bluuebunny/crossref_metadata_2025 huggingface dataset:
Regulatory role and therapeutic prospect of lactate modification in cancer
Authors: Sun Mengdi , Wang Kejing , Lu Fang , Yu Donghua , Liu Shumin
Published: February 2025

Post-translational modifications (PTMs) of proteins refer to the process of adding chemical groups, sugars, or other molecules to specific residues of target proteins following their biosynthesis by ribosomes. PTMs play a crucial role in processes such as signal transduction, epigenetics, and disease development. Lactylation is a newly discovered PTM that, due to its close association with lactate—the end product of glycolytic metabolism—provides a new perspective on the connection between cellular metabolic reprogramming and epigenetic regulation. Studies have demonstrated that lactylation plays a significant role in tumor progression and is associated with poor clinical prognosis. Abnormal histone lactylation can influence gene expression in both tumor cells and immune cells, thereby regulating tumor progression and immunosuppression. Lactylation of non-histone proteins can also modulate processes such as tumor proliferation and drug resistance. This review summarizes the latest research progress in the field of lactylation, highlighting its roles and mechanisms in tumorigenesis, tumor development, the tumor microenvironment, and immunosuppression. It also explores the potential application value of lactylation in tumor-targeted therapy and combined immunotherapy.
The mediating role of job satisfaction in the effect of green transformational leadership on intention to leave the job
Authors: Oktaysoy Onur , Topcuoglu Ethem , Ozgen-Cigdemli Asli Ozge , Kaygin Erdogan , Kosa Gozde , Turan-Torun Burcu , Kobanoglu Mehmet Selman , Uygungil-Erdogan Selen
Published: February 2025

Introduction

The literature on leadership has evolved in accordance with changing global expectations over time and the significance of new leadership approaches which are based on environmental sustainability has increased day by day. Green transformational leaders, one of the results of this evolution, are different from other leadership approaches in terms of their role in motivating employees and increasing their environmental awareness by integrating their environmental sensitivities into the organizational culture.

Methods

This study, which aims to determine the mediating role of job satisfaction in the effect of green transformational leadership on intention to leave, was conducted with 391 people working in 4 and 5 star hotels in Antalya region. Data obtained by convenience sampling method was examined with Smart-PLS program. The study was shaped on the axis of structural equation modeling.

Results

The findings showed that green transformational leadership has a negative effect on intention to leave and job satisfaction plays a mediating role in this relationship. In addition, green transformational leaders were found to increase employees’ job satisfaction, strengthen their commitment to the organization and reduce intention to leave.

Discussion

The study shows that green transformational leadership has an effective structure not only in terms of green and environmental concepts but also in terms of organizational behavior. According to the Social Exchange Theory, green transformational leadership is thought to shape the job satisfaction of employees by interacting with them in accordance with moral and organizational norms.
Design and assessment of a public health course as a general education elective for non-medical undergraduates
Authors: Li Xinyang , Zheng Xiaoxi , Wen Boqiang , Zhang Bowei , Xing Xiaolong , Zhu Liye , Gu Wentao , Wang Shuo
Published: February 2025

Although public health programs among undergraduate students have been increasing and gaining popularity worldwide, few studies have focused on the needs and structure of public health courses for non-medical students. This study aimed to design a public health course as a general education elective for non-medical undergraduates at Nankai University, one of China’s leading multidisciplinary and research-oriented universities. Students’ feedback on the course was collected and analyzed after the completion of the elective course. We designed and developed the course under the general education elective course at Nankai University. The course includes four segments: (a) Public Health Fundamentals and Population Research Methods; (b) Chemical Safety and Health, (c) Diet, Nutrition, and Health, (d) Immunology, Microbiology, and Infectious Diseases, spanning 34 class hours (with 6 class hours designated for a flipped classroom format). The teaching content was divided into five parts: (1) Health and Medicine Knowledge, (2) Public Health Knowledge, (3) Public Health Methodology and Philosophy, (4) Proper View of Health Issues, and (5) Values Education. Students’ feedback after the course indicated that Diet, Nutrition, and Health was the most interesting segment, and the students considered basic biological or medical knowledge to be more important than other public health knowledge. A problem-based learning model was implemented for flipped classrooms, and we found that the problem-based learning questions were not only helpful for students’ knowledge construction but also for educators in understanding and managing the learning expectations of non-medical students. We believe that the lesson may guide other institutions in designing similar curricula.
Assessing the nutrition knowledge, beliefs, and behaviors of food pantry managers: implications for healthier food environments
Authors: Sanchez Sofia O. , Funderburk Katie , Reznicek Erin , Bubb Robert R. , Frugé Andrew D. , Duke-Marks Adrienne , Hinnant J. Benjamin , Parmer Sondra M.
Published: February 2025

Introduction

Food pantry managers play a key role in determining the food environment of the pantry, which can influence their clients’ dietary composition. However, their impact on pantry food environments remains understudied. We sought to understand food pantry managers’ nutrition knowledge, beliefs, and behaviors (KBBs) in this study.

Methods

We surveyed 47 Alabama food pantry managers’ nutrition KBBs from November 2022 to November 2023. Validated and previously published tools used include the Consumer Nutrition Knowledge Scale, a diet beliefs scale, and a dietary-related consumer behavior questionnaire.

Results

The majority of managers were college-educated (54%), white (74%), and female (74%), with a mean age of 60 ± 13 years and an average of 7 ± 5.5 years of experience in managing food pantries. Managers reported positive nutrition beliefs and behaviors but scored lower on objective nutrition knowledge. Exploratory analyses indicated moderate to very strong associations between KBBs, pantry characteristics, and neighborhood characteristics.

Conclusion

Nutrition education for pantry managers, along with improved pantry infrastructural support, could play a key role in improving the healthfulness of the food provided.
Ecodesign and environmental assessment: a synergy for higher education institutions
Authors: Swe Wint Mon , Ribau Teixeira Margarida , Smuleac Laura , Nunes Luís M.
Published: February 2025

This article explores how ecodesign can help enhance environmental assessment methods (EAMs) employed by Higher Education Institutions (HEIs). Current EAMs primarily offer resource management evaluations or sustainability rankings but lack holistic and actionable solutions for institutional improvements. The proposed method integrates ecodesign tools, such as Life Cycle Assessments (LCA), environmental benchmarking, and parametric design tools, with existing EAM frameworks. This synergy allows HEIs to quantitatively assess their environmental and social impacts while identifying targeted strategies for improvement. By embedding ecodesign into institutional practices the approach provides a structured pathway to align HEIs with global sustainability goals, fostering innovation and continuous improvement. The paper emphasizes a multi-tool integration strategy, offering a cohesive solution to advance sustainability comprehensively within HEIs.
Machine Learning for Decision Support and Automation in Games: A Study on Vehicle Optimal Path
Authors: Penelas Gonçalo , Barbosa Luís , Reis Arsénio , Barroso João , Pinto Tiago
Published: February 2025

In the field of gaming artificial intelligence, selecting the appropriate machine learning approach is essential for improving decision-making and automation. This paper examines the effectiveness of deep reinforcement learning (DRL) within interactive gaming environments, focusing on complex decision-making tasks. Utilizing the Unity engine, we conducted experiments to evaluate DRL methodologies in simulating realistic and adaptive agent behavior. A vehicle driving game is implemented, in which the goal is to reach a certain target within a small number of steps, while respecting the boundaries of the roads. Our study compares Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC) in terms of learning efficiency, decision-making accuracy, and adaptability. The results demonstrate that PPO successfully learns to reach the target, achieving higher and more stable cumulative rewards. Conversely, SAC struggles to reach the target, displaying significant variability and lower performance. These findings highlight the effectiveness of PPO in this context and indicate the need for further development, adaptation, and tuning of SAC. This research contributes to developing innovative approaches in how ML can improve how player agents adapt and react to their environments, thereby enhancing realism and dynamics in gaming experiences. Additionally, this work emphasizes the utility of using games to evolve such models, preparing them for real-world applications, namely in the field of vehicles’ autonomous driving and optimal route calculation.
Multi-Strategy Improved Artificial Rabbit Algorithm for QoS-Aware Service Composition in Cloud Manufacturing
Authors: Deng Le , Shu Ting , Xia Jinsong
Published: February 2025

Cloud manufacturing represents a pioneering service paradigm that provides flexible, personalized manufacturing services to customers via the Internet. Service composition plays a crucial role in cloud manufacturing, which focuses on integrating dispersed manufacturing services in the cloud platform into a complete composite service to form an efficient and collaborative manufacturing solution that fulfills the customer’s requirements, having the highest service quality. This research presents the multi-strategy improved artificial rabbit optimization (MIARO) technique, designed to overcome the limitations with the original method, which often risks converging to local optima and have poor solution quality when dealing with optimization problems. MIARO helps the algorithm escape local optimality with Lévy flights, extends local search with the golden sine mechanism, and expands variability with Archimedean spiral mutations. MIARO is experimented on 23 benchmark functions, 3 engineering design problems, and QoS-aware cloud service composition (QoS-CSC) issues at various sizes, and the experimental findings indicate that MIARO delivers outstanding performance and offers a viable solution to the QoS-CSC problem.
Optimized Travel Itineraries: Combining Mandatory Visits and Personalized Activities
Authors: Jewpanya Parida , Nuangpirom Pinit , Pitjamit Siwasit , Nakkiew Warisa
Published: February 2025

Tourism refers to the activity of traveling for pleasure, recreation, or leisure purposes. It encompasses a wide range of activities and experiences, from sightseeing to cultural exploration. In today’s digital age, tourists often organize their excursions independently by utilizing information available on websites. However, due to constraints in designing customized tour routes such as travel time and budget, many still require assistance with vacation planning to optimize their experiences. Therefore, this paper proposes an algorithm for personalized tourism planning that considers tourists’ preferences. For instance, the algorithm can recommend places to visit and suggest activities based on tourist requirements. The proposed algorithm utilizes an extended model of the team orienteering problem with time windows (TOPTW) to account for mandatory locations and activities at each site. It offers trip planning that includes a set of locations and activities designed to maximize the overall score accumulated from visiting these locations. To solve the proposed model, the Adaptive Neighborhood Simulated Annealing (ANSA) algorithm is applied. ANSA is an enhanced version of the well-known Simulated Annealing algorithm (SA), providing an adaptive mechanism to manage the probability of selecting neighborhood moves during the SA search process. The computational results demonstrate that ANSA performs well in solving benchmark problems. Furthermore, a real-world attractive location in Tak Province, Thailand, is used as the case study in this paper to illustrate the effectiveness of the proposed model.
Enumerating Minimal Vertex Covers and Dominating Sets with Capacity and/or Connectivity Constraints
Authors: Kobayashi Yasuaki , Kurita Kazuhiro , Mann Kevin , Matsui Yasuko , Ono Hirotaka
Published: February 2025

In this paper, we consider the minimal vertex cover and minimal dominating sets with capacity and/or connectivity constraint enumeration problems. We develop polynomial-delay enumeration algorithms for these problems on bounded-degree graphs. For the case of minimal connected vertex covers, our algorithms run in polynomial delay, even on the class of d-claw free graphs. This result is extended for bounded-degree graphs and outputs in quasi-polynomial time on general graphs. To complement these algorithmic results, we show that the minimal connected vertex cover, minimal connected dominating set, and minimal capacitated vertex cover enumeration problems in 2-degenerated bipartite graphs are at least as hard as enumerating minimal transversals in hypergraphs.
DLMinTC+: A Deep Learning Based Algorithm for Minimum Timeline Cover on Temporal Graphs
Authors: Lazzarinetti Giorgio , Dondi Riccardo , Manzoni Sara , Zoppis Italo
Published: February 2025

Combinatorial optimization on temporal graphs is critical for summarizing dynamic networks in various fields, including transportation, social networks, and biology. Among these problems, the Minimum Timeline Cover (MinTCover) problem, aimed at identifying minimal activity intervals for representing temporal interactions, remains underexplored in the context of advanced machine learning techniques. Existing heuristic and approximate methods, while effective in certain scenarios, struggle with capturing complex temporal dependencies and scalability in dense, large-scale networks. Addressing this gap, this paper introduces DLMinTC+, a novel deep learning-based algorithm for solving the MinTCover problem. The proposed method integrates Graph Neural Networks for structural embedding, Transformer-based temporal encoding, and Pointer Networks for activity interval selection, coupled with an iterative adjustment algorithm to ensure valid solutions. Key contributions include (i) demonstrating the efficacy of deep learning for temporal combinatorial optimization, achieving superior accuracy and efficiency over state-of-the-art heuristics, and (ii) advancing the analysis of temporal knowledge graphs by incorporating robust, time-sensitive embeddings. Extensive evaluations on synthetic and real-world datasets highlight DLMinTC+’s ability to achieve significant coverage size reduction while maintaining generalization, offering a scalable and precise solution for complex temporal networks.
Numerical Simulation of the Gas Flow of Combustion Products from Ignition in a Solid Rocket Motor Under Conditions of Propellant Creep
Authors: Zhang Yin , Sun Zhensheng , Hu Yu , Zhu Yujie , Xia Xuefeng , Qu Huang , Tian Bo
Published: February 2025

The development of modern solid rocket technology with high-performance and high-loading ratio propellants places higher requirements on the safety and stability of the solid rocket motor. The propellant of the solid rocket motor will creep during long-term vertical storage, which may adversely affect its regular operation. The ignition transient process is a critical phase in the operation of solid rocket motors. The Abaqus v.2022 finite element simulation software is used to analyze the ignition transient under propellant creep conditions and obtain the deformed combustion chamber profile. Then, we use a high-precision finite volume solver developed independently to simulate the flow field during the ignition process. In the simulation, we adopt the surface temperature of the propellant column reaching the ignition threshold as the ignition criterion, considering the heat transfer process of the propellant column instead of using the near-wall gas temperature to obtain the set temperature. Simulation results under different creep conditions reveal that the deformation of the propellant grains progressively intensifies as the solid rocket motor’s storage duration increases. This leads to a delayed initial ignition time of the propellant, an advancement of the overall ignition transient process, and an increased pressurization rate during ignition, which can affect the structure and regular operation of the motor. The research results provide design guidance and theoretical support for the design and life prediction of solid rocket motors.
Streamlining the Identification of the Orange Spiny Whitefly, Aleurocanthus spiniferus (Hemiptera: Aleyrodidae), with Real-Time PCR Probe Technology
Authors: Rizzo Domenico , Zubieta Claudia Gabriela , Moriconi Michela , Carli Marco , Marrucci Andrea , Ranaldi Chiara , Palmigiano Bruno , Bartolini Linda , Pica Feliciana , Carbone Carmela , Massimino Cocuzza Giuseppe Eros , Nugnes Francesco
Published: February 2025

Aleurocanthus spiniferus (Quaintance) (Hemiptera: Aleyrodidae) has rapidly spread, mainly in the central and eastern Mediterranean coastal area, infesting various new host plants alongside known ones. This invasive species poses a significant threat to agricultural ecosystems, necessitating urgent action to monitor and control outbreaks in previously pest-free areas. While entomological and morphological recognitions are crucial for initial detection, challenges often arise in quickly identifying different developmental stages or genus-level distinctions, particularly in surveys conducted by personnel with limited entomological skills. Due to these challenges, a qPCR probe protocol was developed to enhance the diagnostic capacity of laboratories responsible for the territorial control of pests. This biomolecular tool integrates morphological surveys, enabling prompt and reliable proof of A. spiniferus presence in free areas, delimited territories, or during phytosanitary import inspections. The protocol’s high analytical specificity, inclusivity, and exclusivity ensure accurate identification of A. spiniferus, while its low limit of detection and high repeatability and reproducibility reinforce its utility as a standardized diagnostic method. By facilitating prompt and targeted control efforts, this innovative approach strengthens the resilience of agricultural systems against the widespread threat of A. spiniferus infestations.
A Neural Network with Multiscale Convolution and Feature Attention Based on an Electronic Nose for Rapid Detection of Common Bunt Disease in Wheat Plants
Authors: Ren Zhizhou , Liang Kun , Liu Yihe , Wu Xiaoxiao , Zhang Chi , Mei Xiuming , Zhang Yi
Published: February 2025

Common bunt disease in wheat is a serious threat to crops and food security. Rapid assessments of its severity are essential for effective management. The electronic nose (e-nose) system is used to capture volatile organic compounds (VOCs), particularly trimethylamine (TMA), which serves as a key marker of common bunt disease in wheat. In this paper, the GFNN (gas feature neural network) model is proposed for detecting VOCs from the e-nose system, providing a lightweight and efficient approach for assessing disease severity. Multiscale convolution is employed to extract both global and local features from gas data, and three attention mechanisms are used to focus on important features. GFNN achieves 98.76% accuracy, 98.79% precision, 98.77% recall, and an F1-score of 98.75%, with only 0.04 million parameters and 0.42 million floating-point operations per second (FLOPS). Compared to traditional and current deep learning models, GFNN demonstrates superior performance, particularly in small-sample-size scenarios. It significantly improves the deep learning performance of the model in extracting key gas features. This study offers a practical, rapid, and cost-effective method for monitoring and managing common bunt disease in wheat, enhancing crop protection and food security.
Integrated Evaluation of Sustainability and Quality of Italian Tomato Cultivars Grown Under Irrigated and Non-Irrigated Systems
Authors: Vinci Giuliana , Campana Paola , Gobbi Laura , Prencipe Sabrina Antonia , Ruggeri Marco
Published: February 2025

This research aimed to assess how irrigation can affect the sustainability and quality of two Italian tomato cultivars: the “Riccio di Parma Casertano,” which is grown without irrigation, and the “Piennolo del Vesuvio DOP,” which requires irrigation. Life cycle assessment and water footprint analysis were used for sustainability assessment, while, for quality assessment, the content of bioactive compounds was analyzed by UV-Vis spectrophotometric assays. The results indicate that ‘Riccio di Parma Casertano’ is a more sustainable cultivar than ‘Piennolo del Vesuvio DOP’, with lower environmental impacts in all 18 impact categories, showing reductions ranging from 54.55% to 99.90%. This higher sustainability performance of “Riccio di Parma Casertano” is also characterized by increases of +32% in total polyphenol content and +43% in total flavonoid content as an adaptive response to water stress compared with “Piennolo del Vesuvio DOP”. However, “Piennolo del Vesuvio DOP” has a higher yield and better overall nutritional and functional quality, with higher concentrations of hydrophilic and lipophilic compounds, such as lycopene and β-carotene, due to irrigation. The results, therefore, show how the choice between the two cultivars might depend on a trade-off between sustainability and quality. In particular, ’Riccio di Parma Casertano’ could excel in contexts with low water availability while maintaining a good nutritional profile due to the synthesis of bioactive compounds; on the other hand, “Piennolo del Vesuvio DOP” could offer higher yield and nutritional qualities, although it needs improved agricultural practices to reduce overall environmental impacts.
Development of Male and Female Gametophytes in Cannabis sativa L. cv. Helena (Cannabaceae)
Authors: Yankova-Tsvetkova Elina , Semerdjieva Ivanka , Sikora Vladimir , Zheljazkov Valtcho D.
Published: February 2025

This study investigated key aspects of the reproductive potential of C. sativa cv. Helena. It focused on the development of male and female gametophytes, embryos, and endosperm formation. The developmental stages of pollen grains, embryo sacs, and their formation were revealed. The anther and development of the male gametophyte were as follows: tetrasporangiate anther, (whose wall is developed by the Dicotyledonous type and consists of the epidermis, fibrous endothecium, two middle layers, and glandular tapetum) and two-celled mature pollen. The ovule and development of the female gametophyte were characterized by an upper unilocular ovary containing two anatropous, crassinucellate, bitegmic ovules. The female gametophyte follows the Polygonum (monosporic) type. The development also includes nuclear endosperm formation and the presence of an embryo sac haustorium. A high pollen and seed viability was estimated. This fact, combined with the normal running of the processes of formation and development of the female gametophyte, embryo-, and endospermogenesis provide high reproductive potential for the studied cultivar of C. sativa. These findings contribute to a better understanding of C. sativa reproductive biology and provide valuable insights for breeding programs aimed at optimizing cultivar selection.
The Addition of Straw Affects the Response of Labile Soil Organic Carbon to the Freezing and Thawing Process
Authors: Zhu Mengmeng , Ma Qiang , Li Shuailin , Xia Zhuqing , Zhou Changrui , Gao Yun , Zhang Xinhui , An Siyu , Jiang Xiao , Yu Wantai
Published: February 2025

Global warming alters freeze–thaw process frequency and intensity, impacting soil carbon cycles. Four soils from a 12-year straw return experiment were used: S0 (no straw), S1 (low rate of addition), S2 (medium rate), and S3 (high rate). Ten treatments with or without temporary straw addition at different rates were conducted to explore their effects on soil microbial biomass carbon (MBC) and dissolved organic carbon (DOC) under laboratory and field freeze–thaw conditions. Compared to constant temperature, the freeze–thaw process under laboratory conditions reduced MBC (5.79%~29.9%), whereas this trend was mitigated or reversed under field conditions. The alleviating effect of straw addition on the decrease in MBC was greater in S0 than in S1, S2, and S3 by an average of 31.7%. Medium rate straw application (S2 8 t/ha) provided appropriate labile C levels, enhancing microbial activity while keeping DOC low and reducing C loss risk. The results revealed discrepancies in freeze–thaw effects on soil labile OC between laboratory and field conditions, the mitigation of freeze–thaw impacts on MBC by straw addition, and the appropriate straw return rate in Liaohe Plain. Therefore, proper nutrient management can maintain and regulate microbial activity and soil labile C in areas with freeze–thaw cycles.
Dual-Channel Co-Spectroscopy–Based Non-Destructive Detection Method for Fruit Quality and Its Application to Fuji Apples
Authors: Liang Xin , Jiang Tian , Dai Wanli , Xu Sai
Published: February 2025

Visible/near-infrared spectroscopy is widely used for non-destructive fruit quality detection, but the high cost of spectrometers (400–1100 nm range) in sorting equipment limits its accessibility. This study proposes a dual-channel co-spectroscopy method to address this issue. Using apples’ soluble solids content as the research target, a dual-channel platform was constructed to optimize parameters for full-transmission spectral signal acquisition. Spectral data were collected using dual channels (400–700 nm and 700–1100 nm bands, separated by filters) and a single channel (400–1100 nm range). Preprocessing methods (MSC, SNV, FD, SD, SG) and feature extraction algorithms (CARS, SPA, UVE) were applied, followed by PLSR modeling. The dual-channel method with Raw spectrum + FD + CARS + PLSR achieved optimal results, with R2v = 0.88, RMSEP = 0.39 for the 400–700 nm band, and R2v = 0.94, RMSEP = 0.33 for the 700–1100 nm band. The single-channel method with Raw spectrum + MSC + CARS + PLSR achieved R2v = 0.90, RMSEP = 0.36. These findings validate dual-channel co-spectroscopy as a cost-effective, accurate solution for non-destructive fruit quality detection, providing a practical approach to reduce spectrometer costs and enhance sorting system efficiency.
Characterization of Goat Production Systems in the Northern Dry Forest of Peru Using a Multivariate Analysis
Authors: Temoche Victor , Acosta Irene , Gonzales Pablo , Godoy Padilla David , Jibája Omar , Cruz Juancarlos , Corredor Flor-Anita
Published: February 2025

Goat production in the dry forest of northern Peru is essential for rural livelihoods but remains poorly characterized regarding its productivity and sustainability. This study used multivariate techniques—a multiple correspondence analysis (MCA), principal component analysis (PCA), factor analysis of mixed data (FAMD), and hierarchical cluster analysis (HCA)—to analyze data from 284 producers in Tumbes, Piura, and Lambayeque. Surveys captured 48 variables (41 qualitative, seven quantitative) on productivity, socioeconomics, and management. The MCA explained 22.07% of the variability in two dimensions, while the PCA accounted for 63.9%, focusing on productivity and diversification. The FAMD integrated these variables, explaining 51.12% of variability across five dimensions, emphasizing socioeconomic and management differences. The HCA identified three clusters: cluster 1 featured intensive systems with advanced management and commercial focus, cluster 2 included extensive systems limited by water scarcity, and cluster 3 reflected semi-intensive systems with irrigation and diversified production. These findings provide a detailed understanding of goat systems in northern Peru, identifying opportunities to improve resource use and tailor strategies to enhance sustainability. The multivariate analysis proved effective in capturing the complexity of these systems, supporting productivity and improving livelihoods in rural areas.
‘There Is Not a Word’, but Is It Necessary? Analyzing Pragmatic Decisions Regarding Terminology Within Multispecies Family Relationships
Authors: López-Cepero Javier , Español Alicia , Rodríguez-Banda Ángel
Published: February 2025

This study analyzes the decision making that underlies the choice of terms we use to refer to companion animals. Three focus groups were developed, including participants from different demographic backgrounds who answered questions about their experience cohabitating with companion animals. The interviews were transcribed and analyzed using Thematic Analysis, carrying out a progressive refinement of the semantic contents until abstracting general themes. This study organizes the results based on three themes: (1) What you mean to me, contemplating human–animal relationships such as multispecies family, pet–owner relationship, human-like relationship, and objectivization; (2) Others’ surveillance, encompassing the role of social pressure in decision making; and (3) A good solution (here and now), focused on the strategic decisions made to balance the prior questions. The analysis shows that companion animals are usually considered part of the family, but that importance is not always freely communicated outside of the household. Often, participants try to nuance the importance of their companion animals, mask this relationship behind jokes, or tend to isolate themselves to avoid hostile social attention. These findings show the dilemmas faced by people who live with animals and point to the urgency of revising hegemonic discourses to improve the integration of these new family models in Spanish society.
Exploration-Driven Genetic Algorithms for Hyperparameter Optimisation in Deep Reinforcement Learning
Authors: Brzęk Bartłomiej , Probierz Barbara , Kozak Jan
Published: February 2025

This paper investigates the application of genetic algorithms (GAs) for hyperparameter optimisation in deep reinforcement learning (RL), focusing on the Deep Q-Learning (DQN) algorithm. This study aims to identify approaches that enhance RL model performance through the effective exploration of the configuration space. By comparing different GA methods for selection, crossover, and mutation, this study focuses on deep RL models. The results indicate that GA techniques emphasising the exploration of the configuration space yield significant improvements in optimisation efficiency, reducing training time and enhancing convergence. The most effective GA improved the fitness function value from 68.26 (initial best chromosome) to 979.16 after 200 iterations, demonstrating the efficacy of the proposed approach. Furthermore, variations in specific hyperparameters, such as learning rate, gamma, and update frequency, were shown to substantially affect the DQN model’s learning ability. These findings suggest that exploration-driven GA strategies outperform GA approaches with limited exploration, underscoring the critical role of selection and crossover methods in enhancing DQN model efficiency and performance. Moreover, a mini case study on the CartPole environment revealed that even a 5% sensor dropout impaired the performance of a GA-optimised RL agent, while a 20% dropout almost entirely halted improvements.
A Multi-Time Scale Hierarchical Coordinated Optimization Operation Strategy for Distribution Networks with Aggregated Distributed Energy Storage
Authors: Liu Junhui , Niu Chengeng , Zhang Yihan , Xie Anbang , Lu Rao , Yu Shunjiang , Qiao Siyuan , Lin Zhenzhi
Published: February 2025

In recent years, with the steady growth of load demand in distribution networks, the fluctuation and uncertainty of power loads have significantly increased. Meanwhile, the rising penetration of photovoltaic generation has further exacerbated the challenges of power system accommodation capability. To enhance photovoltaic accommodation capability and realize the secure and economic operation of distribution networks, a multi-time scale hierarchical coordinated optimization operation strategy for distribution networks with aggregated distributed energy storage has been proposed. First, the regulation requirements of aggregated distributed energy storage are analyzed, and a distributed energy storage aggregation model is established based on an inner approximate Minkowski Sum. Subsequently, a multi-time scale optimization operation model considering source-load uncertainties for day-ahead, intra-day, and real-time stages is developed based on a stepped carbon emission cost model. Then, a power allocation method within the aggregated distributed energy storage based on the water-filling algorithm is presented. Finally, a practical distribution network in a demonstration county in China is used as a case study to validate the proposed method. The results demonstrate that the proposed strategy effectively reduces system operation costs while improving photovoltaic accommodation capacity and enhancing the reliability of system operation.
Extended Cloud–TODIM Method for Multiple-Attribute Decision-Making Problems in Risk Reduction Schemes: Application in the Tailings Storage Facility Failure
Authors: Zhao Yusong , Chen Congcong
Published: February 2025

Various tailings storage facility (TSF) failures have caused catastrophic consequences, such as life and property losses and environmental destruction. It is crucial to select the optimal risk reduction scheme (RRS) to guarantee the safety and stability of the TSF. Decision-making problems in RRS selection for TSF failure are multiple-attribute decision-making problems. During the RRS selection process, the psychological behavior of the decision makers should be considered. To solve such problems, the cloud–TODIM (abbreviation for interactive and multi-attribute decision-making in Portuguese) method is proposed for RRS selection in this paper. Firstly, the quantitative evaluation information is qualified and converted into clouds based on the cloud model, in which the characteristics of fuzziness, uncertainty, and randomness can be described. Secondly, an improved TODIM method is proposed to select the optimal RRS. Furthermore, a TSF is employed as a case study to examine the superiority of the proposed method. Finally, the sensitivity of the loss aversion coefficient θ, which reflects the attitudes of the decision makers (DMs) to the loss, is analyzed and a comparative analysis is developed therefore illustrating the competitiveness of the multiple-attribute decision-making of the proposed method.
Mathematical Modeling and Electromagnetic Characteristics Analysis of a Six-Phase Distributed Single-Winding BPMSM with 12 Slots and 2 Poles
Authors: Bu Wenshao , Li Jiangdi , Lu Yongfang
Published: February 2025

This work focuses on small bearingless permanent magnet synchronous motors (BPMSMs). In order to enhance its torque control stiffness and improve the stability of its torque and magnetic levitation force dynamic waveforms, a novel six-phase distributed single-winding BPMSM with 12 slots and 2 poles (six-phase DSW-12/2-BPMSM) is proposed and researched in this work. First, the structure and working principle of the six-phase DSW-12/2-BPMSM are analyzed. Subsequently, considering the relative permeability of permanent magnets, mathematical models of the inductance matrix, electromagnetic torque and radial magnetic levitation force are established. Then, using the finite element method (FEM), the control characteristics of the electromagnetic torque and magnetic levitation force of the six-phase DSW-12/2-BPMSM are analyzed, and the mathematical model is verified. Finally, FEM simulation analysis and comparisons are conducted with a commonly used six-phase centralized single-winding BPMSM with 6 slots and 2 poles (six-phase CSW-6/2-BPMSM). The research results show that the established mathematical model is effective and accurate compared with the six-phase CSW-6/2-BPMSM. The six-phase DSW-12/2-BPMSM has greater torque control stiffness, its dynamic waveforms of torque and radial magnetic levitation force have higher quality and stability, and the coupling degree between its torque and radial magnetic levitation force is lower.
AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
Authors: Arévalo-Royo Javier , Flor-Montalvo Francisco-Javier , Latorre-Biel Juan-Ignacio , Tino-Ramos Rubén , Martínez-Cámara Eduardo , Blanco-Fernández Julio
Published: February 2025

This research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, disease diagnosis, and resource management. Utilizing a comprehensive bibliometric analysis of scientific literature from 2020 to 2024, this research outlines the increasing incorporation of AI in Spain and identifies the prevailing trends and obstacles associated with it in the agrifood industry. The findings underscore the extensive application of AI in remote sensing, water management, and environmental sustainability. These areas are particularly pertinent to Spain’s diverse agricultural landscapes. Additionally, the study conducts a comparative analysis between Spain and global research outputs, highlighting its distinctive contributions and the unique challenges encountered within its agricultural sector. Despite the considerable opportunities presented by these technologies, the research identifies key limitations, including the need for enhanced digital infrastructure, improved data integration, and increased accessibility for smaller agricultural enterprises. The paper also outlines future research pathways aimed at facilitating the integration of AI in Spain’s agriculture. It addresses cost-effective solutions, data-sharing frameworks, and the ethical and societal implications inherent to AI deployment.
Sesame Plant Disease Classification Using Deep Convolution Neural Networks
Authors: Nibret Eyerusalem Alebachew , Mequanenit Azanu Mirolgn , Ayalew Aleka Melese , Kusrini Kusrini , Martínez-Béjar Rodrigo
Published: February 2025

Monitoring sesame plant health and detecting disease early are essential to reducing disease spread and facilitate effective management practices. In this research, we developed an image classification model to detect bacterial blight-infected, phyllody-infected, and healthy sesame crops. Since images were necessary to carry out this study, we collected 2300 images at the Gondar and Humera Agriculture Research Centers and directly from the field in Metema. Since the collected images were limited, to increase the number of images in the dataset, we used image augmentation with different variations. In the image preprocessing step, we used a median filter for noise filtering, and contrast stretching techniques were used for image contrast and brightness enhancement. SegNet semantic segmentation, which is deep convolution neural network-based architecture, was used to segment the leaf part of the image from the background. In the feature extraction and classification steps, a deep convolutional neural network was used. Finally, we evaluated the proposed model and compared it with two recent deep convolution neural network models, namely, Xception and InceptionV3. The proposed model for the classification of sesame diseases achieved better accuracy, with 96.67% testing accuracy, 97.78% validation accuracy, and 98% training accuracy.
Antibacterial and Preservative Potential of Eugenol and Isoeugenol in Cosmetics: A Natural Solution for Product Stability
Authors: Fajdek-Bieda Anna , Pawlińska Joanna , Wróblewska Agnieszka , Żwierełło Wojciech , Łuś Agnieszka , Klimowicz Adam
Published: February 2025

Eugenol (EU) and isoeugenol (IZO-EU) are naturally occurring compounds known for their strong antibacterial properties, which makes them ideal candidates for application as preservatives in cosmetic products. Primarily derived from clove oil, EU demonstrates potent antibacterial effects against a wide range of microorganisms, including Gram-positive and Gram-negative bacteria, as well as yeast-like fungi such as Candida albicans. Their antibacterial action is mainly connected with their ability to disrupt microbial cell membranes and inhibit key enzymatic processes. IZO-EU, a structural isomer of EU, also shows significant activity against various pathogens. In these studies, the effectiveness of EU and IZO-EU as preservatives in cosmetic formulations was tested. The antibacterial activity tests, using the disk diffusion method, assessed their effectiveness against Enterococcus faecalis, Staphylococcus aureus (Gram-positive), Escherichia coli, Pseudomonas aeruginosa (Gram-negative), and Candida albicans. Creams containing the different concentrations of EU and IZO-EU (0.5%, 1.5%, 2.5%) were tested for microbiological purity, texture, consistency, and stability over a defined storage period and under controlled conditions (temperature, humidity, and light exposure). Microbiological purity was assessed through the standard culture methods, while the texture and consistency were evaluated using rheological measurements and sensory analysis. The obtained results demonstrated that both compounds effectively preserved the creams, maintaining the microbiological purity without significantly altering the texture, consistency, or stability throughout the storage period. Additionally, EU and IZO-EU not only enhanced the antibacterial protection of the formulations but also contributed to the pleasant fragrance. Furthermore, preliminary studies were conducted on their anticancer properties using the LoVo (colon cancer) and U87MG (glioma) cell lines. These studies revealed the hormetic effects at the low concentrations and cytotoxicity at the higher doses, suggesting that EU and IZO-EU may have therapeutic potential beyond cosmetic applications. These studies support the application of EU and IZO-EU as natural, multifunctional ingredients in cosmetics, offering both preservative and sensory benefits while meeting the growing consumer demand for natural and sustainable solutions in the beauty industry.
GPT-4off: On-Board Traversability Probability Estimation for Off-Road Driving via GPT Knowledge Distillation
Authors: Kim Nahyeong , Choi Seongkyu , Choi Sun , Lee Yejun , Cheong Youngjae , An Jhonghyun
Published: February 2025

This paper proposes a framework for predicting traversability probability in off-road environments by distilling knowledge from large language models (LLMs) such as GPT-4o into lightweight models. The GPT-4off approach utilizes GPT-generated data to train a compact model capable of real-time operation on edge devices, such as the NVIDIA Orin board. Unlike traditional systems that focus on identifying traversable areas, this study emphasizes the prediction of traversability probability to facilitate faster decision-making in complex environments. This is particularly advantageous for unmanned ground vehicles, for which obstacles and terrain variability present significant challenges. The GPT-4off framework improves real-time performance through knowledge distillation and domain-specific optimization, ensuring efficient resource use while maintaining LLM-level performance. Experimental results on the RUGD off-road dataset show that the lightweight model achieves GPT-level performance while being deployable on edge devices. This framework effectively reduces human annotation costs and RAM power consumption, improves the practicality of off-road autonomous driving systems, and demonstrates the potential of leveraging LLM capabilities for low-power real-time applications.
Assessing Design Criteria of University Campus Walkway Systems in the Middle Eastern Arid Environment
Authors: Zami Mohammad Sharif , Alamasi Rawan Emadulden , Hassanain Mohammad A. , Almahdy Omar E.
Published: February 2025

Pedestrian walkways are essential elements of university campus design and urban planning. The function of campus walkways is not only safe walking but also to hold many social activities like interacting and socializing. This paper aims to assess design criteria of successful walkways in the middle eastern arid campus. To achieve the aim, a mixed research methodological approach is adopted whereby a number of qualitative and quantitative methods like naturalistic observation on a case, in-depth interviews, and questionnaire survey are employed to gather data. The study outcomes include a framework of 10 design assessment criteria of successful campus walkways. The shortcomings of middle eastern campus walkways are ranked according to their importance whereby the most and least important shortcomings are identified as the “lack of barrier free environment” and “lack of greeneries along the walkways”, respectively.
Construction and Analysis of the Ecological Security Pattern in Territorial Space in Shaanxi of the Yellow River Basin, China
Authors: Liu Zhengyao , Huang Jing , Liu Xiaokang , Li Yonghong , He Yiping
Published: February 2025

In the context of rapid urbanization and extreme climate change globally, balancing ecological resources and economic development for land spatial planning has become one of the pressing issues that need to be addressed. This study proposes a composite model to construct a spatial ecological security pattern. It identifies restoration areas with different risk levels based on the spatial distribution of land use, offering suggestions for optimizing spatial configuration. Focusing on the central Shaanxi region of the Yellow River Basin in China, ecological sources are identified by integrating ecological factors, and ecological corridors and restoration zones are extracted using the minimum cumulative resistance difference and circuit theory. The results indicate significant improvements in ecological quality and desertification in the study area from 2000 to 2020. Currently, the core area covers 51,649.71 km2, accounting for 62.18% of all landscape types; the total ecological source area covers 31,304.88 km2, representing 18.84% of the entire area. These ecological source areas are mainly distributed in the northern Loess Plateau and the southern mountainous regions. The area has 26 important ecological corridors, identifying 16 ecological pinch points and 12 ecological barriers, presenting an ecological security pattern characterized by a grid-like structure in the northern region and a dispersed pattern in the southern region. Additionally, 273.72 km2 of ecological restoration priority areas and 197.98 square kilometers of ecological restoration encouragement areas are proposed as key planning regions for ecological environmental protection. This study provides references for optimizing spatial configuration to promote the sustainable development of urban and rural living environments in the Yellow River Basin.