Search Results (1,185)

Background/Objectives: Percutaneous dilatational tracheostomy (PDT) is a commonly performed procedure in critically ill patients. Various guidance techniques, including anatomical landmark-guided (ALG), ultrasound-guided (USG) and bronchoscopy-guided (BG), aim to enhance procedural safety and efficacy. This systematic review and meta-analysis aimed to compare the safety and efficacy across ALG, USG, and BG techniques in PDT, focusing on complications and procedure times. Methods: A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted. Studies identified through PubMed, CENTRAL, Scopus, and Web of Science databases up to July 2025 comparing ALG, USG, and BG PDT were included. Primary outcomes were minor and major bleeding, with transient hypoxia, transient hypotension, endotracheal tube cuff puncture, pneumothorax, and procedure time as secondary outcomes. Data were pooled using random-effects models, with risk ratios (RR) and 95% confidence intervals (CI) for complications and mean differences for procedure times. Heterogeneity was assessed using I² statistics, with Bonferroni correction for multiple comparisons. Results: This meta-analysis included five RCTs (568 patients) comparing USG vs. ALG, six RCTs (404 patients) comparing USG vs. BG, and five RCTs (448 patients) comparing ALG vs. BG. USG significantly reduced minor bleeding compared to ALG (RR 2.30, 95% CI 1.38–3.84, p = 0.001) and BG (RR 0.42, 95% CI 0.20–0.91, p = 0.02), and major bleeding compared to ALG (RR 2.62, 95% CI 1.00–6.86, p = 0.04). ALG was associated with higher minor bleeding risk than BG (RR 1.81, 95% CI 1.05–3.12, p = 0.03). No significant differences were found for transient hypoxia, hypotension, endotracheal tube cuff puncture, or pneumothorax across comparisons, though trends suggested lower hypoxia risk with USG and higher pneumothorax risk with ALG. Procedure times were similar (ALG: 10.4 min, USG: 11.7 min, BG: 12.7 min; p = 0.493). Some rare complications, like paratracheal placement and mediastinitis, were too infrequent for analysis. Conclusions: USG PDT significantly reduces the risk of minor and major bleeding relative to ALG and minor bleeding compared to BG, without prolonging procedure time. USG and BG show comparable safety for most non-bleeding outcomes. No significant differences in procedure times. Future research should focus on larger RCTs to assess rare complications and explore hybrid USG-BG approaches to optimize PDT safety and efficacy. Full article

Background: Chronic total occlusion percutaneous coronary intervention (CTO PCI) is a complex revascularization procedure requiring advanced techniques to ensure procedural success and safety. Hybrid strategies combining antegrade dissection/re-entry (ADR) and retrograde approaches have become increasingly adopted in contemporary practice. Objectives: To systematically review and synthesize evidence comparing outcomes of ADR and retrograde CTO PCI techniques, with pooled estimates of success rates and adverse events. Methods: This review followed PRISMA 2020 guidelines. We searched PubMed, Cochrane CENTRAL, and Google Scholar for studies published between January 2015 and June 2025. Eligible studies included randomized controlled trials and observational studies reporting outcomes of ADR and/or retrograde CTO PCI. Data extraction was performed by two independent reviewers. Risk of bias was assessed using the Newcastle–Ottawa Scale and the Cochrane RoB 2.0 tool. A random-effects meta-analysis was conducted for consistently reported outcomes. Results: Twenty studies encompassing over 87,000 CTO PCI procedures were included. Pooled analysis of 16 studies demonstrated a technical success rate of 83.4% and a procedural success rate of 84.6%. The in-hospital major adverse cardiac event (MACE) rate was 3.3%. Hybrid strategies integrating ADR and retrograde approaches yielded the highest success rates (86–91%) with acceptable safety profiles. Use of adjunctive tools such as IVUS, dual arterial access, and re-entry devices was associated with improved outcomes. Discussion: Hybrid CTO PCI techniques are safe, effective, and reproducible across diverse clinical settings. When performed by experienced operators using modern adjuncts, these strategies provide durable benefits and should be considered standard for complex occlusions. Limitations include variation in study quality, heterogeneous procedural definitions, and lack of long-term data in several cohorts. Full article

Background: Locally advanced cervical cancer (LACC) represents a significant challenge in oncology, requiring accurate assessment of local extent and metastatic spread. Multiparametric magnetic resonance imaging (mpMRI) has assumed a central role in the loco-regional characterization of the tumor due to its high soft-tissue resolution and the ability to integrate functional information. Objectives: In this narrative review, we explore the use of mpMRI in the diagnosis, staging, and treatment response of LACC, comparing its performance with that of PET/CT, which remains complementary for remote staging. The potential of whole-body magnetic resonance imaging (WB-MRI) and hybrid PET/MRI techniques is also analyzed, as well as the emerging applications of radiomics and artificial intelligence. The paper also discusses technical limitations, interpretative variability, and the importance of protocol standardization. The goal is to provide an updated and translational summary of imaging in LACC, with implications for clinical practice and future research. Methods: Prospective and retrospective studies, systematic reviews, and meta-analyses on adult patients with cervical cancer were included. Results: Fifty-two studies were included. MRI demonstrated a sensitivity and specificity greater than 80% for parametrial and bladder invasion, but limited sensitivity (45–60%) for lymph node disease, lower than PET/CT. Multiparametric MRI was useful in early prediction of response to chemotherapy and radiotherapy and in distinguishing residual disease from fibrosis. The integration of MRI into Image-Guided Adaptive Brachytherapy (IGABT) resulted in improved oncological outcomes and reduced toxicity. The applications of radiomics and AI demonstrated enormous potential in predicting therapeutic response and lymph node status in the MRI study, but multicenter validation is still needed. Conclusions: MRI is the cornerstone of the local–regional staging of advanced cervical cancer; it has become an essential and crucial tool in treatment planning. Its use, combined with PET/CT for lymph node assessment and metastatic disease staging, is now the standard of care. Future prospects include the use of whole-body MRI and the development of predictive models based on radiomics and artificial intelligence. Full article

Metal anodes promise improvements in energy density and cost; however, their performance is determined within the first several nanometers at the interface. This review reports on how polymer-based artificial solid electrolyte interphases (SEIs) are engineered to stabilize Li and aqueous-Zn anodes, and how these designs are now evaluated against operando readouts rather than post-mortem snapshots. We group the related molecular strategies into three classes: (i) side-chain/ionomer chemistry (salt-philic, fluorinated, zwitterionic) to increase cation selectivity and manage local solvation; (ii) dynamic or covalently cross-linked networks to absorb microcracks and maintain coverage during plating/stripping; and (iii) polymer–ceramic hybrids that balance modulus, wetting, and ionic transport characteristics. We then benchmark these choices against metal-specific constraints—high reductive potential and inactive Li accumulation for Li, and pH, water activity, corrosion, and hydrogen evolution reaction (HER) for Zn—showing why a universal preparation method is unlikely. A central element is a system of design parameters and operando metrics that links material parameters to readouts collected under bias, including the nucleation overpotential (η_nuc), interfacial impedance (charge transfer resistance (R_ct)/SEI resistance (R_SEI)), morphology/roughness statistics from liquid-cell or cryogenic electron microscopy (Cryo-EM), stack swelling, and (for Li) inactive-Li inventory. By contrast, planar plating/stripping and HER suppression are primary success metrics for Zn. Finally, we outline parameters affecting these systems, including the use of lean electrolytes, the N/P ratio, high areal capacity/current density, and pouch-cell pressure uniformity, and discuss closed-loop workflows that couple molecular design with multimodal operando diagnostics. In this view, polymer artificial SEIs evolve from curated “recipes” into predictive, transferable interfaces, paving a path from coin-cell to prototype-level Li- and Zn-metal batteries. Full article

Modern Software-Defined Wide Area Networks (SD-WANs) require adaptive controller placement addressing multi-objective optimization where latency minimization, load balancing, and fault tolerance must be simultaneously optimized. Traditional static approaches fail under dynamic network conditions with evolving traffic patterns and topology changes. This paper presents a novel hybrid framework integrating Gaussian Mixture Model (GMM) clustering with Multi-Agent Reinforcement Learning (MARL) for dynamic controller placement. The approach leverages probabilistic clustering for intelligent MARL initialization, reducing exploration requirements. Centralized Training with Decentralized Execution (CTDE) enables distributed optimization through cooperative agents. Experimental evaluation using real-world topologies demonstrates a noticeable reduction in the latency, improvement in network balance, and significant computational efficiency versus existing methods. Dynamic adaptation experiments confirm superior scalability during network changes. The hybrid architecture achieves linear scalability through problem decomposition while maintaining real-time responsiveness, establishing practical viability. Full article

Constructing accurate Potential Energy Surfaces (PES) is a central task in molecular modeling, as it determines the forces governing nuclear motion and enables reliable quantum dynamics simulations. While ab initio methods can provide accurate PES, they are computationally prohibitive for extensive applications. Alternatively, analytical physics-based models such as the Morse potential offer efficient solutions but are limited by their rigidity and poor generalization to excited states. In recent years, neural networks have emerged as powerful tools for determining PES, due to their universal function approximation capabilities, but they require large training datasets. In this work, we investigate hybrid-residual modeling approaches that combine physics-based potentials with neural network corrections, aiming to leverage both physical priors and data adaptability. Specifically, we compare three hybrid models—APHYNITY, Sequential Phy-ML, and PhysiNet—in their ability to reconstruct the potential energy curve of the ground and first excited states of the hydrogen molecule. Each model integrates a simplified physical representation with a neural component that learns the discrepancies from accurate reference data. Our findings reveal that hybrid models significantly outperform both standalone neural networks and pure physics-based models, especially in low-data regimes. Notably, APHYNITY and Sequential Phy-ML exhibit better generalization and maintain accurate estimation of physical parameters, underscoring the benefits of explicit physics incorporation. Full article

Structure-Modulated Long-Period Fiber Gratings (SM-LPFGs) represent an advancement in fiber optic sensor technology, moving beyond traditional photosensitivity-based fabrication to achieve enhanced performance through the direct physical modification of the geometry of the fiber. This review provides a comprehensive analysis of the primary fabrication techniques enabling this approach, including CO₂ laser inscription, femtosecond laser micromachining, electric-arc discharge, chemical etching, and fusion tapering. The central focus of this work is the elucidation of the definitive structure–performance relationship, systematically detailing how engineered geometries such as helical profiles, micro-tapers, and asymmetric grooves unlock novel sensing capabilities. We demonstrate how these specific structures are strategically designed to induce circular birefringence for torsion measurement, enhance evanescent field interaction for ultra-sensitive refractive index detection, and create localized stress concentrations for high-resolution strain and vector bending sensing. Furthermore, the review surveys the practical implementation of these sensors in critical application domains, including structural health monitoring, biomedical diagnostics, and environmental sensing. Finally, we conclude by summarizing key achievements and identifying promising future research directions, such as the development of hybrid fabrication processes, the integration of machine learning for advanced signal demodulation, and the path towards industrial-scale production. Full article

We recast cooperative localization and scheduling in heterogeneous multi-agent systems through the lens of symmetry and symmetry breaking. On the geometric side, the Fisher Information Matrix (FIM) objective is invariant to rigid Euclidean transformations of the global frame, while its maximization admits symmetric optimal sensor formations; on the algorithmic side, heterogeneity and task constraints break permutation symmetry across agents, requiring policies that are sensitive to role asymmetries. We model communication as a random graph and quantify structural symmetry via topology metrics (average path length, clustering, betweenness) and graph automorphism-related indices, connecting these to estimation uncertainty. We then design a hybrid reward for reinforcement learning (RL) that is equivariant to agent relabeling within roles yet intentionally introduces asymmetry through distance/FIM terms to avoid degenerate symmetric configurations with poor observability. Simulations show that (i) symmetry-aware, FIM-optimized path planning reduces localization error versus symmetric but non-informative placements; and (ii) controlled symmetry breaking in policy learning improves robustness and data rate–reward trade-offs over baselines. Our results position symmetry/asymmetry as first-class design principles that unify estimation-theoretic invariances with learning-based coordination in complex heterogeneous networks. Under DDPG training, the total data rate (SDR) reaches $6.63\pm 0.97$ and the average reward per step (ARPS) is $-80.70\pm 6.94$, representing improvements of approximately $11.8\%$ over the baseline $(5.93\pm 3.51)$ and $11.1\%$ over SAC $(5.97\pm 2.66)$, respectively. The network’s mean shortest-path length is $L=1.721$, and the average betweenness centrality of the coordination nodes is ≈0.098. Moreover, the FIM-optimized path-planning strategy achieves the lowest localization error among all evaluated policies. Full article

Poly (ADP-ribose) polymerase 1 (PARP1) is an important enzyme that plays a central role in the DNA damage response, facilitating repair of single-stranded DNA breaks via the base excision repair (BER) pathway and thus genomic integrity. Its therapeutic relevance is compounded in breast cancer, particularly in BRCA1 or BRCA2 mutant cancers, where compromised homologous recombination repair (HRR) leaves a synthetic lethal dependency on PARP1-mediated repair. This review comprehensively discusses the recent advances in computational chemistry for the discovery of PARP1 inhibitors, focusing on their application in breast cancer therapy. Techniques such as molecular docking, molecular dynamics (MD) simulations, quantitative structure–activity relationship (QSAR) modeling, density functional theory (DFT), time-dependent DFT (TD-DFT), and machine learning (ML)-aided virtual screening have revolutionized the discovery of inhibitors. Some of the most prominent examples are Olaparib (IC₅₀ = 5 nM), Rucaparib (IC₅₀ = 7 nM), and Talazoparib (IC₅₀ = 1 nM), which were optimized with docking scores between −9.0 to −9.3 kcal/mol and validated by in vitro and in vivo assays, achieving 60–80% inhibition of tumor growth in BRCA-mutated models and achieving up to 21-month improvement in progression-free survival in clinical trials of BRCA-mutated breast and ovarian cancer patients. These strategies enable site-specific hopping into the PARP1 nicotinamide-binding pocket to enhance inhibitor affinity and specificity and reduce off-target activity. Employing computation and experimental verification in a hybrid strategy have brought next-generation inhibitors to the clinic with accelerated development, higher efficacy, and personalized treatment for breast cancer patients. Future approaches, including AI-aided generative models and multi-omics integration, have the promise to further refine inhibitor design, paving the way for precision oncology. Full article

This paper presents a novel and computationally efficient numerical method for solving systems of fractional-order differential equations using orthogonal hybrid functions (HFs). The proposed HFs are constructed by combining piecewise constant orthogonal sample-and-hold functions with piecewise linear orthogonal right-handed triangular functions, resulting in a flexible and accurate approximation basis. A central innovation of the method is the derivation of generalized one-shot operational matrices that approximate the Riemann–Liouville fractional integral, enabling direct integration of differential operators of arbitrary order. These matrices act as unified integrators for both integer and non-integer orders, enhancing the method’s applicability and scalability. A rigorous convergence analysis is provided, establishing theoretical guarantees for the accuracy of the numerical solution. The effectiveness and robustness of the approach are demonstrated through several benchmark problems, including fractional-order models related to smoking dynamics, lung cancer progression, and Hepatitis B infection. Comparative results highlight the method’s superior performance in terms of accuracy, numerical stability, and computational efficiency when applied to complex, nonlinear, and high-dimensional fractional-order systems. Full article

The exponential growth of Internet of Things (IoT) devices introduces significant security challenges due to their resource constraints and diverse attack surfaces. To address these issues, this paper proposes the Centralized Two-Tiered Tree-Based Intrusion Detection System (C2T-IDS), a lightweight framework designed for efficient and scalable threat detection in IoT networks. The system employs a hybrid edge-centralized architecture, where the first tier, deployed on edge gateways, performs real-time binary classification to detect anomalous traffic using optimized tree-based models. The second tier, hosted on a centralized server, conducts detailed multi-class classification to diagnose specific attack types using advanced ensemble methods. Evaluated on the realistic CIC-IoT-2023 dataset, C2T-IDS achieves a Macro F1-Score of up to 0.94 in detection and 0.80 in diagnosis, outperforming direct multi-class classification by 5–15%. With inference times as low as 6 milliseconds on edge devices, the framework demonstrates a practical balance between accuracy, efficiency, and deployability, offering a robust solution for securing resource-constrained IoT environments. Full article

Remote northern regions face unique energy challenges due to geographic isolation, harsh climates, and limited access to centralized power grids. In response to growing environmental and economic pressures, there is a rising interest in hybrid energy systems that integrate renewable and conventional sources. This study investigates sustainable and cost-effective energy supply strategies for off-grid northern communities through the modeling and simulation of multi-energy microgrids. Focusing on case studies from Yakutia (Russia), Hordaland (Norway), and Alaska (United States), the research employs a comprehensive methodology that combines a critical literature review, system design using HOMER Pro software (version 3.16.2), and a comparative analysis of simulation outcomes. Three distinct microgrid configurations are proposed, incorporating various combinations of solar photovoltaic (PV), wind energy, diesel generators, and battery storage systems. The findings reveal that integrating solar PV significantly enhances economic efficiency, particularly in regions with high solar irradiance, underscoring its pivotal role in shaping resilient, sustainable energy systems for remote northern areas. This study is innovative in its cross-regional comparative approach, linking techno-economic simulation with climatic variability analysis to identify context-specific energy strategies. The key findings highlight how hybrid microgrids combining PV, wind, and storage systems can reduce both costs and emissions by up to 35% compared to diesel-only systems, offering practical pathways toward sustainable electrification in high-latitude regions. Full article

This research examines how artificial intelligence is reshaping business and labor structures through a sustainability lens. Drawing on survey data from 127 organizations and 14 case studies, we quantify workforce impacts while exposing methodological limitations in current projections. Our analysis reveals implementation variations of 37% across industries and 41% higher user adoption rates for hybrid governance approaches versus centralized models. The evidence supports a three-dimensional strategic framework for sustainable organizational development: comprehensive upskilling fostering behavioral change (2.7× higher implementation success), distributed innovation enabling cross-functional ideation (3.1× more identified use cases), and strategic integration aligning systems across departments (explaining 31% of implementation success variance). Organizations deploying all three dimensions achieved a 74% AI initiative success rate versus 12% for those using none. Implementation barriers include regulatory uncertainty, organizational resistance, and ethical considerations, with data infrastructure maturity (β = 0.32), executive sponsorship (β = 0.29), and change readiness (β = 0.26) explaining 58% of implementation success variance. Our findings indicate that sustainable adaptation capacity—not merely technological investment—determines which organizations successfully navigate this transformation while maintaining long-term organizational viability, workforce resilience, and contribution to broader sustainable development goals. Full article

Modular barges are increasingly applied in inland and nearshore operations for their transportability and flexible assembly, yet the reliability of their connections remains insufficiently studied. This study presents a finite element analysis of a 15-ton-class modular barge in service, focusing on bolted and interlocking joints under still-water and wave-induced loads. A detailed three-dimensional model with explicit contacts was developed, and four load cases combined hydrostatic and deck loads with longitudinal and transverse crest/trough scenarios. Results showed that the highest stresses occurred in central stiffeners and lower interlocking joints, but all were below allowable limits, ensuring adequate safety margins. Bolted joints exhibited low stress, confirming their robustness and redundancy within the hybrid system. By analyzing an operating barge under realistic conditions, this study demonstrates the structural adequacy of the modular concept and provides a basis for future guidelines and larger modular floating platforms. Full article

The transformation of Local Ecological Knowledge (LEK) among minority populations undergoing cultural and linguistic assimilation over time is poorly understood. Arbëreshë communities in Greece, who have preserved Albanian-derived traditions for centuries, offer a unique opportunity to examine how folk plant knowledge adapts over time. This study examines the linguistic labels and culinary uses of wild greens among Arbëreshë (or Arvanites), an ethno-linguistic minority traditionally speaking Arbërisht or Arvanitika, the Tosk dialect of Albanian, who have resided in the Argolis and Corinthia regions of the Peloponnese for several centuries. In 2025, fieldwork was conducted in four rural Arbëreshë villages in the Argolis and Corinthia regions of Greece, combining semi-structured interviews with 24 elderly participants, participant observation, and the collection and identification of botanical specimens. The contemporary dataset was compared with historical ethnobotanical records from the 1970s to assess temporal changes in the use of wild vegetables and folk plant nomenclature. Our results reveal that current Arbëreshë ethnobotanical heritage has undergone profound Hellenisation, with 62% of folk plant names of Greek origin, 14% Albanian, and 24% hybrid, reflecting strong linguistic and cultural assimilation over the past half-century. The traditional boiled green mix (lakra in Arbëreshë, chorta in Greek) remains central to the local cuisine, which is rooted in foraged plants, although its culinary applications have diversified. In total, 37 taxa of wild vegetables across 37 genera and 14 families were documented in 2025, compared with 21 taxa across 21 genera in the filtered 1970 dataset. Core families, such as Asteraceae and Brassicaceae, remained dominant, while new families, like Malvaceae and Portulacaceae, appeared, possibly indicating both ecological and culinary changes. These findings raise questions about whether the Arbëreshë wild vegetable heritage was strongly influenced by the surrounding Greek majority or primarily acquired after migration, potentially facilitated by intermarriages and shared Orthodox Christian affiliation. Overall, our study highlights a largely Hellenised Arbëreshë biocultural heritage and underscores the urgent need for national and regional stakeholders to recognise and celebrate the remaining minority’s linguistic and ethnobotanical diversity. The transformation of local ethnobotanical knowledge over the past fifty years appears influenced by ecological availability, socio-cultural dynamics, and changing taste preferences. Full article