bluuebunny/crossref_metadata_2025 huggingface dataset:
Local Differential Privacy-Based Federated Learning under Personalized Settings
Authors: Wu Xia , Xu Lei , Zhu Liehuang
Published: March 2023

Federated learning is a distributed machine learning paradigm, which utilizes multiple clients’ data to train a model. Although federated learning does not require clients to disclose their original data, studies have shown that attackers can infer clients’ privacy by analyzing the local models shared by clients. Local differential privacy (LDP) can help to solve the above privacy issue. However, most of the existing federated learning studies based on LDP, rarely consider the diverse privacy requirements of clients. In this paper, we propose an LDP-based federated learning framework, that can meet the personalized privacy requirements of clients. We consider both independent identically distributed (IID) datasets and non-independent identically distributed (non-IID) datasets, and design model perturbation methods, respectively. Moreover, we propose two model aggregation methods, namely weighted average method and probability-based selection method. The main idea, is to weaken the impact of those privacy-conscious clients, who choose relatively small privacy budgets, on the federated model. Experiments on three commonly used datasets, namely MNIST, Fashion-MNIST, and forest cover-types, show that the proposed aggregation methods perform better than the classic arithmetic average method, in the personalized privacy preserving scenario.
Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
Authors: Ali Mohsin , Jehangiri Ali Imran , Alramli Omar Imhemed , Ahmad Zulfiqar , Ghoniem Rania M. , Ala’anzy Mohammed Alaa , Saleem Romana
Published: March 2023

The Software-Defined Networking (SDN) paradigm is one that is utilized frequently in data centers. Software-Defined Wireless Networking, often known as SDWN, refers to an environment in which concepts from SDN are implemented in wireless networks. The SDWN is struggling with challenges of scalability and performance as a result of the growing number of wireless networks in its coverage area. It is thought that SDN techniques, such as Mininet-Wi-Fi and Ryu Controller for wireless networks, can overcome the problems with scalability and performance. Existing Wi-Fi systems do not provide SDN execution to end clients, which is one reason why the capability of Wi-Fi is restricted on SDN architecture. Within the scope of this study, we analyzed Wi-Fi networks operating on SDN using the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). By utilizing a testbed consisting of Ryu Controller and Mininet-Wi-Fi, we were able to test Wi-Fi over SDN and evaluate its performance in addition to its scalability. When evaluating the performance of a network, we take into account a number of different metrics, such as bandwidth, round-trip time, and jitter. In order to assess the level of performance, the SDN-based Wi-Fi controller Ryu is linked to an increasing number of access points (1, 2, 3, and 4) as well as stations (10, 30, 50, and 100). The experimental findings obtained using Mininet-Wi-Fi indicate the scalability and dependability of the network performance provided by the SDN Wi-Fi network controller Ryu in an SDN environment. In addition, the round-trip time for TCP packets grows proportionally with the number of hops involved. A single access point is capable of simultaneously supporting up to fifty people at once.
Possibilities of Detecting Damage Due to Osmosis of GFRP Composites Used in Marine Applications
Authors: Swiderski Waldemar , Strag Martyna
Published: March 2023

The marine composites market is driven by the increasing demand for lightweight, corrosion-resistant, and impact-resistant boats. Polymer matrix composites are currently the most popular composite material in marine applications. Fiberglass composites are practically the main type of fiber composites that are used extensively in marine applications. Due to the aggressive sea environment, composite structural elements of ships are exposed to damage due to the phenomenon of osmosis. This damage is also favored by defects that result from impacts and technological errors during the production of these elements. Non-destructive testing methods are necessary to detect damage in the internal structure of the composite. The paper presents a numerical analysis of the possibility of using vibrothermography in the detection of defects in glass–fiber reinforced laminates in marine applications. Numerical simulations have shown that the most favorable method for detecting defects will be acoustic waves. This is an unusual application because, as a rule, the range of ultrasonic waves is used in vibrothermography. In our further works, it is planned to verify numerical calculations through experimental research. The applicability of the terahertz technique was also assessed. During the experimental testing, all defects in the test sample of the glass–fiber reinforced composite were detected using this technique. The presented results indicate the applicability of the presented methods for the detection of defects in composites used in marine applications.
A Non-Destructive Method for Predicting Critical Load, Critical Thickness and Service Life for Corroded Spherical Shells under Uniform External Pressure Based on NDT Data
Authors: Liu Cheng Huijuan , Lacidogna Giuseppe
Published: March 2023

A pressurized spherical shell that is continuously corroded will likely buckle and lose its stability. There are many analytical and numerical methods to study this problem (critical load, critical thickness, and service life), but the friendliness (operability) in engineering test applications is still not ideal. Therefore, in this paper, we propose a new non-destructive method by combining the Southwell non-destructive procedure with the stable analysis method of corroded spherical thin shells. When used carefully, it can estimate the critical load (critical thickness) and service life of these thin shells. Furthermore, its procedure proved to be more practical than existing methods; it can be easily mastered, applied, and generalized in most engineering tests. When used properly, its accuracy is acceptable in the field of engineering estimations. In the context of the high demand for non-destructive analysis in industry, it may be of sufficient potential value to be used as a reference for existing estimating methods based on NDT data.
A Method for Managing Software Assets in the Automotive Industry (Focusing on the Case of Hyundai Motor Company and Parts Makers)
Authors: Ryu Changhan , Do Sungryong
Published: March 2023

We propose a method for managing software assets in the automotive industry to enhance software competitiveness and to reduce development costs. The ownership of software assets in the automotive industry is held by automotive parts companies, making it challenging to exchange these technologies. Moreover, the criteria for determining software assets are often unclear, resulting in difficulties in integrating automotive software and implementing over-the-air updates. To address these issues, we suggest breaking down black-boxed software assets into tradable components, valuating them, and introducing the concept of exchanging software technology assets. Additionally, we provide a structured approach for recycling used software assets and establish a software asset management system for registration and tracking. Our proposed approach can help traditional automotive OEMs narrow the technology gap with automakers such as Tesla and improve their software competitiveness in the automotive industry. This paper contributes to the advancement of software asset management practices in the automotive industry, and provides insights into the integration of automotive software and over-the-air updates.
Device Orientation Independent Human Activity Recognition Model for Patient Monitoring Based on Triaxial Acceleration
Authors: Caramaschi Sara , Papini Gabriele B. , Caiani Enrico G.
Published: March 2023

Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (<20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.11 against a baseline model f1-score equal to 0.49±0.12.
Analytical Method and Analysis of Cold-Joint Interface
Authors: Masėnas Juozas , Šalna Remigijus , Juknevičius Linas , Valivonis Juozas
Published: March 2023

The behaviour of the interface between two concrete layers, subjected to shear, is a complex process that is influenced by many different parameters. Knowledge of concrete interface performance is insufficient to this day. Most of the existing analytical methods are only suitable for determining the highest interface shear resistance and do not consider the interface behaviour at other stages. This article focuses on smooth concrete interfaces, which have their layers cast at different times (cold-joint interface). By analysing the results of different experimental push-off tests, presented in the literature, a novel analytical method was developed for the previously described concrete interface. Furthermore, numerical models of push-off tests were developed. A parametric numerical analysis was employed to determine the influence of various physical parameters that might affect interface shear behaviour. Most importantly, the results acquired using the proposed analytical method were compared with the findings of numerical analysis and experimental results acquired by other authors. The values of shear stress and slippage were found to be relatively close. Therefore, the proposed analytical method is suitable for the analysis of smooth concrete interfaces cast at different times.
A Perfect Decomposition Model for Analyzing Transportation Energy Consumption in China
Authors: Yuan Yujie , Jiang Xiushan , Lai Chun Sing
Published: March 2023

Energy consumption in transportation industry is increasing. Transportation has become one of the fastest energy consumption industries. Transportation energy consumption variation and the main influencing factors of decomposition contribute to reduce transportation energy consumption and realize the sustainable development of transportation industry. This paper puts forwards an improved decomposition model according to the factors of change direction on the basis of the existing index decomposition methods. Transportation energy consumption influencing factors are quantitatively decomposed according to the transportation energy consumption decomposition model. The contribution of transportation turnover, transportation structure and transportation energy consumption intensity changes to transportation energy consumption variation is quantitatively calculated. Results show that there exists great energy-conservation potential about transportation structure adjustment, and transportation energy intensity is the main factor of energy conservation. The research achievements enrich the relevant theory of transportation energy consumption, and help to make the transportation energy development planning and carry out related policies.
Enhancing Biodegradation of Industrial Wastewater into Methane-Rich Biogas Using an Up-Flow Anaerobic Sludge Blanket Reactor
Authors: Ngema Lindokuhle , Sathiyah Devona , Tetteh Emmanuel Kweinor , Rathilal Sudesh
Published: March 2023

Anaerobic digestion (AD), the oldest technology used for treating waste, converts organic matter into biogas in the absence of oxygen. The current efforts focuses on improving the digestion of a local industrial wastewater to produce biogas and treat water for reuse. A lab-scale up-flow anaerobic sludge blanket (UASB) reactor operated at 37 °C was employed for the biodegradation the industrial wastewater. A one-factor-at-a-time (OFAT) approach was used to study the effects of influent chemical oxygen demand (CODin), hydraulic retention time (HRT), and magnetic nanoparticles (magnetite) on UASB biogas and COD elimination from digestate wastewater. The optimum HRT for the biodegradation of municipal wastewater was found to be 21 days with contaminants’ removals of 94%, 90.1%, and 98.9% for COD, color, and turbidity, respectively. The addition of magnetite resulted in 225 mL of cumulative biogas produced with 73% methane content, and treatability efficiency of 85%. The most influential factor was magnetite load, which stimulated the microbial activity via redox catalytic reaction in degrading the high organic wastewater (9590 mg COD/L) into biogas production. The prospects of upgrading lab-scale of this technological concept for bioenergy production is viable to mitigate wastewater management and fossil fuel environmental challenges.
Auditory Perception of Impulsiveness and Tonality in Vocal Fry
Authors: Devaraj Vinod , Roesner Imme , Wendt Florian , Schoentgen Jean , Aichinger Philipp
Published: March 2023

Vocal fry is a voice quality that occurs in a healthy voice, but it can also be a sign of a voice disorder. In this study, we investigated the relationship between the parameters of voice production, a dedicated psychoacoustic feature, and the perceptual aspects of vocal fry. Two perceptual experiments were carried out to determine whether the fundamental frequency, the open quotient, and the glottal area pulse skewness affect the perception of vocal fry in synthetic vowels. Thirteen listeners participated in the perceptual experiments to assess the following attributes: binary fry (yes/no) and impulsiveness, tonality, and naturalness (7-point Likert scales). The results suggest that the perception of vocal fry is mainly triggered by a low fundamental frequency, but the open quotient also plays a role, with narrower glottal area pulses slightly increasing the probability of perceived fry. Perceived tonality is inversely related to perceived impulsiveness. Internal reference standards of listeners appear to have fixed elements but may also be affected by anchoring and the short-term (i.e., within-vowel) context of the stimuli. In addition, the prominence of the peaks observed in the loudness curve over time appears to be related to graduations of fry.
Low-Profile FSS Design Methodology to Increase Isolation between Vehicle-Borne Multifrequency Antennas
Authors: Estévez Ailyn , Pérez Noemí , Valderas Daniel , Sancho Juan I.
Published: March 2023

The present work describes a new approach for the design of a Frequency-Selective Surface (FSS) in the context of frequency filters to increase isolation between two vehicle-borne antennas. A compact FSS design based on nested square meandered resonators is optimized for multifrequency operation. Furthermore, a design workflow is proposed. In general, the measurement of low-profile FSS does not correspond to simulation through Floquet modes based on periodic boundary conditions due to the lack of uniformity of mutual coupling among the FSS unit cells. The proposed method demonstrates the agreement between the infinite simulation and the measurement of the finite prototype once a convenient scale factor is applied, which facilitates the design workflow. In this case, an FSS is used as an efficient filter to increase the isolation between antennas by 6 dB in three representative bands (3GPP, WiFI I and II). In this way, multifrequency antennas can be placed at approximately half their actual distance with the same performance in spatial-constrained vehicular environments.
Characteristics of Dangerous Scenarios between Vehicles Turning Right and Pedestrians under Left-Hand Traffic
Authors: Matsui Yasuhiro , Oikawa Shoko
Published: March 2023

Pedestrian deaths account for the highest percentage of fatality caused by traffic accidents in Japan. Increasing pedestrian safety is a key objective for reducing such deaths. For pedestrian fatality caused by vehicles at low speed, turning the vehicle toward the right was the most common behavior under left-hand traffic. Autonomous emergency braking (AEB) systems for pedestrian safety have great potential to mitigate pedestrian injuries and fatalities in traffic accidents. However, pedestrian-AEB systems, especially for vehicles turning right, are still under development. This study identified the characteristics of dangerous traffic scenarios between vehicles turning right and pedestrians, focusing on two directions of pedestrian crossing: from the left to the right side (Left-Pedestrian) and from the right to the left side (Right-Pedestrian). The ego vehicle recorded near-miss incidents using a drive recorder. The results revealed that the Left-Pedestrian and Right-Pedestrian scenarios had different features for both the width of roads going to and through the intersection and the average of the travel speeds of the ego vehicles. They had similar characteristics in terms of the presence of other vehicle categories, but differences in the relationship of numbers and/or travel directions of other vehicles. The findings of this study will contribute to the development and evaluation of safety systems for preventing collisions between right-turning vehicles and pedestrians at intersections.
A Multipath Transmission System for Information-Centric Networking Based on Standalone Name Resolution
Authors: Jiang Lin , Deng Haojiang , Dang Shoujiang
Published: March 2023

Information-Centric Networking (ICN) essentially supports multipath transmission. However, current multipath schemes in ICN either necessitate major network infrastructure updates or necessitate specific network settings for terminal devices. To solve these problems, we propose MPTS-ICN, a multipath transmission system for ICN that realizes end-to-end multipath transmission. Taking the ICN architecture based on the standalone name resolution approach as a basis, MPTS-ICN is easier to implement and deploy than other ICN multipath schemes. Moreover, we have extended the original network layer protocol to support multipath data transmission in ICN. To set up concurrent transmission multipath efficiently, we propose a heuristic algorithm for the selection of multipath service nodes. Extensive experimental comparisons with existing data transmission methods show that in bandwidth-constrained scenarios, MPTS-ICN outperforms the best-route method by 83.6% and the ECMP method by 79.7% in average flow completion time.
Optimization of the Position and Stiffness of Passive Walking Assistance Devices
Authors: Guzelbulut Cem , Shimono Satoshi , Suzuki Katsuyuki
Published: March 2023

Walking is a fundamental movement in daily life; however, many factors affect walking that may reduce the mobility of the people. Walking assistance devices can help with gaining mobility back for people who suffer from walking problems. In the present study, a computational method to determine the location and stiffness of the assistive walking systems was developed. The human walking model was created by using nine rigid bodies and eight revolute joints connecting them in the sagittal plane. The walking assistance system was considered as a tension spring with both ends attached to the human walking model. A coordinate system was defined along the distal–proximal direction of the human body. The position of the walking assistance system was determined by using four design variables, and the optimal position of the assistive walking system to reduce the total positive joint energy was found around the hip joint at a walking speed of 1.3 m/s. Hip joint moment and power were significantly affected by the walking assistance system, and the total positive joint energy was reduced by 8.8%. Because walking speed significantly affects walking kinematics and kinetics, the effect of walking speed on the optimal walking assistance device was investigated. The position of the device was kept the same, and the optimal stiffness and free length of the spring were found at different walking speeds. Two different cases were considered: a speed-specific design in which stiffness characteristics were separately optimized for each speed and a general design in which stiffness characteristics were optimized by considering all walking speeds. It was found that, in both cases, hip joint moment and power significantly reduced, and the speed-specific design produced a slightly larger reduction in total joint energy. The performance of the walking assistance systems in both cases were found to be higher at faster walking speeds.
Influence of Oxygen Impurity on Nitrogen Atmospheric-Pressure Plasma Jet
Authors: Tsai Jia-Shiuan , Chen Jian-Zhang
Published: March 2023

This study discussed the effect of oxygen impurity in the inlet gas of a nitrogen atmospheric pressure plasma jet (APPJ). A numerical model that takes into account the fluid dynamics, heat transfer, mass transfer, diffusion, and chemical reactions was developed to simulate the nitrogen APPJ. Further, a DC nitrogen APPJ experiment was performed to verify the plasma temperature characteristics on the treated surface. The plasma temperature decreased with an increase in the oxygen impurity. Moreover, the oxygen impurity influenced the related excited and neutral species. Specifically, with added oxygen impurity, N-related species decreased whereas O- and NOx-related species increased. Because the excited state species constitutes the most important reactant in APPJ treatment, this study could serve as a reference for the adjustment of a nitrogen APPJ.
Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree
Authors: Lee Kyoung-Mun , Cho Min-Yang , Kim Jung-Gu , Lee Kyung-Ho
Published: March 2023

This study aimed to enhance the stability and security of power plant control network systems by developing detectable models using artificial intelligence machine learning techniques. Due to the closed system operation policy of facility manufacturers, it is challenging to detect and respond to security threats using standard security systems. With the increasing digitization of control systems, the risk of external malware penetration is also on the rise. To address this, machine learning techniques were applied to extract patterns from network traffic data produced at an average of 6.5 TB per month, and fingerprinting was used to detect unregistered terminals accessing the control network. By setting a threshold between transmission amounts and attempts using one month of data, an anomaly judgment model was learned to define patterns of data communication between the origin and destination. The hypothesis was tested using machine learning techniques if a new pattern occurred and no traffic occurred. The study confirmed that this method can be applied to not only plant control systems but also closed-structured control networks, where availability is critical, and other industries that use large amounts of traffic data. Experimental results showed that the proposed model outperformed existing models in terms of detection efficiency and processing time.
STJA-GCN: A Multi-Branch Spatial–Temporal Joint Attention Graph Convolutional Network for Abnormal Gait Recognition
Authors: Yin Ziming , Jiang Yi , Zheng Jianli , Yu Hongliu
Published: March 2023

Early recognition of abnormal gait enables physicians to determine a prompt rehabilitation plan for patients for the most effective treatment and care. The Kinect depth sensor can easily collect skeleton data describing the position of joints in the human body. However, the default human skeleton model of Kinect includes an excessive number of many joints, which limits the accuracy of the gait recognition methods and increases the computational resources required. In this study, we propose an optimized human skeleton model for the Kinect system and streamline the joints using a center-of-mass calculation. We integrate several techniques to propose an end-to-end, spatial–temporal, joint attention graph convolutional network (STJA-GCN) architecture. We conducted experiments with a fivefold cross-validation on two common datasets of information on abnormal gaits to evaluate the performance of the proposed method. The results show that the STJA-GCN achieved 93.17 and 92.08% accuracy on the two datasets, and compared to the original spatial–temporal graph convolutional network (ST-GCN), the recognition accuracy increases by 9.22 and 20.65%, respectively. Overall, the results demonstrate that the STJA-GCN can accurately recognize abnormal gaits and, thus, can support low-cost rehabilitation assessments at community hospitals or in patients’ homes.
Structure Design and Optimization Algorithm of a Lightweight Drive Rod for Precision Die-Cutting Machine
Authors: Wang Jing , Chen Xian , Li Yong
Published: March 2023

In order to solve the problems of excessive elastic deformation and excessive inertia force existed in the drive mechanism of traditional die-cutting machine, a lightweight drive rod with full symmetrical structure is proposed as the main force bearing component of the drive mechanism based on the kinematics analysis. The elastic deformation and inertia force of the lightweight drive rod are verified by static simulation analysis, and show that the weight of the drive rod is significantly reduced under the same deformation conditions, the traditional one. Further compared with, taking the minimum elastic deformation and lightweight as the optimization objectives, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to optimize the structural parameters of the drive rod. The results show that under the working conditions of 350 T die-cutting force and 125 r/min rotating speed, the elastic deformation of lightweight drive rod after structural optimization is smaller (the maximum deformation is 0.00988 mm) and the weight is lighter (27% less). The research data presented in this paper can be used as the theoretical basis for future research on die-cutting mechanism. The lightweight drive rod proposed in this study can be used in die-cutting devices with high die-cutting speed and high die-cutting accuracy.
Recognition and Pose Estimation Method for Stacked Sheet Metal Parts
Authors: Li Ronghua , Fu Jiaru , Zhai Fengxiang , Huang Zikang
Published: March 2023

To address issues such as detection failure and the difficulty in locating gripping points caused by the stacked placement of irregular parts in the automated sheet metal production process, a highly robust method for the recognition and pose estimation of parts is proposed. First, a decoding framework for parts of a two-dimensional code is established. The morphological closed operation and topology of contours are used to locate the two-dimensional code, and the type of the part is decoded according to the structure of the two-dimensional code extracted by the projection method. Second, the recognition model of the occluded part type is constructed. The edge information of parts is extracted. The contour convex hull is used to split the part contours, and the similarity of segmented contours is calculated based on the Fourier transform. Finally, the occluded parts are located. The corner points of the metal parts are extracted by the adjacency factor of the differential chain code sequence and the contour radius of curvature. The transformation matrix between the part and the standard template is calculated using similar contour segments and contour corner points. A stereo vision system is built to detect and localize the irregular sheet metal parts for experiments, including detection and information extraction experiments of the two-dimensional laser-generated code and detection and positioning experiments of parts under different occlusion rates. The experimental results show that the decoding framework can accurately decode the two-dimensional code made by a laser under low-contrast conditions, the average recognition rate can reach 93% at multiple occlusion rates, the geometric feature extraction algorithm is more accurate than common algorithms and no pseudo-corner points, the localization error is less than 0.8 mm, and the pose angle error is less than 0.6°. The methods proposed in this paper have high accuracy and robustness.
Solutions to the Problem of Freight Transport Flows in Urban Logistics
Authors: Batarlienė Nijolė , Bazaras Darius
Published: March 2023

The operation of a city’s logistics system is associated with many problematic issues, for example, intense pollution and negative impacts on the environment, uneven intensity of traffic flows, and other problems. This article focuses on heavy transport because it causes disruptions in the supply system and affects timely customer service. Optimization processes are associated with route selection, deployment of logistics centers, and the need for cargo consolidation–de-consolidation, which leads to the possibility of using the PPP (public–private partnership) method in practice. A unique aspect of this research is related to the problems of the first and last mile and the use of elements of the “sharing economy”. Therefore, this article aims to analyze the problematic challenges of the first and last mile and the role and significance of heavy transport in urban logistics. For that purpose, in addition to an analysis of scientific sources, an expert survey was conducted and responses related to the questions on optimizing heavy traffic flows in city logistics were analyzed. The study data were processed using Kendall’s correlation coefficient, the concordance method, and the consistency coefficient. During analysis, using the average rank transformation to weights (ARTIW) method, the subjective normalized weights of the main factors that have the greatest influence on a city’s logistics system were determined. Groups of interested parties are also named separately, for whom the results of the study and the formulated decision may be relevant. Based on the results of the research, a recommendation was formulated for the development of small cargo collection and distribution services through self-service terminals located on the outskirts of cities. Proposals are presented to reorganize the system, provide details of new infrastructure elements and suburban terminals, and encourage for the use of environmentally friendly vehicles with a lower carrying capacity.
Personalized Context-Aware Authentication Protocols in IoT
Authors: Bułat Radosław , Ogiela Marek R.
Published: March 2023

The IoT is a specific type of network with its own communication challenges. There are a multitude of low-power devices monitoring the environment. Thus, the need for authentication may be addressed by many available sensors but should be performed on the fly and use the personal characteristics of the device’s owner. Thus, a review and a study of the available authentication methods were performed for use in such a context, and as a result, a preliminary algorithm was proposed as a solution. The algorithm utilizes a variety of independent factors, including the user’s personal characteristics, knowledge, the context in which the authentication is taking place, and the use of steganography, to authenticate users in the dispersed environment. This algorithm encodes all of these factors into a single data vector, which is then used to verify the user’s identity or as a digital signature. Using this personalized context-aware protocol, it is possible to increase the reliability of authentication, given the emphasis on usability in low-computing-power but highly sensor-infused environments and devices. Although more testing is needed to optimize it as an industry solution, personalized protocols seem to have a future in the IoT world.
Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
Authors: Bonaventure Pauline , Guentas Linda , Burtet-Sarramegna Valérie , Amir Hamid
Published: March 2023

Saline ecosystems are often the target of spills and releases of pollutants such as metals, as many industrial companies settle in or around these areas. Metal pollution is a major threat for humans and ecosystems. In line with sustainable development, nature-based solutions and biological tools such as phytoremediation offer eco-friendly and low-cost solutions to remove metals or limit their spread in the environment. Many plant-growth-promoting (PGP) effects are frequently prospected in plant-associated microbes such as the production of auxins, siderophores, or extracellular polymeric substances to enhance phytoremediation. Halophytes are nowadays presented as good phytoremediators for metal-contaminated saline environments such as coastal regions, but little is known about the potential of their associated microbes in the bioaugmentation of this technique. Here, we review the studies that focused on halophytes-associated microbes and their plant-growth-promotion capacities. Moreover, we discuss the limitation and applicability of bioaugmented phytoremediation in saline ecosystems.
Applying Digital Twin and Multi-Adaptive Genetic Algorithms in Human–Robot Cooperative Assembly Optimization
Authors: Xuan Doan Thanh , Huynh Tran Van , Hung Nguyen Thanh , Thang Vu Toan
Published: March 2023

In this study, we utilized digital twin technology in combination with genetic algorithms to optimize human–robot cooperation in a miniature light bulb assembly production line. First, the digital twin was used to find the robot’s motion trajectory; a digital replica of the assembly system and human was created by combining sensors that track the position and activity characteristics of the human in the workspace, which helped to prevent human–robot conflicts. Then, a multi-adaptive genetic algorithm was applied to calculate optimal ergonomics and create a worker’s movement schedule. To ensure continuous operation and no shortage of materials, the worker must observe and move to the input conveyor and material pallets to supply materials to the system. It aimed to provide more input materials for the assembly line while allowing the worker’s task to take place in parallel with the robotic assembly operation. The algorithm was designed to reduce the number of moves required to obtain materials and to ensure that the robot always had enough materials to assemble along the defined trajectory, thus, saving labor and optimizing the manufacturing process. The combination of a digital twin and multi-adaptive genetic algorithm optimized the robot’s movement path and the number of movements performed by the human operator in parallel.
Development of an Artificial Intelligence-Based System for Predicting Weld Bead Geometry
Authors: Tran Ngoc-Hien , Bui Van-Hung , Hoang Van-Thong
Published: March 2023

The prediction of the weld bead geometry parameters is an important aspect of welding processes due to it is related to the strength of the welded joint. This research focuses on using statistical design techniques and a deep learning neural network to predict the weld bead shape parameters of shielded metal arc welding (SMAW), metal inert gas (MIG), and tungsten inert gas (TIG) welding processes. With the statistical design techniques, experiments were carried out to obtain the data for generating the regression models. Establishing mathematical models that shows the relationship between welding process parameters and weld bead size is significant for practical applications. The mathematical model enables the determination of the weld bead size when setting specific welding process parameters. In this research, experimental research results were obtained to build mathematical models showing the relationship between welding process parameters and weld bead geometries for SMAW, MIG, and TIG welding processes. The research results serve as the basis for establishing predictive systems or optimizing welding process parameters. With deep learning neural network techniques, we developed an artificial intelligence-based system for predicting complicated relations between the welding process parameters and the weld bead size. Both a regression model and the deep learning model result in a good correlation between the welding process parameters and the weld bead geometry.
Multi-Scale Feature Learning for Language Identification of Overlapped Speech
Authors: Aysa Zuhragvl , Ablimit Mijit , Hamdulla Askar
Published: March 2023

Language identification is the front end of multilingual speech-processing tasks. The study aims to enhance the accuracy of language identification in complex acoustic environments by proposing a multi-scale feature extraction method. This method replaces the baseline feature extraction network with a multi-scale feature extraction network (SE-Res2Net-CBAM-BILSTM) to extract multi-scale features. A multilingual cocktail party dataset was simulated, and comparative experiments were conducted with various models. The experimental results show that the proposed model achieved language identification accuracies of 97.6% for an Oriental language dataset and 75% for a multilingual cocktail party dataset Furthermore, comparative experiments show that our model outperformed three other models in the accuracy, recall, and F1 values. Finally, a comparison of different loss functions shows that the model performance was better when using focal loss.
Application of Rough Set Theory and Bow-Tie Analysis to Maritime Safety Analysis Management: A Case Study of Taiwan Ship Collision Incidents
Authors: Hsu Shao-Hua , Lee Meng-Tsung , Chang Yang-Chi
Published: March 2023

The surrounding waters of Taiwan are evaluated as a moderate risk environment by Casualty Return, Lloyd’s Registry of Shipping. Among all types of maritime accidents, ship collisions occur most often, which has severe consequences, including ship damage, sinking and death of crews, and destruction of marine environments. It is, therefore, imperative to mitigate the risk of ship collision by exploring the risk factors and then providing preventive measures. This study invited domain experts to form a decision-making group, which helped with the risk assessment. The initial set of risk factors was selected from the literature. The expert group then identified seven representative risk factors using rough set theory (RST). The researchers worked with the experts to delineate the diagram of a bow-tie analysis (BTA), which provided the causes, consequences, and preventive and mitigation measures for ship collision incidents. The results show an integrated research framework for the risk assessment of ship collision that can effectively identify key factors and associated managerial strategies to improve navigation safety, leading to a sound marine environment.
Trait Energy and Fatigue Influence Inter-Individual Mood and Neurocognitive Responses during Work Done While Sitting, Standing, and Intermittent Walking: A Randomized-Controlled Crossover Design
Authors: Gigliotti Hannah M. , Hodgson Cody , Riley Mary , Marshall Brittany , Ward-Ritacco Christie L. , Martin Joel , Boolani Ali
Published: March 2023

College students can be sedentary for a majority of the day, which may exacerbate mental health issues or lead to declines in cognitive task performance; however, interventions to address sedentary behaviors may not positively influence everyone. Therefore, the present study sought to identify inter-individual cognitive performance and mood changes of college students during the performance of a cognitive task battery, while seated, standing and with intermittent bouts of walking. Participants (n = 31, age = 25.80 ± 3.61 yrs, 7 male) completed a series of baseline questionnaires including the Trait Mental and Physical Energy and Fatigue survey. Using a randomized controlled cross-over design, participants completed 3 separate testing sessions. At each session, they performed a series of three rounds of cognitive tasks for 27 min and self-reporting mood states for 1 min in the seated position. Each round of cognitive testing was followed by a 2 min break. Each testing day had participants spend the 2 min break in a different condition: sitting, standing, or walking. A series of mixed ANOVAs were used for the primary analysis and a combination of machine learning regressors and classifiers were used for the secondary analysis. Our results suggest that there are unique inter-individual responses to each of the interventions used during the 2 min break. Participants who were low-trait mental and low-trait physical energy benefited the most from the standing desk intervention, while also reporting significant benefits of intermittent walking. However, participants who were low-trait mental fatigue had significant negative consequences of using both standing desks and walking intermittently, while those who were high-trait mental fatigue saw no change in cognitive responses or moods in those conditions. Post hoc machine learning analyses had modest accuracy rates (MAEs < 0.7 for regressors and accuracy rates >60% for classifiers), suggesting that trait mental and physical energy and fatigue may predict inter-individual responses to these interventions. Incorporating standing desks into college classroom settings may result in some students receiving cognitive benefits when inter-individual variability in mood and cognitive responses are accounted for.
Convergence Rates for Hestenes’ Gram–Schmidt Conjugate Direction Method without Derivatives in Numerical Optimization
Authors: Stein Ivie , Raihen Md Nurul
Published: March 2023

In this work, we studied convergence rates using quotient convergence factors and root convergence factors, as described by Ortega and Rheinboldt, for Hestenes’ Gram–Schmidt conjugate direction method without derivatives. We performed computations in order to make a comparison between this conjugate direction method, for minimizing a nonquadratic function f, and Newton’s method, for solving ∇f=0. Our primary purpose was to implement Hestenes’ CGS method with no derivatives and determine convergence rates.
A Study on Dust-Control Technology Used for Large Mining Heights Based on the Optimization Design of a Tracking Spray Nozzle
Authors: Hu Zuxiang , Zhang Benyi , Chang Haoqian
Published: March 2023

Intense cutting-induced dust production in fully mechanized mining faces (FMMFs) with large mining heights produces a high amount of dust that is difficult to capture and severely affects the working environment, threatening the health of occupational staff. The effective spray range and atomization performance of tracking sprays are counteracted by the influences of the mine’s height and ventilation airflow in FMMFs. Thus, optimizing the spray’s parameters and relationship between the effective spray range and atomization performance to reduce dust levels is the main priority of dust-control techniques. In this study, a new swirl-core atomization nozzle is developed based on fluid mechanics and the solid–liquid coalescence mechanism. The liquid generates a circumferential velocity when passing through the swirl core, which considerably increases the droplet breaking power and reduces the droplet cohesion factor, achieving a remarkable atomization effect. The spray angle of the new nozzle is 57°, which is 80.9% greater than the GZPW-16 mine-use nozzle (31.5°); the effective spray range increases from 5.2 to 5.9 m; and the spray’s mist saturation is significantly better than the GZPW-16 mine-use nozzle. Under different test pressures, the particle size range of the droplets produced by the new nozzle and dust particles on site satisfied the best synergy of droplet–dust coalescence. The total and respirable dust-reduction rates were 78% and 75.1%, respectively, which were 42% and 65% higher than those of the original nozzle. The new nozzle effectively improves the efficiency of the single dust-control technique of the tracking spray, which is significant for the dust-prevention and -control technology of FMMFs with large mining heights.
Climate Elasticity of Annual Runoff: Observation in Fifteen Forested Catchments on a Latitudinal Gradient in East Asia
Authors: Tanaka Nobuaki , Lai Yen-Jen , Im Sangjun , Mahali Maznah Binti , Tuankrua Venus , Kuraji Koichiro , Cleophas Fera , Tantasirin Chatchai , Gomyo Mie , Tseng Chun-Wei , Shiraki Katsushige , Hotta Norifumi , Asano Yuko , Inoue Hiroki , Nainar Anand
Published: March 2023

In order to overview the impact of climate change on runoff from forested catchments over Asian countries, we collected water balance data from fifteen long-term catchment monitoring stations (total monitoring period 1975–2018, not continuous), spanning from Sabah, Malaysia (our southernmost site), to Hokkaido, Japan (our northernmost site). We then employed an elasticity analysis to the dataset to examine how the annual runoff from each catchment responded to inter-annual fluctuations in annual rainfall and annual mean air temperature. As a result, we found that (1) the annual runoff was sensitive to annual rainfall for all the catchments examined. In addition, (2) the annual runoff from seven of the fifteen catchments was sensitive to inter-annual changes in the mean air temperature, which was likely due to changes in forest evapotranspiration. Three catchments, however, exhibited an increased runoff in a hot year. Finally, (3) the annual rainfall from the previous year (carry-over soil moisture) was important in explaining the variation in annual runoff in two tropical montane forest catchments. This study may serve as one of the pilot studies toward a comprehensive understanding of the climate elasticity of runoff in countries over Asia, because the examined catchments are unevenly and sparsely distributed over the area.