bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating the Socioecological Classroom in Full-Service Schools: A Whole-School Approach to the Inclusive Education Context in South Africa
Authors: Mbelu Sifiso Emmanuel , Maguvhe Mbulaheni Obert
Published: October 2024

This paper evaluates the socioecological classroom in the context of a whole-school approach to inclusive education, which has evolved beyond the traditional classroom setting. Inclusive education calls for a paradigm shift to accommodate learners previously marginalised, with the classroom transforming into an ecosystem. The socioecological paradigm steers away from the notion that psychological and physiological elements are the main causes of disability. The importance of addressing multiple layers of influence is emphasised to create a supportive and inclusive learning environment. First, Bronfenbrenner’s Ecological Systems Theory, which emphatically explains how a learner’s development is influenced by the systems in the environment that surrounds them, and then Foucault’s Theory of Power and Knowledge, in which power and knowledge are interconnected and inseparable, with power being exercised through the creation and control of knowledge, guided this study. A qualitative approach was adopted, and semi-structured interviews, focus group discussions and non-participant observation were used to collect data. Phenomenological analysis was used to analyse the data from these data-collecting instruments. It was found that learners with disabilities need support at home, with imbalances being found in the power–knowledge relations between teachers and parents, and teachers occupying a superior position regarding academic knowledge. Some teachers still believe in the Medical-Deficit Model. A strengths-based approach should rather be adopted. Strategies for enhancing community–school collaboration that balances teacher–parent power dynamics should be explored.
Examining the Home Learning Environment Practices for Emergent Bilinguals: Insights from Parental Survey
Authors: Rivera Héctor H. , Chang Heesun , Zhu Yiming , Jimenez David D. , Bemani Mohsen , Taheri Mohammad
Published: October 2024

This study aims to investigate the general practices of bilingual students’ home learning environments and to compare the variation in practices as a function of parents’ length of stay in the USA, educational levels, and household income by analyzing the results of a large-scale parental survey in an urban area in the southwestern United States, serving a predominantly Hispanic student population. The data consisted of 959 parents’ responses. The findings show that parents’ practices of engagement in students’ learning, discussions for students’ future, emotional support, students’ media consumption, disciplinary practice, and support for students’ autonomy vary in multifold ways as a function of their backgrounds. For example, while most parents, regardless of their background, were not prone to supporting children’s autonomy when deciding children’s routines at home, those with higher educational and income levels tend to more frequently participate in children’s classrooms or discuss children’s college entrance and career paths. The study discusses the implications of the findings and limitations.
GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System
Authors: Chen Kun , Liu Ting , Wang Xiaoming
Published: October 2024

The joint beamforming optimization from the perspective of the bit error rate (BER) in a reconfigurable intelligent surface (RIS)–assisted intelligent communication system is studied in this paper. A genetic algorithm (GA) is investigated to address the bottleneck of the system performance based on the dynamic adaptability theory. However, the bottleneck is caused by the interaction between the active and passive beamforming. To tackle the constraints of conventional optimization approaches, the hybrid scheme is proposed to combine the GA optimization (GAO) and fully connected neural network (FCNN) strategy. Specifically, the intelligent collaborative tuning of system parameters is achieved using this proposed technique. Simulation findings indicate that the hybrid scheme not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. Based on this, the signal transmission quality is improved, interference is reduced, and the stable and efficient operation of the RIS–assisted intelligent communication system is ensured in the complex wireless transmission scenario.
The Dynamic Response of Dual Cellular-Connected UAVs for Random Real-Time Communication Requests from Multiple Hotspots: A Deep Reinforcement Learning Approach
Authors: Yang Shengzhi , Zhou Jianming , Meng Xiao
Published: October 2024

It is gradually becoming popular to use multiple cellular-connected UAVs as inspectors to fulfill automatic surveillance and monitoring. However, in actual situations, UAVs should respond to several service requests from different hotspots, whilst the requests usually present randomness in the arrival time, data amount, and the concurrency. This paper proposes a dynamic dual-UAV response policy for multi-hotspot services based on single-agent deep Q-learning, where the UAVs controlled by a ground base station can be dispatched automatically to hotspots and then send videos back. First, this issue is formulated as an optimization problem, whose goal is to maximize the number of successfully served requests with the constraints of both the UAV’s energy limit and request waiting time. Second, a reward function based on service completion is designed to overcome the potential challenges posed by the delay reward. Finally, a simulation was conducted, comparing the conventional time priority algorithm and distance priority algorithm, respectively, to the proposed algorithm. The results illustrate that the proposed algorithm can achieve one more response than the others under different service densities, with the lowest failure number and appropriate average waiting time. This method can give a technical solution for the joint communication-and-control problem of multiple UAVs within complex situations.
Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments
Authors: Gonçalves José Gelson , Ayub Muhammad Shoaib , Zhumadillayeva Ainur , Dyussekeyev Kanagat , Ayimbay Sunggat , Saadi Muhammad , Lopes Rosa Renata , Rodríguez Demóstenes Zegarra
Published: October 2024

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.
FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning
Authors: Liu Teng , Wu Hao , Sun Xidong , Niu Chaojie , Yin Hao
Published: October 2024

Federated Learning (FL), as a distributed machine learning method, is particularly suitable for training models that require large amounts of data while meeting increasingly strict data privacy and security requirements. Although FL effectively protects the privacy of participants by avoiding the sharing of raw data, balancing the risks of privacy leakage with model performance remains a significant challenge. To address this, this paper proposes a new algorithm—FL-APB (Federated Learning with Adversarial Privacy–Performance Balancing). This algorithm combines adversarial training with privacy-protection mechanisms to dynamically adjust privacy and performance budgets, optimizing the balance between the two while enhancing and ensuring performance. The experimental results demonstrate that the FL-APB algorithm significantly improves model performance across various adversarial training scenarios, while effectively protecting the privacy of participants through adversarial training of privacy data.
Research on the Arrangement Scheme of Spirally Twisted Tape Inserts in a Grate Furnace
Authors: Yang Chen , Kong Jingxian , Chen Xinji , Jin Zhijiang , Qian Jinyuan
Published: October 2024

To eliminate the flow dead zone and homogenize the asymmetric flow field of a grate furnace, spirally twisted tape inserts (STTIs) with a pitch ratio of 1.5 were installed in the vertical flues of an SCL1000-13.5/450 grate boiler. The arrangement schemes found to be present inside the chosen 1000 t/d grate furnace, determined using the orthogonal experimental method, included separate installation in chamber II, separate placement in chamber III, and simultaneous arrangement in both chamber II and chamber III. The effects of row spacing H, column spacing W, and mounting angle φ were investigated by means of the practicable and feasible numerical simulation method. With a focus on the uniformity degree of the flue gas, the results showed that temperature distribution is directly correlated with the velocity field. When it comes to the uniformity of the flow field, the exclusive use of STTIs in chamber II was better than that in chamber III. Under the optimal combination scheme of STTIs in both chamber II and chamber III (scheme N323), the exhaust gas temperature reached the minimum value and the uniformity index of temperature increased to the range of 0.994~0.997. The findings in this work could provide a reference for the optimization of the flow field in a grate furnace.
Hardware-in-the-Loop Implementation of an Optimized Energy Management Strategy for Range-Extended Electric Trucks
Authors: Shiledar Ankur , Villani Manfredi , Rizzoni Giorgio
Published: October 2024

The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores a Range-Extended Electric Vehicle (REEV) for medium-duty Class 6 pick-up and delivery trucks. This hybrid architecture combines an electric powertrain with an internal combustion engine range-extender. Maximizing the efficiency of REEVs requires an Energy Management Strategy (EMS) to optimally split the power between the two power sources. In this work, a hierarchical EMS is developed through model-based design and validated via Hardware-In-The-Loop (HIL) simulations. The proposed EMS demonstrated a 7% reduction in fuel consumption compared to a baseline control strategy, while maintaining emissions and engine start frequency comparable to a benchmark globally optimal EMS obtained with dynamic programming. Furthermore, HIL results confirmed the strategy’s real-time implementation feasibility, highlighting the practical viability of the controller. This research underscores the potential of REEVs in significantly reducing emissions and fuel consumption, as well as providing a sustainable alternative for medium-duty truck applications.
Adaptive Variable Universe Fuzzy Droop Control Based on a Novel Multi-Strategy Harris Hawk Optimization Algorithm for a Direct Current Microgrid with Hybrid Energy Storage
Authors: Wang Chen , Jiao Shangbin , Zhang Youmin , Wang Xiaohui , Li Yujun
Published: October 2024

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy Harris Hawk Optimization Algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within ±0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit.
The Influence of the Intake Geometry on the Performance of a Four-Stroke SI Engine for Aeronautical Applications
Authors: Anaclerio Fabio , Viggiano Annarita , Fornarelli Francesco , Caso Paolo , Sparaco Domenico , Magi Vinicio
Published: October 2024

In this work, the influence of plenum and port geometry on the performance of the intake process in a four-stroke spark ignition engine for ultralight aircraft applications is analyzed. Three intake systems are considered: the so-called “standard plenum”, with a relatively small plenum volume, the “V1 plenum”, with a larger plenum volume, and the “standard plenum” equipped with a large curvature manifold called the “G2 port”. Both measurements and 3D CFD simulations, by using Ansys® Academic Fluent, Release 20.2, are performed to characterize and analyze the steady-flow field in the intake system for selected valve lifts. The experimental data and the numerical results are in excellent agreement with each other. The results show that at the maximum valve lift, i.e., 12 mm, the V1 plenum allows an increase in the air mass flow rate of 9.1% and 9.4% compared to the standard plenum and the standard plenum with the “G2 port”, respectively. In addition, the volumetric efficiency has been estimated under unsteady-flow conditions for all geometries at relatively high engine rpms. The difference between numerical results and measurements is less than 1% for the standard plenum, thus proving the accuracy of the model, which is then used to study the other configurations. The V1 plenum shows a fairly constant volumetric efficiency as the engine speed increases, although such an efficiency is lower than that of the other two geometries considered in this work. Specifically, the use of the “G2 port” leads to an increase of 1.5% in terms of volumetric efficiency with respect to the configuration with the original manifold. Furthermore, for the “G2 port” configuration, higher turbulent kinetic energy and higher swirl and tumble ratios are observed. This is expected to result in an improvement of air–fuel mixing and flame propagation.
A Fuzzy Adaptive PID Coordination Control Strategy Based on Particle Swarm Optimization for Auxiliary Power Unit
Authors: Qin Hongyan , Wang Lingfeng , Wang Shilong , Ruan Weitao , Jiang Fachao
Published: October 2024

Range extender hybrid vehicles have the advantages of better dynamics and longer driving range while reducing pollution and fuel consumption. This work focuses on the control strategy of an Auxiliary Power Unit (APU) operating in power generation mode for a range-extender mixer truck. When an operating point is switched, the engine speed and generator torque of the APU will switch accordingly. In order to ensure APU fast and stable adjustment to meet the power demand of the vehicle as well as operate at the lowest fuel consumption, a fuzzy adaptive PID coordination control strategy based on particle swarm optimization (PSO) is proposed to control the APU. The optimal operating curve of APU is calculated by coupling the engine and generator first. Then, the adaptive PID algorithm is used to control the speed and torque of the APU in a dual closed loop. The PSO is used to optimize the PID control parameter. Through hardware-in-the-loop tests under different working conditions, the control strategy is verified to be effective and real-time. The results show that the proposed control strategy can coordinate the operating of engine and generator and control the APU to track target power stably and quickly under minimum fuel consumption. Compared with traditional PID control strategy, the overshoot, regulation time and steady-state error are reduced by 55.1%, 11.1% and 77.3%, respectively.
Loyalty in Islam: A Critical Survey
Authors: Belhaj Abdessamad
Published: October 2024

Up until recently, research on loyalty and Islam—here considered as the discourses and practices of Muslims in regard to the Muslim tradition—had ignored the topic of loyalty. Interest in loyalty in Islam has just lately grown sufficiently and extensively. This entry is both bibliographical and thematic. It seeks to identify the principal themes that have dominated Muslim loyalty studies for the past thirty years or so. Additionally, it provides a thorough synopsis of over 100 studies on loyalty that were published during the same time span in Arabic and European languages. Allegiance in Muslim political ethics always had more than one connotation and the religious component of loyalty, while not strictly defined, allowed and even justified the overlap of multiple loyalties. A person’s or a family’s, ruler’s, or dynasty’s power to govern an area or defend religious institutions and symbols was intimately linked to the loyalty they could claim. Most studies agree on the diversity of political allegiance in modern Islam with regard to perceptions of religion, ethnicity, self-interest, etc.; it is also co-relative in the sense that it is mixed with other allegiances, such as those of family, tribe, leadership, or country.
The Interplay of the Mammalian Brain and Thyroid Hormones, and the Threat of Endocrine-Disrupting Chemicals
Authors: Dsouki Nuha Ahmad , Pereira Bruno Fiorelini , da Silva Roberta Goes , Rodrigues Vinicius Gonçalves , Brito Rafaella da Silva , Kizys Marina Malta Letro , Chiamolera Maria Izabel , Maciel Rui Monteiro , Serrano-Nascimento Caroline , Giannocco Gisele
Published: October 2024

Introduction: During the formation of neural circuits, the developing brain demonstrates extraordinary plasticity, heavily influenced by hormones. These chemical messengers interact with specific receptors to regulate vital physiological functions. The thyroid gland plays a pivotal role in maintaining hormonal balance and guiding brain development. However, emerging threats like endocrine-disrupting chemicals (EDCs) can interfere with this intricate system. EDCs are exogenous substances that can mimic, enhance, or block the actions of endogenous hormones, disrupting hormonal signaling in the brain at various developmental stages. Exposure can impair cognitive function and behavior due to disruptions in thyroid function. Studies indicate that mixtures of EDCs negatively impact brain development, leading to lower IQ and behavioral problems. Reducing EDC exposure through regulations and public awareness is crucial, and further research is needed to elucidate their mechanisms. Conclusions: Protecting vulnerable populations, such as pregnant women and children, is essential through prompt regulatory measures.
Pollarding May Relieve Drought Stress in Black Poplars
Authors: Camarero J. Julio , Valeriano Cristina , Sánchez-Sancho José Antonio , de Jaime Loren Chabier
Published: October 2024

Pollarding has historically been used in broadleaf tree species across European woodlands. However, despite pollarding enhances vigor growth in the short term, it is still unclear how long this effect lasts and whether it can alleviate drought stress in seasonally dry regions. We compared the radial growth and wood δ13C (13C/12C), a proxy of intrinsic water-use efficiency (iWUE), of trees pollarded 10 and 20 years ago in two black poplar (Populus nigra L.) riparian stands located in North Eastern Spain and subjected to different ecohydrological conditions. We also assessed if pollarded trees showed different leaf phenology as compared with uncut trees of coexisting white poplar (Populus alba L.) trees. The relationships between growth, climate variables, drought severity and river flow were quantified. Pollarded and uncut trees showed a similar leaf phenology with a trend towards earlier leaf unfolding as springs become warmer. Pollarding increased growth rates by 54% (ratio between trees pollarded 10 and 20 years ago, respectively), but this enhancement was transitory and lasted ca. 10 years, whereas wood δ13C decreased −5%. The growth of black poplar increased in response to high precipitation in the previous winter, cool wet conditions, and a higher river flow in summer. Pollarding improves growth and relieves drought stress.
A Method for Classifying Wood-Boring Insects for Pest Control Based on Deep Learning Using Boring Vibration Signals with Environment Noise
Authors: Li Juhu , Zhao Xuejing , Li Xue , Ju Mengwei , Yang Feng
Published: October 2024

Wood-boring pests are difficult to monitor due to their concealed lifestyle. To effectively control these wood-boring pests, it is first necessary to efficiently and accurately detect their presence and identify their species, which requires addressing the limitations of traditional monitoring methods. This paper proposes a deep learning-based model called BorerNet, which incorporates an attention mechanism to accurately identify wood-boring pests using the limited vibration signals generated by feeding larvae. Acoustic sensors can be used to collect boring vibration signals from the larvae of the emerald ash borer (EAB), Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae), and the small carpenter moth (SCM), Streltzoviella insularis Staudinger, 1892 (Lepidoptera: Cossidae). After preprocessing steps such as clipping and segmentation, Mel-frequency cepstral coefficients (MFCCs) are extracted as inputs for the BorerNet model, with noisy signals from real environments used as the test set. BorerNet learns from the input features and outputs identification results. The research findings demonstrate that BorerNet achieves an identification accuracy of 96.67% and exhibits strong robustness and generalization capabilities. Compared to traditional methods, this approach offers significant advantages in terms of automation, recognition efficiency, and cost-effectiveness. It enables the early detection and treatment of pest infestations and allows for the development of targeted control strategies for different pests. This introduces innovative technology into the field of tree health monitoring, enhancing the ability to detect wood-boring pests early and making a substantial contribution to forestry-related research and practical applications.
Soil Physicochemical Properties and Carbon Storage Reserve Distribution Characteristics of Plantation Restoration in a Coal Mining Area
Authors: Wang Ruidong , Han Yanlong , Meng Zhongju , Gao Yong , Wu Zhenliang
Published: October 2024

The Bulianta Coal Mine is among the problematic coal mining areas in China that is still creating environmental damage, especially associated with soil destruction. Therefore, a scientific investigation was conducted to establish a scientific basis for evaluating the impact of planted forest on soil physical and chemical properties, as well as the ecological benefits following 15 years of vegetation restoration in the area. The soil physicochemical characteristics and distribution of organic carbon storage in the 0–80 cm layer soils of Pinus sylvestris forests, Prunus sibirica forests, and Hippophae rhamnoides forests restored after 5, 10, and 15 years were investigated. The immersion method was used to determine soil porosity and density followed by the determination of soil indicators, and a statistical ANOVA test was applied to examine the differential effects of different vegetation types and restoration years on soil properties. The results clearly demonstrated the following: (1) The recovery of vegetation was achieved after a period of 15 years, with the average bulk density of the 0–80 cm soil layer as follows: P. sylvestris forest (1.513 g·cm−3) > P. sibirica forest (1.272 g·cm−3) > H. rhamnoides forest (1.224 g·cm−3), and the differences among different forest types were statistically significant (p < 0.05). (2) In planted forests, soil nutrients were predominantly concentrated in the 0–20 cm layer, while soil carbon storage exhibited a decline with an increasing soil depth. (3) The soil carbon storage across the three forest types was as follows: P. sylvestris forest (45.42 t·hm−2) > P. sibirica forest (44.56 t·hm−2) > H. rhamnoides forest (41.87 t·hm−2). In summary, during the ecological vegetation restoration process in the Bulianta Core Mine, both P. sylvestris forest and P. sibirica forest exhibit superior carbon storage capacities compared to H. rhamnoides forest, as well as more effective soil improvement outcomes.
Obtaining and Characterizing Thermostable α-Amylases Secreted by Bacillus subtilis, Originating from Bacillus amyloliquefaciens and Bacillus subtilis
Authors: Soghomonyan Tigran , Hambardzumyan Artur , Mkhitaryan Anna , Khoyetsyan Lev , Paronyan Marina , Izmailyan Marieta , Kinosyan Marina , Bagiyan Valeri , Ghochikyan Vahe , Panosyan Hovik , Paloyan Ani
Published: October 2024

The production of recombinant enzymes, primarily used for obtaining pure and functional target molecules, holds significant importance in modern biotechnology. This study aimed to obtain and characterize recombinant, extracellularly expressed α-amylases (Amy3500 and Amy1974), derived from B. amyloliquefaciens MDC1974 and B. subtilis MDC3500, respectively, using the pBE-S shuttle vector. Both α-amylase genes were molecularly cloned into the E. coli/B. subtilis pBE-S shuttle vector, both with (Amy1974sig and Amy3500sig) and without their signal peptides (Amy1974 and Amy3500), along with a signal peptide originating from the plasmid, and tested in flask fermentations. For recombinant Amy3500, the amylase variants resulted in similar levels of volumetric activity (700–750 U/mL). In contrast, the expression of Amy1974 nearly doubled compared to Amy1974sig with double signal peptides, reaching 2000 U/mL. SDS-PAGE estimated the molecular weight of Amy1974 α-amylase to be 54.6 kDa, which is consistent with the theoretical molecular mass calculations. However, the estimated molecular weight of Amy3500 α-amylase was significantly lower upon exiting the producer cells. Ca2⁺ ions exhibit a modest activating effect on the activities of Amy1974 and Amy3500 amylases, likely due to their tight binding to the protein scaffold. Both enzymes exhibited broad activity peaks between 45 and 70 °C, with a maximum at 65 °C. The Amy1974 and Amy3500 α-amylases demonstrated broad pH optima and pH-dependent thermostability, with optimum pH values at 6.5 and 5.8, and thermal stability peaks at pH 7.6 and 5.9, respectively. Both α-amylases displayed high relative activity against various starches, including corn amylopectin and potato amylose, while showing comparatively lower activity towards α-, β-, and γ-cyclodextrins. The Amy1974 amylase is effective in converting starch into dextrins of varying lengths, while Amy3500 primarily converts starch into glucose. These characteristics make them promising candidate enzymes for industrial applications.
Prediction of Scour Depth for Diverse Pier Shapes Utilizing Two-Dimensional Hydraulic Engineering Center’s River Analysis System Sediment Model
Authors: Al-Jubouri Muhanad , Ray Richard P. , Abbas Ethar H.
Published: October 2024

Examining scouring around bridge piers is crucial for ensuring water-related infrastructure’s long-term safety and stability. Accurate forecasting models are essential for addressing scour, especially in complex water systems where traditional methods fall short. This study investigates the application of the HEC-RAS 2D sedimentation model, which has recently become available for detailed sediment analysis, to evaluate its effectiveness in predicting scoring around various pier shapes and under different water conditions. This study offers a comprehensive assessment of the model’s predictive capabilities by focusing on variables such as water velocity, shear stress, and riverbed changes. Particular attention was paid to the influence of factors like floating debris and different pier geometries on scour predictions. The results demonstrate that while the HEC-RAS 2D model generally provides accurate predictions for simpler pier shapes—achieving up to 85% precision—it shows varied performance for more complex designs and debris-influenced scenarios. Specifically, the model overpredicted scouring depths by approximately 20% for diamond-shaped piers and underpredicted by 15% for square piers in debris conditions. Elliptical piers, in contrast, experienced significantly less erosion, with scour depths up to 30% shallower compared to other shapes. This study highlights the novel application of the HEC-RAS 2D model in this context and underscores its strengths and limitations. Identified issues include difficulties in modeling water flow and debris-induced bottlenecks. This research points to the improved calibration of sediment movement parameters and the development of advanced computational techniques to enhance scour prediction accuracy in complex environments. This work contributes valuable insights for future research and practical applications in civil engineering, especially where traditional scour mitigation methods, such as apron coverings, are not feasible.
Food Waste Biotransformation into Food Ingredients: A Brief Overview of Challenges and Opportunities
Authors: Pinela José , Añibarro-Ortega Mikel , Barros Lillian
Published: October 2024

In today’s global context, challenges persist in preventing agri-food waste due to factors like limited consumer awareness and improper food-handling practices throughout the entire farm-to-fork continuum. Introducing a forward-thinking solution, the upcycling of renewable feedstock materials (i.e., agri-food waste and by-products) into value-added ingredients presents an opportunity for a more sustainable and circular food value chain. While multi-product cascade biorefining schemes show promise due to their greater techno-economic viability, several biotechnological hurdles remain to be overcome at many levels. This mini-review provides a succinct overview of the biotechnological and societal challenges requiring attention while highlighting valuable food-grade compounds derived from biotransformation processes. These bio-based ingredients include organic acids, phenolic compounds, bioactive peptides, and sugars and offer diverse applications as antioxidants, preservatives, flavorings, sweeteners, or prebiotics in foodstuffs and other consumer goods. Therefore, these upcycled products emerge as a sustainable alternative to certain potentially harmful artificial food additives that are still in use or have already been banned from the industry.
Applications of Big Data and Blockchain Technology in Food Testing and Their Exploration on Educational Reform
Authors: Ding Haohan , Xie Zhenqi , Wang Chao , Yu Wei , Cui Xiaohui , Wang Zhenyu
Published: October 2024

This study reviews the applications of big data (BD) and blockchain technology in modern food testing and explores their impact on educational reform. The first part highlights the critical role of BD in ensuring food safety across the supply chain, discussing various data collection methods, such as national and international food safety databases, while addressing the challenges related to data storage and real-time information retrieval. Additionally, blockchain technology has been explored for its ability to enhance transparency, traceability, and security in the food-testing process by creating immutable records of testing data, ensuring data integrity, and reducing the risk of tampering or fraud. The second part focuses on the influence of BD and blockchain on educational reform, particularly within food science curricula. BD enables data-driven curriculum design, supporting personalized learning and more effective educational outcomes, while blockchain ensures transparency in course management and credentials. This study advocates integrating these technologies into curriculum reform to enhance both the efficiency and quality of education.
Unlocking the Potential of Teff for Sustainable, Gluten-Free Diets and Unravelling Its Production Challenges to Address Global Food and Nutrition Security: A Review
Authors: Adepoju Mary , Verheecke-Vaessen Carol , Pillai Laxmi Ravikumar , Phillips Heidi , Cervini Carla
Published: October 2024

Sustainable diets, as defined by the Food and Agriculture Organisation, aim to be nutritionally adequate, safe, and healthy, while optimising natural and human resources. Teff (Eragrostis tef), a gluten-free grain primarily grown in Ethiopia, has emerged as a key contender in this context. Widely regarded as a “supergrain”, teff offers an outstanding nutrition profile, making it an excellent choice for people with gluten-related disorders. Rich with protein, essential amino acids, polyunsaturated fats, and fibre, and abundant in minerals like calcium and iron, teff rivals other popular grains like quinoa and durum wheat in promoting human health. Beyond its nutritional benefits, teff is a hardy crop that thrives in diverse climates, tolerating both drought and waterlogged conditions. Due to its resilience and rich nutrient content, teff holds the potential to address nine of the 17 United Nations’ Sustainable Development Goals (SDGs), including SDG 1 (no poverty), SDG 2 (zero hunger), and SDG 3 (good health and wellbeing), which are tied to improving food and nutrition security. However, teff production in Ethiopia faces significant issues. Traditional farming practices, insufficient storage infrastructure, and food safety challenges, including adulteration, hinder teff’s full potential. This review explores teff’s dual role as a nutritious, sustainable food source and outlines the key challenges in its production to conclude on what needs to be done for its adoption as a golden crop to address global food and nutrition security.
Climate Risks and Real Gold Returns over 750 Years
Authors: Gupta Rangan , Majumdar Anandamayee , Pierdzioch Christian , Polat Onur
Published: October 2024

Using data that cover the annual period from 1258 to 2023, we studied the link between real gold returns and climate risks. We documented a positive contemporaneous link and a negative predictive link. Our findings further show that the predictive link historically gave rise to significant out-of-sample forecasting gains. The positive contemporaneous link is consistent with the view that investors viewed gold as a safe haven in times of elevated climate risks. The negative predictive link, in turn, is consistent with an overshooting scenario in which the real gold price overshot in response to climate risks, only to return subsequently to a lower value. Our findings should provide important implications for investors and policymakers, given that our analysis covered the longest possible data sample involving the gold market, and hence, was independent of any sample selection bias.
Using Machine Deep Learning AI to Improve Forecasting of Tax Payments for Corporations
Authors: Swenson Charles
Published: October 2024

This paper aims to demonstrate how machine deep learning techniques lead to relatively accurate forecasts of quarterly corporate income tax payments. Using quarterly data from Compustat for all U.S. publicly traded corporations from 2000 to 2024, I show that neural nets, the tree method, and random forest models provide robust forecasts despite their encompassing COVID-19 pandemic time periods. The results should be of interest to corporate tax planners, stock analysts, and governments.
The Investigation of Nonlinear Time-Fractional Models in Optical Fibers and the Impact Analysis of Fractional-Order Derivatives on Solitary Waves
Authors: Afridi Muhammad Idrees , Islam Tamanna , Akbar Md Ali , Osman Mohamed S.
Published: October 2024

In this article, we investigate a couple of nonlinear time-fractional evolution equations, namely the cubic-quintic-septic-nonic equation and the Davey–Stewartson (DS) equation, both of which have significant applications in complex physical phenomena such as fiber optical communication, optical signal processing, and nonlinear optics. Using a powerful technique named the extended generalized Kudryashov approach, we extract different rich structured soliton solutions to these models, including bell-shaped, cuspon, parabolic soliton, singular soliton, and squeezed bell-shaped soliton. We also study the impact of fractional-order derivatives on these solutions, providing new insights into the dynamics of nonlinear models. The results are compared with the existing literature, revealing novel and distinct solutions that offer a deeper understanding of these fractional models. The results show that the implemented approach is useful, reliable, and compatible for examining fractional nonlinear evolution equations in applied science and engineering.
Application of an Optimal Fractional-Order Controller for a Standalone (Wind/Photovoltaic) Microgrid Utilizing Hybrid Storage (Battery/Ultracapacitor) System
Authors: Albalawi Hani , Zaid Sherif A. , Alatwi Aadel M. , Moustafa Mohamed Ahmed
Published: October 2024

Nowadays, standalone microgrids that make use of renewable energy sources have gained great interest. They provide a viable solution for rural electrification and decrease the burden on the utility grid. However, because standalone microgrids are nonlinear and time-varying, controlling and managing their energy can be difficult. A fractional-order proportional integral (FOPI) controller was proposed in this study to enhance a standalone microgrid’s energy management and performance. An ultra-capacitor (UC) and a battery, called a hybrid energy storage scheme, were employed as the microgrid’s energy storage system. The microgrid was primarily powered by solar and wind power. To achieve optimal performance, the FOPI’s parameters were ideally generated using the gorilla troop optimization (GTO) technique. The FOPI controller’s performance was contrasted with a conventional PI controller in terms of variations in load power, wind speed, and solar insolation. The microgrid was modeled and simulated using MATLAB/Simulink software R2023a 23.1. The results indicate that, in comparison to the traditional PI controller, the proposed FOPI controller significantly improved the microgrid’s transient performance. The load voltage and frequency were maintained constant against the least amount of disturbance despite variations in wind speed, photovoltaic intensity, and load power. In contrast, the storage battery precisely stores and releases energy to counteract variations in wind and photovoltaic power. The outcomes validate that in the presence of the UC, the microgrid performance is improved. However, the improvement is very close to that gained when using the proposed controller without UC. Hence, the proposed controller can reduce the cost, weight, and space of the system. Moreover, a Hardware-in-the-Loop (HIL) emulator was implemented using a C2000™ microcontroller LaunchPad™ TMS320F28379D kit (Texas Instruments, Dallas, TX, USA) to evaluate the proposed system and validate the simulation results.
The Role of Vanadium in Metallodrugs Design and Its Interactive Profile with Protein Targets
Authors: Chaves Otávio Augusto , Martins Francisco Mainardi , Serpa Carlos , Back Davi Fernando
Published: October 2024

Metallodrugs represent a critical area of medicinal chemistry with the potential to address a wide range of diseases. Their design requires a multidisciplinary approach, combining principles of inorganic chemistry, pharmacology, and molecular biology to create effective and safe therapeutic agents. Vanadium, the element of the fifth group of the first transition series (3d metals), has been already detected as a crucial species in the biological action of some enzymes, e.g., nitrogenases and chloroperoxidase; furthermore, vanadium-based compounds have recently been described as physiologically stable with therapeutic behavior, e.g., having anticancer, antidiabetic (insulin-mimicking), antiprotozoal, antibacterial, antiviral, and inhibition of neurodegenerative disease properties. Since the binding of metallodrugs to serum albumin influences the distribution, stability, toxicity (intended and off-target interactions), and overall pharmacological properties, the biophysical characterization between serum albumin and vanadium-based compounds is one of the hot topics in pharmacology. Overall, since vanadium complexes offer new possibilities for the design of novel metallodrugs, this review summarized some up-to-date biological and medicinal aspects, highlighting proteins as the main targets for the inorganic complexes based on this transition metal.
Genetic Loci Mining and Candidate Gene Analysis for Determining Fatty Acid Composition in Rice
Authors: Ge Yiyun , Wei Yiting , Li Xuan , Zhu Zhenan , Lian Jinjin , Yang Huimin , Lu Tiantian , Li Sanfeng , Huang Jiahui , Ye Yuhan , Wang Yuexing , Rao Yuchun
Published: October 2024

Fatty acid composition and its proportions are critical to the nutritional value and storage quality of rice (Oryza sativa L.) as the third major nutrient component in this staple food. This study involved crossing an indica rice variety, Huazhan (HZ), as the male parent, with a japonica variety, Nekken2, as the female parent, to produce the F1 generation. Subsequently, a population of 120 recombinant inbred lines (RILs) was developed through multiple generations of self-breeding. By utilizing a high-density molecular genetic linkage map and phenotypic data of four fatty acid components, we identified a total of 14 quantitative trait loci (QTLs) related to fatty acid composition across chromosomes 1, 3, 4, 6, 8, and 9. These included two QTLs for C14 content, three for C16:0 content, six for C18:1 content, and three for C18:2 content. Notably, the QTL qCOPT4.2 exhibited a high LOD score of 5.22. Within QTL intervals, genes such as OsACX3 and SLG affecting grain length were identified. The expression of candidate genes within these intervals was assessed and further analyzed by using quantitative real-time PCR. Genes such as LOC_Os01g15000, LOC_Os04g47120, LOC_Os04g49194, LOC_Os06g22080, LOC_Os06g23870, LOC_Os06g24704, LOC_Os06g30780, LOC_Os08g44840, and LOC_Os09g36860 were found to regulate fatty acid synthesis or metabolic pathways, potentially enhancing fatty acid content in rice. These QTLs are indispensable for breeding rice varieties with improved fatty acid profiles, offering new genetic resources for enhancing the nutritional and storage qualities of rice.
Apolipoprotein E Induces Lipid Accumulation Through Dgat2 That Is Prevented with Time-Restricted Feeding in Drosophila
Authors: Moraes Ruan C. M. , Roth Jonathan R. , Mao Hailey , Crawley Savannah R. , Xu Brittney P. , Watson John C. , Melkani Girish C.
Published: October 2024

Background: Apolipoprotein E (ApoE) is the leading genetic risk factor for late-onset Alzheimer’s disease (AD), which is the leading cause of dementia worldwide. Most people have two ApoE-ε3 (ApoE3) alleles, while ApoE-ε2 (ApoE2) is protective from AD, and ApoE-ε4 (ApoE4) confers AD risk. How these alleles modulate AD risk is not clearly defined, and ApoE’s role in lipid metabolism is also not fully known. Lipid droplets increase in AD. However, how ApoE contributes to lipid accumulation in the brain remains unknown. Methods: Here, we use Drosophila to study the effects of ApoE alleles on lipid accumulation in the brain and muscle in a cell-autonomous and non-cell-autonomous manner. Results: We report that pan-neuronal expression of each ApoE allele induces lipid accumulation specifically in the brain, but not in the muscle. However, this was not the case when expressed with muscle-specific drivers. ApoE2- and ApoE3-induced lipid accumulation is dependent on the expression of Dgat2, a key regulator of triacylglycerol production, while ApoE4 still induces lipid accumulation even with knock-down of Dgat2. Additionally, we find that implementation of time-restricted feeding (TRF), a dietary intervention in which food access only occurs in the active period (day), prevents ApoE-induced lipid accumulation in the brain of flies and modulates lipid metabolism genes. Conclusions: Altogether, our results demonstrate that ApoE induces lipid accumulation in the brain, that ApoE4 is unique in causing lipid accumulation independent of Dgat2, and that TRF prevents ApoE-induced lipid accumulation. These results support the idea that lipid metabolism is critical in AD, and that TRF could be a promising therapeutic approach to prevent ApoE-associated dysfunction in lipid metabolism.
Correction: Ferreira-Sánchez et al. Differences in Motor Imagery Ability between People with Parkinson’s Disease and Healthy Controls, and Its Relationship with Functionality, Independence and Quality of Life. Healthcare 2023, 11, 2898
Authors: Ferreira-Sánchez María del Rosario , Moreno-Verdú Marcos , Atín-Arratibel María de los Ángeles , Martín-Casas Patricia
Published: October 2024

In the original publication [...]
High-Affinity Plasma Membrane Ca2+ Channel Cch1 Modulates Adaptation to Sodium Dodecyl Sulfate-Triggered Rise in Cytosolic Ca2+ Concentration in Ogataea parapolymorpha
Authors: Kulakova Maria , Pakhomova Maria , Bidiuk Victoria , Ershov Alexey , Alexandrov Alexander , Agaphonov Michael
Published: October 2024

The cytosolic calcium concentration ([Ca2+]cyt) in yeast cells is maintained at a low level via the action of different transporters sequestrating these cations in the vacuole. Among them, the vacuolar Ca2+ ATPase Pmc1 crucially contributes to this process. Its inactivation in Ogataea yeasts was shown to cause sodium dodecyl sulfate (SDS) hypersensitivity that can be alleviated by the inactivation of the plasma membrane high-affinity Ca2+ channel Cch1. Here, we show that SDS at low concentrations induces a rapid influx of external Ca2+ into cells, while the plasma membrane remains impermeable for propidium iodide. The inactivation of Pmc1 disturbs efficient adaptation to this activity of SDS. The inactivation of Cch1 partially restores the ability of pmc1 mutant cells to cope with an increased [Ca2+]cyt that correlates with the suppression of SDS hypersensitivity. At the same time, Cch1 is unlikely to be directly involved in SDS-induced Ca2+ influx, since its inactivation does not decrease the amplitude of the rapid [Ca2+]cyt elevation in the pmc1-Δ mutant. The obtained data suggest that the effects of CCH1 inactivation on SDS sensitivity and coping with increased [Ca2+]cyt are related to an additional Cch1 function beyond its direct involvement in Ca2+ transport.