Research Institute of Technology (Russia)

Three-dimensional printing (3DP) has gained increasing attention in construction as a means of addressing labor shortages and improving efficiency. Various studies have investigated fiber-reinforced mortars for 3DP. However, only a few studies have examined mixture design strategies aimed at controlling early structural build-up, and the relationships between early structural build-up, printability, and interlayer stability remain largely unexplored. This study aimed to establish a practical method for evaluating the static yield stress and early buildability of 3DP mortars under construction-site conditions. Vane shear and 15-stroke flow tests were conducted to assess the static and dynamic behavior of mortars incorporating polyvinyl alcohol (PVA) fibers, and their compressive and flexural strengths were also evaluated. According to the results, the vane shear test sensitively captured the rheological changes associated with variations in fiber content and superplasticizer dosage. The addition of PVA fibers increased the maximum shear stress of the mortar, resulting in atypical static yield stress development compared to fiber-free mortars. While the 15-stroke flow test further elucidated flowability, the vane shear test revealed a stronger correlation between mechanical properties and overall buildability. Thus, vane shear testing can be reliably used to assess early-age structural build-up and interlayer stability in 3DP mortars for optimizing print performance.

The problem of improving the corrosion conditions during operation of a nuclear power facility (NPF) is directly connected with increasing the corrosion resistance of structural steels to general (uniform) corrosion, chemical transformation and mass transfer of corrosion products in water coolant in conducting different water chemistry (WC) versions. The article gives a description of the generation processes and chemical transformations of the initial ionic forms of structural steel corrosion products at the initial stage of the general corrosion process followed by the generation of the precursors of solid phase oxide-hydroxide particles (sludge and crud) dispersed in coolant and generation of final solid phase forms of corrosion products such as suspensions, deposits, and corrosion oxide films in the nuclear power facility circuits under the conditions of reduction and oxidative water chemistries. The article proposes a physicochemical model of mass exchange and mass transfer of corrosion products in the steel–water coolant system with taking into account the chemical transformations of corrosion product ionic forms up to the generation of solid phase products in water coolant and on the corroding steel surface, on which a two-layer (with topotaxic and epitaxic layers) oxide film of a local origin is produced. On this film, dense corrosion product deposits from the coolant are formed along with loose (dissipative, weakly fixed) corrosion product deposits that are in equilibrium between the circuit surfaces and coolant. The chemical composition of all product existence forms is determined by the compounds of iron with admixtures of structural steel alloying elements, and the radionuclide composition is determined by the activation products of reactor materials (58Co, 60Co, 54Mn, 59Fe, and 51Cr) and other, more short-lived radionuclides. The proposed body of mathematics can be used for determining the numerical values of the indicators characterizing different corrosion process stages – from metal ionization to generation of final corrosion products on the surfaces and in the coolant of the nuclear power facility circuits under the conditions of different water chemistries.

Streaky mark defects on hot dip galvannealed Ti-IF sheets were analyzed using SEM, EBSD, FIB, and TEM. Results revealed that bright streak had slightly larger skin-passed area in correspondence to more uniform GA coating. After the removal of GA coating, more coarse grains on the substrate surface, primarily elongated grains along the rolling direction with the normal direction//<100>, were found in the bright streak area. These coarse grains accelerate the Fe-Zn diffusion reaction during galvannealing and result in a flatter coating surface and an even coating thickness. The source of these coarse grains at the uppermost surface could be traced back to the hot rolling.

<title>Abstract</title> The importance of intelligent vehicle recognition and counting has significantly increased in the context of contemporary highway administration. However, the precision of vehicle counts is directly challenged by the fact that cars vary in size, making it difficult to identify them accurately. This paper introduces a technique for identifying and counting vehicles using vision to tackle this problem. DeepSORT model was used, which is based on the You Only Look Once (YOLO v8) model, to recognize and track vehicles in real time within video sequences. By combining the sophisticated detection capabilities of YOLO v8 with the skilful tracking algorithms of DeepSORT, the suggested method aims to improve accuracy by taking into account the intricacies of various vehicle sizes and movement patterns. The system gives highway management real-time insights into vehicle traffic, enabling them to well-informed information to improve traffic control tactics. Furthermore, the system's capacity to smoothly adjust to intricate traffic situations is enhanced by the combination of YOLO v8 and DeepSORT.

Managing personal finances has become increasingly challenging due to complex income streams, spending behaviors, and dynamic economic conditions. Traditional financial tools offer basic budgeting features but lack the intelligence to provide personalized insights or predict future financial trends accurately. These systems rely on static rules and fixed categorizations, offering limited adaptability and no learning from user history. Consequently, they fail to capture the intricate patterns necessary for effective long-term planning. This project addresses these limitations by introducing an AI-based Personal Finance Analyzer that predicts future savings and expenditure patterns using advanced machine learning models. The system incorporates three core algorithms: Deep Neural Networks (DNN), Gaussian Naive Bayes (GNB), and Gated Recurrent Units (GRU). Each model contributes uniquely—DNN captures complex non-linear relationships, GNB offers probabilistic reasoning with high interpretability, and GRU effectively models time-dependent financial sequences. The system is built with a user-friendly Tkinter GUI and performs comprehensive preprocessing and exploration data analysis before model training. By leveraging diverse machine learning paradigms, the tool provides accurate, adaptable, and insightful predictions. Users can input basic financial information such as income, expenses, and savings, and receive data-driven estimates for future outcomes. Evaluation metrics like MAE, MSE, RMSE, and R² demonstrate the system’s strong performance across models, with GRU excelling in sequential prediction. The proposed system not only enhances personal finance forecasting but also encourages proactive financial planning. It offers a scalable and intelligent solution that adapts user data, making it a significant improvement over conventional method.

Noise reduction plays a vital role in enhancing audio clarity across telecommunications and broadcasting systems, where maintaining speech intelligibility under diverse noise conditions is essential.Traditional approaches like spectral subtraction rely on estimating and removing stationary noise components from the signal spectrum; while effective in stable environments, they often introduce audible artifacts and perform poorly in dynamic or real-time scenarios involving non-stationary noise.To address these challenges, this work proposes a novel Canonical Correlation Deep Learning (CCDL) method for advanced noise reduction that dynamically adapts to varying acoustic environments.The CCDL approach leverages deep learning capabilities alongside canonical correlation analysis to extract meaningful relationships between noisy and clean signals, enabling intelligent parameter adjustment in real time.Its Canonical Correlation Bandpass (CCB) configuration focuses on enhancing critical speech frequency bands while suppressing irrelevant noise, thereby preserving the naturalness and integrity of the audio.Designed for real-time adaptability, CCDL minimizes latency and artifacts, making it highly suitable for applications such as mobile communications, live broadcasts, and voice-based interfaces.Comprehensive evaluations using performance metrics such as Signal-to-Noise Ratio (SNR), Perceptual Evaluation of Speech Quality (PESQ), and Log-Spectral Distance (LSD) demonstrate the proposed method's superiority over conventional techniques, highlighting its effectiveness in delivering clearer, more intelligible audio.CCDL marks a significant advancement in adaptive noise reduction, offering a robust, real-time solution to overcome the limitations of traditional systems in modern communication networks.

The global delivery industry is grappling with severe supply chain inefficiencies, contributing to the annual waste of over 1.3 billion tons of food-approximately 30% of total global production-leading to economic losses of around $940 billion.A significant portion of this issue stems from the fact that 40-50% of inventory-related decisions still rely on manual forecasting, resulting in frequent overstocking or understocking, increased spoilage, and logistical disruptions.Traditional forecasting methods are often static and incapable of adapting to dynamic environmental and market fluctuations, making them unsuitable for real-time driver demand prediction.To address these shortcomings, this research introduces a robust regression-based time series forecasting framework designed to optimize delivery supply chain operations.Utilizing a publicly available driver demand dataset that integrates temporal sales data, weather conditions, and promotional events, the data is preprocessed thoroughly through normalization, imputation of missing values, creation of lag-based features, and outlier handling to ensure high-quality model inputs.Initially, the Light Gradient Boosting Machine (LGBM) Regressor serves as a baseline model due to its strong predictive performance.However, to enhance forecasting precision, a Nonlinear Autoregressive model with Exogenous Inputs (NARX) Regressor is proposed.This model incorporates both historical internal demand data and external variables such as holidays and weather conditions, enabling dynamic and multivariate forecasting.NARX is particularly effective at capturing nonlinear patterns and supports recursive forecasting across multiple time steps.Comparative performance analysis shows that while the LGBM Regressor performs well (MSE: 7.06e-05, MAE: 0.00449, RMSE: 0.00840, R: 0.948), the NARX Regressor significantly improves prediction accuracy, achieving MSE: 6.32e-05, MAE: 0.00350, RMSE: 0.00795, and an R score exceeding 1.00.These results highlight the superior generalization and nearperfect predictive capabilities of the NARX model, demonstrating its potential to substantially reduce forecasting errors, minimize food waste, and enhance decision-making in supply chain management.

This study provides a technical analysis of Land Use and Land Cover (LULC) changes and their impact on stormwater runoff in Dahisar, Mumbai, India, from 2003 to 2023. Utilizing high-resolution Landsat satellite imagery processed through Remote Sensing (RS) and Geographic Information System (GIS) techniques, significant urban expansion is observed, with built-up areas increasing from 40.40% to 58.60%. Despite these changes, there was no significant increase in water bodies. The study identifies a peak stormwater discharge of 1433.44 cubic meters per second, attributed to the rise in impervious surfaces caused by urbanization. This research offers crucial insights into the challenges of rapid urbanization in coastal megacities like Mumbai, where climate change intensifies the risks associated with extreme weather events. The study highlights the urgent need for adaptive urban planning that incorporates sustainable practices, such as green infrastructure and resilient water management systems, to effectively mitigate flood risks and manage stormwater runoff. These findings are particularly significant for policymakers and urban planners, stressing the importance of addressing environmental and hydrological challenges in rapidly urbanizing coastal regions.

The class, characteristics, and possible applications of nanotechnology in medicine are covered in the article below. In order to determine its significance and guide its evolution, it highlights the necessity of collaboration between scientists, governments, civil society sections, and the general public. As a result of their ability to readily penetrate human cells at the nanoscale, makes them valuable for cell target therapy, efficient drug delivery, and disease diagnostics, nanotechnology and medicine are significant disciplines. Additionally, pharmaceuticals are protected from degradation by the shield-like characteristics of nanoparticles. The intention of this study review is to present the next generation of medical scientists with a compilation of well-known attributes of nanomedicine and to highlight established techniques for medical research. The application of nanotechnology in sensitive medical procedures has been the subject of extensive research that have incorporated the most recent data from reliable sources. The possible applications of medically relevant nanotechnology in targeted drug delivery, gene therapy, cancer treatment, and inherited disorder treatment is looked at in this review article. It draws attention to the fact that additional effort is necessary to realize the great potential of nanotechnology in the healthcare sector. With an emphasis on nanomaterials and nanomedicines, this chapter explores the implementation of nanotechnology in clinical research. Their application in drug delivery, optometry, tissue engineering, immunology, neurological treatment, gene therapy, chemotherapy, diagnostic methods, surgical dentistry, wound care, visualization, and molecular repair are all investigated. The multilayer taxonomies of nanoparticles, biosensors, nanotechnology, and nanozymes are discussed in this article, with an emphasis on the internet of nano things (IoBNT) and its possible applications in medicine. It explores the potential to strengthen IoBNT's compatibility with the human body as well as the challenges to its successful deployment.

This study proposed a new structural system for mid-rise wooden building to enhances the strength and ductility using a new steel rod insertion and adhesive bonding joint construction and CLT panel shear walls. To verify the effectiveness of proposed structural system, cyclic loading tests of full scaled specimens were conducted and also conducted lateral load-carrying capacity calculation and time history response analysis. This paper firstly presents the relationship of the strength and ductility of structural system, and then studies the seismic resistance design of the new structural system, introducing an actual project.