Analysis Module Research Articles

Construction of an Intelligent Medical Monitoring System Network for Exercise Rehabilitation Based on the Internet of Things

Exercise rehabilitation refers to a rehabilitation method that restores or improves human motor function through scientific, systematic, and regular exercise training, and it has a very wide range of applications. This paper explored the construction of an intelligent medical monitoring system network for sports rehabilitation based on the Internet of Things (IoT) technology. The system consists of a wireless sensor acquisition module for collecting sports rehabilitation data, a data storage and analysis module for cloud management and deep learning analysis, and a medical monitoring module for remote supervision and patient interaction. The system aimed to achieve personalized exercise rehabilitation plans through real-time monitoring and data collection of patients, and provide remote medical monitoring services to improve the rehabilitation effect and quality of life of patients. The system architecture adopted the IoT technology and cloud storage technology, and combined the deep learning convolutional neural network (CNN) model to achieve remote monitoring and data analysis. The monitoring center of the system can monitor patients’ physiological indicators, exercise status, and rehabilitation effects in real time through IoT wireless sensors, and upload the data to the cloud platform for storage and analysis. Doctors and patients can access data through mobile phones or computers and communicate online. The data showed that when measuring a patient’s heart rate using wireless sensors, the measurement accuracy mostly exceeded 99.8%, and some samples had detection accuracy up to 100%. In addition, traditional sensors can detect heart rates up to 5.1[Formula: see text]s, while wireless sensors can detect heart rates up to 1.28[Formula: see text]s. The CNN had a maximum recall rate of 99.51%, a precision of 99.68%, and an accuracy rate of 99.8% in the classification of psychological data of rehabilitation patients. The experimental results indicated that the system can achieve comprehensive monitoring and effective management of the patient’s rehabilitation process, and its operation is stable and has good practical value.

Вigital transformation of organizational and management processes in the field of physical culture and sports: challenges and opportunities

The article examines the challenges and opportunities of digital transformation in organizational and managerial processes within the field of physical culture and sports. The importance of implementing innovative managerial and organizational practices based on the use of digital technologies and the necessity of transitioning to digital interaction between the Ministry of Youth and Sports of Ukraine and entities in the field of physical culture and sports are substantiated. Research methods: generalization of data from literary sources and information resources on the Internet, content analysis, systematization, modeling. Research results. Changing socio-economic conditions, increasing competition, public expectations regarding the quality of services in the field of physical culture and sports, integration of digital technologies, increasing requirements for transparency and accessibility of information are key prerequisites for developing a model for improving the organizational and managerial principles of the development of the field of physical culture and sports in the context of digital transformation. The foundation of the model is an information and analytical system, which consists of functional subsystems: an electronic calendar of sports competitions and events, an electronic office for organizations and participants in the sports process, an administrative and analytical module for management bodies, an electronic register of sports organizations, an electronic register of sports facilities, an electronic personnel register. The information and analytical system is an integrated platform for data processing and administration of state management processes. Conclusions. The implementation of the tasks of the proposed model will contribute to increasing the efficiency of organizational and management processes, ensure effective interaction and joint functioning of subjects in the field of physical culture and sports in a virtual environment.

Mode Selection for Device to Device Communication in Dynamic Network: A Statistical and Deep Learning Method

A major challenge in device to device (D2D) communications is determining the appropriate communication modes for each potential D2D pair. In dynamic networks, the continuous movement of devices increases the complexity of channel state modeling, which makes it difficult to predict the quality of network service and select appropriate switching thresholds, ultimately affecting the accuracy of D2D mode selection. This paper proposes a novel D2D mode selection method, which integrates deep learning with statistical learning and includes three modules: signal-to-interference-plus-noise-ratio (SINR) prediction, error analysis, and threshold selection. Specifically, the SINR prediction module employs the gated recurrent unit (GRU) method to predict future SINR values; the error analysis module applies a non-parametric method to construct a probability density function of the prediction error. The combination of these two modules provides a significant improvement in prediction value accuracy. Additionally, in the threshold selection module, two constraints are innovatively introduced to mitigate the problem of frequent switching: average reliability (AR) and probably correct reliability (PCR). Simulation results demonstrate that the proposed method achieves higher system throughput, longer D2D mode residence time, and a lower mode switching frequency compared to other methods.

Proactive perceptive road: a digital twin-driven intelligent road infrastructure towards proactive road state perception and deduction

Abstract The emergence of the digital twin (DT) concept provides a new development direction for the digital transformation and upgrading of road health monitoring and maintenance management. Current road digitization technologies primarily focus on the integration and management of easily perceived and measurable asset and maintenance data. However, they struggle to effectively connect the physical state of the road with various computational and analytical modules, making it difficult to sense and integrate complex, hard-to-measure implicit state information and resulting in an inability to support more comprehensive road condition monitoring and precise maintenance decision-making. This paper introduces the concept of proactive perceptive road (PPR), an intelligent road infrastructure driven by DT technology, capable of actively sensing and deducing explicit and implicit road state information. PPR comprises physical space (air, surface, and internal subspaces) entities and DT models and sensing engines in the virtual space. The paper outlines the application logic and core capabilities of PPR and, based on this, proposes an example architecture for its implementation. Two application case studies are presented—one for monitoring construction quality and another for sensing road structure responses—demonstrating the value and potential of PPR in real-time sensing of implicit road state information. The challenges that remain in practical application are also discussed including perceptual, data, and simulation techniques. The proposed concept of PPR represents the next phase of development, offering insights and methodologies for sensing and extracting complex, implicit structural performance data and road health conditions. The presented cases can serve as references for applying DT in the road sector and guide the development of customized solutions for specific scenarios.

Frequency-bin-encoded entanglement-based quantum key distribution in a reconfigurable frequency-multiplexed network

Large-scale quantum networks require dynamic and resource-efficient solutions to reduce system complexity with maintained security and performance to support growing number of users over large distances. Current encoding schemes including time-bin, polarization, and orbital angular momentum, suffer from the lack of reconfigurability and thus scalability issues. Here, we demonstrate the first-time implementation of frequency-bin-encoded entanglement-based quantum key distribution and a reconfigurable distribution of entanglement using frequency-bin encoding. Specifically, we demonstrate a novel scalable frequency-bin basis analyzer module that allows for a passive random basis selection as a crucial step in quantum protocols, and importantly equips each user with a single detector rather than four detectors. This minimizes massively the resource overhead, reduces the dark count contribution, vulnerability to detector side-channel attacks, and the detector imbalance, hence providing an enhanced security. Our approach offers an adaptive frequency-multiplexing capability to increase the number of channels without hardware overhead, enabling increased secret key rate and reconfigurable multi-user operations. In perspective, our approach enables dynamic resource-minimized quantum key distribution among multiple users across diverse network topologies, and facilitates scalability to large-scale quantum networks.

Performance Evaluation and Analysis of a Vehicle

The rapid advancements in technology and the growing demand for personalized solutions have revolutionized the automobile industry. This report introduces a Vehicle Performance and Analysis Application, designed to streamline the process of finding vehicle details based on user-defined parameters. The application provides comprehensive insights into vehicle specifications, performance metrics, and associated company information, enabling users to make well-informed decisions. Additionally, it offers manufacturers and dealers valuable analytics on user preferences and frequently searched criteria. The application features an intuitive search mechanism that allows users to filter vehicles based on parameters such as fuel efficiency, engine power, seating capacity, price range, and more. It also includes a comparison tool to evaluate multiple vehicles simultaneously. For manufacturers and dealers, the app provides real-time data on market trends and user preferences, helping them tailor their offerings and marketing strategies.By integrating advanced technologies such as dynamic database systems and analytical modules, this application enhances the user experience and facilitates data-driven decision-making for companies. The Vehicle Performance and Analysis App thus bridges the gap between consumer demands and market offerings, fostering transparency, efficiency, and innovation in the automobile industry.

Integrated bioinformatics analysis identified cuproptosis-related hub gene Mpeg1 as potential biomarker in spinal cord injury

Spinal cord injury (SCI) is a profound ailment lacking a well-defined molecular mechanism and effective treatments. Cuproptosis, identified as a recently discovered cell death pathway, exhibits diverse roles in various cancers. Nevertheless, its involvement in SCI is yet to be elucidated. Firstly, the RNA sequencing data of 1, 3, 7 dpi SCI samples were collected from GEO database. We performed differential expression analysis on these samples with varying cuproptosis-related scores calculating by ssGSEA. Subsequently, we conducted enrichment analyses with KEGG, GO, and GSEA. Simultaneously, we executed WGCNA analysis using cuproptosis-related scores, selecting the most relevant module for enrichment analysis. Hub genes were identified at the intersection of PPI analysis results from two modules and cuproptosis-related DEGs. Additionally, relying on the immune infiltration landscape associated with cuproptosis, we carried out immune cell correlation analysis on hub genes. Finally, to corroborate our earlier findings, we utilized single-cell RNA-seq analysis and in vitro experimental validation. Based on ssGSEA, differential expression analysis and WGCNA analysis, we identified two modules that were highly relevant to cell division and immune processes, respectively. From these modules, we identified two hub genes, Cd48 and Mpeg1, which exhibited a strong positive correlation (R = 0.92) and shared similar pathways. Furthermore, we observed a positive correlation between M2 macrophages and Cd48/Mpeg1. To validate our findings, we performed external cohort validation using a single-cell RNA sequencing dataset. The results confirmed that Mpeg1 was highly expressed in microglia (macrophages in center nervous system) following spinal cord injury. Additionally, we conducted in vitro experiments to further validate the molecular functions of Mpeg1 in SCI. In summary, targeting Mpeg1, as well as cuproptosis and immune cell infiltration, holds promise as a potential strategy for reducing spinal cord tissue damage and promoting recovery after SCI. These findings provide valuable insights for future therapeutic interventions.

Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem

PurposeThis paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature.Design/methodology/approachThis research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations.FindingsThe study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service.Research limitations/implicationsThe findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties.Practical implicationsThe practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly.Social implicationsThe study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains.Originality/valueUnlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.

Sentiment Analysis for Movie Database Management System Using Naive Bayes Algorithm

Abstract: According to F. Furtado and A. Singh (2021), movie recommendation systems aim to assist viewers by suggesting movies to watch, thereby avoiding the time-consuming and complex process of selecting from a vast library of movies, which can number in the thousands or even millions. This article seeks to reduce human effort by recommending movies based on user interests. To address these challenges, we propose a technique that combines content-based and collaborative approaches. This method produces more precise results than systems that rely solely on content-based methods. Content-based recommendation systems are limited to individual preferences and do not recommend items outside the user’s immediate interests, restricting exploration. To overcome these limitations, we developed a system that addresses these concerns. The proposed movie recommendation system uses Cosine Similarity to suggest movies like the one chosen by the user. While existing algorithms generate recommendations, they often fail to answer the critical question, “Is this a film worth watching?” To enhance the overall experience, our system incorporates sentiment analysis of selected movie reviews using machine learning techniques. This study employs two supervised learning approaches, Naïve Bayes (NB) and Support Vector Machine (SVM), to improve accuracy and efficacy. A comparison of the two methods reveals that SVM achieved an accuracy score of 98.63%, while NB scored 97.33%, as reported by N. Pavitha et al. (2022). The project integrates a sentiment analysis module into the movie management system to enhance decision-making and user experience. The Naïve Bayes approach is used to classify and evaluate user reviews and feedback into positive, negative, and neutral categories. This analysis provides insights into audience preferences, overall satisfaction, and movie reception. The system utilizes this information to refine movie recommendations, analyze trends, and enhance content selection, all of which contribute to a more personalized user experience. The system is evaluated using the ISO 25010 framework, focusing on functionality, reliability, usability, effectiveness, and robustness. Findings show that respondents actively participated in the review process across multiple domains. Key results indicate high satisfaction levels in terms of ease of use, interface design, and system performance. Feedback also highlighted areas for improvement, such as optimizing loading times and enhancing specific features for a smoother user experience. Overall, the findings reflect positive engagement with the system, demonstrating its effectiveness in meeting user needs while identifying opportunities for future enhancements.

Leveraging deep learning for robust EEG analysis in mental health monitoring.

Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal information encoded in EEG signals, which are indicative of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. Despite the potential of these approaches, they often fall short in capturing the intricate spatiotemporal relationships within EEG data, leading to lower classification accuracy and poor adaptability across various populations and mental health scenarios. To overcome these limitations, we introduce the EEG Mind-Transformer, an innovative deep learning architecture composed of a Dynamic Temporal Graph Attention Mechanism (DT-GAM), a Hierarchical Graph Representation and Analysis (HGRA) module, and a Spatial-Temporal Fusion Module (STFM). The DT-GAM is designed to dynamically extract temporal dependencies within EEG data, while the HGRA models the brain's hierarchical structure to capture both localized and global interactions among different brain regions. The STFM synthesizes spatial and temporal elements, generating a comprehensive representation of EEG signals. Our empirical results confirm that the EEG Mind-Transformer significantly surpasses conventional approaches, achieving an accuracy of 92.5%, a recall of 91.3%, an F1-score of 90.8%, and an AUC of 94.2% across several datasets. These findings underline the model's robustness and its generalizability to diverse mental health conditions. Moreover, the EEG Mind-Transformer not only pushes the boundaries of state-of-the-art EEG-based mental health monitoring but also offers meaningful insights into the underlying brain functions associated with mental disorders, solidifying its value for both research and clinical settings.

Aesthetic evaluation underpinning brand love relationship development: an activation likelihood estimation meta-analysis and multivariate analysis.

Brand love is a crucial construct in marketing strategies. Building brand love can generate stable profits for enterprises. Although the marketing literature points out that aesthetic factors contribute to establishing the relationship as a trigger, to what stage of the relationship do they influence the minds of consumers? The present study attempts to reveal the involvement of aesthetic experiences in brand love developmental dynamics. Using the activation likelihood estimation method, we address this issue by assessing overlapping brain regions between brand love at each stage and aesthetic experiences. We adopted three major meta-analytic decoding analysis modules to objectively interpret these brain regions, namely, Neurosynth, NeuroQuery, and the Behavioral Analysis plugin (BrainMap platform). Moreover, we performed a correspondence analysis to identify relationships of mental processes between aesthetic experiences and brand love in each developmental stage of brand love. Our results suggest that the same neural mechanism and mental processes may be underlaid between brand love and aesthetic experiences across all stages. Although reward- and emotion-related mental processes are commonly underlaid between brand love at the first-half stage and aesthetic experiences, exteroceptive and interoceptive signals may drive those mental processes between the early and migration stages of brand love, respectively, and aesthetic experiences. Overlapping regions of brand love at the stable stage and aesthetic experiences may be associated with semantic processing. We demonstrate that several brain regions overlapped between brand love and aesthetic experiences across all the brand love developmental stages. Therefore, aesthetic experiences might be associated with the mental processes of brand love development through all the developmental stages. Our results suggest that aesthetic experiences are essential elements for developing brand-love relationships. Our findings indicate that marketers should recognize that aesthetic experiences play a crucial role in building a bond between brands and consumers, not only when choosing brands. Thus, marketers need to design visual strategies from the view of nurturing brand-love relationships.

Natural Compounds in Clot Waveform Analysis and D-Dimer Modulation: Implications for COVID-19 Diagnosis and Prognosis

Natural Compounds in Clot Waveform Analysis and D-Dimer Modulation: Implications for COVID-19 Diagnosis and Prognosis

자모비(jamovi) 통계 프로그램 환경에서 잠재전이분석 통계 모듈 개발 및 적용

This study presents the development and implementation of a novel statistical module for latent transition analysis within the jamovi software environment. jamovi is an open-source platform designed to make statistical analysis accessible to users with varying levels of expertise. This module integrates latent transition analysis, a complex method used for examining changes in latent variables over time. This integration significantly enhances the software's analytical capabilities. The primary objective of this development is to democratize access to advanced statistical methods, thereby facilitating their use in educational settings and diverse research fields such as psychology, sociology, and education. This paper discusses the technical aspects of the module's integration, evaluates its usability through user feedback, and highlights its potential impact on both teaching and research. The findings suggest that the module not only simplifies the execution of sophisticated analyses but also encourages broader adoption of latent transition analysis by reducing technical barriers. Consequently, this work contributes to the ongoing efforts to make advanced statistical tools more accessible and user-friendly, promoting their integration into everyday research practices.

Study of a Grid-Based Urban Drainage System Analysis Model

As the focus shifts from responding to climate change to adapting to the climate crisis, the need for detailed water adaptation strategies is increasing. Local governments are required to implement measures to address urban flooding, and models such as XP-SWMM are being utilized. However, these models have limitations in terms of precision owing to the resolution of the available data. To address this issue, this study developed a grid-based urban flood analysis model capable of predicting urban flooding using high-resolution data based on a detailed grid of less than 10 m, adequately considering watershed characteristics. It integrates surface runoff analysis using the Clark Unit Hydrograph method and the D8 algorithm, conduit transport analysis linked with EPA-SWMM, and flood analysis modules utilizing the WCA2D method. Unlike existing urban flood analysis models, which require complex parameters and specialized expertise, this model minimizes the number of input parameters to enhance accessibility for practitioners while maintaining reliability in the analysis results. A grid-based model based on high-resolution spatial data can be employed by local governments to establish practical guidelines. It effectively identifies vulnerable flood areas within cities and supports the development of specific flood response strategies, thereby contributing significantly to urban flood management and resilience planning.

HemaCisDB: An Interactive Database for Analyzing Cis-Regulatory Elements Across Hematopoietic Malignancies.

Noncoding cis-regulatory elements (CREs), such as transcriptional enhancers, are key regulators of gene expression programs. Accessible chromatin and H3K27ac are well-recognized markers for CREs associated with their biological function. Deregulation of CREs is commonly found in hematopoietic malignancies yet the extent to which CRE dysfunction contributes to pathophysiology remains incompletely understood. Here, we developed HemaCisDB, an interactive, comprehensive, and centralized online resource for CRE characterization across hematopoietic malignancies, serving as a useful resource for investigating the pathological roles of CREs in blood disorders. Currently, we collected 922 ATAC-seq, 190 DNase-seq, and 531 H3K27ac ChIP-seq datasets from patient samples and cell lines across different myeloid and lymphoid neoplasms. HemaCisDB provides comprehensive quality control metrics to assess ATAC-seq, DNase-seq, and H3K27ac ChIP-seq data quality. The analytic modules in HemaCisDB include transcription factor (TF) footprinting inference, super-enhancer identification, and core transcriptional regulatory circuitry analysis. Moreover, HemaCisDB also enables the study of TF binding dynamics by comparing TF footprints across different disease types or conditions via web-based interactive analysis. Together, HemaCisDB provides an interactive platform for CRE characterization to facilitate mechanistic studies of transcriptional regulation in hematopoietic malignancies. HemaCisDB is available at https://hemacisdb.chinablood.com.cn/.

საქართველოს ელექტროენერგეტიკული სისტემის დინამიკური მდგრადობის ანალიზი ბატარეის ელექტროენერგიის დამაგროვებელ სისტემებში ინტეგრირებული ორმხრივი ნახევარგამტარული გარდამსახების და ენერგიის მართვის სისტემის გამოყენებით

In practice, a directional converter integrated into the battery energy storage systems - an inverter, which only supplies energy to the network for frequency and voltage regulation when necessary, and cannot receive energy from the network. Increasing the reliability of the stability of the electric power system will be especially effective when in the battery energy storage systems will be used a Bi-directional AC/DC Converter, which in turn includes a rectifier and an inverter, which will supply the grid through the energy management system and also consume energy from it if necessary through the upgraded SCADA/EMS. The high-level architecture of the Supervisory Control and Data Acquisition systems/Energy Management System includes a number of capabilities, including a dynamic stability analysis module, also "High numerical aperture extreme ultraviolet lithography - lithography of advanced semiconductor materials" - "High-NA-EUV lithography" provides opportunities for manufacturing such microcircuits, each nanocell includes a nanotransistor and a nanocapacitor, and their chipset size is 1 nanometer. The mentioned rate of scientific development provides an opportunity to create such technologies that will give wide opportunities to modern the battery energy storage systems to fully respond to the requirements that arise during the continuous operational management of the country's electric power system.

Analysis of differential weathering patterns and causes on the top of hollow enemy towers’ facades of the Great Wall – a case study of section in Haigang District, Qinhuangdao City

ABSTRACT The hollow enemy towers are iconic structures of the Great Wall. During a survey of the Ming Great Wall in Haigang District of Qinhuangdao, researchers observed differential weathering phenomena in the facade tops of 21 out of 51 well-preserved hollow enemy towers. Initially, high-precision 3D data of enemy towers was acquired using close-range photogrammetry from drones. The 3D surface metrology analysis software Mountains was introduced, in which the particle analysis module can use false-color images to calibrate and measure depressions on the 3D model surface, thereby enabling the identification and quantitative analysis of weathering and erosion areas on the top of the enemy towers’ facades. The study revealed that the upper regions of facades with the drainage troughs exhibited more pronounced weathering erosion compared to the areas without drainage troughs. Upon conducting an analysis of the structural integrity of the top drainage components and evaluating the slope and surface of the drainage system, it was determined that the primary water collection zones were located near the drainage troughs at both ends of enemy tower tops. Differential weathering at the top of enemy towers’ facades is primarily influenced by the varying amount of water accumulation, which serves as the decisive factor.

A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection

In today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs.

Development of an information and calculation complex for assessing the radioecological situation on radioactively contaminated agricultural land

To organize agriculture on radioactively contaminated agricultural lands, taking into account the levels of contamination and landscape features, an information and calculation complex for assessing the radioecological situation is tested. In the NextGIS Collector mobile application for field operators, a pilot version of the working environment has been created and requirements for the information content of various blocks of the complex have been developed. In the NextGIS QGIS software environment, a GIS project of the test farm of the Tula Research Institute of Agriculture has been created, in which calculation and analytical modules will be created. This project contains attribute data bases, as well as electronic maps of various thematic focus created on their basis, which will be used in the future for a comprehensive analysis of the radioecological situation. Also, the mathematical foundations of calculation and analytical modules have been developed, which are planned to be implemented in the form of built-in software blocks. The presented materials are supposed to be used for practical testing on the territory of a test agricultural enterprise and the subsequent development of recommendations for the design of targeted protective and rehabilitation measures of landscape-ecological focus for different types of agrolandscapes in radioactively contaminated areas.

A Climate Adaptation Asset Risk Management Approach for Resilient Roadway Infrastructure

As climate change intensifies, roadway infrastructure is increasingly at risk from extreme weather events including floods, hurricanes, and wildfires. This paper presents a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. Central to this approach are an Asset Inventory Database and a Risk Registry Database, supported by a Common Reference Location System (GIS). These components are the foundation for analytical modules to assess vulnerability and resilience based on exposure, sensitivity, and adaptive capacity. The approach includes an actionable framework to support a proactive data-driven performance-based management process for prioritizing investments. The project prioritization process consists of four steps: identifying risk factors, integrating climate data, conducting advanced risk assessments, and project prioritization. The goal is to prioritize resource allocation and develop climate-adaptive risk mitigation management strategies. Key performance indicators (KPIs) are recommended for setting goals, monitoring the outcomes of these strategies, and measuring their benefits. A Climate Impact Vulnerability Score (CIVS) is proposed to assess the susceptibility of infrastructure assets to environmental conditions. The approach also leverages artificial intelligence (AI) tools to analyze roadway infrastructure vulnerabilities and climate risk exposure. A case study applied to bridges using k-means clustering and multi-criteria decision analysis (MCDA) demonstrates the potential of advanced analytical methods in improving decision-making. This research concludes that the approach will contribute to enhancing resource allocation, supporting strategic decisions, aligning goals with budgets prioritizing investments, and strengthening the resilience and sustainability of roadway infrastructure.