Search Results (1,347)

Polymer inclusion membranes (PIMs) have been reported to be useful for the selective separation of numerous metal ions, with multiple applications in areas such as analytical chemistry, water quality monitoring, water treatment, and metal recovery. This review aims to update the recent advancements related to PIM technology in metal ion separation, with a particular emphasis on environmentally friendly production and applications. PIMs have many advantages over classical liquid–liquid extraction, such as excellent selectivity, ease of use with simultaneous extraction and back-extraction, stability, and reusability. PIMs typically consist of a base polymer, a carrier, and, if necessary, a plasticizer, and can therefore be tailored to specific analytes and specific matrices. Consequently, numerous studies have been carried out to develop PIMs for specific applications. In analytical chemistry, PIMs have been used mostly for analyte preconcentration, matrix separation, speciation analysis, and sensing. They can be used as passive sampling tools or integrated into automated water monitoring systems. PIMs are also widely studied for the extraction and purification of valuable metals in the frame of the circular economy, as well as for wastewater treatment. Even if they are a greener alternative to classical metal extraction, their production still requires petroleum-based polymers and toxic and volatile solvents. In recent years, there has been a clear trend to replace classical polymers with biodegradable and bio-sourced polymers and to replace the production of PIMs using toxic solvents with those based on green solvents or without solvents. According to the published literature, environmentally friendly PIM-based techniques are a highly recommended area of future research for metal ion separation directed toward a wide range of applications. Full article

Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this paper, we present a crystal structure prediction software based on artificial intelligence, named as CrySPAI, to predict energetically stable crystal structures of inorganic materials given their chemical compositions. The software consists of three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible crystal structure configurations, density functional theory (DFT) that provides the accurate energy values for these structures, and a deep neural network (DNN) that learns the relationship between crystal structures and their corresponding energies. To optimize the process across these modules, a distributed framework is implemented to parallelize tasks, and an automated workflow has been integrated into CrySPAI for seamless execution. This paper reports the development and implementation of the AI-based CrySPAI Crystal Prediction Software tool and its unique features. Full article

River morphology is highly dynamic, requiring accurate datasets and models for effective management, especially in flood-prone regions. Climate change and urbanisation have intensified flooding events, increasing risks to populations and infrastructure. Woody debris, a natural element of river ecosystems, poses a dual challenge: while it provides critical habitats, it can obstruct water flow, exacerbate flooding, and threaten infrastructure. Traditional debris detection methods are time-intensive, hazardous, and limited in scope. This study introduces a novel tool integrating artificial intelligence (AI) and computer vision (CV) to detect woody debris in rivers using aerial drone imagery that is fully integrated into a geospatial Web platform (WebGIS). The tool identifies and segments debris, assigning risk levels based on obstruction severity. When using orthoimages as input data, the tool provides georeferenced locations and detailed reports to support flood mitigation and river management. The methodology encompasses drone data acquisition, photogrammetric processing, debris detection, and risk assessment, and it is validated using real-world data. The results show the tool’s capacity to detect large woody debris in a fully automatic manner. This approach automates woody debris detection and risk analysis, making it easier to manage rivers and providing valuable data for assessing flood risk. Full article

The rapid expansion of digital news sources has necessitated intelligent systems capable of filtering, analyzing, and deriving meaningful insights from vast amounts of information in real time. This study presents an AI-driven chatbot designed for real-time news automation, integrating advanced natural language processing techniques, knowledge graphs, and generative AI models to improve news summarization and correlation analysis. The chatbot processes over 1,306,518 news reports spanning from 25 September 2023 to 17 February 2025, categorizing them into 15 primary event categories and extracting key insights through structured analysis. By employing state-of-the-art machine learning techniques, the system enables real-time classification, interactive query-based exploration, and automated event correlation. The chatbot demonstrated high accuracy in both summarization and correlation tasks, achieving an average F1 score of 0.94 for summarization and 0.92 for correlation analysis. Summarization queries were processed within an average response time of 9 s, while correlation analyses required approximately 21 s per query. The chatbot’s ability to generate real-time, concise news summaries and uncover hidden relationships between events makes it a valuable tool for applications in disaster response, policy analysis, cybersecurity, and public communication. This research contributes to the field of AI-driven news analytics by bridging the gap between static news retrieval platforms and interactive conversational agents. Future work will focus on expanding multilingual support, enhancing misinformation detection, and optimizing computational efficiency for broader real-world applicability. The proposed chatbot stands as a scalable and adaptive solution for real-time decision support in dynamic information environments. Full article

Background: Actinium-225 (²²⁵Ac) has gained interest in nuclear medicine for use in targeted alpha therapy (TAT) for the treatment of cancer. However, the number of suitable chelators for the stable complexation of ²²⁵Ac³⁺ is limited. The promising physical properties of ²²⁵Ac result in an increased demand for the radioisotope that is not matched by its current supply. To expand the possibilities for the development of ²²⁵Ac-based TAT therapeutics, a new hydroxamate-based chelator, DFO*¹², is described. We report the DFT-guided design of dodecadentate DFO*¹² and an efficient and convenient automated solid-phase synthesis for its preparation. To address the limited availability of ²²⁵Ac, a small-scale ²²⁹Th/²²⁵Ac generator was constructed in-house to provide [²²⁵Ac]AcCl₃ for research. Methods: DFT calculations were performed in ORCA 5.0.1 using the BP86 functional with empirical dispersion correction D3 and Becke–Johnson damping (D3BJ). The monomer synthesis over three steps enabled the solid-phase synthesis of DFO*¹². The small-scale ²²⁹Th/²²⁵Ac generator was realized by extracting ²²⁹Th from aged ²³³U material. Radiolabeling of DFO*¹² with ²²⁵Ac was performed in 1 M TRIS pH 8.5 or 1.5 M NaOAc pH 4.5 for 30 min at 37 °C. Results: DFT calculations directed the design of a dodecadentate chelator. The automated synthesis of the chelator DFO*¹² and the development of a small-scale ²²⁹Th/²²⁵Ac generator allowed for the radiolabeling of DFO*¹² with ²²⁵Ac quantitatively at 37 °C within 30 min. The complex [²²⁵Ac]Ac-DFO*¹² indicated good stability in different media for 20 h. Conclusions: The novel hydroxamate-based dodecadentate chelator DFO*¹², together with the developed ²²⁹Th/²²⁵Ac generator, provide new opportunities for ²²⁵Ac research for future radiopharmaceutical development and applications in TAT. Full article

Hepatitis, most importantly hepatitis B and hepatitis C, is a significant global health concern, requiring an accurate and early diagnosis to prevent severe liver damage and ensure effective treatment. The currently employed diagnostic methods, while effective, are often limited in their sensitivity, specificity, and rapidity, and the quest for improved diagnostic tools is ongoing. This review explores the innovative application of Raman spectroscopy combined with a chemometric analysis as a powerful diagnostic tool for hepatitis. Raman spectroscopy offers a non-invasive, rapid, and detailed molecular fingerprint of biological samples, while chemometric techniques enhance the interpretation of complex spectral data, enabling precise differentiation between healthy and diseased states and moreover the severity/stage of disease. This review aims to provide a comprehensive overview of the current research, foster greater understanding, and stimulate further innovations in this burgeoning field. The Raman spectrum of blood plasma or serum provides fingerprints of biochemical changes in the blood profile and the occurrence of disease simultaneously, while Raman analyses of polymerase chain reaction/hybridization chain reaction (PCR/HCR)-amplified nucleic acids and extracted DNA/RNA as the test samples provide more accurate differentiation between healthy and diseased states. Chemometric tools enhance the diagnostic efficiency and allow for quantification of the viral loads, indicating the stage of disease. The incorporation of different methodologies like surface enhancement and centrifugal filtration using membranes provides the ability to target biochemical changes directly linked with the disease. Immunoassays and biosensors based on Raman spectroscopy offer accurate quantitative detection of viral antigens or the immune response in the body (antibodies). Microfluidic devices enhance the speed of detection through the continuous testing of flowing samples. Raman diagnostic studies with massive sample sizes of up to 1000 and multiple reports of achieving a greater than 90% differentiation accuracy, sensitivity, and specificity using advanced multivariate data analysis tools indicate that Raman spectroscopy is a promising tool for hepatitis detection. Its reproducibility and the identification of unique reference spectral features for each hepatic disease are still challenges in the translation of Raman spectroscopy as a clinical tool, however. The development of databases for automated comparison and the incorporation of automated chemometric processors into Raman diagnostic tools could pave the way for their clinical translation in the near future. Full article

Background: Prostate cancer (PCa) diagnosis using MRI is often challenged by lesion variability. Methods: This study introduces Simplatab, an open-source automated machine learning (AutoML) framework designed for, but not limited to, automating the entire machine Learning pipeline to facilitate the detection of clinically significant prostate cancer (csPCa) using radiomics features. Unlike existing AutoML tools such as Auto-WEKA, Auto-Sklearn, ML-Plan, ATM, Google AutoML, and TPOT, Simplatab offers a comprehensive, user-friendly framework that integrates data bias detection, feature selection, model training with hyperparameter optimization, explainable AI (XAI) analysis, and post-training model vulnerabilities detection. Simplatab requires no coding expertise, provides detailed performance reports, and includes robust data bias detection, making it particularly suitable for clinical applications. Results: Evaluated on a large pan-European cohort of 4816 patients from 12 clinical centers, Simplatab supports multiple machine learning algorithms. The most notable features that differentiate Simplatab include ease of use, a user interface accessible to those with no coding experience, comprehensive reporting, XAI integration, and thorough bias assessment, all provided in a human-understandable format. Conclusions: Our findings indicate that Simplatab can significantly enhance the usability, accountability, and explainability of machine learning in clinical settings, thereby increasing trust and accessibility for AI non-experts. Full article

This paper presents an innovative approach to teaching mechatronics at the bachelor’s level, using the design and construction of an Automated Guided Vehicle (AGV) as a comprehensive example of a mechatronic system. The course, titled Laboratory of Electronic Systems, is part of a newly established professionalizing bachelor’s degree program at the University of Pisa, focused on techniques for mechanics and production. This program was developed to meet industry demands for technically skilled personnel with an engineering-related background but without the need for a full traditional engineering education. The course is designed to provide students with hands-on experience, integrating fundamental concepts from mechanical, electronic, and control engineering, along with software development. The curriculum emphasizes practical applications rather than theoretical depth, aligning with the program’s goal of preparing students for operational roles in industrial settings. We present the course structure, educational objectives, and the interdisciplinary nature of mechatronics as addressed in this teaching approach. A dedicated section outlines the critical steps involved in the AGV prototype development, highlighting practical challenges and learning opportunities. The effectiveness of the course is assessed through the evaluation of student projects, specifically via a technical report and a final discussion on the design of a mechatronic system. The results demonstrate the value of a project-based learning approach in equipping students with the practical skills and knowledge required for careers in mechatronics and industrial automation. Full article

This paper examines the use of interactive Google Sheets for the automatic measurement of key performance indicators (KPIs) in higher education, particularly in the context of academic accreditation. As institutions face increasing pressure to demonstrate quality and effectiveness, reliable data tracking and reporting have become essential. Traditional methods of managing academic records and performance metrics can be cumbersome and error-prone, underscoring the need for an automated solution. By leveraging Google Sheets’ dynamic capabilities, institutions can efficiently monitor KPIs related to student performance, curriculum effectiveness, and faculty qualifications. This approach allows for real-time data updates, facilitating timely insights that support accreditation processes and continuous improvement initiatives. The study outlines the design and implementation of a dynamic Google Sheets framework tailored to higher education KPIs, highlighting its benefits in enhancing data accuracy, reducing administrative burden, and fostering collaboration among faculty and administration. Through illustrative case studies, we demonstrate how this tool empowers institutions to meet accreditation standards and drive educational excellence. Ultimately, the research emphasizes the potential of automation to transform academic record management and enhance institutional accountability in higher education. Full article

Flight Operations Quality Assurance (FOQA) is an internationally recognized solution to ensure the safety of civil aircraft flights based on Quick Access Recorder (QAR) data. The traditional approach to anomaly detection in civil aviation is to detect the over-limit values of monitoring parameters for each monitoring event based on the standards issued by civil aviation authorities. Usually, for each anomaly detection operation routine, this only works for one monitoring event. Furthermore, the causal analyses for the detected anomaly events are based on the relevant worker’s expertise. In order to improve the efficiency of FOQA, this paper proposes an automated anomaly detection and causal analysis method called $\mathsf{MAD}\text{-}\mathsf{XFP}$. Due to the unique industry characteristics of QAR data and the requirements of FOQA, feature engineering and hyper-parameter optimization techniques are utilized to enhance the machine learning model. The proposed method can monitor multiple events in one routine and provide a causal analysis. In the causal analysis process, the Shapley additive interpretation method is applied to produce analysis report for detected anomalies. Experimental evaluations are conducted on real civil aviation datasets. The experimental results show that the proposed method can efficiently and automatically detect different abnormal events with high precision in the approach phase and produce preliminary causal analysis. Full article

This study explores possible applications of AI technology in online journalism, given the predictions that speed and adaptation to the new medium will increase the penetration of automation in the production business. The literature shows that while the human supervision of journalistic workflow is still considered vital, the journalistic workflow is changing in nature, with the writing of micro-content being entrusted to ChatGPT-3.5 among the most visible features. This research assesses readers’ reactions to different headline styles as tested on a sample of 624 students from Timisoara, Romania, asked to evaluate the qualities of a mix of human-written vs. AI-generated headlines. The results show that AI-generated, informative headlines were perceived by more than half of the respondents as the most trustworthy and representative of the media content. Clickbait headlines, regardless of their source, were considered misleading and rated as manipulative (44.7%). In addition, 54.5% of respondents reported a decrease in trust regarding publications that frequently use clickbait techniques. A linguistic analysis was conducted to grasp the qualities of the headlines that triggered the registered responses. This study provides insights into the potential of AI-enabled tools to reshape headline writing practices in digital journalism. Full article

Climate change in Eastern Europe requires introducing automated irrigation systems and monitoring agricultural and climatic parameters to ensure food security. The automation of irrigation, together with the generation of climate reports based on AI (artificial intelligence) using OpenAI models for Internet of Things (IoT) data processing, contributes to the optimization of resources by reducing excessive water and energy consumption, supporting plant health through proper irrigation and increasing sustainable agricultural productivity by providing suggestions and statistics to streamline the agricultural process. In this paper, the authors present a system that allows continuous data collection of parameters such as temperature, humidity, and soil moisture, providing detailed information and advanced analytics for each device and area monitored using AI to generate predictive recommendations. The data transmission is performed wirelessly via WebSocket to the central database. This system uses data from all devices connected to the application to assess current climate conditions at a national level, identifying trends and generating reports that aid in adapting to extreme events. The integration of artificial intelligence in the context of monitoring and irrigation of agricultural areas is a step forward in the development of sustainable agriculture and for the adaptation of agriculture to increasingly aggressive climate phenomena, providing a replicable framework for vulnerable regions. Full article

Lead (Pb) is one of the most relevant contaminants due to its high toxicity, even at low concentrations. The growing need for research about real-time Pb analysis in the field has driven advancements in portable, sensitive, and automated analytical methodologies. These innovations are crucial for taking proactive measures against the impacts of Pb pollution on ecosystems and public health. Flow analysis techniques have proven to be very effective in automating procedures for isolating and preconcentrating Pb in surface water and biological samples. Such automation boosts sample throughput and reduces processing time and reagent consumption, aligning with the green chemistry principles by lowering costs and minimizing waste. This review covers 31 recent automated analytical methodologies employing flow analysis techniques such as FIA, SIA, MSFIA, and LOV, emphasizing the trend toward portability and miniaturization, which facilitates in-situ analysis. Additionally, this review examines the pretreatment methods and detection systems used, highlighting the analytical parameters of each technique. The methodologies discussed demonstrate the capability to process up to 55 samples per hour accurately. Limits of quantification as low as 0.014 µg L⁻¹ are reported, enabling environmental monitoring that effectively detects Pb concentrations below the WHO and EPA drinking water reference values of 10 µg L⁻¹ and 15 µg L⁻¹, respectively. Full article

A thermal neutron-absorbing metal matrix composite (MMC) comprised of Al₃Hf particles in an aluminum matrix was developed to filter out thermal neutrons and create a fast flux environment for material testing in a mixed-spectrum nuclear reactor. Intermetallic Al₃Hf particles capture thermal neutrons and are embedded in a highly conductive aluminum matrix that provides conductive cooling of the heat generated due to thermal neutron capture by the hafnium. These Al₃Hf-Al MMCs were fabricated using powder metallurgy via hot pressing. The specimens were neutron-irradiated to between 1.12 and 5.38 dpa and temperatures ranging from 286 °C to 400 °C. The post-irradiation examination included microstructure characterization using transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy. This study reports the microstructural observations of four irradiated samples and one unirradiated control sample. All the samples showed the presence of oxide at the particle–matrix interface. The irradiated specimens revealed needle-like structures that extended from the surface of the Al₃Hf particles into the Al matrix. An automated segmentation tool was implemented based on a YOLO11 computer vision-based approach to identify dislocation lines and loops in TEM images of the irradiated Al-Al₃Hf MMCs. This work provides insight into the microstructural stability of Al₃Hf-Al MMCs under irradiation, supporting their consideration as a novel neutron absorber that enables advanced spectral tailoring. Full article

Electric-connected automated vehicle (CAV) shuttles, as a part of the sustainable microtransit system, have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass-produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centres. Thus, there is a need for increased knowledge on treating risk in such applications. This paper covers the planning and economic feasibility of an advanced technology level 4 automated vehicle-based microtransit system, considering uncertain service and economic feasibility factors. The methods used are advanced for addressing uncertainties in travel demand, service factors, and the economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic, and it is adapted for risk analysis in financial decision-making. The effects of uncertain life-cycle costs on fares and the rate-of-return are described. Results are favourable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on the feasibility of implementing CAV-based first/last mile, and other microtransit services, under uncertainty. Full article