bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing Traditional and “In-Motion” Intense Pulsed Light Techniques for Hair Removal: A Split Study
Authors: Bennardo Luigi , Nisticò Steven Paul , Primavera Grazia , Tolone Martina , Tamburi Federica , Bennardo Stefano , Cannarozzo Giovanni
Published: March 2023

Background and Objectives: Hair removal is a common aesthetic concern for patients referred to dermatologists and aesthetic physicians. Lasers and lights are one of the mainstays in the management of this condition. Among these devices, intense pulsed lights (IPLs) are broadly used in order to reduce the number and width of the hair present. Currently used techniques are associated with a high risk of side effects, such as hyper or hypopigmentation. Materials and Methods: Thirty patients seeking hair removal in one or more body areas with skin phototypes 1 to 4 were recruited to perform this study. All areas to be treated were divided into two equal regions; one side was treated with the standard IPL hair removal procedure, while the other was treated with a new “in-motion” IPL technology. Results and hair removal rates were evaluated six months after the last treatment. Results: Out of the 30 patients treated, all patients experienced hair reduction. No statistically significant difference in hair removal was noted among the two sides. A statistically significant reduction in pain during the procedure was observed in the side treated with the “in-motion” technique. Conclusions: Traditional and “in-motion” IPL techniques have similar results in hair removal; the “in-motion” technology seems to guarantee a better safety profile than the traditional technique, as well as maintains the same results over time and a faster treatment time. A more extensive clinical study will be necessary to confirm our study’s results.
Froth Images from Flotation Laboratory Test in Magotteaux Cell
Authors: Yantén Carlos , Kracht Willy , Díaz Gonzalo , Lois-Morales Pía , Egaña Alvaro
Published: March 2023

Froth flotation is a widely used method for the concentration of sulfide minerals. The structure of the superficial froth is an indicator of the performance of froth flotation alongside with the operational conditions in which this process is carried out. The aim of this study is to explore how the different operational conditions that can be managed in a flotation plant could directly influence the observable characteristics of the superficial froth. For this purpose, a froth image database was created using a special laboratory cell, designed to emulate the conditions seen in an industrial flotation cell. The database contains 2250 images, distributed in 45 categories; each category has a specific combination of testing conditions, and the main visual characteristics are observed. It also includes a methodology used to assess the quality of each corresponding category.
Clinical Trial Data on the Mechanical Removal of 14-Day-Old Dental Plaque Using Accelerated Micro-Droplets of Air and Water (Airfloss)
Authors: Del Rey Yumi C. , Rikvold Pernille D. , Johnsen Karina K. , Schlafer Sebastian
Published: March 2023

Novel strategies to combat dental biofilms aim at reducing biofilm stability with the ultimate goal of facilitating mechanical cleaning. To test the stability of dental biofilms, they need to be subjected to a defined mechanical stress. Here, we employed an oral care device (Airfloss) that emits microbursts of compressed air and water to apply a defined mechanical shear to 14-day-old dental plaque in 20 healthy participants with no signs of oral diseases (clinical trial no. NCT05082103). Exclusion criteria included pregnant or nursing women, users of oral prostheses, retainers or orthodontic appliances, and recent antimicrobial or anti-inflammatory therapy. Plaque accumulation, before and after treatment, was assessed using fluorescence images of disclosed dental plaque on the central incisor, first premolar, and first molar in the third quadrant (120 images). For each tooth, the pre- and post-treatment plaque percentage index (PPI) and Turesky modification of the Quigley-Hein plaque index (TM-QHPI) were recorded. The mean TM-QHPI significantly decreased after treatment (p = 0.03; one-sample sign test), but no significant difference between the mean pre- and post-treatment PPI was observed (p = 0.09; one-sample t-test). These data are of value for researchers that seek to apply a defined mechanical shear to remove and/or disrupt dental biofilms.
A Hybrid Residual Attention Convolutional Neural Network for Compressed Sensing Magnetic Resonance Image Reconstruction
Authors: Hossain Md. Biddut , Kwon Ki-Chul , Shinde Rupali Kiran , Imtiaz Shariar Md , Kim Nam
Published: March 2023

We propose a dual-domain deep learning technique for accelerating compressed sensing magnetic resonance image reconstruction. An advanced convolutional neural network with residual connectivity and an attention mechanism was developed for frequency and image domains. First, the sensor domain subnetwork estimates the unmeasured frequencies of k-space to reduce aliasing artifacts. Second, the image domain subnetwork performs a pixel-wise operation to remove blur and noisy artifacts. The skip connections efficiently concatenate the feature maps to alleviate the vanishing gradient problem. An attention gate in each decoder layer enhances network generalizability and speeds up image reconstruction by eliminating irrelevant activations. The proposed technique reconstructs real-valued clinical images from sparsely sampled k-spaces that are identical to the reference images. The performance of this novel approach was compared with state-of-the-art direct mapping, single-domain, and multi-domain methods. With acceleration factors (AFs) of 4 and 5, our method improved the mean peak signal-to-noise ratio (PSNR) to 8.67 and 9.23, respectively, compared with the single-domain Unet model; similarly, our approach increased the average PSNR to 3.72 and 4.61, respectively, compared with the multi-domain W-net. Remarkably, using an AF of 6, it enhanced the PSNR by 9.87 ± 1.55 and 6.60 ± 0.38 compared with Unet and W-net, respectively.
Comparison of Ultrasound Scores in Differentiating between Benign and Malignant Adnexal Masses
Authors: Pelayo Mar , Pelayo-Delgado Irene , Sancho-Sauco Javier , Sanchez-Zurdo Javier , Abarca-Martinez Leopoldo , Corraliza-Galán Virginia , Martin-Gromaz Carmen , Pablos-Antona María Jesús , Zurita-Calvo Julia , Alcázar Juan Luis
Published: March 2023

Subjective ultrasound assessment by an expert examiner is meant to be the best option for the differentiation between benign and malignant adnexal masses. Different ultrasound scores can help in the classification, but whether one of them is significantly better than others is still a matter of debate. The main aim of this work is to compare the diagnostic performance of some of these scores in the evaluation of adnexal masses in the same set of patients. This is a retrospective study of a consecutive series of women diagnosed as having a persistent adnexal mass and managed surgically. Ultrasound characteristics were analyzed according to IOTA criteria. Masses were classified according to the subjective impression of the sonographer and other ultrasound scores (IOTA simple rules -SR-, IOTA simple rules risk assessment -SRRA-, O-RADS classification, and ADNEX model -with and without CA125 value-). A total of 122 women were included. Sixty-two women were postmenopausal (50.8%). Eighty-one women had a benign mass (66.4%), and 41 (33.6%) had a malignant tumor. The sensitivity of subjective assessment, IOTA SR, IOTA SRRA, and ADNEX model with or without CA125 and O-RADS was 87.8%, 66.7%, 78.1%, 95.1%, 87.8%, and 90.2%, respectively. The specificity for these approaches was 69.1%, 89.2%, 72.8%, 74.1%, 67.9%, and 60.5%, respectively. All methods with similar AUC (0.81, 0.78, 0.80, 0.88, 0.84, and 0.75, respectively). We concluded that IOTA SR, IOTA SRRA, and ADNEX models with or without CA125 and O-RADS can help in the differentiation of benign and malignant masses, and their performance is similar to the subjective assessment of an experienced sonographer.
CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using Optical Coherence Tomography Angiography (OCTA)
Authors: Vali Mahsa , Nazari Behzad , Sadri Saeed , Pour Elias , Riazi-Esfahani Hamid , Faghihi Hooshang , Ebrahimiadib Nazanin , Azizkhani Momeneh , Innes Will , Steel David , Hurlbert Anya , Read Jenny , Kafieh Rahele
Published: March 2023

This paper aims to present an artificial intelligence-based algorithm for the automated segmentation of Choroidal Neovascularization (CNV) areas and to identify the presence or absence of CNV activity criteria (branching, peripheral arcade, dark halo, shape, loop and anastomoses) in OCTA images. Methods: This retrospective and cross-sectional study includes 130 OCTA images from 101 patients with treatment-naïve CNV. At baseline, OCTA volumes of 6 × 6 mm2 were obtained to develop an AI-based algorithm to evaluate the CNV activity based on five activity criteria, including tiny branching vessels, anastomoses and loops, peripheral arcades, and perilesional hypointense halos. The proposed algorithm comprises two steps. The first block includes the pre-processing and segmentation of CNVs in OCTA images using a modified U-Net network. The second block consists of five binary classification networks, each implemented with various models from scratch, and using transfer learning from pre-trained networks. Results: The proposed segmentation network yielded an averaged Dice coefficient of 0.86. The individual classifiers corresponding to the five activity criteria (branch, peripheral arcade, dark halo, shape, loop, and anastomoses) showed accuracies of 0.84, 0.81, 0.86, 0.85, and 0.82, respectively. The AI-based algorithm potentially allows the reliable detection and segmentation of CNV from OCTA alone, without the need for imaging with contrast agents. The evaluation of the activity criteria in CNV lesions obtains acceptable results, and this algorithm could enable the objective, repeatable assessment of CNV features.
Pathomorphological Diagnostic Criteria for Focal Cortical Dysplasias and Other Common Epileptogenic Lesions—Review of the Literature
Authors: Metodiev Dimitar , Minkin Krassimir , Ruseva Margarita , Ganeva Rumiana , Parvanov Dimitar , Nachev Sevdalin
Published: March 2023

Focal cortical dysplasia (FCD) represents a heterogeneous group of morphological changes in the brain tissue that can predispose the development of pharmacoresistant epilepsy (recurring, unprovoked seizures which cannot be managed with medications). This group of neurological disorders affects not only the cerebral cortex but also the subjacent white matter. This work reviews the literature describing the morphological substrate of pharmacoresistant epilepsy. All illustrations presented in this study are obtained from brain biopsies from refractory epilepsy patients investigated by the authors. Regarding classification, there are three main FCD types, all of which involve cortical dyslamination. The 2022 revision of the International League Against Epilepsy (ILAE) FCD classification includes new histologically defined pathological entities: mild malformation of cortical development (mMCD), mild malformation of cortical development with oligodendroglial hyperplasia in frontal lobe epilepsy (MOGHE), and “no FCD on histopathology”. Although the pathomorphological characteristics of the various forms of focal cortical dysplasias are well known, their aetiologic and pathogenetic features remain elusive. The identification of genetic variants in FCD opens an avenue for novel treatment strategies, which are of particular utility in cases where total resection of the epileptogenic area is impossible.
Kinetic Studies on the 2-Oxoglutarate/Fe(II)-Dependent Nucleic Acid Modifying Enzymes from the AlkB and TET Families
Authors: Peng Zhiyuan , Ma Jian , Christov Christo Z. , Karabencheva-Christova Tatyana , Lehnert Nicolai , Li Deyu
Published: March 2023

Nucleic acid methylations are important genetic and epigenetic biomarkers. The formation and removal of these markers is related to either methylation or demethylation. In this review, we focus on the demethylation or oxidative modification that is mediated by the 2-oxoglutarate (2-OG)/Fe(II)-dependent AlkB/TET family enzymes. In the catalytic process, most enzymes oxidize 2-OG to succinate, in the meantime oxidizing methyl to hydroxymethyl, leaving formaldehyde and generating demethylated base. The AlkB enzyme from Escherichia coli has nine human homologs (ALKBH1-8 and FTO) and the TET family includes three members, TET1 to 3. Among them, some enzymes have been carefully studied, but for certain enzymes, few studies have been carried out. This review focuses on the kinetic properties of those 2-OG/Fe(II)-dependent enzymes and their alkyl substrates. We also provide some discussions on the future directions of this field.
The Importance of School Principals’ Values towards the Inclusive Education of Disabled Students: Associations between Their Values and Knowledge, Beliefs, Attitudes and Practices
Authors: Vlachou Anastasia , Tsirantonaki Smaragdi S.
Published: March 2023

The current paper presents part of a broader, large-scale study regarding inclusive education and educational leadership in Greece that highlights the decisive role that school principals’ values play into shaping inclusive education. Its proposed theoretical model, based on extensive bibliographical research, explores the relationships between values, and knowledge, beliefs, attitudes and practices regarding the education of disabled students, highlighting that school principals’ values influence their beliefs and attitudes, and by extension their inclusive practices. Thus, the current paper presents a large-scale research on the proposed theoretical model with a representative sample of Greek primary and secondary education school principals. The data were collected with a composite questionnaire adapted and validated for the Greek context, which was electronically administered to a sample of 582 school principals from 334 primary and 248 secondary schools. The results of a hierarchical multiple regression analysis and a pathway analysis were interpreted based on the strength and direction of the relationships between the examined variables, as well as their significance. The hierarchical multiple regression analysis revealed that school principals’ knowledge and beliefs regarding the education of disabled students, and to a lesser extent their values, predicted whether school principals implement practices regarding the education of said students in their school units. However, school principals’ values were indeed the strongest predictive factor for their attitudes towards the education of disabled students and along with their knowledge, play a catalytic role in shaping their beliefs, attitudes and by extension, their practices regarding the education of disabled students. The pathway analysis confirmed the schematic representation of the regression relationships between the examined variables which showed that the theoretical model captures the predictive relationships among the variables, identifies potential causal pathways and showcases their decisive role in inclusive educational leadership. In short, the theoretical model demonstrates a very good fit to the research data coming in agreement with the results from both statistical analyses. Thus, it outlines a consistent and coherent outcome, which highlights multiple relationships between the variables, but primarily the multilayered effect of values in the case of inclusive educational leadership.
Fostering Project-Based Learning through Industry Engagement in Capstone Design Projects
Authors: Badir Ashraf , O’Neill Robert , Kinzli Kristoph-Dietrich , Komisar Simeon , Kim Jong-Yeop
Published: March 2023

Extensive studies have indicated that real-life project-based learning through industry involvement in capstone design courses provides benefits to students, faculty, and industry practitioners. This paper presents the contributions of industry participants to student experiential and project-based learning in the civil and environmental engineering senior design courses at Florida Gulf Coast University (FGCU). Surveys were conducted to obtain insights into the contributions of industry involvement in the capstone design course from the perspectives of both students and practitioners. Practitioners have been involved in various roles, including project mentors for capstone design projects and/or judges for students’ capstone design project presentations. Practitioners, through the students, are provided with new ways of looking at and solving problems. Practitioners, through their involvement, provide valuable feedback to the faculty and students that enriches the overall experience gained in the capstone design course. This feedback helps improve student performance on their projects and provides them with additional tools to carry forward into their engineering careers. However, there was a gap in perception between students and practitioners with regard to the benefits of industry involvement. This paper also describes two successful capstone design projects and culminates success drivers from the reflection of instructors teaching these courses. The results of this study have substantial implications for faculty teaching these courses. They showed what students did well and pinpointed areas for improvement through the lens of industry practitioners.
Muscular and Prefrontal Cortex Activity during Dual-Task Performing in Young Adults
Authors: Saraiva Marina , Castro Maria António , Vilas-Boas João Paulo
Published: March 2023

Postural control depends on attentional resources besides automatic processes. The dual-task paradigm is a possible approach to analyzing the interference and performance between motor and/or cognitive tasks. Various studies showed that, when individuals simultaneously perform two tasks, the postural stability can decline during a dual-task compared with a single-task due to the attentional resources required performing the tasks. However, little is known about the cortical and muscular activity pattern during dual-task performance. Therefore, this study aims to analyze the muscular and prefrontal activity under dual-task performance in healthy young adults. Thirty-four healthy young adults (mean age ± SD = 22.74 ± 3.74 years) were recruited to perform a postural task (standing posture) and a dual-task (maintaining standing posture while performing a cognitive task). Lower-limb muscle activity was bilaterally collected from five muscles using surface electromyography (sEMG), and the co-contraction index (CCI) was also calculated for selected muscle pairings. The oxy- and deoxyhemoglobin concentrations (prefrontal cortex activity) were recorded using functional near-infrared spectroscopy (fNIRS). Data were compared between single- and dual-task performance. Prefrontal activity increased (p < 0.05), and muscle activity decreased in most analyzed muscles (p < 0.05), from the single-task to cognitive dual-task performing. The co-contraction index patterns changed from single- to dual-task conditions in most selected muscle pairs (p < 0.05). We conclude that the cognitive task negatively interfered with motor performance once the muscle activity decreased and the prefrontal cortex activity increased under a dual-task, suggesting that young adults prioritized cognitive task performance, and they allocated more attentional resources to the cognitive task over the motor performance. Understanding the neuromotor changes can help adopt a better clinical practice to prevent injuries. However, future studies are recommended to assess and monitor muscular and cortical activity during the dual-task performance to provide additional information about the cortical and muscular activity patterns in postural control while performing a dual-task.
Impact of In-Air Gestures on In-Car Task’s Diver Distraction
Authors: Cui Chengyong , Shen Guojiang , Wang Yu , Xu Yile , Du Hao , Zhang Wenyi , Kong Xiangjie
Published: March 2023

As in-vehicle information systems (IVIS) grow increasingly complex, the demand for innovative artificial intelligence-based interaction methods that enhance cybersecurity becomes more crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness, potentially improving security in human–computer interactions. However, the impact of in-air gestures on driver distraction during in-vehicle tasks and the scarcity of skeleton-based in-air gesture recognition methods in IVIS remain largely unexplored. To address these challenges, we developed a skeleton-based framework specifically tailored for IVIS that recognizes in-air gestures, classifying them as static or dynamic. Our gesture model, tested on the large-scale AUTSL dataset, demonstrates accuracy comparable to state-of-the-art methods and increased efficiency on mobile devices. In comparative experiments between in-air gestures and touch interactions within a driving simulation environment, we established an evaluation system to assess the driver’s attention level during driving. Our findings indicate that in-air gestures provide a more efficient and less distracting interaction solution for IVIS in multi-goal driving environments, significantly improving driving performance by 65%. The proposed framework can serve as a valuable tool for designing future in-air gesture-based interfaces for IVIS, contributing to enhanced cybersecurity.
EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
Authors: Xu Tong , Chen Yazhou , Wang Yuming , Zhang Dongxiao , Zhao Min
Published: March 2023

In this work, a multi-task convolutional neural network with multi-input (MIMT-CNN) is proposed for electromagnetic interference (EMI) signals recognition and electromagnetic environment risk evaluation of the data link of unmanned aerial vehicle (UAV). The visualized performance parameters, short-time Fourier transform (STFT) spectrograms, and constellation diagrams are obtained by experiment on the electromagnetic susceptibility of UAV’s datalink. In particular, the constellation diagram is further enhanced by calculating the density distribution of sampling points to obtain the normalized density constellation. Taking the above different categories of images as the input of the expected model, the multi-element and high correlation EMI features are extracted and fused in the MIMT-CNN. Besides, the structure of series-parallel connection is adopted in the trained model and the Bayesian optimization is also used to select hyperparameters. In this case, the perception model with higher reliability can be obtained. On this basis, the performance and complexity of the obtained model with different input channels are compared. The results show that with the input of constellation diagram, especially the normalized density constellation, can significantly improve the accuracy of the model. Besides the normalized density constellation, the model with visualized performance parameters and STFT spectrogram as inputs has a much better performance.
An SVPWM Algorithm for a Novel Multilevel Rectifier with DC-Side Capacitor Voltage Balance
Authors: Cheng Hong , Yang Daokuan , Wang Cong , Tian Changgeng
Published: March 2023

The recently proposed novel unidirectional multilevel rectifier, a three single-phase star-connected multilevel rectifier, has the characteristic of having a large number of DC-side capacitors and a complex capacitor voltage balancing control structure under conventional carrier-based phase-shift sine wave pulse-width modulation (SPWM). Hence, a space vector pulse-width modulation (SVPWM) algorithm for the novel multilevel rectifier is proposed in this paper, which can quickly balance the capacitor voltage without an additional voltage balancing control structure. Firstly, it divides the space vectors of the rectifier, then it determines the two basis voltage vectors that synthesize the output reference voltage. After that, based on the analysis of the relationship between switching states and the charge–discharge of capacitors, the final action sequences of redundant vectors are determined according to the principle of keeping the capacitor charge–discharge time consistent. Thus, the capacitor voltages can be automatically balanced without an additional voltage balancing control structure. Finally, simulation and experimental results validated the feasibility and effectiveness of the proposed SVPWM algorithm. The results also show improvements in current quality, capacitor voltage balance and the fluctuation of the neutral point voltage on the DC-link, allowing for further reduction in the overall volume and cost of the rectifier.
An In-Vehicle Behaviour-Based Response Model for Traffic Monitoring and Driving Assistance in the Context of Smart Cities
Authors: Anjum Mohd , Shahab Sana , Dimitrakopoulos George , Guye Habib
Published: March 2023

Intelligent transportation systems (ITS) are pivotal to the development of smart cities, as they aim to enhance traffic flow, reduce traffic congestion, improve road safety, and increase social inclusion. Intelligent vehicles can sense, actuate, and process information that has been gathered from the environment to provide reliable services. During communication, congestion is a major issue that affects driving behaviour. This paper proposes a behaviour-based response model for analysing the roadside traffic in a smart city environment. In this model, the vehicles leverage the benefits of connected cloud technology and smart computational capabilities to analyse traffic conditions and provide assisted driving to users. The proposed model employs a regression model for computing and analysing the information that is gathered from the environment. It also generates recommendations for its users and provides traffic congestion-free driving assistance, with a reduced reaction time and improved driving efficiency. Lastly, the model also intends to provide real-time information and actionable insights for drivers so that they can make informed decisions and improve the road safety in smart environments. The performance of the proposed model is validated by using the appropriate experiments, and the results are validated for the varying set of inputs and intervals for the metrics response delay, processing time, and precision errors.
Pixel-Coordinate-Induced Human Pose High-Precision Estimation Method
Authors: Sun Xuefei , Adamu Mohammed Jajere , Zhang Ruifeng , Guan Xin , Li Qiang
Published: March 2023

Accurately estimating human pose is crucial for providing feedback during exercises or musical performances, but the complex and flexible nature of human joints makes it challenging. Additionally, traditional methods often neglect pixel coordinates, which are naturally present in high-resolution images of the human body. To address this issue, we propose a novel human pose estimation method that directly incorporates pixel coordinates. Our method adds a coordinate channel to the convolution process and embeds pixel coordinates into the feature map, while also using coordinate attention to capture position- and structure-sensitive features. We further reduce the network parameters and computational cost by using small-scale convolution kernels and a smooth activation function in residual blocks. We evaluate our model on the MPII Human Pose and COCO Keypoint Detection datasets and demonstrate improved accuracy, highlighting the importance of directly incorporating coordinate location information in position-sensitive tasks.
NACDA: Naming-Based Access Control and Decentralized Authorization for Secure Many-to-Many Data Sharing
Authors: Li Minghui , Xue Jingfeng , Wang Yong , Ma Rui , Huo Wei
Published: March 2023

The rapid development of wearable technology has facilitated the collection and sharing of health data, allowing patients to benefit from caretakers and medical research. However, these personal health data often contain sensitive information and it is typically not known in advance with whom the information will be shared. Therefore, messages must be encrypted and shared while adhering to the decoupled communication model. This paper presents NACDA, a secure many-to-many data-sharing service on the Named Data Network (NDN). NACDA uses Identity-Based Encryption with Wildcard Key Derivation (WKD-IBE) to allow naming-based access control, enabling data subjects to share data securely and flexibly regardless of the data processor. In addition, NACDA supplements a decentralized authorization mechanism with blockchain to ensure data subjects’ data ownership and enforce access policies. We developed an NDN-based prototype and performed a security analysis to demonstrate NACDA’s feasibility.
An Ice-Penetrating Signal Denoising Method Based on WOA-VMD-BD
Authors: Lu Danping , Shen Shaoxiang , Li Yuxi , Zhao Bo , Liu Xiaojun , Fang Guangyou
Published: March 2023

Chang’E-7 will be launched around 2026 to explore resources at the lunar south pole. Glaciers are suitable scenes on the earth for lunar penetrating radar verification. In the verification experiment, ice-penetrating signals are inevitably polluted by noise, affecting the accuracy and reliability of glacier detection. This paper proposes a denoising method for ice-penetrating signals based on the combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and the improved Bhattacharyya distance (BD). Firstly, a fitness function for WOA is established based on permutation entropy (PE), and the number of decomposition modes K and the quadratic penalty factor α in the VMD are optimized using WOA. Then, VMD is performed on the signal to obtain multiple intrinsic mode functions (IMFs). Finally, according to the BD, the relevant IMFs are selected for signal reconstruction and denoising. The simulation results indicate the strengths of this method in enhancing the signal-to-noise ratio (SNR), and its performance is better than empirical mode decomposition (EMD). Experiments on the detected signals of the Mengke Glacier No. 29 indicate that the WOA-VMD-BD method can efficiently eliminate noise from the data and procure well-defined layered profiles of the glacier. The research in this paper helps observe the layered details of the lunar regolith profile and interpret the data in subsequent space exploration missions.
A Clustered Federated Learning Method of User Behavior Analysis Based on Non-IID Data
Authors: Zhang Jianfei , Li Zhongxin
Published: March 2023

Federated learning (FL) is a novel distributed machine learning paradigm. It can protect data privacy in distributed machine learning. Hence, FL provides new ideas for user behavior analysis. User behavior analysis can be modeled using multiple data sources. However, differences between different data sources can lead to different data distributions, i.e., non-identically and non-independently distributed (Non-IID). Non-IID data usually introduce bias in the training process of FL models, which will affect the model accuracy and convergence speed. In this paper, a new federated learning algorithm is proposed to mitigate the impact of Non-IID data on the model, named federated learning with a two-tier caching mechanism (FedTCM). First, FedTCM clustered similar clients based on their data distribution. Clustering reduces the extent of Non-IID between clients in a cluster. Second, FedTCM uses asynchronous communication methods to alleviate the problem of inconsistent computation speed across different clients. Finally, FedTCM sets up a two-tier caching mechanism on the server for mitigating the Non-IID data between different clusters. In multiple simulated datasets, compared to the method without the federated framework, the FedTCM is maximum 15.8% higher than it and average 12.6% higher than it. Compared to the typical federated method FedAvg, the accuracy of FedTCM is maximum 2.3% higher than it and average 1.6% higher than it. Additionally, FedTCM achieves more excellent communication performance than FedAvg.
AMFF-YOLOX: Towards an Attention Mechanism and Multiple Feature Fusion Based on YOLOX for Industrial Defect Detection
Authors: Chen Yu , Tang Yongwei , Hao Huijuan , Zhou Jun , Yuan Huimiao , Zhang Yu , Zhao Yuanyuan
Published: March 2023

Industrial defect detection has great significance in product quality improvement, and deep learning methods are now the dominant approach. However, the volume of industrial products is enormous and mainstream detectors are unable to maintain a high accuracy rate during rapid detection. To address the above issues, this paper proposes AMFF-YOLOX, an improved industrial defect detector based on YOLOX. The proposed method can reduce the activation function and normalization operation of the bottleneck in the backbone network, and add an attention mechanism and adaptive spatial feature fusion within the feature extraction network to enable the network to better focus on the object. Ultimately, the accuracy of the prediction is enhanced without excessive loss of speed in network prediction, with competitive performance compared to mainstream detectors. Experiments show that the proposed method in this paper achieves 61.06% (85.00%) mAP@0.5:0.95 (mAP@0.5) in the NRSD-MN dataset, 51.58% (91.09%) is achieved in the PCB dataset, and 49.08% (80.48%) is achieved in the NEU-DET dataset. A large number of comparison and ablation experiments validate the effectiveness and competitiveness of the model in industrial defect detection scenarios.
Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study
Authors: Cocianu Cătălina-Lucia , Uscatu Cristian Răzvan , Kofidis Konstantinos , Muraru Sorin , Văduva Alin Gabriel
Published: March 2023

Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages.
Design of Parameter-Optimized Spiral Arrays with Ultra-Wideband Grating Lobe Suppression
Authors: Liu Siqi , Chen Liyang , Liu Yanhui
Published: March 2023

An effective method is presented to design the layout of spiral array with ultra-wideband (UWB) grating lobe suppression. In this method, the general array factor expression of spiral array is formulated. Then, the synthesis problem of spiral array layout is converted to an optimization problem of three geometric parameters: s, r and Δϕ. These parameters can determine the exact position of each element. The optimization problem is solved by particle swarm optimization (PSO) algorithm where the constraints of element spacing and array aperture are introduced. For different-sized spiral arrays, there are always only three variables that need to be optimized in the PSO algorithm, which allows the proposed method to handle the layout design of large-scale spiral arrays with UWB grating lobe suppression. Two examples with different synthesis requirements are conducted to verify the effectiveness and advantages of the proposed method.
Rapid and Effective Technology Development for 3D-Model-Based Solar Access Analysis and Comparative Study with Fish-Eye Camera
Authors: Lee Chung-Hyun , Lee Woo-Hyuk , Choi Yosoon , Suh Jangwon , Kim Sung-Min
Published: March 2023

In this study, we developed a 3D-model-based technology that can evaluate solar access by analyzing solar radiation and shade to find the optimal location for a solar system. We developed an algorithm that can quickly calculate viewshed by applying ray-casting technology, which is useful in the field of computer graphics. To apply the developed technology, an unmanned aerial vehicle (DJI MAVIC 3) was used to create a 3D model by taking 320 photos of the Kangwon National University Samcheok campus. To verify the developed technology, a comparison with image-based analysis using a 360-degree camera was performed for 30 points. As a result of applying the developed technology to the study area, it was possible to calculate the solar access for each point. In general, image-based analysis exaggerates the effects of objects such as trees, whereas the developed technique can produce realistic results if the 3D objects were well built. If the technology is further developed in the future, it can be used to increase the efficiency of solar power generation.
An IoT-Based Solution for Monitoring and Controlling Battery Energy Storage Systems at Residential and Commercial Levels
Authors: Burgio Alessandro , Cimmino Domenico , Nappo Andrea , Smarrazzo Luigi , Donatiello Giuseppe
Published: March 2023

Today, increasing numbers of batteries are installed in residential and commercial buildings; by coordinating their operation, it is possible to favor both the exploitation of renewable sources and the safe operation of electricity grids. However, how can this multitude of battery storage systems be coordinated? Using the Application Programming Interfaces of the storage systems’ manufacturers is a feasible solution, but it has a huge limitation: communication to and from storage systems must necessarily pass through the manufacturers’ cloud infrastructure. Therefore, this article presents an IoT-based solution which allows monitoring/controlling battery storage systems, independently from the manufacturers’ cloud infrastructure. More specifically, a home gateway locally controls the battery storage using local APIs via Wi-Fi on the condition that the manufacturer enables them. If not, an auxiliary device allows the home gateway to establish a wired communication with the battery storage via the SunSpec protocol. Validations tests demonstrate the effectiveness of the proposed IoT solution in monitoring and controlling ABB, Sonnen and SolarEdge storage systems.
Dual-Extended State Observer-Based Feedback Linearizing Control for a Nonlinear System with Mismatched Disturbances and Uncertainties
Authors: Gandhi Ravi , Adhyaru Dipak , Sharma Gulshan , Bokoro Pitshou N.
Published: March 2023

This research article presents the nonlinear control framework to estimate and reject the mismatched lumped disturbances acting on the nonlinear uncertain system. It is an unfortunate fact that the conventional extended state observer (ESO) is not capable of simultaneously estimating the mismatched lumped disturbance and its derivative for the systems. Moreover, the basic ESO is only suitable for systems with integral chain form (ICF) structures. Similarly, the conventional feedback linearizing control (FLC) approach is not robust enough to stabilize systems in the presence of disturbances and uncertainties. Hence, the nonlinear control framework is proposed to overcome the above issues, which are composed of (a) a dual-extended state observer (DESO), and (b) a DESO-based FLC. The DESO provides information on the unmeasured state, mismatched disturbance, and its derivatives. The DESO-FLC utilizes the information from the DESO to counter the effects of such disturbances and to stabilize the nonlinear systems around the reference point. The detailed closed-loop analysis is presented for the proposed control framework in the presence of lumped disturbances. The performance robustness of the presented design was validated for the third-order, nonlinear, unstable, and disturbed magnetic levitation system (MLS). The results of the DESO-FLC approach are compared with the most popular linear quadratic regulator (LQR) and nonlinear FLC approaches based on the integral error criterion and the average electrical energy consumption.
Energy Supply Systems Predicting Model for the Integration of Long-Term Energy Planning Variables with Sustainable Livelihoods Approach in Remote Communities
Authors: Pereyra-Mariñez Carlos , Andrickson-Mora José , Ocaña-Guevera Victor Samuel , Santos García Félix , Vallejo Diaz Alexander
Published: March 2023

The Sustainable Development Goals (SDGs) of the United Nations Organization pursue the provision of affordable and quality energy for all human beings, which is why the correct planning of Energy Supply Systems (ESS) in communities that present levels of energy poverty, that is, the impossibility to satisfy their minimum needs for energy services. This work proposes a methodology to evaluate the contribution to development by the adequate provision of the demand of ESS in remote communities through the approach of Sustainable Livelihoods (SLs). The methodology starts from the initial evaluation of the sustainable livelihoods or capitals of the communities and the analysis of their interaction. Then, a capital improvement process is proposed by selecting the indicator values that optimize the model in each period, through an evolutionary algorithm that guarantees that the indicators evolve to a rich scenario as a result of planning to evolve the key variables based on a quantitative model with the indicators that empower evaluating the contribution of the ESS to them.
Robust Subsynchronous Damping Control of PMSG-Based Wind Farm
Authors: Wang Yun , Luo Fengyun , Long Chaoyang , Tao Guoqing , Xu Ying , Yang Rong
Published: March 2023

This paper provides an H∞ robust control strategy for a permanent magnet synchronous generator (PMSG)-based wind farm to realize subsynchronous resonance suppression (SSR) subject to uncertain system distortions and parameter perturbation. Firstly, an eighth-order state space mathematical model of a PMSG-based wind farm is established, including the grid-side converter (GSC), GSC controller, and phase-locked loop (PLL) model. Secondly, the SSR characteristics of a PMSG-based wind farm are analyzed through eigenvalue analysis. Thirdly, a robust subsynchronous damping controller is designed based on eigenvalue analysis of SSR. Finally, the designed robust subsynchronous damping controller is validated with case studies of wind farms. The results show that the controller can increase the stability of PMSG-based wind farm systems and restrain SSR.
Co-Pyrolysis of Woody Biomass and Oil Shale in a Batch Reactor in CO2, CO2-H2O, and Ar Atmospheres
Authors: Lyons Cerón Alejandro , Konist Alar
Published: March 2023

The partial replacement of fossil fuels with biomass provides an alternative to producing cleaner and more sustainable energy and fuels. Conventional shale oil production infrastructure can potentially be used in co-pyrolysis with biomass to reduce the use of oil shale and decrease its environmental impact. The effect of adding 10 and 30 wt% woody biomasses (spruce, alder, pine, and birch) into oil shale was studied through intermediate co-pyrolysis. The experiments were carried out in a batch reactor at 520 °C, with a 20 min residence time, in CO2, CO2-H2O 1:1, and Ar gas atmospheres. The solid products were collected and analyzed for elemental composition and surface area, while the composition of the gases was determined through gas chromatography. The difference in experimental and theoretical mass balances of fuel blends was lower than 2.5 wt% in all gas environments, indicating slight interactions between the fuels. CO2 atmospheres contributed to increased decomposition, with up to 2.6 wt% lower solid products. Biomass increased the production of combustible gases, especially CO yields, from 0.42 to 1.30 vol%. The addition of biomass and the use of alternative atmospheres can improve pyrolysis through increased fuel decomposition and a lower share of residual mass from 74.4 wt% for oil shale to 58–70 wt% for oil shale and biomass blends.
Determination of Thermocline Heat Transfer Coefficient by Using CFD Simulation
Authors: Szczęśniak Arkadiusz , Milewski Jarosław , Dybiński Olaf , Futyma Kamil , Skibiński Jakub , Martsinchyk Aliaksandr , Szabłowski Łukasz
Published: March 2023

This article deals with a thermal energy storage system in the form of a water tank with a thermocline. The well-known thermocline phenomenon is modeled using computational fluid dynamics (CFD). However, the reservoir model proposed in this article is zero-dimensional. This is due to the fact that the aim of this article is to build a mathematical model that will be more useful in mathematical models of complex energy systems in which a hot water tank is one of many elements of the system. In such a zero-dimensional mathematical model, the hot water tank will be modeled using equations describing heat transfer, and the thermocline itself will be treated as a heat transfer surface with known dimensions and heat transfer coefficient. A novelty of this paper is that it addresses heat loss across the thermocline as defined in this manner. A CFD model of a thermal storage tank is created, validated with available experimental data, and used to obtain the heat transfer coefficient U. The resulting value is then analyzed quantitatively and qualitatively and the changes in the thickness of the thermocline are accounted for in the equation. The results from this groundbreaking work can be used to analyze heat storage in the form of thermocline water tanks at the level of system modeling, e.g., for the purpose of configuring the structure of other devices and control systems.