Search Results (19,373)

Background/Objectives: Long-term exposure to high-risk human papilloma virus (HPV) leads to high-grade squamous intraepithelial lesions (HSILs), which may develop into cancer. Various proteins and metabolites change during the development of cervical cancer; thus, assessing the dysregulated molecules and pathways in HSILs is important to elucidate early pathological mechanisms and identify potential intervention targets. Methods: In this study, we performed proteomic and metabolomic analyses in five pairs of HPV-positive HSIL tissues and paired normal tissues. Immunohistochemistry (IHC) was applied to validate the levels of carnitine palmitoyltransferase 1A (CPT1A) in HSIL tissues. Quantitative real-time PCR and Western blot were used to detect the expression levels of CPT1A in cervical cancer cell lines. Results: In proteomic analysis, 836 proteins showed significant changes. Functional analyses of the differentially expressed proteins indicated that metabolic pathways, oxidative phosphorylation and ribosome are the top three enriched pathways. In metabolomic analysis, 105 metabolites were differentially altered. Most metabolites were involved in lipid metabolism, such as phosphatidylethanolamine (PE), phosphatidylinositol (PI) and L-palmitoylcarnitine. Integrated proteomics and metabolomics revealed that the metabolic pathway was the most enriched pathway that contained the maximum number of differentially expressed metabolites and proteins. In vitro, we found CPT1A was upregulated in HSIL tissues and in cervical cancer cell lines. Conclusions: Our findings characterize the protein and metabolite alterations in HSILs, which may represent molecular features associated with disease progression. Full article

Developing far-offshore wind power integrated with hydrogen production represents a critical pathway for China’s energy decarbonization. However, the investment prospects of off-grid offshore wind-to-hydrogen projects remain highly uncertain due to volatile technology costs and hydrogen prices, complicating the evaluation of project value and optimal timing. To address the oversimplified treatment of electrolyzer operation and the limited consideration of alkaline electrolyzers in the existing studies, this paper proposes an integrated assessment framework that combines time-series operational simulation with real options analysis. A detailed dynamic model of an alkaline (ALK)–proton exchange membrane (PEM) hybrid configuration is developed to simulate the coordinated hydrogen production under fluctuating wind power. Technical learning effects and stochastic hydrogen price processes are incorporated, and the least-squares Monte Carlo method is applied to determine the optimal investment strategies. A case study of a planned far-offshore wind farm in Guangdong indicates that, compared with a standalone PEM configuration, the hybrid configuration reduces the levelized hydrogen cost by about 15%, increases the investment value by up to 17 times under slow technological progress, and brings forward the optimal investment year by five years, from 2039 to 2034. Sensitivity analysis shows that expected hydrogen prices and discount rates dominate the investment outcomes. Full article

Background: Glioblastoma (GBM) remains the most aggressive primary brain tumor, and intraoperative frozen section analysis is the current standard for rapid histopathological assessment. However, this approach is time-consuming and resource-intensive. Stimulated Raman scattering (SRS) imaging has emerged as a label-free technique enabling near real-time microscopic evaluation of fresh tissue. This study compares the visualization of selected histopathological features in a newly developed intraoperative SRS system with conventional hematoxylin–eosin (HE) staining in confirmed GBM. Methods: Tumor samples from 30 patients with neuropathologically confirmed GBM were analyzed. For each case, both HE-stained frozen sections and SRS-generated virtual HE-like images were prepared from separate portions of the specimen. Twelve neuropathologists with varying levels of experience assessed 60 images according to seven predefined GBM criteria, resulting in 720 image evaluations. Feature detection was analyzed using cluster-adjusted generalized estimating equation models, and interobserver agreement was assessed using Fleiss’ κ. Results: Descriptively, hypercellularity and hypervascularization were identified at similar frequencies in both modalities, whereas pleomorphism, endothelial proliferation, mitotic activity, and necrosis were more often recognized in HE images. In cluster-adjusted analyses, SRS showed significantly lower detection rates for hypercellularity, pleomorphism, endothelial proliferation, and mitotic activity, while no significant difference was observed for hypervascularization, necrosis, or pseudopalisading after false discovery rate correction. Interobserver agreement was feature-dependent and generally higher for HE than SRS, particularly for hypercellularity. Conclusions: In this feature-level analysis of neuropathologically confirmed GBM, SRS imaging provided rapid, label-free morphological information and showed comparable visualization of selected histopathological features, particularly hypervascularization. While conventional HE-stained frozen sections remained superior for certain WHO-defining features, SRS represents a promising intraoperative adjunct that may complement established neuropathological workflows. Further studies including non-tumor tissue and a broader range of glioma grades are needed to determine the full diagnostic accuracy and clinical applicability of this technique. Full article

Search and rescue (SAR) operations in mountainous terrain present significant challenges due to complex environments, time-critical decisions, and the need for effective human–AI collaboration. Existing approaches typically employ either fully autonomous systems that lack adaptability to varying task requirements, or fixed human–AI authority allocations that fail to leverage the distinct strengths of humans and AI across different mission phases. This paper proposes Phase-Aware Hierarchical Reinforcement Learning (PAHRL), a novel framework that dynamically allocates decision-making authority between human operators and AI agents based on identified task phases. First, we formulate the mountain SAR problem as a three-phase task structure: Wide Search (WS), Target Confirmation (TC), and Rescue Coordination (RC), and examine the consistency of this decomposition through unsupervised clustering analysis, supported by bootstrap stability (ARI = 0.983 ± 0.083) and multiple clustering metrics. Second, we design an adaptive authority mechanism with four levels (L1: Human-Led to L4: Full-Auto) that automatically adjusts human involvement based on current phase characteristics and environmental uncertainty estimates. Third, we introduce a priority-based task execution module that ensures efficient resource allocation across multiple rescue objectives while respecting authority constraints. Extensive experiments demonstrate that PAHRL outperforms baseline methods, achieving a 20.9% higher success rate compared to standard PPO (59.0% vs. 48.8%) and 66.7% improvement over heuristic approaches. PAHRL maintains 96.9% precision even under 60% noise conditions with only 0.09 false rescues per episode. Ablation studies further reveal that phase awareness serves as a critical robustness mechanism; removing phase detection causes complete mission failure under noisy conditions. These results evaluate that phase-aware dynamic authority allocation significantly enhances both efficiency and robustness in human–AI collaborative SAR missions. While demonstrated in a proof-of-concept simulation with computational human models, validation with real operators and more complex environments remains essential before operational deployment. Full article

The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved from geometry-driven to semantics-driven and, more recently, knowledge-driven approaches. With the introduction of Large Language Models (LLMs) and Vision-Language Models (VLMs), recent methods have achieved substantial improvements in instruction interpretation, cross-modal alignment, and reasoning-based planning. However, existing surveys primarily focus on traditional VLN settings and offer limited coverage of LLM-based VLN, particularly in relation to Sim2Real transfer and edge-oriented deployment. This paper presents a structured review of LLM-enabled VLN, covering four core components: instruction understanding, environment perception, high-level planning, and low-level control. Edge deployment and implementation requirements, datasets, and evaluation protocols are summarized, along with an analysis of task evolution from path-following to goal-oriented and demand-driven navigation. Key challenges, including reasoning complexity, spatial cognition, real-time efficiency, robustness, and Sim2Real adaptation, are examined. Future research directions, such as knowledge-enhanced navigation, multimodal integration, and world-model-based frameworks, are discussed. Overall, LLM-driven VLN is progressing toward deeper cognitive integration, supporting the development of more explainable, generalizable, and deployable embodied navigation systems. Full article

Floating roof seal integrity is critical for safety and emission control in petroleum storage tanks, yet current detection methods suffer from spark risks and operational inefficiencies. This study proposes an intrinsically safe, non-contact leakage detection system utilizing oil-swellable rubber actuators coupled with a linear magnetic transmission mechanism. By integrating quasi-static experiments with finite element simulations, we investigated the impact of permanent magnet geometry on transmission performance. The results establish a “thickness priority principle”, revealing that increasing magnet thickness nonlinearly enhances shear force and transmission efficiency, whereas increasing width yields diminishing returns due to magnetic flux leakage and added mass. Furthermore, comparative analysis demonstrates that optimized monolithic magnets significantly outperform arrayed configurations, achieving a 471% increase in shear force and a 3.7-fold improvement in transmission efficiency. Based on these findings, a practical detection device was designed and verified against API 650 standards. The proposed solution offers a reliable, electricity-free, and real-time monitoring method for early leakage detection in hazardous tank environments. Full article

Background and objectives: Odontogenic abscess represents a serious infection in the head and neck region with the necessity of immediate treatment. Due to the fast pacing and progression, as well possibly severe consequences of this condition, it is important to have a fast and reliable biomarker to adequately monitor these patients. Since serum procalcitonin and C-reactive protein (CRP) are the most commonly used clinical biomarkers to monitor serious infections, the aim of this study was to investigate their temporal profiles in hospitalized patients undergoing surgical management of odontogenic abscesses. Materials and methods: This longitudinal, multicentric study was conducted on 65 patients with odontogenic abscesses at the University Hospital of Split and Dubrava University Hospital. Biomarker levels were assessed at admission and at four time points during the early and middle postoperative periods to evaluate initial elevations, treatment-associated changes, and differences in kinetic behavior. Results: After converting real procalcitonin and CRP values to proportions, a Δ between the time points was calculated. There was a statistically significant difference in the Δ proportion between procalcitonin and CRP in the 0–6 h time frame (19.3 (10.6–27.8)% vs. 7.2 (−3.0–20.4)%, p < 0.001) and the 24–48 h time frame (30.8 (24.5–35.0)% vs. 51.7 (30.5–57.7)%, p < 0.001). Furthermore, multiple linear regression analysis showed that procalcitonin at time point 0 (p = 0.037), 6 h (p = 0.009) and 24 h (p = 0.038) significantly predicted hospitalization duration after model adjustment for age, gender, BMI and pre-admission antibiotic treatment. Conclusions: The findings of this study show that procalcitonin exhibits a faster and more pronounced decrease in the early postoperative period compared with the CRP values. Following the middle postoperative period both biomarkers decreased in association with clinical improvement; however, procalcitonin demonstrated an earlier and more consistent decline. The observed pattern indicates a rapid dynamic of procalcitonin values during the early postoperative phase and supports its potential value for early monitoring of surgical treatment response in odontogenic abscesses. Full article

Modern-day urban warehouses face exploding large inventory and tight spaces requiring fast, accurate, and safe stocktaking in a narrow aisle in a GPS-denied environment. This paper proposes a complete UAV-enabled framework performing real-time QR code scanning with inventory synchronization through a safety-aware trajectory generation for obtaining collision-free motion. A novel hybrid workflow integrating MATLAB/Simulink R2024b and Unreal Engine is used for dynamics and photorealistic rendering, alongside a real-time warehouse setup using drone cameras and 3D LiDAR coupled with a ground control station and live dashboard. The system in this paper was evaluated by testing with single and multi-UAV models across high-fidelity simulations and experiments. Results demonstrate simulated QR accuracy of approximately 95 to 96%, with experimental validation achieving between 86 and 90.5% due to real-world environmental factors. In experimental and simulation analysis, mean end-to-end latency remained under half a second, trajectory error range between 8 and 10 cm, and safety margins were consistently maintained throughout the test. It was further observed that multi-UAV coordination halved mission time compared to single-drone tests while keeping duplicate reads negligible, indicating a scalable and safe pipeline for industry application. Full article

This study investigated the antibiofilm and anti-quorum-sensing (QS) potential of endophyte extracts isolated from medicinal plants and their validation against Pseudomonas aeruginosa. Endophytes were isolated from the plants using the serial dilution method, and the extracts produced by these endophytes were screened for antimicrobial and biofilm-inhibition activity using assays. The efficient extract was biochemically characterized, followed by validation of its secondary metabolite content. Furthermore, QS-regulatory gene expression levels and microscopy were used to confirm inhibition of biofilm formation. A total of 12 cultures, including 8 bacterial and 4 fungal, were isolated and screened, demonstrating efficient antimicrobial activity (zone of inhibition of 18.8 mm) and 64.3% antibiofilm activity. The efficient endophyte isolated from Paederia foetida was identified as Bacillus xiamenensis MM07 by 16S rRNA gene sequencing. MM07 extract analyses by biochemical and Fourier transform infrared methods revealed the presence of diverse biomolecules. A dose-dependent inhibition was observed, achieving up to 83.5, 60.3, 73.2, 82.7, 83.2, and 15.1 in biofilm formation and exopolysaccharide, violacein, pyocyanin, protease, and alginate production, along with 63.2% swimming ability at 30 µg/mL, respectively. Gas chromatography-mass spectrometry analyses validated the presence of major secondary metabolites, including 3,3-dimethyl-4-methylamino-butan-2-one, 6-amino-2-methyl-, 1-iodo-2-methylundecane, and hexadecanoic acid, with the potential to inhibit biofilm and QS activity. Quantitative real-time polymerase chain reaction of QS regulatory genes (lasI, lasR, rhlI, and rhlR) and microscopy analysis confirmed the anti-QS properties, evidenced by a 40.3% decline in gene expression and biofilm inhibition by MM07 extract. These findings highlight the potential of novel B. xiamenensis MM07 endophyte from P. foetida as a sustainable source of biomolecules for combating biofilm-associated infections. Full article

Continuous monitoring of sitting posture is crucial for ergonomic assessment and fatigue prevention, yet many existing approaches rely on vision-based systems or single-modality sensing that are limited in capturing spatial and temporal biomechanical dynamics. This paper presents a multimodal smart-skin sensing system for spatial and temporal ergonomic fatigue analysis in sitting postures. The proposed platform integrates 42 distributed pressure, temperature, and vibration sensors arranged in 14 trimodal sensing nodes embedded across anatomical seating and back regions to enable real-time multimodal acquisition of human–chair interaction patterns. The study introduces an analytical framework combining anatomical heatmap visualization, temporal evolution analysis, delta pressure mapping, fatigue intensity estimation, and hotspot detection to characterize dynamic pressure redistribution during prolonged sitting. Experimental evaluations were conducted using a biomechanical mannequin and a single human participant with identical anthropometric characteristics (165 cm height and 62 kg body mass) across nine seated conditions, including neutral sitting, reclining, leaning, periodic shifting, and vibration-induced motion. Each posture condition was recorded as a time-series session and segmented into temporal phases to analyze fatigue evolution during prolonged sitting. Statistical analysis of pressure redistribution dynamics indicates significantly higher pressure drift in human measurements compared with the mechanically stable mannequin baseline (p < 0.001). The proposed framework provides a scalable sensing approach for ergonomic monitoring, intelligent seating systems, and human–machine interface applications. Full article

Agapanthus africanus (L.) Hoffmanns. is a medicinal plant traditionally used in South Africa for its promise as a source of bioactive compounds with anticancer properties. This study aimed to investigate the apoptotic effects of A. africanus fractions on cancer cell lines and to identify the bioactive phytochemical constituents using gas chromatography-mass spectrometry analysis. To test for cytotoxicity, MCF-7, A549, and HeLa cancer cells were treated with crude extract, n-hexane, n-butanol, dichloromethane, and aqueous fractions of A. africanus extracts at different concentrations (0.00–1000 µg/mL). Total apoptosis was quantified using Annexin V/PI staining. The 4′,6-diamidino-2-phenylindole was used to detect nuclear morphological changes and the Caspase-GLO 3/7 assay was employed to check the caspase activation in the cancer cells. Expression of apoptosis-related (caspase-3, bax, bcl-2) genes was evaluated using real time-polymerase chain reaction. The crude extract of A. africanus exhibited dose-dependent cytotoxicity against MCF-7, A549, and HeLa cells, with IC₅₀ values of 130 µg/mL, 380 µg/mL, and <125 µg/mL, respectively. Among the tested fractions, the n-butanol fraction showed cytotoxicity towards MCF-7 cells with an IC₅₀ value of <870 µg/mL. In contrast, n-hexane, dichloromethane and the aqueous fractions exhibited higher IC₅₀ values against cancer cells. Flow cytometry analysis, which was applied to quantify total apoptosis, revealed that the crude extract of A. africanus induced apoptosis by (~60%) compared to the n-butanol fraction, which exhibited a moderate apoptotic effect (~27%). DAPI nuclear staining showed nuclear shrinkage and chromatin condensation in the MCF-7 cell line, whereas in Caspase-GLO 3/7, the crude extract and n-butanol fraction resulted in significant luminescence, indicating activation of caspase-3/7. Caspase-3/7 analysis showed A. africanus treatments produced varying levels of apoptotic activation. The crude extract increased caspase activity by 2.9-fold, while the n-butanol fraction induced a 1.7-fold rise compared with untreated cells. GC-MS chromatograms detected and identified 16 compounds in the fractionated n-butanol and 23 compounds from the crude extract of A. africanus. The major compounds identified from the n-butanol fraction included n-hexadecanoic acid; α-tocopherol and 9,12,15-octadecatrienoic acid, while the GC–MS profile of the crude extract was dominated by 6,10,14-trimethylpentadecan-2-one; 1,3,5-Triphenylcyclohexane and phytol. The study indicates the pro-apoptotic potential of A. africanus, particularly in its crude form, supporting its ethnopharmacological use and suggesting its relevance as a candidate for anticancer drug discovery. Full article

Heterogeneous multi-core systems (HMCSs) typically face a dilemma: heuristics (e.g., Linux CFS) are fast but blind to global constraints, while meta-heuristics (e.g., GAs) are globally optimal but too slow for real-time OS interaction. To bridge this gap without relying on “black-box” neural networks, we introduce the Tissue P System-Inspired Task Allocator (TPSTA). By mapping HMCS and parallel task scheduling to Tissue P System models and vectorized linear algebra problems, TPSTA achieves a computational complexity of $\mathrm{O}\left(\mathrm{M}/\mathrm{W}\right)$, effectively compressing the decision space. Our rigorous evaluation across four dimensions reveals a system strictly bound by physical constraints rather than algorithmic heuristics. (1) Under sufficient resource provisioning (four chips), TPSTA achieves a 0.00% Deadline Miss Ratio (DMR). Crucially, stress tests on constrained hardware (two chips) show graceful degradation to a 12.88% DMR, matching the optimal theoretical bound of EDF, whereas standard heuristics collapse to failure rates > 68%. On a massive 4096-core cluster, TPSTA outperforms the Linux GTS scalar baseline by 14.4×, maintaining low latency where traditional algorithms fail (>8 s). (3) Adaptability: The system demonstrates adaptive routing in handling hardware heterogeneity; without explicit rule-coding, it autonomously prioritizes data locality during NUMA transfers and migrates compute-bound tasks during thermal throttling events. (4) Physical Limits: Finally, our roofline analysis confirms that while the algorithmic speedup is theoretically linear, practical performance saturates at ~375× due to the Memory Wall, validating the isomorphism between synaptic bandwidth and hardware memory channels. Full article

Manufacturing is going through a significant shift propelled by Industry 4.0 and smart manufacturing infrastructures, requiring sophisticated production control techniques that can adaptively adjust to fluctuating operational situations. This paper presents a novel five-step hybrid simulation framework for adaptive real-time production speed control in smart manufacturing lines, integrating conceptual modelling, hybrid simulation, algorithm redefinition, design of experiments, optimisation, and real-system implementation. The framework transforms the speed management systems into online digital twins capable of optimising system performance and mitigating unforeseen fluctuations, faults, and congestion. A comprehensive case study from the beverage manufacturing sector demonstrates the framework’s effectiveness, utilising a universal simulation platform to model both continuous fluid flow and discrete event processes. The proposed stepwise, multi-threshold algorithm employs multiple distinct logical thresholds evaluated sequentially to optimise both upstream and downstream station speeds, with decision thresholds independently adjustable for each production line segment. The experimental results show significant improvements, including around an 18% increase in overall throughput and a 95.7% reduction in work-in-process inventory. A comprehensive resiliency analysis and statistical tests under various disruption scenarios further validated the approach, demonstrating its superiority. Beyond the studied case, the framework provides a transferable pathway for real-time adaptive control across a wide range of smart manufacturing environments, enabling enhancements to operational efficiency without requiring additional capital investment in new equipment or infrastructure. Full article

This systematic review evaluates recent scientific and technological advances in water quality monitoring and pollution alarms for ports, based on records retrieved from seven databases following the PRISMA protocol. A total of 414 documents were screened, resulting in 141 articles (TRL 3) and 56 patents (TRL 4–5). Bibliometric, patentometric, and thematic analyses were conducted using Bibliometrix and ORBIT^®. Results show sustained growth in both academic and technological outputs, with a patent Compound Annual Growth Rate (CAGR) of 32%, compared with 13% for scientific publications, indicating accelerated translation from research to innovation. The conversion rate from scientific research to patenting increased from 14% (2010–2015) to 47% (2020–2023). Analysis of patent legal status reveals that 52% of patent families remain valid (48% granted; 4% pending), while 33% are lapsed, 13% revoked, and 2% expired, reflecting the dynamic and emerging character of the field. Technological ownership is highly concentrated, with China accounting for nearly all active patents, whereas scientific production is more geographically distributed. Thematic analysis identifies four main scientific clusters: environmental monitoring, chemical pollutants, seashore hazards, and eutrophication. The main technological domains of the patents are analysis of biological materials, control, and environmental technologies. Emerging areas of focus at TRL 3 and TRL 4–5 include microplastics, climate-change impacts, aquaculture risks, real-time sensing, IoT-enabled platforms, machine-learning analytics, autonomous monitoring systems, and bioindicator-based early-warning tools. This review provides a quantitative roadmap to support sustainable port operations, coastal ecosystem protection, and progress toward multiple synergistic United Nations Sustainable Development Goals (SDGs). Full article

Many countries rely on coal for energy security during renewable transitions. This study conducts a technical, economic, and environmental analysis of hybridizing a supercritical coal-fired power unit with photovoltaics (PV) to create a sustainable hybrid system at a plant in Silesian Voivodeship, Poland. The goal is to assess costs and optimal operating conditions for a coal–PV hybrid under varying scenarios, using a decision-support model that integrates fuel prices, CO₂ emission charges (EUA), and technical parameters. Two main scenarios are modeled. In auxiliary-only PV (112 MW system), real-time power supplies pumps and fans, cutting coal consumption without storage; LCOE decreases with annual hours (2800–7000), outperforming conventional coal across EUA prices (20–50 EUR/t). In PV surplus export, excess generation (1300 h/year) is grid-fed for revenue, amplifying LCOE reductions—hybrid superiority emerges above 34 EUR/t EUA, per equivalence thresholds. Results show coal electricity exceeds low-emission costs above 34 EUR/t CO₂, with maximum disparity at 50 EUR/Mg. The hybrid leverages existing infrastructure, mitigates solar intermittency via auxiliary supply, ensures baseload continuity, boosts flexibility, and prolongs asset life—reducing >123,000 EUA/year at 145,000 MWh PV output. This sustainable hybrid promotes energy transition, reduces fossil fuel dependence, and aligns with global sustainability goals. Full article