bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach
Authors: Shiraga Takeru , Makimoto Hisaki , Kohlmann Benita , Magnisali Christofori-Eleni , Imai Yoshie , Itani Yusuke , Makimoto Asuka , Schölzel Fabian , Bejinariu Alexandru , Kelm Malte , Rana Obaida
Published: December 2023

Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications.
Sensor Integration in a Forestry Machine
Authors: Pereira Tiago , Gameiro Tiago , Viegas Carlos , Santos Victor , Ferreira Nuno
Published: December 2023

This paper presents the integration of multimodal sensor systems for an autonomous forestry machine. The utilized technology is housed in a single enclosure which consolidates a set of components responsible for executing machine control actions and comprehending its behavior in various scenarios. This sensor box, named Sentry, will subsequently be connected to a forestry machine from MDB, model LV600 PRO. The article outlines previous work in this field and then details the integration and operation of the equipment, integrated into the forest machine, providing descriptions of the adopted architecture at both the hardware and software levels. The gathered data enables the assessment of the forestry machine’s orientation and position based on the information collected by the sensors. Finally, practical experiments are presented to demonstrate the system’s behavior and to analyze the methods to be employed for autonomous navigation, thereby assessing the performance of the established architecture. The novel aspects of this work include the physical and digital integration of a multimodal sensor system on a forestry machine, its use in a real case scenario, namely, forest vegetation removal, and the strategies adopted to improve the machine localization and navigation performance on unstructured environments.
Agreement between Optoelectronic System and Wearable Sensors for the Evaluation of Gait Spatiotemporal Parameters in Progressive Supranuclear Palsy
Authors: Ricciardi Carlo , Pisani Noemi , Donisi Leandro , Abate Filomena , Amboni Marianna , Barone Paolo , Picillo Marina , Cesarelli Mario , Amato Francesco
Published: December 2023

The use of wearable sensors for calculating gait parameters has become increasingly popular as an alternative to optoelectronic systems, currently recognized as the gold standard. The objective of the study was to evaluate the agreement between the wearable Opal system and the optoelectronic BTS SMART DX system for assessing spatiotemporal gait parameters. Fifteen subjects with progressive supranuclear palsy walked at their self-selected speed on a straight path, and six spatiotemporal parameters were compared between the two measurement systems. The agreement was carried out through paired data test, Passing Bablok regression, and Bland-Altman Analysis. The results showed a perfect agreement for speed, a very close agreement for cadence and cycle duration, while, in the other cases, Opal system either under- or over-estimated the measurement of the BTS system. Some suggestions about these misalignments are proposed in the paper, considering that Opal system is widely used in the clinical context.
Concurrent Validity of Cervical Movement Tests Using VR Technology—Taking the Lab to the Clinic
Authors: Forsberg Karin , Jirlén Johan , Jacobson Inger , Röijezon Ulrik
Published: December 2023

Reduced cervical range of motion (ROM) and movement velocity are often seen in people with neck pain. Objective assessment of movement characteristics is important to identify dysfunction, to inform tailored interventions, and for the evaluation of the treatment effect. The purpose of this study was to investigate the concurrent validity of a newly developed VR technology for the assessment of cervical ROM and movement velocity. VR technology was compared against a gold-standard three-dimensional optical motion capture system. Consequently, 20 people, 13 without and 7 with neck pain, participated in this quantitative cross-sectional study. ROM was assessed according to right/left rotation, flexion, extension, right/left lateral flexion, and four diagonal directions. Velocity was assessed according to fast cervical rotation to the right and left. The correlations between VR and the optical system for cervical ROM and velocity were excellent, with intraclass correlation coefficient (ICC) values > 0.95. The mean biases between VR and the optical system were ≤ 2.1° for the ROM variables, <12°/s for maximum velocity, and ≤3.0°/s for mean velocity. In conclusion, VR is a useful assessment device for ROM and velocity measurements with clinically acceptable biases. It is a feasible tool for the objective measurement of cervical kinematics in the clinic.
Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments
Authors: Aldaej Abdulaziz , Ahanger Tariq Ahamed , Ullah Imdad
Published: December 2023

The Internet of Things (IoT) technology has seen substantial research in Deep Learning (DL) techniques to detect cyberattacks. Critical Infrastructures (CIs) must be able to quickly detect cyberattacks close to edge devices in order to prevent service interruptions. DL approaches outperform shallow machine learning techniques in attack detection, giving them a viable alternative for use in intrusion detection. However, because of the massive amount of IoT data and the computational requirements for DL models, transmission overheads prevent the successful implementation of DL models closer to the devices. As they were not trained on pertinent IoT, current Intrusion Detection Systems (IDS) either use conventional techniques or are not intended for scattered edge–cloud deployment. A new edge–cloud-based IoT IDS is suggested to address these issues. It uses distributed processing to separate the dataset into subsets appropriate to different attack classes and performs attribute selection on time-series IoT data. Next, DL is used to train an attack detection Recurrent Neural Network, which consists of a Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (LSTM). The high-dimensional BoT-IoT dataset, which replicates massive amounts of genuine IoT attack traffic, is used to test the proposed model. Despite an 85 percent reduction in dataset size made achievable by attribute selection approaches, the attack detection capability was kept intact. The models built utilizing the smaller dataset demonstrated a higher recall rate (98.25%), F1-measure (99.12%), accuracy (99.56%), and precision (99.45%) with no loss in class discrimination performance compared to models trained on the entire attribute set. With the smaller attribute space, neither the RNN nor the Bi-LSTM models experienced underfitting or overfitting. The proposed DL-based IoT intrusion detection solution has the capability to scale efficiently in the face of large volumes of IoT data, thus making it an ideal candidate for edge–cloud deployment.
Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals
Authors: Vieira Jusciaane Chacon , Guedes Luiz Affonso , Santos Mailson Ribeiro , Sanchez-Gendriz Ignacio
Published: December 2023

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models—without resorting to deep learning—is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.
Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers
Authors: Shomal Zadeh Fatemeh , Koh Ryan G. L. , Dilek Banu , Masani Kei , Kumbhare Dinesh
Published: December 2023

Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants (left and/or right side(s)). Four texture feature approaches (individually and a combination of them) were employed that focused on identifying spots, and edges were used to explore the discrimination between the three groups: active MTrPs (n = 30), latent MTrPs (n = 30), and healthy muscle (n = 30). Machine learning (ML) and one-way analysis of variance were used to investigate the discrimination ability of the different approaches. Statistically significant results were seen in almost all examined features for each texture feature approach, but, in contrast, ML techniques struggled to produce robust discrimination. The ML techniques showed that two texture features (i.e., correlation and mean) within the combination of texture features were most important in classifying the three groups. This discrepancy between traditional statistical analysis and ML techniques prompts the need for further investigation of texture-based approaches in US for the discrimination of MTrPs.
Research on the Surface Deformation, Fault Rupture, and Coseismic Geohazard of the 2022 Luding Mw 6.8 Earthquake
Authors: Lu Yiling , Yang Yinghui , Zeng Li , Xu Wanfu , Song Jiawei , Li Xiaoyun
Published: December 2023

An Mw 6.8 earthquake occurred in Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, on 5 September 2022. This seismic event triggered numerous coseismic geohazards in the seismic zone. In this study, the ascending- and descending-track synthetic aperture radar (SAR) images observed by the Sentinel-1A satellite are utilized to extract the coseismic surface deformation of the Luding earthquake. Subsequently, a faulting model is estimated based on the elastic dislocation theory, under the constraint of the InSAR observation. Additionally, the POT technique was employed to detect coseismic geohazards. High-spatial-resolution optical remote sensing images served to validate the reliability of the detection results. The coseismic interferometric synthetic aperture radar (InSAR) deformation field indicated a maximum deformation of ~190 mm and ~140 mm along the ascending and descending tracks, respectively. The estimated best-fitting faulting model suggests that the optimal seismogenic fault strike and dip angles are 169.3° and 70°, respectively. The fault slip predominantly exhibits left-lateral strike-slip characteristics and is concentrated at depths of 3–12 km. The estimated maximum fault slip was 2.67 m, occurring at a depth of 7 km. The pixel offset tracking (POT) result derived from the pre- and post-earthquake SAR images found a total of 245 medium- to large-scale coseismic geohazards, with a verification rate from optical images exceeding 64%. The distribution of these geohazards is notably dense within the significant fault rupture segment. Geohazards on the fault hanging wall are densely packed, whereas landslides along the Dadu River’s fault footwall are also notably frequent.
Online Quantitative Analysis of Perception Uncertainty Based on High-Definition Map
Authors: Yang Mingliang , Jiao Xinyu , Jiang Kun , Cheng Qian , Yang Yanding , Yang Mengmeng , Yang Diange
Published: December 2023

Environmental perception plays a fundamental role in decision-making and is crucial for ensuring the safety of autonomous driving. A pressing challenge is the online evaluation of perception uncertainty, a crucial step towards ensuring the safety and the industrialization of autonomous driving. High-definition maps offer precise information about static elements on the road, along with their topological relationships. As a result, the map can provide valuable prior information for assessing the uncertainty associated with static elements. In this paper, a method for evaluating perception uncertainty online, encompassing both static and dynamic elements, is introduced based on the high-definition map. The proposed method is as follows: Firstly, the uncertainty of static elements in perception, including the uncertainty of their existence and spatial information, was assessed based on the spatial and topological features of the static environmental elements; secondly, an online assessment model for the uncertainty of dynamic elements in perception was constructed. The online evaluation of the static element uncertainty was utilized to infer the dynamic element uncertainty, and then a model for recognizing the driving scenario and weather conditions was constructed to identify the triggering factors of uncertainty in real-time perception during autonomous driving operations, which can further optimize the online assessment model for perception uncertainty. The verification results on the nuScenes dataset show that our uncertainty assessment method based on a high-definition map effectively evaluates the real-time perception results’ performance.
Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data
Authors: Minic Ana , Jovanovic Luka , Bacanin Nebojsa , Stoean Catalin , Zivkovic Miodrag , Spalevic Petar , Petrovic Aleksandar , Dobrojevic Milos , Stoean Ruxandra
Published: December 2023

Monitoring heart electrical activity is an effective way of detecting existing and developing conditions. This is usually performed as a non-invasive test using a network of up to 12 sensors (electrodes) on the chest and limbs to create an electrocardiogram (ECG). By visually observing these readings, experienced professionals can make accurate diagnoses and, if needed, request further testing. However, the training and experience needed to make accurate diagnoses are significant. This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. The performance of the optimized models is compared to models created by other contemporary optimizers, and the results show significant potential for real-world applications. Further analyses are carried out on the best-performing models to determine feature importance.
New Shear Horizontal (SH) Surface-Plasmon-Polariton-like Elastic Surface Waves for Sensing Applications
Authors: Kiełczyński Piotr
Published: December 2023

The advent of elastic metamaterials at the beginning of the 21st century opened new venues and possibilities for the existence of new types of elastic (ultrasonic) surface waves, which were deemed previously impossible. In fact, it is not difficult to prove that shear horizontal (SH) elastic surface waves cannot exist on the elastic half-space or at the interface between two conventional elastic half-spaces. However, in this paper we will show that SH elastic surface waves can propagate at the interface between two elastic half-spaces, providing that one of them is a metamaterial with a negative elastic compliance s44(ω). If in addition, s44(ω) changes with frequency ω as the dielectric function ε(ω) in Drude’s model of metals, then the proposed SH elastic surface waves can be considered as an elastic analogue of surface plasmon polariton (SPP) electromagnetic waves, propagating at a metal-dielectric interface. Due to inherent similarities between the proposed SH elastic surface waves and SPP electromagnetic waves, the new results developed in this paper can be readily transferred into the SPP domain and vice versa. The proposed new SH elastic surface waves are characterized by a strong subwavelength confinement of energy in the vicinity of the guiding interface; therefore, they can potentially be used in subwavelength ultrasonic imaging, superlensing, and/or acoustic (ultrasonic) sensors with extremely high mass sensitivity.
DYNet: A Printed Book Detection Model Using Dual Kernel Neural Networks
Authors: Wang Lubin , Xie Xiaolan , Huang Peng , Yu Qiang
Published: December 2023

Target detection has always been a hotspot in image processing/computer vision research, and small-target detection is a frequently encountered problem in the field of target detection. With the continuous innovation of target detection technology, people always hope that the detection of small targets can reach the real-time accuracy of large-target detection. In this paper, a small-target detection model based on dual-core convolutional neural networks (CNN) is proposed, which is mainly used for the intelligent detection of books in the production line of printed books. The model is mainly composed of two modules, including a region prediction module and suspicious target search module. The region prediction module uses a CNN to predict suspicious region blocks in a large context. The suspicious target search module uses a different CNN from the above to find tiny targets in the predicted region blocks. Comparative testing of four small book target samples using this model shows that this model has better book small-target detection accuracy compared to other models.
Photoplethysmography for the Assessment of Arterial Stiffness
Authors: Karimpour Parmis , May James M. , Kyriacou Panicos A.
Published: December 2023

This review outlines the latest methods and innovations for assessing arterial stiffness, along with their respective advantages and disadvantages. Furthermore, we present compelling evidence indicating a recent growth in research focused on assessing arterial stiffness using photoplethysmography (PPG) and propose PPG as a potential tool for assessing vascular ageing in the future. Blood vessels deteriorate with age, losing elasticity and forming deposits. This raises the likelihood of developing cardiovascular disease (CVD), widely reported as the global leading cause of death. The ageing process induces structural modifications in the vascular system, such as increased arterial stiffness, which can cause various volumetric, mechanical, and haemodynamic alterations. Numerous techniques have been investigated to assess arterial stiffness, some of which are currently used in commercial medical devices and some, such as PPG, of which still remain in the research space.
Colorimetric Sensors: Methods and Applications
Authors: Yang Feng-Qing , Ge Liya
Published: December 2023

Colorimetric sensors have attracted considerable attention in many sensing applications because of their specificity, high sensitivity, cost-effectiveness, ease of use, rapid analysis, simplicity of operation, and clear visibility to the naked eye [...]
Correction: Todorov et al. Electromagnetic Sensing Techniques for Monitoring Atopic Dermatitis—Current Practices and Possible Advancements: A Review. Sensors 2023, 23, 3935
Authors: Todorov Alexandar , Torah Russel , Wagih Mahmoud , Ardern-Jones Michael R. , Beeby Steve P.
Published: December 2023

**Mahmoud Wagih** was not included as an author in the original publication [...]
Design and Development of an Imitation Detection System for Human Action Recognition Using Deep Learning
Authors: Alhakbani Noura , Alghamdi Maha , Al-Nafjan Abeer
Published: December 2023

Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain. This paper presents a study on the design and development of an imitation detection system using an HAR algorithm based on deep learning. This study explores the use of deep learning models, such as a single-frame convolutional neural network (CNN) and pretrained VGG-16, for the accurate classification of human actions. The proposed models were evaluated using a benchmark dataset, KTH. The performance of these models was compared with that of classical classifiers, including K-Nearest Neighbors, Support Vector Machine, and Random Forest. The results showed that the VGG-16 model achieved higher accuracy than the single-frame CNN, with a 98% accuracy rate.
A PAD-Based Unmanned Aerial Vehichle Route Planning Scheme for Remote Sensing in Huge Regions
Authors: Shao Tianyi , Li Yuxiang , Gao Weixin , Lin Jiayuan , Lin Feng
Published: December 2023

Unmanned aerial vehicles (UAVs) have been employed extensively for remote-sensing missions. However, due to their energy limitations, UAVs have a restricted flight operating time and spatial coverage, which makes remote sensing over huge regions that are out of UAV flight endurance and range challenging. PAD is an autonomous wireless charging station that might significantly increase the flying time of UAVs by recharging them in the air. In this work, we introduce PADs to simplify UAV-based remote sensing over a huge region, and then we explore the UAV route planning problem once PADs have been predeployed throughout a huge remote sensing region. A route planning scheme, named PAD-based remote sensing (PBRS), is proposed to solve the problem. The PBRS scheme first plans the UAV’s round-trip routes based on the location of the PADs and divides the whole target region into multiple PAD-based subregions. Between adjacent subregions, the UAV flight subroute is planned by determining piggyback points to minimize the total time for remote sensing. We demonstrate the effectiveness of the proposed scheme by conducting several sets of simulation experiments based on the digital orthophoto model of Hutou Village in Beibei District, Chongqing, China. The results show that the PBRS scheme can achieve excellent performance in three metrics of remote sensing duration, the number of trips to charging stations, and the data-storage rate in UAV remote-sensing missions over huge regions with predeployed PADs through effective planning of UAVs.
High-Precision Corrosion Detection via SH1 Guided Wave Based on Full Waveform Inversion
Authors: Wen Jiawei , Jiang Can , Chen Hao
Published: December 2023

Corrosion detection for industrial settings is crucial for safe and efficient operations. Due to its high imaging resolution, the guided–wave full–waveform inversion tomography technique has significant potential for corrosion detection of plate metals. Limited by the long wavelengths of A0 and S0 mode waves, this method exhibits inadequate detection resolution for the earlier shallow and small corrosion defects. Based on the relatively short wavelength characteristics of the SH1 mode wave, we propose a high–precision corrosion detection method via SH1 guided wave using the full waveform inversion algorithms. By conducting finite element simulations of ultrasonic–guided waves on aluminum plates with varying corrosion defects, a comparison was made to assess the detection precision across A0, S0, and SH1 modes. The comparison results showed that, whether for regular or irregular defects, the SH1 mode wave always exhibited higher imaging accuracy than the A0 and S0 mode waves for shallow and small–sized defects. The corresponding experiments were conducted on an aluminum plate with simple or complex defects. The results of the experiments reconfirmed that the full waveform inversion method using SH1 guided wave can effectively reconstruct the shape and size of small and shallow corrosion defects within aluminum plates.
A Glove-Wearing Detection Algorithm Based on Improved YOLOv8
Authors: Li Shichu , Huang Huiping , Meng Xiangyin , Wang Mushuai , Li Yang , Xie Lei
Published: December 2023

Wearing gloves during machinery operation in workshops is essential for preventing accidental injuries, such as mechanical damage and burns. Ensuring that workers are wearing gloves is a key strategy for accident prevention. Consequently, this study proposes a glove detection algorithm called YOLOv8-AFPN-M-C2f based on YOLOv8, offering swifter detection speeds, lower computational demands, and enhanced accuracy for workshop scenarios. This research innovates by substituting the head of YOLOv8 with the AFPN-M-C2f network, amplifying the pathways for feature vector propagation, and mitigating semantic discrepancies between non-adjacent feature layers. Additionally, the introduction of a superficial feature layer enriches surface feature information, augmenting the model’s sensitivity to smaller objects. To assess the performance of the YOLOv8-AFPN-M-C2f model, this study conducted multiple experiments using a factory glove detection dataset compiled for this study. The results indicate that the enhanced YOLOv8 model surpasses other network models. Compared to the baseline YOLOv8 model, the refined version shows a 2.6% increase in mAP@50%, a 63.8% rise in FPS, and a 13% reduction in the number of parameters. This research contributes an effective solution for the detection of glove adherence.
Identifying the Sweet Spot of Padel Rackets with a Robot
Authors: Blanes Carlos , Correcher Antonio , Martínez-Turégano Jaime , Ricolfe-Viala Carlos
Published: December 2023

Although the vibration of rackets and the location of the sweet spot for players when hitting the ball is crucial, manufacturers do not specify this behavior precisely. This article analyses padel rackets, provides a solution to determine the sweet spot position (SSP), quantifies its behavior, and determines the level of vibration transmitted along the racket handle. The proposed methods serve to locate the SSP without quantifying it. This article demonstrates the development of equipment capable of analyzing the vibration behavior of padel rackets. To do so, it employs a robot that moves along the surface of the padel racket, striking it along its central line. Accelerometers are placed on a movable cradle where rackets are positioned and adjusted. A method for analyzing accelerometer signals to quantify vibration severity is proposed. The SSP and vibration behavior along the central line are determined and quantified. As a result of the study, 225 padel rackets are analyzed and compared. SSP is independent of the padel racket shape, balance, weight, moment of inertia, and padel racket shape (tear, diamond, or round) and is not located at the same position as the center of percussion.
Research on Pneumatic Control of a Pressurized Self-Elevating Mat for an Offshore Wind Power Installation Platform
Authors: Cui Junguo , Shi Qi , Lin Yunfei , Shi Haibin , Yuan Simin , Xiao Wensheng
Published: December 2023

Efficient deep-water offshore wind power installation platforms with a pressurized self-elevating mat are a new type of equipment used for installing offshore wind turbines. However, the unstable internal pressure of the pressurized self-elevating mat can cause serious harm to the platform. This paper studies the pneumatic control system of the self-elevating mat to improve the precision of its pressure control. According to the pneumatic control system structure of the self-elevating mat, the pneumatic model of the self-elevating mat is established, and a conventional PID controller and fuzzy PID controller are designed and established. It can be seen via Simulink simulation that the fuzzy PID controller has a smaller adjustment time and overshoot, but its anti-interference ability is relatively weak. The membership degree and fuzzy rules of the fuzzy PID controller are optimized using a neural network algorithm, and a fuzzy neural network PID controller based on BP neural network optimization is proposed. The simulation results show that the overshoot of the optimized controller is reduced by 9.71% and the stability time is reduced by 68.9% compared with the fuzzy PID. Finally, the experiment verifies that the fuzzy neural network PID controller has a faster response speed and smaller overshoot, which improves the pressure control accuracy and robustness of the self-elevating mat and provides a scientific basis for the engineering applications of the self-elevating mat.
Recent Advancements in Graphene-Based Implantable Electrodes for Neural Recording/Stimulation
Authors: Alahi Md Eshrat E. , Rizu Mubdiul Islam , Tina Fahmida Wazed , Huang Zhaoling , Nag Anindya , Afsarimanesh Nasrin
Published: December 2023

Implantable electrodes represent a groundbreaking advancement in nervous system research, providing a pivotal tool for recording and stimulating human neural activity. This capability is integral for unraveling the intricacies of the nervous system’s functionality and for devising innovative treatments for various neurological disorders. Implantable electrodes offer distinct advantages compared to conventional recording and stimulating neural activity methods. They deliver heightened precision, fewer associated side effects, and the ability to gather data from diverse neural sources. Crucially, the development of implantable electrodes necessitates key attributes: flexibility, stability, and high resolution. Graphene emerges as a highly promising material for fabricating such electrodes due to its exceptional properties. It boasts remarkable flexibility, ensuring seamless integration with the complex and contoured surfaces of neural tissues. Additionally, graphene exhibits low electrical resistance, enabling efficient transmission of neural signals. Its transparency further extends its utility, facilitating compatibility with various imaging techniques and optogenetics. This paper showcases noteworthy endeavors in utilizing graphene in its pure form and as composites to create and deploy implantable devices tailored for neural recordings and stimulations. It underscores the potential for significant advancements in this field. Furthermore, this paper delves into prospective avenues for refining existing graphene-based electrodes, enhancing their suitability for neural recording applications in in vitro and in vivo settings. These future steps promise to revolutionize further our capacity to understand and interact with the neural research landscape.
A Grating Interferometric Acoustic Sensor Based on a Flexible Polymer Diaphragm
Authors: Xiong Linsen , Qi Zhi-mei
Published: December 2023

This study presents a grating interferometric acoustic sensor based on a flexible polymer diaphragm. A flexible-diaphragm acoustic sensor based on grating interferometry (GI) is proposed through design, fabrication and experimental demonstration. A gold-coated polyethylene terephthalate diaphragm was used for the sensor prototype. The vibration of the diaphragm induces a change in GI cavity length, which is converted into an electrical signal by the photodetector. The experimental results show that the sensor prototype has a flat frequency response in the voice frequency band and the minimum detectable sound pressure can reach 164.8 µPa/√Hz. The sensor prototype has potential applications in speech acquisition and the measurement of water content in oil. This study provides a reference for the design of optical interferometric acoustic sensor with high performance.
Enhancing the Competitiveness of the Show Business Industry for Cultural Export as a Means of Creative Economy Development and Tourism Promotion in the Future New Normal Era: The Case of Thailand
Authors: Lee-Anant Chayapoj , Choibamroong Therdchai , Srikhwan Boonnawat
Published: December 2023

The tourism industry is considered the greatest economic generator in Thailand. Nonetheless, the impact of the COVID-19 pandemic thoroughly shifted its image of tourism stability and trustworthiness. The show business industry is an element of Thai tourism that attracts more than a million tourists nationwide. The show business industry not only attracts quality multinational tourists as visitors, but also indirectly establishes ‘soft power’ with audiences. This research aimed to (1) study the alternative future scenario of Thailand’s cultural show business industry with respect to cultural export and the creative economy; (2) determine the competitive potential of Thailand’s show business industry by comparing it with the global show business industry; (3) examine the market demands and behaviors within Thailand’s cultural show business industry for cultural export and the creative economy; and (4) present a strategy for elevating the competitive potential of the show business industry for cultural export, promoting a creative economy and fostering tourism in the post-pandemic ‘New Normal’ era. The results of this study found that the ‘Alternative Future Scenario’ of Thailand’s cultural performance industry with respect to cultural exports and the creative economy consists of three scenarios: (1) a mass show business tourism industry, (2) an exclusive show business tourism industry, and (3) a new mega show business tourism industry. It also revealed the competitive potential of the Thai show business industry. By comparing it with the international performing arts industry in terms of competitiveness, it was found that the show business industry in Thailand meets world-class standards. Given its clear, outstanding, and unique culture and traditions, Thailand is ready to serve as a major cultural exporter via cultural performance. Furthermore, the data analysis revealed a total of 10 significant strategies for enhancing the competitiveness of the show business industry in Thailand.
Developing a Pedagogical Approach with the Aim of Empowering Educators and Students to Address Emerging Global Issues such as Climate Change and Social Justice: A Case Study
Authors: Predan Barbara , Černe Oven Petra
Published: December 2023

The “Green Nudges” program, developed within the framework of the United Nations Environment Programme (UNEP), is based on behavioral science and nudge theory. Aimed primarily at universities, it explores ways to adopt a more environmentally friendly lifestyle. Studies show that many young people recognize climate change as a major problem but that it leaves them feeling helpless and anxious—something that our teaching practice confirms. As we had had no success teaching sustainability using conventional approaches, we used the pedagogical design capacity (PDC) principle to develop a novel workshop format and implement it in a pilot series of three repeated workshops. The workshop concept is based on empowering educators and students to tackle emerging global issues while also boosting critical thinking, field research, and teamwork skills. An important part of the integration of different tools was based on supporting students’ self-direction and knowledge- and evidence-based decision making. The results demonstrate that the proposed pedagogical framework resonates with and empowers students. At the same time, the workshop empowers educators to competently navigate complex and sustainability-oriented topics within the field of education for sustainable development (ESD).
Assessing Pro-Environmental Behaviour towards Plastics among Staff and Students at a South African University
Authors: Dalu Mwazvita T. B. , Ngobeni Bonginkosi S. , Dalu Tatenda
Published: December 2023

Plastic pollution is a global issue of concern, with all environmental realms contaminated by plastics. Human consumption as well as management of daily plastics use can play a significant role to control this global environmental problem. Thus, understanding public perceptions about plastic consumption and pollution is vital to engage society in solutions to reduce the associated environmental impact. This study explores the perceptions, attitudes, and beliefs of a university community towards plastic consumption and pollution. We employed online questionnaires, which had 248 respondents in the end, and data was analysed using a combination of Chi-Squared and Spearman correlation tests. We observed that staff members (i.e., lecturers) had a greater awareness of long-term plastic waste impacts and were more likely to take steps to reduce their plastic use in their daily routines. About 50% of staff members and 14.8% of students indicated that their main source of information was scientific publications, followed by Google (8.3% of staff members, 24% of students). Furthermore, they were also more likely to implement policies and procedures that promote sustainability and reduce waste. Approximately 54.5% of staff members and 56.4% of students highlighted that they did not know of any tools to help reduce plastic pollution, with 36.4% of staff members and 20.5% of students indicating that they did not know of any intervention methods. In comparison, students were more focused on convenience and did not have the same level of understanding or concern about the environmental impact of plastics as they were more likely to use single-use plastics, without considering the potential environmental harm. Hence, to bridge this gap in perception, educational institutions should take proactive steps to educate both staff and students about the importance of reducing plastic waste and promoting sustainability through the provision of educational materials, organising awareness campaigns and events, and implementing policies that encourage responsible plastic use.
Discussion of the Standards System for Sustainable Aviation Fuels: An Aero-Engine Safety Perspective
Authors: Gan Chenyu , Ma Qinglin , Bao Shengyu , Wang Xinming , Qiu Tian , Ding Shuiting
Published: December 2023

Sustainable aviation fuels (SAFs) are considered an important solution for reducing carbon emissions. Safety is the most important prerequisite for a new fuel to be used in an aero-engine. As a special component in aero-engines, fuel is required to comply with both airworthiness and technical standards. These two types of standard work together to guide SAF development. In this paper, the SAF safety issues related to aero-engines are first analyzed. Subsequently, SAF-related standard systems are analyzed in detail, and the different safety responsibilities of airworthiness authorities and industry associations are explained. Moreover, the relationships between airworthiness and technical standards are determined from the perspective of actual SAF certification. Furthermore, the revision of the standards is reviewed to summarize the historical evolution and outline the revision intention. Finally, the future SAF certification standards are discussed and prospected, including the blending ratio, property specifications, and testing equipment. According to the discussion, increased safety and fewer constraints are the principal objectives for the development of SAF standards. Analysis, review, and discussion of the SAF standards systems from an aero-engine safety perspective will contribute to the establishment of the next generation of standards to release the fuel potential and improve safety.
Prediction and Early Warning of Extreme Winds for High-Speed Railway Bridge Construction Using Machine-Learning Methods
Authors: Xie Yishun , Chang Xiangyu , Mao Jianxiao , Ni Youhao , Wang Hao
Published: December 2023

Measuring the impact of extreme winds is important in high-speed railway bridge construction to avoid the risk of engineering accidents. This study presents an early-warning framework for high-speed railway (HSR) bridge construction under extreme wind conditions, using a long-span continuous beam bridge constructed in a typhoon-prone area as a case study. Specifically, on-site wind environment measurements during a typhoon are utilized, and parameters such as fluctuating wind turbulence intensity, gust factor, turbulence integral scale, and power spectral density are employed to characterize the wind environment. Multi-step prediction of gust wind speed during the bridge construction is performed using the hybrid Back Propagation-Genetic Algorithm (BP-GA). Hierarchical warnings and control actions are proposed and implemented based on the prediction results. The analysis results of wind parameters revealed a discrepancy between the measured and specified typhoon turbulence intensities, with the windward turbulence intensity being lower than specified. The longitudinal average gust factor exceeded the transverse value. The prediction curve based on the BP-GA algorithm closely resembles the actual curve, meeting accuracy requirements. Notably, the one-step prediction provided the most accurate results. Based on the predicted wind speed trend for the upcoming half-hour, appropriate hierarchical warnings and control measures were conducted.
Research Progress on Iron- and Steelmaking Iste Slag-Based Glass-Ceramics: Preparation and GHG Emission Reduction Potentials
Authors: Wei Zichao , Liu Xiaomin , Hu Guangwen , Xue Kai , Wu Yufeng
Published: December 2023

Promoted by carbon neutrality and solid iste policies, iron- and steelmaking iste slag (ISWS)-based glass-ceramics have drawn attention because of their contribution to achieving the net-zero carbon emissions goal for the iron- and steelmaking industry. However, a holistic estimation of the preparation, property and GHG (greenhouse gas) emission abatement of ISWS-based glass-ceramics is still under exploration. In this paper, research progress on preparing glass-ceramics from ISWS discharged from the traditional iron- and steelmaking industry is reviewed. Then, the influence of ISWS’s chemical characteristics on the preparation of glass-ceramics and the products’ performance are discussed. In addition, the potential of GHG emission reduction related to the promotion of ISWS-based glass-ceramics is measured. It is found that ISWS-based glass-ceramics can avoid 0.87–0.91 tons of CO2 emissions compared to primary resource routes. A scenario simulation is also conducted. If the technology could be fully applied in the ironmaking and steelmaking industries, the results suggest that 2.07 and 0.67 tons of indirect CO2 reductions can be achieved for each ton of crude steel production from blast furnace–basic oxygen furnace (BF-BOF) and electric arc furnace (EAF) routes, respectively. Finally, a “dual promotion” economic mode based on national policy orientation and the high demands on metallurgical iste slag (MWS)-based glass-ceramics is proposed, and the application prospects of MWS-based glass-ceramics are examined. These application prospects will deepen the fundamental understanding of glass-ceramic properties and enable them to be compounded with other functional materials in various new technologies.
Determining the Appropriate Minimum Effort Levels for Use in Fisheries Dynamic Bioeconomic Models
Authors: Pascoe Sean , Deng Roy Aijun , Hutton Trevor , Parker Denham
Published: December 2023

Managing fisheries to achieve ecological, economic and social sustainability is complex. The use of dynamic bioeconomic models can be and have been used to assist in determining management targets. However, optimizing profits over time can result in large reductions in fishing effort in the short term with adverse social consequences. There exist other benefits from maintaining fishing effort even in adverse conditions (e.g., maintain crew and fleet capacity). For this reason, many bioeconomic models have included some form of minimum effort, catch or short-term profit constraint. In this paper, we consider a range of approaches to assess an appropriate minimum fishing effort, including the estimation of fishery breakeven effort levels, and approaches based on historical fishing levels. These are tested using a bioeconomic model currently used for fishery management. We find that breakeven approaches tend to result in the most conservative effort levels and the highest net present value of profits. In contrast, using a proportion of the moving average of the observed fishing effort results in less conservative change in effort, while resulting in positive changes in the net present value of fishery profits. The approach also has the advantage of being dynamic, adjusting with recent fishery conditions.
Towards Smart Parking Management: Econometric Analysis and Modeling of Public-Parking-Choice Behavior in Three Cities of Binh Duong, Vietnam
Authors: Long Nguyen Viet , Linh Hoang Thuy , Tuan Vu Anh
Published: December 2023

In developing cities, newly emerging cities have started facing the problem of insufficient public parking facilities and ineffective regulations. To support the planning, design and management of the public parking system towards a smart and sustainable city vision, it is necessary to study deeply parking behaviors. This paper presents an empirical study on parking-choice behaviors of motorcycle users and car users in the emerging cities of developing countries through a case study of three cities in Binh Duong, Vietnam. To explore the behavioral mechanisms and influential factors, the multinomial logit parking choice models are developed using revealed preference and stated preference data. The users’ overall satisfaction and perceived importance of parking lot design and service aspects are analyzed using order logistic regression. The revealed choices show no trade-off between parking fee and walking distance, as the users are not fully aware of parking locations and service features. However, the stated choice experiments prove a potential existence of the trade-off mechanism and differentiate significant factors in the decision of choices for the two user groups. The results bring insightful implications for the development of a smart public parking system.