bluuebunny/crossref_metadata_2025 huggingface dataset:
Examining the Mechanical and Thermal Properties of a Novel Hybrid Thermoplastic Rubber Composite Made with Polypropylene, Polybutadiene, S-Glass Fibre, and Flax Fibre
Authors: Diwahar Periasamy , Prakalathan Karuppiah , Bhuvana K. Periyasamy , Senthilkumar Krishnasamy
Published: December 2024

In this work, twin-screw extruder and compression moulding techniques were utilized to fabricate polymer blends: polypropylene (PP), polybutadiene (PB), and composites using glass fibre (GF) and flax fibre (FF). During fabrication, the polymer ratios maintained between PP and PB were 90:10, 80:20, and 70:30. Likewise, the composites were fabricated by varying the ratios between the PP, PB, and GF, which were 90PP:10PB:10GF, 80PP:20PB:10GF, and 70PP:30PB:10GF. Additionally, a hybrid composite was fabricated by adding 20% FF to the 90PP/10PB/10GF blend. The mechanical characterization revealed that the tensile strength and modulus increased by approximately 24% and 23%, respectively, for the hybrid combination (90PP/10PB/10GF/20FF) compared to pure PP (from 21.47 MPa and 1123 MPa to 26.54 MPa and 1382 MPa). Similarly, flexural strength and impact resistance showed significant improvements in hybrid samples, with flexural strength increasing by approximately 15%. Scanning electron microscopy (SEM) was also carried out for impact-tested samples to understand the fibre-to-matrix adhesion behaviour. Regarding the DSC results, PP exhibited a melting peak between 160 °C and 170 °C. When incorporating PP into PB, a reduction in crystallinity was observed. Further, by adding GF to polymer blends, the crystallinity was increased. HDT and Vicat softening temperature results reported that the hybrid samples showed higher values of 79.3 °C and 88.2 °C, respectively, resulting in improvements of approximately 3.9% and 2.9% over standard PP. Findings from this study suggest that the novel combinations offer a promising synergy of flexibility, strength, and thermal resistance, making them suitable for medium engineering applications.
Designing Microparticles of Luteolin and Naringenin in Different Carriers via Supercritical Antisolvent Process
Authors: Mottola Stefania , De Marco Iolanda
Published: December 2024

Antioxidants are contained in fruits and vegetables and are commonly obtained through food. However, it is frequently necessary to supplement the diet with substances that are often poorly soluble in water and sensitive to light and oxygen. For this reason, in this work, luteolin (LUT) and naringenin (NAR), two compounds with antioxidant activity and potential health benefits, were precipitated through the supercritical antisolvent technique using polyvinylpyrrolidone and β-cyclodextrin as the carriers. The precipitation occurred from dimethylsulfoxide using supercritical carbon dioxide as the antisolvent. The influence of pressure (9–12 MPa), active substance/carrier concentration in the solution (20–200 mg/mL), and their ratio (1/1 and 1/2 mol/mol) on morphology, particle mean size, and distribution were investigated. Under the optimized operating conditions, spherical microparticles with a mean diameter equal to 2.7 ± 0.9 μm (for LUT) and 5.5 ± 1.9 μm (for NAR) were obtained. The active ingredients were protected from the external environment by the presence of the carrier, and the dissolution rate was notably increased by processing them with β-cyclodextrin. It was sixty times faster and three times faster than that of the antioxidant alone for LUT and NAR, respectively.
A Novel High Vacuum MSF/MED Hybrid Desalination System for Simultaneous Production of Water, Cooling and Electrical Power, Using Two Barometric Ejector Condensers
Authors: Caballero-Talamantes Francisco J. , Velázquez-Limón Nicolás , Aguilar-Jiménez Jesús Armando , Casares-De la Torre Cristian A. , López-Zavala Ricardo , Ríos-Arriola Juan , Islas-Pereda Saúl
Published: December 2024

This work presents a novel trigeneration system for the simultaneous production of desalinated water, electrical energy, and cooling, addressing the challenges of water scarcity and climate change through an integrated and efficient approach. The proposed system combines an 8-stage Multi Stage Flash Distillation (MSF) process with a 6-effect Multiple Effect Distillation (MED) process, complemented by an expander-generator to optimize steam utilization. Cooling production is achieved through a dual ejectocondensation mechanism, which enhances energy recovery and expands operational flexibility. The system’s performance was analyzed using Aspen Plus simulations, demonstrating technical feasibility across a broad operating range: 28.3 to 0.8 kPa and 68 to 4 °C. In cogeneration mode, the system achieves a Performance Ratio (PR) of 12.06 and a Recovery Ratio (RR) of 54%, producing 67,219.2 L/day of desalinated water and reducing electrical consumption by 12.03%. In trigeneration mode, it achieves a PR of 17.81 and an RR of 80%, with a cooling capacity of 1225 kW, generating 99,273.6 L/day of desalinated water while reducing electrical consumption by 3.69%. These results underscore the system’s capability to significantly enhance the efficiency and capacity of thermal desalination technologies, offering a sustainable and high-performing solution for coastal communities worldwide.
Upcycling Salmon Skin Waste: Sustainable Bio-Sequins and Guanine Crystals for Eco-Friendly Textile Accessories
Authors: Clavel Valentina , Salazar Sandoval Sebastián , Silva Nataly , Araya-Hermosilla Rodrigo , Amenábar Alejandra , Contreras Paulina
Published: December 2024

The significant environmental impact from fashion and textile industries has spurred interest in sustainable alternatives, especially for accessories like sequins and beads, whose usage has surged post-pandemic. This study explores the potential of utilizing salmon industry waste from Chile to produce bio-sequins (BS) and guanine crystals (GC) from salmon skin. The production of BS offers a strategy to reduce reliance on non-renewable resources and support sustainable waste management, as these materials decompose naturally without harmful residues. Physicochemical and mechanical characterization of the BS by using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), thermogravimetric analysis (TGA), X-ray powder diffraction (XRPD), and Fourier transform infrared spectroscopy (FT-IR), evaluated their feasibility for textile, design, and fashion applications. Additionally, GC were extracted from salmon scales using less hazardous solvents such as acetone, ethanol, and acetic acid, and subsequently immobilized on the BS for decorative purposes. Notably, tensile mechanical properties of the BS improved up to 75% after guanine decoration and exposure to simulated environmental factors like UV radiation. This work addresses the dual challenge of pollution and resource depletion, demonstrating that BS from salmon skin offer an eco-friendly alternative. It underscores the importance of adopting sustainable practices throughout the fashion industry’s production chain.
Socioeconomic Variation in Motivations for Ritual Practice
Authors: Xygalatas Dimitris , Maňo Peter
Published: December 2024

This paper investigates socioeconomic variation in motivations for ritual practices among Mauritian Hindus. Using cultural domain analysis, we explore individuals’ reported reasons for engaging in a variety of religious rituals. Our findings demonstrate significant intra-cultural diversity driven by social stratification. Specifically, we observe that those of lower social standing appear primarily motivated by existential concerns related to material security and safety, while higher-status individuals view these practices as platforms for personal and social enrichment, as they are more preoccupied with self-actualization, spiritual connection, and social affirmation, reflecting a more abstract engagement with religious practices. Our findings reveal the adaptability of ritual practices to meet a wide range of human needs across varying life circumstances, as rituals can be differentially negotiated by individuals within the same cultural context depending on the specific socioecological niches they occupy. Moreover, they highlight the role of culture as a dynamic and distributed system with important implications for anthropological theory and practice.
Judicial Training in Saudi Arabia: From an Uncodified to Codified System
Authors: Yakar Sümeyra , Yakar Emine
Published: December 2024

The precise description of the roles and qualifications of Saudi judges (qāḍī) in the legal process assist in understanding the actual practice of jurisprudence. This paper aims to shed light on the jurisprudential procedure and the responsibilities of judges in the past and present Saudi legal system. Although the Saudi judges had freedom to exercise independent reasoning in the process of evaluating cases during the uncodified period before the 2020s, they were required to follow the classical regulations that were transmitted by the previous Ḥanbalī scholars’ textual sources. On the other hand, recent codification attempts provide Saudi scholars with a kind of set of systematized traditional rules and bring standardization in final decisions. Since the rules of codification are directly derived from the main sources (the Qur’an and Sunna) of Islamic law, the Saudi legal system is supposedly governed by the traditional framework of Islamic law, and this semi-independent nature separates it from its counterparts’ dependent codified legal systems. This article elucidates the transformational process of the Saudi legal system from classical implementation to codification. In applying analytical and descriptive methods, the objective of this paper is to investigate the responsibilities and training process of the judges and the jurisprudential procedure in the Saudi legal system.
The Flash Vacuum Expansion Process Increases the Bioaccessibility and Stability of Antioxidant Compounds in Papaya Puree During Digestion
Authors: de Jesús Castillo-Romero Teresita , López-Martínez Leticia Xochitl , Salgado-Cervantes Marco Antonio , Quintana-Obregón Eber Addí , González-Aguilar Gustavo Adolfo , Vargas-Ortiz Manuel
Published: December 2024

Among all fruits, the papaya ranks among the most significant, occupying fourth place in terms of marketing volumes. The papaya encounters various deterioration issues throughout the marketing chain, which results in the loss of bioactive phytochemicals in the fruit’s pulp. Making puree is the best way to make papaya pulp last longer, but processing can break down antioxidants like phenolic compounds (which are mostly stored in cell vacuoles) and carotenoids (which are stored in chromoplasts). The flash vacuum expansion process (FVE) promotes an expansion of the water present in the vacuoles, which generates cell rupture and consequently, the release of intracellular components. Because cell rupture is promoted from within the cell, the expulsion of bioactive compounds is more efficient and can therefore increase their bioaccessibility. Our results show that the FVE process increased the antioxidant capacity of the purees (measured by TEAC, FRAP, and DPPH assays) before and during digestion. Our results show that the FVE process significantly enhances the bioaccessibility and stability of antioxidant compounds, providing a clear advantage over the conventional method. Fruit purees generated by FVE can be an ingredient that increases the functionality of foods (juices, nectars, purees, ice creams, and yogurt) aimed at people with digestive problems, baby food, or the use of fruits with low commercial value.
Dairy Goat Production: Socioeconomic, Environmental, and Cultural Importance Across Time (1970–2022) and Possible Scenarios (2050)
Authors: Navarrete-Molina Cayetano , Meza-Herrera Cesar A. , De Santiago-Miramontes Angeles , Valenzuela-Núñez Luis M. , Marin-Tinoco Ruben I. , Soto-Cardenas Miguel A. , Sariñana-Navarrete Maria A.
Published: December 2024

Inequality, malnutrition, poverty, and environmental degradation are some of the global challenges facing humanity. These are aggravated in the context of climate change (CC), envisioning as a utopia to guarantee food security without risking sustainability. Considering the increase in scientific attention on dairy goat production (DGP), we aimed to carry out an exhaustive analysis regarding the evolution of DGP to determine both its socioeconomic and cultural importance during the period 1970–2022 and its possible scenarios for 2050. Over the last half century (1970–2022), dairy goats (DG; 214.01 million heads) have shown an inventory growth of 182%, and this is estimated to increase by 53.37% over the next 28 years (2023–2050). While DGP increased 196% during 1970–2022, it is projected to increase around 71.29% by 2050. Notably, however, the economic value of DGP almost quadrupled (+375%) during 1991–2022, and the same trend is estimated for 2023–2050. Historically, Asia has excelled in both goat inventory and goat milk production volume. This research highlights the crucial role of both goats and DG in the socioeconomic issues in various regions of the world, as they most often represent the only source of income for millions of smallholder families, particularly in developing countries. In the face of CC challenges, goats in general, and DG in particular, show an exceptional potential to be considered the “animal of the future” due to their refined and sophisticated ethological, adaptive, and physiological plasticity under generally clean, green, and ethical production schemes, mainly in marginal contexts in the arid and semi-arid zones of the world.
Assessment of the Impact of Coastal Wetland Saltmarsh Vegetation Types on Aboveground Biomass Inversion
Authors: Wu Nan , Zhang Chao , Zhuo Wei , Shi Runhe , Zhu Fengquan , Liu Shichang
Published: December 2024

Coastal wetlands play an important carbon sequestration role in China’s “carbon peaking” and “carbon neutrality” goals. Monitoring aboveground biomass (AGB) is crucial for wetland management. Satellite remote sensing enables efficient retrieval of AGB. However, a variety of statistical models can be used for biomass inversion, depending on factors such as the vegetation type and inversion method. In this study, Landsat 8 Operational Land Imager (OLI) images were preprocessed in the study area through radiation calibration and atmospheric correction for modeling. In terms of model selection, 13 different models, including the univariate regression model, multiple regression model, and machine learning regression model, were compared in terms of their accuracy in estimating the biomass of various wetland vegetation types under their respective optimal parameters. The findings revealed that: (1) the regression models varied across vegetation types, with the accuracy of the biomass estimates decreasing in the order of Scirpus spp. > Spartina alterniflora > Phragmites australis; (2) overall modeling, without distinguishing vegetation types, addressed the challenges of limited samples availability and sampling difficulty. Among them, the random forest regression model outperformed the others in estimating wet and dry AGB with R2 values of 0.806 and 0.839, respectively. (3) Comparatively, individual modeling of vegetation types can better reflect the biomass of each wetland vegetation type, especially the dry AGB of Scirpus spp., whose R2 and RMSE values increased by 0.248 and 11.470 g/m2, respectively. This study evaluates the impact of coastal saltmarsh vegetation types on biomass estimation, providing insights into biomass dynamics and valuable support for wetland conservation and restoration, with potential contributions to global habitat assessment models and international policies like the 30x30 Conservation Agenda.
Piping Plover Habitat Changes and Nesting Responses Following Post-Tropical Cyclone Fiona on Prince Edward Island, Canada
Authors: Guild Ryan , Wang Xiuquan
Published: December 2024

Climate change is driving regime shifts across ecosystems, exposing species to novel challenges of extreme weather, altered disturbances, food web disruptions, and habitat loss. For disturbance-dependent species like the endangered piping plover (Charadrius melodus), these shifts present both opportunities and risks. While most piping plover populations show net growth following storm-driven habitat creation, similar gains have not been documented in the Eastern Canadian breeding unit. In September 2022, post-tropical cyclone Fiona caused record coastal changes in this region, prompting our study of population and nesting responses within the central subunit of Prince Edward Island (PEI). Using satellite imagery and machine learning tools, we mapped storm-induced change in open sand habitat on PEI and compared nest outcomes across habitat conditions from 2020 to 2023. Open sand areas increased by 9–12 months post-storm, primarily through landward beach expansion. However, the following breeding season showed no change in abundance, minimal use of new habitats, and mixed nest success. Across study years, backshore zones, pure sand habitats, and sandspits/sandbars had lower apparent nest success, while washover zones, sparsely vegetated areas, and wider beaches had higher success. Following PTC Fiona, nest success on terminal spits declined sharply, dropping from 45–55% of nests hatched in pre-storm years to just 5%, partly due to increased flooding. This suggests reduced suitability, possibly from storm-induced changes to beach elevation or slope. Further analyses incorporating geomorphological and ecological data are needed to determine whether the availability of suitable habitat is limiting population growth. These findings highlight the importance of conserving and replicating critical habitat features to support piping plover recovery in vulnerable areas.
A High-Resolution Spotlight Imaging Algorithm via Modified Second-Order Space-Variant Wavefront Curvature Correction for MEO/HM-BiSAR
Authors: Ren Hang , Lu Zheng , Li Gaopeng , Zhang Yun , Yang Xueying , Guo Yalin , Li Long , Qi Xin , Hua Qinglong , Ding Chang , Mu Huilin , Du Yong
Published: December 2024

A bistatic synthetic aperture radar (BiSAR) system with a Medium-Earth-Orbit (MEO) SAR transmitter and high-maneuvering receiver (MEO/HM-BiSAR) can achieve a wide swath and high resolution. However, due to the complex orbit characteristics and the nonlinear trajectory of the receiver, MEO/HM-BiSAR high-resolution imaging faces two major challenges. First, the complex geometric configuration of the BiSAR platforms is difficult to model accurately, and the ‘non-stop-go’ effects should also be considered. Second, non-negligible wavefront curvature caused by the nonlinear trajectories introduces residual phase errors. The existing spaceborne BiSAR imaging algorithms often suffer from image defocusing if applied to MEO/HM-BiSAR. To address these problems, a novel high-resolution imaging algorithm named MSSWCC (Modified Second-Order Space-Variant Wavefront Curvature Correction) is proposed. First, a high-precision range model is established based on an analysis of MEO SAR’s orbital characteristics and the receiver’s curved trajectory. Based on the echo model, the wavefront curvature error is then addressed by two-dimensional Taylor expansion to obtain the analytical expressions for the high-order phase errors. By analyzing the phase errors in the wavenumber domain, the compensation functions can be designed. The MSSWCC algorithm not only corrects the geometric distortion through reverse projection, but it also compensates for the second-order residual spatial-variant phase errors by the analytical expressions for the two-dimensional phase errors. It can achieve high-resolution imaging ability in large imaging scenes with low computational load. Simulations and real experiments validate the high-resolution imaging capabilities of the proposed MSSWCC algorithm in MEO/HM-BiSAR.
Monitoring Spatial-Temporal Variability of Vegetation Coverage and Its Influencing Factors in the Yellow River Source Region from 2000 to 2020
Authors: Wang Boyang , Si Jianhua , Jia Bing , He Xiaohui , Zhou Dongmeng , Zhu Xinglin , Liu Zijin , Ndayambaza Boniface , Bai Xue
Published: December 2024

As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR.
Temporal Vine Water Status Modeling Through Machine Learning Ensemble Technique and Sentinel-2 Multispectral Images Under Semi-Arid Conditions
Authors: Giannico Vincenzo , Garofalo Simone Pietro , Brillante Luca , Sciusco Pietro , Elia Mario , Lopriore Giuseppe , Camposeo Salvatore , Lafortezza Raffaele , Sanesi Giovanni , Vivaldi Gaetano Alessandro
Published: December 2024

New challenges will be experienced by the agriculture sector in the near future, especially due to the effects of climate change. For example, rising temperatures could result in increased evapotranspiration demand, causing difficulties in the management of irrigation practices. Generally, an important predictor of plant water status to be taken into account for irrigation monitoring and management is the stem water potential. However, it requires a huge amount of time-consuming fieldwork, particularly when an adequate data amount is necessary to fully investigate the spatial and temporal variability of large areas under monitoring. In this study, the integration of machine learning and satellite remote sensing (Sentinel-2) was investigated to obtain a model able to predict the stem water potential in viticulture using multispectral imagery. Vine water status data were acquired within a Montepulciano vineyard in the south of Italy (Puglia region), under semi-arid conditions; data were acquired over two years during the irrigation seasons. Different machine learning algorithms (lasso, ridge, elastic net, and random forest) were compared using vegetation indices and spectral bands as predictors in two independent analyses. The results show that it is possible to remotely estimate vine water status with random forest from vegetation indices (R2 = 0.72). Integrating machine learning techniques and satellite remote sensing could help farmers and technicians manage and plan irrigation, avoiding or reducing fieldwork.
Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i
Authors: Rodríguez-Puerta Francisco , Perroy Ryan L. , Barrera Carlos , Price Jonathan P. , García-Pascual Borja
Published: December 2024

The generation of cloud-free satellite mosaics is essential for a range of remote sensing applications, including land use mapping, ecosystem monitoring, and resource management. This study focuses on remote sensing across the climatic diversity of Hawai’i Island, which encompasses ten Köppen climate zones from tropical to Arctic: periglacial. This diversity presents unique challenges for cloud-free image generation. We conducted a comparative analysis of three cloud-masking methods: two Google Earth Engine algorithms (CloudScore+ and s2cloudless) and a new proprietary deep learning-based algorithm (L3) applied to Sentinel-2 imagery. These methods were evaluated against the best monthly composite selected from high-frequency Planet imagery, which acquires daily images. All Sentinel-2 bands were enhanced to a 10 m resolution, and an advanced weather mask was applied to generate monthly mosaics from 2019 to 2023. We stratified the analysis by cloud cover frequency (low, moderate, high, and very high), applying one-way and two-way ANOVAs to assess cloud-free pixel success rates. Results indicate that CloudScore+ achieved the highest success rate at 89.4% cloud-free pixels, followed by L3 and s2cloudless at 79.3% and 80.8%, respectively. Cloud removal effectiveness decreased as cloud cover increased, with clear pixel success rates ranging from 94.6% under low cloud cover to 79.3% under very high cloud cover. Additionally, seasonality effects showed higher cloud removal rates in the wet season (88.6%), while no significant year-to-year differences were observed from 2019 to 2023. This study advances current methodologies for generating reliable cloud-free mosaics in tropical and subtropical regions, with potential applications for remote sensing in other cloud-dense environments.
Identification of Green Tide Decomposition Regions in the Yellow Sea, China: Based on Time-Series Remote Sensing Data
Authors: Zhang Guangzong , He Yufang , Niu Lifeng , Wu Mengquan , Kaufmann Hermann , Liu Jian , Liu Tong , Kong Qinglei , Chen Bo
Published: December 2024

Approximately 1 million tons of green tides decompose naturally in the Yellow Sea of China every year, releasing large quantities of nutrients that disrupt the marine ecological balance and cause significant environmental consequences. Currently, the identification of areas affected by green tides primarily relies on certain methods, such as ground sampling and biochemical analysis, which limit the ability to quickly and dynamically identify decomposition regions at large spatial and temporal scales. While multi-source remote sensing data can monitor the extent of green tides, accurately identifying areas of algal decomposition remains a challenge. Therefore, satellite data were integrated with key biochemical parameters, such as the carbon-to-nitrogen ratio (C/N), to develop a method for identifying green tide decomposition regions (DRIM). The DRIM shows a high accuracy in identifying green tide decomposition areas, validated through regional repetition rates and UAV measurements. Results indicate that the annual C/N threshold for green tide decomposition regions is 1.2. The method identified the primary decomposition areas in the Yellow Sea from 2015 to 2020, concentrated mainly in the southeastern region of the Shandong Peninsula, covering an area of approximately 1909.4 km2. In 2015, 2016, and 2017, the decomposition areas were the largest, with an average annual duration of approximately 35 days. Our method provides a more detailed classification of the dissipation phase, offering reliable scientific support for accurate and detailed monitoring and management of green tide disasters.
Towards Real-Time Detection of Wakes for Various Sea States with Lightweight Deep Learning Model in Synthetic Aperture Radar Images
Authors: Zhou Xixuan , Zheng Fengjie , Wang Haoyu , Yang Haitao
Published: December 2024

Synthetic aperture radar (SAR) is an essential tool for monitoring and managing maritime traffic and ensuring safety. It is particularly valuable because it can provide surveillance in all weather conditions. Ship wake detection has attracted considerable attention in offshore management as it has potential for widespread use in ship positioning and motion parameter inversion, surpassing conventional ship detection methods. Traditional wake detection methods depend on linear feature extraction through image transformation processing techniques, which are often ineffective and time-consuming when applied to large-scale SAR data. Conversely, deep learning (DL) algorithms have been infrequently utilized in wake detection and encounter significant challenges due to the complex ocean background and the effect of the sea state. In this study, we propose a lightweight rotating target detection network designed for detecting ship wakes under various sea states. For this purpose, we initially analyzed the features of wake samples across various frequency domains. In the framework, a YOLO structure-based deep learning is implemented to achieve wake detection. Our network design enhances the YOLOv8’s structure by incorporating advanced techniques such as deep separation convolution and combined frequency domain–spatial feature extraction modules. These modules are used to replace the usual convolutional layer. Furthermore, it integrates an attention technique to extract diverse features. By conducting experiments on the OpenSARWake dataset, our network exhibited outstanding performance, achieving a wake detection accuracy of 66.3% while maintaining a compact model size of 51.5 MB and time of 14 ms. This model size is notably less than the existing techniques employed for rotating target detection and wake detection. Additionally, the algorithm exhibits excellent generalization ability across different sea states, addressing to a certain extent the challenge of wake detection being easily influenced by varying sea states.
Validation of CRU TS v4.08, ERA5-Land, IMERG v07B, and MSWEP v2.8 Precipitation Estimates Against Observed Values over Pakistan
Authors: Abbas Haider , Song Wenlong , Wang Yicheng , Xiang Kaizheng , Chen Long , Feng Tianshi , Linghu Shaobo , Alam Muneer
Published: December 2024

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies.
Region-Focusing Data Augmentation via Salient Region Activation and Bitplane Recombination for Target Detection
Authors: Zhang Huan , Han Xiaolin , Sun Weidong
Published: December 2024

As the performance of a convolutional neural network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years. Although the current data augmentation methods are efficient because they force the network to learn multiple parts of a given training image through occlusion or re-editing, most of them can damage the internal structures of targets and ultimately affect the results of subsequent application tasks. To this end, region-focusing data augmentation via salient region activation and bitplane recombination for the target detection of optical satellite images is proposed in this paper to solve the problem of internal structure loss in data augmentation. More specifically, to boost the utilization of the positive regions and typical negative regions, a new surroundedness-based strategy for salient region activation is proposed, through which new samples with meaningful focusing regions can be generated. And to generate new samples of the focusing regions, a region-based strategy for bitplane recombination is also proposed, through which internal structures of the focusing regions can be reserved. Thus, a multiplied effect of data augmentation by the two strategies can be achieved. In addition, this is the first time that data augmentation has been examined from the perspective of meaningful focusing regions, rather than the whole sample image. Experiments on target detection with public datasets have demonstrated the effectiveness of this proposed method, especially for small targets.
Detecting Ocean Eddies with a Lightweight and Efficient Convolutional Network
Authors: Sun Haochen , Li Hongping , Xu Ming , Xia Tianyu , Yu Hao
Published: December 2024

As a ubiquitous mesoscale phenomenon, ocean eddies significantly impact ocean energy and mass exchange. Detecting these eddies accurately and efficiently has become a research focus in ocean remote sensing. Many traditional detection methods, rooted in physical principles, often encounter challenges in practical applications due to their complex parameter settings, while effective, deep learning models can be limited by the high computational demands of their extensive parameters. Therefore, this paper proposes a new approach to eddy detection based on the altimeter data, the Ghost Attention Deeplab Network (GAD-Net), which is a lightweight and efficient semantic segmentation model designed to address these issues. The encoder of GAD-Net consists of a lightweight ECA+GhostNet and an Atrous Spatial Pyramid Pooling (ASPP) module. And the decoder integrates an Efficient Attention Network (EAN) module and an Efficient Ghost Feature Integration (EGFI) module. Experimental results show that GAD-Net outperforms other models in evaluation indices, with a lighter model size and lower computational complexity. It also outperforms other segmentation models in actual detection results in different sea areas. Furthermore, GAD-Net achieves detection results comparable to the Py-Eddy-Tracker (PET) method with a smaller eddy radius and a faster detection speed. The model and the constructed eddy dataset are publicly available.
Machine Learning Recognizes Stages of Parkinson’s Disease Using Magnetic Resonance Imaging
Authors: Chudzik Artur
Published: December 2024

Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans (N = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated. Models used volumes, Euclidean, and Cosine distances of subcortical brain structures relative to the thalamus to differentiate among control (HC), prodromal (PR), and PD groups. Based on three separate experiments, the Logistic Regression approach was optimal, providing low feature complexity and strong predictive performance (accuracy: 85%, precision: 88%, recall: 85%) in PD-stage recognition. Using interpretable metrics, such as the volume- and centroid-based spatial distances, models achieved high diagnostic accuracy, presenting a promising framework for early-stage PD identification based on MRI scans.
Concentration vs. Optical Density of ESKAPEE Bacteria: A Method to Determine the Optimum Measurement Wavelength
Authors: Wacogne Bruno , Belinger Podevin Marine , Vaccari Naïs , Koubevi Claudia , Codjiová Céline , Gutierrez Emilie , Davoine Lucie , Robert-Nicoud Marjorie , Rouleau Alain , Frelet-Barrand Annie
Published: December 2024

Optical density measurement has been used for decades to determine the microorganism concentration and more rarely for mammalian cells. Although this measurement can be carried out at any wavelength, studies report a limited number of measurement wavelengths, mainly around 600 nm, and no consensus seems to be emerging to propose an objective method for determining the optimum measurement wavelength for each microorganism. In this article, we propose a method for analyzing the absorbance spectra of ESKAPEE bacteria and determining the optimum measurement wavelength for each of them. The method is based on the analysis of the signal-to-noise ratio of the relationships between concentrations and optical densities when the measurement wavelength varies over the entire spectral range of the absorbance spectra measured for each bacterium. These optimum wavelengths range from 612 nm for Enterococcus faecium to 705 nm for Acinetobacter baumannii. The method can be directly applied to any bacteria, any culture method, and also to any biochemical substance with an absorbance spectrum without any particular feature such as an identified maximum.
Influence of Sudden Changes in Foot Strikes on Loading Rate Variability in Runners
Authors: Chabot Maxime , Thibault-Piedboeuf Alexandre , Nault Marie-Lyne , Roy Jean-Sébastien , Dixon Philippe C. , Simoneau Martin
Published: December 2024

Foot strike patterns influence vertical loading rates during running. Running retraining interventions often include switching to a new foot strike pattern. Sudden changes in the foot strike pattern may be uncomfortable and may lead to higher step-to-step variability. This study evaluated the effects of running with an imposed and usual foot strike on vertical loading rate variability and amplitude. Twenty-seven participants (16 men and 11 women; age range: 18–30 years) ran on an instrumented treadmill with their usual foot strike for 10 min. Then, the participants were instructed to run with an unusual foot strike for 6 min. We calculated the vertical instantaneous and vertical average loading rates and their variances over 200 steps to quantify vertical loading rate variability. We also calculated the amplitude and variability of the shank acceleration peak using an inertial measurement unit. The vertical loading rate and shank acceleration peak amplitudes were higher when running with a rearfoot strike, regardless of the foot strike conditions (i.e., usual or imposed). The vertical loading rate and shank acceleration peak variability were higher when running with an imposed rearfoot strike than when running with a usual forefoot strike. No differences were found in the vertical loading rate and shank acceleration peak variabilities between the imposed forefoot strike and usual rearfoot strike conditions. This study offers compelling evidence that adopting an imposed (i.e., unusual) rearfoot strike amplifies loading rate and shank acceleration peak variabilities.
InCrowd-VI: A Realistic Visual–Inertial Dataset for Evaluating Simultaneous Localization and Mapping in Indoor Pedestrian-Rich Spaces for Human Navigation
Authors: Bamdad Marziyeh , Hutter Hans-Peter , Darvishy Alireza
Published: December 2024

Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual–inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments. Recorded using Meta Aria Project glasses, it captures realistic scenarios without environmental control. InCrowd-VI features 58 sequences totaling a 5 km trajectory length and 1.5 h of recording time, including RGB, stereo images, and IMU measurements. The dataset captures important challenges such as pedestrian occlusions, varying crowd densities, complex layouts, and lighting changes. Ground-truth trajectories, accurate to approximately 2 cm, are provided in the dataset, originating from the Meta Aria project machine perception SLAM service. In addition, a semi-dense 3D point cloud of scenes is provided for each sequence. The evaluation of state-of-the-art visual odometry (VO) and SLAM algorithms on InCrowd-VI revealed severe performance limitations in these realistic scenarios. Under challenging conditions, systems exceeded the required localization accuracy of 0.5 m and the 1% drift threshold, with classical methods showing drift up to 5–10%. While deep learning-based approaches maintained high pose estimation coverage (>90%), they failed to achieve real-time processing speeds necessary for walking pace navigation. These results demonstrate the need and value of a new dataset to advance SLAM research for visually impaired navigation in complex indoor environments.
Testing and Validation of the Vehicle Front Camera Verification Method Using External Stimulation
Authors: Langer Robin , Bauder Maximilian , Moghariya Ghanshyam Tukarambhai , Eckert Michael Clemens Georg , Kubjatko Tibor , Schweiger Hans-Georg
Published: December 2024

The perception of the vehicle’s environment is crucial for automated vehicles. Therefore, environmental sensors’ reliability and correct functioning are becoming increasingly important. Current vehicle inspections and self-diagnostics must be adapted to ensure the correct functioning of environmental sensors throughout the vehicle’s lifetime. There are several promising approaches for developing new test methods for vehicle environmental sensors, one of which has already been developed in our previous work. A method for testing vehicle front cameras was developed. In this work, the method is improved and applied again. Various test vehicles, including the Tesla Model 3, Volkswagen ID.3, and Volkswagen T-Cross, are stimulated by simulating driving scenarios. The stimulation is carried out via a tablet positioned before the camera. The high beam assist is used to evaluate the vehicle’s reaction. It was observed whether the vehicle switched from high to low beam as expected in response to the stimulation. Although no general statement can be made, the principle of stimulation works. A vehicle reaction can be successfully induced using this method. In further test series, the influence of display brightness is examined for the first time in this work. The results show that the display brightness significantly influences the test procedure. In addition, the method is validated by stimulation with colored images. It is shown that no complex traffic simulation is necessary to trigger a vehicle reaction. In the following validation approach, the CAN data of the Tesla Model 3 is analyzed during the tests. Here, too, the assumption that the vehicle reaction is based solely on the detected brightness instead of identifying road users is confirmed. The final validation approach examines the method’s applicability to other vehicles and high beam assist technologies. Although the method could not be used on the Volkswagen T-Cross due to a fault detected by the vehicle’s self-diagnosis, it worked well on the Volkswagen ID.3. This vehicle has a dynamic light assist in which individual segments of the high beam are dimmed during stimulation. Although the method developed to stimulate vehicle front cameras is promising, the specific factors that trigger the vehicle responses remain to be seen. This uncertainty suggests that further research is needed better to understand the interaction of stimulation and sensor detection.
An Ensemble Network for High-Accuracy and Long-Term Forecasting of Icing on Wind Turbines
Authors: Dai Jiazhi , Rotea Mario , Kehtarnavaz Nasser
Published: December 2024

Freezing of wind turbines causes loss of wind-generated power. Forecasting or prediction of icing on wind turbine blades based on SCADA sensor data allows taking appropriate actions before icing occurs. This paper presents a newly developed deep learning network model named PCTG (Parallel CNN-TCN GRU) for the purpose of high-accuracy and long-term prediction of icing on wind turbine blades. This model combines three networks, the CNN, TCN, and GRU, in order to incorporate both the temporal aspect of SCADA time-series data as well as the dependencies of SCADA variables. The experimentations conducted by using this model and SCADA data from three wind turbines in a wind farm have generated average prediction accuracies of about 97% for prediction horizons of up to 2 days ahead. The developed model is shown to maintain at least 95% prediction accuracy for long prediction horizons of up to 22 days ahead. Furthermore, for another wind farm SCADA dataset, it is shown that the developed PCTG model achieves over 99% icing prediction accuracy 10 days ahead.