bluuebunny/crossref_metadata_2025 huggingface dataset:
Lipidomic Characterization and Antioxidant Activity of Macro- and Microalgae Blend
Authors: Marques Francisca , Lopes Diana , Conde Tiago , Melo Tânia , Silva Joana , Abreu Maria Helena , Domingues Pedro , Domingues Maria Rosário
Published: January 2023

Macro- and microalgae are currently recognized sources of lipids with great nutritional quality and attractive bioactivities for human health promotion and disease prevention. Due to the lipidomic diversity observed among algae species, giving rise to different nutritional and functional characteristics, the mixture of macro- and microalgae has the potential to present important synergistic effects resulting from the complementarity among algae. The aim of this work was to characterize for the first time the lipidome of a blend of macro- and microalgae and evaluate the antioxidant capacity of its lipid fraction. Fatty acids were profiled by GC-MS, the polar lipidome was identified by high resolution LC-MS, and ABTS+• and DPPH• assays were used to assess the antioxidant potential. The most abundant fatty acids were oleic (18:1 n-9), α-linolenic (18:3 n-3), and linoleic (18:2 n-6) acids. The lipid extract presented a beneficial n-6/n-3 ratio (0.98) and low values of atherogenic (0.41) and thrombogenic indices (0.27). The polar lipidome revealed 462 lipid species distributed by glycolipids, phospholipids, and betaine lipids, including some species bearing PUFA and a few with reported bioactivities. The lipid extract also showed antioxidant activity. Overall, the results are promising for the valorization of this blend for food, nutraceutical, and biotechnological applications.
Sample Size Calculation in Genetic Association Studies: A Practical Approach
Authors: Politi Cristina , Roumeliotis Stefanos , Tripepi Giovanni , Spoto Belinda
Published: January 2023

Genetic association studies, testing the relationship between genetic variants and disease status, are useful tools for identifying genes that grant susceptibility to complex disorders. In such studies, an inadequate sample size may provide unreliable results: a small sample is unable to accurately describe the population, whereas a large sample makes the study expensive and complex to run. However, in genetic association studies, the sample size calculation is often overlooked or inadequately assessed for the small number of parameters included. In light of this, herein we list and discuss the role of the statistical and genetic parameters to be considered in the sample size calculation, show examples reporting incorrect estimation and, by using a genetic software program, we provide a practical approach for the assessment of the adequate sample size in a hypothetical study aimed at analyzing a gene–disease association.
Gamma Knife Radiosurgery Irradiation of Surgical Cavity of Brain Metastases: Factor Analysis and Gene Mutations
Authors: Huang Yi-Han , Yang Huai-Che , Chiang Chi-Lu , Wu Hsiu-Mei , Luo Yung-Hung , Hu Yong-Sin , Lin Chung-Jung , Chung Wen-Yuh , Shiau Cheng-Ying , Guo Wan-Yuo , Lee Cheng-Chia
Published: January 2023

(1) Background: Surgical resection for the removal of brain metastases often fails to prevent tumor recurrence within the surgical cavity; hence, researchers are divided as to the benefits of radiation treatment following surgical resection. This retrospective study assessed the effects of post-operative stereotactic radiosurgery (SRS) on local tumor control and overall survival. (2) Methods: This study examined the demographics, original tumor characteristics, and surgical outcomes of 97 patients who underwent Gamma Knife Radiosurgery (GKRS) treatment (103 brain metastases). Kaplan–Meier plots and Cox regression were used to correlate clinical features to tumor control and overall survival. (3) Results: The overall tumor control rate was 75.0% and overall 12-month survival was 89.6%. Tumor control rates in the radiation group versus the non-radiation group were as follows: 12 months (83.1% vs. 57.7%) and 24 months (66.1% vs. 50.5%). During the 2-year follow-up period after SRS, the intracranial response rate was higher in the post-craniotomy radiation group than in the non-radiation group (p = 0.027). Cox regression multivariate analysis determined that post-craniotomy irradiation of the surgical cavity is predictive of tumor control (p = 0.035). However, EGFR mutation was not predictive of overall survival or tumor control. (4) Conclusions: Irradiating the surgical cavity after surgery can enhance local tumor control; however, it does not have a significant effect on overall survival.
Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review
Authors: Almhdie-Imjabbar Ahmad , Toumi Hechmi , Lespessailles Eric
Published: January 2023

Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren–Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models’ performance were not possible due to the lack of a unique definition of each of the three outcomes.
Differential Accumulation of sHSPs Isoforms during Desiccation of the Resurrection Plant Haberlea rhodopensis Friv. under Optimal and High Temperature
Authors: Mihailova Gergana , Tchorbadjieva Magdalena , Rakleova Goritsa , Georgieva Katya
Published: January 2023

Haberlea rhodopensis belongs to the small group of angiosperms that can survive desiccation to air-dry state and quickly restore their metabolism upon rehydration. In the present study, we investigated the accumulation of sHSPs and the extent of non-photochemical quenching during the downregulation of photosynthesis in H. rhodopensis leaves under desiccation at optimum (23 °C) and high temperature (38 °C). Desiccation of plants at 38 °C caused a stronger reduction in photosynthetic activity and corresponding enhancement in thermal energy dissipation. The accumulation of sHSPs was investigated by Western blot. While no expression of sHPSs was detected in the unstressed control sample, exposure of well-hydrated plants to high temperature induced an accumulation of sHSPs. Only a faint signal was observed at 50% RWC when dehydration was applied at 23 °C. Several cross-reacting polypeptide bands in the range of 16.5–19 kDa were observed in plants desiccated at high temperature. Two-dimensional electrophoresis and immunoblotting revealed the presence of several sHSPs with close molecular masses and pIs in the range of 5–8.0 that differed for each stage of treatment. At the latest stages of desiccation, fourteen different sHSPs could be distinguished, indicating that sHSPs might play a crucial role in H. rhodopensis under dehydration at high temperatures.
Exploring the Bioactive Mycocompounds (Fungal Compounds) of Selected Medicinal Mushrooms and Their Potentials against HPV Infection and Associated Cancer in Humans
Authors: Rokos Tomas , Pribulova Terezia , Kozubik Erik , Biringer Kamil , Holubekova Veronika , Kudela Erik
Published: January 2023

Medicinal mushrooms have been used as a medicinal tool for many centuries and, nowadays, are used in the prevention and therapy of various diseases, including as an adjunct to cancer treatment. It is estimated that 14–16% of global cancer cases are caused by infectious events; one well-known infectious agent that leads to cancer is the human papillomavirus (HPV). HPV is responsible for more than 99.7% of cervical cancer cases and also may play a role in vaginal, vulvar, penile, anal, rectal, and oropharyngeal carcinogenesis. Coriolus versicolor, a basidiomycetes class mushroom, consists of glycoproteins called polysaccharide-K (PSK) and polysaccharopeptide (PSP), which are mainly responsible for its effectiveness in the fight against a variety of cancers. Its beneficial effect lies in its ability to arrest different phases of the cell cycle, immunomodulation or induction of apoptosis. Coriolus versicolor extractcan reduces BCL-2 expression or increases the expression of p53 tumour suppressor genes in breast tumour cell lines. Inhibition of proliferation was also demonstrated with HeLa cells, while cervical cytology abnormalities improved in patients who locally applied Coriolus versicolor-based vaginal gel. Coriolus versicolor extract itself, and also its combination with another medicinal mushroom, Ganoderma lucidum, leads to improved HPV clearance in HPV cervical or oral-positive patients. Medicinal mushrooms can also increase the effectiveness of vaccination. This review considers the use of medicinal mushrooms as a suitable adjunct to the treatment of many cancers or precanceroses, including those caused by the HPV virus.
Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images
Authors: Li Jianqiang , Xu Qinlan , Cheng Wenxiu , Zhao Linna , Liu Suqin , Gao Zhengkai , Xu Xi , Ye Caihua , You Huanling
Published: January 2023

Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and classification in a weakly supervised manner. It first locates pollen regions from the raw images based on the detection model. To improve the classification performance, we segmented the pollen grains through a pre-trained U-Net using unsupervised pollen contour features. The segmented pollen regions were fed into a deep convolutional neural network to obtain the activation maps, which were used to further refine the segmentation masks. In this way, both segmentation and classification models can be collaboratively trained, supervised by just pollen contour features and class-specific information. Extensive experiments on real-world datasets were conducted, and the results prove that our method effectively avoids impurity interference and improves pollen identification accuracy (86.6%) under the limited supervision (around 1000 images with image-level labels).
Evaluation Soybean Cultivars for Reaction to Heterodera glycines Populations HG Types 7 and 1.3.4.7 in Northeast China
Authors: Chen Jingsheng , Zhou Yuanyuan , Hu Yanfeng , Zhao Di , Zhou Changjun , Shi Rujie , Sun Miao , Zhang Li , Chen Guowei , Li Haiyan , Chen Lijie , Xiao Guosheng
Published: January 2023

Soybean cyst nematode Heterodera glycines (SCN) is a major threat to global soybean production. Effective management of this disease is dependent on the development of resistant cultivars. Two SCN HG Types, 7 and 1.3.4.7. were previously identified as prevalent H. glycines populations in Northeast China. In order to evaluate soybean cultivars resistant to local SCN populations, 110 domestic commercial soybeans from different regions of Northeast China were assessed in the greenhouse to determine their potential as novel sources of resistance. The results suggested that cultivars responded differently to the two HG types. Of the 110 soybean cultivars evaluated, 24 accessions were classified as resistant or moderately resistant to HG Type 7, and five cultivars were classified as resistant or moderately resistant to HG Type 1.3.4.7. Among the tested cultivars, Kangxian 12 and Qingdou 13 had resistance response to both HG types 7 and 1.3.4.7. Thus, these broad-based SCN cultivars will be the valuable materials in the SCN resistance breeding program.
Lights and Shadows of Paracentesis: Is an Ultrasound Guided Approach Enough to Prevent Bleeding Complications?
Authors: Patturelli Marta , Pignata Luca , Venetucci Pietro , Guarino Maria
Published: January 2023

Paracentesis is a validated procedure for diagnosing and managing ascites. Although paracentesis is a safe procedure with a 1–2% risk of complications such as bleeding, it is necessary to inform the patient about the possible adverse events. We would like to share our experience with two cases of bleeding after paracentesis. In our unit, two major hemorrhagic complications occurred in 162 procedures performed over the year 2020 (frequency of bleeding complications: 1.2%). We report two clinical cases of post-paracentesis abdominal wall hematomas. Despite a similar clinical presentation, the management approach was different: in the first case, embolization of the epigastric artery supplying the hematoma was performed. In the second case, conservative treatment was adopted. Our report aims to provide food for thought about a potentially challenging hemorrhagic complication, even with the risk of adverse outcomes.
5,5,7,7-Tetrametyl-6,7-dihydro-5H-dibenzo[c,e]azepine
Authors: Bisaccia Roberto , Scafato Patrizia , Casarini Daniele , Superchi Stefano
Published: January 2023

5,5,7,7-Tetrametyl-6,7-dihydro-5H-dibenzo[c,e]azepine has been synthesized as a possible pro-chiral (or tropos) unit for the construction of a chiral catalyst and as a molecular chirality sensor for the absolute configuration assignment by chiroptical spectroscopy. A straightforward synthetic strategy for the preparation of the title compound in high overall yield through sequential addition of the four methyl groups on benzylic positions has been described. A VT-NMR study was used to determine the rotational barrier of the aryl–aryl bond in this biphenylazepine, revealing its torsional flexibility at room temperature, which makes the biphenylazepine suitable as both a chirality probe and a tropos moiety in chiral ligands.
Fault Location in Distribution Network by Solving the Optimization Problem Based on Power System Status Estimation Using the PMU
Authors: Dashtdar Masoud , Hussain Arif , Al Garni Hassan Z. , Mas’ud Abdullahi Abubakar , Haider Waseem , AboRas Kareem M. , Kotb Hossam
Published: January 2023

Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s.
Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples
Authors: Liu Jiang , Xu Kangzhi , Cai Baigen , Guo Zhongbin
Published: January 2023

On-board train control equipment is an important component of the Train Control System (TCS) of railway trains. In order to guarantee the safe and efficient operation of the railway system, Predictive Maintenance (PdM) is significantly required. The operation data of the on-board equipment allow us to build fault prediction models using a data-driven approach. However, the problem of unbalanced fault samples makes it difficult to achieve the expected modeling performance. In this paper, a Conditional Generative Adversarial Network (CGAN) is adopted to solve the unbalancing problem by generating synthetic samples corresponding to specific fault labels that belong to the minority classes. With this basis, a CGAN-enhanced eXtreme Gradient Boosting (XGBoost) solution is presented for training the fault prediction models. From the pre-processing to the field data, artificial fault samples are generated and integrated into the training sample sets, and the XGBoost models can be derived with multiple decision trees. Both the feature importance sequence list and the knowledge graph are derived to describe the characteristics obtained by the models. Filed data sets from practical operation are utilized to validate the proposed solution. By comparison with conventional machine learning algorithms, it can be found that higher accuracy, precision, recall, and F1 scores, which are up to 99.76%, can be achieved by the proposed solution. By involving the CGAN strategy, the maximum enhancement to the F1 score with the XGBoost approach reaches 6.13%. The advantages of the proposed solution show great potential in implementing equipment health management and intelligent condition-based maintenance.
Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Authors: Alwan Waseem , Ngadiman Nor Hasrul Akhmal , Hassan Adnan , Saufi Syahril Ramadhan , Mahmood Salwa
Published: January 2023

Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such advances. Among these techniques, statistical process controls (SPC), in particular the control chart pattern (CCP), have become a popular choice for monitoring process variance, being utilized in numerous industrial and manufacturing applications. This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. Before advancing to the classification step, Nelson’s Rus Rules were utilized as a monitoring rule to distinguish between stable and unstable processes. The study’s findings indicate that the proposed method improves classification performance for patterns with mean changes of less than 1.5 sigma, and confirm that the performance of the ensemble classifier is superior to that of the individual classifier. The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.
The Graph Neural Network Detector Based on Neighbor Feature Alignment Mechanism in LIDAR Point Clouds
Authors: Liu Xinyi , Zhang Baofeng , Liu Na
Published: January 2023

Three-dimensional (3D) object detection has a vital effect on the environmental awareness task of autonomous driving scenarios. At present, the accuracy of 3D object detection has significant improvement potential. In addition, a 3D point cloud is not uniformly distributed on a regular grid because of its disorder, dispersion, and sparseness. The strategy of the convolution neural networks (CNNs) for 3D point cloud feature extraction has the limitations of potential information loss and empty operation. Therefore, we propose a graph neural network (GNN) detector based on neighbor feature alignment mechanism for 3D object detection in LiDAR point clouds. This method exploits the structural information of graphs, and it aggregates the neighbor and edge features to update the state of vertices during the iteration process. This method enables the reduction of the offset error of the vertices, and ensures the invariance of the point cloud in the spatial domain. For experiments performed on the KITTI public benchmark, the results demonstrate that the proposed method achieves competitive experimental results.
Early Fault Diagnosis of Rolling Bearing Based on Threshold Acquisition U-Net
Authors: Zhang Dongsheng , Zhang Laiquan , Zhang Naikang , Yang Shuo , Zhang Yuhao
Published: January 2023

Considering the problem that the early fault signal of rolling bearing is easily interfered with by background information, such as noise, and it is difficult to extract fault features, a method of rolling bearing early fault diagnosis based on the threshold acquisition U-Net (TA-UNet) is proposed. First, to improve the feature extraction ability of U-Net, the channel spatial threshold acquisition network (CS-TAN) and the dilated convolution module (DCM) based on different dilated rate combinations are introduced into the U-Net to construct the TA-UNet. Among them, the CS-TAN can adaptively learn the threshold, reduce the interference of noise in the signal, and the DCM can improve the multi-scale feature extraction ability of the network. Then, the TA-UNet is used for early fault diagnosis, and the method is divided into two steps: The model training phase and the vibration signal fault feature extraction phase. In the first step, additive gaussian white noise is added to the vibration signal to obtain the noise-added vibration signal, and the TA-UNet is trained to learn how to denoise the noise-added vibration signal. In the second step, the trained TA-UNet is used to extract the fault features of vibration signals and diagnose the early fault types of rolling bearing. The two-step method solves the problem that U-Net, as a supervised neural network, needs corresponding labeled data to be trained, as it realizes the fault diagnosis of unlabeled data. The feature extraction capability of the TA-UNet is evaluated by denoising the simulated signal of rolling bearing. The effectiveness of the proposed diagnostic method is demonstrated by the early fault diagnosis of open-source datasets.
Numerical Investigation on the Combustion and Emission Characteristics of Diesel Engine with Flexible Fuel Injection
Authors: Mei Qihao , Naruemon Intarat , Liu Long , Wu Yue , Ma Xiuzhen
Published: January 2023

As the main engineering power plant, diesel engines are irreplaceable in the future. However, the stringent emission regulations impose many tough requirements to their developments. Recently, flexible fuel injection strategy has been recognized as an effective technology in creating an advanced spray and mixture formation and improving combustion efficiency indirectly. However, the detailed combustion and emission behaviors under flexible fuel injection are still unknown. Therefore, this paper aims to investigate the combustion and emission characteristics under flexible fuel injection and explore an optimal injection strategy for high-efficiency combustion. A numerical simulation method is conducted by coupling the large-eddy simulation (LES) model and the SAGE combustion model. Then, the spray mixing, combustion flame propagation and emissions formation under various multiple-injection strategies are investigated. Results reveal that initial an ultrahigh injection pressure has a significant influence on the spray’s axial penetration while dwell time mainly affects the spray’s radial expansion. Under an initial ultrahigh injection pressure, the turbulence kinetic energy (TKE) becomes larger, and the vortex motions are stronger, contributing to a better spray turbulent mixing. Meanwhile, a snatchier flame structure with a favorable level of equivalence ratio and a homogeneous temperature distribution is obtained. In this way, the peak heat release rate (HRR) could increase by 46.7% with a 16.7% reduction in soot formation and a 31.4% reduction in NOx formation.
Research on Edge Detection Model of Insulators and Defects Based on Improved YOLOv4-tiny
Authors: Li Boqiang , Qin Liang , Zhao Feng , Liu Haofeng , Yu Jinyun , He Min , Wang Jing , Liu Kaipei
Published: January 2023

Edge computing can avoid the long-distance transmission of massive data and problems with large-scale centralized processing. Hence, defect identification for insulators with object detection models based on deep learning is gradually shifting from cloud servers to edge computing devices. Therefore, we propose a detection model for insulators and defects designed to deploy on edge computing devices. The proposed model is improved on the basis of YOLOv4-tiny, which is suitable for edge computing devices, and the detection accuracy of the model is improved on the premise of maintaining a high detection speed. First, in the neck network, the inverted residual module is introduced to perform feature fusion to improve the positioning ability of the insulators. Then, a high-resolution detection output head is added to the original model to enhance its ability to detect defects. Finally, the prediction boxes are post-processed to incorporate split object boxes for large-scale insulators. In an experimental evaluation, the proposed model achieved an mAP of 96.22% with a detection speed of 10.398 frames per second (FPS) on an edge computing device, which basically meets the requirements of insulator and defect detection scenarios in edge computing devices.
Slow Methyl Axes Motions in Perdeuterated Villin Headpiece Subdomain Probed by Cross-Correlated NMR Relaxation Measurements
Authors: Vugmeyster Liliya , Nichols Parker J. , Ostrovsky Dmitry , McKnight C. James , Vögeli Beat
Published: January 2023

Protein methyl groups can participate in multiple motional modes on different time scales. Sub-nanosecond to nano-second time scale motions of methyl axes are particularly challenging to detect for small proteins in solutions. In this work we employ NMR relaxation interference between the methyl H-H/H-C dipole-dipole interactions to characterize methyl axes motions as a function of temperature in a small model protein villin headpiece subdomain (HP36), in which all non-exchangeable protons are deuterated with the exception of methyl groups of leucine and valine residues. The data points to the existence of slow motional modes of methyl axes on sub-nanosecond to nanosecond time scales. Further, at high temperatures for which the overall tumbling of the protein is on the order of 2 ns, we observe a coupling between the slow internal motion and the overall molecular tumbling, based on the anomalous order parameters and their temperature-dependent trends. The addition of 28% (w/w) glycerol-d8 increases the viscosity of the solvent and separates the timescales of internal and overall tumbling, thus permitting for another view of the necessity of the coupling assumption for these sites at high temperatures.
DVIT—A Decentralized Virtual Items Trading Forum with Reputation System
Authors: Ying Zuobin , Lan Wusong , Deng Chen , Liu Lu , Liu Ximeng
Published: January 2023

The metaverse provides us with an attractive virtual space in which the value of the virtual property has been increasingly recognized. However, the lack of effective cross-metaverse trading tools and the reputation guarantee makes it difficult to trade items among different metaverses. To this end, a decentralized reputation system for virtual items trading forum named DVIT is devised. To the best of our knowledge, DVIT is the first decentralized cross-metaverse item trading prototype inspired by the online-game trading system. We designed the corresponding transaction function and realized the autonomous governance of the community by introducing the reputation mechanism. An improved election mechanism is proposed to improve efficiency based on Delegated Proof-of-Stake (DPoS). Through token rewards associated with activity levels, users’ motivation can be stimulated. The experiments indicate that our proposed scheme could dynamically measure the trustworthiness degree of the users through the dynamic reputation value and thereby exclude malicious users from the blockchain within 20 epochs.
New Modified Couple Stress Theory of Thermoelasticity with Hyperbolic Two Temperature
Authors: Kaur Iqbal , Singh Kulvinder , Craciun Eduard-Marius
Published: January 2023

This paper deals with the two-dimensional deformation in fibre-reinforced composites with new modified couple stress thermoelastic theory (nMCST) due to concentrated inclined load. Lord Shulman heat conduction equation with hyperbolic two temperature (H2T) has been used to form the mathematical model. Fourier and Laplace transform are used for obtaining the physical quantities of the mathematical model. The expressions for displacement components, thermodynamic temperature, conductive temperature, axial stress, tangential stress and couple stress are obtained in the transformed domain. A mathematical inversion procedure has been used to obtain the inversion of the integral transforms using MATLAB software. The effects of hyperbolic and classical two temperature are shown realistically on the various physical quantities.
Statistical Beamforming for Multi-Set Space–Time Shift-Keying-Based Full-Duplex Millimeter Wave Communications
Authors: Aljohani Abdulah Jeza , Moinuddin Muhammad , Al-Saggaf Ubaid M. , El-Hajjar Mohammed , Ng Soon Xin
Published: January 2023

Full-duplex (FD) communication has been shown to provide an increased achievable rate, while millimeter wave (mmWave) communications benefit from a large available bandwidth that further improves the achievable rate. On the other hand, the concept of multi-set space-time shift keying (MS-STSK) has been proposed to provide a flexible design trade-off between throughput and performance. Hence, in this work, we consider the design of an FD-aided MS-STSK transceiver for millimeter wave communications. However, a major challenge is that channel reciprocity is not valid in mmWave communications due to shorter channel coherence time. Thus, the uplink (UL) pilots cannot be utilized to estimate the downlink (DL) channel. To overcome this challenge, we propose a beamforming technique based on channel statistics without assuming channel reciprocity. For this purpose, a closed-form expression for the outage probability of the system is derived by employing the characterization of the ratio of the Indefinite Quadratic Form (IQF). The derived analytical expression is then utilized to design optimum beamforming weights using the Sequential Quadratic Programming (SQP)-based heuristic method. Moreover, an Iterative Statistical Method (ISM) of joint transmit and receive beamforming algorithm is also developed by utilizing Principle Eigenvector (PE) and Generalized Rayleigh Quotient (G-RQ) optimization techniques. Finally, we verify our simulation results with the theoretical analysis.
Reliability and Service Life Analysis of Airbag Systems
Authors: Dui Hongyan , Song Jiaying , Zhang Yun-an
Published: January 2023

Airbag systems are important to a car’s safety protection system. To further improve the reliability of the system, this paper analyzes the failure mechanism of automotive airbag systems and establishes a dynamic fault tree model. The dynamic fault tree model is transformed into a continuous-time Bayesian network by introducing a unit step function and an impulse function, from which the failure probability of the system is calculated. Finally, the system reliability and average life are calculated and analyzed and compared with the sequential binary decision diagram method. The results show that the method can obtain more accurate system reliability and effectively identify the weak parts of the automotive airbag system, to a certain extent compensating for the lack of computational complexity of dynamic Bayesian networks in solving system reliability problems with continuous failure processes.
Contextual Urdu Lemmatization Using Recurrent Neural Network Models
Authors: Hafeez Rabab , Anwar Muhammad Waqas , Jamal Muhammad Hasan , Fatima Tayyaba , Espinosa Julio César Martínez , López Luis Alonso Dzul , Thompson Ernesto Bautista , Ashraf Imran
Published: January 2023

In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. This paper presents a lemmatization algorithm based on recurrent neural network models for the Urdu language. However, lemmatization techniques for resource-scarce languages such as Urdu are not very common. The proposed model is trained and tested on two datasets, namely, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU). The datasets are lemmatized with the help of recurrent neural network models. The Word2Vec model and edit trees are used to generate semantic and syntactic embedding. Bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), bidirectional gated recurrent neural network (BiGRNN), and attention-free encoder–decoder (AFED) models are trained under defined hyperparameters. Experimental results show that the attention-free encoder-decoder model achieves an accuracy, precision, recall, and F-score of 0.96, 0.95, 0.95, and 0.95, respectively, and outperforms existing models.
A Survey on High-Dimensional Subspace Clustering
Authors: Qu Wentao , Xiu Xianchao , Chen Huangyue , Kong Lingchen
Published: January 2023

With the rapid development of science and technology, high-dimensional data have been widely used in various fields. Due to the complex characteristics of high-dimensional data, it is usually distributed in the union of several low-dimensional subspaces. In the past several decades, subspace clustering (SC) methods have been widely studied as they can restore the underlying subspace of high-dimensional data and perform fast clustering with the help of the data self-expressiveness property. The SC methods aim to construct an affinity matrix by the self-representation coefficient of high-dimensional data and then obtain the clustering results using the spectral clustering method. The key is how to design a self-expressiveness model that can reveal the real subspace structure of data. In this survey, we focus on the development of SC methods in the past two decades and present a new classification criterion to divide them into three categories based on the purpose of clustering, i.e., low-rank sparse SC, local structure preserving SC, and kernel SC. We further divide them into subcategories according to the strategy of constructing the representation coefficient. In addition, the applications of SC methods in face recognition, motion segmentation, handwritten digits recognition, and speech emotion recognition are introduced. Finally, we have discussed several interesting and meaningful future research directions.
A Parallel Compact Gannet Optimization Algorithm for Solving Engineering Optimization Problems
Authors: Pan Jeng-Shyang , Sun Bing , Chu Shu-Chuan , Zhu Minghui , Shieh Chin-Shiuh
Published: January 2023

The Gannet Optimization Algorithm (GOA) has good performance, but there is still room for improvement in memory consumption and convergence. In this paper, an improved Gannet Optimization Algorithm is proposed to solve five engineering optimization problems. The compact strategy enables the GOA to save a large amount of memory, and the parallel communication strategy allows the algorithm to avoid falling into local optimal solutions. We improve the GOA through the combination of parallel strategy and compact strategy, and we name the improved algorithm Parallel Compact Gannet Optimization Algorithm (PCGOA). The performance study of the PCGOA on the CEC2013 benchmark demonstrates the advantages of our new method in various aspects. Finally, the results of the PCGOA on solving five engineering optimization problems show that the improved algorithm can find the global optimal solution more accurately.
Smart Production System with Random Imperfect Process, Partial Backordering, and Deterioration in an Inflationary Environment
Authors: Yadav Dharmendra , Chand Umesh , Goel Ruchi , Sarkar Biswajit
Published: January 2023

In today’s digital age, industrial methods are shifting away from humans and toward machines. We choose automated systems for various jobs related to production systems, such as screening, manufacturing. A smart manufacturing system is one in which machines take the place of humans. Under the influence of inflation, this study proposes a smart production-inventory model with partial backlogging, and an imperfect manufacturing process where the deterioration rate is constant. Every production system, in reality, has a random defect rate. A screening procedure is required due to the manufacture of some defective items, which is carried out by machine, i.e., by an automated system. Carbon is released during the manufacturing process due to actions such as holding deterioration. As a result, carbon emissions are taken into account in the current study. The goal of this study is to reduce total inventory costs as much as possible. To demonstrate the proposed model’s practical application, many numerical examples and sensitivity assessments with graphs are provided.
Sample Size Determination for Two-Stage Multiple Comparisons for Exponential Location Parameters with the Average
Authors: Wu Shu-Fei
Published: January 2023

In this paper, the design constant is determined based on a criterion of probability of correct detection for a given deviation of k location parameters from the average being at least γ for the two-stage multiple comparisons for location parameters of exponential distributions when scale parameters are unequal. All required values for determining the design constant and then the total sample size for a two-stage procedure are listed in tables for practical use. For illustrative purposes, two examples are given to demonstrate the sample size determination for this two-stage procedure.
Application of Fractional Differential Model in Image Enhancement of Strong Reflection Surface
Authors: Ruiyin Tang , Bo Liu
Published: January 2023

Combined with advanced fractional differential mask operation, this paper used a fractional differential to normalize the 5 × 5 mask and conducted experiments to select fractional v = 0.7 to determine the equation. The position of the center of the light band was obtained by the gray centroid method, and the center of the light band was enhanced by the fractional differential algorithm. Three samples of hard disk substrate, roller, and printed circuit board were selected. The traditional processing was compared to the fractional differential algorithm in this paper, and several advanced algorithms were compared with the algorithm in this paper. Experimental data showed that fractional differential enhancement can effectively improve the accuracy of extracting the center of light fringes. It can be found that the average error of extracting the center by fractional differential processing was relatively small, and the image outline was clearer.
On Modeling of Sorted Cost Consensus Negotiation Considering Efficiency and Time Based on the Stochastic Programming
Authors: Zhou Yi , Guo Chonglan , Wei Guo , Herrera-Viedma Enrique
Published: January 2023

In the consensus reaching process (CRP) permitting negotiation, the efficiency of negotiation is affected by the order of negotiation with decision makers (DMs), the time, and the number of moderators. In this paper, the sorted negotiation against DMs considering efficiency and time is initiated into consensus decision making, which can improve the speed and effectiveness of consensus. Based on the opinion dynamics (opinion evolution), uniform and normal distributions are used to describe the uncertainty of DMs’ opinions and negotiation time, the opinion order efficiency and cost coefficient are coined, and the cost-constrained optimal efficiency sorted negotiation model and the optimal efficiency sorted negotiation model involving multiple moderators and time constraints are respectively constructed. The optimal solution of the chance-constrained model is obtained in the context of China’s urban demolition negotiation using an improved genetic algorithm, and an optimum set of influential individuals based on opinion similarity is introduced so that assessment criteria for validating the reasonableness of the sorting sequence are determined. Sorted consensus negotiation combined with complex scenarios such as different representation formats of opinions, characteristics of DMs, other solving algorithms, Bayesian dynamics, etc. can be included in future works.
Enhancement of Non-Permutation Binomial Power Functions to Construct Cryptographically Strong S-Boxes
Authors: Isa Herman , Syed Junid Syed Alwee Aljunid , Z’aba Muhammad Reza , Endut Rosdisham , Ammar Syed Mohammad , Ali Norshamsuri
Published: January 2023

A Substitution box (S-box) is an important component used in symmetric key cryptosystems to satisfy Shannon’s property on confusion. As the only nonlinear operation, the S-box must be cryptographically strong to thwart any cryptanalysis tools on cryptosystems. Generally, the S-boxes can be constructed using any of the following approaches: the random search approach, heuristic/evolutionary approach or mathematical approach. However, the current S-box construction has some drawbacks, such as low cryptographic properties for the random search approach and the fact that it is hard to develop mathematical functions that can be used to construct a cryptographically strong S-box. In this paper, we explore the non-permutation function that was generated from the binomial operation of the power function to construct a cryptographically strong S-box. By adopting the method called the Redundancy Removal Algorithm, we propose some enhancement in the algorithm such that the desired result can be obtained. The analytical results of our experiment indicate that all criteria such as bijective, nonlinearity, differential uniformity, algebraic degree and linear approximation are found to hold in the obtained S-boxes. Our proposed S-box also surpassed several bijective S-boxes available in the literature in terms of cryptographic properties.
Oscillation of Neutral Differential Equations with Damping Terms
Authors: Dassios Ioannis , Muhib Ali , El-Marouf Sobhy A. A. , Elagan Sayed K.
Published: January 2023

Our interest in this paper is to study and develop oscillation conditions for solutions of a class of neutral differential equations with damping terms. New oscillation criteria were obtained by using Riccati transforms. The criteria we obtained improved and completed some of the criteria in previous studies mentioned in the literature. Examples are provided to illustrate the applicability of our results.
Assessment of the Relationships between Prospective Mathematics Teachers’ Classroom Management Anxiety, Academic Self-Efficacy Beliefs, Academic Amotivation and Attitudes toward the Teaching Profession Using Structural Equation Modelling
Authors: Karakose Turgut , Polat Hakan , Yirci Ramazan , Tülübaş Tijen , Papadakis Stamatios , Ozdemir Tuncay Yavuz , Demirkol Murat
Published: January 2023

Academic self-efficacy, academic amotivation, attitude toward the teaching profession, and classroom management anxiety are four of the most significant factors for both teacher training and performance because these psychological and behavioral constructs are first developed during the initial training, and reflect on the actual teaching quality of teachers. Therefore, investigation into their development and relationships, particularly with regard to prospective teachers, is significant both for the theory and practice of teaching. Hence, the current study aims to explore the casual relationships between these variables with a sample of prospective mathematics teachers, using the structural equation modelling (SEM). The participants were selected using simple random sampling method from prospective mathematics teachers studying at educational faculties of seven universities in different regions of Turkey. The data were collected using the academic amotivation scale, academic self-efficacy scale, attitude toward the teaching profession scale, classroom management anxiety scale, and a personal information form developed by the researchers. Data obtained from 581 participants were analyzed using path analysis. The findings showed that prospective mathematics teachers had a positive attitude toward the profession, and were eager to teach. Their academic self-efficacy predicted their attitude toward the teaching profession. Similarly, prospective mathematics teachers’ attitude toward the teaching profession correlated negatively with their academic amotivation. In other words, as prospective mathematics teachers’ attitude scores toward the profession increased, their academic amotivation scores decreased. However, prospective mathematics teachers had a high level of classroom management anxiety. Interestingly, prospective mathematics teachers with a positive attitude toward the profession experienced higher levels of classroom management anxiety. The findings mostly supported previous results in the literature. Implications were suggested both for teacher training and practice of quality teaching.
Self-Organization in Randomly Forced Diffusion Systems: A Stochastic Sensitivity Technique
Authors: Kolinichenko Alexander , Bashkirtseva Irina , Ryashko Lev
Published: January 2023

The problem with the analysis of noise-induced transitions between patterns in distributed stochastic systems is considered. As a key model, we use the spatially extended dynamical “phytoplankton-herbivore” system with diffusion. We perform the parametric bifurcation analysis of this model and determine the Turing instability zone, where non-homogeneous patterns are generated by diffusion. The multistability of this deterministic model with the coexistence of several waveform pattern–attractors is found. We study how noise affects these non-homogeneous patterns and estimate the dispersion of random states using a new technique based on stochastic sensitivity function (SSF) analysis and the confidence domain method. To investigate the preferences in noise-induced transitions between patterns, we analyze and compare the results of this theoretical approach with the statistics extracted from the direct numerical simulation.