bluuebunny/crossref_metadata_2025 huggingface dataset:
Bridging Formal Shape Models and Deep Learning: A Novel Fusion for Understanding 3D Objects
Authors: Zhang Jincheng , Willis Andrew R.
Published: June 2024

This article describes a novel fusion of a generative formal model for three-dimensional (3D) shapes with deep learning (DL) methods to understand the geometric structure of 3D objects and the relationships between their components, given a collection of unorganized point cloud measurements. Formal 3D shape models are implemented as shape grammar programs written in Procedural Shape Modeling Language (PSML). Users write PSML programs to describe complex objects, and DL networks estimate the configured free parameters of the program to generate 3D shapes. Users write PSML programs to enforce fundamental rules that define an object class and encode object attributes, including shapes, components, size, position, etc., into a parametric representation of objects. This fusion of the generative model with DL offers artificial intelligence (AI) models an opportunity to better understand the geometric organization of objects in terms of their components and their relationships to other objects. This approach allows human-in-the-loop control over DL estimates by specifying lists of candidate objects, the shape variations that each object can exhibit, and the level of detail or, equivalently, dimension of the latent representation of the shape. The results demonstrate the advantages of the proposed method over competing approaches.
Using AI-Based Virtual Companions to Assist Adolescents with Autism in Recognizing and Addressing Cyberbullying
Authors: Ferrer Robinson , Ali Kamran , Hughes Charles
Published: June 2024

Social media platforms and online gaming sites play a pervasive role in facilitating peer interaction and social development for adolescents, but they also pose potential threats to health and safety. It is crucial to tackle cyberbullying issues within these platforms to ensure the healthy social development of adolescents. Cyberbullying has been linked to adverse mental health outcomes among adolescents, including anxiety, depression, academic underperformance, and an increased risk of suicide. While cyberbullying is a concern for all adolescents, those with disabilities are particularly susceptible and face a higher risk of being targets of cyberbullying. Our research addresses these challenges by introducing a personalized online virtual companion guided by artificial intelligence (AI). The web-based virtual companion’s interactions aim to assist adolescents in detecting cyberbullying. More specifically, an adolescent with ASD watches a cyberbullying scenario in a virtual environment, and the AI virtual companion then asks the adolescent if he/she detected cyberbullying. To inform the virtual companion in real time to know if the adolescent has learned about detecting cyberbullying, we have implemented fast and lightweight cyberbullying detection models employing the T5-small and MobileBERT networks. Our experimental results show that we obtain comparable results to the state-of-the-art methods despite having a compact architecture.
A Review of Deep Learning-Based LiDAR and Camera Extrinsic Calibration
Authors: Tan Zhiguo , Zhang Xing , Teng Shuhua , Wang Ling , Gao Feng
Published: June 2024

Extrinsic parameter calibration is the foundation and prerequisite for LiDAR and camera data fusion of the autonomous system. This technology is widely used in fields such as autonomous driving, mobile robots, intelligent surveillance, and visual measurement. The learning-based method is one of the targetless calibrating methods in LiDAR and camera calibration. Due to its advantages of fast speed, high accuracy, and robustness under complex conditions, it has gradually been applied in practice from a simple theoretical model in just a few years, becoming an indispensable and important method. This paper systematically summarizes the research and development of this type of method in recent years. According to the principle of calibration parameter estimation, learning-based calibration algorithms are divided into two categories: accurate calibrating estimation and relative calibrating prediction. The evolution routes and algorithm frameworks of these two types of algorithms are elaborated, and the methods used in the algorithms’ steps are summarized. The algorithm mechanism, advantages, limitations, and applicable scenarios are discussed. Finally, we make a summary, pointing out existing research issues and trends for future development.
A Simulation Method for Underwater SPAD Depth Imaging Datasets
Authors: Lu Taoran , Qiu Su , Wang Hui , Zhu Shihao , Jin Weiqi
Published: June 2024

In recent years, underwater imaging and vision technologies have received widespread attention, and the removal of the backward-scattering interference caused by impurities in the water has become a long-term research focus for scholars. With the advent of new single-photon imaging devices, single-photon avalanche diode (SPAD) devices, with high sensitivity and a high depth resolution, have become cutting-edge research tools in the field of underwater imaging. However, the high production costs and small array areas of SPAD devices make it very difficult to conduct underwater SPAD imaging experiments. To address this issue, we propose a fast and effective underwater SPAD data simulation method and develop a denoising network for the removal of backward-scattering interference in underwater SPAD images based on deep learning and simulated data. The experimental results show that the distribution difference between the simulated and real underwater SPAD data is very small. Moreover, the algorithm based on deep learning and simulated data for the removal of backward-scattering interference in underwater SPAD images demonstrates effectiveness in terms of both metrics and human observation. The model yields improvements in metrics such as the PSNR, SSIM, and entropy of 5.59 dB, 9.03%, and 0.84, respectively, demonstrating its superior performance.
The Highly Sensitive Refractive Index Sensing of Seawater Based on a Large Lateral Offset Mach–Zehnder Interferometer
Authors: Zhou Jingwen , Sun Yue , Liu Haodong , Li Haibin , Wang Yuye , Jiang Junfeng , Xu Degang , Yao Jianquan
Published: June 2024

A novel fiber sensor for the refractive index sensing of seawater based on a Mach–Zehnder interferometer has been demonstrated. The sensor consisted of a single-mode fiber (SMF)–no-core fiber (NCF)–single-mode fiber structure (shortened to an SNS structure) with a large lateral offset spliced between the two sections of a multimode fiber (MMF). Optimization studies of the multimode fiber length, offset SNS length, and vertical axial offset distance were performed to improve the coupling efficiency of interference light and achieve the best extinction ratio. In the experiment, a large lateral offset sensor was prepared to detect the refractive index of various ratios of saltwater, which were used to simulate seawater environments. The sensor’s sensitivity was up to −13,703.63 nm/RIU and −13,160 nm/RIU in the refractive index range of 1.3370 to 1.3410 based on the shift of the interference spectrum. Moreover, the sensor showed a good linear response and high stability, with an RSD of only 0.0089% for the trough of the interference in air over 1 h.
Postural Sway Velocity of Deaf Children with and without Vestibular Dysfunction
Authors: Melo Renato S. , Lemos Andrea , Wiesiolek Carine Carolina , Soares Lucas Gallindo Martins , Raposo Maria Cristina Falcão , Lambertz Daniel , Belian Rosalie Barreto , Ferraz Karla Mônica
Published: June 2024

Background: Sensory information obtained from the visual, somatosensory, and vestibular systems is responsible for regulating postural control, and if damage occurs in one or more of these sensory systems, postural control may be altered. Objective: To evaluate and compare the postural sway velocity between children with normal hearing and with sensorineural hearing loss (SNHL), matched by sex and age group, and to compare the postural sway velocity between children with normal hearing and with SNHL, with and without vestibular dysfunction. Methods: Cross-sectional study that evaluated 130 children (65 with normal hearing and 65 with SNHL), of both sexes and aged between 7 and 11 years, from public schools of the city of Caruaru, Pernambuco state, Brazil. The postural sway velocity of the center of pressure (COP) was assessed by a force platform, in two directions, anteroposterior (AP) and mediolateral (ML)), in three positions, namely bipedal support with feet together and parallel (parallel feet (PF)), bipedal support with one foot in front of the other (tandem foot (TF)), and single-leg support (one foot (OF)), evaluated with the eyes open and closed. Results: Children with SNHL demonstrated greater postural sway velocity compared to children with normal hearing in all the positions evaluated, with significant differences in the AP direction, with the eyes open (PF: p = 0.001; TF: p = 0.000; OF: p = 0.003) and closed (PF: p = 0.050; TF: p = 0.005). The same occurred in the ML direction, with the eyes open (PF: p = 0.001; TF: p = 0.000; OF: p = 0.001) and closed (PF: p = 0.002; TF: p = 0.000). The same occurred in relation to vestibular function, where the children with SNHL with an associated vestibular dysfunction demonstrated greater postural sway velocity compared to children with normal hearing in all the positions evaluated, demonstrating significant differences in the AP direction, with the eyes open (TF: p = 0.001; OF: p = 0.029) and eyes closed (PF: p = 0.036; TF: p = 0.033). The same occurred in the ML direction, with the eyes open (TF: p = 0.000) and with the eyes closed (PF: p = 0.008; TF: p = 0.009). Conclusions: Children with SNHL demonstrated greater instability of postural control than children with normal hearing in all the directions assessed. Children with SNHL and an associated vestibular dysfunction demonstrated the greatest instability of postural control in this study.
Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics
Authors: Giannakopoulou Olympia , Kakkos Ioannis , Dimitrakopoulos Georgios N. , Tarousi Marilena , Sun Yu , Bezerianos Anastasios , Koutsouris Dimitrios D. , Matsopoulos George K.
Published: June 2024

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals’ brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.
Forecasting a Short-Term Photovoltaic Power Model Based on Improved Snake Optimization, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network
Authors: Wang Yonggang , Yao Yilin , Zou Qiuying , Zhao Kaixing , Hao Yue
Published: June 2024

The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutional neural network–bidirectional long short-term memory network to predict short-term photovoltaic power. Firstly, K-means clustering is utilized to categorize weather scenarios into three categories: sunny, cloudy, and rainy. The Pearson correlation coefficient method is then utilized to determine the inputs of the model. Secondly, the snake optimization algorithm is improved by introducing Tent chaotic mapping, lens imaging backward learning, and an optimal individual adaptive perturbation strategy to enhance its optimization ability. Then, the multi-strategy improved snake optimization algorithm is employed to optimize the parameters of the convolutional neural network–bidirectional long short-term memory network model, thereby augmenting the predictive precision of the model. Finally, the model established in this paper is utilized to forecast photovoltaic power in diverse weather scenarios. The simulation findings indicate that the regression coefficients of this method can reach 0.99216, 0.95772, and 0.93163 on sunny, cloudy, and rainy days, which has better prediction precision and adaptability under various weather conditions.
Smart Robotics for Automation
Authors: Martins Felipe N.
Published: June 2024

In recent years, the demand for efficient automation across various sectors has accelerated significantly [...]
Quality-Aware Signal Processing Mechanism of PPG Signal for Long-Term Heart Rate Monitoring
Authors: Beh Win-Ken , Yang Yu-Chia , Wu An-Yeu
Published: June 2024

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.
Biomechanical Investigation of Lower Limbs during Slope Transformation Running with Different Longitudinal Bending Stiffness Shoes
Authors: Lu Runhan , Chen Hairong , Huang Jialu , Ye Jingyi , Gao Lidong , Liu Qian , Quan Wenjing , Gu Yaodong
Published: June 2024

Background: During city running or marathon races, shifts in level ground and up-and-down slopes are regularly encountered, resulting in changes in lower limb biomechanics. The longitudinal bending stiffness of the running shoe affects the running performance. Purpose: This research aimed to investigate the biomechanical changes in the lower limbs when transitioning from level ground to an uphill slope under different longitudinal bending stiffness (LBS) levels in running shoes. Methods: Fifteen male amateur runners were recruited and tested while wearing three different LBS running shoes. The participants were asked to pass the force platform with their right foot at a speed of 3.3 m/s ± 0.2. Kinematics data and GRFs were collected synchronously. Each participant completed and recorded ten successful experiments per pair of shoes. Results: The range of motion in the sagittal of the knee joint was reduced with the increase in the longitudinal bending stiffness. Positive work was increased in the sagittal plane of the ankle joint and reduced in the keen joint. The negative work of the knee joint increased in the sagittal plane. The positive work of the metatarsophalangeal joint in the sagittal plane increased. Conclusion: Transitioning from running on a level surface to running uphill, while wearing running shoes with high LBS, could lead to improved efficiency in lower limb function. However, the higher LBS of running shoes increases the energy absorption of the knee joint, potentially increasing the risk of knee injuries. Thus, amateurs should choose running shoes with optimal stiffness when running.
Study on the Fast Search Planning Problem of Lost Targets for Maritime Emergency Response Based on an Improved Adaptive Immunogenetic Algorithm
Authors: Yu Tianyue , Zhang Yasheng , Yang Jie
Published: June 2024

This study investigates the problem of rapid search planning for moving targets in maritime emergencies using an improved adaptive immune genetic algorithm. Given the complexity and uncertainty inherent in searching for moving targets in maritime emergency situations, a task planning method based on the improved adaptive immunogenetic algorithm (IAIGA) is proposed to enhance search efficiency and accuracy. This method utilizes a priori information to construct the potential regions of the target and the distribution probability within each region. It establishes a “prediction-scheduling” search strategy model, planning a rapid search task for disconnected targets based on overlapping probability through the IAIGA. By incorporating an immune mechanism, the algorithm enhances its global search capability and robustness. Additionally, the adaptive strategy enables dynamic adjustment of the algorithm’s parameters to accommodate varying search scenarios. The experimental results demonstrate that the proposed IAIGA significantly outperforms traditional methods, providing higher search speeds and more accurate search results in the context of maritime emergency response. These findings offer effective technical support for maritime emergency operations.
A Pressure and Proximity Sensor Based on Laser-Induced Graphene
Authors: Ye Jiatong , Zhao Tiancong , Zhang Hangyu
Published: June 2024

Smart wearable devices are extensively utilized across diverse domains due to their inherent advantages of flexibility, portability, and real-time monitoring. Among these, flexible sensors demonstrate exceptional pliability and malleability, making them a prominent focus in wearable electronics research. However, the implementation of flexible wearable sensors often entails intricate and time-consuming processes, leading to high costs, which hinder the advancement of the entire field. Here, we report a pressure and proximity sensor based on oxidized laser-induced graphene (oxidized LIG) as a dielectric layer sandwiched by patterned LIG electrodes, which is characterized by high speed and cost-effectiveness. It is found that in the low-frequency range of fewer than 0.1 kHz, the relative dielectric constant of the oxidized LIG layer reaches an order of magnitude of 104. The pressure mode of this bimodal capacitive sensor is capable of detecting pressures within the range of 1.34 Pa to 800 Pa, with a response time of several hundred milliseconds. The proximity mode involves the application of stimulation using an acrylic probe, which demonstrates a detection range from 0.05 mm to 37.8 mm. Additionally, it has a rapid response time of approximately 100 ms, ensuring consistent signal variations throughout both the approach and withdrawal phases. The sensor fabrication method proposed in this project effectively minimizes expenses and accelerates the preparation cycle through precise control of laser processing parameters to shape the electrode-dielectric layer-electrode within a single substrate material. Based on their exceptional combined performance, our pressure and proximity sensors exhibit significant potential in practical applications such as motion monitoring and distance detection.
SHA-256 Hardware Proposal for IoT Devices in the Blockchain Context
Authors: Santos Carlos E. B. , Silva Lucileide M. D. da , Torquato Matheus F. , Silva Sérgio N. , Fernandes Marcelo A. C.
Published: June 2024

This work proposes an implementation of the SHA-256, the most common blockchain hash algorithm, on a field-programmable gate array (FPGA) to improve processing capacity and power saving in Internet of Things (IoT) devices to solve security and privacy issues. This implementation presents a different approach than other papers in the literature, using clustered cores executing the SHA-256 algorithm in parallel. Details about the proposed architecture and an analysis of the resources used by the FPGA are presented. The implementation achieved a throughput of approximately 1.4 Gbps for 16 cores on a single FPGA. Furthermore, it saved dynamic power, using almost 1000 times less compared to previous works in the literature, making this proposal suitable for practical problems for IoT devices in blockchain environments. The target FPGA used was the Xilinx Virtex 6 xc6vlx240t-1ff1156.
Remote Sensing Image Classification Based on Canny Operator Enhanced Edge Features
Authors: Zhou Mo , Zhou Yue , Yang Dawei , Song Kai
Published: June 2024

Remote sensing image classification plays a crucial role in the field of remote sensing interpretation. With the exponential growth of multi-source remote sensing data, accurately extracting target features and comprehending target attributes from complex images significantly impacts classification accuracy. To address these challenges, we propose a Canny edge-enhanced multi-level attention feature fusion network (CAF) for remote sensing image classification. The original image is specifically inputted into a convolutional network for the extraction of global features, while increasing the depth of the convolutional layer facilitates feature extraction at various levels. Additionally, to emphasize detailed target features, we employ the Canny operator for edge information extraction and utilize a convolution layer to capture deep edge features. Finally, by leveraging the Attentional Feature Fusion (AFF) network, we fuse global and detailed features to obtain more discriminative representations for scene classification tasks. The performance of our proposed method (CAF) is evaluated through experiments conducted across three openly accessible datasets for classifying scenes in remote sensing images: NWPU-RESISC45, UCM, and MSTAR. The experimental findings indicate that our approach based on incorporating edge detail information outperforms methods relying solely on global feature-based classifications.
High-Efficiency Frequency Doubling Blue-Laser VECSEL Based on Intracavity Beam Control
Authors: Zhang Zhuo , Zhang Jianwei , Du Ziye , Chen Chao , Zhou Yinli , Sun Jingjing , Liu Tianjiao , Zhang Jiye , Zhang Xing , Ning Yongqiang , Wang Lijun
Published: June 2024

Blue lasers are integral to a variety of applications, including marine communication, underwater resource exploration, cold laser processing, laser medicine, and beyond. Vertical external cavity surface-emitting lasers (VECSELs) have the advantages of high output power and tunable wavelength, and can output blue laser via frequency doubling. In this article, a new type of intracavity beam control external-cavity structure is introduced. The laser beam waist is effectively adjusted by intracavity beam control, and the frequency conversion efficiency is improved. A laser cavity stability analysis model was developed to investigate the impact of laser cavity lens parameters and relative positions on stability. The external resonant cavity of VECSELs utilizes two optical lenses to position the beam waist near the laser output coupling mirror and locates the frequency doubling crystal at a high optical power density position to optimize frequency conversion efficiency. The VECSEL straight external-cavity structure achieves a frequency conversion efficiency of up to 60.2% at 488 nm, yielding a blue laser output exceeding 1.3 W. The full width at half maximum of the 488 nm spectrum measures approximately 0.23 nm. This intracavity beam-controlled direct external-cavity structure effectively mitigates laser mode leakage and shows potential for the development of an efficient and compact blue laser source.
A Wideband Circularly Polarized Dipole Antenna with Compact Size and Low-Pass Filtering Response
Authors: Lin Xianjing , Weng Zhangrun , Hong Yibin , Zhang Yao
Published: June 2024

This paper presents a compact wideband circularly polarized cross-dipole antenna with a low-pass filter response. It consists of two pairs of folded cross-dipole arms printed separately on both sides of the top substrate, and the two dipole arms on the same surface are connected by an annular phase-shifting delay line to generate circular polarization. A bent metal square ring and four small metal square rings around the cross-dipoles are employed to introduce new resonant frequencies, effectively extending the impedance and axial-ratio bandwidth. Four square patches printed on the middle substrate are connected to the ground plane by the vertical metal plates in order to reduce the antenna height. Thus, a compact wideband circularly polarized antenna is realized. In addition, a transmission zero can be introduced at the upper frequency stopband by the bent metal square rings, without using extra filter circuits. For verification, the proposed model is implemented and tested. The overall size of the model is 90mm×90mm×33mm (0.37λ0×0.37λ0×0.14λ0; λ0 denotes the center operating frequency). The measured impedance bandwidth and 3 dB axial-ratio (AR) bandwidth are 53.3% and 41%, respectively. An upper-band radiation suppression level greater than 15 dB is realized, indicating a good low-pass filter response.
Spectral Behavior of Fiber Bragg Gratings during Embedding in 3D-Printed Metal Tensile Coupons and Cyclic Loading
Authors: Ahmed Farid , Forhad Md Shahriar , Porag Mahmudul Hasan
Published: June 2024

Additive manufacturing (AM) enables the spatially configurable 3D integration of sensors in metal components to realize smart materials and structures. Outstanding sensing capabilities and size compatibility have made fiber optic sensors excellent candidates for integration in AM components. In this study, fiber Bragg grating (FBG) sensors were embedded in Inconel 718 tensile coupons printed using laser powder bed fusion AM. On-axis (fiber runs through the coupon’s center of axis) and off-axis (fiber is at 5° and 10° to the coupon’s center of axis) sensors were buried in epoxy resin inside narrow channels that run through the coupons. FBGs’ spectral evolutions during embedment in the coupons were examined and cyclic loading experiments were conducted to analyze and evaluate the sensor integration process, complex strain loading, process flaws, and sensing performance. This study also demonstrates that the AM process-born deficiencies such as poor surface finish and staircase effects can be detrimental to the embedded sensors and their sensing performance.
An Integrated Method for Microwave Absorption and External Thermal Flow Simulation in SAR Antenna Vacuum Thermal Tests
Authors: Pan Shangjie , Zhang Yuchang , Liu Chun , An Wanqing , Zhang Yu
Published: June 2024

The simulation of microwave absorption and external thermal flow is an essential aspect of the vacuum thermal testing process for Synthetic Aperture Radar (SAR) antenna. This paper proposes a novel integrated method for simulating microwave absorption and external thermal flow, specifically designed for vacuum thermal testing. The method employs a non-woven fabric square pyramid assembly as the primary structure to establish a low electromagnetic scattering environment. External heat flow simulation is achieved by arranging carbon fiber heating wires between square cones. Through numerical analysis and experimental tests, the influence of the position of the carbon fiber heating wire on the uniformity of heat flow and reflectivity was revealed. A prototype system is developed based on these findings. The external thermal flow is adjustable in the range of 80–550 W/m2, with a uniformity better than 5%. The reflectivity in the L to X microwave frequency band is basically better than −25 dB, and in local frequency bands, it is better than −30 dB. The system has been successfully applied in SAR antenna component and satellite vacuum thermal tests, meeting all ground simulation test requirements and exhibiting significant potential for widespread application.
Attention-Based Variational Autoencoder Models for Human–Human Interaction Recognition via Generation
Authors: Banerjee Bonny , Baruah Murchana
Published: June 2024

The remarkable human ability to predict others’ intent during physical interactions develops at a very early age and is crucial for development. Intent prediction, defined as the simultaneous recognition and generation of human–human interactions, has many applications such as in assistive robotics, human–robot interaction, video and robotic surveillance, and autonomous driving. However, models for solving the problem are scarce. This paper proposes two attention-based agent models to predict the intent of interacting 3D skeletons by sampling them via a sequence of glimpses. The novelty of these agent models is that they are inherently multimodal, consisting of perceptual and proprioceptive pathways. The action (attention) is driven by the agent’s generation error, and not by reinforcement. At each sampling instant, the agent completes the partially observed skeletal motion and infers the interaction class. It learns where and what to sample by minimizing the generation and classification errors. Extensive evaluation of our models is carried out on benchmark datasets and in comparison to a state-of-the-art model for intent prediction, which reveals that classification and generation accuracies of one of the proposed models are comparable to those of the state of the art even though our model contains fewer trainable parameters. The insights gained from our model designs can inform the development of efficient agents, the future of artificial intelligence (AI).
Design and Development Considerations of a Cyber Physical Testbed for Operational Technology Research and Education
Authors: Hosseinzadeh Salaheddin , Voutos Dionysios , Barrie Darren , Owoh Nsikak , Ashawa Moses , Shahrabi Alireza
Published: June 2024

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion.
Distance- and Angle-Based Hybrid Localization Integrated in the IEEE 802.15.4 TSCH Communication Protocol
Authors: Morano Grega , Simončič Aleš , Kocevska Teodora , Javornik Tomaž , Hrovat Andrej
Published: June 2024

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space.
Enlarged Eye-Box Accommodation-Capable Augmented Reality with Hologram Replicas
Authors: Moon Woonchan , Hahn Joonku
Published: June 2024

Augmented reality (AR) technology has been widely applied across a variety of fields, with head-up displays (HUDs) being one of its prominent uses, offering immersive three-dimensional (3D) experiences and interaction with digital content and the real world. AR-HUDs face challenges such as limited field of view (FOV), small eye-box, bulky form factor, and absence of accommodation cue, often compromising trade-offs between these factors. Recently, optical waveguide based on pupil replication process has attracted increasing attention as an optical element for its compact form factor and exit-pupil expansion. Despite these advantages, current waveguide displays struggle to integrate visual information with real scenes because they do not produce accommodation-capable virtual content. In this paper, we introduce a lensless accommodation-capable holographic system based on a waveguide. Our system aims to expand the eye-box at the optimal viewing distance that provides the maximum FOV. We devised a formalized CGH algorithm based on bold assumption and two constraints and successfully performed numerical observation simulation. In optical experiments, accommodation-capable images with a maximum horizontal FOV of 7.0 degrees were successfully observed within an expanded eye-box of 9.18 mm at an optimal observation distance of 112 mm.
Analysis of the Boss Structure of Type Ⅳ Composite Vessel for a High-Pressure Hydrogen Tube Trailer
Authors: Meng Zhiwen , Jin Suke , Yu Meng , Mehari Abel , Jiang Long
Published: June 2024

Currently, large-volume type IV composite vessel tube trailers garner significant attention and development within the hydrogen energy storage and transportation industry due to their cost-effectiveness and practicality. This study aims to assess the static strength and sealing performance of the boss structure in order to optimize its design. Firstly, a model of the mouth structure of type IV vessels was constructed to analyze the stress distributions in the boss and liner. Subsequently, innovative boss and liner structures were developed based on the primary mouth structure to investigate the impact of geometric dimensions through finite element analysis. This study revealed that changes in geometrical dimensions led to significant alterations in the stresses of the plastic liner in comparison to metallic bosses. Building upon these findings, the structural safety and sealing performance of the boss and liner structure were further validated through finite element analysis. The outcomes of this research can serve as a reference for guiding the structural design of bosses and aiding in the development of hydrogen storage vessels.
Sustainability Commitment Versus Earnings Management Practices: Saudi Insights
Authors: Al Barrak Thamir , Kouaib Amel
Published: June 2024

This paper aims to examine the impact of corporate sustainable management (CSM) on earnings management (EM) activities using annual data from 2018 to 2022 for 37 non-financial Saudi indexed firms. A multi-measure approach was utilized to proxy for EM (AEM and REM) and CSM (CSR sustainability reporting, CSR sustainability committee, CSR sustainability external audit, GRI report guidelines, ESG performance index). The empirical analysis employed pooled ordinary least squares (POLS) regression. The results suggest that CSM plays a significant role in reducing both AEM and REM practices, indicating that sustainability-oriented organizations mitigate EM activities. Furthermore, the study reveals a negative correlation between CSM and sales manipulation, overproduction, and cutting discretionary expenditures. This research supports the notion that companies prioritize sustainable management due to a focus on long-term strategies and transparency. This is the first work in the Middle East and Arab region, particularly in Saudi Arabia, investigating this association.
Investigating the Energy Potential and Degradation Kinetics of Nine Organic Substrates: Promulgating Sustainability in Developing Economies
Authors: Onu Peter , Pradhan Anup
Published: June 2024

To standardize, systematize, and improve the efficiency of the evaluation of biodegradable materials for large-scale biogas projects to support clean and sustainable energy development in emerging economies from a sub-Saharan African perspective, this paper analyzes and fits the potential for methane production (biochemical methane potential, BMP) and degradation kinetics of materials based on the gas production and degradation dynamics obtained from methane potential experiments. The first-order, modified first-order, and Gompertz models are used for analysis and fitting. The Gompertz model shows higher accuracy in fitting the methane production potential curve of screened materials, and the fitted methane potential values are close to the experimental values. When using BMP1% (cumulative gas production reaching 1% of cumulative gas production per day) as a quantitative indicator for the methane production potential of materials, the cumulative methane production reaches over 85% of the cumulative methane production at the end of the experiment. The BMP test time is shortened by 26.98% to 72.06%. Among the screened materials, the methane production potential (calculated using BMP1%) of dry rice straw, maize leaves, fresh rice, soybean straw, maize stalks, chicken manure hydrolysate, chicken feathers, kitchen/food waste, and chicken offal are 234.14, 241.01, 253.34, 331.40, 305.80, 508.41, 510.10, 630.7, and 621.32 mL/g, respectively. The kinetic parameters show that among the nine materials, cellulose materials (except for maize stalks and soybean straw), chicken manure, and kitchen waste are easily degradable materials. In contrast, chicken feathers and offal are slowly degradable materials. The study posits that comparing standardized methane production potential and methane production kinetic parameters among materials improves the efficiency of screening materials and is critical for biogas projects.
Sustainable Geoinformatic Approaches to Insurance for Small-Scale Farmers in Colombia
Authors: Abd Rabuh Ahmad , Teeuw Richard M. , Oakey Doyle Ray , Argyriou Athanasios V. , Foxley-Marrable Max , Wilkins Alan
Published: June 2024

This article presents a low-cost insurance system developed for smallholder farms in disaster-prone regions, primarily using free Earth observation (EO) data and free open source software’s (FOSS), collectively termed “sustainable geoinformatics.” The study examined 30 farms in Risaralda Department, Colombia. A digital elevation model (12.5 m pixels) from the ALOS PALSAR satellite sensor was used with a geographic information system (GIS) to map the terrain, drainage, and geohazards of each farming district. Google Earth Engine (GEE) was used to carry out time-series analysis of 15 EO and weather datasets for 1998 to 2020. This analysis enabled the levels of risk from hydrometeorological hazards to be determined for each farm of the study, providing key data for the setting of insurance premiums. A parametric insurance product was developed using a proprietary mobile phone app that collected GPS-tagged, time-stamped mobile phone photos to verify crop damage, with further verification of crop health also provided by daily near-real-time satellite imagery (e.g., PlanetScope with 3 m pixels). Machine learning was used for feature identification with the photos and the satellite imagery. Key features of this insurance system are its low operational cost and rapid damage verification relative to conventional approaches to farm insurance. This relatively fast, low-cost, and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policyholder farmers to recover more quickly from disasters.
Stealing from Phytotherapy—Heritage Conservation with Essential Oils: A Review, from Remedy to Sustainable Restoration Product
Authors: Reale Rita , Medeghini Laura , Botticelli Michela
Published: June 2024

Essential oils represent a good alternative to chemical biocides as they have antiseptic, antibacterial and antifungal properties, inhibiting the formation and proliferation of biofilms which can occur on outdoor and indoor surfaces of ancient and modern artworks. In this review, we illustrate how their antimicrobial properties, known since antiquity in phytotherapy, have been studied and tested for conservation purposes since the 1970s. In vitro tests on a wide range of plant extracts and in situ applications of specific volatile compounds have shown selective antibacterial and antifungal properties after the individual action of pure components or as the synergic effects of pre-determined mixtures. The review emphasizes the broad spectrum of materials—organic and inorganic—that essential oils can be applied to as biocides and finally emphasizes how the demand for commercial solutions has rapidly grown in bioconservation. The review demonstrates how research on the subject has been powerfully boosted by the ecofriendly and harmless character of essential oil applications, which makes them one of the most sustainable options in heritage conservation nowadays. The review elucidates how research is developing novel solutions for the application of EO blends—like encapsulation and microemulsions—and their optimization in commercial products for heritage conservation.
Big Data Analytics, Strategic Capabilities, and Innovation Performance: Mediation Approach of Organizational Ambidexterity
Authors: Alaskar Thamir H. , Alsadi Amin K. , Aloulou Wassim J. , Ayadi Faouzi M.
Published: June 2024

Our study explores the critical role played by organizational ambidexterity capabilities in the link between big data analytics, strategic innovation capabilities, and innovation performance. We developed a conceptual framework based on resource-based and dynamic capability views to examine the direct and indirect relationships among main variables. We used a quantitative approach to collect data from 172 Saudi IT and Telecom firms. We then employed structural equation modeling through Smart-PLS to test the study hypotheses. Our findings revealed that big data analytics and strategic innovation capabilities have a significant impact on organizational ambidexterity and then on innovation performance. Ambidexterity capability mediates between big data analytics capabilities and innovation performance and between strategic innovation capabilities and innovation performance. Our study contributes to the literature on big data and innovation. It offers valuable insights into the potential impacts of big data analytics, strategic innovation, and ambidexterity capabilities on innovation performance. It demonstrates how significantly boosting a firm’s capabilities for improved firms’ innovation performance can potentially enhance performance outcomes (e.g., competitiveness and sustainability). These findings provide managers with meaningful implications regarding the innovation performance that can be achieved by leveraging these important resources and capabilities.
Susceptibility Mapping of Thaw Slumps Based on Neural Network Methods along the Qinghai–Tibet Engineering Corridor
Authors: Li Pengfei , Dong Tianchun , Wang Yanhe , Luo Jing , Wang Huini , Zhang Huarui
Published: June 2024

Climate warming has induced the thawing of permafrost, which increases the probability of thaw slump occurrences in permafrost regions of the Qinghai–Tibet Engineering Corridor (QTEC). As a key and important corridor, thaw slump distribution is widespread, but research into effectively using neural networks to predict thaw slumping remains insufficient. This study automated the identification of thaw slumps within the QTEC and investigated their environmental factors and susceptibility assessment. We applied a deep learning-based semantic segmentation method, combining U-Net with ResNet101, to high spatial and temporal resolution images captured by the Gaofen-1 images. This methodology enabled the automatic delineation of 455 thaw slumps within the corridor area, covering 40,800 km², with corresponding precision, recall, and F1 scores of 0.864, 0.847, and 0.856, respectively. Subsequently, employing a radial basis function neural network model on this inventory of thaw slumps, we investigated environmental factors that could precipitate the occurrence of thaw slumps and generated sensitivity maps of thaw slumps along the QTEC. The model demonstrated high accuracy, and the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve reached 0.95. The findings of the study indicate that these thaw slumps are predominantly located on slopes with gradients of 1–18°, distributed across mid-elevation regions ranging from 4500 to 5500 m above sea level. Temperature and precipitation were identified as the predominant factors that influenced the distribution of thaw slumps. Approximately 30.75% of the QTEC area was found to fall within high to extremely high susceptibility zones. Moreover, validation processes confirmed that 82.75% of the thaw slump distribution was located within areas of high or higher sensitivity within the QTEC.