bluuebunny/crossref_metadata_2025 huggingface dataset:
An Integrated Strategy to Treat and Control Acid Mine Drainage from Waste Rock and Underground Workings at the Former Franklin Mine in Nova Scotia, Canada: Field Performance Monitoring
Authors: Power Christopher
Published: January 2025

Acid mine drainage (AMD), which is primarily caused by the exposure of sulfidic minerals to oxygen and water during mining operations, remains a significant contributor to environmental pollution. Numerous technologies have been developed to prevent/control and treat AMD, including the isolation of waste from the atmosphere and treatment systems for AMD-impacted water. Many field studies on mine site reclamation have involved an individual AMD source and/or technology, with a limited number of studies looking at reclamation programs integrating multiple approaches to manage AMD stemming from both surface and underground sources. The former Franklin mine site in Nova Scotia, Canada, was impacted by the deposition of waste rock across the site and the discharge of mine water from underground workings, with the adjacent Sullivan’s Pond serving as the main environmental receptor. Site reclamation was completed in 2010 and involved the following: (1) excavation of the dispersed waste rock (117,000 m2) and backfilling with clean soil; (2) consolidation of the excavated waste rock into a covered, compact waste rock pile (WRP) (25,000 m2); and (3) construction of a passive treatment system for the discharging underground mine water. An extensive field sampling program was conducted between 2011 and 2018 to monitor a range of meteorological, cover material, waste rock, groundwater, and surface water quality parameters. The results confirm that the multi-layer, geomembrane-lined WRP cover system is an extremely effective barrier to air and water influx, thereby minimizing the rate of AMD generation and seepage into groundwater and eliminating all contaminated surface water runoff. A small AMD groundwater plume emanates from the base of the WRP, with 50% captured by the underground mine workings over the long term and 50% slowly migrating towards Sullivan’s Pond. Excavation of the former waste disposal area eliminated the AMD source from the previously dispersed waste, with only clean surface water runoff and a diminishing legacy groundwater plume remaining. Finally, the passive treatment system, which contains a series of treatment technologies such as a limestone leach bed and settling pond, successfully treats all mine water loading (~50 kg/day) discharging from the underground workings and surface runoff. Its additional treatment capacity (up to ~150 kg/day) ensures it will be able to manage any potential drop in treatment efficiency and/or increased AMD loading from long-term WRP seepage. This comprehensive study of mine site reclamation and AMD management at an abandoned mining site can be of great reference value for environmental management and policymakers in the mining sector.
Recent Advancements of Bio-Derived Flame Retardants for Polymeric Materials
Authors: Chen Min , Guo Qinhe , Yuan Yao , Li Ao , Lin Bo , Xiao Yi , Xu Lulu , Wang Wei
Published: January 2025

The sustainable flame retardancy of polymeric materials is a key focus for the direction of the next generation in the field of fire safety. Bio-derived flame retardants are gaining attention as environmentally friendly additives due to their low ecological impact and decreasing costs. These compounds can enhance char formation in polymeric materials by swelling upon heating, attributed to their functional groups. This review explores various biomolecules used as flame retardants, including phytic acid, chitosan, lignin, tannic acid, and bio-derived phosphorus and nitrogen compounds, emphasizing their flame-retardant properties and compatibility with different polymer matrices. The primary focus is on the structural characteristics, modifications, and flame-retardant behaviors of these bio-derived additives, particularly regarding their mechanisms of action within polymeric materials. Finally, the review explores the opportunities, current challenges, and future directions for the practical application of bio-derived flame retardants in polymer materials.
Characterization of Self-Cured Silicone Oils for Encapsulation of Ultraviolet-C Light-Emitting Diodes
Authors: Qiu Xing , Yu Qianhang , Cheng Yuanjie , Lo Jeffery C. C. , Lee Shi-wei Ricky
Published: January 2025

The effectiveness of ultraviolet-C light-emitting diodes (UVC LEDs) is currently limited by the lack of suitable encapsulation materials, restricting their use in sterilization, communication, and in vivo cancer tumor inhibition. This study evaluates various silicone oils for UVC LED encapsulation. A material aging experiment was conducted on CF1040 (octamethylcyclotetrasiloxane), HF2020 (methyl hydro polysiloxanes), and MF2020-1000 (polydimethylsiloxane) under UVC radiation for 1000 h. The analysis assessed transmittance changes and chemical composition alterations throughout the aging process. Notably, HF2020 showed an increase in transmittance before 500 h, indicating a curing process attributed to the photolysis of Si-H, leading to the formation of Si-O-Si. Further testing on 265 nm UVC LEDs, both with and without HF2020 encapsulation, showed that the encapsulated LEDs exhibited a remarkable maximum increase of 27% in radiant power compared to their unencapsulated counterparts. Additionally, these encapsulated LEDs sustained higher radiant power levels during the first 200 h of operation. Notably, its potential application in photodynamic therapy is significant; by activating photosensitizers with higher UVC exposure, it facilitates the rapid production of reactive oxygen species, leading to effective cancer cell destruction within a short timeframe.
Buriti Oil (Mauritia flexuosa L.) as Functional Feed for Broiler Chickens
Authors: Dourado Leilane R. B. , Brauna Adriana S. A. , Bezerra Roseane M. , Sousa Iara S. , Carvalho Franscica Luana A. , Silva Gabriela G. , Moura Francinete A. S. , Sousa Samara C. , Santos Renato A. , Silva Silvokleio C. , Silva Lucilene S. , Gonçalves Larissa Maria F. , Miranda Rafael S.
Published: January 2025

This experiment evaluated the effects of including buriti oil (BO) in the diet of broilers on growth performance, nutrient digestibility, carcass yield, meat quality, blood parameters, intestinal morphometry, the proliferation of collagen fibers in the skin and collagen concentration in the breast muscle. A total of 180 one-day-old Ross male chicks were distributed in a completely randomized experimental design with three treatments (0%, 1% and 2% BO) and six replications of 10 birds each. Nutrient digestibility was evaluated from 8 to 21 days, while the performance, carcass yield, meat cut yields, abdominal fat, meat quality, blood parameters, intestinal morphometry and skin histology of the birds were evaluated after 21 days. The inclusion of BO significantly affected (p ≤ 0.05) performance, digestibility, meat color, lymphocyte count, duodenal morphometry and collagen concentration. In addition, it increased the metabolizable energy of the diet and reduced the birds’ performance, indicating that the nutraceutical effect of BO improved nutrient use. However, it caused an excess of energy that had to be metabolized by the birds, consequently affecting their performance. BO increased the pigmentation of the breast meat and reduced the lymphocyte count, probably due to its antimicrobial action, consequently decreasing the recruitment of defense cells. An increase in the height of the crypt of the duodenum was observed as the inclusion of BO increased, with the opposite being observed in the jejunum. The inclusion of BO increased the histological proliferation of collagen from minimal (0%) to medium (1%) and moderate (2%), as well as the collagen concentration (CC = 35.933 + 4.677BO; R2 = 0.80). Thus, due to functional/nutraceutical effects, adding BO in the diet of broilers can be a promising alternative to antibiotic growth promoters to improve poultry production.
Biochar-Aided Heat Transfer in Ground Source Heat Pumps: Effects on Water Capillary Rise and Carbon Storage Capability
Authors: Morselli Nicolò , Ottani Filippo , Puglia Marco , Pedrazzi Simone , Tartarini Paolo , Allesina Giulio
Published: January 2025

Ground source heat pump systems rely on soil conductivity for optimal performance, and soil conductivity is primarily influenced by soil moisture content. In this study, we investigate how biochar, a porous material derived from biomass gasification and pyrolysis, influences capillary water rise and moisture retention in soil. Mixtures of biochar with soil and sand in varying ratios, along with control mixtures, were prepared and tested on lab-scale equipment. The results showed that biochar-amended samples exhibited a significantly higher capillary water rise. At a height of 0.25 m above the water level, the minimum moisture content in the biochar-treated samples was 43.5%, much higher than the 6.5% recorded in the control group, which consisted of soil or soil and sand only. Even in the long term, mixtures with biochar maintained high moisture content, ranging from 36% to 57%, compared to the control’s 8%, at heights near 0.5 m over the free surface of the water. Moreover, the utilization of biochar as a soil improver in geothermal application is an innovative way for carbon sequestration which, in the analyzed conditions, leads to the storage of up to 0.7 tons of CO2eq per square meter of geothermal field.
An Optimization Approach to Select Koopman Observables for Data-Based Modeling Using Dynamic Mode Decomposition with Control
Authors: Martí-Coll Amanda , Rodríguez-Ramos Adrián , Llanes-Santiago Orestes
Published: January 2025

The advent and evolution of Industry 4.0 have been driven by technologies such as the Industrial Internet of Things, Big Data, and Cloud Computing. Within this framework, digital twins have gained significant popularity and are now employed across a wide range of industries and processes. A crucial step in developing a digital twin is deriving the system model, for which numerous methods are available. Among these, the Koopman operator and Dynamic Mode Decomposition with control have demonstrated their effectiveness and are widely recognized in the scientific community. This paper proposes a procedure for the automatic selection of Koopman observables by solving an optimization problem. The objective is to identify the minimal set of observables, belonging to a predefined dictionary, that minimize the error between actual process observations and predictions made by the estimated linear model—a key requirement for digital twin development. To tackle the optimization challenge, any algorithm available in the literature can be utilized. In this paper, the evolutive algorithms, including Genetic Algorithm and Differential Evolution Algorithm, are applied to evaluate the proposed approach in a benchmark problem. In both cases, the algorithms obtained the minimum set of observable functions from the dictionary used that achieve the lowest error obtained between the real process and the model, confirming the validity of the proposed method.
Transient Energy-Based Ultra-High-Speed Unit Protection for Overhead Outgoing Lines in Inverter-Interfaced Renewable Energy Plants
Authors: Kong Yuan , Wu Yumeng , Liang Zhenfeng , Jia Rong
Published: January 2025

The transient fault characteristics of an inverter-interfaced renewable energy (IIRE) overhead outgoing line may cause misoperations of existing protection schemes. This paper uses directional comparison of the post-fault transient energy to construct the ultra-high-speed (UHS) unit protection for an IIRE plant’s overhead outgoing line. The proposed method could identify the internal faults accurately in various fault scenarios. Compared to conventional traveling wave-based protection, distance protection and differential protection, the proposed scheme can overcome the high sampling rate difficulty and remain effective when the fault inception angle is zero. The scheme is also tested under conditions of noise, parallel lines, and CT saturation. The simulation results demonstrate that the proposed method is immune to these factors, which make the scheme more reliable and applicable in commercial industries.
Effects of Attitudes Toward Remembering on Metamemory and Memory Performance in College Students
Authors: Provost Jacob A. , Otani Hajime , Franks Andrew S.
Published: January 2025

As modern technology enables instant access to virtually limitless information, students may perceive memorization of information as lacking in practical importance. The current study investigated the relationship between attitudes toward remembering and metamemory as well as objective memory performance. University students (N = 108, MAge = 19.39, 77% women) completed the Importance of Remembering questionnaire (IORQ) as a measure of attitudes toward remembering. Subjective components of memory were measured by immediate and delayed judgments of learning (JOLs), global judgments of learning (global JOLs), retroactive confidence judgments (RCJs), and subjective mental workload. Objective memory performance was measured using a cued recall test using word pairs and picture pairs. The IORQ was only significantly correlated with absolute accuracy of delayed judgments of learning for words and pictures such that higher IORQ ratings were associated with less accurate judgments about how well they learned the items. No other correlations were significant. This suggests that a student’s lack of belief in the importance of remembering, at least as conceptualized on the IORQ, may not affect most aspects of memory performance, including those related to academic outcomes.
Model for Proton Acceleration in Strongly Self-Magnetized Sheath Produced by Ultra-High-Intensity Sub-Picosecond Laser Pulses
Authors: Korzhimanov Artem V.
Published: January 2025

Recently, it has been experimentally shown that the sheath acceleration of protons from ultra-thin metal targets irradiated by sub-picosecond laser pulses of intensities above 1021 W/cm2 is suppressed compared to well-established models. This detrimental effect has been attributed to a self-generation of gigagauss-level quasi-static magnetic fields in expanded plasmas on the rear side of a target. Here we present a set of numerical simulations which support this statement. Based on 2D full-scale PIC simulations, it is shown that the scaling of a cutoff energy of the accelerated protons with intensity deviates from a well-established Mora model for laser pulses with a duration exceeding 500 fs. This deviation is showed to be connected to effective magnetization of the hottest electrons producing at the maximum of the laser pulse intensity. We propose a modification of the Mora model which incorporates the effect of the possible electron magnetization. Comparing it to the simulation results shows that by appropriately choosing a single fitting parameter, the model produces results that quantitatively coincide with simulations.
A 3D Superposition Approximation for Gamma Knife Dose Calculation
Authors: Stone Payton H. , Lay Lam M. , Ramirez Raymi , Neck Daniel , Chu Connel , Dey Joyoni , Solis David
Published: January 2025

Effective dose calculation is essential for optimizing Gamma Knife (GK) stereotactic radiosurgery (SRS) treatment plans. Modern GK systems allow independent sector activation, enabling complex dose distributions per shot. This study presents a dose approximation method designed to account for shot flexibility and generate 3D doses external to GammaPlan. A treatment plan was created with the TMR10 calculation for individual sector activations using a Radiosurgery Head Phantom. The resulting dose arrays established a basis set of sector-specific distributions, which were then referenced by shot parameters from the plan, allowing dose accumulation through superposition. This superposition approximation (SA) was compared to the original TMR10 using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and GK deliverability metrics: coverage, selectivity, and gradient index, across an isodose normalization range from 10% to 90%. In a cohort of 30 patients with 71 targets, strong agreement was observed between TMR10 and SA in the clinically used 50–60% isodose range, with DSC above 85% and HD95 under 2.18 mm. The average differences for the coverage, selectivity, and gradient index were 0.014, 0.008, and 0.118, respectively. This method accurately approximates TMR10 calculations within clinically relevant ranges, offering an external tool to assess 3D dose distributions for GK treatment plans.
INterpolated FLOod Surface (INFLOS), a Rapid and Operational Tool to Estimate Flood Depths from Earth Observation Data for Emergency Management
Authors: Poterek Quentin , Caretto Alessandro , Braun Rémi , Clandillon Stephen , Huber Claire , Ceccato Pietro
Published: January 2025

The INterpolated FLOod Surface (INFLOS) tool was developed to meet the operational needs of the Copernicus Emergency Management Service (CEMS) Rapid Mapping (RM) component, which delivers critical crisis information within hours during and after disasters. With increasing demand for accurate and real-time flood depth estimates, INFLOS provides a rapid, adaptable solution for estimating floodwater depth across diverse flood scenarios, using remotely sensed data and high-resolution Digital Terrain Models (DTMs). INFLOS calculates flood depth by interpolating water surface elevation from sample points along flooded area boundaries, derived from satellite imagery. This tool is capable of delivering flood depth estimates in a rapid mapping context, leveraging a multistep interpolation and filtering process for improved accuracy. Tested across fourteen regions in Europe and South America, INFLOS has been successfully integrated into CEMS RM operations. The tool’s computational optimisations further enhance efficiency, improving computation times by up to 15-fold, compared to similar techniques. Indeed, it is able to process areas of up to 6000 ha in a median time of 5.2 min, and up to 30 min at most. In conclusion, INFLOS is currently operational and consistently generates flood depth products quickly, supporting real-time emergency management and reinforcing the CEMS RM portfolio.
Dynamic Calibration Method of Multichannel Amplitude and Phase Consistency in Meteor Radar
Authors: Jin Yujian , Chen Xiaolong , Huang Songtao , Chen Zhuo , Li Jing , Hao Wenhui
Published: January 2025

Meteor radar is a widely used technique for measuring wind in the mesosphere and lower thermosphere, with the key advantage of being unaffected by terrestrial weather conditions, thus enabling continuous operation. In all-sky interferometric meteor radar systems, amplitude and phase consistencies between multiple channels exhibit dynamic variations over time, which can significantly degrade the accuracy of wind measurements. Despite the inherently dynamic nature of these inconsistencies, the majority of existing research predominantly employs static calibration methods to address these issues. In this study, we propose a dynamic adaptive calibration method that combines normalized least mean square and correlation algorithms, integrated with hardware design. We further assess the effectiveness of this method through numerical simulations and practical implementation on an independently developed meteor radar system with a five-channel receiver. The receiver facilitates the practical application of the proposed method by incorporating variable gain control circuits and high-precision synchronization analog-to-digital acquisition units, ensuring initial amplitude and phase consistency accuracy. In our dynamic calibration, initial coefficients are determined using a sliding correlation algorithm to assign preliminary weights, which are then refined through the proposed method. This method maximizes cross-channel consistencies, resulting in amplitude inconsistency of <0.0173 dB and phase inconsistency of <0.2064°. Repeated calibration experiments and their comparison with conventional static calibration methods demonstrate significant improvements in amplitude and phase consistency. These results validate the potential of the proposed method to enhance both the detection accuracy and wind inversion precision of meteor radar systems.
Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
Authors: Perfetti Nicola , Taddia Yuri , Pellegrinelli Alberto
Published: January 2025

The study of the Earth’s ionosphere is a topic that has increased in relevance over the past few decades. The ability to predict the ionosphere’s behavior, as well as to mitigate the effects of its rapid changes, is a matter of primary importance in satellite communications, positioning, and navigation applications at present. Ionosphere perturbations can be produced by geomagnetic storms correlated with the solar activity or by earthquakes, volcanic activities, and so on. The monitoring of space weather is achieved through analyzing the Vertical Total Electron Content (VTEC) and its anomalies by means of time series, maps, and other derived parameters. In this study, we outline a strategy to estimate the VTEC in real-time, its rate of change, and the corresponding Signal-to-Noise Ratio (SNR) based on dual-frequency GNSS Doppler observations. We describe how to compute these parameters from GNSS data for a regional network using Adjusted Spherical Harmonic Analysis (ASHA) applied to a local model. The proposed method was tested to study ionospheric anomalies for two seismic events: the 2015 Nepal and 2023 Turkey earthquakes. In both cases, anomalies were detected in the maps of the differential VTEC (DTEC), differential VTEC rate, and SNR of the VTEC produced close to the earthquake zone. The robustness of the results is strongly related to the availability of a dense Ionosphere Pierce Point (IPP) cloud on the ionospheric layer and surrounding the studied area. At present, the distribution of Continuously Operating Reference Stations (CORSs) around the world is insufficiently dense and homogeneous in certain regions (e.g., the oceans). Robustness can be improved by increasing the number of CORSs and developing new models involving measurements over ocean surfaces.
Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images
Authors: Zhang Xiaoning , Yu Yi , Li Daqun , Wang Yuqing
Published: January 2025

With the continuous advancement of deep neural networks, salient object detection (SOD) in natural images has made significant progress. However, SOD in optical remote sensing images (ORSI-SOD) remains a challenging task due to the diversity of objects and the complexity of backgrounds. The primary challenge lies in generating robust features that can effectively integrate both global semantic information for salient object localization and local spatial details for boundary reconstruction. Most existing ORSI-SOD methods rely on pre-trained CNN- or Transformer-based backbones to extract features from ORSIs, followed by multi-level feature aggregation. Given the significant differences between ORSIs and the natural images used in pre-training, the generalization capability of these backbone networks is often limited, resulting in suboptimal performance. Recently, prompt engineering has been employed to enhance the generalization ability of networks in the Segment Anything Model (SAM), an emerging vision foundation model that has achieved remarkable success across various tasks. Despite its success, directly applying the SAM to ORSI-SOD without prompts from manual interaction remains unsatisfactory. In this paper, we propose a novel progressive self-prompting model based on the SAM, termed PSP-SAM, which generates both internal and external prompts to enhance the network and overcome the limitations of SAM in ORSI-SOD. Specifically, domain-specific prompting modules, consisting of both block-shared and block-specific adapters, are integrated into the network to learn domain-specific visual prompts within the backbone, facilitating its adaptation to ORSI-SOD. Furthermore, we introduce a progressive self-prompting decoder module that performs prompt-guided multi-level feature integration and generates stage-wise mask prompts progressively, enabling the prompt-based mask decoders outside the backbone to predict saliency maps in a coarse-to-fine manner. The entire network is trained end-to-end with parameter-efficient fine-tuning. Extensive experiments on three benchmark ORSI-SOD datasets demonstrate that our proposed network achieves state-of-the-art performance.
A Method of Arrival Angle Optimization in Single-Station Positioning Based on Statistical Features
Authors: Li Ting , Liu Tongxin , Yang Xuehai , Yang Guobin , Jiang Chunhua , Lao Chongzhe
Published: January 2025

Aiming to mitigate the substantial dispersion in arrival angle estimation due to colored and white noise interference, which may seriously affect the accuracy of short-wave single-station positioning, this paper introduces an approach to optimizing angles based on the statistical features. By utilizing the extraction of the main peak area of the probability density distribution of the measured angle, as well as the two-dimensional Gaussian fitting and confidence ellipse bounding, the angle measurement results affected by colored noise interference and the noise points with large deviations can be sequentially filtered out. Combining experimental scenarios and confirmed by actual measurement data, the dispersion of arrival angle estimation results has been significantly constrained, and, correspondingly, the positioning accuracy has also been significantly improved by about 3%.
Multi-Class Guided GAN for Remote-Sensing Image Synthesis Based on Semantic Labels
Authors: Niu Zhenye , Li Yuxia , Gong Yushu , Zhang Bowei , He Yuan , Zhang Jinglin , Tian Mengyu , He Lei
Published: January 2025

In the scenario of limited labeled remote-sensing datasets, the model’s performance is constrained by the insufficient availability of data. Generative model-based data augmentation has emerged as a promising solution to this limitation. While existing generative models perform well in natural scene domains (e.g., faces and street scenes), their performance in remote sensing is hindered by severe data imbalance and the semantic similarity among land-cover classes. To tackle these challenges, we propose the Multi-Class Guided GAN (MCGGAN), a novel network for generating remote-sensing images from semantic labels. Our model features a dual-branch architecture with a global generator that captures the overall image structure and a multi-class generator that improves the quality and differentiation of land-cover types. To integrate these generators, we design a shared-parameter encoder for consistent feature encoding across two branches, and a spatial decoder that synthesizes outputs from the class generators, preventing overlap and confusion. Additionally, we employ perceptual loss (LVGG) to assess perceptual similarity between generated and real images, and texture matching loss (LT) to capture fine texture details. To evaluate the quality of image generation, we tested multiple models on two custom datasets (one from Chongzhou, Sichuan Province, and another from Wuzhen, Zhejiang Province, China) and a public dataset LoveDA. The results show that MCGGAN achieves improvements of 52.86 in FID, 0.0821 in SSIM, and 0.0297 in LPIPS compared to the Pix2Pix baseline. We also conducted comparative experiments to assess the semantic segmentation accuracy of the U-Net before and after incorporating the generated images. The results show that data augmentation with the generated images leads to an improvement of 4.47% in FWIoU and 3.23% in OA across the Chongzhou and Wuzhen datasets. Experiments show that MCGGAN can be effectively used as a data augmentation approach to improve the performance of downstream remote-sensing image segmentation tasks.
RockCloud-Align: A High-Precision Benchmark for Rock-Mass Point-Cloud Registration
Authors: Wang Yunbiao , Yu Dongbo , Liu Lupeng , Xiao Jun
Published: January 2025

Rock-mass point-cloud registration is a critical yet challenging task in the fields of geology and engineering. Currently, the lack of dedicated datasets for rock-mass point-cloud registration significantly limits the development and application of advanced algorithms in this area. To address this gap, we introduce RockCloud-Align, a large-scale dataset specifically designed for rock-mass point-cloud registration. Created using high-resolution LiDAR scans, this dataset covers a wide range of geological scenarios with varying densities and includes over 14,000 meticulously curated point-cloud pairs. RockCloud-Align provides a comprehensive benchmark for evaluating registration algorithms, along with a robust evaluation protocol to standardize the assessment of these methods. Building upon this dataset, we propose a novel registration method that eliminates the dependence on feature points and random sampling consensus, ensuring high efficiency and precision across diverse scenes and densities. Extensive experiments demonstrate that the proposed method significantly outperforms existing approaches in both accuracy and computational efficiency.
Geometry-Based Synchrosqueezing S-Transform with Shifted Instantaneous Frequency Estimator Applied to Gearbox Fault Diagnosis
Authors: Zhu Xinping , Shi Wuxi , Huang Zhongxing , Shi Liqing
Published: January 2025

This paper introduces a novel geometry-based synchrosqueezing S-transform (GSSST) for advanced gearbox fault diagnosis, designed to enhance diagnostic precision in both planetary and parallel gearboxes. Traditional time-frequency analysis (TFA) methods, such as the Synchrosqueezing S-transform (SSST), often face challenges in accurately representing fault-related features when significant mode closely spaced components are present. The proposed GSSST method overcomes these limitations by implementing an intuitive geometric reassignment framework, which reassigns time-frequency (TF) coefficients to maximize energy concentration, thereby allowing fault components to be distinctly isolated even under challenging conditions. The GSSST algorithm calculates a new instantaneous frequency (IF) estimator that aligns closely with the ideal IF, thus concentrating TF coefficients more effectively than existing methods. Experimental validation, including tests on simulated signals and real-world gearbox fault data, demonstrates that GSSST achieves high robustness and diagnostic accuracy across various types of gearbox faults even in the presence of noise. Moreover, unlike conventional reassignment method, GSSST supports partial signal reconstruction, a key advantage for applications requiring accurate signal recovery. This research highlights GSSST as a promising and versatile tool for diagnosing complex mechanical faults and provides new insights for the future development of TFA methods in mechanical fault analysis.
Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification
Authors: Li Jiachen , Gong Xiaojin
Published: January 2025

Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attracted growing attention due to its practical relevance. While numerous methods have been proposed, most rely on discriminative or contrastive learning frameworks to learn generalizable feature representations. However, these approaches often fail to mitigate shortcut learning, leading to suboptimal performance. In this work, we propose a novel method called diffusion model-assisted representation learning with a correlation-aware conditioning scheme (DCAC) to enhance DG Re-ID. Our method integrates a discriminative and contrastive Re-ID model with a pre-trained diffusion model through a correlation-aware conditioning scheme. By incorporating ID classification probabilities generated from the Re-ID model with a set of learnable ID-wise prompts, the conditioning scheme injects dark knowledge that captures ID correlations to guide the diffusion process. Simultaneously, feedback from the diffusion model is back-propagated through the conditioning scheme to the Re-ID model, effectively improving the generalization capability of Re-ID features. Extensive experiments on both single-source and multi-source DG Re-ID tasks demonstrate that our method achieves state-of-the-art performance. Comprehensive ablation studies further validate the effectiveness of the proposed approach, providing insights into its robustness.
Development of Robust MWCNT Hydrogel Electrochemical Biosensor for Pyocyanin Detection by Phosphotungstic Acid Modification
Authors: Xue Ting , Gao Lei , Dai Xianying , Ma Shenhui , Bu Yuyu , Wan Yi
Published: January 2025

The trace detection of pyocyanin (PCN) is crucial for infection control, and electrochemical sensing technology holds strong potential for application in this field. A pivotal challenge in utilizing carbon materials within electrochemical sensors lies in constructing carbon-based films with robust adhesion. To address this issue, a novel composite hydrogel consisting of multi-walled carbon nanotubes/polyvinyl alcohol/phosphotungstic acid (MWCNTs/PVA/PTA) was proposed in this study, resulting in the preparation of a highly sensitive and stable PCN electrochemical sensor. The sensor is capable of achieving stable and continuous detection of PCN within the range of 5–100 μM across a variety of complex electrolyte environments. The limit of detection (LOD) is as low as 1.67 μM in PBS solution, 2.71 μM in LB broth, and 3.63 μM in artificial saliva. It was demonstrated that the introduction of PTA can complex with PVA through hydrogen bonding to form a stabilized hydrogel architecture, effectively addressing issues related to inadequate film adhesion and unstable sensing characteristics observed with MWCNTs/PVA alone. By adjusting the content of PTA within the hydrogel, an increase followed by a subsequent decrease in sensing current response was observed, elucidating how PTA regulates the active sites and conductive network of MWCNTs on the sensor surface. This study provides a new strategy for constructing stable carbon-based electrochemical sensors and offers feasible assistance towards advancing PCN electrochemical sensors for practical applications.
Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview
Authors: Visconti Paolo , Rausa Giuseppe , Del-Valle-Soto Carolina , Velázquez Ramiro , Cafagna Donato , De Fazio Roberto
Published: January 2025

In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management.
Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest
Authors: Sonalcan Hamza , Bilen Enes , Ateş Bahar , Seçkin Ahmet Çağdaş
Published: January 2025

In this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes discomfort for athletes. Data were collected from 21 collegiate basketball players, and movements such as dribbling, passing, shooting, layup, and standing still were recorded. The collected IMU data underwent preprocessing and feature extraction, followed by the application of machine learning algorithms including KNN, decision tree, Random Forest, AdaBoost, and XGBoost. Among these, the XGBoost algorithm with a window size of 250 and a 75% overlap yielded the highest accuracy of 96.6%. The system demonstrated superior performance compared to other single-sensor systems, achieving an overall classification accuracy of 96.9%. This research contributes to the field by presenting a new dataset of basketball movements, comparing the effectiveness of various feature extraction and machine learning methods, and offering a scalable, efficient, and accurate action recognition system for basketball.
Research on Fire Detection of Cotton Picker Based on Improved Algorithm
Authors: Shi Zhai , Wu Fangwei , Han Changjie , Song Dongdong
Published: January 2025

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers.
Identifying the Primary Kinetic Factors Influencing the Anterior–Posterior Center of Mass Displacement in Barbell Squats: A Factor Regression Analysis
Authors: Chen Diwei , Sun Dong , Li Fengping , Wang Dongxu , Zhou Zhanyi , Gao Zixiang , Gu Yaodong
Published: January 2025

Background: Barbell squats are commonly used in strength training, but the anterior–posterior displacement of the Center of Mass (COM) may impair joint stability and increase injury risk. This study investigates the key factors influencing COM displacement during different squat modes.; Methods: This study recruited 15 male strength training enthusiasts, who performed 60% of their one-repetition maximum (1RM) in the Front Barbell Squat (FBS), High Bar Back Squat (HBBS), and Low Bar Back Squat (LBBS). Joint moments at both the hip, knee, and ankle were collected using a motion capture system and force plates, and a factor regression analysis was conducted using SPSS.; Results: In the FBS, primary factors influencing COM displacement included right knee adduction–abduction (38.59%), knee flexion–extension (31.08%), and hip internal–external rotation (29.83%). In the HBBS, they were right ankle internal–external rotation (19.13%), hip flexion–extension (−19.07%), and left knee flexion–extension (19.05%). In the LBBS, the key factors were left knee adduction–abduction (27.82%), right ankle internal–external rotation (27.59%), and left ankle internal–external rotation (26.12%).; Conclusion: The study identifies key factors affecting COM displacement across squat modes, with knee flexion–extension being dominant in the FBS and hip moments more significant in the HBBS and LBBS. These findings have implications for optimizing squat training and injury prevention strategies.
Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period
Authors: Yoon Seonjun , Kim Hyunsoo
Published: January 2025

In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study proposes an integrated monitoring framework that combines computer vision-based object detection and document recognition techniques. The system utilizes YOLOv5 for detecting jack supports in both construction drawings and on-site images captured through wearable cameras, while optical character recognition (OCR) and natural language processing (NLP) extract installation and dismantling timelines from work orders. The proposed framework enables continuous monitoring and ensures compliance with retention periods by aligning on-site data with documented requirements. The analysis includes 23 jack supports monitored daily over 28 days under varying environmental conditions, including lighting changes and structural configurations. The results demonstrate that the system achieves an average detection accuracy of 94.1%, effectively identifying discrepancies and reducing misclassifications caused by structural similarities and environmental variations. To further enhance detection reliability, methods such as color differentiation, construction plan overlays, and vertical segmentation were implemented, significantly improving performance. This study validates the effectiveness of integrating visual and textual data sources in dynamic construction environments. The study supports the development of automated monitoring systems by improving accuracy and safety measures while reducing manual intervention, offering practical insights for future construction site management.
Ground-Target Recognition Method Based on Transfer Learning
Authors: Zhou Qiuzhan , Hu Jikang , Wu Huinan , Wang Cong , Liu Pingping , Yao Xinyi
Published: January 2025

A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small size, low power consumption, strong concealment, easy installation, and low power consumption. However, existing recognition algorithms generally suffer from problems such as the inability to recognize long-distance moving targets and adapt to new environments, as well as low recognition accuracy. Here, we demonstrate that applying transfer learning to recognition algorithms can adapt to new environments and improve accuracy. We proposed a new moving ground-target recognition algorithm based on CNN and domain adaptation. We used convolutional neural networks (CNNS) to extract depth features from target vibration signals to identify target types. We used transfer learning to make the algorithm more adaptable to environmental changes. Our results show that the proposed moving ground-target recognition algorithm can identify target types, improve accuracy, and adapt to a new environment with good performance. We anticipate that our algorithm will be the starting point for more complex recognition algorithms. For example, target recognition algorithms based on multi-modal fusion and transfer learning can better meet actual needs.
A Piecewise Linearization Based Method for Crossed Frequency Admittance Matrix Model Calculation of Harmonic Sources
Authors: Yang Youhang , Wang Shaorong , Shi Mingming , Zheng Xian
Published: January 2025

The integration of large-scale power electronic equipment has intensified harmonic issues in power systems. Accurate harmonic models are fundamental for evaluating and mitigating harmonic problems, but existing models still exhibit deficiencies in harmonic mechanism, model complexity and accuracy. This work proposes a calculation method of crossed frequency admittance matrix (CFAM) analytical model based on piecewise linearization, aiming to achieve accurate modeling of phase-controlled power electronic harmonic sources. Firstly, the traditional CFAM model construction methods are introduced, and the shortcomings in harmonic modeling are discussed. Subsequently, the parameter-solving process of the CFAM analytical model based on piecewise linearization is proposed. This method improves the accuracy of harmonic modeling and simplifies the construction process of the analytical model. Furthermore, taking single-phase and three-phase bridge rectifiers as examples, CFAM analytical models under intermittent and continuous load current conditions are established, respectively, and the unified harmonic models for both conditions are summarized. Finally, case studies of rectifier harmonic sources under varying circuit control parameters and supply voltage distortions are conducted through Matlab/Simulink and experiments. The results demonstrate that the proposed method provides higher accuracy and more stable performance for harmonic current estimation compared with the traditional CFAM model and constant current source model.
Impact of the “21st Century Maritime Silk Road” Construction on the Efficiency of China’s Coastal Ports
Authors: Huang Shunquan , Huo Yongsheng , Xiao Guangnian
Published: January 2025

The “Belt and Road” initiative centrally embodies the Chinese government’s new concept of further strengthening regional cooperation and opening up to the outside world. The “21st Century Maritime Silk Road” (MSR) is a key component of this initiative. This study uses the MSR policy as a quasi-natural experiment to explore its impact on the efficiency of China’s coastal ports and the mechanisms underlying this effect, employing a difference-in-differences (DID) model based on panel data from major coastal ports in China between 2011 and 2022. The study finds that the policy of the MSR can significantly contribute to the efficiency of ports along the route, and this conclusion still holds after a series of robustness tests that take into account endogeneity issues and the exclusion of other policy interferences. From the analysis of heterogeneity, the construction of the MSR can effectively promote port efficiency in the southern region and large cities. From the perspective of the impact mechanism, this policy mainly promotes port efficiency by mentioning the optimization of the human capital structure, the improvement of the level of industrialization, and the construction of infrastructure. The conclusions of the study are of great significance in the advancement of high-quality sustainable development of ports along China’s routes.
Correction: Gül, E.; Esen, S. High Desertification Susceptibility in Forest Ecosystems Revealed by the Environmental Sensitivity Area Index (ESAI). Sustainability 2024, 16, 10409
Authors: Gül Ebru , Esen Serhat
Published: January 2025

The authors would like to make the following corrections about the published paper [...]