bluuebunny/crossref_metadata_2025 huggingface dataset:
Cross-Modality Data Augmentation for Aerial Object Detection with Representation Learning
Authors: Wei Chiheng , Bai Lianfa , Chen Xiaoyu , Han Jing
Published: December 2024

Data augmentation methods offer a cost-effective and efficient alternative to the acquisition of additional data, significantly enhancing data diversity and model generalization, making them particularly favored in object detection tasks. However, existing data augmentation techniques primarily focus on the visible spectrum and are directly applied to RGB-T object detection tasks, overlooking the inherent differences in image data between the two tasks. Visible images capture rich color and texture information during the daytime, while infrared images are capable of imaging under low-light complex scenarios during the nighttime. By integrating image information from both modalities, their complementary characteristics can be exploited to improve the overall effectiveness of data augmentation methods. To address this, we propose a cross-modality data augmentation method tailored for RGB-T object detection, leveraging masked image modeling within representation learning. Specifically, we focus on the temporal consistency of infrared images and combine them with visible images under varying lighting conditions for joint data augmentation, thereby enhancing the realism of the augmented images. Utilizing the masked image modeling method, we reconstruct images by integrating multimodal features, achieving cross-modality data augmentation in feature space. Additionally, we investigate the differences and complementarities between data augmentation methods in data space and feature space. Building upon existing theoretical foundations, we propose an integrative framework that combines these methods for improved augmentation effectiveness. Furthermore, we address the slow convergence observed with the existing Mosaic method in aerial imagery by introducing a multi-scale training strategy and proposing a full-scale Mosaic method as a complement. This optimization significantly accelerates network convergence. The experimental results validate the effectiveness of our proposed method and highlight its potential for further advancements in cross-modality object detection tasks.
Drone-Based Wildfire Detection with Multi-Sensor Integration
Authors: Abdusalomov Akmalbek , Umirzakova Sabina , Bakhtiyor Shukhratovich Makhkamov , Mukhiddinov Mukhriddin , Kakhorov Azamat , Buriboev Abror , Jeon Heung Seok
Published: December 2024

Wildfires pose a severe threat to ecological systems, human life, and infrastructure, making early detection critical for timely intervention. Traditional fire detection systems rely heavily on single-sensor approaches and are often hindered by environmental conditions such as smoke, fog, or nighttime scenarios. This paper proposes Adaptive Multi-Sensor Oriented Object Detection with Space–Frequency Selective Convolution (AMSO-SFS), a novel deep learning-based model optimized for drone-based wildfire and smoke detection. AMSO-SFS combines optical, infrared, and Synthetic Aperture Radar (SAR) data to detect fire and smoke under varied visibility conditions. The model introduces a Space–Frequency Selective Convolution (SFS-Conv) module to enhance the discriminative capacity of features in both spatial and frequency domains. Furthermore, AMSO-SFS utilizes weakly supervised learning and adaptive scale and angle detection to identify fire and smoke regions with minimal labeled data. Extensive experiments show that the proposed model outperforms current state-of-the-art (SoTA) models, achieving robust detection performance while maintaining computational efficiency, making it suitable for real-time drone deployment.
SSUM: Spatial–Spectral Unified Mamba for Hyperspectral Image Classification
Authors: Lu Song , Zhang Min , Huo Yu , Wang Chenhao , Wang Jingwen , Gao Chenyu
Published: December 2024

How to effectively extract spectral and spatial information and apply it to hyperspectral image classification (HSIC) has been a hot research topic. In recent years, the transformer-based HSIC models have attracted much interest due to their advantages in long-distance modeling of spatial and spectral features in hyperspectral images (HSIs). However, the transformer-based method suffers from high computational complexity, especially in HSIC tasks that require processing large amounts of data. In addition, the spatial variability inherent in HSIs limits the performance improvement of HSIC. To handle these challenges, a novel Spectral–Spatial Unified Mamba (SSUM) model is proposed, which introduces the State Space Model (SSM) into HSIC tasks to reduce computational complexity and improve model performance. The SSUM model is composed of two branches, i.e., the Spectral Mamba branch and the Spatial Mamba branch, designed to extract the features of HSIs from both spectral and spatial perspectives. Specifically, in the Spectral Mamba branch, a nearest-neighbor spectrum fusion (NSF) strategy is proposed to alleviate the interference caused by the spatial variability (i.e., same object having different spectra). In addition, a novel sub-spectrum scanning (SS) mechanism is proposed, which scans along the sub-spectrum dimension to enhance the model’s perception of subtle spectral details. In the Spatial Mamba branch, a Spatial Mamba (SM) module is designed by combining a 2D Selective Scan Module (SS2D) and Spatial Attention (SA) into a unified network to sufficiently extract the spatial features of HSIs. Finally, the classification results are derived by uniting the output feature of the Spectral Mamba and Spatial Mamba branch, thus improving the comprehensive performance of HSIC. The ablation studies verify the effectiveness of the proposed NSF, SS, and SM. Comparison experiments on four public HSI datasets show the superior of the proposed SSUM.
An Unsupervised Remote Sensing Image Change Detection Method Based on RVMamba and Posterior Probability Space Change Vector
Authors: Song Jiaxin , Yang Shuwen , Li Yikun , Li Xiaojun
Published: December 2024

Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods.
Research on Downhole MTATBOT Positioning and Autonomous Driving Strategies Based on Odometer-Assisted Inertial Measurement
Authors: Hao Mingrui , Yuan Xiaoming , Ren Jie , Bi Yueqi , Ji Xiaodong , Zhao Sihai , Wu Miao , Shen Yang
Published: December 2024

In response to the current situation of backward automation levels, heavy labor intensities, and high accident rates in the underground coal mine auxiliary transportation system, the mining trackless auxiliary transportation robot (MTATBOT) is presented in this paper. The MTATBOT is specially designed for long-range, space-constrained, and explosion-proof underground coal mine environments. With an onboard perception and autopilot system, the MTATBOT can perform automated and unmanned subterranean material transportation. This paper proposes an integrated odometry-based method to improve position estimation and mitigate location ambiguities for simultaneous localization and mapping (SLAM) in large-scale, GNSS-denied, and perceptually degraded subterranean transport roadway scenarios. Additionally, this paper analyzes the robot dynamic model and presents a nonlinear control strategy for the robot to autonomously track a planned trajectory based on the path-following error dynamic model. Finally, the proposed algorithm and control strategy are tested and validated both in a virtual transport roadway environment and in an active underground coal mine. The test results indicate that the proposed algorithm can obtain more accurate and robust robot odometry and better large-scale underground roadway mapping results compared with other SLAM solutions.
Characteristic Analysis and Error Compensation Method of Space Vector Pulse Width Modulation-Based Driver for Permanent Magnet Synchronous Motors
Authors: Chen Qihang , Wu Wanzhen , He Qianen
Published: December 2024

Permanent magnet synchronous motors (PMSMs) are widely used in a variety of fields such as aviation, aerospace, marine, and industry due to their high angular position accuracy, energy conversion efficiency, and fast response. However, driving errors caused by the non-ideal characteristics of the driver negatively affect motor control accuracy. Compensating for the errors arising from the non-ideal characteristics of the driver demonstrates substantial practical value in enhancing control accuracy, improving dynamic performance, minimizing vibration and noise, optimizing energy efficiency, and bolstering system robustness. To address this, the mechanism behind these non-ideal characteristics is analyzed based on the principles of space vector pulse width modulation (SVPWM) and its circuit structure. Tests are then conducted to examine the actual driver characteristics and verify the analysis. Building on this, a real-time compensation method is proposed, physically matched to the driver. Using the volt–second equivalence principle, an input–output voltage model of the driver is derived, with model parameters estimated from test data. The driving error is then compensated with a voltage method based on the model. The results of simulations and experiments show that the proposed method effectively mitigates the influence of the driver’s non-ideal characteristics, improving the driving and speed control accuracies by 88.07% (reducing the voltage error from 0.7345 V to 0.0879 V for a drastic command voltage with a sinusoidal amplitude of 10 V and a frequency of 50 Hz) and 53.08% (reducing the speed error from 0.0130°/s to 0.0061°/s for a lower command speed with a sinusoidal amplitude of 20° and a frequency of 0.1 Hz), respectively, in terms of the root mean square errors. This method is cost-effective, practical, and significantly enhances the control performance of PMSMs.
The Impact of Poor Waste Management on Public Health Initiatives in Shanty Towns in Tanzania
Authors: Kitole Felician Andrew , Ojo Temitope O. , Emenike Chijioke U. , Khumalo Nolwazi Z. , Elhindi Khalid M. , Kassem Hazem S.
Published: December 2024

Poor waste management in shanty towns across developing countries has significantly impacted public health, contributing to widespread outbreaks of diseases such as cholera, malaria, and typhoid due to unsanitary living conditions and contaminated environments. Limited efforts by residents and governments to implement effective waste disposal practices exacerbate these health risks, perpetuating a cycle of poor sanitation, increased disease transmission, and environmental degradation. This study investigates the impact of poor waste management on public health in informal settlements and explores strategies to mitigate these risks through improved practices and collaborative efforts. The study employed a cross-sectional research design and collected data using semi-structured questionnaires to collect data from 217 households in Tandale, Manzese, and Tandika in the Dar es Salaam region. The findings confirm that inadequate waste collection services, lack of proper disposal sites, high costs of waste management, and poor public awareness are key contributors to the accumulation of waste and the prevalence of diseases. Hypothesis testing further reveals that inadequate waste collection services significantly impact public health challenges, while public health initiatives on waste management significantly improve health outcomes and reduce disease prevalence. The study’s recommendations include increasing the frequency of waste collection, fostering community-led waste management initiatives, enhancing public education on the health risks of poor waste disposal, and providing subsidized resources such as waste bins and bags. Additionally, strengthened collaboration between local governments, NGOs, and community members is essential for mobilizing resources and implementing sustainable waste management practices. These measures are vital to reducing public health risks and creating healthier living conditions in underserved communities.
Carbon and Environmental Labelling of Food Products: Insights into the Data on Display
Authors: Trebbin Anika , Geburt Katrin
Published: December 2024

The food system has been in focus as one of the major drivers behind the environmental and climate crisis. In this context, there is a growing need for more transparent and reliable information on the environmental impacts of food production and consumption as part of the transition process towards more sustainable food systems. Stakeholders along the food supply chain are confronted with multiple requirements and systems as the demand for environmental reporting at the product, company, and country level increases all at the same time. Simultaneously, consumers are often more interested in the sustainability of the food products they consume. While there is currently a lack of coherent supranational or even national legislation regulating methodological procedures, private initiatives for the environmental and carbon labelling of food products have developed rapidly. This article finds that most labels are characterised by a lack of transparency, clarity, and comprehensibility. Examining 14 labels, mainly from the German food retail market, we found a puzzling variety of data sources and methodologies used to calculate the values and claims displayed. We highlight this variety in data sources and footprint values by looking at milk and beef as case studies.
Assessing Negative Externalities of Rural Abandoned Houses in South Korea: Insights from Discrete Choice Experiments
Authors: Shin Seongyong , Kim Tae-Hwa
Published: December 2024

The proliferation of abandoned houses in rural South Korea poses significant challenges to sustainable rural development, driven by declining birth rates, aging populations, and urban migration. However, effective policy implementation is hindered by the lack of understanding of the negative externalities caused by abandoned houses. This study fills this gap by estimating the negative externalities associated with abandoned rural houses using discrete choice experiments. Surveys targeting individuals planning rural relocations and potential tourists considering rural stays were conducted to quantify the external costs. Our findings reveal that the marginal willingness to pay associated with abandoned houses is negative and decreases with an increasing number of abandoned houses nearby, both in the context of house purchases and rural stays. Extrapolating these results to the national level, we estimate the aggregate negative externalities value to be approximately 4.2 trillion KRW per year, highlighting significant negative externalities in rural areas nationwide. The implications of our analysis underscore the severity of negative externalities from abandoned houses, which may surpass the value of housing services, discourage migration, and prompt residents to leave rural communities, thus exacerbating the issue. Our study emphasizes the necessity for further research and policy interventions to address the negative externalities associated with abandoned rural houses, offering insights into the potential use of discrete choice experiments in similar contexts.
Quantitative Evaluation and Evolution of Overpressure in the Deep Layers of a Foreland Basin: Examples from the Lower Cretaceous Bashijiqike Formation in the Keshen Area, Kuqa Depression, Tarim Basin, China
Authors: Wen Chenxi , Wang Zhenliang
Published: December 2024

The Kuqa area comprises a foreland basin located near the southern border of the South Tianshan Mountains which is considered as a major hydrocarbon-producing basin in NW China. The Keshen area is an important zone for hydrocarbon accumulation. The main oil-bearing reservoirs in the Keshen area are documented in the Bashijiqike Formation (Lower Cretaceous), located at depths ranging from 6000 to 8000 m, where overpressure (maximum up to 85 MPa) is prevalent. The origin of overpressure in the Bashijiqike Formation (Lower Cretaceous) includes mudstone disequilibrium compaction, tectonic compression, and fracture transfer overpressures. In this work, mathematical modeling is key to evaluate different types of overpressure quantitatively. The coupling evolution of different overpressures is also crucial. The results showed that the overpressures due to disequilibrium compaction, tectonic compression, and fracture transfer were 5–10, 25–30, and 10–15 MPa. The evolution characteristics of polygenic pressure are as follows: before 23.3 Ma, the overpressure was almost 0; from 23.3–10 Ma, the overpressure was mainly caused by disequilibrium compaction, and the residual pressure gradually increased to 18 MPa; and after 10 Ma, the overpressure was mainly caused by the combination of the disequilibrium compaction type of overpressure, tectonic extrusion type of overpressure, and fracture transfer overpressure. The residual pressure rapidly increased to 60 Mpa and then slowly released due to formation uplift. This research is of great significance for the quantitative evaluation of different origins of overpressure and the study of the evolution of multi-cause overpressure in deep layers of foreland basins. This research of deep oil and gas exploration provides the possibility to realize further sustainable oil.
Factors Influencing Changes in Forestry Carbon Sinks Under the ‘Dual Carbon’ Framework in Southwest China: Evidence from Satellite Remote Sensing Data
Authors: Cao Yang , Xing Haoyue , Wang Zeen
Published: December 2024

This study, grounded in the Ecological Environmental Systems Theory, commenced by extracting structured proxy variables from satellite remote sensing imagery spanning 2013 to 2022. Subsequently, a research data set was constructed by integrating annual statistical data from 38 cities in Southwest China with meteorological data sets. Finally, a Panel Vector Autoregression (PVAR) model was employed to examine the ecological and socioeconomic factors influencing forestry carbon sinks. The results demonstrate that annual average precipitation and economic development level positively influence forestry carbon sinks, whereas annual average temperature, forestry production value, urban heat island effects, urban scale, population urbanization rate, and road mileage exert significant negative impacts. In the short term, forestry production value, annual average temperature, and annual average precipitation account for the greatest proportion of variance, with forestry production value exhibiting a notable lag effect. Over the long term, population urbanization rate and economic development level emerge as the primary determinants of forestry carbon sinks, whereas road mileage and urban scale exhibit relatively stable effects. This study offers a rigorous analysis of the factors influencing forestry carbon sinks and provides practical implications, thereby laying a solid foundation for future research in this domain.
Informal Caregivers’ Goal Orientation at Work: The Role of Rational Fit Resources and Psychological Conditions
Authors: Vinarski-Peretz Hedva
Published: December 2024

Within the demographic shifts associated with an aging society, the provision of informal unpaid care for relatives may affect the goal orientation and well-being of an increasing proportion of employees in the workforce. A significant gap in the management and organizational behavior empirical literature is the absence of research on this topic. This article responds to and focuses on one resource mechanism that supports working informal caregivers’ well-being by promoting a performance goal orientation in the workplace. Drawing on conservation of resources (COR) theory, the research model examines the relationship between two personal resources, two psychological conditions, and performance goal orientation among employees combining paid work with informal caregiving responsibilities. The results show that psychological meaningfulness and psychological availability mediate the effects of two rational fit resources—person–organization (P–O) fit and person–job (P–J) fit—to increase caregivers’ performance orientation at work. Based on a sample of 383 primary informal caregivers who participate in the Israeli labor market, and using data drawn from two time points, the results show that high P–O fit and P–J fit perceptions increase both psychological meaningfulness and availability, which, in turn, resulted in a higher performance goal orientation.
Life Cycle Assessment of Crude Oil-Contaminated Soil Treated by Low-Temperature Thermal Desorption and Its Beneficial Reuse for Soil Amendment
Authors: Song Young Ho , Kim Geon Yong , Kim Da Yeon , Hwang Yong Woo
Published: December 2024

The effectiveness of thermal treatment technologies for the remediation of soils contaminated with heavy hydrocarbons has been extensively documented in the scientific literature. In general, high-concentration crude-oil-contaminated soil is treated with high-temperature thermal desorption (HTTD) in order to achieve high remediation efficiency. However, this process has the unintended consequence of destroying soil fertility. Low-temperature thermal desorption (LTTD) represents an alternative approach that has been developed with the objective of remediating heavily crude-oil-contaminated soil in a more rapid and cost-effective manner while simultaneously enhancing soil fertility. The thermal desorption unit (TDU) was employed using both LTTD and HTTD, operating at 300 °C and 500 °C, respectively, with a 30 min residence time in the kiln. The concentration of total petroleum hydrocarbons (TPH) in both the LTTD- and HTTD-treated soils was found to be less than 1% by weight, thereby below regulatory standards. The environmental impacts of both processes were assessed using the OpenLCA software version 2.0. The HTTD process exhibited a total abiotic depletion potential (ADP) impact of 1.63 × 10−4 MJ and a global warming potential (GWP) of 414 kg CO2-eq. In contrast, LTTD demonstrated lower impacts, with an ADP of 1.29 × 10−4 MJ and a GWP of 278 kg CO2-eq. The transition from HTTD to LTTD resulted in a notable reduction in ADP by 20.5% and in GWP by 32.9%. The application of LTTD-treated soil coated with coke or carbonized residues has been demonstrated to serve as an effective soil amendment, with the capacity to sequester approximately 50% of organic hydrocarbon contaminants. The results of this study illustrate the potential of LTTD for not only economical and rapid soil remediation but also the enhancement of soil quality through beneficial reuse.
Demographic Change and the Housing Stock of Large and Medium-Sized Cities in the Context of Sustainable Development
Authors: Blaszke Małgorzata , Oleńczuk-Paszel Anna , Sompolska-Rzechuła Agnieszka , Śpiewak-Szyjka Monika
Published: December 2024

The changing demographics of the global population represent a significant challenge for humanity. Such changes have an impact on the functioning of the economy, including the housing market, and necessitate constant monitoring. This study evaluated the spatial diversity of all the large and medium-sized cities in the West Pomeranian Voivodeship, situated in the north-west of Poland, in terms of three key factors: demographic potential, housing stock and their price levels. Furthermore, the interactions between the cities’ positions in the rankings, which were created on the basis of the aforementioned phenomena, were identified. In order to achieve the objectives of the study, the linear object ordering method, the Hellwig pattern method and Kendall’s tau rank correlation coefficient were employed. The research was conducted using data from the years 2018 to 2022, sourced from the databases of the Polish Statistical Office and the Analysis and Monitoring System of the Real Estate Market. The study observed a relatively strong positive correlation between the positions of cities in the ranking created for demographic potential and the level of residential property prices for the year 2020. The correlation between the positions of cities in the rankings for demographic potential and housing real estate stock was found to be very weak. The case of Koszalin was identified as an optimal location for residence due to the existing residential property stock and its prices. This was corroborated by the city’s residents, who also enabled the city to be ranked at the top of a ranking created for this phenomenon through the diagnostic variables for demographic potential. This article addresses a research gap, as, to the best of our knowledge, the indicated relationships have not yet been analysed in the contexts presented in the article.
Impact of Graphite-Associated Waste as a Sustainable Aggregate on UHPC Performance
Authors: Jiang Chengjin , Li Tianyu , Yang Ao , Qi Jiahui , Liu Hongbo
Published: December 2024

In this study, graphite tailings produced in Luobei County, Heilongjiang Province, were used as a sustainable aggregate to replace river sand (0%, 25%, 50%, 75%, 100%) in UHPC for the preparation of ultra-high-performance concrete (UHPC). The experimental results showed that the incorporation of 75% graphite tailings caused a significant increase in the wet bulk density of UHPC mortar. The workability of UHPC decreased monotonically with the increase in graphite tailing substitution rate, and wet bulk density decreased by 28.42% with 100% graphite tailings (compared with no graphite tailings). The incorporation of 75% graphite tailings helped to improve the compressive and flexural strengths as well as the durability of UHPC. Compared with the UHPC without graphite tailings, the 28 d compressive and flexural strengths increased by 8.82% and 7.28%, respectively, and the chloride ion electrical flux decreased by 19.49%. XRD and thermogravimetric analysis data indicate that the incorporation of graphite tailings did not change the type of hydration and that the incorporation of graphite tailings helped to increase the degree of hydration within the UHPC matrix. MIP and SEM showed that 75% graphite tailings helped to reduce the porosity and the number of harmful pores inside the matrix. The 100% graphite tailings treatment replacing river sand decreased the economic cost of UHPC by up to 23.78%.
Unmanned Aerial Vehicle (UAV) Hyperspectral Imagery Mining to Identify New Spectral Indices for Predicting the Field-Scale Yield of Spring Maize
Authors: Zhang Yue , Wang Yansong , Hao Hang , Li Ziqi , Long Yumei , Zhang Xingyu , Xia Chenzhen
Published: December 2024

A nondestructive approach for accurate crop yield prediction at the field scale is vital for precision agriculture. Considerable progress has been made in the use of the spectral index (SI) derived from unmanned aerial vehicle (UAV) hyperspectral images to predict crop yields before harvest. However, few studies have explored the most sensitive wavelengths and SIs for crop yield prediction, especially for different nitrogen fertilization levels and soil types. This study aimed to investigate the appropriate wavelengths and their combinations to explore the ability of new SIs derived from UAV hyperspectral images to predict yields during the growing season of spring maize. In this study, the hyperspectral canopy reflectance measurement method, a field-based high-throughput method, was evaluated in three field experiments (Wang-Jia-Qiao (WJQ), San-Ke-Shu (SKS), and Fu-Jia-Jie (FJJ)) since 2009 with different soil types (alluvial soil, black soil, and aeolian sandy soil) and various nitrogen (N) fertilization levels (0, 168, 240, 270, and 312 kg/ha) in Lishu County, Northeast China. The measurements of canopy spectral reflectance and maize yield were conducted at critical growth stages of spring maize, including the jointing, silking, and maturity stages, in 2019 and 2020. The best wavelengths and new SIs, including the difference spectral index, ratio spectral index, and normalized difference spectral index forms, were obtained from the contour maps constructed by the coefficient of determination (R2) from the linear regression models between the yield and all possible SIs screened from the 450 to 950 nm wavelengths. The new SIs and eight selected published SIs were subsequently used to predict maize yield via linear regression models. The results showed that (1) the most sensitive wavelengths were 640–714 nm at WJQ, 450–650 nm and 750–950 nm at SKS, and 450–700 nm and 750–950 nm at FJJ; (2) the new SIs established here were different across the three experimental fields, and their performance in maize yield prediction was generally better than that of the published SIs; and (3) the new SIs presented different responses to various N fertilization levels. This study demonstrates the potential of exploring new spectral characteristics from remote sensing technology for predicting the field-scale crop yield in spring maize cropping systems before harvest.
The Impact of Digital Technology Application on Agricultural Low-Carbon Transformation—A Case Study of the Pesticide Reduction Effect of Plant Protection Unmanned Aerial Vehicles (UAVs)
Authors: Deng Qian , Zhang Yuhan , Lin Zhuyu , Gao Xueping , Weng Zhenlin
Published: December 2024

Reducing pesticide use is a crucial step toward achieving the green and low-carbon transformation of agriculture. Analyzing the role and mechanisms of agricultural digital technologies—particularly plant protection unmanned aerial vehicles (UAVs) for aerial spraying—is essential for identifying viable strategies to reduce pesticide application intensity among farming households. This analysis is critical for facilitating the low-carbon transformation of rice production and advancing sustainable agricultural development. This study, using survey data from 455 farming households in Jiangxi Province, China, employs Ordinary Least Squares (OLS) and Propensity Score Matching (PSM) methods to investigate the relationship between plant protection UAVs and pesticide application intensity. The findings reveal that adopting plant protection UAVs significantly reduces pesticide application intensity in rice production by 24.9%. Further analysis indicates that the reduction effect is more pronounced among non-aged, large-scale, and part-time farming households. To achieve the low-carbon transformation of rice production, it is vital to enhance agricultural support policies and develop effective market promotion and application mechanisms to encourage the adoption of UAV-based aerial spraying and other digital agricultural technologies.
Integrating Safety and Efficiency: Design and Evaluation of Dynamic Emergency Evacuation Sign System in Urban Rail Transit
Authors: Zhang Yu , Bian Yang , Zhao Xiaohua , Zhao Xuena
Published: December 2024

The emergency evacuation sign system is crucial for the safety and sustainable development of urban rail transit. Dynamic emergency evacuation signs, which offer real-time guidance during emergencies, are gaining prominence. There is an urgent need to develop a dynamic system that balances perceptual and cognitive visibility. Against this backdrop, this study built a simulation experiment platform based on BIM, Unity, and VR. Eight experimental scenarios were created using the platform: two emergency events (fire/fire accompanied by power outage) × four emergency evacuation signage systems (static emergency signage system/dynamic dissuasion emergency signage system/dynamic dissuasion emergency signage system with flashing/dynamic dissuasion emergency signage system with flashing and auxiliary information), and experimental testing was completed. The evacuation behavior parameters of 39 passengers were extracted and used to construct a multidimensional indicator system. Subsequently, generalized estimation equations were applied to investigate the impact mechanism of signage systems on passengers’ evacuation behavior. Finally, the coupling coordination degree model was used to quantitatively evaluate the coupling coordination level of four emergency evacuation signage systems under different emergencies. Results indicate that compared to static signage system, the three sets of dynamic identification system schemes have a positive impact on passenger evacuation behavior and significantly reduce the number of decision-making errors. Particularly in high-risk scenarios involving fire accompanied by power outages, the dynamic dissuasion emergency signage system with flashing and auxiliary information outperforms others by achieving a better balance of reliability, efficiency, and safety. This study investigates the efficacy of various emergency evacuation signage systems across diverse emergency scenarios, offering insights for the enhanced design of such systems and thereby fostering the sustainable development of urban rail transit infrastructure.
Weighted Multiview K-Means Clustering with L2 Regularization
Authors: Hussain Ishtiaq , Nataliani Yessica , Ali Mehboob , Hussain Atif , Mujlid Hana M. , Almaliki Faris A. , Rahimi Nouf M.
Published: December 2024

In the era of big data, cloud, internet of things, virtual communities, and interconnected networks, the prominence of multiview data is undeniable. This type of data encapsulates diverse feature components across varying perspectives, each offering unique insights into the same underlying samples. Despite being sourced from diverse settings and domains, these data serve the common purpose of describing the same samples, establishing a significant interrelation among them. Thus, there arises a necessity for the development of multiview clustering methodologies capable of leveraging the wealth of information available across multiple views. This study introduces two novel weighted multiview k-means algorithms, W-MV-KM and weighted multiview k-means using L2 regularization, W-MV-KM-L2, designed specifically for clustering multiview data. These algorithms incorporate feature weights and view weights within the k-means (KM) framework. Our approach emphasizes a weighted multiview learning strategy, which assigns varying degrees of importance to individual views. We evaluate the clustering performance of our algorithms on seven diverse benchmark datasets spanning dermatology, textual, image, and digit domains. Through extensive experimentation and comparisons with existing methods, we showcase the superior effectiveness and utility of our newly introduced W-MV-KM-L2 algorithm.
Particle Movement in DEM Models and Artificial Neural Network for Validation by Using Contrast Points
Authors: Černilová Barbora , Kuře Jiří , Chotěborský Rostislav , Linda Miloslav
Published: December 2024

The calibration and validation of input parameters in the Discrete Element Method (DEM) are crucial for accurately simulating physical processes, typically achieved through experimental particle behavior analysis. Enhancing the accuracy of DEM models allows for more reliable predictions of material behavior, which is essential for optimizing engineering applications that involve particulate materials. In this study, we present a methodology for analyzing the movement properties of particulate materials, employing a combination of Caliscope software to obtain the real-world co-ordinates based on pixel values from both cameras and artificial neural networks for regression as straightforward and efficient tools. This approach enables the validation and calibration of digital twins of particulate matter systems with respect to motion characteristics. The method of contrast points was utilized to acquire spatial co-ordinates of particulate material movement from experimental measurements, facilitating precise trajectory determination and the subsequent verification of simulation predictions. The neural network analysis demonstrated high accuracy, achieving R2 values of 0.9988, 0.9972, and 0.9982 for the X–, Y–, and Z–axes, respectively. The standard deviation between the predicted and actual co-ordinates was found to be 1.8 mm. A comparative analysis of particle trajectories from both the model and experimental data indicated strong agreement, underscoring the soundness and reliability of this approach.
CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound
Authors: Wang Yi-Ming , Wang Chi-Yuan , Liu Kuo-Ying , Huang Yung-Hui , Chen Tai-Been , Chiu Kon-Ning , Liang Chih-Yu , Lu Nan-Han
Published: December 2024

Background/Objectives: Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures. Materials and Methods: Breast images were sourced from public datasets, including the RSNA, the PAS, and Kaggle, and categorized into malignant and benign groups. Data augmentation techniques were used to address imbalances in the ultrasound dataset. Three models were developed: (1) pre-trained CNNs integrated with machine learning classifiers, (2) transfer learning-based CNNs, and (3) a custom-designed 17-layer CNN for direct classification. The performance of the models was evaluated using metrics such as accuracy and the Kappa score. Results: The custom 17-layer CNN outperformed the other models, achieving an accuracy of 0.964 and a Kappa score of 0.927. The transfer learning model achieved moderate performance (accuracy 0.846, Kappa 0.694), while the pre-trained CNNs with machine learning classifiers yielded the lowest results (accuracy 0.780, Kappa 0.559). Cross-modality fusion proved effective in leveraging the complementary strengths of mammography and ultrasound imaging. Conclusions: This study demonstrates the potential of cross-modality imaging and tailored CNN architectures to significantly improve diagnostic accuracy and reliability in breast cancer detection. The custom-designed model offers a practical solution for early detection, potentially reducing false positives and false negatives, and improving patient outcomes through timely and accurate diagnosis.
Antimicrobial Resistance Profiling of Pathogens from Cooked Donkey Meat Products in Beijing Area in One Health Context
Authors: Liu Yiting , Duan Hongyun , Yang Luo , Chen Hong , Wu Rongzheng , Li Yi , Zhu Yiping , Li Jing
Published: December 2024

The prevalence of foodborne diseases has raised concerns due to the potential transmission of zoonotic bacterial pathogens through meat products. The objective of this study was to determine the occurrence and antimicrobial resistance (AMR) profiles of pathogenic bacteria in cooked donkey meat products from Beijing. Twenty-one cooked donkey meat samples were collected from different delis, subjected to homogenization, and analyzed for bacterial contamination. Molecular identification was performed through polymerase chain reaction (PCR) amplification and sequencing targeting the 16S rDNA gene. The antimicrobial susceptibility of the isolates was evaluated using the disk diffusion method. A total of forty bacterial isolates were identified, with Proteus mirabilis being the predominant species, followed by Klebsiella pneumoniae and Novosphingobium. Both Proteus mirabilis and Klebsiella pneumoniae exhibited high levels of resistance to several antibiotics, including penicillin, ampicillin, and erythromycin. This study’s findings underscore the public health risk posed by antimicrobial-resistant foodborne pathogens and emphasize the necessity for enhanced food safety surveillance within the One Health context.
Metagenomic Meta-Analysis of Antibiotic-Resistance Genes in Wastewater: A Perspective from the COVID-19 Pandemic
Authors: Alhazmi Shaima M. , BaniMustafa Ala’a , Alindonosi Abrar R. , Almutairi Adel F.
Published: December 2024

Antibiotic resistance is a silent global crisis intensified by the recent pandemic of coronavirus disease 2019 (COVID-19). To address this growing threat, wastewater-based surveillance (WBS) is emerging as a promising public health tool for monitoring antibiotic resistance within communities. Our meta-analysis aims to reveal the landscape of antibiotic-resistance genes (ARGs) in global wastewater during and after the COVID-19 pandemic. The analysis included wastewater samples collected between 2020 and 2024 from five countries across three continents: Asia (China), Europe (United Kingdom and Russia), and North America (United States and Canada). Our findings showed higher observed ARGs in Russia and China despite their small sample size, while the USA showed more diverse ARGs. Distinct patterns of ARGs were observed in European and North American wastewater samples (p-value < 0.001). We identified 2483 ARGs, with multidrug-resistant (MDR) genes dominating most regions and accounting for almost 45% of all ARGs detected in Europe. Country-specific indicator ARGs showed 22 unique ARGs for Russia, 3 for each of the UK and Canada, and 2 were specific for China. Continentally, 100 indicator ARGs were specific to Asia, 38 to Europe, and 18 to North America. These findings highlight the regional variations in ARG profiles, emphasizing the urgent need for region-specific strategies to combat antibiotic-resistance threat. Additionally, our study further supports the value of WBS as a valuable public health tool for monitoring antibiotic resistance.
Optimizing Suspended Sediment Models: A Novel Expert System with Spatial Probabilities and Isolated Points
Authors: Sabat Mira , Terfous Abdelali , Ghenaim Abdellah , Sabat Macole , Draybi Michel , Romanos Jimmy
Published: December 2024

Predicting suspended sediment concentration (SSC) profiles with high accuracy remains a critical challenge for environmental and engineering applications. This study presents a novel, data-driven expert system that leverages a knowledge-based framework to select optimal SSC models based on diverse flow conditions. The system utilizes model function ranges and spatial relationships between data points as key decision factors. This methodology is applied to study vertical velocity profiles and SSC distribution in steady and uniform river flows. The system systematically extracts and categorizes influencing parameters, generating weighted averages to interpolate and extrapolate profiles where single models exhibit limitations. Two weight calculation methods are implemented: (1) a spatial conditional probability approach utilizing a uniform distribution within control cells, and (2) an isolated point analysis based on distances to cell centers. This approach exhibits some similarities to Voronoi tessellations and associated Laplace and Sibson weights, offering a robust and innovative method for SSC modeling. The proposed expert system empowers hydrologists and engineers by selecting and applying the most suitable SSC models for different scenarios, leading to enhanced prediction accuracy and reliability. This work represents a significant advancement in the field of sediment transport modeling, providing a valuable tool for improved water resource management and environmental protection.
Bearing Characteristics and Negative Skin Friction Preventive Measures for Highway Bridge Pile Foundations in Collapsible Loess Areas Under Water Immersion
Authors: Bian Haiding , Wei Jin
Published: December 2024

In collapsible loess sites, large-scale collapsible settlement may occur after water immersion, which will reduce the bearing capacity of existing highway bridge pile foundations and pose serious potential safety hazards. Given this, a large-scale field pile foundation immersion–loading test was conducted in a collapsible loess site. The settlement law of collapsible loess during the immersion was obtained, the bearing characteristics of pile foundations under the loading and immersion–loading conditions were compared and analyzed, and the formation mechanism of negative skin friction was discussed. The results show that the degree of collapsible deformation is related to the duration of immersion, external load, boundary conditions, and soil layer depth. Whether the collapsible loess site is immersed or not can only change the value and transfer rate of the axial force of the pile foundation but cannot change its transfer law. The collapsible deformation will increase the utilization rate of the pile tip resistance. During the collapsible settlement process, part of the gravity of the soil around the pile will be transferred to the pile, generating negative skin friction on the pile shaft. On this basis, eight preventive measures for reducing the negative skin friction of pile foundations in collapsible loess sites were proposed. The research findings can serve as a valuable reference for the design and construction of highway bridge pile foundations in collapsible loess areas.
Empowering Intrusion Detection Systems: A Synergistic Hybrid Approach with Optimization and Deep Learning Techniques for Network Security
Authors: Chinnasamy Ramya , Subramanian Malliga , Sengupta Nandita
Published: December 2024

Over last decade, there is a rapid advancement in networking and computing technologies that produced large volume of sensitive data. Clearly, protecting those data from intrusions and attack is of paramount importance. Researchers have proposed many cyber security solutions and tools to protect the data. One such technique for safeguarding data is the Intrusion Detection System (IDS). This research introduces a hybrid optimization-based Feature Selection (FS) and deep learning-driven categorization namely Honey Badger Optimization-Artificial Neural Network (HBO-ANN) to identify intrusions. The Honey Badger Optimization (HBO) is an optimization technique that is utilized to choose the dataset’s most important features. The Artificial Neural Network (ANN) receives reduced features dataset and classifies it as benign or attack. Additionally, a well-known CIC-IDS 2017 dataset is employed to construct and validate the suggested system. Performance metrics for assessing the effectiveness of the suggested system are the false alarm rate, Mean Squared Error (MSE), precision, accuracy and recall. The testing and training MSEs are 0.009 and 0.00317, respectively. The model’s accuracy is 97.66%. The model has a precision of 98.03% and a recall of 97.18%. There is a 1.97% false alarm rate. The outcomes have been compared with bench mark models such as Grey Wolf Optimizer-Support Vector Machine (GWO-SVM), Particle Swarm Optimization-Support Vector Machine (PSO-SVM), Fuzzy Clustering-Artificial Neural Network (FC-ANN), Bidirectional Long-Short-Term-Memory (BiDLSTM) and Feed-Forward Deep Neural Network (FFDNN). As demonstrated by the experimental results, the suggested model outperforms the benchmark algorithms