bluuebunny/crossref_metadata_2025 huggingface dataset:
Diet and Nutrients in Rare Neurological Disorders: Biological, Biochemical, and Pathophysiological Evidence
Authors: Briglia Marilena , Allia Fabio , Avola Rosanna , Signorini Cinzia , Cardile Venera , Romano Giovanni Luca , Giurdanella Giovanni , Malaguarnera Roberta , Bellomo Maria , Graziano Adriana Carol Eleonora
Published: September 2024

Background/Objectives: Rare diseases are a wide and heterogeneous group of multisystem life-threatening or chronically debilitating clinical conditions with reduced life expectancy and a relevant mortality rate in childhood. Some of these disorders have typical neurological symptoms, presenting from birth to adulthood. Dietary patterns and nutritional compounds play key roles in the onset and progression of neurological disorders, and the impact of alimentary needs must be enlightened especially in rare neurological diseases. This work aims to collect the in vitro, in vivo, and clinical evidence on the effects of diet and of nutrient intake on some rare neurological disorders, including some genetic diseases, and rare brain tumors. Herein, those aspects are critically linked to the genetic, biological, biochemical, and pathophysiological hallmarks typical of each disorder. Methods: By searching the major web-based databases (PubMed, Web of Science Core Collection, DynaMed, and Clinicaltrials.gov), we try to sum up and improve our understanding of the emerging role of nutrition as both first-line therapy and risk factors in rare neurological diseases. Results: In line with the increasing number of consensus opinions suggesting that nutrients should receive the same attention as pharmacological treatments, the results of this work pointed out that a standard dietary recommendation in a specific rare disease is often limited by the heterogeneity of occurrent genetic mutations and by the variability of pathophysiological manifestation. Conclusions: In conclusion, we hope that the knowledge gaps identified here may inspire further research for a better evaluation of molecular mechanisms and long-term effects.
Food Avoidance and Aversive Goal Value Computation in Anorexia Nervosa
Authors: Weider Siri , Shott Megan E. , Nguyen Tyler , Swindle Skylar , Pryor Tamara , Sternheim Lot C. , Frank Guido K. W.
Published: September 2024

Anorexia nervosa (AN) is associated with food restriction and significantly low body weight, but the neurobiology of food avoidance in AN is unknown. Animal research suggests that food avoidance can be triggered by conditioned fear that engages the anterior cingulate and nucleus accumbens. We hypothesized that the neural activation during food avoidance in AN could be modeled based on aversive goal value processing. Nineteen females with AN and thirty healthy controls matched for age underwent functional magnetic resonance brain imaging while conducting a food avoidance task. During active control free-bid and computer-generated forced-bid trials, participants bid money to avoid eating food items. Brain activation was parametrically modulated with the trial-by-trial placed bids. During free-bid trials, the AN group engaged the caudate nucleus, nucleus accumbens, ventral anterior cingulate, and inferior and medial orbitofrontal cortex more than the control group. High- versus low-bid trials in the AN group were associated with higher caudate nucleus response. Emotion dysregulation and intolerance of uncertainty scores were inversely associated with nucleus accumbens free-bid trial brain response in AN. This study supports the idea that food avoidance behavior in AN involves aversive goal value computation in the nucleus accumbens, caudate nucleus, anterior cingulate, and orbitofrontal cortex.
Optimizing Nutritional Decisions: A Particle Swarm Optimization–Simulated Annealing-Enhanced Analytic Hierarchy Process Approach for Personalized Meal Planning
Authors: Sarani Rad Fatemeh , Amiri Maryam , Li Juan
Published: September 2024

Background/Objective: Nutritionists play a crucial role in guiding individuals toward healthier lifestyles through personalized meal planning; however, this task involves navigating a complex web of factors, including health conditions, dietary restrictions, cultural preferences, and socioeconomic constraints. The Analytic Hierarchy Process (AHP) offers a valuable framework for structuring these multi-faceted decisions but inconsistencies can hinder its effectiveness in pairwise comparisons. Methods: This paper proposes a novel hybrid Particle Swarm Optimization–Simulated Annealing (PSO-SA) algorithm to refine inconsistent AHP weight matrices, ensuring a consistent and accurate representation of the nutritionist’s expertise and client preferences. Our approach merges PSO’s global search capabilities with SA’s local search precision, striking an optimal balance between exploration and exploitation. Results: We demonstrate the practical utility of our algorithm through real-world use cases involving personalized meal planning for individuals with specific dietary needs and preferences. Results showcase the algorithm’s efficiency in achieving consistency and surpassing standard PSO accuracy. Conclusion: By integrating the PSO-SA algorithm into a mobile app, we empower nutritionists with an advanced decision-making tool for creating tailored meal plans that promote healthier dietary choices and improved client outcomes. This research represents a significant advancement in multi-criteria decision-making for nutrition, offering a robust solution to the inconsistency challenge in AHP and paving the way for more effective and personalized dietary interventions.
HPLC-PDA Analysis of Polyacetylene Glucosides from Launaea capitata and Their Antibacterial and Antibiofilm Properties against Klebsiella pneumoniae
Authors: Aljarba Tariq M. , Abdel Bar Fatma M. , Sherif Asmaa E. , Elekhnawy Engy , Magdy Galal , Samra Reham M.
Published: September 2024

Background/Objectives: Bacterial resistance and virulence are challenges in treating bacterial infections, especially in Klebsiella pneumoniae. Plants of the Launaea Cass. genus are used traditionally to address a variety of diseases, including infections, but the potential bioactive compounds are unknown. Our goals were to verify the potential contribution of two major polyacetylene glycosides isolated from our previous study, (3S,6E,12E)-6,12-tetradecadiene-8,10-diyne-1-ol 3-O-β-D-glucopyranoside (1) and bidensyneoside A (syn. gymnasterkoreaside A) [(3R,8E)-3-hydroxy-8-decene-4,6-diyn-1-yl β-D-glucopyranoside] (2), to the anti-infective properties of Launaea capitata and to develop a dependable HPLC method for their quantification; Methods: On a panel of K. pneumoniae clinical isolates, the antibacterial action of 1, 2, and the methanol extract of the whole L. capitata plant were evaluated by broth microdilution assay, while their antibiofilm action was evaluated by the crystal violet assay. qRT-PCR investigated luxS, mrkA, wzm, and wbbm genes that encode biofilm formation and quorum sensing (QS). The antibacterial activity of 1 was revealed by employing mice infection. Chromatographic separation was conducted using isocratic elution on a Hypersil BDS C18 column using a photodiode array (PDA) detector; Results: Compound 1 showed antibacterial activity with MIC values of 16–128 µg/mL. It remarkably reduced strong and moderate biofilm-forming bacterial isolates from 84.21% to 42.1% compared with the extract (68.42%) and 2 (78.95%). Compound 1 also downregulated the QS genes, luxS, mrkA, wzm, and wbbm, and exhibited in vivo antibacterial action through the enhancement of the histological construction of the liver and spleen, decreased TNF-α immunoreaction, bacterial burden, and the inflammatory mediators IL-1β and IL-6. A successful HPLC-PDA approach was developed to separate the binary mixture of 1 and 2 in less than 10 min with high sensitivity, with detection limits down to 0.518 and 0.095 µg/mL for 1 and 2, respectively; Conclusions: Compound 1 exhibited remarkable antibacterial and antibiofilm properties and may contribute to the anti-infectious traditional uses of L. capitata, meriting further clinical studies and serving as a reliable quality control biomarker for the plant.
Complex Diagnostic Challenges in Glioblastoma: The Role of 18F-FDOPA PET Imaging
Authors: Sipos David , Debreczeni-Máté Zsanett , Ritter Zsombor , Freihat Omar , Simon Mihály , Kovács Árpád
Published: September 2024

Glioblastoma multiforme (GBM) remains one of the most aggressive and lethal forms of brain cancer, characterized by rapid proliferation and diffuse infiltration into the surrounding brain tissues. Despite advancements in therapeutic approaches, the prognosis for GBM patients is poor, with median survival times rarely exceeding 15 months post-diagnosis. An accurate diagnosis, treatment planning, and monitoring are crucial for improving patient outcomes. Core imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are indispensable in the initial diagnosis and ongoing management of GBM. Histopathology remains the gold standard for definitive diagnoses, guiding treatment by providing molecular and genetic insights into the tumor. Advanced imaging modalities, particularly positron emission tomography (PET), play a pivotal role in the management of GBM. Among these, 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (18F-FDOPA) PET has emerged as a powerful tool due to its superior specificity and sensitivity in detecting GBM and monitoring treatment responses. This introduction provides a comprehensive overview of the multifaceted role of 18F-FDOPA PET in GBM, covering its diagnostic accuracy, potential as a biomarker, integration into clinical workflows, impact on patient outcomes, technological and methodological advancements, comparative effectiveness with other PET tracers, and its cost-effectiveness in clinical practice. Through these perspectives, we aim to underscore the significant contributions of 18F-FDOPA PET to the evolving landscape of GBM management and its potential to enhance both clinical and economic outcomes for patients afflicted with this formidable disease.
Synthesis and Evaluation of 99mTc(CO)3 Complexes with Ciprofloxacin Dithiocarbamate for Infection Imaging
Authors: Papasavva Afroditi , Pirmettis Nektarios N. , Shegani Antonio , Papadopoulou Eleni , Kiritsis Christos , Georgoutsou-Spyridonos Maria , Mastellos Dimitrios C. , Chiotellis Aristeidis , Kyprianidou Patricia , Pelecanou Maria , Papadopoulos Minas , Pirmettis Ioannis
Published: September 2024

Background: The accurate diagnosis of bacterial infections remains a critical challenge in clinical practice. Traditional imaging modalities like computed tomography (CT) and magnetic resonance imaging (MRI) often fail to distinguish bacterial infections from sterile inflammation. Nuclear medicine, such as technetium-99m (99mTc) radiopharmaceuticals, offers a promising alternative due to its ideal characteristics. Methods: This study explores the development of [2 + 1] mixed-ligand 99mTc-labeled ciprofloxacin dithiocarbamate (Cip-DTC) complexes combined with various phosphine ligands, including triphenylphosphine (PPh3), tris(4-methoxyphenyl)phosphine (TMPP), methyl(diphenyl)phosphine (MePPh2), dimethylphenylphosphine (DMPP), and 1,3,5-triaza-7-phosphaadamantane (ADAP). The characterization of 99mTc-complexes was conducted using rhenium analogs as structural models to ensure similar coordination. Results: Stability studies demonstrated the high integrity (97–98%) of the complexes under various conditions, including cysteine and histidine challenges. Lipophilicity studies indicated that complexes with higher logD7.4 values (1.6–2.7) exhibited enhanced tissue penetration and prolonged circulation. Biodistribution studies in Swiss Albino mice with induced infections and aseptic inflammation revealed distinct patterns. Specifically, the complex fac-[99mTc(CO)3(Cip-DTC)(PPh3)] (2′) showed high infected/normal muscle ratios (4.62 at 120 min), while the complex fac-[99mTc(CO)3(Cip-DTC)(TMPP)] (3′) demonstrated delayed but effective targeting (infected/normal muscle ratio of 3.32 at 120 min). Conclusions: These findings highlight the potential of 99mTc-labeled complexes as effective radiopharmaceuticals for the differential diagnosis of bacterial infections, advancing nuclear medicine diagnostics. Future studies will focus on optimizing molecular weight, lipophilicity, and stability to further enhance the diagnostic specificity and clinical utility of these radiopharmaceuticals.
Study on Thermal Cycling Reliability of Epoxy-Enhanced SAC305 Solder Joint
Authors: Zhang Peng , Xue Songbai , Liu Lu , Wang Jianhao , Tatsumi Hiroaki , Nishikawa Hiroshi
Published: September 2024

In this work, epoxy was added into commercial Sn-3.0Ag-0.5Cu (SAC305) solder paste to enhance the thermal cycling reliability of the joint. The microstructure and fracture surface were observed using a scanning electron microscope/energy dispersive spectrometer (SEM/EDS), and a shear test was performed on the thermally cycled joint samples. The results indicated that during the thermal cycling test, the epoxy protective layer on the surface of the epoxy-enhanced SAC305 solder joint could significantly alleviate the thermal stress caused by coefficients of thermal expansion (CTE) mismatch, resulting in fewer structural defects. The interfacial compound of the original SAC305 solder joints gradually coarsened due to the accelerated atomic diffusion, but epoxy-enhanced SAC305 solder joints demonstrated a thinner interfacial layer and a smaller IMC grain size. Due to the reduced stress concentration and the additional mechanical support provided by the cured epoxy layer, epoxy-enhanced SAC305 solder joints displayed superior shear performance compared to the original joint during the thermal cycling test. After 1000 thermal cycles, Cu-Sn IMC regions were observed on the fracture surfaces of the original SAC305 solder joint, exhibiting brittle fracture characteristics. However, the fracture of the SAC305 solder joint with 8 wt.% epoxy remained within the solder bulk and exhibited a ductile fracture mode. This work indicates that epoxy-enhanced SAC305 solder pastes display high thermal cycling reliability and could meet the design needs of advanced packaging technology for high-performance electronic packaging materials.
A Review on the Application of Deep Eutectic Solvents in Polymer-Based Membrane Preparation for Environmental Separation Technologies
Authors: Marco-Velasco Gorka , Gálvez-Subiela Alejandro , Jiménez-Robles Ramón , Izquierdo Marta , Cháfer Amparo , Badia José David
Published: September 2024

The use of deep eutectic solvents (DESs) for the preparation of polymer membranes for environmental separation technologies is comprehensively reviewed. DESs have been divided into five categories based on the hydrogen bond donor (HBD) and acceptor (HBA) that are involved in the production of the DESs, and a wide range of DESs’ physicochemical characteristics, such as density, surface tension, viscosity, and melting temperature, are initially gathered. Furthermore, the most popular techniques for creating membranes have been demonstrated and discussed, with a focus on the non-solvent induced phase separation (NIPS) method. Additionally, a number of studies have been reported in which DESs were employed as pore formers, solvents, additives, or co-solvents, among other applications. The addition of DESs to the manufacturing process increased the presence of finger-like structures and macrovoids in the cross-section and, on numerous occasions, had a substantial impact on the overall porosity and pore size. Performance data were also gathered for membranes made for various separation technologies, such as ultrafiltration (UF) and nanofiltration (NF). Lastly, DESs provide various options for the functionalization of membranes, such as the creation of various liquid membrane types, with special focus on supported liquid membranes (SLMs) for decarbonization technologies, discussed in terms of permeability and selectivity of several gases, including CO2, N2, and CH4.
Rheological Characterization of Genipin-Based Crosslinking Pigment and O-Carboxymethyl Chitosan–Oxidized Hyaluronic Acid In Situ Formulable Hydrogels
Authors: Beserra Junior Ivo Marquis , de Sousa Lopes Débora , da Silva Barbosa Milena Costa , da Silva Neto João Emídio , da Silva Henrique Nunes , Fook Marcus Vinícius Lia , Navarro Rômulo Feitosa , Silva Suédina Maria de Lima
Published: September 2024

The aim of this study was to develop a material capable of rapidly absorbing bodily fluids and forming a resilient, adhesive, viscoelastic hydrogel in situ to prevent post-surgical adhesions. This material was formulated using O-carboxymethyl chitosan (O-CMCS), oxidized hyaluronic acid (OHA), and a crosslinking pigment derived from genipin and glutamic acid (G/GluP). Both crosslinked (O-CMCS/OHA-G/GluP) and non-crosslinked hydrogels (O-CMCS/OHA) were evaluated using a HAAKE™ MARS™ rheometer for their potential as post-surgical barriers. A rheological analysis, including dynamic oscillatory measurements, revealed that the crosslinked hydrogels exhibited significantly higher elastic moduli (G′), indicating superior gel formation and mechanical stability compared to non-crosslinked hydrogels. The G/GluP crosslinker enhanced gel stability by increasing the separation between G′ and G″ and achieving a lower loss tangent (tan δ < 1.0), indicating robustness under dynamic physiological conditions. The rapid hydration and gelation properties of the hydrogels underscore their effectiveness as physical barriers. Furthermore, the O-CMCS/OHA-G/GluP hydrogel demonstrated rapid self-healing and efficient application via spraying or spreading, with tissue adherence and viscoelasticity to facilitate movement between tissues and organs, effectively preventing adhesions. Additionally, the hydrogel proved to be both cost effective and scalable, highlighting its potential for clinical applications aimed at preventing post-surgical adhesions.
Elimination of Delamination during the Drilling of Biocomposite Materials with Flax Fibers
Authors: Váňa Martin , Dvořáčková Štěpánka , Knápek Tomáš , Kroisová Dora
Published: September 2024

The present study focuses on the elimination of delamination during the drilling of a linen-based biocomposite material in epoxy resin used for the manufacture of sports kayaks, depending on the tool material, cutting conditions, and the use of additional wooden support plates. In the present study, HSS (high-speed steel) and Carbide cutting tools without coatings, with the same geometry and two types of cutting conditions (n = 1500 rpm, fn = 0.05 and 0.1 mm/rev) were used. A Sololite-type wooden backing plate was used to aid in reducing delamination. The results show that the additional support plates significantly reduced delamination by up to 80% both at the material inlet and especially at the drill hole outlet. In this study, the use of a lower feed rate (fn = 0.05 mm/rev) per tooth was shown to have a significant effect on reducing the delamination of biocomposite materials with flax fibers, which are generally known to be difficult to machine. The Carbide cutting tool shows significantly better results both in terms of its wear and in terms of delamination of the biocomposite material. The highest delamination was obtained without the use of a backing board at the tool exit after 50 drilled holes of 3509 µm. With the use of a backing board, this delamination decreased to 693 µm after 50 drilled holes.
The China Coastal Front from Himawari-8 AHI SST Data—Part 2: South China Sea
Authors: Belkin Igor M. , Lou Shang-Shang , Zang Yi-Tao , Yin Wen-Bin
Published: September 2024

High-resolution (2 km) high-frequency (hourly) SST data from 2015 to 2021 provided by the Advanced Himawari Imager (AHI) onboard the Japanese Himawari-8 geostationary satellite were used to study spatial and temporal variability of the China Coastal Front (CCF) in the South China Sea. The SST data were processed with the Belkin and O’Reilly (2009) algorithm to generate monthly maps of the CCF’s intensity (defined as SST gradient magnitude GM) and frontal frequency (FF). The horizontal structure of the CCF was investigated from cross-frontal distributions of SST along 11 fixed lines that allowed us to determine inshore and offshore boundaries of the CCF and calculate the CCF’s strength (defined as total cross-frontal step of SST). Combined with the results of Part 1 of this study, where the CCF was documented in the East China Sea, the new results reported in this paper allowed the CCF to be traced from the Yangtze Bank to Hainan Island. The CCF is continuous in winter, when its intensity peaks at 0.15 °C/km (based on monthly data). In summer, when the Guangdong Coastal Current reverses and flows eastward, the CCF’s intensity is reduced to 0.05 °C/km or less, especially off western Guangdong, where the CCF vanishes almost completely. Owing to its breadth (50–100 km, up to 200 km in the Taiwan Strait), the CCF is a very strong front, especially in winter, when the total SST step across the CCF peaks at 9 °C in the Taiwan Strait. The CCF’s strength decreases westward to 6 °C off eastern Guangdong, 5 °C off western Guangdong, and 2 °C off Hainan Island, all in mid-winter.
Interannual Glacial Mass Changes in High Mountain Asia and Connections to Climate Variability
Authors: Wang Yifan , Zhan Jingang , Shi Hongling , Chen Jianli
Published: September 2024

We use data from the Gravity Recovery and Climate Experiment and its Follow-On mission (GRACE/GRACE-FO) from April 2002 to December 2022 to analyze interannual glacial mass changes in High Mountain Asia (HMA) and its subregions and their driving factors. Glacial mass changes in the HMA subregions show clear regional characteristics. Interannual glacial mass changes in the HMA region are closely related to interannual oscillations of precipitation and temperature, and are also correlated with El Niño–Southern Oscillation (ENSO). Glacial mass changes in the regions (R1–R6) are dominated by precipitation, and ENSO affects interannual glacial mass changes mainly by affecting precipitation. In region (R7) and region (R8), the glacial mass changes are mainly controlled by temperature. ENSO also affects the interannual glacial mass changes by affecting interannual changes in temperature. The interannual glacial mass changes in regions (R9–R11) are jointly dominated by temperature and precipitation, and also related to ENSO. Another interesting finding of this study is that glacial mass changes in the western part of HMA (R1–R6) show a clear 6–7-year oscillation, strongly correlated with a similar oscillation in precipitation, while in the eastern part (R9–R11), a 2–3-year oscillation was found in both glacial mass change and precipitation, as well as temperature. These results verify the response of interannual HMA glacial mass changes to climate processes, crucial for understanding regional climate dynamics and sustainable water resource management.
Snow Cover Extraction from Landsat 8 OLI Based on Deep Learning with Cross-Scale Edge-Aware and Attention Mechanism
Authors: Yu Zehao , Gong Hanying , Zhang Shiqiang , Wang Wei
Published: September 2024

Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this study, a snow cover extraction algorithm integrating cross-scale edge perception and an attention mechanism on the U-net model architecture is proposed. The cross-scale edge perception module replaces the original jump connection of U-net, enhances the low-level image features by introducing edge detection on the shallow feature scale, and enhances the detail perception via branch separation and fusion features on the deep feature scale. Meanwhile, parallel channel and spatial attention mechanisms are introduced in the model encoding stage to adaptively enhance the model’s attention to key features and improve the efficiency of utilizing global semantic information. The method was evaluated on the publicly available CSWV_S6 optical remote sensing dataset, and the accuracy of 98.14% indicates that the method has significant advantages over existing methods. Snow extraction from Landsat 8 OLI images of the upper reaches of the Irtysh River was achieved with satisfactory accuracy rates of 95.57% (using two, three, and four bands) and 96.65% (using two, three, four, and six bands), indicating its strong potential for automated snow cover extraction over larger areas.
Efficient On-Board Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization
Authors: Wang Keyan , Jia Jia , Zhou Peicheng , Ma Haoyi , Yang Liyun , Liu Kai , Li Yunsong
Published: September 2024

Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images.
Denoising of Photon-Counting LiDAR Bathymetry Based on Adaptive Variable OPTICS Model and Its Accuracy Assessment
Authors: Li Peize , Xu Yangrui , Zhao Yanpeng , Liang Kun , Si Yuanjie
Published: September 2024

Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its strong performance under high background noise. However, this algorithm’s fixed input variables can lead to inaccurate photon distribution parameters in areas near the water bottom, which results in inadequate denoising in these areas, affecting bathymetric accuracy. To address this issue, an Adaptive Variable OPTICS (AV-OPTICS) model is proposed in this paper. Unlike the traditional OPTICS model with fixed input variables, the proposed model dynamically adjusts input variables based on point cloud distribution. This adjustment ensures accurate measurement of photon distribution parameters near the water bottom, thereby enhancing denoising effects in these areas and improving bathymetric accuracy. The findings indicate that, compared to traditional OPTICS methods, AV-OPTICS achieves higher F1-values and lower cohesions, demonstrating better denoising performance near the water bottom. Furthermore, this method achieves an average MAE of 0.28 m and RMSE of 0.31 m, indicating better bathymetric accuracy than traditional OPTICS methods. This study provides a promising solution for shallow-water bathymetry based on photon-counting LiDAR data.
Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark
Authors: Matellon Antonio , Maset Eleonora , Beinat Alberto , Visintini Domenico
Published: September 2024

The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems.
Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing
Authors: Marković Dušan , Stamenković Zoran , Đorđević Borislav , Ranđić Siniša
Published: September 2024

The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional Neural Networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%).
Inter-Mode Crosstalk Estimation between Cores for LPmn Modes in Weakly Coupled Few-Mode Multicore Fiber with Perturbations
Authors: Liu Shuangmeng , Xiang Lian
Published: September 2024

A novel inter-mode crosstalk (IMXT) model of LPmn mode for weakly coupled few-mode multicore fiber is proposed based on the coupled mode theory (CMT) with bending and twisting perturbations. A universal expression of the mode coupling coefficient (MCC) between LPmn modes is derived. By employing this MCC, the universal semi-analytical model (USAM) of inter-core crosstalk (ICXT) can be applied to calculate the IMXT. Simulation results show that our model is generally consistent with previous theories when stochastic perturbations are absent. Moreover, our model can work effectively when stochastic perturbations are present, where former theories are not able to work properly. It has been theoretically found that the MCC has an intimate relationship with core pitch. Our model, based on the CMT, can provide physical characteristics in detail, which has not been reported clearly by former theories. In addition, our model is applicable to phase-matching and non-phase-matching regions of both real homogeneous and heterogeneous few-mode multicore fibers (FM-MCFs) with a wider range of applications.
ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines
Authors: Khujamatov Halimjon , Davronbekov Dilmurod , Khayrullaev Alisher , Abdullaev Mirjamol , Mukhiddinov Mukhriddin , Cho Jinsoo
Published: September 2024

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line. Many methods of analysis and comparison have been employed to examine the reliability of wireless sensor devices used in real-time remote monitoring. A reliability model was developed to evaluate the reliability of the monitoring system in various situations. Based on the developed models, it was found that the reliability indicators of the proposed monitoring system were 98% in 1 month. In addition, it has been proven that the reliability of the system remains high even when an optional sensor in the network fails. This study investigates various IoT technologies, their integration into monitoring systems, and their effectiveness in enhancing the reliability and efficiency of electrical transmission infrastructure. The analysis includes data from field deployments, case studies, and simulations to assess performance metrics, such as accuracy, latency, and fault detection capabilities.
MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network
Authors: Zhang Yi , Song Wei , Shao Mingyue , Liu Xiangchun
Published: September 2024

This study focuses on the problem of dense object counting. In dense scenes, variations in object scales and uneven distributions greatly hinder counting accuracy. The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively. Lower-resolution features are more sensitive to larger objects closer to the camera, while higher-resolution features are more efficient for smaller objects further away. Thus, preserving features that carry the most relevant information at each scale is crucial for improving counting precision. Motivated by this, we propose a multi-resolution scale feature fusion-based universal density counting network (MRSNet). It utilizes independent modules to process high- and low-resolution features, adaptively adjusts receptive field sizes, and incorporates dynamic sparse attention mechanisms to optimize feature information at each resolution, by integrating optimal features across multiple scales into density maps for counting evaluation. Our proposed network effectively mitigates issues caused by large variations in object scales, thereby enhancing counting accuracy. Furthermore, extensive quantitative analyses on six public datasets demonstrate the algorithm’s strong generalization ability in handling diverse object scale variations.
FusionOpt-Net: A Transformer-Based Compressive Sensing Reconstruction Algorithm
Authors: Zhang Honghao , Chen Bi , Gao Xianwei , Yao Xiang , Hou Linyu
Published: September 2024

Compressive sensing (CS) is a notable technique in signal processing, especially in multimedia, as it allows for simultaneous signal acquisition and dimensionality reduction. Recent advancements in deep learning (DL) have led to the creation of deep unfolding architectures, which overcome the inefficiency and subpar quality of traditional CS reconstruction methods. In this paper, we introduce a novel CS image reconstruction algorithm that leverages the strengths of the fast iterative shrinkage-thresholding algorithm (FISTA) and modern Transformer networks. To enhance computational efficiency, we employ a block-based sampling approach in the sampling module. By mapping FISTA’s iterative process onto neural networks in the reconstruction module, we address the hyperparameter challenges of traditional algorithms, thereby improving reconstruction efficiency. Moreover, the robust feature extraction capabilities of Transformer networks significantly enhance image reconstruction quality. Experimental results show that the FusionOpt-Net model surpasses other advanced methods on various public benchmark datasets.
AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI
Authors: Rakhimzhanova Tomiris , Kuzdeuov Askat , Varol Huseyin Atakan
Published: September 2024

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.
Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results
Authors: Thenginthody Hassan Sabna , Chen Peng , Rong Yue , Chan Kit Yan
Published: September 2024

In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions.
Investigation of the Reduction in Distributed Acoustic Sensing Signal Due to Perforation Erosion by Using CFD Acoustic Simulation and Lighthill’s Acoustic Power Law
Authors: Hamanaka Yasuyuki , Zhu Ding , Hill A. D.
Published: September 2024

Distributed Acoustic Sensing (DAS), widely adopted in hydraulic fracturing monitoring, continuously measures sound from perforation holes due to fluid flow through the perforation holes during fracturing treatment. DAS has the potential to monitor perforation Tulsa, OK 74136erosion, a phenomenon of increasing perforation size due to sand (referred to as proppant) injection during treatment. Because the sound generated by fluid flow at a perforation hole is negatively related to the perforation diameter, by detecting the decay of the DAS signal, the perforation erosion level can be estimated, which is critical information for fracture design. We used a Computation Fluid Dynamics (CFD) acoustic simulator to calculate the acoustic pressure induced by turbulence inside a wellbore and investigated the relationship between the acoustic response from fluid flow through a perforation and the perforation size by running the simulator for various perforation diameters and flow rates. The results show that if the perforation size is constant, the plot between the calculated sound pressure level and the logarithm of flow rate follows a straight line relationship. However, with different perforation sizes, the intercept of the linear relationship changes, reducing the sound pressure level. Lighthill’s power law indicates that the change in intercept corresponds to the logarithm of the ratio of the increased diameter to the original diameter. The reduction in sound pressure level observed in the CFD simulation correlates with the reduction in the DAS signal in field data. The findings of this study help to evaluate perforation diameter growth using DAS and interpret fluid distribution in fracture stimulation.
Two-Step Contrast Source Learning Method for Electromagnetic Inverse Scattering Problems
Authors: Si Anran , Wang Miao , Fang Fuping , Dai Dahai
Published: September 2024

This article is devoted to solving full-wave electromagnetic inverse scattering problems (EM-ISPs), which determine the geometrical and physical properties of scatterers from the knowledge of scattered fields. Due to the intrinsic ill-posedness and nonlinearity of EM-ISPs, traditional non-iterative and iterative methods struggle to meet the requirements of high accuracy and real-time reconstruction. To overcome these issues, we propose a two-step contrast source learning approach, cascading convolutional neural networks (CNNs) into the inversion framework, to tackle 2D full-wave EM-ISPs. In the first step, a contrast source network based on the CNNs architecture takes the determined part of the contrast source as input and then outputs an estimate of the total contrast source. Then, the recovered total contrast source is directly converted into the initial contrast. In the second step, the rough initial contrast obtained beforehand is input into the U-Net for refinement. Consequently, the EM-ISPs can be quickly solved with much higher accuracy, even for high-contrast objects, almost achieving real-time imaging. Numerical examples have demonstrated that the proposed two-step contrast source learning approach is able to improve accuracy and robustness even for high-contrast scatterers. The proposed approach offers a promising avenue for advancing EM-ISPs by integrating strengths from both traditional and deep learning-based approaches, to achieve real-time quantitative microwave imaging for high-contrast objects.
Development and Assessment of Artificial Intelligence-Empowered Gait Monitoring System Using Single Inertial Sensor
Authors: Zhou Jie , Mao Qian , Yang Fan , Zhang Jun , Shi Menghan , Hu Zilin
Published: September 2024

Gait instability is critical in medicine and healthcare, as it has associations with balance disorder and physical impairment. With the development of sensor technology, despite the fact that numerous wearable gait detection and recognition systems have been designed to monitor users’ gait patterns, they commonly spend a lot of time and effort to extract gait metrics from signal data. This study aims to design an artificial intelligence-empowered and economic-friendly gait monitoring system. A pair of intelligent shoes with a single inertial sensor and a smartphone application were developed as a gait monitoring system to detect users’ gait cycle, stand phase time, swing phase time, stride length, and foot clearance. We recruited 30 participants (24.09 ± 1.89 years) to collect gait data and used the Vicon motion capture system to verify the accuracy of the gait metrics. The results show that the gait monitoring system performs better on the assessment of the gait metrics. The accuracy of stride length and foot clearance is 96.17% and 92.07%, respectively. The artificial intelligence-empowered gait monitoring system holds promising potential for improving gait analysis and monitoring in the medical and healthcare fields.
Architecture for Enhancing Communication Security with RBAC IoT Protocol-Based Microgrids
Authors: Shin SooHyun , Park MyungJoo , Kim TaeWan , Yang HyoSik
Published: September 2024

In traditional power grids, the unidirectional flow of energy and information has led to a decrease in efficiency. To address this issue, the concept of microgrids with bidirectional flow and independent power sources has been introduced. The components of a microgrid utilize various IoT protocols such as OPC-UA, MQTT, and DDS to implement bidirectional communication, enabling seamless network communication among different elements within the microgrid. Technological innovation, however, has simultaneously given rise to security issues in the communication system of microgrids. The use of IoT protocols creates vulnerabilities that malicious hackers may exploit to eavesdrop on data or attempt unauthorized control of microgrid devices. Therefore, monitoring and controlling security vulnerabilities is essential to prevent intrusion threats and enhance cyber resilience in the stable and efficient operation of microgrid systems. In this study, we propose an RBAC-based security approach on top of DDS protocols in microgrid systems. The proposed approach allocates roles to users or devices and grants various permissions for access control. DDS subscribers request access to topics and publishers request access to evaluations from the role repository using XACML. The overall implementation model is designed for the publisher to receive XACML transmitted from the repository and perform policy decision making and enforcement. By applying these methods, security vulnerabilities in communication between IoT devices can be reduced, and cyber resilience can be enhanced.