bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Risk Analysis Method of Maglev Train Suspension System Based on Fuzzy Multi-Attribute Decision-Making
Authors: Chen Xiang , Li Xiaolong , Feng Yilu
Published: February 2025

As a new type of rail transit vehicle, maglev trains have extremely high requirements for safety and reliability. With the gradual commercial operation of maglev trains, how to scientifically and effectively assess the safety and analyze the risks of train equipment has become an urgent issue to be addressed. Against the backdrop of the practical application of maglev train projects, this paper integrates domestic and international risk analysis models, proposes the steps for conducting the risk analysis of maglev rail transit, and establishes a risk analysis system for the entire lifecycle of maglev rail transit. Based on the results of fault analysis, a risk analysis of the levitation system is carried out. The theory of multi-attribute decision-making is studied, new risk evaluation indicators are established using triangular fuzzy numbers, the risk levels of the levitation system are determined, and the weak links within the system and the relationships between the pieces of equipment are identified. These efforts provide guidance for enhancing the safety and reliability of train equipment and for carrying out train maintenance work.
Minimum-Fuel Trajectories and Near-Optimal Explicit Guidance for Pinpoint Landing from Low Lunar Orbit
Authors: Caruso Matteo , De Angelis Giulio , Leonardi Edoardo Maria , Pontani Mauro
Published: February 2025

This research addresses minimum-fuel pinpoint lunar landing at the South Pole, focusing on trajectory design and near-optimal guidance aimed at driving a spacecraft from a circular low lunar orbit (LLO) to an instantaneous hovering state above the lunar surface. Orbit dynamics is propagated in a high-fidelity ephemeris-based framework, which employs spherical coordinates as the state variables and includes several harmonics of the selenopotential, as well as third-body gravitational perturbations due to the Earth and Sun. Minimum-fuel two-impulse descent transfers are identified using Lambert problem solutions as initial guesses, followed by refinement in the high-fidelity model, for a range of initial LLO inclinations. Then, a feedback Lambert-based impulsive guidance algorithm is designed and tested through a Monte Carlo campaign to assess the effectiveness under non-nominal conditions related to injection and actuation errors. Because the last braking maneuver is relatively large, a finite-thrust, locally flat, near-optimal guidance is introduced and applied. Simplified dynamics is assumed for the purpose of defining a minimum-time optimal control problem along the last thrust arc. This admits a closed-form solution, which is iteratively used until the desired instantaneous hovering condition is reached. The numerical results in non-nominal flight conditions testify to the effectiveness of the guidance approach at hand in terms of propellant consumption and precision at landing.
Combining Machine Learning Algorithms with Earth Observations for Crop Monitoring and Management
Authors: Piekutowska Magdalena , Niedbała Gniewko , Kujawa Sebastian , Wojciechowski Tomasz
Published: February 2025

Combining machine learning algorithms with Earth observations has great potential in the context of crop monitoring and management, which is essential in the face of global challenges related to food security and climate change [...]
Excavation and Functional Verification of Plant Height Gene BnGPI2-A03 in Brassica napus L.
Authors: Liu Hailiang , Ren Rui , Liu Wei , Yang Bo , He Xin , Liu Zhongsong , Qian Lunwen , Xiong Xinghua
Published: February 2025

Plant height is an important agronomic trait that affects the establishment of the ideal plant type and yield formation of rapeseed. However, the genetic mechanism affecting plant height has not been fully elucidated. Building on previous genome-wide association studies (GWASs), we performed differential gene expression analysis using 14 shoot apex transcriptome datasets derived from materials exhibiting extreme seedling height phenotypes at the bud stage. The promoter and the first exon regions of the Glycosylphosphatidylinositol 2 gene (BnGPI2-A03) of rapeseed were identified as harboring two significant SNPs (Bn-A03-p717776 and Bn-A03-p7178917) associated with plant height. Co-expression network analysis shows that BnGPI2-A03 was associated with many hormone pathway genes, cell wall synthesis pathway genes, and transcription factor genes and formed a potential network regulating the plant height of rapeseed. To examine the function of BnGPI2-A03 on plant height in Brassica napus L., we generated transgenic plants in which BnGPI2-A03 was overexpressed using a 35 s promoter. Overexpression of BnGPI2-A03 elevated the height and main inflorescence length of the transgenic plant by an average of 17.95% and 25.92%, respectively, which showed a positive relationship with the level of BnGPI2-A03. Transcriptome sequencing analysis of the overexpressed transgenic rapeseed stem-tip tissue during the bud stage shows that BnGPI2-A03 is associated with plant cell wall synthesis and cell division pathways. The requirement of BnGPI2-A03 for plant height was further evidenced in natural germplasms by the higher expression levels of BnGPI2-A03 in the stem-tip tissue of extremely high genotypes (Hap1) relative to their ultra-short counterparts (Hap3). These discoveries highlight potential targets for the plant height genetic improvement of rapeseed in the future.
Evaluating the Impact of Green Manure Incorporation on Cotton Yield, Soil Fertility, and Net Eco–Economic Benefits
Authors: Wei Xuewen , Qin Dulin , Yin Zujun , Wang Guoping , Li Lin , Feng Lu , Xu Qinqing
Published: February 2025

Incorporating green manure is a vital strategy for optimizing cropping systems and improving soil quality. However, it is unclear whether the effects of different types of green manure on subsequent cotton yield and soil fertility improvement are uniform. This study evaluated the effects of three green manure incorporation treatments over a two-year cropping cycle (Chinese violet-cotton-Chinese violet-cotton (T1), rapeseed-cotton-rapeseed-cotton (T2), and ryegrass-cotton-hairy vetch-cotton (T3)) on cotton yield and yield components. These treatments were also compared with the winter fallow-cotton (T0) to analyze differences in soil nutrients and net ecological–economic benefits. No significant differences in cotton yield or yield components were observed among the green manure incorporation treatments. However, averaged across two years, T1 produced a seed cotton yield 8.1% higher than T2 and 3.9% higher than T3. T2 and T3 significantly enhanced soil alkali-hydrolyzed nitrogen, organic matter, and total humus content compared to T0. Notably, T3 increased these parameters by 18.7, 23.9, and 26.8%, respectively. Additionally, T3 achieved the highest net ecological–economic benefit, exceeding T0 by $405/ha. This study highlights the potential of green manure to enhance soil fertility and ecological–economic sustainability in cotton fields. Further research is required to evaluate its long-term benefits and broader implications for sustainable agriculture.
Correction: Li et al. Environmental Simulation Model for Rapid Prediction of Tea Seedling Growth. Agronomy 2022, 12, 3165
Authors: Li He , Mao Yilin , Wang Yu , Fan Kai , Shi Hongtao , Sun Litao , Shen Jiazhi , Shen Yaozong , Xu Yang , Ding Zhaotang
Published: February 2025

The journal’s Editorial Office and Editorial Board are jointly issuing a resolution and removal of the Journal Notice linked to this article [...]
Crop–Mushroom Rotation: A Comprehensive Review of Its Multifaceted Impacts on Soil Quality, Agricultural Sustainability, and Ecosystem Health
Authors: Dou Tingting , Zhang Kaixuan , Shi Xiaofei , Liu Wei , Yu Fuqiang , Liu Dong
Published: February 2025

Cross-kingdom rotation offers several agronomic and ecological benefits, including enhanced soil nutrient availability, reduced pest and disease prevalence, improved soil structure, and minimized chemical inputs, which contribute to a dynamic and resilient soil ecosystem, thereby fostering biodiversity and ecological balance. Additionally, crop diversity encourages plant root exudates that feed a wider range of beneficial soil microbes, ultimately leading to a balanced soil food web. Integrating rice cultivation with the edible mushroom Stropharia rugosoannulata further improves soil fertility and enhances organic carbon sequestration. This rotation introduces organic matter into the soil, affecting microbial community structure and supporting the decomposition of complex organic materials via lignocellulose-decomposing fungi. These processes contribute to soil organic carbon accumulation, nutrient cycling, and long-term soil health. The study emphasizes the importance of microbial communities (including live biomass and necromass) in maintaining ecosystem stability and highlights the potential of the rice–S. rugosoannulata rotation model as a sustainable agricultural practice. Further research is needed to clarify how fungal necromass contributes to soil carbon accumulation and to optimize agricultural practices for improving soil health and carbon sequestration in response to climate change. These findings provide valuable insights for developing sustainable agricultural strategies that balance productivity with environmental conservation.
Genetic Evaluation of Resilience Indicators in Holstein Cows
Authors: Kašná Eva , Zavadilová Ludmila , Vařeka Jan
Published: February 2025

The analysis of resilience indicators was based on daily milk yields recorded from 3347 lactations of 3080 Holstein cows located on 10 farms between 2022 and 2024. Six farms used an automatic milking system. A random regression function with a fourth-degree Legendre polynomial was used to predict the lactation curve. The indicators were the natural log-transformed variance (LnVar), lag-1 autocorrelation (r-auto), and skewness (skew) of daily milk yield (DMY) deviations from the predicted lactation curve, as well as the log-transformed variance of DMY (Var). The single-step genomic prediction method (ssGBLUP) was used for genomic evaluation. A total of 9845 genotyped animals and 36,839 SNPs were included. Heritability estimates were low (0.02–0.13). The strongest genetic correlation (0.87) was found between LnVar and Var. The genetic correlation between r-auto and skew was also strong but negative (−0.73). Resilience indicators showed a negative correlation with milk yield per lactation and a positive correlation with fat and protein contents. The negative correlation between fertility and two resilience indicators may be due to the evaluation period (50th–150th day of lactation) being when cows are most often bred after calving, and a decrease in production may accompany a significant oestrus. The associations between resilience indicators and health traits (clinical mastitis, claw health) were weak but mostly favourable.
Human Recombinant Interleukin-6 and Leukemia Inhibitory Factor Improve Inner Cell Mass Cell Number but Lack Cryoprotective Activities on In Vitro-Produced Bovine Blastocysts
Authors: Oliver Mary A. , Alward Kayla J. , Rhoads Michelle L. , Ealy Alan D.
Published: February 2025

This work explored whether supplementing recombinant human interleukin-6 (IL6), interleukin-11 (IL11), or leukemia inhibitory factor (LIF) improves IVP bovine embryo development, morphology, and cryosurvivability. Embryos were treated from day 5 to 8 post-fertilization with either the carrier only (control) or 100 ng/mL of IL6, IL11, or LIF. Blastocyst formation and stage were determined on day 7 and 8. A subset of day 8 blastocysts was processed for immunofluorescence to count trophectoderm (TE) and inner cell mass (ICM) cell numbers and another subset was slow frozen and stored in liquid nitrogen until thawing. No differences in the blastocyst rate or blastocyst stage of development were detected. Increases in ICM cell numbers were observed for IL6 and LIF but not the IL11 treatment. None of the cytokine treatments applied before freezing affected post-thaw survival, TE or ICM cell number, or cell death 24 h after thawing. In conclusion, supplementing IL6 and LIF improves ICM cell numbers in non-frozen blastocysts, but there was no evidence that any of these cytokine treatments contain cryoprotective properties in bovine embryos.
Carmofur Exhibits Antimicrobial Activity Against Streptococcus pneumoniae
Authors: Lyu Wenting , Zhang Yuqing , Zhang Zhen , Lu Hao
Published: February 2025

Background/Objectives: Streptococcus pneumoniae (S. pneumoniae) is a major pathogen causing severe infectious diseases, with an escalating issue of antimicrobial resistance that threatens the efficacy of existing antibiotics. Given the challenges in developing traditional antibiotics, drug repurposing strategies offer a novel approach to address the resistance crisis. This study aims to evaluate the antibacterial and anti-biofilm activities of the approved non-antibiotic anticancer drug carmofur against multidrug-resistant S. pneumoniae, and investigate the mechanism of action, and assess therapeutic potential in vivo. Methods/Results: Antimicrobial tests revealed that carmofur exhibited strong antibacterial activity against multidrug-resistant S. pneumoniae strains, with minimum inhibitory concentrations (MICs) ranging from 0.25 to 1 µg/mL. In the biofilm detection experiments, carmofur not only inhibited the formation of biofilms, but also effectively removed biofilms under high concentration conditions. Mechanistic studies showed that carmofur disrupted bacterial membrane permeability and decreased intracellular ATP levels. Molecular docking and dynamics simulation assays indicated that carmofur could stably bind to thymidylate synthase through hydrogen bonding and hydrophobic interactions, thereby exerting antibacterial effects. Meanwhile, carmofur was able to repress the expression of the thyA gene at the mRNA level. In a mouse infection model, the carmofur treatment group showed a reduction of approximately two log levels in bacterial load in lung tissue and blood, a significant decrease in the levels of inflammatory cytokines TNF-α and IL-6, and an improvement in survival rate to 60%. Conclusions: In summary, carmofur demonstrated significant antibacterial and anti-biofilm activities against multidrug-resistant S. pneumoniae and showed good anti-infective effects in vivo, suggesting its potential clinical application as a therapeutic agent against drug-resistant bacteria.
Editorial for Special Issue “Antimicrobial Treatment of Lower Respiratory Tract Infections”
Authors: Moschopoulos Charalampos D. , Kotanidou Anastasia , Tsiodras Sotirios , Fragkou Paraskevi C.
Published: February 2025

Lower respiratory tract infections (LRTIs) are highly prevalent, and severe LRTIs are associated with significant mortality and morbidity worldwide [...]
Oxidative Stress and Redox Signaling in Gastric Cancer: From Mechanisms to Therapeutic Implications
Authors: Chen Zehua , Fan Jiawu , Chen Xiaolong , Yang Kun , Wang Kui
Published: February 2025

Oxidative stress, which is characterized by an imbalance between reactive oxygen species (ROS) production and antioxidant defenses, has critical roles in the initiation, progression, and treatment of gastric cancer. On the one hand, an excessive ROS accumulation induces oxidative damage and cancer cell death. On the other hand, moderate levels of ROS cause genetic mutations and dysregulation of signaling pathways to promote proliferation, inflammation, angiogenesis, and metastasis in gastric cancer. Notably, emerging evidence has revealed that ROS also mediate oxidative post-translational modifications (oxPTMs) of redox-sensitive proteins, which can directly affect protein functions and regulate redox signaling in cancer cells. Therefore, elucidating the regulatory mechanisms of oxidative stress and redox signaling in gastric cancer holds great promise to identify novel therapeutic targets or redox-targeting strategies. This review will summarize the mechanisms of oxidative stress in regulating the hallmarks of gastric cancer and highlight the roles of ROS-mediated oxPTMs in gastric cancer. In addition, we will discuss emerging strategies targeting oxidative stress for the treatment of gastric cancer, with an emphasis on the use of bioactive natural products and nanomaterials.
Natural Antioxidants Reduce Oxidative Stress and the Toxic Effects of RNA-CUG(exp) in an Inducible Glial Myotonic Dystrophy Type 1 Cell Model
Authors: Morales Fernando , Vargas Dayana , Palma-Jiménez Melissa , Rodríguez Esteban J. , Azofeifa Gabriela , Hernández-Hernández Oscar
Published: February 2025

The toxic gain-of-function of RNA-CUG(exp) in DM1 has been largely studied in skeletal muscle, with little focus on its effects on the central nervous system (CNS). This study aimed to study if oxidative stress is present in DM1, its relationship with the toxic RNA gain-of-function and if natural antioxidants can revert some of the RNA-CUG(exp) toxic effects. Using an inducible glial DM1 model (MIO-M1 cells), we compared OS in expanded vs. unexpanded cells and investigated whether antioxidants can mitigate OS and RNA-CUG(exp) toxicity. OS was measured via superoxide anion and lipid peroxidation assays. RNA foci were identified using FISH, and the mis-splicing of selected exons was analyzed using semi-quantitative RT-PCR. Cells were treated with natural antioxidants, and the effects on OS, foci formation, and mis-splicing were compared between treated and untreated cells. The results showed significantly higher superoxide anion and lipid peroxidation levels in untreated DM1 cells, which decreased after antioxidant treatment (ANOVA, p < 0.001). Foci were present in 51% of the untreated cells but were reduced in a dose-dependent manner following treatment (ANOVA, p < 0.001). Antioxidants also improved the splicing of selected exons (ANOVA, p < 0.001), suggesting OS plays a role in DM1, and antioxidants may offer therapeutic potential.
The Effects of Velocity- Versus Percentage-Based Resistance Training on Lower Limb Explosive Power and Footwork Movement Speed in Elite University Badminton Players
Authors: Lu Tianfeng , Peng Weiqi , Yi Mingxian , Chen Ni , Han Yue , Huang Junpei , Chen Jingyuan
Published: February 2025

Purpose: Footwork speed is closely linked to explosive strength, and both percentage- (PBRT) and velocity-based resistance training (VBRT) are popular methods for developing muscle strength. This study aimed to compare the effects of PBRT and VBRT on lower limb explosive power and footwork movement speed in elite university badminton players over a 6-week training period. Methods: A total of 20 elite badminton players (12 males, 8 females) from Tongji University were randomly divided into VBRT (n = 10) and PBRT groups (n = 10). The VBRT group trained with loads determined by target speed and velocity loss, while the PBRT participants used fixed loads based on a percentage of their one-repetition maximum (1RM). Both the groups performed free-weight back squats with relative loads ranging from 65% to 95% of 1RM over 6 weeks. The pre- and post-training measurements included back squat 1RM; countermovement (CMJ), squat (SJ), and standing long jumps (SLJs); self-weighted squat jump speed (SJS); left and right touch line (LRF), full-field four-point (FF), and front and back touch net footwork (FBF). Results: (1) The baseline measurements showed no significant differences between the groups (p > 0.05). (2) Post-training, both VBRT and PBRT improved the participants’ lower limb explosive power and footwork movement (p < 0.05). (3) The VBRT group demonstrated significantly greater improvements than the PBRT group in all the measures (p < 0.05). Conclusions: VBRT was superior to PBRT in boosting lower limb explosive power and footwork speed in badminton players over 6 weeks, leading to more significant strength–related and neural adaptations.
Research on High-Precision Gas Concentration Inversion for Imaging Fourier Transform Spectroscopy Based on Multi-Scale Feature Attention Model
Authors: Luo Jianhao , Zhao Wei , Ouyang Feipeng , Sheng Kaiyang , Wang Shurong
Published: February 2025

The accurate monitoring of greenhouse gas (GHG) concentrations is crucial in mitigating global warming. The imaging Fourier transform spectrometer (IFTS) is an effective tool for measuring GHG concentrations, offering high throughput and a wide spectral measurement range. In order to address the issue of spectral inconsistency during the detection process of the target gas, which is influenced by external environmental factors, making it difficult to achieve high-precision gas concentration inversion, this paper proposes a multi-scale feature attention (MDISE) model. The model uses a multi-scale dilated convolution (MD) module to retain both global and local shallow features of the spectra; introduces the one-dimensional Inception (1D Inception) module to further extract multi-scale deep features; and incorporates the channel attention mechanism (SE) module to enhance attention to important spectral wavelengths, suppressing redundant and interfering information. A target gas detection system was built in the laboratory, and the proposed model was tested on gas samples collected by two channels of a short and medium-wavelength infrared imaging Fourier transform spectrometer (SMWIR-IFTS). The experimental results show that the MDISE model reduces the root mean square error (RMSE) in both channels by 79.14%, 76.59%, and 69.80%, and 81.45%, 82.65%, and 74.01%, respectively, compared to the partial least squares regression (PLSR), support vector regression (SVR), and conventional one-dimensional convolutional neural network (1D-CNN) models. Additionally, the MDISE model achieved average coefficient of determination (R2) values of 0.997 and 0.995 for the concentration intervals in both channels. The MDISE model demonstrates excellent performance and significantly improves the accuracy of GHG concentration inversion.
A Bibliometric and Systematic Review of Manufacturing-as-a-Service: Literature Insights, Challenges, and Future Trends
Authors: Karamanli Anastasia , Xanthopoulos Alexandros , Gasteratos Antonios , Koulouriotis Dimitrios
Published: February 2025

Manufacturing-as-a-service consists of an Industry 4.0 innovation and has been a focal point of research and business attention in recent years. MaaS provides a matching connection between manufacturing resources or capabilities as service and organizations that need to utilize them, via a digital thread. Through this approach, not all companies have to invest in heavy capital, which allows them to focus on other critical capacities. This review provides bibliometric insights, such as quantitative results regarding the annual trends, dominant publications, journals, institutions, and VOS-viewer-software-extracted networks that highlight co-occurrence of keywords and journal co-citations. Moreover, the publications are presented concisely and are critically evaluated and categorized in pilar categories based on their core concept. The current limitations and future trends in the MaaS field are identified as well. Even though it is an established, important Industry 4.0 innovation, the available literature may not provide such a holistic, comprehensive overview of MaaS. The current study not only presents critical observations of contemporary and distinguished scientific papers but also guides researchers in a systematic examination of MaaS theoretically, architecturally represented, or regarding its data-driven opportunities.
Investigating Land Suitability for PV Farm and Existing Sites Using a Multi-Criteria Decision Approach in Gaziantep, Türkiye
Authors: Akay Semih Sami
Published: February 2025

Nowadays, renewable energy facilities are coming to the forefront in order to protect nature and prevent climate change. In this context, location-based analyses are carried out for the most optimal use of renewable energy resources. This study aims to identify suitable locations for photovoltaic (PV) farms in Gaziantep using the Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) technologies. The research incorporates various criteria, including solar radiation, land use, slope, aspect, distance to road, fault line proximity, distance to powerlines, and wind speed to evaluate potential sites for solar energy production. The AHP method is applied to prioritize these criteria through a pairwise comparison matrix and to calculate the weight values for each factor. The analysis reveals that approximately 80% of Gaziantep’s land is suitable for PV farm installation, with the southern region being the most favorable. Furthermore, the comparison between existing PV installations and the identified suitable areas highlights a high degree of alignment, with most of the current PV farms located in areas classified as suitable or highly suitable. Additionally, it was determined that 92% of the existing PV farms have been established within suitable areas. This indicates a high alignment between the identified suitable zones and the locations of the current PV installations, reflecting an effective site selection process based on the applied criteria. The study concludes that GIS-based AHP is an effective tool for rapid and reliable decision-making in renewable energy site selection, offering a valuable approach for future solar energy projects in Gaziantep and similar regions.
Stability of 3D Tunnel Faces in Inclined Layered Soils Under Steady-State Unsaturated Seepage Based on Grey Relational Analysis
Authors: Wu Huanjiang , Wu Yimin , Liao Hong , Zhu Jianqun
Published: February 2025

This paper aims to estimate the stability of tunnel faces in inclined layered soils under steady unsaturated seepage conditions, comparing the degree of correlation between the stability of the tunnel face and the influence of three types of parameters: unsaturated soil parameters, spatial geometric parameters of soil stratification, and other relevant factors. The study leverages the suction head distribution in multi-layered unsaturated soils, deriving a new formula to compute the effects of steady-state seepage forces with matric suction distribution in inclined stratified soil layers. A three-dimensional discrete rotational failure mechanism model is employed to calculate the critical support force at the tunnel face. The corresponding parametric analysis involves unsaturated soil parameters, spatial geometric parameters, and the variations in these parameters across different soil layers. Based on this analysis, the varying degrees of correlation between tunnel stability and changes in spatial angle parameters considering soil stratification, and the variations in unsaturated parameters across different soil layers are thoroughly investigated. The proposed framework serves as a valuable tool for quantitatively assessing the effects of unsaturated seepage forces on the stability of tunnel faces situated within complex, inclined, layered soil formations.
Reducing Calculation Times for Seismic Hazard Using Non-Ergodic Ground-Motion Models for Areal Source Zones
Authors: Lacour Maxime , Abrahamson Norman
Published: February 2025

Using non-ergodic ground-motion models (GMMs) in probabilistic seismic hazard analysis (PSHA) for areal sources can lead to large increases in calculation time compared to PSHA based on ergodic GMMs due to the large number of branches on the logic tree required to capture the spatial correlation of the non-ergodic terms. To reduce the computation time, a Polynomial Chaos (PC) expansion with a Taylor series approximation to capture the effects of the spatial correlation effects of the non-ergodic terms is used for the hazard calculations. With these approximate analytical methods, the calculation time for a logic tree with 100 branches for the non-ergodic terms can be reduced by a factor of 50 to 100. Using the proposed analytical approximations, the loss of accuracy of the mean hazard and the epistemic fractiles of the hazard is about 2%.
Study of Airfoil Deflections for Unsteady Aerodynamics Optimization in Pitching Airfoils
Authors: Refling William , Fabijanic Charles , Sprengeler Thomas , Suzen Yildirim Bora , Estevadeordal Jordi
Published: February 2025

Camber deflection concepts for a VR-12 rotorcraft airfoil were studied for the optimization of unsteady aerodynamics, including dynamic stall conditions and wing–wing interactions during pitching. The designs are based on deflections of the leading edge and trailing edge sections of the airfoil. The deflection parameters were initially established using Computational Fluid Dynamics (CFD). Results from CFD and Particle Image Velocimetry (PIV) were generated for various leading and trailing edge deflection combinations for comparison of their performances. The conditions of this study are for a Reynolds number of 250,000 and pitching reduced frequency of 0.04, representing a medium regime of rotorcraft operations. Linear tandem tests were performed to simulate unsteady wing–wing interactions. The effects of the deflections are discussed and compared to the baseline. Significant benefits are observed, notably dynamic stall mitigation from the leading edge (LE) deflected wing for certain angles of attack and decrease in the separation regions. Overall, from the numerical simulations and the experimental data fields, the LE deflection provides about 10% improvement, followed by the combined LE&TE deflections (8%). It is also found that combining various deflections can provide a performance increase over drastically different areas of the range of angle of attack.
Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
Authors: Fernández-Bustamante Pablo , Artetxe Eneko , Calvo Isidro , Barambones Oscar
Published: February 2025

Maximum power point tracking (MPPT) algorithms are necessary to optimize the power generation in solar photovoltaic (PV) power plants. Typically, MPPT control systems depend on the wired connections among sensors, processing nodes, and DC–DC power converters. However, Low-Power Wide-Area Networks (LPWANs) allow for centralizing the execution of MPPT algorithms wirelessly, achieving more flexibility and reducing costs. In particular, LoRa/LoRaWAN is a low-cost/low-consumption technology with an excellent immunity to interference, which is able to operate over tens of kilometers. This article presents a centralized MPPT control architecture for PV systems based on the LoRa/LoRaWAN technology. This technology provides long-range/low-cost wireless connectivity with PV plants located far away. The presented approach allows for executing in parallel, on a central computing node, different MPPT algorithms for distinct PV subsystems. A proof-of-concept prototype was implemented to experimentally validate the architecture. It involved a rooftop PV system and a DC–DC converter connected to a computer, which executes the MPPT algorithms, by means of a point-to-point LoRa network. For validation purpose, two MPPT control techniques were implemented: Perturb and Observe (P&O) and Sliding Mode Control (SMC). However, the presented approach allows for the implementation of more sophisticated MPPT algorithms for optimizing energy production. The obtained results prove the validity of the concept and suggest that the proposed low-cost approach can be extrapolated to be used with LoRaWAN networks.
Improvement of a Subpixel Convolutional Neural Network for a Super-Resolution Image
Authors: Ağalday Muhammed Fatih , Çinar Ahmet
Published: February 2025

Super-resolution technologies are one of the tools used in image restoration, which aims to obtain high-resolution content from low-resolution images. Super-resolution technology aims to increase the quality of a low-resolution image by reconstructing it. It is a useful technology, especially in content where low-resolution images need to be enhanced. Super-resolution applications are used in areas such as face recognition, medical imaging, and satellite imaging. Deep neural network models used for single-image super-resolution are quite successful in terms of computational performance. In these models, low-resolution images are converted to high resolution using methods such as bicubic interpolation. Since the super-resolution process is performed in the high-resolution area, it adds a memory cost and computational complexity. In our proposed model, a low-resolution image is given as input to a convolutional neural network to reduce computational complexity. In this model, a subpixel convolution layer is presented that learns a series of filters to enhance low-resolution feature maps to high-resolution images. In our proposed model, convolution layers are added to the efficient subpixel convolutional neural network (ESPCN) model, and in order to prevent the lost gradient value, we transfer the feature information of the current layer from the previous layer to the next upper layer. The efficient subpixel convolutional neural network (R-ESPCN) model proposed in this paper is remodeled to reduce the time required for the real-time subpixel convolutional neural network to perform super-resolution operations on images. The results show that our method is significantly improved in accuracy and demonstrates the applicability of deep learning methods in the field of image data processing.
Construction of a Geological Fault Corpus and Named Entity Recognition
Authors: Wang Huainuo , Niu Ruiqing , Han Yongyao , Deng Qinglu
Published: February 2025

The rapid and effective extraction of fault entities is a fundamental process in constructing a fault knowledge graph. As a key method for recording and preserving fault data, a fault investigation report holds significant potential for extracting valuable information. This paper proposes a fault knowledge annotation system that incorporates geographic information, fault attribute, fault structure, fault activity, fault geomorphology, and fault hazard. The system is developed based on a comprehensive analysis of the textual characteristics of fault investigation reports. Additionally, we establish a fine-grained corpus tailored for this task and apply a combination of BERT and BiLSTM-CRF for named entity recognition in the fault domain. We compare the performance of our model with a non-pre-training baseline model. The experimental results demonstrate that (1) the F1 value of entity recognition based on the faulty corpus exceeds 80%, which validates the efficacy of the faulty corpus; (2) the BERT model can effectively utilize available information. The corpus to adjust the subsequent tasks, thus improving the model output; (3) the proposed BERT-BiLSTM-CRF model and ALBERT-BiLSTM-CRF models have superior extraction performance in comparison to the no-pre-training model. This study not only provides a theoretical basis for the effectiveness of the BERT-BiLSTM-CRF model in fault entity identification, but also establishes a solid data foundation for the subsequent construction of the fault knowledge map. In addition, it offers reliable technical support for practical application areas such as geological surveys, disaster early warning, and urban planning, thereby promoting the advancement of data-driven research in the field of geology.
Be Sure to Use the Same Writing Style: Applying Authorship Verification on Large-Language-Model-Generated Texts
Authors: Weerasinghe Janith , Seepersaud Ovendra , Smothers Genesis , Jose Julia , Greenstadt Rachel
Published: February 2025

Recently, there have been significant advances and wide-scale use of generative AI in natural language generation. Models such as OpenAI’s GPT3 and Meta’s LLaMA are widely used in chatbots, to summarize documents, and to generate creative content. These advances raise concerns about abuses of these models, especially in social media settings, such as large-scale generation of disinformation, manipulation campaigns that use AI-generated content, and personalized scams. We used stylometry (the analysis of style in natural language text) to analyze the style of AI-generated text. Specifically, we applied an existing authorship verification (AV) model that can predict if two documents are written by the same author on texts generated by GPT2, GPT3, ChatGPT and LLaMA. Our AV model was trained only on human-written text and was effectively used in social media settings to analyze cases of abuse. We generated texts by providing the language models with fanfiction snippets and prompting them to complete the rest of it in the same writing style as the original snippet. We then applied the AV model across the texts generated by the language models and the human written texts to analyze the similarity of the writing styles between these texts. We found that texts generated with GPT2 had the highest similarity to the human texts. Texts generated by GPT3 and ChatGPT were very different from the human snippet, and were similar to each other. LLaMA-generated texts had some similarity to the original snippet but also has similarities with other LLaMA-generated texts and texts from other models. We then conducted a feature analysis to identify the features that drive these similarity scores. This analysis helped us answer questions like which features distinguish the language style of language models and humans, which features are different across different models, and how these linguistic features change over different language model versions. The dataset and the source code used in this analysis have been made public to allow for further analysis of new language models.
Integrating Stamping-Induced Material Property Variations into FEM Models for Structural Performance Simulation of Sheet-Metal Components
Authors: Elia Burello , Rezvanpour Hamed , Cimolino Dario , Capaccioli Francesco , Vergnano Alberto
Published: February 2025

The accurate prediction of structural performance in sheet-metal components is critical for optimizing design and ensuring reliability in engineering applications. This study emphasizes the necessity of incorporating non-uniformities induced by stamping processes, such as thickness variation and work-hardening effects, into Finite Element Method (FEM) simulations. Experimental and computational analyses reveal that neglecting these variations results in significant discrepancies, particularly in displacement predictions, where deviations exceeding 50% were observed at specific points. While elastic behavior showed reasonable agreement with experimental results, plastic deformation predictions were notably less accurate due to the inherent inhomogeneities of the real work-hardening model compared to the uniform assumptions in standard FEM models. These findings underscore the need for improved methodologies in mapping stamping-induced material properties and validating simulation results. Further refinement of mapping accuracy and validation techniques is essential for enhancing the predictive capabilities of FEM simulations for complex sheet-metal components.
Exploring Red Wines from the Drama Region: A Chemical, Sensory, and Terroir Insight
Authors: Bouloumpasi Elisavet , Skendi Adriana , Karampatea Aikaterini , Tseine Georgia , Irakli Maria , Mamalis Spyridon , Soufleros Evangelos H.
Published: February 2025

Drama represents one of the distinct winegrape regions in the continuous growth of northern Greece. Yet, little is known about grape, must, and wine quality in various parts of the region. Determining the physicochemical characteristics of the Merlot, Cabernet Sauvignon, and Agiorgitiko grape varieties grown in different locations in the area across two vintages, their chromatic characteristics, phenolic profile, and organoleptic characteristics were the goal of this study. The results reveal a high variation in the physicochemical characteristics of must and wine among the locations. The location affects the profile of phenolic compounds with procyanidin B2, catechin, procyanidin B3, procyanidin B1, epicatechin, and gallic acid being present in high amounts. Merlot wines from the location with the highest altitude showed a higher amount of quantified phenolics. Altitude positively affected the yeast assimilable nitrogen (YAN) levels and negatively the wine’s purple hue. Moreover, the grape weight negatively affects the levels of some of the quantified phenolic compounds. On the other hand, both altitude and YAN positively impact the taste of wines. The altitude represents a significant parameter in the location studied that influences the quality parameters and taste of the wines. As part of climate adaptation strategies for viticulture in the Drama region and other comparable regions, high-altitude vineyard development should be studied to preserve the grape’s acidity and counteract warming trends.
Energy Metabolism-Related Arginine Kinase Gene Expression in Mud Crab (Macrophthalmus japonicus) Following Irgarol Exposure
Authors: Kim Won-Seok , Park Kiyun , Park Jiwon , Kwak Ihn-Sil
Published: February 2025

Antifouling paints are essentially used to prevent biological fouling of marine vessels and structures, but their release into the environment has resulted in various marine ecosystem problems. Irgarol, a representative antifouling paint substance, is well known for its direct effects on plankton productivity, but studies on its harmful effects on estuarine organisms are limited. This study aimed to determine the impact of irgarol exposure at the transcriptional level in Macrophthalmus japonicus, a highly dominant crab species in estuaries. To this end, we characterized the M. japonicus arginine kinase (AK) gene, which plays a role in energy metabolism in invertebrates, and examined its expression levels in response to irgarol exposure. Consequently, AK was identified as a highly prevalent enzyme in invertebrate species, including crustaceans and insects. Following irgarol exposure, the gills of M. japonicus exhibited relatively elevated AK gene expression compared to the control group at 4 and 7 days of exposure. In addition, elevated AK gene expression was observed in the hepatopancreas. Notably, the relatively low irgarol exposure concentrations of 1 and 10 μg L−1 demonstrated comparatively higher AK gene expression in the hepatopancreas than the relatively high irgarol concentration of 30 μg L−1. The results of this study imply that irgarol exposure may disrupt the equilibrium of energy metabolic processes regulated by AK gene expression in the M. japonicus crab. Moreover, the sustained environmental accumulation of irgarol indicates that it may serve as a significant disturbance factor within estuarine ecosystems.
A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism
Authors: Luo Bixiong , Zuo Peng , Zhu Lijun , Hua Wei
Published: February 2025

Wind power, as a pivotal renewable energy source, is anticipated to play a critical role in ensuring the reliability, security, and stability of the global energy supply system. Accurate prediction of wind power density (WPD) holds significant practical importance for wind farms, grid operators, and the entire wind power industry, as it facilitates informed decision-making, optimized resource allocation, and enhanced system performance. This paper proposes a novel WPD forecasting model based on RF-DBO-VMD feature selection and BiGRU optimized by an attention mechanism. The proposed model consists of three main stages. First, critical physical features relevant to WPD are identified using random forest (RF), effectively eliminating data redundancy and enhancing prediction efficiency. Second, the variational mode decomposition (VMD) parameters are optimized via the dung beetle optimizer (DBO) algorithm to extract independent intrinsic mode functions (IMFs), which, alongside the original data, serve as temporal feature inputs. Finally, an attention mechanism is employed to identify important information from the outputs of the BiGRU model, and the Grid Search (GS) method is used to optimize the BiGRU-Attention model, yielding optimal predictions. The experimental results demonstrate the model’s high predictive accuracy, evidenced by an R2 value of 0.9754. Notably, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) are substantially minimized compared to alternative models. These results highlight the model’s potential to provide effective forecasting insights for future applications, such as energy trading and power system management, which will be further explored in real-world scenarios.
PM2.5 Concentration Prediction in the Beijing–Tianjin–Hebei Region Based on ERA5 Stratified PWV and Atmospheric Pollutants
Authors: Shang Jun , Zhang Peixuan , Wang Yong , Liu Yanping , Wang Hongsheng , Li Suo
Published: February 2025

Accurate prediction of PM2.5 (particle pollution from fine particulate) concentration is crucial for environmental protection and public health. Precipitable water vapor (PWV) in the atmosphere is an important meteorological element with stratification properties, which plays a crucial role in energy transfer, weather dynamics, and PM2.5 generation. However, past studies tend to use total PWV as an input parameter, neglecting the impact of PWV variations in different altitude layers on PM2.5 concentration. To overcome this limitation, this study proposes an innovative approach that employs stratified water vapor data (ERA5-PWV) calculated from the ERA5 reanalysis data instead of the total PWV obtained using the traditional method. This approach provides a more accurate representation of the vertical distribution of atmospheric PWV and enhances the prediction of PM2.5 content. In this study, the stratified ERA5 PWV in the Beijing–Tianjin–Hebei region is integrated with other meteorological elements and atmospheric pollutants, and the FFT-ConvLSTM method, characterized by its spatio-temporal properties, is utilized to predict the PM2.5 concentration by incorporating the spatio-temporal correlation. The FFT-ConvLSTM model is modeled by extracting spatio-temporal features through ConvLSTM, following the identification of the optimal common change period of each element using the FFT technique. This process mitigates the problem of spatio-temporal heterogeneity among elements, thus, realizing the high-precision prediction of gridded PM2.5 concentration in the next 24 h. The research results show that among the results of different layers of ERA5-PWV combinations involved in the prediction of PM2.5 concentrations in the research region, divided into three parts of the research region—plains, mountains, and plateaus—the stratified ERA5-PWV from layers 1–4 with pressure levels consistently outperformed the total ERA5-PWV in accuracy, and the RMSEs of the predicted results for the PM2.5 concentrations were each reduced by 0.862 μg/m3, 5.384 μg/m3 and 1.706 μg/m3.