bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Parameter-Variabled and Coupled Chaotic System and Its Application in Image Encryption with Plaintext-Related Key Concealment
Authors: Wang Zuxi , Wang Siyang , Chen Zhong , Zhou Boyun
Published: September 2024

The design of a chaotic system and pseudo-random sequence generation method with excellent performance and its application in image encryption have always been attractive and challenging research fields. In this paper, a new model of parameter-variabled coupled chaotic system (PVCCS) is established by interaction coupling between parameters and states of multiple low-dimensional chaotic systems, and a new way to construct more complex hyperchaotic systems from simple low-dimensional systems is obtained. At the same time, based on this model and dynamical DNA codings and operations, a new pseudo-random sequence generation method (PSGM-3DPVCCS/DNA) is proposed, and it is verified that the generated pseudo-random sequence of PSGM-3DPVCCS/DNA has excellent random characteristics. Furthermore, this paper designs a novel pixel chain diffusion image encryption algorithm based on the proposed parameter-variabled coupled chaotic system (PVCCS) in which the hash value of plaintext image is associated with the initial key to participate in the encryption process so that the encryption key is closely associated with plaintext, which improves the security of the algorithm and effectively resists the differential cryptanalysis risk. In addition, an information hiding method is designed to hide the hash value of plaintext image in ciphertext image so that the hash value does not need to be transmitted in each encryption, and the initial key can be reused, which solves the key management problem in application and improves the application efficiency of the encryption algorithm. The experimental analysis shows that the chaotic system constructed in this paper is creative and universal and has more excellent chaotic characteristics than the original low-dimensional system. The sequence generated by the pseudo-random sequence generation method has excellent pseudo-random characteristics and security, and the image encryption algorithm can effectively resist differential cryptanalysis risk, showing advanced encryption performance.
Stakeholder Experiences and Perspectives on the Nueva Escuela Mexicana Reform in Mexican Primary Education
Authors: Bremner Nicholas , Sakata Nozomi , Bórquez Morales Lilia Sulema
Published: September 2024

Mexico has recently introduced the Nueva Escuela Mexicana (NEM) in Basic Education, which aims to create a more meaningful, contextualised, inclusive learning environment. This study examined stakeholder experiences and perspectives on the NEM in its first year of implementation. A total of 79 semi-structured individual and group interviews were conducted in 12 primary schools in three Mexican states. A total of 168 participants were interviewed: learners, teachers, head teachers, teacher trainers, and local and regional supervisors. This study found that stakeholders held a range of positive and negative views about the NEM reform. Participants reported several concerns, such as the lack of foundational knowledge developed through the new approach, doubts about certain curricular content (e.g., gender and sexuality), and a lack of explicit guidance and training. This paper offers policy recommendations, which may also be relevant to policymakers in other countries. Limitations of this study and recommendations for future studies are discussed.
Developing Pedagogical Principles for Digital Assessment
Authors: Jurāne-Brēmane Anžela
Published: September 2024

Digitalization has been a widely discussed topic in recent years, and it has entered various areas, including education. The issue of identifying and applying pedagogical aspects in digitalization has not been sufficiently discussed in the literature. This deficiency is particularly obvious in terms of assessment, an integral part of the education. Assessment is one of the most important aspects in managing education environments. The research data were obtained by combining two methods: ten pedagogical practices were examined that utilized various technologies in assessment already in use before the pandemic; data from the previous focus group discussions were reviewed regarding to pedagogical principles. A concept map was used in formulating the principles. Finally, the Delphi method with five experts from four counties was applied to obtain an expert view. As a result, five pedagogical principles of digital assessment were developed: (1) the clear purpose of the assessment and explicit criteria; (2) choice of adequate technology; (3) sufficient digital competence and technological equipment; (4) use of technological opportunities; (5) consistent analysis and use of assessment data. This is especially important given the need to demonstrate the appropriate and full use of technology. Those pedagogical principles contribute to a shared understanding between stakeholders in education.
How Professional Learning Networks Can Support Teachers’ Data Literacy: In Conversation with Experts
Authors: Warmoes Ariadne , Decabooter Iris , Van Gasse Roos , Struyven Katrien , Consuegra Els
Published: September 2024

In the last decade data-based decision making has been promoted to stimulate school improvement and student learning. However, many teachers struggle with one or more elements of data-based decision making, as they are often not data literate. In this exploratory study, professional learning networks are presented as a way to provide access to data literacy that is not available in schools. Through interviews with scientific experts (n = 14), professional learning networks are shown to contribute to data-based decision making in four ways: (1) by regulating motivation and emotions throughout the process, (2) by encouraging cooperation by sharing different perspectives and experiences, (3) increasing collaboration to solve complex educational problems, and (4) encouraging both inward and outward brokering of knowledge. The experts interviewed have varying experiences on whether professional learning networks should have a homogenous and heterogenous composition, the degree of involvement of the school leaders, and which competencies a facilitator needs to facilitate the process of data-based decision making in a professional learning network.
The Role of Growth Mindset on the Relationships between Students’ Perceptions of English Language Teachers’ Feedback and Their ESL Learning Performance
Authors: Muthukrishnan Priyadarshini , Fung Lan Loo , Anandhan Hariharasudan , Swamy D Premila
Published: September 2024

The importance of growth mindset and teachers’ feedback has been widely recognised to improve the English language performance of students; however, the impact of growth mindset as a mediator is least explored. Therefore, the study aimed to empirically analyse the interrelationships between growth mindset and teachers’ feedback levels on secondary school students’ English as a Second Language (ESL) performance and to study the mediation effects of growth mindset in the relationships. The research model examined growth mindset along with four types of feedback. The levels of feedback include task, process, self-regulation and self-based feedback that teachers provide to improve the ESL performance of students. Survey questionnaires were administered to 301 secondary school students in Class 9 from two private schools in India. The data were analysed using PLS-SEM 4.0 software. The results indicated that the direct effect of feedback that emphasised process and self-regulation fosters a growth mindset in ESL students. Feedback levels focused on task, process, self-regulation, and growth mindset significantly impact ESL performance. Moreover, growth mindset mediated the relationships between process and self-regulation-focused feedback and ESL performance. However, no evidence supports the relationship between self-focused feedback, growth mindset, and ESL performance. The study concludes with implications and directions for future research.
A Bio-Psycho-Social Approach to Understanding Optimism and Pessimism in Response to Stress
Authors: Paat Yok-Fong , Hope Trina L. , Ferreira-Pinto João B. , Olvera Alvarez Hector
Published: September 2024

Stress is widely known to have debilitating effects on physical health and mental wellbeing, particularly on one’s coping styles, personality traits, and outlook on life. Cumulative and chronic stress, which can serve as a triggering or aggravating factor for many pathological disorders if left unaddressed, has been linked to many life-threatening diseases. While many studies have looked at how optimism and pessimism are used as a form of coping mechanism, few have examined how different bio-psycho-social reactions to stress shape the level of optimism and pessimism. Using a sample of adult individuals aged 18 and older in the United States (n = 3361), this study addressed the following research questions: (1) What types of stress are predictive of optimism and pessimism? (2) Which responses to stress and coping mechanisms are most predictive of optimism and pessimism? (3) Do optimism and pessimism share the same stress-related risk and protective factors? Overall, this study found that while optimism and pessimism share conceptual similarities, they are not necessarily influenced by the same stress mechanisms. Stress, whether personal or financial, was associated with a negative outlook on life. This study showed that having good sleep quality and a lower number of psychological stress symptoms was linked to increasing optimism and reducing pessimism, while overeating or eating unhealthily was connected to both optimism and pessimism. Additionally, this study found that exercise/walking and emotional support mediated the effects of the responses to stress on the respondents’ level of optimism and pessimism.
Blockchain-Based Privacy Preservation for the Internet of Medical Things: A Literature Review
Authors: Alsadhan Afnan , Alhogail Areej , Alsalamah Hessah
Published: September 2024

The Internet of Medical Things (IoMT) is a rapidly expanding network comprising medical devices, sensors, and software that collect and exchange patient health data. Today, the IoMT has the potential to revolutionize healthcare by offering more personalized care to patients and improving the efficiency of healthcare delivery. However, the IoMT also introduces significant privacy concerns, particularly regarding data privacy. IoMT devices often collect and store large amounts of data about patients’ health. These data could be used to track patients’ movements, monitor their health habits, and even predict their future health risks. This extensive data collection and surveillance could be a major invasion of patient privacy. Thus, privacy-preserving research in an IoMT context is an important area of research that aims to mitigate these privacy issues. This review paper comprehensively applies the PRISMA methodology to analyze, review, classify, and compare current approaches of preserving patient data privacy within IoMT blockchain-based healthcare environments.
Modulation Format Recognition Scheme Based on Discriminant Network in Coherent Optical Communication System
Authors: Yang Fangxu , Tian Qinghua , Xin Xiangjun , Pan Yiqun , Wang Fu , Lázaro José Antonio , Fàbrega Josep M. , Zhou Sitong , Wang Yongjun , Zhang Qi
Published: September 2024

In this paper, we skillfully utilize the discriminative ability of the discriminator to construct a conditional generative adversarial network, and propose a scheme that uses few symbols to achieve high accuracy recognition of modulation formats under low signal-to-noise ratio conditions in coherent optical communication. In the one thousand kilometres G.654E optical fiber transmission system, transmission experiments are conducted on the PDM-QPSK/-8PSK/-16QAM/-32QAM/-64QAM modulation format at 8G/16G/32G baud rates, and the signal-to-noise ratio parameters are traversed under experimental conditions. As a key technology in the next-generation elastic optical networks, the modulation format recognition scheme proposed in this paper achieves 100% recognition of the above five modulation formats without distinguishing signal transmission rates. The optical signal-to-noise ratio thresholds required to achieve 100% recognition accuracy are 12.4 dB, 14.3 dB, 15.4 dB, 16.2 dB, and 17.3 dB, respectively.
Graph Neural Network-Based Modeling with Subcategory Exploration for Drug Repositioning
Authors: Lu Rong , Liang Yong , Lin Jiatai , Chen Yuqiang
Published: September 2024

Drug repositioning is a cost-effective approach to identifying new indications for existing drugs by predicting their associations with new diseases or symptoms. Recently, deep learning-based models have become the mainstream for drug repositioning. Existing methods typically regard the drug-repositioning task as a binary classification problem to find the new drug–disease associations. However, drug–disease associations may encompass some potential subcategories that can be used to enhance the classification performance. In this paper, we propose a prototype-based subcategory exploration (PSCE) model to guide the model learned with the information of a potential subcategory for drug repositioning. To achieve this, we first propose a prototype-based feature-enhancement mechanism (PFEM) that uses clustering centroids as the attention to enhance the drug–disease features by introducing subcategory information to improve the association prediction. Second, we introduce the drug–disease dual-task classification head (D3TC) of the model, which consists of a traditional binary classification head and a subcategory-classification head to learn with subcategory exploration. It leverages finer-grained pseudo-labels of subcategories to introduce additional knowledge for precise drug–disease association classification. In this study, we conducted experiments on four public datasets to compare the proposed PSCE with existing state-of-the-art approaches and our PSCE achieved a better performance than the existing ones. Finally, the effectiveness of the PFEM and D3TC was demonstrated using ablation studies.
A Single-Transmitter Multi-Receiver Wireless Power Transfer System with High Coil Misalignment Tolerance and Variable Power Allocation Ratios
Authors: Luo Yanting , Dai Zhuoyue , Yang Yongmin
Published: September 2024

This article proposes a single-transmitter multi-receiver wireless power transfer (STMR-WPT) system, which uses a cross-overlapped bipolar coil as the transmitter and multiple square unipolar coils as the receivers. By using this structure, the magnetic field of the system can be adjusted to accommodate different coil misalignment conditions. In addition, the proposed system uses C-CLCs networks to achieve separate load power allocation. Thus, relay coils, complex multi-frequency transmission channels and multiple independent power supplies can be avoided. A mapping impedance-based circuit model was established to analyze the characteristics of the system, and then a single-frequency power allocation method was presented. Through this method, the STMR-WPT system can achieve load power allocation at any specified ratios under different mutual inductance and load impedance conditions. Finally, an experimental STMR-WPT system was built. The side lengths of the transmitter and receiver coils are 400 mm and 160 mm, respectively. The measurement results indicated that when the lateral or longitudinal coil misalignment varies within the range of 0~200 mm, the coupling coefficient decreases by a maximum of 6% compared to the initial value, and when the angular coil misalignment varies within the range of 0~90 degrees, the coupling coefficient decreases by a maximum of 22% compared to the initial value. In four different power allocation scenarios, the experimental STMR-WPT system successfully achieved the expected power allocation goals.
Study on the Design of Series-Type All-DC Wind Farms Based on Half-Bridge Voltage Balancing Circuits
Authors: Su Xiaochen , Wang Haiyun , Li Zhanlong , Ma Qianyu
Published: September 2024

Offshore wind farms connected in series, with each wind turbine connected in series with one another, enhance the coupling between them. Significant differences in wind speeds between neighboring DC wind turbines (DCWTs) might result in a substantial disparity in the output voltage, hence posing a risk of overvoltage. Nevertheless, implementing voltage-limiting configurations for DCWTs might lead to the dissipation of wind energy, thereby diminishing the wind farm’s capacity to deliver electricity. This work introduces a half-bridge voltage balancing circuit (HVBC) topology as a solution to the issue of DCWT output voltage changes affecting the stable operation of wind farms. The proposed HVBC topology is designed specifically for large-capacity series-connected all-DC wind farms where wind speed variations occur. This design achieves power decoupling for series-connected all-DC wind farms by providing current compensation to the series-connected DCWTs. A control strategy is devised by examining the decoupling principle and operational characteristics of the HVBC. A 60 kV/48 MW tandem-type all-DC wind farm model consisting of six DCWTs in series is built in Matlab/Simulink. The model is then simulated to evaluate its performance under conditions of unequal wind speed, rapid changes in wind speed, and wind turbine failure shutdown. This research verifies the feasibility of the HVBC topology and improves the stability of the series-type all-DC wind farm.
Multi-Objective Combinatorial Optimization Algorithm Based on Asynchronous Advantage Actor–Critic and Graph Transformer Networks
Authors: Jia Dongbao , Cao Ming , Hu Wenbin , Sun Jing , Li Hui , Wang Yichen , Zhou Weijie , Yin Tiancheng , Qian Ran
Published: September 2024

Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity.
A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements
Authors: Shao Zhimin , Ding Qingyang , Meng Lingzhen , Yang Tao , Chen Shengpeng , Li Yapeng
Published: September 2024

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.
Simulation-Based Hybrid Energy Storage Composite-Target Planning with Power Quality Improvements for Integrated Energy Systems in Large-Building Microgrids
Authors: He Chunguang , Tan Xiaolin , Liu Zixuan , An Jiakun , Li Xuejun , Li Gengfeng , Zhang Runfan
Published: September 2024

In this paper, we present an optimization planning method for enhancing power quality in integrated energy systems in large-building microgrids by adjusting the sizing and deployment of hybrid energy storage systems. These integrated energy systems incorporate wind and solar power, natural gas supply, and interactions with electric vehicles and the main power grid. In the optimization planning method developed, the objectives of cost-effective and low-carbon operation, the lifecycle cost of hybrid energy storage, power quality improvements, and renewable energy utilization are targeted and coordinated by using utility fusion theory. Our planning method addresses multiple energy forms—cooling, heating, electricity, natural gas, and renewable energies—which are integrated through a combined cooling, heating, and power system and a natural gas turbine. The hybrid energy storage system incorporates batteries and compressed-air energy storage systems to handle fast and slow variations in power demand, respectively. A sensitivity matrix between the output power of the energy sources and the voltage is modeled by using the power flow method in DistFlow, reflecting the improvements in power quality and the respective constraints. The method proposed is validated by simulating various typical scenarios on the modified IEEE 13-node distribution network topology. The novelty of this paper lies in its focus on the application of integrated energy systems within large buildings and its approach to hybrid energy storage system planning in multiple dimensions, including making co-location and capacity sizing decisions. Other innovative aspects include the coordination of hybrid energy storage combinations, simultaneous siting and sizing decisions, lifecycle cost calculations, and optimization for power quality enhancement. As part of these design considerations, microgrid-related technologies are integrated with cutting-edge nearly zero-energy building designs, representing a pioneering attempt within this field. Our results indicate that this multi-objective, multi-dimensional, utility fusion-based optimization method for hybrid energy storage significantly enhances the economic efficiency and quality of the operation of integrated energy systems in large-building microgrids in building-level energy distribution planning.
Pixel-Level Decision Fusion for Land Cover Classification Using PolSAR Data and Local Pattern Differences
Authors: Papadopoulos Spiros , Anastassopoulos Vassilis , Koukiou Georgia
Published: September 2024

Combining various viewpoints to produce coherent and cohesive results requires decision fusion. These methodologies are essential for synthesizing data from multiple sensors in remote sensing classification in order to make conclusive decisions. Using fully polarimetric Synthetic Aperture Radar (PolSAR) imagery, our study combines the benefits of both approaches for detection by extracting Pauli’s and Krogager’s decomposition components. The Local Pattern Differences (LPD) method was employed on every decomposition component for pixel-level texture feature extraction. These extracted features were utilized to train three independent classifiers. Ultimately, these findings were handled as independent decisions for each land cover type and were fused together using a decision fusion rule to produce complete and enhanced classification results. As part of our approach, after a thorough examination, the most appropriate classifiers and decision rules were exploited, as well as the mathematical foundations required for effective decision fusion. Incorporating qualitative and quantitative information into the decision fusion process ensures robust and reliable classification results. The innovation of our approach lies in the dual use of decomposition methods and the application of a simple but effective decision fusion strategy.
Optimal Placement of HVDC-VSC in AC System Using Self-Adaptive Bonobo Optimizer to Solve Optimal Power Flows: A Case Study of the Algerian Electrical Network
Authors: Alouache Houssam Eddine , Sayah Samir , Bosisio Alessandro , Hamouda Abdellatif , Kouadri Ramzi , Shirvani Rouzbeh
Published: September 2024

Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy.
Zero-Shot Image Caption Inference System Based on Pretrained Models
Authors: Zhang Xiaochen , Shen Jiayi , Wang Yuyan , Xiao Jiacong , Li Jin
Published: September 2024

Recently, zero-shot image captioning (ZSIC) has gained significant attention, given its potential to describe unseen objects in images. This is important for real-world applications such as human–computer interaction, intelligent education, and service robots. However, the zero-shot image captioning method based on large-scale pretrained models may generate descriptions containing objects that are not present in the image, which is a phenomenon termed “object hallucination”. This is because large-scale models tend to predict words or phrases with high frequency, as seen in the training phase. Additionally, the method set a limitation to the description length, which often leads to an improper ending. In this paper, a novel approach is proposed to address and reduce the object hallucination and improper ending problem in the ZSIC task. We introduce additional emotion signals as guidance for sentence generation, and we find that proper emotion will filter words that do not appear in the image. Moreover, we propose a novel strategy that gradually extends the number of words in a sentence to confirm the generated sentence is properly completed. Experimental results show that the proposed method achieves the leading performance on unsupervised metrics. More importantly, the subjective examples illustrate the effect of our method in improving hallucination and generating properly ending sentences.
Advances in Human–Machine Interaction, Artificial Intelligence, and Robotics
Authors: Solanes Juan Ernesto , Gracia Luis , Valls Miro Jaime
Published: September 2024

The convergence of artificial intelligence (AI), robotics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and extended reality (XR) is transforming the way humans interact with machines [...]
Blockchain Handshaking with Software Assurance: Version++ Protocol for Bitcoin Cryptocurrency
Authors: Sarker Arijet , Wuthier Simeon , Kim Jinoh , Kim Jonghyun , Chang Sang-Yoon
Published: September 2024

Cryptocurrency software implements cryptocurrency operations (including the distributed consensus protocol and peer-to-peer networking) and often involves the open-source community. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol by providing the verification capability of the Bitcoin software by peers and preventing any potential peer from establishing a connection with modified Bitcoin software. Since we focus on Bitcoin (the most popular cryptocurrency) for implementation and integration, we call our scheme Version++, built on and advancing the current Bitcoin handshaking protocol based on the Version message. Our Version++ protocol providing software assurance is distinguishable from previous research because it is permissionless, distributed, and lightweight for its cryptocurrency application. Our scheme is permissionless since it does not require a centralized trusted authority (unlike the remote software attestation techniques from trusted computing); it is distributed since the peer checks the software assurances of its own peer connections; and it is designed for efficiency/lightweight to support the dynamic nature of the peer connections and large-scale broadcasting in cryptocurrency networking. Utilizing Merkle Tree for the efficiency of the proof verification, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. Our prototype-based performance analyses demonstrate the lightweight design of Version++. The peer-specific verification grows logarithmically with the number of software files in processing time and in storage. Furthermore, the Version++ verification overhead is small compared to the version-verack handshaking process; we measure the overhead to be 0.524% in our local networking environment between virtual machines and between 0.057% and 0.282% (depending on the peer location) in our more realistic cloud-based experiments with remote peer machines.
Learning Underwater Intervention Skills Based on Dynamic Movement Primitives
Authors: Yang Xuejiao , Zhang Yunxiu , Li Rongrong , Zheng Xinhui , Zhang Qifeng
Published: September 2024

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments.
Modeling the Influence of Thermal Phenomena in Inductors and Capacitors on the Characteristics of the SEPIC Converter
Authors: Detka Kalina , Górecki Krzysztof , Downar-Zapolski Michał
Published: September 2024

The paper presents the results of measurements and calculations of the SEPIC converter characteristics, taking into account thermal phenomena in semiconductor devices and passive elements. Compact electrothermal models of the MOSFET transistor, diode, capacitor, and inductor are proposed. Parasitic phenomena are also included in these models. The form of the developed models and the method of determining the values of their parameters are presented. The correctness of the formulated models was verified experimentally. Calculations and measurements of the characteristics of SEPIC converters containing inductors with ferromagnetic cores made of different materials were carried out. The obtained results of the investigations are discussed, and the range of applicability of the formulated models is described. It was shown that, at the considered operating conditions at an ambient temperature equal to 22 °C, the temperature of capacitors can exceed 40 °C, whereas the temperatures of inductors can even reach 50 °C.
Development of a T2D App for Elderly Users: Participatory Design Study via Heuristic Evaluation and Usability Testing
Authors: Liu Zhengyang , Yu Xinran
Published: September 2024

Diabetes management applications effectively help patients monitor blood glucose levels and adjust diet and exercise plans. However, most diabetes management apps, including the SugarShift application developed in 2023, use generalized designs that often fail to consider the unique cognitive and physiological characteristics of elderly users, thereby impacting their acceptance and usage. This paper proposes an enhancement for the SugarShift application by introducing a mode specifically designed for elderly users. This proposed enhancement aims to directly involve elderly patients so as to meet their needs better through a participatory design approach. A heuristic evaluation conducted with six experts identified 126 usability issues and 179 heuristic violations in the current version, which has led to the recommendation to develop optimization strategies tailored for elderly users. Subsequent usability testing with 28 elderly patients and six experts emphasized the importance of interface simplicity, logical operation, and interaction quality in enhancing user experience. These factors improve usability, facilitate information processing, and reduce cognitive load. Despite the overall usability of SUS scores, significant challenges still need to be addressed in information display, system feedback, and user interaction. Recommendations for future enhancements include simplifying data entry and presentation, improving readability, and providing timely feedback to enhance usability and user satisfaction, ultimately increasing user retention.
An 88 dB SNDR 100 kHz BW Sturdy MASH Delta-Sigma Modulator Using Self-Cascoded Floating Inverter Amplifiers
Authors: Hao Xirui , Yuan Yidong , Pan Jie , Lu Zhaonan , Song Shuang , Yu Xiaopeng , Zhao Menglian
Published: September 2024

Battery-powered Internet-of-Things applications require high-resolution, energy-efficient analog-to-digital converters (ADCs). There are still limited works on sub-MHz-bandwidth ADC designs. This paper presents a sturdy multi-stage shaping (SMASH) discrete-time (DT) delta-sigma modulator (DSM) structure using a self-cascoded floating-inverter-based dynamic amplifier (FIA). The proposed structure removes the explicit quantization error extraction of the first loop and all the feedback DACs in the cascaded loop, decreasing the design complexity of the circuit. This enables the proposed DT DSM to operate at a higher speed, which is suitable for achieving high-order noise at a low oversampling ratio (OSR). The proposed self-cascoded FIA is more power-efficient and can acquire more than 45 dB DC gain under a 1.2 V supply. The DT DSM implemented in a piece of 55 nm CMOS technology measures an 88.0 dB peak signal-to-noise-and-distortion ratio (SNDR) in a 100 kHz bandwidth (BW) and an 85.3 dB dynamic range (DR), consuming 249.1 μW from a 1.2 V supply at 10 MS/s. The obtained 174.0 dB SNDR-based Schreier figure-of-merit (FoMs) is competitive within state-of-art high-resolution (SNDR > 85 dB) and general-purpose (sub-MHz-bandwidth) ΔΣ ADCs.
A Study on Designing Cyber Training and Cyber Range to Effectively Respond to Cyber Threats
Authors: Shin Yongjoo , Kwon Hyukjin , Jeong Jaeyeong , Shin Dongkyoo
Published: September 2024

As cyberattacks become increasingly sophisticated with advancements in information and communication technology, the impact of cyberspace threats is growing in both civilian and defense sectors. The utilization of cyber capabilities in operations is on the rise, prompting major nations to continuously enhance their cyber capabilities. This study aims to establish a systematic approach to cyber operations training and propose a framework for the development of cyber training. A hybrid cyber training system is designed as a plan for temporal and spatial integration to simultaneously combine simulation-based training with real-world target training. To develop this concept, a literature review was conducted, expert consultations were held, and data were collected and analyzed through visits to relevant organizations and units. Additionally, the fundamental components of cyber training were examined from environmental, scenario-based, and operational perspectives, leading to the presentation of a development direction for effective cyber training. This study is anticipated to enhance response capabilities to evolving cyber threats and attacks, improve cyber operational proficiency, and secure cyber power to achieve dominance in cyberspace.
Unfixed Seasonal Partition Based on Symbolic Aggregate Approximation for Forecasting Solar Power Generation Using Deep Learning
Authors: Kwak Minjin , Chuluunsaikhan Tserenpurev , Marakhimov Azizbek , Kim Jeong-Hun , Nasridinov Aziz
Published: September 2024

Solar energy is an important alternative energy source, and it is essential to forecast solar power generation for efficient power management. Due to the seasonal characteristics of weather features, seasonal data partition strategies help develop prediction models that perform better in extreme weather-related situations. Most existing studies rely on fixed season partitions, such as meteorological and astronomical, where the start and end dates are specific. However, even if the countries are in the same Northern or Southern Hemisphere, seasonal changes can occur due to abnormal climates such as global warming. Therefore, we propose a novel unfixed seasonal data partition based on Symbolic Aggregate Approximation (SAX) to forecast solar power generation. Here, symbolic representations generated by SAX are used to select seasonal features and obtain seasonal criteria. We then employ two-layer stacked LSTM and combine predictions from various seasonal features and partitions through ensemble methods. The datasets used in the experiments are from real-world solar panel plants such as in Gyeongju, South Korea; and in California, USA. The results of the experiments show that the proposed methods perform better than non-partitioned or fixed-partitioned solar power generation forecasts. They outperform them by 2.2% to 3.5%; and 1.6% to 6.5% in the Gyeongju and California datasets, respectively.
Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review
Authors: Agrahari Anurag , Dhabu Meera M. , Deshpande Parag S. , Tiwari Ashish , Baig Mogal Aftab , Sawarkar Ankush D.
Published: September 2024

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.
Diamond FET Biosensor Fabrication and Application
Authors: Zou Fengling , Wang Zimin , Lin Zelong , Wang Chengyong , Yuan Zhishan
Published: September 2024

Diamond is renowned as the ultimate semiconductor thanks to its exceptional physical properties, including unmatched hardness, exceptional wear resistance, superior mechanical and tribological characteristics, and high fracture strength. Diamond solution-gate field-effect transistors (D-SGFETs) leverage these advantages, along with their outstanding high-power and high-frequency performance, excellent thermal conductivity, wide bandgap, high carrier mobility, and rapid saturation speed. These features make D-SGFETs highly promising for fast and precise biomedical detection applications. This paper provides a comprehensive review of the fabrication techniques for diamond SGFETs, encompassing diamond film synthesis, surface conduction layer formation, source/drain fabrication, and FET packaging. Furthermore, the study delves into the surface functionalization of diamond SGFETs and their diverse applications in biomedical detection. Finally, the paper discusses the future outlook of diamond SGFETs in advancing biomedical detection technologies.
Multi-Modal Fusion Network with Multi-Head Self-Attention for Injection Training Evaluation in Medical Education
Authors: Li Zhe , Kanazuka Aya , Hojo Atsushi , Nomura Yukihiro , Nakaguchi Toshiya
Published: September 2024

The COVID-19 pandemic has significantly disrupted traditional medical training, particularly in critical areas such as the injection process, which require expert supervision. To address the challenges posed by reduced face-to-face interactions, this study introduces a multi-modal fusion network designed to evaluate the timing and motion aspects of the injection training process in medical education. The proposed framework integrates 3D reconstructed data and 2D images of hand movements during the injection process. The 3D data are preprocessed and encoded by a Long Short-Term Memory (LSTM) network to extract temporal features, while a Convolutional Neural Network (CNN) processes the 2D images to capture detailed image features. These encoded features are then fused and refined through a proposed multi-head self-attention module, which enhances the model’s ability to capture and weigh important temporal and image dynamics in the injection process. The final classification of the injection process is conducted by a classifier module. The model’s performance was rigorously evaluated using video data from 255 subjects with assessments made by professional physicians according to the Objective Structured Assessment of Technical Skill—Global Rating Score (OSATS-GRS)[B] criteria for time and motion evaluation. The experimental results demonstrate that the proposed data fusion model achieves an accuracy of 0.7238, an F1-score of 0.7060, a precision of 0.7339, a recall of 0.7238, and an AUC of 0.8343. These findings highlight the model’s potential as an effective tool for providing objective feedback in medical injection training, offering a scalable solution for the post-pandemic evolution of medical education.
Enhanced Data Processing and Machine Learning Techniques for Energy Consumption Forecasting
Authors: Shin Jihye , Moon Hyeonjoon , Chun Chang-Jae , Sim Taeyong , Kim Eunhee , Lee Sujin
Published: September 2024

Energy consumption plays a significant role in global warming. In order to achieve carbon neutrality and enhance energy efficiency through a stable energy supply, it is necessary to pursue the development of innovative architectures designed to optimize and analyze time series data. Therefore, this study presents a new architecture that highlights the critical role of preprocessing in improving predictive performance and demonstrates its scalability across various energy domains. The architecture, which discerns patterns indicative of time series characteristics, is founded on three core components: data preparation, process optimization methods, and prediction. The core of this architecture is the identification of patterns within the time series and the determination of optimal data processing techniques, with a strong emphasis on preprocessing methods. The experimental results for heat energy demonstrate the potential for data optimization to achieve performance gains, thereby confirming the critical role of preprocessing. This study also confirms that the proposed architecture consistently enhances predictive outcomes, irrespective of the model employed, through the evaluation of five distinct prediction models. Moreover, experiments extending to electric energy validate the architecture’s scalability and efficacy in predicting various energy types using analogous input variables. Furthermore, this research employs explainable artificial intelligence to elucidate the determinants influencing energy prediction, thereby contributing to the management of low-carbon energy supply and demand.