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The integration of radar technology into smart furniture represents a practical approach to health monitoring, circumventing the concerns regarding user convenience and privacy often encountered by conventional smart home systems. Radar technology's inherent non-contact methodology, privacy-preserving features, adaptability to diverse environmental conditions, and high precision characteristics collectively establish it a compelling alternative for comprehensive health monitoring within domestic environments. In this paper, we introduce a millimeter (mm)-wave radar system positioned strategically behind a seat, featuring an algorithm capable of identifying unique cardiac waveform patterns for healthy subjects. These patterns are characterized by two peaks followed by a valley in each cycle, which can be correlated to Electrocardiogram (ECG), enabling effective cardiac waveform monitoring. The provided algorithm excels in discerning variations in heart patterns, particularly in individuals with prolonged corrected QT intervals, by minimizing high frequency breathing interference and ensuring accurate pattern recognition. Additionally, this paper addresses the influence of body movements in seated individuals, conducting a comprehensive study on heart rate variability and estimation. Experiment results demonstrate a maximum interbeat intervals (IBI) error of 30 milliseconds and an average relative error of 4.8% in heart rate estimation, showcasing the efficacy of the proposed method utilizing variational mode decomposition and a multi-bin approach., Competing Interests: Declarations. Competing Interests: None of the authors have conflicts of interest to declare., (© 2025. The Author(s).)

Objective: To identify the early predictors of a self-reported persistence of long COVID syndrome (LCS) at 12 months after hospitalisation and to propose the prognostic model of its development., Design: A combined cross-sectional and prospective observational study., Setting: A tertiary care hospital., Participants: 221 patients hospitalised for COVID-19 who have undergone comprehensive clinical, sonographic and survey-based evaluation predischarge and at 1 month with subsequent 12-month follow-up. The final cohort included 166 patients who had completed the final visit at 12 months., Main Outcome Measure: A self-reported persistence of LCS at 12 months after discharge., Results: Self-reported LCS was detected in 76% of participants at 3 months and in 43% at 12 months after discharge. Patients who reported incomplete recovery at 1 year were characterised by a higher burden of comorbidities (Charlson index of 0.69±0.96 vs 0.31±0.51, p=0.001) and residual pulmonary consolidations (1.56±1.78 vs 0.98±1.56, p=0.034), worse blood pressure (BP) control (systolic BP of 138.1±16.2 vs 132.2±15.8 mm Hg, p=0.041), renal (estimated glomerular filtration rate of 59.5±14.7 vs 69.8±20.7 mL/min/1.73 m 2 , p=0.007) and endothelial function (flow-mediated dilation of the brachial artery of 10.4±5.4 vs 12.4±5.6%, p=0.048), higher in-hospital levels of liver enzymes (alanine aminotransferase (ALT) of 76.3±60.8 vs 46.3±25.3 IU/L, p=0.002) and erythrocyte sedimentation rate (ESR) (34.3±12.1 vs 28.3±12.6 mm/h, p=0.008), slightly higher indices of ventricular longitudinal function (left ventricular (LV) global longitudinal strain (GLS) of 18.0±2.4 vs 17.0±2.3%, p=0011) and higher levels of Hospital Anxiety and Depression Scale anxiety (7.3±4.2 vs 5.6±3.8, p=0.011) and depression scores (6.4±3.9 vs 4.9±4.3, p=0.022) and EFTER-COVID study physical symptoms score (12.3±3.8 vs 9.2±4.2, p<0.001). At 1 month postdischarge, the persisting differences included marginally higher LV GLS, mitral E/e' ratio and significantly higher levels of both resting and exertional physical symptoms versus patients who reported complete recovery. Logistic regression and machine learning-based binary classification models have been developed to predict the persistence of LCS symptoms at 12 months after discharge., Conclusions: Compared with post-COVID-19 patients who have completely recovered by 12 months after hospital discharge, those who have subsequently developed 'very long' COVID were characterised by a variety of more pronounced residual predischarge abnormalities that had mostly subsided by 1 month, except for steady differences in the physical symptoms levels. A simple artificial neural networks-based binary classification model using peak ESR, creatinine, ALT and weight loss during the acute phase, predischarge 6-minute walk distance and complex survey-based symptoms assessment as inputs has shown a 92% accuracy with an area under receiver-operator characteristic curve 0.931 in prediction of LCS symptoms persistence at 12 months., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Polymeric materials are commonly used as the outermost layer in spacecraft passive thermal control. However, in geostationary earth orbit environments, the polymeric layer is susceptible to environmental hazards, particularly electrostatic charges. In this study, we develop a graphene-based coating on a polymeric polyimide (Kapton ® ) and discuss its suitability in simulated harsh space environments for electrostatic dissipation. An about 80-100 nm thick conducting reduced graphene oxide (rGO) coating was developed on Kapton® by a simple and cost-effective spray technique while ensuring minimal variation in the thermo-optical properties and hence the equilibrium temperature. The spaceworthiness and stability of the coating were evaluated through simulated space environment tests, including thermal cycling, thermal vacuum, relative humidity, adhesion, and aging tests. Structural, optical, and electrical properties were found to be preserved after spaceworthiness tests, demonstrating the durability of the coating in harsh space environments. Furthermore, field emission scanning electron microscopy demonstrated significant electron charging on uncoated Kapton ® , with a gradual reduction in charge buildup for GO-coated Kapton ® , and almost negligible charging on rGO-coated Kapton ® when subjected to electron bombardment at 10, 15, and 20 kV. Kelvin probe force microscopy further confirmed the enhanced electrostatic dissipative properties, showing a notable decrease in surface potential from 300 mV for uncoated Kapton ® to 60 mV for rGO-coated Kapton ® . These findings suggest that the developed graphene-based coating holds promise as a space-survivable solution for electrostatic dissipation in a spacecraft.

Modern artificial intelligence (AI) solutions often face challenges due to the "black box" nature of deep learning (DL) models, which limits their transparency and trustworthiness in critical medical applications. In this study, we propose and evaluate a scalable approach based on a transition matrix to enhance the interpretability of DL models in medical signal and image processing by translating complex model decisions into user-friendly and justifiable features for healthcare professionals. The criteria for choosing interpretable features were clearly defined, incorporating clinical guidelines and expert rules to align model outputs with established medical standards. The proposed approach was tested on two medical datasets: electrocardiography (ECG) for arrhythmia detection and magnetic resonance imaging (MRI) for heart disease classification. The performance of the DL models was compared with expert annotations using Cohen's Kappa coefficient to assess agreement, achieving coefficients of 0.89 for the ECG dataset and 0.80 for the MRI dataset. These results demonstrate strong agreement, underscoring the reliability of the approach in providing accurate, understandable, and justifiable explanations of DL model decisions. The scalability of the approach suggests its potential applicability across various medical domains, enhancing the generalizability and utility of DL models in healthcare while addressing practical challenges and ethical considerations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Barmak, Krak, Yakovlev, Manziuk, Radiuk and Kuznetsov.)

Introduction: Recent advancements in generative AI, exemplified by ChatGPT, hold promise for healthcare applications such as decision-making support, education, and patient engagement. However, rigorous evaluation is crucial to ensure reliability and safety in clinical contexts. This scoping review explores ChatGPT's role in clinical inquiry, focusing on its characteristics, applications, challenges, and evaluation., Methods: This review, conducted in 2023, followed PRISMA-ScR guidelines (Supplemental Digital Content 1, http://links.lww.com/MS9/A636). Searches were performed across PubMed, Scopus, IEEE, Web of Science, Cochrane, and Google Scholar using relevant keywords. The review explored ChatGPT's effectiveness in various medical domains, evaluation methods, target users, and comparisons with other AI models. Data synthesis and analysis incorporated both quantitative and qualitative approaches., Results: Analysis of 41 academic studies highlights ChatGPT's potential in medical education, patient care, and decision support, though performance varies by medical specialty and linguistic context. GPT-3.5, frequently referenced in 26 studies, demonstrated adaptability across diverse scenarios. Challenges include limited access to official answer keys and inconsistent performance, underscoring the need for ongoing refinement. Evaluation methods, including expert comparisons and statistical analyses, provided significant insights into ChatGPT's efficacy. The identification of target users, such as medical educators and nonexpert clinicians, illustrates its broad applicability., Conclusion: ChatGPT shows significant potential in enhancing clinical practice and medical education. Nevertheless, continuous refinement is essential for its successful integration into healthcare, aiming to improve patient care outcomes, and address the evolving needs of the medical community., Competing Interests: The authors declare no conflict of interest.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Dry reforming of methane (DRM) was extensively studied on Cu-doped LaNiO 3 catalysts. The main findings of this work are as follows: (i) thermal switching of the catalyst phase between the parent perovskite and molecular-level nanocomposite of individual components formed in situ during DRM, (ii) reusability of the catalyst with enhanced activity, and (iii) regeneration of the catalyst phase at a lower temperature than that required for the formation of the parent perovskite. The present investigation provides an extensive analysis and understanding of the DRM reaction using Cu-doped LaNiO 3 compared to the result reported by Moradi et al. , ( Chin. J. Catal. , 2012, 33 , 797-801) and hence provides new insights into its catalytic activity. Phase-pure LaNi 1- x Cu x O 3 catalysts, specifically LaNi 0.8 Cu 0.2 O 3 , exhibited high catalytic activity towards the DRM reaction (97% CH 4 and 99% CO 2 conversion with an H 2 /CO ratio of ∼1.4-0.9). Remarkably, although the initial perovskite phase primarily decomposed into its component phases after DRM, its catalytic activity was barely affected and maintained even after 100 h. The regeneration of the initial perovskite from the disintegrated binary phases via annealing at temperatures even lower than the synthesis temperature together with the amazing retention of activity was very intriguing. The parallel activity of the pristine perovskite and its degraded binary mixtures makes it difficult to identify the actual components responsible for the DRM activity. Accordingly, we have explained the sustained activity of the degraded perovskite catalyst in the context of nanocomposite formation at the molecular level in the reforming atmosphere with the availability of Ni 0 and NiO, as revealed by the thoroughly characterized samples in the as-prepared, aged, and regenerated forms.

Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience becomes crucial. A promising approach for reducing the resource consumption of AI systems during test-time involves applying the concepts and methods of dynamic neural networks. Nevertheless, the resilience of dynamic neural networks against various disturbances remains underexplored. This paper proposes a model architecture and training method that integrate dynamic neural networks with a focus on resilience. Compared to conventional training methods, the proposed approach yields a 24% increase in the resilience of convolutional networks and a 19.7% increase in the resilience of visual transformers under fault injections. Additionally, it results in a 16.9% increase in the resilience of convolutional network ResNet-110 and a 21.6% increase in the resilience of visual transformer DeiT-S under adversarial attacks, while saving more than 30% of computational resources. Meta-training the neural network model improves resilience to task changes by an average of 22%, while achieving the same level of resource savings.

The accurate and rapid identification of explosives and their toxic by-products is an important aspect of safety protocols, forensic investigations and pollution studies. Herein, surface-enhanced Raman scattering (SERS) is used to detect different explosive molecules using an improved substrate design by controllable oxidation of the tungsten surface and deposition of Au layers. The resulting furrow-like morphology formed at the intersection of the tungsten Wulff facets increases nanoroughness and improves the SERS response by over 300 % compared to the untreated surface. The substrate showed excellent reproducibility with a relative standard deviation of less than 15 % and a signal recovery of over 95 % after ultrafast Ar/O 2 plasma cleanings. The detection limit for the "dried on a surface" measurement case was better than 10 -8 M using the moving scanning regime and an acquisition time of 10 s, while for the "water droplets on a surface" scenario the LoD is 10 -7 , which is up to 2 orders of magnitude better than the UV-Vis spectroscopy method. The substrates were successfully used to classify the molecular fingerprints of HMX, Tetryl, TNB and TNT, demonstrating the efficiency of a sensor for label-free SERS screening in the practice of monitoring traces of explosives in the water medium., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The analysis of utilising unmanned aerial vehicles (UAVs) to form flying networks in obstacle conditions and various algorithms for obstacle avoidance is conducted. A planning scheme for deploying a flying LiFi network based on UAVs in a production facility with obstacles is developed and described. Such networks are necessary to ensure reliable data transmission from sensors or other sources of information located in dangerous or hard-to-reach places to the crisis centre. Based on the planning scheme, the following stages are described: (1) laying the LiFi signal propagation route in conditions of interference, (2) placement of the UAV at the specified points of the laid route for the deployment of the LiFi network, and (3) ensuring the reliability of the deployed LiFi network. Strategies for deploying UAVs from a stationary depot to form a flying LiFi network in a room with obstacles are considered, namely the strategy of the first point for the route, the strategy of radial movement, and the strategy of the middle point for the route. Methods for ensuring the uninterrupted functioning of the flying LiFi network with the required level of reliability within a given time are developed and discussed. To implement the planning stages for deploying the UAV flying LiFi network in a production facility with obstacles, the "Simulation Way" and "Reliability Level" software tools are developed and described. Examples of utilising the proposed software tools are given.

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community's understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Salvagno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI models, such as ChatGPT, have significantly impacted healthcare through the strategic use of prompts to enhance precision, relevance, and ethical standards. This perspective explores the application of prompt engineering to tailor outputs specifically for healthcare stakeholders: patients, providers, policymakers, and researchers. A nine-stage process for prompt engineering in healthcare is proposed, encompassing identifying applications, understanding stakeholder needs, designing tailored prompts, iterative testing and refinement, ethical considerations, collaborative feedback, documentation, training, and continuous updates. A literature review focused on "Generative AI" or "ChatGPT," prompts, and healthcare informed this study, identifying key prompts through qualitative analysis and expert input. This systematic approach ensures that AI-generated prompts align with stakeholder requirements, offering valuable insights into symptoms, treatments, and prevention, thereby supporting informed decision-making among patients.

Wrinkled coatings are a potential drug-free method for mitigating bacterial attachment and biofilm formation on materials such as medical and food grade steel. However, their fabrication typically requires multiple steps and often the use of a stimulus to induce wrinkle formation. Here, we report a facile plasma-based method for rapid fabrication of thin (<250 nm) polymer coatings from a single environmentally friendly precursor, where wrinkle formation and fractal pattern development are controlled solely by varying the deposition time from 3 s to 60 s. We propose a mechanism behind the observed in situ development of wrinkles in plasma, as well as demonstrate how introducing specific topographical features on the surface of the substrata can result int the formation of even more complex, ordered wrinkle patterns arising from the non-uniformity of plasma when in contact with structured surfaces. Thus-fabricated wrinkled surfaces show good adhesion to substrate and an antifouling activity that is not observed in the equivalent smooth coatings and hence is attributed to the specific pattern of wrinkles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Magnetic sensor with spin valve-GMR technology with medium dynamic range is designed for a diversity of applications, including linear and rotary position measurements, proximity switches, and current sensors. For this, the sensing layer (SL) of the spin valve stack was modified by a soft pinning layer (SPL) through an exchange bias field created by an antiferromagnetic layer which has a lower blocking temperature than the one that is kept adjacent to the pinned layer. Numerical simulation was carried out to control the bias field by keeping a non-magnetic Ru spacer layer between the SPL and SL layers and the results were experimentally verified. The magnetic sensor was fabricated with linear operating field range of the order ±100 Oe having a sensitivity of the order of 0.1 m V V -1 Oe -1 near zero field. The thermal performance confirms that the device can be operated in the temperature range of -40 ∘ C to 125 ∘ C and it has a thermal coefficient of voltage around 15 µ V V -1∘ C -1 ., (© 2024 IOP Publishing Ltd.)

Conifer shoots exhibit intricate geometries at an exceptionally detailed spatial scale. Describing the complete structure of a conifer shoot, which contributes to a radiation scattering pattern, has been difficult, and the previous respective components of radiative transfer models for conifer stands were rather coarse. This paper presents a dataset aimed at models and applications requiring detailed 3D representations of needle shoots. The data collection was conducted in the Järvselja RAdiation transfer Model Intercomparison (RAMI) pine stand in Estonia. The dataset includes 3-dimensional surface information on 10 shoots of two conifer species present in the stand (5 shoots per species) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 26th July 2022, and subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. For each of these samples, the dataset comprises of a photo of the sampled shoot and its obtained 3-dimensional surface reconstruction. Scanned shoots may replace previous, artificially generated models and contribute to the more realistic representation of 3D forest representations and, consequently, more accurate estimates of related parameters and processes by radiative transfer models., (© 2024 The Author(s).)

Importance: Since the full-scale Russian invasion, hospitals in Ukraine have been compelled to close or operate at reduced capacity due to inadequate supplies, damage, or destruction caused by war., Objective: To analyze hospital services in Ukraine during the period before and after the Russian invasion., Design, Setting, and Participants: Of the 450 hospitals currently functioning in Ukraine, a cross-sectional survey was carried out with the participation of 74 hospitals from 12 oblasts. Hospital administrators responded to an online survey with questions on the use of hospital services. Data were abstracted from hospital databases for the prewar period (before February 23, 2022) and during the war (February 23, 2022, to May 30, 2023)., Main Outcomes and Measures: Hospital services (including emergency services, preventive services, screenings, laboratory tests, obstetrics, telehealth, pharmacy, and rehabilitation services) were compared during the prewar and war periods., Results: Of 450 Ukrainian hospitals in operation, 74 hospitals (16.0%) across 12 oblasts provided data for the current analyses. During the war, daily emergency admissions increased to 2830, compared with 2773 before the war. At the same time, hospitals reported reduced laboratory testing (72 [97%] vs 63 [85%]), tobacco education (52 [70%] vs 36 [49%]), cancer screening (49 [66%] vs 37 [50%]), gynecological services (43 [58%] vs 32 [43%]), rehabilitation services (37 [50%] vs 27 [36%]), pharmacy services (36 [49%] vs 27 [36%]), and telehealth programs (33 [45%] vs 21 [28%]). Hospitals reported additional difficulties during the war, including disruptions in the supply chain for essential equipment and pharmaceuticals, shortages of laboratory test kits, delays in the delivery of crucial medications, and problems around appropriate medication storage due to power outages., Conclusions and Relevance: The ongoing war has inflicted profound devastation on Ukraine's hospitals. The findings of this cross-sectional survey offer valuable insights into the formidable challenges that hospitals confront in war-affected regions and underscore the pressing necessity for bolstering support to sustain and enhance hospital services during wartime.

Background: The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies., Objective: The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home., Methods: A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis., Results: Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used., Conclusion: Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: The healthcare sector demands a higher degree of responsibility, trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems. Machine learning operations (MLOps) for AI-based medical diagnostic systems are primarily focused on aspects such as data quality and confidentiality, bias reduction, model deployment, performance monitoring, and continuous improvement. However, so far, MLOps techniques do not take into account the need to provide resilience to disturbances such as adversarial attacks, including fault injections, and drift, including out-of-distribution. This article is concerned with the MLOps methodology that incorporates the steps necessary to increase the resilience of an AI-based medical diagnostic system against various kinds of disruptive influences., Methods: Post-hoc resilience optimization, post-hoc predictive uncertainty calibration, uncertainty monitoring, and graceful degradation are incorporated as additional stages in MLOps. To optimize the resilience of the AI based medical diagnostic system, additional components in the form of adapters and meta-adapters are utilized. These components are fine-tuned during meta-training based on the results of adaptation to synthetic disturbances. Furthermore, an additional model is introduced for post-hoc calibration of predictive uncertainty. This model is trained using both in-distribution and out-of-distribution data to refine predictive confidence during the inference mode., Results: The structure of resilience-aware MLOps for medical diagnostic systems has been proposed. Experimentally confirmed increase of robustness and speed of adaptation for medical image recognition system during several intervals of the system's life cycle due to the use of resilience optimization and uncertainty calibration stages. The experiments were performed on the DermaMNIST dataset, BloodMNIST and PathMNIST. ResNet-18 as a representative of convolutional networks and MedViT-T as a representative of visual transformers are considered. It is worth noting that transformers exhibited lower resilience than convolutional networks, although this observation may be attributed to potential imperfections in the architecture of adapters and meta-adapters., Сonclusion: The main novelty of the suggested resilience-aware MLOps methodology and structure lie in the separating possibilities and activities on creating a basic model for normal operating conditions and ensuring its resilience and trustworthiness. This is significant for the medical applications as the developer of the basic model should devote more time to comprehending medical field and the diagnostic task at hand, rather than specializing in system resilience. Resilience optimization increases robustness to disturbances and speed of adaptation. Calibrated confidences ensure the recognition of a portion of unabsorbed disturbances to mitigate their impact, thereby enhancing trustworthiness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Moskalenko and Kharchenko.)

Application of nanomaterials is gaining tremendous interest in the field of wood science and technology for value addition and enhancing performance of wood and wood-based composites. This review focuses on the use of nanomaterials in improving the properties of wood and wood-based materials and protecting them from weathering, biodegradation, and other deteriorating agents. UV-resistant, self-cleaning (superhydrophobic) surfaces with anti-microbial properties have been developed using the extraordinary features of nanomaterials. Scratch-resistant nano-coatings also improve durability and aesthetic appeal of wood. Moreover, nanomaterials have been used as wood preservatives for increasing the resistance against wood deteriorating agents such as fungi, termites and borers. Wood can be made more resistant to ignition and slower to burn by introducing nano-clays or nanoparticles of metal-oxides. The use of nanocellulose and lignin nanoparticles in wood-based products has attracted huge interest in developing novel materials with improved properties. Nanocellulose and lignin nanoparticles derived/synthesized from woody biomass can enhance the mechanical properties such as strength and stiffness and impart additional functionalities to wood-based products. Cellulose nano-fibres/crystals find application in wide areas of materials science like reinforcement for composites. Incorporation of nanomaterials in resin has been used to enhance specific properties of wood-based composites. This review paper highlights some of the advancements in the use of nanotechnology in wood science, and its potential impact on the industry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Artificial intelligence (AI) chatbots have the potential to revolutionize online health information-seeking behavior by delivering up-to-date information on a wide range of health topics. They generate personalized responses to user queries through their ability to process extensive amounts of text, analyze trends, and generate natural language responses. Chatbots can manage infodemic by debunking online health misinformation on a large scale. Nevertheless, system accuracy remains technically challenging. Chatbots require training on diverse and representative datasets, security to protect against malicious actors, and updates to keep up-to-date on scientific progress. Therefore, although AI chatbots hold significant potential in assisting infodemic management, it is essential to approach their outputs with caution due to their current limitations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Morita, Lotto, Kaur, Chumachenko, Oetomo, Espiritu and Hussain.)

This work emphasizes the dry reforming of methane (DRM) reaction on citrate sol-gel-synthesized double perovskite oxides. Phase pure La 2 NiMnO 6 shows very impressive DRM activity with H 2 /CO = 0.9, hence revealing a high prospect of next-generation catalysts. Although the starting double perovskite phase gets degraded into mostly binary oxide phases after a few hours of DRM activity, the activity continues up to 100 h. The regeneration of the original double perovskite out of decomposed phases by annealing at near synthesis temperature, followed by the spectacular retention of activity, is rather interesting and hitherto unreported. This result unravels unique reversible thermal switching between the original double perovskite phase and decomposed phases during DRM without compromising the activity and raises challenge to understand the role of decomposed phases evolved during DRM. We have addressed this unique feature of the catalyst via structure-property relationship using the in situ generated molecular level nanocomposite.

Objective: Aim: To study the peculiarities of self-realization of women with different states of family interaction, to build a mathematical model that allows to identify the probability of self-realization of women, depending on their psycho-emotional, individual-psychological, behavioral and partnership patterns, to consider the family as a whole system, to define the phenomenon of family interaction as a leading the construct of the process of family functioning, to distinguish levels of family crisis and states of family interaction., Patients and Methods: Materials and Methods: Women of different social status and level of self-realization participated in this study., Results: Results: In the given sample, it was displayed what percentages of divorced women compared to those living in families are self-actualized in their lives., Conclusion: Conclusions: A mathematical model was developed to explore the probability/lack of self-realization in women, which allowed the studying of the main indicators that affect the determination of the probability of self-realization in women depending on their psycho-emotional state and individual psychological or behavioral patterns.

Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions., (© 2023. The Author(s).)

Inhospitable, inaccessible, and extremely remote alike the famed pole of inaccessibility, aka Point Nemo, the isolated locations in deserts, at sea, or in outer space are difficult for humans to settle, let alone to thrive in. Yet, they present a unique set of opportunities for science, economy, and geopolitics that are difficult to ignore. One of the critical challenges for settlers is the stable supply of energy both to sustain a reasonable quality of life, as well as to take advantage of the local opportunities presented by the remote environment, e.g., abundance of a particular resource. The possible solutions to this challenge are heavily constrained by the difficulty and prohibitive cost of transportation to and from such a habitat (e.g., a lunar or Martian base). In this essay, the advantages and possible challenges of integrating Fischer-Tropsch, artificial photosynthesis, and plasma catalysis into a robust, scalable, and efficient self-contained system for energy harvesting, storage, and utilization are explored., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. Global Challenges published by Wiley‐VCH GmbH.)

Background: Post-COVID-19 syndrome (PCS) has been increasingly recognized as an emerging problem: 50% of patients report ongoing symptoms 1 year after acute infection, with most typical manifestations (fatigue, dyspnea, psychiatric and neurological symptoms) having potentially debilitating effect. Early identification of high-risk candidates for PCS development would facilitate the optimal use of resources directed to rehabilitation of COVID-19 convalescents., Objective: To study the in-hospital clinical characteristics of COVID-19 survivors presenting with self-reported PCS at 3 months and to identify the early predictors of its development., Methods: 221 hospitalized COVID-19 patients underwent symptoms assessment, 6-min walk test, and echocardiography pre-discharge and at 1 month; presence of PCS was assessed 3 months after discharge. Unsupervised machine learning was used to build a SANN-based binary classification model of PCS development., Results: PCS at 3 months has been detected in 75% patients. Higher symptoms level in the PCS group was not associated with worse physical functional recovery or significant echocardiographic changes. Despite identification of a set of pre-discharge predictors, inclusion of parameters obtained at 1 month proved necessary to obtain a high accuracy model of PCS development, with inputs list including age, sex, in-hospital levels of CRP, eGFR and need for oxygen supplementation, and level of post-exertional symptoms at 1 month after discharge (fatigue and dyspnea in 6MWT and MRC Dyspnea score)., Conclusion: Hospitalized COVID-19 survivors at 3 months were characterized by 75% prevalence of PCS, the development of which could be predicted with an 89% accuracy using the derived neural network-based classification model., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Honchar, Ashcheulova, Chumachenko, Chumachenko, Bobeiko, Blazhko, Khodosh, Matiash, Ambrosova, Herasymchuk, Kochubiei and Smyrnova.)

Introduction: Non-Fungible Tokens (NFTs) are digital assets that are verified using blockchain technology to ensure authenticity and ownership. NFTs have the potential to revolutionize healthcare by addressing various issues in the industry., Method: The goal of this study was to identify the applications of NFTs in healthcare. Our scoping review was conducted in 2023. We searched the Scopus, IEEE, PubMed, Web of Science, Science Direct, and Cochrane scientific databases using related keywords. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)., Results: After applying inclusion and exclusion criteria, a total of 13 articles were chosen. Then extracted data was summarized and reported. The most common application of NFTs in healthcare was found to be in health data management with 46% frequency, followed by supply chain management with 31% frequency. Furthermore, Ethereum is the main blockchain platform that is applied in NFTs in healthcare with 70%., Discussion: The findings from this review indicate that the NFTs that are currently used in healthcare could transform it. Also, it appears that researchers have not yet investigated the numerous potentials uses of NFTs in the healthcare field, which could be utilized in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Abhari, Morita, Miranda, Garavand, Hanjahanja-Phiri and Chumachenko.)

Super-hydrophobic electrospun membranes are very essential barrier materials to physically isolate the wound site in order to prevent adhesions and for restoring the normal functioning of the surrounding tissues and organs. In the present study, poly (ε-caprolactone) (PCL)/beeswax (BW) based nanofibrous anti-adhesion membranes were fabricated by electrospinning technique. The BW concentration was varied from 10 to 30 wt.%. The nanofibers were evaluated for their morphological and physio-chemical properties. The electrospun mats demonstrate random distribution of nanofibers. Surface wettability was evaluated using static water contact angle method. PCL/BW (70/30) membrane had shown super-hydrophobicity (contact angle = 150°). From the cell culture studies, it was evident that cell viability, adhesion and proliferation of L929 cells on PCL/BW (70/30) membrane were comparatively lower than those on pure PCL membrane due to its super-hydrophobic nature. Consequently, PCL/BW (70/30) membrane was found as a potential candidate for fibroblast (L929) cell anti-adhesion applications., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.