bluuebunny/crossref_metadata_2025 huggingface dataset:
Formation of Superhydrophobic Coatings Based on Dispersion Compositions of Hexyl Methacrylate Copolymers with Glycidyl Methacrylate and Silica Nanoparticles
Authors: Klimov Viktor V. , Shilin Alexey K. , Kusakovskiy Daniil A. , Kolyaganova Olga V. , Kharlamov Valentin O. , Rudnev Alexander V. , Le Manh D. , Bryuzgin Evgeny V. , Navrotskii Alexander V.
Published: November 2024

In the last decade, the task of developing environmentally friendly and cost-effective methods for obtaining stable superhydrophobic coatings has become topical. In this study, we examined the effect of the concentrations of filler and polymer binder on the hydrophobic properties and surface roughness of composite coatings made from organic–aqueous compositions based on hexyl methacrylate (HMA) and glycidyl methacrylate (GMA) copolymers. Silicon dioxide nanoparticles were used as a filler. A single-stage “all-in-one” aerosol application method was used to form the coatings without additional intermediate steps for attaching the adhesive layer or texturing the substrate surface, as well as pre-modification of the surface of filler nanoparticles. As the ratio of the mass fraction of polymer binder (Wn) to filler (Wp) increases, the coatings show the lowest roll-off angles among the whole range of samples studied. Coatings with an optimal mass fraction ratio (Wn/Wp = 1.2 ÷ 1.6) of the filler to polymer binder maintained superhydrophobic properties for 24 h in contact with a drop of water in a chamber saturated with water vapor and exhibited roll-off angles of 6.1° ± 1°.
Three-Dimensional Thermohaline Reconstruction Driven by Satellite Sea Surface Data Based on Sea Ice Seasonal Variation in the Arctic Ocean
Authors: Wu Xiangyu , Li Jinlong , Wang Xidong , He Zikang , Chen Zhiqiang , Ren Shihe , Liang Xi
Published: November 2024

This study investigates and evaluates methods for the three-dimensional thermohaline reconstruction of the Arctic Ocean using multi-source observational data. A multivariate statistical regression model based on sea ice seasonal variation is developed, driving by satellite data, and in situ data is used to validate the model output. The study indicates that the multivariate statistical regression model effectively captures the characteristics of the three-dimensional thermohaline structure of the Arctic Ocean. Areas with large reconstruction errors are primarily observed in the salinity values of ice-free regions and the temperature values of ice-covered regions. The statistical regression experiments reveal that salinity errors in ice-free regions are caused by inaccuracies in the satellite salinity data, while temperature errors in ice-covered areas mainly result from the inadequate representation of the under-ice temperature structure of the reanalysis data. The continuous and stable thermohaline data produced using near real-time satellite data as input provide an important foundation for studying Arctic marine environmental characteristics and assessing climate change.
Testing the Limits of Atmospheric Correction over Turbid Norwegian Fjords
Authors: Tessin Elinor , Hamre Børge , Kristoffersen Arne Skodvin
Published: November 2024

Atmospheric correction, the removal of the atmospheric signal from a satellite image, still poses a challenge over optically complex coastal water. Here, we present the first atmospheric correction validation study performed in optically complex Norwegian fjords. We compare in situ reflectance measurements and chlorophyll-a concentrations from Western Norwegian fjords with atmospherically corrected Sentinel-3 Ocean and Land Colour Instrument observations and chlorophyll-a retrievals. Measurements were taken in Hardangerfjord, Bjørnafjord and Møkstrafjord during a bright green coccolithophore bloom in May 2022, and during a period of no apparent discoloration in April 2023. Coccolithophore blooms generally peak in the blue region (490 nm), but spectra measured in this bloom peaked in the green region (559 nm), possibly due to absorption by colored dissolved organic matter (aCDOM(440) = 0.18 ± 0.01 m−1) or due to high cell counts (up to 15 million cells/L). We tested a wide range of atmospheric correction algorithms, including ACOLITE, BAC, C2RCC, iCOR, L2gen, POLYMER and the SNAP Rayleigh correction. Surprisingly, atmospheric correction algorithms generally performed better during the bloom (average MAE = 1.25) rather than in the less scattering water in the following year (average MAE = 4.67), possibly because the high water-leaving radiances due to the high backscattering by coccolithophores outweighed the adjacency effect. However, atmospheric correction algorithms consistently underestimated water-leaving reflectance in the bloom. In non-bloom matchups, most atmospheric correction algorithms overestimated the water-leaving reflectance. POLYMER appears unsuitable for use over coccolithophore blooms but performed well in non-bloom matchups. Neither BAC, used in the official Level-2 OLCI products, nor C2RCC performed well in the bloom. Nine chlorophyll-a retrieval algorithms, including two algorithms based on neural nets, four based on red and near-infrared bands and three maximum band-ratio algorithms, were also tested. Most chlorophyll-a retrieval algorithms did not perform well in either year, although several did perform within the 70% accuracy threshold for case-2 waters. A red-edge algorithm performed best in the coccolithophore blooms, while a maximum band-ratio algorithm performed best in the following year.
In-Depth Development of a Versatile Rumen Bolus Sensor for Dairy Cattle
Authors: Vakulya Gergely , Hajnal Éva , Udvardy Péter , Simon Gyula
Published: November 2024

Precision agriculture and the increasing automation efforts in animal husbandry requires continuous and complex monitoring of the animals. Rumen bolus sensors, which are cutting-edge pieces of technology and a rapidly developing research field, present an exceptional opportunity for monitoring the health status, physiological parameters, and estrus of the animals. The objective of this paper is to provide a comprehensive overview of the development process of a new sensor development. We address the issues of conceptual design, an overview of applicable sensor modalities, mechanical design, power supply design, applicable hardware solutions, applicable communication solutions and finally the sensor detection algorithms proved in field tests. In conclusion, we present a summary of the current opportunities in the field and provide an analysis of the foreseeable trends.
Effectiveness of Sensors-Based Augmented Feedback in Ergonomics to Reduce Adverse Biomechanical Exposure in Work-Related Manual Handling—A Rapid Review of the Evidence
Authors: Lind Carl M.
Published: November 2024

Manual handling is a major risk factor for work-related musculoskeletal disorders and one of the leading causes of disability-adjusted life years globally, necessitating multifaceted risk reduction measures. One potential intervention for manual handling tasks is work technique training assisted by augmented feedback on biomechanical exposures. However, there is a research gap regarding its effectiveness specifically for manual handling tasks in both real work environments and controlled settings, as well as its ability to induce retained reductions in biomechanical exposure. The gap was investigated using a rapid review comprising a literature search using two databases and 11 reviews/overviews to identify studies from the past 20 years, up to studies published by 1 June 2024. Sixteen studies were identified, with 14 of them being of high or moderate methodological quality and were included. Three studies were conducted in real work environments and eleven in controlled settings. Most studies (n = 9) used auditory feedback, followed by vibration feedback (n = 6). In real work environments, the evidence for the effectiveness of sensor-based augmented feedback in reducing biomechanical exposure during administration was considered to be inconsistent and very limited directly after administration. For longer periods after administration, ranging from one week to more than six months, there is currently no evidence demonstrating the effectiveness of the feedback. In controlled settings, there was strong evidence for its effectiveness during and immediately after administration, and limited evidence for effectiveness up to six months post-administration when considering the tasks included in the training. Future research needs are discussed.
Lineshape of Amplitude-Modulated Stimulated Raman Spectra
Authors: Lamperti Marco , Rutkowski Lucile , Vesco Guglielmo , Moretti Luca , Gatti Davide , Cerullo Giulio , Polli Dario , Marangoni Marco
Published: November 2024

The amplitude modulation of a pump field and the phase-sensitive detection of a pump-induced intensity change of a probe field encompass a common practice in nonlinear spectroscopies to enhance the detection sensitivity. A drawback of this approach arises when the modulation frequency is comparable to the width of the spectral feature of interest, since the presence of sidebands in the amplitude-modulated pump field provides distortion to the observed spectral lineshape. This represents a problem when accurate measurements of spectral lineshapes and line positions are pursued, as recently happened in our group with the metrology of the Q(1) line in the 1-0 band of molecular hydrogen. The measurement was performed with a Stimulated Raman Scattering spectrometer that was calibrated, for the first time, against an optical frequency comb. In this work, we develop an analytical tool for nonlinear Stimulated Raman spectroscopies that allows us to precisely quantify spectral distortions arising from high-frequency amplitude modulation in one of the interacting fields. Once they are known, spectral distortions can be deconvolved from the measured spectra to retrieve unbiased data. The application of this tool to the measured spectra is discussed.
A Safe Fiber-Optic-Sensor-Assisted Industrial Microwave-Heating System
Authors: Yüksel Kivilcim , Merdin Oguz Deniz , Kinet Damien , Merdin Murat , Guyot Corentin , Caucheteur Christophe
Published: November 2024

Industrial microwave-heating systems are pivotal in various sectors, including food processing and materials manufacturing, where precise temperature control and safety are critical. Conventional systems often struggle with uneven heat distribution and high fire risks due to the intrinsic properties of microwave heating. In this work, a fiber-optic-sensor-assisted monitoring system is presented to tackle the pressing challenges associated with uneven heating and fire hazards in industrial microwave systems. The core innovation lies in the development of a sophisticated fiber-optic 2D temperature distribution sensor and a dedicated fire detector, both designed to significantly mitigate risks and optimize the heating process. Experimental results set the stage for future innovations that could transform the landscape of industrial heating technologies toward better process quality.
From Fiber Layout to the Sensor: Preparation Methods as Key Factors for High-Quality Coupled-Core-Fiber Sensors
Authors: Lindner F. , Bierlich J. , Alonso-Murias M. , Maldonado-Hurtado D. , Flores-Bravo J. A. , Sales S. , Villatoro J. , Wondraczek K.
Published: November 2024

During recent years, the optical-fiber-based simultaneous sensing of strain and temperature has attracted increased interest for different applications, e.g., in medicine, architecture, and aerospace. Specialized fiber layouts further enlarge the field of applications at much lower costs and with easier handling. Today, the performance of many sensors fabricated from conventional fibers suffers from cross-sensitivity (temperature and strain) and relatively high interrogation costs. In contrast, customized fiber architectures would make it possible to circumvent such sensor drawbacks. Here, we report on the development of a high-quality coupled-core fiber and its performance for sensors—from the initial fiber layout via elaboration of the preform and fiber up to the sensor evaluation. A compact, high-speed, and cost-effective interrogation unit using such a specialized coupled-core fiber has been designed to monitor reflectivity changes while even being able to distinguish the direction of the force or impact. Several fiber core material techniques and approaches were investigated, which made it possible to obtain a sufficient volume of material for the required fiber core number and a specialized fiber core geometry in terms of core distances and radial refractive index profile, whilst handling the non-symmetrical fiber architectures of such modeled, complex structures and balancing resources and efforts.
Isotopic and Geophysical Investigations of Groundwater in Laiyuan Basin, China
Authors: Wang Weiqiang , Meng Zilong , Wang Chenglong , Gui Jianye
Published: November 2024

Due to the complex intersection and control of multiple structural systems, the hydrogeological conditions of the Laiyuan Basin in China are complex. The depth of research on the relationship between geological structure and groundwater migration needs to be improved. The supply relationship of each aquifer is still uncertain. This paper systematically conducts research on the characteristics of hydrogen and oxygen isotopes, and combines magnetotelluric impedance tensor decomposition and two-dimensional fine inversion technology to carry out fine exploration of the strata and structures in the Laiyuan Basin, as well as comprehensive characteristics of groundwater migration and replenishment. The results indicate the following: (i) The hydrogen and oxygen values all fall near the local meteoric water line, indicating that precipitation is the main groundwater recharge source. (ii) The excess deuterium decreased gradually from karst mountain to basin, and karst water and pore water experienced different flow processes. (iii) The structure characteristics of three main runoff channels are described by MT fine processing and inversion techniques. Finally, it is concluded that limestone water moved from the recharge to the discharge area, mixed with the deep dolomite water along the fault under the control of fault F2, and eventually rose to the surface of the unconsolidated sediment blocked by fault F1 to emerge into an ascending spring.
Portable Detection of Copper Ion Using Personal Glucose Meter
Authors: Du Bin , Chen Taoying , Huang Anqi , Chen Haijun , Liu Wei
Published: November 2024

A simple and sensitive method for Cu2+ detection was developed using the Cu+-catalyzed alkyne–azide cycloaddition reaction, Fe3O4 magnetic nanoparticles (MNPs) as the reaction platform, and a portable blood glucose meter (PGM) as the detection method. Gold nanoparticles (AuNPs) were labeled with glucose oxidase (GOx) and alkyne-functionalized, terminally thiolated ssDNA (C2). In the presence of Cu2+ and ascorbate, the functionalized AuNPs were captured onto MNPs modified with azide-functionalized ssDNA (C1) via the Cu+-catalyzed alkyne–azide cycloaddition reaction. The GOx on the AuNPs’ surface oxidized glucose (Glu) into gluconic acid and H2O2, reducing the Glu content in the reaction solution, which was quantitatively detected by the PGM. Under optimal conditions, the PGM response of the system showed a good linear relationship with the logarithm of Cu2+ concentration in the range of 0.05 to 10.00 μmol/L, with a detection limit of 0.03 μmol/L (3σ). In actual tap water samples, the spiked recovery rate of Cu2+ was between 92.30% and 113.33%, and the relative standard deviation was between 0.14% and 0.34%, meeting the detection requirements for Cu2+ in real water samples.
Leveraging Environmental Contact and Sensor Feedback for Precision in Robotic Manipulation
Authors: Šifrer Jan , Petrič Tadej
Published: November 2024

This paper investigates methods that leverage physical contact between a robot’s structure and its environment to enhance task performance, with a primary emphasis on improving precision. Two main approaches are examined: solving the inverse kinematics problem and employing quadratic programming, which offers computational efficiency by utilizing forward kinematics. Additionally, geometrical methods are explored to simplify robot assembly and reduce the complexity of control calculations. These approaches are implemented on a physical robotic platform and evaluated in real-time applications to assess their effectiveness. Through experimental evaluation, this study aims to understand how environmental contact can be utilized to enhance performance across various conditions, offering valuable insights for practical applications in robotics.
Optimizing Open Radio Access Network Systems with LLAMA V2 for Enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and Massive Machine-Type Communications: A Framework for Efficient Network Slicing and Real-Time Resource Allocation
Authors: Tahir H. Ahmed , Alayed Walaa , Hassan Waqar ul , Do Thuan Dinh
Published: November 2024

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN’s dynamic slicing and LLAMA_V2’s optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries.
ZnO Nanowires/Self-Assembled Monolayer Mediated Selective Detection of Hydrogen
Authors: Singh Mandeep , Kaur Navpreet , Comini Elisabetta
Published: November 2024

We are proposing a novel self-assembled monolayer (SAM) functionalized ZnO nanowires (NWs)-based conductometric sensor for the selective detection of hydrogen (H2). The modulation of the surface electron density of ZnO NWs due to the presence of negatively charged terminal amine groups (−NH2) of monolayers leads to an enhanced electron donation from H2 to ZnO NWs. This, in turn, increases the relative change in the conductance (response) of functionalized ZnO NWs as compared to bare ones. In contrast, the sensing mechanism of bare ZnO NWs is determined by the chemisorbed oxygen ions. The functionalized ZnO NWs exhibit an eight times higher response compared to bare ZnO NWs at an optimal working temperature of 200 °C. Finally, in comparison to studies in the literature involving strategies to enhance the sensing performance of metal oxides toward H2, like decoration with metal nanoparticles, heterostructures, and functionalization with a metal–organic framework, etc., SAM functionalization showed superior sensing results.
Automatic Apple Detection and Counting with AD-YOLO and MR-SORT
Authors: Yang Xueliang , Gao Yapeng , Yin Mengyu , Li Haifang
Published: November 2024

In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations in complex orchard environments, and it is difficult to realize automatic and accurate apple counting. In this paper, a video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed based on the improved YOLOv8 and BoT-SORT. First, we propose the AD-YOLO model, which aims to reduce the number of incorrect detections during object tracking. In the YOLOv8s backbone network, an Omni-dimensional Dynamic Convolution (ODConv) module is used to extract local feature information and enhance the model’s ability better; a Global Attention Mechanism (GAM) is introduced to improve the detection ability of a foreground object (apple) in the whole image; a Soft Spatial Pyramid Pooling Layer (SSPPL) is designed to reduce the feature information dispersion and increase the sensory field of the network. Then, the improved BoT-SORT algorithm is proposed by fusing the verification mechanism, SURF feature descriptors, and the Vector of Local Aggregate Descriptors (VLAD) algorithm, which can match apples more accurately in adjacent video frames and reduce the probability of ID switching in the tracking process. The results show that the mAP metrics of the proposed AD-YOLO model are 3.1% higher than those of the YOLOv8 model, reaching 96.4%. The improved tracking algorithm has 297 fewer ID switches, which is 35.6% less than the original algorithm. The multiple-object tracking accuracy of the improved algorithm reached 85.6%, and the average counting error was reduced to 0.07. The coefficient of determination R2 between the ground truth and the predicted value reached 0.98. The above metrics show that our method can give more accurate counting results for apples and even other types of fruit.
Comprehensive Review: High-Performance Positioning Systems for Navigation and Wayfinding for Visually Impaired People
Authors: Feghali Jean Marc , Feng Cheng , Majumdar Arnab , Ochieng Washington Yotto
Published: November 2024

The global increase in the population of Visually Impaired People (VIPs) underscores the rapidly growing demand for a robust navigation system to provide safe navigation in diverse environments. State-of-the-art VIP navigation systems cannot achieve the required performance (accuracy, integrity, availability, and integrity) because of insufficient positioning capabilities and unreliable investigations of transition areas and complex environments (indoor, outdoor, and urban). The primary reason for these challenges lies in the segregation of Visual Impairment (VI) research within medical and engineering disciplines, impeding technology developers’ access to comprehensive user requirements. To bridge this gap, this paper conducts a comprehensive review covering global classifications of VI, international and regional standards for VIP navigation, fundamental VIP requirements, experimentation on VIP behavior, an evaluation of state-of-the-art positioning systems for VIP navigation and wayfinding, and ways to overcome difficulties during exceptional times such as COVID-19. This review identifies current research gaps, offering insights into areas requiring advancements. Future work and recommendations are presented to enhance VIP mobility, enable daily activities, and promote societal integration. This paper addresses the urgent need for high-performance navigation systems for the growing population of VIPs, highlighting the limitations of current technologies in complex environments. Through a comprehensive review of VI classifications, VIPs’ navigation standards, user requirements, and positioning systems, this paper identifies research gaps and offers recommendations to improve VIP mobility and societal integration.
MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks
Authors: Catalano Rachele , Tibaldi Myriam Giusy , Lombardi Lucia , Santone Antonella , Cesarelli Mario , Mercaldo Francesco
Published: November 2024

Breast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep learning, specifically convolutional neural networks. The approach classifies histological images of breast tissue as either tumor-positive or tumor-negative. We utilize several deep learning models, including a custom-built CNN, EfficientNet, ResNet50, VGG-16, VGG-19, and MobileNet. Fine-tuning was also applied to VGG-16, VGG-19, and MobileNet to enhance performance. Additionally, we introduce a novel deep learning model called MR_Net, aimed at providing a more accurate network for breast cancer detection and localization, potentially assisting clinicians in making informed decisions. This model could also accelerate the diagnostic process, enabling early detection of the disease. Furthermore, we propose a method for explainable predictions by generating heatmaps that highlight the regions within tissue images that the model focuses on when predicting a label, revealing the detection of benign, atypical, and malignant tumors. We evaluate both the quantitative and qualitative performance of MR_Net and the other models, also presenting explainable results that allow visualization of the tissue areas identified by the model as relevant to the presence of breast cancer.
FCSwinU: Fourier Convolutions and Swin Transformer UNet for Hyperspectral and Multispectral Image Fusion
Authors: Li Rumei , Zhang Liyan , Wang Zun , Li Xiaojuan
Published: November 2024

The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). Existing methods primarily based on convolutional neural networks (CNNs) struggle to capture global features and do not adequately address the significant scale and spectral resolution differences between LR-HSI and HR-MSI. To tackle these challenges, our novel FCSwinU network leverages the spectral fast Fourier convolution (SFFC) module for spectral feature extraction and utilizes the Swin Transformer’s self-attention mechanism for multi-scale global feature fusion. FCSwinU employs a UNet-like encoder–decoder framework to effectively merge spatiospectral features. The encoder integrates the Swin Transformer feature abstraction module (SwinTFAM) to encode pixel correlations and perform multi-scale transformations, facilitating the adaptive fusion of hyperspectral and multispectral data. The decoder then employs the Swin Transformer feature reconstruction module (SwinTFRM) to reconstruct the fused features, restoring the original image dimensions and ensuring the precise recovery of spatial and spectral details. Experimental results from three benchmark datasets and a real-world dataset robustly validate the superior performance of our method in both visual representation and quantitative assessment compared to existing fusion methods.
Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
Authors: Nkemeni Valery , Mieyeville Fabien , Kuaban Godlove Suila , Czekalski Piotr , Tokarz Krzysztof , Nsanyuy Wirnkar Basil , Deussom Djomadji Eric Michel , Katche Musong L. , Tsafack Pierre , Zieliński Bartłomiej
Published: November 2024

Battery-powered sensor nodes encounter substantial energy constraints, especially in linear wireless sensor network (LWSN) applications like border surveillance and road, bridge, railway, powerline, and pipeline monitoring, where inaccessible locations exacerbate battery replacement challenges. Addressing these issues is crucial for extending a network’s lifetime and reducing operational costs. This paper presents a comprehensive analysis of the factors affecting WSN energy consumption at the node and network levels, alongside effective energy management strategies for prolonging the WSN’s lifetime. By categorizing existing strategies into node energy reduction, network energy balancing, and energy replenishment, this study assesses their effectiveness when implemented in LWSN applications, providing valuable insights to assist engineers during the design of green and energy-efficient LWSN monitoring systems.
Digitizing the Appearance of 3D Printing Materials Using a Spectrophotometer
Authors: Pranovich Alina , Hannemose Morten Rieger , Jensen Janus Nørtoft , Tran Duc Minh , Aanæs Henrik , Gooran Sasan , Nyström Daniel , Frisvad Jeppe Revall
Published: November 2024

The conventional approach to appearance prediction for 3D printed parts is to print a thin slab of material and measure its reflectance or transmittance with a spectrophotometer. Reflectance works for opaque printing materials. Transmittance works for transparent printing materials. However, the conventional approach does not work convincingly for translucent materials. For these, we need to separate scattering and absorption. We suggest printing a collection of thin slabs of different thicknesses and using these in a spectrophotometer to obtain the scattering and absorption properties of the material. A model is fitted to the measured data in order to estimate the scattering and absorption properties. To this end, we compare the use of Monte Carlo light transport simulation and the use of an analytic model that we developed from the theory of radiative transfer in plane-parallel media. We assess the predictive capabilities of our method through a multispectral photo-render comparison based on the estimated optical properties.
A Review of Potential Exoskeletons for the Prevention of Work-Related Musculoskeletal Disorders in Agriculture
Authors: Arachchige Sanura Dunu , Piyathilaka Lasitha , Sul Jung-Hoon , Preethichandra D. M. G.
Published: November 2024

Exoskeletons possess a high potential for assisting the human workforce while eliminating or reducing the risk of Work-Related Musculoskeletal Disorders (WMSDs). However, their usage in agricultural work, where there is a plethora of reported WMSD cases, seems limited. Since agricultural tasks are complex and performed in harsh environments, developing novel exoskeleton-based solutions could be challenging. However, commercial exoskeletons are already being used in various other industries, such as logistics, military, medicine, and manufacturing. Thus, it is expected that those existing exoskeleton solutions could be applied to agricultural tasks. Nevertheless, prior to implementation, assessing the feasibility, efficacy, and necessary modifications for these exoskeletons is imperative to supporting agricultural activities prone to WMSDs. In this review, prevalent exoskeletons documented in scientific literature are identified, and their potential relevance to agricultural tasks with elevated WMSD risks is evaluated. The review further highlights and deliberates on exoskeletons that could be applicable in an agricultural context. This comprehensive examination serves as a foundational step towards the conceptualization and development of exoskeleton-based approaches tailored explicitly for agricultural tasks.
An Improved Vital Signal Extraction Method Based on Laser Doppler Effect
Authors: Li Yu , Zhang Haiyang , Zhang Bowen , Qi Yujiao , Chen Si
Published: November 2024

The mixed signal of respiratory waveform and heartbeat waveform detected by the Laser-Doppler system is processed with an intermediate-frequency (IF) interference filtering method, an enhanced extraction method and a waveform-fixing method. To filter the IF interference signals and the noise scatters in the time-frequency graph, the filtering method based on coefficient of variation (CoV) values and the enhanced curve extraction method based on noise-scatter theory are utilized in vital signal analysis. To decouple the respiratory signal and the heartbeat signal in time domain, the waveform-fixing method based on second-order difference theory is utilized in signal decoupling. This method as an algorithm is applied in the computer simulation and laboratory environments. The results show that the above methods can extract the mixed waveforms and identify the respiratory rates and heart rates in real experimental data. The IF interference signal can be filtered adaptively, and the accuracy of the analyzed rates can be improved to about 95%.
Filtering-Assisted Airborne Point Cloud Semantic Segmentation for Transmission Lines
Authors: Yan Wanjing , Ma Weifeng , Wu Xiaodong , Wang Chong , Zhang Jianpeng , Deng Yuncheng
Published: November 2024

Point cloud semantic segmentation is crucial for identifying and analyzing transmission lines. Due to the number of point clouds being huge, complex scenes, and unbalanced sample proportion, the mainstream machine learning methods of point cloud segmentation cannot provide high efficiency and accuracy when extending to transmission line scenes. This paper proposes a filter-assisted airborne point cloud semantic segmentation for transmission lines. First, a large number of ground point clouds is identified by introducing the well-developed cloth simulation filter to alleviate the impact of the imbalance of the target object proportion on the classifier’s performance. The multi-dimensional features are then defined, and the classification model is trained to achieve the multi-element semantic segmentation of the transmission line scene. The experimental results and analysis indicate that the proposed filter-assisted algorithm can significantly improve the semantic segmentation performance of the transmission line point cloud, enhancing both the point cloud segmentation efficiency and accuracy by more than 25.46% and 3.15%, respectively. The filter-assisted point cloud semantic segmentation method reduces the volume of sample data, the number of sample classes, and the sample imbalance index in power line scenarios to a certain extent, thereby improving the classification accuracy of classifiers and reducing time consumption. This research holds significant theoretical reference value and engineering application potential for scene reconstruction and intelligent understanding of airborne laser point cloud transmission lines.
Enhancing Underwater SLAM Navigation and Perception: A Comprehensive Review of Deep Learning Integration
Authors: Merveille Fomekong Fomekong Rachel , Jia Baozhu , Xu Zhizun , Fred Bissih
Published: November 2024

Underwater simultaneous localization and mapping (SLAM) is essential for effectively navigating and mapping underwater environments; however, traditional SLAM systems have limitations due to restricted vision and the constantly changing conditions of the underwater environment. This study thoroughly examined the underwater SLAM technology, particularly emphasizing the incorporation of deep learning methods to improve performance. We analyzed the advancements made in underwater SLAM algorithms. We explored the principles behind SLAM and deep learning techniques, examining how these methods tackle the specific difficulties encountered in underwater environments. The main contributions of this work are a thorough assessment of the research into the use of deep learning in underwater image processing and perception and a comparison study of standard and deep learning-based SLAM systems. This paper emphasizes specific deep learning techniques, including generative adversarial networks (GANs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and other advanced methods to enhance feature extraction, data fusion, scene understanding, etc. This study highlights the potential of deep learning in overcoming the constraints of traditional underwater SLAM methods, providing fresh opportunities for exploration and industrial use.
MEMS and ECM Sensor Technologies for Cardiorespiratory Sound Monitoring—A Comprehensive Review
Authors: Torabi Yasaman , Shirani Shahram , Reilly James P. , Gauvreau Gail M.
Published: November 2024

This paper presents a comprehensive review of cardiorespiratory auscultation sensing devices (i.e., stethoscopes), which is useful for understanding the theoretical aspects and practical design notes. In this paper, we first introduce the acoustic properties of the heart and lungs, as well as a brief history of stethoscope evolution. Then, we discuss the basic concept of electret condenser microphones (ECMs) and a stethoscope based on them. Then, we discuss the microelectromechanical systems (MEMSs) technology, particularly focusing on piezoelectric transducer sensors. This paper comprehensively reviews sensing technologies for cardiorespiratory auscultation, emphasizing MEMS-based wearable designs in the past decade. To our knowledge, this is the first paper to summarize ECM and MEMS applications for heart and lung sound analysis.
Novel Applications in Controlled Drug Delivery Systems by Integrating Osmotic Pumps and Magnetic Nanoparticles
Authors: Navarro-Tumar David , García-Merino Belén , González-Fernández Cristina , Ortiz Inmaculada , San-Román Ma.-Fresnedo , Bringas Eugenio
Published: November 2024

The alarming rise in chronic diseases worldwide highlights the urgent need to overcome the limitations of conventional drug delivery systems. In this context, osmotic pumps are able to release drugs by differential osmotic pressure, achieving a controlled rate independent of physiological factors and reducing the dosing frequency. As osmotic pumps are based on the phenomenon of osmosis, the choice of high osmolality draw solutions (DSs) is a critical factor in the successful delivery of the target drug. Therefore, one alternative that has received particular attention is the formulation of DSs with magnetic nanoparticles (MNPs) due to their easy recovery, negligible reverse solute flux (RSF), and their possible tailor-made functionalization to generate high osmotic gradients. In this work, the possible integration of DSs formulated with MNPs in controlled drug delivery systems is discussed for the first time. In particular, the main potential advantages that these novel medical devices could offer, including improved scalability, regeneration, reliability, and enhanced drug delivery performance, are provided and discussed. Thus, the results of this review may demonstrate the potential of MNPs as osmotic agents, which could be useful for advancing the design of osmotic pump-based drug delivery systems.
A Study of Mixed Non-Motorized Traffic Flow Characteristics and Capacity Based on Multi-Source Video Data
Authors: Gu Guobin , Sun Xin , Lou Benxiao , Wang Xiang , Yang Bingheng , Chen Jianqiu , Zhou Dan , Huang Shiqian , Hu Qingwei , Bao Chun
Published: November 2024

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of “last-mile” transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060–3297 bikes/(h·m) and a pure bicycle capacity of 1538–2460 bikes/(h·m). The density–flow model calculates a pure EV capacity of 2349–2897 bikes/(h·m) and a pure bicycle capacity of 1753–2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion.
Framework for Microdosing Odors in Virtual Reality for Psychophysiological Stress Training
Authors: Anheuer Daniel , Karacan Brid , Herzog Lara , Weigel Nora , Meyer-Nieberg Silja , Gebhardt Thomas , Freiherr Jessica , Richter Martin , Leopold Armin , Eder Monika , Hofmann Marko , Renner Karl-Heinz , Küsel Cornelia
Published: November 2024

To better cope with stress in emergencies, emergency personnel undergo virtual reality (VR) stress training. Such training typically includes visual, auditory and sometimes tactile impressions, whereas olfactory stimuli are mostly neglected. This concept paper therefore examines whether odors might be beneficial for further enhancing the experience of presence and immersion into a simulated environment. The aim is to demonstrate the benefits of VR civilian stress training for emergency personnel and to investigate the role of odors as stressors by manipulating the degree of perceived psychophysiological stress via olfactory impressions. Moreover, the current paper presents the development and validation of a convenient and portable fragrance dosing system that allows personalized odor presentation in VR. The presented system can transport reproducible small quantities of an air-fragrance mixture close to the human nose using piezoelectric stainless steel micropumps. The results of the fluidic system validation indicate that the micropump is suitable for releasing odors close to the nose with constant amounts of odor presentation. Furthermore, the theoretical background and the planned experimental design of VR stress training, including odor presentation via olfactory VR technology, are elucidated.
Applicability of Small and Low-Cost Magnetic Sensors to Geophysical Exploration
Authors: Accomando Filippo , Florio Giovanni
Published: November 2024

In the past few decades, there has been a notable technological advancement in geophysical sensors. In the case of magnetometry, several sensors were used, having the common feature of being miniaturized and lightweight, thus idoneous to be carried by UAVs in drone-borne magnetometric surveys. A common feature is that their sensitivity ranges from 0.1 to about 200 nT, thus not comparable to that of optically pumped, standard fluxgate or even proton magnetometers. However, their low cost, volume and weight remain very interesting features of these sensors. In fact, such sensors have the common feature of being very inexpensive, so new ways of making surveys using many of these sensors could be devised, in addition to the possibility, even with limited resources, of creating gradiometers by combining two or more of them. In this paper, we explore the range of applicability of small tri-axial magnetometers commonly used for attitude determination in several devices. We compare the results of surveys performed with standard professional geophysical instruments with those obtained using these sensors and find that in the presence of strongly magnetized sources, they succeeded in identifying the main anomalies.
Smartphone Light Sensors as an Innovative Tool for Solar Irradiance Measurements
Authors: Di Laccio José Luis , Monetta Andrés , Alonso-Suárez Rodrigo , Monteiro Martín , Marti Arturo C.
Published: November 2024

In recent years, the teaching of experimental science and engineering has been revolutionized by the integration of smartphone sensors, which are widely used by a large portion of the population. Concurrently, interest in solar energy has surged. This raises the important question of how smartphone sensors can be harnessed to incorporate solar energy studies into undergraduate education. We provide comprehensive guidelines for using smartphone sensors in various conditions, along with detailed instructions on how to calibrate them with widely accessible clear-sky satellite data. This smartphone-based method is also compared with professional reference measurements to ensure consistency. This experiment can be easily conducted with most smartphones, basic materials, and a clear, open location over a few hours (methods). The findings demonstrate that smartphones, combined with simple resources, can accurately measure solar irradiance and support experiments on solar radiation physics, atmospheric interactions, and variations in solar energy across locations, cloud cover, and time scales. This approach provides a practical and accessible tool for studying solar energy, offering an innovative and engaging method for measuring solar resources.
N-Type Nanocomposite Films Combining SWCNTs, Bi2Te3 Nanoplates, and Cationic Surfactant for Pn-Junction Thermoelectric Generators with Self-Generated Temperature Gradient Under Uniform Sunlight Irradiation
Authors: Hoshino Koki , Yamamoto Hisatoshi , Tamai Ryota , Nakajima Takumi , Miyake Shugo , Takashiri Masayuki
Published: November 2024

Flexible thermoelectric generators (TEGs) with pn-junction single-walled carbon nanotube (SWCNT) films on a polyimide substrate have attracted considerable attention for energy harvesting. This is because they generate electricity through the photo-thermoelectric effect by self-generated temperature gradient under uniform sunlight irradiation. To increase the performance and durability of the pn-junction TEGs, n-type films need to be improved as a priority. In this study, bismuth telluride (Bi2Te3) nanoplates synthesized by the solvothermal method were added to the n-type SWCNT films, including a cationic surfactant to form the nanocomposite films because Bi2Te3 has high n-type thermoelectric properties and high durability. The performances of the pn-junction TEGs were investigated by varying the heat treatment times. When the artificial sunlight was uniformly irradiated to the pn-junction TEGs, a stable output voltage of 0.47 mV was observed in the TEG with nanocomposite films heat-treated at 1 h. The output voltage decreased with increasing heat treatment time due to the decrease in the p-type region. The output voltage of TEG at 1 h is higher than that of the TEGs without Bi2Te3 nanoplates under the same conditions. Therefore, the addition of Bi2Te3 nanoplates was found to improve the performance of the pn-junction TEGs. These findings may aid in the development of facile and flexible optical devices, including photodetectors and hybrid devices integrating solar cells.
Fast and Smart State Characterization of Large-Format Lithium-Ion Batteries via Phased-Array Ultrasonic Sensing Technology
Authors: Zhou Zihan , Hua Wen , Peng Simin , Tian Yong , Tian Jindong , Li Xiaoyu
Published: November 2024

Lithium-ion batteries (LIBs) are widely used in electric vehicles and energy storage systems, making accurate state transition monitoring a key research topic. This paper presents a characterization method for large-format LIBs based on phased-array ultrasonic technology (PAUT). A finite element model of a large-format aluminum shell lithium-ion battery is developed on the basis of ultrasonic wave propagation in multilayer porous media. Simulations and comparative analyses of phased array ultrasonic imaging are conducted for various operating conditions and abnormal gas generation. A 40 Ah ternary lithium battery (NCMB) is tested at a 0.5C charge-discharge rate, with the state of charge (SOC) and ultrasonic data extracted. The relationship between ultrasonic signals and phased array images is established through simulation and experimental comparisons. To estimate the SOC, a fully connected neural network (FCNN) model is designed and trained, achieving an error of less than 4%. Additionally, phased array imaging, which is conducted every 5 s during overcharging and overdischarging, reveals that gas bubbles form at 0.9 V and increase significantly at 0.2 V. This research provides a new method for battery state characterization.