bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impacts of Perceived Safety and Service Quality on Perceived Accessibility by Public Transport in Melbourne
Authors: Chau Hing-Wah , Chan Melissa , Jamei Elmira , Lättman Katrin
Published: November 2024

In recent years, accessibility to services and activities using sustainable transport modes has become an important goal for land use and transport planning policies. Traditional approaches rely on objective measures, such as travel time, distance, costs, and other spatial data, overlooking personal preferences, experiences, demographic features, and socio-cultural and economic dynamics. Attributes that affect perceptions of accessibility require attention but our understanding of the factors influencing perceived accessibility is inadequate, particularly in Australia’s expanding suburbs. Through a literature review and questionnaire surveys, this study aims to take into account various travel characteristics and enhance the understanding of how perceived safety and service quality impact mobility behaviour and perceived accessibility within the Australian context. The study found that perceived safety and service quality have significant impacts on perceived accessibility and hence influence the use of public transport. We have identified critical factors that require proper consideration in transport and land use planning and policies to enable a better contribution from public transport to the liveability and well-being of residents in Australian suburbs.
A Constantly Updated Flood Hazard Assessment Tool Using Satellite-Based High-Resolution Land Cover Dataset Within Google Earth Engine
Authors: Gemitzi Alexandra , Kopsidas Odysseas , Stefani Foteini , Polymeros Aposotolos , Bellos Vasilis
Published: November 2024

This work aims to develop a constantly updated flood hazard assessment tool that utilizes readily available datasets derived by remote sensing techniques. It is based on the recently released global land use/land cover (LULC) dataset Dynamic World, which is readily available, covering the period from 2015 until now, as an open data source within the Google Earth Engine (GEE) platform. The tool is updated constantly following the release rate of Sentinel-2 images, i.e., every 2 to 5 days depending on the location, and provides a near-real-time detection of flooded areas. Specifically, it identifies how many times each 10 m pixel is characterized as flooded for a selected time period. To investigate the fruitfulness of the proposed tool, we provide two different applications; the first one in the Thrace region, where the flood hazard map computed with the presented herein approach was compared against the flood hazard maps developed in the frames of the EU Directive 2007/60, and we found several inconsistencies between the two approaches. The second application focuses on the Thessaly region, aiming to assess the impacts of a specific, unprecedented storm event that affected the study area in September 2023. Moreover, a new economic metric is proposed, named maximum potential economic loss, to assess the socioeconomic implications of the flooding. The innovative character of the presented methodology consists of the use of remotely sensed-based datasets, becoming available at increasing rates, for developing an operational instrument that defines and updates the flood hazard zones in real-time as required.
Natural and Organic Input-Based Integrated Nutrient-Management Practices Enhance the Productivity and Soil Quality Index of Rice–Mustard–Green Gram Cropping System
Authors: Sarkar Sukamal , Dhar Anannya , Dey Saikat , Chatterjee Sujan Kr. , Mukherjee Shibasis , Chakraborty Argha , Chatterjee Gautam , Ravisankar Natesan , Mainuddin Mohammed
Published: November 2024

The effects of integrated nutrient-management (INM) practices on soil quality are essential for sustaining agro-ecosystem productivity. The soil quality index (SQI) serves as a tool to assess the physical, chemical, and biological potential of soils as influenced by various edaphic and agronomic practices. A multiyear (2018–2021) field experiment was performed at the University Organic Research Farm, Narendrapur, West Bengal, India, to investigate the influence of integrated and sole applications of different conventional fertilizers, organic (e.g., vermicompost), and natural farming inputs (e.g., Dhrava Jeevamrit and Ghana Jeevamrit) on SQIs and crop productivity of rice–mustard–green gram-based cropping systems. A total of 12 parameters were selected for the assessment of SQI, amongst which only four, namely pH, organic carbon %, total actinomycetes, and bulk density, were retained for the minimum data set based on principal component analysis (PCA). In this study, the maximum SQI value (0.901) of the experimental soil was recorded in the INM practice of 25% organic and 25% inorganic nutrient inputs, and the rest with natural farming inputs, which augments the SQI by 24% compared to the 100% inorganic nutrient treatment. Amongst the different soil parameters, the highest contribution was from the pH (35.18%), followed by organic carbon % (26.77%), total actinomycetes (10.95%), and bulk density (6.98%). The yields in different cropping systems varied year-wise across treatments. Notably, the highest yield in rainy rice was estimated in the 100% organic treatment, followed by INM practices in the subsequent years, and finally, the combination of organic and natural inputs in the final year. In the case of mustard, the combination of organic and natural inputs resulted in the highest productivity in the initial and last years of study, while the 100% organic treatment resulted in higher productivity in subsequent years. Green gram showed a dynamic shift in yield between the 100% organic and integrated treatments over the years. Further, a strong correlation was also established between the soil physico-chemical parameters and the SQI. Overall, this study concludes that the natural and organic input-based INM practice enhances the soil quality and crop productivity of the rice–mustard–green gram cropping system under the coastal saline zone.
Bridging Matera’s Fragmented Identity: Unifying Disconnected Urban Spaces
Authors: Tzortzi Julia Nerantzia , Saxena Ishita
Published: November 2024

The city, like a piece of architecture, is a structure in space, but one of gigantic scale, something perceived only over a long period. A space is termed a place when it acquires an identity. The entirety of urban personality, urban communication, urban conduct, and urban design constitute the urban identity. This research delves into divided urban identities and examines how urban and architectural design influence the fragmentation of the cityscape. It explores the connection between urban environments’ physical attributes and the divide of social, cultural, and political identities within cities. This study uses a multidisciplinary method to acquire thorough knowledge by combining architectural studies, urban planning theories, and social-cultural perspectives. The case study of reference is the city of Matera, in southern Italy, which has a unique history of a slow shaping of its urban and productive landscape throughout centuries due to heavy environmental constraints and resource availability and which has suffered forced evacuation and major discontinuities in the past century. Here, the opposing traits of the historical Sassi district and the new town are examined, focusing on their resulting separated urban identities. The study also looks at how divided cities may be reconciled and integrated, emphasizing the relevance of a holistic urban approach for the framing of complex issues. The research proposes methods and best practices for developing inclusive urban settings that promote cohesion and shared identities through the analysis of successful cases of urban regeneration, adaptive reuse of spaces, and participatory design processes. The findings of this research contribute to both academic and practical knowledge by deepening the understanding of the relationship between urban design, architecture, and divided urban identities. It emphasizes the value of comprehensive approaches to urban planning that take into account the social, cultural, and historical settings of cities to foster inclusivity, rapprochement, and the development of common urban identities.
Mechanism of Population Outflow and Ecological Pressure Reflux in China’s Pastoral Area: S-C Gacha’s Case
Authors: Fan Mingming , Zeren Gongbu
Published: November 2024

In China’s rural areas, as populations flow into cities, dependence on land has significantly decreased, leading to widespread land abandonment; however, the relationship between population and land in pastoral areas differs. This study conducted a five-year tracking survey on Sunite Right Banner in Xilingol League (Inner Mongolia), where macro data and in-depth home interviews were used for the mechanisms’ analysis. The findings demonstrate that the pastoral regions’ ecological pressure has increased with population immigration. There are several factors influencing such interaction between people and rangeland: First is the cost mechanism. The increasing cost of livestock production leads herders to increase their reliance on grazing rental systems to increase the number of livestock. The grazing rental system generates income for those migrating to adjacent urban regions. Second is the institutional mechanism. The “three-separating property right systems”, a recent rural land institutional reform policy, has marketized rural individualized rangelands, leading herders to focus more on the exchange value of rangelands rather than the use value, including ecological functions. Consequently, both lessors and lessees engage in high-intensity utilization of their individualized grazing lands. Third is the natural mechanism. Climate fluctuations influence herders’ migration patterns, prompting them to leave and return to grazing lands. These ecological dynamics force herders to oscillate between survival and development, intensifying pressure on the grasslands during favorable years and preventing them from recovering while increasing the occurrence of poor years, such as droughts. Fourth is the consumption mechanism. Herders who have migrated to urban regions must continue relying on rangeland resources to sustain their livelihoods. However, lower economic outputs of livestock production hardly catch up with the higher consumption levels in urban regions, which leads migrated herders to increase their pressure on rural rangelands. The final factor is a combination of the previous four mechanisms. The interplay between these previously mentioned four mechanisms encouraged higher herder population outflow, though their reliance on rangeland resources has continued. Based on the results, we argue that these dynamic relations between rural population change and land use patterns are becoming common issues across pastoral regions in Inner Mongolia, demanding innovative solutions.
Global Urbanization and Habitat Quality: Interactive Coercive Relationships
Authors: Li Weisong , Wu Jiahui , Yuan Yanghaoyue , Duan Binqiao , Pan Sipei , Chen Wanxu , Chen Yan
Published: November 2024

Urbanization is inevitably accompanied by drastic changes in regional land use and therefore presents an evident influence on ecosystems. Habitat quality (HQ) reflects the ability of a habitat to provide suitable conditions for the survival of an individual or population, and clarifying the interrelationships between urbanization level (UL) and HQ provides insights into sustainable urbanzation and ecosystems conservation. Much attention has focused on how urbanization related to HQ at multi-scales, but few studies have analyzed the interactive coercive relationships between UL and HQ on the global county level. To address this gap, we adopted the bivariate spatial autocorrelation and coupling coordination degree (CCD) model to determine their interactive coercive relationships at the county level globally. Results showed that the global average UL was 0.0807, 0.0838, and 0.0857 in 2000, 2010, and 2020, respectively, with a continuously increasing trend. The global average HQ was 0.6186, 0.6133, and 0.6111 for 2000, 2010, and 2020, reflecting opposite declining trends. The Moran’s I of population urbanization and HQ in 2000, 2010, and 2020 globally remained negative but showed an increasing trend, with values of −0.189, −0.228, and −0.254, respectively, while those of economic urbanization and HQ and land urbanization and HQ also remained negative and exhibited a similar increasing trend. The spatial autocorrelations for UL and HQ in different dimensions indicated that deserts and plateaus functioned as catchment areas with low UL and HQ. The CCD between UL and HQ in each county globally ranged from 0 to 0.70, while CCD between land urbanization and HQ was the most optimal at 0–0.90. The CCD between population urbanization and HQ was similar to that of economic urbanization in terms of spatial distribution. Nevertheless, the CCD of land urbanization in HQ exhibited the greatest degree of coordination among the three urbanization dimensions during the study period. These findings provided important support for sustainable urbanization and ecosystem protection globally.
Perception of European Portuguese Mid-Vowels by Ukrainian–Russian Bilinguals
Authors: Kogan Vita V. , Tavares Gabriela
Published: November 2024

Mid-vowel contrasts often present perceptual challenges for speakers of languages that lack these distinctions. However, bilingual speakers, who have access to two phonological systems and exhibit greater metalinguistic awareness, might not necessarily encounter such difficulties. In this study, 27 Ukrainian–Russian bilinguals listened to an unfamiliar language, European Portuguese, and completed two tasks: an identification task where they assimilated the seven stressed oral Portuguese vowels to the closest Ukrainian categories and a discrimination task featuring the Portuguese vowel contrasts /ɛ/–/e/, /e/–/i/, /ɔ/–/o/, and /o/–/u/. No bilingual advantage was observed: the discrimination performance on all contrasts was slightly above or near a chance level (A-prime scores varied between 0.55 and 0.20). These perceptual difficulties may be attributed to the acoustic similarities between the vowels within the contrasts rather than to the differences between the phonological inventories of the languages (the most challenging contrast was not a mid-vowel contrast but acoustically similar /o/–/u/). Although with the back mid-vowel contrast, the difficulty seems to also stem from the possibility that both Ukrainian and Russian have only one back mid-vowel, /o/, and this category occupies a wider area in the vowel space of Ukrainian–Russian bilinguals. The results suggest that bilingual advantage does not always manifest itself in the perception of a new language, especially if two typologically close languages are involved.
Machine Learning-Based Software for Predicting Pseudomonas spp. Growth Dynamics in Culture Media
Authors: Tarlak Fatih
Published: November 2024

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R2adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R2adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, Bf: 0.998 to 1.047; accuracy factor, Af: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology.
A Web-Interface Based Decision Support System for Optimizing Home Healthcare Waste Collection Vehicle Routing
Authors: Sar Kubra , Ghadimi Pezhman
Published: November 2024

Background: The significant increase in home healthcare (HHC) driven by technological advancements, an ageing population, and heightened disease outbreaks—especially evident during the COVID-19 pandemic—has created an urgent need for improved medical waste management. Methods: This paper presents the development of a decision support system with a web-based interface designed for efficient medical waste collection in the HHC sector. Results: The system utilises Flask for backend operations, with HTML and CSS for the user interface, and manages data using JSON files. Its flexible design supports real-time adjustments for various vehicle types and changing waste production locations. It incorporates dynamic routing by employing two sophisticated metaheuristic algorithms: the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This setup supports different dataset sizes and vehicle fleets, including Internal Combustion Engine (ICE) vehicles and Electric Vehicles (EVs). Conclusions: The automation reduces uncertainties in waste collection by minimising human intervention. The system is built to be easily adaptable for other sectors with minor modifications and can be expanded to test various scenarios with new selectable parameters.
Optimal Control of Hydrocarbons’ Hydrogenation with Catalysts
Authors: Sakharov Maxim , Koledina Kamila , Gubaydullin Irek
Published: November 2024

In this paper, the optimal control problem of hydrocarbons’ hydrogenation was investigated in the presence of two catalysts—Nickel–Kieselguhr and Raney Nickel. This multistage chemical reaction holds significant practical importance, particularly in the production of high-density fuels. The optimal control problem was reformulated as a nonlinear global optimization problem and addressed using a modified Mind Evolutionary Computation algorithm. The proposed modifications include methods designed to ensure solution feasibility and ease of practical implementation. Using the proposed method, the performance of the two catalysts was compared under constant temperature conditions and with optimal control strategies. The results demonstrate that selecting an effective catalyst has a greater impact on the reaction’s efficiency than temperature control alone, with the Raney Nickel catalyst consistently outperforming the Nickel–Kieselguhr catalyst by at least 17%. Additionally, the optimization approach was applied to identify a new set of catalyst parameters. The newly obtained catalyst parameters allowed for the improvement of the results of the Raney Nickel catalyst by 18%. The results of all numerical experiments and implementation details are described in the paper.
Enhanced Semi-Supervised Medical Image Classification Based on Dynamic Sample Reweighting and Pseudo-Label Guided Contrastive Learning (DSRPGC)
Authors: Liu Kun , Liu Ji , Liu Sidong
Published: November 2024

In semi-supervised learning (SSL) for medical image classification, model performance is often hindered by the scarcity of labeled data and the complexity of unlabeled data. This paper proposes an enhanced SSL approach to address these challenges by effectively utilizing unlabeled data through a combination of pseudo-labeling and contrastive learning. The key contribution of our method is the introduction of a Dynamic Sample Reweighting strategy to select reliable unlabeled samples, thereby improving the model’s utilization of unlabeled data. Additionally, we incorporate multiple data augmentation strategies based on the Mean Teacher (MT) model to ensure consistent outputs across different perturbations. To better capture and integrate multi-scale features, we propose a novel feature fusion network, the Medical Multi-scale Feature Fusion Network (MedFuseNet), which enhances the model’s ability to classify complex medical images. Finally, we introduce a pseudo-label guided contrastive learning (PGC) loss function that improves intra-class compactness and inter-class separability of the model’s feature representations. Extensive experiments on three public medical image datasets demonstrate that our method outperforms existing SSL approaches, achieving 93.16% accuracy on the ISIC2018 dataset using only 20% labeled data, highlighting the potential of our approach to advance medical image classification under limited supervision.
A Matheuristic Approach Based on Variable Neighborhood Search for the Static Repositioning Problem in Station-Based Bike-Sharing Systems
Authors: Daza-Escorcia Julio Mario , Álvarez-Martínez David
Published: November 2024

In this paper, we study a novel static bike-sharing repositioning problem. There is a set of stations spread over a given area, each containing a number of operative bikes, damaged bikes, and free slots. The customers may pick up an operative bike from a station, use it, and return it to another station. Each station should have a target number of operative bikes to make it likely to meet customer demands. Furthermore, the damaged bikes should be removed from the stations. Given a fleet of available vehicles, the repositioning problem consists of designing the vehicles’ routes and calculating the number of operative (usable) and damaged (unusable) bikes that will be moved (loading instructions/loading policy) between stations and/or the depot. The objective is to minimize the weighted sum of the deviation from the target number of bikes for each station, the number of damaged bikes not removed, and the total time used by vehicles. To solve this problem, we propose a matheuristic based on a variable neighborhood search combined with several improving algorithms, including an integer linear programming model to optimize loading instructions. The algorithm was tested in instances based on real-world data and could find good solutions in reasonable computing times.
On the Evolution Operators of a Class of Linear Time-Delay Systems
Authors: De la Sen Manuel
Published: November 2024

This paper studies the properties of the evolution operators of a class of time-delay systems with linear delayed dynamics. The considered delayed dynamics may, in general, be time-varying and associated with a finite set of finite constant point delays. Three evolution operators are defined and characterized. The basic evolution operator is the so-called point delay operator, which generates the solution trajectory under point initial conditions at t0=0. Furthermore, this paper also considers the whole evolution operator and the delay strip evolution operator, which define the solution trajectory, respectively, at any time instant and along a strip of time whose size is that of the maximum delay. These operators are defined for any given bounded piecewise continuous function of initial conditions on an initialization time interval of measure being identical to the maximum delay. It is seen that the semigroup property of the time-invariant undelayed dynamics, which is generated by a C0-semigroup, becomes lost by the above evolution operators in the presence of the delayed dynamics. This fact means that the point evolution operator is not a strongly and uniformly continuous one-parameter semigroup, even if its undelayed part has a time-invariant associated dynamics. The boundedness and the stability properties of the time-delay system, as well as the strong and uniform continuity properties of the evolution operators, are also discussed.
Sample Inflation Interpolation for Consistency Regularization in Remote Sensing Change Detection
Authors: Jiang Zuo , Chen Haobo , Tang Yi
Published: November 2024

Semi-supervised learning has gained significant attention in the field of remote sensing due to its ability to effectively leverage both a limited number of labeled samples and a large quantity of unlabeled data. An effective semi-supervised learning approach utilizes unlabeled samples to enforce prediction consistency under minor perturbations, thus reducing the model’s sensitivity to noise and suppressing false positives in change-detection tasks. This principle underlies consistency regularization-based methods. However, while these methods enhance noise robustness, they also risk overlooking subtle but meaningful changes, leading to information loss and missed detections. To address this issue, we introduce a simple yet efficient method called Sample Inflation Interpolation (SII). This method leverages labeled sample pairs to mitigate the information loss caused by consistency regularization. Specifically, we propose a novel data augmentation strategy that generates additional change samples by combining existing supervised change samples with calculated proportions of change areas. This approach increases both the quantity and diversity of change samples in the training set, effectively compensating for potential information loss and reducing missed detections. Furthermore, to prevent overfitting, small perturbations are applied to the generated sample pairs and their labels. Experiments conducted on two public change detection (CD) datasets validate the effectiveness of our proposed method. Remarkably, even with only 5% of labeled training data, our method achieves performance levels that closely approach those of fully supervised learning models.
FEBE-Net: Feature Exploration Attention and Boundary Enhancement Refinement Transformer Network for Bladder Tumor Segmentation
Authors: Nie Chao , Xu Chao , Li Zhengping
Published: November 2024

The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. We propose FEBE-Net, which aims to effectively capture global and remote semantic features, preserve more local detail information, and provide clearer and more precise boundaries. Specifically, first, we use PVT v2 backbone to learn multi-scale global feature representations to adapt to changes in bladder tumor size and shape. Secondly, we propose a new feature exploration attention module (FEA) to fully explore the potential local detail information in the shallow features extracted by the PVT v2 backbone, eliminate noise, and supplement the missing fine-grained details for subsequent decoding stages. At the same time, we propose a new boundary enhancement and refinement module (BER), which generates high-quality boundary clues through boundary detection operators to help the decoder more effectively preserve the boundary features of bladder tumors and refine and adjust the final predicted feature map. Then, we propose a new efficient self-attention calibration decoder module (ESCD), which, with the help of boundary clues provided by the BER module, gradually and effectively recovers global contextual information and local detail information from high-level features after calibration enhancement and low-level features after exploration attention. Extensive experiments on the cystoscopy dataset BtAMU and five colonoscopy datasets have shown that FEBE-Net outperforms 11 state-of-the-art (SOTA) networks in segmentation performance, with higher accuracy, stronger robust stability, and generalization ability.
Image Processing Application for Pluripotent Stem Cell Colony Migration Quantification
Authors: Chibyshev Timofey , Krasnova Olga , Chabina Alina , Gursky Vitaly V. , Neganova Irina , Kozlov Konstantin
Published: November 2024

Human pluripotent stem cells (hPSCs) attract tremendous attention due to their unique properties. Manual extraction of trajectories of cell colonies in experimental image time series is labor intensive and subjective, thus the aim of the work was to develop a computer semi-automated protocol for colony tracking. The developed procedure consists of three major stages, namely, image registration, object detection and tracking. Registration using discrete Fourier transform and tracking based on the solution of a linear assignment problem was implemented as console programs in the Python 3 programming language using a variety of packages. Object detection was implemented as a multistep procedure in the ProStack in-house software package. The procedure consists of more than 40 elementary operations that include setting of several biologically relevant parameters, image segmentation and performing of quantitative measurements. The developed procedure was applied to the dataset containing bright-field images from time-lapse recording of the human embryonic cell line H9. The detection step took about 6 h for one image time series with a resolution of 2560 by 2160; about 1 min was required for image registration and trajectories extraction. The developed procedure was effective in detecting and analyzing the time series of images with “good” and “bad” phenotypes. The differences between phenotypes in the distance in pixels between the starting and finishing positions of trajectories, in the path length along the trajectory, and the mean instant speed and mean instant angle of the trajectories were identified as statistically significant by Mann–Whitney and Student’s tests. The measured area and perimeter of the detected colonies differed, on average, for different phenotypes throughout the entire time period under consideration. This result confirms previous findings obtained by analyzing static images.
Influence of Transfer Epidemiological Processes on the Formation of Endemic Equilibria in the Extended SEIS Model
Authors: Karimov Alexander R. , Solomatin Michael A. , Bocharov Alexey N.
Published: November 2024

In the present paper, a modification of the standard mean-field model is considered, allowing for the description of the formation of a dynamic equilibrium between infected and recovered persons in a population of constant size. The key point of this model is that it highlights two-infection transfer mechanisms depending on the physical nature of the contact between people. We separate the transfer mechanism related directly to the movement of people (the so-called transport processes) from the one occurring at zero relative speed of persons (the so-called social contacts). Under the framework of a physical chemical analogy, the dependencies for the infection transfer rate constants are proposed for both purely transport and social mechanisms of spread. These dependencies are used in discussing the formation of quasi-stationary states in the model, which can be interpreted as endemic equilibrium states. The stability of such endemic equilibria is studied by the method of Lyapunov function.
Interpretable Embeddings for Next Point-of-Interest Recommendation via Large Language Model Question–Answering
Authors: Chen Jiubing , Wang Haoyu , Shang Jianxin , Chaomurilige
Published: November 2024

Next point-of-interest (POI) recommendation provides users with location suggestions that they may be interested in, allowing them to explore their surroundings. Existing sequence-based or graph-based POI recommendation methods have matured in capturing spatiotemporal information; however, POI recommendation methods based on large language models (LLMs) focus more on capturing sequential transition relationships. This raises an unexplored challenge: how to leverage LLMs to better capture geographic contextual information. To address this, we propose interpretable embeddings for next point-of-interest recommendation via large language model question–answering, named QA-POI, which transforms the POI recommendation task into obtaining interpretable embeddings via LLM prompts, followed by lightweight MLP fine-tuning. We introduce question–answer embeddings, which are generated by asking LLMs yes/no questions about the user’s trajectory sequence. By asking spatiotemporal questions about the trajectory sequence, we aim to extract as much spatiotemporal information from the LLM as possible. During training, QA-POI iteratively selects the most valuable subset of potential questions from a set of questions to prompt the LLM for the next POI recommendation. It is then fine-tuned for the next POI recommendation task using a lightweight Multi-Layer Perceptron (MLP). Extensive experiments on two datasets demonstrate the effectiveness of our approach.
A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space
Authors: Luo Zheng , Mai Jiahao , Feng Caihong , Kong Deyao , Liu Jingyu , Ding Yunhong , Qi Bo , Zhu Zhanbo
Published: November 2024

The prediction and analysis of students’ academic performance are essential tools for educators and learners to improve teaching and learning methods. Effective predictive methods assist learners in targeted studying based on forecast results, while effective analytical methods help educators design appropriate educational content. However, in actual educational environments, factors influencing student performance are multidimensional across both temporal and spatial dimensions. Therefore, a student performance prediction and analysis method incorporating multidimensional spatiotemporal features has been proposed in this study. Due to the complexity and nonlinearity of learning behaviors in the educational process, predicting students’ academic performance effectively is challenging. Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. Initially, a multidimensional spatiotemporal feature dataset was constructed by combining three categories of features: students’ basic information, performance at various stages of the semester, and educational indicators from their places of origin (considering both temporal aspects, i.e., performance at various stages of the semester, and spatial aspects, i.e., educational indicators from their places of origin). Subsequently, six machine learning models were trained using this dataset to predict student performance, and experimental results confirmed their accuracy. Furthermore, SHAP analysis was utilized to extract factors significantly impacting the experimental outcomes. Subsequently, this study conducted data ablation experiments, the results of which proved the rationality of the feature selection in this study. Finally, this study proposed a feasible solution for guiding teaching strategies by integrating spatiotemporal multi-dimensional features in the analysis of student performance prediction in actual teaching processes.
Drone-Captured Wildlife Data Encryption: A Hybrid 1D–2D Memory Cellular Automata Scheme with Chaotic Mapping and SHA-256
Authors: Belazi Akram , Migallón Héctor
Published: November 2024

In contemporary wildlife conservation, drones have become essential for the non-invasive monitoring of animal populations and habitats. However, the sensitive data captured by drones, including images and videos, require robust encryption to prevent unauthorized access and exploitation. This paper presents a novel encryption algorithm designed specifically for safeguarding wildlife data. The proposed approach integrates one-dimensional and two-dimensional memory cellular automata (1D MCA and 2D MCA) with a bitwise XOR operation as an intermediate confusion layer. The 2D MCA, guided by chaotic rules from the sine-exponential (SE) map, utilizes varying neighbor configurations to enhance both diffusion and confusion, making the encryption more resilient to attacks. A final layer of 1D MCA, controlled by pseudo-random number generators, ensures comprehensive diffusion and confusion across the image. The SHA-256 hash of the input image is used to derive encryption parameters, providing resistance against plaintext attacks. Extensive performance evaluations demonstrate the effectiveness of the proposed scheme, which balances security and complexity while outperforming existing algorithms.
Off-Policy Temporal Difference Learning with Bellman Residuals
Authors: Yang Shangdong , Sun Dingyuanhao , Chen Xingguo
Published: November 2024

In reinforcement learning, off-policy temporal difference learning methods have gained significant attention due to their flexibility in utilizing existing data. However, traditional off-policy temporal difference methods often suffer from poor convergence and stability when handling complex problems. To address these issues, this paper proposes an off-policy temporal difference algorithm with Bellman residuals (TDBR). By incorporating Bellman residuals, the proposed algorithm effectively improves the convergence and stability of the off-policy learning process. This paper first introduces the basic concepts of reinforcement learning and value function approximation, highlighting the importance of Bellman residuals in off-policy learning. Then, the theoretical foundation and implementation details of the TDBR algorithm are described in detail. Experimental results in multiple benchmark environments demonstrate that the TDBR algorithm significantly outperforms traditional methods in terms of both convergence speed and solution quality. Overall, the TDBR algorithm provides an effective and stable solution for off-policy reinforcement learning with broad application prospects. Future research can further optimize the algorithm parameters and extend its application to continuous state and action spaces to enhance its applicability and performance in real-world problems.
A Filippov-Type Lemma for Stieltjes Differential Inclusions with Supremum
Authors: Satco Bianca
Published: November 2024

The aim of this paper is to generalize the classical Filippov lemma to the framework of Cauchy differential set-valued problems involving the supremum of the unknown function on a past interval and its Stieltjes derivative with respect to a left-continuous non-decreasing function. To highlight the wide spectrum of the result (coming from the fact that this setting covers the theories of ordinary differential or difference problems, impulsive problems or problems on time scales), as a consequence, a Filippov-type lemma for dynamic inclusions on time scales with supremum is obtained.
Impact of a Physical Exercise and Health Education Program on Metabolic Syndrome and Quality of Life in Postmenopausal Breast Cancer Women Undergoing Adjuvant Treatment with Aromatase Inhibitors
Authors: Cespedes Pedro , Martínez-Arnau Francisco M. , Torregrosa María Dolores , Cauli Omar , Buigues Cristina
Published: November 2024

Background and Objectives: Adjuvant treatment with aromatase inhibitors (AIs) in breast cancer (BC) survivors can cause adverse effects such as metabolic syndrome (MS) (insulin resistance, central obesity, atherogenic dyslipidemia, and hypertension) associated with morbidity and premature mortality. We evaluate the effect of a multimodal program based on physical exercise and health education on MS and health-related quality of life (QoL) in postmenopausal women with BC under AIs. Methods: A total of 56 postmenopausal women, diagnosed with BC, aged 60 years or older (mean age 67.2 years) and on hormonal treatment with AIs, were included in the multimodal physical exercise and health education program, and evaluated before and after their participation. The assessment of the five criteria of the MS included the following: waist circumference, high blood pressure, fasting glucose, triglycerides, and high-density lipoprotein cholesterol. Two main instruments were used to evaluate the impact of the intervention on QoL: the EORTC QLQ C30 (questionnaire for cancers in general) and the EORTC QLQ BR23 (specifically for breast cancer patients). The EuroQol 5D (EQ-5D) was also used to compare these results. Results: The percentage of women meeting the MS criteria was 37.7% at baseline and fell to 15.1% at 3 months after the intervention (p = 0.02). The intervention significantly reduced hypertension (p < 0.001), central obesity (p < 0.001), and the concentration of triglycerides (p = 0.016). No significant changes were observed in fasting glucose and HDL concentration. A statistically significant improvement was found in QoL (on both the QLQ30 and BR23 scales). A multivariate regression model analysis identified marital status (being married) (95% CI: 1.728–131.615, p = 0.014), and percentage of attendance at health education sessions (95% CI: 1.010–1.211, p = 0.029) as positive predictive variables of improvement in MS. Conclusions: The implementation of multimodal, community-based programs of physical exercise and health education improve the prevalence of MS and specific criteria of MS and QoL in postmenopausal women with breast cancer receiving AI treatment.
High-Entropy Alloy Laser Cladding with Cable-Type Welding Wire: Experimental Study and First-Principles Calculations
Authors: Wang Wenjun , Zheng Yifei , Cai Zhihui , Zheng Wenjian , Zhang Cai , Wang Yu , Zhao Zhiyong , Feng Daochen , Ma Yinghe , Yang Jianguo
Published: November 2024

The Co-Cr-Fe-Ni high-entropy alloy (HEA) is particularly suitable for preparing coatings due to its excellent comprehensive properties. In this study, we use the laser cladding method to prepare Co-Cr-Fe-Ni HEA coatings with Co-Cr-Fe-Ni cable-type welding wire (CTWW) as the filling material and investigated the dilution rates of the coatings by experimental studies and first-principles calculations. The dilution rate is reduced to about 50% by changing the wire feeding speed, and a Co-Cr-Fe-Ni HEA coating with near nominal composition was prepared by multi-layer cladding. The HEA coating with near nominal composition is successfully prepared in the fourth layer of cladding. The coating is dense and uniform, with good metallurgical bonding. The mechanical properties of the coating were explored using first-principles calculations. All four coatings exhibit a single face-centered cubic (FCC) phase with good mechanical stability in the ground state. The bulk modulus B, shear modulus G, and Young’s modulus E of the four layers of coatings are gradually decreasing from B = 202 GPa, G = 136 GPa, and E = 334 GPa to B = 239 GPa, G = 154 GPa, and E = 380 GPa. The brittleness of the coating shows a trend of first decreasing and then increasing, and the coating closest to the nominal composition has the highest brittleness.
Evaluation of Continuous GMA Welding Characteristics Based on the Copper-Plating Method of Solid Wire Surfaces
Authors: Kim Dong-Yoon , Yu Jiyoung
Published: November 2024

Gas metal arc welding (GMAW) is widely used in various industries, such as automotive and heavy equipment manufacturing, because of its high productivity and speed, with solid wires being selected based on the mechanical properties required for welded joints. GMAW consists of various components, among which consumables such as the contact tip and continuously fed solid wire have a significant impact on the weld quality. In particular, the copper-plating method can affect the conductivity and arc stability of the solid wire, causing differences in the continuous welding performance. This study evaluated the welding performance during 60 min continuous GMAW using an AWS A5.18 ER70S-3 solid wire, which was copper-plated using chemical plating (C-wire) and electroplating (E-wire). The homogeneity and adhesion of the copper-plated surface of the E-wire were superior to those of the C-wire. The E-wire exhibited better performance in terms of arc stability. The wear rate of the contact tip was approximately 45% higher when using the E-wire for 60 min of welding compared with the C-wire, which was attributed to the larger variation rate in the cast and helix in the E-wire. Additionally, the amount of spatter adhered to the nozzle during 60 min, with the E-wire averaging 5.9 g, approximately half that of the C-wire at 12.9 g. The E-wire exhibits superior arc stability compared with the C-wire based on the spatter amount adhered to the nozzle. This study provides an important reference for understanding the impact of copper plating methods and wire morphology on the replacement cycles of consumable welding parts in automated welding processes such as continuous welding and wire-arc additive manufacturing.
Effects of Deep Tillage on Rhizosphere Soil and Microorganisms During Wheat Cultivation
Authors: Sui Junkang , Wang Chenyu , Hou Feifan , Shang Xueting , Zhao Qiqi , Zhang Yuxuan , Hou Yongqiang , Hua Xuewen , Chu Pengfei
Published: November 2024

The production of wheat is fundamentally interconnected with worldwide food security. The practice of deep tillage (DT) cultivation has shown advantages in terms of soil enhancement and the mitigation of diseases and weed abundance. Nevertheless, the specific mechanisms behind these advantages are unclear. Accordingly, we aimed to clarify the influence of DT on rhizosphere soil (RS) microbial communities and its possible contribution to the improvement of soil quality. Soil fertility was evaluated by analyzing several soil characteristics. High-throughput sequencing techniques were utilized to explore the structure and function of rhizosphere microbial communities. Despite lowered fertility levels in the 0–20 cm DT soil layer, significant variations were noted in the microbial composition of the DT wheat rhizosphere, with Acidobacteria and Proteobacteria being the most prominent. Furthermore, the abundance of Bradyrhizobacteria, a nitrogen-fixing bacteria within the Proteobacteria phylum, was significantly increased. A significant increase in glycoside hydrolases within the DT group was observed, in addition to higher abundances of amino acid and carbohydrate metabolism genes in the COG and KEGG databases. Moreover, DT can enhance soil quality and boost crop productivity by modulating soil microorganisms’ carbon and nitrogen fixation capacities.
Zero-Valent Iron and Sand Filtration Reduces Levels of Cyclospora cayetanensis Surrogates, Eimeria tenella and Eimeria acervulina, in Water
Authors: Gutierrez Alan , Tucker Matthew S. , Yeager Christina , Fournet Valsin , Jenkins Mark C. , Dubey Jitender P. , Kniel Kalmia E. , Rosenthal Benjamin M. , Sharma Manan
Published: November 2024

Recurring outbreaks of cyclosporiasis linked to fresh produce demonstrate the need to develop interventions to reduce C. cayetanensis in irrigation water. C. cayetanensis is resistant to commonly used irrigation water treatments, such as chemical sanitizers, making removal of oocysts by filtration the most suitable intervention. This study evaluated the reduction of Eimeria tenella and E. acervulina, as surrogates for C. cayetanensis, in water using filters packed with sand alone or mixtures of sand and zero-valent iron (ZVI). Water inoculated with Eimeria spp. oocysts was filtered through laboratory-scale (PVC column) and field-scale (swimming pool filter) filters packed with either 100% sand or 50% ZVI/50% sand (v/v). Filtered and backflush water was examined microscopically for oocysts. Laboratory-scale filters with 50% ZVI significantly (p < 0.05) reduced 99.9% of E. tenella oocysts compared to 55.3% with filters containing 100% sand. At the field-scale level, 50% ZVI filters significantly (p < 0.05) reduced 70.5% of E. acervulina oocysts compared to 54.5% by 100% sand filters. Filters were backflushed to examine the recovery of these parasites during routine filter-media cleaning procedures. Backflush recovery of oocysts ranged from 4.42–16.7%. The addition of ZVI significantly improved the reduction of Eimeria spp. oocysts at both filter scales. and should be further investigated as a potential irrigation water intervention to reduce C. cayetanensis.