Search Results (2,678)

This work presents a dynamic flowsheet model that predicts residual stress from shot peening. The peening medium is characterized by size and shape, and evolves dynamically with abrasion, fracture, classification, and replenishment. Because particle size and impact location vary stochastically, the resulting residual stress field is spatially heterogeneous. Residual stress fields are predicted in real time through a convolutional long short-term memory (ConvLSTM) neural network trained on finite element simulations, enabling fast, mechanistically grounded prediction of surface stress evolution under industrial shot peening conditions. We deploy the model in a pair of 10,000-cycle production peening case studies, demonstrating that media recharge strategy has a measurable effect on residual stress outcomes. Full article

This study investigates the spatiotemporal evolution and key obstacle factors of urban flood resilience in the Shenyang Metropolitan Area, aiming to inform regional flood resilience planning and management. A comprehensive assessment indicator system was established, integrating natural, economic, social, and infrastructure dimensions to capture the multifaceted nature of flood resilience. The long short-term memory (LSTM) network with an attention mechanism, combined with the obstacle degree model, was employed to analyze resilience trends and diagnose limiting factors from 2001 to 2023. The findings reveal a sustained increase in the regional flood resilience index, rising from 0.255 in 2001 to 0.574 in 2023. Spatially, the resilience pattern evolved from a monocentric core diffusion to a dual-core leadership and multi-city collaborative structure, driven by basin-wide management and differentiated development between mountainous and plain areas. Disparities in resilience levels across cities narrowed over time. At the criterion level, infrastructure was the primary obstacle before 2010, while social factors became increasingly significant thereafter. At the indicator level, the main limiting factors varied among cities and shifted over time, reflecting local development dynamics. These results provide a theoretical basis and practical guidance for enhancing urban flood resilience in the Shenyang Metropolitan Area and offer insights applicable to other rapidly urbanizing regions. Full article

Collective intelligence within a quantum-informed cybernetic paradigm presents a transformative perspective to examine adaptability and resilience in Internet of Things (IoT) systems. This paper introduces Cogitor5, a fifth-order cybernetic system that builds upon the foundational principles of the fourth-order COgITOR framework, a liquid computational system designed for complex adaptive processes. The term COgITOR is etymologically linked to the Latin passive verb cogĭtur, translating to “He is gathered,” in contrast to the more commonly recognized active form cogito, meaning “I gather” or “I think,” as famously articulated by Descartes. In contrast to conventional binary systems, Cogitor5 functions as a simulation-based complex adaptive system, inspired by a population of nano agents represented by nanoparticles suspended in a colloidal medium. These agents exhibit autonomous interactions within the solvent, featuring quantum-enabled properties that facilitate advanced self-organization and coevolutionary dynamics. This intricate model captures the complexities of agent interaction, offering a refined representation of their evolving collective intelligence. The study redefines collective intelligence as emergent process intelligence, relevant to the adaptive capacities of both biological and cybernetic systems. By utilizing metacybernetic principles in conjunction with theories of complex adaptive systems, this paper investigates how IoT networks can evolve to enhance agency trajectory formation and increase adaptability. Cogitor5 serves as an innovative computational framework for addressing the inherent complexities of IoT, providing clarity in examining self-organization, self-regulation, self-maintenance, and sustainability, thus elevating system viability. The methodology encompasses the modeling of collective and process intelligence within the scope of Mindset Agency Theory (MAT), an advanced metacybernetic model that allows for evaluable characteristics. Furthermore, this approach integrates theoretical modelling and a practical case study implemented in Matlab^® to illustrate agency functionality within a dynamic system simulating failures in the nodes of an electric grid. Full article

Background/Objectives: Artificial intelligence (AI) has rapidly evolved from experimental algorithms to transformative tools in clinical dentistry. Between 2020 and 2025, advances in machine learning (ML) and deep learning (DL) have reshaped diagnostic imaging, caries detection, prosthodontic design, and teledentistry, while raising new ethical and regulatory challenges. This study aimed to provide a comprehensive bibliometric and conceptual review of AI applications in dental care, highlighting research trends, thematic clusters, and future directions for equitable and responsible integration of AI technologies. In addition, the review further considers the implications of AI adoption for patient-centered care, including its potential role in supporting shared decision-making processes in oral healthcare. Methods: A comprehensive search was conducted in PubMed, Scopus and Embase for articles published between January 2020 and October 2025 using AI-related keywords in dentistry. Eligible records were analyzed using VOSviewer (v.1.6.20) to map co-occurrence networks of keywords, authors, and citations. A narrative synthesis complemented the bibliometric mapping, emphasizing conceptual and ethical dimensions of AI adoption in oral health care. Results: A total of 50 documents met the inclusion criteria. Bibliometric network visualization identified that the largest and most interconnected clusters were centered around the keywords “artificial intelligence,” “machine learning,” and “deep learning,” reflecting the technological backbone of AI-based applications in dentistry. Thematic evolution analysis indicated increasing interest in generative and multimodal AI models, explainability, and fairness in clinical deployment. Conclusions: AI has become a core driver of innovation in dentistry, enabling precision diagnostics and personalized care. However, responsible translation requires robust validation, transparency, and ethical oversight. Future research should integrate interdisciplinary approaches linking AI performance, patient outcomes, and equity in oral health. Full article

Brain–Computer Interface (BCI) systems are rapidly evolving and increasingly integrated into interactive environments such as gaming and Virtual/Augmented Reality. In such applications, user adaptability and engagement are critical. This study applies deep learning to predict user performance in a 3D BCI-controlled game using pre-game Motor Imagery (MI) electroencephalographic (EEG) recordings. A total of 72 EEG recordings were collected from 36 participants, 17 using the Muse 2 headset and 19 using the Emotiv Insight device, during left and right hand MI tasks. The signals were preprocessed and transformed into time–frequency spectrograms, which served as inputs to a custom convolutional neural network (CNN) designed to classify users into three performance levels: low, medium, and high. The model achieved classification accuracies of 83% and 95% on Muse 2 and Emotiv Insight data, respectively, at the epoch level, and 75% and 84% at the subject level, using LOSO-CV. These findings demonstrate the feasibility of using deep learning on MI EEG data to forecast user performance in BCI gaming, enabling adaptive systems that enhance both usability and user experience. Full article

Background: The maritime industry, vital for global trade, faces escalating cyber threats in 2025. Critical port infrastructures are increasingly vulnerable due to rapid digitalization and the integration of IT and operational technology (OT) systems. Methods: Using 112 incidents from the Maritime Cyber Attack Database (MCAD, 2020–2025), we developed a novel quantitative risk assessment model based on a Threat-Vulnerability-Impact (T-V-I) framework, calibrated with MITRE ATT&CK techniques and validated against historical incidents. Results: Our analysis reveals a 150% rise in incidents, with OT compromise identified as the paramount threat (98/100 risk score). Ports in Poland and Taiwan face the highest immediate risk (95/100), while the Panama Canal is assessed as the most probable next target (90/100). State-sponsored actors from Russia, China, and Iran are responsible for most high-impact attacks. Conclusions: This research provides a validated, data-driven framework for prioritizing defensive resources. Our findings underscore the urgent need for engineering-grade solutions, including network segmentation, zero-trust architectures, and proactive threat intelligence integration to enhance maritime cyber resilience against evolving threats. Full article

Blockchain technology has rapidly evolved as a cornerstone of decentralized computing, transforming how trust, data integrity, and transparency are achieved in digital ecosystems. However, despite extensive adoption, significant gaps remain in understanding how key blockchain variables, such as block size, consensus mechanisms, and network latency, affect system vulnerabilities and susceptibility to cyberattacks. This survey addresses this gap by combining qualitative and quantitative analyses across multiple blockchain environments. Using simulation tools such as Ganache and Bitcoin Core, and reviewing peer-reviewed studies from 2016 to 2024, the research systematically maps blockchain parameters to cyberattack vectors including 51% attacks, Sybil attacks, and double-spending. Findings indicate that design choices like block size, block interval, and consensus type substantially influence resilience against attacks. The Blockchain Variable Quantitative Risk Framework (BVQRF) introduced here integrates NIST’s cybersecurity principles with quantitative scoring to assess risks. This framework represents a novel contribution by operationalizing theoretical security constructs into actionable evaluation metrics, enabling predictive modeling and adaptive risk mitigation strategies for blockchain systems. Full article

Anthracnose is one of the major diseases affecting Dalbergia odorifera T. Chen. However, the soil microbial mechanisms underlying D. odorifera responses to anthracnose remain largely unexplored. This study investigated three planting systems: a Dalbergia odorifera monoculture (J); a mixed plantation of D. odorifera and Pterocarpus macrocarpus (JD); and a composite mixed plantation of D. odorifera, P. macrocarpus, and Clinacanthus nutans (JDY). Using amplicon sequencing technology for soil microbial analysis and combining soil physical and chemical properties with disease severity, we comprehensively analyzed changes in soil microbial community structure and function across different planting modes. The results showed that the diverse mixed mode (JD, JDY) significantly improved soil physicochemical properties and promoted soil nutrient cycling. Redundancy analysis (RDA) indicated that soil organic matter (SOM) and disease severity, quantified by the area under the disease progress curve (AUDPC), were the primary environmental drivers of microbial community variation. Genera positively correlated with SOM and negatively correlated with AUDPC were significantly enriched in JDY and JD, whereas genera showing opposite relationships were predominantly enriched in J. Functional predictions revealed enhanced nutrient-cycling capacities in JD and JDY, with JDY uniquely harboring functional groups such as Arbuscular Mycorrhizal, Epiphyte, and Lichenized taxa. In contrast, microbial functions in the J plantation were mainly limited to environmental amelioration. Co-occurrence network analysis further showed that as planting patterns shifted from J to JDY, microbial communities evolved from competition-dominated networks to cooperative defensive networks, integrating efficient decomposition with strong pathogen suppression potential. The study demonstrates that complex mixed planting systems regulate soil properties, enhance the enrichment of key functional microbial taxa, reshape community structure and function, and ultimately enable ecological control of anthracnose disease. This study provides new perspectives and theoretical foundations for ecological disease management in plantations of rare tree species and for microbiome-based ecological immunization strategies. Full article

Climate change has intensified the frequency, scale, and interconnection of disasters, challenging the resilience of urban social–ecological systems. Progress remains fragmented because studies on climate adaptation, disaster risk, and resilience often evolve in isolation. Using an integrated methodological approach that combines bibliometric and knowledge mapping analyses of 2396 climate change, 1228 disaster risk, and 989 climate-related disaster risk publications (1994–2024) from the Web of Science Core Collection, this study explores global trends, collaboration networks, and thematic evolution. Results show that (1) disaster risk research remains centered on emergency management; (2) climate change resilience emphasizes adaptive governance and nature-based transformation; and (3) climate-related disaster studies increasingly address compound hazards and cross-sectoral feedback. Synthesizing these strands, this study develops a Dynamic Resilience Framework integrating multi-level feedbacks, governance coordination, and spatiotemporal coupling across robustness, redundancy, transformability, and learnability. The framework identifies future research priorities in multi-risk governance, urban transformability, and justice-oriented adaptation. Full article

In control systems, chaos is a natural dualistic phenomenon that can be both a beneficial resource to be used and a negative phenomenon to be avoided. The study examines two opposing paradigms: positive chaotic control, which aims to enhance performance, and negative chaos management, which aims to stabilize a system. More sophisticated suppression methods, including adaptive neural networks, sliding mode control, and model predictive control, can decrease convergence times. Controlled chaotic dynamics have significantly impacted the domain of embedded control systems. Specialized controller designs include fractal-based systems and hybrid switching systems that offer better control of chaotic behavior in many situations. The paper highlights the key issues that are related to chaos-based systems, such as the need to implement them in real time, parameter sensitivity, and safety. Recent research suggests an increased interdependence between artificial intelligence, quantum computing, and sustainable technology. The synthesis shows that chaos control has evolved into an engineering field, significantly impacting the industry, which was initially a theoretical concept. It also offers exclusive ideas in the design and improvement of complex control systems. Full article

The evolving landscape of the liquid fuels market, together with changing legal regulations, has prompted consideration of using artificial intelligence methods for the physicochemical analysis of fuel and biofuel blends. The objective of the study was to determine the dynamic viscosity of diesel fuel and its blends with vegetable oils derived from rapeseed, camelina, flax, and mustard. These oils were selected due to their previous applications in the petrochemical industry. The oils used in the study were obtained by cold pressing with a screw press. The measurements were performed over a temperature range of 5 to 85 °C at mass ratios containing 20%, 40%, 60%, and 80% vegetable oil in diesel fuel. A Brookfield-type rotational viscometer was employed for the measurements. Based on the resulting laboratory data, mathematical models of dynamic viscosity were developed. Furthermore, the experimental results were used to train a neural network to analyse relationships among dynamic viscosity, temperature, and vegetable oil content in the blend. Both the empirical (mathematical) models and the models describing changes in dynamic viscosity as a function of temperature and component content in the vegetable oil–diesel blends achieved coefficients of determination (R²) exceeding 0.99. Full article

Hypertrophic cardiomyopathy (HCM) is a common and deadly cardiac disease characterized by enlarged myocytes, increased myocardial wall thickening, and fibrosis. A majority of HCM cases are associated with mutations in the β-myosin heavy chain (MYH7) converter domain locus, which leads to varied pathophysiological and clinical manifestations. Using base-editing technology, we generated mutant human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) harboring HCM-causing myosin converter domain mutations (MYH7 c.2167C>T [R723C]; MYH6 c.2173C>T [R725C]) to define HCM pathogenesis in vitro. In this study, we integrated transcriptomic analysis with phenotypic and molecular analyses to dissect the HCM disease mechanisms using MYH6/7 myosin mutants. Our KEGG analysis of bulk RNA-sequencing data revealed significant upregulation of transcripts associated with HCM in the mutant hiPSC-CMs. Further, in-depth transcriptomic analysis using Gene-Ontology (GO-term) analysis for biological process showed upregulation of several transcripts associated with heart development and disease. Notably, our analysis showed robust upregulation of cytoskeletal transcripts, including actin-cytoskeleton networks, sarcomere components, and other structural proteins in the mutant CMs. Furthermore, cellular and nuclear morphological analysis showed that the MYH6/7 mutation induced cellular hypertrophy and increased aspect ratio compared to the isogenic control. Immunostaining experiments showed marked sarcomere disorganization with lower sarcomeric order and higher dispersion in the mutant hiPSC-CMs, highlighting the remodeling of the myofibril arrangement. Notably, the MYH6/7 mutant showed reduced cortical F-actin expression and increased central F-actin expression compared to the isogenic control, confirming the cytoskeletal remodeling and sarcomeric organization during HCM pathogenesis. These pathological changes accumulated progressively over time, underscoring the chronic and evolving nature of HCM driven by the MYH6/7 mutations. Together, our findings provide critical insights into the cellular and molecular underpinnings of MYH6/7-mutation-associated disease. These findings offer valuable insights into HCM pathogenesis, aiding in future therapies. Full article

Background and Objectives: Infantile spasms (ISs), or West syndrome (WS), represent an early-onset epileptic encephalopathy in which diverse structural, genetic, metabolic, infectious, and neurocutaneous conditions converge on a shared pattern of hypsarrhythmia, clustered spasms, and later developmental impairment. Growing use of genomic diagnostics has revealed that variants in STXBP1, KCNQ2, GRIN2A, GRIN2B, and TSC-related genes are more common than previously recognized and can be linked to partially actionable pathways. This review aimed to synthesize current evidence on the multifactorial etiology, network-based pathogenesis, and evolving targeted therapies for ISs, with particular attention to TSC-related forms. Materials and Methods: A structured narrative review was undertaken of publications from 1990 to 2025 in PubMed, Scopus, Web of Science, and Embase using terms related to ISs, WS, genetics, mTOR, ACTH, vigabatrin, ketogenic diet, and precision therapies. Authoritative guidance from ILAE and AAN was incorporated. Clinical, molecular, and therapeutic data were grouped under etiological, pathogenetic, and management domains. Results: Structural causes remained the largest group, but combined genetic, genetic–structural, and metabolic etiologies accounted for about one third of contemporary cohorts. Early network disruption involving cortex, thalamus, basal ganglia, and brainstem, together with imbalances in NGF, BDNF, and IGF-1, explained why distinct primary insults produce a uniform electroclinical phenotype. Early treatment with ACTH or high dose prednisolone, with or without vigabatrin, was consistently associated with higher electroclinical remission and better developmental outcome. Everolimus and related mTOR inhibitors showed benefit in TSC-associated ISs, while agents directed at NMDA receptors or KCNQ channels are emerging for genotype defined subgroups. Conclusions: ISs should be approached as a heterogeneous but mechanistically convergent disorder in which rapid diagnosis, parallel genetic testing, and early disease modifying therapy improve prognosis. Integration of molecular profiling with standardized outcome monitoring is likely to move management from symptomatic seizure control to pathway-specific intervention. Full article

The increasing proportion of HIV-1 infections transmitted via non-marital non-commercial heterosexual contact (NMNCHC) in China necessitates a deeper understanding of its local characteristics. This study investigated the epidemiological, molecular network, and drug-resistant profiles among 400 newly diagnosed HIV-1 patients infected via non-marital heterosexual contact (NMHC), specifically its non-commercial subtype, in Lishui from 2020–2024. HIV-1 pol gene sequences were analyzed for subtypes, drug resistance mutations, and transmission clusters using phylogenetic and network methods (genetic distance threshold: 0.9%). The overall prevalence of transmitted drug resistance (TDR) was 13.3%, an intermediate level exceeding the national average, driven predominantly by NNRTI resistance (6.3%). High-level resistance to NVP (3.0%) and EFV (2.75%) was observed. CRF08_BC (43.8%) was the dominant subtype. Multivariate analysis identified female gender and higher education as significant risk factors for NMNCHC acquisition. Molecular network analysis incorporated 55.3% of cases, revealing clusters predominantly composed of middle-aged and elderly males, with CRF08_BC and CRF01_AE showing higher NMNCHC transmission risk within networks. These findings underscore an evolving epidemic with significant TDR and highlight the urgent need for targeted interventions, including enhanced resistance surveillance and focused strategies for the concealed NMNCHC population, to curb local HIV-1 transmission. Full article

Background/Objectives: Since the invention of MRI, analytical methods for MRI data have continuously evolved. In recent years, the rapid development of artificial intelligence has transformed MRI data analysis—from functional MRI (fMRI) techniques to deep learning-based image segmentation, and from traditional machine learning to radiomics for clinical applications. Methods: This review provides a succinct summary of recent progress in fMRI and structural MRI analysis. The discussed techniques include fMRI, quantitative MRI (qMRI) methods such as T1 and T2 relaxation time mapping, and proton density imaging. Approaches for diffusion, perfusion, and the Dixon method are also described. Furthermore, studies published between 2012 and 2025 on MRI radiomics were reviewed. Different neural network architectures related to radiomics-based segmentation are compared and discussed. Results: A major trend in both fMRI and MRI analysis is the increasing use of quantitative methods, which enable better cross-study comparison and reproducibility. Deep learning remains to progress rapidly in MRI research, particularly in segmentation tasks, with new loss functions and network architectures developed to improve performance. These methods are expected to undergo further optimization and find broader applications in clinical practice. Conclusions: Despite substantial progress, challenges remain in standardization, validation, and clinical translation. Continued efforts are necessary before these advanced analytical techniques can be fully integrated into routine medical practice. Full article