bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Pyrazolo[3,4-d]pyrimidine-Based Kinase Inhibitors in The Attenuation of CCl4-Induced Liver Fibrosis in Rats
Authors: Ghobrial Diana K. , El-Nikhely Nefertiti , Sheta Eman , Ragab Hanan M. , Rostom Sherif A. F. , Saeed Hesham , Wahid Ahmed
Published: March 2023

Liver Fibrosis can be life-threatening if left untreated as it may lead to serious, incurable complications. The common therapeutic approach is to reverse the fibrosis while the intervention is still applicable. Celecoxib was shown to exhibit some antifibrotic properties in the induced fibrotic liver in rats. The present study aimed to investigate the possible antifibrotic properties in CCl4-induced liver fibrosis in male Sprague–Dawley rats compared to celecoxib of three novel methoxylated pyrazolo[3,4-d]pyrimidines. The three newly synthesized compounds were proved to be safe candidates. They showed a therapeutic effect against severe CCl4-induced fibrosis but at different degrees. The three compounds were able to partially reverse hepatic architectural distortion and reduce the fibrotic severity by showing antioxidant properties reducing MDA with increasing GSH and SOD levels, remodeling the extracellular matrix proteins and liver enzymes balance, and reducing the level of proinflammatory (TNF-α and IL-6) and profibrogenic (TGF-β) cytokines. The results revealed that the dimethoxy-analog exhibited the greatest activity in all the previously mentioned parameters compared to celecoxib and the other two analogs which could be attributed to the different methoxylation patterns of the derivatives. Collectively, the dimethoxy-derivative could be considered a safe promising antifibrotic candidate.
Effects of Physical Exercise and Motor Activity on Oxidative Stress and Inflammation in Post-Mastectomy Pain Syndrome
Authors: Calapai Marco , Puzzo Luisa , Bova Giuseppe , Vecchio Daniele Alfio , Blandino Rosario , Barbagallo Alessia , Ammendolia Ilaria , Cardia Luigi , De Pasquale Maria , Calapai Fabrizio , Esposito Emanuela , Trimarchi Fabio , Di Mauro Debora , Calapai Gioacchino , Mannucci Carmen
Published: March 2023

It is estimated that 10–50% of interventions can generate persistent post-surgical pain. Chronic post-mastectomy pain is a condition persisting for at least three months after surgery. It has been shown that physical activity in the cancer patient allows the improvement of the pain symptom. The aim of this study was to evaluate the effects of physical activity on the intensity and interference of chronic pain in the quality of life of women underwent mastectomy needed for breast cancer removal. The secondary objective was to measure the effects of physical activity on inflammatory and oxidative markers in the same population. A Numeric Rating Scale (NRS) was used to assess pain intensity, and Brief Inventory Pain (BIP) was used for assessing interference of pain in quality of life. Physical activity was measured with the International Physical Activity Questionnaire (IPAQ). Inflammatory mediators such as interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α, c-reactive protein (CRP), and biomarkers of oxidative stress malondialdehyde (MDA), superoxide dismutase (SOD), and catalase (CAT) were evaluated in the blood of patients. All the evaluations were performed after three and six months after surgery. Results showed that adequate physical activity can diminish intensity and interference of pain and that these effects are associated with a reduction of blood biomarkers of inflammation.
Mitochondrial ATP Synthase and Mild Uncoupling by Butyl Ester of Rhodamine 19, C4R1
Authors: Zorova Ljubava D. , Pevzner Irina B. , Khailova Ljudmila S. , Korshunova Galina A. , Kovaleva Marina A. , Kovalev Leonid I. , Serebryakova Marina V. , Silachev Denis N. , Sudakov Roman V. , Zorov Savva D. , Rokitskaya Tatyana I. , Popkov Vasily A. , Plotnikov Egor Y. , Antonenko Yuri N. , Zorov Dmitry B.
Published: March 2023

The homeostasis of the transmembrane potential of hydrogen ions in mitochondria is a prerequisite for the normal mitochondrial functioning. However, in different pathological conditions it is advisable to slightly reduce the membrane potential, while maintaining it at levels sufficient to produce ATP that will ensure the normal functioning of the cell. A number of chemical agents have been found to provide mild uncoupling; however, natural proteins residing in mitochondrial membrane can carry this mission, such as proteins from the UCP family, an adenine nucleotide translocator and a dicarboxylate carrier. In this study, we demonstrated that the butyl ester of rhodamine 19, C4R1, binds to the components of the mitochondrial ATP synthase complex due to electrostatic interaction and has a good uncoupling effect. The more hydrophobic derivative C12R1 binds poorly to mitochondria with less uncoupling activity. Mass spectrometry confirmed that C4R1 binds to the β-subunit of mitochondrial ATP synthase and based on molecular docking, a C4R1 binding model was constructed suggesting the binding site on the interface between the α- and β-subunits, close to the anionic amino acid residues of the β-subunit. The association of the uncoupling effect with binding suggests that the ATP synthase complex can provide induced uncoupling.
AD−1 Small Molecule Improves Learning and Memory Function in Scopolamine-Induced Amnesic Mice Model through Regulation of CREB/BDNF and NF-κB/MAPK Signaling Pathway
Authors: Balakrishnan Rengasamy , Park Ju-Young , Cho Duk-Yeon , Ahn Jae-Yong , Yoo Dong-Sun , Seol Sang-Ho , Yoon Sung-Hwa , Choi Dong-Kug
Published: March 2023

Cognitive decline and memory impairment induced by oxidative brain damage are the critical pathological hallmarks of Alzheimer’s disease (AD). Based on the potential neuroprotective effects of AD−1 small molecule, we here explored the possible underlying mechanisms of the protective effect of AD-1 small molecule against scopolamine-induced oxidative stress, neuroinflammation, and neuronal apoptosis. According to our findings, scopolamine administration resulted in increased AChE activity, MDA levels, and decreased antioxidant enzymes, as well as the downregulation of the antioxidant response proteins of Nrf2 and HO-1 expression; however, treatment with AD−1 small molecule mitigated the generation of oxidant factors while restoring the antioxidant enzymes status, in addition to improving antioxidant protein levels. Similarly, AD−1 small molecule significantly increased the protein expression of neuroprotective markers such as BDNF and CREB and promoted memory processes in scopolamine-induced mice. Western blot analysis showed that AD−1 small molecule reduced activated microglia and astrocytes via the attenuation of iba-1 and GFAP protein expression. We also found that scopolamine enhanced the phosphorylation of NF-κB/MAPK signaling and, conversely, that AD−1 small molecule significantly inhibited the phosphorylation of NF-κB/MAPK signaling in the brain regions of hippocampus and cortex. We further found that scopolamine promoted neuronal loss by inducing Bax and caspase-3 and reducing the levels of the antiapoptotic protein Bcl-2. In contrast, AD−1 small molecule significantly decreased the levels of apoptotic markers and increased neuronal survival. Furthermore, AD−1 small molecule ameliorated scopolamine-induced impairments in spatial learning behavior and memory formation. These findings revealed that AD−1 small molecule attenuated scopolamine-induced cognitive and memory dysfunction by ameliorating AChE activity, oxidative brain damage, neuroinflammation, and neuronal apoptosis.
Aging Hallmarks and the Role of Oxidative Stress
Authors: Maldonado Edio , Morales-Pison Sebastián , Urbina Fabiola , Solari Aldo
Published: March 2023

Aging is a complex biological process accompanied by a progressive decline in the physical function of the organism and an increased risk of age-related chronic diseases such as cardiovascular diseases, cancer, and neurodegenerative diseases. Studies have established that there exist nine hallmarks of the aging process, including (i) telomere shortening, (ii) genomic instability, (iii) epigenetic modifications, (iv) mitochondrial dysfunction, (v) loss of proteostasis, (vi) dysregulated nutrient sensing, (vii) stem cell exhaustion, (viii) cellular senescence, and (ix) altered cellular communication. All these alterations have been linked to sustained systemic inflammation, and these mechanisms contribute to the aging process in timing not clearly determined yet. Nevertheless, mitochondrial dysfunction is one of the most important mechanisms contributing to the aging process. Mitochondria is the primary endogenous source of reactive oxygen species (ROS). During the aging process, there is a decline in ATP production and elevated ROS production together with a decline in the antioxidant defense. Elevated ROS levels can cause oxidative stress and severe damage to the cell, organelle membranes, DNA, lipids, and proteins. This damage contributes to the aging phenotype. In this review, we summarize recent advances in the mechanisms of aging with an emphasis on mitochondrial dysfunction and ROS production.
A Novel Implicit Neural Representation for Volume Data
Authors: Sheibanifard Armin , Yu Hongchuan
Published: March 2023

The storage of medical images is one of the challenges in the medical imaging field. There are variable works that use implicit neural representation (INR) to compress volumetric medical images. However, there is room to improve the compression rate for volumetric medical images. Most of the INR techniques need a huge amount of GPU memory and a long training time for high-quality medical volume rendering. In this paper, we present a novel implicit neural representation to compress volume data using our proposed architecture, that is, the Lanczos downsampling scheme, SIREN deep network, and SRDenseNet high-resolution scheme. Our architecture can effectively reduce training time, and gain a high compression rate while retaining the final rendering quality. Moreover, it can save GPU memory in comparison with the existing works. The experiments show that the quality of reconstructed images and training speed using our architecture is higher than current works which use the SIREN only. Besides, the GPU memory cost is evidently decreased.
Multi-Feature Fusion Method for Chinese Pesticide Named Entity Recognition
Authors: Ji Wenqing , Fu Yinghua , Zhu Hongmei
Published: March 2023

Chinese pesticide named-entity recognition (NER) aims to identify named entities related to pesticide properties from unstructured Chinese pesticide information texts. In view of the characteristics of massive, fragmented, professional, and complex semantic relationships of pesticide information data, a deep learning method based on multi-feature fusion was applied to improve the accuracy of pesticide NER. In this study, the pesticide data set is manually annotated by the begin inside outside (BIO) sequence annotation scheme. Bi-directional long short-term memory (BiLSTM) and iterated dilated convolutional neural networks (IDCNN) combined with conditional random field (CRF) form the model BiLSTM-IDCNN-CRF, and it is applied to implement named-entity recognition in Chinese pesticide data sets. IDCNN is introduced to enhance the semantic representation ability and local feature capture ability of the text. BiLSTM network and IDCNN network are combined to obtain the long-distance dependence relationship and context features of different granularity of pesticide data text. Finally, CRF is used to implement the sequence labeling task. According to the experiment results, the accuracy rate, recall rate, and F1 score of the BiLSTM-IDCNN-CRF model in the Chinese pesticide data set were 78.59%, 68.71%, and 73.32%, respectively, which are significantly better than other compared models. Experiments show that the BiLSTM-IDCNN-CRF model can effectively identify and extract entities from Chinese pesticide information text data, which is helpful in constructing the pesticide information knowledge graph and intelligent question-answering.
Emotional Health Detection in HAR: New Approach Using Ensemble SNN
Authors: Bibbo’ Luigi , Cotroneo Francesco , Vellasco Marley
Published: March 2023

Computer recognition of human activity is an important area of research in computer vision. Human activity recognition (HAR) involves identifying human activities in real-life contexts and plays an important role in interpersonal interaction. Artificial intelligence usually identifies activities by analyzing data collected using different sources. These can be wearable sensors, MEMS devices embedded in smartphones, cameras, or CCTV systems. As part of HAR, computer vision technology can be applied to the recognition of the emotional state through facial expressions using facial positions such as the nose, eyes, and lips. Human facial expressions change with different health states. Our application is oriented toward the detection of the emotional health of subjects using a self-normalizing neural network (SNN) in cascade with an ensemble layer. We identify the subjects’ emotional states through which the medical staff can derive useful indications of the patient’s state of health.
Comparative Insights into the Microbial Diversity and Community Structure of Striga hermonthica-Infested Maize Rhizosphere
Authors: Olowe Olumayowa Mary , Ayangbenro Ayansina Segun , Akanmu Akinlolu Olalekan , Kutu Funso Raphael , Odhiambo Jude J. O. , Babalola Olubukola Oluranti
Published: March 2023

The damaging competition between crops and parasitic weeds has a negative impact on agricultural productivity; however, the impact of disturbance on the soil’s microbial community has received less attention. Hence, this study investigates the microbial composition and diversity of the maize rhizosphere infected with Striga hermonthica using a shotgun sequencing approach from two maize-growing fields (Eruwa, Nigeria and Mbuzini, South Africa). The rhizosphere soil DNA was extracted from infested soil using a Nucleospin soil genomic DNA extraction kit and sequenced on an Illumina platform. The dominant phyla were Actinobacteria, Bacteroidetes, Deinococcus-Thermus, Acidobacteria, Chloroflexi, Cyanobacteria, Planctomycetes, Verrucomicrobia, Chlorobi, Proteobacteria, Firmicutes, Nitrospirae, Thermotogae, Synergistetes, Ascomycota, Euryarchaeota, and Crenarchaeota. Bacteria phyla were observed to be of higher proportion in the rhizosphere soil samples obtained from Striga-infested maize field in Eruwa (Es) than those recovered from Mbuzini (Ms). The alpha diversity of microbial communities indicated insignificance differences (p > 0.05) between the five taxonomical groups (phylum, class, order, family, and genus), while the beta diversity produced a significant (p = 0.01, R = 0.52) difference in the microbial diversity of the infested soil. In summary, the study sheds light on the diversity and composition of the microbiome of Striga hermonthica-infested soil, which influences the microbial functions in the management and sustenance of plant health against parasitic weeds.
Numerical Study on the Coupling Effect on a Retaining Structure of a Complex Deep Foundation Pit Group Excavation in a Soft-Soil Area
Authors: You Xinyu , Zhou Qiulong , Xiao Yu , Tong Liyuan , Yang Qiang
Published: March 2023

Based on the complex deep foundation pit group process of the Huimin Avenue Comprehensive Reconstruction Project in Nanjing, the finite element numerical simulation method is used to analyze the deformation characteristics of the retaining structure and the coupling effect between foundation pits during the whole process of excavation and support construction of the complex foundation pit group. Meanwhile, the differences in the deformation characteristics and coupling effects of the retaining structure under different construction time sequence schemes of the foundation pit group are studied. Finally, some suggestions are put forward for the monitoring and construction in this project, which provides a reference case for the design and construction of similar foundation pit groups.
PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
Authors: Zaikis Dimitrios , Stylianou Nikolaos , Vlahavas Ioannis
Published: March 2023

Media analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, through the computational treatment of opinions, sentiments, and subjectivity on mostly highly subjective text types. These texts can come from social media, the internet, and news articles with clearly defined and unique targets. Additionally, MA-related fields include emotion, irony, and hate speech detection, which are usually tackled independently from one another without leveraging the contextual similarity between them, mainly attributed to the lack of annotated datasets. In this paper, we present a unified framework to the complete intelligent media analysis, where we propose a shared parameter layer architecture with a joint learning approach that takes advantage of each separate task for the classification of sentiments, emotions, irony, and hate speech in texts. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, and internet articles such as blog posts and opinion pieces. The results show that this joint classification approach improves the classification effectiveness of each task in terms of the micro-averaged F1-score.
Phenolic Compounds and Pyrrolizidine Alkaloids of Two North Bluebells: Mertensia stylosa and Mertensia serrulata
Authors: Kashchenko Nina , Olennikov Daniil , Chirikova Nadezhda
Published: March 2023

Two North bluebells, Mertonian stylosa and M. serrulata, are plants used in the traditional medicine of the Buryats as wound healing and antitumor remedies. Both mertensias have been used by local healers as substitutes for the rare Tibetan raw material Cynoglossum amabile. The lack of information on the chemical composition of M. stylosa and M. serrulata herbs has prompted the study of metabolites, in particular phenolic compounds and alkaloids, as components with high biological activity. In this study, the application of liquid chromatography–mass spectrometry for the metabolite profiling of both Mertensia species resulted in the identification of 30 compounds, including hydroxycinnamates, flavonoids, and pyrrolizidine alkaloids. In particular, lycopsamine N-oxide was the dominant alkaloid in M. stylosa (5.27 mg/g) and M. serrulata (2.14 mg/g) herbs, 5-O-caffeoylquinic acid (43.41 mg/g) and rutin (42.40 mg/g) prevailed among the phenolic compounds in M. stylosa herb, while rutin (25.72 mg/g) was the dominant compound of the M. serrulata herb. The investigated extracts of M. stylosa and M. serrulata herb revealed good scavenging capacity against DPPH•, ABTS•+, and DMPD•+ radicals. To our knowledge, this is the first study of M. stylosa and M. serrulata alkaloids and phenolic compounds and antioxidativity.
The Influence of Fatigue Loading on the Durability of the Conveyor Belt
Authors: Ilanković Nikola , Živanić Dragan , Zuber Ninoslav
Published: March 2023

The conveyor belt is by its structure a textile composite. As a load-supporting element of the conveyor, the belt withstands variable loads during its operations. In order to investigate the influence of the level and variability of loading on the life of the belt, tests were carried out on specimens in laboratory conditions. A testing device was specially designed and made for these tests that enabled precise control and monitoring of the loading as well as number of loading cycles up to fracture. This research provides an overview of the influence of fatigue loading on the fatigue life of the belt. The methodology of the conducted research is explained with a description of important technical parameters of the testing device. A physical experiment and a corresponding numerical simulation using the FEM method were carried out with multiple loading levels of belt specimens. Based on the obtained results, appropriate conclusions were made; at loads less than 70% of the breaking strength, the lifetime of the belt is very long. Attention was drawn to additional influences that could not be covered by the experiment and possible directions for further research were indicated.
Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
Authors: Inayathullaah M. A. , Sivakumar N. , Balasundaram Ananthakrishnan , Arul R. , Angalaeswari S.
Published: March 2023

This paper presents a modeling and performance evaluation of a five-phase PMBLDC motor with different controllers to analyze its transients and dynamic response with time domain specifications. Four different types of speed controllers, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS), an Adaptive Hybrid fuzzy-PI, a Proportional Integral (PI) control and a Fuzzy Logic Control (FLC), were considered and compared for this purpose. The mathematical model of the five-phase Permanent Magnet Brushless Direct Current (PMBLDC) motor was developed and simulated using MATLAB/Simulink to analyze its performance. The simulation results for all controllers with step and linear load fluctuations were evaluated. It was evident that the ANFIS controller provided a better dynamic time domain response than the other controllers. It gave eight times the peak torque at starting, and settled at 92.6% of the rated speed, with negligible overshoot, a very low rise time and a quick settling time compared to other controllers, which are ideally suited for an electric vehicle (EV). A real-time experimental setup was also developed, and experiments were carried out to validate the simulation results.
VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment
Authors: Auricchio Silvia , Cirotto Francesco , Giannini Antonio
Published: March 2023

A novel machine learning (ML) approach based on a recurrent neural network (RNN) for event topology identification in high energy physics (HEP) is presented. The vector-boson fusion (VBF) production mechanism arising in proton-to-proton collisions is predicted both from the current theoretical model of the particle physics, the standard model, and from its extensions that foresee potential new physics phenomena. This physical process has a well-defined event topology in the final state and a distinctive detector signature. In this work, an ML approach based on the RNN architecture is developed to deal with hadronic-only event information in order to enhance the acceptance of this production mechanism in physics analysis of the data. This technique was applied to a physics analysis in the context of high-mass diboson resonance searches using data collected by the ATLAS experiment.
Human Pose Estimation Based on a Spatial Temporal Graph Convolutional Network
Authors: Wu Meng , Shi Pudong
Published: March 2023

To address the problem of poor detection and under-utilization of the spatial relationship between nodes in human pose estimation, a method based on an improved spatial temporal graph convolutional network (ST-GCN) model is proposed. Firstly, upsampling and segmented random sampling strategies are used to effectively solve the problems of class imbalance and the large sequence length of the dataset. Secondly, an improved detection transformer (DETR) structure is added to effectively suppress the generation of non-maximal suppression (NMS) and anchor points, a multi-head attention (M-ATT) module is introduced into each ST-GCN cell to capture richer feature information, and a residual module is introduced into the 9th ST-GCN cell to avoid possible network degradation in deep networks. In addition, strategies such as warmup, regularization, loss functions, and optimizers are configured to improve the model’s performance. The experimental results show that the average percentage of correct keypoints (PCK) of this method are 93.2% and 92.7% for the FSD and MPII datasets, respectively, which is 1.9% and 1.7% higher than the average PCK of the original ST-GCN method. Moreover, the confusion matrix corresponding to this method also indicated that the model has high recognition accuracy. In addition, comparison experiments with ST-GCN and other methods show that the computation of the model corresponding to this method is about 1.7 GFLOPs and the corresponding MACs are about 6.4 GMACs, which is a good performance.
Hard Disk Failure Prediction Based on Blending Ensemble Learning
Authors: Zhang Mingyu , Ge Wenqiang , Tang Ruichun , Liu Peishun
Published: March 2023

As the most widely used storage device today, hard disks are efficient and convenient, but the damage incurred in the event of a failure can be very significant. Therefore, early warnings before hard disk failure, allowing the stored content to be backed up and transferred in advance, can reduce many losses. In recent years, an endless stream of research on the prediction of hard disk failure prediction has emerged. The detection accuracy of various methods, from basic machine learning models, such as decision trees and random forests, to deep learning methods, such as BP neural networks and recurrent neural networks, has also been improving. In this paper, based on the idea of blending ensemble learning, a novel failure prediction method combining machine learning algorithms and neural networks is proposed on the publicly available BackBlaze hard disk datasets. The failure prediction experiment is conducted only with S.M.A.R.T., that is, the learned characteristics collected by self-monitoring analysis and reporting technology, which are internally counted during the operation of the hard disk. The experimental results show that this ensemble learning model is able to outperform other independent models in terms of evaluation criterion based on the Matthews correlation coefficient. Additionally, through the experimental results on multiple types of hard disks, an ensemble learning model with high performance on most types of hard disks is found, which solves the problem of the low robustness and generalization of traditional machine learning methods and proves the effectiveness and high universality of this method.
Comparison of Perioperative Electrophysiological Measurements and Postoperative Results in Cochlear Implantation with a Slim Straight Electrode
Authors: Gabrovec Tina , Dragar Jana , Guzelj Domen , Bržan Petra Povalej , Rebol Janez
Published: March 2023

This research aims to determine whether a neural response telemetry (NRT) threshold determines the success of surgery. Furthermore, we examined whether the patient’s age, the etiology of their hearing loss, the depth of the electrode insertion, and a slow electrode insertion affect the result of postoperative speech audiometry (PSA). A total of 23 patients that had operations in a tertiary medical centre were included in the research. All of them received a slim straight electrode that was inserted through the round window into the lateral part of scala tympani The duration of the insertion was consistently 2 min in 52.2% and less than 2 min in 47.8% of cases. Statistical analyses were performed in the IBM SPSSTM program. Patients that were diagnosed with sensorineural hearing loss (SNHL) had statistically lower average NRT threshold values in comparison to patients diagnosed with otosclerosis (t = 3.069; p = 0.034). The depth of electrode insertion is inversely proportional to the average of all NRT thresholds (r = −0.464; p = 0.026). No correlation was found between slow electrode insertion and postoperative average values of tone audiometry (U = 44.000; p = 0.300). No statistically significant correlation could be drawn between the average of all NRT thresholds and postoperative speech audiometry (rho = −0.070; p = 0.751).
Experimental Detection of Nonlinear Dynamics Using a Laser Profilometer
Authors: Bonisoli Elvio , Dimauro Luca , Venturini Simone , Cavallaro Salvatore Paolo
Published: March 2023

This paper investigates a cantilever beam nonlinear dynamic behaviour, on which the nonlinearity is introduced with permanent magnet interactions or with a non-holonomic contact. The experimental time domain responses obtained from non-zero initial conditions are measured using a laser profilometer, conventionally adopted for product shape detections in online industrial applications. The Fourier transform, Continuous Wavelet transform, and Hilbert transform are used to investigate nonlinear phenomena in the frequency content, highlighting advantages and drawbacks of the three methods in catching instantaneous phenomena. Then, a Multi-Phi approach is proposed to describe the time evolution of nonlinear systems by means of a discrete number of linearised systems. Therefore, two linearised models have been developed and tuned to describe the dynamic behaviour of different Euler–Bernoulli cantilever beam configurations. The experimental data of nonlinear systems are compared with the corresponding ones of the linear system to evaluate the effects of introduced nonlinearities on the overall dynamic properties.
Modelling Analysis of a Four-Way Shuttle-Based Storage and Retrieval System on the Basis of Operation Strategy
Authors: Mao Jia , Cheng Jinyuan , Li Xiangyu , Zhao Honggang , Lin Ciyun
Published: March 2023

In the context of sustainable development, this paper rationalises the outbound process of a four-way shuttle system with a focus on their modelling, performance evaluation and configuration using a parallel operation strategy to reduce resource waste, thus achieving sustainable development. The parallelism of the hoist and shuttle is innovatively incorporated into the four-way shuttle system, so the modelling content is divided into parallel and serial types. In the parallel operation strategy model, a separation–aggregation queueing network model is constructed, and the open-loop queueing network is innovatively solved using the maximum entropy method. In the serial operation strategy model, a semi-open-loop queuing network is constructed and solved using the geometric matrix method. By varying different parameters, the accuracy of the model is verified by Arena simulation with an error range of 10% or less, and the error of the system performance index calculation is reduced by 20% compared with the existing methods. Setting up 18 different sizes of shuttle systems provided a better performance than a single serial-operation strategy through the addition of parallel strategies, with an average reduction of 12.6% in the system response time and a minimum reduction of 1.8%. The conclusions of this paper were verified on the basis of an arithmetic case analysis.
A Multivariate Local Descriptor Registration Method for Surface Topography Evaluation
Authors: Kong Chao , Xu Yuanping , Li Zhuowei , Zhang Chaolong , Li Tukun , Macleod Iain , Jiang Xiangqian , Tang Dan , Lu Jun
Published: March 2023

This paper illustrates a systematical surface topography measurement and evaluation method based on a 3D optical system. Firstly, the point cloud data of the workpiece are extracted by the use of a 3D structured light measurement system, and the STEP file of the design model is converted into point cloud data. Secondly, the local measurement point cloud (LMPC) and digital model point cloud (DMPC) are registered by a multivariate local descriptor registration scheme proposed in this study. Thirdly, the surface shapes extracted from the STEP file are applied as a reference to segment the measuring point cloud. Finally, an error analysis scheme is conducted on specific functional surfaces. An experiment was conducted to analyse the flatness, cylindricity and roughness to demonstrate the effectiveness and advantage of the method. The comparison results show that the proposed method outperforms other 3D optical surface topography analysis methods.
Luminosity and Contrast Adjustment of Fundus Images with Reflectance
Authors: AlRowaily Mofleh Hannuf , Arof Hamzah , Ibrahim Imanurfatiehah
Published: March 2023

This paper presents an automatic correction method for luminosity and contrast variation in fundus images. Sixty retina or fundus images with different levels of reflectance are selected from online databases and used to assess the effectiveness of the proposed method. There are five stages in the approach, and they are image input, filtering, luminosity correction, histogram stretching and post-processing. First, a color fundus image is read as input, and its three color components, red (R), green (G) and blue (B), are separated into different channels or arrays. Next, the eye region, or the region of interest (ROI), is identified along with its border via thresholding. After that, the original ratios of red-to-green and blue-to-green for every pixel in the ROI are computed and kept together with copies of the three channels. Then, the ROI for the three channels is subjected to lowpass filtering, row-wisely in the horizontal direction and column-wisely in the vertical direction, to create a smooth background luminosity surface. This surface does not contain foreground objects such as blood vessels, optic discs, lesions, microaneurysms and others. Three lowpass filters are tested for this purpose, and their efficacy is compared. The outcome is a smooth luminosity surface that estimates the background illumination of the entire ROI. Once the background illumination is established, the luminosity is equalized for all pixels in the ROI, such that every pixel will have the same background brightness. Afterward, the histogram of the ROI is stretched or equalized to enhance the contrast between the foreground objects and the background. Next, the green channel is further improved by adding details from the blue and red channels. Finally, in the post-filtering stage, the intensities of the blue and red channels are adjusted according to their original ratios to the green channel. When all three channels are recombined, the resulting color image looks similar to the original image but shows improved luminosity and contrast. The method is tested on 60 test images. It reduces luminosity variation and increases the contrast of all images. On average, this method achieves a 30% reduction in luminosity variation and a 90% increment in contrast. The proposed method was executed on AMD 5900HS CPU using MATLAB R2021b, and the mean execution time was nearly 2 s on average.
Research on a Service Load Prediction Method Based on VMD-GLRT
Authors: Zhang Jin , Huang Yiqi , Pi Yu , Sun Cheng , Cai Wangyang , Huang Yuanyuan
Published: March 2023

In this paper, a deep learning-based prediction model VMD-GLRT is proposed to address the accuracy problem of service load prediction. The VMD-GLRT model combines Variational Mode Decomposition (VMD) and GRU-LSTM. At the same time, the model incorporates residual networks and self-attentive mechanisms to improve accuracy of the model. The VMD part decomposes the original time series into several intrinsic mode functions (IMFs) and a residual part. The other part uses a GRU-LSTM structure with ResNets and Self-Attention to learn the features of the IMF and the residual part. The model-building process focuses on three main aspects: Firstly, a mathematical model is constructed based on the data characteristics of the service workload. At the same time, VMD is used to decompose the input time series into multiple components to improve the efficiency of the model in extracting features from the data. Secondly, a long and short-term memory (LSTM) network unit is incorporated into the residual network, allowing the network to correct the predictions more accurately and improve the performance of the model. Finally, a self-focus mechanism is incorporated into the model, allowing the model to better capture features over long distances. This improves the dependence of the output vector on these features. To validate the performance of the model, experiences were conducted using open-source datasets. The experimental results were compared with other deep learning and statistical models, and it was found that the model proposed in this paper achieved improvements in mean absolute percentage error (MAPE).
The Saturation of the Response to an Electron Beam of Ce- and Tb-Doped GYAGG Phosphors for Indirect β-Voltaics
Authors: Karpyuk Petr , Korzhik Mikhail , Fedorov Andei , Kamenskikh Irina , Komendo Ilya , Kuznetsova Daria , Leksina Elena , Mechinsky Vialy , Pustovarov Vladimir , Smyslova Valentina , Retivov Vasilii M. , Talochka Yauheni , Tavrunov Dmitry , Vasil’ev Andrei
Published: March 2023

GYAGG:Tb (Ce) scintillators have been confirmed to be promising sources of light emission when excited by an intense 150 keV electron beam. The saturation of the scintillation yield under such excitation conditions has been studied. To explain the results obtained, a model that considers the Auger quenching mechanism was used. The Ce-doped material did not show saturation, whereas a moderate 30% drop of the yield was measured in the Tb-doped sample at the highest excitation beam intensity ~1 A/cm2. This put forward a way to exploit the Tb-doped scintillator for indirect β-voltaic batteries.
Anaerobic Digestion of Microalga Chlorella protothecoides and Metagenomic Analysis of Reddish-Colored Digestate
Authors: Eusébio Ana , Santos Carla A. , Marques Isabel Paula
Published: March 2023

Microalga Chlorella protothecoides materials were assessed as substrates for anaerobic digestion (AD) aiming at the simultaneous production of biogas/methane and pigments: whole autotrophic (AA) and heterotrophic algae (H); extracted heterotrophic microalgae from lipid production (HExt); and pretreated heterotrophic microalgae through enzymatic (HPEnz), autoclave (HPA), and ultrasound (HPU) processes. AA was more suitable for AD than H, as it was more efficiently converted into methane (279 vs. 180 L CH4/kg VSin). In comparison, the pretreatment of heterotrophic microalgae had a positive effect on AD, with registered methane yield increases from 263 to 290 L CH4/kg VSin (HPU, HPA, HExt). Reddish pigmentation developed in H and HPU units due to the presence of purple non-sulfur bacteria (PNSB). This phenomenon and the changes in microbiota structure during AD were confirmed by metagenomic analysis. At the end of the process, the relative abundance of Clostridiales and Bacillales increased, enhancing the hydrolysis of compounds in acetate. Consistently, Methanosaeta became the comparatively dominant methanogen, meaning that methane was produced through the acetoclastic methanogenesis pathway. The obtained results indicate for AD biorefinery feasibility—regarding the simultaneous production of biogas/methane—a digestate flow and pigments (bacteriochlorophyll a and carotenoids).
Toward the Utilization of 3D Blading in the Cantilevered Stator from Highly Loaded Compressors
Authors: Xu Xiaobin , Wang Ruoyu , Yu Xianjun , An Guangfeng , Qiu Ying , Liu Baojie
Published: March 2023

Three-dimensional blading is an efficient technique in compressor aerodynamic design, and its function mechanism in the cantilevered stator needs to be addressed. This paper focuses on the sweep and dihedral in the cantilevered stator and seeks to expose their effects through detailed flow field analysis. Results show that the forward sweep could alleviate the corner flow separation by preventing the accumulation of the secondary flow toward the corner region, resulting in stronger flow separation at the blade trailing edge; in summary, forward sweep with appropriate parameters could increase static pressure rise by 14.3%. The positive dihedral will carry the endwall flow to the upper-span sections, thereby reducing blade corner separation; hence, as much as 23.5% improvement in static pressure rise could be obtained with the appropriate dihedral. Moreover, the combination of a relatively large sweep height and a moderate sweep angle with a low dihedral height and a moderate sweep angle provides optimum aerodynamic performance; the static pressure rise coefficient sees an increment of 25.5% at the near stall point. An experiment is then performed to further validate the theory, which shows a 2% improvement in efficiency of 3D blading at small mass flow rates. However, the secondary leakage should be given attention at high mass flow coefficients, while the corner separation needs further elimination at small mass flow rates.
Multi-Dimensional Evaluation of Land Comprehensive Carrying Capacity Based on a Normal Cloud Model and Its Interactions: A Case Study of Liaoning Province
Authors: Yu Huisheng , Zhang Xinyue , Yu Wenbo , Gao Yanpeng , Xue Yuyu , Sun Wei , Sun Dongqi
Published: March 2023

Studying land comprehensive carrying capacity (LCCC) is the foundational and key requirement for determining land development planning and urban spatial development patterns of a region. However, the traditional evaluation method discounts the fuzziness and randomness of the evaluation index and its results. The cloud model combines randomness and fuzziness to reveal the correlation between randomness and fuzziness using numerical feature entropy and is used to represent the granularity of a qualitative concept. This study used the Liaoning Province as the study area, and developed a multi-dimensional evaluation index system for LCCC using a normal cloud model. Based on this, the relationship between the different elements of geological condition, resources and environment, economic scale and urban construction were studied using the coupling coordination degree model that reflected not only the system interactions but also the strengths of its degree of coordination. Our results were as follows: (1) numerical feature entropy were evaluated to determine the carrying capacity level of the land, and comprehensive land carrying capacity evaluations were conducted in terms of both quantitative results and the reliability of the results, promoting the scientific application of uncertainty theory in the field of comprehensive land evaluation as well as carrying capacity. (2) Liaoning Province’s prefecture-level cities had distinctly different LCCC, demonstrating “low in the west and high in the east” spatial distribution characteristics. Cities with established economies and relatively strong infrastructures had larger comprehensive land carrying capacities. Overall, there was considerable consistency across the region, though the “low in the west and high in the east” spatial distribution characteristics affected the degree of coordination.
A Flexible Session-Based Recommender System for e-Commerce
Authors: Salampasis Michail , Katsalis Alkiviadis , Siomos Theodosios , Delianidi Marina , Tektonidis Dimitrios , Christantonis Konstantinos , Kaplanoglou Pantelis , Karaveli Ifigeneia , Bourlis Chrysostomos , Diamantaras Konstantinos
Published: March 2023

Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods to embeddings and SotA deep learning methods. This paper analyzes theoretical and practical issues in developing and evaluating methods for SBSR in e-commerce applications, where user profiles and purchase data do not exist. The major tasks of SBRS are reviewed and studied, namely: prediction of next-item, next-basket and purchase intent. For physical retail shopping where no information about the current session exists, we treat the previous baskets purchased by the user as previous sessions drawn from a loyalty system. Mobile application scenarios such as push notifications and calling tune recommendations are also presented. Recommender models using graphs, embeddings and deep learning methods are studied and evaluated in all SBRS tasks using different datasets. Our work contributes a number of very interesting findings. Among all tested models, LSTMs consistently outperform other methods of SBRS in all tasks. They can be applied directly because they do not need significant fine-tuning. Additionally, they naturally model the dynamic browsing that happens in e-commerce web applications. On the other hand, another important finding of our work is that graph-based methods can be a good compromise between effectiveness and efficiency. Another important conclusion is that a “temporal locality principle” holds, implying that more recent behavior is better suited for prediction. In order to evaluate these systems further in realistic environments, several session-based recommender methods were integrated into an e-shop and an A/B testing method was applied. The results of this A/B testing are in line with the experimental results, which represents another important contribution of this paper. Finally, important parameters such as efficiency, application of business rules, re-ranking issues, and the utilization of hybrid methods are also considered and tested, providing comprehensive useful insights into SBRS and facilitating the transferability of this research work to other domains and recommendation scenarios.
Hannah Arendt’s Action Theory, Aesthetics and Feminist Curatorial Praxis
Authors: Mohamadi Neda
Published: March 2023

This article considers the relationship between action (Arendt) and aesthetics in curatorial projects with feminist concepts. I suggest that Hannah Arendt’s theory of action provides the connection between aesthetics and the notion of action in feminist curatorial praxis. The vision of feminism discussed here refers to the desire to understand matters from the specific point of view of women and considers the distribution of power and potentiality in various levels of life. The feminist theory in this research aims to reveal, show, and transform cultural, historical and social structures. From a broader perspective, living in the neoliberal realities alongside capitalist and patriarchal state structures provides multiple reasons and a rationale for collectively forming a new foundation of resistance. Feminism emerges in and through curatorial actions involving varied artistic expressions of freedom, discontent, etc. Four case studies concerning women as subjects are investigated, whose subject is migration and border-crossing, and both works and exhibitions are compared in terms of their curatorial approach, the level of action and their aesthetics methods.
Interlaboratory Study on Brake Particle Emissions—Part I: Particulate Matter Mass Emissions
Authors: Grigoratos Theodoros , Mathissen Marcel , Vedula RaviTeja , Mamakos Athanasios , Agudelo Carlos , Gramstat Sebastian , Giechaskiel Barouch
Published: March 2023

The Particle Measurement Programme Informal Working Group (PMP-IWG) coordinated a global interlaboratory study (ILS) on brake wear particle emissions with the participation of 16 testing facilities. Two articles present the main outcomes of the ILS: (I) Particulate matter mass (PM), and (II) Particle Number (PN) emissions. The test matrix covered a wide variety of brake systems and configurations. The tested disc brakes were found to emit PM2.5 and PM10 that varied between 0.8–4.0 mg/km and 2.2–9.5 mg/km per brake, respectively, depending on the type of brake and the applied testing load. The drum brake emitted much lower PM due to its enclosed nature. Almost 37–45% of the emitted PM falls in the fine particle size with this fraction being higher for the drum brake. On the other hand, almost 50–65% of the total brake mass loss falls in particle sizes larger than 10 μm or gets lost before being measured. The most important loss mechanisms for PM in the proposed layout are being discussed. Finally, the PM measurement variability and lab-to-lab reproducibility are investigated.