bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing
Authors: Dong Haitao , Suo Jian , Zhu Zhigang , Wang Haiyan , Ji Hongbing
Published: May 2024

Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work.
Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms
Authors: Lee Kangsan , van Leeuwen Willem J. D. , Gillan Jeffrey K. , Falk Donald A.
Published: May 2024

Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and remotely sensed data from Landsat 8 and LiDAR, we analyzed the effects of structural and functional vegetation traits and environmental factors on burn severity. This analysis revealed that the difference normalized burn ratio (dNBR) was a more reliable indicator of burn severity compared to the relative dNBR (RdNBR). Stepwise regression identified pre-fire normalized difference vegetation index (NDVI), canopy cover, and tree density as significant variables across all land cover types that explained burn severity, suggesting that denser areas with higher vegetation greenness experienced more severe burns. Interestingly, residuals between the actual and estimated dNBR were lower in herbaceous zones compared to denser forested areas at similar elevations, suggesting potentially more predictable burn severity in open areas. Spatial analysis using Geary’s C statistics further revealed a strong negative autocorrelation: areas with high burn severity tended to be clustered, with lower severity areas interspersed. Overall, this study demonstrates the potential of readily available remote sensing data to predict potential burn severity values before a fire event, providing valuable information for forest managers to develop strategies for mitigating future wildfire damage.
Remote Sensing of Forests in Bavaria: A Review
Authors: Coleman Kjirsten , Müller Jörg , Kuenzer Claudia
Published: May 2024

In recent decades, climatic pressures have altered the forested landscape of Bavaria. Widespread loss of trees has unevenly impacted the entire state, of which 37% is covered by forests (5% more than the national average). In 2018 and 2019—due in large part to drought and subsequent insect infestations—more tree-covered areas were lost in Bavaria than in any other German state. Moreover, the annual crown condition survey of Bavaria has revealed a decreasing trend in tree vitality since 1998. We conducted a systematic literature review regarding the remote sensing of forests in Bavaria. In total, 146 scientific articles were published between 2008 and 2023. While 88 studies took place in the Bavarian Forest National Park, only five publications covered the whole of Bavaria. Outside of the national park, the remaining 2.5 million hectares of forest in Bavaria are understudied. The most commonly studied topics were related to bark beetle infestations (24 papers); however, few papers focused on the drivers of infestations. The majority of studies utilized airborne data, while publications utilizing spaceborne data focused on multispectral; other data types were under-utilized- particularly thermal, lidar, and hyperspectral. We recommend future studies to both spatially broaden investigations to the state or national scale and to increase temporal data acquisitions together with contemporaneous in situ data. Especially in understudied topics regarding forest response to climate, catastrophic disturbances, regrowth and species composition, phenological timing, and in the sector of forest management. The utilization of remote sensing data in the forestry sector and the uptake of scientific results among stakeholders remains a challenge compared to other heavily forested European countries. An integral part of the Bavarian economy and the tourism sector, forests are also vital for climate regulation via atmospheric carbon reduction and land surface cooling. Therefore, forest monitoring remains centrally important to attaining more resilient and productive forests.
KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X
Authors: Lee Taesam , Hwang Seonghyeon , Singh Vijay P.
Published: May 2024

Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from the motion technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps and lined shapes. Therefore, a nonparametric estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with synthetically simulated trapezoidal, U-shape and V-shape channels. In addition, the proposed KLR model was compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). The experimental study was performed with the river experiment center in Andong, South Korea. Furthermore, the KLR model was applied to two real case studies in the Migok-cheon stream on Hapcheon-gun and Pori-cheon stream on Yecheon-gun and compared to the other models. With the extensive applications to the feasible river channels, the results indicated that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps as well as different shapes. Finally, the limitation of the UAV-driven demarcation approach was also discussed due to the penetrability of RGB sensors to water.
Efficacy of a Single-Bout of Auditory Feedback Training on Gait Performance and Kinematics in Healthy Young Adults
Authors: Tomita Yosuke , Sekiguchi Yoshihiro , Mayo Nancy E.
Published: May 2024

This study investigated the immediate effects of auditory feedback training on gait performance and kinematics in 19 healthy young adults, focusing on bilateral changes, despite unilateral training. Baseline and post-training kinematic measurements, as well as the feedback training were performed on a treadmill with a constant velocity. Significant improvements were seen in step length (trained: 590.7 mm to 611.1 mm, 95%CI [7.609, 24.373]; untrained: 591.1 mm to 628.7 mm, 95%CI [10.698, 30.835]), toe clearance (trained: 13.9 mm to 16.5 mm, 95%CI [1.284, 3.503]; untrained: 11.8 mm to 13.7 mm, 95%CI [1.763, 3.612]), ankle dorsiflexion angle at terminal stance (trained: 8.3 deg to 10.5 deg, 95%CI [1.092, 3.319]; untrained: 9.2 deg to 12.0 deg, 95%CI [1.676, 3.573]), hip flexion angular velocity, (trained: −126.5 deg/s to −131.0 deg/s, 95%CI [−9.054, −2.623]; untrained: −130.2 deg/s to −135.3 deg/s, 95%CI [−10.536, −1.675]), ankle angular velocity at terminal stance (trained: −344.7 deg/s to −359.1 deg/s, 95%CI [−47.540, −14.924]; untrained: −340.3 deg/s to −376.9 deg/s, 95%CI [−37.280, −13.166s]), and gastrocnemius EMG activity (trained: 0.60 to 0.66, 95%CI [0.014, 0.258]; untrained: 0.55 to 0.65, 95%CI [0.049, 0.214]). These findings demonstrate the efficacy of auditory feedback training in enhancing key gait parameters, highlighting the bilateral benefits from unilateral training.
Artificial Intelligence Approach for Classifying Images of Upper-Atmospheric Transient Luminous Events
Authors: Aguilera Axi , Manian Vidya
Published: May 2024

Transient Luminous Events (TLEs) are short-lived, upper-atmospheric optical phenomena associated with thunderstorms. Their rapid and random occurrence makes manual classification laborious and time-consuming. This study presents an effective approach to automating the classification of TLEs using state-of-the-art Convolutional Neural Networks (CNNs) and a Vision Transformer (ViT). The ViT architecture and four different CNN architectures, namely, ResNet50, ResNet18, GoogLeNet, and SqueezeNet, are employed and their performance is evaluated based on their accuracy and execution time. The models are trained on a dataset that was augmented using rotation, translation, and flipping techniques to increase its size and diversity. Additionally, the images are preprocessed using bilateral filtering to enhance their quality. The results show high classification accuracy across all models, with ResNet50 achieving the highest accuracy. However, a trade-off is observed between accuracy and execution time, which should be considered based on the specific requirements of the task. This study demonstrates the feasibility and effectiveness of using transfer learning and pre-trained CNNs for the automated classification of TLEs.
Research on the Multiple Small Target Detection Methodology in Remote Sensing
Authors: Zou Changman , Jeon Wang-Su , Rhee Sang-Yong
Published: May 2024

This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement (MFE) method of YOLOv7, enhancing its adaptability and precision in detecting small targets and complex backgrounds. Secondly, we design a modified YOLOv7 global information DP-MLP module to effectively capture and integrate global information, thereby improving target detection accuracy and robustness, especially in handling large-scale variations and complex scenes. Lastly, we explore a semi-supervised learning model (SSLM) target detection algorithm incorporating unlabeled data, leveraging information from unlabeled data to enhance the model’s generalization ability and performance. Experimental results demonstrate that despite the outstanding performance of YOLOv7, the mean average precision (MAP) can still be improved by 1.9%. Specifically, under testing on the TGRS-HRRSD-Dataset, the MFE and DP-MLP models achieve MAP values of 93.4% and 93.1%, respectively. Across the NWPU VHR-10 dataset, the three models achieve MAP values of 93.1%, 92.1%, and 92.2%, respectively. Significant improvements are observed across various metrics compared to the original model. This study enhances the adaptability, accuracy, and generalization of remote sensing image object detection.
ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method
Authors: You Shuai , Lin Shijun , Feng Yujian , Fan Jianhua , Yan Zhenzheng , Liu Shangdong , Ji Yimu
Published: May 2024

The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method.
Design and Modeling of a Terahertz Transceiver for Intra- and Inter-Chip Communications in Wireless Network-on-Chip Architectures
Authors: Paudel Biswash , Li Xue Jun , Seet Boon-Chong
Published: May 2024

This paper addresses the increasing demand for computing power and the challenges associated with adding more core units to a computer processor. It explores the utilization of System-on-Chip (SoC) technology, which integrates Terahertz (THz) wave communication capabilities for intra- and inter-chip communication, using the concept of Wireless Network-on-Chips (WNoCs). Various types of network topologies are discussed, along with the disadvantages of wired networks. We explore the idea of applying wireless connections among cores and across the chip. Additionally, we describe the WNoC architecture, the flip-chip package, and the THz antenna. Electromagnetic fields are analyzed using a full-wave simulation software, Ansys High Frequency Structure Simulator (HFSS). The simulation is conducted with dipole and zigzag antennas communicating within the chip at resonant frequencies of 446 GHz and 462.5 GHz, with transmission coefficients of around −28 dB and −33 to −41 dB, respectively. Transmission coefficient characterization, path loss analysis, a study of electric field distribution, and a basic link budget for transmission are provided. Furthermore, the feasibility of calculated transmission power is validated in cases of high insertion loss, ensuring that the achieved energy expenditure is less than 1 pJ/bit. Finally, employing a similar setup, we study intra-chip communication using the same antennas. Simulation results indicate that the zigzag antenna exhibits a higher electric field magnitude compared with the dipole antenna across the simulated chip structure. We conclude that transmission occurs through reflection from the ground plane of a printed circuit board (PCB), as evidenced by the electric field distribution.
Polycations as Aptamer-Binding Modulators for Sensitive Fluorescence Anisotropy Assay of Aflatoxin B1
Authors: Samokhvalov Alexey V. , Mironova Alena A. , Eremin Sergei A. , Zherdev Anatoly V. , Dzantiev Boris B.
Published: May 2024

Fluorescence induced by the excitation of a fluorophore with plane-polarized light has a different polarization depending on the size of the fluorophore-containing reagent and the rate of its rotation. Based on this effect, many analytical systems have been implemented in which an analyte contained in a sample and labeled with a fluorophore (usually fluorescein) competes to bind to antibodies. Replacing antibodies in such assays with aptamers, low-cost and stable oligonucleotide receptors, is complicated because binding a fluorophore to them causes a less significant change in the polarization of emissions. This work proposes and characterizes the compounds of the reaction medium that improve analyte binding and reduce the mobility of the aptamer–fluorophore complex, providing a higher analytical signal and a lower detection limit. This study was conducted on aflatoxin B1 (AFB1), a ubiquitous toxicant contaminating foods of plant origins. Eight aptamers specific to AFB1 with the same binding site and different regions stabilizing their structures were compared for affinity, based on which the aptamer with 38 nucleotides in length was selected. The polymers that interact reversibly with oligonucleotides, such as poly-L-lysine and polyethylene glycol, were tested. It was found that they provide the desired reduction in the depolarization of emitted light as well as high concentrations of magnesium cations. In the selected optimal medium, AFB1 detection reached a limit of 1 ng/mL, which was 12 times lower than in the tris buffer commonly used for anti-AFB1 aptamers. The assay time was 30 min. This method is suitable for controlling almond samples according to the maximum permissible levels of their contamination by AFB1. The proposed approach could be applied to improve other aptamer-based analytical systems.
Shear Wave Velocity Determination of a Complex Field Site Using Improved Nondestructive SASW Testing
Authors: Kim Gunwoong , Hwang Sungmoon
Published: May 2024

The nondestructive spectral analysis of surface waves (SASW) technique determines the shear wave velocities along the wide wavelength range using Rayleigh-type surface waves that propagate along pairs of receivers on the surface. The typical configuration of source-receivers consists of a vertical source and three vertical receivers arranged in a linear array. While this approach allows for effective site characterization, laterally variable sites are often challenging to characterize. In addition, in a traditional SASW test configuration system, where sources are placed in one direction, the data are collected more on one side, which can cause an imbalance in the interpretation of the data. Data interpretation issues can be resolved by moving the source to opposite ends of the original array and relocating receivers to perform a second complete set of tests. Consequently, two different Vs profiles can be provided with only a small amount of additional time at sites where lateral variability exists. Furthermore, the testing procedure can be modified to enhance the site characterization during data collection. The advantages of performing SASW testing in both directions are discussed using a real case study.
Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks
Authors: Alshehri Ali , Badr Mahmoud M. , Baza Mohamed , Alshahrani Hani
Published: May 2024

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers’ privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors’ parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers’ privacy.
Antijamming Schemes for the Generalized MIMO Y Channel
Authors: Lenarska Karolina , Wesołowski Krzysztof
Published: May 2024

Signal space alignment (SSA) is a promising technique for interference management in wireless networks. However, despite the excellent work done on SSA, its robustness against jamming attacks has not been considered in the literature. In this paper, we propose two antijamming strategies for the SSA scheme applied in the multiple-input–multiple-output (MIMO) Y channel. The first scheme involves projecting the jamming signal into the null space of each source’s precoding vectors, effectively eliminating it entirely. The second scheme removes interference originating from the jammer by subtracting the disturbance estimate from the incoming signal. The estimate is derived on the basis of the criterion of minimizing the received signal energy. The block error rate (BLER) performance of the proposed strategies in various channel configurations is verified by link level simulations and is presented to show the efficiency in mitigating jamming signals within the SSA-based MIMO Y channel.
PrivShieldROS: An Extended Robot Operating System Integrating Ethereum and Interplanetary File System for Enhanced Sensor Data Privacy
Authors: Wang Tianhao , Chen Ke , Zheng Zhaohua , Guo Jiahao , Zhao Xiying , Zhang Shenhui
Published: May 2024

With the application of robotics in security monitoring, medical care, image analysis, and other high-privacy fields, vision sensor data in robotic operating systems (ROS) faces the challenge of enhancing secure storage and transmission. Recently, it has been proposed that the distributed advantages of blockchain be taken advantage of to improve the security of data in ROS. Still, it has limitations such as high latency and large resource consumption. To address these issues, this paper introduces PrivShieldROS, an extended robotic operating system developed by InterPlanetary File System (IPFS), blockchain, and HybridABEnc to enhance the confidentiality and security of vision sensor data in ROS. The system takes advantage of the decentralized nature of IPFS to enhance data availability and robustness while combining HybridABEnc for fine-grained access control. In addition, it ensures the security and confidentiality of the data distribution mechanism by using blockchain technology to store data content identifiers (CID) persistently. Finally, the effectiveness of this system is verified by three experiments. Compared with the state-of-the-art blockchain-extended ROS, PrivShieldROS shows improvements in key metrics. This paper has been partly submitted to IROS 2024.
Single-Shot 3D Reconstruction via Nonlinear Fringe Transformation: Supervised and Unsupervised Learning Approaches
Authors: Nguyen Andrew-Hieu , Wang Zhaoyang
Published: May 2024

The field of computer vision has been focusing on achieving accurate three-dimensional (3D) object representations from a single two-dimensional (2D) image through deep artificial neural networks. Recent advancements in 3D shape reconstruction techniques that combine structured light and deep learning show promise in acquiring high-quality geometric information about object surfaces. This paper introduces a new single-shot 3D shape reconstruction method that uses a nonlinear fringe transformation approach through both supervised and unsupervised learning networks. In this method, a deep learning network learns to convert a grayscale fringe input into multiple phase-shifted fringe outputs with different frequencies, which act as an intermediate result for the subsequent 3D reconstruction process using the structured-light fringe projection profilometry technique. Experiments have been conducted to validate the practicality and robustness of the proposed technique. The experimental results demonstrate that the unsupervised learning approach using a deep convolutional generative adversarial network (DCGAN) is superior to the supervised learning approach using UNet in image-to-image generation. The proposed technique’s ability to accurately reconstruct 3D shapes of objects using only a single fringe image opens up vast opportunities for its application across diverse real-world scenarios.
Analysis of E2E Delay and Wiring Harness in In-Vehicle Network with Zonal Architecture
Authors: Park Chulsun , Cui Chengyu , Park Sungkwon
Published: May 2024

With recent advances in vehicle technologies, in-vehicle networks (IVNs) and wiring harnesses are becoming increasingly complex. To solve these challenges, the automotive industry has adopted a new zonal-based IVN architecture (ZIA) that connects electronic control units (ECUs) according to their physical locations. In this paper, we evaluate how the number of zones in the ZIA affects the end-to-end (E2E) delay and the characteristics of the wiring harnesses. We evaluate the impact of the number of zones on E2E delay through the OMNeT++ network simulator. In addition, we theoretically predict and analyze the impact of the number of zones on the wiring harnesses. Specifically, we use an asymptotic approach to analyze the total length and weight evolution of the wiring harnesses in ZIAs with 2, 4, 6, 8, and 10 zones by incrementally increasing the number of ECUs. We find that as the number of zones increases, the E2E delay increases, but the total length and weight of the wiring harnesses decreases. These results confirm that the ZIA effectively uses the wiring harnesses and mitigates network complexity within the vehicle.
Cost-Effective Optical Wireless Sensor Networks: Enhancing Detection of Sub-Pixel Transmitters in Camera-Based Communications
Authors: Rodríguez-Yánez Idaira , Guerra Víctor , Rabadán José , Pérez-Jiménez Rafael
Published: May 2024

In the domain of the Internet of Things (IoT), Optical Camera Communication (OCC) has garnered significant attention. This wireless technology employs solid-state lamps as transmitters and image sensors as receivers, offering a promising avenue for reducing energy costs and simplifying electronics. Moreover, image sensors are prevalent in various applications today, enabling dual functionality: recording and communication. However, a challenge arises when optical transmitters are not in close proximity to the camera, leading to sub-pixel projections on the image sensor and introducing strong channel dependence. Previous approaches, such as modifying camera optics or adjusting image sensor parameters, not only limited the camera’s utility for purposes beyond communication but also made it challenging to accommodate multiple transmitters. In this paper, a novel sub-pixel optical transmitter discovery algorithm that overcomes these limitations is presented. This algorithm enables the use of OCC in scenarios with static transmitters and receivers without the need for camera modifications. This allows increasing the number of transmitters in a given scenario and alleviates the proximity and size limitations of the transmitters. Implemented in Python with multiprocessing programming schemes for efficiency, the algorithm achieved a 100% detection rate in nighttime scenarios, while there was a 89% detection rate indoors and a 72% rate outdoors during daylight. Detection rates were strongly influenced by varying transmitter types and lighting conditions. False positives remained minimal, and processing times were consistently under 1 s. With these results, the algorithm is considered suitable for export as a web service or as an intermediary component for data conversion into other network technologies.
Silicon Microring Resonator Biosensor for Detection of Nucleocapsid Protein of SARS-CoV-2
Authors: Uchida Yusuke , Arakawa Taro , Higo Akio , Ishizaka Yuhei
Published: May 2024

A high-sensitivity silicon microring (Si MRR) optical biosensor for detecting the nucleocapsid protein of SARS-CoV-2 is proposed and demonstrated. In the proposed biosensor, the surface of a Si MRR waveguide is modified with antibodies, and the target protein is detected by measuring a resonant wavelength shift of the MRR caused by the selective adsorption of the protein to the surface of the waveguide. A Si MRR is fabricated on a silicon-on-insulator substrate using a CMOS-compatible fabrication process. The quality factor of the MRR is approximately 20,000. The resonant wavelength shift of the MRR and the detection limit for the environmental refractive index change are evaluated to be 89 nm/refractive index unit (RIU) and 10−4 RIU, respectively. The sensing characteristics are examined using a polydimethylsiloxane flow channel after the surface of the Si MRR waveguide is modified with the IgG antibodies through the Si-tagged protein. First, the selective detection of the protein by the MRR sensor is experimentally demonstrated by the detection of bovine serum albumin and human serum albumin. Next, various concentrations of nucleocapsid protein solutions are measured by the MRR, in which the waveguide surface is modified with the IgG antibodies through the Si-tagged protein. Although the experimental results are very preliminary, they show that the proposed sensor has a potential nucleocapsid sensitivity in the order of 10 pg/mL, which is comparable to the sensitivity of current antigen tests. The detection time is less than 10 min, which is much shorter than those of other antigen tests.
DOxy: A Dissolved Oxygen Monitoring System
Authors: Shaghaghi Navid , Fazlollahi Frankie , Shrivastav Tushar , Graham Adam , Mayer Jesse , Liu Brian , Jiang Gavin , Govindaraju Naveen , Garg Sparsh , Dunigan Katherine , Ferguson Peter
Published: May 2024

Dissolved Oxygen (DO) in water enables marine life. Measuring the prevalence of DO in a body of water is an important part of sustainability efforts because low oxygen levels are a primary indicator of contamination and distress in bodies of water. Therefore, aquariums and aquaculture of all types are in need of near real-time dissolved oxygen monitoring and spend a lot of money on purchasing and maintaining DO meters that are either expensive, inefficient, or manually operated—in which case they also need to ensure that manual readings are taken frequently which is time consuming. Hence a cost-effective and sustainable automated Internet of Things (IoT) system for this task is necessary and long overdue. DOxy, is such an IoT system under research and development at Santa Clara University’s Ethical, Pragmatic, and Intelligent Computing (EPIC) Laboratory which utilizes cost-effective, accessible, and sustainable Sensing Units (SUs) for measuring the dissolved oxygen levels present in bodies of water which send their readings to a web based cloud infrastructure for storage, analysis, and visualization. DOxy’s SUs are equipped with a High-sensitivity Pulse Oximeter meant for measuring dissolved oxygen levels in human blood, not water. Hence a number of parallel readings of water samples were gathered by both the High-sensitivity Pulse Oximeter and a standard dissolved oxygen meter. Then, two approaches for relating the readings were investigated. In the first, various machine learning models were trained and tested to produce a dynamic mapping of sensor readings to actual DO values. In the second, curve-fitting models were used to produce a successful conversion formula usable in the DOxy SUs offline. Both proved successful in producing accurate results.
Plasma Diagnostics
Authors: Gonçalves Bruno
Published: May 2024

Plasma science and engineering is a multidisciplinary area encompassing some of the most exciting fundamental and applied research themes in today’s scientific landscape, with an extraordinarily broad impact in science, technology, and industry [...]
Bioactive Components Analysis and Pharmacological Properties of Extracts and Metabolites of Lichen Umbilicaria crustulosa
Authors: Tomović Jovica , Kočović Aleksandar , Anđić Marijana , Bradić Jovana , Zubić Nevena , Jakovljević Vladimir , Sovrlić Miroslav , Vasiljević Perica , Manojlović Nedeljko
Published: May 2024

Lichens, a diverse group of organisms, have a unique structure consisting of fungal filaments and photosynthetic partner cells. This research conducted a comprehensive chemical analysis and evaluation of the anti-inflammatory and antioxidant properties of methanolic and acetone extracts from Umbilicaria crustulosa lichen, along with its isolated metabolites. The process involved separating atranorin and chloratranorin fractions, physodic acid, and gyrophoric acid. Secondary metabolites were identified using chromatographic and spectroscopic data. The total polyphenols content was determined spectrophotometrically. This study examined the antioxidant activity of extracts of the lichen U. crustulosa and the isolated fractions using three methods: DPPH scavenging activity, ABTS scavenging activity, and reducing power. This study also evaluated the acute oral toxicity and the anti-inflammatory activity of the extracts in Wistar albino rats. A higher content of the total phenolic compounds was found in the acetone extract, but antioxidant and anti-inflammatory activities were more prominent in the methanolic extract. The isolated atranorin and chloratranorin fractions and compound physodic acid showed the highest antioxidant activity. No toxic effects were noted in the acute oral toxicity study. This study highlights the potential of the investigated lichen as a valuable source of novel biological agents.
Study of Body Composition and Motor Skills of Futsal Athletes of Different Competitive Levels
Authors: Belo João , Valente-dos-Santos João , Pereira João R. , Duarte-Mendes Pedro , M. Gamonales José , Paulo Rui
Published: May 2024

This study aimed to verify whether there are differences in the body composition, functionality, lower-limb power, agility, and cardiorespiratory capacity in futsal players, comparing futsal athletes by competitive level. The athletes (N = 84) were divided into three groups: group Elite (N = 29), group Sub-Elite (N = 29), and group Non-Elite (N = 26). Anthropometric variables were analyzed through a bioimpedance scale (Inbody 270), and functionality was analyzed through a functional movement screen battery. The power of the lower limbs was tested with the Abalakov jump, the agility with the zigzag agility test, and the cardiorespiratory capacity through the futsal intermittent endurance test. Anthropometric data from futsal athletes revealed a homogeneity in relation to the variables analyzed, regardless of the level of competition in which they operate. In performance variables, the power of the members and functionality was considered a discriminating factor of the level of competitiveness of the athletes, with the Elite group athletes presenting the best values. We concluded that there were no differences in relation to the body composition of the athletes. However, the athletes of higher levels, as a rule, present better performances in physiological aspects, results that can be explained by the fact that there is a better periodization in terms of training, with more intense loads and more complex competitive calendars, thus resulting in a greater specialization of these athletes.
Scrap Steel Recycling: A Carbon Emission Reduction Index for China
Authors: Hao Hao , Wu Haolong , Wei Fangfang , Xu Zhaoran , Xu Yi
Published: May 2024

Accurately assessing carbon emissions from recycling scrap steel is essential for reducing emissions in the steel industry, especially in China, the world’s largest crude steel producer. In this study, a carbon emission reduction index was introduced to evaluate the effectiveness of recycling scrap steel in reducing emissions. The index considers the three processes used in scrap steel recycling: blast furnace ironmaking, converter steelmaking, and electric arc furnace steelmaking. This study developed an evaluation model using fuzzy analytic hierarchy process and iterative cluster analysis to determine the reduction of carbon emission. From a life cycle perspective, this study identified primary factors contributing to emissions, including fuel, raw materials, electric energy, and auxiliary materials. Then, the carbon emission reduction index for scrap recycling was developed by examining the production of one ton of steel and each additional ton of scrap steel, which can provide valuable insights into the environmental impact of scrap recycling. Finally, the study forecasts the future Carbon Emission Reduction Index for steel scrap recycling. The study indicates an increase in the carbon emission reduction index for scrap recycling prior to 2017, followed by a decrease about 11.8% from 2017 to 2018 and increases from 2018 to 2021. Finally, it dropped by 8.7% per cent in 2022. Similarly, the carbon emission reduction index for electric furnace steelmaking increased prior to 2019, then subsequently decreased. It is changing by ten per cent a year. Additionally, the scrap recycling index experienced a significant decrease of 90% in 2015, followed by a gradual increase until 2017 and then a consistent decrease every year thereafter. The index suddenly rose in 2021 and then decreased change for policy reasons. The forecast results suggest a gradual increase in the carbon emission reduction index per ton of steel scrap in the future. In conclusion, the practicable modeling methodology has the ability to assist government organizations and private enterprises in devising efficient green and low-carbon development tactics.
Sustainability in Public Relations Campaigns: Diagnosis of the Phenomenon Using the Example of PR Industry Research in Poland
Authors: Tworzydło Dariusz , Gawroński Sławomir , Opolska-Bielańska Agata , Szuba Przemysław
Published: May 2024

This article is based on a study of the public relations environment in Poland and contains many references to the problems that Polish PR professionals have to deal with while performing their daily professional duties. The purpose of the article is to identify the factors that determine the frequency with which sustainable development elements are used in PR campaigns. The unit of analysis consists of the responses of 315 PR professionals who represent various organizations and provide services both to their clients and themselves, contributing to the PR structures of private and public companies. The main research technique was an online CAWI survey conducted in 2022. The main hypothesis of the article assumes that the professional experience of Polish PR professionals determines the frequency of using sustainable development elements in PR campaigns. In light of the above considerations, this article can make a valuable contribution to the discussion on the evolution of the PR specialist’s role in the context of sustainable development.
Dual Environmental, Social, and Governance (ESG) Index for Corporate Sustainability Assessment Using Blockchain Technology
Authors: Liu Xinlai , Liang Wenbiao , Fu Yelin , Huang George Q.
Published: May 2024

Investors are increasingly relying on Environmental, Social, and Governance (ESG) indexes to obtain a third-party assessment of corporate sustainability performance. Various ESG indexes are, therefore, released by prominent rating agencies, including MSCI, Sustainalytics, Refinitiv, etc. However, existing ESG indexes overvalue the usage of massive ESG metrics while ignoring various ESG disclosure levels, leading to critical issues such as limited company coverage, inflexible ESG framework, and obscure assessment processes. This paper proposes a novel Dual ESG Index (DESGI) model using blockchain technology to provide a flexible and transparent corporate sustainability assessment. Firstly, the DESGI model is developed by analogy to the rationale and concepts of the academic credit system due to its advantages of scalability and flexibility. Secondly, blockchain is used to build a transparent environment for ESG assessment. Thirdly, the smart contract and crypto token, as the core blockchain constructs, are used to achieve the dual-dimensional ESG depth and width assessment using ESG GPA and ESG credit, respectively. Finally, a case study is carried out to validate the DESGI by using real-life ESG data and comparing it with four existing ESG indexes. Several managerial implications are also found: (1) DESGI can expand the scope of companies evaluated by ESG criteria regardless of company size or scale; (2) DESGI provides a good potential to fight against greenwashing through the blockchain-based traceability; (3) DESGI can identify the ESG elites who disclose fewer ESG metrics but with excellent ESG performances, which can hardly be achieved using traditional ESG indexes.
Application of Aggregates from Construction and Demolition Wastes in Concrete: Review
Authors: Luo Hua , Aguiar José , Wan Xiaoqi , Wang Yinggu , Cunha Sandra , Jia Zhiyou
Published: May 2024

In the current century, urbanization and the development of the construction industry have led to the generation of construction and demolition waste (CDW), imposing pressure on ecology and the environment. This has attracted the attention of industry personnel and researchers. This work discusses the current research on recycled coarse or fine aggregate, mainly focusing on the physical, mechanical and durability properties of sustainable concrete with recycled coarse or fine aggregate. Furthermore, it also summarizes CDW recycling and classification in major countries, the production processes of recycled aggregate, and the physical properties. This review will provide a reference for the application of concrete with recycled coarse or fine aggregate. Moreover, this review notes that replacing natural aggregates with both coarse and fine recycled aggregates awaits further experimental exploration.
Systemic Risk Arising from Shadow Banking and Sustainable Development: A Study of Wealth Management Products in China
Authors: Pan Hongjie , Fan Hong
Published: May 2024

Shadow banking is a main way for the financial market to serve the real economy today, and this process is closely related to systemic risk. This study examines the impact of shadow banking associated with sustainable development in China’s banking on systemic risk. We analyze the data obtained from a rich sample of 31 listed commercial banks in China and shadow banking represented by wealth management products (WMPs) by constructing a dynamic complex interbank network model. The results show that the risks and vulnerabilities generated by shadow banking spread out through the interbank network and cause systemic risk to increase. The effect operates through increasing the number of default banks, reducing banks’ survival rate and profit, and forcing central bank bailout funds expansion. However, it has a positive impact in terms of augmenting liquidity and enhancing investment opportunities. Furthermore, the variability in the influence of different categories of shadow banking is assessed, emphasizing that short-term shadow banking exerts a more pronounced impact on systemic risk. In addition, the heterogeneity of the shadow banking effect on different types of commercial banks is explored, revealing that local and rural commercial banks experience a more conspicuous effect compared to state-owned and joint-stock banks. Our findings highlight that improving external supervision, promoting financial internal governance, and constraining credit linkages are vital for alleviating the increase in risks in shadow banking and maintaining the sustainable development of banking.
Driving Economic Growth through Transportation Infrastructure: An In-Depth Spatial Econometric Analysis
Authors: Shi Jianwei , Bai Tongyuan , Zhao Zhihong , Tan Huachun
Published: May 2024

This research investigates the crucial role of transportation infrastructure in influencing economic activity, thus employing advanced econometric methods including Moran’s I index, LM, Hausman, and LR tests to ensure analytical accuracy and select the appropriate spatial model. Our findings reveal that freight volumes across road, waterway, and civil aviation significantly enhance economic activity by bolstering domestic trade, industrial production, and supply chains. Conversely, the impact of passenger turnover is comparatively minor, although it still contributes to labor mobility and urban accessibility. This study highlights the need for strategic investment in transportation infrastructure and efficient public transport systems to foster economic growth and sustainable development. We recommend that policymakers focus on optimizing transportation networks and integrating intelligent transport technologies to boost economic competitiveness and societal well-being. This analysis not only sheds light on the direct economic impacts of transportation but also underscores the broader social implications, thus advocating for a holistic approach to transportation planning and policymaking.