bluuebunny/crossref_metadata_2025 huggingface dataset:
Bovine Ephemeral Fever Viruses in Israel 2014–2023: Genetic Characterization of Local and Emerging Strains
Authors: Golender Natalia , Hoffmann Bernd , Kenigswald Gabriel , Scheinin Shani , Kedmi Maor , Gleser Dan , Klement Eyal
Published: July 2024

Bovine ephemeral fever (BEF) is an arthropod-borne viral disease, which frequently causes significant epizootics in susceptible water buffalo and cattle in Africa, Australia, Asia and the Middle East. In the current study, a two-stage protocol for BEFV viral isolation was developed. Data on the clinical signs, geographic distribution and phylogenetic analysis of BEFV strains isolated in Israel in 2015, 2018, 2021 and 2023 were summarized. It was found that during 2015–2021, all BEF outbreaks were caused by local BEFV strains, whereas the epizootic of BEFV in 2023 was caused by a new “Mayotte-like” BEFV strain. A comparison of bluetongue (BT) and BEF outbreaks during 2023 in Israel demonstrated that the incidence of BEFV was 2.21 times higher and its pathogenicity was more serious for the cattle population compared to that caused by BTVs. A phylogenetic analysis of Israeli and global BEFV revealed the emergence of non-local strains in new areas. This finding suggests that BEFV can no longer be classified based only upon geographic distribution. Considering a phylogenetic, genetic and proteomic analysis of all available BEFV strains, we suggest classifying them as a single serotype, which includes four lineages.
High Performance Thin Layer Chromatography (HPTLC) Analysis of Anti-Asthmatic Combination Therapy in Pharmaceutical Formulation: Assessment of the Method’s Greenness and Blueness
Authors: AlSalem Huda Salem , Algethami Faisal K. , Magdy Maimana A. , Ali Nourudin W. , Zaazaa Hala E. , Abdelkawy Mohamed , Abdelrahman Maha M. , Gamal Mohammed
Published: July 2024

A cost-effective, selective, sensitive, and operational TLC-densitometric approach has been adapted for the concurrent assay of Hydroxyzine Hydrochloride (HYX), Ephedrine Hydrochloride (EPH), and Theophylline (THP) in their pure powder and pharmaceutical forms. In the innovative TLC-densitometric approach, HYX, EPH, and THP were efficaciously separated and quantified on a 60F254 silica gel stationary phase with chloroform–ammonium acetate buffer (9.5:0.5, v/v) adjusted to pH 6.5 using ammonia solution as a mobile liquid system and UV detection at 220 nm. The novel TLC method validation has been performed in line with the international conference for harmonization (ICH) standards and has been effectively used for the estimation of the researched medicines in their pharmaceutical formulations without intervention from excipients. Additionally, parameters affecting the chromatographic analysis have been investigated. The new TLC approach’s functionality and greenness were appraised using three modern and automated tools, namely the Blue Applicability Grade Index (BAGI), the Analytical Greenness metric (AGREE), and the Green Analytical Procedure Index (GAPI) tools. In short, the greenness characteristics were not achieved as a result of using mandatory, non-ecofriendly solvents such as ammonia and chloroform. On the contrary, the applicability and usefulness of the novel TLC approach were attained via concurrent estimation for the three drugs using simple and straightforward procedures. Moreover, the novel TLC method outperforms previously published HPLC ones in terms of the short run time per sample and moderate pH value for the liquid system. According to the conclusions of comparisons with previously recorded TLC methods, our novel HPTLC method has the highest AGREE score, so it is the greenest HPTLC strategy. Moreover, its functionality and applicability are very appropriate because of the simultaneous assessment of three drugs in one TLC run. Furthermore, no tedious and complicated extraction and evaporation processes are prerequisites.
Hydrogen Bonding in Amorphous Indomethacin
Authors: Benmore C. J. , Yarger J. L. , Davidowski S. K. , Shrader C. D. , Smith P. A. , Byrn S. R.
Published: July 2024

Amorphous Indomethacin has enhanced bioavailability over its crystalline forms, yet amorphous forms can still possess a wide variety of structures. Here, Empirical Potential Structure Refinement (EPSR) has been used to provide accurate molecular models on the structure of five different amorphous Indomethacin samples, that are consistent with their high-energy X-ray diffraction patterns. It is found that the majority of molecules in amorphous Indomethacin are non-bonded or bonded to one neighboring molecule via a single hydrogen bond, in contrast to the doubly bonded dimers found in the crystalline state. The EPSR models further indicate a substantial variation in hydrogen bonding between different amorphous forms, leading to a diversity of chain structures not found in any known crystal structures. The majority of hydrogen bonds are associated with the carboxylic acid group, although a significant number of amide hydrogen bonding interactions are also found in the models. Evidence of some dipole–dipole interactions are also observed in the more structurally ordered models. The results are consistent with a distribution of Z-isomer intramolecular type conformations in the more disordered structures, that distort when stronger intermolecular hydrogen bonding occurs. The findings are supported by 1H and 2H NMR studies of the hydrogen bond dynamics in amorphous Indomethacin.
Allelopathic Effects of Sugarcane Leaves: Optimal Extraction Solvent, Partial Separation of Allelopathic Active Fractions, and Herbicidal Activities
Authors: Krumsri Ramida , Kato-Noguchi Hisashi , Poonpaiboonpipat Thanatsan
Published: July 2024

The inhibitory potential of allelopathic plants is the subject of increasing research attention for their application in weed management. The sugarcane leaf is an agricultural waste product that has been reported to have allelopathic potential. Therefore, the present study determined the optimal organic solvent system and fractionation procedure to enhance the quantity of this extract and its allelopathic efficiency. Sugarcane leaves were extracted using five ethanol/water solvent ratios (v/v): 00:100, 25:75, 50:50, 75:25, and 100:00. Their allelopathic effects on seed germination and seedling growth were assayed in two major weeds, Echinochloa crus-galli (L.) Beauv. and Amaranthus viridis L. The results showed that the extract concentration, solvent ratio, and their interaction significantly inhibited the growth parameters in A. viridis. Consequently, a crude ethanol/water ratio of 00:100 was used to separate the active fraction via acid–base solvent partitioning. The acidic fraction (AE) exerted the greatest inhibitory effect and completely (100%) inhibited A. viridis at all concentrations, followed by the original crude fraction, neutral fraction, and aqueous fraction. Moreover, all of the fractions had selective effects, inhibiting A. viridis much more than E. crus-galli in the laboratory tests. The chemical analysis using gas chromatography/mass spectrometry indicated that the AE fraction contained 20 different compounds. The five major compounds included alkaloids, organic acids, and phenols. Therefore, the AE fraction was selected for formulation in a concentrated suspension and tested for its herbicidal characteristics. The formulation exhibited early post-emergence activities and had a stronger effect on A. viridis compared to E. crus-galli. The physiological mechanism of the formulation was tested against A. viridis. The thiobarbituric acid reactive substances and H2O2 occurred in the A. viridis leaf, which suggests lipid peroxidation and cell disruption.
Precision Detection of Salt Stress in Soybean Seedlings Based on Deep Learning and Chlorophyll Fluorescence Imaging
Authors: Deng Yixin , Xin Nan , Zhao Longgang , Shi Hongtao , Deng Limiao , Han Zhongzhi , Wu Guangxia
Published: July 2024

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models—AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2—are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.
Challenges and Opportunities in the Sustainable Improvement of Carrot Production
Authors: Paparella Antonello , Kongala Prasada Rao , Serio Annalisa , Rossi Chiara , Shaltiel-Harpaza Liora , Husaini Amjad M. , Ibdah Mwafaq
Published: July 2024

From an agricultural perspective, carrots are a significant tap root vegetable crop in the Apiaceae family because of their nutritional value, health advantages, and economic importance. The edible part of a carrot, known as the storage root, contains various beneficial compounds, such as carotenoids, anthocyanins, dietary fiber, vitamins, and other nutrients. It has a crucial role in human nutrition as a significant vegetable and raw material in the nutraceutical, food, and pharmaceutical industries. The cultivation of carrot fields is susceptible to a wide range of biotic and abiotic hazards, which can significantly damage the plants’ health and decrease yield and quality. Scientific research mostly focuses on important biotic stressors, including pests, such as nematodes and carrot flies, as well as diseases, such as cavity spots, crown or cottony rot, black rot, and leaf blight, caused by bacteria, fungi, and oomycetes. The emerging challenges in the field include gaining a comprehensive understanding of the interaction between hosts and pathogens in the carrot–pathogen system, identifying the elements that contribute to disease development, expanding knowledge of systemic treatments, exploring host resistance mechanisms, developing integrated control programs, and enhancing resistance through breeding approaches. In fact, the primary carrot-growing regions in tropical and subtropical climates are experiencing abiotic pressures, such as drought, salinity, and heat stress, which limit carrot production. This review provides an extensive, up-to-date overview of the literature on biotic and abiotic factors for enhanced and sustainable carrot production, considering the use of different technologies for the shelf-life extension of carrots. Therefore, it addresses the current issues in the carrot production chain, opening new perspectives for the exploration of carrots both as a food commodity and as a source of natural compounds.
Tolerance Mechanisms of Olive Tree (Olea europaea) under Saline Conditions
Authors: El Yamani Mohamed , Cordovilla María del Pilar
Published: July 2024

The olive tree (Olea europaea L.) is an evergreen tree that occupies 19% of the woody crop area and is cultivated in 67 countries on five continents. The largest olive production region is concentrated in the Mediterranean basin, where the olive tree has had an enormous economic, cultural, and environmental impact since the 7th century BC. In the Mediterranean region, salinity stands out as one of the main abiotic stress factors significantly affecting agricultural production. Moreover, climate change is expected to lead to increased salinization in this region, threatening olive productivity. Salt stress causes combined damage by osmotic stress and ionic toxicity, restricting olive growth and interfering with multiple metabolic processes. A large variability in salinity tolerance among olive cultivars has been described. This paper aims to synthesize information from the published literature on olive adaptations to salt stress and its importance in salinity tolerance. The morphological, physiological, biochemical, and molecular mechanisms of olive tolerance to salt stress are reviewed.
Two New Steroidal Saponins with Potential Anti-Inflammatory Effects from the Aerial Parts of Gnetum formosum Markgr.
Authors: Hieu Ngo Van , Vinh Le Ba , Phong Nguyen Viet , Cong Pham Van , Dat Nguyen Tien , Dan Nguyen Van , Duc Ngo Viet , Tao Hoang Minh , Tam Le Thi , Anh Le Tuan , Cuong Nguyen Cao , Tai Bui Huu , Yang Seo Young , Tuan Anh Hoang Le
Published: July 2024

Gnetum formosum Markgr., a member of the Gnetaceae family, is distributed in Vietnam. This plant remains a botanical enigma with an unexplored diversity of chemical constituents and pharmacological effects. In this study, two new steroidal saponins, namely gnetumosides A (1) and B (2), were isolated from the aerial parts of G. formosum. Their chemical structures were elucidated using spectroscopic techniques, including high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) and NMR, along with chemical hydrolysis and comparison with the reported literature. The potential anti-inflammatory effects of the isolated compounds were evaluated by measuring lipopolysaccharide-stimulated nitric oxide (NO) production in murine macrophage cells. Notably, compound 1 exhibited the most potent inhibitory activity (IC50 = 14.10 ± 0.75 µM), comparable to dexamethasone. Additionally, the mechanisms underlying the observed anti-inflammatory effects were investigated through molecular docking and molecular dynamics simulations on inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) proteins. This study is the first to investigate the chemical constituents and pharmacological effects of G. formosum.
Exploring Conventional and Green Extraction Methods for Enhancing the Polyphenol Yield and Antioxidant Activity of Hyssopus officinalis Extracts
Authors: Polaki Sofia , Stamatelopoulou Vasiliki , Kotsou Konstantina , Chatzimitakos Theodoros , Athanasiadis Vassilis , Bozinou Eleni , Lalas Stavros I.
Published: July 2024

Hyssopus officinalis L. (HO) is, as one of the most prevalently utilized plants, used in traditional medicine to cure various diseases as well as the in food and cosmetic industries. Moreover, HO is a rich source of polyphenols with potent antioxidant properties. However, the studies on the extraction of such compounds from HO are scanty and sparse. This study aims to optimize the extraction of polyphenols and maximize the antioxidant activity in HO extracts. A comprehensive experimental design was employed, encompassing varied extraction parameters to determine the most effective ones. Alongside conventional stirring (ST), two green approaches, the ultrasonic treatment (US) and the pulsed electric field (PEF), were explored, either alone or in combination. The extracted polyphenolic compounds were identified with a high-performance liquid chromatography–diode array detector (HPLC-DAD). According to the results, the employment of ST along with an ethanolic solvent at 80 °C for 150 min seems beneficial in maximizing the extraction of polyphenols from HO, resulting in extracts with enhanced antioxidant activity. The total polyphenol was noted at 70.65 ± 2.76 mg gallic acid equivalents (GAE)/g dry weight (dw) using the aforementioned techniques, and the antioxidant activity was noted as 582.23 ± 16.88 μmol ascorbic acid equivalents (AAE)/g dw (with FRAP method) and 343.75 ± 15.61 μmol AAE/g dw (with the DPPH method). The as-prepared extracts can be utilized in the food and cosmetics industries to bestow or enhance the antioxidant properties of commercial products.
Development, Testing, and Thermoforming of Thermoplastics Reinforced with Surface-Modified Aramid Fibers for Cover of Electronic Parts in Small Unmanned Aerial Vehicles Using 3D-Printed Molds
Authors: Sonmez Maria , Pelin Cristina-Elisabeta , Pelin George , Rusu Bogdan , Stefan Adriana , Stelescu Maria Daniela , Ignat Madalina , Gurau Dana , Georgescu Mihai , Nituica Mihaela , Oprea Ovidiu-Cristian , Motelica Ludmila , Waśniewski Bartłomiej , Ortyl Paweł , Trușcă Roxana Doina
Published: July 2024

This paper presents the development, characterization, and testing of PP/PE-g-MA composites with 10 and 15 wt% surface-modified aramid fibers, and aluminum-based pigment, as covers for a small drone body for collision protection. The successful fiber surface modification with SiO2 by the sol–gel method using TEOS was confirmed by FTIR, SEM, and EDS analyses. The composites were characterized by FTIR and SEM analyses and surface energy and water contact angle measurements and tested in terms of tensile, flexural, impact, and thermal properties. The materials exhibited hydrophobic character and compact and uniform morphostructures, with increased surface energy with fiber content owed to improved adhesion between modified fibers and the matrix. Compared to the control sample, composites with modified fibers showed an increase by 20% in tensile strength, and 36–52% in the modulus, and an increase by 26–33% in flexural strength and 30–47% in the modulus, with higher values at room temperature. Impact resistance of modified fiber composites showed an increase by 20–40% compared to the control sample, due to improved interaction between SiO2-modified fibers and maleic anhydride, which inhibits crack formation, allowing higher energies’ absorption. The composites were vacuum-thermoformed on 3D-printed molds as a two-part cover for the body of a drone, successfully withstanding the flight test.
Influence of Montmorillonite Organoclay Fillers on Hygrothermal Response of Pultruded E-Glass/Vinylester Composites
Authors: Karbhari Vistasp M.
Published: July 2024

Pultruded fiber reinforced polymer composites used in civil, power, and offshore/marine applications use fillers as resin extenders and for process efficiency. Although the primary use of fillers is in the form of an extender and processing aid, the appropriate selection of filler can result in enhancing mechanical performance characteristics, durability, and multifunctionality. This is of special interest in structural and high voltage applications where the previous use of specific fillers has been at levels that are too low to provide these enhancements. This study investigates the use of montmorillonite organoclay fillers of three different particle sizes as substitutes for conventional CaCO3 fillers with the intent of enhancing mechanical performance and hygrothermal durability. The study investigates moisture uptake and kinetics and reveals that uptake is well described by a two-stage process that incorporates both a diffusion dominated initial phase and a second slower phase representing relaxation and deterioration. The incorporation of the organoclay particles substantially decreases uptake levels in comparison to the use of CaCO3 fillers while also enhancing stage I, diffusion, dominated stability, with the use of the 1.5 mm organoclay fillers showing as much as a 41.5% reduction in peak uptake as compared to the CaCO3 fillers at the same 20% loading level (by weight of resin). The mechanical performance was characterized using tension, flexure, and short beam shear tests. The organoclay fillers showed a significant improvement in each, albeit with differences due to particle size. Overall, the best performance after exposure to four different temperatures of immersion in deionized water was shown by the 4.8 mm organoclay filler-based E-glass/vinylester composite system, which was the only one to have less than a 50% deterioration over all characteristics after immersion for a year in deionized water at the highest temperature investigated (70 °C). The fillers not only enhance resistance to uptake but also increase tortuosity in the path, thereby decreasing the overall effect of uptake. The observations demonstrate that the use of the exfoliated organoclay particles with intercalation, which have been previously used in very low amounts, and which are known to be beneficial in relation to enhanced thermal stability, flame retardancy, and decreased flammability, provide enhanced mechanical characteristics, decreased moisture uptake, and increased hygrothermal durability when used at particle loading levels comparable to those of conventional fillers, suggesting that these novel systems could be considered for critical structural applications.
Easer Hole Design Method Based on the Principle of Minimum Burden at the Hole Bottom and Its Application in Tunnel Blasting
Authors: Wu Xiaodong , Jia Jiayin , Wang Likun , Zhou Shijun , Wu Haojun , Zhao Xinxiang , Gong Min
Published: July 2024

Current tunnel blasting hole layouts are mostly designed based on a two-dimensional plane at the workface, without considering the distribution of the minimum burden at the bottom of the blast holes. This results in a significant number of residual holes at the bottom, reducing excavation efficiency. To address this issue, this study proposes an easer hole design method based on the principle of minimum burden at the hole bottom. The method involved the arithmetic distribution for the minimum burden at the bottom of easer holes, using the difficulty of rock breaking as the design principle for hole positioning. Through theoretical analysis, numerical simulation, and field tests, it is proposed that the minimum burden at the bottom of the holes should increase progressively with the initiation sequence, and the relationship between burden distribution and blasting effect was investigated. This study indicates that using the new design principle achieves better blasting results than the model with an evenly distributed burden. When the control ratio of the minimum burden at the bottom of each row of easer holes is 1.3, an average residual hole depth of 36.7 cm and a maximum damage volume of 4.638 m3 can be achieved, yielding the best overall blasting effect. The application of this blasting scheme in the field significantly improved the residual hole problem, reducing the average residual hole depth to 39.5 cm, which is a 43.4% reduction compared to the previous scheme. Additionally, the utilization rate of blast holes in the new scheme increased to 91.3%, an improvement of 11.0% over the previous scheme. This study provides new insights and methods for tunnel blasting hole layout design, offering significant engineering application value.
Missing Region Completion Network for Large-Scale Laser-Scanned Point Clouds: Application to Transparent Visualization of Cultural Heritage
Authors: Li Weite , Pan Jiao , Hasegawa Kyoko , Li Liang , Tanaka Satoshi
Published: July 2024

The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses and applications. Point cloud completion techniques, by predicting and filling these missing regions, are vital for restoring the integrity of cultural heritage structures, enhancing restoration accuracy and efficiency. In this paper, for challenges in processing large-scale cultural heritage point clouds, particularly the slow processing speed and visualization impairments from uneven point density during completion, we propose a point cloud completion employing centroid-based voxel feature extraction, which significantly accelerates feature extraction for massive point clouds. Coupled with an efficient upsampling module, it achieves a uniform point distribution. Experimental results show that the proposed method matches SOTA performance in completion accuracy while surpassing in point density uniformity, demonstrating capability in handling larger-scale point cloud data, and accelerating the processing of voluminous point clouds. In general, the proposed method markedly enhances the efficiency and quality of large-scale point cloud completion, holding significant value for the digital preservation and restoration of cultural heritage.
Estimation of Coastal Wetland Vegetation Aboveground Biomass by Integrating UAV and Satellite Remote Sensing Data
Authors: Niu Xiaomeng , Chen Binjie , Sun Weiwei , Feng Tian , Yang Xiaodong , Liu Yangyi , Liu Weiwei , Fu Bolin
Published: July 2024

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R2 value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R2 = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m2, 1508.65 g/m2, and 1545.11 g/m2, respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems.
Flood Mapping of Synthetic Aperture Radar (SAR) Imagery Based on Semi-Automatic Thresholding and Change Detection
Authors: Lang Fengkai , Zhu Yanyin , Zhao Jinqi , Hu Xinru , Shi Hongtao , Zheng Nanshan , Zha Jianfeng
Published: July 2024

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method.
Characterizing the Macro and Micro Properties of Precipitation during the Landfall of Typhoon Lekima by Using GPM Observations
Authors: Wang Zhimin , Yang Jing , Chen Fengjiao , Liu Yiting , Shi Lijuan
Published: July 2024

Understanding the macro and micro characteristics of precipitation in landfall typhoons is crucial to predicting the path and intensity of tropical cyclones by using numerical models. In this study, we use observations from the Global Precipitation Measurement Mission to analyze the microphysics of convection and stratiform precipitation during the landfall of Typhoon Lekima. The statistical results show that the correlation coefficient of the reflectivity factors of the spaceborne and ground-based radars is 0.77 and that the water content detected by the 18.7 GHz low-frequency vertical channel is positively correlated with the intense-precipitation rate. The storm top height is generally consistent with the location of heavy precipitation. The average near-surface precipitation rate and liquid water content of convective precipitation are higher than those of stratiform precipitation. The average mass-weighted raindrop diameter and particle number concentration of convective (stratiform) precipitation at a distance of 2 km above ground level are 1.52 mm (1.29 mm) and 39.52 (36.44, in decibel scale), respectively. Below the melting layer, there is a significant increase in average particle diameter, indicating that the collision aggregation growth process of raindrops is dominant. These results are potentially helpful in validating and improving microphysics parameterization in numerical models.
A Comparison of Landforms and Processes Detection Using Multisource Remote Sensing Data: The Case Study of the Palinuro Pine Grove (Cilento, Vallo di Diano and Alburni National Park, Southern Italy)
Authors: Valiante Mario , Di Benedetto Alessandro , Aloia Aniello
Published: July 2024

The automated recognition of landforms holds significant importance within the framework of digital geomorphological mapping, serving as a pivotal focal point for research and practical applications alike. Over the last decade, various methods have been developed to achieve this goal, ranging from grid-based to object-based approaches, covering a range from supervised to completely unsupervised techniques. Furthermore, the vast majority of the methods mentioned depend on Digital Elevation Models (DEMs) as their primary input, highlighting the crucial significance of meticulous preparation and rigorous quality assessment of these datasets. In this study, we compare the outcomes of grid-based methods for landforms extraction and surficial process type assessment, leveraging various DEMs as input data. Initially, we employed a photogrammetric Digital Terrain Model (DTM) generated at a regional scale, along with two LiDAR datasets. The first dataset originates from an airborne survey conducted by the national government approximately a decade ago, while the second dataset was acquired by UAV as part of this study’s framework. The results highlight how the higher resolution and level of detail of the LiDAR datasets allow the recognition of a higher number of features at higher scales; but, in contrast, generally, a high level of detail corresponds with a higher risk of noise within the dataset, mostly due to unwanted natural features or anthropogenic disturbance. Utilizing these datasets for generating geomorphological maps harbors significant potential in the framework of natural hazard assessment, particularly concerning phenomena associated with geo-hydrological processes.
Capturing How the Accelerometer Measured Physical Activity Profile Differs in People with Diabetic Foot Ulceration
Authors: Neal Liam , McCarthy Matthew , Dempsey Paddy , Zaccardi Francesco , Berrington Rachel , Brady Emer M. , Edwardson Charlotte L. , Game Frances , Hall Andrew , Henson Joseph , Khunti Kamlesh , Turner Bethany , Webb David , Davies Melanie J. , Rowlands Alex V. , Yates Tom
Published: July 2024

Diabetic Foot Ulcers (DFUs) are a major complication of diabetes, with treatment requiring offloading. This study aimed to capture how the accelerometer-assessed physical activity profile differs in those with DFUs compared to those with diabetes but without ulceration (non-DFU). Participants were requested to wear an accelerometer on their non-dominant wrist for up to 8days. Physical activity outcomes included average acceleration (volume), intensity gradient (intensity distribution), the intensity of the most active sustained (continuous) 5–120 min of activity (MXCONT), and accumulated 5–120 min of activity (MXACC). A total of 595 participants (non-DFU = 561, DFU = 34) were included in the analysis. Average acceleration was lower in DFU participants compared to non-DFU participants (21.9 mg [95%CI:21.2, 22.7] vs. 16.9 mg [15.3, 18.8], p < 0.001). DFU participants also had a lower intensity gradient, indicating proportionally less time spent in higher-intensity activities. The relative difference between DFU and non-DFU participants was greater for sustained activity (MXCONT) than for accumulated (MXACC) activity. In conclusion, physical activity, particularly the intensity of sustained activity, is lower in those with DFUs compared to non-DFUs. This highlights the need for safe, offloaded modes of activity that contribute to an active lifestyle for people with DFUs.
Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application
Authors: Duobiene Shathya , Simniškis Rimantas , Račiukaitis Gediminas
Published: July 2024

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.
Unobtrusive Skin Temperature Estimation on a Smart Bed
Authors: Garcia-Molina Gary , Winger Trevor , Makaram Nikhil , Rao Megha Rajam , Chernega Pavlo , Shcherbakov Yehor , McGhee Leah , Chellamuthu Vidhya , Veneros Erwin , Mills Raj , Aloia Mark , Reid Kathryn J.
Published: July 2024

The transition from wakefulness to sleep occurs when the core body temperature decreases. The latter is facilitated by an increase in the cutaneous blood flow, which dissipates internal heat into the micro-environment surrounding the sleeper’s body. The rise in cutaneous blood flow near sleep onset causes the distal (hands and feet) and proximal (abdomen) temperatures to increase by about 1 °C and 0.5 °C, respectively. Characterizing the dynamics of skin temperature changes throughout sleep phases and understanding its relationship with sleep quality requires a means to unobtrusively and longitudinally estimate the skin temperature. Leveraging the data from a temperature sensor strip (TSS) with five individual temperature sensors embedded near the surface of a smart bed’s mattress, we have developed an algorithm to estimate the distal skin temperature with a minute-long temporal resolution. The data from 18 participants who recorded TSS and ground-truth temperature data from sleep during 14 nights at home and 2 nights in a lab were used to develop an algorithm that uses a two-stage regression model (gradient boosted tree followed by a random forest) to estimate the distal skin temperature. A five-fold cross-validation procedure was applied to train and validate the model such that the data from a participant could only be either in the training or validation set but not in both. The algorithm verification was performed with the in-lab data. The algorithm presented in this research can estimate the distal skin temperature at a minute-level resolution, with accuracy characterized by the mean limits of agreement [−0.79 to +0.79 °C] and mean coefficient of determination R2=0.87. This method may enable the unobtrusive, longitudinal and ecologically valid collection of distal skin temperature values during sleep. Therelatively small sample size motivates the need for further validation efforts.
A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising
Authors: Zhou Qiting , Xue Longxian , He Jie , Jia Sixiang , Li Yongbo
Published: July 2024

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance.
Proactive Threat Hunting in Critical Infrastructure Protection through Hybrid Machine Learning Algorithm Application
Authors: Shan Ali , Myeong Seunghwan
Published: July 2024

Cyber-security challenges are growing globally and are specifically targeting critical infrastructure. Conventional countermeasure practices are insufficient to provide proactive threat hunting. In this study, random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), AdaBoost, and hybrid models were applied for proactive threat hunting. By automating detection, the hybrid machine learning-based method improves threat hunting and frees up time to concentrate on high-risk warnings. These models are implemented on approach devices, access, and principal servers. The efficacy of several models, including hybrid approaches, is assessed. The findings of these studies are that the AdaBoost model provides the highest efficiency, with a 0.98 ROC area and 95.7% accuracy, detecting 146 threats with 29 false positives. Similarly, the random forest model achieved a 0.98 area under the ROC curve and a 95% overall accuracy, accurately identifying 132 threats and reducing false positives to 31. The hybrid model exhibited promise with a 0.89 ROC area and 94.9% accuracy, though it requires further refinement to lower its false positive rate. This research emphasizes the role of machine learning in improving cyber-security, particularly for critical infrastructure. Advanced ML techniques enhance threat detection and response times, and their continuous learning ability ensures adaptability to new threats.
A Super-Resolution and 3D Reconstruction Method Based on OmDF Endoscopic Images
Authors: Sun Fujia , Song Wenxuan
Published: July 2024

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning.
Improving Nowcasting of Intense Convective Precipitation by Incorporating Dual-Polarization Radar Variables into Generative Adversarial Networks
Authors: Cai Pengjie , Huang He , Liu Taoli
Published: July 2024

The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the “regression to average” issue of autoregressive model leads to the “blurring” phenomenon. The evolution method’s generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method’s generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the “regression to average” issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1.
A Multi-Sensing IoT System for MiC Module Monitoring during Logistics and Operation Phases
Authors: Arshad Husnain , Zayed Tarek
Published: July 2024

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module’s structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module’s structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module’s structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module’s structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations.