bluuebunny/crossref_metadata_2025 huggingface dataset:
Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models
Authors: Pop Mădălin-Dorin , Pescaru Dan , Micea Mihai V.
Published: October 2023

The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi–Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi–Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi–Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.
CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach
Authors: Chai Guangzhao , Li Shiming , Yang Yu , Zhou Guohui , Wang Yuhe
Published: October 2023

The traditional Transformer model primarily employs a self-attention mechanism to capture global feature relationships, potentially overlooking local relationships within sequences and thus affecting the modeling capability of local features. For Support Vector Machine (SVM), it often requires the joint use of feature selection algorithms or model optimization methods to achieve maximum classification accuracy. Addressing the issues in both models, this paper introduces a novel network framework, CTSF, specifically designed for Industrial Internet intrusion detection. CTSF effectively addresses the limitations of traditional Transformers in extracting local features while compensating for the weaknesses of SVM. The framework comprises a pre-training component and a decision-making component. The pre-training section consists of both CNN and an enhanced Transformer, designed to capture both local and global features from input data while reducing data feature dimensions. The improved Transformer simultaneously decreases certain training parameters within CTSF, making it more suitable for the Industrial Internet environment. The classification section is composed of SVM, which receives initial classification data from the pre-training phase and determines the optimal decision boundary. The proposed framework is evaluated on an imbalanced subset of the X-IIOTID dataset, which represent Industrial Internet data. Experimental results demonstrate that with SVM using both “linear” and “rbf” kernel functions, CTSF achieves an overall accuracy of 0.98875 and effectively discriminates minor classes, showcasing the superiority of this framework.
Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices
Authors: Félix Ramón A. , Ochoa-Brust Alberto , Mata-López Walter , Martínez-Peláez Rafael , Mena Luis J. , Valdez-Velázquez Laura L.
Published: October 2023

Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm’s performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal’s isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts.
Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters
Authors: Wang Haibin , Ge Hongjuan , Zhang Zhihui , Bu Zonghao
Published: October 2023

To clarify the reasons for inaccurate fire detection in aircraft cargo holds, this article depicts research from the perspective of a single type of sensor detection. In terms of fire smoke, we select dual-wavelength photoelectric smoke sensors for fire-data collection and a genetic algorithm to optimize the classification and detection of random forest fires. From the perspective of fire CO concentration, we use PSO-LSTM to train a CO concentration compensation model to reduce sensor measurement errors. Research is then conducted from the perspective of various types of sensor detection, using the improved BP-AdaBoost algorithm to train a fire-detection model and achieve the high-precision identification of complex environments and fire situations.
Automatic Calibration of Microscopic Traffic Simulation Models Using Artificial Neural Networks
Authors: Daguano Rodrigo F. , Yoshioka Leopoldo R. , Netto Marcio L. , Marte Claudio L. , Isler Cassiano A. , Santos Max Mauro Dias , Justo João F.
Published: October 2023

Traffic simulations are valuable tools for urban mobility planning and operation, particularly in large cities. Simulation-based microscopic models have enabled traffic engineers to understand local transit and transport behaviors more deeply and manage urban mobility. However, for the simulations to be effective, the transport network and user behavior parameters must be calibrated to mirror real scenarios. In general, calibration is performed manually by traffic engineers who use their knowledge and experience to adjust the parameters of the simulator. Unfortunately, there is still no systematic and automatic process for calibrating traffic simulation networks, although some methods have been proposed in the literature. This study proposes a methodology that facilitates the calibration process, where an artificial neural network (ANN) is trained to learn the behavior of the transport network of interest. The ANN used is the Multi-Layer Perceptron (MLP), trained with back-propagation methods. Based on this learning procedure, the neural network can select the optimized values of the simulation parameters that best mimic the traffic conditions of interest. Experiments considered two microscopic models of traffic and two psychophysical models (Wiedemann 74 and Wiedemann 99). The microscopic traffic models are located in the metropolitan region of São Paulo, Brazil. Moreover, we tested the different configurations of the MLP (layers and numbers of neurons) as well as several variations of the backpropagation training method: Stochastic Gradient Descent (SGD), Adam, Adagrad, Adadelta, Adamax, and Nadam. The results of the experiments show that the proposed methodology is accurate and efficient, leading to calibration with a correlation coefficient greater than 0.8, when the calibrated parameters generate more visible effects on the road network, such as travel times, vehicle counts, and average speeds. For the psychophysical parameters, in the most simplified model (W74), the correlation coefficient was greater than 0.7. The advantage of using ANN for the automatic calibration of simulation parameters is that it allows traffic engineers to carry out comprehensive studies on a large number of future scenarios, such as at different times of the day, as well as on different days of the week and months of the year.
Swin Transformer-Based Edge Guidance Network for RGB-D Salient Object Detection
Authors: Wang Shuaihui , Jiang Fengyi , Xu Boqian
Published: October 2023

Salient object detection (SOD), which is used to identify the most distinctive object in a given scene, plays an important role in computer vision tasks. Most existing RGB-D SOD methods employ a CNN-based network as the backbone to extract features from RGB and depth images; however, the inherent locality of a CNN-based network limits the performance of CNN-based methods. To tackle this issue, we propose a novel Swin Transformer-based edge guidance network (SwinEGNet) for RGB-D SOD in which the Swin Transformer is employed as a powerful feature extractor to capture the global context. An edge-guided cross-modal interaction module is proposed to effectively enhance and fuse features. In particular, we employed the Swin Transformer as the backbone to extract features from RGB images and depth maps. Then, we introduced the edge extraction module (EEM) to extract edge features and the depth enhancement module (DEM) to enhance depth features. Additionally, a cross-modal interaction module (CIM) was used to integrate cross-modal features from global and local contexts. Finally, we employed a cascaded decoder to refine the prediction map in a coarse-to-fine manner. Extensive experiments demonstrated that our SwinEGNet achieved the best performance on the LFSD, NLPR, DES, and NJU2K datasets and achieved comparable performance on the STEREO dataset compared to 14 state-of-the-art methods. Our model achieved better performance compared to SwinNet, with 88.4% parameters and 77.2% FLOPs. Our code will be publicly available.
Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments
Authors: Kim Tae-Hyeong , Jo Gi-Hwan , Yun Hyeong-Seok , Yun Kyung-Su , Park Tae-Hyoung
Published: October 2023

Sensors on autonomous vehicles have inherent physical constraints. To address these limitations, several studies have been conducted to enhance sensing capabilities by establishing wireless communication between infrastructure and autonomous vehicles. Various sensors are strategically positioned within the road infrastructure, providing essential sensory data to these vehicles. The primary challenge lies in sensor placement, as it necessitates identifying optimal locations that minimize blind spots while maximizing the sensor’s coverage area. Therefore, to solve this problem, a method for positioning multiple sensor systems in road infrastructure is proposed. By introducing a voxel grid, the problem is formulated as an optimization challenge, and a genetic algorithm is employed to find a solution. Experimental findings using lidar sensors are presented to demonstrate the efficacy of this proposed approach.
Vehicle Detection and Attribution from a Multi-Sensor Dataset Using a Rule-Based Approach Combined with Data Fusion
Authors: Bowman Lindsey A. , Narayanan Ram M. , Kane Timothy J. , Bradley Eliza S. , Baran Matthew S.
Published: October 2023

Vehicle detection using data fusion techniques from overhead platforms (RGB/MSI imagery and LiDAR point clouds) with vector and shape data can be a powerful tool in a variety of fields, including, but not limited to, national security, disaster relief efforts, and traffic monitoring. Knowing the location and number of vehicles in a given area can provide insight into the surrounding activities and patterns of life, as well as support decision-making processes. While researchers have developed many approaches to tackling this problem, few have exploited the multi-data approach with a classical technique. In this paper, a primarily LiDAR-based method supported by RGB/MSI imagery and road network shapefiles has been developed to detect stationary vehicles. The addition of imagery and road networks, when available, offers an improved classification of points from LiDAR data and helps to reduce false positives. Furthermore, detected vehicles can be assigned various 3D, relational, and spectral attributes, as well as height profiles. This method was evaluated on the Houston, TX dataset provided by the IEEE 2018 GRSS Data Fusion Contest, which includes 1476 ground truth vehicles from LiDAR data. On this dataset, the algorithm achieved a 92% precision and 92% recall. It was also evaluated on the Vaihingen, Germany dataset provided by ISPRS, as well as data simulated using an image generation model called DIRSIG. Some known limitations of the algorithm include false positives caused by low vegetation and the inability to detect vehicles (1) in extremely close proximity with high precision and (2) from low-density point clouds.
Photonic Crystal Surface Modes for Trapping and Waveguiding of Ultracold Atoms
Authors: Konopsky Valery
Published: October 2023

The design of a photonic system for the trapping and waveguiding of ultracold atoms far above a dielectric surface is proposed and analyzed. The system consists of an optical rib waveguide deposited on a planar one-dimensional photonic crystal, which sustains two wavelengths of photonic crystal surface modes tuned in the red and blue sides relative to the atomic transition of the neutral atom. The addition of a third blue-tuned wavelength to the system allows the neutral atoms to be stabilized in the lateral dimension above the rib waveguide. Trapping atoms at relatively large distances, more than 600 nm above the dielectric surface, allows to reduce the influence of Casimir–Polder forces in this system. The detailed design methodology and specifications of the photonic system are provided. The presented design can be employed in atomic chips and quantum sensors.
Self-Aware Cybersecurity Architecture for Autonomous Vehicles: Security through System-Level Accountability
Authors: Adu-Kyere Akwasi , Nigussie Ethiopia , Isoaho Jouni
Published: October 2023

The inherent dynamism of recent technological advancements in intelligent vehicles has seen multitudes of noteworthy security concerns regarding interactions and data. As future mobility embraces the concept of vehicles-to-everything, it exacerbates security complexities and challenges concerning dynamism, adaptiveness, and self-awareness. It calls for a transition from security measures relying on static approaches and implementations. Therefore, to address this transition, this work proposes a hierarchical self-aware security architecture that effectively establishes accountability at the system level and further illustrates why such a proposed security architecture is relevant to intelligent vehicles. The article provides (1) a comprehensive understanding of the self-aware security concept, with emphasis on its hierarchical security architecture that enables system-level accountability, and (2) a deep dive into each layer supported by algorithms and a security-specific in-vehicle black box with external virtual security operation center (VSOC) interactions. In contrast to the present in-vehicle security measures, this architecture introduces characteristics and properties that enact self-awareness through system-level accountability. It implements hierarchical layers that enable real-time monitoring, analysis, decision-making, and in-vehicle and remote site integration regarding security-related decisions and activities.
Tool Frame Calibration for Robot-Assisted Ultrasonic Testing
Authors: Zhang Hanming , Wang Jingpin , Guo Canzhi
Published: October 2023

Tool frame calibration has been widely used in robot-assisted printing, welding, and grinding, but it is not suitable for ultrasonic testing because the robot is submerged in water. The purpose of this paper is to present a tool frame calibration method, which is suitable for improving the precision of ultrasonic testing. In uniform mediums, sound travels along a straight line like ray. A reflector is fixed in water to reflect ultrasound, which makes it possible to measure distances between incidence points on a reflector and tool center point (TCP) on an ultrasound transducer. In addition, the positions and poses of the end flange are recorded through a robot controller. Finally, an optimization method is applied to calculate the position and pose errors of the tool frame relative to the end flange according to such records. The presented method was implemented in an ultrasonic testing system. We selected 100 incidence points on the reflector to calculate the assembly errors of the transducer. The pulse amplitude rose obviously after calibration, which verifies that this is an effective method. Considering that ultrasonic transducers can be used as a measuring tool, this paper proposes a tool frame calibration method for ultrasonic testing robots without introducing other measuring devices, which draws the conclusion that tool frame can be calibrated through ultrasound.
PMIndoor: Pose Rectified Network and Multiple Loss Functions for Self-Supervised Monocular Indoor Depth Estimation
Authors: Chen Siyu , Zhu Ying , Liu Hong
Published: October 2023

Self-supervised monocular depth estimation, which has attained remarkable progress for outdoor scenes in recent years, often faces greater challenges for indoor scenes. These challenges comprise: (i) non-textured regions: indoor scenes often contain large areas of non-textured regions, such as ceilings, walls, floors, etc., which render the widely adopted photometric loss as ambiguous for self-supervised learning; (ii) camera pose: the sensor is mounted on a moving vehicle in outdoor scenes, whereas it is handheld and moves freely in indoor scenes, which results in complex motions that pose challenges for indoor depth estimation. In this paper, we propose a novel self-supervised indoor depth estimation framework-PMIndoor that addresses these two challenges. We use multiple loss functions to constrain the depth estimation for non-textured regions. We introduce a pose rectified network that only estimates the rotation transformation between two adjacent frames of images for the camera pose problem, and improves the pose estimation results with the pose rectified network loss. We also incorporate a multi-head self-attention module in the depth estimation network to enhance the model’s accuracy. Extensive experiments are conducted on the benchmark indoor dataset NYU Depth V2, demonstrating that our method achieves excellent performance and is better than previous state-of-the-art methods.
Correction: Kumar et al. Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System. Sensors 2022, 22, 5160
Authors: Kumar Sandeep , Rani Shilpa , Jain Arpit , Verma Chaman , Raboaca Maria Simona , Illés Zoltán , Neagu Bogdan Constantin
Published: October 2023

In the original publication [...]
Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction
Authors: Tan Dongmei , Guo Tai , Luo Hao , Ji Baifeng , Tao Yu , Li An
Published: October 2023

The early health warning of a cable-stayed bridge is of great significance for discovering the abnormal condition of the structure, eliminating the risk factors, and ensuring the normal operation of the bridge in order to set a reasonable safety monitoring threshold to ensure the safety warning and condition assessment of the bridge structure. A method of dynamic early warning by considering the temperature effect is adopted in this paper on the basis of the benchmark threshold. Based on the long-term deflection monitoring data of a bridge in Wuhan, the generalized Pareto distribution (GPD) extreme value analysis theory is used to set the benchmark threshold. Then, by constructing the seasonal autoregressive integrated moving average (SARIMA) long-span bridge temperature effect prediction model, the reference threshold is dynamically adjusted. Finally, it is compared with the traditional fixed threshold monitoring system. The results show that the dynamic threshold has stronger adaptability to the monitoring of cable-stayed bridges and can also achieve effective monitoring of local mutations in other periods. Dynamic threshold early warning can reduce the shortcomings of traditional early warning methods such as underreporting and misreporting. At the same time, the GPD extreme value analysis theory overcomes the disadvantage that the extreme value information is not fully utilized. It has an important application value for bridge health monitoring.
Room Temperature Chemiresistive Gas Sensors Based on 2D MXenes
Authors: Mirzaei Ali , Lee Myoung Hoon , Safaeian Haniyeh , Kim Tae-Un , Kim Jin-Young , Kim Hyoun Woo , Kim Sang Sub
Published: October 2023

Owing to their large surface area, two-dimensional (2D) semiconducting nanomaterials have been extensively studied for gas-sensing applications in recent years. In particular, the possibility of operating at room temperature (RT) is desirable for 2D gas sensors because it significantly reduces the power consumption of the sensing device. Furthermore, RT gas sensors are among the first choices for the development of flexible and wearable devices. In this review, we focus on the 2D MXenes used for the realization of RT gas sensors. Hence, pristine, doped, decorated, and composites of MXenes with other semiconductors for gas sensing are discussed. Two-dimensional MXene nanomaterials are discussed, with greater emphasis on the sensing mechanism. MXenes with the ability to work at RT have great potential for practical applications such as flexible and/or wearable gas sensors.
Damage Identification of Railway Bridges through Temporal Autoregressive Modeling
Authors: Anastasia Stefano , García-Macías Enrique , Ubertini Filippo , Gattulli Vincenzo , Ivorra Salvador
Published: October 2023

The damage identification of railway bridges poses a formidable challenge given the large variability in the environmental and operational conditions that such structures are subjected to along their lifespan. To address this challenge, this paper proposes a novel damage identification approach exploiting continuously extracted time series of autoregressive (AR) coefficients from strain data with moving train loads as highly sensitive damage features. Through a statistical pattern recognition algorithm involving data clustering and quality control charts, the proposed approach offers a set of sensor-level damage indicators with damage detection, quantification, and localization capabilities. The effectiveness of the developed approach is appraised through two case studies, involving a theoretical simply supported beam and a real-world in-operation railway bridge. The latter corresponds to the Mascarat Viaduct, a 20th century historical steel truss railway bridge that remains active in TRAM line 9 in the province of Alicante, Spain. A detailed 3D finite element model (FEM) of the viaduct was defined and experimentally validated. On this basis, an extensive synthetic dataset was constructed accounting for both environmental and operational conditions, as well as a variety of damage scenarios of increasing severity. Overall, the presented results and discussion evidence the superior performance of strain measurements over acceleration, offering great potential for unsupervised damage detection with full damage identification capabilities (detection, quantification, and localization).
Development of a Methodology for Low-Cost 3D Underwater Motion Capture: Application to the Biomechanics of Horse Swimming
Authors: Giraudet Chloé , Moiroud Claire , Beaumont Audrey , Gaulmin Pauline , Hatrisse Chloé , Azevedo Emeline , Denoix Jean-Marie , Ben Mansour Khalil , Martin Pauline , Audigié Fabrice , Chateau Henry , Marin Frédéric
Published: October 2023

Hydrotherapy has been utilized in horse rehabilitation programs for over four decades. However, a comprehensive description of the swimming cycle of horses is still lacking. One of the challenges in studying this motion is 3D underwater motion capture, which holds potential not only for understanding equine locomotion but also for enhancing human swimming performance. In this study, a marker-based system that combines underwater cameras and markers drawn on horses is developed. This system enables the reconstruction of the 3D motion of the front and hind limbs of six horses throughout an entire swimming cycle, with a total of twelve recordings. The procedures for pre- and post-processing the videos are described in detail, along with an assessment of the estimated error. This study estimates the reconstruction error on a checkerboard and computes an estimated error of less than 10 mm for segments of tens of centimeters and less than 1 degree for angles of tens of degrees. This study computes the 3D joint angles of the front limbs (shoulder, elbow, carpus, and front fetlock) and hind limbs (hip, stifle, tarsus, and hind fetlock) during a complete swimming cycle for the six horses. The ranges of motion observed are as follows: shoulder: 17 ± 3°; elbow: 76 ± 11°; carpus: 99 ± 10°; front fetlock: 68 ± 12°; hip: 39 ± 3°; stifle: 68 ± 7°; tarsus: 99 ± 6°; hind fetlock: 94 ± 8°. By comparing the joint angles during a swimming cycle to those observed during classical gaits, this study reveals a greater range of motion (ROM) for most joints during swimming, except for the front and hind fetlocks. This larger ROM is usually achieved through a larger maximal flexion angle (smaller minimal angle of the joints). Finally, the versatility of the system allows us to imagine applications outside the scope of horses, including other large animals and even humans.
Graph Analysis of TMS–EEG Connectivity Reveals Hemispheric Differences following Occipital Stimulation
Authors: Siviero Ilaria , Bonfanti Davide , Menegaz Gloria , Savazzi Silvia , Mazzi Chiara , Storti Silvia Francesca
Published: October 2023

(1) Background: Transcranial magnetic stimulation combined with electroencephalography (TMS–EEG) provides a unique opportunity to investigate brain connectivity. However, possible hemispheric asymmetries in signal propagation dynamics following occipital TMS have not been investigated. (2) Methods: Eighteen healthy participants underwent occipital single-pulse TMS at two different EEG sites, corresponding to early visual areas. We used a state-of-the-art Bayesian estimation approach to accurately estimate TMS-evoked potentials (TEPs) from EEG data, which has not been previously used in this context. To capture the rapid dynamics of information flow patterns, we implemented a self-tuning optimized Kalman (STOK) filter in conjunction with the information partial directed coherence (iPDC) measure, enabling us to derive time-varying connectivity matrices. Subsequently, graph analysis was conducted to assess key network properties, providing insight into the overall network organization of the brain network. (3) Results: Our findings revealed distinct lateralized effects on effective brain connectivity and graph networks after TMS stimulation, with left stimulation facilitating enhanced communication between contralateral frontal regions and right stimulation promoting increased intra-hemispheric ipsilateral connectivity, as evidenced by statistical test (p < 0.001). (4) Conclusions: The identified hemispheric differences in terms of connectivity provide novel insights into brain networks involved in visual information processing, revealing the hemispheric specificity of neural responses to occipital stimulation.
Online Multi-Contact Motion Replanning for Humanoid Robots with Semantic 3D Voxel Mapping: ExOctomap
Authors: Tsuru Masato , Escande Adrien , Kumagai Iori , Murooka Masaki , Harada Kensuke
Published: October 2023

This study introduces a rapid motion-replanning technique driven by a semantic 3D voxel mapping system, essential for humanoid robots to autonomously navigate unknown territories through online environmental sensing. Addressing the challenges posed by the conventional approach based on polygon mesh or primitive extraction for mapping, we adopt semantic voxel mapping, utilizing our innovative Extended-Octomap (ExOctomap). This structure archives environmental normal vectors, outcomes of Euclidean Cluster Extraction, and principal component analysis within an Octree structure, facilitating an O(log N) efficiency in semantic accessibility from a position query x∈R3. This strategy reduces the 6D contact pose search to simple 3D grid sampling. Moreover, voxel representation enables the search of collision-free trajectories online. Through experimental validation based on simulations and real robotic experiments, we demonstrate that our framework can efficiently adapt multi-contact motions across diverse environments, achieving near real-time planning speeds that range from 13.8 ms to 115.7 ms per contact.
LC/MS-Based Profiling of Hedyotis aspera Whole-Plant Methanolic Extract and Evaluation of Its Nephroprotective Potential against Gentamicin-Induced Nephrotoxicity in Rats Supported by In Silico Studies
Authors: Prasanth Dsnbk , Reddy Lingareddygari Siva Sanker , Dasari Tharani , Bhavanam Pamula Reddy , Ahmad Sheikh F. , Nalluri Rahul , Pasala Praveen Kumar
Published: October 2023

Many high-altitude plants, such as Hedyotis aspera, need to be explored for their possible medicinal value. The current study explored the protective effect of Hedyotis aspera methanolic extract whole plant (HAME) against gentamicin-induced nephrotoxicity in rats. It profiled their phytocontents using HPLC-QTOF-MS/MS analytic methods. The LC-MS analysis of HAME revealed 27 compounds. Eight compounds followed Lipinski’s rule of five and were found to be potential TNF-α inhibitors with binding affinities of −6.9, −6.3, −6.3, and −6.3 Kcal/mol, such as 14,19-Dihydroaspidospermatine, coumeroic acid, lycocernuine and muzanzagenin. All potential compounds were found to be safe according to the ADMET analysis. The in vitro 2,2-diphenyl-1-picrlhydrazyl (DPPH) assay assessed the antioxidant activity. The nephroprotective activity was assessed in rats using a gentamicin-induced nephrotoxicity model. The in vivo analysis involved histological examination, tissue biochemical evaluation, including a kidney function test, catalase activity (CAT), reduced glutathione (GSH) levels, superoxide dismutase (SOD), and the inflammatory mediator TNF-α. Based on DPPH activity, HAME showed a scavenging activity IC50 of 264.8 ± 1.2 µg/mL, while results were compared with a standard vitamin C IC50 of 45 ± 0.45 µg/mL. Nephrotoxicity was successfully induced, as shown by elevated creatinine and uric acid levels, decreased kidney antioxidant levels, and increased TNF-α in gentamicin-treated rats. The HAME treatment significantly reduced serum creatinine and uric acid levels, increased GSH (p < 0.01 **), CAT (p < 0.01 **), and SOD (p < 0.001 ***), and decreased TNF-α (p < 0.001 ***) in nephrotoxic rats. The histopathological examination of the groups treated with HAME revealed a notable enhancement in the structural integrity of the kidneys as compared to the group exposed to gentamicin. Biochemical, histopathological, and phytochemical screening of HAME suggests that it has nephroprotective potential, owing to the presence of 14,19-Dihydroaspidospermatine, coumeroic acid, lycopene, and muzanzagenin.
Research on Optimal Scheduling Strategy of Microgrid Considering Electric Vehicle Access
Authors: Wu Zhimin , Zou Yang , Zheng Feng , Liang Ning
Published: October 2023

The random output of renewable energy and the disorderly grid connection of electric vehicles (EV) will pose challenges to the safe and stable operation of the power system. In order to ensure the reliability and symmetry of the microgrid operation, this paper proposes a microgrid optimization scheduling strategy considering the access of EVs. Firstly, in order to reduce the impact of random access to EVs on power system operation, a schedulable model of an EV cluster is constructed based on the Minkowski sum. Then, based on the wavelet neural network (WNN), the renewable energy output is predicted to reduce the influence of its output fluctuation on the operation of the power system. Considering the operation constraints of each unit in the microgrid, the network active power loss and node voltage deviation are taken as the optimization objectives, and the established microgrid model is equivalently transformed via second-order cone relaxation to improve its solution efficiency. Based on network reconfiguration and flexible load participation in demand response, the economy and reliability of system operation are improved. Finally, the feasibility and effectiveness of the proposed method are verified based on the simulation examples.
On Some Combinatorial Properties of Oresme Hybrationals
Authors: Włoch Iwona , Paja Natalia , Szynal-Liana Anetta
Published: October 2023

In this paper, we study the Oresme hybrationals that generalize Oresme hybrid numbers and Oresme rational functions. We give a reccurence relation and a generating function for Oresme hybrationals. Moreover, we give some of their properties, among others, Binet formulas and general bilinear index-reduction formulas, through which we can obtain Catalan-, Cassini-, Vajda-, and d’Ocagne-type identities.
Euclidean Jordan Algebras, Symmetric Association Schemes, Strongly Regular Graphs, and Modified Krein Parameters of a Strongly Regular Graph
Authors: Vieira Luís Almeida
Published: October 2023

In this paper, in the environment of Euclidean Jordan algebras, we establish some inequalities over the Krein parameters of a symmetric association scheme and of a strongly regular graph. Next, we define the modified Krein parameters of a strongly regular graph and establish some admissibility conditions over these parameters. Finally, we introduce some relations over the Krein parameters of a strongly regular graph.
Alternative Derivation of the Non-Abelian Stokes Theorem in Two Dimensions
Authors: Ariwahjoedi Seramika , Zen Freddy Permana
Published: October 2023

The relation between the holonomy along a loop with the curvature form is a well-known fact, where the small square loop approximation of aholonomy Hγ,O is proportional to Rσ. In an attempt to generalize the relation for arbitrary loops, we encounter the following ambiguity. For a given loop γ embedded in a manifold M, Hγ,O is an element of a Lie group G; the curvature Rσ∈g is an element of the Lie algebra of G. However, it turns out that the curvature form Rσ obtained from the small loop approximation is ambiguous, as the information of γ and Hγ,O are insufficient for determining a specific plane σ responsible for Rσ. To resolve this ambiguity, it is necessary to specify the surface S enclosed by the loop γ; hence, σ is defined as the limit of S when γ shrinks to a point. In this article, we try to understand this problem more clearly. As a result, we obtain an exact relation between the holonomy along a loop with the integral of the curvature form over a surface that it encloses. The derivation of this result can be viewed as an alternative proof of the non-Abelian Stokes theorem in two dimensions with some generalizations.
Developing and Managing Film-Related Tourism in the All-for-One Model at a Tourism Destination: The Case of Hengdian Town (China)
Authors: Cui Xin
Published: October 2023

All-for-one tourism is a new planning concept proposed by Chinese tourism scholars and practitioners, which has been formally regarded as a new tourism model to develop the country’s tourism industries since 2016. It aims to stimulate the growth of the tourism industries across the entire region, encompassing all tourism and tourism-related resources at a destination. Hengdian Town is a tourism destination in China that has implemented the model of all-for-one tourism to develop and manage its film-related tourism. Based on the data collected from ethnographic methods (participant observations and individual interviews) in Hengdian Town, this paper examines the ways that the destination manages its film-related tourism, as well as the outcomes of such approaches, by applying the model of all-for-one tourism. The findings reveal that Hengdian Town has leveraged the model to manage its tourism resources and provided tourists with a comprehensive travel experience. This paper also explores the benefits and drawbacks of managing film-related tourism in the all-for-one tourism model. By focusing on film-related tourism, this study provides a unique perspective on the all-for-one tourism model.
Mercury Contamination as an Indicator of Fish Species’ Trophic Position in the Middle Araguaia River, Brazil
Authors: de Castro Moraes Lilian , Bernardi José Vicente Elias , de Souza João Pedro Rudrigues , Portela Joelma Ferreira , Pereira Hasley Rodrigo , de Oliveira Barbosa Hugo , Pires Nayara Luiz , Monteiro Lucas Cabrera , Rodrigues Ygor Oliveira Sarmento , Vieira Ludgero Cardoso Galli , Sousa Passos Carlos José , de Souza Jurandir Rodrigues , Bastos Wanderley Rodrigues , Dórea José Garrofe
Published: October 2023

This study evaluates the use of mercury (Hg) concentrations in fish muscle tissue to determine a species’ trophic position (TP) in its environment. A campaign conducted in 2019 along 375 km in the middle Araguaia River basin, Brazil, resulted in 239 organisms from 20 species collected. The highest total mercury (THg) concentrations were found in Pellonacastelnaeana (6.93 µg·g−1, wet weight) and in Triportheus elongatus (3.18 µg·g−1, wet weight), whose TPs were different according to the FishBase database. However, they occupied the same trophic level in this study. The intra-specific comparison showed a difference in Hg concentrations between individuals captured in distinct sites. The study of the biota–sediment accumulation factor (BSAF) showed that spatiality interferes with a species’ TP. Statistical analyses revealed that when we used a predicted species’ TP based on each individual’s size, it explained 72% of the variability in THg concentration across all fish species. Multiple regression analysis confirmed that standard length and FishBase values are positively associated with THg (R2 = 0.943). These results point to Hg as a viable indicator of a fish species’ TP since it reflects regional, biological, and environmental factors, as demonstrated here for the middle Araguaia River.
Distribution Characteristics of Microplastics in Surface Seawater off the Yangtze River Estuary Section and Analysis of Ecological Risk Assessment
Authors: Ji Xiao , Yan Shuaishuai , He Yanlong , He Haisheng , Liu Hanqi
Published: October 2023

Microplastics are widespread in the oceans as a new type of pollutant. Due to the special geographical environment characteristics, the Yangtze River estuary region become hotspot for microplastics research. In 2017 and 2019, surface seawater microplastics samples were collected from five stations off the Yangtze River estuary during four seasons (spring, summer, autumn, and winter). The abundance and characteristics of microplastics in seawater were researched. The results showed that microplastics widely existed in surface seawater; the average abundance of microplastics in seawater was (0.17 ± 0.14) items/m3 (0.00561 ± 0.00462) mg/m3; and accounting for 80% of the total plastic debris, the abundance of microplastics was at moderately low levels compared to national and international studies. The particle size of most microplastics was between 1 mm to 2 mm, accounting for 36.1% of the total microplastics. The main shapes of microplastics were fiber, flake, and line, accounting for 39.5%, 28.4%, and 20.8%, respectively. Polypropylene, polyethylene terephthalate, and polyethylene were the main components of microplastics, accounting for 41.0%, 25.1%, and 24.9%, respectively. Yellow, green, black, and transparent were the most common colors, accounting for 21.9%, 19.6%, 16.5%, and 15.7%, respectively. This study shows that the spatial distribution of microplastics in the surface waters off the Yangtze River estuary shows a decreasing trend from nearshore to farshore due to the influence of land-based inputs, hydrodynamics, and human activities; the distribution of microplastics has obvious seasonal changes, and the level of microplastic pollution is higher in summer. The potential ecological risk of microplastics in the surface waters off the Yangtze River estuary is relatively small.
Removal of Particulate Matter by a Non-Powered Brush Filter Using Electrostatic Forces
Authors: Heo Jaeseok , Lee Jooyeon , Yoon Minyoung , Park Duckshin
Published: October 2023

In urban areas, a major source of harmful particulate matter is generated by vehicles. In particular, bus stops, where people often stay for public transportation, generate high concentrations of particulate matter compared to the general atmosphere. In this study, a non-powered type brush filter that generates electrostatic force without using a separate power source was developed to manage the concentration of particulate matter exposed at bus stops, and the removal performance of particulate matter was evaluated. The dust collection performance of the non-motorized brush filter varied by material, with particle removal efficiencies of 82.1 ± 3.4, 76.1 ± 4.7, and 73.7 ± 4.5% for horse hair, nylon, and stainless steel, respectively. In conditions without the fan running to see the effect of airflow, the particle removal efficiency was relatively low at 58.2 ± 8.4, 53.6 ± 9.2, and 58.0 ± 7.3%. Then, to check the dust collection performance according to the density, the number of brushes was increased to densify the density, and the horse hair, nylon, and stainless steel brush filters showed a maximum dust collection performance of 89.6 ± 2.2, 88.3 ± 3.2, and 82.1 ± 3.8%, respectively. To determine the replacement cycle of the non-powered brush filter, the particulate removal performance was initially 88.0 ± 3.2% when five horse hair brushes were used. Over time, particulate matter tended to gradually decrease, but after a period of time, particulate matter tended to increase again. The purpose of this study is to evaluate the particulate matter removal performance using a brush filter that generates electrostatic force without a separate power source. This study’s brush filter is expected to solve the maintenance problems caused by the purchase and frequent replacement of expensive HEPA filters that occur with existing abatement devices, and the ozone problems caused by abatement devices that use high voltages.
Effects of Ambient O3 on Respiratory Mortality, Especially the Combined Effects of PM2.5 and O3
Authors: Deng Ye , Wang Junlong , Sun Li , Wang Yue , Chen Jiaoyang , Zhao Zhixin , Wang Tianyun , Xiang Yuting , Wang Yuting , Chen Jiamei , He Miao
Published: October 2023

Background: In China, the increasing concentration of ozone (O3) has emerged as a significant air pollution issue, leading to adverse effects on public health, particularly the respiratory system. Despite the progress made in managing air pollution in China, it is crucial to address the problem of environmental O3 pollution at present. Methods: The connection between O3 exposure and respiratory mortality in Shenyang, China, from 2014 to 2018 was analyzed by a time-series generalized additive regression model (GAM) with quasi-Poisson regression. Additionally, the potential combined effects of fine particulate matter (PM2.5) and O3 were investigated using the synergy index (SI). Results: Our findings indicate that each 10 μg/m3 increase in O3 at lag 2 days was associated with a maximum relative risk (RR) of 1.0150 (95% CI: 1.0098–1.0202) for respiratory mortality in the total population. For individuals aged ≥55 years, unmarried individuals, those engaged in indoor occupations, and those with low educational attainment, each 10 μg/m3 increase in O3 at lag 07 days was linked to RR values of 1.0301 (95% CI: 1.0187–1.0417), 1.0437 (95% CI: 1.0266–1.0610), 1.0317 (95% CI: 1.0186–1.0450), and 1.0346 (95% CI: 1.0222–1.0471), respectively. Importantly, we discovered a synergistic effect of PM2.5 and O3, resulting in an SI of 2.372 on the occurrence of respiratory mortality. Conclusions: This study confirmed a positive association between O3 exposure and respiratory mortality. Furthermore, it highlighted the interaction between O3 and PM2.5 in exacerbating respiratory deaths.
The Effectiveness of Topical Dimethicone Together with a One Health Approach for the Control of Tungiasis in the Sanumás Communities, Yanomami Territory, Amazon Rainforest: A Real-World Study
Authors: Santana Yago Ranniere Teixeira , Andrade Débora Dornelas Belchior Costa , Barroso Daniel Holanda , Silva Andressa Vieira , Oliveira Lucas Felipe Carvalho , Timbó Renata Velôzo , Araújo David Dias , de Andrade Rafael Rocha , Pellegrini Marcos Antonio , de Carvalho Fabiola Christian Almeida , Martins Luciana Pereira Freire , Gomes Ciro Martins
Published: October 2023

Background: The success of tungiasis treatment is highly dependent on adequate environmental control. Methods: This is a real-world observational cohort study designed to monitor the effectiveness of topical dimethicone together with a One Health approach for the control of tungiasis in the Sanumás communities, Amazon rainforest, Brazil. We followed up on 562 indigenous people and 81 domestic dogs for 1.5 years in a 3-month interval. A new molecular method for large-scale soil evaluation was also tested. The control of tungiasis was independently conducted by the Brazilian Ministry of Health and comprised topical dimethicone application (NYDA®) for humans, single-dose oral afoxolaner for dogs, and in-house soil fumigation with fipronil. The main outcome was the occurrence of tungiasis after the use of topical dimethicone together with the One Health approach. Results: A total of 49 of the 562 indigenous people had active tungiasis at enrollment (8.72%). Only three cases of tungiasis resulted in active lesions after the use of topical dimethicone together with the One Health approach, with two cases of recurrence. From the 6-month follow-up and after, soil infestation was not detected. Conclusions: We conclude that the use of NYDA® together with animal and environmental interventions are effective measures for the control of tungiasis.