bluuebunny/crossref_metadata_2025 huggingface dataset:
Biomimetic Active Stereo Camera System with Variable FOV
Authors: Zhou Yanmiao , Wang Xin
Published: December 2024

Inspired by the biological eye movements of fish such as pipefish and sandlances, this paper presents a novel dynamic calibration method specifically for active stereo vision systems to address the challenges of active cameras with varying fields of view (FOVs). By integrating static calibration based on camera rotation angles with dynamic updates of extrinsic parameters, the method leverages relative pose adjustments between the rotation axis and cameras to update extrinsic parameters continuously in real-time. It facilitates epipolar rectification as the FOV changes, and enables precise disparity computation and accurate depth information acquisition. Based on the dynamic calibration method, we develop a two-DOF bionic active camera system including two cameras driven by motors to mimic the movement of biological eyes; this compact system has a large range of visual data. Experimental results show that the calibration method is effective, and achieves high accuracy in extrinsic parameter calculations during FOV adjustments.
Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
Authors: Shao Junhu , Liu Zaiping , Liu Yishuo , Xie Tianjiao
Published: December 2024

By utilizing chirp-BOK (binary orthogonal keying) modulation into a troposphere scattering communication system, a lower demodulation threshold can be achieved with excellent linear frequency modulation properties in a strong noise and weak signal environment. Firstly, the bit error rate (BER) formula of chirp-BOK modulation over a Rayleigh fading channel was derived theoretically. Then, the BER performance with different chirp-BOK parameters were numerically calculated. In order to investigate the performance of chirp-BOK over deeping fading troposphere scattering link, a seven-path equal-delay Rayleigh fading model was employed. Finally, the system BER performance was simulated under different tap delay and time–bandwidth product parameters. The results demonstrate that when BER reaches 10−4, the optimal configuration of the system achieves a gains approximately from 1.7 dB to 10 dB compared to non-optimized configuration under different Path-Gain-Vector with varying tap delays.
GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital Substations
Authors: Tobar-Rosero Oscar A. , Roa-Romero Omar A. , Rueda-Carvajal Germán D. , Leal-Piedrahita Alexánder , Botero-Vega Juan F. , Gutierrez-Betancur Sergio A. , Branch-Bedoya John W. , Zapata-Madrigal Germán D.
Published: December 2024

Cybersecurity in Critical Infrastructures, especially Digital Substations, has garnered significant attention from both the industrial and academic sectors. A commonly adopted approach to support research in this area involves the use of datasets, which consist of network traffic samples gathered during the operation of an infrastructure. However, creating such datasets from real-world electrical systems presents some challenges: (i) These datasets are often generated under controlled or idealized conditions, potentially overlooking the complexities of real-world operations within a digital substation; (ii) the captured data frequently contain sensitive information, making it difficult to share openly within the research community. This paper presents the creation of a new dataset aimed at advancing cybersecurity research, specifically focused on GOOSE spoofing attacks, given the crucial role of the GOOSE protocol in managing operational and control tasks within Digital Substations. The dataset highlights the real-world impacts of these attacks, demonstrating the execution of unintended operations under different operational scenarios, including both stable conditions and situations involving system failures. The data were collected from a laboratory testbed that replicates the actual functioning of a real digital substation with two bays. The experiments provided insights into key characteristics of GOOSE protocol traffic and the vulnerability of DS infrastructure to Spoofing Attacks.
Rule-Based Energy Management System to Enhance PV Self-Consumption in a Building: A Real Case
Authors: Camblong Haritza , Zapirain Irati , Curea Octavian , Ugartemendia Juanjo , Boussaada Zina , Zamora Ramon
Published: December 2024

The building sector has an important role in the decarbonization of energy. Buildings energy management systems can act on flexible loads to contribute to the integration of renewable energies. This article presents the design, implementation and evaluation of a rule-based energy management systems (RB-EMS) in the frame of a collective self-consumption (CSC) system based on photovoltaic (PV) energy in the Technology Park of Izarbel, in Bidart, France. The RB-EMS acts on the heat ventilation and air conditioning (HVAC) system of one of the buildings involved in the CSC in order to maximize the PV energy self-consumption rate (SCR). Internet of things (IoT) has been developed and implemented in order to retrieve consumption, production and temperature data, and to be able to act on the HVAC. A new 5-step methodology has been developed to design and adjust the RB-EMS. This methodology is mainly based on tests carried out to find the relationship between ON/OFF states of the internal units of the HVAC and the heat pumps’ consumption. Finally, the RB-EMS is tested in heating operating mode. The results show that the RB-EMS allows obtaining a SCR of 89%, and that the users comfort limits are respected.
An Experimental Study on the Heat Transfer and Flow Characteristics of Aluminum Heating Elements Coated with Graphene
Authors: Hou Nana , Li Shuqian , Feng Lianyuan , Shi Jinyu , Guo Meng , Zhou Pengcheng
Published: December 2024

With the rapid development of clean heating, electric heating has received increasing attention. As a common electric heating device, heaters are also widely used in residential and industrial heating, as well as in other fields. In order to improve the reliability and heat transfer flow characteristics of the electric heating element of the heater, a new type of structural electric heating element was designed and developed based on the surface heat transfer enhancement and pit drag reduction characteristics of graphene. The heat transfer and flow characteristics of the electric heating element were analyzed through experiments, and the results showed that the average surface emissivity increases from 0.25 with the non-graphene-coated sample to 0.94 with the graphene-coated one, and the surface temperature of the electric heating element decreased from 289 °C to 237.5 °C. Through wind-tunnel experiments, it was found that the PEC value of the electric heating element with pits was 1.0693, and the Nusselt number increased by 6.22% compared with the smooth surface. Furthermore, after coating with graphene, the Nusselt number increased by 30.3% compared with the smooth surface.
Circular Economy Models in Sustainability Reports of the Polish Electric Power Industry
Authors: Garbol Jacek , Ciechan-Kujawa Marlena
Published: December 2024

The purpose of this article is the identification of circular economy implementation models characteristic of the Polish electric power industry based on the scope and degree of progress of activities included in companies’ sustainability reports. A comprehensive four-level model was used in the study, and it was completed through an assessment of the progress of CE projects implemented in the companies. The scope and level of progress of activities undertaken in the companies of the analysed industry are relatively similar, which makes it possible to determine the model of CE implementation in the electric power industry. Companies are engaged in 2 of the 13 defined areas, energy efficiency and environmental improvements, and, on average, in 4 further ones (industrial waste recycling, dematerialisation, renewables, and industrial symbiosis), which means that they focus mainly on activities of level I and partially level II. Activities at levels III and IV are currently omitted or not advanced.
Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger
Authors: Qin Xiaogang , Yan Shiwei , Xu Haibo , Gao Yi , Yu Yanbing , Wang Jinjiang
Published: December 2024

Heat exchangers play essential roles in the oil and gas production process for convective heat transfer and heat conduction. The health management of heat exchangers stays in the direct monitoring of performance parameters. Aiming at the difficulty of precise fault identification and quantification for heat exchangers in multiple unknown failure modes, a data-model fusion-driven fault quantitative diagnosis method is proposed. Firstly, based on the monitoring data such as temperature, pressure and flow rate, the secondary parameters characterizing the heat exchanger running state are constructed combined with structural physical parameters. Then, by analyzing the correlation among parameter variation, failure modes and deterioration degree, a qualitative inference model of heat exchanger is formed for fault identification, where weights of parameters are introduced based on their sensitivity for different failure modes. After the fault mode is identified, to achieve quantitative analysis of the failure degree, an index-integrated mechanism equation is constructed using monitoring data and secondary parameters, where the index is dynamically modified by online data. Finally, a heat exchanger experiment is carried out to demonstrate the robustness and accuracy of the proposed method.
Sodium, Potassium-Adenosine Triphosphatase as a Potential Target of the Anti-Tuberculosis Agents, Clofazimine and Bedaquiline
Authors: Mmakola Khomotso , Balmith Marissa , Steel Helen , Said Mohamed , Potjo Moliehi , van der Mescht Mieke , Hlatshwayo Nomsa , Meyer Pieter , Tintinger Gregory , Anderson Ronald , Cholo Moloko
Published: December 2024

Multidrug-resistant tuberculosis (MDR-TB) patients are treated with a standardised, short World Health Organization (WHO) regimen which includes clofazimine (CFZ) and bedaquiline (BDQ) antibiotics. These two antibiotics lead to the development of QT prolongation in patients, inhibiting potassium (K+) uptake by targeting the voltage-gated K+ (Kv)11.1 (hERG) channel of the cardiomyocytes (CMs). However, the involvement of these antibiotics to regulate other K+ transporters of the CMs, as potential mechanisms of QT prolongation, has not been explored. This study determined the effects of CFZ and BDQ on sodium, potassium–adenosine triphosphatase (Na+,K+-ATPase) activity of CMs using rat cardiomyocytes (RCMs). These cells were treated with varying concentrations of CFZ and BDQ individually and in combination (1.25–5 mg/L). Thereafter, Na+,K+-ATPase activity was determined, followed by intracellular adenosine triphosphate (ATP) quantification and cellular viability determination. Furthermore, molecular docking of antibiotics with Na+,K+-ATPase was determined. Both antibiotics demonstrated dose–response inhibition of Na+,K+-ATPase activity of the RCMs. The greatest inhibition was demonstrated by combinations of CFZ and BDQ, followed by BDQ alone and, lastly, CFZ. Neither antibiotic, either individually or in combination, demonstrated cytotoxicity. Molecular docking revealed an interaction of both antibiotics with Na+,K+-ATPase, with BDQ showing higher protein-binding affinity than CFZ. The inhibitory effects of CFZ and BDQ, individually and in combination, on the activity of Na+,K+-ATPase pump of the RCMs highlight the existence of additional mechanisms of QT prolongation by these antibiotics.
A Rod-like Bi2O3 Photocatalyst Derived from Bi-Based MOFs for the Efficient Adsorption and Catalytic Reduction of Cr(VI)
Authors: Fang Qin , Chen Luying , Fu Qiucheng , Chen Yongjuan , He Jiao , Jiang Liang , Yan Zhiying , Wang Jiaqiang
Published: December 2024

Heavy metal ion pollution poses a serious threat to the natural environment and human health. Photoreduction through Bi-based photocatalysts is regarded as an advanced green technology for solving environmental problems. However, their photocatalytic activity is limited by the rapid recombination of photogenerated e− and h+ pairs and a low photo-quantum efficiency. In this work, an optimal precursor of Bi-based MOFs was identified by using different solvents, and rod-like Bi2O3 materials were derived by in situ oxidation of Bi atoms in the precursor. The adsorption and photocatalytic reduction efficiency of the prepared Bi2O3 materials for Cr(VI) were evaluated under visible light irradiation. The results showed that the prepared materials had a large specific surface area and enhanced visible light absorption. Bi2O3(DMF/MeOH-3)-400 had a large specific surface area and many active adsorption sites, and it had the highest adsorption of Cr(VI) (49.13%) among the materials. Bi2O3(DMF/MeOH-3)-400 also had the highest photocatalytic reduction efficiency, and it achieved 100% removal of 10 mg·L−1 Cr(VI) within 90 min under light. In addition, the material showed remarkable stability after three consecutive photocatalytic cycles. The enhanced photocatalytic performance was mainly attributed to the fast separation of electron–hole pairs and efficient electron transfer in the MOF-derived materials, which was confirmed by electrochemical tests and PL spectroscopy. Reactive species trapping experiments confirmed that electrons were the main active substances; accordingly, a possible photocatalytic mechanism was proposed. In conclusion, this work provides a new perspective for designing novel photocatalysts that can facilitate the removal of Cr(VI) from water.
Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing
Authors: Amorim Marcio Luís Munhoz , Lima Jorge Gomes , Torres Norah Nadia Sánchez , Afonso Jose A. , Lopes Sérgio F. , Carmo João P. P. do , Hartmann Lucas Vinicius , Souto Cicero Rocha , Salvadori Fabiano , Ando Junior Oswaldo Hideo
Published: December 2024

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO2 levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO2 and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.
Dynamic Analysis of Radial Journal Bearing-Rotor System Based on the Meshless Barycentric Rational Interpolation Collocation Method
Authors: Zhang Hongwei , Imin Rahmatjan
Published: December 2024

This study focuses on a rigid rotor supported by radial journal bearings. Initially, models for the unsteady oil film force in bearing lubrication and the dynamics of the bearing-rotor system are established. Subsequently, the Reynolds equation for dynamic lubricating oil films is discretely solved using the meshless barycentric rational interpolation collocation method. By combining this with the equation of motion for the axis orbit, the oil film pressure distribution, the dynamic response of the rotor, and the axis orbit are calculated. Furthermore, the study investigates the dynamic response of the rotor at different rotational speeds, both with and without considering unbalanced loads. Finally, the influence of step load on the stability of rotor motion is analyzed, revealing that applying an appropriate step load to the rotor can effectively mitigate the lubricating oil films oscillation conditions. The findings of this study hold significant reference value and practical utility for engineering applications.
Evaluation of the Influence of Tanacetum vulgare Extract on Cognitive Functions and Hippocampal BDNF Expression
Authors: Lechkova Borislava , Shishmanova-Doseva Michaela , Benbassat Niko , Gevrenova Reneta , Atanassova Pepa , Penkova Nadya , Peychev Lyudmil , Hrischev Petar , Peychev Zhivko , Ivanova Stanislava
Published: December 2024

Tanacetum vulgare L. has been traditionally applied as a remedy for headaches, rheumatism, digestion, respiratory and neurological problems, and other medical conditions. However, the literature data on its effects on cognitive function are scarce. The aim of the present work was to evaluate the effects of two doses of T. vulgare alcohol extract on cognitive functions, hippocampal brain-derived neurotrophic factor (BDNF) expression, and organ toxicity in rats. Rats were treated with T. vulgare 200 mg/kg (TV 200) and 1000 mg/kg (TV 1000) for 28 days. After one week of pre-treatment, the animals were subjected to a series of tests. We found that in the active avoidance test, only TV 200 improved learning and memory, while in the passive avoidance test, both doses facilitated these processes. In addition, the two doses enhanced spatial memory. In the elevated plus maze test, only the higher dose of TV 1000 resulted in anxiolytic-like behavior. Both doses of the extract significantly increased the hippocampal expression of BDNF. We suggest that increased neurotrophic factor expression could be one of the important mechanisms underlying the cognition-enhancing effects of T. vulgare extract.
Insights into the Metabolism, Disposition, and Quantitative Profile of mGlu5 NAM AE90015 with Metabolite Identification and a Novel Integration Method
Authors: Zou Zhiyang Zack , Han Ming-Jie , Chang Yu , Li Guiying
Published: December 2024

AE90015 is a highly specific and effective negative allosteric modulator (NAM) for the human mGlu5 receptor, showing significant promise for treating Parkinson’s disease. An in vivo rat oral dose study was conducted on AE90015, which involved the collection of urine and bile samples over a 24 h period. At the study’s endpoint, plasma, liver, brain, and renal tissues were also collected. A total of 30 metabolites of AE90015 were identified and structurally characterized or detected using high-resolution LC-MS/MSn. These metabolites fall into four categories: mono-hydroxyl, di-hydroxyl, mono-hydroxyl glucuronide, and di-hydroxyl glucuronide. This study provided a comprehensive overview of the metabolism, excretion, and disposition of AE90015, a promising NAM. The primary clearance pathway for AE90015 is mono-oxidation, accounting for 96% of the total, while direct excretion via renal and bile routes accounted for only 0.5%. Bile emerged as the predominant excretion route, at 65%, for metabolites and a minor amount of parent compound, which contrasts with the common assumption that urine would be the primary excretion pathway, which accounted for 26%. Each adamantyl and pyrazine moiety of AE90015 undergoes a one-time oxidation, while the pyridyl portion remains unmetabolized. Secondary metabolites, such as di-hydroxylated forms and glucuronide conjugates, do not contribute to clearance. In this work, a new quantification method combining UV and mass spectra integration was developed, allowing for the quantification of overlapping metabolite peaks. This novel approach proved to be highly effective for metabolite identification in early preclinical studies.
Enhanced Degradation of Oxytetracycline Antibiotic Under Visible Light over Bi2WO6 Coupled with Carbon Quantum Dots Derived from Waste Biomass
Authors: Ren Haitao , Qi Fan , Zhao Ke , Lv Du , Ma Hao , Ma Cheng , Padervand Mohsen
Published: December 2024

Improving the photogenerated carrier separation efficiency of individual semiconductor materials has always been a key challenge in photocatalysis. In this study, we synthesized a novel photocatalytic material, N-CQDs/UBWO, in situ by combining nitrogen-doped carbon quantum dots (N-CQDs) derived from discarded corn stover with ultrathin Bi2WO6 nanosheets (UBWO). Detailed characterization indicates that the random distribution of N-CQDs on the UBWO surface increases the specific surface area of UBWO, which is beneficial for the adsorption and degradation of oxytetracycline (OTC). More importantly, N-CQDs act as electron acceptors, promoting the effective separation of photogenerated charges, prolonging the lifetime of charge carriers in UBWO, and thereby enhancing the degradation efficiency of OTC. As a result, the optimized 3wt%N-CQDs/UBWO could degrade 85% of OTC within 40 min under visible light, with a removal rate four times that of pure Bi2WO6. The performance of photocatalytic degradation over OTC by 3wt%N-CQDs/UBWO exceeds that of most reported Bi2WO6-based photocatalysts. The EPR analysis confirmed that ∙O2− and ∙OH are the main active species in the photocatalytic degradation of OTC on 3wt%N-CQDs/UBWO. This study provides insight into designing green, low-cost, and efficient photocatalysts using CQDs derived from waste biomass and the degradation of emerging pollutants like antibiotics.
Adhesive, Biocompatible, Antibacterial, and Degradable Collagen-Based Conductive Hydrogel as Strain Sensor for Human Motion Monitoring
Authors: Liao Lixia , Zhang Jiyuan , Ding Jiaqi , Xu Chengzhi , Zhu Lian , Hou Yuanjing , Li Sheng , Zhang Juntao , Wei Benmei , Wang Haibo
Published: December 2024

The conductive hydrogels (CHs) are promising for developing flexible energy storage devices, flexible sensors, and electronic skin due to the unique features of excellent flexibility and high conductivity. However, poor biocompatibility and antibacterial properties seriously limit their application in the biomedical field. Collagen, one of the main components of the extracellular Matrix (ECM), is the ideal matrix for constructing hydrogels due to good biocompatibility with human tissue. Here, dopamine–polypyrrole–collagen (DA-PPY-COL) hydrogel was constructed by dopamine-mediated pyrrole in situ polymerization in a collagen matrix. As a strain sensor, it can be affixed to different parts of the human body to monitor large-scale motion movements and fine micro-expressions in real time. The performance was attributed to its good self-adhesion, flexibility, and electrical conductivity. Biological experiments have shown that it has good antimicrobial properties, biocompatibility, and degradability, allowing the hydrogel to safely monitor human motor behavior. This work not only offers a material preparation strategy for constructing biomimetic electronic skin and wearable sensors but also demonstrates the great potential prospect for implantable degradable medical device applications.
Radar-Based Heart Cardiac Activity Measurements: A Review
Authors: Frazao Alvaro , Pinho Pedro , Albuquerque Daniel
Published: December 2024

In recent years, with the increased interest in smart home technology and the increased need to remotely monitor patients due to the pandemic, demand for contactless systems for vital sign measurements has also been on the rise. One of these kinds of systems are Doppler radar systems. Their design is composed of several choices that could possibly have a significant impact on their overall performance, more specifically those focused on the measurement of cardiac activity. This review, conducted using works obtained from relevant scientific databases, aims to understand the impact of these design choices on the performance of systems measuring either heart rate (HR) or heart rate variability (HRV). To that end, an analysis of the performance based on hardware architecture, carrier frequency, and measurement distance was conducted for works focusing on both of the aforementioned cardiac parameters, and signal processing trends were discussed. What was found was that the system architecture and signal processing algorithms had the most impact on the performance, with FMCW being the best performing architecture, whereas factors like carrier frequency did not have an impact.This means that newer systems can focus on cheaper, lower-frequency systems without any performance degradation, which will make research easier.
Low-Cost Sensor Network for Air Quality Assessment in Cabo Verde Islands
Authors: da Costa Anedito Zico , Aniceto José P. S. , Lopes Myriam
Published: December 2024

This study explores the application of low-cost sensor networks for air quality monitoring in Cabo Verde islands, utilizing Clarity Node-S sensors to measure fine particulate matter with diameters equal to or smaller than 10 µm (PM10) and 2.5 µm (PM2.5) and nitrogen dioxide (NO2) gasses, across various locations. The sensors were strategically placed and calibrated to ensure coverage of the whole archipelago and accurate data collection. The results consistently revealed seasonal patterns of dust variation across the archipelago, with concentrations of particulate matter exceeding World Health Organization (WHO) limits in all regions. However, Praia frequently exhibits the highest levels of air pollution, exceeding a 200 µg/m3 daily average, particularly during the dry season. Seasonal variations indicated that pollutants are significantly higher from November to March due to Saharan dust flux (a phenomenon locally know as Bruma Seca). Other cities showed more stable and lower pollutant concentrations. This study highlights the potential of low-cost sensors to provide extensive and real-time air quality data, enabling better environmental assessment and policy formulation. However, the variability in equipment accuracy and the limited geographical coverage remain the main limitations to be overcome. Future research should focus on these issues, and a sensor network integrated with reference methods could be a great asset to enhance data accuracy and improve outcomes of air quality monitoring in the country.
A Novel Multi-Objective Trajectory Planning Method for Robots Based on the Multi-Objective Particle Swarm Optimization Algorithm
Authors: Wang Jiahui , Zhang Yongbo , Zhu Shihao , Wang Junling
Published: December 2024

The three performance indexes of the space robot, travel time, energy consumption, and smoothness, are the key to its important role in space exploration. Therefore, this paper proposes a multi-objective trajectory planning method for robots. Firstly, the kinematics and dynamics of the Puma560 robot are analyzed to lay the foundation for trajectory planning. Secondly, the joint space trajectory of the robot is constructed with fifth-order B-spline functions, realizing the continuous position, velocity, acceleration, and jerk of each joint. Then, the improved multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the trajectory, and the distribution uniformity, convergence, and diversity of the obtained Pareto front are good. The improved MOPSO algorithm can realize the optimization between multiple objectives and obtain the trajectory that meets the actual engineering requirements. Finally, this paper implements the visualization of the robot’s joints moving according to the optimal trajectory.
Development of a Zynq-Based Seismic Acquisition Station for the Exploration of Antarctic Subglacial Lakes
Authors: Zhou Keyu , Zhang Qisheng , Guo Linyan , Feng Guangkun , Li Changhong , Zhang Jinhang , Zhang Qifei
Published: December 2024

The Antarctic region holds significant scientific research value and potential resources. Currently, limited research exists on the use of seismic exploration methods for Antarctic subglacial lakes compared to their use on other continents. Moreover, few reports are available on systems capable of multi-channel seismic data acquisition, remote data quality monitoring, and high-speed real-time data recycling in the extremely low temperatures of Antarctica. In this study, we developed a Zynq-based seismic acquisition station for polar exploration. The system features a compact design, lightweight construction, high data collection accuracy, excellent cold resistance, low power consumption, and real-time control. The software and hardware design of the system are described here, and validity testing is presented. The main controller utilizes a Zynq series system-on-chip integrated with an FPGA (Field-Programmable Gate Array) and an ARM (Advanced RISC Machine), enabling functions such as local data storage on a secure digital card, Wi-Fi wireless human–machine interaction, and high-speed Ethernet data transmission. Furthermore, to enhance data acquisition accuracy under low-temperature conditions, a neural network was employed for the temperature drift correction of the analog-to-digital converter chip. The validity test results showed that the station operated stably, was easy to use, and met the high-standard requirements for polar exploration.
Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare
Authors: Alqulaity Malak , Yang Po
Published: December 2024

The generation of synthetic tabular data has emerged as a critical task in various fields, particularly in healthcare, where data privacy concerns limit the availability of real datasets for research and analysis. This paper presents an enhanced Conditional Generative Adversarial Network (GAN) architecture designed for generating high-quality synthetic tabular data, with a focus on cardiovascular disease datasets that encompass mixed data types and complex feature relationships. The proposed architecture employs specialized sub-networks to process continuous and categorical variables separately, leveraging metadata such as Gaussian Mixture Model (GMM) parameters for continuous attributes and embedding layers for categorical features. By integrating these specialized pathways, the generator produces synthetic samples that closely mimic the statistical properties of the real data. Comprehensive experiments were conducted to compare the proposed architecture with two established models: Conditional Tabular GAN (CTGAN) and Tabular Variational AutoEncoder (TVAE). The evaluation utilized metrics such as the Kolmogorov–Smirnov (KS) test for continuous variables, the Jaccard coefficient for categorical variables, and pairwise correlation analyses. Results indicate that the proposed approach attains a mean KS statistic of 0.3900, demonstrating strong overall performance that outperforms CTGAN (0.4803) and is comparable to TVAE (0.3858). Notably, our approach shows lowest KS statistics for key continuous features, such as total cholesterol (KS = 0.0779), weight (KS = 0.0861), and diastolic blood pressure (KS = 0.0957), indicating its effectiveness in closely replicating real data distributions. Additionally, it achieved a Jaccard coefficient of 1.00 for eight out of eleven categorical variables, effectively preserving categorical distributions. These findings indicate that the proposed architecture captures both distributions and dependencies, providing a robust solution in supporting mobile personalized cardiovascular disease prevention systems.
Empowering Efficient Spatio-Temporal Learning with a 3D CNN for Pose-Based Action Recognition
Authors: Ren Ziliang , Xiao Xiongjiang , Nie Huabei
Published: December 2024

Action recognition based on 3D heatmap volumes has received increasing attention recently because it is suitable for application to 3D CNNs to improve the recognition performance of deep networks. However, it is difficult for models to capture global dependencies due to their restricted receptive field. To effectively capture long-range dependencies and balance computations, a novel model, PoseTransformer3D with Global Cross Blocks (GCBs), is proposed for pose-based action recognition. The proposed model extracts spatio-temporal features from processed 3D heatmap volumes. Moreover, we design a further recognition framework, RGB-PoseTransformer3D with Global Cross Complementary Blocks (GCCBs), for multimodality feature learning from both pose and RGB data. To verify the effectiveness of this model, we conducted extensive experiments on four popular video datasets, namely FineGYM, HMDB51, NTU RGB+D 60, and NTU RGB+D 120. Experimental results show that the proposed recognition framework always achieves state-of-the-art recognition performance, substantially improving multimodality learning through action recognition.
An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model
Authors: Megalingam Rajesh Kannan , Sankardas Kariparambil Sudheesh , Manoharan Sakthiprasad Kuttankulangara
Published: December 2024

Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain–computer interface (BCI) systems, utilizing EEG signals from motor imagery tasks. BCI is very useful for people with severe mobility issues like quadriplegics, spinal cord injury patients, stroke patients, etc., giving them the freedom to a certain extent to perform activities without the need for a caretaker, like driving a wheelchair. However, motion artifacts can significantly affect the quality of EEG recordings. The conventional EEG enhancement algorithms are effective in removing ocular and muscle artifacts for a stationary subject but not as effective when the subject is in motion, e.g., a wheelchair user. In this research study, we propose an empirical error model-based artifact removal approach for the cross-subject classification of motor imagery (MI) EEG data using a modified CNN-based deep learning algorithm, designed to assist wheelchair users with severe mobility issues. The classification method applies to real tasks with measured EEG data, focusing on accurately interpreting motor imagery signals for practical application. The empirical error model evolved from the inertial sensor-based acceleration data of the subject in motion, the weight of the wheelchair, the weight of the subject, and the surface friction of the terrain under the wheelchair. Three different wheelchairs and five different terrains, including road, brick, concrete, carpet, and marble, are used for artifact data recording. After evaluating and benchmarking the proposed CNN and empirical model, the classification accuracy achieved is 94.04% for distinguishing between four specific classes: left, right, front, and back. This accuracy demonstrates the model’s effectiveness compared to other state-of-the-art techniques. The comparative results show that the proposed approach is a potentially effective way to raise the decoding efficiency of motor imagery BCI.
Engineered Porosity ZnO Sensor Enriched with Oxygen Vacancies Enabled Extraordinary Sub-ppm Sensing of Hydrogen Sulfide and Nitrogen Dioxide Air Pollution Gases at Low Temperature in Air
Authors: Ciftyurek Engin , Li Zheshen , Schierbaum Klaus
Published: December 2024

We report the results of a zinc oxide (ZnO) low-power microsensor for sub-ppm detection of NO2 and H2S in air at 200 °C. NO2 emission is predominantly produced by the combustion processes of fossil fuels, while coal-fired power plants are the main emitter of H2S. Fossil fuels (oil, natural gas, and coal) combined contained 74% of USA energy production in 2023. It is foreseeable that the energy industry will utilize fossil-based fuels more in the ensuing decades despite the severe climate crises. Precise NO2 and H2S sensors will contribute to reducing the detrimental effect of the hazardous emission gases, in addition to the optimization of the combustion processes for higher output. The fossil fuel industry and solid-oxide fuel cells (SOFCs) are exceptional examples of energy conversion–production technologies that will profit from advances in H2S and NO2 sensors. Porosity and surface activity of metal oxide semiconductor (MOS)-based sensors are both vital for sensing at low temperatures. Oxygen vacancies (VO••) act as surface active sites for target gases, while porosity enables target gases to come in contact with a larger MOS area for sensing. We were able to create an open porosity network throughout the ZnO microstructure and simultaneously achieve an abundance of oxygen vacancies by using a heat treatment procedure. Surface chemistry and oxygen vacancy content in ZnO were examined using XPS and AES. SEM was used to understand the morphology of the unique characteristics of distinctive grain growth during heat treatment. Electrical resistivity measurements were completed. The valance band was examined by UPS. The Engineered Porosity approach allowed the entire ZnO to act as an open surface together with the creation of abundant oxygen vacancies (VO••). NO2 detection is challenging since both oxygen (O2) and NO2 are oxidizing gases, and they coexist in combustion environments. Engineered porosity ZnO microsensor detected sub-ppm NO2 under O2 interference, which affects mimicking realistic sensor operation conditions. Engineered porosity ZnO performed better than the previous literature findings for H2S and NO2 detection. The exceptionally high sensor response is attributed to the high number of oxygen vacancies (VO••) and porosity extending through the thickness of the ZnO with a high degree of tortuosity. These features enhance gas adsorption and diffusion via porosity, leading to high sensor response.
Research on Environmental Risk Monitoring and Advance Warning Technologies of Power Transmission and Distribution Projects Construction Phase
Authors: Sun Xiaohu , Liu Fei , Zhao Yu , Liu Fang , Wang Jian , Zhu Shu , He Qiang , Bai Yu , Zhang Jiyong
Published: December 2024

The threat power transmission and distribution projects pose to the ecological environment has been widely discussed by researchers. The scarcity of early environmental monitoring and supervision technologies, particularly the lack of effective real-time monitoring mechanisms and feedback systems, has hindered the timely quantitative identification of potential early-stage environmental risks. This study aims to comprehensively review the literature and analyze the research context and shortcomings of the advance warning technologies of power transmission and distribution projects construction period using the integrated space–sky–ground system approach. The key contributions of this research include (1) listing ten environmental risks and categorizing the environmental risks associated with the construction cycle of power transmission and distribution projects; (2) categorizing the monitoring data into one-dimensional, two-dimensional, and three-dimensional frameworks; and (3) constructing the potential environmental risk knowledge system by employing the knowledge graph technology and visualizing it. This review study provides a panoramic view of knowledge in a certain field and reveals the issues that have not been fully explored in the research field of monitoring technologies for potential environmental damage caused by power transmission and transformation projects.
A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles
Authors: Zhu Yuchang , Ke Li , Wei Yijing , Zheng Xiao
Published: December 2024

The unique magnetothermal properties of magnetic nanoparticles enable the development of a high-precision, real-time, noninvasive temperature measurement method with significant potential in the biomedical field. Based on a low-frequency alternating magnetic field excitation model, we construct two additional magnetic field excitation models—alternating current–direct current superposition and dual-frequency superposition—to extract harmonic amplitude components from the magnetization response. To increase the accuracy of harmonic information acquisition, the effects of the truncation error, excitation magnetic field frequency, and amplitude are thoroughly analyzed, and optimal parameter values are selected to minimize the error. A single algorithm is designed for temperature inversion, and a joint algorithm is proposed to optimize the performance of the single algorithm. Under low-frequency alternating-current magnetic field excitation, the autonomous group particle swarm optimization method achieves superior real-time performance in terms of temperature inversion and running time. Compared with the opposition learning gray wolf optimizer and particle swarm optimization–gray wolf optimization, the proposed method achieves reductions of 52% and 68%, respectively. Additionally, under dual-frequency superimposed magnetic field excitation, a higher temperature inversion accuracy is achieved compared with that of the particle swarm optimization–gray wolf optimization algorithm, reducing the error from 0.237 K to 0.094 K.
A Review of SiC Sensor Applications in High-Temperature and Radiation Extreme Environments
Authors: Zhang Quanwei , Liu Yan , Li Huafeng , Wang Jue , Wang Yuan , Cheng Fabin , Han Haijun , Zhang Peng
Published: December 2024

Sensors operating in extreme environments are currently a focal point of global research. Extreme environmental conditions, such as overload, vibration, corrosion, high pressure, high temperature, and radiation, can affect the performance of sensors to the point of failure. It is noteworthy that, compared to the resistance to overload and vibration achieved through structural design, the application of sensors under high-temperature and radiation extreme conditions poses a greater challenge. Silicon carbide (SiC) material, due to its excellent physical and chemical properties, such as a large band gap and high atomic critical displacement energy, demonstrates outstanding potential for application in high-temperature and radiation extreme environments. This review presents the current status and research progress of SiC sensors in high-temperature and radiation extreme environments. Finally, given the limited research on the radiation resistance of SiC sensors, it identifies several challenges and research deficiencies in the application of SiC sensors under radiation extreme environments and discusses the future development direction of SiC-based substrate sensors.
Do Similar Objects Have Similar Grasp Positions?
Authors: Sun Qi , He Lili
Published: December 2024

In robotic grasping tasks, shape similarity has been widely adopted as a reference in grasp positions prediction for unknown objects. However, to the best of our knowledge, the issue “do similar objects have similar grasp positions?” has not been quantitatively analyzed before. This work aims to confirm or disprove the question by analyzing the relationship between the object shape similarity and grasp positions similarity. To this end, we constructed a similarity-estimation plane (SE-Plane), whose horizontal and vertical axes indicate the objects similarity and grasp similarity, respectively. Then, the proof of the issue is equal to the confirmation of the inference that “the points with higher objects similarity accordingly own higher grasps similarity in the proposed SE-Plane”. We adopted several classical shape descriptors and two kinds of widely recognized deep neural network (DNN) architectures as objects similarity strategies. Furthermore, we employed the widely adopted intersection-over-union (IoU) of grasp anchors to measure the grasp similarity between objects. The experiments were carried out on a dozen objects with commonly seen primitive shapes selected from two well-known open grasp datasets: Cornell and Jacquard. It was found that the IoU values of grasp anchors are generally proportional to those of objects similarity in the SE-Plane. In addition, we obtained several primitive shapes from the commonly seen shapes, which are more suitable references in grasp positions prediction for unknown objects. We also constructed a realistic object dataset that included the objects with commonly seen primitive shapes. With the IoU prediction strategy learned from Cornell and Jacquard, the IoU predicted for realistic objects yielded similar results in the proposed SE-Plane. These discussions indicate that “similar objects have similar grasp positions” is reasonably correct. The proposed SE-Plane presents a new strategy to measure the relationship between objects similarity and grasp similarity.
Assessing the Feasibility of Using Remote Sensing Data and Vegetation Indices in the Estimation of Land Subject to Consolidation
Authors: Kocur-Bera Katarzyna , Małek Anna
Published: December 2024

The values of vegetation indices can provide a new source of data for use in the estimation of land to be consolidated. The results of research work carried out so far indicate a significant advantage of low-volume imaging over satellite methods when it comes to calculating vegetation index values. This paper analyses multispectral images for the areas of selected croplands acquired via the Sentinel-2 satellite and an unmanned aerial vehicle (UAV) equipped with a multispectral camera. The research work consisted of evaluating NDVI (Normalised Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) values depending on the type of crop grown, the size of the cultivated area and the method of data acquisition. The data obtained were used to assess their potential use in the estimation of land to be consolidated. The effect of land consolidation is primarily to create more favourable living conditions and increase agricultural productivity. The results of the study showed that it would be preferable to use multispectral images acquired using UAVs rather than those from Sentinel satellites. This is due to the insufficient resolution of the satellite data, the correlation of NDVI and SAVI values at only a satisfactory level and the low accuracy of the data obtained for small registered plots of land.