bluuebunny/crossref_metadata_2025 huggingface dataset:
Distinct Clusters of Testosterone Levels, Symptoms, and Serum Trace Elements in Young Men: A Cross-Sectional Analysis
Authors: Tanaka Takazo , Kojo Kosuke , Suetomi Takahiro , Nagumo Yoshiyuki , Midorikawa Haruhiko , Matsuda Takaaki , Nakazono Ayumi , Shimizu Takuya , Fujimoto Shunsuke , Ikeda Atsushi , Kandori Shuya , Negoro Hiromitsu , Takayama Tatsuya , Nishiyama Hiroyuki
Published: February 2025

Background/Objectives: Modern societal stressors have been linked to declining testosterone levels among young men, contributing to somatic, psychological, and sexual health problems. Despite growing evidence suggesting a link between trace elements and testosterone-related symptoms, there are only a few comprehensive analyses on younger populations. This study’s aim was to examine how serum trace elements modulate the relationship between testosterone levels and symptom severity. Methods: This cross-sectional study included 225 young men seeking infertility consultation in Japan. Serum total and free testosterone levels were measured, along with self-reported symptoms using the Aging Males’ Symptoms scale (somatic, psychological, sexual) and the Erection Hardness Score. The serum concentrations of 20 trace elements were measured. We used unsupervised clustering to classify participants based on testosterone levels and symptom severity and then compared the distribution of trace elements among the resulting clusters. Results: Three distinct clusters emerged: (1) lowest testosterone with highest symptom severity, (2) intermediate, and (3) highest testosterone with minimal symptoms. Interestingly, the intermediate cluster displayed low testosterone levels but minimal symptoms. Eleven trace elements (phosphorus, sulfur, potassium, calcium, iron, zinc, arsenic, rubidium, strontium, molybdenum, and cesium) were identified as potential contributors to testosterone dynamics. Weighted quantile sum regression indicated that phosphorus, strontium, and molybdenum negatively influenced testosterone outcomes, whereas iron, sulfur, and zinc were beneficial. Conclusions: Serum trace element profiles are significantly associated with testosterone levels and symptom severity in young men. Targeted interventions may address testosterone decline and its implications. These findings may help develop tailored strategies for optimizing male health.
Dietary and Hygiene-Related Knowledge Versus Reported Behaviors of Eighteen-Year-Olds: A Nationwide Cross-Sectional Study
Authors: Olczak-Kowalczyk Dorota , Studnicki Marcin , Turska-Szybka Anna
Published: February 2025

Background/Objectives: The aim of the present study was to examine eighteen-year-olds’ self-assessed knowledge about diet and hygiene, behaviors that they report, and education they receive during dental appointments. Methods: Questionnaires distributed among 1611 subjects included questions concerning sociodemographic factors, dietary and hygiene knowledge and behaviors, and dental visits. Spearman’s rank correlation and a bivariate and multivariate logistic regression analysis was conducted; odds ratios (ORs) and adjusted odds ratios (AORs) were calculated (p ≤ 0.05). Results: Respondents self-assessed their oral health knowledge; 63.1% of them considered it to be limited, and 40.7% understood the cariogenic effect of frequent snacking. Dentists and a dental team (DT) were the main source of information (55.7%). Dietary advice was recommended by 10.8% of dentists and the DT, while check-up appointments were advised by 49.2%. Parents’ higher education contributed to a lower intake of cariogenic food. This effect was stronger for the mother’s education. Dental appointments scheduled twice a year increased the chances of consuming healthy food (A2OR = 1.21 (1.07–2.11); p = 0.0028)). Being informed increased the chances of toothbrushing ≥ 2 times (OR: 1.21, CI 1.10–1.46), using fluoridated toothpaste (OR: 1.26 CI1.05–1.55), and the frequency of appointments (A1OR: 1.56 CI 1.21–1.87; A2OR: 1.78 CI 1.54–1.91). Conclusions: Knowledge and oral health related behaviors, as well as the involvement of the dental team in education, are inadequate. Education and instruction in the dental office has a favorable influence on oral-health-related behaviors. It is, therefore, necessary to undertake systemic solutions so that dental practitioners are more involved. Providing oral health knowledge should be the standard of care.
Electrochemical Analysis and Inhibition Assay of Immune-Modulating Enzyme, Indoleamine 2,3-Dioxygenase
Authors: Mie Yasuhiro , Mikami Chitose , Yasutake Yoshiaki , Shigemura Yuki , Yamashita Taku , Tsujino Hirofumi
Published: February 2025

Background: An accurate and rapid analysis of human indoleamine 2,3-dioxygenase (hIDO) is crucial for the development of anticancer pharmaceuticals because of the role of hIDO in promoting tumoral immune escape. However, the conventional assay of hIDO is limited by interference from reductants, which are used to reduce the heme iron to begin the hIDO catalytic reaction. Methods: A direct electrochemical method was applied to drive the hIDO reaction. Results: The nanostructured gold electrode enabled the electrochemical reduction of the heme iron of hIDO1. In the presence of substrates (tryptophan and oxygen), a bioelectrocatalytic current was observed, confirming an electrochemically driven hIDO reaction. A well-known inhibitor of hIDO, epacadostat, hindered this catalytic signal according to its concentration, demonstrating the rapid evaluation of its inhibition activity for the hIDO reaction. Through an in silico study using the proposed electrochemical assay system, we discovered a strong inhibitor candidate with a half-maximal inhibitory concentration of 10 nM. Conclusions: An accurate and rapid assay system in drug discovery for hIDO and kynureine pathway-targeted immunotherapy has been developed.
Special Issue “Pharmaceutical Solid Forms: From Crystal Structure to Formulation”
Authors: Espeau Philippe
Published: February 2025

This Special Issue aims to highlight the interest of characterizing the structural aspects of an API before its formulation, as much work is required between the discovery of a molecule with a therapeutic effect and its formulation [...]
Compact Polarization-Insensitive 2 × 2 3 dB Quasi-Adiabatic Coupler Based on Shape Optimization
Authors: Su Guangchen , Chen Hongliang , Fu Xin , Yang Lin
Published: February 2025

In this study, we designed and experimentally demonstrated a compact polarization-insensitive 2 × 2 3 dB quasi-adiabatic coupler based on B-spline curves and shape optimization. By using the supermode to enable the segmented shape optimization of the coupler, we significantly reduced the computational cost of the optimization process. The numerical simulation results exhibited a power imbalance below ±0.46 dB and an insert loss (IL) of less than 0.09 dB over a broad bandwidth of 140 nm, ranging from 1490 nm to 1630 nm for both the TE and TM polarizations, with a compact coupling length of 12 µm. The experimental results showed a power splitting ratio within 3 ± 0.46 dB over the range of 1525 nm–1600 nm for the TM mode and 1576 nm–1610 nm for the TE mode. This broadband and low-loss 3 dB coupler is suitable for microwave photonic (MPW) applications, enabling efficient polarization-independent signal processing in integrated photonic systems.
Quantum Dot Waveguide Array for Broadband Light Sources
Authors: Li Dongyang , Chu Yufei , Xu Qingbo , Liu Dong , Ruan Junying , Sun Hao , Li Jianwei , Guo Chengde , Pu Xiaoyun , Zhang Yuanxian
Published: February 2025

In this paper, we demonstrate a broadband and simultaneous waveguide array light source based on water-soluble CdSe/ZnS quantum dots (QDs). We initially measure the fluorescence intensity for various cladding solution concentrations along the fiber axis to assess their impact on the propagation loss; the experimental results show that the fluorescent intensity decreases with fiber length, with higher concentrations showing a more pronounced decrease. Then, we showcase a synchronous QD light source in an optofluidic chip that fluoresces in red, green, and blue (RGB) within a microfluidic channel. Finally, a 3 × 3 QD array of a fluorescent display on a single PDMS chip is demonstrated. The QD waveguide represents a compact and stable structure that is readily manufacturable, making it an ideal light source for advancing high-throughput biochemical sensing and on-chip spectroscopic analysis.
Transient Heat Transfer Modeling of SERF Co-Magnetometer Atomic Ensemble Based on the Thermoelectric Analogy Method
Authors: Wu Jiaqi , Liu Feng , Duan Lihong , Lv Shaowei , Dou Xihui , Quan Wei
Published: February 2025

Bias instability is one of the most critical factors in the performance of spin-exchange relaxation-free (SERF) co-magnetometers. Previous studies on SERF co-magnetometers have shown that changes in the atomic ensemble temperature can lead to variations in the alkali metal atom density, which in turn affect the optical rotation angle and light shift, ultimately influencing the system’s stability. Building on this understanding, this paper introduces the thermoelectric analogy method for the first time in the transient heat transfer analysis of SERF co-magnetometer atomic ensembles. Using this method, the primary factors affecting the atomic ensemble temperature in a SERF co-magnetometer were analyzed, and transient heat transfer models were established for the following processes: the interaction between the non-magnetic electric heating system and the atomic ensemble temperature, laser heating of the atomic ensemble by the optical system, and the effect of environmental temperature changes on the non-magnetic electric heating system. These models were experimentally validated through active temperature variation experiments. The experimental results show that the proposed transient heat transfer models accurately describe the related heat transfer processes of the atomic ensemble, with model fitting accuracy exceeding 98%. This lays a solid foundation for the high-precision closed-loop control of the atomic ensemble temperature in SERF co-magnetometers and provides valuable insights for the structural design and engineering applications of SERF co-magnetometers.
Photobiological and Biochemical Characterization of Conchocelis and Blade Phases from Porphyra linearis (Rhodophyta, Bangiales)
Authors: Pereira Débora Tomazi , Figueroa Félix L.
Published: February 2025

Porphyra specimens are red macroalgae with significant economic importance for food and pharmaceutical industries due to their physiological activities resulting from their bioactive compounds (BACs). Due to its economic importance, this research aimed to characterize the photosynthetic and biochemical aspects of the conchocelis and blade phases of Porphyra linearis to understand and help improve production of this algae. The algae were cultured for 7 days with nutrients for blade phase measurements, while another portion was cultured without nutrients for 21 days to release carpospores, which were cultivated for 4 months. For both phases, the content of BACs (chlorophyll a, carotenoids, phycobiliproteins, phenols, carbohydrates, proteins, mycosporine-like amino acids), antioxidant activity, and photosynthetic parameters were analyzed. Most of the parameters showed the blade phase had better results than conchocelis, except for carbohydrates. Phycobiliproteins showed no statistical differences between the phases. These findings highlight that conchocelis is not a good BACs source compared to the blade phase, but it is a crucial phase in the life cycle of Porphyra. Understanding the key parameters for maintaining the cultivation of conchocelis stocks for the development of the blade phase is a way to produce macroscopic biomass of this economically important algae throughout the year.
An Implicit Flux-Corrected Transport Algorithm Used for Gas Discharge Calculations
Authors: Morrow Richard
Published: February 2025

An implicit flux-corrected transport (FCT) and diffusion algorithm was developed and used in many gas discharge calculations. Such calculations require the use of a fine mesh where the electric field changes rapidly; that is, near electrodes or in a streamer front. If diffusion is included using an explicit method, then the von Neumann stability condition severely limits the time-step that can be used; however, this limitation does not apply to implicit methods. Further, for gas discharge calculations including space-charge effects, it is necessary to solve the continuity equations with no negative number densities nor point-by-point oscillation in the number density. This is because the electron number densities are finely balanced with the ion number densities to determine the space-charge distribution and hence the electric field which drives the motion of the particles. An efficient way to solve the particle transport equation, with the required properties, is to use FCT. The most accurate form of FCT developed by the author is implicit fourth-order FCT; hence, the method presented incorporates implicit diffusion into the implicit fourth-order FCT scheme to produce a robust algorithm that has been successfully used in many calculations.
Deep-Neural-Networks-Based Data-Driven Methods for Characterizing the Mechanical Behavior of Hydroxyl-Terminated Polyether Propellants
Authors: Han Ruohan , Fu Xiaolong , Qu Bei , Shi La , Liu Yuhang
Published: February 2025

Hydroxyl-terminated polyether (HTPE) propellants are attractive in the weapons materials and equipment industry for their insensitive properties. Storage, combustion, and explosion of solid propellants are affected by their mechanical properties, so accurate mechanical modeling is vital. In this study, deep neural networks are applied to model composite solid-propellant mechanical behavior for the first time. A data-driven framework incorporating a novel training–testing splitting strategy is proposed. By building Neural Networks (FFNNs), Kolmogorov–Arnold Networks (KANs) and Long Short-Term Memory (LSTM) networks and optimizing the model framework and parameters using a Bayesian optimization algorithm, the results show that the LSTM model predicts the stress–strain curve of HTPE propellant with an RMSE of 0.053 MPa, which is 62.7% and 48.5% higher than the FFNNs and the KANs, respectively. The R2 values of the LSTM model for the testing set exceed 0.99, which can effectively capture the effects of tensile rate and temperature changes on tensile strength, and accurately predict the yield point and the slope change of the stress–strain curve. Using the interpretable Shapley Additive Explanations (SHAP) method, fine-grained ammonium perchlorate (AP) can increase its tensile strength, and plasticizers can increase their elongation at break; this method provides an effective approach for HTPE propellant formulation.
Processing and Characterization of Unidirectional Flax Fiber-Reinforced Bio-Based Polyamide 11 Biocomposites
Authors: Hirsch Patrick , Tillner Benjamin , Henkel André , Teuscher Nico , Jahn Ivonne , Feldmann Maik
Published: February 2025

Within this study, the impregnation behavior and resulting mechanical properties of unidirectional flax fiber-reinforced polyamide 11 biocomposites were investigated. Therefore, different grades of bio-based polyamide 11 have been evaluated regarding their eligibility as composite matrix material. The production of the unidirectional flax fiber-reinforced biocomposites was investigated using a continuous film-stacking method. It was found that the flow behavior of the polyamide 11 matrix polymer significantly affected the impregnation quality and the resulting mechanical properties as tested by tensile and bending tests. A lower shear viscosity and stronger shear thinning behavior led to better impregnation, a 15% higher stiffness, and 18% higher strength. This was also analyzed with morphological analysis by scanning electron microscopy. Additionally, the effect of the fiber volume content of the flax fibers on the mechanical properties was tested, showing a positive correlation between the fiber content and the resulting stiffness and strength, leading to an increase of 48% and 55%, respectively. In result, a maximum Young’s modulus of 16.9 GPa and tensile strength of 175 MPa at a fiber volume content of 33% was achieved. Thus, the unidirectional flax fiber-reinforced polyamide 11 biocomposites investigated can be a sustainable construction material for lightweight applications, e.g., in the automotive industry.
Optimized Dispatch of Integrated Energy Systems in Parks Considering P2G-CCS-CHP Synergy Under Renewable Energy Uncertainty
Authors: Zhang Zhiyuan , Li Xiqin , Zhang Lu , Zhao Hu , Wang Ziren , Li Wei , Wang Baosong
Published: February 2025

To enhance low-carbon economies within Park Integrated Energy Systems (PIES) while addressing the variability of wind power generation, an innovative optimization scheduling strategy is proposed, incorporating a reward-and-punishment ladder carbon trading mechanism. This method effectively mitigates the unpredictability of wind power output and integrates Power-to-Gas (P2G), Carbon Capture and Storage (CCS), and Combined Heat and Power (CHP) systems. This study develops a CHP model that combines P2G and CCS, focusing on electric-heat coupling characteristics and establishing constraints on P2G capacity, thereby significantly enhancing electric energy flexibility and reducing carbon emissions. The carbon allowance trading strategy is refined through the integration of reward and punishment coefficients, yielding a more effective trading model. To accurately capture wind power uncertainty, the research employs kernel density estimation and Copula theory to create a representative sequence of daily wind and photovoltaic power scenarios. The Dung Beetle Optimization (DBO) algorithm, augmented by Non-Dominated Sorting (NSDBO), is utilized to solve the resulting multi-objective model. Simulation results indicate that the proposed strategy increases the utilization rates of renewable energy in PIES by 28.86% and 19.85%, while achieving a reduction in total carbon emissions by 77.65% and a decrease in overall costs by 36.91%.
Optimized Layout for High-Level Drilling in a High-Gas Coal Seam to Prevent Gas Overrun
Authors: Gao Kun , Ma Shuonan
Published: February 2025

With the popularization of comprehensive mechanized mining methods and the increase in coal mining intensity, production has become more concentrated and efficient, which inevitably leads to Coal seam accumulates a large amount of gas The existence of huge goaf and mining overburden cracks that form behind the working face provides favorable conditions for the migration of gas to the goaf and its subsequent accumulation. The high concentration of gas that accumulates in the goaf gradually flows toward the working face under the action of pressure and concentration gradients, which can easily cause gas overrun accidents at the working face. Therefore, effective relief of the gas pressure in the goaf is important to guarantee safe and efficient mining at the coal mine working face. One of the most used gas drainage methods in such mines is high-level borehole gas drainage. This method can effectively reduce the gas content of coal seams, ensure the safe production of working faces, and reduce carbon emissions. In this study, the mining of a high-gas and low-permeability extra-thick coal seam in the Shanxi mining area is taken as the engineering background. In order to optimize the extraction design and improve the efficiency of gas extraction, according to the dual characteristics of coal seam pores and cracks, the permeability, and migration form of the gas in the coal body are analyzed, and a COMSOL coal seam gas migration model is established. By controlling different gas extraction horizons, pressure, and the number of boreholes and by optimizing the trajectory of the boreholes, the law of gas migration during high-level borehole gas extraction and the variation law with extraction time and pressure are studied. From this, the effective extraction calculation formula is fitted and statistical analyses are carried out. Through on-site extraction and simulation verification, the gas concentration was found to reach a maximum of 86% at a distance of 23 m from the floor. When using similar extraction times, 20 MPa gas extraction was found to have the best effect. The highest gas concentration in the upper corner was only 0.71%, and the extraction efficiency is higher when the high-level borehole trajectory angle is 30 degrees. The research results have important reference value for gas disaster control in the fully mechanized caving face of high-gas low-permeability and extra-thick coal seams.
Disease Infection Classification in Coconut Tree Based on an Enhanced Visual Geometry Group Model
Authors: Huang Xiaocun , Alobaedy Mustafa Muwafak , Fazea Yousef , Goyal S. B. , Deng Zilong
Published: February 2025

The coconut is a perennial, evergreen tree in the palm family that belongs to the monocotyledonous group. The coconut plant holds significant economic value due to the diverse functions served by each of its components. Any ailment that impacts the productivity of the coconut plantation will ultimately have repercussions on the associated industries and the sustenance of the families reliant on the coconut economy. Deep learning has the potential to significantly alter the landscape of plant disease detection. Convolutional neural networks are trained using extensive datasets that include annotated images of plant diseases. This training enables the models to develop high-level proficiency in identifying complex patterns and extracting disease-specific features with exceptional accuracy. To address the need for a large dataset for training, an Enhanced Visual Geometry Group (EVGG16) model utilizing transfer learning was developed for detecting disease infections in coconut trees. The EVGG16 model achieves effective training with a limited quantity of data, utilizing the weight parameters of the convolution layer and pooling layer from the pre-training model to perform transfer Visual Geometry Group (VGG16) network model. Through hyperparameter tuning and optimized training batch configurations, we achieved enhanced recognition accuracy, facilitating the development of more robust and stable predictive models. Experimental results demonstrate that the EVGG16 model achieved a 97.70% accuracy rate, highlighting its strong performance and suitability for practical applications in disease detection for plantations.
Coal Mining Machine Localization Method Based on Non-Gaussian Summation Parallel Kalman Filter Group
Authors: Xi Chenrong , Zhang Fan , Yu Yang , Song Hui
Published: February 2025

Coal mining machine positioning technology is the key to realizing unmanned and intelligent mining of the comprehensive mining zone. Based on the traditional Strapdown Inertial Navigation System combined with Kalman-filtering coal mining machine positioning technology, non-integrity constraints are introduced, and the error of the output of the above system is filtered by an optimized Kalman filtering method proposed in this paper: non-Gaussian summation and a parallel Kalman filter bank. This method decomposes the non-Gaussian system into a linear combination of multiple Gaussian systems through the parallel Kalman filter group, then fuses the states occupying different weight coefficients and designs a method of Gaussian-term number trimming to solve the problem of parameter explosion in the filtering process, and ultimately obtains the optimal estimation of the positioning information of the coal mining machine. Experiments show that, for the coal mining machine positioning issue in the complex noise interference environment of intelligent mines, the non-Gaussian summation and parallel Kalman filter group method in this paper, compared with the traditional particle filtering method, greatly reduces the three-dimensional attitude error, three-dimensional velocity error, three-dimensional position error in the nine dimensional parameters of the estimation error, and the average estimation error. The average estimation error is reduced by 49%, 52%, 50%, 53%, 51%, 48.8%, 50.1%, 54%, and 51.3%, respectively, which significantly improves the positioning accuracy of coal mining machines, and has stronger real-time performance, stability, and accuracy in the coal mining machine positioning system.
Experimental Study on the Acid Fracturing Fracture Propagation Law of a Fractured Carbonate Reservoir in the Majiagou Formation
Authors: Zhang Yongchun , Kuang Jianchao , Zhang Hao , Zhong Ying , Dong Shijie
Published: February 2025

Acid fracturing is a crucial method for reservoir reconstruction in carbonate reservoirs, and the propagation pattern of acid-etched fractures plays a key role in determining the scope of reservoir enhancement and post-fracturing productivity. However, large-scale physical simulations directly using acid solutions in fracturing experiments are limited, and the fracture propagation patterns under acid fracturing remain unclear. To address this gap, in this study, we collected carbonate rock samples from the Majiagou Formation in the Daniudi area, preparing large-scale fracturing specimens with side lengths of 30 cm. The propagation of acid fracturing fractures was investigated using self-developed true-triaxial acid fracturing equipment. Based on post-fracturing fracture morphology and pressure curves, the effects of fracturing fluid type, injection rate, injection mode, and natural fractures (NFs) on acid fracturing fracture propagation were analyzed. The experimental results showed that the acid solution effectively weakens the mechanical properties of the open-hole section, creating multiple mechanical weak points and promoting the initiation of fractures. Pre-fracturing treatment with low-viscosity acid can significantly enhance fracture complexity near the wellbore and expand the near-well stimulation zone. Lowering the injection rate increases the acid solution’s filtration loss into natural fractures, weakening the cementation strength of these fractures and encouraging the formation of complex fracture networks. Furthermore, employing a multi-stage alternating injection of high-viscosity and low-viscosity acids can reduce fracture temperature and acid filtration loss while also enhancing differential etching through viscous fingering. This approach improves the conductivity and conductivity retention of the acid-etched fractures. The results of this study can provide a reference for the acid fracturing stimulation of fractured carbonate reservoirs.
Development of an LC-MS Method for the Quantitative Determination of Six Organic Acids Produced by Bacterial Fermentation In Vitro and In Vivo
Authors: Wang Jing , Tang Xuxiao , Qu Chao , Zhang Yingzhong , Zhang Chaoqun , Chu Qiulu , Wang Hao
Published: February 2025

Due to the strong polarity and volatility of organic acids, there is often a lack of effective quantitative methods for organic acids in vivo or in vitro. This study introduced a robust and scientifically validated LC-MS methodology for quantitatively analyzing six organic acids, encompassing five short-chain fatty acids and lactate, observed in in vitro fermentation and human fecal samples. This method was able to achieve precise quantification through the monitoring of mass-to-charge (m/z) ratios of the deprotonated negative ions. After optimization, a 2:1 (v/v) dichloromethane/acetonitrile mixture was utilized to extract the crude acid mix from fermentation or fecal samples. Subsequently, the organic acids were isolated using 3 mL of 3.5 M ammonium hydroxide solution from mixtures. The calibration curves for six organic acids demonstrated linearity with R2 > 0.991 across the concentration ranges of 0.01–5.0 or 0.01–20 mM. The coefficient of variation and accuracy were 2–13% and 95–128%, respectively, which exhibited good precision and sensitivity. Accurate quantification of organic acids would aid in metabolic analysis in bacterial culture supernatants and human fecal matter.
Risk Assessment Method for Power Distribution Systems Based on Spatiotemporal Characteristics of the Typhoon Disaster Chain
Authors: Chen Bin , Sun Nuoling , Chen Hao , Zhang Linyao , Wan Jiawei , Su Jie
Published: February 2025

In recent years, power outages due to typhoon-induced rainstorms, waterlogging, and other extreme weather events have become increasingly common, and accurately assessing the risk of damage to the distribution system during a disaster is critical to enhancing the resilience of the power system. Therefore, a risk assessment method for power distribution systems considering the spatiotemporal characteristics of the typhoon disaster chain is proposed. The mechanism of forming the typhoon disaster chain is first analyzed and its spatiotemporal characteristics are modeled. Secondly, the failure probability of the distribution system equipment during the evolution process of the disaster chain is modeled. Then, the non-sequential Monte Carlo state sampling method combined with the distribution system risk assessment index is proposed to establish the disaster risk assessment system of the distribution system. Finally, based on the IEEE 33-bus power system, the proposed distribution system disaster risk assessment method is verified. Simulation solutions show that the proposed assessment method can effectively assess the disaster risk of the distribution system under the influence of the typhoon disaster chain. The simulation results show that at the time step of typhoon landfall, the load shedding reaches 1315.3 kW with a load shedding rate of 35.4%. The total economic loss at the time step is 2,289,200 CNY. These results demonstrate the effectiveness of the proposed method in assessing disaster risks and improving the resilience of power systems during typhoon events.
Quantitative Identification of Emission Sources and Emission Dynamics of Pressure-Relieved Methane Under Variable Mining Intensities
Authors: Chen Xuexi , Chen Xingyu , Hu Jiaying , Xiao Jian , Sun Jihong , Yan Zhilong
Published: February 2025

This study addresses the abnormal emission of pressure-relieved methane under high-intensity mining conditions by integrating geostatistical inversion, FLAC3D-COMSOL coupled numerical simulations, and stable carbon–hydrogen isotopic tracing. Focusing on the 12023 working face at Wangxingzhuang Coal Mine, we established a heterogeneous methane reservoir model to analyze the mechanical responses of surrounding rock, permeability evolution, and gas migration patterns under mining intensities of 2–6 m/d. Key findings include the following: (1) When the working face advanced 180 m, vertical stress in concentration zones increased significantly with mining intensity, peaking at 12.89% higher under 6 m/d compared to 2 m/d. (2) Higher mining intensities exacerbated plastic failure in floor strata, with a maximum depth of 47.9 m at 6 m/d, enhancing permeability to 223 times the original coal seam. (3) Isotopic fingerprinting and multi-method validation identified adjacent seams as the dominant gas source, contributing 77.88% of total emissions. (4) Implementing targeted long directional drainage boreholes in floor strata achieved pressure-relief gas extraction efficiencies of 34.80–40.95%, reducing ventilation air methane by ≥61.79% and maintaining return airflow methane concentration below 0.45%. This research provides theoretical and technical foundations for adaptive gas control in rapidly advancing faces through stress–permeability coupling optimization, enabling the efficient interception and resource utilization of pressure-relieved methane. The outcomes support safe, sustainable coal mining practices and advance China’s Carbon Peak and Neutrality goals.
Electric Load Prediction of Electric Vehicle Charging Stations Based on Moving Average–Gated Recurrent Unit
Authors: Huang Wei , Ru Chuanhong , Qin Jian , Lin Yong , Cai Qingxi , Song Bing
Published: February 2025

The load prediction of electric vehicle charging stations is the basis of their static safety, which directly affects the safety of operation, the rationality of planning, and the economy of supply. However, various factors lead to drastic changes in short-term power consumption, which makes the data more complicated and difficult to predict. In this paper, the moving average–gated recurrent unit method is proposed to predict the electric load of electric vehicle charging stations. A prediction model is established based on the historical data of electric load of electric vehicle charging stations to realize the accurate prediction of future electric loads. Firstly, considering the problems of noise in the historical data of electric vehicle charging stations, the moving average method is used for smoothing. Secondly, the smoothed data are modeled by the gated recurrent unit, and the future prediction results are obtained. Finally, the validity and practicability of the proposed method are proved by the research and testing of the actual electric vehicle charging station power load dataset. Compared with the classic LSTM prediction model, the proposed MA-GRU method can achieve more accurate prediction performance.
Assessing Safety in Physical Human–Robot Interaction in Industrial Settings: A Systematic Review of Contact Modelling and Impact Measuring Methods
Authors: Mudiyanselage Buddhika Piyumal Bandara Samarathunga Samarathunga , Valori Marcello , Legnani Giovanni , Fassi Irene
Published: February 2025

As collaborative robots (cobots) increasingly share workspaces with humans, ensuring safe physical human–robot interaction (pHRI) has become paramount. This systematic review addresses safety assessment in pHRI, focussing on the industrial field, with the objective of collecting approaches and practices developed so far for modelling, simulating, and verifying possible collisions in human–robot collaboration (HRC). To this aim, advances in human–robot collision modelling and test-based safety evaluation over the last fifteen years were examined, identifying six main categories: human body modelling, robot modelling, collision modelling, determining safe limits, approaches for evaluating human–robot contact, and biofidelic sensor development. Despite the reported advancements, several persistent challenges were identified, including the over-reliance on simplified quasi-static models, insufficient exploration of transient contact dynamics, and a lack of inclusivity in demographic data for establishing safety thresholds. This analysis also underscores the limitations of the biofidelic sensors currently used and the need for standardised validation protocols for the impact scenarios identified through risk assessment. By providing a comprehensive overview of the topic, this review aims to inspire researchers to address underexplored areas and foster innovation in developing advanced, but suitable, models to simulate human–robot contact and technologies and methodologies for reliable and user-friendly safety validation approaches. Further deepening those topics, even combined with each other, will bring about the twofold effect of easing the implementation while increasing the safety of robotic applications characterised by pHRI.
Burned Areas Mapping Using Sentinel-2 Data and a Rao’s Q Index-Based Change Detection Approach: A Case Study in Three Mediterranean Islands’ Wildfires (2019–2022)
Authors: Tiengo Rafaela , Merino-De-Miguel Silvia , Uchôa Jéssica , Guiomar Nuno , Gil Artur
Published: February 2025

This study explores the application of remote sensing-based land cover change detection techniques to identify and map areas affected by three distinct wildfire events that occurred in Mediterranean islands between 2019 and 2022, namely Sardinia (2019, Italy), Thassos (2022, Greece), and Pantelleria (2022, Italy). Applying Rao’s Q Index-based change detection approach to Sentinel-2 spectral data and derived indices, we evaluate their effectiveness and accuracy in identifying and mapping burned areas affected by wildfires. Our methodological approach implies the processing and analysis of pre- and post-fire Sentinel-2 imagery to extract relevant indices such as the Normalized Burn Ratio (NBR), Mid-infrared Burn Index (MIRBI), Normalized Difference Vegetation Index (NDVI), and Burned area Index for Sentinel-2 (BAIS2) and then use (the classic approach) or combine them (multidimensional approach) to detect and map burned areas by using a Rao’s Q Index-based change detection technique. The Copernicus Emergency Management System (CEMS) data were used to assess and validate all the results. The lowest overall accuracy (OA) in the classical mode was 52%, using the BAIS2 index, while in the multidimensional mode, it was 73%, combining NBR and NDVI. The highest result in the classical mode reached 72% with the MIRBI index, and in the multidimensional mode, 96%, combining MIRBI and NBR. The MIRBI and NBR combination consistently achieved the highest accuracy across all study areas, demonstrating its effectiveness in improving classification accuracy regardless of area characteristics.
CSMR: A Multi-Modal Registered Dataset for Complex Scenarios
Authors: Li Chenrui , Gao Kun , Hu Zibo , Yang Zhijia , Cai Mingfeng , Cheng Haobo , Zhu Zhenyu
Published: February 2025

Complex scenarios pose challenges to tasks in computer vision, including image fusion, object detection, and image-to-image translation. On the one hand, complex scenarios involve fluctuating weather or lighting conditions, where even images of the same scenarios appear to be different. On the other hand, the large amount of textural detail in the given images introduces considerable interference that can conceal the useful information contained in them. An effective solution to these problems is to use the complementary details present in multi-modal images, such as visible-light and infrared images. Visible-light images contain rich textural information while infrared images contain information about the temperature. In this study, we propose a multi-modal registered dataset for complex scenarios under various environmental conditions, targeting security surveillance and the monitoring of low-slow-small targets. Our dataset contains 30,819 images, where the targets are labeled as three classes of “person”, “car”, and “drone” using Yolo format bounding boxes. We compared our dataset with those used in the literature for computer vision-related tasks, including image fusion, object detection, and image-to-image translation. The results showed that introducing complementary information through image fusion can compensate for missing details in the original images, and we also revealed the limitations of visual tasks in single-modal images with complex scenarios.
Adaptive Differential Event Detection for Space-Based Infrared Aerial Targets
Authors: Guo Lan , Rao Peng , Gao Cong , Su Yueqi , Li Fenghong , Chen Xin
Published: February 2025

Space resources are of economic and strategic value. Infrared (IR) remote sensing, unaffected by geography and weather, is widely used in weather forecasting and defense. However, detecting small IR targets is challenging due to their small size and low signal-to-noise ratio, and the resulting low detection rates (DRs) and high false alarm rates (FRs). Existing algorithms struggle with complex backgrounds and clutter interference. This paper proposes an adaptive differential event detection method for space-based aerial target observation, tailored to the characteristics of target motion. The proposed IR differential event detection mechanism uses trigger rate feedback to dynamically adjust thresholds for strong, dynamic radiation backgrounds. To accurately extract targets from event frames, a lightweight target detection network is designed, incorporating an Event Conversion and Temporal Enhancement (ECTE) block, a Spatial-Frequency Domain Fusion (SFDF) block, and a Joint Spatial-Channel Attention (JSCA) block. Extensive experiments on simulated and real datasets demonstrate that the method outperforms state-of-the-art algorithms. To advance research on IR event frames, this paper introduces SITP-QLEF, the first remote-sensing IR event dataset designed for dim and moving target detection. The algorithm achieves an mAP@0.5 of 96.3%, an FR of 4.3 ×10−5, and a DR of 97.5% on the SITP-QLEF dataset, proving the feasibility of event detection for small targets in strong background scenarios.
Sparse Regularization Least-Squares Reverse Time Migration Based on the Krylov Subspace Method
Authors: Peng Guangshuai , Gong Xiangbo , Wang Shuang , Cao Zhiyu , Xu Zhuo
Published: February 2025

Least-squares reverse time migration (LSRTM) is an advanced seismic imaging technique that reconstructs subsurface models by minimizing the residuals between simulated and observed data. Mathematically, the LSRTM inversion of the sub-surface reflectivity is a large-scale, highly ill-posed sparse inverse problem, where conventional inversion methods typically lead to poor imaging quality. In this study, we propose a regularized LSRTM method based on the flexible Krylov subspace inversion framework. Through the strategy of the Krylov subspace projection, a basis set for the projection solution is generated, and then the inversion of a large ill-posed problem is expressed as the small matrix optimization problem. With flexible preconditioning, the proposed method could solve the sparse regularization LSRTM, like with the Tikhonov regularization style. Sparse penalization solution is implemented by decomposing it into a set of Tikhonov penalization problems with iterative reweighted norm, and then the flexible Golub–Kahan process is employed to solve the regularization problem in a low-dimensional subspace, thereby finally obtaining a sparse projection solution. Numerical tests on the Valley model and the Salt model validate that the LSRTM based on Krylov subspace method can effectively address the sparse inversion problem of subsurface reflectivity and produce higher-quality imaging results.
Responses of Terrestrial GPP to Extreme Compound Heatwave and Drought Events of Different Intensities in the Yangtze River Basin
Authors: Zhu Jiawen , Guo Haokun , Sun Yankun
Published: February 2025

In 2022, a record-breaking extreme compound heatwave–drought (CHD) event occurred in China’s Yangtze River Basin (YRB), which significantly reduced terrestrial ecosystem Gross Primary Production (GPP), as many previous studies have shown. However, it remains uncertain how GPP responds to extreme CHD events of varying intensities, as well as the differences in GPP responses among different vegetation types in the YRB. This study used two independent GPP products (GPPGOSIF and GPPFluxSat) and Enhanced Vegetation Index (EVIMODIS) data to investigate these uncertainties during three identified CHD years: 2003, 2013, and 2022. In 2022, when the CHD event intensity was the strongest, the YRB experienced the most significant GPP reductions, with amplitudes of −0.37 gC m−2 day−1 (−3.05 standard deviation, hereafter STD) and −0.57 gC m−2 day−1 (−5.97 STD) for GPPGOSIF and GPPFluxSat, respectively. In the less intense years, the year 2003 had less than one-third of GPP reductions in 2022, while the year 2013 even showed an overall slight GPP increase. Moreover, the year 2022 also showed the widest extent with significant GPP reductions exceeding one STD, which was more than twice the grid points in 2003 and 2013. This study also revealed significant differences in GPP responses across different vegetation types. In 2022, GPP reductions were the most pronounced in shrub-dominated areas, followed by evergreen forests, while deciduous forests and mixed forests experienced relatively smaller decreases. However, there were no significant differences among vegetation types in 2003 and 2013. These findings enhance our understanding of the variability in the GPP responses to extreme CHD events of varying intensities in the YRB terrestrial ecosystems, and this enhancement provides an important scientific basis for optimizing related vegetation and carbon cycle models.
Impact and Compensation of Rainfall Propagation Effect on Pol-ISAR Imaging of Parabolic Antenna
Authors: Ju  Xinjie , Li Xinda , Gan  Lin , Yin  Jiapeng , Shen  Chun , Li  Jianbing
Published: February 2025

Orientation is an important parameter to identify the working status of a parabolic antenna. Polarimetric inverse synthetic aperture radar (Pol-ISAR) is an effective approach to sense the parabolic antenna, but the imaging process may be seriously deteriorated by the propagation effect under the rainfall condition. This paper proposes a method to compensate for the propagation effect of rainfall for accurately estimating the orientation parameter of a parabolic antenna from Pol-ISAR images. Numerical simulations show that the impact of the propagation effect for the Pol-ISAR imaging of a parabolic antenna is serious, but it can be well compensated for by the method proposed in this paper.
A Novel Azimuth Channel Errors Estimation Algorithm Based on Characteristic Clusters Statistical Treatment
Authors: Yang Wensen , Tao Ran , Huan Hao , Feng Jing , Chen Longyong , Xu Yihao , Yang Junhua
Published: February 2025

Azimuth multi-channel techniques show great promise in high-resolution, wide-swath synthetic aperture radar systems. However, in practical engineering applications, errors between channels can significantly affect the reconstruction of multi-channel echo data, leading to a degraded synthetic aperture radar image. To address this issue, this article derives the formula expression in the two-dimensional time domain after single-channel processing under the assumption of an insufficient azimuth sampling rate and proposes a novel algorithm based on the statistical treatment of characteristic clusters. In this algorithm, channel imaging is first performed separately; then, the image is divided into a predefined number of sub-images. The characteristic clusters and points within each sub-image are identified, and their positions, amplitude, and phase information are used to obtain the range synchronization errors, amplitude errors, and phase errors between channels. Compared with traditional methods, the proposed method does not require iteration or the complex eigenvalue decomposition of the covariance matrix. Furthermore, it can utilize existing imaging tools and software in single-channel synthetic aperture radar systems. The effectiveness of the proposed method is validated through simulation experiments and real-world data processing.
Enhanced Phase Optimization Using Spectral Radius Constraints and Weighted Eigenvalue Decomposition for Distributed Scatterer InSAR
Authors: Feng Jun , Fan Hongdong , Yuan Yuan , Liu Ziyang
Published: February 2025

Eigenvalue decomposition (EVD) of covariance matrices or coherence matrices has been employed to suppress noise in phase information, and this approach has shown some effectiveness in data processing. However, while this method helps attenuate noisy phase components, it also tends to significantly degrade the true deformation phase information, which can be detrimental in certain applications. To address this issue, this paper proposes an optimal eigenvalue decomposition phase optimization method, incorporating a spectral radius-constrained covariance matrix construction, named SREVD. This method constructs a covariance matrix using spectral radius constraints and then selects optimal eigenvectors from the covariance matrix for weighted combination, yielding the final optimized phase. The advantages of this approach (1) include the use of spectral radius constraints to obtain a stable covariance matrix, and (2) rather than using the eigenvector associated with the maximum eigenvalue for phase optimization, the interferometric phase is reconstructed by a weighted combination of eigenvectors selected through eigenvalue-based optimization. Experimental analysis conducted in a mining area in Datong, Shanxi Province, China, yields the following conclusions: compared to the original interferogram and the traditional EVD-optimized interferogram, the proposed SREVD method demonstrates superior noise suppression. After optimization with SREVD, the density of monitoring points has been significantly improved. The final number of selected points is 9.06 times that of StaMPS and 1.3 times that of EVD optimization, which can better reflect the topographic changes in the study area.