bluuebunny/crossref_metadata_2025 huggingface dataset:
Bionic Optimal Design and Performance Study of Soil Loosening Shovels for Degraded Grasslands
Authors: Wang Zhaoyu , You Yong , Zhang Xuening , Wang Decheng , Pan Chengzhong
Published: February 2025

To improve the soil loosening effects of degraded grasslands, this study investigates the performance of a bionic loosening shovel designed based on the claws of prairie zokor. A single-factor simulation test of the bionic loosening shovel was conducted using EDEM software to analyze the effects of loosening depth (H) and operating speed (V) on key parameters, including the ridge disturbance area (As), furrow disturbance area (Af), loosening resistance (Fr), and trench specific resistance (Fc). Additionally, field tests were performed to validate the simulation results of the bionic loosening shovel. The findings indicate that the difference ratio (Da1) between the simulated and test values for the bionic loosening shovel remained consistently low, confirming the reliability of the simulation model in predicting variations in response parameters. Furthermore, comparative field tests were conducted to evaluate the loosening performance of the bionic loosening shovel against standard loosening shovels (the diamond-shaped loosening shovel and the arrow-shaped loosening shovel). The results show that the bionic loosening shovel achieved the lowest values for As, Af, and Fr under the same operating parameters. However, its effect on improving Af was limited. These findings provide valuable technical support for the enhancement and optimization of loosening shovels for degraded grasslands.
The Impact of Vibrations on the Hand–Arm System and Body of Agricultural Tractor Operators in Relation to Operational Parameters, Approach: Analytical Hierarchical Process (AHP)
Authors: Barač Željko , Plaščak Ivan , Jurić Tomislav , Marković Monika
Published: February 2025

This paper presents research on the impact of vibrations on the hand–arm and body system of agricultural tractor operators as ergonomic indicators in relation to certain operational parameters. The measurements were conducted on a LANDINI POWERFARM 100 tractor on agricultural production areas and access roads of the Agricultural and Veterinary School in Osijek. The measurements followed the ISO 5008:2015 standard, which describes the creation of test tracks: a smooth track of 100 m in length and a rough track of 35 m in length. Body vibration measurements were conducted according to the prescribed standards HRN ISO 2631-1: 1999/A1:2019 and HRN ISO 2631-4:2010. Hand–arm system vibration measurements were performed according to the prescribed standards HRN ISO 5349-1:2008 and HRN ISO 5349-2:2008/A1:2015. After the measured data were processed, a three-factor analysis of variance was performed, where some operational parameters were designated as A—agrotechnical surfaces (6 types), B—tractor speed (6 speeds), and C—tire air pressure (3 pressures), along with multiple regression analysis and the AHP (analytical hierarchical process). This research determined that none of the measured hand–arm system vibrations exceeded the warning (2.5 ms−2) or limit (5 ms−2) values of daily exposure. Furthermore, vibrations affecting the operator’s body in the x-axis at higher speeds and pressures C2 and C3, in the y-axis at higher speeds and pressures C1 and C2, and in the z-axis at the highest speed and pressures C1 and C2 were found to exceed the daily exposure warning value of 0.5 ms−2. It was concluded that the operator’s health is at risk, and it is recommended that the seat’s air suspension system be inspected to prevent further complications in a timely manner.
DoubleNet: A Method for Generating Navigation Lines of Unstructured Soil Roads in a Vineyard Based on CNN and Transformer
Authors: Cui Xuezhi , Zhu Licheng , Zhao Bo , Wang Ruixue , Han Zhenhao , Lu Kunlei , Feng Xuguang , Ni Jipeng , Cui Xiaoyi
Published: February 2025

Navigating unstructured roads in vineyards with weak satellite signals presents significant challenges for robotic systems. This research introduces DoubleNet, an innovative deep-learning model designed to generate navigation lines for such conditions. To improve the model’s ability to extract image features, DoubleNet incorporates several key innovations, such as a unique multi-head self-attention mechanism (Fused-MHSA), a modified activation function (SA-GELU), and a specialized operation block (DNBLK). Based on them, DoubleNet is structured as an encoder–decoder network that includes two parallel subnetworks: one dedicated to processing 2D feature maps and the other focused on 1D tensors. These subnetworks interact through two feature fusion networks, which operate in both the encoder and decoder stages, facilitating a more integrated feature extraction process. Additionally, we utilized a specially annotated dataset comprising images fused with RGB and mask, with five navigation points marked to enhance the accuracy of point localization. As a result of these innovations, DoubleNet achieves a remarkable 95.75% percentage of correct key points (PCK) and operates at 71.16 FPS on our dataset, with a combined performance that outperformed several well-known key point detection algorithms. DoubleNet demonstrates strong potential as a competitive solution for generating effective navigation routes for robots operating in vineyards with unstructured roads.
Boosting Aeroponic System Development with Plasma and High-Efficiency Tools: AI and IoT—A Review
Authors: Qureshi Waqar Ahmed , Gao Jianmin , Elsherbiny Osama , Mosha Abdallah Harold , Tunio Mazhar Hussain , Qureshi Junaid Ahmed
Published: February 2025

Sustainable agriculture faces major issues with resource efficiency, nutrient distribution, and plant health. Traditional soil-based and soilless farming systems encounter issues including excessive water use, insufficient nutrient uptake, nitrogen deficiency, and restricted plant development. According to the previous literature, aeroponic systems accelerate plant growth rates, improve root oxygenation, and significantly enhance water use efficiency, particularly when paired with both low- and high-pressure misting systems. However, despite these advantages, they also present certain challenges. A major drawback is the inefficiency of nitrogen fixation, resulting in insufficient nutrient availability and heightened plant stress from uncontrolled misting, which ultimately reduces yield. Many studies have investigated plasma uses in both soil-based and soilless plant cultures; nevertheless, however, its function in aeroponics remains unexplored. Therefore, the present work aims to thoroughly investigate and review the integration of plasma-activated water (PAW) and plasma-activated mist (PAM) in aeroponics systems to solve important problems. A review of the current literature discloses that PAW and PAM expand nitrogen fixation, promote nutrient efficiency, and modulate microbial populations, resulting in elevated crop yields and enhanced plant health, akin to soil-based and other soilless systems. Reactive oxygen and nitrogen species (RONS) produced by plasma treatments improve nutrient bioavailability, root development, and microbial equilibrium, alleviating critical challenges in aeroponics, especially within fine-mist settings. This review further examines artificial intelligence (AI) and the Internet of Things (IoT) in aeroponics. Models driven by AI enable the accurate regulation of fertilizer concentrations, misting cycles, temperature, and humidity, as well as real-time monitoring and predictive analytics. IoT-enabled smart farming systems employ sensors for continuous nutrient monitoring and gas detection (e.g., NO2, O3, NH3), providing automated modifications to enhance aeroponic efficiency. Based on a brief review of the current literature, this study concludes that the future integration of plasma technology with AI and IoT may address the limitations of aeroponics. The integration of plasma technology with intelligent misting and data-driven control systems can enhance aeroponic systems for sustainable and efficient agricultural production. This research supports the existing body of research that advocates for plasma-based innovations and intelligent agricultural solutions in precision farming.
Deep Reinforcement Learning: A Chronological Overview and Methods
Authors: Terven Juan
Published: February 2025

Introduction: Deep reinforcement learning (deep RL) integrates the principles of reinforcement learning with deep neural networks, enabling agents to excel in diverse tasks ranging from playing board games such as Go and Chess to controlling robotic systems and autonomous vehicles. By leveraging foundational concepts of value functions, policy optimization, and temporal difference methods, deep RL has rapidly evolved and found applications in areas such as gaming, robotics, finance, and healthcare. Objective: This paper seeks to provide a comprehensive yet accessible overview of the evolution of deep RL and its leading algorithms. It aims to serve both as an introduction for newcomers to the field and as a practical guide for those seeking to select the most appropriate methods for specific problem domains. Methods: We begin by outlining fundamental reinforcement learning principles, followed by an exploration of early tabular Q-learning methods. We then trace the historical development of deep RL, highlighting key milestones such as the advent of deep Q-networks (DQN). The survey extends to policy gradient methods, actor–critic architectures, and state-of-the-art algorithms such as proximal policy optimization, soft actor–critic, and emerging model-based approaches. Throughout, we discuss the current challenges facing deep RL, including issues of sample efficiency, interpretability, and safety, as well as open research questions involving large-scale training, hierarchical architectures, and multi-task learning. Results: Our analysis demonstrates how critical breakthroughs have driven deep RL into increasingly complex application domains. We highlight existing limitations and ongoing bottlenecks, such as high data requirements and the need for more transparent, ethically aligned systems. Finally, we survey potential future directions, highlighting the importance of reliability and ethical considerations for real-world deployments.
The Use of Natural Sorbents in Cow Feed to Reduce Gaseous Air Pollutants and Faecal Biogenic Compounds
Authors: Nowakowicz-Dębek Bożena , Wlazło Łukasz , Król Jolanta , Karpińska Katarzyna , Ossowski Mateusz , Bis-Wencel Hanna , Ospałek Wojciech
Published: February 2025

The implementation of new technologies and best practices on factory farms is crucial for reducing the environmental pollution burden. This study aimed to evaluate the use of natural sorbents in the cows’ diet to reduce gaseous pollutants released on the farm and the content of faecal biogenic compounds. To this end, a mixture of natural sorbents (65% beechwood biochar, 25% aluminosilicate, and 10% glycerine) was added to cows’ feed. Pollutants released from all groups of cows were continually monitored on the farm during the experiment. Blood samples were also collected from the cows for haematological and biochemical analysis to determine the sorbents’ impact on their health. The average level of gaseous pollutants in the air on the farm were highest in the control group and lowest in the experimental groups. The levels of NH3, CH4, and H2S were statistically significant at p < 0.05. The results demonstrated that the added sorbents effectively reduced gaseous pollutants without adversely affecting the health of cows. Natural additives in the cows’ diet, including sorbents, bind harmful substances such as NH3 and CH4, which are common gaseous by-products of digestion. This leads to improvements in animal welfare and the natural environment.
The Creation of a Systematic Framework to Assess Dog Laws and Their Relationship to Societal Changes in the United Kingdom
Authors: Weir Sarah A. , McDevitt Lynsey , Andrews Clare P. , Kessler Sharon E.
Published: February 2025

Dogs and humans have shared a complex relationship throughout history, with law serving as an important tool to manage dogs’ integration into human societies. As dogs increasingly become regarded as family members in Western countries, and as similar trends emerge globally, it is vital to understand how legislation balances the interests of stakeholders. Existing studies often focus on localised disputes and fragmented legal areas, limiting understanding of how dog-related laws interact and potentially conflict. We developed a conceptual framework to systematically analyse dog-related legislation, using the United Kingdom as a case study. Identified through a systematic search, laws were evaluated using content analysis based on the benefits provided to stakeholders, the regulated aspects of dog ownership, and whether benefits afforded to stakeholders occur in public or private spaces. We found that the greatest legislative focus was on dog welfare, dangerous dogs, and dog control, with little emphasis on areas like the breeding and sale of dogs. Eighty-two percent of laws that manage dogs in public space predominantly benefit the general public, often disadvantaging dogs and their owners, while 81% of laws that govern dogs within the home favour dogs. Owners consistently face legal obligations, but gain few benefits. These findings highlight misalignments between the law and dogs’ evolving societal roles, potentially contributing to public space conflicts and low compliance. The framework offers a tool for cross-country comparisons and assessing legislation for other species with similarly shifting roles.
Methionine Alters the Fecal Microbiota and Enhances the Antioxidant Capacity of Lactating Donkeys
Authors: Huang Fei , Ma Zongjie , Du Xinyi , Wang Changfa , Liu Guiqin , Zhou Miaomiao
Published: February 2025

This study looked at how methionine (Met) affects on serum biochemical indicators, antioxidant indicators, and the fecal microbiota of lactating donkeys. A total of 18 healthy donkeys at the mid-lactation stage were used in this study. They were given concentrate meals with varying concentrations of Met after being randomly split into three groups: Met group I (M1) received 5 g/d of Met, Met group II (M2) received 15 g/d of Met, and the control group (C) received 0 g/d of Met. The trial lasted for five weeks in total. Fecal microbes, antioxidant markers, and blood biochemical indicators were examined. The findings showed that while the levels of malondialdehyde (MDA) decreased (p < 0.05), the addition of Met (15 g/d) significantly enhanced the total antioxidant capacity (T-AOC) and catalase (CAT) activity. Milk samples from the different groups trended towards having significant differences in the levels of the serum biochemical indicators albumin (ALB) and lactate dehydrogenase (LDH) (0.05 < p < 0.1). A fecal microbiome analysis revealed that the addition of Met (5 g/d) increased the abundance of Methanocorpusculum and Ruminococcus, and the addition of 15 g/d of Met increased the abundance of Ruminococcus, Peptococcus, and Anaeroplasma while decreasing the abundance of the [Eubacterium]_ruminantium_group. A correlation analysis revealed that Ruminococcus and Methanocorpusculum were positively correlated with the T-AOC and CAT activity (M1 group). Peptococcus was significantly negatively correlated with MDA levels and positively correlated with the T-AOC (M2 group). Anaeroplasma was positively correlated with CAT activity (M2 group). The above results indicated that Met increased donkey antioxidant levels and modulated the abundance of the fecal microbial community. These findings lay the groundwork for boosting the production performance and general health of nursing donkeys.
Comparative Analysis of Feeding Largemouth Bass (Micropterus salmoides) with Trash Fish and a Compound Diet: Effects on the Growth Performance, Muscle Quality and Health Condition
Authors: Yang Yuanhao , Xing Yangfen , Zhang Niankun , Li Fenggang , Yan Xianfang , Zhang Mingyue , Zhu Zilin , Gisbert Enric , Zhou Jishu
Published: February 2025

Two feeding strategies based on the use of trash fish (TF) and an artificial compound feed (ACF) were compared in terms of growth performance, feed efficiency, muscle quality and health status in Micropterus salmoides. For this purpose, fish (128 ± 14 g; n = 102) were divided into two groups and fed with the TF and ACF in triplicate for 90 days. Results showed that the growth performance and condition factor were not affected by the diet, whereas the viscerosomatic and hepatosomatic indexes in the ACF group were higher than in the TF group. The muscle from the TF group had higher levels of 20:5n-3, 22:6n-3, and total n-3 PUFA contents, which resulted in lower thrombogenicity index values. No differences in the amino acid profile were found. Regarding muscle texture properties, only the gumminess and chewiness were significantly lower in the ACF. The use of histological and gene expression biomarkers showed that fish fed TF had a healthier hepatic condition compared to the ACF. The only disadvantage of TF in the current study was the higher values of FCR in comparison to ACF.
Functional and Economic Role of Some Mediterranean Medicinal Plants in Dairy Ruminants’ Feeding: A Review of the Effects of Garlic, Oregano, and Rosemary
Authors: Iommelli Piera , Spina Anna Antonella , Vastolo Alessandro , Infascelli Lorenzo , Lotito Daria , Musco Nadia , Tudisco Raffaella
Published: February 2025

Botanical and herbal supplements contain diverse compounds that support health and prevent disease by influencing biological pathways. In ruminant nutrition, such supplements are rarely used as most integrations focus on production improvements using synthetic additives. This review explores the potential of Mediterranean herbs, namely, garlic, oregano, and rosemary, in dairy ruminant diets, emphasizing their effects on milk production, antioxidant activity, weaning management, in vitro studies, and economic impacts. Using medicinal plants reduces reliance on synthetic additives, lowers costs, enhances animal health, and boosts productivity. These plants align with sustainable farming practices, enable premium market access, and support local resources to improve performance and reduce livestock’s environmental footprint.
Fading Blue: Exploring the Causes of Locus Coeruleus Damage Across the Lifespan
Authors: Galgani Alessandro , Scotto Marco , Faraguna Ugo , Giorgi Filippo S.
Published: February 2025

Locus Coeruleus (LC) is a brain nucleus that is involved in a variety of key functions (ranging from attention modulation to sleep–wake cycle regulation, to memory encoding); its proper function is necessary both during brain development and for brain integrity maintenance, and both at the microscale and macroscale level. Due to their specific intrinsic and extrinsic features, LC cells are considered particularly susceptible to damage concerning a variety of insults. This explains LC involvement in degenerative diseases not only in adults (in the context of neurodegenerative disease, mainly), but also in children (in relation to early hypoxic damage and Down’s Syndrome, among others). In this narrative review, we dissect the potential mechanisms through which LC is affected in different diseases, with a special emphasis on the high rate of activity it is subjected to and the oxidative stress associated with it. Further research aimed at deepening our understanding of these mechanisms is needed to enable the development of potential strategies in the future that could slow down LC degeneration in subjects predisposed to specific brain disorders.
Endothelial Dysfunction: Redox Imbalance, NLRP3 Inflammasome, and Inflammatory Responses in Cardiovascular Diseases
Authors: Penna Claudia , Pagliaro Pasquale
Published: February 2025

Endothelial dysfunction (ED) is characterized by an imbalance between vasodilatory and vasoconstrictive factors, leading to impaired vascular tone, thrombosis, and inflammation. These processes are critical in the development of cardiovascular diseases (CVDs) such as atherosclerosis, hypertension and ischemia/reperfusion injury (IRI). Reduced nitric oxide (NO) production and increased oxidative stress are key contributors to ED. Aging further exacerbates ED through mitochondrial dysfunction and increased oxidative/nitrosative stress, heightening CVD risk. Antioxidant systems like superoxide-dismutase (SOD), glutathione-peroxidase (GPx), and thioredoxin/thioredoxin-reductase (Trx/TXNRD) pathways protect against oxidative stress. However, their reduced activity promotes ED, atherosclerosis, and vulnerability to IRI. Metabolic syndrome, comprising insulin resistance, obesity, and hypertension, is often accompanied by ED. Specifically, hyperglycemia worsens endothelial damage by promoting oxidative stress and inflammation. Obesity leads to chronic inflammation and changes in perivascular adipose tissue, while hypertension is associated with an increase in oxidative stress. The NLRP3 inflammasome plays a significant role in ED, being triggered by factors such as reactive oxygen and nitrogen species, ischemia, and high glucose, which contribute to inflammation, endothelial injury, and exacerbation of IRI. Treatments, such as N-acetyl-L-cysteine, SGLT2 or NLRP3 inhibitors, show promise in improving endothelial function. Yet the complexity of ED suggests that multi-targeted therapies addressing oxidative stress, inflammation, and metabolic disturbances are essential for managing CVDs associated with metabolic syndrome.
How Generative AI Enables an Online Project-Based Learning Platform: An Applied Study of Learning Behavior Analysis in Undergraduate Students
Authors: Dai Yi , Xiao Jia-Ying , Huang Yizhe , Zhai Xuesong , Wai Fan-Chun , Zhang Ming
Published: February 2025

Using Generative Artificial Intelligence (GAI) in education has opened new avenues for innovation, yet its role as an interactive tool with learners remains underexplored. Research in this domain faces challenges from pedagogical complexities and the variability of AI tools. To address these gaps, this study developed an online project-based learning (PBL) platform incorporating a GAI plug-in and conducted a year-long experiment to analyze its impact. Three sets of experimental analyses were performed to examine learners’ methods, cognitive processes, and learning effectiveness. The findings reveal that GAI significantly influenced students’ learning approaches, cognitive engagement, and learning effectiveness. Additionally, the study demonstrates that PBL offers an effective framework for investigating the educational implications of GAI, providing new insights for future research in this evolving field.
Interrelationships Between Topology and Wettability of Nanostructured Composite Wide Bandgap Metal Oxide Films Prepared by Spray Pyrolysis
Authors: Morari Vadim , Monaico Elena I. , Monaico Eduard V. , Rusu Emil V. , Ursaki Veaceslav V.
Published: February 2025

The interrelationships between the topological features, such as surface roughness deduced from atomic force microscopy (AFM), and wettability properties expressed by the contact angle of a water droplet on the surface of nanostructured wide bandgap oxide films prepared by spray pyrolysis are investigated for a wide range of compositions. A direct relationship between the surface roughness and the value of the contact angle was found for nanocomposite (In2O3)1−x(MgO)x, (In1−xGax)2O3, and Zn1−xMgxO films, for which both the surface roughness and the contact angle increase with the increasing x-value. On the other hand, in ITO films doped with Ga, it was found that the surface roughness increases by increasing the Ga doping, while the contact angle decreases. Both the surface roughness and the contact angle proved to increase in Ga2O3 films when they were alloyed with Al2O3, similar to other nanocomposite films. An inverse relationship was revealed for a nanocomposite formed from Ga2O3 and SnO2. The contact angle for a (Ga2O3)0.75(SnO2)0.25 film was larger as compared to that of the Ga2O3 film, while the surface roughness was lower, similar to ITO films. The highest value of the contact angle equal to 128° was found for a (In2O3)1−x(MgO)x film with an x-value of 0.8, and the largest RMS roughness of 20 nm was showed by a Ga1.75Al0.25O3 film. The optical properties of the prepared films were also analyzed from optical absorption spectroscopy, demonstrating their bandgap variation in the range of (4 to 4.85) eV, corresponding to the middle ultraviolet spectral range.
Effect of High-Pressure Processing and Long-Time Storage on Color, Lipid Oxidation, and Free Amino Acids Profile of Beef
Authors: Chmiel Marta , Świder Olga , Roszko Marek , Adamczak Lech , Cegiełka Aneta , Pietrzak Dorota , Florowski Tomasz , Bryła Marcin , Florowska Anna
Published: February 2025

This study aimed to evaluate the effect of high-pressure processing (HPP, 300, 400, or 500 MPa, 5 min, 5 °C) on the color, lipid oxidation, and free amino acids profile of skin-packed beef stored under cooling conditions (0–4 °C) for 56 days. The pH of the HPP-treated beef (500 MPa) was stable during storage (5.6–5.7) and significantly higher (p = 0.0010) than that of the control meat at the end of the storage time. During storage, the pH of the control meat decreased up to 5.3 on day 56. The HPP, especially 500 MPa, triggered faster lipid oxidation, leading to higher TBARS (thiobarbituric acid reactive substance) index values than the control meat (1.9 vs. 0.6 mg MAD/ kg of product at the end of storage time). Also, the extension of the storage time resulted in an increase in the TBARS index values of beef, which was greater when higher pressures were used. Moreover, HPP significantly modified the free amino acids profile of the meat. The use of HPP reduced the content of most of the free amino acids analyzed (except leucine, phenylalanine, and lysine) compared to the control meat, and the exposure to higher pressures resulted in greater losses (even a 2–3-fold lower content at the end of storage time). Furthermore, the dynamics of the increase in the free amino acids content during beef storage were higher for the control and HPP300 and lower for HPP400 and HPP500 meat. It seems that knowledge of lipid oxidation, color changes, and the content of free amino acids in beef, especially those constituting biogenic amine precursors, may help optimize process parameters such as pressure and beef storage time, to minimize undesirable quality changes. Considering the high costs of HPP, it seems also necessary to conduct a sensory evaluation to assess its potential usefulness in extending the shelf-life of culinary beef. Additionally, the changes occurring in beef within the first few hours after HPP treatment also need further investigation.
A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
Authors: Cui Longfei , Niu Xinyu , Qian Haizhong , Wang Xiao , Xu Junkui
Published: February 2025

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. This study introduces a novel approach called “Pre-Trained Shape Feature Representations from Transformers (PSRT)”, which utilizes transformer encoders designed with three self-supervised pre-training tasks: coordinate masking prediction, coordinate offset correction, and coordinate sequence rearrangement. This approach enables the extraction of general shape features applicable to both line and polygon elements, generating high-dimensional embedded feature vectors. These vectors facilitate downstream tasks like shape classification, pattern recognition, and cartographic generalization. Our experimental results show that PSRT can extract vector shape features effectively without needing labeled samples and is adaptable to various types of vector features. Compared to the methods without pre-training, PSRT enhances training efficiency by over five times and improves accuracy by 5–10% in tasks such as line element matching and polygon shape classification. This innovative approach offers a more unified, efficient solution for processing vector shape data across different applications.
Impact of Local Soil Conditions on the Seismic Performance of Reinforced Concrete Structures: In the Context of the 2023 Kahramanmaraş Earthquakes
Authors: Işık Ercan , Avcil Fatih , Büyüksaraç Aydın , Arkan Enes , Harirchian Ehsan
Published: February 2025

Devastating earthquakes around the world highlight the crucial need to understand the seismic performance of structures. Local soil conditions are among the most significant factors influencing a structure’s seismic behavior. Earthquake–soil–structure interactions directly affect seismic damage levels. In performance-based earthquake engineering, accurate target displacements enable a more realistic estimation of the expected performance levels for structures. This depends on obtaining realistic local soil conditions. This study conducted structural analyses on seven different variables, considering four different local soil conditions specified in Eurocode 8. The variables selected were importance class, peak ground acceleration (PGA), damping ratio, ground storey height, frame openings, number of storeys, and storey height, applied to a symmetrical and regular reinforced concrete structure. Period, base shear, stiffness, and target displacements were obtained for each variable through pushover analyses for the four various local soil conditions. All structural results were compared with one another and with other variables. This paper also aimed to reveal the effect of local soil conditions in the context of the 6 February 2023 Kahramanmaraş (Türkiye) earthquakes. The study confirms that variations in soil types, as classified in Eurocode 8, have a major impact on the seismic behavior of reinforced-concrete structures. Weaker soils amplify seismic effects, increasing target displacements and structural vulnerability.
A Computational Sketch-Based Approach Towards Optimal Product Design Solutions
Authors: Charalampous Paschalis
Published: February 2025

This paper presents a numerical sketch-based methodology to achieve optimal product design solutions, bridging the gap between initial conceptual sketches and advanced engineering analyses. The proposed approach enables the transformation of simple hand-drawn sketches into digital models suitable for complex computational simulations and design optimization. Using computer vision algorithms, sketches are processed to generate digital design components that serve as inputs for Finite Element Analysis (FEA). In order to further enhance the overall design process, topology optimization (TO) is also performed, iteratively refining the geometry to achieve optimal material distribution for improved structural performance. Additionally, Adaptive Mesh Refinement (AMR) techniques are applied to ensure computational efficiency and accuracy by dynamically refining the mesh in regions of high complexity or stress concentration. The synergy of sketch-based modeling, FEA, TO, and AMR demonstrates significant potential in reducing design cycles while maintaining high-performance standards. Finally, it should be noted that the proposed pipeline consists of a fully automated procedure, hence it could reduce the learning curve for the designers, enabling companies to onboard employees faster and integrate advanced design techniques into their workflows without extensive training. The above-mentioned modules render the introduced approach particularly suitable for applications in product design development that can be utilized in several industries like mechanical, manufacturing, and furniture.
Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
Authors: Zhou Suhua , Han Wenjie , Huang Minghua , Xu Zhiwen , Li Jinfeng , Zhang Jiuchang
Published: February 2025

In predicting slope stability, updating datasets with new cases necessitates retraining traditional machine learning models, consuming substantial time and resources. This paper introduces the Incremental Learning Bayesian (ILB) model, combining incremental learning theory with the naive Bayesian model, to address this issue. Key slope parameters—height; slope angle; unit weight; cohesion; internal friction angle; and pore water ratio—are used as predictive indicators. A dataset of 242 slope cases from existing literature is compiled for training and evaluation. The ILB model’s performance is assessed using accuracy, area under the ROC curve (AUC), generalization ability, and computation time and compared to four common batch learning models: Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). Variable importance and partial dependence plots are used to explore the relationship between prediction results and parameters. Validation is performed with real slope cases from the Lala Copper Mine in Sichuan Province, China. Results show that (1) The ILB model’s accuracy and AUC improve as the dataset grows. (2) The ILB model outperforms GBM, SVM, and MLP in accuracy and AUC, similar to RF. (3) It demonstrates superior generalization and lower computation time than batch learning models. (4) Internal friction angle, slope angle, and pore water ratio are the most important predictors.
Probing Petroleum Sources Using Geochemistry, Multivariate Analysis, and Basin Modeling: A Case Study from the Dibei Gas Field in the Northern Kuqa Foreland Basin, NW China
Authors: Wei Xinzhuo , Liu Keyu , Yang Xianzhang , Liu Jianliang , Zhou Lu , Ding Xiujian
Published: February 2025

The Dibei Gas Field, located in the northern Kuqa Foreland Basin, Tarim Basin, western China, is one of the most important condensate gas-producing areas in China, with over one trillion cubic feet of gas reserves discovered in the Jurassic terrestrial reservoirs. However, further hydrocarbon exploration and development in the area is hampered by uncertainties on the petroleum sources. A robust oil–source and gas-source correlation analysis was carried out in the Dibei area to enhance our understanding of the gas accumulation potential. An integrated molecular geochemical analysis, multivariate analysis, and basin modeling were conducted to investigate source rocks, inclusion oils, reservoir oils, and gas from the Dibei area. Two types of source rocks have been identified in the Dibei area: a Jurassic coaly source rock and a Triassic lacustrine source rock based on multivariate analysis. The compositions of the n-alkanes, steranes, and terpanes and the carbon isotope ratios of individual n-alkanes in the inclusion oil extracts and reservoir oils from Jurassic Yangxia and Ahe reservoirs show distinct differences when compared with the two types of source extracts. Multiple oil sources are revealed in the Dibei area, with various degrees of mixing between reservoir oil (present) and inclusion oil (paleo), reflecting evolving oil sources. Basin modeling shows that during the late Himalayan orogeny, the Jurassic strata in the Dibei area experienced a rapid burial within ~20 Ma, with the oil generation window of the source rocks expanding greatly. This caused the shallowly buried Jurassic source rocks to enter the oil generation window, resulting in the occurrence of two oil sources for the inclusion oils and reservoir oils, and an increasing degree of mixing over time. Our finding confirms that the accumulated condensate gas in the Dibei area is mainly derived from the Jurassic source rocks. This allows the extent of prospective exploration to be better defined.
Multiple-Valued Logic, Vocabulary Structure, and Linked List for Data Verification in Dialog Communications of Agents
Authors: Bykovsky Alexey
Published: February 2025

Distant verification of the autonomous agent’s parameters in the dialog mode is a difficult multi-parametric task if the large-scale scene of action is characterized by a large number of collaborative and rival robots. The possible scheme to realize it for mass robots is to use non-exhaustive and selective data verification, combining the polling of internal subsystems and external data storage in collaborating network agents. Selective extraction of data for such checks is proposed to involve the special ordered set of vocabularies, containing coded digital words and classifying parameters of agents, tasks, objects, and events. The structure of such vocabularies is to be combined with various versions of the linked list scheme, known in blockchain and actual for protective documenting of critical data. Multiple-valued logic is used here as the convenient method to provide autonomous navigation in a multi-parametric structure of data and verification variables.
A Modified Kalman Filter Based on Radial Basis Function Neural Networks for the Improvement of Numerical Weather Prediction Models
Authors: Donas Athanasios , Galanis George , Pytharoulis Ioannis , Famelis Ioannis Th.
Published: February 2025

This study introduces a novel enhancement to the Kalman filter algorithm by integrating it with Radial Basis Function neural networks to improve numerical weather prediction models. Traditional Kalman filters frequently underperform when used by dynamical systems due to their reliance on fixed covariance matrices, resulting in inaccuracies and forecast uncertainty. The proposed modified Kalman filter utilizes Radial Basis Function neural networks to estimate covariance matrices adaptively during the filtering process. This self-adaptive computational system enables the simultaneous targeting of the systematic and the remaining non-systematic parts of the forecast error, producing an innovative and efficient post-process strategy. The suggested methodology is evaluated on predictions of 10-meter wind speed and 2-meter air temperature obtained from the Weather Research and Forecasting model for observation stations in northern Greece. The derived results demonstrate a significant reduction in systematic error, as the bias decreased by up to 88% for 10-meter wind speed and 58% for 2-meter air temperature. Additionally, the forecast variability was successfully mitigated, with the RMSE reduced by 39% and 40%, respectively. Compared to the traditional Kalman filter, which exhibited increased RMSE in several cases and failed to control forecast uncertainty, the proposed approach consistently outperformed by providing stable and reliable predictions across all examined scenarios. These improvements validate the robustness of the method in comparison to conventional techniques, highlighting its potential to produce reliable and stable predictions for environmental applications.
Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas
Authors: Oliveira Santos Victor , Costa Rocha Paulo Alexandre , Thé Jesse Van Griensven , Gharabaghi Bahram
Published: February 2025

Keeping track of air quality is paramount to issue preemptive measures to mitigate adversarial effects on the population. This study introduces a new quantum–classical approach, combining a graph-based deep learning structure with a quantum neural network to predict ozone concentration up to 6 h ahead. The proposed architecture utilized historical data from Houston, Texas, a major urban area that frequently fails to comply with air quality regulations. Our results revealed that a smoother transition between the classical framework and its quantum counterpart enhances the model’s results. Moreover, we observed that combining min–max normalization with increased ansatz repetitions also improved the hybrid model’s performance. This was evident from evaluating the assessment metrics root mean square error (RMSE), coefficient of determination (R2) and forecast skill (FS). Values for R2 and FS for the horizons considered were 94.12% and 31.01% for the 1 h, 83.94% and 48.01% for the 3 h, and 75.62% and 57.46% for the 6 h forecasts. A comparison with the existing literature for both classical and QML models revealed that the proposed methodology could provide competitive results, and even surpass some well-established forecasting models, proving to be a valuable resource for air quality forecasting, and thus validating this approach.
Analysis of Cross-Polarization Discrimination Due to Rain for Earth–Space Satellite Links Operating at Millimetre-Wave Frequencies in Pretoria, South Africa
Authors: Lawal Yusuf Babatunde , Owolawi Pius Adewale , Tu Chunling , Van Wyk Etienne , Ojo Joseph Sunday
Published: February 2025

This study investigates the impact of rain-induced attenuation on cross-polarization discrimination (XPD) in Earth–space satellite links operating at millimeter-wave frequencies in Pretoria, South Africa. The traditional method of computing XPD employs a constant annual mean rain height and annual mean co-polar attenuation (CPA) over a certain location. This research utilized seasonal rain height data obtained from a recent study and the latest ITU-R P.618-14 guidelines, to compute and analyze XPD variations across six selected frequencies (11.7 GHz to 35 GHz) for different percentages of time exceedance in Pretoria. The study reveals significant seasonal dependencies of rain heights, with XPD reaching its maximum during winter due to lower rain height, and lower rain-induced attenuation and its minimum during summer, characterized by intense convective rainfall and maximum rain height. For instance, the estimated XPD for a 35 GHz signal at 0.01% of the time in the summer, spring, winter, and autumn are 13, 14, 15, and 14 dB, respectively. This implies that radio signals suffer severe attenuation caused by low XPD in the summer. The relationship between CPA and XPD highlights the need for increased XPD margins at higher frequencies to mitigate signal degradation caused by rain depolarization. Practical recommendations include the adoption of adaptive modulation and coding schemes to maintain link reliability during adverse weather conditions, particularly in summer. This research highlights the significance of incorporating frequency-dependent parameters and rain height variability in XPD estimation to enhance the design of satellite communication systems, ensuring optimized performance and reliable operation in a tropical climate.
Theoretical Investigation on Vortex Electron Impact Excitation of a Mg Atom Confined in a Solid-State Environment
Authors: Strnat Sophia , Sahoo Aloka K. , Sharma Lalita , Sommerfeldt Jonas , Park Daesung , Bick Christian , Surzhykov Andrey
Published: February 2025

We present a theoretical investigation of the inelastic scattering of vortex electrons by many-electron atoms embedded in a solid-state environment. Special emphasis is placed on the probability of exciting a target atom and on the relative population of its magnetic substates as described by the set of alignment parameters. These parameters are directly related to the angular distribution of the subsequent radiative decay. To demonstrate the application of the developed theoretical approach, we present calculations for the 3s2 S01→3s3p P13 excitation of a Mg atom and its subsequent 3s3p P13→3s2 S01 radiative decay. Our results highlight the significance of the orbital angular momentum (OAM) projection as well as the relative position of the vortex electron with respect to the target atom.
Sustainable Wine Fining: Evaluating Grape Pomace as a Natural Alternative to Commercial Agents
Authors: Kokkinomagoulos Evangelos , Kandylis Panagiotis
Published: February 2025

Winemaking generates significant amounts of byproducts, mainly grape pomace, which is composed of skins, seeds, and stems. Rich in phenolic compounds with antioxidant properties, grape pomace is often underutilized in low-value applications such as compost or animal feed. Recent research highlights its potential as a sustainable alternative to conventional fining agents, such as bentonite, commonly used to improve wine clarity, stability, and sensory attributes. However, previous studies have been limited in scope, focusing on selected wine parameters or narrow experimental conditions. This study explored the use of red and white grape pomace as fining agents for Mavrodaphne red wine, evaluating their effects on anthocyanin level, tannin content, total polyphenol index, chromatic properties, and aromatic profile across varied dosages and contact times. The results indicated that grape pomace, either from red or white grapes, achieved comparable or superior tannin and anthocyanin removal and chromatic enhancement relative to commercial fining agents, without significantly altering aromatic complexity. The findings underscore grape pomace’s suitability as a natural, vegan-friendly, and sustainable fining option, aligning with consumer preferences for cleaner-label wines. This study promotes the adoption of grape pomace in winemaking, supporting waste valorization and advancing sustainable practices within the industry.
Sensitivity of Lumbar Total Joint Replacement Contact Stresses Under Misalignment Conditions—Finite Element Analysis of a Spine Wear Simulator
Authors: Kurtz Steven M. , Rundell Steven A. , Spece Hannah , Yarbrough Ronald V.
Published: February 2025

A novel total joint replacement (TJR) that treats lumbar spine degeneration was previously assessed under Mode I and Mode IV conditions. In this study, we relied on these previous wear tests to establish a relationship between finite element model (FEM)-based bearing stresses and in vitro wear penetration maps. Our modeling effort addressed the following question of interest: Under reasonably worst-case misaligned conditions, do the lumbar total joint replacement (L-TJR) polyethylene stresses and strains remain below values associated with Mode IV impingement wear tests? The FEM was first formally verified and validated using the risk-informed credibility assessment framework established by ASME V&V 40 and FDA guidance. Then, based on criteria for unreasonable misuse outlined in the surgical technique guide, a parametric analysis of reasonably worst-case misalignment using the validated L-TJR FEM was performed. Reasonable misalignment was created by altering device positioning from the baseline condition in three scenarios: Axial Plane Convergence (20–40°), Axial Plane A-P Offset (0–4 mm), and Coronal Plane Tilt (±20°). We found that, for the scenarios considered, the contact pressures, von Mises stresses, and effective strains of the L-TJR-bearing surfaces remained consistent with Mode I (clean) conditions. Specifically, the mechanical response values fell below those determined under Mode IV (worst-case) boundary conditions, which provided the upper-bound benchmarks for the study (peak contact pressure 83.3 MPa, peak von Mises stress 32.2 MPa, and peak effective strain 42%). The L-TJR was judged to be insensitive to axial and coronal misalignment under the in vitro boundary conditions imposed by the study.