bluuebunny/crossref_metadata_2025 huggingface dataset:
Vascular Enlargement as a Predictor of Nodal Involvement in Bladder Cancer
Authors: Borgheresi Alessandra , Agostini Andrea , Sternardi Francesca , Cesari Elisa , Ventura Fiammetta , Ottaviani Letizia , Delle Fave Rocco Francesco , Pretore Eugenio , Cimadamore Alessia , Filosa Alessandra , Galosi Andrea Benedetto , Giovagnoni Andrea
Published: June 2023

In bladder cancer (BC), the evaluation of lymph node (LN) involvement at preoperative imaging lacks specificity. Since neoangiogenesis is paired with lymphatic involvement, this study aims to evaluate the presence of perivesical venous ectasia as an indirect sign of LN involvement, together with other conventional CT findings. All the patients who underwent radical cystectomy (RC) for BC between January 2017 and December 2019 with available preoperative contrast-enhanced CT (CECT) within 1 month before surgery were included. Patients without available pathological reports (and pTNM stage) or who underwent neoadjuvant treatments and palliative RC were excluded. Two readers in blind assessed the nodal shape and hilum, the short axis, and the contrast enhancement of suspicious pelvic LNs, the Largest Venous Diameter (LVD) efferent to the lesion, and the extravesical tumor invasion. In total, 38 patients (33 males) were included: 17 pT2, 17 pT3, 4 pT4; pN+: 20/38. LN short axis > 5 mm, LN enhancement, and LVD > 3 mm were significantly correlated with N+ at pathology. LVD > 3 mm had a significantly higher sensitivity and specificity (≥90%, AUC = 0.949) and was an independent predictor (p = 0.0016).
Can Low-Iodine, Low-Radiation-Dose CT Aortogram Reliably Detect Endoleak after Endovascular Aneurysm Repair of the Aorta?
Authors: Chen Po-An , Huang Eric P. , Chen Yi-Chun , Chuo Chiung-Chen , Huang Shu-Tin , Wu Ming-Ting
Published: June 2023

Objective: Double-low CT aortography (DLCTA) is increasingly used in follow-up studies of aortic aneurysm after endovascular aneurysm repair (EVAR). However, whether DLCTA can reliably detect the presence of endoleak is not clear. Methods: From February 2014 to October 2019, patients who received EVAR, underwent CT surveillance, and had at least one standard CTA protocol (120 kVp, 400 mg I/kg) and one DLCTA (70–80 kVp, 200 mg I/kg) were included. The integrated findings of the standard CTA and sequential change were considered as the reference standard for the presence of endoleak. Results: In all, 36 patients received TEVAR and 24 patients received EVAR; 62 standard CTA and 167 DLCTA results were analyzed. There were 2 type I (3.3%) and 12 type II (20.0%) endoleaks in 14 patients (23.3%). The performance of DLCTA in the diagnosis of endoleak reached 100% accuracy compared to that of standard CTA in case of the correction of CT findings by an expert second reading. Compared to the standard CTA, DLCTA scan reduced the radiation dose by 71% and the iodine dose by 50%. Conclusions: DLCTA with 70–80 kVp and 200 mg I/kg can reliably detect the presence of endoleak after TEVAR/EVAR.
Cytogenomic Investigation of Syndromic Brazilian Patients with Differences of Sexual Development
Authors: Faria José Antonio Diniz , Moraes Daniela R. , Kulikowski Leslie Domenici , Batista Rafael Loch , Gomes Nathalia Lisboa , Nishi Mirian Yumie , Zanardo Evelin , Nonaka Carolina Kymie Vasques , de Freitas Souza Bruno Solano , Mendonca Berenice Bilharinho , Domenice Sorahia
Published: June 2023

Background: Cytogenomic methods have gained space in the clinical investigation of patients with disorders/differences in sexual development (DSD). Here we evaluated the role of the SNP array in achieving a molecular diagnosis in Brazilian patients with syndromic DSD of unknown etiology. Methods: Twenty-two patients with DSD and syndromic features were included in the study and underwent SNP-array analysis. Results: In two patients, the diagnosis of 46,XX SRY + DSD was established. Additionally, two deletions were revealed (3q29 and Xp22.33), justifying the syndromic phenotype in these patients. Two pathogenic CNVs, a 10q25.3-q26.2 and a 13q33.1 deletion encompassing the FGFR2 and the EFNB2 gene, were associated with genital atypia and syndromic characteristics in two patients with 46,XY DSD. In a third 46,XY DSD patient, we identified a duplication in the 14q11.2-q12 region of 6.5 Mb associated with a deletion in the 21p11.2-q21.3 region of 12.7 Mb. In a 46,XY DSD patient with delayed neuropsychomotor development and congenital cataracts, a 12 Kb deletion on chromosome 10 was found, partially clarifying the syndromic phenotype, but not the genital atypia. Conclusions: The SNP array is a useful tool for DSD patients, identifying the molecular etiology in 40% (2/5) of patients with 46,XX DSD and 17.6% (3/17) of patients with 46,XY DSD.
18F-FDG PET/CT and Whole-Body Bone Scan Findings in Gorlin–Goltz Syndrome
Authors: Cheon Miju , Yoo Jang , Kang Kyu-Bok
Published: June 2023

Gorlin–Goltz syndrome (basal cell nevus syndromes) is an uncommon, autosomal dominant inherited disorder characterized by developing basal cell carcinomas from a young age. Other distinct clinical features include keratocystic odontogenic tumors, dyskeratotic palmar and plantar pitting, and skeletal abnormalities. Clinicopathological findings of the syndrome are very diverse, and many symptoms manifest during a certain period of life. We present the compelling whole-body bone scan and 18F-FDG PET/CT findings in a 32-year-old man with odontogenic keratocyst, early-onset basal cell carcinoma, multiple ectopic calcifications in extremities, calcified falx cerebri, spinal scoliosis, macrocephaly, and ocular hypertelorism.
Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks
Authors: Ali Muhammad Danish , Saleem Adnan , Elahi Hubaib , Khan Muhammad Amir , Khan Muhammad Ijaz , Yaqoob Muhammad Mateen , Farooq Khattak Umar , Al-Rasheed Amal
Published: June 2023

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model’s learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model’s feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model’s classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model’s performance will be compared with state-of-the-art approaches in other existing systems’ accuracy, precision, recall, and F1 score.
Neurofilament Expression as a Biomarker of Post-COVID-19 Sudden Sensorineural Hearing Loss
Authors: Zoccali Federica , Petrella Carla , Zingaropoli Maria Antonella , Fiore Marco , Ralli Massimo , Minni Antonio , Barbato Christian
Published: June 2023

Sudden sensorineural hearing loss (SSHL) affects a patient’s quality of life and requires rapid treatment. The etiology is viral, vascular, and autoimmune, even though, in most cases, it remains idiopathic SSHL. Since 2019, several different complications have been identified following COVID-19 infection. The post-COVID-19 ENT manifestations reported in the literature are sore throat, headache, pharyngeal erythema, nasal obstruction, rhinorrhea, upper respiratory tract infection, and tonsil enlargement. Cases of SSHL, vestibular neuronitis, and audio-vestibular disorders (such as tinnitus, dizziness, and vertigo) have also been reported, albeit in a smaller percentage of patients. We reported our experience of a case of post-COVID-19 SSHL in the absence of any other type of post-COVID symptoms or brain and internal auditory canal magnetic resonance imaging and magnetic resonance angiography modifications. We aimed to identify a serological biomarker of sudden sensorineural hearing loss, and we also dosed and monitored the value of the serum neurofilament light (NfL). the best of our knowledge, this is the first report that associates SSHL and the serological increase in NfL as a potential biomarker of neuronal-disease-related damage.
Dental Teacher Feedback and Student Learning: A Qualitative Study
Authors: Fine Peter , Leung Albert , Tonni Ingrid , Louca Chris
Published: June 2023

Introduction: Feedback is essential to improve student learning and motivation and to encourage curriculum development by teachers. This study looked at feedback to and from dental students from a qualitative perspective. Methods: Dental teachers were recruited exclusively to this study from the membership of the Association for Dental Education in Europe (ADEE). Delegates from each of the four annual ADEE conferences were invited to attend focus groups to discuss aspects of feedback. Focus groups established an individual theme for the respective conferences: (i) the role of the teacher in delivering feedback; (ii) feedback from the students’ perspective; (iii) changes to feedback due to the COVID-19 pandemic; and (iv) integrating feedback with assessments. Results: Qualitative data collected from the conference delegates were diverse and thought provoking. Delegates reported different styles of feedback varying from individual, personal feedback to no feedback at all. An enforced and mostly positive adaptation to online delivery during the COVID-19 pandemic was reported. A partial return to pre-pandemic practices was described. Conclusions: Feedback is well recognized by students and teachers as contributing to learning. A universal approach to delivering feedback as part of the student learning process can be challenging due to a multitude of variables. Many aspects of changes in dental education, teaching, and feedback practices adopted as a result of the COVID-19 pandemic have been retained post-pandemic, thereby accelerating the anticipated progression to online teaching.
Variable-Step Multiscale Fuzzy Dispersion Entropy: A Novel Metric for Signal Analysis
Authors: Li Yuxing , Wu Junxian , Zhang Shuai , Tang Bingzhao , Lou Yilan
Published: June 2023

Fuzzy dispersion entropy (FuzDE) is a newly proposed entropy metric, which combines the superior characteristics of fuzzy entropy (FE) and dispersion entropy (DE) in signal analysis. However, FuzDE only reflects the feature from the original signal, which ignores the hidden information on the time scale. To address this problem, we introduce variable-step multiscale processing in FuzDE and propose variable-step multiscale FuzDE (VSMFuzDE), which realizes the characterization of abundant scale information, and is not limited by the signal length like the traditional multiscale processing. The experimental results for both simulated signals show that VSMFuzDE is more robust, more sensitive to dynamic changes in the chirp signal, and has more separability for noise signals; in addition, the proposed VSMFuzDE displays the best classification performance in both real-world signal experiments compared to the other four entropy metrics, the highest recognition rates of the five gear signals and four ship-radiated noises reached 99.2% and 100%, respectively, which achieves the accurate identification of two different categories of signals.
HeteEdgeWalk: A Heterogeneous Edge Memory Random Walk for Heterogeneous Information Network Embedding
Authors: Liu Zhenpeng , Zhang Shengcong , Zhang Jialiang , Jiang Mingxiao , Liu Yi
Published: June 2023

Most Heterogeneous Information Network (HIN) embedding methods use meta-paths to guide random walks to sample from HIN and perform representation learning in order to overcome the bias of traditional random walks that are more biased towards high-order nodes. Their performance depends on the suitability of the generated meta-paths for the current HIN. The definition of meta-paths requires domain expertise, which makes the results overly dependent on the meta-paths. Moreover, it is difficult to represent the structure of complex HIN with a single meta-path. In a meta-path guided random walk, some of the heterogeneous structures (e.g., node type(s)) are not among the node types specified by the meta-path, making this heterogeneous information ignored. In this paper, HeteEdgeWalk, a solution method that does not involve meta-paths, is proposed. We design a dynamically adjusted bidirectional edge-sampling walk strategy. Specifically, edge sampling and the storage of recently selected edge types are used to better sample the network structure in a more balanced and comprehensive way. Finally, node classification and clustering experiments are performed on four real HINs with in-depth analysis. The results show a maximum performance improvement of 2% in node classification and at least 0.6% in clustering compared to baselines. This demonstrates the superiority of the method to effectively capture semantic information from HINs.
Feature-Based Complexity Measure for Multinomial Classification Datasets
Authors: Erwin Kyle , Engelbrecht Andries
Published: June 2023

Machine learning algorithms are frequently used for classification problems on tabular datasets. In order to make informed decisions about model selection and design, it is crucial to gain meaningful insights into the complexity of these datasets. Feature-based complexity measures are a set of complexity measures that evaluates how useful features are at discriminating instances of different classes. This paper, however, shows that existing feature-based measures are inadequate in accurately measuring the complexity of various synthetic classification datasets, particularly those with multiple classes. This paper proposes a new feature-based complexity measure called the F5 measure, which evaluates the discriminative power of features for each class by identifying long sequences of uninterrupted instances of the same class. It is shown that the F5 measure better represents the feature complexity of a dataset.
The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data
Authors: Wang Yangyang , Gao Xiaoguang , Ru Xinxin , Sun Pengzhan , Wang Jihan
Published: June 2023

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.
An Image Encryption Transmission Scheme Based on a Polynomial Chaotic Map
Authors: Zhang Yanpeng , Dong Wenjie , Zhang Jing , Ding Qun
Published: June 2023

Most existing chaotic systems have many drawbacks in engineering applications, such as the discontinuous range of chaotic parameters, weak chaotic properties, uneven chaotic sequence outputs, and dynamic degradation. Therefore, based on the above, this paper proposes a new method for the design of a three-dimensional chaotic map. One can obtain the desired number of positive Lyapunov exponents, and can also obtain the desired value of positive Lyapunov exponents. Simulation results show that the proposed system has complex chaotic behavior and high complexity. Finally, the method is implemented into an image encryption transmission scheme and experimental results show that the proposed image encryption scheme can resist brute force attacks, correlation attacks, and differential attacks, so it has a higher security.
Schizophrenia MEG Network Analysis Based on Kernel Granger Causality
Authors: Wang Qiong , Yao Wenpo , Bai Dengxuan , Yi Wanyi , Yan Wei , Wang Jun
Published: June 2023

Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (p=0.001). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (p=0.0018). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (p=0.012); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.
Fractal Derivatives, Fractional Derivatives and q-Deformed Calculus
Authors: Deppman Airton , Megías Eugenio , Pasechnik Roman
Published: June 2023

This work presents an analysis of fractional derivatives and fractal derivatives, discussing their differences and similarities. The fractal derivative is closely connected to Haussdorff’s concepts of fractional dimension geometry. The paper distinguishes between the derivative of a function on a fractal domain and the derivative of a fractal function, where the image is a fractal space. Different continuous approximations for the fractal derivative are discussed, and it is shown that the q-calculus derivative is a continuous approximation of the fractal derivative of a fractal function. A similar version can be obtained for the derivative of a function on a fractal space. Caputo’s derivative is also proportional to a continuous approximation of the fractal derivative, and the corresponding approximation of the derivative of a fractional function leads to a Caputo-like derivative. This work has implications for studies of fractional differential equations, anomalous diffusion, information and epidemic spread in fractal systems, and fractal geometry.
Ergodic Measure and Potential Control of Anomalous Diffusion
Authors: Wen Bao , Li Ming-Gen , Liu Jian , Bao Jing-Dong
Published: June 2023

In statistical mechanics, the ergodic hypothesis (i.e., the long-time average is the same as the ensemble average) accompanying anomalous diffusion has become a continuous topic of research, being closely related to irreversibility and increasing entropy. While measurement time is finite for a given process, the time average of an observable quantity might be a random variable, whose distribution width narrows with time, and one wonders how long it takes for the convergence rate to become a constant. This is also the premise of ergodic establishment, because the ensemble average is always equal to the constant. We focus on the time-dependent fluctuation width for the time average of both the velocity and kinetic energy of a force-free particle described by the generalized Langevin equation, where the stationary velocity autocorrelation function is considered. Subsequently, the shortest time scale can be estimated for a system transferring from a stationary state to an effective ergodic state. Moreover, a logarithmic spatial potential is used to modulate the processes associated with free ballistic diffusion and the control of diffusion, as well as the minimal realization of the whole power-law regime. The results presented suggest that non-ergodicity mimics the sparseness of the medium and reveals the unique role of logarithmic potential in modulating diffusion behavior.
Robust Z-Estimators for Semiparametric Moment Condition Models
Authors: Toma Aida
Published: June 2023

In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that realizes the supremum in the dual form of the divergence. A linear relationship between the influence function of a minimum empirical divergence estimator and the influence function of the estimator of the element that realizes the supremum in the dual form of the divergence led to the idea of defining new Z-estimators for the parameter of the model, by using robust estimators in the dual form of the divergence. The asymptotic properties of the proposed estimators were proven, including here the consistency and their asymptotic normality. Then, the influence functions of the estimators were derived, and their robustness is demonstrated.
Highly Integrated Ka-Band Multi-Beam Network Design Based on a Low-Cost Multi-Layer Printed Circuit Board
Authors: Zhang Xiao Yang , Yang Jun , Zhang Bo , Li Yan , Li Ruo Xin , Qin Xu Rong , Long Mao
Published: June 2023

A Ka-band low-profile and low-loss tile-type four-beam network based on high-density interconnection printed circuit board (PCB) technology is proposed, which integrates a power supply network, serial peripheral interface (SPI) control network, and four-beam radio frequency (RF) feeding network. In order to realize the fast optimal design, an integrated multi-beam network topology and fast optimization methods are established. The proposed feed networks can be integrated with antenna array structures directly, which not only realizes low-profile, light, and miniaturized design but also achieves efficient heat dissipation and low-loss interconnection. The designed multi-beam network has an amplitude balance better than ±0.5 dB and a phase balance better than ±6° in the Ka-band with a 4 GHz working bandwidth. A prototype was fabricated and measured, and good performance was observed.
MFDANet: Multi-Scale Feature Dual-Stream Aggregation Network for Salient Object Detection
Authors: Ge Bin , Pei Jiajia , Xia Chenxing , Wu Taolin
Published: June 2023

With the development of deep learning, significant improvements and optimizations have been made in salient object detection. However, many salient object detection methods have limitations, such as insufficient context information extraction, limited interaction modes for different level features, and potential information loss due to a single interaction mode. In order to solve the aforementioned issues, we proposed a dual-stream aggregation network based on multi-scale features, which consists of two main modules, namely a residual context information extraction (RCIE) module and a dense dual-stream aggregation (DDA) module. Firstly, the RCIE module was designed to fully extract context information by connecting features from different receptive fields via residual connections, where convolutional groups composed of asymmetric convolution and dilated convolution are used to extract features from different receptive fields. Secondly, the DDA module aimed to enhance the relationships between different level features by leveraging dense connections to obtain high-quality feature information. Finally, two interaction modes were used for dual-stream aggregation to generate saliency maps. Extensive experiments on 5 benchmark datasets show that the proposed model performs favorably against 15 state-of-the-art methods.
IDGAN: Information-Driven Generative Adversarial Network of Coverless Image Steganography
Authors: Zhang Chunying , Gao Xinkai , Liu Xiaoxiao , Hou Wei , Yang Guanghui , Xue Tao , Wang Liya , Liu Lu
Published: June 2023

Traditional image steganography techniques complete the steganography process by embedding secret information into cover images, but steganalysis tools can easily detect detectable pixel changes that lead to the leakage of confidential information. The use of a generative adversarial network (GAN) makes it possible to embed information using a combination of information and noise in generating images to achieve steganography. However, this approach is usually accompanied by issues such as poor image quality and low steganography capacity. To address these challenges, we propose a steganography model based on a novel information-driven generative adversarial network (IDGAN), which fuses a GAN, attention mechanisms, and image interpolation techniques. We introduced an attention mechanism on top of the original GAN model to improve image accuracy. In the generation model, we replaced some transposed convolution operations with image interpolation for better quality of dense images. In contrast to traditional steganographic methods, the IDGAN generates images containing confidential information without using cover images and utilizes GANs for information embedding, thus having better anti-detection capability. Moreover, the IDGAN uses an attention mechanism to improve the image details and clarity and optimizes the steganography effect through an image interpolation algorithm. Experimental results demonstrate that the IDGAN achieves an accuracy of 99.4%, 95.4%, 93.2%, and 100% on the MNIST, Intel Image Classification, Flowers, and Face datasets, respectively, with an embedding rate of 0.17 bpp. The model effectively protects confidential information while maintaining high image quality.
Underwater Image Enhancement Method Based on Improved GAN and Physical Model
Authors: Chang Shuangshuang , Gao Farong , Zhang Qizhong
Published: June 2023

Underwater vision technology is of great significance in marine investigation. However, the complex underwater environment leads to some problems, such as color deviation and high noise. Therefore, underwater image enhancement has been a focus of the research community. In this paper, a new underwater image enhancement method is proposed based on a generative adversarial network (GAN). We embedded the channel attention mechanism into U-Net to improve the feature utilization performance of the network and used the generator to estimate the parameters of the simplified underwater physical model. At the same time, the adversarial loss, the perceptual loss, and the global loss were fused to train the model. The effectiveness of the proposed method was verified by using four image evaluation metrics on two publicly available underwater image datasets. In addition, we compared the proposed method with some advanced underwater image enhancement algorithms under the same experimental conditions. The experimental results showed that the proposed method demonstrated superiority in terms of image color correction and image noise suppression. In addition, the proposed method was competitive in real-time processing speed.
A Multi-Feature Fusion and Situation Awareness-Based Method for Fatigue Driving Level Determination
Authors: Wei Fei-Fei , Chi Tao , Chen Xuebo
Published: June 2023

The detection and evaluation of fatigue levels in drivers play a crucial role in reducing traffic accidents and improving the overall quality of life. However, existing studies in this domain often focus on fatigue detection alone, with limited research on fatigue level evaluation. These limitations include the use of single evaluation methods and relatively low accuracy rates. To address these issues, this paper introduces an innovative approach for determining fatigue driving levels. We employ the Dlib library and fatigue state detection algorithms to develop a novel method specifically designed to assess fatigue levels. Unlike conventional approaches, our method adopts a multi-feature fusion strategy, integrating fatigue features from the eyes, mouth, and head pose. By combining these features, we achieve a more precise evaluation of the driver’s fatigue state level. Additionally, we propose a comprehensive evaluation method based on the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation, combined with situational prediction. This approach effectively evaluates the fatigue state level of drivers at specific moments or stages and provides accurate predictions. Furthermore, we optimize the gated recurrent unit (GRU) network using an enhanced marine predator algorithm (MAP), which results in significant improvements in predicting fatigue levels during situational prediction. Experimental results demonstrate a classification accuracy of 92% across various scenarios while maintaining real-time performance. In summary, this paper introduces a novel approach for determining fatigue driving levels through multi-feature fusion. We also incorporate AHP-fuzzy comprehensive evaluation and situational prediction techniques, enhancing the accuracy and reliability of fatigue level evaluation. This research holds both theoretical and practical significance in the field of fatigue driving.
A Sub-Synchronous Oscillation Suppression Strategy Based on Active Disturbance Rejection Control for Renewable Energy Integration System via MMC-HVDC
Authors: Chao Wujie , Deng Chaoping , Huang Junwei , Dai Liyu , Min Yangxi , Cheng Yangfan , Wang Yuhong , Liao Jianquan
Published: June 2023

To realize the consumption of renewable energy such as wind power and photovoltaics in the power system, renewable energy integration system via modular multilevel converter (MMC)-based high voltage direct current (MMC-HVDC) has been widely applied. However, with the large-scale grid connection of renewable energy units, sub-synchronous oscillation (SSO) is prone to occur. Aiming at the problem, this paper proposes an SSO suppression strategy for renewable energy integration system via MMC-HVDC based on active disturbance rejection control (ADRC) theory. Using the direct drive permanent magnet synchronous generators (PMSG)-based wind farm integration system via MMC-HVDC as an example, firstly the topology and control system principles of the system are described, and a simulation model is built in PSCAD/EMTDC. Moreover, the SSO mechanism of the system is revealed by Nyquist stability criterion, and the major factors affecting the SSO of the system are simulated and analyzed. Subsequently, an additional sub-synchronous damping controller (ASSDC) is proposed based on ADRC theory. Compared to traditional additional damping controllers, the proposed controller considers disturbances of the system during the designing process and has stronger robustness. In addition, when faults happen, the speed of the system with ASSDC reaching a steady-state operating point rises by 33.7% as compared to the system without ASSDC. Finally, the effectiveness of the proposed suppression strategy is verified through simulation analysis.
Modeling and Analyzing the Impact of Different Operating Conditions for Electric and Conventional Vehicles in Malaysia on Energy, Economic, and the Environment
Authors: Muzir Nur Ayeesha Qisteena , Hasanuzzaman Md. , Selvaraj Jeyraj
Published: June 2023

Given the significance of the transportation sector to the economy of a country, major companies and government-linked entities have invested in infrastructure and transportation services. Nonetheless, the sector faces issues relating to traffic congestion, energy consumption, and environmental impacts such as air pollution and carbon emissions. To address and analyze these issues, the current study employed microscopic modeling using the AIMSUN software, which allowed for detailed modeling and simulation. The current study examined the impacts of different operating conditions, namely: internal combustion engine vehicles (ICEVs) and electric vehicles (EVs), on energy consumption, energy savings, cost savings, and emissions traveling on a total of six (6) routes: (i) long-distance highway travel, (ii) short-distance highway travel, (iii) long-distance urban travel, (iv) short-distance urban travel, (v) long-distance suburban travel, and (vi) short-distance suburban travel. The impacts of the traffic management systems, such as traffic lights, roundabouts, and road altitude, were also analyzed in this research. The current study discovered that, on average, EVs consumed 30 percent less energy than ICEVs and a 26 percent energy cost saving for long-distance highway travel. On long-distance urban travel, EVs experienced higher energy and cost savings than ICEVs, with 86 percent and 64 percent, respectively. In addition, EVs had lower carbon dioxide emissions than ICEVs. This study concludes that EVs offer positive impacts on energy cost savings and carbon dioxide emissions reduction for all six (6) simulated routes in Malaysia compared to ICEVs, thereby contributing to the existing literature on EVs in Malaysia.
A Review on Mode Transition Strategies between Grid-Connected and Standalone Operation of Voltage Source Inverters-Based Microgrids
Authors: Hmad Jihed , Houari Azeddine , Bouzid Allal El Moubarek , Saim Abdelhakim , Trabelsi Hafedh
Published: June 2023

Microgrids technologies are seen as a cost effective and reliable solution to handle numerous challenges, mainly related to climate change and power demand increase. This is mainly due to their potential for integrating available on-site renewable energy sources and their flexibility and scalability. The particularity of microgrids is related to their capacity to operate in synchronization with the main grid or in islanded mode to secure the power supply of nearby end-users after a grid failure thanks to storage solutions and an intelligent control system. The most critical operating case occurs when a sudden transition from grid-connected (GC) to stand-alone operation (SA) happens. During the transition, the system experiences abrupt changes that can result in a malfunction of the control system and a possible failure of the power system. The transition issue attracted considerable attention from researchers. Indeed, many research works are proposed to address this issue by proposing detection and transition techniques that ensure a smooth transition at the islanding time. Although there are several approaches to dealing with this issue, a categorization of the proposed methods in the literature and their differences is useful to assist engineers and researchers working on this topic. Thus, this study proposes a comprehensive review to summarize these approaches and point out their advantages and limitations.
Experimental Study on the Effects of Applied Electric Field on Liquid Infiltration into Hydrophobic Zeolite
Authors: Zhang Yafei , Zhang Jiahua , Luo Rui , Dou Yihua
Published: June 2023

A nanofluidic energy absorption system (NEAS) is composed of nanoporous material and functional liquid with high energy absorption density. Applying an electric field to adjust the energy absorption characteristics of a nanofluidic system will open broader prospects for its application. In the current work, ZSM-5 zeolite was adopted as the nanoporous material and water, a 25% KCl solution, and a saturated KCl solution were adopted as functional liquids to configure NEASs. Pressure-induced infiltration experiments were carried out to study the infiltration and defiltration characteristics of the NEASs under the action of an applied electric field. The results show that the introduction of an applied electric field can weaken the hydrogen bonds between molecules, thus reducing the equivalent surface tension and contact angle, changing the infiltrability of liquid molecules into the nanopores, and reducing the infiltration pressure of the system. In an electrolyte solution/zeolite system, the anions and cations move close to the two plate electrodes under the action of an external electric field, and the fluid properties in the central zone of the pressure chamber are close to the water/zeolite system. For both an ultra-low conductivity liquid and an electrolyte solution/zeolite system, applying an electric field can effectively improve the relative outflow rate of liquid, thus improving the reusability of the system.
Design and Implementation of a Control System for an Autonomous Reforestation Machine Using Finite State Machines
Authors: Rossander Morgan , Lideskog Håkan
Published: June 2023

Reforestation is performed after the final felling as an important and often law-mandated step to ensure that wood production is sustainable. In Sweden alone, over 400 millions seedlings are planted annually. This work is physically demanding and the quality is uneven. Therefore, automatic production systems are under research and development. A necessary effort in this endeavor is presented in this paper: the development and evaluation of a mission supervisor utilized to control the mission and behavior of a full-scale autonomous forest regeneration machine tested in realistic environments. The mission supervisor is implemented in the Robot Operating System framework using a finite state machine package called SMACH. A terrain machine built as a research platform with an added full-scale forwarder crane is used as a base machine. First, we describe the scenario in which planting is conducted, whereupon we develop the composite tasks required as states. A simplified simulator then enables an intermediate step before field experiments. The system is implemented and operated in real time on a full-scale machine. Results show that the developed SMACH mission supervisor can be used as a sound basis for an autonomous forest regeneration machine and the chosen communication solution between different systems works well. The simulations show good agreement with the experiments. The results also show that crane movements take 70% of the machine time, emphasizing the importance of limiting crane movement, improving the actuator movement speed and integrating the composite solutions. Further development with a holistic approach is required before the concept can reach the prototype level.
Impacting Factors of Changes in Dynamic Viscosity and Interfacial Tension of Wood Xylem Sap
Authors: Wen Liang , Wang Xuan , Xu Qin , Tong Jiewei , Zhao Wanwan , Zhang Yaoli , Lv Jianxiong , Cai Liping , Xia Changlei
Published: June 2023

The growth of trees is inseparable from the water transpiration in the xylem. To explore the mechanism of sap rising in the xylem, the monthly variation of the dynamic viscosity and interfacial tension of the xylem sap of different tree species and their impacting factors were analyzed. In this experiment, the dynamic viscosity and interfacial tension of the xylem sap of poplar and metasequoia were measured within one year, as well as the sap velocity of poplar. Gas chromatography–mass spectrometry and atomic absorption spectroscopy were used to detect the organic components and inorganic cations of the xylem sap of poplar and metasequoia. By analyzing the influence of organic components and the inorganic cation concentration of xylem sap on the dynamic viscosity and interfacial tension of xylem sap, this study revealed that the dynamic viscosity and the interfacial tension of poplar and metasequoia samples in different months changed in basically the same manner. However, the dynamic viscosity and the interfacial tension of the metasequoia samples were generally higher than those of the poplar samples. The dynamic viscosity of the xylem sap had an obvious exponential relationship with temperature, while the interfacial tension of the xylem sap had an inconspicuous linear relationship with temperature. In addition, disparate xylem structures of the broad-leaved tree poplar and the coniferous tree metasequoia led to different concentrations of organic components and inorganic cations in their xylem sap, which made a difference in the dynamic viscosity and interfacial tension between poplar and metasequoia samples.
Analysis of the Heart Rate of Operators of Forwarding Machines during Work Activities
Authors: Sláma David , Mergl Václav , Pavlíková Eva Abramuszkinová
Published: June 2023

The aim of this work was to determine the dependence of the heart rate of operators of forwarding machines on the activities performed during the working day within the framework of timber forwarding and to compare individual activities in terms of the level of physical workload. For this purpose, the work shift of operators carrying out timber forwarding was divided into individual activities: driving, maintenance, forwarding, and break. During these work activities, the heart rate of each operator was taken for subsequent evaluation. A portable device, a Garmin smartwatch, was used to measure their heart rate. The results show that the highest pulse rates of the operators occurred during the maintenance of the entrusted machine, while the highest pulse fluctuations were recorded during forwarding. During this activity, the highest heart rate of the entire measurement process was recorded (132.0000 bpm), but also the lowest (42.0000 bpm). Furthermore, it was proven that both the operator and the activity he performs affect the pulse rate. The activities themselves did not differ from each other in only one of the six cases of comparison, specifically, between driving and forwarding.
Effects of Nitrogen Deposition on Leaf Litter Decomposition and Soil Organic Carbon Density in Arid and Barren Rocky Mountainous Regions: A Case Study of Yimeng Mountain
Authors: Kong Baishu , Zhou Jilei , Qi Liguo , Jiao Shuying , Ma Lujie , Geng Wenwen , Zhao Yuhao , Gao Ting , Gong Jie , Li Kun , Li Chuanrong
Published: June 2023

The ecological impact of nitrogen (N) deposition has gained significance since the advent of the industrial revolution. Although numerous studies have examined the impact of N deposition on soil organic carbon (SOC), certain arid and barren rocky mountainous regions, which experience more pronounced N limitations, have been overlooked. This study was conducted in the Yimeng Mountains, examining eight treatments created by four N addition levels (0 kg N ha−1 yr−1, 50 kg N ha−1 yr−1, 100 kg N ha−1 yr−1 and 200 kg N ha−1 yr−1) and two tree species (Quercus acutissima Carruth. and Pinus thunbergii Parl.). The research revealed variations in the effect of N addition on leaf litter decomposition and SOC density (SOCD) between different tree species. Notably, N addition stimulated the decomposition of leaf litter from Quercus acutissima Carruth. However, the decomposition of Pinus thunbergii Parl. leaf litter was enhanced at N addition levels below 100 kg N ha−1 yr−1, while it was hindered at levels exceeding 100 kg N ha−1 yr−1. In the Quercus acutissima Carruth. forest, the N addition levels of 50 kg N ha−1 yr−1, 100 kg N ha−1 yr−1 and 200 kg N ha−1 yr−1 resulted in decreases in SOCD by 10.57%, 22.22% and 13.66%, respectively, compared to 0 kg N ha−1 yr−1. In the Pinus thunbergii Parl. forest, the N addition levels of 50 kg N ha−1 yr−1, 100 kg N hm−2 ha−1 and 200 kg N ha−1 yr−1 led to increases in SOCD by 49.53%, 43.36% and 60.87%, respectively, compared to 0 kg N ha−1 yr−1. Overall, N addition decreases the SOCD of Quercus acutissima Carruth., but it increases the SOCD of Pinus thunbergii Parl., attributed to the alteration in soil enzyme stoichiometry and nutrient cycling by N addition. This study fills a theoretical gap concerning leaf litter decomposition and SOC sequestration in arid and barren rocky mountainous regions under global climate change.
A New AI-Based Semantic Cyber Intelligence Agent
Authors: Sufi Fahim
Published: June 2023

The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-reaching financial, social, and psychological repercussions on individuals. Beyond inflicting monetary losses, cyber-attacks exert adverse effects on the social fabric and psychological well-being of the affected individuals. In order to mitigate the deleterious consequences of cyber threats, adoption of an intelligent agent-based solution to enhance the speed and comprehensiveness of cyber intelligence is advocated. In this paper, a novel cyber intelligence solution is proposed, employing four semantic agents that interact autonomously to acquire crucial cyber intelligence pertaining to any given country. The solution leverages a combination of techniques, including a convolutional neural network (CNN), sentiment analysis, exponential smoothing, latent Dirichlet allocation (LDA), term frequency-inverse document frequency (TF-IDF), Porter stemming, and others, to analyse data from both social media and web sources. The proposed method underwent evaluation from 13 October 2022 to 6 April 2023, utilizing a dataset comprising 37,386 tweets generated by 30,706 users across 54 languages. To address non-English content, a total of 8199 HTTP requests were made to facilitate translation. Additionally, the system processed 238,220 cyber threat data from the web. Within a remarkably brief duration of 6 s, the system autonomously generated a comprehensive cyber intelligence report encompassing 7 critical dimensions of cyber intelligence for countries such as Russia, Ukraine, China, Iran, India, and Australia.
Comment on Laming et al. The Curse of Conservation: Empirical Evidence Demonstrating That Changes in Land-Use Legislation Drove Catastrophic Bushfires in Southeast Australia. Fire 2022, 5, 175
Authors: Penna Ian
Published: June 2023

In 1970, the Victorian state government in Australia established the Land Conservation Council (LCC) to study the state’s publicly-owned land and make recommendations for its use [...]