articles from ScienceDirect, PubMed, and Google Scholar databases met the inclusion criteria. China, Brazil, and India are more active in conducting studies about land restoration compared to other regions, particularly in megadiverse countries in Asia with high degradation rates. There is a strong emphasis on practical and adaptive restoration strategies, including soil and water management (e.g., agroforestry), the use of native plant species, and weed and invasive species management. The prevalence of Acacia, Leucaena, and Eucalyptus species in restoration programs can inform decisions about effective species selection. Here, a holistic understanding of the complex ecological processes is crucial for the development of effective restoration strategies. Although policy frameworks have received less attention in restoration research, their incorporation into restoration projects can help address policy implications for land-based restoration. Overall, successful restoration necessitates a thorough understanding of the optimal strategies and environmental and socioeconomic factors impacting restoration success. Future restoration initiatives can leverage such an understanding to ensure successful implementation.
Enhancing Water Condensation on Hybrid Surfaces by Optimizing Wettability Contrast
Authors: Chi Do-Thuy , Nguyen Thanh-Binh
Published: July 2024

This study uses a hybrid concept to propose an optimal textured surface morphology for enhancing water condensation. The natural phenomenon-inspired morphology, which combined different degrees of wettability presented on the surface, documented their advantage in water harvesting compared to untreated surfaces. These superiorities might be explained by the appropriate combination of nucleation and water-driven ability facilitated by the superhydrophobic surrounding area. The uniform condensed droplets are effectively agglomerated to achieve the critical size. The best combination was found on a superhydrophobic-hydrophilic hybrid sample that improved water collection efficiency by up to 50% compared to bare Al. Condensation performance also illustrated an interesting tendency that revealed the great contribution of wettability on hydrophilic dots and the water-driven ability of the high-hydrophobicity area. The results were supported by a theoretical model which predicts the critical volume of a single droplet before it has departed from the surface. The findings reveal a good level of agreement between theory and real-time measurement, demonstrating the potential of combinations of hybrid samples to induce water collection efficiency.
Group-Action-Based S-box Generation Technique for Enhanced Block Cipher Security and Robust Image Encryption Scheme
Authors: Baowidan Souad Ahmad , Alamer Ahmed , Hassan Mudassir , Yousaf Awais
Published: July 2024

Data security is one of the biggest concerns in the modern world due to advancements in technology, and cryptography ensures that the privacy, integrity, and authenticity of such information are safeguarded in today’s digitally connected world. In this article, we introduce a new technique for the construction of non-linear components in block ciphers. The proposed S-box generation process is a transformational procedure through which the elements of a finite field are mapped onto highly nonlinear permutations. This transformation is achieved through a series of algebraic and combinatorial operations. It involves group actions on some pairs of two Galois fields to create an initial S-box Pr Sbox, which induces a rich algebraic structure. The post S-box Po Sbox, which is derived from heuristic group-based optimization, leads to high nonlinearity and other important cryptographic parameters. The proposed S-box demonstrates resilience against various attacks, making the system resistant to statistical vulnerabilities. The investigation reveals remarkable attributes, including a nonlinearity score of 112, an average Strict Avalanche Criterion score of 0.504, and LAP (Linear Approximation Probability) score of 0.062, surpassing well-established S-boxes that exhibit desired cryptographic properties. This novel methodology suggests an encouraging approach for enhancing the security framework of block ciphers. In addition, we also proposed a three-step image encryption technique comprising of Row Permutation, Bitwise XOR, and block-wise substitution using Po Sbox. These operations contribute to adding more levels of randomness, which improves the dispersion across the cipher image and makes it equally intense. Therefore, we were able to establish that the approach works to mitigate against statistical and cryptanalytic attacks. The PSNR, UACI, MSE, NCC, AD, SC, MD, and NAE data comparisons with existing methods are also provided to prove the efficiency of the encryption algorithm.
Performance Analysis of a Sound-Based Steganography Wireless Sensor Network to Provide Covert Communications
Authors: Rodriguez-Gomez Ariadna I. , Rivero-Angeles Mario E. , Orea Flores Izlian Y. , Gallegos-García Gina
Published: July 2024

Given the existence of techniques that disrupt conventional RF communication channels, the demand for innovative alternatives to electromagnetic-based communications is clear. Covert communication, which claims to conceals the communication channel, has been explored using bio-inspired sounds in aquatic environments, but its application in terrestrial areas is largely unexplored. This work develops a mathematical analysis of a wireless sensor network that operates stealthily in outdoor environments by using birdsong audio signals from local birds for covert communication. Stored bird sounds are modified to insert sensor data while altering the sound minimally, both in characteristics and random silence/song patterns. This paper introduces a technique that modifies a fourth-level coefficient detail with a wavelet transform, then applies an inverse transform to achieve imperceptible audio modifications. The mathematical analysis includes a statistical study of the On/Off periods of different birds’ songs and a Markov chain capturing the system’s main dynamics. We derive the system throughput to highlight the potential of using birdsong as a covert communication medium in terrestrial environments. Additionally, we compare the performance of the sound-based network to the RF-based network to identify the proposed system’s capabilities.
Structural and Functional Insights into the Delivery Systems of Bacillus and Clostridial Binary Toxins
Authors: Sevdalis Spiridon E. , Varney Kristen M. , Cook Mary E. , Gillespie Joseph J. , Pozharski Edwin , Weber David J.
Published: July 2024

Pathogenic Bacillus and clostridial (i.e., Clostridium and Clostridioides) bacteria express a diverse repertoire of effector proteins to promote disease. This includes production of binary toxins, which enter host epithelial cells and seriously damage the intestinal tracts of insects, animals, and humans. In particular, binary toxins form an AB-type complex composed of a catalytic subunit that is toxic (A) and an oligomeric cell-binding and delivery subunit (B), where upon delivery of A into the cytoplasm of the host cell it catalytically ADP-ribosylates actin and rapidly induces host cell death. In this review, binary toxins expressed by Bacillus thuringiensis, Clostridioides difficile, and Clostridium perfringens will be discussed, with particular focus placed upon the structural elucidations of their respective B subunits and how these findings help to deconvolute how toxic enzyme delivery into target host cells is achieved by these deadly bacteria.
Development of a Diagnostic Microfluidic Chip for SARS-CoV-2 Detection in Saliva and Nasopharyngeal Samples
Authors: Sharma Sandhya , Caputi Massimo , Asghar Waseem
Published: July 2024

The novel coronavirus SARS-CoV-2 was first isolated in late 2019; it has spread to all continents, infected over 700 million people, and caused over 7 million deaths worldwide to date. The high transmissibility of the virus and the emergence of novel strains with altered pathogenicity and potential resistance to therapeutics and vaccines are major challenges in the study and treatment of the virus. Ongoing screening efforts aim to identify new cases to monitor the spread of the virus and help determine the danger connected to the emergence of new variants. Given its sensitivity and specificity, nucleic acid amplification tests (NAATs) such as RT-qPCR are the gold standard for SARS-CoV-2 detection. However, due to high costs, complexity, and unavailability in low-resource and point-of-care (POC) settings, the available RT-qPCR assays cannot match global testing demands. An alternative NAAT, RT-LAMP-based SARS-CoV-2 detection offers scalable, low-cost, and rapid testing capabilities. We have developed an automated RT-LAMP-based microfluidic chip that combines the RNA isolation, purification, and amplification steps on the same device and enables the visual detection of SARS-CoV-2 within 40 min from saliva and nasopharyngeal samples. The entire assay is executed inside a uniquely designed, inexpensive disposable microfluidic chip, where assay components and reagents have been optimized to provide precise and qualitative results and can be effectively deployed in POC settings. Furthermore, this technology could be easily adapted for other novel emerging viruses.
VOGDB—Database of Virus Orthologous Groups
Authors: Trgovec-Greif Lovro , Hellinger Hans-Jörg , Mainguy Jean , Pfundner Alexander , Frishman Dmitrij , Kiening Michael , Webster Nicole Suzanne , Laffy Patrick William , Feichtinger Michael , Rattei Thomas
Published: July 2024

Computational models of homologous protein groups are essential in sequence bioinformatics. Due to the diversity and rapid evolution of viruses, the grouping of protein sequences from virus genomes is particularly challenging. The low sequence similarities of homologous genes in viruses require specific approaches for sequence- and structure-based clustering. Furthermore, the annotation of virus genomes in public databases is not as consistent and up to date as for many cellular genomes. To tackle these problems, we have developed VOGDB, which is a database of virus orthologous groups. VOGDB is a multi-layer database that progressively groups viral genes into groups connected by increasingly remote similarity. The first layer is based on pair-wise sequence similarities, the second layer is based on the sequence profile alignments, and the third layer uses predicted protein structures to find the most remote similarity. VOGDB groups allow for more sensitive homology searches of novel genes and increase the chance of predicting annotations or inferring phylogeny. VOGD B uses all virus genomes from RefSeq and partially reannotates them. VOGDB is updated with every RefSeq release. The unique feature of VOGDB is the inclusion of both prokaryotic and eukaryotic viruses in the same clustering process, which makes it possible to explore old evolutionary relationships of the two groups. VOGDB is freely available at vogdb.org under the CC BY 4.0 license.
Updating and Refining of Economic Evaluation of Rotavirus Vaccination in Spain: A Cost–Utility and Budget Impact Analysis
Authors: Imaz-Iglesia Iñaki , Carmona Montserrat , García-Carpintero Esther E. , Pedrosa-Pérez Lucía , Martínez-Portillo Alejandro , Alcalde-Cabero Enrique , Linertová Renata , García-Pérez Lidia
Published: July 2024

Two vaccines against rotavirus diseases, Rotarix® and RotaTeq®, are being marketed in Spain; but rotavirus is not presently among the diseases covered by universal vaccination in Spain. The aim of this study was to assess the efficiency of extending Spain’s current targeted rotavirus vaccination strategy including only preterm babies, to a policy of universal vaccination. A de novo cohort-based Markov model was built to evaluate the efficiency of three compared rotavirus vaccination strategies in Spain: targeted, universal, and no vaccination. Using Rotarix® or RotaTeq®, we compared the cost–utility of these strategies from both a societal perspective and Spanish National Health System (SNHS) perspective. The model represents the most important clinical events conceivably linked to rotavirus infection. Efficacy, effectiveness, safety, costs, and utilities were identified by systematic reviews. Incremental cost–utility ratio (ICUR) is EUR 23,638/QALY (Quality-Adjusted Life Year) for targeted vaccination with Rotarix® compared with no vaccination. The ICUR for the rest of the strategies evaluated are above EUR 30,000/QALY. The sensitivity analysis shows price as the only parameter that could make the universal vaccination strategy efficient. Considering a threshold of EUR 25,000/QALY, only targeted vaccination with Rotarix® would be efficient from societal perspective. Price drops of 36.9% for Rotarix® and 44.6% for RotaTeq® would make universal vaccination efficient.
Biodiversity and Ecosystem Services in Rivers
Authors: Khosrovyan Alla
Published: July 2024

Rivers are complex networks of aquatic–terrestrial interactions where changes in one part (e [...]
A Study on the Maximum Scour Depth of River-Crossing Tunnels
Authors: Yang Meiqing , Feng Luojie , Xu Feng , Yang Fencheng , Zhang Junhong , Xu Bingqing , Lv Yuan , Huang Yongjun
Published: July 2024

As urbanization progresses and city populations grow, river-crossing tunnels assume a crucial role in transportation networks, with the maximum scour depth constituting a critical parameter influencing tunnel safety. Using Line 6 of the Nanning Metro in Guangxi, China as a case study, a two-dimensional hydrosediment mathematical model was employed to investigate variations in maximum bedrock scouring. This study introduces the concept of critical frequency floods and compares it with urban flood control standards to determine the appropriate flood frequency for calculating maximum bedrock scour depth. The impact of bed sediment particle size on maximum scour depth is quantified, revealing a decrease in scour depth of 0.3 to 0.6 m for every 1 mm increase in particle size. The relationship between bed sedimentation and the Froude number demonstrates an upward-opening parabolic symmetry: lower Froude numbers correspond to relatively stable beds, while higher numbers correlate with an increased amplitude of bed erosion or deposition. The curve’s nadir identifies the critical threshold of the Froude number, facilitating calculation of the channel’s critical water depth. In practical engineering applications, a bed under conditions of critical water depth tends to be more stable, thereby favoring the selection of sites for river-crossing tunnels.