Construction Characteristics Research Articles

Усовершенствование схемы блока управления электродвигателя малогабаритного стрелочного универсального

The article is dedicated to the research and improvement of the design of a compact universal point motor (EMSU) and the fundamental control block scheme of the electric motor. The universal voltage and rotor rotation frequency motor EMSU has been developed as a replacement for commonly used direct current and alternating current motors, such as MSP, DPS, MST, and MSA types. The universality of EMSU leads to its widespread implementation in the railway networks of the Russian Federation. The lack of publicly available information on the construction features of the electric motor and the fundamental control block scheme has led to the necessity of conducting an independent analysis of its design. In the process of work, the EMSU has been disassembled, followed by an analysis of its mechanical and electrical components. Assembly and analysis of statistical information on its various types of failures have been performed, and suggestions for improving the design of the electric motor have been developed.

A geometric semantic macro-crossover operator for evolutionary feature construction in regression

A geometric semantic macro-crossover operator for evolutionary feature construction in regression

Comparing univariate filtration preceding and succeeding PLS-DA analysis on the differential variables/metabolites identified from untargeted LC-MS metabolomics data

BackgroundPLS-DA of high-dimensional metabolomics data is frequently employed to capture the most pertinent features to sample classification. But the presence of numerous insignificant input features could distort the PLS-DA model, blow up and scramble the selected differential features. Usually, univariate filtration is subsequently complemented to refine the selected features, but often giving unstable results. Whereas by precluding insignificant features through univariate data prefiltration assessed by FDR adjusted p-value, PLS-DA can generate more stable and reliable differential features. We explored and compared these two data analysis procedures to gain insights into the underlying mechanisms responsible for the disparate results. ResultsThe effect of univariate data filtration preceding and succeeding PLS-DA analysis on the identified discriminative features/metabolites was investigated using LC-MS data acquired on the samples of human serum and C. elegans extracts, with and without metabolite standards spiked to simulate the treated and control groups of biological samples. It was shown that the univariate data prefiltration before PLS-DA usually gave less but more stable and likely more reliable and meaningful differential features, while PLS-DA applied directly to the original data could be affected by the presence of insignificant features and orthogonal noise. Large number of insignificant variables and orthogonal noise could distort the generated PLS-DA model and affect the p(corr) value, and artificially inflate the calculated VIP values of relevant features due to the increased total number of input features for model construction, thus leading to more false positives selected by the conventional VIP threshold of 1.0. Significance and noveltyUnivariate data filtration preceding PLS-DA was important for the identification of reliable differential features if using a conventional threshold of VIP of 1.0. Presence of insignificant features could distort the PLS-DA model and inflate VIP values. Appropriate VIP threshold is associated with the numbers of input features and the model components. For PLS-DA without univariate prefiltration, threshold of VIP larger than 1.0 is recommended for the selection of discriminative features to reduce the false positives.

The Application of Bamboo in the Railway Industry: A Sustainable Solution for Track Construction

The railway industry has shown a strong interest in utilizing sustainable materials, including recycled materials and composites, in construction. Bamboo, as a highly renewable natural resource, has been proposed as a construction material for the railway industry. This material offers several advantages, such as high strength and durability, sustainability, low embodied energy, and ease of handling. It has been used in various construction materials like plywood, scrimber, laminates, and fibers. This paper aims to review the application of bamboo as a material in the railway industry and provide suggestions for its future use as railway sleepers. The mechanical properties of bamboo and its desirable features for sleeper construction, such as versatility, durability, low embodied energy and carbon footprint, lightweight, and ease of handling, are discussed. Bamboo-based products like plywood and scrimber can offer higher mechanical properties compared to traditional timber sleepers. Moreover, due to its rapid growth rate, bamboo is considered an environmentally friendly material. However, there are certain factors that limit the widespread deployment of bamboo in the railway industry. For instance, the lightweight nature of bamboo can reduce the lateral resistance of sleepers. Additionally, long-term performance studies and its performance in regions with varying weather conditions need to be further investigated. This review paper aims to promote the increased utilization of bamboo in the railway industry, contributing to the development of sustainable railway tracks. By considering the mechanical properties and advantageous characteristics of bamboo, it is possible to explore its potential as a viable and eco-friendly material for railway sleepers.

Analyzing the Emotional Characteristics of British and American Literature by Combining Semiotics and the Operational Model of Thinking

Abstract This paper first analyzes the emotional features of English and American literary works by combining semiotics of interactivity, language class emotional context and thinking operation model. Then, a B-Feature-BP text emotion feature construction model is constructed on the basis of the BRET model combined with the BP neural network, and the emotion features of English and American literary works are constructed by combining semiotics and the thought operation model. Then, based on the multi-task learning method in deep learning, a multi-task MT-GSU model is proposed to classify and recognize the emotional features of constructed text. Finally, the performance of constructing, classifying and recognizing the emotional features of English and American literary works in this paper is analyzed so as to analyze the emotional features of English and American literary works. The results show that the performance of the constructed emotional features is all greater than 0.8, and the emotional features of the characters, the environment, and the whole of the English and American literary works are above 0.87, and the classification time is between [0.338,0.721]s. The intensity of tendency of the characteristics of the emotions of the works of English and American literature is [0.68,0.78], the intensity of stability is [0.6,0.74], the intensity of profundity is [0.71,0.79], and the intensity of efficacy is [0.72,0.82]. This study has a positive impact on the appreciation and translation of English and American literary works.

SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING AN ENHANCED ENCODER-DECODER ARCHITECTURE

Abstract. Semantic segmentation is one of most the important computer vision tasks for the analysis of aerial imagery in many remote sensing applications, such as resource surveys, disaster detection, and urban planning. This area of research still faces unsolved challenges, especially in cluttered environments and complex sceneries. This study presents a repurposed Robust UNet (RUNet) architecture for semantic segmentation, and embeds the architecture with attention mechanism in order to enhance feature extraction and construction of segmentation maps. The attention mechanism is achieved using Squeeze-and-Excitation (SE) block. The resulting network is referred to as SE-RUNet. SE is also tested with the classical UNet, termed SE-UNet, to verify the efficiency of introducing SE. The proposed approach is trained and tested using “Semantic Segmentation of Aerial Imagery” dataset. The results are evaluated using Accuracy, Precision, Recall, F-score and mean Intersection over Union (mIoU) metrics. Comparative evaluation and experimental results show that using SE to embed attention mechanism into UNet and RUNet significantly improves the overall performance.

AttCON: With better MSAs and attention mechanism for accurate protein contact map prediction

Protein contact map prediction is a critical and vital step in protein structure prediction, and its accuracy is highly contingent upon the feature representations of protein sequence information and the efficacy of deep learning models. In this paper, we propose an algorithm, DeepMSA+, to generate protein multiple sequence alignments (MSAs) and to construct feature representations based on co-evolutionary information and sequence information derived from MSAs. We also propose an improved deep learning model, AttCON, for training input features to predict protein contact map. The model incorporates an attention module, and by comparing different attention modules, we find a parameter-free attention module suitable for contact map prediction. Additionally, we use the Focal Loss function to better address the data imbalance issue in protein contact map. We also developed a weighted evaluation index (W score) for model evaluation, which takes into account a wide range of metrics. W score is comprehensive in its scope, with a particular focus on the precision of predictions for medium-range and long-range contacts. Experimental results show that AttCON achieves good precision results on datasets from CASP11 to CASP15. Compared to some state-of-the-art methods, it achieves an average improvement of over 5% in both medium-range and long-range predictions, and W score is improved by an average of 2 points.

A Multi-Perspective Feature Approach to Few-Shot Classification of IoT Traffic

The realization of Internet of Things (IoT) traffic classification is crucial to the management and monitoring of the IoT network. Performing IoT traffic classification under a few-shot scenario is vital due to extremely rapid growth in the number of IoT devices and necessity to save computational costs. In this paper, we propose MAFFIT, a multi-perspective feature approach to few-shot classification of IoT traffic. The purpose is to comprehensively consider the information of network traffic and to achieve accurate classification of IoT traffic using a limited number of samples. MAFFIT is based on our key observation that traffic behaviour and traffic composition are highly consistent across IoT traffic of the same class. For a flow, MAFFIT will first extract the packet length sequences and packet byte sequence, then encodes the features of the corresponding sequences using feature construction, and finally uses comparative learning to obtain the class of the flow without the additional cost of training a comparison model. We conduct extensive experiments on two real-world IoT traffic datasets, the results demonstrate that MAFFIT can achieve accurate IoT traffic classification using a limited number of flow samples and MAFFIT outperforms three existing network traffic classification methods.

Prohibition of the glorification of the crimes condemned by the verdict of the International Military Tribunal of the European Axis Countries: problems of interpretation and law enforcement

The subject of this study is the criminal law prohibition of the approval of crimes condemned by the verdict of the International Military Tribunal of the European Axis countries. This act of glorifying of such crimes is an element of the objective side of the corpus delicti provided for in Article 354.1 of the Criminal Code of the Russian Federation. The methodology of the research consists of such methods as formal-legal, logical, systematic, as well as the method of analysis. The author emphasizes the importance of considering the aspects of the qualification of this act in the context of the development of the information society, since due to the active processes of digitalization, the present crime is often committed in the Internet environment. Special attention is paid to such a feature of the subjective side of the crime as its goal: the author argues about the need to consolidate the goal as a constructive feature of the subjective side of the considered corpus delicti. The novelty of this study lies in the fact that this norm is analyzed in relation to the constitutional principle of freedom of speech. The author comes to the conclusion that in this matter it is advisable to be guided by part 3 of Article 55 of the Constitution of the Russian Federation, according to which constitutional rights and freedoms can be restricted only to the extent necessary to protect the constitutional foundations and ensure the security of the state.

FAMM: Facial Muscle Motions for Detecting Compressed Deepfake Videos Over Social Networks

As a face manipulation technique, the misuse of Deepfakes poses potential threats to the state, society, and individuals. Several countermeasures have been proposed to reduce the negative effects produced by Deepfakes. Current detection methods achieve satisfactory performance in dealing with uncompressed videos. However, videos are generally compressed when spread over social networks because of limited bandwidth and storage space, which generates compression artifacts and the detection performance inevitably decreases. Hence, how to effectively identify compressed Deepfake videos over social networks becomes a significant problem in video forensics. In this paper, we propose a facial-muscle-motions-based (FAMM) framework to solve the problem of compressed Deepfake video detection. Specifically, we first locate faces from consecutive frames and extract landmarks from the face images. Then, continuous facial landmarks are utilized to construct facial muscle motion features by modeling the five sensory and face regions. Finally, we fuse the diverse forensic knowledge using Dempster-Shafer theory and provide the final detection results. Furthermore, we demonstrate the effectiveness of FAMM through analyzing mutual information, compression procedure, and facial landmarks for compressed Deepfake videos. Theoretical analyses illustrate that compression does not affect facial muscle motion feature construction and the differences in designed features exist between the real and Deepfake videos. Extensive experimental results conclude that the proposed method outperforms the state-of-the-art methods in detecting compressed Deepfake videos. More importantly, FAMM achieves comparable detection performance on compressed videos that are over real-world social networks.

Prediction and customized design of Curie temperature of Fe-based amorphous alloys based on interpretable machine learning

Predicting the Curie temperature (Tc) is a crucial problem in the field of amorphous alloys. In this study, Fe-based amorphous alloys are taken as an example, and the composition and corresponding Tc are collected through a literature review. Three feature construction strategies are employed to establish the relationship between the composition and Tc using machine learning. The research findings demonstrate that the combination of the Meredig rule and the GBT algorithm yields the highest prediction accuracy. The features are constructed using recursive elimination and enumeration methods, ultimately resulting in an optimal 8-dimensional feature subset. Furthermore, the Shapley Additive exPlanations (SHAP) values are introduced to analyze the interpretability of the prediction model, providing a feature importance ranking and their critical values. Finally, by replacing the Fe and P atoms with Mn and Si atoms, respectively, in the Fe80P13C7 alloy, Fe62Mn18P(13-x)C7Six (x = 4,5,6) alloys are successfully designed to exhibit a Tc close to room temperature (335 K), enabling customized Tc design for Fe-based amorphous alloys.

Three-dimension object detection and forward-looking control strategy for non-destructive grasp of thin-skinned fruits

The dynamic non-destructive grasp of thin-skinned fruits using flexible robotic hands, which requires obtaining three-dimension(3D) spatial structure information along with adaptive planning and motion control toward the target object, is a challenging topic in agricultural intelligence. To tackle the issue of 3D detection, we utilize RGB images and LiDAR point clouds for feature extraction and construct a multi-modal depth fusion convolution neural network (MDF-CNN) to obtain the classification information and perform image segmentation. Incorporating the advantages of a variable palm structure, we establish an evaluation mechanism of the optimal grasping stability (EM-OGS) using a hybrid method of the best configuration and force closure to build a new comprehensive performance optimal configuration planning (CPO-CP) method that is based on the multiple grasping performance indexes. We also create three cross-related nonlinear prediction models P-MGF, P-ODAP, and P-OBA along with a forward-looking non-destructive grasp control algorithm (FL-NGCA) using minimum grasping force to grasp thin-skinned fruits. The control algorithm carries out online, self-directed learning in the actual grasping process of the flexible hand and constantly optimizes the accuracy of the prediction model. Experimental results show that our proposed approach greatly improves the flexible hand comprehensive performance of grasping, outperforming state-of-the-art methods for non-destructive grasping of delicate fruits in most areas.

AMSF: attention-based multi-view slice fusion for early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease with a high prevalence in the elderly population over 65 years of age. Intervention in the early stages of AD is of great significance to alleviate the symptoms. Recent advances in deep learning have shown extreme advantages in computer-aided diagnosis of AD. However, most studies only focus on extracting features from slices in specific directions or whole brain images, ignoring the complementarity between features from different angles. To overcome the above problem, attention-based multi-view slice fusion (AMSF) is proposed for accurate early diagnosis of AD. It adopts the fusion of three-dimensional (3D) global features with multi-view 2D slice features by using an attention mechanism to guide the fusion of slice features for each view, to generate a comprehensive representation of the MRI images for classification. The experiments on the public dataset demonstrate that AMSF achieves 94.3% accuracy with 1.6-7.1% higher than other previous promising methods. It indicates that the better solution for AD early diagnosis depends not only on the large scale of the dataset but also on the organic combination of feature construction strategy and deep neural networks.

CrossFuse-XGBoost: accurate prediction of the maximum recommended daily dose through multi-feature fusion, cross-validation screening and extreme gradient boosting.

In the drug development process, approximately 30% of failures are attributed to drug safety issues. In particular, the first-in-human (FIH) trial of a new drug represents one of the highest safety risks, and initial dose selection is crucial for ensuring safety in clinical trials. With traditional dose estimation methods, which extrapolate data from animals to humans, catastrophic events have occurred during Phase I clinical trials due to interspecies differences in compound sensitivity and unknown molecular mechanisms. To address this issue, this study proposes a CrossFuse-extreme gradient boosting (XGBoost) method that can directly predict the maximum recommended daily dose of a compound based on existing human research data, providing a reference for FIH dose selection. This method not only integrates multiple features, including molecular representations, physicochemical properties and compound-protein interactions, but also improves feature selection based on cross-validation. The results demonstrate that the CrossFuse-XGBoost method not only improves prediction accuracy compared to that of existing local weighted methods [k-nearest neighbor (k-NN) and variable k-NN (v-NN)] but also solves the low prediction coverage issue of v-NN, achieving full coverage of the external validation set and enabling more reliable predictions. Furthermore, this study offers a high level of interpretability by identifying the importance of different features in model construction. The 241 features with the most significant impact on the maximum recommended daily dose were selected, providing references for optimizing the structure of new compounds and guiding experimental research. The datasets and source code are freely available at https://github.com/cqmu-lq/CrossFuse-XGBoost.

Developing a systematic performance measurement framework for benchmarking steel modular building construction

PurposeModular construction is an innovative method that enhances the performance of building construction projects. However, the performance of steel modular construction has not been systematically understood, and the existing measurement methods exhibit limitations in effectively addressing the features of steel modular building construction. Therefore, this study aims to develop a new performance measurement framework for systematically examining the performance of steel modular construction in building projects.Design/methodology/approachThis study was conducted through a mixed-method research design that combines a comprehensive review of the state-of-the-art practices of construction performance measurement and a case study with a 17-story steel modular apartment building project in Hong Kong. The case project was measured with data collected from the project teams and other reliable channels, and the measurement practices and findings were referenced to establish a systematic performance measurement framework for steel modular construction.FindingsConsidering steel modular construction as a complex socio-technical system, a systematic performance measurement framework was developed, which considers the features of steel modular construction, focuses on the construction stage, incorporates the views of various stakeholders, integrates generic and specific key performance indicators and provides a benchmarking process. Multifaceted benefits of adopting steel modular construction were demonstrated with case study, including improved economic efficiency (e.g. nearly 10% cost savings), improved environmental friendliness (e.g. approximately 90% waste reduction) and enhanced social welfare (e.g. over 60% delivery trips reduction).Originality/valueThis paper extends the existing performance measurement methods with a new framework proposed and offers experience for future steel modular construction. The measured performance of the case project also contributes in-depth understanding on steel modular construction with benefits demonstrated. The study is expected to accelerate an effective uptake of steel modular construction in building projects.

Generalizable pipeline for constructing HIV risk prediction models across electronic health record systems.

The HIV epidemic remains a significant public health issue in the United States. HIV risk prediction models could be beneficial for reducing HIV transmission by helping clinicians identify patients at high risk for infection and refer them for testing. This would facilitate initiation on treatment for those unaware of their status and pre-exposure prophylaxis for those uninfected but at high risk. Existing HIV risk prediction algorithms rely on manual construction of features and are limited in their application across diverse electronic health record systems. Furthermore, the accuracy of these models in predicting HIV in females has thus far been limited. We devised a pipeline for automatic construction of prediction models based on automatic feature engineering to predict HIV risk and tested our pipeline on a local electronic health records system and a national claims data. We also compared the performance of general models to female-specific models. Our models obtain similarly good performance on both health record datasets despite difference in represented populations and data availability (AUC = 0.87). Furthermore, our general models obtain good performance on females but are also improved by constructing female-specific models (AUC between 0.81 and 0.86 across datasets). We demonstrated that flexible construction of prediction models performs well on HIV risk prediction across diverse health records systems and perform as well in predicting HIV risk in females, making deployment of such models into existing health care systems tangible.

A Decoding Method Using Riemannian Local Linear Feature Construction for a Lower-Limb Motor Imagery Brain–Computer Interface System

Recently, motor imagery brain–computer interfaces (BCIs) have been developed for use in motor function assistance and rehabilitation engineering. In particular, lower-limb motor imagery BCI systems are receiving increasing attention in the field of motor rehabilitation, because these systems could accurately and rapidly identify a patient’s lower-limb movement intention, which could improve the practicability of the motor rehabilitation. In this study, a novel lower-limb BCI system combining visual stimulation, auditory stimulation, functional electrical stimulation, and proprioceptive stimulation was designed to assist patients in lower-limb rehabilitation training. In addition, the Riemannian local linear feature construction (RLLFC) algorithm is proposed to improve the performance of decoding by using unsupervised basis learning and representation weight calculation in the motor imagery BCI system. Three in-house experiment were performed to demonstrate the effectiveness of the proposed system in comparison with other state-of-the-art methods. The experimental results indicate that the proposed system can learn low-dimensional features and correctly characterize the relationship between the testing trial and its k-nearest neighbors.

Multi-Objective artificial bee colony optimized hybrid deep belief network and XGBoost algorithm for heart disease prediction.

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

The Impact of Anisotropic Properties of the Rail Track Superstructure on the Parameters of Dynamic Settlement of Bridge Approaches

Numerous artificial structures, bridges, overpasses, tubes, channels, chutes are operated on railway networks. Artificial structures are considered to have a longer life cycle and thus will need during it overhaul and reconstruction with the use of modern technology. Currently, there is an increase in axial load and traffic speed, the makes even more relevant the solution of tasks referring to dynamic impact of trains on artificial structures and superstructure. See the features of construction and maintenance of rail track sections at the approaches to bridges, the respective problems merit special consideration.The research which results are presented in the article was dedicated to the impact of the anisotropy of the track superstructure on the parameters of the dynamic settlement of sections of transitional stiffness located at the approaches to bridges and built using bottomless box culverts.The research proposed to model the railway track within the transitional section as a set of flat elements of constant thickness, while each of them is regarded as a anisotropic plate supported by deformable foundation with its own deformability rates. By setting boundary conditions for individual fragments of the track structure and varying the anisotropy coefficients that determine the ratio of mechanical characteristics in different directions of anisotropy, it is possible to select necessary rail track stiffness rates for a section at bridge approaches, that paves the way to further research on structural features of designing and arrangement of those structures.

Construction of iron metabolism-related prognostic features of gastric cancer based on RNA sequencing and TCGA database.

Researches have manifested that the disorder of iron metabolism is participated in Gastric cancer (GC), but whether iron metabolism-relevant genes (IMRGs) is related to the survival outcome of GC remain unknown. Eleven tumor as well as nine adjacent normal tissues from GC patients were underwent mRNA sequencing, and the The Cancer Genome Atlas Stomach Cancer (TCGA-STAD) datasets were acquired from the TCGA database. Cox analyses and least absolute shrinkage and selection operator (LASSO) regression were applied to build a IMRGs signature. The relationship between signature genes and the infiltration profiling of 24 immune cells were investigated using single-sample GSEA (ssGSEA). Meanwhile, the potential biological significance, genes that act synergistically with signature genes, and the upstream regulatory targets were predicted. Finally, the abundance of the signature genes were measured via the quantitative real-time PCR (qRT-PCR). A IMRGs signature was constructed according to the expression and corresponding coefficient of DOHH, P4HA3 and MMP1 (The Schoenfeld individual test showed risk score was not significant with P values = 0.83). The prognostic outcome of patients in the high-risk group was terrible (p < 0.05). Receiver operating characteristic (ROC) curves confirmed that the IMRGs signature presented good efficiency for predicting GC prognosis (AUC > 0.6). The nomogram was performed well for clinical utilize (C-index = 0.60), and the MMP1 expression significantly increased in the cohorts at age > 60 and Stage II-IV (p < 0.05). The positive correlation of P4HA3 and MMP1 expression as well as the negative correlation of DOHH expression with risk score (p < 0.0001) and worse prognosis (p < 0.05) were detected as well. Furthermore, 11 differential immune cells were associated with these signature genes (most p < 0.01). Finally, qRT-PCR revealed that the abundance of DOHH, P4HA3 and MMP1 were high in tumor cases, indicating the complex mechanism between the high expression of DOHH as a protective factor and the high expression of P4HA3 and MMP1 as the risk factors in the development of GC. An iron metabolism-related signature was constructed and has significant values for foretelling the OS of GC.