Search Results (141)

Federated learning (FL), which enables collaborative learning across distributed nodes, confronts a significant heterogeneity challenge, primarily including resource heterogeneity induced by different hardware platforms, and statistical heterogeneity originating from non-IID private data distributions among clients. Neural architecture search (NAS), particularly one-shot NAS, holds great promise for automatically designing optimal personalized models tailored to such heterogeneous scenarios. However, the coexistence of both resource and statistical heterogeneity destabilizes the training of the one-shot supernet, impairs the evaluation of candidate architectures, and ultimately hinders the discovery of optimal personalized models. To address this problem, we propose a heterogeneity-aware personalized federated NAS (HAPFNAS) method. First, we leverage lightweight knowledge models to distill knowledge from clients to server-side supernet, thereby effectively mitigating the effects of heterogeneity and enhancing the training stability. Then, we build random-forest-based personalized performance predictors to enable the efficient evaluation of candidate architectures across clients. Furthermore, we develop a model-heterogeneous FL algorithm called heteroFedAvg to facilitate collaborative model training for the discovered personalized models. Comprehensive experiments on CIFAR-10/100 and Tiny-ImageNet classification datasets demonstrate the effectiveness of our HAPFNAS, compared to state-of-the-art federated NAS methods. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

Objectives: With the growing importance of mobile technology and artificial intelligence (AI) in healthcare, the development of automated cardiac diagnostic systems has gained strategic significance. This review aims to summarize the current state of knowledge on the use of AI in the analysis of electrocardiographic (ECG) signals obtained from wearable devices, particularly smartwatches, and to outline perspectives for future clinical applications. Methods: A narrative literature review was conducted using PubMed, Web of Science, and Scopus databases. The search focused on combinations of keywords related to AI, ECG, and wearable technologies. After screening and applying inclusion criteria, 152 publications were selected for final analysis. Conclusions: Modern AI algorithms—especially deep neural networks—show promise in detecting arrhythmias, heart failure, prolonged QT syndrome, and other cardiovascular conditions. Smartwatches without ECG sensors, using photoplethysmography (PPG) and machine learning, show potential as supportive tools for preliminary atrial fibrillation (AF) screening at the population level, although further validation in diverse real-world settings is needed. This article explores innovation trends such as genetic data integration, digital twins, federated learning, and local signal processing. Regulatory, technical, and ethical challenges are also discussed, along with the issue of limited clinical evidence. Artificial intelligence enables a significant enhancement of personalized, mobile, and preventive cardiology. Its integration into smartwatch ECG analysis opens a path toward early detection of cardiac disorders and the implementation of population-scale screening approaches. Full article

Root-knot nematodes, Meloidogyne spp., are widespread phytoparasites that cause a significant reduction in the yield of tomato Solanum lycopersicum. In the Russian Federation, where the use of chemical nematicides is limited due to environmental and toxicological risks, the cultivation of resistant varieties and hybrids remains the most effective and environmentally safe method to control Meloidogyne. In the course of this study, the resistance screening of 20 tomato varieties/hybrids and 21 mutant lines from the collection of the FSBSI FRCBPP to M. hapla was carried out using a comprehensive approach that included morphological and biochemical analysis methods. Resistance was assessed by calculating the gall formation index, the degree of root system damage, and biochemical parameters of fruits—vitamin C content and titratable acidity. In addition, molecular screening was carried out using the SCAR marker Mi23 to identify the Mi-1.2 gene, known as a key factor in resistance to a number of Meloidogyne spp. Although Mi-1.2 is not typically associated with resistance to M. hapla, all genotypes carrying this gene showed phenotypic resistance. This unexpected correlation suggests the possible involvement of Mi-associated or parallel mechanisms and highlights the need for further investigation into noncanonical resistance pathways. It was found that when susceptible genotypes were infected with M. hapla, there was a tendency for the vitamin C content to decrease, while resistant lines retained values close to the control. The presence of the Mi-1.2 gene was confirmed in 9.5% of samples. However, the phenotypic resistance of some lines, such as Volgogradets, which do not contain a marker for the Mi-1.2 gene, indicates a polygenic nature of resistance, alternative genetic mechanisms, or the possible influence of epigenetic mechanisms. The obtained data highlight the potential of using the identified resistant genotypes in breeding programs and the need for further studies of the molecular mechanisms of resistance, including the search for new markers specific to M. hapla, to develop effective strategies for tomato protection in sustainable agriculture. Full article

This research focuses on the ideological sphere of criminals with the highest status in the Russian Federation. This ideological sphere was studied in literary sources of various kinds on the basis of repeatability (the existence of linguistic facts) and averaging (external and internal confrontation of sources). It is suggested that, in speech, there exist some selective overinterpretations of world religions that neglect basic elements of the traditional law-abiding picture of the world and that are directly based on literary fiction instead of the scientific literature. On the other hand, there can be some search for faith connected with the belief in spiritual knowledge from the dead, divine beings, and God. Full article

In recent years, artificial intelligence (AI) has achieved significant progress and remarkable advancements across various disciplines, including biology, computer science, and industry. However, the increasing complexity of AI network structures and the vast number of associated parameters impose substantial computational and storage demands, severely limiting the practical deployment of these models on resource-constrained edge devices. Although edge intelligence methods have been proposed to alleviate the computational and storage burdens, they still face multiple persistent challenges, such as large-scale model deployment, poor interpretability, privacy and security vulnerabilities, and energy efficiency constraints. This article systematically reviews the current advancements in edge intelligence technologies, highlights key enabling techniques including model sparsity, quantization, knowledge distillation, neural architecture search, and federated learning, and explores their applications in industrial, automotive, healthcare, and consumer domains. Furthermore, this paper presents a comparative analysis of these techniques, summarizes major trade-offs, and proposes decision frameworks to guide deployment strategies under different scenarios. Finally, it discusses future research directions to address the remaining technical bottlenecks and promote the practical and sustainable development of edge intelligence. Standing at the threshold of an exciting new era, we believe edge intelligence will play an increasingly critical role in transforming industries and enabling ubiquitous intelligent services. Full article

Objectives: Limited variety in children’s diets impairs lifelong nutrition and health. Head Start is a federal program serving expectant families and children in the United States living at or below the poverty line to the age of five. Head Start children face barriers to nutrient intake. Many nutrition education curricula are implemented in Head Start settings; however, few have addressed whether taste or non-taste food exposures are more effective and appropriate for improving dietary intake in this population. This review evaluates if taste or non-taste exposures are more effective at increasing willingness to try, consume, and like food in children participating in Head Start. Methods: PubMed was searched for studies published in the last 10 years with children aged 2 to 12 years. Included studies had an intervention with exposure to food or its likeness, focusing on those studying Head Start or similar samples. Articles were excluded if they referenced exposure to marketing, disease, or foodborne illness. Results: Searches yielded 903 results. 51 articles were screened, and 15 were included in the narrative. Studies revealed that combinations of taste and non-taste exposures improved children’s willingness to try, consume, and like food. Conclusions: Taste and non-taste exposures, when used independently, inconsistently affect children’s willingness to try, consume, and like food; exposures are most effective when combined, although research on the topic faces limitations of study design and environmental controls. With federal standards for nutrition, Head Start programs should implement food exposure activities. Additional studies with improved designs and controls for exposure to the environment should be completed in this population to increase the validity and reliability of food exposure research. Full article

An industrial-scale increase in applications of the Internet of Things (IoT), a significant number of which are based on the concept of federation, presents unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. A federation may be created for the duration of a mission, such as military operations or Humanitarian Assistance and Disaster Relief (HADR) operations. These missions often occur in very difficult or even hostile environments, posing additional challenges for ensuring reliability and security. The heterogeneity of devices, protocols, and security requirements in different domains further complicates the requirements for the secure distribution of data streams in federated IoT environments. The effective dissemination of data streams in federated environments also ensures the flexibility to filter and search for patterns in real-time to detect critical events or threats (e.g., fires and hostile objects) with changing information needs of end users. The paper presents a novel and practical framework for secure and reliable data stream dissemination in federated IoT environments, leveraging blockchain, Apache Kafka brokers, and microservices. To authenticate IoT devices and verify data streams, we have integrated a hardware and software IoT gateway with the Hyperledger Fabric (HLF) blockchain platform, which records the distinguishing features of IoT devices (fingerprints). In this paper, we analyzed our platform’s security, focusing on secure data distribution. We formally discussed potential attack vectors and ways to mitigate them through the platform’s design. We thoroughly assess the effectiveness of the proposed framework by conducting extensive performance tests in two setups: the Amazon Web Services (AWS) cloud-based and Raspberry Pi resource-constrained environments. Implementing our framework in the AWS cloud infrastructure has demonstrated that it is suitable for processing audiovisual streams in environments that require immediate interoperability. The results are promising, as the average time it takes for a consumer to read a verified data stream is in the order of seconds. The measured time for complete processing of an audiovisual stream corresponds to approximately 25 frames per second (fps). The results obtained also confirmed the computational stability of our framework. Furthermore, we have confirmed that our environment can be deployed on resource-constrained commercial off-the-shelf (COTS) platforms while maintaining low operational costs. Full article

Cyanobacterial harmful blooms (CyanoHABs) in lakes, estuaries, and freshwater reser-voirs represent a significant risk to water authorities worldwide due to their cyanotoxins and economic impacts. The duration, spread, and severity of CyanoHABs have markedly increased over the past decades. The article addresses CyanoHABs, cyanotoxins, and monitoring methodologies in post-Soviet and Central Asian countries. This particular region was selected for the systematic review due to its relative lack of representation in global CyanoHABs reporting, particularly in Central Asia. The main aim of this systematic review was to analyze the primary literature available from 2010–2024 to examine the current situation of CyanoHAB detection, monitoring, and management in Central Asia and post-Soviet countries. Following a detailed database search in several selected data-bases (Google Scholar, Pubmed, Web of Science (WOS), Scopus, Elibrary, ENU, and KazNU) along with additional hand searching and citation searching, 121 primary articles reporting 214 local cyanobacterial bloom cases were selected for this review. Aquatic cyanotoxins were reported in water bodies of eight countries, including high concentrations of microcystins that often exceeded reference values established by the World Health Organization (WHO). Advancing monitoring efforts in Baltic countries, Belarus, and the Russian Federation differed from only a few Central Asian reports. However, Central Asian aquatic ecosystems are especially threatened by rising anthropogenic pressures (i.e., water use, intensive agriculture, and pollution), climate change, and the lack of adequate ecological surveillance. We hypothesize that recent Caspian seal mass mortality events have been caused by a combination of infection (viral or bacterial) and exposure to algal neurotoxins resulting from harmful algal blooms of Pseudo-nitzschia. We conclude that there is an urgent need to improve the assessment of cyanobacterial blooms in Central Asia and post-Soviet countries. Full article

Federated learning is effective for Internet of Things data privacy and non-independent and identically distributed issues but not device heterogeneity. Neural Architecture Search can alleviate this by constructing multiple model structures to optimize federated learning performance across diverse edge devices. However, existing methods, whether lightweight networks or client grouping, face a tradeoff between scaling to larger federations and utilizing more powerful structures. We decompose residual network blocks, reformulating them as a Neural Architecture Search task. Furthermore, we propose a method for reinterpreting any sequential architecture into a supernet and developed a training pipeline tailored to this reinterpretated architecture, mitigating this frustrating tradeoff. We conduct pretraining on ImageNet1K and federated training on the CIFAR-100, CIFAR-10, and CINIC-10 datasets under both the ring-based federated learning and FedAvg framework. In less constrained environments, our method maintains performance comparable to another top-two method, which varies across experimental settings, while maintaining a margin of at least 1% Top-1 accuracy over the third-best method. Under balanced settings, our method outperforms the second-best approach by more than 1%, and this advantage increases to over 5% as the task difficulty further rises. Under the most challenging setting, our method outperformed AdaptiveFL, a state-of-the-art dynamic network method for federated learning, by 18.3% on CIFAR-100 with 100 clients under a ResNet backbone. Full article

Background/Objectives: Early childhood caries (ECC) is a significant global public health issue with economic and psychosocial consequences, impacting families and pediatric dentists. It affects children’s quality of life, causing pain and infection. Despite increasing research on ECC cross-sectional studies worldwide, inconsistencies and gaps remain in terms of geographical disparities. This study aimed to conduct a bibliometric analysis of cross-sectional surveys on ECC by examining the co-authorship, citation analysis, co-citation networks, and keyword co-occurrence. Methods: An advanced search was performed using relevant terms in the Dimensions database from 2005 to 2024. Bibliometric parameters were retrieved through the database’s analytical view tool and VOSviewer software. Results: A total of 571 documents were identified, with the highest output between 2019 and 2023 (355 records). Saul Martins Paiva authored the most articles (10), with 294 citations and a total link strength of 19. Brazil and the U.S. had the highest numbers of publications (56 and 52) and total link strengths, i.e., a measure of collaborative ties (21 and 50). The Universidade Federal de Minas Gerais in Brazil had the most published documents (15). BMC Oral Health led in terms of citations (44 articles, 899 citations, average 20.43%). The frequently co-occurring terms included ECC (1147 occurrences), oral health (417), and preschool child (301). Conclusions: This bibliometric analysis highlights the global interest in cross-sectional ECC studies beyond pediatric dentistry, helping researchers understand the field’s scope and progress. Full article

Background: Artificial intelligence (AI) is rapidly transforming thoracic surgery by enhancing diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative management. AI-driven technologies, including machine learning (ML), deep learning, computer vision, and robotic-assisted surgery, have the potential to optimize clinical workflows and improve patient outcomes. However, challenges such as data integration, ethical concerns, and regulatory barriers must be addressed to ensure AI’s safe and effective implementation. This review aims to analyze the current applications, benefits, limitations, and future directions of AI in thoracic surgery. Methods: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed using PubMed, Scopus, Web of Science, and Cochrane Library for studies published up to January 2025. Relevant articles were selected based on predefined inclusion and exclusion criteria, focusing on AI applications in thoracic surgery, including diagnostics, robotic-assisted surgery, intraoperative guidance, and postoperative care. A risk of bias assessment was conducted using the Cochrane Risk of Bias Tool and ROBINS-I for non-randomized studies. Results: Out of 279 identified studies, 36 met the inclusion criteria for qualitative synthesis, highlighting AI’s growing role in diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative care in thoracic surgery. AI-driven imaging analysis and radiomics have improved pulmonary nodule detection, lung cancer classification, and lymph node metastasis prediction, while robotic-assisted thoracic surgery (RATS) has enhanced surgical accuracy, reduced operative times, and improved recovery rates. Intraoperatively, AI-powered image-guided navigation, augmented reality (AR), and real-time decision-support systems have optimized surgical planning and safety. Postoperatively, AI-driven predictive models and wearable monitoring devices have enabled early complication detection and improved patient follow-up. However, challenges remain, including algorithmic biases, a lack of multicenter validation, high implementation costs, and ethical concerns regarding data security and clinical accountability. Despite these limitations, AI has shown significant potential to enhance surgical outcomes, requiring further research and standardized validation for widespread adoption. Conclusions: AI is poised to revolutionize thoracic surgery by enhancing decision-making, improving patient outcomes, and optimizing surgical workflows. However, widespread adoption requires addressing key limitations through multicenter validation studies, standardized AI frameworks, and ethical AI governance. Future research should focus on digital twin technology, federated learning, and explainable AI (XAI) to improve AI interpretability, reliability, and accessibility. With continued advancements and responsible integration, AI will play a pivotal role in shaping the next generation of precision thoracic surgery. Full article

Background: The heterogeneity of sarcomas and resulting distinct sub-type specific characteristics, their high recurrence rates, and tendency for distant metastasis, continue to present significant challenges to providing optimal treatments. Objective: To provide a comprehensive review of current literature and clinical trials in gene and cell therapies for sarcomas. Methods: A comprehensive literature search was conducted utilizing the following databases: PubMed, Medline, Google Scholar and clinicaltrials.gov. Search terms included “gene therapy”, “cell therapy”, “NK cell therapy, “CAR-T therapy”, “virotherapy”, “sarcoma”, “gene therapy”, and “solid tumors”. Additional sources were identified through manual searching for references of relevant studies. No language restrictions were set. The NCT number, study status, condition, and phase were noted for clinical trials. Results: There are only three gene and cell therapies for sarcomas that have been approved by a federal regulatory agency. Rexin-G: the first tumor-targeted gene therapy vector designed to target all advanced solid malignancies, including chemo-refractory osteosarcomas and soft tissue sarcomas, was approved by the Philippine FDA in 2007. Gendicine was the first oncolytic virus approved for intratumoral delivery in China in 2003. Afami-cel, an innovative chimeric antigen receptor (CAR) T cell therapy, was approved for synovial sarcoma in the United States in 2024. Other promising therapies are discussed in the text. Conclusions: The future of gene and cell therapy for sarcomas holds great promise, as research moves to late-stage clinical development. The integration of gene and cell therapies into standard sarcoma treatment protocols has the potential to significantly improve the quality of life and outcomes for patients with this rare and challenging group of cancers. Full article

Background and Objectives: Numerous studies have explored the biomarker atherogenic index of plasma (AIP) in relation to metabolic syndrome (MetS), showing its potential utility in assessing this condition. However, the existing evidence remains inconsistent and inconclusive. Therefore, this study aimed to evaluate the association between AIP and MetS and assess its predictive accuracy. Materials and Methods: A comprehensive search of PubMed, EMBASE, and Scopus was conducted using a predefined search strategy to identify relevant studies. Eligible studies diagnosed MetS based on the International Diabetes Federation criteria. The primary outcomes were the mean difference (MD) in AIP between MetS patients and healthy controls, as well as the area under the curve (AUC) for AIP in predicting MetS. Results: Thirteen studies involving 17,689 participants met the inclusion criteria and were included in the systematic review and meta-analysis. AIP levels were significantly higher in MetS patients compared to healthy controls, with an MD of 0.309 (95% CI 0.214, 0.405). In contrast, the difference in AIP levels between type 2 diabetes mellitus (T2DM) patients with MetS and normoglycemic MetS patients was not statistically significant (MD 0.142, 95% CI −0.091, 0.376). The predictive accuracy of AIP for MetS yielded an AUC of 0.864 (95% CI 0.856, 0.871). Conclusions: AIP levels are significantly elevated in MetS patients compared to healthy individuals, supporting AIP’s potential role as a biomarker for MetS. However, AIP levels did not differ significantly between T2DM patients with MetS and normoglycemic MetS patients. The predictive accuracy of AIP for MetS is acceptable, indicating that AIP may serve as a useful tool in MetS diagnosis. Further research is warranted to clarify its diagnostic and prognostic significance in clinical settings. Full article

Based on the mobility requirements of electric vehicles, four-wheel drive (4WD) can significantly enhance driving capability and increase operational flexibility in handling. If the output of different drive motors can be effectively controlled, energy losses during the distribution process can be reduced, thereby greatly improving overall efficiency. This study presents a simulation platform for an electric vehicle with four motors as power sources. This platform also consists of the driving cycle, driver, lithium-ion battery, vehicle dynamics, and energy management system models. Two rapid-prototyping controllers integrated with the required circuit to process analog-to-digital signal conversion for input and output are utilized to carry out a hardware-in-the-loop (HIL) simulation. The driving cycle, called NEDC (New European Driving Cycle), and FTP-75 (Federal Test Procedure 75) are used for evaluating the performance characteristics and response relationship among subsystems. A control strategy, called ECMS (Equivalent Consumption Minimization Strategy), is simulated and compared with the four-wheel average torque mode. The ECMS method considers different demanded powers and motor speeds, evaluating various drive motor power distribution combinations to search for motor power consumption and find the minimum value. As a result, it can identify the global optimal solution. Simulation results indicate that, compared to the average torque mode and rule-based control, in the pure simulation environment and HIL simulation during the UDDS driving cycle, the maximum improvement rates for pure electric energy efficiency for the 45 kW and 95 kW power systems are 6.1% and 6.0%, respectively. In the HIL simulation during the FTP-75 driving cycle, the maximum improvement rates for pure electric energy efficiency for the 45 kW and 95 kW power systems are 5.1% and 4.8%, respectively. Full article