bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Common Prosperity Effect of Integrated Regional Expansion: An Empirical Study Based on the Yangtze River Delta
Authors: Li Mengfan , Wen Wanzhen , Ma Wenwu , Jin Yihang
Published: February 2025

Addressing the urban–rural income disparity and fostering coordinated urban–rural development pose critical challenges for China in pursuit of its common prosperity strategy during its new phase of development. Regional integration emerges as a pivotal policy tool, which is extensively utilized to facilitate regional development coordination and significantly contributing to overall regional economic growth. This study delves into whether the implementation of regional integration policies generates a common prosperity effect, thereby reducing the disparity in income levels between urban and rural regions. Utilizing city-level panel data spanning from 2000 to 2019 within the Yangtze River Delta region, we treat the expansion of regional integration as a quasi-natural experiment and employ a time-varying Difference-in-Differences model to identify the integration’s common prosperity effect. Furthermore, we leverage mediation effect models to unravel the mechanisms through which integration influences the urban–rural divide in income. Our findings reveal that the expansion of integrated regions contributes to narrowing the urban–rural income gap with these results remaining robust across multiple tests. Urbanization and marketization are pivotal mechanisms driving the reduction in the urban–rural income disparity in integrated regions. Additionally, heterogeneity analysis uncovers significant spatial and temporal variations in the urban–rural income gap narrowing effect of integration expansion. Specifically, over time, the effect transitions from a significant negative impact to an insignificant positive one, while spatially, significant negative effects are observed in Jiangsu and Zhejiang provinces, contrasting with insignificant positive effects in Anhui province. This study offers fresh perspectives on the nexus between regional integration and the urban–rural income disparity, laying a scientific groundwork to evaluate the impacts of urban agglomeration integration and optimize policies aimed at fostering regional integration and coordinated urban–rural development.
Spatial Variability in Soil Hydraulic Properties Under Different Vegetation Conditions in a Coastal Wetland
Authors: Zhang Yu , Wang Tiejun , Han Qiong , Zuo Yutao , Bai Qinling , Li Xun
Published: February 2025

Understanding the spatial variability in soil hydraulic properties (SHPs) and their influencing variables is critical for ecohydrological and biogeochemical studies in coastal wetlands, where complex landscapes make it challenging to accurately delineate the spatial patterns of SHPs. In this study, soil samples were collected from two transects covered by Suaeda salsa (S. salsa) and Phragmites australis (P. australis) from the Beidagang Wetland Nature Reserve in northern China, and a comprehensive dataset on soil physical properties and SHPs was obtained by laboratory experiments. The results showed that soil physical properties (e.g., soil particle size, bulk density (BD), and soil organic matter (SOM)) displayed significant spatial variability, which was related to the physiological characteristics of S. salsa and P. australis and to soil depth. As a result, SHPs, including saturated hydraulic conductivity (Ks) and parameters of the van Genuchten model (θs-saturated soil water content, including α, the reciprocal of the air-entry value, and n, the pore size distribution index) varied considerably along the two transects. Specifically, Ks, θs, and α were negatively correlated with BD and pH, while positively correlated with SOM, which promoted soil aggregation to enlarge soil pores. Soil depth was shown to significantly affect SHPs, whereas the differences in SHPs between the two transects were not statistically significant, suggesting vegetation type did not directly impact SHPs. Soil water retention capacities were noticeably higher in surface soils, especially when soil suctions were less than 1000 cm, whereas their differences between depths largely diminished with further increasing soil suctions. This study highlights the complex interplay of SHPs with surrounding environments, providing critical insight for characterizing the spatial patterns of SHPs in coastal wetlands.
Correction: Zafar et al. Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey. Life 2023, 13, 146
Authors: Zafar Mehwish , Sharif Muhammad Imran , Sharif Muhammad Irfan , Kadry Seifedine , Bukhari Syed Ahmad Chan , Rauf Hafiz Tayyab
Published: February 2025

In the published publication [...]
Distributed Estimation for ℓ0-Constrained Quantile Regression Using Iterative Hard Thresholding
Authors: Zhao Zhihe , Lian Heng
Published: February 2025

Distributed frameworks for statistical estimation and inference have become a critical toolkit for analyzing massive data efficiently. In this paper, we present distributed estimation for high-dimensional quantile regression with ℓ0 constraint using iterative hard thresholding (IHT). We propose a communication-efficient distributed estimator which is linearly convergent to the true parameter up to the statistical precision of the model, despite the fact that the check loss minimization problem with an ℓ0 constraint is neither strongly smooth nor convex. The distributed estimator we develop can achieve the same convergence rate as the estimator based on the whole data set under suitable assumptions. In our simulations, we illustrate the convergence of the estimators under different settings and also demonstrate the accuracy of nonzero parameter identification.
Predicting T Cell Mitochondria Hijacking from Tumor Single-Cell RNA Sequencing Data with MitoR
Authors: Jiang Anna , Lyu Chengshang , Zhao Yue
Published: February 2025

T cells play a crucial role in the immune system by identifying and eliminating tumor cells. Malignant cancer cells can hijack mitochondria (MT) from nearby T cells, affecting their metabolism and weakening their immune functions. This phenomenon, observed through co-culture systems and fluorescent labeling, has been further explored with the development of the MERCI algorithm, which predicts T cell MT hijacking in cancer cells using single-cell RNA (scRNA) sequencing data. However, MERCI is limited by its reliance on a linear model and its inability to handle data sparsity. To address these challenges, we introduce MitoR, a computational algorithm using a Poisson–Gamma mixture model to predict T cell MT hijacking from tumor scRNA data. In performance comparisons, MitoR demonstrated improved performance compared to MERCI’s on gold-standard benchmark datasets scRNA-bench1 (top AUROC: 0.761, top accuracy: 0.769) and scRNA-bench2 (top AUROC: 0.730, top accuracy: 0.733). Additionally, MitoR showed an average 4.14% increase in AUROC and an average 3.86% increase in accuracy over MERCI in all rank strategies and simulated datasets. Finally, MitoR revealed T cell MT hijacking events in two real-world tumor datasets (basal cell carcinoma and esophageal squamous-cell carcinoma), highlighting their role in tumor immune evasion.
Spatiotemporal Dynamics of Bacterial Community Assembly and Co-Occurrence Patterns in Biological Soil Crusts of Desert Ecosystems
Authors: Bao Runze , Tang Kai , Ji Yanfu , Zhang Shengnan , Wang Chunying , Liang Yungang , Zhao Xiujuan , Meng Jianyu
Published: February 2025

Biological soil crusts (BSCs) play a fundamental role in desert ecosystems by stabilizing soil, cycling nutrients, and retaining moisture. However, the assembly processes governing bacterial communities within BSCs remain largely unknown. This study aimed to reveal the spatiotemporal variations in the bacterial community diversity, co-occurrence patterns, and ecological assembly processes of BSCs and their underlying soils across different desert and seasonal conditions. We systematically analyzed the spatial differences in the bacterial diversity, co-occurrence networks, and community assembly processes of BSCs and their underlying soils using samples collected at various soil depths from different BSC types in different deserts. We discovered that BSC type and soil depth were the primary factors driving bacterial community assembly, while seasonal effects were weaker and more indirect, and mainly regulated community dynamics through changes in resource availability and environmental conditions. The underlying soils of moss- and lichen-BSCs exhibited higher bacterial diversity and richness than those of algae BSCs. In contrast, cyano-BSCs exhibited a lower diversity, but Cyanobacteria demonstrated the highest photosynthetic function. Among the different deserts, the community assembly of samples from the eastern Inner Mongolia deserts was largely influenced by environmental selection, whereas stochastic processes were more prominent in the central and western desert regions. A β-nearest taxon index (βNTI) analysis indicated that stochastic processes were dominant in surface BSC samples, while environmental selection played a stronger role in deeper layers. A co-occurrence network analysis revealed that surface BSC samples had a high degree of network connectivity, with those from moss- and lichen-BSCs being particularly high, and they also exhibited high modularity and local clustering that promoted the functional stability of the microbial communities. This study revealed the integrated effects of soil depth, BSC type, desert type, and resource availability on microbial community assembly in desert ecosystems. These findings provide a theoretical basis for the microbial management of BSCs and scientific insights to support restoration strategies in desert ecosystems.
Construction and Regulation of Polymer@Silica Microspheres with Double-Shell Hollow Structures
Authors: Jiang Mingxiu , Yang Yuanyuan , Feng Jiawei , Wang Zhaopan , Deng Wei
Published: February 2025

Microspheres with well-defined hollow structures have been attracting interest due to their unique morphology and fascinating properties. Herein, a strategy for morphology and size control of hollow polymer@silica microspheres is proposed. Multilayer core–shell polymer microspheres, containing substantial carboxyl groups inside, evolve into microspheres with a 304 nm hollow structure after alkali treatment, which are used to construct hollow polymer@silica microspheres by coating the inorganic layer using the layer-by-layer (LBL) and sol–gel methods, respectively. The inorganic shell thickness of hollow polymer@silica microspheres can be adjusted from 15 nm to 33 nm by the self-assembled layers in the LBL method and from 15 nm to 63 nm by the dosage of precursor in the sol–gel method. Compared to the LBL method, the hollow polymer@silica microspheres prepared via the sol–gel method have a uniform and dense inorganic shell, thus ensuring the complete spherical morphology of the microspheres after calcination, even if the inorganic shell thickness is only 15 nm. Moreover, the hollow polymer@silica microspheres prepared via the sol–gel method exhibit improved compression resistance and good opacity, remaining intact at 16,000 psi and providing the corresponding coating with transmittance lower than 35.1%. This work highlights the morphology regulation of microspheres prepared by different methods and provides useful insights for the design of composites microspheres with controllable structures.
Discovery of Sphingosine Kinase Inhibition by Modified Quinoline-5,8-Diones
Authors: Kruschel Ryan D. , Malone Kyle , Walsh Alison N. , Waeber Christian , McCarthy Florence O.
Published: February 2025

Background: Sphingosine kinase (SphK) overexpression is observed in many cancers, including breast, renal and leukaemia, which leads to increased cellular proliferation, survival and growth. SphK inhibition has been an attractive target for anticancer drug development for the past decade, with SphK inhibitors such as PF-543 and opaganib exhibiting clinical antitumour effects. By exploiting both CB5468139 and PF-543 as structural leads, we hereby report on the first quinoline-5,8-dione-based SphK inhibitor using a fragment-based approach. Methods: The quinoline-5,8-dione framework was developed to incorporate two defined regions, namely a polar quinoline core, which links to an aryl lipophilic chain. All synthetic molecules were characterized by NMR and HRMS and assayed against SphK 1 and 2, and molecular docking studies were performed. A subset of compounds was screened for anticancer activity. Results: As the binding site of SphK accommodates the lipophilic tail of sphingosine, we initially set out to explore the substitution of the C(7) aryl moiety to attain eight novel C(7) ether-linked quinoline-5,8-diones, which were screened for SphK1 and SphK2 activity with good potency identified. To improve SphK binding, structural fragments were adapted from PF-543 to participate in hydrogen bonding within the binding site of SphK1. A model study was performed to yield novel compounds through activated C(2) formyl intermediates. Two pyrrolidine-based quinoline-5,8-diones were assayed for SphK activity, with 21 revealing an improvement of SphK1 binding efficacy relative to the parent compound and 20 (and its precursor 4). Molecular modelling on the pyrrolidine quinoline-5,8-dione construct revealed favourable docking, low binding energies and opportunities for further improvement. Conclusions: Although the screening of anticancer activity was inconclusive, low micromolar dual SphK1/2 inhibition with the quinoline-5,8-dione framework has been identified for the first time, and a plausible new binding mode has been identified.
The Potential of Using Screening Tools for Bipolar Disorder to Predict Lithium Response
Authors: Janiri Delfina , Pinto Mario , Montanari Silvia , Marzo Ester Maria , Sfratta Greta , Donofrio Guglielmo , Koukopoulos Alexia , Camardese Giovanni , Simonetti Alessio , Moccia Lorenzo , Autullo Gianna , Sani Gabriele
Published: February 2025

Background/Objectives: Lithium is the gold standard for treating Bipolar Disorder (BD), but its effectiveness varies widely. While clinical and environmental factors may influence response, it remains unclear if screening tools can reliably predict lithium response outcomes. This study explores this potential using two widely used screening instruments for BD. Methods: A total of 146 patients with BD were evaluated. Lithium response was assessed using the Alda Scale, while hypomanic and manic symptoms were characterized through the Hypomania Checklist-32 (HCL-32) and the Mood Disorder Questionnaire (MDQ). Group differences in HCL-32 and MDQ scores were analyzed using ANOVA, and a multivariate model was employed to identify predictors of lithium response. Results: Of the total sample, 46 (31.5%) patients were identified as lithium responders based on the Alda Scale. Responders exhibited significantly higher HCL-32 scores compared to non-responders (p = 0.023), while no differences were observed in MDQ scores or other sociodemographic characteristics. Linear regression analysis revealed that HCL-32 scores were a significant predictor of Alda Scale scores, with no associations found for age, gender, or MDQ scores. Conclusions: Our study underscores the importance of considering hypomanic symptoms when estimating lithium response in BD, particularly by utilizing the HCL-32 during screening.
The Mentorship Blueprint: A Comprehensive Review for the Development of Programs in Pharmacy Education
Authors: Guy Jason W. , Smart Shelby , Harber Mollie , Oestreich Julie H.
Published: February 2025

Background: Mentorship has benefits for students and faculty, helping to support their professional development, connectedness, and career endeavors. While the value of mentorship programs is well documented in the literature, there is less practical guidance and few compiled resources to start a program. This paper reviews different mentorship practices in pharmacy education and provides a list of strategies to develop high-functioning mentorship programs or groups. Methods: A review of the literature was conducted through PubMed and other databases. If the titles and abstracts met the initial criteria for relevance to the topic, the complete article was reviewed in the context of the inclusion and exclusion criteria. Included articles focused on mentorship, mentorship programs, mentorship development, mentoring faculty or students, or mentoring in the workplace. Results: Twenty-three studies were included in the final review. Summaries and key points from the studies were reviewed and discussed. The advantages of mentorship programs include increased social connection, goal setting, and professional development. Challenges include increased time commitments and difficulty in determining objective markers of success. Critical components have been extracted from the literature, and key resources and templates have been provided to aid in mentorship program development. Conclusions: This review summarizes the pharmacy mentorship literature and provides user-friendly tables to quickly locate resources to build a mentorship program in pharmacy education.
Fulvic Acid, Amino Acids, and Vermicompost Enhanced Yield and Improved Nutrient Profile of Soilless Iceberg Lettuce
Authors: Keskin Beyza , Akhoundnejad Yelderem , Dasgan Hayriye Yildiz , Gruda Nazim S.
Published: February 2025

Soilless cultivation systems are sustainable innovations in modern agriculture, promoting high efficiency per unit area, supporting food sustainability, and addressing the growing demand for high-quality produce with minimal environmental impact. This study evaluates the effects of fulvic acid, amino acid, and vermicompost biostimulants on the growth, yield, and nutrient profile of soilless-grown iceberg lettuce (Lactuca sativa var. capitata) in floating culture under controlled glasshouse conditions. Two experiments were conducted to determine the most effective concentrations and combinations of biostimulants. In the first experiment, varying doses of fulvic acid (40 and 80 ppm), amino acid (75 and 100 ppm), and vermicompost (1 and 2 mL L−1) were tested alongside a control. Optimal doses were identified based on their positive effects on lettuce growth and yield. The second experiment examined combinations of fulvic acid, amino acid, and vermicompost extract compared to a control. Biostimulants improved lettuce growth, nutrient uptake, and antioxidants. Vermicompost boosted root biomass and leaf area, while fulvic acid and amino acid reduced nitrates and increased dry matter. Fulvic acid and vermicompost resulted in the highest yield (17.15 kg/m2, 18.2% increase), and the combined treatment maximized antioxidants, increasing vitamin C by 17.16%, total phenols by 52.54%, and flavonoids by 52.38%. These findings highlight the potential of biostimulants as eco-friendly solutions for optimizing lettuce production in soilless systems.
Calibration and Validation of the CSM-CROPGRO-Peanut Model Under Mulched Drip Irrigation Conditions in Xinjiang
Authors: Chen Junwei , Li Qiang , Zhang Xiaopei , Dong Jianshu , Hou Xianfei , Miao Haocui , Li Haiming , Zhang Yuchao , Shen Xiaojun , Si Zhuanyun , Shan Zhijie
Published: February 2025

In order to explore the applicability of the peanut growth simulation model CSM-CROPGRO-Peanut under conditions of mulched drip irrigation in Xinjiang, and to determine the optimal scenario for parameter estimation and model validation, field experiments were conducted in 2022 and 2023 on the water and nitrogen regulation of peanut. Based on the water requirements during the stages of peanut growth, three irrigation levels (low, medium, and high) and two nitrogen application levels (100% N and 50% N) were set, resulting in six treatments. An additional control treatment (CK) with a medium irrigation level and no nitrogen application was also included. In this study, four different parameter estimation and validation protocols were designed, and different parameter estimation results were obtained using the DSSAT-GLUE parameter estimation module. The results showed that the FL-SH (time between first flower and first pod), FL-SD (time between first flower and first seed), SIZLF (time between first flower and first seed), XFRT (maximum size of full leaf), and WTPSD (maximum weight per seed) parameters exhibited strong variability, with coefficients of variation of 24.33%, 22.9%, 19.78%, 14.47%, and 23.82%, respectively, and were significantly affected by environment–management interactions. Other parameters showed weaker variability, with coefficients of variation that were all less than 10%. The model outputs varied significantly among different parameter estimation protocols. Scenario 3, which used data from the adequate irrigation and adequate fertilization treatment (W3N2) environment across both years for parameter estimation and data from other treatments for validation, showed the highest model calibration and validation accuracy. The average absolute relative error (ARE) and normalized root mean square error (nRMSE) for model calibration and validation were the lowest at 9.1% and 10.1%, respectively. The CSM-CROPGRO-Peanut model effectively simulated peanut growth and development as well as soil moisture dynamics under mulched drip irrigation conditions in Xinjiang, with the highest simulation accuracy observed under full irrigation conditions. The findings provide a basis for using the CSM-CROPGRO-Peanut model to develop suitable irrigation and nitrogen application regimes for peanuts under mulched drip irrigation in Xinjiang.
Unveiling Drought Tolerant Cotton Genotypes: Insights from Morpho-Physiological and Biochemical Markers at Flowering
Authors: Shani Muhammad Yousaf , Ashraf Muhammad Yasin , Ramzan Muhammad , Khan Zafran , Batool Nimra , Gul Nimra , Bauerle William L.
Published: February 2025

Drought stress substantially restricts cotton growth, decreasing cotton production potential worldwide. This study evaluated cotton genotypes at the flowering stage to identify drought-resilient genotypes under moderate and severe drought conditions using physio-morphic and biochemical markers. Five genotypes were examined in a completely randomized design with three replicates across three treatments. Growth and biochemical traits were measured after 14 days of drought stress. The Multi-trait Genotype–Ideotype Distance Index (MGIDI) identified the most drought-tolerant genotypes. Severe drought had a pronounced negative effect on growth and biochemical traits, followed by moderate drought. Among the genotypes, FH-912 exhibited the strongest resilience, with significant increases in proline, peroxidase, catalase, and total chlorophyll. In contrast, chlorophyll a and transpiration rates were largely unaffected. Genotypes VH-351, VH-281, and GH-99 showed moderate drought tolerance, while FH-556 was highly sensitive to water stress. Statistical analyses, including ANOVA, PCA, and heatmaps, confirmed FH-912’s superior performance under drought stress. The drought-resilient genotype, FH-912, holds promise for breeding drought-tolerant cotton varieties to sustain cotton productivity in water-limited environments, especially in drought-prone regions.
Plastic Devolatilisation Kinetics During Isothermal High-Temperature Pyrolysis: Focus on Solid Products (Part I)
Authors: Kiminaitė Ieva , Wilhelm Sebastian , Martetschläger Lukas , Eckert Clara Leonie Brigitte , Berenguer Casco Marcos , Striūgas Nerijus , Fendt Sebastian
Published: February 2025

Incineration remains Europe’s main practice for plastic packaging waste treatment, primarily due to the limitations of mechanical recycling technology. Consequently, research and development of more sustainable and flexible approaches are of high importance. Thermochemical conversion of polypropylene, polystyrene, and municipal plastic packaging mix via high-temperature flash pyrolysis (1000 °C/s) is studied in this research, focusing on the kinetics and yields of the devolatilisation stage. The primary stage results in the formation of volatile organic compounds considered intermediate products for carbon black production. The experiments were conducted in a pressurised wire mesh reactor, investigating the influence of temperature (600–1200 °C), residence time (0.5–10 s), and pressure (1–25 bar). The positive effect of temperature on the volatile yield was observed up to 2–5 s. The devolatilisation stage was completed within a maximum of 5 s at temperatures ranging from 800 to 1200 °C. The pressure was determined to be a kinetically limiting factor of the process to up to 800 °C, and the effect was not present at ≥1000 °C. Raman spectroscopy measurements revealed that pyrolytic carbon deposited on the post-experimental meshes is structurally similar to the industrially produced carbon black. The kinetic data and developed model can be further applied in the upscale reactor design.
Statistical Analysis of the Effect of Simulation Time on the Results of Molecular Dynamics Studies of Food Proteins: A Study of the Ara h 6 Peanut Protein
Authors: Smith Andrea , Raghavan Vijaya
Published: February 2025

GROMACS MD simulations of food proteins and processes are often run over relatively short simulation lengths due to their high computational power demand. As long-timescale simulations are not always feasible, the purpose of this study was to determine, statistically, how simulation time affects conclusions drawn from GROMACS MD studies of food proteins. The Ara h 6 peanut allergen, undergoing heat processing at 300 K, 350 K, 400 K and 450 K, was used as the model in this study, and 2 ns, 20 ns and 200 ns GROMACS MD simulation lengths were investigated. The statistical analysis performed, using both one-way and two-way ANOVA tests, suggested that, depending on the selected simulation length, different final conclusions may be drawn regarding the effect that thermal processing temperature has on the geometric features of the Ara h 6 allergen. This was observed for many of the geometric features used to characterize the Ara h 6 allergen in this study, including RMSD, Rg, total number of intra-peptide hydrogen bonds and SASA. An inadequate sample size was, however, identified as a major limitation in this study.
Adolescent Aggression: A Narrative Review on the Potential Impact of Violent Video Games
Authors: Borrego-Ruiz Alejandro , Borrego Juan J.
Published: February 2025

Background: Exposure to violent content through video games can shape perceptions of aggression as normative or acceptable, potentially desensitizing adolescents to violence and increasing the likelihood that they will engage in aggressive behavior in their real-world interactions. This narrative review examines the relationship between violent video games (VVGs) and youth aggression, addressing (i) VVG exposure through the lens of Social Learning Theory and the General Aggression Model; (ii) the biological determinants of aggressive behavior, including neurophysiological correlates and the potential modulatory role of the gut microbiome; (iii) and current evidence on the influence of VVG exposure on adolescent aggression. Results: VVG exposure may promote moral disengagement and reinforce aggressive behavior through rewarding violent actions, potentially leading to real-life aggression. Exposure to VVGs increases aggression by elevating cortisol levels, activating the sympathetic nervous system, stimulating the brain’s reward system, and disrupting neurotransmitter balance. Alterations in gut microbiome composition could occur as a result of engaging in VVGs, potentially mirroring the physiological responses observed in violent real-world activities. The relationship between VVGs and aggression among adolescents has been widely explored, with studies generally indicating that VVG exposure can lead to desensitization to violence, reduced empathy, and diminished prosocial behavior. VVGs have also been linked to both bullying perpetration and victimization dynamics among adolescents, contributing to mental health issues, which in turn exacerbate problematic gaming behaviors. Conclusions: While there is evidence suggesting that exposure to VVGs is associated with increased adolescent aggression, discrepancies in the literature highlight the need for further research aimed at improving our understanding of this phenomenon. The studies on this topic reveal significant limitations, including a predominance of cross-sectional designs, methodological shortcomings, small sample sizes, and challenges in generalizing results. Factors such as the developmental stages of the participants, the exclusion of females, and unmeasured variables during long follow-up periods further complicate interpretations. Moreover, parental responsibility and the effectiveness of age ratings require closer examination.
Smart Organization of Imbalanced Traffic Datasets for Long-Term Traffic Forecasting
Authors: Kara Mustafa M. , Turkmen H. Irem , Guvensan M. Amac
Published: February 2025

Predicting traffic speed is an important issue, especially in urban regions. Precise long-term forecasts would enable individuals to conserve time and financial resources while diminishing air pollution. Despite extensive research on this subject, to our knowledge, no publications investigate or tackle the issue of imbalanced datasets in traffic speed prediction. Traffic speed data are often biased toward high numbers because low traffic speeds are infrequent. The temporal aspect of traffic carries two important factors for low-speed value. The daily population movement, captured by the time of day, and the weather data, recorded by month, are both considered in this study. Hour-wise Pattern Organization and Month-wise Pattern Organization techniques were devised, which organize the speed data using these two factors as a metric with a view to providing a superior representation of data characteristics that are in the minority. In addition to these two methods, a Speed-wise Pattern Organization strategy is proposed, which arranges train and test samples by setting boundaries on speed while taking the volatile nature of traffic into consideration. We evaluated these strategies using four popular model types: long short-term memory (LSTM), gated recurrent unit networks (GRUs), bi-directional LSTM, and convolutional neural networks (CNNs). GRU had the best performance, achieving a MAPE (Mean Absolute Percentage Error) of 13.51%, whereas LSTM demonstrated the lowest performance, with a MAPE of 13.74%. We validated their robustness through our studies and observed improvements in model accuracy across all categories. While the average improvement was approximately 4%, our methodologies demonstrated superior performance in low-traffic speed scenarios, augmenting model prediction accuracy by 11.2%. The presented methodologies in this study are applied in the pre-processing steps, allowing their application with various models and additional pre-processing procedures to attain comparable performance improvements.
A Study on Systematic Improvement of Transformer Models for Object Pose Estimation
Authors: Lee Jungwoo , Suh Jinho
Published: February 2025

Transformer architecture, initially developed for natural language processing and time series analysis, has been successfully adapted to various generative models in several domains. Object pose estimation, which uses images to determine the 3D position and orientation of an object, is essential for tasks such as robotic manipulation. This study introduces a transformer-based deep learning model for object pose estimation in computer vision, which determines the 3D position and orientation of objects from images. A baseline model derived from an encoder-only transformer faces challenges with high GPU memory usage when handling multiple objects. To improve training efficiency and support multi-object inference, it reduces memory consumption by adjusting the transformer’s attention layer and incorporates low-rank weight decomposition to decrease parameters. Additionally, GQA and RMS normalization enhance multi-object pose estimation performance, resulting in reduced memory usage and improved training accuracy. The improved model implementation with an extended matrix dimension reduced the GPU memory usage to only 2.5% of the baseline model, although it increased the number of model weight parameters. To mitigate this, the number of weight parameters was reduced by 28% using low-rank weight decomposition in the linear layer of attention. In addition, a 17% improvement in rotation training accuracy over the baseline model was achieved by applying GQA and RMS normalization.
Deep Reinforcement Learning-Based Routing Method for Low Earth Orbit Mega-Constellation Satellite Networks with Service Function Constraints
Authors: Chen Yan , Cao Huan , Wang Longhe , Chen Daojin , Liu Zifan , Zhou Yiqing , Shi Jinglin
Published: February 2025

Low-orbit satellite communication networks have gradually become the research focus of fifth-generation (5G) beyond and sixth generation (6G) networks due to their advantages of wide coverage, large communication capacity, and low terrain influence. However, the low earth orbit mega satellite network (LEO-MSN) also has difficulty in constructing stable traffic transmission paths, network load imbalance and congestion due to the large scale of network nodes, a highly complex topology, and uneven distribution of traffic flow in time and space. In the service-based architecture proposed by 3GPP, the introduction of service function chain (SFC) constraints exacerbates these challenges. Therefore, in this paper, we propose GDRL-SFCR, an end-to-end routing decision method based on graph neural network (GNN) and deep reinforcement learning (DRL) which jointly optimize the end-to-end transmission delay and network load balancing under SFC constraints. Specifically, this method constructs the system model based on the latest NTN low-orbit satellite network end-to-end transmission architecture, taking into account the SFC constraints, transmission delays, and network node loads in the end-to-end traffic transmission, uses a GNN to extract node attributes and dynamic topology features, and uses the DRL method to design specific reward functions to train the model to learn routing policies that satisfy the SFC constraints. The simulation results demonstrate that, compared with graph theory-based methods and reinforcement learning-based methods, GDRL-SFCR can reduce the end-to-end traffic transmission delay by more than 11.3%, reduce the average network load by more than 14.1%, and increase the traffic access success rate and network capacity by more than 19.1% and two times, respectively.
Systematic Analysis of Driving Modes and NiFe Layer Thickness in Planar Hall Magnetoresistance Sensors
Authors: Jeon Changyeop , Kim Mijin , Kim Jinwoo , Yang Sunghee , Choi Eunseo , Lim Byeonghwa
Published: February 2025

Planar Hall magnetoresistance (PHMR) sensors are widely utilized due to their high sensitivity, simple structure, and cost-effectiveness. However, their performance is influenced by both the driving mode and the thickness of the ferromagnetic layer, yet the combined effects of these factors remain insufficiently explored. This study systematically investigates the impact of Ni80Fe20 thickness (5–35 nm) on PHMR sensor performance under constant current (CC) and constant voltage (CV) modes, with a focus on optimizing the peak-to-peak voltage (Vp-p). In CC mode, electron surface scattering at 5–10 nm increases resistance, leading to a sharp rise in Vp-p, followed by a decline as the thickness increases. In contrast, CV mode minimizes resistance-related effects, with sensor signals predominantly governed by magnetization-dependent resistivity. Experimentally, the optimal Vp-p was observed at 25 nm in CV mode. However, for thicknesses beyond this point, the reduction in sensor resistance suggests that voltage distribution across both the sensor and external load resistance significantly influences performance. These findings provide practical insights into optimizing PHMR sensors by elucidating the interplay between driving modes and material properties. The results contribute to the advancement of high-performance PHMR sensors with enhanced signal stability and sensitivity for industrial and scientific applications.
Numerical Simulation Study on the Impact of Blind Zones in Ground Penetrating Radar
Authors: Wang Wentian , Du Wei , Cheng Siyuan , Zhuo Jia
Published: February 2025

Ground-penetrating radar (GPR) is an effective geophysical method for rapid and non-destructive detection. Directional borehole radar is the application of GPR in a borehole, which can determine the depth, orientation, and distance of the target from the borehole. The borehole radar azimuth recognition algorithm is based on the assumption of far-field plane waves. Therefore, in the near-field area where the target is closer to the borehole, the electromagnetic waves reflected by the target cannot be regarded as plane waves but will have a certain curvature. The plane wave assumption is not valid in this area, so the azimuth recognition algorithm will have significant errors, forming blind zones for directional borehole radar detection. This article uses the finite-difference time-domain (FDTD) algorithm to numerically simulate how blind zones affect directional borehole radar systems, identify the impact patterns, and minimize them. After calculation and numerical simulation verification, it has been found that when the center frequency of the antenna is 1 GHz, within 2 m of the target from the borehole, there is a significant error in azimuth recognition, which can be defined as the near-field region. Similarly, through numerical simulation verification, the optimal antenna center frequency is between 600 MHz and 1100 MHz. Oil-based mud is superior to water-based mud. The optimal antenna center frequency decreases as the target distance increases.
Data Transfer Reliability from Building Information Modeling (BIM) to Life Cycle Assessment (LCA)—A Comparative Case Study of an Industrial Warehouse
Authors: Fernández Rodríguez Juan Francisco , Picardo Alberto , Aguilar-Planet Teresa , Martín-Mariscal Amanda , Peralta Estela
Published: February 2025

The automation of environmental assessment processes aimed at reducing the ecological footprint of industrial facilities, buildings, and infrastructure is one of the main challenges currently faced by the construction sector. In this context, Building Information Modeling (BIM) is a comprehensive methodology that enables the creation of digital models, facilitating the analysis of environmental performance throughout the life cycle of built assets. In addition to the capabilities offered by BIM, specialized tools for environmental impact analysis implement the standardized life cycle assessment (LCA) methodology. However, one of the current limitations is the integration of BIM models and LCA tools. Few software solutions enable automated data transfer, complicating the environmental assessment process. The objective of this study is to evaluate the reliability of data transfer from BIM models to LCA tools, using an industrial warehouse as a case study. The research compares two LCA tools: Athena Impact Estimator, specialized in the construction sector, and SimaPro, a professional tool with advanced capabilities. This study is structured in two phases: (i) the development of a BIM model in Revit, including the definition of structural and functional components and the export of data to LCA tools, and (ii) the execution of the life cycle assessment in compliance with ISO 14040 and EN 15804 standards. The results show methodological and interoperability differences between the two tools, highlighting their strengths and limitations in terms of the precision of the results, resource consumption, training and expertise requirements, scope of environmental impact calculations, and adaptability to the construction sector in the integration of an LCA with a BIM model.
Lagged and Instantaneous Effects Between Vegetation and Surface Water Storage in the Yellow River Basin
Authors: Teng Jian , Chang Jun , Zhai Yongbo , Qin Xiaomin , Yin Zuotang , Guo Liangjie , Liu Wei
Published: February 2025

In recent years, large-scale afforestation in the Yellow River Basin (YRB) has attracted widespread attention due to its significant impact on surface water, playing a crucial role in the ecological sustainability and high-quality development of the basin. In this study, we used a combination of Theil–Sen and Mann–Kendall trend analysis to detect the spatiotemporal dynamic changes of NDVI, surface water storage (SWS), and its components in the YRB from 2001 to 2020, and explored the time lag and instantaneous effects between them using methods such as cross-correlation. The results show that from 2001 to 2020, NDVI and SWS in the YRB increased at rates of 0.41%/year and 1.95 mm/year, respectively, with fluctuations. Spatially, NDVI exhibited a significant upward trend in most areas of the YRB, while regions with significant increases in SWS, canopy surface water (CSW), snow water equivalent (SWE), and soil moisture (SM) were primarily located in the upper reaches. There was a time lag effect of about 2 months between NDVI and SWS in the YRB, and the time lags between SWE, SM, and NDVI were 5 months and 2 months, respectively. Except for CSW, the lag between NDVI and SWE was longer than that between NDVI and SWS or SM across all land cover types. Regarding the instantaneous effect, we found that the effect of vegetation on SWS in the upstream area is mainly the water storage function. In some areas of the middle and lower reaches, vegetation intensifies the consumption of SWS. Our study provides valuable insights into the response mechanism between vegetation restoration and SWS changes, facilitating better coordination between water resource management and ecological conservation in the YRB, thereby achieving sustainable regional economic and ecological development.
Prediction of Construction Waste Generation in China Based on Grey Model and Management Recommendations
Authors: Gao Xiuxiu , Yuan Ying , Wang Yizhi , Yang Ting , Chen Tan
Published: February 2025

As urbanization and construction activities in China continue to accelerate, the management of construction waste has become crucial. This study comprehensively investigated the current status and challenges in construction waste management in China. Through the application of building area estimation methodology combined with the Grey Prediction GM (1,1) model, we analyzed historical waste generation patterns from 2000 to 2022 and projected future trends for the next 10 years. The results revealed significant regional disparities in waste generation, with the East China region contributing over 50% of the national total, while maintaining continuous growth. National construction waste generation is projected to reach 3.084 billion tons in 2032, highlighting escalating management challenges. This study identified several critical issues in China’s current management system, including incomplete statistical data, weak implementation of source reduction measures, underdeveloped classification systems, and a notably low resource utilization rate (below 10% as of 2022). Drawing on successful international practices and domestic pilot experiences, we proposed a comprehensive management framework emphasizing full-process supervision, enhanced data collection systems, improved classification management, advanced resource utilization technologies, and strengthened policy mechanisms. These proposals will foster the development of sustainable construction waste management in China’s transition, in parallel with the realization of circular economy principles within the construction sector.
Exploration and Deconstruction of Correlation Cycles in Multidimensional Datasets
Authors: Dudáš Adam , Kršák Emil , Kvaššay Miroslav
Published: February 2025

Correlation analysis is one of the most prolific statistical methods used in data analysis problems, mining of knowledge focused on relationships of attributes in large datasets, and in various predictive tasks utilizing statistical, machine learning, and deep learning models. This approach to the analysis of functional relationships in multidimensional datasets is commonly used in conjunction with visual analysis approaches, which offer novel context for the relationships in data and clarify the results presented in large correlation matrices. One of such visualization methods uses graphical models called correlation graphs and chains, which visualize individual direct and indirect relationships between pairs of attributes in a dataset of interest as a graph structure, where vertices of the graph represent attributes of the dataset and edges between vertices represent the correlation of these attributes. This work focuses on the definition, identification, and exploration of so-called correlation cycles, which can be—through their deconstruction—used as an approach to lower error values in regression tasks. After the implementation of the correlation cycle identification and deconstruction, the proposed concept is evaluated on predictive analysis tasks in the context of three benchmarking datasets from the engineering field—the Sensor dataset, Superconductivity dataset, and Energy Farm dataset. The results obtained in this study show that when using simple, explainable regressors, the method utilizing deconstructed correlation cycles reaches a lower error rate in 83.3% of regression cases compared to the same regression models without the cycle incorporation.
Accelerated and Energy-Efficient Galaxy Detection: Integrating Deep Learning with Tensor Methods for Astronomical Imaging
Authors: Farias Humberto , Damke Guillermo , Solar Mauricio , Jaque Arancibia Marcelo
Published: February 2025

Addressing the astronomical challenges posed by the interplay of data volume, AI sophistication, and energy consumption is crucial for the future of astronomy. As astronomical surveys continue to produce vast amounts of data, the computational and energy demands for galaxy classification have escalated, necessitating more efficient and sustainable approaches. This study presents a novel application of tensor factorization within the Faster R-CNN framework, resulting in the development of our model, T-Faster R-CNN, designed to enhance both the energy efficiency and computational performance of deep learning models used in galaxy classification. By integrating tensor factorization, our T-Faster R-CNN significantly reduces the model’s complexity, memory footprint, and CO2 emissions, while maintaining, and in some cases even improving, the accuracy of morphological classification. The effectiveness of this optimized model is validated using data from the Galaxy Zoo DECaLS, where it demonstrates substantial improvements in computational efficiency without compromising classification precision. Furthermore, this research incorporates green code principles, emphasizing reductions in energy consumption and environmental impact in computational astronomy. The T-Faster R-CNN model offers a resource-efficient, sustainable methodology for analyzing large-scale astronomical data, addressing the critical need for greener computational practices in the era of big data.