Data Analysis System Research Articles

Development of an intelligent platform for predicting curriculum management in higher education under the AUN-QA framework

This research aims to study and define the user requirements and operational capabilities required to develop an intelligent platform for forecasting and managing higher education courses under the AUN-QA framework using documentary research, platform design, and technology suitability assessment. The samples studied include personnel, instructors, course administrators, and IT specialists selected by the purposive method. Data were analyzed using statistics and standard deviation calculations. The results show that the developed platform should have five main components: user data control with AWS IAM and React, access security, SmartEduQA system for data storage and analysis, data presentation with Microsoft Power BI, and a serverless storage system. The platform is designed to meet the needs of administrators in forecasting the labor market and managing courses, instructors in analyzing data according to AUN-QA standards, and staff in managing data quickly and supporting interactive display. This platform can help make higher education course management efficient and aligned with AUN-QA standards.

Application of Multidimensional Data Analysis in Network Intrusion Detection System

It is important to understand the behavior of an information network and its features. In this research, we explore this idea by applying a multidimensional data analysis system, a system that is significant in enhancing data intrusion detection. To accomplish this, we gather data from various sources, like network traffic, user behavior, and system logs. Using the examples mentioned, the proposed system detects and prevents cyber threats more accurately. Artificial Intelligence techniques including deep learning, clustering, and principal component analysis (PCA), are used. These are essential in analyzing complex patterns within the data and enabling the detection of sophisticated and evolving intrusion techniques. Multidimensional data allows for the capture of intricate, non-linear relationships. This improves the system’s ability to differentiate between normal and abnormal activities. Real-time data processing and AI-driven algorithms enhance detection speed and enable faster responses to potential intrusions. We tested this system on benchmark datasets. Results showed significant improvements in detection rates and a reduction in false positives compared to traditional NIDS. The integration of AI gave us a more adaptive and scalable approach to intrusion detection. Also, it allowed the system to learn from new attack patterns and continuously refine its capabilities.

Legal regulation of social dialogue in Ukraine: current challenges and prospects for improvement

The article comprehensively examines the legal regulation of social dialogue in Ukraine, analyzing its current state, challenges, and prospects for improvement in the context of modern socio-economic transformations. The research focuses on the complex interplay between historical developments and contemporary trends in social dialogue regulation, particularly emphasizing the challenges posed by the digitalization of labor relations and Ukraine’s European integration aspirations. The study provides a detailed analysis of how the COVID-19 pandemic and Russian aggression have significantly impacted Ukraine’s labor market and social dialogue mechanisms. These extraordinary circumstances have exposed existing vulnerabilities in the system while simultaneously creating new challenges for worker representation and collective bargaining processes. The research particularly highlights the declining trade union membership and the emergence of precarious employment forms as critical factors undermining traditional negotiation processes. Special attention is devoted to examining the legal framework’s adaptation to new forms of employment, especially in the digital economy sector. The article identifies significant gaps in the current regulatory framework regarding platform workers and those employed through algorithmic management systems, who often lack effective representation and protection mechanisms. This analysis is complemented by an assessment of recent legislative reforms aimed at harmonizing Ukrainian labor law with EU directives and international standards. The research methodology combines historical analysis with contemporary legal and socio-economic perspectives, allowing for a comprehensive understanding of the challenges facing social dialogue in Ukraine. The study examines various stakeholders’ positions, including trade unions, employers’ organizations, and government bodies, providing a multi-faceted view of the current situation and potential solutions. The article proposes a comprehensive set of measures to improve the legal regulation of social dialogue. These recommendations include modernizing the legislative framework to accommodate modern forms of employment, strengthening the institutional capacity of social partners, and developing social dialogue mechanisms at the local level within the context of decentralization. Particular emphasis is placed on the need to enhance data collection and analysis systems to better inform policy decisions and monitor the effectiveness of collective agreements. The research findings suggest that effective legal regulation of social dialogue is crucial for ensuring social stability and economic development in Ukraine. The proposed improvements aim to create a more resilient and inclusive system of labor relations that can better protect workers’ rights while promoting economic growth. The article emphasizes the importance of these reforms in the context of Ukraine’s post-pandemic recovery and post-conflict reconstruction, highlighting social dialogue as a key mechanism for successful economic reconstruction and integration into the European socio-economic space. The study contributes to the broader academic discourse on labor law reform and social dialogue in transitional economies, offering insights that may be valuable for other countries facing similar challenges. It concludes by emphasizing the need for a balanced approach that considers both immediate labor concerns and long-term structural reforms, ensuring that Ukraine’s social dialogue framework can effectively address current challenges while preparing for future developments in the world of work.

Smart Sensor Technologies Shaping the Future of Precision Agriculture: Recent Advances and Future Outlooks

The rapid advancement of smart sensor technologies has transformed the landscape of precision agriculture, ushering in a new era of data‐driven and sustainable farming practices. This study presents a comprehensive review of recent innovations and future perspectives in the domain of smart sensors for precision agriculture. By harnessing the power of the Internet of Things (IoT) and artificial intelligence, these sensors have revolutionized how farmers collect, analyze, and utilize data to optimize crop yield, conserve resources, and enhance overall agricultural efficiency. The review delves into various types of smart sensors used in precision agriculture, including soil sensors for monitoring moisture, nutrient levels, and soil health; crop health sensors for detecting diseases, pests, and stress factors; weather and environmental sensors for real‐time climate monitoring; and automated irrigation systems for precise water management. Cutting‐edge technologies such as drone‐based sensors, hyperspectral imaging, and sensor fusion are explored, highlighting their potential to revolutionize data acquisition and decision‐making in agriculture. Furthermore, the manuscript addresses the incorporation of smart sensors with data analysis systems. This enables farmers to acquire practical observations and render knowledgeable choices regarding watering schedules, fertilizing regimens, and safeguarding approaches for crops. The role of data‐driven agriculture in mitigating environmental impact, optimizing resource utilization, and promoting sustainable practices is emphasized. Despite the significant strides made in smart sensor technology, challenges such as sensor calibration, data privacy, interoperability, and adoption barriers remain pertinent. To address these hurdles, the manuscript outlines potential research directions and emerging trends in sensor development, including miniaturization, energy efficiency, and wireless communication advancements. In conclusion, this manuscript provides a comprehensive assessment of the recent advancements and future prospects of smart sensors in precision agriculture. With the potential to revolutionize traditional farming practices and address global food security challenges, smart sensors offer an unprecedented opportunity for farmers to enhance productivity, conserve resources, and foster sustainable agricultural practices regardless of an ever‐changing climate and growing demand for food.

Assessing the impact of air quality on solar energy production

In studying fires and other natural disasters, air quality is often used to assess their severity. This study explores the relationship between air quality and solar energy production, focusing on how air pollutants affect solar output. We analyze four air quality indicators—ozone (O₃), nitrogen dioxide (NO₂), carbon monoxide (CO), and sulfur dioxide (SO₂)—and their effects on photovoltaic performance using data analysis and geographic information systems. This research highlights the importance of understanding this connection to improve solar panel placement and efficiency. Hypothesis testing confirms a negative correlation between poor air quality and solar energy production.

Talk to the Wall: The Role of Speech Interaction in Collaborative Visual Analytics.

We present the results of an exploratory study on how pairs interact with speech commands and touch gestures on a wall-sized display during a collaborative sensemaking task. Previous work has shown that speech commands, alone or in combination with other input modalities, can support visual data exploration by individuals. However, it is still unknown whether and how speech commands can be used in collaboration, and for what tasks. To answer these questions, we developed a functioning prototype that we used as a technology probe. We conducted an in-depth exploratory study with 10 participant pairs to analyze their interaction choices, the interplay between the input modalities, and their collaboration. While touch was the most used modality, we found that participants preferred speech commands for global operations, used them for distant interaction, and that speech interaction contributed to the awareness of the partner's actions. Furthermore, the likelihood of using speech commands during collaboration was related to the personality trait of agreeableness. Regarding collaboration styles, participants interacted with speech equally often whether they were in loosely or closely coupled collaboration. While the partners stood closer to each other during close collaboration, they did not distance themselves to use speech commands. From our fndings, we derive and contribute a set of design considerations for collaborative and multimodal interactive data analysis systems. All supplemental materials are available at https://osf.io/8gpv2.

Data Processing and Reporting System for Chemical Analytical Laboratories of Environmental Control

The relevance of environmental monitoring and environmental pollution control is justified in the introduction of the article in order to monitor the state of the environment and its protection, on the basis of existing regulatory legal acts. Fundamental principles of these types of environmental activities are given. In order to justify the task, a brief description of chemical analytical laboratories (hereinafter referred to as HAL) activities and the tasks of their information systems is provided. Having analyzed a number of HAL information systems operating in the Russian Federation the authorscame to the conclusion that they mainly perform the functions of automated collecting, storing and processing information. The availability of decision-making support systems (DSS) function in the systems has not been established, for example, none of the software systems issues recommendations on changing the measurement regulations in cases of exceeding permissible values. An information system for data collection, processing and analysis from a chemical analytical laboratory with the DSS functions (monitoring changes in measurement regulations and monitoring the timing of metrologicaldevice verification) is proposed. The article describes the main functions of the proposed system available to the user and the administratorin detail. An example and a detailed description of the regulation changing process based on the MPC excess in samples are given. Regulations, the main blocks of the system, their functions and capabilities are described. The program structure and the system algorithm are presented. All system functions have been successfully tested. The figures show the algorithms of user’s and system administratoractivity when testing the database function, the function of reporting on specified parameters to obtain information about the discrepancy between samplenumbers and devices to the specified parameters, the function of automatic sampling and adding samples from the selected device and the function 'Changing the rules'. In conclusion, it is emphasized that this information system allows not only data processing from research results of chemical analytical laboratories in an automated mode, but also to perform certain functions of decision-making support systems.

ТЕОРЕТИЧНІ ВИКЛИКИ ТА ПРАКТИЧНІ МОЖЛИВОСТІ ОЦІНЮВАННЯ ЕФЕКТИВНОСТІ ЗАХОДІВ ПСИХОЛОГІЧНИХ ОПЕРАЦІЙ

The article discusses theoretical challenges and practical possibilities of assessing the effectiveness of psychological operations in modern armed conflicts. The author analyses the role of psychological operations, which are an important component of hybrid warfare aimed at manipulating the information space and the psychological state of the enemy. The main attention is paid to the use of modern technologies, such as electronic means, unmanned aerial vehicles, cyber tools and social networks for information and psychological influence. Modern armed conflicts are characterised by the intensive use of technology, which allows for a significant expansion of psychological operations. Thanks to innovative communication tools such as automated data analysis systems, artificial intelligence algorithms and big data technologies, new opportunities are emerging to assess enemy reactions in real time. This approach allows for a more accurate and timely correction of psychological influence measures depending on the results. The article examines various methods of psychological influence: demoralisation of the enemy, spreading disinformation, manipulation of public opinion, creating a negative information background among the enemy's civilian population, and maintaining the morale of own troops. Particular importance is attached to information and analytical operations, which can influence not only individual decisions of the enemy command, but also strategic management processes. In addition, the importance of evaluating the effectiveness of such operations is emphasised, in particular through the use of quantitative and qualitative metrics that allow to determine the impact on the enemy, changes in his behaviour and morale. In particular, quantitative indicators include the level of information coverage of the target audience, the number of changes in the behaviour or decision-making of the enemy command. Qualitative indicators analyse deeper aspects, such as the emotional stability of personnel, changes in confidence in the command, and demoralisation of the population in the combat zone. Particular attention is paid to methods of assessing the effectiveness of psychological operations, including analysing the enemy's reactions, monitoring changes in his behaviour, and measuring the impact on the enemy's information and communication systems. An important component is the analysis of the results of disinformation campaigns, in particular, the assessment of their long-term impact on public opinion. The article emphasises the importance of a comprehensive approach to determining the results of psychological operations, which combines different types of analysis to achieve strategic goals in modern military conflicts.

Design and Implementation of College Students' Physical Health Data Analysis System Based on Multiple Computing Big Data

Design and Implementation of College Students' Physical Health Data Analysis System Based on Multiple Computing Big Data

KEBOLEHGUNAAN MODUL PENGAJARAN SMART-KQ DI TADIKA ISLAM MENGGUNAKAN TEKNIK KUMPULAN NOMINAL

This study aims to evaluate the usability of the SMART-KQ teaching module developed for Arabic vocabulary instruction through Quranic memorization at the kindergarten level. The module is a combination of three teaching models: Gagne's Model (1985), TABA Curriculum Model (1962), and Sidek Module Development Model (2005). The research methodology employed a modified Nominal Group Technique (NGT), involving 23 Islamic Education teachers as experts. Data was analyzed using the NGT Plus data analysis system. Findings showed that all main module constructs achieved high agreement levels (exceeding 70%) among experts. The highest agreement percentages were achieved by the color selection construct (88%) and image selection construct (86%). However, challenges were encountered in teaching the differences between nouns and verbs, with related items achieving only 77% agreement. The overall module usability evaluation involved eight items covering aspects of utility, efficiency, and suitability, with all items achieving agreement levels exceeding 85%. This study demonstrates that the SMART-KQ module has high usability and potential to enhance the quality of Arabic vocabulary teaching through Quranic memorization at the kindergarten level. Research implications suggest the potential for widespread module implementation in kindergartens and as a teacher professional development tool. Recommendations for future research include evaluating long-term impact, comparative studies with traditional approaches, and further research on effective teaching strategies for challenging grammatical aspects in the context of Arabic language teaching for children.

Skin-Friendly Large Matrix Iontronic Sensing Meta-Fabric for Spasticity Visualization and Rehabilitation Training via Piezo-Ionic Dynamics

Rehabilitation training is believed to be an effectual strategy that can reduce the risk of dysfunction caused by spasticity. However, achieving visualization rehabilitation training for patients remains clinically challenging. Herein, we propose visual rehabilitation training system including iontronic meta-fabrics with skin-friendly and large matrix features, as well as high-resolution image modules for distribution of human muscle tension. Attributed to the dynamic connection and dissociation of the meta-fabric, the fabric exhibits outstanding tactile sensing properties, such as wide tactile sensing range (0 ~ 300kPa) and high-resolution tactile perception (50Pa or 0.058%). Meanwhile, thanks to the differential capillary effect, the meta-fabric exhibits a "hitting three birds with one stone" property (dryness wearing experience, long working time and cooling sensing). Based on this, the fabrics can be integrated with garments and advanced data analysis systems to manufacture a series of large matrix structure (40 × 40, 1600 sensing units) training devices. Significantly, the tunability of piezo-ionic dynamics of the meta-fabric and the programmability of high-resolution imaging modules allow this visualization training strategy extendable to various common disease monitoring. Therefore, we believe that our study overcomes the constraint of standard spasticity rehabilitation training devices in terms of visual display and paves the way for future smart healthcare.

Computer-Aided System for Customs Fraud Analytics Based on Artificial Intelligence Techniques

Globalization has stimulated the opening of the market and the accumulation of huge amounts of data, which consequently leads to an increase in the importance of the control of customs operations. However, customs data is highly imbalanced and this poses challenges in its integration and processing. Therefore, it is of prime importance to find automatic computationally intelligent solutions for customs management. The purpose of the research presented in this paper is to propose a computer-aided system for customs fraud analytics based on artificial intelligence techniques, ensuring the application and verification of methods and algorithms for integration, management, analysis and visualization of data on customs violations. The architecture of the customs violation data analysis system consists of the following components: data sources, data storage, data integration and preprocessing, real-time data flow, modeling, analysis and storage of analytical data, and visualization of the results. A machine learning approach for detecting customs fraud through unstructured data analysis is proposed. An artificial neural network designed for data analysis is designed, and the input data is divided into training data and testing data. A reduced set of statistical records related to the analysis of heterogeneous databases of different institutions, which is stored in a data warehouse, are used as experimental data. The first 80% of the data are used to train the neural network and the remaining 20% to test the trained network. Experimental results show that the calculated accuracy increases with increasing epochs and is higher for the training data and lower for the validation test data. Thus, the trained model can be saved and used to monitor for anomalies. The trained model is applied to the system to calculate new input parameters that were not used in either training or validation.

Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants

Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems in the dairy ruminant sector feasible. The objectives of the current review were (i) to describe the most widely applied spectroscopy-based and supervised machine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drug residues, (ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and (iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain.

A low-cost system for continuous dynamic monitoring and autonomous data analysis using the Internet of Things and Machine Learning

Structural health monitoring using the Internet of Things (IoT) is the latest tech-nique employed in the field of structural damage detection. Although conventional systems based on commercial sensors, such as piezoelectric accelerometers, pro-vide high accuracy, their high cost often limits their application. To address this is-sue, one possibility has been the development of strategies using low-cost sen-sors. However, there are several challenges to be addressed, such as the optimiza-tion of the low-cost devices to increase their reliability in reading and processing data, and the development of data storage and data transmission strategies, mainly for dynamic monitoring applications, which requires working with large amounts of data. In this regard, this paper presents the development of a low-cost SHM so-lution, which is able to collect, store, process and transmit vibration data to the cloud from an instrumented aluminum beam. For this purpose, a prototype is de-veloped using an ESP32 board, an MPU6050 triaxial accelerometer and a mi-croSD card. A completely low-cost system is adopted, where the data processing and availability of results to the final user is performed in the free version of ThingSpeak IoT platform from MathWorks. As a result, a fully automatic dynam-ic monitoring strategy able to collect, store and transmit raw vibration data to the cloud is developed. Then, the raw acceleration data is processed and analyzed in the cloud, where the Fast Fourier Transforms (FFT) are computed and visualized in quasi-real time.

Imaging Spectroscopy: Earth and Planetary Remote Sensing with the PSI Tetracorder and Expert Systems from Rovers to EMIT and Beyond

A system for rapid analysis of spectroscopy data with emphasis on planetary surfaces, both imaging and single-spectrum data, is described. The system, called Tetracorder, is commanded by an expert system developed by expert spectroscopists. The Tetracorder and the expert system apply multiple algorithms to analyze a spectrum in segments, leveraging the advantages of each spectral region’s sensitivity to detecting different compounds, whether solid, liquid, or gas. The algorithms compare measured spectra to the spectral properties of materials in spectral libraries. The libraries include pure minerals, mineral mixtures that include areal mixtures, intimate mixtures, coatings, and molecular mixtures and other compounds such as organics, vegetation, liquids, and gases. Absorption bands of a particulate surface change shape with grain size, and shape changes are used in some cases to constrain grain size of each component in the surface. The different algorithm results are compared for each spectral region, and specific material composition and average grain size (when possible) are identified. The system is operational analyzing real-time data on a new generation of rovers for future planetary missions, as well as identifying materials using an imaging spectrometer on the International Space Station. Four abundance models are presented, each with increasing sophistication, that are computationally fast on imaging spectrometer data and use Tetracorder identifications to produce maps of mineral abundances. A fifth full radiative model that includes multilayer surfaces is presented but is computationally intensive. The system is open source and available on GitHub.

Fear of missing out: Drug availability in the United States vs Canada.

Per capita spending on drugs in the United States is double that of Canada. One commonly debated point when comparing the 2 countries is whether this additional spending allows residents of the United States access to valuable therapies not available in Canada. To characterize the therapeutic value of prescription drugs used in the United States that are not marketed in Canada. This cross-sectional study used IQVIA Multinational Integrated Data Analysis System data to identify drugs purchased in the United States but not in Canada from 2017 to 2021. Drug listing and regulatory review statuses were obtained. We categorized the drugs into 8 mutually exclusive groups: listing status in Canada ("cancelled post-market" or "dormant; approved but not marketed; cancelled pre-market"), other alternatives available ("formulation unavailable," "existing drug class," or "therapeutically similar"), "pre-approval," "atypical access available," or "unavailable without alternatives marketed" in Canada. Therapeutic value assessments of drugs in the last category were obtained from 3 international organizations. 2,084 products were purchased in the United States but not in Canada from 2017 to 2021; 1,685 were excluded because they were not prescription drugs, were combinations in which each active pharmaceutical ingredient was already available in the United States as a separate drug, had been discontinued in the United States by August 30, 2023, or were marketed in Canada by August 30, 2023. After exclusions, there were 399 drugs; 120 (30%) were "cancelled post-market," 38 (10%) were "dormant; approved but not marketed; cancelled pre-market," 49 (12%) were "formulation unavailable," 130 (33%) were "existing drug class," 35 (9%) were "therapeutically similar," 3 (1%) were "preapproval," 15 (4%) were "atypical access available," and 9 (2%) were "unavailable" in Canada. 6 of the 9 drugs had been evaluated by 1 or more independent organizations, and all 6 were rated as offering minor to no additional therapeutic value compared with existing drugs. There was similar access to important prescription drug therapies in the United States and Canada. Overall, the additional spending in the United States may not have necessarily translated into access to important therapeutic innovations.

Algorithm-informed treatment from EEG patterns improves outcomes for patients with major depressive disorder.

Selecting the right medication for major depressive disorder (MDD) is challenging, and patients are often on several medications before an effective one is found. Using patient EEG patterns with computer models to select medications is a potential solution, however, it is not widely performed. Therefore, we evaluated a commercially available EEG data analysis system to help guide medication selection in a clinical setting. Patients with MDD were recruited, and their physicians used their own judgment to select medications (Control; n = 115) or relied on computer-guided selection (PEER n = 165) of medications. Quick Inventory of Depressive Symptomatology (QIDS SR-16) scores were obtained from patients, before the start of the study (day 0) and again at ~90 and ~180 d. Patients in the PEER arm were classified into one of 4 groups depending on if the report was followed throughout (RF/RF), the first 90 days only (RF/RNF), the second 90 days only (RNF/RF), or not at all (RNF/RNF). Outcomes were then compared with controls whose physician performed the EEG and submitted data but did not receive the PEER report. Patients in the controls, RF/RF and RNF/RNF groups had fewer depressive symptoms at 90 and 180 days, but the response was significantly stronger for patients in the RF/RF group. Lower rates of suicidal ideation were also noted in the RF/RF group than the control group at 90 and 180 days of treatment. Computational analysis of EEG patterns may augment physicians' skills at selecting medications for the patients.

An IoT-based data analysis system: A case study on tomato cultivation under different irrigation regimes

The exploitation of modern technologies in heterogeneous farming scenarios with different crops cultivation is nowadays an effective solution to implement the concept of Smart Agriculture (SA). Following this approach, in this study the tomato plants’ response to different irrigation regimes is investigated through the implementation of an Internet of Things (IoT)-oriented SA data collection and monitoring system. In particular, the experimentation is conducted on tomatoes grown at three different irrigation regimes: namely, at 100%, 60%, and 30% of the Italian irrigation recommendation service, denoted as Irriframe. The proposed platform, denoted as Agriware, is able to: (i) evaluate information from heterogeneous data sources, (ii) calculate agronomic indicators (e.g., Growing Degree Days, GDD), and (iii) monitor on-field parameters (e.g., water consumption). Different plant-related parameters have been collected to assess the response to water stress (e.g., Soil Plant Analysis Development (SPAD), chlorophyll content, fluorescence, and others), along with leaf color and final production evaluations. The obtained results show that the best irrigation regime, in terms of plant health and productivity, corresponds to 60% of Irriframe, allowing significant water savings for the cultivation.

Geotechnical monitoring of the hospital Chernigov

Summary. Geotechnical monitoring is a system of observation, collection and data analysis on the condition of soils, basements and structural elements of facilities. It is a key tool for safety assurance and control during the reconstruction of structures. This is extremely important while old buildings reconstruction, where unexpected reactions to changes in loads are possible. The monitoring of emergency facilities is especially important, where even minor changes can lead to serious consequences. Geotechnical monitoring of the Chernihiv`s hospital was conducted. The building was put into operation in 1982. Since 1984, the building began to deform. There were opening of temperature joints with displacement of transverse walls, separation of the longitudinal wall from the transverse ones, minor deformation cracks in other blocks were detected. In the early 1990s, the works for strengthening of the structures was carried out, but they were not fully completed. In 2016, a major repair of the roofing and the drainage system from the building were carried out. The deformation of the building did not stop. In 2018, the SE NIIBK conducted a study and, based on the recommendations of «Eurotechindustry» LLC, a reinforcement of the stairwell was developed. However, in 2022, during active hostilities in the Chernihiv city, the hospital was severely damaged. There were direct hits and the collapse of structure`s elements. The meticulous inspection of the facility in order to establish the residual resource and reliability of the whole structures becomes necessary. An inspection of the hospital building as well as geotechnical monitoring, particularly engineering and geodetic observations, and verification calculations of the basement were performed.

HTBPPS: A high-throughput behavioral phenotyping platform for shrimp

In shrimp breeding, phenotypic measurement is crucial for identifying key traits. Meanwhile, behavioral traits typically exhibiting moderate to high heritability are gaining recognition as novel traits in aquatic species breeding. However, traditional shrimp behavioral phenotype measurement is unsuitable for large-scale evaluations as it relies primarily on time-consuming and error-prone manual observations. With advances in computer vision, various behavior recognition and tracking algorithms have effectively overcome these limitations. Yet, such algorithms often prove inadequate for long-duration behavioral video data. Inspired by high-throughput phenotyping platforms in plant research, this study designed specialized temporary housing and observation apparatus to track shrimp behavior and the shrimp.tracker software based on a boundary-constrained Kalman filter algorithm. Additionally, this study established an analysis system for shrimp behavioral phenotypic data and a framework for long-term high-throughput behavioral data acquisition. Ultimately, these hardware and software systems form a high-throughput behavioral phenotyping platform for shrimp. The platform's algorithm scored 92.4 in sMOTSA, 99.5 in MOTA, and 95.5 in MOTSP, with a detection accuracy of 99.79 %, surpassing the deep learning-based Track-RCNN algorithm. Case studies illustrated the platform's capability to track various shrimp species, multiple individuals over extended periods, and shrimp under high- temperature stress. The experimental results revealed significant behavioral phenotype differences among different types and sizes of shrimp. During long-term behavioral observations, shrimp behaviors exhibited 24-h cyclical rhythm changes. Different types of shrimp exhibited the same M-shaped trend in mobility during linear temperature increases, with the last peak of the M shape moderately correlating with shrimp heat tolerance. These findings emphasize the importance of behavioral phenotypes in shrimp breeding, serving as potential indicators for assessing heat tolerance in shrimp breeding. Thus, the proposed platform has significant potential for future applications in shrimp breeding.