bluuebunny/crossref_metadata_2025 huggingface dataset:
Permanent Maxillary Lateral Incisors’ Agenesis Managed by Mini-Screw Implant-Supported Pontics: A Scoping Review
Authors: Caramaschi Elena , Lalli Elisabetta , Garau Valentino , Verdecchia Alessio , Spinas Enrico
Published: February 2025

Background/Objectives: The Agenesis of maxillary lateral incisors occurs with a variable prevalence in different ethnic groups, and there is a need for a temporary replacement until maturity has been reached in patients for whom the replacement solution has been chosen. This study aims to analyze the scientific evidence available to date concerning the use of mini-screw implant (MSI)-supported pontics for the transitional management of permanent maxillary lateral incisors’ agenesis in developmental age subjects. Methods: Electronic research was conducted using four databases: PubMed, Clarivate Analytics/Web of Science Core Collection, Scopus, and the Wiley/Cochrane Library. Six studies were included in the final review. Data were extracted based on the first and second author, year of publication, study design, sample characteristics, mini-screw implant (MSI) characteristics, MSI insertion and loading protocol, characteristics of the prosthetic component, and outcomes during the follow-up time. Results: Clinical outcomes were proven positive in all studies. In only one study did MSIs show mobility and consequent failure after one month. Discoloration of the prosthetic part proved to be the main complication. Conclusions: The comparison with conventional removable prostheses and fixed dental prostheses revealed that MSI-supported pontics are a viable alternative and a promising temporary solution until the end of growth. Further studies to standardize protocols and assess long-term outcomes are needed.
Unveiling the Molecular Crosstalk Between Periodontal and Cardiovascular Diseases: A Systematic Review
Authors: Dhungana Gunaraj , Srisai Dollada , Sampath Chethan , Soliman Jeremiah , Kelly Regan M. , Saleh Honar Y. , Sedik Abdelrahman , Raynes Edilberto , Ferguson Alexys , Alluri Leela Subhashini Choudary , Gangula Pandu R.
Published: February 2025

Background/Objectives: Periodontal disease (PD) is a chronic inflammatory condition caused by dysbiosis of the oral microbiome. PD is linked to systemic inflammation and endothelial dysfunction, which associate it with cardiovascular disease (CVD). This systematic review explores the molecular and microbial mechanisms through which periodontal pathogens, including “Red Complex” bacteria (Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola) and Fusobacterium nucleatum, influence cardiovascular health via inflammatory pathways, immune modulation, and microbial dissemination. Methods: A systematic review was conducted following PRISMA guidelines. A literature search was conducted in the PubMed and ScienceDirect databases using relevant keywords, with strict inclusion and exclusion criteria, from the first week of September 2024 to the first week of October 2024. Studies addressing the relationship between PD and CVD were assessed for methodological rigor, relevance, and data availability. The outcomes were synthesized using a descriptive narrative approach. Out of 591 records screened, 421 full-text articles were sought for retrieval. The final review included 58 articles providing supplementary aggregated data after eligibility assessment. Results: The pathogenesis of PD involves the activation of immune cells and the release of pro-inflammatory cytokines (such as IL-1, IL-6, TNF-α, and PGE2) and chemokines (including IL-8 and MCP-1) along with oxidative stress driven by reactive oxygen species (ROS). Periodontal pathogens trigger endothelial oxidative stress and systemic inflammation via Toll-like receptors (TLRs), NF-κB signaling, and nitric oxide (NO) dysregulation, contributing to endothelial dysfunction and atherogenesis. Biomarkers, such as C-reactive protein, interleukins, and matrix metalloproteinases (MMPs), further highlight the systemic inflammatory response. Conclusions: This review underscores the significant role of periodontal pathogens and inflammatory mediators in systemic health, particularly in the progression of CVD. Although existing evidence illustrates these associations, the underlying molecular mechanisms remain inadequately understood, indicating a need for further research to advance precision medicine and therapeutic strategies.
UAV Path Planning Using an Adaptive Strategy for the Particle Swarm Optimization Algorithm
Authors: Rosas-Carrillo Ary Shared , Solís-Santomé Arturo , Silva-Sánchez Carlos , Camacho-Nieto Oscar
Published: February 2025

The path planning process for an unmanned aerial vehicle is a complex process due to different operating environment conditions and aircraft constraints. The particle swarm optimization algorithm is one of several heuristic techniques used to solve the path planning problem. However, it has been observed that this algorithm tends to converge prematurely to a local optimum, resulting in poor quality paths. This paper proposes an adaptive strategy for calculating the inertia weight (KPSO) of the particles in the PSO algorithm for UAV path planning in a real-world environment. The strategy was applied to a reconnaissance mission during an eruptive event based on the Popocatepetl volcano area in Mexico and was found to outperform other adaptive strategies from the literature in a specific scenario.
Simulating the Evolution of von Neumann Entropy in Black Hole Hawking Radiation Using Biphoton Entanglement
Authors: Li Zhuoying , Fan Haoshen , Zhao Xingwen , Wu Qinfei , Bian Ji , Liu Yang , Luo Le
Published: February 2025

Addressing the black hole information paradox necessitates the exploration of various hypotheses and theoretical frameworks. Among these, the proposition to utilize quantum entanglement, as introduced by Don N. Page, shows great promise. This study builds upon Page’s theoretical foundation and proposes a simplified model for elucidating the evolution of black hole von Neumann entropy. This model simulates the process of Hawking radiation using entangled photon pairs. Our experiment suggests that quantum entanglement may offer a plausible avenue for resolving the paradox, thereby lending support to Page’s proposal. The results suggest that this model may contribution to the exploration of one of the most profound puzzles in theoretical physics.
In-Motion Initial Alignment Method Based on Multi-Source Information Fusion for Special Vehicles
Authors: Chang Zhenjun , Zhang Zhili , Zhou Zhaofa , Li Xinyu , Hao Shiwen , Sun Huadong
Published: February 2025

To address the urgent demand for autonomous rapid initial alignment of vehicular inertial navigation systems in complex battlefield environments, this study overcomes the technical limitations of traditional stationary base alignment methods by proposing a robust moving-base autonomous alignment approach based on multi-source information fusion. First, a federal Kalman filter-based multi-sensor fusion architecture is established to effectively integrate odometer, laser Doppler velocimeter, and SINS data, resolving the challenge of autonomous navigation parameter calculation under GNSS-denied conditions. Second, a dual-mode fault diagnosis and isolation mechanism is developed to enable rapid identification of sensor failures and system reconfiguration. Finally, an environmentally adaptive dynamic alignment strategy is proposed, which intelligently selects optimal alignment modes by real-time evaluation of motion characteristics and environmental disturbances, significantly enhancing system adaptability in complex operational scenarios. The experimental results show that the method proposed in this paper can effectively improve the accuracy of vehicle-mounted alignment in motion, achieve accurate identification, effective isolation, and reconstruction of random incidental faults, and improve the adaptability and robustness of the system. This research provides an innovative solution for the rapid deployment of special-purpose vehicles in GNSS-denied environments, while its fault-tolerant mechanisms and adaptive strategies offer critical insights for engineering applications of next-generation intelligent navigation systems.
Development and Validation of a Machine Learning Model That Predicts Short Inpatient Stays Among Urgent Admissions
Authors: Gao Yan , Srivatsava Sunku , Oh Hong Choon , Goh Siang Hiong , Lim Hoon Chin Steven
Published: February 2025

Background/Objectives: This study aimed to explore the feasibility of predicting short stays among urgent admissions to an acute hospital in Singapore. With an increase in the average length of stay (LOS) in hospitals in recent years, accurately predicting short stays could enable hospitals to better manage inpatient demand and reduce emergency department (ED) overcrowding. Methods: This was a retrospective study of urgent admissions to Changi General Hospital, Singapore, from 1 January 2016 to 30 June 2022. To identify potential short stayers, a total of 25 features comprising demographic characteristics, admission and clinical characteristics, and healthcare utilization history were analyzed for each admitted patient at the point when the ED physician decided to admit the patient. The dataset was further split into a development dataset and an external validation dataset based on the year of admission. A CatBoost classifier was trained using 75% of the development dataset. Apart from reporting the model’s prediction accuracy, we conducted various analyses and simulations to study the effects of the features crucial to the prediction output. Results: The prediction accuracy of the model was evaluated on both the development test dataset (25%) and the external validation dataset. On the former, the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC) were 0.803 (95% CI: 0.799, 0.808) and 0.755 (95% CI: 0.749, 0.762), respectively, with the precision = 0.700 (95% CI: 0.694, 0.707) and recall = 0.692 (95% CI: 0.685, 0.699). On the external validation dataset, the performance was similar. The ED diagnosis and whether the admission required a surgical procedure were the most important features for making the prediction. Conclusions: The LOS prediction model could help providers to identify short stayers early in the course of their inpatient journeys so they could make interventions to better manage the overall utilization of hospital beds.
Adversarial Sample Generation Based on Model Simulation Analysis in Intrusion Detection Systems
Authors: Sun Jiankang , Yang Shujie
Published: February 2025

The explosive development of artificial intelligence technology is profoundly affecting the strategic landscape of cyberspace security, demonstrating enormous potential in the field of intrusion detection. Recent research has found that machine learning models have serious vulnerabilities, and adversarial samples derived from this vulnerability can significantly reduce the accuracy of model detection by adding slight perturbations to the original samples. In our article, we propose a novel method called adversarial sample generation based on model simulation that quickly generates adversarial samples in a short period of time and improves the model’s generalization and robustness after adversarial training. Extensive experiments on the CICIDS-2017 dataset demonstrated that the method consistently outperforms other current research methods.
A Comparative Study of YOLO Series (v3–v10) with DeepSORT and StrongSORT: A Real-Time Tracking Performance Study
Authors: Alkandary Khadijah , Yildiz Ahmet Serhat , Meng Hongying
Published: February 2025

Many previous studies have explored the integration of a specific You Only Look Once (YOLO) model with real-time trackers like Deep Simple Online and Realtime Tracker (DeepSORT) and Strong Simple Online and Realtime Tracker (StrongSORT). However, few have conducted a comprehensive and in-depth analysis of integrating the family of YOLO models with these real-time trackers to study the performance of the resulting pipeline and draw critical conclusions. This work aims to fill this gap, with the primary objective of investigating the effectiveness of integrating the YOLO series, in light-sized versions, with the real-time DeepSORT and StrongSORT tracking algorithms for real-time object tracking in a computationally limited environment. This work will systematically compare various lightweight YOLO versions, from YOLO version 3 (YOLOv3) to YOLO version 10 (YOLOv10), combined with both tracking algorithms. It will evaluate their performance using detailed metrics across diverse and challenging real-world datasets: the Multiple Object Tracking 2017 (MOT17) and Multiple Object Tracking 2020 (MOT20) datasets. The goal of this work is to assess the robustness and accuracy of these light models in multiple complex real-world environments in scenarios with limited computational resources. Our findings reveal that YOLO version 5 (YOLOv5), when combined with either tracker (DeepSORT or StrongSORT), offers not only a solid baseline in terms of the model’s size (enabling real-time performance on edge devices) but also competitive overall performance (in terms of Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP)). The results suggest a strong correlation between the choice regarding the YOLO version and the tracker’s overall performance.
Semi-Global Observer-Based Secure Consensus of Multi-Agent Systems with Input Saturation and Denial-of-Service Attacks
Authors: Dong Xinyue , Ouyang Quan
Published: February 2025

This paper addresses the problem of achieving secure consensus control in multi-agent systems (MASs) operating on directed graphs under the challenges of input saturation and periodic denial-of-service (DoS) attacks. The motivation behind this study is to ensure robust performance and security in MASs. To tackle input saturation, a distributed controller is designed using a parametric algebraic Riccati equation-based low-gain output-feedback control approach. This method ensures that control inputs remain within operational limits while maintaining stability. Additionally, leveraging the Lyapunov stability theory, this paper presents a comprehensive analysis of the impact of DoS attacks, focusing on their frequency and average duration, and guarantees semi-global secure consensus under these conditions. Simulation results including a circuit system demonstrate the effectiveness and advantages of the proposed approach.
Measuring Model Parameter Setting Errors’ Effects in the Control of an Order 4 Underactuated System
Authors: Atinga Awudu , Kósi Krisztián , Tar József K.
Published: February 2025

In the control-based approach of medical treatment of various illnesses such as diabetes mellitus, certain angiogenic cancers, or in anesthesia, the starting point used to be some “patient model” on the basis of which the appropriate administration of the drugs can be designed. The identification of the “patient model’s parameters” is always a hard and sometimes unsolvable mathematical task. Furthermore, these parameters have wide variability between patients. In principle, either robust or adaptive techniques can be used to tackle the problem of modeling imprecisions. In this paper, the potential application of a variant of Fixed Point Iteration-Based Adaptive Controllers was investigated in model-based control. The main point was the introduction of a “parameter estimation error significance metric” through the use of which the individual model parameter estimation can be avoided, and even the consequences of the deficiencies of the approximate model as a whole can be estimated. The adaptive controller forces the system to track the prescribed nominal trajectory; therefore, it brings about the “actual control situation” in which the consequences of the estimation errors are of interest. One component of the adaptive control is a “rotational block” that creates a multidimensional orthogonal (rotation) matrix that rotates arrays of identical Frobenius norms into each other. Since in a recent publication under review it was proved that the angle of the necessary rotation satisfies the mathematical criteria of metrics in a metric space, even in quite complicated nonlinear and multidimensional cases, this simple value can serve as a metric for this purpose. To exemplify the method, an under-actuated nonlinear system of 2 degree of freedom and relative order 4 was controlled by a special adaptive backstepping controller that was designed on a purely kinematic basis. From this point of view, it has a strong relationship with the PID controllers. This simple model was rich enough to exemplify parameters that require precise identification because their error produces quite significant consequences, and other parameters that do not require very precise identification. It was found that the method provided the dynamic models with reliable parameter sensitivity estimation metrics.
Designing a Remote Photoplethysmography-Based Heart Rate Estimation Algorithm During a Treadmill Exercise
Authors: Nam Yusang , Lee Junghwan , Lee Jihong , Lee Hyuntae , Kwon Dongwook , Yeo Minsoo , Kim Sayup , Sohn Ryanghee , Park Cheolsoo
Published: February 2025

Remote photoplethysmography is a technology that estimates heart rate by detecting changes in blood volume induced by heartbeats and the resulting changes in skin color through imaging. This technique is fundamental for the noncontact acquisition of physiological signals from the human body. Despite the notable progress in remote-photoplethysmography algorithms for estimating heart rate from facial videos, challenges remain in accurately assessing heart rate during cardiovascular exercises such as treadmill or elliptical workouts. To address these issues, research has been conducted in various fields. For example, an understanding of optics can help solve these issues. Careful design of video production is also crucial. Approaches in computer vision and deep learning with neural networks can also be applied. We focused on developing a practical approach to improve heart rate estimation algorithms under constrained conditions. To address the limitations of motion blur during high-motion activities, we introduced a novel motion-based algorithm. While existing methods like CHROM, LGI, OMIT, and POS incorporate correction processes, they have shown limited success in environments with significant motion. By analyzing treadmill data, we identified a relationship between motion changes and heart rate. With an initial heart rate provided, our algorithm achieved over a 15 bpm improvement in mean absolute error and root mean squared error compared to existing methods, along with more than double the Pearson correlation. We hope this research contributes to advancements in healthcare and monitoring.
Anthropological Insights into Emotion Semantics in Intangible Cultural Heritage Museums: A Case Study of Eastern Sichuan, China
Authors: Li Jiaman , He Maoen , Yang Zi , Siu Kin Wai Michael
Published: February 2025

The preservation of intangible cultural heritage (ICH) has transitioned from “static” and “living” approaches to a “digital ecosystem”, becoming a significant topic of anthropological research. This study, adopting an anthropological perspective, integrates sentiment semantic analysis with user identity classification to propose the Identity and Sentiment-Centered Framework for Intangible Cultural Heritage (ISC-ICH). Drawing on four types of ICH museums in Eastern Sichuan, China—Nanchong Langzhong Wang Shadow Puppetry Museum, Bazhong Pingchang Fanshan Jiaozi Base, Guang’an Eastern Sichuan Folk Museum, and Dazhou ICH Exhibition Hall—as case studies, this research analyzes the core factors contributing to the audience’s sense of local identity, including its composition, emotional needs, and cultural interaction. The findings reveal that: (1) “Explorers” and “Experience Seekers” constitute the primary audience groups, with their emotional evaluations closely tied to cultural depth and interactivity. (2) The digital transformation of ICH museums faces challenges such as resource limitations, festival-centric phenomena, the rise of “internet celebrity” trends, and technological homogenization. This paper introduces a culturally tailored corpus and a comprehensive evaluation framework, highlighting the dynamic interaction between ICH and its audience. Additionally, it proposes effective digital strategies to enhance the social and cultural identity of ICH museums in peripheral regions.
Finite Time ESO-Based Line-of-Sight Following Method with Multi-Objective Path Planning Applied on an Autonomous Marine Surface Vehicle
Authors: Han Bingheng , Sun Jinhong
Published: February 2025

The multi-objective path planning and robust continuous path-following method for the autonomous marine surface vehicle (AMSV) is employed. By incorporating the position and direction constraints into the optimization cost function, the spiral path planner obtains a continuous path with smooth path tangency and curvature and ensures strict adherence to the desired multi-objective points. An improved A* and optimization algorithm are combined with the global path planning to avoid obstacles in real-time. For the path-following controller, the unknown sideslip angle and uncertainties are added to build the system model, based on which observation technique is adopted to estimate all the uncertainties online. Based on the kinematic system, a finite time extended state observer (ESO) is put forward to estimate the sideslip angle accurately. The nonlinear line-of-sight (LOS) guidance scheme is designed for the model, effectively compensating for the observed values and achieving convergence in a finite time. The finite-time ESO is adopted to estimate the uncertainty for the surge and heading controller design, and the terminal sliding mode technique is introduced to achieve the final finite-time convergence. Through extensive experiments, the proposed approach demonstrates its effectiveness, feasibility, and the advantage of fast convergence and accurate control.
GNSS Spoofing Modeling and Consistency-Check-Based Spoofing Mitigation with Android Raw Data
Authors: Medina Enrique Takiguchi , Lohan Elena Simona
Published: February 2025

Spoofing events are increasingly affecting the performance of devices and operations relying on Global Navigation Satellite Systems (GNSSs). Developing powerful and robust GNSS spoofing detection and mitigation algorithms is an important endeavor in the GNSS community nowadays; some of the challenges in this field are limited access to spoofing measurement data, as spoofing over wireless channels is not legally allowed and in-lab spoofing emulators are not necessarily able to precisely capture the effects of radio channels, and the fact that classical Receiver Autonomous Integrity Monitoring approaches are typically quite complex, especially when dealing with complex or targeted spoofers. Our paper addresses these two challenges, first, by proposing a targeted spoofing model with a variable number of spoofed satellites, starting from Android raw pseudorange measurements, and second, by introducing a consistency-check-based iterative approach for spoofing detection and mitigation. We test our solution in various dynamic scenarios (bus, walk, ferry, car, flight, and bike), and we show that the positioning error correction rates depend on the number of spoofing pseudorandom (PRN) codes, as well as on the spoofing error introduced by our model. We also show that a large part of the spoofing errors can be mitigated with the proposed algorithms if the number of spoofed satellites (or pseudoranges) is sufficiently low with respect to the total number of visible satellites.
MultiDiffEditAttack: A Multi-Modal Black-Box Jailbreak Attack on Image Editing Models
Authors: Chen Peihong , Chen Feng , Guo Lei
Published: February 2025

In recent years, image editing models have made notable advancements and gained widespread use. However, these technologies also present significant security risks by enabling the creation of Not Safe For Work (NSFW) content. This study introduces MDEA (MultiDiffEditAttack), an innovative multi-modal black-box jailbreak attack framework designed to evaluate and challenge the security of image editing models. MDEA leverages large language models and genetic algorithms to generate adversarial prompts that modify sensitive vocabulary structures, thereby bypassing prompt filters. Additionally, MDEA employs transfer learning to optimize input image features, effectively bypassing post-hoc safety checks. By integrating prompt attacks and safety checker attacks, MDEA utilizes a multimodal attack strategy to target image editing models in a black-box setting. Experimental results demonstrate that MDEA significantly improves the attack efficiency against image editing models compared to current black-box methods. These results demonstrate the effectiveness of MDEA in multi-modal attacks and reveal numerous vulnerabilities in current defense mechanisms.
Lamb Wave Near-Field Source Localization Method for Corrosion Monitoring
Authors: Xin Zengnian , Bao Qiao , Zheng Fei
Published: February 2025

Corrosion is one of the main causes of aircraft structural damage. The deepening of the corrosion depth will greatly endanger the safety of the crew. The Lamb wave array signal processing method can be used to estimate the direction of arrival (DOA) of the signal source. As a form of the Lamb wave array signal processing method, multiple-signal classification (MUSIC) has been gradually applied to the corrosion monitoring of aluminum plates. However, when MUSIC is used for Lamb wave DOA estimation, it has a low resolution and poor anti-interference ability. To improve it, the Lamb wave near-field source location (LWNFL) method is proposed in this paper. The new method adopts a double-sensor array arrangement. Firstly, the compressed sensing (CS) theory is combined with the Lamb wave near-field array model to obtain a DOA estimation of the corrosion. Here, the corrosion angle can be obtained using a CS reconstruction algorithm, and the noise interference can be suppressed by limiting a minimization of the l2 norm. Then, the corrosion distance is calculated according to the Lamb wave arrival time difference between different sensors. Finally, the average of the positioning results from multiple excitation sensors is used as the final location of the corrosion. The proposed LWNFL method is verified on an aluminum plate. The experimental results show that the new method can accurately obtain the location of corrosion and has good resolution and strong anti-interference ability.
Evaluation of Indoor Power Performance of Emerging Photovoltaic Technology for IoT Device Application
Authors: Olzhabay Yerassyl , Idu Ikenna Henry , Hamidi Muhammad Najwan , Ishak Dahaman , Marzuki Arjuna , Ng Annie , Ukaegbu Ikechi A.
Published: February 2025

The rapid rise in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) has opened the door for diverse potential applications in powering indoor Internet of Things (IoT) devices. An energy harvesting system (EHS) powered by a PSC module with a backup Li-ion battery, which stores excess power at moments of high irradiances and delivers the stored power to drive the load during operation scenarios with low irradiances, has been designed. A DC-DC boost converter is engaged to match the voltage of the PSC and Li-ion battery, and maximum power point tracking (MPPT) is achieved by a perturb and observe (P&O) algorithm, which perturbs the photovoltaic (PV) system by adjusting its operating voltage and observing the difference in the output power of the PSC. Furthermore, the charging and discharging rate of the battery storage is controlled by a DC-DC buck–boost bidirectional converter with the incorporation of a proportional–integral (PI) controller. The bidirectional DC-DC converter operates in a dual mode, achieved through the anti-parallel connection of a conventional buck and boost converter. The proposed EHS utilizes DC-DC converters, MPPT algorithms, and PI control schemes. Three different case scenarios are modeled to investigate the system’s behavior under varying irradiances of 200 W/m2, 100 W/m2, and 50 W/m2. For all three cases with different irradiances, MPPT achieves tracking efficiencies of more than 95%. The laboratory-fabricated PSC operated at MPP can produce an output power ranging from 21.37 mW (50 W/m2) to 90.15 mW (200 W/m2). The range of the converter’s output power is between 5.117 mW and 63.78 mW. This power range can sufficiently meet the demands of modern low-energy IoT devices. Moreover, fully charged and fully discharged battery scenarios were simulated to study the performance of the system. Finally, the IoT load profile was simulated to confirm the potential of the proposed energy harvesting system in self-sustainable IoT applications. Upon review of the current literature, there are limited studies demonstrating a combination of EHS with PSCs as an indoor power source for IoT applications, along with a bidirectional DC-DC buck–boost converter to manage battery charging and discharging. The evaluation of the system performance presented in this work provides important guidance for the development and optimization of new-generation PV technologies like PSCs for practical indoor applications.
Investigating Coastal Effects on Offshore Wind Conditions in Japan Using Unmanned Aerial Vehicles
Authors: Goto Kazutaka , Uchida Takanori , Kishida Takeshi , Nohara Daisuke , Nakao Keisuke , Sato Ayumu
Published: February 2025

Wind conditions play a significant role in wind power generation. Offshore wind turbines in Japan are located in areas with a shorter fetch compared with those in Europe, raising concerns about more significant coastal effects on offshore wind conditions. Therefore, we conducted observations using unmanned aerial vehicles (UAVs) to investigate coastal effects on offshore wind conditions in Japan, measuring the vertical structure of meteorological parameters at multiple nearshore locations. We explored the application of data pre-processing methods to focus on the spatial variations caused by coastal effects and minimize short-term fluctuations. The results indicated that using ensemble averages of multiple vertical profiles effectively reduced short-term fluctuations. Our UAV observations revealed that stable stratification developed even within the 1300 m fetch region, with rapid growth rates. Additionally, we found that wind speeds were independent of height in some cases, suggesting that the wind profile power law is not suitable for expressing the vertical profiles of wind speed.
Diagnostics of Inter-Turn Short Circuit Fault in Dry-Type Air-Core Reactor Based on Lissajous Graph and Lightweight Network Model
Authors: Xiang Binglong , Dang Xiaojing , Zhu Junlin , Chen Lian , Tang Chao , Zhao Zhongyong
Published: February 2025

Dry-type air-core reactors (DARs) often have inter-turn short circuit (ITSC) faults. However, traditional fault detection methods for DARs generally demonstrate poor timeliness and low sensitivity, and few methods combine intelligent algorithms for objective and accurate diagnosis. Therefore, a novel online diagnosis method for ITSC faults was proposed. First, the “field-circuit” coupling 2D model of reactors was established to simulate the impact of ITSC faults on the characteristics of various state parameters; accordingly, the Lissajous graph was introduced to characterize the short circuit fault. Then, the variation law of the Lissajous graph under different inter-turn fault layers, turns, and degrees was explored to verify the feasibilities of the proposed method. Finally, to achieve rapid diagnosis and fulfill the requirements of edge computing, a lightweight network model named MobileNetV3-Small was used and combined as a classifier to achieve accurate diagnosis of ITSC faults. The results robustly validate that the Lissajous graphical method can significantly reflect ITSC faults through observing the variation in the graph and feature parameters. Furthermore, the MobileNetV3-Small model achieves a diagnostic accuracy of up to 95.91%, which can further enhance the diagnostic accuracy of the ITSC fault degree.
Assessing the Impact of Conservation Practices on Post-Wildfire Recovery of Evergreen and Conifer Forests Using Remote Sensing Data
Authors: Bahramvash Shams Shima , Boehnert Jennifer , Wilhelmi Olga
Published: February 2025

The intensity of wildfires has increased dramatically in recent decades; thus, better understanding the impact of land-management efforts in biodiversity conservation on post-wildfire recovery could highlight the value of these interventions. Field assessments are often costly; therefore, monitoring the effectiveness of applied conservation practices using remote sensing tools is critical. The main goal of this study is to develop and apply a remote sensing framework to assess the impact of conservation practices on post-fire recovery. We focused on a study area in northern California and southern Oregon, a region with diverse conservation practices and increased wildfire activity in the past decade. The proposed framework uses the MODIS dataset to identify fire burn events and Landsat to analyze the time series of an area-aggregated vegetation index, the Normalized Burn Ratio (NBR). Using the remote sensing framework, we confirmed our hypothesis that in areas lacking conservation protection practices, post-fire recovery is slower and more lingering. The median 5-year dNBR recovery for unprotected burn events was around 27%, compared to 37% across all other burn areas. Along with our primary goal of recovery analysis, we also examined fire severity across different conservation practices to identify moderate-to-severe fire events and to capture differences in fire characteristics for the areas under different conversation practices. This analysis revealed that unprotected areas experienced more severe fire events. We also investigated the impact of conservation practices across three dominant forest types in our study area: Dry-Mesic Conifer, Mesic Conifer, and Evergreen Forests. The disparity in post-wildfire recovery between protected and non-protected areas was most pronounced in burn areas dominated by Evergreen Forests. Using the proposed aggregated remote sensing framework, this study highlights the importance of conservation practices in wildfire recovery. This approach could provide a cost-efficient tool for assessing the effectiveness of land-management practices on wildfire recovery across the globe.
Comparing Grassland Fire Drivers and Models in Inner Mongolia Using Field and Remote Sensing Data
Authors: Zhang Heng , Liang Yansen , Ren Haiyan , Ban Qingyu
Published: February 2025

Frequent and intense grassland fires represent a significant threat to the stability and sustainability of grassland ecosystems. Therefore, understanding the driving factors of grassland fire and the occurrence of fire is key to formulating effective fire management policies and management plans. Based on the fire dataset (manually recorded data, satellite remote sensing data) from 2001 to 2022, this study uses six models to analyze the differences in grassland fire driving factors in different regions and fire prevention periods in the study area, determine the relative importance of fire driving factors, and draw a probability map of grassland fire. The results show that both types of data selected the Boosted Regression Trees (BRT) model as the optimal model for predicting grassland fires in the Inner Mongolia Autonomous Region. Meteorological factors are the main driving factors of grassland fire in the Inner Mongolia Autonomous Region, and topographic factors and socio-economic factors are important factors. The number and probability of fires gradually decreased from east to west, and fires were mainly concentrated in the northeast and middle of the study area. Therefore, our study functioned to explore the spatio-temporal pattern of grassland fire, accurately predict the probability of grassland fire at different scales, and provide a scientific basis for the rational allocation of grassland fire prevention resources in the study area.
Open Access to Burn Severity Data—A Web-Based Portal for Mainland Portugal
Authors: Castro Pedro , Gonçalves João , Mota Diogo , Marcos Bruno , Alves Cristiana , Alonso Joaquim , Honrado João P.
Published: February 2025

With the rising frequency and severity of wildfires that cause significant threats to ecosystems, public health and livelihoods, it is essential to have tools for evaluating and monitoring their impacts and the effectiveness of policy initiatives. This paper presents the development and implementation of a new calculation pipeline integrated with a web-based platform designed to provide georeferenced data on the burn severity of wildfires in mainland Portugal. The platform integrates a modular architecture that comprises a module in R and Google Earth Engine to compute standardized satellite-derived datasets on observed/historical severity for burned areas, integrated with a web portal module to facilitate the access, search, visualization, and downloading of the generated data. The platform provides open-access, multisource data from satellite missions, including MODIS, Landsat-5, -7, and -8, and Sentinel-2. It offers multitemporal burn severity products, covering up to 12 months post-fire, and incorporates three severity indicators, the delta NBR, relative difference NBR, and relativized burn ratio, derived from Normalized Burn Ratio (NBR) quarterly median composites. The platform’s modular and scalable framework also allows the integration of more spectral indices, burn severity indicators, and other wildfire perimeter databases. These design features also enable the platform to adapt to other contexts or regions beyond its current scope and regularly update burn severity products. Results from exploratory data analyses revealed the ability of satellite-based severity products to diagnose trends, assess interannual variability, and enable regional comparisons of burn severity, providing a basis for further research. In the face of climate change and societal challenges, the platform aims to support decision-making processes by providing authorities with standardized and updated information while promoting public awareness of wildfire challenges and, ultimately, contributing to the sustainability of rural landscapes.
The Acute-Phase Serum Amyloid A Promotes Cytokines Production in Oyster Crassostrea gigas
Authors: Cheng Junlei , Wang Weilin , Cheng Xuemei , Li Xuesong , Lun Luyi , Lian Xingye , Wang Lingling , Song Linsheng
Published: February 2025

Serum amyloid A (SAA) is one of the most abundant acute-phase response proteins and has been extensively studied in vertebrates for its role in modulation of the inflammatory response and as a marker of disease diagnosis. By comparison, SAA is rarely identified in aquatic species and its physical functions are also not well studied. The present study identified the only one gene encoding SAA protein in oyster Crassostrea gigas. The open reading frame (ORF) of CgSAA was of 417 bp, encoding a putative polypeptide of 138 amino acid residues with a predicted molecular weight of 15.66 kDa. CgSAA was composed of a signal peptide (residues 1–22) and a conserved SAA domain (residues 36–138). The mRNA expression of CgSAA in normal individuals was detectable but at a low level, with the lowest expression level in the tissue of labial palp and a slightly higher expression level in hemocytes. The mRNA expression level of CgSAA was significantly up-regulated at 6 h (2.76-fold of that in control group, p < 0.01) post V. splendidus stimulation. It was also significantly induced under environmental stress at high temperature (34 °C) or low salinity (15‰ salinity). The recombinant protein rCgSAA was expressed in Escherichia coli and purified by affinity chromatography. After rCgSAA was injected into oysters or incubated with culture primary hemocytes, the mRNA expressions of the cytokines CgIL17-1, CgIL17-5, and CgTNF were all significantly up-regulated. The results collectively suggested that CgSAA, as a conserved acute-phase response protein in oyster, was quickly induced under environmental stress and promoted the expressions of cytokines, which provide fresh ideas for understanding the roles of SAA proteins in aquatic invertebrates.
Evidence for the Functional Relevance of vgll3 and six6 οn Developmental Stages of Commercially Important Fish Species: Gilthead Seabream (Sparus aurata Linnaeus) and European Seabass (Dicentrarchus labrax Linnaeus)
Authors: Moulistanos Aristotelis , Kaitetzidou Elisavet , Minoudi Styliani , Gkagkavouzis Konstantinos , Kallimanis Athanasios , Antonopoulou Efthimia , Triantafyllidis Alexandros , Papakostas Spiros
Published: February 2025

The six6 and vgll3 genes play evolutionarily conserved roles in developmental processes and life history traits across species, including teleosts. Notable differences in genotype and allele frequencies of these genes have been observed between farmed and wild populations of European seabass and gilthead seabream, suggesting potential roles in traits associated with domestication. Here, we hypothesized that genetic variations in the six6 and vgll3 are associated with distinct expression profiles that underlie domestication-related traits in the two species. Using quantitative PCR (qPCR), we examined the expression profiles of these genes in early developmental stages across genotypes linked to domestication. Our results confirmed that vgll3 and six6 genotypes significantly influenced their expression in gilthead seabream, with statistically significant differences between genotypes. In European seabass, six6 expression did not significantly differ among genotypes, although heterozygous larvae showed higher variability that decreased at the juvenile stage. Meanwhile, no genetic variation was observed in vgll3, precluding genotype-specific expression analysis. Altogether, our findings provide the first evidence confirming that prior DNA-based associations of these genes with domestication traits are reflected at the mRNA level, particularly in vgll3 in gilthead seabream. This highlights the potential functional relevance of these genetic variations in shaping expression profiles linked to domestication in the species.
Transcriptomic Analysis of Endocrine System Responses in Zebrafish Embryos Following Exposure to Environmentally Relevant Concentrations of Arsenate
Authors: Li Tao , Zhang Di , Ding Liang , Zhou Hongyan , Hou Yizhong , Hong Huachang , Sun Hongjie , Yu Xinwei
Published: February 2025

Water environments contaminated with arsenic (As) have become a significant environmental concern. Previous research has highlighted the detrimental effects of As on fish, but limited knowledge exists regarding its impacts on endocrine systems. To address this gap, zebrafish embryos were exposed to various concentrations (0, 25, 50, 75, and 150 μg/L) of arsenate (AsV) for 120 h post-fertilization (hpf). Our findings indicate that exposure to AsV significantly increases cortisol- and thyroid-stimulating hormone (TSH) levels while decreasing estradiol (E2) and testosterone (T) levels. Additionally, it initially decreases and then increases thyroxine (T4) contents. Furthermore, several key genes relevant to these endocrine systems also show significant influences. The results from principal component analysis demonstrate that TRH, TSH, TRHRb, and TRβ primarily affect the level of T4 while Cyp11b, StAR, hmgrb MC2R, and GR mainly influence cortisol levels. On the other hand, Cyp19a, Cyp17, 17βhsd, ERβ, LHR, hmgrb, and AR predominantly impact E2 and T levels. Transcriptomics and enrichment analysis reveal that these pathways are primarily associated with steroid hormone synthesis and transport. Furthermore, it was found that AsV stimulates the cAMP signaling pathway through a compensation mechanism. These results suggest that AsV may potentially act as environmental endocrine-disrupting chemicals with non-negligible interference effects on the endocrine system in zebrafish. This study holds theoretical value in assessing the environmental risk posed by As overall as well as providing an important basis for addressing human health issues and implementing preventive measures.
Mixture Design and Doehlert Matrix for Optimization of Energized Dispersive Guided Extraction (EDGE) of Theobromine and Caffeine from Cocoa Bean Shells
Authors: Lordêlo Nascimento Luciana , Alves dos Santos Paulo Natan , Granja Honnara Santos , da Silveira Ferreira Larissa , Ferreira Lima João Victor , de Moura Pita Bruna Louise , dos Santos Polidoro Allan , dos Santos Freitas Lisiane , Caramão Elina Bastos , de Souza Dias Fabio , Fricks Alini Tinoco
Published: February 2025

This work describes the development of a method for the extraction of methylxanthines from cocoa bean shell (CBS) by employing the novel Energized Dispersive Guided Extraction (EDGE) system. The mixtures were composed of ethanol–methanol–water and the ratio was optimized using a simplex-centroid design. Doehlert design (DD) was used to optimize the variables of temperature and time while using methylxanthine content obtained by HPLC-DAD as an analytical response. The optimized mixture consisted of water–ethanol in a 3:2 ratio. The optimum operating conditions for extraction were achieved at a temperature of 148.5 °C and 382 s. Under optimal conditions, 20.14 mg g−1 DM of theobromine and 3.53 mg g−1 DM of caffeine were found in the CBS extract. Methylxanthines were quantified with good linearity, LOQs, LODs, precision, and accuracy. The EDGE system, a newly automated extraction instrument, has proven to be very efficient for the recovery of theobromine and caffeine, and is considered a green extraction procedure, as demonstrated by the analytical greenness metric for sample preparation.
Structure Characterization and Treatment Effect of Zingiber officinale Polysaccharide on Dextran Sulfate Sodium-Induced Ulcerative Colitis
Authors: Jing Yongshuai , Wang Ziying , Cheng Wenjing , Fan Hanju , Zheng Kaiyan , Zheng Yuguang , Wu Lanfang
Published: February 2025

Background: Ulcerative colitis (UC) is on the rise all over the world. Zingiber officinale polysaccharide (ZOP-1) has good anti-inflammatory and antioxidant effects, but the therapeutic effect and mechanism of ZOP-1 on UC are still unclear. Methods: ZOP-1 obtained by water extraction and alcohol precipitation was analyzed by methylation and NMR. At the same time, the mechanism of ZOP-1 in the treatment of UC was clarified by hematoxylin-eosin (HE) staining, metagenomics, immunohistochemistry, and protein blot (Wb). Results: ZOP-1 was the structure of the by →4,6)-β-Glcp-1→ and →3,6)-α-Galp-(1→ constitute the main chain, there were two branched chain by →4)-β-Glcp(1→, and α-Araf(1→ as the end group. ZOP-1 significantly improved the shortening and thickening of the colon, changed the index of immune organs, inhibited the production of inflammatory factors in mice with ulcerative colitis, changed the intestinal flora of mice, increased the content of short-chain fatty acids (SCFAs) in the intestine, and controlled the TLR4/NF-κB/MAPK signaling pathway, thus preventing and treating DSS-induced ulcerative colitis in mice. Conclusions: ZOP-1 alleviated UC by controlling the expression of cytokines, thereby reducing intestinal inflammation and oxidative stress, enhancing intestinal integrity, modulating intestinal flora, and regulating the levels of SCFAs.