Search Results (687)

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots. Full article

Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring. Full article

Carbonyl stress, chronic inflammation, and progressive tissue injury accompany type 2 diabetes mellitus (T2DM) and obesity. Yet, the molecular systems that connect these processes with cardiac, vascular and neuronal complications are incompletely defined. This review examines the AGE–RAGE–DIAPH1 axis as a mechanistic link between metabolic dysfunction and diabetic myocardial and neuronal injury, with emphasis on vascular and myocardial remodeling and emerging implications for autonomic neuronal vulnerability. We summarize current evidence on the formation and accumulation of advanced glycation end-products and other RAGE ligands in metabolic disease, DIAPH1’s structural and signaling role as an intracellular effector of RAGE, and the cellular consequences of pathway activation in vascular, neural, and cardiac tissues. Across experimental models, this signaling axis promotes oxidative stress and inflammatory activation, leading to endothelial dysfunction and barrier failure. Subsequent fibrotic remodeling provides a biologically plausible route through which metabolic stress may be translated into persistent organ injury. In the heart, these mechanisms are linked to coronary microvascular dysfunction, altered cardiomyocyte phenotype, calcium handling abnormalities, and myocardial fibrosis. In the autonomic nervous system, limited but emerging data connect RAGE activation to oxidative injury and mitochondrial dysfunction, abnormal neuronal excitability, and structural vulnerability. Direct evidence linking DIAPH1 to autonomic neurons is lacking. We also review biomarker candidates related to this pathway, including circulating AGEs and soluble RAGE isoforms, skin AGE measurements, imaging markers of myocardial remodeling, and autonomic functional measures. Finally, we discuss pharmacological and natural compounds that target AGE formation, ligand accumulation, RAGE signaling, or intracellular protein interactions linked to this axis. Overall, the available evidence supports the AGE–RAGE–DIAPH1 axis as a credible mechanistic concept and a potentially informative translational hypothesis in T2DM. However, the AGE–RAGE component is supported more strongly than DIAPH1-specific involvement in human diabetic myocardial disorder or cardiovascular autonomic neuropathy. The value of DIAPH1 as a biomarker or therapeutic target in these neurocardiac complications remains to be established. Full article

Sudden cardiac death (SCD) in athletes often represents the first manifestation of an underlying inherited cardiovascular disorder exposed by adrenergic stress, altered calcium cycling, mechanical loading, and metabolic demand during intense exercise. This review focuses on the molecular architecture that links genotype to arrhythmogenic phenotype in athletes, emphasizing sarcomeric force generation and energetic inefficiency in hypertrophic cardiomyopathy, desmosomal failure and Hippo/Wnt/transforming growth factor-beta (TGF-β) signaling in arrhythmogenic cardiomyopathy, and ion-channel and calcium/calmodulin-dependent protein kinase II (CaMKII)calcium handling abnormalities in inherited channelopathies. This review further examines how exercise-induced physiological remodeling intersects with these pathways through insulin-like growth factor-1 (IGF-1)/phosphoinositide 3-kinase (PI3K)/ protein kinase B (AKT) signaling, mitochondrial biogenesis, oxidative stress, inflammatory signaling, and epigenetic regulation. Attention is given to the molecular basis of genotype-positive/phenotype-negative states, variable penetrance, and exercise-mediated disease expression. Finally, the integration of molecular biology with genomic data, polygenic risk, and emerging digital phenotyping is discussed to refine mechanism-based risk stratification and identify future therapeutic targets for prevention of SCD in athletes. Full article

Sensors and biosensors designed for biomarker detection in biological samples often suffer from performance loss caused by surface fouling, interface blocking, and matrix interference. Although these effects are frequently discussed separately, in real sensing systems they are strongly interconnected and they determine analytical reliability, especially in body fluids like serum, plasma, whole blood, sweat, and other complex media. This review provides a practical and mechanism-oriented overview of how these processes originate, how they differ, and how they ultimately lead to signal drift, reduced sensitivity, false-positive responses, and shortened sensor lifetime. We first discuss the molecular origins of interface failure, including protein adsorption, conditioning film formation, nonspecific binding, ionic strength effects, pH fluctuations, viscosity-related diffusion changes, and electroactive interferents. The impact of these phenomena is then compared across major sensing platforms, including electrochemical, potentiometric, optical, capacitive sensors, field-effect transistors and wearable biosensors. A central part of this review focuses on practical prevention strategies already employed in real biomarker sensing platforms. These include hydration-driven antifouling coatings, zwitterionic and hydrogel interfaces, post-immobilization blocking with bovine serum albumin, mercaptohexanol and ethanolamine, ionophore and membrane engineering in ion-selective electrodes, hydrophobic solid-contact layers for water-layer suppression, regeneration workflows, membrane and microfluidic pre-treatment, and AI-assisted drift correction. By combining advances in materials engineering, surface chemistry, sample handling, and algorithmic correction, this review highlights strategies to improve sensor stability in complex biological fluids. Overall, it offers a practical guide for developing next-generation low-fouling, drift-resistant, and self-correcting sensing systems for reliable biomarker analysis at the point of care. Full article

Heart failure (HF) remains a major cause of morbidity and mortality despite substantial therapeutic progress, and important phenotype-specific treatment gaps persist. Protein S-nitrosylation (SNO) is a reversible cysteine-centered post-translational modification (PTM) whose reported associations with selected HF-relevant contexts, including vascular–endothelial dysfunction, mitochondrial–energetic remodeling, Ca²⁺-handling abnormalities, and selected receptor- or stress-related signaling observations, are supported to varying degrees. In this review, we evaluate reported mechanisms that may regulate cardiac SNO and define the evidentiary boundaries that constrain interpretation across HF-relevant settings. Available studies suggest that altered SNO homeostasis is associated with selected HF-related processes, but the strength of support varies substantially across targets, phenotypes, and disease contexts. Many mechanistic observations derive from animal models, cultured systems, donor-based perturbations, or non-HF settings. These should, therefore, be interpreted as hypothesis-generating rather than as established mechanisms in human HF. We accordingly distinguish findings supported by human HF tissue or HF-relevant in vivo evidence from more preliminary observations and highlight the need for human, site-resolved, and, where feasible, quantitatively grounded datasets. Future studies should prioritize stronger tissue anchoring, better integration of circulating and myocardial readouts, and closer alignment between mechanistic claims and the strength of the supporting evidence. Full article

Background: Myocardial infarction triggers a complex remodeling process involving inflammation, hypertrophy, fibrosis, and electrical adaptation, ultimately predisposing the heart to failure. Krüppel-like factors (KLFs) are transcriptional regulators implicated in cardiovascular development and disease; however, a comprehensive temporal characterization of their coordinated activity during post-injury remodeling remains lacking. Objective: To define the temporal orchestration of the KLF family during myocardial injury and hypertrophy, and to integrate these dynamics within regulatory networks associated with cardiac remodeling. Methods: Myocardial injury was induced in rats using intraperitoneal isoproterenol. Left ventricular tissue was collected over a 21-day period. Cardiac morphometry, histology, immunohistochemistry, and quantitative gene expression analyses were performed to evaluate structural and transcriptional changes. Publicly available human cardiac and fibroblast datasets were analyzed for translational comparison, and protein–protein interaction networks were constructed to identify functional associations. Results: Isoproterenol treatment induced progressive hypertrophy, structural disorganization, and sustained fibrotic remodeling. KLFs displayed coordinated, phase-specific regulation, characterized by early activation of inflammation-associated members, intermediate engagement of factors linked to transforming growth factor signaling and hypertrophy modulation, and late induction of regulators associated with apoptosis and scar formation. These temporal patterns paralleled changes in inflammatory mediators, cardiac transcription factors, and genes involved in electrical and calcium handling pathways. Human expression analyses supported tissue-specific specialization of key KLFs. Conclusions: KLFs exhibit a coordinated and temporally structured regulatory program during myocardial remodeling, functioning as a transcriptional network that integrates inflammation, fibrosis, hypertrophy, and electrical adaptation. These findings position KLFs as key regulatory nodes in cardiac remodeling and potential targets for therapeutic intervention. Full article

We develop a closed-form analysis of the joint distribution for cumulative shock reliability systems with phase-type inter-shock times. The analytical literature on shock-driven reliability has hitherto been split into two largely separate traditions: scalar fluctuation theory, which delivers closed-form joint distributions of pre-failure and failure-time observables but cannot accommodate matrix phase structure; and matrix-analytic methods, which handle phase-type dynamics naturally but focus on stationary indicators rather than first-passage distributions. We bridge these traditions by introducing a matrix-valued reliability functional ${\mathbf{\Phi }}_{\nu }(\xi ,u,v,\vartheta ,\theta )$ that encodes the joint distribution of the failure index, pre-failure damage and time, failure-time damage and time, and the operational phase at the moment of failure. We derive ${\mathbf{\Phi }}_{\nu }$ in closed form via Sherman–Morrison reduction of the matrix Laplace–Stieltjes transform together with the Dshalalow $\mathfrak{D}$-operator, and establish a span-reduction theorem showing that ${\mathbf{\Phi }}_{\nu }$ lies in a three-dimensional matrix subspace generated by the identity and two matrix LSTs. The functional simultaneously generalizes the scalar fluctuation functional of Dshalalow and White and the phase-tagged first excess functional of Tadj, recovering both as projections. We extract twelve closed-form reliability indices, including the reliability function, mean time to failure, mean overshoot, joint pre-failure and failure transforms, and, new to the cumulative shock literature, the phase distribution at failure and the phase-resolved failure-time distribution. Two structural identities of Wald type emerge as corollaries. The framework reduces to elementary arithmetic for rational model primitives and is verified against $2\times {10}^5$ Monte Carlo trajectories in a worked example. Full article

The black shinner Pseudopungtungia nigra is an endangered freshwater fish endemic to Korea, and standardized anesthetic protocols are needed for conservation-related hatchery handling. This study evaluated the effects of water temperature and MS-222 concentration on anesthetic induction and recovery responses in hatchery-reared juvenile P. nigra of approximately 3 cm total length. Juveniles were exposed to four MS-222 concentrations, 80, 100, 150, and 200 mg L⁻¹, at three water temperatures, 21, 24, and 27 °C. Induction time, recovery time, and recovery success within 600 s were assessed using behavioral endpoints. The 80 mg L⁻¹ treatment induced anesthesia within 600 s only at 27 °C, whereas fish exposed at 21 and 24 °C failed to reach the defined anesthetic stage within 600 s; therefore, this treatment was treated as a low-concentration induction-failure condition. In the main 3 × 3 factorial analysis using 100, 150, and 200 mg L⁻¹, induction time decreased significantly with increasing MS-222 concentration and water temperature, with significant effects of temperature, concentration, and their interaction. In contrast, recovery time increased with increasing MS-222 concentration, indicating a clear trade-off between rapid induction and recovery stability. Although 200 mg L⁻¹ produced the shortest induction times, it also resulted in the longest recovery times and delayed recovery at 24 and 27 °C. The 100 mg L⁻¹ treatment showed stable recovery but required prolonged induction, especially at lower temperatures. Overall, 150 mg L⁻¹ provided the most balanced behavioral response by substantially reducing induction time compared with 100 mg L⁻¹ while avoiding the greater recovery burden observed at 200 mg L⁻¹. These findings suggest that 150 mg L⁻¹ MS-222 is a practical concentration for routine short-term handling of hatchery-reared juvenile P. nigra under the tested temperature and handling conditions. However, this recommendation should be interpreted as a behavioral handling guideline because physiological stress responses and long-term post-anesthetic outcomes were not evaluated. Full article

Cardiopulmonary exercise testing (CPET) is the reference method for the objective assessment of exercise capacity because it provides an integrated appraisal of cardiovascular, respiratory and metabolic responses to exertion. However, CPET alone quantifies the magnitude of functional impairment without fully resolving the central and peripheral mechanisms that determine exercise intolerance. The integration of CPET with exercise stress echocardiography and near-infrared spectroscopy (NIRS) has therefore emerged as a clinically relevant multimodal strategy. Stress echocardiography provides real-time information on ventricular reserve, filling pressures, pulmonary pressure response, valvular function, pulmonary congestion and dynamic outflow obstruction, whereas NIRS provides continuous insight into skeletal muscle oxygen delivery, extraction and utilization. This narrative review summarizes the physiological rationale, practical workflow, methodological limitations and clinical applications of combined CPET, stress echocardiography and NIRS across heart failure, pulmonary hypertension, peripheral artery disease, cardiomyopathies and sports cardiology. By linking systemic gas exchange, central hemodynamics and peripheral oxygen handling, this approach may move exercise evaluation from a descriptive measure of performance toward a mechanism-based framework for phenotyping, risk stratification and individualized therapeutic decision-making. Further studies are needed to harmonize protocols, validate reproducible multimodal indices and demonstrate incremental prognostic value over conventional testing. Full article

Group projects are considered a fundamental component of higher education, as they enhance students’ competencies and problem-solving abilities within professional learning environments. Therefore, ensuring student success and providing effective supervision is essential. However, this remains a challenging task due to the reliance on supervisors’ expertise and the diverse characteristics and backgrounds of student groups. In this paper, we introduce a novel theoretical and practical interval type-2 fuzzy logic system (IT2FLS) for early prediction and guidance for novice supervisors by correlating and learning expert supervisors’ assessments according to the required level of support and the risk of failure for student groups needing early intervention. Experimental evaluation was performed based on assessments of 33 graduation projects conducted by expert supervisors, which served as the input–output data for developing interpretable white-box models that allow both novice and expert supervisors to transparently analyse reasoning processes and outcomes. The results demonstrate that the developed IT2FLS predicts the required level of support and the risk of failure for student groups with lower average error and standard deviation, outperforming the encountered Type-1 fuzzy logic systems. This study thus indicates the IT2FLS’s effectiveness in handling linguistic and numerical uncertainties in supervisors’ evaluations of students’ required early interventions. Full article

Operational failures in continuous material-handling systems are usually evaluated through failure counts; however, failure frequency alone may underestimate the true operational burden when downtime severity is unevenly distributed across devices and fault mechanisms. This study develops an integrated statistical framework for analysing operational failures and downtime in a continuous material-handling and technological transport process. The empirical dataset consists of 6605 anonymised failure events recorded between 2017 and 2025, covering 108 monthly observations, three technological device categories, and 42 classified fault types. The methodology combines frequency–severity analysis, inferential testing, time-series forecasting, and cluster-based identification of monthly operating regimes. The results show a strong disproportionality between failure frequency and downtime burden. Conveyor belts accounted for 51.40% of all failures but generated 83.22% of total downtime, confirming their dominant role in system-level operational losses. Several fault types, including Belt Slip, Off-Track Belt, Tear, Motor Failure, and Transfer Chute, also exhibited high downtime severity despite lower occurrence frequency. Inferential testing confirmed statistically significant and operationally meaningful differences in downtime severity across machine categories, whereas the calendar month was not a significant determinant of monthly failure counts or total downtime. Among the candidate forecasting models, Seasonal and Trend decomposition using Loess combined with exponential smoothing (STL-ETS) achieved the best holdout performance for both failure counts and total downtime. Cluster analysis further identified six interpretable monthly operating regimes differing in failure intensity, downtime burden, equipment involvement, fault-type composition, and temporal growth dynamics. The study contributes to downtime-oriented maintenance analytics by demonstrating that operational risk should be assessed through combined frequency–severity and regime-based perspectives rather than through aggregate failure counts alone. Full article

Machine-learning-based intrusion detection for Internet of Things systems has often been evaluated through model-centered pipelines that use weakly governed partitioning, limited leakage auditing, and closed-set assumptions. Consequently, reported performance could reflect data-handling artifacts rather than reliable security intelligence. This paper introduces a scientific integrity framework that treats preprocessing as a primary research object for open-set Internet of Things intrusion detection. The framework integrated device-disjoint split governance, feasibility-aware zero-day isolation, quantified leakage control, train-only preprocessing, shared-safe feature selection, diagnostic-harness verification, baseline split comparison, and auditable artifact generation. Applied to the CICIoT-DIAD 2024 corpus with Institute of Electrical and Electronics Engineers Organizationally Unique Identifier-based vendor enrichment, the protocol locked 28 canonical classes, eight semantic attack families, and five policy labels before constructing a device-disjoint, vendor-aware grouped split. When strict device-level zero-day holdout was infeasible, the framework activated an audited row-level fallback that preserved contamination-free holdout isolation without claiming strict device-novel zero-day evaluation. On 35,672,407 flows from 180 files, the accepted run achieved zero device overlap, zero flow-signature Jaccard leakage risk, 100 percent zero-day purity, a Feature Distribution Stability Score of 0.00518, a Device-Feature Dependency Index of 0.00000, an Attack Invariance Score of 0.92964, and an Attack Semantic Consistency Score of 0.90714. The diagnostic harness produced zero hard failures and zero warnings, while baseline comparison showed stronger preprocessing integrity than random stratified and simple device-disjoint splitting. This study did not claim downstream classifier superiority; rather, it established an auditable preprocessing substrate for later classifier-level experiments. Full article

Heart failure (HF) is a complex and heterogeneous clinical syndrome defined by progressive structural, functional, and molecular alterations in the myocardium, representing a significant global health challenge. Beyond haemodynamic compromise, HF arises from intricate interactions among neurohormonal activation, chronic inflammation, oxidative stress, mitochondrial dysfunction, impaired calcium handling, and extracellular matrix remodelling. These processes drive maladaptive cardiac remodelling and progressive functional decline across multiple HF phenotypes, including HF with reduced (HFrEF), mildly reduced (HFmrEF), and preserved ejection fraction (HFpEF). Recent advances in molecular biology have highlighted the critical roles of genomic, epigenetic, and transcriptomic mechanisms in the progression of HF. DNA methylation, histone modifications, chromatin remodelling, and non-coding RNAs regulate gene expression in response to environmental and metabolic stimuli, thereby connecting systemic risk factors to cardiac dysfunction. Proteomic and post-translational modifications, such as phosphorylation, acetylation, and redox signalling, modulate protein function and contribute to contractile impairment and metabolic dysregulation. Metabolomic studies have revealed significant changes in myocardial energy metabolism, including reduced oxidative capacity, decreased metabolic flexibility, and limited bioenergetic reserves. The integration of multi-omics approaches—including genomics, transcriptomics, proteomics, metabolomics, and epigenomics—has provided unprecedented insight into the biological heterogeneity of HF, facilitating the identification of distinct molecular subtypes and novel therapeutic targets. Systems biology and network-based analyses, supported by artificial intelligence and machine learning, enable the synthesis of complex datasets and enhance risk classification, prognosis, and personalised treatment approaches. This narrative review synthesises the current understanding of the molecular mechanisms underlying HF, with particular emphasis on the interplay between metabolic and epigenetic regulation in disease progression. It also highlights emerging translational opportunities, including omics-based biomarkers, targeted therapies, and precision medicine approaches. Despite significant advances, challenges remain in translating these findings into clinical practice, underscoring the need for standardised methodologies, extensive validation, and integrative frameworks. Ultimately, a systems-level, multi-omics perspective is crucial for redefining HF as a biologically stratified condition in the landscape of advancing tailored cardiovascular medicine. Full article

The rapid integration of AI-powered coding assistants into developer workflows has raised significant privacy and trust concerns. As developers entrust proprietary code to services like OpenAI’s GPT, Google’s Gemini, and GitHub Copilot, the unclear data handling practices of these tools create security and compliance risks. This paper addresses this challenge by introducing and applying a novel, expert-validated privacy scorecard. The methodology involves a detailed analysis of four document types—from legal policies to external audits—to score five leading assistants against 14 weighted criteria. A legal expert and a data protection officer refined these criteria and their weighting. The results reveal a distinct hierarchy of privacy protections, with a 20-point gap between the highest- and lowest-ranked tools. The analysis uncovers common industry weaknesses, including the pervasive use of opt-out consent for model training and a near-universal failure to filter secrets from user prompts proactively. The resulting scorecard provides actionable guidance for developers and organizations, enabling evidence-based tool selection. This work establishes a new benchmark for transparency and advocates for a shift towards more user-centric privacy standards in the AI industry. Full article