Search Results (2,769)

There is no approved drug therapy for schwannomas associated with NF2-related schwannomatosis (NF2-SWN). Neither life-saving surgical resection or radiation are curative and can compound the debilitating neurological effects of the schwannomas. We previously identified fimepinostat, a dual histone deacetylase (HDAC)/phosphoinositide-3 kinase (PI3K) inhibitor, as a promising drug candidate with pro-apoptotic effects on NF2-related schwannomas. This preclinical study used the pharmaceutical formulation of fimepinostat to confirm its efficacy in schwannomas and identify pro-apoptotic signaling pathways. Fimepinostat was tested in human schwannoma model cells, patient-derived primary vestibular and non-vestibular schwannoma cells, and in a sciatic nerve allograft model. The signaling pathways leading to caspase-3-dependent apoptosis were elucidated using immune assays, flow cytometry, imaging, proteome, and acetylome analysis. Acute exposure to fimepinostat led to p21-dependent cell cycle inhibition, upregulation of tumor necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL R2), and downregulation of tumor necrosis factor receptor 1 (TNFR1), Yes-associated protein (YAP), and inhibitors of apoptosis. Moreover, fimepinostat downregulated cytokine and chemokine secretion increased by merlin loss in schwannoma cells. Fimepinostat is a promising new drug intervention for NF2-SWN patients with the potential to promote tumor regression. Full article

Objectives: This systematic review and meta-analysis compared freehand and navigation-assisted spine surgery with robot-assisted techniques, focusing on radiation dose, fluoroscopy time, and factors influencing these outcomes. Methods: Following the PRISMA and PROSPERO protocols, we searched major databases for comparative studies on radiation exposure or fluoroscopy duration. Non-comparative, cadaveric, and animal studies were excluded. Bias was assessed with RoB 2 and MINORS. The data were pooled using random-effects models, with subgroup, meta-regression, sensitivity, and publication-bias analyses. Results: Twenty-eight studies (3205 patients) were included. Compared with freehand surgery, robotic assistance did not significantly reduce radiation dose (SMD −0.81; p = 0.07) or fluoroscopy time (SMD −0.56; p = 0.06), with substantial heterogeneity. Subgroup analyses revealed lower exposure with specific robotic systems (e.g., Tianji^®), in degenerative and trauma indications, and at cervical, lumbar, and thoracolumbar levels. No differences were observed between robotic-assisted and navigation-assisted techniques. A meta-regression showed increasing an fluoroscopy time and radiation dose in more recent freehand studies, while trends were stable in robotic cohorts. No publication bias was detected. Conclusions: Robotic-assisted surgery was not associated with statistically significant reductions in overall radiation dose or fluoroscopy time compared with freehand techniques. Effects may vary by robotic platform, indication, and anatomical level; however, substantial heterogeneity limits certainty. Further randomized controlled trails with standardized reporting are warranted. Full article

The global maritime industry, a critical pillar of international trade, continues to face persistent challenges in ensuring the integrity, security, and transparency of containerized cargo data, particularly during ocean transport. Traditional container tracking systems at sea often lack the reliability and resilience required to prevent data tampering, cyber threats, and operational inefficiencies. As supply chains become more complex and interconnected, the demand for robust, end-to-end data security solutions becomes more pressing. A promising technological advancement in this area is the convergence of smart containers, equipped with Internet of Things (IoT) sensors for real-time condition monitoring, and blockchain technology (BCT) for secure data validation. These IoT devices facilitate continuous tracking of critical parameters such as location, temperature, humidity, tilt, and the like. However, the data they generate remains vulnerable to cyberattacks, signal disruptions, and unauthorized alterations. Blockchain’s decentralized and tamper-evident architecture addresses these vulnerabilities by enabling secure data immutability, transparent audit trails, and enhanced stakeholder trust. Despite its potential, the practical integration of blockchain with smart container systems in maritime logistics remains largely underexplored. To bridge this gap, this paper proposes a blockchain-enabled smart container monitoring system that combines container real-time data with secure physical tracking. Furthermore, to ensure scalability and efficient in data storage, hybrid on/off-chain architecture is introduced, balancing blockchain integrity with performance and resource optimization. Full article

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy is highly sensitive to panel discretization strategies, which are often determined heuristically. This study proposes a bio-inspired optimization framework for VLM panel discretization and evaluates it through a systematic case study on a representative wing geometry. A grid-convergence analysis was initially carried out to ensure solution independence across various spanwise-to-chordwise panel ratios. Subsequently, a novel Hybrid Response Surface Methodology (HRSM), integrating Box–Behnken and Central Composite experimental designs, was employed to enable a more comprehensive exploration of the factor space while quantifying the effects of clustering parameters at the leading-edge, trailing-edge, root, and tip regions of the wing. The HRSM dataset was further utilized to train Ensemble Machine-Learning surrogate models, which were coupled with bio-inspired Crayfish and Aquila optimization algorithms, alongside a classical Genetic Algorithm (GA) as a performance benchmark, to identify the optimal discretization strategy and to enable a comparative assessment of their convergence behavior and robustness against the numerical noise of the ensemble-based landscape. Compared to base (i.e., uniform) panel distribution, the optimally clustered discretization enhanced overall aerodynamic prediction accuracy by approximately 33%, particularly at low angles of attack, while maintaining robust performance at higher angles. Both algorithms converged to similar minima; however, the Aquila algorithm achieved higher solution consistency, whereas the Crayfish algorithm exhibited greater dispersion despite faster convergence, revealing a multimodal optimization landscape. The variance decomposition revealed that trailing-edge clustering dominated aerodynamic accuracy at low angles of attack, contributing up to 90% of the total variance, whereas tip clustering became increasingly influential at higher angles, exceeding 30%, highlighting the need for adaptive discretization strategies to ensure reliable VLM-based aerodynamic analyses. Full article

Algorithmic management systems increasingly coordinate work, allocate resources, and support decisions in corporate, public sector, and research environments. Yet many such systems remain opaque: they optimize and score effectively but struggle to communicate rationales that are contextual, auditable, and defensible under emerging governance expectations. Large language models (LLMs) can help bridge this gap by translating quantitative signals into human-readable explanations and enabling interactive clarification. However, LLM integration also introduces new risks—hallucinated rationales, bias amplification, prompt-based security failures, and automation dependence—that must be governed rather than merely engineered. This article proposes a governance-oriented architecture for LLM-augmented algorithmic management. The model combines the following elements: an algorithmic decision core; an LLM-based cognitive interface for explanation and dialogue, and a verification and governance layer that enforces policy constraints, provenance, audit trails, and human-in-command oversight. The framework is developed through targeted conceptual synthesis and normative alignment with key governance instruments (e.g., the EU AI Act, GDPR, and ISO/IEC 42001). It is illustrated through cross-domain scenarios and complemented by a demonstrative synthetic-trace simulation that highlights transparency–latency trade-offs under verification controls. Using the demonstrative simulation (n = 120 decision events), the framework illustrates a mean baseline latency of 100.3 ms and a mean LLM-augmented latency of 115.8 ms (≈15.5% increase), a mean explanation validity proxy of 85.6%, and a simulated constraint-satisfaction rate of 94.2% (113/120 events), with failed cases routed to review. These values are presented as design-level indicators of operational plausibility and governance trade-offs, not empirical performance benchmarks or state-of-the-art comparisons. The paper contributes a conceptual and governance-oriented architectural blueprint for integrating generative AI into organisational decision systems without sacrificing accountability, compliance, or operational reliability. Full article

Ecotourism in protected areas creates a conservation paradox: tourism revenue funds protection, yet tourism infrastructure simultaneously degrades the wildlife it protects. We examined this paradox in white-faced capuchins (Cebus imitator) in Tortuguero National Park, comparing behaviour across a high-tourism accommodation site (2152 monthly guests) and a strictly regulated terrestrial trail. Using focal animal and sweep sampling methods, we recorded 477 behavioural units across 261 min, analysing locomotion, feeding, and agonistic behaviours through generalized linear models. Primates in accommodation areas exhibited significantly reduced high substrate use (p = 0.005), showed a trend toward increased anthropogenic food reliance (p = 0.070), and higher—but not statistically significant—rates of agonistic behaviours (p > 0.05). The negative correlation between natural foraging and active food supply (r = −0.31) is consistent with anthropogenic provisioning that may alter primate ecological functions. These findings demonstrate that effective conservation in tourism contexts requires integrated management addressing three interconnected challenges: (1) habituation to human presence, (2) food provisioning with cascading consequences, and (3) ecosystem-level degradation through altered primate functions. We recommend evidence-based interventions including secured waste management, enforcement of wildlife feeding prohibitions, and environmental education programs with community participation. Ecotourism sustainability requires managing human–wildlife interactions and integrating local stakeholder perspectives to preserve animal welfare and ecosystem functions essential for conservation. Full article

To enhance the aerodynamic performance of organic Rankine cycle (ORC) turbines under increasing energy demands, surrogate-based optimization was applied to a 100 kW ORC turbine rotor. Four representative surrogate models—a radial basis neural network (RBNN), Kriging, response surface approximation (RSA), and a PRESS-based weighted (PBW) ensemble—were comparatively evaluated under identical numerical conditions. Independent optimizations of the first- and second-stage rotors enabled an examination of how different design variable space characteristics influenced surrogate predictive behavior. A fractional factorial sampling strategy was used to construct the training dataset, and learning curve analysis was conducted to assess sample size adequacy. Sensitivity estimation revealed distinct response surface characteristics between stages, allowing the interpretation of variations in surrogate stability. In both stages, geometric modifications were primarily concentrated near the outlet blade angle, identified as a dominant variable influencing efficiency. CFD validation confirmed that surrogate-based exploration successfully identified improved rotor geometries. Flow-field analysis indicated reduced entropy generation near the trailing edge region, suggesting the mitigation of aerodynamic losses. The results demonstrate that surrogate-based optimization can reliably improve turbine performance within a bounded design space, while the relative effectiveness of surrogate models depends on the sensitivity structure of the underlying problem. Full article

Modern organizations increasingly rely on heterogeneous database environments that combine relational, document-oriented, and key-value storage systems to optimize performance for diverse application requirements. However, this technological diversity creates significant challenges for implementing consistent data governance policies, regulatory compliance, and access control across disparate systems. Traditional governance approaches that operate within individual database silos fail to provide unified policy enforcement and create compliance gaps that expose organizations to regulatory and operational risks. This paper presents a novel API-driven architecture that enables unified data governance across heterogeneous database environments without requiring database-specific modifications or vendor lock-in. The proposed framework implements a centralized governance layer that coordinates policy enforcement across PostgreSQL, MongoDB, and Amazon DynamoDB systems through RESTful API interfaces. Key architectural components include differentiated access control through hierarchical API key management, automated compliance workflows for regulatory requirements such as GDPR, real-time audit trail generation, and comprehensive data quality monitoring with automated improvement mechanisms. Comprehensive experimental evaluation demonstrates the framework’s effectiveness across multiple operational dimensions. The system achieved 95.2% accuracy in access control enforcement across different data classification levels, while automated GDPR compliance workflows demonstrated 98.6% success rates with average processing times of 2.9 h. Performance evaluation reveals acceptable overhead characteristics with linear scaling patterns for PostgreSQL operations (R² = 0.89), consistent sub-20ms response times for MongoDB logging operations, and sustained throughput rates ranging from 38.9 to 142.7 requests per second across the integrated system. Data quality improvements ranged from 16.1% to 34.3% across accuracy, completeness, consistency, and timeliness dimensions over a 12-week monitoring period, with accuracy improving by 17.8 percentage points, completeness by 13.2 percentage points, consistency by 19.7 percentage points, and timeliness by 24.5 percentage points. The duplicate detection system achieved 94.6% precision and 95.6% recall across various duplicate types, including cross-database redundancy identification. The results demonstrate that API-driven governance architectures can effectively address the persistent challenges of policy fragmentation in multi-database environments while maintaining operational performance and enabling measurable improvements in data quality and regulatory compliance. The framework provides a practical migration path for organizations seeking to implement comprehensive governance capabilities without replacing existing database infrastructure investments. Full article

Stringent cleanliness standards govern process fluid transport in integrated circuit (IC) manufacturing. Cavitation-induced surface defects on flow control components promote bacterial adhesion, thereby compromising wafer fabrication. To elucidate the coupling mechanisms among surface topography, hydrodynamics, and bacterial retention, this study utilizes a one-way coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach integrated with extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) theory. We constructed a numerical model of rod-shaped Pseudomonas aeruginosa, integrated with a customized API-based coupling scheme to resolve temporal scale disparities, and systematically simulated flow evolution and adhesion behaviors across varying groove geometries (quadrilateral, triangular, and semicircular) and inlet velocities (1–3 m/s). The results indicate that groove-induced flow separation and recirculation vortices drive bacterial accumulation at the trailing edge. Triangular profiles exhibited superior flow stability, yielding significantly lower adhesion than quadrilateral and semicircular shapes. Bacterial retention scaled inversely with flow velocity due to enhanced hydrodynamic shear. These findings provide theoretical and engineering insights for the anti-contamination design of ultra-clean flow control components in IC manufacturing. Full article

Airframe noise generated at wing trailing edges and high-lift devices, such as flaps, remains a major challenge during landing, with significant contributions in the low-frequency band of 500–1500 Hz. While solid surfaces reflect this acoustic energy, metallic porous materials can effectively absorb it through viscous and thermal dissipation within their internal pore structure. To address this, the present study examines the acoustic absorption characteristics of open-cell AlSi porous cylinders featuring controlled pore diameters between 0.3 mm and 2.25 mm. Measurements were conducted in an acoustic impedance tube according to the ISO 10534-2:2023 standard, using six cylindrical samples (28 mm diameter, 70 mm length). Two sets of measurements were performed for each sample (front and rear faces), and the average values were used. The findings indicate that the normal-incidence sound absorption coefficient α rises as pore size increases, reaching 0.93–0.97 at low frequencies of 500–700 Hz for the samples with the largest pores (1.8–2.25 mm). These results indicate that open-cell AlSi alloys offer strong low-frequencies sound absorption, positioning them as promising options for aeroacoustic noise mitigation, including applications such as porous trailing edge and hybrid flap designs. Full article

Cantilevered unstable rock masses constitute a prevalent geological hazard, with their stability intrinsically governed by the depth of trailing edge cracks. Traditional stability assessment methods, which largely rely on static calculations or displacement monitoring, often suffer from poor timeliness and insufficient early warning capabilities. To address these limitations, this study employs the Extended Finite Element Method (XFEM) to simulate the natural crack propagation trajectory and investigate the associated dynamic response characteristics under loading. The simulation results demonstrate that XFEM effectively captures the natural “vertical-to-oblique” fracture morphology, overcoming the limitations of pre-defined crack models. A critical correlation is established between crack evolution and natural frequency: the first-order natural frequency exhibits a staged decline, characterized by a precipitous drop of approximately 7 Hz during the late stage of fracture development (80–97% depth). Consequently, a “crack evolution–frequency response” model is proposed. This model confirms that natural frequency is a significantly more sensitive indicator of internal damage than displacement, providing a novel theoretical foundation and technical pathway for the early identification and dynamic evaluation of rock mass stability. Full article

To explore the influence of inter-formation variables on swimming performance during fish schooling, this paper adopts the sharp interface immersed boundary method based on virtual cells to conduct numerical research on the swimming of three-fish and four-fish swarms with different formations and spacings. The study finds that both streamwise spacing and lateral spacing have significant impacts on the swimming performance of fish schools. In the three-fish formation, when the tandem arrangement has a streamwise spacing of 1.3 times the body length (L), the trailing fish achieve the highest swimming efficiency; when the parallel arrangement has a lateral spacing of 0.25L, the fish in the middle position exhibits the optimal swimming performance. In the four-fish formation model, fish in symmetric positions within the same swarm have similar hydrodynamic performance. For the diamond formation, under the configuration of streamwise spacing 1.2L and lateral spacing 0.5L, the propulsive efficiency of the trailing fish is markedly diminished; however, for the rectangular formation, all trailing fish obtain lower swimming efficiency, and a stable 2S-type vortex structure appears in the wake under the configuration of streamwise spacing 1.5L and lateral spacing 0.5L, which is conducive to thrust generation. The conclusions of this paper can provide certain hydrodynamic advantages and support the development of bionic underwater vehicles and robot technology. Full article

Current research in consent management techniques focuses on isolated aspects of data security, privacy, or auditability, but important issues like (i) dynamically integrating regulatory updates into form generation, (ii) support in content generation with verifiable audit trails, and (iii) tools that make compliance reasoning transparent for non-legal users are not yet addressed. This paper introduces CONSENT, an architecture that integrates AI-based consent reasoning using Large Language Models (LLMs) for automated consent-form drafting and compliance evaluation, alongside blockchain technology for secure and auditable storage. The architecture builds on prior work to address the aforementioned issues by introducing three supporting mechanisms: (a) Specialized AI models coordinated through expert routing which coordinate subtasks such as automation in form generation and regulatory compliance, (b) Retrieval-Augmented Generation (RAG) that supports the integration of regulatory updates into forms, and (c) Explainable AI (XAI) for the reasoning behind form content and compliance assessments. CONSENT architecture is evaluated through 250 test cases and a pilot case study for clinical trial consent management involving 20 engineers and attorneys, who evaluated the prototype on form quality (i.e., coherence, conciseness, factuality, fluency, and relevance) as well as time and effort efficiency. Results show that CONSENT substantially reduces the manual effort in consent-form creation while providing transparent, audit-ready compliance assessments, highlighting its potential for dynamic, user-centric consent management. Full article

White-tailed Deer (Odocoileus virginianus) overabundance has emerged as a significant ecological concern in recent decades. With current populations exceeding 30 million, White-tailed Deer (WTD) are now one of the most spatially abundant ungulate species across both natural and human-altered environments. High densities have led to considerable ecological and economic impacts, including forest understory degradation, biodiversity loss, and increased deer-vehicle collisions. This study examines the spatiotemporal distribution of WTD within three sites at Binghamton University, a heavily wooded campus in the Appalachian Upland region of New York State. To monitor population densities and movement patterns, a combination of remote sensing techniques was employed, including six Assark PH960W trail cameras and a DJI Mavic 3T UAV equipped with an uncooled VOx microbolometer thermal infrared (IR) sensor. Data were collected between 31 October 2024 and 10 March 2025, in relation to three deer culling events on 18 December 2024, 2 January 2025, and 9 January 2025. While Unoccupied Aerial Vehicle (UAV) based thermal imaging proved effective for estimating population dynamics, its utility is constrained by environmental and logistical limitations. In contrast, WiFi-enabled trail cameras provide a cost-efficient approach for capturing high-temporal resolution data at localized sites. Density estimates were derived from UAV thermal imaging and Random Encounter and Staying Time (REST) model calculations, ranging from 13.2 to 26.8 deer/km² across the region. Findings underscore the need for ongoing deer management strategies on campus to support long-term forest ecosystem health. Full article

This study examines the ripple effects of parkification, the transformation of post-industrial landscapes into public parks and green infrastructure, in Greenville at the Upper State region of South Carolina. As many Southern mill towns contend with industrial decline, environmental degradation, and complex land-use legacies, parkification has emerged as a pragmatic response to constraint rather than a conventional redevelopment strategy. Framed as a process rather than an isolated design outcome, parkification is understood here as a generative mechanism that produces cumulative spatial, ecological, and institutional change beyond individual project boundaries. Using a mixed-methods approach that integrates spatial and temporal mapping, archival research, site analysis, and semi-structured interviews with key stakeholders and decision-makers, this study traces how parkification unfolds across time and scale. Three interconnected case studies in Greenville, Falls Park on the Reedy, Conestee Nature Preserve, and the Swamp Rabbit Trail, are examined to address how post-industrial parkification contributes to greenway network formation and broader urban–regional transformation in the American South. The findings reveal that parkification consistently emerged from conditions of environmental constraint, including contamination, flooding, infrastructural legacies, and limited redevelopment feasibility. Early parkification projects functioned as generative landscape nodes that catalyzed the expansion of green space and connectivity rather than remaining isolated amenities. By establishing visible, accessible, and publicly valued landscapes, these projects enabled the extension of trails, river corridors, and preserved infrastructures, contributing to the formation of an interconnected regional greenway system. Institutional alignment among civic organizations, public agencies, and landscape professionals further supported the scaling and replication of parkification. Together, these findings position parkification as a process-based landscape strategy capable of driving the spread of green areas and long-term urban connectivity in post-industrial regions. Full article