Adsorption of REEs from aqueous solutions using modified polystyrene- di (2-ethylhexyl) phosphoric acid electrospun nanofibers

• Thallium accumulates readily in lettuce in a concentration-dependent manner • Rare earth elements show moderate and concentration-dependent uptake • Gallium uptake by lettuce is low but rises sharply at high soil concentrations • Neodymium reduces seed germination, affecting crop productivity The increasing use of Technology-critical elements (TCEs) in modern technologies is expected to raise their concentrations in soils of many areas, prompting concerns about their mobility and entry into the food chain. This study examines pore water availability and plant uptake of gallium (Ga), thallium (Tl), and the rare earth elements gadolinium (Gd), neodymium (Nd), and ytterbium (Yb). Lettuce ( Lactuca sativa ) was grown under controlled greenhouse conditions in a native agricultural soil from Sweden, both at the baseline concentrations of the elements and after spiking in a concentration series in which the highest level corresponded to a 40-fold enrichment of the original concentrations. Our findings indicate that Tl poses the greatest risk among the investigated TCEs, as it was readily taken up by lettuce even at moderate concentrations in the soil, with a clear dose-dependent increase in accumulation (Bioconcentration factor, BCF = 0.084–1.5). Gallium demonstrated a low concentration in pore water and limited accumulation in lettuce (BCF = 0.00010–0.00066), suggesting a low immediate concern. However, the Ga uptake increased substantially at the highest contamination level, indicating a potential risk threshold. Relative to Ga and Tl, the REEs demonstrated intermediate levels of both pore water availability and lettuce uptake, although the uptake increased with rising soil concentrations (BCF = 0.0013–0.0071, 0.0013–0.012, and 0.0011–0.0020, for Gd, Nd, and Yb respectively). Notably, Nd was also found to negatively affect seed germination, suggesting a possible risk to crop productivity. Together, our findings highlight the need for targeted risk assessments of specific TCEs, as these elements may increasingly find their way onto our plates through soil-to-plant transfer under potential future soil contamination.

Managing large volumes of documents remains a challenging task for individuals and organizations, especially when data exists in unstructured formats such as scanned files, PDFs, and images. Traditional document management approaches rely heavily on manual effort for sorting, searching, and extracting information, which leads to inefficiencies and increased processing time. To address these challenges, this paper introduces Neural Docs, an AI-enabled document management system that focuses on automating document understanding and interaction. The system is designed to transform static documents into interactive and searchable knowledge sources. It utilizes Optical Character Recognition (OCR) to convert visual document content into machine-readable text. This extracted information is further processed using Natural Language Processing (NLP) techniques to identify key entities, generate metadata, and organize documents intelligently. In addition, a Retrieval-Augmented Generation (RAG) mechanism is incorporated to enable users to query documents through a conversational interface, providing responses based on relevant contextual information rather than simple keyword matching. The overall architecture is divided into multiple layers, including a user interface for interaction, a backend system for processing and coordination, and a dedicated AI module for advanced analysis. A vector-based storage mechanism is used to maintain semantic representations of documents, allowing efficient similarity-based retrieval. The system is implemented using modern technologies such as Node.js, FastAPI, and containerized deployment for flexibility and scalability. The developed solution demonstrates improved accessibility and reduced manual workload in document handling tasks. It allows users to quickly retrieve meaningful insights from stored documents and interact with them in a more intuitive way. The approach presented in this work highlights the potential of combining multiple AI techniques to create smarter and more efficient document management systems suitable for real-world applications

Agriculture is the foundation of our economy, and modern technologies are changing how farming is done. One of the biggest problems in farming is the manual application of pesticides and fertilizers, which is time-consuming, labor intensive, and harmful due to chemical exposure. To fix this, this project introduces a drone-based agricultural spraying system that automates the spraying process, cutting down on human work and making things more efficient. The system uses an Arduino as its main control unit to manage all the parts. The drone has several features: an ultrasonic sensor to maintain the spraying height, a flow sensor to monitor the amount of pesticide sprayed, and a GPS module to track its position. With these tools, the drone can cover big areas efficiently and ensure even spraying, helping to make farming smarter and more sustainable.

The rapid growth of digital education platforms has increased the demand for intelligent systems capable of delivering personalized learning experiences. Traditional online learning systems often provide static course structures that fail to adapt to individual student needs, learning styles, and knowledge levels. This paper presents Edumentor – Multi-Agent AI Study Assistant, an intelligent educational platform designed to provide personalized learning support using a multi-agent architecture powered by large language models. The system analyzes student learning requirements and dynamically generates customized learning roadmaps, recommended learning resources, practice quizzes, and interactive tutoring. In addition, the platform incorporates Retrieval-Augmented Generation (RAG) to enable document-based learning, allowing students to upload study materials and receive context-aware explanations. The system is implemented using a modular multi-agent framework where specialized agents perform tasks such as student analysis, curriculum planning, resource discovery, quiz generation, and tutoring. A vector database is used to store semantic embeddings of uploaded documents, enabling efficient similarity-based retrieval during question answering. The platform integrates modern AI technologies with an intuitive web interface to create a flexible and adaptive learning environment. The results demonstrate that the proposed system can effectively assist learners by providing structured learning guidance, improving access to educational resources, and supporting interactive knowledge exploration.

The rapid growth of online transactions and digital banking has significantly increased the use of credit cards for financial activities. However, this convenience has also led to a rise in credit card fraud, causing major financial losses for banks and customers. Traditional fraud detection systems mainly rely on rule-based methods, which are often inefficient in identifying new and complex fraud patterns. To address this challenge, this project proposes a Machine Learning Based Credit Card Fraud Detection System that automatically analyzes transaction data and identifies fraudulent activities with high accuracy.The system utilizes modern technologies such as Python for backend development and machine learning algorithms to analyze transaction patterns and detect anomalies. Transaction data is first preprocessed to remove noise and extract important features such as transaction amount, time, and location. Machine learning algorithms like Logistic Regression, Random Forest, and Isolation Forest are then applied to classify transactions as legitimate or fraudulent. The proposed system aims to improve fraud detection accuracy, reduce financial losses, and enhance the security of online transactions. By leveraging data-driven techniques and intelligent algorithms, the system can identify suspicious activities in real time and assist financial institutions in preventing fraudulent transactions. Experimental results show that the machine learning model can effectively detect fraudulent behavior and improve the efficiency of fraud detection systems.Furthermore, the system provides a scalable architecture for handling large volumes of transaction data and supports faster decision-making in financial security systems. By integrating machine learning with modern data processing techniques, the proposed system offers a reliable and efficient solution for credit card fraud detection in digital payment environments.

This paper presents CAB5, a web-based platform that combines cab booking and package delivery services into a single unified system. The platform addresses critical frag- mentation problems in current urban mobility solutions, where users depend on multiple separate applications. CAB5 features a multi-role architecture with Customer, Driver, and Admin modules, enabling users to book rides, track packages, and manage logistics seamlessly. The system incorporates transpar- ent pricing, real-time vehicle tracking, and a novel offline AI assistant called POKO that functions without continuous internet connectivity. Built using modern technologies including Next.js, TypeScript, and Tailwind CSS, CAB5 is designed for scalability, maintainability, and high performance. The results demonstrate that CAB5 provides a reliable, cost-efficient, and scalable solution for smart urban mobility and logistics, effectively eliminating the need for multiple disjointed platforms.

A significant increase in the number of patent publications in recent years has created difficulties in conducting classical manual analysis and searching for patent analogues. Automation of the search for patent analogues is a key tool for reducing time and financial costs at the stages of patent application formation and patent examination. The use of Big Data technologies and distributed systems makes it possible to build an effective architecture of a system of patent analogues and improve the quality of patent search results. The theoretical significance of the work lies in the development of the architecture and concept of a full-text patent search system based on a comparative analysis of the effectiveness of various distributed systems for searching and processing textual Russian-language information, taking into account its morphological and syntactic features. The practical significance of the work lies in the implemented software, which includes tools for parsing patent documents into a distributed file system, searching taking into account the features of the natural Russian language, as well as a web interface for visualizing search results. Modern frameworks and technologies are used in the process of work: Apache Hadoop, Spark, Hive, Elasticsearch, PostgreSQL, ClickHouse. Elasticsearch showed the best results in both response time and search quality (accuracy – 0.87, completeness – 0.82, F-measure – 0.84) for complex queries reflecting the specifics of the Russian language.

Abstract: The integration of multimedia elements and interactive content into educational environments has fundamentally transformed teaching approaches and learning outcomes. The research paper explores the effects of multimedia technology on the education system in Kosovo, covering thinking and inclusion, interactive learning, and environment. Videos, animations, virtual environments, and interactive multimedia have transformed the way lecturers teach by adopting a teaching approach that changes from hearing information to actual involvement in acquiring knowledge, where the learner is central. This study analyzes how interactive multimedia content and video-based teaching affect student motiva¬tion, participation levels, and academic achievement. The research use qualitative field research methods, including classroom observation, teacher consultations, and student questionnaires. In addition, a quasi-experimental methodology was implemented using pre-test and post-test assessments to rigorously assess edu-cational achievements. The study involved two distinct groups of Information Tech¬nology students – one using multimedia and the other using traditional methods – with each group divided into treatment and comparison groups. This study provides strong evidence that carefully conceived multimedia implemen¬tations can significantly improve educational achievement, offering important perspectives for the development of innovative teaching methodologies tailored to diverse learning needs. Multimedia implementation is an essential element with¬in the teaching methodology, as it prepares learners to thrive in technology-intensive learning environments. Keywords: academic and pedagogical communication, interactive learning, video instruction, multimedia, virtual reality.

Background: Precision agriculture has transformed the face of farming with modern technologies, including GPS guided sowing. Legume crops-which are critical for the purpose of sustainable agriculture owing to their nitrogen-dropping capacity, could be improved substantially with fine placement of seeds. However, the traditional sowing method usually causes non-uniform seed distribution that will influence germination rate and the efficiency of a crop in general. The other study reviewed the yield and efficiency of legume crop production using GPS guided precision sowing. Methods: A field experiment with GPS and precision seed GPS enabled for the planting of legume crops was done versus direct conventional sowing. Collected parameters, reproduced on seed placement, germination, plant population and final yield were recorded. The data was processed by statistical software for the evaluation of precision sowing impact on the performance of the crop. Result: The study found that GPS precision sowing drastically changed the seed uniform distribution, providing the highest germination and best plant spacing. Thus, legume yields increased on average by 15-20% over general sowing practices. It also increased resource efficiency with less seed going to waste and improved soil health. Overall, these results imply that the adoption of GPS guided sowing for legumes leads to higher productivity and sustainability.

Colorectal cancer is a malignant tumor arising from mucosa of large intestine (colon, rectosigmoid part and rectum). Currently, surgery is the primary treatment for colorectal cancer regarding oncological perspective. However, it is associated with high risk of postoperative complications, and one of these is anastomotic leakage (AL). According to various data, the incidence of AL can reach 35%. One of the main factors for AL is impairment of perfusion in anastomotic area. We hypothesize that preserving the left colic artery in rectosigmoid and rectal surgeries, as well as the first sigmoid artery in rectal surgeries will reduce the incidence of AL. Modern technologies, including CT perfusion, indocyanine green dye, photoplethysmography, perfusion flowmetry, and MASP (marginal artery stump pressure) allow us to assess perfusion preoperatively and intraoperatively. This review analyzes the main causes of AL from physiological and metabolic-biochemical perspective. It presents statistical data reflecting the relevance of intestinal perfusion assessment and proposes hypotheses that could significantly reduce the incidence of anastomotic leakage. Study objective was to select the optimal method for assessing intestinal perfusion. We analyzed the articles published in PubMed and Sci-Hub databases.

Introduction . The article is examined a model of cross-border migration discourse based on the English language on the example of the US-Mexican border. The relevance of the proposed research is related to the increasing interest of linguists in the complex migration phenomenon, as well as the fact that this type of discourse is first identified by the authors independently. The aim of the study is to build a linguistic model of cross-border migration discourse by using corpus technologies. Methodology and sources . Interpretation, categorization, systematization, and contextual and definitional analysis were used as the main research methods. The material for the work was the chapter “The border” from the book “Sovereign Violence: Migrants, Borders, and the Brutal Logic of Nationhood” by Lily Ana Chavez [1], with a volume of 65,600 pp. without spaces. Results and discussion . This study was carried out within the framework of migration linguistics, one of the tasks of which is to study the migration narrative, through which a certain attitude of the host society towards different categories of participants in migration processes and their images are formed. Based on the concepts of G.G. Slyshkin and V.I. Karasik, the authors identify the cross-border migration discourse as a special type of discourse and argue their position. The article is presented a model of this type of discourse, including 1560 lexemes, 15 semantic modules and 13 micromodules. Conclusion . As a result, the semantic modules “Migration participants” and “Border” belong to the core of the model of cross-border migration discourse. The near periphery includes “Law”, “Citizenship”, “Migration Policy” and “Security”. The far periphery includes “Theatricality”, “Economy”, “Ethnicity“, “Communication”, “Documents” and “Modern technologies”.

The article is devoted to the study of the problem of soft skills development in future IT specialists as a scientific phenomenon and pedagogical category. The relevance of the study is determined by the growing role of soft skills in the professional activities of IT specialists who work in conditions of rapid technological evolution, team interaction and global competition. It is argued that the formation of soft skills in the IT education system is not only a practical task but also a scientific problem that requires theoretical understanding, the definition of pedagogical conditions and the development of effective methodological approaches. The paper analyses the key contradictions between the requirements of the modern labour market and the real possibilities of higher education institutions to develop soft skills in IT students. A review of modern pedagogical approaches, models and technologies for the formation of soft skills is carried out, among which the following are highlighted: competence-based, communicative-activity-based, project-oriented, integration-based and personality-development-based. It is argued that their comprehensive implementation contributes to the development of communication, critical thinking, creativity, emotional intelligence and adaptability – skills that determine the competitiveness of future IT specialists. The direction of further scientific research has been determined, in particular the creation of a comprehensive pedagogical model for integrating soft skills into the content of professional training, the development of mechanisms for assessing their formation, and the improvement of the system of methodological support for professional training. The direction of further scientific research has been determined, in particular, the creation of a comprehensive pedagogical model for integrating soft skills into the content of professional training, the development of mechanisms for assessing their formation, and the improvement of the system of methodological support for the professional training of students in technical specialities.

The article discusses the pedagogical aspects of integrating mathematical methods of signal analysis into the training of technical university students in the context of modern energy-efficient technologies and Internet of Things (IoT) systems. The relevance of the study is due to the growing demands on engineers to be able to work with large amounts of real data, perform analytical processing, and make informed engineering decisions in the field of computer-integrated heating control systems for “smart homes.” Particular attention is paid to the problem of insufficient practical orientation of teaching mathematical disciplines in technical education and the gap between theoretical knowledge and real engineering tasks. The purpose of the article is to justify and experimentally verify a step-by-step methodology for teaching student’s mathematical methods of analyzing heating parameters based on real IoT data using Fourier and wavelet transforms. The work applies competency-based, interdisciplinary, and practice-oriented approaches to teaching. The proposed methodology involves a sequential transition from the analysis of time temperature signals to spectral and time-frequency analysis, with the subsequent use of the results obtained to optimize the operation of heating systems. The article defines a list of key mathematical methods necessary for analyzing heating parameters in IoT systems and establishes their connection with specific laboratory work. A complete cycle of classes based on real or near-real data from a “smart home” is described. Considerable attention is paid to the physical and engineering interpretation of the spectral characteristics of temperature signals, which contributes to the formation of systematic engineering thinking in students. The pedagogical effectiveness of the methodology is confirmed by the results of an experimental study using quantitative indicators: levels of professional competence, results of educational testing, and assessment of practical skills. The data obtained indicate a statistically significant increase in the level of student training, an improvement in the quality of mastery of mathematical methods, and the ability to apply them to solve real engineering problems in the field of energy-efficient IoT systems.

ABSTRACT IdeaCrafter is an intelligent AI-based platform designed to provide personalized mentorship for aspiring entrepreneurs through a conversational chatbot interface. In today’s fast-growing startup ecosystem, many individuals struggle to access expert guidance, especially in the early stages of their business journey. This project addresses that gap by offering real-time, data-driven, and context-aware suggestions using machine learning and natural language processing techniques. The system analyzes user inputs such as business ideas, startup stage, and specific requirements, and generates meaningful recommendations related to funding, product validation, and market strategies. It integrates modern technologies including AI models, APIs, and cloud-based databases to ensure scalability, efficiency, and real-time interaction. by combining chatbot intelligence with structured data analysis, IdeaCrafter aims to democratize entrepreneurial mentorship, making it accessible, affordable, and scalable for a wide range of users including students and startup founders. Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Chatbot System, Entrepreneurial Mentorship, Personalized Recommendations, Startup Guidance, Conversational Interface, Data-Driven Decision Making, Cloud-Based Platform

Product Engineering is a fundamental component of modern engineering education. In the Chemical Engineering sector, professional roles have been evolving from traditional commodities production toward product development—particularly in countries such as Portugal, where industrial activity is better suited to customized or medium-scale production rather than mass manufacturing. Recognizing this trend, the Master’s in Chemical Engineering program at the Faculty of Engineering of the University of Porto (FEUP) introduced a Product Engineering course in 2009. In 2022, a Special Issue of the U.Porto Journal of Engineering showcased a selection of works developed within this course (Silva et al., 2022). The course follows a structured methodology for product development based on four key cornerstones: Identification of market needs Generation of technical solutions to address those needs (ideas) Screening and selection of the most promising ideas Implementation analysis, including time-to-market considerations, industrial process design, regulatory aspects, intellectual property rights, and economic feasibility (manufacturing) In the 2023/24 academic year, the Product Engineering course incorporated advanced process simulation tools, namely SuperPro Designer and Aspen (Silva et al., 2024). These additions enabled students to apply the technical knowledge acquired throughout the Chemical Engineering curriculum to achieve more accurate process designs and to estimate capital (CAPEX) and operational (OPEX) expenditures. This third Special Issue presents a collection of works that already integrate these new features, demonstrating the continuous evolution and enhancement of the Product Engineering course at FEUP.

Abstract The vibration control in civil structures is a primary objective in modern engineering. Among the different vibration control systems, active mass dampers (AMDs) represent a highly effective solution due to their limited invasiveness, optimized use of masses, and thus suitability for both new and existing buildings. When vibrations are detected, an actuator moves the mass in order to generate a force on the structure that is proportional and opposite to the floor velocity: this allows to mitigate oscillations over a broad range of frequencies. Despite their potential, the current design approaches are mostly restricted to regular structures where the cantilever vibration mode is predominant. In this work, a computational framework for the linear dynamic analysis of structures equipped with AMDs is presented. A configuration of AMDs is modelled as a viscous damping having with both translational and rotation contributions and lumped at some floors, leading to non-classical damping conditions. A generalized modal superposition procedure which takes into account phase shift effects is adopted. This allows to evaluate the AMD-induced shear reduction for each vibration mode under a given dynamic action, providing significant insights on the AMD efficiency for superior vibration modes and contributing to their practical implementation in vibration control and seismic protection.

The relevance of this study is driven by the increasing volume of online meetings and public discussions in digital formats, creating a demand for automated tools to analyze group communication. Traditional manual coding and transcription methods are highly labor-intensive and subjective, limiting large-scale research on communication patterns. The aim of this research is to develop and validate a comprehensive system for automated analysis of communicative behavior that integrates modern speaker diarization technologies, automatic speech recognition, and statistical analysis to provide a detailed picture of group dynamics in public discussions. Methods. The system is implemented using a microservice architecture with Python 3.10+, FastAPI, and React. Speaker diarization is performed using the pyannote.audio algorithm, which combines convolutional encoders with pre-trained WavLM models. Automatic speech recognition is carried out using transformer architectures (Whisper, AssemblyAI, Conformer). Communicative behavior analysis includes calculation of activity statistics, network analysis of interactions, and assessment of communication style. Results. The developed system successfully integrates speaker diarization with 0.5-second precision, automatic transcription, and multidimensional analysis of communication patterns. The modular architecture ensures flexibility for adaptation to various application domains. The system generates detailed timestamps of participant activity, visualizes speaking time distribution, and provides comprehensive analytics to improve decision-making processes. Prospects. Further development of the system includes integration of multimodal analysis considering non-verbal communication, improvement of stability in noisy conditions, domain adaptation for specific sectors, and implementation of real-time analysis of live discussions. The system opens new opportunities for studying group dynamics in corporate, educational, and governmental sectors.Received 2026-02-20Accepted 2026-03-27

This paper investigates the current problem of finding the optimal path in dynamic environments modeled as cellular labyrinths with obstacles. The problem of finding the optimal path between two points of a cellular labyrinth is relevant due to its versatility, applied value and fundamental importance for the development of modern technologies. The main attention is paid to the development and software implementation of a system for visualizing algorithms on graphs, which allows real-time observation of the decision-making process of an autonomous agent. The paper provides a detailed comparative analysis of classical methods, such as Dijkstra's algorithm and A*, as well as specialized incremental approaches, in particular D* Lite. The advantages of using graph theory for solving problems in environmental management, logistics and robotics, where the environment can change unpredictably, are substantiated. The purpose of the presented work is to develop a program for visualizing the algorithm for finding the optimal path on a field, which can be represented as a labyrinth with surmountable and insurmountable obstacles. The task is to find the optimal path between two points on the field and display it. The practical part of the research includes the development of software in C#, which demonstrates the process of rerouting the route when new obstacles arise without the need for a complete recalculation of the entire network. This is critically important for minimizing computational costs in complex information and analytical systems. A special emphasis is placed on the educational aspect: the developed visualization is integrated into the educational process for teaching the disciplines "Algorithms and Data Structures" and "Object-Oriented Programming", which significantly improves the assimilation of complex mathematical concepts by students. The results of the work confirm that the combination of theoretical methods of pathfinding with interactive visualization provides high reliability and transparency of the functioning of modern navigation and environmental monitoring systems. The program for visualizing the algorithm for finding the optimal path in a maze has great practical importance and can be used in robotics and autonomous systems for robot navigation in a real environment, in networks and telecommunications it searches for the optimal data transmission route. Visualizing the algorithm for finding the optimal path in a cellular maze with dynamic obstacles allows for a deeper understanding of the principles of operation of algorithms, to assess their effectiveness in real time, and to experiment with different strategies for rerouting the route.Received 2026-02-23Accepted 2026-04-01

This study aims to analyze the factors that influence this low interest of young people in working in the agricultural sector in Lendang Nangka Utara Village, Masbagik District, East Lombok Regency. This study uses a qualitative approach. There are three paths of qualitative data analysis, namely data reduction, data presentation, and conclusion drawing. With data obtained through interviews, observations, and documentation with triangulation source validity testing. The research informants consisted of young people, hamlet heads, village secretaries, and the patrents of the interviewed youth.The results of the study show that the interest of the younger generation in Lendang Nangka Utara Village in the agricultural sector is relatively low. This low interest is influenced by several key factors: wages/income, continuity, education, family land assets, and family environment. Government support is needed in the form of training, capital, and modern technology to increase the interest of the younger generation in the agricultural sector.