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The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette packaging paper samples. Employing multivariate statistical methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Statistical Quality Control (SQC), and Similarity-based Independent Modeling of Class Analogy (SIMCA), we analyzed and processed the collected data. Comprehensive evaluation and quality control models were constructed to assess sample stability and distinguish odors. Results indicate that our electronic nose system rapidly detects odors and effectively performs quality control. By establishing models for quality stability control, we successfully identified samples with acceptable quality and those with odors. To further validate the system’s performance and extend its applications, we collected two types of cigarette packaging paper samples with odor data. Using data augmentation techniques, we expanded the dataset and achieved an accuracy rate of 0.9938 through classification and discrimination. This highlights the significant potential of our self-developed electronic nose system in recognizing cigarette packaging paper odors and odorous samples.

Objective: To assess the current trends in the field of artificial intelligence in medicine by analysing 100 most cited original articles relevant to the field. Methods: The systematic review was conducted in September 2022, and comprised literature search on Scopus database for original articles only. Google and Medical Subject Headings databases were used as resources to extract key words. In order to cover a broad range of articles, original studies comprising human as well as non-human subjects, studies without abstract and studies in languages other than English were part of the inclusion criteria. There was no specific time period applied to the search and no specific selection was done regarding the journals in the database. The screening was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to extract the top 100 most cited articles in the field of artificial intelligence usage in medicine. Data was analysed using SPSS 23. Results: Of the 11,571 studies identified, 100(0.86%) were analysed in detail. The studies were published between 1986 and 2021, with a median of 43 citations (IQR 53) per article. The journal ‘Artificial Intelligence in Medicine’ accounted for the highest number 9(9%)) of articles, and the United States was the country of origin for most of the articles 36(36%). Conclusion: The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.

Abstract Wearable sensors are establishing themselves as options for real‐time continuous health monitoring in health care and wellness. In particular, the use of flexible interfaces that conform to the skin have attracted considerable interest for the extraction of meaningful pathophysiological information through continuous and painless sampling and analysis of biofluids. In contrast, conventional techniques for biomarkers analysis are difficult to adapt to real‐time portable monitoring due to their invasive sampling protocols, biosample preparation and reagent stabilization. Here a shelf‐stable, non‐invasive, paper‐based colorimetric wearable lactate sensor is reported. This sensor exploits the ability of silk to control the concentration, print, and functionally preserve labile transducing biomolecules in the format of a shelf‐stable digital patch for optical readout. This novel approach overcomes major challenges associated with the commercialization of colorimetric wearable sensors (e.g., enzyme thermal instability, narrow sensing range, low sensitivity, and qualitative response) by showing a combination of unprecedented stability (i.e., up to 2 years in refrigerated conditions), wide sensing range, and high sensitivity. Additionally, real‐time quantitative signal readouts are achieved using machine learning‐driven image analysis enabling physiological status evaluation with a simple smartphone camera.

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research.

Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C2H4) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms.

In the dynamic landscape of advanced manufacturing, the confluence of laser powder bed fusion (LPBF) and machine learning (ML) has recently garnered significant attention in many applications. This review investigates the confluence of LPBF and ML, specifically within the specific domain of stainless steel. Firstly, it delves into LPBF principles, including an overview of critical process parameters and associated defects. Secondly, the paper meticulously addresses the distinct challenges posed by steel in additive manufacturing (AM), highlighting factors such as chemical composition, anisotropic microstructure, and oxide film formation, all of which require specialized considerations. Thirdly, the spotlight shifts to the pivotal role of ML, covering predictive modeling for process parameters, real-time defect detection, and quality control. This paper highlights recent advances, revealing how data-driven approaches can accelerate process understanding and part qualification. Eventually, this review offers insights into the future integration of ML in LPBF for steel, providing valuable perspectives on potential advancements in the field of AM.

In 2023, Hungary produced notable scientific publications focused on cardiac arrhythmias. This review highlights the most impactful papers, authored at least in part by Hungarians, featuring work conducted at Hungarian research sites and published in international peer-reviewed journals. We give a short overview regarding papers dealing with the role of imaging (different types of ultrasound, cardiac magnetic resonance imaging and electroanatomical mapping) in treating cardiac arrhythmias. We summarize the novelties of ischemic complications of atrial fibrillation (AF), cerebral safety of AF ablation, and current AF ablation techniques. Optimizing risk stratification of ventricular tachycardia (VT) ablation was a landmark paper of our “machine learning” research. Further studies have made significant strides in cardiac device therapy, notably through the groundbreaking BUDAPEST-CRT Upgrade Trial for heart failure, which demonstrated marked reductions in deaths and hospitalizations following Cardiac Resynchronization Therapy (CRT) upgrade procedures. Research on the impact of frailty on CRT outcomes has underscored the importance of tailored treatments. Artificial intelligence, particularly topological data analysis, has been crucial in personalizing CRT therapy. Additionally, advancements in remote monitoring and lead extraction techniques have mirrored the rapid evolution of cardiac technology and care.

Abstract Background The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Methods The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022. Results We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships. Conclusions The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. This has underlined the need for advanced Deep Learning and Machine Learning approaches to effectively spot and manage the virus's spread. Indeed, researchers worldwide have dynamically participated in the field by publishing an important number of papers across various databases. In this context, we present a bibliometric analysis focused on the detection and classification of COVID-19 using Deep Learning and Machine Learning techniques, based on X-Ray and CT images. We analyzed published documents of the six prominent databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect) during the period between 2019 and November 2023. Our results showed that rising forces in economy and technology, especially India, China, Turkey, and Pakistan, began to compete with the great powers in the field of scientific research, which could be seen from their number of publications. Moreover, researchers contributed to Deep Learning techniques more than the use of Machine Learning techniques or the use of both together and preferred to submit their works to Springer Database. An important result was that more than 57% documents were published as Journal Articles, which was an important portion compared to other publication types (conference papers and book chapters). Moreover, the PubMed journal "Multimedia Tools and Applications" tops the list of journals with a total of 29 published articles.

Hydro-meteorological hazards are a major issue in many regions of the world, including Romania. Among these, flash floods are the most frequent phenomena, generating significant annual socio-economic and environmental damages. In recent years, flash flood forecasting using machine learning algorithms has become an useful tool for data-based hydrologic modeling. Machine learning allows to create mathematical relationships between the river discharge and other climatic and physico-geographic parameters from the training dataset. This paper aims to perform a scientometric analysis using open-source programs, namely ScientoPyGui and VOSviewer. The expression ‘flash flood forecasting AND machine learning’ was searched in the Web of Science and Scopus databases. After merging and removing duplicates, 112 publications were retained for analysis. Their number has increased by 60% in the past three years (after 2021) with a trend towards a sub-branch of machine learning, namely deep learning. The spatial distribution of the papers showed that China is a global leader with 25% of the total. These findings highlight the increasing role of machine learning based models (particularly deep learning) in enhancing flash flood forecasting, a nonstructural measure for the flash flood risk mitigation