bluuebunny/crossref_metadata_2025 huggingface dataset:
Dietary Adherence to Recommendations among a Cohort of Adults and Teens with Celiac Disease Maintaining a Gluten-Free Diet Compared to a Nationally Representative Sample: A Cross-Sectional Study
Authors: Cadenhead Jennifer W. , Lee Anne R. , Nguyen Thanh Thanh T. , Lebwohl Benjamin , Green Peter H. R. , Wolf Randi L.
Published: September 2024

Celiac disease (CeD) is a common autoimmune condition, with a prevalence of ~1%. Currently, a gluten-free diet (GFD) is the only treatment option. Due to fortification rules excluding gluten-free products in the United States of America (U.S.A.), understanding the nutritional adequacy of a GFD is important for promoting optimal health among those with CeD. Cross-sectional examination of multiple 24 h dietary recalls from a study sample of 50 adults and 30 teens with CeD was used to determine nutritional adequacy and excesses according to U.S.A. recommendations. The results were compared with those of 15,777 adults and 2296 teens from a nationally representative sample not reporting CeD, the National Health and Nutrition Examination Survey (NHANES) 2009–2014. Compared with NHANES, our study population was more at risk of low folate and carbohydrate (adults) consumption, and of excessive niacin and vitamin A (teens), as well as saturated and total fat consumption (adults). Overall, though, compared with NHANES, our study participants had similar nutrient concerns but fewer nutritional imbalances, with some notable exceptions. In addition to maintaining a GFD, individuals with CeD should be counseled to maintain a balanced diet and to pay attention to nutrient-dense foods. Special attention should be given to teens in providing dietary counseling to potentially mitigate the risk of future morbidity.
An Unexpected Small Biodiversity Oasis of Sea Slugs (Mollusca, Gastropoda, Heterobranchia) in the Largest Petrochemical Hub of Italy (Central Mediterranean)
Authors: Lombardo Andrea , Marletta Giuliana
Published: September 2024

The Magnisi peninsula is a small portion of land located near the largest Italian petrol-chemical pole of Augusta–Priolo–Melilli (40 km2), which, since the 1950s, devastated the local environment and landscape and unloaded directly into the sea an impressive quantity of pollutants. Unlike the terrestrial part of the area, where a Natural Oriented Reserve (NOR) called “Saline di Priolo” was established in the 2000s, no concrete legislative action has been implemented or proposed for the marine environment. At the same time, the fauna of the marine environment has not been studied in the same way as that of the terrestrial environment. As concerns the molluscan fauna, most of the information dates back to the 1800s. These studies were exclusively focused on the shells of some mollusks in the area. Instead, no study related to this area has ever been carried out on the group of sea slugs. This study conducted between 2022 and 2023, through snorkeling activities, allowed to provide the first faunistic list of the sea slugs of this area, together with information on the biology and ecology of these gastropods, highlighting the potential biodiversity present in this small stretch of coastline affected by high industrial pollution.
Prevalence of Anisakiasis in Madrid (Spain) after 20 Years of Preventive Legislation
Authors: Blanco-Costales Eva , González-Quevedo Alejandra L. , Lorenzo-Bernardo Laura , de la Hoz-Martín María P. , Rodero Marta , Puente Pilar , Moreno-Torres Irene , Cuéllar Carmen , González-Fernández Juan
Published: September 2024

Historical seroprevalence data for Anisakis in Spain vary greatly depending on the sampling region owing to different fish consumption habits. As a result of European Regulation (EC) No. 853/2004, the Royal Decree 1420/2006 on the prevention of parasitosis by Anisakis in fishery products supplied by establishments that serve food to final consumers or to communities came into force in Spain. In this study, a prevalence study of Anisakis in Madrid has been conducted to verify the prophylactic effects of the application of the law. Sera from 500 blood donors from the Fundación Jiménez Díaz University Hospital (Madrid/2021–2023) were collected, and the levels of anti-Anisakis IgG, IgA, and IgE were analyzed by ELISA, comparing them with those obtained with 110 donors from the Red Cross and the “Gómez Ulla” Central Defense Hospital (Madrid/2001–2002). The percentages of positivity in the 2021–2023 donor group were IgG (13.6%), IgA (13.6%), and IgE (2.2%), while in the 2001–2002 donors they were positive for IgG (15.45%), IgA (14.54%), and IgE (11.65%). A reduction of more than 80% was observed in the prevalence of anti-Anisakis IgE in the healthy population of Madrid, which confirmed the positive effect of RD1420/2006, which was later incorporated into RD1021/2022.
Physcion Mitigates LPS-Induced Neuroinflammation, Oxidative Stress, and Memory Impairments via TLR-4/NF-кB Signaling in Adult Mice
Authors: Ahmad Sareer , Choe Kyonghwan , Badshah Haroon , Ahmad Riaz , Ali Waqar , Rehman Inayat Ur , Park Tae Ju , Park Jun Sung , Kim Myeong Ok
Published: September 2024

Alzheimer’s disease (AD) is the most predominant cause of dementia, considered a progressive decline in cognitive function that ultimately leads to death. AD has posed a substantial challenge in the records of medical science over the past century, representing a predominant etiology of dementia with a high prevalence rate. Neuroinflammation is a common characteristic of various central nervous system (CNS) pathologies like AD, primarily mediated by specialized brain immune and inflammatory cells, such as astrocytes and microglia. The present study aims to elucidate the potential mechanism of physcion that mitigates LPS-induced gliosis and assesses oxidative stress in mice. Physcion reduced the reactivity of Iba-1- and GFAP-positive cells and decreased the level of inflammatory cytokines like TNF-α and IL-1β. Physcion also reversed the effect of LPS-induced oxidative stress by upregulating the expression of Nrf2 and HO-1. Moreover, physcion treatment reversed LPS-induced synaptic disorder by increasing the level of presynaptic protein SNAP-23 and postsynaptic protein PSD-95. Our findings may provide a contemporary theoretical framework for clinical investigations aimed at examining the pathogenic mechanisms and therapeutic approaches for neuroinflammation and AD.
Editorial on Special Issue “Recent Advances in Hydrogels for Biomedical Applications”
Authors: Babić Radić Marija M.
Published: September 2024

The significant progress in design and engineering of advanced hydrogels for biomedical applications is fundamentally driven by the increasing complexity and specificity of challenges in the biomedical field [...]
Powder Self-Emulsifying Drug Delivery System for Mitotane: In Vitro and In Vivo Evaluation
Authors: Skiba Mohamed , Lefébure Valentin , Bounoure Frederic , Milon Nicolas , Thomas Michael , Lefebvre Herve , Malika Lahiani-Skiba
Published: September 2024

Drug Delivery Systems (DDSs) of known drugs are prominent candidates for new and more effective treatments of various diseases, as they may increase drug solubility, dissolution velocity, and bioavailability. Mitotane (o,p′-dichlorodimethyl dichloroethane [o,p′-DDD]) is used for the treatment of adrenocortical cancer and, occasionally, Cushing’s syndrome. However, the efficacy of mitotane is limited by its low oral bioavailability, caused by its extremely poor aqueous solubility. This research explores the development of a new powder self-emulsifying drug delivery system (P-SEDDS) for mitotane to improve its oral bioavailability. The study focuses on the new concept of a mitotane-loaded P-SEDDS to overcome the challenges associated with its limited solubility and high logP, thereby improving its therapeutic efficacy, reducing off-target toxicity, and avoiding first-pass metabolism. The P-SEDDS formulations were meticulously designed using only α-cyclodextrin and oil, with the goal of achieving a stable and efficient P-SEDDS. The optimized formulation was characterized for pharmaceutical properties, and its pharmacokinetic behavior was examined in rats. The results demonstrated a significant enhancement in the bioavailability of mitotane when delivered through the P-SEDDS, attributed to the increased dissolution velocity and improved absorption of the poorly water-soluble drug. The results suggest that a mitotane-loaded P-SEDDS has distinctly enhanced in vitro and in vivo performance compared with conventional mitotane formulations (Lysodren®), which leads to the conclusion that the P-SEDDS formulation could be a viable and effective strategy for improving the dissolution rate and bioavailability of poorly aqueous-soluble ingredients.
Advances in Nanoparticles as Non-Viral Vectors for Efficient Delivery of CRISPR/Cas9
Authors: Kim Minse , Hwang Youngwoo , Lim Seongyu , Jang Hyeon-Ki , Kim Hyun-Ouk
Published: September 2024

The clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system is a gene-editing technology. Nanoparticle delivery systems have attracted attention because of the limitations of conventional viral vectors. In this review, we assess the efficiency of various nanoparticles, including lipid-based, polymer-based, inorganic, and extracellular vesicle-based systems, as non-viral vectors for CRISPR/Cas9 delivery. We discuss their advantages, limitations, and current challenges. By summarizing recent advancements and highlighting key strategies, this review aims to provide a comprehensive overview of the role of non-viral delivery systems in advancing CRISPR/Cas9 technology for clinical applications and gene therapy.
Whole-Genome Resequencing Identifies SNPs in Sucrose Synthase and Sugar Transporter Genes Associated with Sweetness in Coconut
Authors: Khongmaluan Manlika , Aesomnuk Wanchana , Dumhai Reajina , Pitaloka Mutiara K. , Xiao Yong , Xia Rui , Kraithong Tippaya , Phonsatta Natthaporn , Panya Atikorn , Ruanjaichon Vinitchan , Wanchana Samart , Arikit Siwaret
Published: September 2024

Coconut (Cocos nucifera L.) is an important agricultural commodity with substantial economic and nutritional value, widely used for various products, including coconut water. The sweetness is an important quality trait of coconut water, which is influenced by genetic and environmental factors. In this study, we utilized next-generation sequencing to identify genetic variations in the coconut genome associated with the sweetness of coconut water. Whole-genome resequencing of 49 coconut accessions, including diverse germplasm and an F2 population of 81 individuals, revealed ~27 M SNPs and ~1.5 M InDels. Sugar content measured by °Bx was highly variable across all accessions tested, with dwarf varieties generally sweeter. A comprehensive analysis of the sugar profiles revealed that sucrose was the major sugar contributing to sweetness. Allele mining of the 148 genes involved in sugar metabolism and transport and genotype–phenotype association tests revealed two significant SNPs in the hexose carrier protein (Cnu01G018720) and sucrose synthase (Cnu09G011120) genes associated with the higher sugar content in both the germplasm and F2 populations. This research provides valuable insights into the genetic basis of coconut sweetness and offers molecular markers for breeding programs aimed at improving coconut water quality. The identified variants can improve the selection process in breeding high-quality sweet coconut varieties and thus support the economic sustainability of coconut cultivation.
Genomic Sequence of a Czech Isolate of Erysimum Latent Virus from Sisymbrium altissimum
Authors: Ben Mansour Karima , Špak Josef , Komínek Petr , Zouhar Miloslav , Ryšánek Pavel , Gibbs Adrian J.
Published: September 2024

The Erysimum latent virus (ELV), a tymovirus, was first isolated from several wild and cultivated brassicas in Germany. Its virions were shown to be serologically distinct from those of the turnip yellow mosaic virus (TYMV), which is also found in wild and cultivated plants in several European countries but also in other parts of the world. TYMV and ELV were among the first plant viruses to have had their genomes sequenced, and when other tymovirus genomes were sequenced, it was found that, in phylogenies, ELV is probably the basal outlier to all other tymoviruses. Here, we report the near-complete genomic sequence of another isolate of ELV from Czechia. This isolate was found in 1990 in Sisymbrium altissimum plants showing mosaic symptoms. It was detected using ELISA tests and electron microscopy. We have now sequenced the full coding sequence of this isolate using contemporary high throughput methods and found that the German and Czech isolates of ELV are closely related and are of the same virus species.
Extreme Weather Events and Pathogen Pollution Fuel Infectious Diseases: The 2024 Flood-Related Leptospirosis Outbreak in Southern Brazil and Other Red Lights
Authors: Ziliotto Marina , Chies José Artur Bogo , Ellwanger Joel Henrique
Published: September 2024

The emergence of infectious disease outbreaks and ‘superbugs’ related to pollution combined with climate change is a current problem, not just a future threat. In May 2024, an extreme flood hit the Rio Grando Sul State, southern Brazil, triggering an important leptospirosis outbreak in urban settings with deficient sanitation systems. This and other cases discussed in this article exemplify how extreme weather events exacerbate the consequences of environmental pollution by multiple classes of pathogens in the global scenario of increasing anthropogenic pressures on the environment. A combination of actions to combat climate change and improvements in sanitation systems is essential to mitigate this problem.
“I Discovered Such a Lay Force That I Could Not Remove Them”: Sacred and Secular Space and Ecclesiastical and Secular Authority in the Parish in the Fourteenth-Century Diocese of York
Authors: Hoskin Philippa
Published: September 2024

In 1309, the parish church of Harewood in Yorkshire, England, was invaded by a group of armed parishioners opposing the decisions of the church courts. The story of this invasion and the ways in which Church and State attempted and failed to remove it demonstrate how an apparently local quarrel could be part of national political events and of the intertwining of the laws of Church and State. It also demonstrates, importantly, a largely overlooked aspect of the relationship between the laity at a local level and the Church as an institution.
Harnessing the Power of Large Language Models for Automated Code Generation and Verification
Authors: Antero Unai , Blanco Francisco , Oñativia Jon , Sallé Damien , Sierra Basilio
Published: September 2024

The cost landscape in advanced technology systems is shifting dramatically. Traditionally, hardware costs took the spotlight, but now, programming and debugging complexities are gaining prominence. This paper explores this shift and its implications, focusing on reducing the cost of programming complex robot behaviors, using the latest innovations from the Generative AI field, such as large language models (LLMs). We leverage finite state machines (FSMs) and LLMs to streamline robot programming while ensuring functionality. The paper addresses LLM challenges related to content quality, emphasizing a two-fold approach using predefined software blocks and a Supervisory LLM.
Four-Dimensional Parameter Estimation for Mixed Far-Field and Near-Field Target Localization Using Bistatic MIMO Arrays and Higher-Order Singular Value Decomposition
Authors: Zhang Qi , Jiang Hong , Zheng Huiming
Published: September 2024

In this paper, we present a novel four-dimensional (4D) parameter estimation method to localize the mixed far-field (FF) and near-field (NF) targets using bistatic MIMO arrays and higher-order singular value decomposition (HOSVD). The estimated four parameters include the angle-of-departure (AOD), angle-of-arrival (AOA), range-of-departure (ROD), and range-of-arrival (ROA). In the method, we store array data in a tensor form to preserve the inherent multidimensional properties of the array data. First, the observation data are arranged into a third-order tensor and its covariance tensor is calculated. Then, the HOSVD of the covariance tensor is performed. From the left singular vector matrices of the corresponding module expansion of the covariance tensor, the subspaces with respect to transmit and receive arrays are obtained, respectively. The AOD and AOA of the mixed FF and NF targets are estimated with signal-subspace, and the ROD and ROA of the NF targets are achieved using noise-subspace. Finally, the estimated four parameters are matched via a pairing method. The Cramér–Rao lower bound (CRLB) of the mixed target parameters is also derived. The numerical simulations demonstrate the superiority of the tensor-based method.
Relationship between Vegetation and Soil Moisture Anomalies Based on Remote Sensing Data: A Semiarid Rangeland Case
Authors: Martín-Sotoca Juan José , Sanz Ernesto , Saa-Requejo Antonio , Moratiel Rubén , Almeida-Ñauñay Andrés F. , Tarquis Ana M.
Published: September 2024

The dynamic of rangelands results from complex interactions between vegetation, soil, climate, and human activity. This scenario makes rangeland’s condition challenging to monitor, and degradation assessment should be carefully considered when studying grazing pressures. In the present work, we study the interaction of vegetation and soil moisture in semiarid rangelands using vegetation and soil moisture indices. We aim to study the feasibility of using soil moisture negative anomalies as a warning index for vegetation or agricultural drought. Two semiarid agricultural regions were selected in Spain for this study: Los Vélez (Almería) and Bajo Aragón (Teruel). MODIS images, with 250 m and 500 m spatial resolution, from 2002 to 2019, were acquired to calculate the Vegetation Condition Index (VCI) and the Water Condition Index (WCI) based on the Normalised Difference Vegetation Index (NDVI) and soil moisture component (W), respectively. The Optical Trapezoid Model (OPTRAM) estimated this latter W index. From them, the anomaly (Z-score) for each index was calculated, being ZVCI and ZWCI, respectively. The probability of coincidence of their negative anomalies was calculated every 10 days (10-day periods). The results show that for specific months, the ZWCI had a strong probability of informing in advance, where the negative ZVCI will decrease. Soil moisture content and vegetation indices show more similar dynamics in the months with lower temperatures (from autumn to spring). In these months, given the low temperatures, precipitation leads to vegetation growth. In the following months, water availability depends on evapotranspiration and vegetation type as the temperature rises and the precipitation falls. The stronger relationship between vegetation and precipitation from autumn to the beginning of spring is reflected in the feasibility of ZWCI to aid the prediction of ZVCI. During these months, using ZWCI as a warning index is possible for both areas studied. Notably, November to the beginning of February showed an average increase of 20–30% in the predictability of vegetation anomalies, knowing moisture soil anomalies four lags in advance. We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón.
Mapping Building Heights at Large Scales Using Sentinel-1 Radar Imagery and Nighttime Light Data
Authors: Kakooei Mohammad , Baleghi Yasser
Published: September 2024

Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1’s signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability.
ANN-Based Filtering of Drone LiDAR in Coastal Salt Marshes Using Spatial–Spectral Features
Authors: Liu Kunbo , Liu Shuai , Tan Kai , Yin Mingbo , Tao Pengjie
Published: September 2024

Salt marshes provide diverse habitats for a wide range of creatures and play a key defensive and buffering role in resisting extreme marine hazards for coastal communities. Accurately obtaining the terrains of salt marshes is crucial for the comprehensive management and conservation of coastal resources and ecology. However, dense vegetation coverage, periodic tide inundation, and pervasive ditch distribution create challenges for measuring or estimating salt marsh terrains. These environmental factors make most existing techniques and methods ineffective in terms of data acquisition resolution, accuracy, and efficiency. Drone multi-line light detection and ranging (LiDAR) has offered a fire-new perspective in the 3D point cloud data acquisition and potentially exhibited great superiority in accurately deriving salt marsh terrains. The prerequisite for terrain characterization from drone multi-line LiDAR data is point cloud filtering, which means that ground points must be discriminated from the non-ground points. Existing filtering methods typically rely on either LiDAR geometric or intensity features. These methods may not perform well in salt marshes with dense, diverse, and complex vegetation. This study proposes a new filtering method for drone multi-line LiDAR point clouds in salt marshes based on the artificial neural network (ANN) machine learning model. First, a series of spatial–spectral features at the individual (e.g., elevation, distance, and intensity) and neighborhood (e.g., eigenvalues, linearity, and sphericity) scales are derived from the original data. Then, the derived spatial–spectral features are selected to remove the related and redundant ones for optimizing the performance of the ANN model. Finally, the reserved features are integrated as input variables in the ANN model to characterize their nonlinear relationships with the point categories (ground or non-ground) at different perspectives. A case study of two typical salt marshes at the mouth of the Yangtze River, using a drone 6-line LiDAR, demonstrates the effectiveness and generalization of the proposed filtering method. The average G-mean and AUC achieved were 0.9441 and 0.9450, respectively, outperforming traditional geometric information-based methods and other advanced machine learning methods, as well as the deep learning model (RandLA-Net). Additionally, the integration of spatial–spectral features at individual–neighborhood scales results in better filtering outcomes than using either single-type or single-scale features. The proposed method offers an innovative strategy for drone LiDAR point cloud filtering and salt marsh terrain derivation under the novel solution of deeply integrating geometric and radiometric data.
Thorough Understanding and 3D Super-Resolution Imaging for Forward-Looking Missile-Borne SAR via a Maneuvering Trajectory
Authors: Gu Tong , Guo Yifan , Zhao Chen , Zhang Jian , Zhang Tao , Liao Guisheng
Published: September 2024

For missile-borne platforms, traditional SAR technology consistently encounters two significant shortcomings: geometric distortion of 2D images and the inability to achieve forward-looking imaging. To address these issues, this paper explores the feasibility of using a maneuvering trajectory to enable forward-looking and three-dimensional imaging by analyzing the maneuvering characteristics of an actual missile-borne platform. Additionally, it derives the corresponding resolution characterization model, which lays a theoretical foundation for future applications. Building on this, the paper proposes a three-dimensional super-resolution imaging algorithm that combines axis rotation with compressed sensing. The axis rotation not only realizes the dimensionality reduction of data, but also can expand the observation scenario in the cross-track dimension. The proposed algorithm first focuses on the track-vertical plane to extract 2D position parameters. Then, a compressed sensing-based process is applied to extract reflection coefficients and super-resolution cross-track position parameters, thereby achieving precise 3D imaging reconstruction. Finally, numerical simulation results confirm the effectiveness and accuracy of the proposed algorithm.
Enhancing Alfalfa Biomass Prediction: An Innovative Framework Using Remote Sensing Data
Authors: Lucero Matias F. , Hernández Carlos M. , Carcedo Ana J. P. , Zajdband Ariel , Guillevic Pierre C. , Houborg Rasmus , Hamilton Kevin , Ciampitti Ignacio A.
Published: September 2024

Estimating pasture biomass has emerged as a promising avenue to assist farmers in identifying the best cutting times for maximizing biomass yield using satellite data. This study aims to develop an innovative framework integrating field and satellite data to estimate aboveground biomass in alfalfa (Medicago sativa L.) at farm scale. For this purpose, samples were collected throughout the 2022 growing season on different mowing dates at three fields in Kansas, USA. The satellite data employed comprised four sources: Sentinel-2, PlanetScope, Planet Fusion, and Biomass Proxy. A grid of hyperparameters was created to establish different combinations and select the best coefficients. The permutation feature importance technique revealed that the Planet’s PlanetScope near-infrared (NIR) band and the Biomass Proxy product were the predictive features with the highest contribution to the biomass prediction model’s. A Bayesian Additive Regression Tree (BART) was applied to explore its ability to build a predictive model. Its performance was assessed via statistical metrics (r2: 0.61; RMSE: 0.29 kg.m−2). Additionally, uncertainty quantifications were proposed with this framework to assess the range of error in the predictions. In conclusion, this integration in a nonparametric approach achieved a useful predicting tool with the potential to optimize farmers’ management decisions.
Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method
Authors: Cao Zongqing , Liu Bing , Yang Jianchao , Tan Ke , Dai Zheng , Lu Xingyu , Gu Hong
Published: September 2024

Interrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as unrealistic assumptions, susceptibility to noise, and suboptimal performance limits. This study proposes a unified framework to address the challenges of associating interrupted and multi-source track segments by measuring trajectory similarity. We present TSA-cTFER, a novel network utilizing contrastive learning and TransFormer Encoder to accurately assess trajectory similarity through learned Representations by computing distances between high-dimensional feature vectors. Additionally, we tackle dynamic association scenarios with a two-stage online algorithm designed to manage tracks that appear or disappear at any time. This algorithm categorizes track pairs into easy and hard groups, employing tailored association strategies to achieve precise and robust associations in dynamic environments. Experimental results on real-world datasets demonstrate that our proposed TSA-cTFER network with the two-stage online algorithm outperforms existing methods, achieving 94.59% accuracy in interrupted track segment association tasks and 94.83% in multi-source track segment association tasks.
Enhanced IoT Spectrum Utilization: Integrating Geospatial and Environmental Data for Advanced Mid-Band Spectrum Sharing
Authors: Brown Colin , Rong Bo
Published: September 2024

The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks.
Multimodal Social Sensing for the Spatio-Temporal Evolution and Assessment of Nature Disasters
Authors: Yu Chen , Wang Zhiguo
Published: September 2024

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems.
RT-CBAM: Refined Transformer Combined with Convolutional Block Attention Module for Underwater Image Restoration
Authors: Ye Renchuan , Qian Yuqiang , Huang Xinming
Published: September 2024

Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global receptive field and adaptability. Although transformers excel in capturing global features, they lag behind CNNs in handling fine local features, especially when dealing with underwater images containing complex and delicate structures. In order to tackle this challenge, we propose a refined transformer model by improving the feature blocks (dilated transformer block) to more accurately compute attention weights, enhancing the capture of both local and global features. Subsequently, a self-supervised method (a local and global blind-patch network) is embedded in the bottleneck layer, which can aggregate local and global information to enhance detail recovery and improve texture restoration quality. Additionally, we introduce a multi-scale convolutional block attention module (MSCBAM) to connect encoder and decoder features; this module enhances the feature representation of color channels, aiding in the restoration of color information in images. We plan to deploy this deep learning model onto the sensors of underwater robots for real-world underwater image-processing and ocean exploration tasks. Our model is named the refined transformer combined with convolutional block attention module (RT-CBAM). This study compares two traditional methods and six deep learning methods, and our approach achieved the best results in terms of detail processing and color restoration.
Tiny Security Hole: First-Order Vulnerability of Masked SEED and Its Countermeasure
Authors: Kim Ju-Hwan , Han Dong-Guk
Published: September 2024

Side-channel analysis is a type of cryptanalysis that utilizes the physical leakage of a cryptographic device. An adversary exploits the relationship between a physical leakage and the secret intermediate value of an encryption algorithm. In order to prevent side-channel analysis, the masking method was proposed. Several masking methods of the ISO/IEC 18033-3 standard encryption algorithm SEED have been proposed, as the Korean financial IC (integrated circuit) card standard (CFIP.ST.FINIC-01-2021) mandates using a robust implementation of SEED as an encryption algorithm against side-channel analyses. However, vulnerabilities were reported, except for with only one masking method. This study proposes the first-order vulnerability of that masking method. That is, an adversary is able to perform a side-channel analysis with the same complexity as an unprotected implementation. In order to fix this vulnerability, we revise the masking method with negligible additional overhead. Its vulnerability and security are theoretically verified and experimentally demonstrated. The round key of the existing masking method is revealed with only 210 power consumption traces, while that of the proposed masking method is not disclosed with 10,000 traces.
An Algorithm for Strapdown Airborne Gravity Disturbance Vector Measurement Based on High-Precision Navigation and EGM2008
Authors: Fang Ke , Cai Tijing
Published: September 2024

Attitude errors, accelerometer bias, the gravity disturbance vector, and their coupling are the primary factors obstructing strapdown airborne vector gravimetry. This paper takes the geocentric inertial frame as a reference and solves the kinematic equations of its motion and its errors of the body frame and local geographic frame in the Lie group, respectively; the attitude accuracy is improved through a high-precision navigation algorithm. The constant accelerometer bias is estimated through Kalman filtering and is deducted from the accelerometer output to eliminate its influence. Based on the EGM2008 model, the low-frequency components of the gravity disturbance vector are corrected. The gravity disturbance vectors after model data fusion were low-pass filtered to obtain the ultimate results. This method was applied to flight experimental data in the South China Sea, and a gravity anomaly accuracy of better than 0.5 mGal, a northward gravity disturbance accuracy of 0.85 mGal, and an eastward gravity disturbance accuracy of 4.0 mGal were obtained, with a spatial resolution of approximately 4.8 km.
Enhancing Reliability and Stability of BLE Mesh Networks: A Multipath Optimized AODV Approach
Authors: Ghori Muhammad Rizwan , Wan Tat-Chee , Sodhy Gian Chand , Aljaidi Mohammad , Rizwan Amna , Sadiq Ali Safaa , Kaiwartya Omprakash
Published: September 2024

Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads. However, the packet delivery ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose the Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in the case of node failures, compared with other forwarding-based protocols that require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments.
Spectroscopic Analyses of Blue Pigments in the Manoscritto Parmense 3285 from the 14th Century
Authors: Marghella Giuseppe , Bruni Stefania , Gessi Alessandro , Tireni Lorena , Ubaldini Alberto , Cicconi Flavio
Published: September 2024

During the restoration and digitalization intervention of the Manoscritto Parmense (Ms. Parm.) 3285 codex, a 14th-century illuminated volume that contains the three books of Dante Alighieri’s Divina Commedia belonging to the Biblioteca Palatina of Parma, the deposits present in the center of some bifoliums were removed using soft bristle brushes and collected. A preliminary observation of these deposits with a stereomicroscope allowed the detection of some pigment grains of different colors, likely detached from the full-page illuminations at the beginning of each book. These grains of the pigments were then analyzed in the Bologna ENEA Research Centre through Scanning Electron Microscopy (SEM), EDX microanalysis, and micro-Raman spectroscopy, allowing the identification of the minerals or the compounds used as pigments. Consequently, some pigments that were commonly used in the Middle Ages such as gold leaf, typically employed in illuminations and the decoration of heading initials, cinnabar red, and different types of blue pigments, including traces of lapis lazuli and azurite, were identified.
Public Perception of Drought and Extreme Rainfall Impacts in a Changing Climate: Aconcagua Valley and Chañaral, Chile
Authors: Aldunce Paulina , Haverbeck Fernanda , Sapiains Rodolfo , Quilaqueo Antonio , Castro Carmen Paz
Published: September 2024

Droughts and extreme rainfall events are two of the hazards that affect many people in the world and are frequent and complex hazards, the rate of occurrence and magnitude of which are expected to increase in a changing climate. In this context, understanding how different actors perceive changes in climate, drought, and extreme rainfall events and their impacts is relevant in contributing to successfully implementing adaptation strategies to reduce their impacts. This research seeks to explore the main changes the climate has undergone and the impacts of drought and precipitation events, as perceived at local levels by different stakeholders. A multi-method approach was applied, including qualitative methods such as observation, 51 semi-structured interviews, and document reviews in Chañaral and the Aconcagua Valley, Chile. This research shows what the perceived changes in climate are and that drought and extreme rainfall events have affected the well-being of the local people by severely impacting the economy, the environment, social interactions, quality of life, and human health. Additionally, the perception of climate change and its impacts vary depending on the type of hazard and the social, geographical, and environmental contexts in which communities live. This study is useful as it has generated knowledge relevant to inform policy decisions, practice, and theory.
Augmentation of Reclaimed Water with Excess Urban Stormwater for Direct Potable Use
Authors: Ikehata Keisuke , Espindola Carlos A. , Ashraf Anjumand , Adams Hunter
Published: September 2024

Groundwater and surface water have been the primary sources of our public water supply around the world. However, rapid population and economic growth, as well as global climate change, are posing major threats to the quality and quantity of these water resources. Treated wastewater (reclaimed water) and stormwater are becoming more important water resources. Use/reuse of these unconventional water resources can enable a truly sustainable, closed-loop, circular water system. However, these two sources are not usually mixed with each other. In this study, we propose the use of combined excess urban stormwater and reclaimed water as a source of potable water supply. One of the most pronounced benefits of this proposed scheme is the possible elimination of costly and energy-intensive processes like reverse osmosis. Reclaimed water tends to have high concentrations of dissolved solids (>500 mg/L) and nitrate-N (>10 mg/L), which can be lowered by blending with stormwater or rainwater. Despite technical and engineering challenges, this approach can benefit various communities—small, medium, large, upstream, downstream, urban, and rural—in diverse climates. Our study suggests that this new holistic approach is feasible, enabling the combined water to be directly used as a sustainable drinking water source.
The Effect of Corporate Social Responsibility on Corporate Performance in the Food Industry in Saudi Arabia: A Partial Least Squares Structural Equation Modeling Approach
Authors: Eledum Hussein , Elmahgop Faiza Omer
Published: September 2024

Although there has been a growing interest in understanding the influence of corporate social responsibility (CSR) on business outcomes, the specific mechanisms through which CSR impacts financial performance (FP) and competitive advantage (CA) remain underexplored, particularly in the context of the food industry. This study contributes to the literature by looking into the mediating effect of innovation and the moderating role of CA in the relationship between CSR and FP in Saudi Arabian food industry companies. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from executive managers of these companies to assess how CSR practices foster innovation and, consequently, enhance financial outcomes and competitive positioning. The results reveal that CSR significantly improves FP and CA, primarily when focused on innovation. Moreover, CA does not influence the strength or direction of the relationship between CSR and FP. This research offers essential guidance for food industry managers to integrate CSR into core business strategies, foster innovation, and strengthen stakeholder relationships, thereby achieving sustainable growth and profitability.
Impacts of Digital Entrepreneurial Ecosystems on Sustainable Development: Insights from Latin America
Authors: Pigola Angélica , Fischer Bruno , Moraes Gustavo Hermínio Salati Marcondes de
Published: September 2024

Digital Entrepreneurial Ecosystems (DEEs) are transforming the economic landscape through their integration of digital technologies, offering new opportunities for innovation and growth. This study explores the impact of DEEs on sustainable development, focusing specifically on Latin America. As DEEs continue to evolve, understanding their influence on economic, environmental, and social sustainability becomes crucial, particularly in a region characterized by significant developmental challenges. Utilizing a data panel from two different periods of analysis, from 2013 to 2017 and from 2018 to 2022, within the adapted DEE framework provided by the Global Entrepreneurship Development Institute (GEDI), we employ Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), and fuzzy-set Qualitative Comparative Analysis (fsQCA 3.0) to analyze DEE components across 14 Latin American countries. These countries may not have the full spectrum of digital capabilities, yet they are still able to harness the digital elements they do possess effectively. This suggests that even partial digitalization, when strategically utilized, can lead to substantial gains in sustainable development. Additionally, Networking, Digital Protection, and Digital Tech Transfer are DEE components that present a higher magnitude in social, environmental, and economic development in Latin American countries. This study not only contributes to a deeper understanding of a DEE’s role in fostering sustainable development, but it also offers actionable insights for policymakers and entrepreneurs to leverage DEEs for broader societal benefits. The implications of the findings present perspectives under the existing literature, and the conclusion shows recommendations for future research and strategy development.
Study on the Evolution of SCO Agricultural Trade Network Pattern and Its Influencing Mechanism
Authors: Abudukeremu Abudureyimu , Youliwasi Asiyemu , Abula Buwajian , Yiming Abulaiti , Wang Dezhen
Published: September 2024

Investigating the evolution of the agricultural trade network pattern of Shanghai Cooperation Organisation (SCO) countries and its influencing mechanism is of vital importance for clarifying each country’s trade position, ensuring China’s food security, and stabilizing the supply of major agricultural products. This paper adopts complex network analysis and the time-indexed random graph model (TERGM) to systematically study the evolution trajectory of the Shanghai Cooperation Organisation (SCO) agricultural trade network and its influencing factors during the period from 2003 to 2022. The results show that the SCO agricultural trade network has undergone significant evolution and development over the past two decades, forming an increasingly close, interconnected, and diversified trade network structure. In particular, China has played a crucial role in the trade network, and the adjustment of its trade strategy and the shift of its role from export orientation to import orientation have had a profound impact on the overall trade network structure. Moreover, over time, the number of core countries in the trade network has gradually increased, and the network structure has gradually developed in a more diversified direction. Through empirical analysis, it is found that the formation of the SCO agricultural trade network is the result of a combination of factors, including intrinsic reciprocity, multiple connectivity, and stability mechanisms, as well as extrinsic geographic, cultural, and economic factors. Among them, China, as the leading country, has played a pivotal role in promoting the development of the trade network.