bluuebunny/crossref_metadata_2025 huggingface dataset:
Increased Epicardial Adipose Tissue and Heart Characteristics Are Correlated with BMI and Predict Silent Myocardial Infarction in Sudden Cardiac Death Subjects: An Autopsy Study
Authors: Hogea Timur , Noemi Nagy , Suciu Bogdan Andrei , Brinzaniuc Klara , Chinezu Laura , Arbănași Emil Marian , Kaller Réka , Carașca Cosmin , Arbănași Eliza Mihaela , Vunvulea Vlad , Hălmaciu Ioana , Mureșan Adrian Vasile , Russu Eliza , Ciucanu Claudiu Constantin , Radu Casandra Maria , Radu Corina Carmen
Published: June 2023

Background: Sudden cardiac death (SCD) is a significant global public health issue and the leading cause of death worldwide. Its etiopathogenesis is complex and multilayered, involving dynamic factors interacting with a preexistent cardiovascular pathology, frequently unknown, and resulting in cardiac rhythm disorders and cardiac arrest; Methods: This study conducted a retrospective descriptive analysis over a one-year period, identifying 321 autopsy cases of sudden deaths from the Institute of Legal Medicine in Mures County, Romania, in 2019. From the 321 sudden death cases, 189 autopsy reports were selected for analysis based on inclusion and exclusion; Results: The autopsies had a mean age of 61.16 years and included 140 males and 49 females. No significant differences were found between the silent myocardial infarction (SMI) and no-SMI groups regarding demographic data. The SMI group exhibited higher thickness of LV (left ventricle), IV (interventricular septum), EAT LCx (epicardial adipose tissue at left circumflex artery), EAT LAD (epicardial adipose tissue at left anterior descending artery), heart weight, and BMI (body mass index). The left coronary artery showed a higher incidence of type V plaques, while the right coronary artery showed higher incidences of type V and type VI plaque. The SMI group also exhibited a higher incidence of moderate and severe valvular atherosclerosis, severe left ventricle dilatation, and a lower incidence of mild left ventricle dilatation. In addition, the SMI group showed a higher presence of contraction band necrosis on histological examination. Multivariate analysis revealed that type V and type VI plaques for the right and left coronary arteries, moderate and severe valvular atherosclerosis, severe left ventricle dilatation, heart weight, EAT LCx, EAT LAD, LV thickness, IV thickness, BMI, and the presence of contraction band necrosis are all independent predictors of SMI; Conclusions: The findings suggest that SCD is a complex condition, and its etiopathogenesis involves dynamic factors interacting with pre-existing cardiovascular pathology. The risk factors of SCD are similar to those of ischemic heart disease. The findings of this study could guide clinicians in identifying patients at risk of SCD and implementing preventive measures.
Self-Reported Systemic Sclerosis-Related Symptoms Are More Prevalent in Subjects with Raynaud’s Phenomenon in the Lifelines Population: Focus on Pulmonary Complications
Authors: van de Zande Saskia Corine , Abdulle Amaal Eman , Al-Adwi Yehya , Stel Alja , de Leeuw Karina , Brouwer Elisabeth , Arends Suzanne , Gan Christiaan Tji , van Goor Harry , Mulder Douwe Johannes
Published: June 2023

Puffy fingers and Raynaud’s phenomenon (RP) are important clinical predictors of the development of systemic sclerosis (SSc). We aim to assess the prevalence of SSc-related symptoms, explore pulmonary symptoms, and test the usefulness of skin autofluorescence (SAF) as a non-invasive marker for Advanced Glycation Endproducts (AGEs). Subjects from the Lifelines Cohort Study with known connective tissue disease (CTD) were excluded. Patient characteristics, SAF, self-reported pulmonary symptoms, and spirometry were obtained. Subjects (n = 73,948) were categorized into definite RP (5.3%) with and without SSc-related symptoms and non-RP. Prevalence of at least one potential SSc-related symptom (other than RP) was 8.7%; 23.5% in subjects with RP and 7.1% without RP (p < 0.001). Subjects with RP and additional SSc-related symptoms more frequently reported dyspnea at rest, dyspnea after exertion, and self-reported pulmonary fibrosis, and had the lowest mean forced vital capacity compared to the other groups (RP without SSc-related symptoms and no RP, both p < 0.001). In multivariate regression, dyspnea at rest/on exertion remained associated with an increased risk of SSc-related symptoms in subjects with RP (both p < 0.001). SAF was higher in subjects with RP and SSc-related symptoms compared to the other groups (p < 0.001), but this difference was not significant after correction for potential confounders. The prevalence of SSc-related symptoms was approximately three-fold higher in subjects with RP. Pulmonary symptoms are more prevalent in subjects with RP who also reported additional potential SSc-related symptoms. This might suggest that (suspected) early SSc develops more insidiously than acknowledged. According to this study, SAF is no marker for early detection of SSc.
Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution
Authors: Zeng Xueqiang , Guo Yingwei , Zaman Asim , Hassan Haseeb , Lu Jiaxi , Xu Jiaxuan , Yang Huihui , Miao Xiaoqiang , Cao Anbo , Yang Yingjian , Chen Rongchang , Kang Yan
Published: June 2023

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse attention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edged features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features’ expression ability without increasing the model’s complexity. Finally, the proposed training sample cropping strategy reduces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation techniques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway structures. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare professionals.
Cryptographically Secure PseudoRandom Bit Generator for Wearable Technology
Authors: Melosik Michał , Galan Mariusz , Naumowicz Mariusz , Tylczyński Piotr , Koziol Scott
Published: June 2023

This paper presents a prototype wearable Cryptographically Secure PseudoRandom Bit Generator CSPRBG (wearable CSPRBG). A vest prototype has been fabricated to which an evaluation board with a ZYBO (ZYnq BOard) Zynq Z-7010 has been mounted using tailoring technology. In this system, a seed generator and block cryptographic algorithms responsible for the generation of pseudo-random values were implemented. A microphone and an accelerometer recorded sound and acceleration during the use of the prototype vest, and these recordings were passed to the seed generator and used as entropy sources. Hardware implementations were made for several selected Block Cryptographic algorithms such as Advanced Encryption Standard (AES), Twofish and 3DES. The random binary values generated by the wearable CSPRBG were analyzed by National Institute of Standards and Technology (NIST) statistical tests as well as ENT tests to evaluate their randomness, depending on the configuration of the entropy sources used. The idea of possible development of the wearable CSPRBG as a System on Chip (SoC) solution is also presented.
Heterogeneous Retirement Savings Strategy Selection with Reinforcement Learning
Authors: Ozhamaratli Fatih , Barucca Paolo
Published: June 2023

Saving and investment behaviour is crucial for all individuals to guarantee their welfare during work-life and retirement. We introduce a deep reinforcement learning model in which agents learn optimal portfolio allocation and saving strategies suitable for their heterogeneous profiles. The environment is calibrated with occupation- and age-dependent income dynamics. The research focuses on heterogeneous income trajectories dependent on agents’ profiles and incorporates the parameterisation of agents’ behaviours. The model provides a new flexible methodology to estimate lifetime consumption and investment choices for individuals with heterogeneous profiles.
Student Adaptation, Loneliness and Mental Health Profiles during the Second Wave of the Pandemic COVID-19
Authors: Lampropoulou Aikaterini , Georgakakou-Koutsonikou Niki , Hatzichristou Chryse , Roussos Petros
Published: June 2023

Since the COVID-19 pandemic, adolescent mental health difficulties have increased. To understand adolescent adjustment, it is important to explore both mental health difficulties and indicators of resilience and well-being. The primary aim of this study was to explore the associations among resilience, subjective well-being (SWB), fear of COVID-19, and loneliness among adolescents during the second wave of the pandemic. Additionally, the study aimed to identify student profiles based on these variables. The sample consisted of 469 high school students (61% girls, 29% junior high school). Resilience was positively correlated with SWB and negatively with fear of COVID-19 and loneliness. Three student profiles were identified. The Resilient and Satisfied group (38%) consisted of students with the most positive adaptation, and the Average group (41%) involved students with middle scores in all variables, while the Vulnerable and Distressed group (21%) included those who struggled the most. The need for a tiered approach in providing school-based mental health support is discussed.
Using Psychometric Testing Procedures for Scale Validity, Reliability, and Invariance Analysis: The PRETIE-Q Portuguese Version
Authors: Rodrigues Filipe , Morouço Pedro , Antunes Raul , Monteiro Diogo , Jacinto Miguel , Figueiredo Nuno , Santos Filipe , Bastos Vasco , Teixeira Diogo
Published: June 2023

This study investigated the psychometric nature of preference for and tolerance of exercise intensity in physical activity. It initially re-examined the Preference for and Tolerance of the Intensity of Exercise Questionnaire (PRETIE-Q) among Portuguese exercisers, looking at its applicability to different exercise activities and exercise experiences. Then, to investigate the applicability of the measure in different groups, its invariance was examined. The sample consisted of 1117 participants (528 male, 589 female) aged 18–81 years old (Mage = 36.81, SD = 11.89). All participants reported at baseline that they were exercising, on average, 3.93 days (SD = 1.36) per week. The exploratory structural equation modeling (ESEM) displayed the best fit. The ESEM did show invariance when tested for multigroup analysis. The conclusion of this research is that the ESEM demonstrated the best fit, displaying invariance in multigroup analysis. Furthermore, when assessing preference and tolerance in various exercise modalities, the PRETIE-Q should be primarily used as a multidimensional instrument due to the differential recognition of preference and tolerance in seemingly similar physical activity circumstances, highlighting the importance of employing context-verified measures to evaluate exercise-intensity preference and tolerance based on sample characteristics or real-time context.
High Efficiency Converters Based on Modular Partial Power Processing for Fully Electric Maritime Applications
Authors: Anzola Jon , Garayalde Erik , Urkizu June , Alacano Argiñe , Lopez-Erauskin Ramon
Published: June 2023

This paper proposes an approach for analyzing the benefits that partial-power-processing-based converters can bring to fully electric maritime applications. With the aim of making the system modular and scalable to different powers/energies, series-connected partial power converters are proposed. Serializing these converters entails significant overvoltage issues, and this paper tackles them for one series-connected module failure case. A reliability analysis has been carried out considering that the components of the battery system follow an independent and identical distribution in terms of failure probability. Furthermore, a redundancy factor has been added to allow a certain failure rate in what is known as a fault-tolerant system. Finally, to demonstrate the high efficiency of partial power converters, a 3 kW prototype is tested at different working points that model the charging process of a battery. The experimental results show a peak efficiency of 99.36%.
High Dimensional Data Differential Privacy Protection Publishing Method Based on Association Analysis
Authors: Shi Wei , Zhang Xiaolei , Chen Hao , Zhang Xing
Published: June 2023

In order to solve the problem of privacy disclosure when publishing high-dimensional data and to protect the privacy of frequent itemsets in association rules, a high-dimensional data publishing method based on frequent itemsets of association rules (PDP Growth) is proposed. This method, in a distributed framework, utilizes rough set theory to improve the mining of association rules. It optimizes association analysis while reducing the dimensionality of high-dimensional data, eliminating more redundant attributes, and obtaining more concise frequent itemsets, and uses the exponential mechanism to protect the differential privacy of the simplest frequent itemset obtained, and effectively protects the privacy of the frequent itemset by adding Laplace noise to its support. The theory validates that the method satisfies the requirement of differential privacy protection. Experiments on multiple datasets show that this method can improve the efficiency of high-dimensional data mining and meet the privacy protection. Finally, the association analysis results that meet the requirements are published.
Deep Neural Networks-Based Direct-Current Operation Prediction and Circuit Migration Design
Authors: Wu Qingsen , Liu Haixu , Xin Jian , Li Lin , Ye Zuochang , Wang Yan
Published: June 2023

Recently, design methods based on gm/Id parameters have attracted attention in analog integrated circuit design and have been automated with computer assistance. However, the look-up tables (LUTs) in the gm/Id method have the problem of high hardware resource overhead. To address this issue, this paper proposes a multi-output deep neural network (DNN) structure for modeling the direct-current parameters of transistors and replacing LUTs for circuit design. The proposed DNN models’ performance is verified using mainstream design technologies such as TSMC 40 nm (T40), TSMC 65 nm (T65), TSMC 180 nm (T180), and SMIC 180 nm (S180). Compared with LUTs, the proposed DNN models are able to reduce at least 99.9% storage space occupation and 95.62% prediction time overhead with a mean absolute percentage error of less than 0.2%. In addition, we propose an automated circuit migration design method using DNN models in different technologies, combined with gm/Id parameters. The method generates circuit design databases in different technologies and obtains device design results according to performance requirements. The experimental results show that using DNN models can reduce the time overhead by more than 40% compared to using LUTs. The simulation results of circuit transplantation design show that the circuit performance of T40, T65, S180, and T180 meets the requirements, which verifies the proposed DNN-based automated circuit design method.
iMouse: Augmentative Communication with Patients Having Neuro-Locomotor Disabilities Using Simplified Morse Code
Authors: Kim Hyeonseok , Han Seungjae , Cho Jeongho
Published: June 2023

Patients with amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, an incurable disease in which motor neurons are selectively destroyed, gradually lose their mobility as organ dysfunction begins, and eventually, patients find it challenging to make even minor movements and simple communications. To communicate with patients with quadriplegia, researchers have focused on movements of the eye, the only moving organ for patients with ALS, and they have investigated the detection of eyeblinks using brainwaves or cameras or other ways to select letters on a screen via eyeball movements based on eye-tracking cameras. However, brainwave-based techniques, which use the electrical signals of eye movements to determine patient’s intentions, are sensitive to noise, often resulting in the inaccurate identification of intent. Alternatively, a camera-based method that uses letter selection detects the movement of eye feature-points, and this method makes it easy to identify a patient’s intentions using a predefined decision-making process. However, it has long processing time and is prone to inaccuracy due to errors in either the Morse code implementation assigned to all alphabets or the sequential selection methods. Therefore, we have proposed iMouse-sMc, a simplified Morse code-based user interface model using an eye mouse for faster and easier communication with such patients. Furthermore, we improved the detection performance of the eye mouse by applying image contrast techniques to enable communication with patients even at night. To verify the excellent performance of the proposed eye mouse for a user interface, we conducted comparative experiments with existing camera-based communication models based on various words. The results revealed that the time of communication was reduced to 83 s and the intention recognition accuracy was improved by ~28.16%. Additionally, even in low-light environments, where existing models are unable to communicate with the patients due to difficulties with eye detection, the proposed model demonstrated its eye detection capability and proved that it can be used universally for communication with patients during the day and at night.
Recommender System for Arabic Content Using Sentiment Analysis of User Reviews
Authors: Al-Ajlan Amani , Alshareef Nada
Published: June 2023

Recommender systems are used as effective information-filtering techniques to automatically predict and identify sets of interesting items for users based on their preferences. Recently, there have been increasing efforts to use sentiment analysis of user reviews to improve the recommendations of recommender systems. Previous studies show the advantage of integrating sentiment analysis with recommender systems to enhance the quality of recommendations and user experience. However, limited research has been focused on recommender systems for Arabic content. This study, therefore, sets out to improve Arabic recommendation systems and investigate the impact of using sentiment analysis of user reviews on the quality of recommendations. We propose two collaborative filtering recommender systems for Arabic content: the first depends on users’ ratings, and the second uses sentiment analysis of users’ reviews to enhance the recommendations. These proposed models were tested using the Large-Scale Arabic Book Reviews dataset. Our results show that, when the user review sentiment analysis is combined with recommender systems, the quality of the recommendations is improved. The best model was the singular value decomposition (SVD) with the Arabic BERT–mini model, which yielded minimum errors in terms of RMSE and MAE values and outperformed the performance of other previous studies in the literature.
Efficient Hardware-in-the-Loop Models Using Automatic Code Generation with MATLAB/Simulink
Authors: Saralegui Roberto , Sanchez Alberto , de Castro Angel
Published: June 2023

Hardware-in-the-loop testing is usually a part of the design cycle of control systems. Efficient and fast models can be created in a Hardware Description Language (HDL), which is implemented in a Field-Programmable Gate Array (FPGA). Control engineers are more skilled in higher-level approaches. HDL models derived automatically from schematics have noticeably lower performance, while HDL models derived from their equations are faster and smaller. However, even models translated automatically into HDL using the equations might be worse than manually coded models. A design workflow is proposed to achieve manual-like performance with automatic tools. It consists of the identification of similar operations, forcing signal signedness, and adjusting to multiplier input sizes. A detailed comparison was performed between three workflows: (1) translation of high-level MATLAB code, (2) translation of a Simulink model, and (3) working directly in the HDL. Sources of inefficiency were shown in a buck converter, and the process was validated in a full-bridge with electrical losses using a Runge–Kutta method. The results showed that the proposed approach delivered code that performed very close to a reference VHDL implementation, even for complex designs. Finally, the model was implemented in an off-the-shelf FPGA board suitable for a hardware-in-the-loop test setup.
A Bicycle-Embedded Electromagnetic Harvester for Providing Energy to Low-Power Electronic Devices
Authors: Urbina Robert , Baron Luis , Carvajal Juan-Pablo , Pérez Manuel , Paez-Rueda Carlos-Ivan , Fajardo Arturo , Yamhure Germán , Perilla Gabriel
Published: June 2023

Bicycles are rapidly gaining popularity as a sustainable mode of transportation around the world. Furthermore, the smart bicycle paradigm enables increased use through the Internet of Things applications (e.g., GPS tracking systems). This new paradigm introduces energy autonomy as a new challenge. The energy harvesting technology can capture the energy present in the cycling environment (e.g., kinetic or solar) to give this autonomy. The kinetic energy source is more stable and dense in this environment. There are several wheel kinetic harvesters on the market, ranging from low-complexity dynamos used to power bicycle lights to smart harvester systems that harvest kinetic energy while braking and cycling and store it for when it is needed to power sensors and other electronics loads. Perhaps the hub and the “bottle” dynamos are the most commercially successful systems because of their cost-effective design. Furthermore, the bottle generator is very inexpensive, yet it suffers from significant energy losses and is unreliable in wet weather due to mechanical friction and wheel slippage in the wheel/generator contact. This paper proposes a cost-effective bicycle harvester based on a novel kinetic-electromagnetic transducer. The proposed harvester allows for the generation and storage of harnessed kinetic energy to power low-power electronics loads when the user requires it (e.g., cell phone charging, lighting). The proposed harvester is made up of a power processing unit, a battery, and an optimized transducer based on a Halbach magnet array. An extensive full-wave electromagnetic simulation was used to evaluate the proposed transducer. Circuit simulation was also used to validate the proposed power unit. The proposed harvester generates a simulated output power of 1.17 W with a power processing unit efficiency of 45.6% under a constant bicycle velocity of 30 km/h.
Web-Based 3D Virtual Environments Utilization in Primary and Secondary Education of Children with Multiple Impairments
Authors: Sobota Branislav , Korečko Štefan , Mattová Miriama
Published: June 2023

The technological advances we are witnessing today have stimulated the creation of many 3D virtual environments for various purposes, from entertainment to industry to education. While the majority of these environments are perfectly suited for the healthy population, we should not forget about impaired people living among us. Regarding children’s education, one may wonder how impaired children handle them. Do they find them usable and attractive? How well do they handle basic activities in 3D environments, including orientation and interaction with objects? The experiment presented in this article provides answers to these questions within a specific setup. The experiment used a custom web application with several 3D virtual environments in a desktop virtual reality setting. The participants were 12 children, aged 8–14, with multiple impairments, predominantly hearing impairment, borderline and mild degree of mental retardation, and inferior communication skills. The answers can be regarded as positive and are based on results gathered in the form of completion times and the System Usability Scale questionnaire scores. The article also reports on a significant relation found between completion times and questionnaire scores. Future research directions, including those related to the Metaverse concept, are discussed, too.
Physically Plausible Realistic Grip-Lift Interaction Based on Hand Kinematics in VR
Authors: Nam Hyeongil , Kim Chanhee , Kim Kangsoo , Park Jong-Il
Published: June 2023

Immersive technology, refers to various novel ways of creating and interacting with applications and experiences, e.g., virtual reality (VR), has been used in various simulations and training where preparing real/physical settings is not ideal or possible, or where the use of virtual contents is otherwise beneficial. Realizing realistic interactions with virtual content is crucial for a quality experience and the effectiveness of such simulation and training. In this paper, we propose a kinematics-based realistic hand interaction method to enable a physically plausible grip-lifting experience in VR. The method reflects three kinematic characteristics of the hand: the force at contact points, finger flexion, and the speed of hand/finger motion, and we developed a grip-lift interaction prototype using the proposed method. To examine the sense of realism and hand poses during the grip-lift interaction, we conducted a human subjects experiment using the prototype, resulting in positive effects on the perceived realism and usefulness of the interaction. Grip-lifting is a fundamental interaction technique that is involved in most embodied interaction scenarios. Our method would contribute to the design and development of realistic virtual experiences, of which we will discuss the implications and potential based on our findings.
Building Change Detection in Remote Sensing Imagery with Focal Self-Attention and Multi-Level Feature Fusion
Authors: Shen Peiquan , Mei Liye , Ye Zhaoyi , Wang Ying , Zhang Qi , Hong Bo , Yin Xiliang , Yang Wei
Published: June 2023

Accurate and intelligent building change detection greatly contributes to effective urban development, optimized resource management, and informed decision-making in domains such as urban planning, land management, and environmental monitoring. Existing methodologies face challenges in effectively integrating local and global features for accurate building change detection. To address these challenges, we propose a novel method that uses focal self-attention to process the feature vector of input images, which uses a “focusing” mechanism to guide the calculation of the self-attention mechanism. By focusing more on critical areas when processing image features in different regions, focal self-attention can better handle both local and global information, and is more flexible and adaptive than other methods, improving detection accuracy. In addition, our multi-level feature fusion module groups the features and then constructs a hierarchical residual structure to fuse the grouped features. On the LEVIR-CD and WHU-CD datasets, our proposed method achieved F1-scores of 91.62% and 89.45%, respectively. Compared with existing methods, ours performed better on building change detection tasks. Our method therefore provides a framework for solving problems related to building change detection, with some reference value and guiding significance.
Design of UWB Filtering Impedance Transformers and Power Dividers Using Stepped-Impedance Resonators
Authors: Cao Ziheng , Liu Yun , Liang Chen , Majid Irfan
Published: June 2023

This study presents a novel design of ultra-wideband (UWB) impedance transformers and power dividers with filtering capabilities. Based on the UWB impedance matching network, the UWB filtering impedance transformers are designed, and the UWB filtering power dividers are achieved by impedance transforming one port of one or more impedance transformers. The transformers consist of a multi-mode stepped-impedance resonator (SIR) and defected ground structures (DGSs). The SIR is tightly coupled to two ports of different impedance levels via the unsymmetrical two-sided coupled lines and DGSs. In addition, two transformers that convert impedance from 50 Ω to 100 Ω are connected to form a UWB power divider with filtering function. The 25 Ω port of an impedance transformer with a 50 Ω to 25 Ω conversion is impedance matched to two 50 Ω ports connected in parallel, resulting in another power divider with filtering function. Thus, two prototype UWB impedance transformers from 50 Ω to 100 Ω and from 50 Ω to 25 Ω are designed and their corresponding power dividers are also designed and fabricated. The simulated and measured results are consistent, demonstrating good features, such as return loss greater than 10 dB and insertion loss less than 4.5 dB in the passband, UWB filtering capacity with out-of-band rejection greater than 20 dB, and compact size smaller than 1.2λ × 2.1λ (λ is the wavelength of the central frequency).
Time Series Prediction Methodology and Ensemble Model Using Real-World Data
Authors: Kim Mintai , Lee Sungju , Jeong Taikyeong
Published: June 2023

Time series data analysis and forecasting have recently received considerable attention, supporting new technology development trends for predicting load fluctuations or uncertainty conditions in many domains. In particular, when the load is small, such as a building, the effect of load fluctuation on the total load is relatively large compared to the power system, except for specific factors, and the amount is very difficult to quantify. Recently, accurate power consumption prediction has become an important issue in the Internet of Things (IoT) environment. In this paper, a traditional time series prediction method was applied and a new model and scientific approach were used for power prediction in IoT and big data environments. To this end, to obtain data used in real life, the power consumption of commercial refrigerators was continuously collected at 15 min intervals, and prediction results were obtained by applying time series prediction methods (e.g., RNN, LSTM, and GRU). At this time, the seasonality and periodicity of electricity use were also analyzed. In this paper, we propose a method to improve the performance of the model by classifying power consumption into three classes: weekday, Saturday, and Sunday. Finally, we propose a method for predicting power consumption using a new type of ensemble model combined with three time series methods. Experimental results confirmed the accuracy of RNN (i.e., 96.1%), LSTM (i.e., 96.9%), and GRU (i.e., 96.4%). In addition, it was confirmed that the ensemble model combining the three time series models showed 98.43% accuracy in predicting power consumption. Through these experiments and approaches, scientific achievements for time series data analysis through real data were accomplished, which provided an opportunity to once again identify the need for continuous real-time power consumption monitoring.
LLM-Informed Multi-Armed Bandit Strategies for Non-Stationary Environments
Authors: de Curtò J. , de Zarzà I. , Roig Gemma , Cano Juan Carlos , Manzoni Pietro , Calafate Carlos T.
Published: June 2023

In this paper, we introduce an innovative approach to handling the multi-armed bandit (MAB) problem in non-stationary environments, harnessing the predictive power of large language models (LLMs). With the realization that traditional bandit strategies, including epsilon-greedy and upper confidence bound (UCB), may struggle in the face of dynamic changes, we propose a strategy informed by LLMs that offers dynamic guidance on exploration versus exploitation, contingent on the current state of the bandits. We bring forward a new non-stationary bandit model with fluctuating reward distributions and illustrate how LLMs can be employed to guide the choice of bandit amid this variability. Experimental outcomes illustrate the potential of our LLM-informed strategy, demonstrating its adaptability to the fluctuating nature of the bandit problem, while maintaining competitive performance against conventional strategies. This study provides key insights into the capabilities of LLMs in enhancing decision-making processes in dynamic and uncertain scenarios.
Image Cryptosystem in Optical Gyrator Transform Domain Using Audio Keys
Authors: Tian Miao , Sun Gege , Song Wenbo , Liu Zhengjun , Chen Hang
Published: June 2023

Two remote sensing image encryption algorithms based on the randomness of audio channel sequences are proposed and their robustness is verified by many attack experiments. The first encryption algorithm uses the randomness of audio to encrypt image pixels in RMD. Compared with traditional image encryption algorithms, this algorithm has high randomness and security and can resist stronger password attacks. However, the encryption algorithm takes a long time. Considering that information sometimes needs to be transmitted urgently, a second encryption algorithm is proposed. By combining vocal tract and random phase to form new public and private keys, the number of computation amount and dislocations is reduced. The second algorithm is short in time but low in security.
Structural Design and Sealing Performance Analysis of a Nanofluidic Self-Heating Unsealing Rubber Cylinder
Authors: Zhang Yafei , Fan Taihao , Zhang Pengbo , Dou Yihua
Published: June 2023

As a crucial component for temporary blocking of layer segments in the segmental fracturing process, bridge plugs are difficult to unseal by conventional methods and may cause major downhole accidents if not handled properly. In this paper, a nanofluidic self-heating unsealing rubber cylinder is designed, which is equipped with a nanofluidic self-heating unsealing sandwich inside the conventional rubber cylinder, consisting of a nanofluidic system and an annular flexible heater. When unsealing, the nanofluidic self-heating unsealing sandwich is heated by the annular flexible heater, and the nanofluidic system can help the bridge plug rubber cylinder shrink in volume and unseal smoothly by the characteristics of heat shrinkage and cold expansion. The nanofluidic system, consisting of porous carbon with an exceptionally large specific surface area and glycerol, serves as a prime example for filling the sandwich layer, and the design parameters calculation was carried out. The sealing performance of the designed nanofluidic self-heating unsealing rubber cylinder was analyzed based on the Mooney–Rivlin principal structure by finite element modeling. The results show that the maximum contact stress between the nanofluidic self-heating unsealing rubber cylinder and the casing wall increases by 9.73%, the compression distance reduces by 24.47%, and the maximum equivalent force decreases by 12.17% on average compared with a conventional rubber cylinder under the same seating load. The designed nanofluidic self-heating unsealing rubber cylinder can satisfy the requirements of pressure-bearing capacity and sealing performance and performs better than a conventional rubber cylinder.
Evaluation of Voltage Stability in Microgrid-Tied Photovoltaic Systems
Authors: Stanchev Plamen , Vacheva Gergana , Hinov Nikolay
Published: June 2023

These days, with the significant increase in the use of renewable energy sources as additional energy sources connected to the distribution network, many challenges and difficulties arise in ensuring sustainability and reliability. The generation, transmission and distribution, in the current state of the electricity system, are facing quite dynamic changes. They are the result of the liberalization of the energy market, the increased use of renewable energy sources such as photovoltaic systems, wind turbines and the charging stations for hybrid and electric vehicles. The most important factors are related to the balancing of the energy system, the analysis of voltage stability, overcoming the consequences of the increase in short-circuit currents, increasing the transmission capacities of the system forming and distribution networks, as well as the accurate forecasting of the development of loads and consumption over the coming years. This article presents an analysis of the voltage stability in a smart microgrid for two different scenarios. The studied cases describe a linear low-voltage p-type microgrid with loads connected to it at different nodes. Data on the type and cross-section of the conductors of the studied power line are presented. Simulation studies were carried out to determine the limits of grid voltage stability when connecting photovoltaic plants with a set power. The simulation results are commented on and an analysis of the optimal operating mode of the system is realized. The model studies were implemented in the NEPLAN program environment. The research carried out allows an evaluation of the permissible limits for network stability when connecting photovoltaic plants. Through this evaluation, it can be determined how many and at which node the loads should be connected without causing an imbalance in the network. This is useful from the point of view of ensuring the sustainability and reliability of electrical energy in a microgrid.
Wind Tunnel Experimental Study on the Efficiency of Vertical-Axis Wind Turbines via Analysis of Blade Pitch Angle Influence
Authors: Szczerba Zygmunt , Szczerba Piotr , Szczerba Kamil , Szumski Marek , Pytel Krzysztof
Published: June 2023

This paper presents results of experimental investigations and numerical simulations of a vertical-axis H-type wind turbine, considering the influence of propeller blade pitch angle on turbine characteristics. An innovative airfoil profile based on a modified symmetric NACA0015 airfoil profile was used as the designed blade profile, which was tested in a wind tunnel over a range of Reynolds numbers from 50,000 to 300,000. The phenomenon of angle-of-attack variation and the resulting forces acting on the blades, particularly in the horizontal configuration and vertical axis of rotation, were discussed. Series of experiments were conducted on a 1:1 scale four-bladed turbine model in the wind tunnel to determine the characteristics, specifically the power coefficient distribution over the tip speed ratio for various Reynolds numbers and blade pitch angles. Subsequently, the turbine was modeled using Qblade software, and a series of calculations were performed under the same conditions. The numerical results were validated with the experimental data.
Disperse Partial Shading Effect of Photovoltaic Array by Means of the Modified Complementary SuDoKu Puzzle Topology
Authors: Ye Cheng-En , Tai Cheng-Chi , Huang Yu-Pei
Published: June 2023

This paper presents a novel modified Complementary SuDoKu puzzle (MC-SDKP) topology for the static reconfiguration of photovoltaic (PV) arrays. It was developed with the aim of enhancing the power output of a PV array which is exposed to partially shaded conditions (PSCs). To disperse patterns of both center shading and corner shading, the MC-SDKP technique modified and combined the Optimal SDKP and the Complementary SDKP (C-SDKP) topologies. An 8 × 8 PV array configured with the MC-SDKP topology was exposed to nine different shading patterns, and its performance was compared with that of the other four topologies. The results of the performance evaluation confirmed that, when configured according to the MC-SDKP, the PV array produced the highest average power output among all five topologies, with a 15.07% higher output on average than the total-cross tied. The PV array with the MC-SDKP topology also exhibited the lowest average power loss (1.34%). This study clearly established the effectiveness of the MC-SDKP topology at mitigating the effects of both center and corner shading. The advantages of the MC-SDKP reconfiguration technique are: an increase in extracted power, a reduction in current mismatch losses, an improvement in shade dispersion under conditions of center shading, and good scalability.
Flexible Smart Energy-Management Systems Using an Online Tendering Process Framework for Microgrids
Authors: Selseleh Jonban Mansour , Romeral Luis , Rakhshani Elyas , Marzband Mousa
Published: June 2023

Currently, modern power grids are evolving into complex cyber-physical systems integrated with distributed energy resources that can be controlled and monitored by computer-based algorithms. Given the increasing prevalence of artificial intelligence algorithms, it is essential to explore the possibility of energy management in microgrids by implementing control methodologies with advanced processing centers. This study proposes a novel smart multi-agent-based framework under a tendering process framework with a bottom-up approach to control and manage the flow of energy into a grid-connected microgrid (MG). The tendering organization in this structure as an upstream agent allocates demand among generators, creates a balance between supply and demand, and provides optimal energy cost for the MG. To optimize the electricity cost and decrease the use of grid power, the first-price sealed-bid (FPSB) algorithm is implemented over the tendering process. The proposed approach from one side optimally allocates energy among generators, and, from the other side, guarantees the system from blackouts. Theoretical analysis and results demonstrate that the proposed technique is easy to implement and provides a robust and stable control for MGs, which can guarantee energy management as well as flexible and online control. Furthermore, results show the proposed framework besides the real-time allocation of power among providers to optimize the injected power from the grid so that the total injected power by the grid is 146.92 kWh and the injected power to the grid is 214.34 kWh.
Design for a Heat Pump with Sink Temperatures of 200 °C Using a Radial Compressor
Authors: Unterluggauer Julian , Sulzgruber Verena , Kroiss Clemens , Riedl Johannes , Jentsch Reinhard , Willinger Reinhard
Published: June 2023

To reduce CO2 emissions in the industrial sector, high-temperature heat pumps are a key technology. This work presents an approach to design such an industrial heat pump system capable of supplying 200 °C sink temperature and a capacity of approximately 1 MW. Today’s market-available heat pumps using displacement compressors are not suitable for reaching that high sink temperatures as they need lubricating oil, which is not temperature resistant enough. As a consequence, in this study a transcritical heat pump cycle using a two-stage oil-free radial compressor is investigated. Based on preliminary studies, R1233zd(E) is chosen as a refrigerant. The procedure couples 1D thermodynamic cycle simulations with a radial compressor mean-line design model. A preliminary geometry for a compressor with and without inlet guide vanes is presented, and compressor maps including the compressors behaviour in off-design are calculated. The compressor design is then imported into a 1D simulation to analysis the performance of the heat pump in the whole operating range. In the analysis, the application of a fixed inlet is evaluated, and an improvement of approximately 21% and 16% of the isentropic efficiency is achieved. The thermodynamic simulations showed a maximum COP of approximately 2.8 and a possible operating range of 0.5 to 1.3 MW thermal power. Furthermore, a techno-economical analysis by means of a deep-fryer use case showed reasonable payback times of between 2 and 10 years, depending on the electricity to gas price ratio.
Predictions for Bending Strain at the Tower Bottom of Offshore Wind Turbine Based on the LSTM Model
Authors: Lee Songjune , Kang Seungjin , Lee Gwang-Se
Published: June 2023

In recent years, the demand and requirement for renewable energy have significantly increased due to concerns regarding energy security and the climate crisis. This has led to a significant focus on wind power generation. As the deployment of wind turbines continues to rise, there is a growing need to assess their lifespan and improve their stability. Access to accurate load data is crucial for enhancing safety and conducting remaining life assessments of wind turbines. However, maintaining and ensuring the reliability of measurement systems for long-term load data accumulation, stability assessments, and residual life evaluations can be challenging. As a result, numerous studies have been conducted on load prediction for wind turbines. However, existing load prediction models based on 10 min statistical data cannot adequately capture the short-term load variations experienced by wind turbines. Therefore, it is essential to develop models capable of predicting load with a high temporal resolution to enhance reliability, especially with the increasing scale and development of floating wind turbines. In this paper, we developed prediction models with a 50 Hz resolution for the bending strain at the tower bottom of offshore wind turbines by combining SCADA data and acceleration data using machine learning techniques and analyzed the results. The load prediction models demonstrated high accuracy, with a mean absolute percentage error below 4%.
Financial Analysis of a Desalination–Wastewater Recycle Plant Powered by a DC-DC Photovoltaic-Batteries System on the Aeolian Islands, Italy
Authors: Mosconi Gabriele , Acciarri Maurizio F.
Published: June 2023

The scarcity of drinking water is an increasingly pressing issue in many regions of the world, even in areas up till now considered developed. Climate change deprives many populations of the amount of water necessary for human consumption and traditional crops. Therefore, finding new water sources and making their usage more efficient and able to adapt to new environmental conditions without worsening the situation with further pollution is becoming mandatory. In the case study considered here, set on the Italian island of Vulcano in the Central Mediterranean, we propose the economic analysis and financial sustainability of plants for the desalination and recycled wastewater, powered by a DC-DC photovoltaic tracker system with silicon crystal panels sustained by a daily pack of lithium batteries. We present an estimation of the necessary budget, and propose a mix of traditional and innovative financial instruments to construct and analyse the economic trends of the installations over 30 years, considering the specific area price levels, salaries and interest rates. Finally, through the net present value index, we evaluate the financial sustainability of the entire operation, namely, identifying the circumstances when funding and building these plants in areas as remote as the one considered here is cost-effective.