Main Part Research Articles

Simple Contrastive Graph Clustering.

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to its promising performance. However, complicated data augmentations and time-consuming graph convolutional operations undermine the efficiency of these methods. To solve this problem, we propose a simple contrastive graph clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function. As to the architecture, our network includes two main parts, that is, preprocessing and network backbone. A simple low-pass denoising operation conducts neighbor information aggregation as an independent preprocessing, and only two multilayer perceptrons (MLPs) are included as the backbone. For data augmentation, instead of introducing complex operations over graphs, we construct two augmented views of the same vertex by designing parameter unshared Siamese encoders and perturbing the node embeddings directly. Finally, as to the objective function, to further improve the clustering performance, a novel cross-view structural consistency objective function is designed to enhance the discriminative capability of the learned network. Extensive experimental results on seven benchmark datasets validate our proposed algorithm's effectiveness and superiority. Significantly, our algorithm outperforms the recent contrastive deep clustering competitors with at least seven times speedup on average. The code of SCGC is released at SCGC. Besides, we share a collection of deep graph clustering, including papers, codes, and datasets at ADGC.

An Adaptive Image Segmentation Network for Surface Defect Detection.

Surface defect detection plays an essential role in industry, and it is challenging due to the following problems: 1) the similarity between defect and nondefect texture is very high, which eventually leads to recognition or classification errors and 2) the size of defects is tiny, which are much more difficult to be detected than larger ones. To address such problems, this article proposes an adaptive image segmentation network (AIS-Net) for pixelwise segmentation of surface defects. It consists of three main parts: multishuffle-block dilated convolution (MSDC), dual attention context guidance (DACG), and adaptive category prediction (ACP) modules, where MSDC is designed to merge the multiscale defect features for avoiding the loss of tiny defect feature caused by model depth, DACG is designed to capture more contextual information from the defect feature map for locating defect regions and obtaining clear segmentation boundaries, and ACP is used to make classification and regression for predicting defect categories. Experimental results show that the proposed AIS-Net is superior to the state-of-the-art approaches on four actual surface defect datasets (NEU-DET: 98.38% ± 0.03%, DAGM: 99.25% ± 0.02%, Magnetic-tile: 98.73% ± 0.13%, and MVTec: 99.72% ± 0.02%).

A Hybrid Controller for Musculoskeletal Robots Targeting Lifting Tasks in Industrial Metaverse.

In manufacturing, musculoskeletal robots have gained more attention with the potential advantages of flexibility, robustness, and adaptability over conventional serial-link rigid robots. Focusing on the fundamental lifting tasks, a hybrid controller is proposed to overcome control challenges of such robots for widely applications in industry. The metaverse technology offers an available simulated-reality-based platform to verify the proposed method. The hybrid controller contains two main parts. A muscle-synergy-based radial basis function (RBF) network is proposed as the feedforward controller, which is able to characterize the phasic and the tonic muscle synergies simultaneously. The adaptive dynamic programming (ADP) is applied as the feedback controller to address the optimal control problem. The actor-critic structure is applied in the ADP-based controller, where the critic network is trained to approximate the optimal performance index and the actor network is trained to compute the optimal muscle excitations. Furthermore, the convergence and stability of the ADP algorithm are also analyzed. Finally, experiments have been designed to verify the effectiveness of this hybrid controller on an upper limb musculoskeletal system, and the comparisons with other controllers are also illustrated. The results show that the proposed controller can obtain a satisfactory performance for lifting tasks.

Facilitators and barriers to the development of giftedness in children

Giftedness can be interpreted as a multifactorial set of special abilities and skills, that the individual begins to express from an early developmental age. Many believe that the concept of giftedness goes beyond and includes not only IQ but also other factors. The purpose of this literature review is to collect recent data according to factors other than socio/cultural and economic that may influence the expression of giftedness in children. In the introduction giftedness is discussed according to several elements that may intervene in the acceptance of a universal definition and identification of this specific population and the main part explores several factors that may prevent giftedness through inhibition of cognitive and emotional aspects of daily living of children including children with Neurodevelopmental Disorders.

Classification of Mushroom Data Set by Ensemble Methods

Due to disease of mushrooms, it is very important to classify mushrooms for predicting the best quality mushrooms. There are many methods to analyse the main parts of mushrooms. For above mentioned descriptions; in this simulation study five types of classification algorithm are employed to predict the structure of mushrooms. Mushroom dataset is used to predict the classes of mushrooms. The results are improved that these methods will be used to predict exact mushrooms features and classifications in real time approaches.

Stylistic and syntactic functions of parceling (on the material of B.L. Vasilyev’s novella "The Dawns Here Are Quiet")

The article describes one of expressive syntax techniques, namely parcelled constructions, identified in B.L. Vasilyev’s novella "The Dawns Here Are Quiet". The stylistic and syntactic functions of parcelling are determined and analysed using structural-semantic and functional stylistic analysis. The article concludes that parcelling as a stylistic device is a universal phenomenon of a multifunctional nature. It facilitates the implementation of the author’s various pragmatic ideas. Besides the main stylistic function, i. e. the function of expressively highlighting a certain part of the utterance, parcelled sentences perform several additional functions in a literary text: figurative, characterological, emotional-emphatic, expressive-grammatical. It is revealed that the syntactic functions of parcels in the novella are diverse. In simple sentences, they can be both their main and secondary parts. In composite sentences, they function as the predicative part which is attached to the main utterance using a subordinating, coordinating, or asyndetic connection. The variety of stylistic and syntactic functions of parcelled constructions enabled B. Vasilyev to realistically convey the atmosphere of the pre-war and war periods. He managed to describe the protagonists’ characters and their actions in a true to life way, making the narrative sound natural.

Design and Experiment of Oil-Electric Hybrid Air-Suction Sorghum Plot Seeder

To tackle the issues of low seeding accuracy and seed injury caused by the seeders utilized at a small scale and in the plot seeding of sorghum in mountainous or hilly regions, this study presents the design of an oil–electric hybrid air suction sorghum plot seeder. The main working parts of the seeder are described, and the performance of the seed-mixing device is simulated using EDEM software. An oil–electric hybrid drive mode is used to provide power for operation and to the seed-metering device and fan. Additionally, a sowing control and monitoring system is designed using a single-chip microcomputer controller to ensure uniform plant spacing at different forward speeds. A multi-factor experiment is conducted using the central synthesis method to determine the optimal operating parameters of the seed-metering device through bench tests. The results show that a profile hole diameter of 2.5 mm on the seed tray, a negative-pressure chamber vacuum of 8.0 kPa, and a seed-metering device speed of 28 r/min result in a 95.95% pass rate, 0.5% missing rate, and 3.55% reseeding rate. The deviation between the experimental and analytical results that validate the optimum parameters is kept within acceptable limits. Field tests are conducted at different forward speeds using the optimum parameter combinations, and a comparison is made with the widely used duckbill planter. The results show pass, missing, and reseeding rates of 94.41%, 2.3%, and 3.29%, respectively. The missing monitoring error is less than 7.19%. All of the indices of the oil–electric hybrid air suction sorghum plot seeder are superior to those of the duckbill planter; thus, it fulfills the agronomic requirements for seeding a sorghum plot.

Research on the development and application of digital twin for the full life cycle of bridge engineering

Bridges play an important role in modern society, whereas many bridges are in poor condition and are characterized by complexity, randomness, and risk no matter what phase the bridges are in. Meanwhile, with advanced technologies such as IoT and Big Data, Digital Twin has been developed in different industries, such as aerospace. With multiple superiorities of real-time data detection, big data processing, full life-cycle management, etc., it is appropriate for bridge engineering. The paper aims to present a state-of-the-art literature review on the full life cycle of the application of Digital Twin in Bridge Engineering. The article discusses the relationship between Digital Twin and Building Information Model (BIM) and constructs a comprehensive framework for Digital Twin in Bridge Engineering. The review is classified into three main parts design and construction phase, operation and maintenance phase, and demolition phase based on different stages of the full life cycle. The results show the applications high potential and feasibility, and show that most of the current research focuses on the operation and maintenance phases. However, intensive research is still necessary to improve the accuracy, quality, efficiency, timeliness, and versatility of the Digital Twin in Bridge Engineering.

COMPARATIVE ANALYSIS OF YANDEX.METRICS AND GOOGLE ANALYTICS AS POLICY TOOLS FOR MANAGING URBAN DIGITAL INFRASTRUCTURE

This article is a review of the electronic tools Yandex.Metrica and Google Analytics for potential possible use in urban digital infrastructure facilities. A comparative analysis of the data from two tools in search of an optimal choice was chosen as the research method. The work consists of an introduction, which provides a brief description of the abovementioned electronic platforms, the main part and a conclusion, which presents a likely partial confirmation of the hypothesis. The article may be useful for students, postgraduates and research teachers in the fields of political science, information technology and economic sciences, as well as be of interest to specialists in promoting products to the market and lobbying interests within the framework of government contracts.

Accumulation mechanism of metabolite markers identified by machine learning between Qingyuan and Xiushui counties in Polygonatum cyrtonema Hua

Polygonatum cyrtonema Hua is a traditional Chinese medicinal plant acclaimed for its therapeutic potential in diabetes and various chronic diseases. Its rhizomes are the main functional parts rich in secondary metabolites, such as flavonoids and saponins. But their quality varies by region, posing challenges for industrial and medicinal application of P. cyrtonema. In this study, 482 metabolites were identified in P. cyrtonema rhizome from Qingyuan and Xiushui counties. Cluster analysis showed that samples between these two regions had distinct secondary metabolite profiles. Machine learning methods, specifically support vector machine-recursive feature elimination and random forest, were utilized to further identify metabolite markers including flavonoids, phenolic acids, and lignans. Comparative transcriptomics and weighted gene co-expression analysis were performed to uncover potential candidate genes including CHI, UGT1, and PcOMT10/11/12/13 associated with these compounds. Functional assays using tobacco transient expression system revealed that PcOMT10/11/12/13 indeed impacted metabolic fluxes of the phenylpropanoid pathway and phenylpropanoid-related metabolites such as chrysoeriol-6,8-di-C-glucoside, syringaresinol-4'-O-glucopyranosid, and 1-O-Sinapoyl-D-glucose. These findings identified metabolite markers between these two regions and provided valuable genetic insights for engineering the biosynthesis of these compounds.

The Role of Transformational Leadership and Psychological Empowerment Toward Employee Creativity

Employee creativity is a fundamental cognitive skill; according to the latest statistics, employee creativity is the most crucial factor for future success and is integrated into the bottom line in all organisations. Previous research reported a significant relationship between transformational leadership and employee creativity; however, the mechanism of this relationship is still unfolding, and the mediatory role of these variables is largely unexplored. Thus, this study aims to examine the mediating effect of psychological empowerment between transformational leadership and employee creativity within Malaysia's customer service industry due to its crucial role in meeting changing demands through excellent service in a socio-cultural context. A total of 429 responses were collected through questionnaires consist of four main parts including demographic information, transformational leadership, employee creativity and psychological empowerment. The data were analysed using measurement and structural models through smart-PLS using structural equation modelling with bootstrapping. The findings revealed that transformational leadership does not have a significant positive relationship with employee creativity. Meanwhile, the result demonstrates that there is a significant relationship between transformational leadership and psychological empowerment as well as between psychological empowerment and employee creativity. Interestingly, the findings revealed that psychological empowerment mediates the relationship between transformational leadership and employee creativity. This study contributes to the theoretical perspective in terms of the mediating role of psychological empowerment in the relationship between transformational leadership and employee creativity. As for the practical implications, this study provides empirical evidence for the organisation to design interventions that enhance creativity by empowering psychology among the employees.

Multi-class center dynamic contrastive learning for unsupervised domain adaptation person re-identification

Unsupervised domain adaptation person re-identification (UDA Re-ID) aims to leverage the pedestrian knowledge learned from labeled source domain to assist in learning the pedestrian knowledge in the unlabeled target domain. Most of existing investigations typically utilize single-class center clustering algorithms to group unlabeled target domain instances. Unfortunately, single-class center clustering algorithms tend to cluster pedestrian pictures from different identities into the same cluster, leading to inaccurate labels. Training with these noisy labels can undesirably deteriorate the accuracy of UDA Re-ID. Responding to the problem, we propose a multi-class center dynamic contrastive learning (MCC-DCL) for UDA Re-ID, which includes three main parts: multi-center clustering (MCC), dynamic pseudo-labeling (DPL), and dynamic contrastive learning (DCL). In order to reduce noisy labels generated during clustering, we introduce MCC method to generates reliable pseudo-labels for instances. Furthermore, to fully utilize the knowledge learned by the network during each iteration, we propose DPL method to optimizes the pseudo-labels of instances. Finally, for improving the discriminative performance of model and its tolerance to noisy labels, we propose DCL method that utilizes dynamic pseudo-labels and dynamic contrastive loss for supervised training. Comprehensive experiments and analyses demonstrate that MCC-DCL significantly outperforms existing approaches in UDA Re-ID tasks.

A material world: how can materiality assessments be used to define organizational sustainability priorities, while taking into account the United Nations’ SDGs?

Purpose This paper aims to contribute to existing academic work and business practice by presenting original empirical findings and by providing insights into priority setting on Sustainable Development Goals (SDGs) in organizations. From an academic viewpoint, it not only adds to previous work on the topic of SDG materiality (e.g. Van Tulder and Lucht, 2019) but also aims to contribute new insights into the steps that are crucial and influence the adoption of the SDGs in materiality assessments. It may also add to the literature by providing new knowledge on the strategic considerations that organizations may make and institutional dynamics that encourage organizations to implement the SDG materiality method. Design/methodology/approach By executing a national survey research in Belgium through a collaboration between academics of Antwerp Management School, Louvain School of Management (UCLouvain) and the University of Antwerp, and supported by Belgium’s Federal Institute of Sustainable Development, the authors have obtained several insights into the SDG landscape in Belgium for various types of organizations, including companies, governmental and nongovernmental organizations and educational institutions. This research builds further on a first national survey (SDG Barometer Belgium, 2018) on the adoption and implementation of the SDGs. However, an important aim of this research is to shift the emphasis to more prominent new elements, such as whether or not organizations use the SDGs in materiality assessments. While the main part of the data for this research were collected through an online questionnaire, document analyses were conducted based on the sustainability reports of BEL 20 companies, the benchmark stock market index of Euronext Brussels consisting of 20 companies traded at the Brussels Stock Exchange, and seven interviews were held to obtain additional insights. Findings A total of 386 organizations across sectors responded to the question “Does your organization perform a materiality analysis”, of which 210 organizations completed the question “Does your organization align the materiality analysis with the SDGs,”after an “exit route” based on a positive answer to the first question. When diving into the survey results, the authors see that no more than 12% of the 210 organizations performing a materiality analysis align their materiality analysis with the SDGs, while 14% indicate that they do not account for the SDGs at all in their materiality analyses. The results show that 41% of the organizations take into account the SDGs to a certain degree when performing their materiality analysis. Speculating on an explanation for these results, it may be the case that organizations do not yet think about coupling the SDGs to their materiality assessment, experience difficulties in practice or generally lack the knowledge for relating the SDGs to the sustainability topics that are relevant to them. This seems in line with other research (e.g. Van Tulder and Lucht, 2019), as the results of this study indicate that it seems to be difficult for organizations to relate the SDGs to the existing sustainability priorities or materiality analyses of companies. Originality/value The real contribution of this paper essentially lies in the description of the Janssen Pharmaceuticals case. The company recognized that today’s internally focused approach to goal setting is not enough to address global challenges. Hence, looking at what is needed externally from a global perspective, taking into account sustainability thresholds and setting ambitions accordingly, is needed to bridge the gap between current performance and required performance. From the Janssen Pharmaceuticals case, the authors learned that external stakeholders are an extremely useful source of information to address the required performance by using the SDG framework. For sure, SDG materiality analyses are still in an early phase of development and knowledge on how to conduct such an analysis may be lacking. Future efforts – or the lack thereof – may indicate whether or not companies consider such analyses as sufficiently relevant. Although the uptake of the SDGs is in progress, it remains to be seen which, if any, materiality method will eventually turn out as a new dominant way of defining material issues. The findings presented in this study hopefully serve as a basis for further investigation of the topic.

Heat Transfer and Flow Characteristics of Channel Impingement Cooling Structure at Leading Edge Inside Turbine Blades Using Large Eddy Simulation

Abstract As a main part of multichannel wall jet cooling structure, channel impingement cooling is a cooling strategy of great concern at the leading edge inside of the turbine blade. In this paper, heat transfer and flow behavior in the channel impingement cooling structure are investigated by large eddy simulation (LES). The results imply that impingement created by curvature-induced centrifugal instabilities in the turning region of the cooling channel is dominated by a streamwise vortex system containing a counter-rotating Dean vortex, which presents high heat transfer streaks along the streamwise direction on the target wall. The intensely unsteady nature of the cooling jet induced by a lack of equilibrium between the pressure gradient and the centrifugal force is precisely captured herein by LES. An attaching-wall jet formed on the outer wall downstream of the cooling channel has highly three-dimensional characteristics not observed by Reynolds-averaged Navier–Stokes equations (RANS). Heat transfer augmentation on the target wall of the cooling channel is mainly due to the intensifying streamwise vortex system developing in the turning region as driven by the centrifugal force. This research work will provide a reference for the optimization and application of multichannel wall jet cooling for gas turbine blades.

Enhancing gust load alleviation performance in an optimized composite wing using passive wingtip devices: Folding and Twist approaches

This paper introduces an innovative numerical method for the design and optimization of high-aspect-ratio composite wings equipped with passive control systems, specifically, Folding WingTip (FWT) and Twist WingTip (TWT) devices. The aim is to enhance Gust Load Alleviation (GLA) performance in the baseline wing. Recent numerical studies have indicated that the inclusion of spring devices and wingtip modifications can offer additional benefits in alleviating gust loads during flight. The baseline wing is designed using a comprehensive multi-disciplinary optimization framework, taking into account aerostructural constraints and exploiting the anisotropic properties of composite materials. The proposed methodology integrates Finite Element (FE) software, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses, and Particle Swarm Optimization (PSO). This method, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, facilitated the streamlined design of composite wings with optimized aeroelastic and structural performance. The paper is divided into two main parts. Part 1 introduces a Multidisciplinary Design Optimization (MDO) approach for high-aspect-ratio composite wings, leading to the development of a baseline wing model. Part 2 evaluates the effectiveness of the FWT and TWT devices in alleviating gust loads on the baseline wing, with a focus on the Root Bending Moment (RBM) as a critical criterion for comparison. In wingtip modeling, geometrical nonlinearity is incorporated, and elastic trim is adjusted in each iteration to accommodate shape changes under load and aerodynamic panel movement is synchronized with structural adjustments.

Hawthorn leaf flavonoids alleviate the deterioration of atherosclerosis by inhibiting SCAP-SREBP2-LDLR pathway through sPLA2-ⅡA signaling in macrophages in mice

Ethnopharmacological relevanceHawthorn leaves are a combination of the dried leaves of the Rosaceae plants, i.e., Crataegus pinnatifida Bge. or Crataegus pinnatifida Bge. var. major N. E. Br., is primarily cultivated in East Asia, North America, and Europe. hawthorn leaf flavonoids (HLF) are the main part of extraction. The HLF have demonstrated potential in preventing hypertension, inflammation, hyperlipidemia, and atherosclerosis. However, the potential pharmacological mechanism behind its anti-atherosclerotic effect has yet to be explored. Aim of the studyThe in vivo and in vitro effects of HLF on lipid-mediated foam cell formation were investigated, with a specific focus on the levels of secreted phospholipase A2 type IIA (sPLA2-II A) in macrophage cells. Materials and methodsThe primary constituents of HLF were analyzed using ultra-high performance liquid chromatography and liquid chromatography-tandem mass spectrometry. In vivo, HLF, at concentrations of 5 mg/kg, 20 mg/kg, and 40 mg/kg, were administered to apolipoprotein E knockout mice (ApoE−/−) fed by high-fat diet (HFD) for 16 weeks. Aorta and serum samples were collected to identify lesion areas and lipids through mass spectrometry analysis to dissect the pathological process. RAW264.7 cells were incubated with oxidized low-density lipoprotein (ox-LDL) alone, or ox-LDL combined with different doses of HLF (100, 50, and 25 μg/ml), or ox-LDL plus 24-h sPLA2-IIA inhibitors, for cell biology analysis. Lipids and inflammatory cytokines were detected using biochemical analyzers and ELISA, while plaque size and collagen content of plaque were assessed by HE and the Masson staining of the aorta. The lipid deposition in macrophages was observed by Oil Red O staining. The expression of sPLA2-IIA and SCAP-SREBP2-LDLR was determined by RT-qPCR and Western blot analysis. ResultsThe chemical profile of HLF was studied using UPLC-Q-TOF-MS/MS, allowing the tentative identification of 20 compounds, comprising 1 phenolic acid, 9 flavonols and 10 flavones, including isovitexin, vitexin-4″-O-glucoside, quercetin-3-O-robibioside, rutin, vitexin-2″-O-rhamnoside, quercetin, etc. HLF decreased total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) levels in ApoE−/− mice (P < 0.05), reduced ox-LDL uptake, inhibited level of inflammatory factors, such as IL-6, IL-8, TNF-α, and IL-1ꞵ (P < 0.001), and alleviated aortic plaques with a thicker fibrous cap. HLF effectively attenuated foam cell formation in ox-LDL-treated RAW264.7 macrophages, and reduced levels of intracellular TC, free cholesterol (FC), cholesteryl ester (CE), IL-6, TNF-α, and IL-1β (P < 0.001). In both in vivo and in vitro experiments, HLF significantly downregulated the expression of sPLA2-IIA, SCAP, SREBP2, LDLR, HMGCR, and LOX-1 (P < 0.05). Furthermore, sPLA2-IIA inhibitor effectively mitigated inflammatory release in RAW264.7 macrophages and regulated SCAP-SREBP2-LDLR signaling pathway by inhibiting sPLA2-IIA secretion (P < 0.05). ConclusionHLF exerted a protective effect against atherosclerosis through inhibiting sPLA2-IIA to diminish SCAP-SREBP2-LDLR signaling pathway, to reduce LDL uptake caused foam cell formation, and to slow down the progression of atherosclerosis in mice.

Clinical, Pathological and Epidemiological Aspects of Cephalopina titillator Larval, Exciter in Camelus dromedaris from the Rafsanjan Region, Iran

The camel nasal bot fly is an obligate parasite which its larvae causes nasopharyngeal myiasis and the 3rd generation of larvae may cause serious nasal mucosal damage. The present study was conducted on Camelus dromedaris to determine the clinical assessment, seasonal prevalence and also pathological adverse effect of Cephalopina titillator in Rafsanjan region (South east of Iran). Due to history and dental formula, camels were divided into four age groups: less than two years (62 cases), two to five years (86 cases), five to nine (42 cases) and more than nine years (10 cases), which positive cases confirmed in 12 (19.4%%), 31 (36%), 40 (95.2%) and 10 (100%) cases, respectively. The major clinical signs of naturally infected camel was categorized in three main parts; Respiratory signs, nervous behavior and general symptoms which the main ones were related to stridor and nasal discharge. Suspected larvae with average 14.83±4.35 was recovered from 93 infested camels and evaluated microscopically for determining the larval stage. Furthermore, the gross and histopathological findings were recorded by using tissue samples. These non sex related findings showed that with increasing the age, susceptibility to this specific parasite infestation increased as well (p&lt;0.001). No significant seasonal changes were recorded, but the incidence rate was higher in March, April and September. Different larval stage were mostly found in nasal cavity, nasopharyngeal and laryngeal areas which was accompanied by local inflammation, regional edema and serosanguinous exudate. Microscopical findings were showed the epithelial damage along with lymphoid hyperplasia, accumulation of inflammatory cells and remnants of necrotic tissue. However, fibroblasts proliferation, connective tissue formation along with some degrees of atrophy and focal calcification were the significant findings of chronic infestation. These findings indicate the presence of heavy larval infestation in camel of Rafsanjan region which imposes severe economic losses and affect their health, growth and related productions. Therefore, it is recommended to carry out the necessary measures for the biological control of the camel nasal bot fly.

РАБОЧАЯ ТЕТРАДЬ КАК СПОСОБ ОРГАНИЗАЦИИ ИЗУЧЕНИЯ ТВОРЧЕСТВА П. ВАСИЛЬЕВА НА ФАКУЛЬТАТИВНЫХ ЗАНЯТИЯХ ПО ЛИТЕРАТУРНОМУ КРАЕВЕДЕНИЮ

This article is devoted to the problem of insufficiency of the material and methodological base for conducting optional classes in literary local history of the Pavlodar Irtysh region at school. The main part of the work is aimed at describing a way to overcome this problem through the creation of workbooks dedicated to the work of local authors. The developed notebook on the work of Pavel Nikolaevich Vasiliev is presented; a description of the principles by which the concept of the notebook was created, works for analysis and the tasks themselves were selected. The authors name the advantages of this method of work in an optional lesson: accessibility, versatility, flexibility in use, manifestation of creativity of the teacher and students. The authors point out the relevance of this issue in connection with the growing interest in the study of the local literary heritage and the direction of this work on the patriotic education of the younger generation. This article may be useful for methodologists, practicing teachers of the Russian language and literature, and students of pedagogical specialties

Numerical method for reducing the effect of spectral leakage on the frequency normalisation results of signals obtained in an asphalt pavement test using a FWD device

ABSTRACT This paper provides an evaluation of the numerical method for limiting the effect of spectral leakage on the frequency normalisation results of the pavement load and displacement functions which were induced with a dynamic impulse generated during FWD tests. The main part of the paper is concerned with the presentation of a method, which can be used to effectively reduce the effect of spectral leakage on the frequency normalisation of signals obtained through pavement testing with FWD. The method developed is supported by the observed differences between the results of the backcalculations carried out for the measurement results of the test section pavement deflections, which were subjected to frequency normalisation with and without spectral leakage reduction. The signals, where the proposed spectral leakage mitigation method was applied, were the only ones for which the identified values of the elastic modulus of the asphalt layer package model increase as the frequency increases, which agrees with the generally accepted relationship observed under laboratory conditions.

Pneumonia prediction on chest x-ray images using deep learning approach

&lt;span lang="EN-US"&gt;Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest x-ray images. In deep learning, computers can automatically identify useful features for the model, directly from the raw data, bypassing the difficult step of manual information refinement. The main part of the deep learning method is the focus on automatically learning data representations. Visual geometry group 16 (VGG16) and DenseNet121 are methods in deep learning. The data used is a chest x-ray of pneumonia. Data is divided into training, testing, and validation. The best method for this research case is VGG16 with 93% accuracy training and 90% accuracy testing. In this study, DenseNet121 obtained accuracy below VGG16, with 92% accuracy in training and 88% for accuracy testing. Parameters have a significant influence on the accuracy of each model, and with the parameters that have been used, the VGG16 is a method that has high accuracy and can be used to predict chest x-ray images aimed at checking pneumonia in patients.&lt;/span&gt;