bluuebunny/crossref_metadata_2025 huggingface dataset:
Applications of Entropy in Data Analysis and Machine Learning: A Review
Authors: Sepúlveda-Fontaine Salomé A. , Amigó José M.
Published: December 2024

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann–Gibbs, von Neumann, Shannon, Kolmogorov–Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), another commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond. Therefore, it is not surprising that, in the course of time, many other instances of the overarching concept of entropy have been proposed, most of them tailored to specific purposes. Following the current usage, we will refer to all of them, whether classical or new, simply as entropies. In particular, the subject of this review is their applications in data analysis and machine learning. The reason for these particular applications is that entropies are very well suited to characterize probability mass distributions, typically generated by finite-state processes or symbolized signals. Therefore, we will focus on entropies defined as positive functionals on probability mass distributions and provide an axiomatic characterization that goes back to Shannon and Khinchin. Given the plethora of entropies in the literature, we have selected a representative group, including the classical ones. The applications summarized in this review nicely illustrate the power and versatility of entropy in data analysis and machine learning.
Digital and Mediation Competence for Students’ Safe Use of the Internet: Enhancing Teacher Training
Authors: Gómez-Puerta Marcos , Chiner Esther , Villegas-Castrillo Esther , Suriá-Martínez Raquel
Published: December 2024

Teachers need digital skills to optimise the educational benefits of the Internet and mediate its risks. This study investigates digital and mediation competencies among teachers, focusing on their preparation for guiding students in the safe use of the Internet. Using a descriptive, cross-sectional survey design, data were collected from 550 Spanish teachers across various educational settings and levels. The findings reveal that while nearly half of the teachers received some online safety training, the average training duration was relatively low. Notably, differences emerged based on school type and educational stage, with secondary school and special education teachers receiving more training. Teachers generally reported moderate to high digital competence, though those with greater teaching experience perceived themselves as less digitally competent. Additionally, teachers felt only moderately prepared to mediate risks. Special education teachers expressed a higher perceived competence in addressing certain risks than their counterparts in regular education. These findings underscore the need for enhanced, context-specific training in digital safety and mediation skills across educational contexts, addressing both technical aspects and broader digital safety competencies. The study concludes by recommending ongoing, accessible training, particularly for experienced teachers, to align with evolving digital challenges in education.
AI-Generated Context for Teaching Robotics to Improve Computational Thinking in Early Childhood Education
Authors: Hijón-Neira Raquel , Pizarro Celeste , Borrás-Gené Oriol , Cavero Sergio
Published: December 2024

This study investigates the impact of AI-generated contexts on preservice teachers’ computational thinking (CT) skills and their acceptance of educational robotics. This article presents a methodology for teaching robotics based on AI-generated contexts aimed at enhancing CT. An experiment was conducted with 122 undergraduate students enrolled in an Early Childhood Education program, aged 18–19 years, who were training in the Computer Science and Digital Competence course. The experimental group utilized a methodology involving AI-generated practical assignments designed by their lecturers to learn educational robotics, while the control group engaged with traditional teaching methods. The research addressed five key factors: the effectiveness of AI-generated contexts in improving CT skills, the specific domains of CT that showed significant improvement, the perception of student teachers regarding their ability to teach with educational robots, the enhancement in perceived knowledge about educational robots, and the overall impact of these methodologies on teaching practices. Findings revealed that the experimental group exhibited higher engagement and understanding of CT concepts, with notable improvements in problem-solving and algorithmic thinking. Participants in the AI-generated context group reported increased confidence in their ability to teach with educational robots and a more positive attitude toward technology integration in education. The findings highlight the importance of providing appropriate context and support when encouraging future educators to build confidence and embrace educational technologies. This study adds to the expanding research connecting AI, robotics, and education, emphasizing the need to incorporate these tools into teacher training programs. Further studies should investigate the lasting impact of such approaches on computational thinking skills and teaching methods in a variety of educational environments.
Perception of Symmetry and Spatial Reasoning in 11–12-Year-Old Pupils
Authors: Kmetová Mária , Rumanová Lucia
Published: December 2024

Visual perception and visualization are crucial in mathematical thinking and, more importantly, in geometric thinking. Our research study aimed to follow the link of geometric transformations, mental rotation, spatial ability, and visualisation in geometric thinking. During a longitudinal study, we examined the spatial ability-related geometric competencies of 11–12-year-old students from three lower secondary schools. We analysed their test results from axial symmetry and its application in practical problems, orientation in spatial labyrinths, and building spatial constructions with three views. During a detailed analysis of the written results, a grounded theory hypothesis arose that there was a possible relationship between symmetry perception and spatial thinking. This hypothesis was tested with CHIC statistical analysis. The analysis, however, did not show any deeper connection between symmetry perception and spatial thinking from the given data. We can conclude that this connection occurs in individual cases but not in general.
Hierarchical Reinforcement Learning-Based Adaptive Initial QP Selection and Rate Control for H.266/VVC
Authors: He Shuqian , Jin Biao , Tian Shangneng , Liu Jiayu , Deng Zhengjie , Shi Chun
Published: December 2024

In video encoding rate control, adaptive selection of the initial quantization parameter (QP) is a critical factor affecting both encoding quality and rate control precision. Due to the diversity of video content and the dynamic nature of network conditions, accurately and efficiently determining the initial QP remains a significant challenge. The optimal setting of the initial QP not only influences bitrate allocation strategies but also impacts the encoding efficiency and output quality of the encoder. To address this issue in the H.266/VVC standard, this paper proposes a novel hierarchical reinforcement learning-based method for adaptive initial QP selection. The proposed method introduces a hierarchical reinforcement learning framework that decomposes the initial QP selection task into high-level and low-level strategies, handling coarse-grained and fine-grained QP decisions, respectively. The high-level strategy quickly determines a rough QP range based on global video features and network conditions, while the low-level strategy refines the specific QP value within this range to enhance decision accuracy. This framework integrates spatiotemporal video complexity, network conditions, and rate control objectives to form an optimized model for adaptive initial QP selection. Experimental results demonstrate that the proposed method significantly improves encoding quality and rate control accuracy compared to traditional methods, confirming its effectiveness in handling complex video content and dynamic network environments.
A High-Speed Train Traction Motor State Prediction Method Based on MIC and Improved SVR
Authors: Wang Hui , Li Chaoxu , Liu Yuchen , Li Man
Published: December 2024

The traction motor realizes the mutual conversion of electrical energy and mechanical energy during the train traction and braking process and is a key component of high-speed trains. The normal operation of the motor is directly related to the safety of high-speed train operation. Changes in temperature signals can reflect faults in the traction motor. By analyzing the internal and external influencing factors of temperature signals, a multi-factor prediction model for traction motors is established based on the maximal information coefficient and improved support vector regression. In this model, highly relevant features selections are performed based on time-delayed sequences and the maximal information coefficient. Using the adaptive particle swarm algorithm to optimize the improved support vector regression algorithm can enhance its accuracy and efficiency. Furthermore, using the K-nearest neighbor algorithm for error prediction will yield more accurate results. By comparing the RMSE, MBE, MAE, and other evaluation metrics of different algorithms under various working conditions, the results show that the prediction method proposed in this paper performs well across different working conditions. This method demonstrates greater adaptability to varying conditions and is more suitable for applications involving high-speed trains.
The Development of Fast DST-I Algorithms for Short-Length Input Sequences
Authors: Raciborski Mateusz , Cariow Aleksandr , Bandach Jakub
Published: December 2024

The subject of this paper is the development of rationalized algorithms of discrete sinusoidal transform of type I for short sequences of length N = 2, 3, 4, 5, 6, 7, and 8. Here, by the word “rationalization”, we mean the reduction of the number of arithmetic operations required to implement the algorithms. The arithmetic complexity of the developed algorithms is presented in the final table. For each algorithm, we also provide data flow graphs demonstrating the space–time structure of the computational processes. The algorithms were tested to verify their validity using MATLAB software (version R2023).
Application of DS-DFT to the Fine Spectral Estimation of High-Noise Signals
Authors: Qin Lin , Dang Suihu , Fu Di , Feng Yutao
Published: December 2024

This paper presented an extended double-subsegment discrete Fourier transform (DS-DFT) algorithm as a tool for the fine spectral estimation of high-noise environments, which was previously effective in low-noise scenarios, and as such, its application to the analysis of noisy signals observed by a satellite-based interferometer was investigated. The observation of satellite-borne Doppler asymmetric spatial heterodyne spectroscopy (DASH) was first simulated to obtain the signals of low- and high-noise levels; then, a practical criterion to classify noise levels in the interferograms based on the DS-DFT results was introduced and validated by calculating the SNR. For high-noise signals, DS-DFT remains robust by employing phase differences and amplitude ratios for fine frequency estimation.
Identification of Strike-Slip Faults and Their Control on the Permian Maokou Gas Reservoir in the Southern Sichuan Basin (SW China): Fault Intersections as Hydrocarbon Enrichment Zones
Authors: Liu Jiawei , Wu Guanghui , Li Hai , Zhang Wenjin , Zheng Majia , Long Hui , Li Chenghai , Deng Min
Published: December 2024

The Middle Permian Maokou Formation carbonate rocks in the southern Sichuan Basin are import targets for hydrocarbon exploration, with numerous gas fields discovered in structural traps. However, as exploration extends into slope and syncline zones, the limestone reservoirs become denser, and fluid distribution becomes increasingly complex, limiting efficient exploration and development. Identifying the key factors controlling natural gas accumulation is therefore critical. This study is the first to apply deep learning techniques to fault detection in the southern Sichuan Basin, identifying previously undetected WE-trending subtle strike-slip faults (vertical displacement < 20 m). By integrating well logging, seismic, and production data, we highlight the primary factors influencing natural gas accumulation in the Maokou Formation. The results demonstrate that 80% of production comes from less than 30% of the well, and that high-yield wells are strongly associated with faults, particularly in slope and syncline zones where such wells are located within 200 m of fault zones. The faults can increase the drilling leakage of the Maokou wells by (7–10) times, raise the reservoir thickness to 30 m, and more than double the production. Furthermore, 73% of high-yield wells are concentrated in areas of fault intersection with high vertical continuity. Based on these insights, we propose four hydrocarbon enrichment models for anticline and syncline zones. Key factors controlling gas accumulation and high production include fault intersections, high vertical fault continuity, and local structural highs. This research demonstrates the effectiveness of deep learning for fault detection in complex geological settings and enhances our understanding of fault systems and carbonate gas reservoir exploration.
Energy in Smart Cities: Technological Trends and Prospects
Authors: Szpilko Danuta , Fernando Xavier , Nica Elvira , Budna Klaudia , Rzepka Agnieszka , Lăzăroiu George
Published: December 2024

Energy management in smart cities has gained particular significance in the context of climate change and the evolving geopolitical landscape. It has become a key element of sustainable urban development. In this context, energy management plays a central role in facilitating the growth of smart and sustainable cities. The aim of this article is to analyse existing scientific research related to energy in smart cities, identify technological trends, and highlight prospective directions for future studies in this field. The research involves a literature review based on the analysis of articles from the Scopus and Web of Science databases to identify and evaluate studies concerning energy in smart cities. The findings suggest that future research should focus on the development of smart energy grids, energy storage, the integration of renewable energy sources, as well as innovative technologies (e.g., Internet of Things, 5G/6G, artificial intelligence, blockchain, digital twins). This article emphasises the significance of technologies that can enhance energy efficiency in cities, contributing to their sustainable development. The recommended practical and policy directions highlight the development of smart grids as a cornerstone for adaptive energy management and the integration of renewable energy sources, underpinned by regulations encouraging collaboration between operators and consumers. Municipal policies should prioritise the adoption of advanced technologies, such as the IoT, AI, blockchain, digital twins, and energy storage systems, to improve forecasting and resource efficiency. Investments in zero-emission buildings, renewable-powered public transport, and green infrastructure are essential for enhancing energy efficiency and reducing emissions. Furthermore, community engagement and awareness campaigns should form an integral part of promoting sustainable energy practices aligned with broader development objectives.
Metaheuristic Optimization of Wind Turbine Airfoils with Maximum-Thickness and Angle-of-Attack Constraints
Authors: Radi Jinane , Sierra-García Jesús Enrique , Santos Matilde , Armenta-Déu Carlos , Djebli Abdelouahed
Published: December 2024

The shape of the blade strongly influences the aerodynamic behavior of wind turbines; therefore, it is essential to optimize its design to maximize the energy harvested from the wind. Some works address this optimized design problem using CFD, a tool that requires a lot of computational resources and time and starts from scratch. This work describes a new automated design method to generate aerodynamic profiles of wind turbines using existing blades as a base, which speeds up the design process. The optimization is performed using heuristic techniques, and the aim is to improve the characteristics of the blade shape which impact resilience and durability. Specifically, the glide ratio is maximized to capture maximum energy while ensuring specific design parameters, such as maximum thickness or optimal angle of attack. This methodology can obtain results more quickly and with lower computational cost, in addition to integrating these two design parameters into the optimization process, aspects that have been largely neglected in previous works. The analytical model of the blades is described by a class of two-dimensional shapes suitable for representing airfoils. The drag and lift coefficients are estimated, and a metaheuristic optimization technique, genetic algorithm, is applied to maximize the glide ratio while reducing the difference from the desired design parameters. Using this methodology, three new airfoils have been generated and compared with the existing starting models, S823, NACA 2424, and NACA 64418, achieving improvements in the maximum lift and maximum glide ratio of up to 13.8% and 39%, respectively. For validation purposes, a small 10 kW horizontal-axis wind turbine is simulated using the best design of the blades. The comparison with the existing blades focuses on the calculation of the generated power, the power coefficient, torque, and torque coefficient. For the new airfoils, improvements of 6.7% in the power coefficient and 5.5% in the torque coefficient were achieved. This validates the methodology for optimizing the blade airfoils.
Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data
Authors: Anan Muhammad , Kanaan Khalid , Benhaddou Driss , Nasser Nidal , Qolomany Basheer , Talei Hanaa , Sawalmeh Ahmad
Published: December 2024

Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Although many studies have been conducted on building energy predictions, the impact of occupancy on energy prediction models for office-type commercial buildings remains insufficiently explored, despite its potential to improve energy efficiency by 20%. This study investigates energy prediction using a Long Short-Term Memory (LSTM) model that incorporates time-series power consumption data and considers occupancy. A real-world dataset containing the per-minute electricity consumption of various appliances in an office building in Houston, TX, USA, is utilized. The proposed machine learning models forecast future energy consumption based on hourly, 3-hourly, daily, and quarterly predictions for individual appliances and total energy usage. The model’s performance is evaluated using the following three metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results demonstrate the superiority of the proposed system.
Thermal Management of Cubesat Subsystem Electronics
Authors: Kuta Kacper , Nowak Grzegorz , Nowak Iwona
Published: December 2024

The temperature field of an electronic optical instrument can affect the image quality realised by the instrument and, in extreme cases, lead to damage. This is particularly true for instruments operating in harsh environments such as space. The hyperspectral imaging optical instrument (OI) designed for the Intuition-1 (I-1) nanosatellite, currently in low Earth orbit, has been subjected to a numerical analysis of its thermal state under different operating conditions, and some preliminary experimental tests have been carried out to determine the maximum operating temperatures of its sensitive components and the risk of thermal damage. This work was part of a testing campaign prior to the deployment of Intuition-1. Three operational cases were analysed: (1) behaviour in the Earth’s atmospheric conditions when the OI is pointed at the Sun, (2) the end of the de-tumbling process in orbit with the Sun crossing the diagonal of the OI’s field of view, and (3) identification of the maximum possible number of consecutive Earth imaging cycles in orbit. The ultimate goal of this work was to validate the numerical approach used for these cases and to deepen the understanding of the thermal safety of the CMOS image sensor placed in the OI. For these cases, transient thermal field analyses were performed for the OI to determine the temperature distribution and its variability in the most thermally sensitive CMOS image sensor. The components of the OI and its overall structure were experimentally tested, and the results were used to validate the numerical models. The study showed that the built-in temperature sensor does not always reflect the actual CMOS temperature, and in some extreme cases the current temperature monitoring does not ensure its safe operation.
Research on Thyristor Reverse Recovery Behavior in High-Voltage Direct Current Transmission Converter Valves and Its Application in Integrated Protection Systems
Authors: Wen Cao , Song Liang , Huang Yu , Peng Dong , Zhang Peng , Liao Jianquan , Yang Longjie , Gao Shilin
Published: December 2024

The performance of converter valves is essential for the reliability and efficiency of high-voltage direct current (HVDC) transmission systems. Converter valves consist of multiple thyristor levels, each requiring regular testing to ensure proper functionality. Protective triggering tests play a crucial role in evaluating the safety and performance of these thyristors during maintenance. This study introduces a high-power experimental setup designed to investigate the effects of varying current levels and thermal stresses on the reverse recovery behavior of thyristors—a key performance indicator. Results indicate that the reverse recovery time increases rapidly with higher current levels before reaching a saturation point. Additionally, prolonged exposure to high temperatures significantly reduces both the storage time and the amount of charge recovered during the reverse recovery process. These findings enable the optimization of protective test settings, thereby enhancing the effectiveness of the Thyristor Control Unit (TCU) in protecting converter valves. Improved testing methodologies derived from this research contribute to more reliable maintenance practices and increased overall stability of HVDC transmission systems.
The Method for Assessing the Causes of Damage to a Bearing in a Rotary Air Preheater
Authors: Maślak Paweł , Przybyłek Grzegorz
Published: December 2024

This article presents a method for identifying the cause of damage to a rotary air preheater on one of the fluidized bed boilers operating in a power plant. The bearing in question operates under harsh conditions with the exhaust gas temperature reaching 287 °C and causing its casing to heat up intensively. It is therefore important to ensure that the bearing is constantly cooled by water, which lowers the operating temperature and thus extends its service life. Unfortunately, after a short period of operation, the upper double-row spherical roller bearing was damaged, and the tests presented in the assessment method helped to determine the cause of damage to its casing.
Impact of Temperature on the Hygroscopic Behavior and Mechanical Properties of Expansive Mudstone
Authors: Meng Lingdong , Zhang Wenxiu , Yang Huakui , Xu Shaoyun , Xu Ming , Zhang Lina , Dong Wenlong
Published: December 2024

Aiming at the large-scale nonlinear deformation of the roadway in Liangjia Coal Mine, Shandong Province, the mudstone of the 1602 working face is taken as the research object. A high-precision mudstone weathering test system integrating monitoring and control was developed, and the weathering tests of expanded mudstone were carried out at 10 °C, 20 °C, 30 °C and 40 °C. The results show that the hygroscopic curves of expanded mudstone demonstrate a nonlinear growth trend at different temperatures, and the influence of temperature on the hygroscopic curves is less than 20%. From the overall law, it can be roughly divided into three stages: the strong hygroscopic stage, the hygroscopic deceleration stage and the stable hygroscopic stage. The maximum expansion rates of the samples were 4.1%, 5.3%, 6.2% and 6.8%, respectively, and the water content and expansion rates corresponding to different ambient temperature and humidity were generally “concave”. The mechanical tests show that the mechanical properties of mudstone decrease as the ambient temperature increases, and the corresponding compressive strength decreases by 16~50%. The linear degradation is obvious, and the gradual expansion of peak strain indicates that the plasticity of the rock increases and the elastic modulus decreases linearly.
Effect of Silver Nanoparticles (AgNPs) on Aquatic and Wetland Plants
Authors: López-Herrera Amelia , Gómez-Merino Fernando Carlos , Zavaleta-Mancera Hilda Araceli , Avalos-Borja Miguel , García-Nava José Rodolfo , Trejo-Téllez Libia Iris
Published: December 2024

Among nanomaterials, silver nanoparticles (AgNPs) are cost-effective and exhibit unique physicochemical properties that enable them to become the most used agents for the manufacture of various products known as nano-enabled, including those for personal care, drugs, fabrics, sprays, disinfectants, vacuum cleaners, and air conditioners, with a continuous expansion to different sectors. Industrial discharges, the disposal of wastewater treatment effluents, and indirect runoff from the soil are some factors that are increasing the accumulation of AgNPs in aquatic and wetland ecosystems. Herewith, we critically analyze the progress in the research of the uptake and translocation of AgNPs in aquatic and wetland plants and their phytotoxic effect that depends on the concentration, size, distribution, morphological shape, surface characteristics and chemical composition of the nanoparticles, as well as the plant genotypes, among other factors. Due to biological plasticity, the toxicity level of AgNPs may vary among plant species, which may be further affected by the mode of application, time of exposure, and plant conditions (e.g., agronomic management, growth rate, phenological stage, etc.). Therefore, it is possible to identify and select competent plants for phytoremediation purposes, including superior capabilities for phytoextraction, phytofiltration, and phytostabilization. The review also identifies the main gaps that require attention in future research in order to elucidate a more integrative map aimed to reduce the potential threats to the environment and living organisms including humans.
Long- and Mid-Term Trends in the Waterbird Community: Functional and Ecological Turnovers After Restoration of Freshwater and Brackish Habitats in a Mediterranean Coastal Wetland
Authors: Vera Pablo , Dies José Ignacio , Ferrís Diana , Valentín Anna
Published: December 2024

In the Mediterranean, urban and agricultural development has led to the degradation of coastal habitats and a decline in wetland biodiversity. L’Albufera de Valencia is a paradigmatic case-study, with intensive rice fields, scarcity of natural aquatic habitats, and an unfavorable conservation status. Since the 1990s, restoration efforts have been made in freshwater and brackish environments. In this study, long-, medium-, and short-term dynamics and ecological and functional traits were analyzed for waterbird communities. We found that restoration of these areas had facilitated the establishment of resilient communities with higher specialization and diversity than the rest of the protected area, including threatened species. Overall, restoration has improved population dynamics and ecological traits in L’Albufera compared to the surrounding hyper-eutrophic lagoon and rice fields. The findings reveal two complementary conservation paradoxes linked to ecological succession. Initial dynamics affected small, pioneer species of high conservation value, while larger species of conservation concern displaced them over time. Dynamics were influenced by body mass, with less competition in freshwater species. Notably, large waders and vegetation gleaners nesting in restored freshwater wetlands exhibited greater sensitivity to ecological succession, reversing regional declines. These paradoxes highlight the importance of increasing restoration efforts in various stages of succession to meet the ecological needs of threatened species.
Seasonal and Spatial Discrimination of Sandy Beaches Using Energy-Dispersive X-Ray Fluorescence Spectroscopy Analysis: A Comparative Study of Maltese Bays
Authors: Costa Christine , Lia Frederick , Sinagra Emmanuel
Published: December 2024

The general increase in awareness of environmental pollutants and typical sources reflects the application of sustainability and development goals. Energy-Dispersive X-Ray Fluorescence spectroscopy analysis has been used to analyse sand samples collected from five different beaches located on the east and north-eastern coasts of Malta and Gozo during two summers and two winters. Samples were collected along linear transects perpendicular to the shoreline at three different depths. Chemometrics were used to discriminate between four latent variables, including season, location, depth, and distance from shoreline. The highest concentrations were attributed to Fe2O3, Al2O3, SrO, and SnO2. Principal Components Analysis and Factor Analysis classified distributions of Fe2O3, CoO, As2O3, MnO, SrO, SeO2, and CaCO3 under Principal Component 1. However, since no loading value dominance was observed, such distributions most likely represent a combination of lithogenic and anthropogenic natures. Discrimination using Stepwise Linear Canonical Discriminant Analysis (SLC-DA) and Partial Least Squares Discriminant Analysis (PLS-DA) using Leave-One-Out-Cross-Validation with Variance Importance Plots proved highly effective in classifying data by location, followed by seasonal variability. It follows that concentrations are not affected by depth and distance from shoreline variability, proving that accumulation and anthropogenic effects from land are not concentrated in specific zones but are spatially spread out along the bays and do not increase with depth.
Multisilva: A Web-Based Decision Support System to Assess and Simulate the Provision of Forest Ecosystem Services at the Property Level
Authors: Petucco Claudio , Chion Laurent , Ludwig Jérémy , Navarrete Gutiérrez Tomás , Rugani Benedetto , Stankiewicz Jacek
Published: December 2024

Forests provide a variety of ecosystem services (ESs) that contribute to a society’s wellbeing. ES provision depends on the structure and evolution of forest ecosystems and is influenced by forest management. Society’s increasing need for ESs requires these complex ecological dynamics to be understood and integrated in forest management and planning. We present the decision support system (DSS) Multisilva for multifunctional forest management. The Multisilva DSS is a web-based application that comprises two tools: the Mapping tool and the Simulation tool. The first tool provides spatial statistics and maps of the current provision of ESs at the forest property level. The Simulation tool compares two alternative, user-defined management scenarios over time and returns the biophysical estimations of ESs and the economic costs for each alternative. Multisilva is calibrated for Luxembourg, though it can be adapted for other temperate forest regions.
MaxEnt-Based Habitat Suitability Assessment for Vaccinium mandarinorum: Exploring Industrial Cultivation Opportunities
Authors: Bao Xuxu , Zhou Peng , Zhang Min , Fang Yanming , Zhang Qiang
Published: December 2024

Vaccinium mandarinorum Diels, a wild blueberry species distributed in the south of the Yangtze River in China, holds significant ecological and commercial value. Understanding its potential distribution and response to climate change is crucial for effective resource utilization and scientific introduction. By using the Maximum Entropy (MaxEnt) model, we evaluated V. mandarinorum’s potential distribution under current (1970–2000) and future climate change scenarios (2041–2060, 2061–2080, and 2081–2100) based on 216 modern distribution records and seven bioclimatic variables. The results showed that the MaxEnt model could effectively simulate the historical distribution and suitability degree of V. mandarinorum. The top two major environmental variables were precipitation of the driest quarter and annual precipitation, considering their contribution rates of 61.3% and 23.4%, respectively. Currently, the high suitability areas were mainly concentrated in central and northern Jiangxi province, central and southern Zhejiang province, southern Anhui province, central and northern Fujian province, and the border areas of Hunan and Guangxi provinces, covering 21.5% of the total suitable area. Future projections indicate that habitat will shift to higher latitudes and altitudes and that habitat quality will decline. Strategies are required to protect current V. mandarinorum populations and their habitats. The study results could provide an important theoretical reference for the optimization of planting distribution and ensure the sustainable production of the blueberry industry.
A Survey of Scenario Generation for Automated Vehicle Testing and Validation
Authors: Wang Ziyu , Ma Jing , Lai Edmund M-K
Published: December 2024

This survey explores the evolution of test scenario generation for autonomous vehicles (AVs), distinguishing between non-adaptive and adaptive scenario approaches. Non-adaptive scenarios, where dynamic objects follow predetermined scripts, provide repeatable and reliable tests but fail to capture the complexity and unpredictability of real-world traffic interactions. In contrast, adaptive scenarios, which adapt in real time to environmental changes, offer a more realistic simulation of traffic conditions, enabling the assessment of an AV system’s adaptability, safety, and robustness. The shift from non-adaptive to adaptive scenarios is increasingly emphasized in AV research, to better evaluate system performance in complex environments. However, generating adaptive scenario is more complex and faces challenges. These include the limited diversity in behaviors, low model interpretability, and high resource requirements. Future research should focus on enhancing the efficiency of adaptive scenario generation and developing comprehensive evaluation metrics to improve the realism and effectiveness of AV testing.
Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques
Authors: Kamal Hesham , Mashaly Maggie
Published: December 2024

Network and cloud environments must be fortified against a dynamic array of threats, and intrusion detection systems (IDSs) are critical tools for identifying and thwarting hostile activities. IDSs, classified as anomaly-based or signature-based, have increasingly incorporated deep learning models into their framework. Recently, significant advancements have been made in anomaly-based IDSs, particularly those using machine learning, where attack detection accuracy has been notably high. Our proposed method demonstrates that deep learning models can achieve unprecedented success in identifying both known and unknown threats within cloud environments. However, existing benchmark datasets for intrusion detection typically contain more normal traffic samples than attack samples to reflect real-world network traffic. This imbalance in the training data makes it more challenging for IDSs to accurately detect specific types of attacks. Thus, our challenges arise from two key factors, unbalanced training data and the emergence of new, unidentified threats. To address these issues, we present a hybrid transformer-convolutional neural network (Transformer-CNN) deep learning model, which leverages data resampling techniques such as adaptive synthetic (ADASYN), synthetic minority oversampling technique (SMOTE), edited nearest neighbors (ENN), and class weights to overcome class imbalance. The transformer component of our model is employed for contextual feature extraction, enabling the system to analyze relationships and patterns in the data effectively. In contrast, the CNN is responsible for final classification, processing the extracted features to accurately identify specific attack types. The Transformer-CNN model focuses on three primary objectives to enhance detection accuracy and performance: (1) reducing false positives and false negatives, (2) enabling real-time intrusion detection in high-speed networks, and (3) detecting zero-day attacks. We evaluate our proposed model, Transformer-CNN, using the NF-UNSW-NB15-v2 and CICIDS2017 benchmark datasets, and assess its performance with metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that our method achieves an impressive 99.71% accuracy in binary classification and 99.02% in multi-class classification on the NF-UNSW-NB15-v2 dataset, while for the CICIDS2017 dataset, it reaches 99.93% in binary classification and 99.13% in multi-class classification, significantly outperforming existing models. This proves the enhanced capability of our IDS in defending cloud environments against intrusions, including zero-day attacks.
Active Firefighting Systems Against Flashover Fires in Big Halls
Authors: Chow Cheuk-Lun , Cheng Chi-Honn , Chow Wan-Ki
Published: December 2024

The number of big fires in buildings appears to be increasing [...]
Diversification of Aquaculture in the Sub-Saharan Region—The Obscure Snakehead
Authors: Wuertz Sven , Amoutchi Amien Isaac , Ogunji Johnny
Published: December 2024

The sub-Saharan region shows fast growth in aquaculture, but current production is dominated by two species, the African catfish and tilapia. In order to support the expansion of the industry and ensure food resilience of the sector, diversification is desirable. Indeed, several candidates have been identified, among them the African snakehead Parachanna obscura. In contrast to the fast expansion of Asian snakehead farming, African aquaculture does not anticipate this trend. Still, looking at Asia, recent scientific literature provides impulses and solutions for the most pressing problems such as dry feed, cannibalism of juvenile stages and reproduction. In this review, we illustrate future research needs, integrating the recent progress in snakehead farming. Based on the recent progress in commercial diets in Clarias and protocols established for the reproduction of Channa species, an expansion of African snakehead farming seems feasible in the near future.
Effect of Genetic Polymorphism of Bovine β-Casein Variants (A1 and A2) on Yoghurt Characteristics
Authors: Juan Godoy Bibiana , Codina-Torrella Idoia , Trujillo Mesa Antonio-José
Published: December 2024

The present study aims to evaluate the physicochemical and sensory characteristics of A2 yoghurts (made with A2A2 β-CN milk), in comparison with Control yoghurts (elaborated from conventional milk, a mixture of A1 and A2 β-CN milk). The pH, acidity, water-holding capacity, spontaneous syneresis, firmness and color of yoghurts were monitored during their cold storage (4 °C) for 35 days. Two independent sensory tests (with expert judges and consumers) were also performed. The A2 yoghurts showed only minor differences in some of their physicochemical and sensory characteristics compared to those made with conventional milk. At specific storage times, the A2 yoghurt exhibited higher levels of acidity, luminosity (L*) and firmness, compared to the Control. No differences were observed in the growth curves of the starter (Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus salivarius subsp. thermophilus) during the yoghurt production, nor in the water-holding capacity or spontaneous syneresis of the two types of gels. Regarding the sensory evaluation of samples, the A2 yoghurts were described as firmer and more adherent (by the expert panel), and brighter and more homogeneous (by the consumers) than the Control. In all cases, both consumers and expert sensory panels showed a preference for the A2 yoghurts. Therefore, these results demonstrate the suitability of A2A2 β-CN milk for producing yoghurts with similar characteristics to those obtained with conventional milk.
Development of a Colloidal Gold Immunochromatographic Assay Strip Using a Monoclonal Antibody for the Rapid Detection of Ofloxacin
Authors: Li Xiaolan , Huang Jin , Li Na , Salah Mahmoud , Guan Shuoning , Pan Wenwen , Wang Ziyi , Zhou Xinghua , Wang Yun
Published: December 2024

The livestock industry uses ofloxacin, an antibiotic, to prevent several animal diseases; however, the overdose of ofloxacin used in animal farming treatments may appear in food products and cause some adverse human health effects. Hence, there is an immediate need to develop a method suitable for on site large-scale detection of ofloxacin residues in animal-derived foods. This study aimed to prepare a monoclonal antibody with high sensitivity and affinity for ofloxacin by re-synthesizing the ofloxacin hapten and synthesizing the corresponding complete antigen. The IC50 of the enzyme-linked immunosorbent assay (ic-ELISA) was 0.13 ng/mL, and the detection limit was 0.033 ng/mL. The visual detection limit of the established colloidal gold immunochromatographic test strip, for the visual detection of actual samples, was 1 ng/g. In summary, this work establishes a rapid detection method of ofloxacin residues on the basis of colloidal gold immunochromatography that is suitable for actual detection.
Effect of Nutmeg (Myristica fragrans) and Tea Tree (Melaleuca alternifolia) Essential Oils on the Oxidative and Microbial Stability of Chicken Fillets During Refrigerated Storage
Authors: Moirangthem Sushmita , Patra Gopal , Biswas Subhasish , Das Annada , Nath Santanu , Verma Arun K. , Pal Srija , Chatterjee Niloy , Bandyopadhyay Samiran , Nanda Pramod K. , Sharma Geetanjali , Das Arun K.
Published: December 2024

The current study investigated the impact of nutmeg essential oil (NEO) and tea tree essential oil (TTEO) on the preservation of raw chicken fillets during nine days of refrigerated storage study. The primary aim was to explore the antioxidant and antimicrobial properties of these essential oils (EOs) and assess their ability to extend the shelf life of poultry meat. Gas chromatography–mass spectrometry (GC-MS) was utilized to identify the chemical compositions of NEO and TTEO, revealing the presence of compounds like myristicin and terpenoids, known for their antimicrobial and antioxidant activities. Antioxidant properties were evaluated using DPPH and ABTS radical scavenging assays, where both oils exhibited potent free radical scavenging abilities, with NEO showing higher efficacy than TTEO. The EOs showed their antimicrobial potential, exhibiting significant antibacterial activities against tested Gram-positive and Gram-negative pathogens, such as Staphylococcus aureus and Escherichia coli, respectively. Raw chicken fillets treated with either NEO or TTEO at 1% were analyzed for physico-chemical, microbiological, and sensory attributes. Results demonstrated that both NEO- and TTEO-treated samples maintained better microbiological qualities, with lower total viable counts and enhanced sensory attributes, such as color and odor, compared to the control samples. Furthermore, NEO and TTEO effectively delayed spoilage, extending the shelf life of chicken fillets by up to seven days. This study concludes that both the test’s essential oils can be considered natural preservatives for enhancing the safety and quality of meat.
Flavor Wheel Development from a Machine Learning Perspective
Authors: Rodríguez-Mendoza Anggie V. , Arbeláez-Parra Santiago , Amaya-Gómez Rafael , Ratkovich Nicolas
Published: December 2024

The intricate relationships between chemical compounds and sensory descriptors in distilled spirits have long intrigued distillers, sensory experts, and consumers alike. The importance and complexity of this relation affect the production, quality, and appreciation of spirits, and the success of a product. Because of that, profoundly investigating the different flavor and aroma combinations that the chemical compounds can give to a desired beverage takes an essential place in the industry. This study aims to study these relationships by employing machine learning techniques to analyze a comprehensive dataset with 3051 chemical compounds and their associated aroma descriptors for seven distilled spirit categories: Baijiu, cachaça, gin, mezcal, rum, tequila, and whisk(e)y. The study uses principal component analysis (PCA) to reduce the dimensionality of the dataset and a clustering machine learning model to identify distinct clusters of aroma descriptors associated with each beverage category. Based on these results, an aroma wheel that encapsulates the diverse olfactory landscapes of various distilled spirits was developed. This flavor wheel is a valuable tool for distillers, sensory experts, and consumers, providing a comprehensive reference for understanding and appreciating the complexities of distilled spirits.
The Antidiabetic Effect of Grape Skin Extracts of Selected Indigenous Croatian White Grapevine Varieties
Authors: Rastija Vesna , Drenjančević Mato , Kujundžić Toni , Zmaić Luka , Karnaš Maja
Published: December 2024

Grape skin is an excellent bioactive compound source with numerous beneficial health effects. This study aimed to determine and compare the antidiabetic potential of the grape skin of indigenous Croatian white grapevine varieties. The grape skin extracts (GSEs) were assessed for total polyphenols, antioxidant activity, and inhibition potential against α-amylase and α-glucosidase, enzymes responsible for carbohydrate metabolism. GSE of variety “Svetokriška belina” has the highest total phenols (TP) content (1404.87 mg of gallic acid equivalent), the highest antioxidant capacity against DPPH (544.82 mg ascorbic acid equivalent), and the highest inhibitory activity against α-amylase (99.60%). α-glucosidase was best inhibited by the variety “Kozjak” (93.53%), followed by a significantly lower inhibition by the GSE of “Svetokriška belina” (89.64%). The principal component analysis (PCA) revealed the relationship among the grape varieties by their inhibition potential, where the first PC explained 71.34% of the variation. Indigenous Croatian white grapevine varieties have great potential for developing new natural supplements to prevent and treat diabetes.