Search Results (21,720)

Connected and Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are critical components of future Intelligent Transportation Systems (ITS), yet their deployment remains constrained by fragmented charging infrastructures and the lack of coordinated reservation and trip planning across static, dynamic wireless, and vehicle-to-vehicle (V2V) charging networks using magnetic resonance and laser-based power transfer. Existing solutions often struggle with misalignment sensitivity, unpredictable arrivals, and disconnected ground–aerial scheduling. This work introduces a three-layer architecture that integrates a handshake protocol for coordinated charging and billing, a misalignment correction algorithm for magnetic resonance and laser-based systems, and three scheduling strategies: Static Heuristic Charging Scheduling and Planning (SH-CSP), Dynamic Heuristic Charging Scheduling and Planning (DH-CSP), and the Safety, Scheduling, and Sustainability-Aware Feasibility-Enhanced Deep Deterministic Policy Gradient (SAFE-DDPG). SAFE-DDPG extends vanilla DDPG with feasibility-aware action filtering, prioritized replay, and adaptive exploration to enable real-time scheduling in heterogeneous and congested charging networks. Results show that SAFE-DDPG significantly improves scheduling efficiency, reducing average wait times by over 70% compared to DH-CSP and over 85% compared to SH-CSP, demonstrating its potential to support scalable and coordinated ground–aerial charging ecosystems. Full article

Cholesterol plays a fundamental role in the human body—it stabilizes cell membranes, modulates gene expression, and is a precursor to steroid hormones, vitamin D, and bile salts. Its correct level is crucial for homeostasis, while both excess and deficiency are associated with serious metabolic and health consequences. Excessive accumulation of cholesterol leads to the development of atherosclerosis, while its deficiency disrupts the transport of fat-soluble vitamins. Magnetic resonance spectroscopy (MRS) enables the detection of cholesterol esters and the differentiation between their liquid and crystalline phases, but the technical limitations of clinical MRI systems require the use of dedicated coils and sequence modifications. This study demonstrates the feasibility of using MRS to identify cholesterol-specific spectral signatures in atherosclerotic plaque through ex vivo analysis. Using a custom-designed experimental coil adapted for small-volume samples, we successfully detected characteristic cholesterol peaks from plaque material dissolved in chloroform, with spectral signatures corresponding to established NMR databases. To further enhance spectral quality, a deep-learning denoising framework based on a 1D U-Net architecture was implemented, enabling the recovery of low-intensity cholesterol peaks that would otherwise be obscured by noise. The trained U-Net was applied to experimental MRS data from atherosclerotic plaques, where it significantly outperformed traditional denoising methods (Gaussian, Savitzky–Golay, wavelet, median) across six quantitative metrics (SNR, PSNR, SSIM, RMSE, MAE, correlation), enhancing low-amplitude cholesteryl ester detection. This approach substantially improved signal clarity and the interpretability of cholesterol-related resonances, supporting more accurate downstream spectral assessment. The integration of MRS with NMR-based lipidomic analysis, which allows the identification of lipid signatures associated with plaque progression and destabilization, is becoming increasingly important. At the same time, the development of high-resolution techniques such as μOCT provides evidence for the presence of cholesterol crystals and their potential involvement in the destabilization of atherosclerotic lesions. In summary, nanotechnology-assisted MRI has the potential to become an advanced tool in the proof-of-concept of atherosclerosis, enabling not only the identification of cholesterol and its derivatives, but also the monitoring of treatment efficacy. However, further clinical studies are necessary to confirm the practical usefulness of these solutions and their prognostic value in assessing cardiovascular risk. Full article

Cancer stem cells (CSCs) represent a small but highly resilient tumor subpopulation responsible for sustained growth, metastasis, therapeutic resistance, and recurrence. Their survival is supported by aberrant activation of developmental and inflammatory pathways, including Wnt/β-catenin, Notch, Hedgehog, PI3K/Akt/mTOR, STAT3, and NF-κB, as well as epithelial–mesenchymal transition (EMT) programs and niche-driven cues. Increasing evidence shows that phytochemicals, naturally occurring bioactive compounds from medicinal plants, can disrupt these networks through multi-targeted mechanisms. This review synthesizes current findings on prominent phytochemicals such as curcumin, sulforaphane, resveratrol, EGCG, genistein, quercetin, parthenolide, berberine, and withaferin A. Collectively, these compounds suppress CSC self-renewal, reduce sphere-forming capacity, diminish ALDH⁺ and CD44⁺/CD24⁻ fractions, reverse EMT features, and interfere with key transcriptional regulators that maintain stemness. Many phytochemicals also sensitize CSCs to chemotherapeutic agents by downregulating drug-efflux transporters (e.g., ABCB1, ABCG2) and lowering survival thresholds, resulting in enhanced apoptosis and reduced tumor-initiating potential. This review further highlights the translational challenges associated with poor solubility, rapid metabolism, and limited bioavailability of free phytochemicals. Emerging nanotechnology-based delivery systems, including polymeric nanoparticles, lipid carriers, hybrid nanocapsules, and ligand-targeted formulations, show promise in improving stability, tumor accumulation, and CSC-specific targeting. These nanoformulations consistently enhance intracellular uptake and amplify anti-CSC effects in preclinical models. Overall, the consolidated evidence supports phytochemicals as potent modulators of CSC biology and underscores the need for optimized delivery strategies and evidence-based combination regimens to achieve meaningful clinical benefit. Full article

The transition toward sustainable energy systems necessitates innovative solutions that reduce greenhouse gas emissions while improving fuel efficiency in existing combustion technologies. Hydrogen has emerged as a promising clean energy carrier; however, its widespread deployment is limited by challenges associated with large-scale transportation and storage. This study investigates a practical alternative in which hydrogen-rich syngas produced via steam methane reforming (SMR) is directly integrated into the diesel engine intake, thereby eliminating the need for fuel transport, storage, and separation while supporting a more sustainable fuel pathway. A validated computational fluid dynamics (CFD) model was developed to examine the effects of varying SMR gas mixture ratios (0–20%) on engine combustion, performance, and emissions. The findings reveal that increasing the SMR fraction enhances in-cylinder pressure by up to 15.7%, heat release rate by 100%, and engine power output by 102.5% compared to conventional diesel operation. Additionally, under SMR20 conditions, CO₂ emissions are reduced by approximately 12%, demonstrating the potential contribution of this approach to decarbonization and climate mitigation efforts. However, the rise in in-cylinder temperatures was found to increase NO_x formation, indicating the necessity for complementary emission control strategies. Overall, the results suggest that direct SMR syngas integration offers a promising pathway to improve the environmental and performance characteristics of conventional diesel engines while supporting cleaner energy transitions. Full article

The aviation industry, responsible for approximately 2.5–3.5% of global greenhouse gas emissions, faces increasing pressure to adopt sustainable energy solutions. Hydrogen, with its high gravimetric energy density and zero carbon emissions during use, has emerged as a promising alternative fuel to support aviation decarbonization. However, its large-scale implementation remains hindered by cryogenic storage requirements, safety risks, infrastructure adaptation, and economic constraints. This study aims to identify and evaluate the primary technical and operational risks associated with hydrogen utilization in aviation through a comprehensive Monte Carlo Simulation-based risk assessment. The analysis specifically focuses on four key domains—hydrogen leakage, cryogenic storage, explosion hazards, and infrastructure challenges—while excluding economic and lifecycle aspects to maintain a technical scope only. A 10,000-iteration simulation was conducted to quantify the probability and impact of each risk factor. Results indicate that hydrogen leakage and explosion hazards represent the most critical risks, with mean risk scores exceeding 20 on a 25-point scale, whereas investment costs and technical expertise were ranked as comparatively low-level risks. Based on these findings, strategic mitigation measures—including real-time leak detection systems, composite cryotank technologies, and standardized safety protocols—are proposed to enhance system reliability and support the safe integration of hydrogen-powered aviation. This study contributes to a data-driven understanding of hydrogen-related risks and provides a technological roadmap for advancing carbon-neutral air transport. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

Background/Objectives: [¹⁸F]Fluproxadine (formerly [¹⁸F]AF78) is a PET radiotracer targeting the norepinephrine transporter (NET) with potential applications in cardiac, neurological, and oncological imaging. Its guanidine moiety, while essential for NET binding, presents major radiosynthetic challenges due to high basicity and the harsh deprotection conditions required for protected precursors. Previous methods relied on multistep procedures, strong acids, and complex purification, limiting clinical translation. This study aimed to develop a practical one-step radiosynthesis suitable for routine and automated production. Methods: A direct SN2-type nucleophilic [¹⁸F]fluorination was performed using an unprotected guanidine precursor to eliminate deprotection steps. Reaction parameters, including the base system, solvent composition, precursor concentration, and temperature, were optimized under conventional and microwave heating. Radiochemical conversion (RCC) and operational robustness were evaluated, and purification strategies were assessed for automation compatibility. Results: Direct [¹⁸F]fluorination using the unprotected precursor reduced the total synthesis time to 60–70 min. Optimal conditions employed a tert-butanol/acetonitrile (4:1) solvent system with K₂CO₃/Kryptofix222, affording RCC up to 33% under conventional heating. Microwave irradiation further improved efficiency, achieving RCC of up to 64% within 1.5 min at 140 °C. The method showed broad tolerance to variations in the base molar ratio and precursor concentration and enabled isocratic HPLC purification. Conclusions: This one-step radiosynthesis overcomes longstanding challenges in [¹⁸F]fluproxadine production by eliminating harsh deprotection and enabling high-yield, automation-ready synthesis, thereby improving clinical feasibility. Full article

Against the backdrop of global climate change and China’s “dual-carbon” goals, carbon emissions from the construction phase of transportation infrastructure, particularly the rapidly expanding railway network, have garnered significant attention. However, systematic research and general evaluation models targeting the factors influencing carbon emissions during the railway bridge construction phase remain insufficient. To address this gap, this study presents a novel low-carbon evaluation model that integrates the analytic network process (ANP) and the fuzzy comprehensive evaluation (FCE) method. First, a carbon accounting model covering four stages—material production, transportation, construction, and maintenance—is established based on life cycle assessment (LCA) theory, providing a data foundation. Second, an innovative low-carbon evaluation index system for railway bridges, comprising 5 criterion layers and 23 indicator layers, is constructed. The ANP method is employed to calculate weights, effectively capturing the interdependencies among indicators, while the FCE method handles assessment ambiguities, forming a comprehensive evaluation framework. A case study of the bridge demonstrates the model’s effectiveness, yielding an evaluation score of 82.38 (“excellent” grade), which is consistent with expert judgement. The ranking of indicator weights from the model is highly consistent with the actual carbon emission inventory ranking (Spearman coefficient of 0.714). Key indicators—C21 (use of high-performance materials), C22 (concrete consumption), and C25 (transportation energy consumption)—collectively account for approximately 60% of the total impact, accurately identifying the major emission sources. This research not only verifies the model’s efficacy in pinpointing critical carbon sources but also provides a scientific theoretical basis and practical tool for low-carbon decision-making and optimization in the planning and design stages of railway bridge projects. Full article

Occupational exposure to sharps waste represents a critical challenge for public health systems, directly affecting healthcare workers’ safety, institutional costs, and environmental sustainability. This study aimed to analyze sharps waste management practices and to structure improvement actions for biosafety governance in Brazilian Emergency Care Units (ECUs) through the application of the Lean Six Sigma (LSS) and DMAIC method (Define, Measure, Analyze, Improve, and Control). A single multiple-case study was conducted across three public units in different regions of Brazil, combining direct observation, regulatory checklists based on ANVISA Resolution No. 222/2018 (RDC), and cause–and–effect (5M) analysis. The diagnostic phase identified recurrent nonconformities in labeling, documentation, and internal transport routes, primarily due to managerial and behavioral gaps. Based on these findings, the DMAIC framework supported the development of a low-cost, evidence-based action plan that outlined proposed interventions, including visual checklists, standardized internal routes, and key performance indicators (KPIs), intended to strengthen biosafety traceability and occupational safety. The se proposed actions are expected to support continuous learning, staff engagement, and a culture of shared responsibility for safe practices. Overall, the study provides a structured basis for future implementation and empirical validation of continuous improvement initiatives, aimed at enhancing public health governance and occupational safety in resource-constrained healthcare environments. Full article

Molten salts are widely used in high-temperature energy systems because of their thermal properties. In such applications, natural circulation provides a passive means of heat transport in systems that require passive safety features. Many studies have examined the thermal–hydraulic behavior of molten salts in natural circulation configurations. This work develops a two-dimensional CFD model of a molten salt natural circulation loop and evaluates two formulations—a laminar model and the Transition SST (γ–Reθ) model. The models were verified through mesh-independence studies and validated against experimental benchmark data. Both models reproduced the measured temperature rise across the loop, but significant differences appeared in velocity and Reynolds-number prediction. The laminar model underpredicted circulation by about 30%, whereas the Transition SST model shows 4.2% for velocity and 11.8% for Reynolds number. Local comparison showed that the Transition SST model captured developing wall-peaked structures in the vertical legs, whereas the laminar model misinterprets these regions as stagnant core flow. These findings apply only to the 2D model, and the use of the CFD models follows a benchmark experiment rather than universal validation for all molten salt loops. Overall, the results show that transitional turbulence modeling is needed to capture the mixed-regime behavior in molten salt natural circulation. Full article

This paper addresses critical issues related to the structural design of a micro-factory housing a mobile 3D printing system for plastic recycling. Rather than a simple comparison, it quantifies the “modification penalty”, the structural and economic cost of retrofitting a repurposed ISO shipping container (ISCC) versus deploying a purpose-built cold-formed steel (CFS) volumetric structure. Finite Element Analysis of a standard 20-foot shipping container revealed a serviceability failure in its roof under standard imposed loads. Concurrently, an initial analysis of an equivalent CFS structure also indicated non-compliance, with significant floor and roof deflections. Both platforms were subsequently redesigned with structural reinforcements to achieve full compliance with Australian Standards. The comparative evaluation moves beyond static analysis to incorporate critical performance metrics. While the CFS structure proved to be 575 kg lighter with a lifespan 300–400% longer, the modified ISCC was 47% cheaper in initial capital outlay ($7161 vs. $13,549). However, when considering the totality of performance factors, specifically the ISCC’s inherent vulnerability to resonance (8–18 Hz), which overlaps with transport frequencies, and the logistical burden of losing CSC certification upon modification, the CFS platform is conclusively identified as the superior engineering solution. Its design flexibility, predictable performance, and amenability to purpose-built optimization make it a more reliable and operationally secure platform for this specialized application. Full article

Understanding particle transport in rough-walled fractures is essential for predicting flow behavior, clogging, and permeability evolution in natural and engineered subsurface systems. This study develops a fully coupled CFD–DEM framework to investigate how self-affine fractal roughness, represented by the Joint Roughness Coefficient (JRC), governs fluid–particle interactions across multiple scales. Nine fracture geometries with controlled roughness were generated using a fractal-based surface model, enabling systematic isolation of roughness effects. The results show that increasing JRC introduces a hierarchy of geometric perturbations that reorganize the flow field, amplify shear and velocity-gradient fluctuations, and enhance particle–wall interactions. Particle migration exhibits a nonlinear response to roughness due to the competing influences of disturbance amplification and the formation of preferential high-velocity pathways. Furthermore, roughness-controlled scaling relations are identified for mean particle velocity, residence time, and energy dissipation, revealing JRC as a fundamental parameter linking geometric complexity to transport efficiency. Based on these findings, a unified mechanistic framework is established that conceptualizes fractal roughness as a multiscale geometric forcing mechanism governing hydrodynamic heterogeneity, particle dynamics, and dissipative processes. This framework provides new physical insight into transport behavior in rough fractures and offers a scientific basis for improved prediction of clogging, proppant placement, and transmissivity evolution in subsurface engineering applications. Full article

To improve the safety of road transportation of Spent Nuclear Fuel (SNF), this paper proposes a novel approach for risk identification and chaotic synchronous control in SNF road transportation systems. Firstly, a dynamic risk evolution model for the road transportation of SNF is developed by analyzing the nonlinear interactions among vehicles, environmental conditions, and human factors using complex network analysis and nonlinear dynamics. Secondly, an enhanced K-shell decomposition method is applied to identify key risk nodes and assess the relative importance of different risk factors, providing a basis for targeted risk control. Finally, a chaotic synchronization control strategy based on Lyapunov stability is proposed to suppress risk divergence and restore system stability. Three targeted control schemes are evaluated by varying the control gain coefficients across the ‘Vehicle–Environment–Human’ dimensions. Simulation results indicate that the strategy prioritizing environmental and human risk control yields the fastest convergence, significantly outperforming vehicle-centric approaches. The results show that prioritizing both environmental and human-factor control is most effective for suppressing chaotic divergence. This provides a solid quantitative basis for the strategic shift from passive defense to active environmental warning, thereby significantly optimizing the dynamic risk management of the SNF transportation system. Full article

With rapid urbanization and population growth, efficient transportation systems are increasingly crucial, particularly in sectors like healthcare and pharmaceutical logistics, which face unique challenges. In Morocco, there is a lack of studies on pharmaceutical transport, especially regarding costs and delivery conditions, creating a need for a specialized model. This research presents the development and validation of a predictive model for optimizing urban transport in Morocco. Tested across key sectors—pharmaceuticals, agri-food, electronics, and manufactured goods—the model demonstrated strong performance, though variations emerged based on product complexity. Notably, the agri-food sector presented greater logistical challenges, while the manufacturing and electronics sectors yielded higher prediction accuracy. By integrating statistical process control (SPC) and Lean Six Sigma principles, the model ensures ongoing performance monitoring and continuous improvement. It supports cost reduction, time optimization, and lower environmental impact through enhanced route planning and delivery efficiency. The pharmaceutical sector was selected as a case study due to its critical logistical constraints, such as cold chain requirements and the need for high reliability. Python was used for model development, enabling rapid iteration and collaborative validation. The results confirm the model’s adaptability and generalizability to similar urban environments across North and Sub-Saharan Africa. The study offers a robust and scalable framework for improving transport efficiency while aligning with sustainability and smart mobility goals. Full article

Counterfactual quantum control is a novel control method, in which no actual material particles or energy are transported and exchanged between the controller and the controlled. By introducing the quantum Zeno effect where the evolution of a quantum system can be suppressed by continuous observation, this paper presents a review of research progress in counterfactual quantum control. The basic concept of counterfactual quantum control is presented and macro counterfactual quantum control is thoroughly discussed. In addition, related experimental verification and applied exploration are also discussed. This review paper covers the progress toward counterfactual quantum communication, non-invasive imaging and specific applications. Full article