bluuebunny/crossref_metadata_2025 huggingface dataset:
New Antibacterial and Antioxidant Chitin Derivatives: Ultrasonic Preparation and Biological Effects
Authors: Egorov Anton R. , Khubiev Omar M. , Golubev Roman A. , Semenkova Daria I. , Nikolaev Andrey A. , Maharramov Abel M. , Mammadova Gunay Z. , Liu Wanjun , Tskhovrebov Alexander G. , Kritchenkov Andreii S.
Published: September 2024

This work focuses on the first use of ultrasonic phenol-ene coupling as a polymer analogous transformation. The ultrasonic reaction was introduced into chitin chemistry, resulting in the fast and convenient preparation of new water-soluble cationic chitin derivatives. Since water-soluble derivatives of fully deacetylated chitin are poorly described in the literature, the synthesis of each new type of these derivatives is a significant event in polysaccharide chemistry. Polycations, or cationic polymers, are of particular interest as antibacterial agents. Consequently, the resulting polymers were tested for their antibacterial activity and toxicity. We found that the highly substituted polymer of medium molecular weight exhibited the most pronounced in vitro antibacterial effect. We prepared nanoparticles using the ionic gelation technique. The most effective in vitro antibacterial chitin-based systems were tested in vivo in rats. These tests demonstrated outstanding antibacterial effects combined with an absence of toxicity. Additionally, we found that the resulting polymers, unlike their nanoparticle counterparts, also exhibited strong antioxidant effects. In summary, we demonstrated the effectiveness of ultrasound in polymer chemistry and highlighted the importance of the sonochemical approach in the chemical modification of polysaccharides. This approach enables the synthesis of derivatives with improved physicochemical and biological properties.
Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection
Authors: Wang Shigang , Zhang Xiangyuan , Zhao Yifan , Yu Haozi , Li Bin
Published: September 2024

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.
A New Data Fusion Method for GNSS/INS Integration Based on Weighted Multiple Criteria
Authors: Jiang Chen , Zhang Qiuzhao , Zhao Dongbao
Published: September 2024

The standard Kalman filter and most of its enhancements are typically designed based on the criterion that minimizes the mean squared error, with little discussion of multiple criteria in the positioning and navigation fields. Therefore, a novel data fusion method that takes into account weighted multiple criteria is proposed in this paper, implementing a filtering algorithm based on integrated criteria with different weights determined by a weight adjustment factor. The proposed algorithm and conventional filtering algorithms were utilized for data fusion in GNSS/INS integration. Experiments were conducted using actual data collected from an urban environment. Comparative analysis revealed that, when utilizing the proposed algorithm, the precision of the position, velocity, and attitude of the tested land vehicle could be improved by approximately 24%, 48%, and 35%, respectively. Furthermore, a series of filtering algorithms with different weight adjustment factors was performed to test their influence on the filtering. The application of the proposed algorithm should be accompanied by an appropriate weight adjustment factor.
Evaluating Thermal Infrared Drone Flight Parameters on Spider Monkey Detection in Tropical Forests
Authors: Pinel-Ramos Eduardo José , Aureli Filippo , Wich Serge , Longmore Steven , Spaan Denise
Published: September 2024

Geoffroy’s spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission–fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (−45°, −90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was “substantial” (Fleiss’ kappa coefficient = 0.61–0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a −90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model.
Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature
Authors: Li Xueyuan , Shang Wenjing
Published: September 2024

In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty in the mapping direction makes the mapped features ineffective in distinguishing anomalous targets from the background. To address this problem, a hyperspectral anomaly detection algorithm based on the spectral similarity variability feature (SSVF) is proposed. First, the high-dimensional similar neighborhoods are fused into similar features using AE networks, and then the SSVF are obtained using residual autoencoder. Finally, the final detection of SSVF was obtained using Reed and Xiaoli (RX) detectors. Compared with other comparison algorithms with the highest accuracy, the overall detection accuracy (AUCODP) of the SSVFRX algorithm is increased by 0.2106. The experimental results show that SSVF has great advantages in both highlighting anomalous targets and improving separability between different ground objects.
Advanced Machine Learning Models for Estimating the Distribution of Sea-Surface Particulate Organic Carbon (POC) Concentrations Using Satellite Remote Sensing Data: The Mediterranean as an Example
Authors: Li Chao , Wu Huisheng , Yang Chaojun , Cui Long , Ma Ziyue , Wang Lejie
Published: September 2024

Accurate estimation of the distribution of POC in the sea surface is an important issue in understanding the carbon cycle at the basin scale in the ocean. This study explores the best machine learning approach to determine the distribution of POC in the ocean surface layer based on data obtained using satellite remote sensing. In order to estimate and verify the accuracy of this method, it is necessary to obtain a large amount of POC data from field observations, so this study was conducted in the Mediterranean Sea, where such data have been obtained and published. The research initially utilizes the Geographic Detector (GD) method to identify spatial correlations between POC and 47 environmental factors in the region. Four machine learning models of a Bayesian optimized random forest (BRF), a backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Model validation yielded that the BRF exhibited superior performance in estimating sea-surface POC. To build a more accurate tuneRanger random forest (TRRF) model, we introduced the tuneRanger R package for further optimization, resulting in an R2 of 0.868, a mean squared error of 1.119 (mg/m3)2, and a mean absolute error of 1.041 mg/m3. It was employed to estimate the surface POC concentrations in the Mediterranean for May and June 2017. Spatial analysis revealed higher concentrations in the west and north and lower concentrations in the east and south, with higher levels near the coast and lower levels far from the coast. Additionally, we deliberated on the impact of human activities on the surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in this area.
Disturbance Estimation and Predefined-Time Control Approach to Formation of Multi-Spacecraft Systems
Authors: Zhang Zhicheng , Bao Weimin , Hou Qimin , Ju Yinhao , Gao Yabin
Published: September 2024

Accurate sensing and control are important for high-performance formation control of spacecraft systems. This paper presents a strategy of disturbance estimation and distributed predefined-time control for the formation of multi-spacecraft systems with uncertainties based on a disturbance observer. The process begins by formulating a kinematics model for the relative motion of spacecraft, with the formation’s communication topology represented by a directed graph for the formation system of the spacecraft. A disturbance observer is then developed to estimate the disturbances, and the estimation errors can be convergent in fixed time. Following this, a disturbance-estimation-based sliding mode control is proposed to guarantee the predefined-time convergence of the multi-spacecraft formation system, regardless of initial conditions. It allows each spacecraft to reach its desired position within a set time frame. The results of the analysis of the multi-spacecraft formation system are also provided. Finally, an example simulation of a five-spacecraft formation flying system is provided to demonstrate the presented formation control method.
Screen-Printed Electrodes as Low-Cost Sensors for Breast Cancer Biomarker Detection
Authors: Shen Yin , Sun Zhuang , Zhao Shichao , Chen Fei , Shi Peizheng , Zhao Ningbin , Sun Kaiqiang , Ye Chen , Lin Chengte , Fu Li
Published: September 2024

This review explores the emerging role of screen-printed electrodes (SPEs) in the detection of breast cancer biomarkers. We discuss the fundamental principles and fabrication techniques of SPEs, highlighting their adaptability and cost-effectiveness. The review examines various modification strategies, including nanomaterial incorporation, polymer coatings, and biomolecule immobilization, which enhance sensor performance. We analyze the application of SPEs in detecting protein, genetic, and metabolite biomarkers associated with breast cancer, presenting recent advancements and innovative approaches. The integration of SPEs with microfluidic systems and their potential in wearable devices for continuous monitoring are explored. While emphasizing the promising aspects of SPE-based biosensors, we also address current challenges in sensitivity, specificity, and real-world applicability. The review concludes by discussing future perspectives, including the potential for early screening and therapy monitoring, and the steps required for clinical implementation. This comprehensive overview aims to stimulate further research and development in SPE-based biosensors for improved breast cancer management.
Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell
Authors: Singh Maulshree , Kapukotuwa Jayasekara , Gouveia Eber Lawrence Souza , Fuenmayor Evert , Qiao Yuansong , Murry Niall , Devine Declan
Published: September 2024

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework’s versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments.
An Audiovisual Correlation Matching Method Based on Fine-Grained Emotion and Feature Fusion
Authors: Su Zhibin , Feng Yiming , Liu Jinyu , Peng Jing , Jiang Wei , Liu Jingyu
Published: September 2024

Most existing intelligent editing tools for music and video rely on the cross-modal matching technology of the affective consistency or the similarity of feature representations. However, these methods are not fully applicable to complex audiovisual matching scenarios, resulting in low matching accuracy and suboptimal audience perceptual effects due to ambiguous matching rules and associated factors. To address these limitations, this paper focuses on both the similarity and integration of affective distribution for the artistic audiovisual works of movie and television video and music. Based on the rich emotional perception elements, we propose a hybrid matching model based on feature canonical correlation analysis (CCA) and fine-grained affective similarity. The model refines KCCA fusion features by analyzing both matched and unmatched music–video pairs. Subsequently, the model employs XGBoost to predict relevance and to compute similarity by considering fine-grained affective semantic distance as well as affective factor distance. Ultimately, the matching prediction values are obtained through weight allocation. Experimental results on a self-built dataset demonstrate that the proposed affective matching model balances feature parameters and affective semantic cognitions, yielding relatively high prediction accuracy and better subjective experience of audiovisual association. This paper is crucial for exploring the affective association mechanisms of audiovisual objects from a sensory perspective and improving related intelligent tools, thereby offering a novel technical approach to retrieval and matching in music–video editing.
Human-in-the-Loop Trajectory Optimization Based on sEMG Biofeedback for Lower-Limb Exoskeleton
Authors: Li Ling-Long , Zhang Yue-Peng , Cao Guang-Zhong , Li Wen-Zhou
Published: September 2024

Lower-limb exoskeletons (LLEs) can provide rehabilitation training and walking assistance for individuals with lower-limb dysfunction or those in need of functionality enhancement. Adapting and personalizing the LLEs is crucial for them to form an intelligent human–machine system (HMS). However, numerous LLEs lack thorough consideration of individual differences in motion planning, leading to subpar human performance. Prioritizing human physiological response is a critical objective of trajectory optimization for the HMS. This paper proposes a human-in-the-loop (HITL) motion planning method that utilizes surface electromyography signals as biofeedback for the HITL optimization. The proposed method combines offline trajectory optimization with HITL trajectory selection. Based on the derived hybrid dynamical model of the HMS, the offline trajectory is optimized using a direct collocation method, while HITL trajectory selection is based on Thompson sampling. The direct collocation method optimizes various gait trajectories and constructs a gait library according to the energy optimality law, taking into consideration dynamics and walking constraints. Subsequently, an optimal gait trajectory is selected for the wearer using Thompson sampling. The selected gait trajectory is then implemented on the LLE under a hybrid zero dynamics control strategy. Through the HITL optimization and control experiments, the effectiveness and superiority of the proposed method are verified.
Using Light Polarization to Identify Fiber Orientation in Carbon Fiber Components: Metrological Analysis
Authors: Chiominto Luciano , D’Emilia Giulio , Natale Emanuela
Published: September 2024

In this work, a method for measuring tow angles in carbon fiber components, based on the use of a polarized camera, is analyzed from a metrological point of view. Carbon fibers alter the direction of the reflected light’s electrical field, so that in each point of the surface of a composite piece, the angle of polarization of reflected light matches the fiber orientation. A statistical analysis of the angle of linear polarization (AoLP) in each pixel of each examined area allows to evaluate the average winding angle. An evaluation of the measurement uncertainty of the method on a cylinder obtained by a filament winding process is carried out, and the result appears adequate for the study of the distribution of angles along the surface of the piece, in order to optimize the process.
Photonic Dipstick Immunosensor to Detect Adulteration of Ewe, Goat, and Donkey Milk with Cow Milk through Bovine κ-Casein Detection
Authors: Kourti Dimitra , Angelopoulou Michailia , Makarona Eleni , Economou Anastasios , Petrou Panagiota , Misiakos Konstantinos , Kakabakos Sotirios
Published: September 2024

The quality and authenticity of milk are of paramount importance. Cow milk is more allergenic and less nutritious than ewe, goat, or donkey milk, which are often adulterated with cow milk due to their seasonal availability and higher prices. In this work, a silicon photonic dipstick sensor accommodating two U-shaped Mach–Zehnder Interferometers (MZIs) was employed for the label-free detection of the adulteration of ewe, goat, and donkey milk with cow milk. One of the two MZIs of the chip was modified with bovine κ-casein, while the other was modified with bovine serum albumin to serve as a blank. All assay steps were performed by immersion of the chip side where the MZIs are positioned into the reagent solutions, leading to a photonic dipstick immunosensor. Thus, the chip was first immersed in a mixture of milk with anti-bovine κ-casein antibody and then in a secondary antibody solution for signal enhancement. A limit of detection of 0.05% v/v cow milk in ewe, goat, or donkey milk was achieved in 12 min using a 50-times diluted sample. This fast, sensitive, and simple assay, without the need for sample pre-processing, microfluidics, or pumps, makes the developed sensor ideal for the detection of milk adulteration at the point of need.
Dual-Wave ZnO Film Ultrasonic Transducers for Temperature and Stress Measurements
Authors: Liu Wentao , Shan Longlong , Lin Zhongliang , Wan Binghua , Yang Bin , Zeng Xiaomei , Yang Bing , Pelenovich Vasiliy
Published: September 2024

ZnO film ultrasonic transducers for temperature and stress measurements with dual-mode wave excitation (longitudinal and shear) were deposited using the reactive RF magnetron sputtering technique on Si and stainless steel substrates and construction steel bolts. It was found that the position in the substrate plane had a significant effect on the structure and ultrasonic performance of the transducers. The transducers deposited at the center of the deposition zone demonstrated a straight columnar structure with a c-axis parallel to the substrate normal and the generation of longitudinal waves. The transducers deposited at the edge of the deposition zone demonstrated inclined columnar structures and the generation of dominant shear or longitudinal shear waves. Transducers deposited on the bolts with dual-wave excitation were used to study the effects of high temperatures in the range from 25 to 525 °C and tensile stress in the range from 0 to 268 MPa on ultrasonic response. Dependencies between changes in the relative time of flight and temperature or axial stress were obtained. The dependencies can be described by second-order functions of temperature and stress. An analysis of the contributions of thermal expansion, strain, and the speed of sound to changes in the time of flight was performed. At high temperatures, a decrease in the signal amplitude was observed due to the decreasing resistivity of the transducer. The ZnO ultrasonic transducers can be used up to temperatures of ~500 °C.
Measuring Transverse Relaxation with a Single-Beam 894 nm VCSEL for Cs-Xe NMR Gyroscope Miniaturization
Authors: Zhao Qingyang , Zhang Ruochen , Liu Hua
Published: September 2024

The spin-exchange-pumped nuclear magnetic resonance gyroscope (NMRG) is a pivotal tool in quantum navigation. The transverse relaxation of atoms critically impacts the NMRG’s performance parameters and is essential for judging normal operation. Conventional methods for measuring transverse relaxation typically use dual beams, which involves complex optical path and frequency stabilization systems, thereby complicating miniaturization and integration. This paper proposes a method to construct a 133Cs parametric resonance magnetometer using a single-beam vertical-cavity surface-emitting laser (VCSEL) to measure the transverse relaxation of 129Xe and 131Xe. Based on this method, the volume of the gyroscope probe is significantly reduced to 50 cm3. Experimental results demonstrate that the constructed Cs-Xe NMRG can achieve a transverse relaxation time (T2) of 8.1 s under static conditions. Within the cell temperature range of 70 °C to 110 °C, T2 decreases with increasing temperature, while the signal amplitude inversely increases. The research lays the foundation for continuous measurement operations of miniaturized NMRGs.
Compressing and Recovering Short-Range MEMS-Based LiDAR Point Clouds Based on Adaptive Clustered Compressive Sensing and Application to 3D Rock Fragment Surface Point Clouds
Authors: Li Lin , Wang Huajun , Wang Sen
Published: September 2024

Short-range MEMS-based (Micro Electronical Mechanical System) LiDAR provides precise point cloud datasets for rock fragment surfaces. However, there is more vibrational noise in MEMS-based LiDAR signals, which cannot guarantee that the reconstructed point cloud data are not distorted with a high compression ratio. Many studies have illustrated that wavelet-based clustered compressive sensing can improve reconstruction precision. The k-means clustering algorithm can be conveniently employed to obtain clusters; however, estimating a meaningful k value (i.e., the number of clusters) is challenging. An excessive quantity of clusters is not necessary for dense point clouds, as this leads to elevated consumption of memory and CPU resources. For sparser point clouds, fewer clusters lead to more distortions, while excessive clusters lead to more voids in reconstructed point clouds. This study proposes a local clustering method to determine a number of clusters closer to the actual number based on GMM (Gaussian Mixture Model) observation distances and density peaks. Experimental results illustrate that the estimated number of clusters is closer to the actual number in four datasets from the KEEL public repository. In point cloud compression and recovery experiments, our proposed approach compresses and recovers the Bunny and Armadillo datasets in the Stanford 3D repository; the experimental results illustrate that our proposed approach improves reconstructed point clouds’ geometry and curvature similarity. Furthermore, the geometric similarity increases to 0.9 above in our complete rock fragment surface datasets after selecting a better wavelet basis for each dimension of MEMS-based LiDAR signals. In both experiments, the sparsity of signals was 0.8 and the sampling ratio was 0.4. Finally, a rock outcrop point cloud data experiment is utilized to verify that the proposed approach is applicable for large-scale research objects. All of our experiments illustrate that the proposed adaptive clustered compressive sensing approach can better reconstruct MEMS-based LiDAR point clouds with a lower sampling ratio.
Three-Dimensional Gravity Inversion Based on Attention Feature Fusion
Authors: Chen Chen , Li Houpu , Zhang Yujie , Jin Xiaomei , Liu Jianfeng
Published: September 2024

Three-dimensional gravity inversion is a process of obtaining the location, shape, and physical property parameters of underground anomaly sources using gravity anomaly data observed on the surface. In recent years, with the rapid development of data-driven methods, the application of deep learning (DL) to 3D gravity inversion has also attracted wide attention and achieved certain results. In this paper, based on the U-Net network, a three-dimensional gravity inversion method using an attention feature fusion mechanism is proposed. Using U-Net as the basic framework, the coarse-grained semantic features and fine-grained semantic features in the encoder and decoder are connected by long hops, and the global and local semantic features are aggregated through the attention feature fusion module, which avoids feature loss in the network training process. Compared with the inversion results of the U-Net network, the proposed method has a higher vertical resolution and effectively alleviates the influence of the skin effect on three-dimensional gravity inversion. Ablation experiments show that the attention feature fusion module is the key to improving the vertical resolution and prediction accuracy of inversion results. Noise experiments show that the inversion network in this study has a strong anti-noise ability and good generalization performance. The experimental results of the inversion network used in the prediction of the SAN Nicolas deposit in Mexico show that the inversion network can clearly predict the basic location and general shape of the sulfur deposit, and the results are in good agreement with the known geological data.
Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network
Authors: Yoo Jaehyun
Published: September 2024

Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network.
A Cooperative Optimization Model for Variable Approach Lanes at Signaled Intersections Based on Real-Time Flow
Authors: Zhu Zhiqiang , Zhu Mingyue , Liu Miaomiao , Li Pengrui , Tang Renjing , Zhang Xuechi
Published: September 2024

To resolve the congestion caused by imbalanced traffic at intersections, this paper establishes a model of the average delay deviation with the minimization of the average delay in the approach as the optimization objective. Then, the signal control scheme is further optimized based on the variable approach lanes setting. First, we investigate the threshold conditions for setting the VALs under different flows in a single approach direction. The results show that when the ratio of left-turn traffic exceeds the threshold range of 0.20~0.28, the function of the VALs needs to be changed from straight to left-turn. Then, based on the improved Webster’s formula, an optimal timing method that aims at minimizing the average vehicle delay, minimizing the queue length, and maximizing the capacity, is proposed. Finally, taking the actual Huangke intersection in the Hefei demonstration area as an example, three schemes are compared and analyzed in the case of a VAL at the intersection. The results show that under the cooperative optimization scheme proposed in this paper, the travel time and the efficiency of the intersection could be reduced by 18.7% and 9.9%, respectively, when compared with the original and Webster’s schemes.
Fusion of PCA and ICA in Statistical Subset Analysis for Speech Emotion Recognition
Authors: Kingeski Rafael , Henning Elisa , Paterno Aleksander S.
Published: September 2024

Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal–Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases—Berlin EmoDB, SAVEE, and RAVDES—were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%.
A Crowdsourced AI Framework for Atrial Fibrillation Detection in Apple Watch and Kardia Mobile ECGs
Authors: Bahrami Rad Ali , Kirsch Miguel , Li Qiao , Xue Joel , Sameni Reza , Albert Dave , Clifford Gari D.
Published: September 2024

Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple’s algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor’s Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices.
Ultrasound Control of Pickering Emulsion-Based Capsule Preparation
Authors: Ratajczak Filip , Jameel Bassam , Bielas Rafał , Józefczak Arkadiusz
Published: September 2024

Capsules with microparticle shells became of great interest due to their potential in many fields. Those capsules can be fabricated at high temperatures from particle-stabilized emulsions (Pickering emulsions) by sintering together particles that cover droplets. One of the problems with such an approach is accurately controlling whether particles are already sintered and creating the rigid capsule shell of a capsule. Here, we propose using a non-destructive ultrasound method for monitoring Pickering emulsion-based capsules prepared using heating under an alternating magnetic field. The polyethylene microparticles that were responsive to temperatures higher than 112 °C were used as droplet stabilizers together with iron oxide nanoparticles. During the coalescence of the droplets, facilitated by an external electric field, the ultrasonic attenuation increased, giving evidence that the ultrasound method detects structural changes in Pickering emulsions. The main change was the difference in the droplets’ size, which was also observed via optical microscopy. The attenuation of ultrasound increased even more when measured after magnetic heating for the same concentration of particle stabilizers. Simultaneously, the values of ultrasonic velocity did not exhibit similar variety. The results show that the values of the attenuation coefficient can be used for a quantitative evaluation of the capsule formation process.
Analysis of Head Movement in KPSIT Dummies and the Impact of Seats and Seat Belts during Low-Speed Collisions 20 km/h
Authors: Poliak Milos , Frej Damian , Jaśkiewicz Marek , Caban Jacek , Górniak Aleksander , Gidlewski Mirosław , Hajduk Iwona Ewa , Kubiak Przemysław , Tarnapowicz Dariusz
Published: September 2024

The aim of this study was to compare the head displacement of the KPSIT C50 dummy, representing a 50th percentile male, with the KPSIT C5 dummy, representing a 5th percentile female, during low-speed collisions. Low-speed collisions, such as those occurring in urban traffic jams, are increasingly common. The research was conducted on a dedicated educational workstation designed to measure forces in seat belts. This study is part of a comprehensive research project on crash tests involving both volunteers and physical KPSIT dummies. The tests were conducted at a speed of 20 km/h to simulate real-world low-speed collision scenarios. The findings demonstrate that using a sports bucket seat with four-point or five-point harnesses significantly reduces head displacement compared with standard car seats. Such seating configurations enhance safety by minimizing the risk of head injuries, which can occur when airbags do not deploy during low-speed collisions. Moreover, the study highlights that standard three-point seat belts allow for greater forward head movement, increasing the risk of head contact with the vehicle’s interior during collisions at speeds too low to trigger airbag deployment.
Overnight Sleep Staging Using Chest-Worn Accelerometry
Authors: Schipper Fons , Grassi Angela , Ross Marco , Cerny Andreas , Anderer Peter , Hermans Lieke , van Meulen Fokke , Leentjens Mickey , Schoustra Emily , Bosschieter Pien , van Sloun Ruud J. G. , Overeem Sebastiaan , Fonseca Pedro
Published: September 2024

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform “proxy” sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13–83 years, with BMI 18–47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen’s kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.
Advances and Challenges in the Hunting Instability Diagnosis of High-Speed Trains
Authors: Liang Jiayi , Sun Jianfeng , Jiang Yonghua , Pan Weifang , Jiao Weidong
Published: September 2024

With the continuous increase in train running speeds and the rapid complexity of operation environments, running stability of the high-speed train is facing significant challenges. A series of abnormal vibration issues, caused by hunting instability, have emerged, including bogie instability alarm, carbody swaying, and carbody shaking, posing a significant threat to the safe and stable operation of high-speed trains. Therefore, the monitoring and diagnosis of hunting instability have become important research topics in rail transit. This review follows the development of fault diagnosis for bogie hunting instability and carbody hunting instability. It first summarizes the existing evaluation standards and innovative diagnostic methods. Due to the current limitation of hunting instability evaluation standards, which can only detect large-amplitude hunting, this paper addresses the gap in evaluation criteria for early-stage, small amplitude hunting instability diagnosis. A thorough overview of the progress made by researches in this field of research is given, emphasizing three primary facets: diagnostic signal sources, diagnostic features, and diagnostic targets. Furthermore, given that existing methods only classify faults into small and large amplitudes, which does not meet the practical need for quickly and accurately identifying fault types and severity during operation, this review introduces existing works on the detailed assessment and fault tracing of hunting instability, as well as the mechanisms underlying its occurrence, with the aim of achieving a comprehensive diagnosis of hunting instability. Finally, the limitations of current methods and the future development trends in hunting instability diagnostics are discussed and summarized. This paper provides readers with a framework for the research process of hunting instability diagnosis, offering valuable references and innovative perspectives for their future research efforts.
Echoes of Madness: Exploring Disability and Mental Illness in Hellblade: Senua’s Sacrifice
Authors: Torabi Sina , Preston Jeff
Published: September 2024

Video games are known for many things, but nuanced portrayals of characters with mental illness might not be one of them. This trend, however, has gradually started to shift with games like Hellblade: Senua’s Sacrifice, which aim to convey a genuine experience of mental illness to the player. Through a close reading of different instances in the game, this paper shows how Hellblade complicates the usual sanist ideas seen in most other games by taking an ambiguous stance, using mental illness as a representational tool. Furthermore, it avoids some of the more sensationalist and problematic tropes often employed in such representations, like the supercrip and the Cartesian divide of the body and mind. In order to show this, we have employed Mitchel and Snyder’s concept of narrative prosthesis to demonstrate how the game does not in fact rely on Senua’s disability as an exotic feature of the narrative to hook players in. By combining insights from disability and mad studies, we show how this game is a step in the right direction when it comes to challenging the perceptions of mental illness prevalent in pop culture.
Hiding the Hate—Contextual Effects on Hate Crime Reports
Authors: Küchler Armin C. D.
Published: September 2024

This study examines the influence of local norm shifts driven by white supremacist ideology on hate crime reporting by US law enforcement agencies. Results show a substantial association, indicating a threefold increase in expected hate crimes reports in counties experiencing a spike in local hate group activity. Specifically, Republican vote share acts as a moderator, reducing reported hate crimes by 23% in counties with strong Republican support and right-wing hate group presence. Adjacent Republican counties also show a 13% reduction in expected reports, suggesting a spillover effect. Beyond local politics, a regional impact is evident; Northeastern counties with higher right-wing hate group presence show a 23% lower incidence rate. Using longitudinal data from the FBI UCR, SPLC, MIT Election Lab, and the US Census (2010–2020), through negative binomial regressions, this study highlights how right-wing hate groups can affect law enforcement’s enforcement of general civil rights depending on the local context.
Building a Resilient Digital Entrepreneurship Landscape: The Importance of Ecosystems, Decent Work, and Socioeconomic Dynamics
Authors: Alzamel Samar
Published: September 2024

This study explores the relationship between the entrepreneurship ecosystem and decent work in digital entrepreneurship, raising essential questions about the roles of economic growth and socioeconomic status. By examining this relationship, the research aims to clarify how these factors influence opportunities, inclusivity, and sustainable development in the digital entrepreneurship landscape. It investigates both the direct impacts of the entrepreneurship ecosystem and decent work and the moderating effects of economic growth and socioeconomic disparities on digital entrepreneurship. These moderating factors are crucial for understanding how broader economic conditions can enhance or limit the entrepreneurship ecosystem’s effectiveness. Data were collected from 624 entrepreneurs in the Kingdom of Saudi Arabia (KSA) to empirically test this model, providing a solid foundation for the proposed hypotheses. The findings significantly affect knowledge advancement and policymaking, particularly concerning KSA Vision 2030. The results confirm the vital roles of a well-developed entrepreneurship ecosystem and the promotion of decent work in supporting digital entrepreneurship. Moreover, the moderating effects of economic growth and socioeconomic status indicate that policy interventions should consider these broader contextual factors to be effective. For policymakers, this study emphasizes the need to foster an environment that strengthens the entrepreneurship ecosystem while addressing economic inequalities and promoting sustained growth. Aligning these initiatives with KSA Vision 2030 could advance digital entrepreneurship as a pivotal element of the country’s economic diversification and development strategies. Ultimately, the study aims to inform policies that enhance inclusivity and sustainability in the digital entrepreneurship sector, ensuring that the advantages of digital innovation are widely accessible across society.
A Study on the Impact of China’s Prefabricated Building Policy on the Carbon Reduction Benefits of China’s Construction Industry Based on a Difference-in-Differences Method
Authors: Wang Xiangxiang , Xie Shasha , Wei Zhe , Wang Jinjing
Published: September 2024

The construction industry is a significant contributor to carbon emissions in China. To effectively meet the “dual carbon” targets, several provincial regions within the country started to implement policies promoting prefabricated buildings. This study examines data from 18 provinces in China over the period from 2012 to 2021, treating the introduction of prefabricated building policies as a quasi-natural experiment. Utilizing the difference-in-differences methodology, this research assesses the impact of these policies on the carbon emission performance of China’s construction sector and evaluates the robustness of the findings. The results indicate that the prefabricated building policies positively influenced the carbon emission efficiency of the construction industry. Specifically, these policies enhance carbon emission efficiency by increasing labor productivity, optimizing the allocation of mechanical resources, and improving the utilization rate of building materials. Additionally, the effectiveness of these policies is positively correlated with the level of regional technological innovation, environmental protection efforts, and the advancement of energy structure optimization. The study concludes with several policy recommendations aimed at further enhancing the effectiveness of prefabricated building policies.
An Empirical Study on Lightweight CNN Models for Efficient Classification of Used Electronic Parts
Authors: Chand Praneel , Assaf Mansour
Published: September 2024

The problem of electronic waste (e-waste) presents a significant challenge in our society as outdated electronic devices are frequently discarded rather than recycled. To tackle this issue, it is important to embrace circular economy principles. One effective approach is to desolder and reuse electronic components, thereby reducing waste buildup. Automated vision-based techniques, often utilizing deep learning models, are commonly employed to identify and locate objects in sorting applications. Artificial intelligence (AI) and deep learning processes often require significant computational resources to perform automated tasks. These computational resources consume energy from the grid. Consequently, a rise in the use of AI can lead to higher demand for energy resources. This research empirically develops a lightweight convolutional neural network (CNN) model by exploring models utilising various grayscale image resolutions and comparing their performance with pre-trained RGB image classifier models. The study evaluates the lightweight CNN classifier’s ability to achieve an accuracy comparable to pre-trained red–green–blue (RGB) image classifiers. Experiments demonstrate that lightweight CNN models using 100 × 100 pixels and 224 × 224 pixels grayscale images can achieve accuracies on par with more complex pre-trained RGB classifiers. This permits the use of reduced computational resources for environmental sustainability.