Search Results (17,710)

Based on the follow-up of patients who recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, several reports of people who re-tested positive have been described. This may result from viral reactivation, true reinfection, superinfection, or an initial infection by more than one virus (multiple infection). These scenarios can only be correctly distinguished through viral quasispecies analysis. Herein, 26 cancer patients under extended follow-up for SARS-CoV-2 infection were submitted to multiple longitudinal analyses through nucleic acid isolation, PCR amplification and high-throughput sequencing. SARS-CoV-2 classification and the definition of cases as persistent or repeated infections were based on phylogenetic reconstruction. Supported by their viral complete genomes and intrahost quasispecies over time, the different scenarios were identified. Nine confirmed and 12 plausible persistence cases were identified. Virus evolution dynamics in the intrahost population from patients with persistent infection was shown for the first time. Regarding reinfection, three confirmed and two plausible cases were identified, including one case of multiple infection. Altogether, this is the first study that analyzes the plethora of SARS-CoV-2 within-host minor variants and describes reinfections, multiple infections and viral evolution across time in cancer patients, contributing to the understanding of SARS-CoV-2’s within-host population dynamics in the natural history of COVID-19. Full article

To address the inherent limitations in the robustness of fully autonomous unmanned aerial vehicle (UAV) visual perception and the high cognitive workload associated with manual control, this paper proposes a human-in-the-loop brain–computer interface (BCI) control framework. The system integrates steady-state visual evoked potential (SSVEP) with deep learning techniques to create a spatio-temporally dynamic interaction paradigm, enabling real-time alignment between visual targets and frequency stimuli. At the perception level, an enhanced YOLOv11 network incorporating partial convolution (PConv) and shape intersection over union (Shape-IoU) loss is developed and coupled with the DeepSort multi-object tracking algorithm. This configuration ensures high-speed execution on edge computing platforms while maintaining stable stimulus coverage over dynamic targets, thus providing a robust visual induction environment for EEG decoding. At the neural decoding level, an enhanced task-discriminant component analysis (TDCA-V) algorithm is introduced to improve signal detection stability within non-stationary flight conditions. Experimental results demonstrate that within the predefined fixation task window, the system achieves 100% success in maintaining target identity (ID). The BCI system achieved an average command recognition accuracy of 91.48% within a 1.0 s time window, with the TDCA-V algorithm significantly outperforming traditional spatial filtering methods in dynamic scenarios. These findings demonstrate the system’s effectiveness in decoupling human cognitive intent from machine execution, providing a robust solution for human–machine collaborative control. Full article

With the deep integration of pharmacy and materials science, functional materials are increasingly applied in drug development, environmental remediation of pharmaceutical pollutants, and clinical diagnosis and treatment. This article focuses on multiple application scenarios of functional materials, including drug degradation, drug adsorption, drug analysis and detection, electrochemical detection, and bioimaging. It systematically reviews the structural characteristics, modification strategies, and latest research progress of typical functional materials such as metal–organic framework materials, nanocomposites and bio-based materials in various application fields. The article also analyzes key challenges faced by functional materials in multi-scenario applications, such as biocompatibility, stability, and large-scale preparation. In light of the trends in precision medicine, it outlines future directions for the application of functional materials in the field of pharmacy, aiming to provide references for the design and development of multifunctional materials and innovative applications in pharmaceuticals. Full article

The rapid increase in point density and acquisition rate of UAV laser scanning (ULS) systems has shifted the primary bottleneck of LiDAR workflows from data acquisition to post-processing, particularly during direct georeferencing of massive LiDAR measurements. This study presents a systematic evaluation of parallel computing strategies for accelerating ULS direct georeferencing while preserving geodetic accuracy. Two georeferencing models are investigated: (1) a rigorous model that strictly follows the full geodetic transformation chain from sensor owned coordinates system (SOCS) to projected map coordinates, and (2) an approximate model that incorporates meridian convergence angle compensation and preprocessing of platform trajectories to reduce per-point computational complexity. For each model, a shared-memory multicore CPU implementation based on OpenMP and a heterogeneous GPU implementation based on CUDA are designed. Experiments were conducted on seven real-world ULS datasets, ranging from 2.9 × 10⁷ to 7.0 × 10⁸ points and covering diverse terrain types. Accuracy analysis shows that, in typical urban, plain, and industrial scenarios, the approximate model achieves millimeter-level mean errors and centimeter-level RMSEs relative to the rigorous model, satisfying the requirements of most engineering surveying applications. Performance evaluation demonstrates that parallelization yields substantial speedups: OpenMP-based method achieves 7–9 times acceleration, while GPU computing attains up to 24.6 times acceleration for the rigorous model and up to 16.7 times for the approximate model. The results highlight the complementary strengths of the two models and provide practical guidance for selecting accuracy-efficiency trade-offs in large-scale ULS production workflows. Full article

DC motors are widely used in mechatronic systems; however, their performance degrades significantly in the presence of nonlinear mechanical loads, parameter variations and sensing uncertainties. This paper proposes three control strategies (i.e., PID, optimal, and hybrid controllers) for discrete-time DC motor systems to overcome the disturbances caused by nonlinear mechanical loads and parameter variations. Optimal control of nonlinear discrete-time systems is formally characterized by the Hamilton–Jacobi–Bellman (HJB) equation, whose analytical solution is generally intractable. To address this challenge, a learning-based optimal control strategy based on the Heuristic Dynamic Programming (HDP) framework is developed to approximate the HJB equation, supported by a formal convergence proof. For that purpose, Neural Networks (NNs) are employed to approximate both the cost function and the optimal control policy, enabling near-optimal performance with manageable computational complexity. Although the resulting optimal control achieves fast convergence, it may introduce overshoot and steady-state offset under nonlinear disturbances. To address this limitation, a hybrid control framework is proposed, where nonlinear optimal corrections are integrated with the robustness and adaptability of Proportional–Integral–Derivative (PID) control through error-dependent gating and gain-scheduling mechanisms. A structured evaluation framework is conducted, including nominal analysis, motor-parameter stress testing across nine nonlinear scenarios, controller-design sensitivity analysis, and stochastic measurement-noise assessment under filtered sensing conditions. Results demonstrate that the hybrid controller preserves transient speeds within 5–10% of the optimal controller while effectively eliminating overshoot and steady-state offset under nominal conditions. The hybrid design reduces the accumulated tracking error by more than 95% compared to the optimal controller, while incurring only negligible additional control effort. Under aggressive supply-sag disturbances, the hybrid controller significantly limits peak deviation and reduces accumulated tracking error by over 90%, while maintaining comparable control cost. Overall, the hybrid framework provides a convergence-proven and practically deployable control solution that combines near-optimal convergence speed with robust, overshoot-free performance for intelligent motion-control and robotics applications. Full article

The analysis of water and emission efficiency in cropping systems is vital for sustainable agriculture in Mediterranean regions, which face increasing water shortages. This study offers a site-specific assessment of the Water Footprint (WFP) and Carbon Footprint (CFP) of organic cotton grown under Mediterranean conditions, integrating environmental indicator measurements with economic valuation of greenhouse gas (GHG) emissions via the EU Emissions Trading System (ETS) and the Social Cost of Carbon (SCC). Experiments were carried out at three sites with different soil types, testing two cultivars (Armonia and ST-318) under three irrigation scenarios: severe water deficit (I30), moderate water deficit (I70), and full irrigation (I100). The results reveal significant site-specific variability, with average WFP_lint values ranging from about 1.440 m³ per ton at the most productive site to over 4.100 m³ per ton at the least productive site. Similarly, CFP_lint is lower under high-yield conditions, emphasizing the strong influence of yield on mass-based indicators. At the Carboj and Primosole sites, shifting from (I30) to I100 results in roughly a 50% reduction in emissions, while at Buonfornello, increased irrigation does not consistently produce benefits. The cultivar response is key: Armonia shows greater resilience to water stress, while ST-318 performs best with full irrigation. Overall, the findings highlight that the sustainability of the Mediterranean cotton system depends on factors such as yield performance, site-specific conditions, and cultivar choice. Full article

Indoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO₂ levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO₂ levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation. Full article

The combined impacts of urban pluvial flooding and traffic congestion can severely delay emergency response. However, existing studies often focus on isolated scenarios, failing to systematically quantify the reduction in overall service capability and specific route disruptions to critical functional nodes under compound hazards. To address this problem, this study proposes a three-tier analytical framework to systematically evaluate the resilience of emergency services under compound hazards. The framework first utilizes spatial network analysis to simulate the overall spatial evolution of service capabilities for Emergency Medical Service (EMS) and Fire and Rescue Service (FRS) across various return periods and traffic conditions. It then delves into the emergency response coverage for vulnerable population places. Finally, the fastest-route analysis is employed to identify variations in rescue routing. The study reveals several critical insights. (1) As rainfall intensity and traffic congestion intensify, the coverage areas of EMS and FRS exhibit significant contraction and boundary erosion. Notably, the service areas of FRS show a distinct fragmentation pattern. (2) The protection levels for vulnerable population places (e.g., kindergartens, primary and secondary schools, and nursing homes) show a pronounced stepwise decline. Under extreme rainfall and the heaviest congestion, the 5 min coverage for these sites drops from 89.9% to 23.6% for EMS, and from 72.4% to only 15.1% for FRS, revealing a severe risk exposure for vulnerable groups. (3) Road inundation leads to a substantial extension of rescue routes and even results in the complete isolation of 141 primary and secondary schools. Overall, the framework provides actionable decision support to enhance urban emergency response under compound hazards. Full article

Widespread acceptance of collaborative robots in human-involved scenarios requires accessible and intuitive interfaces for lay workers and non-expert users. Existing interfaces often rely on users to plan and issue low-level commands, necessitating extensive knowledge of robot control. This study proposes a multimodal agentic AI framework integrating natural user interfaces (NUIs) to foster effortless human-like partnerships in human–robot collaboration (HRC), which enhance intuitiveness and operational efficiency. First, it allows users to instruct robots using plain language verbally, coupled with gaze, revealing objects precisely. Second, it offloads users’ workload for robot motion planning by understanding context and reasoning task decomposition. Third, coordinating with AI agents built on large language models (LLMs), the system interprets users’ requests effectively and provides feedback to establish transparent communication. This proof-of-concept study included experiments to demonstrate a practical implementation of the agentic AI framework on a mobile manipulation robot in the collaborative task of human–robot wood assembly. Seven participants were recruited to interact with this AI-integrated agentic robotic system. Task performance and user experience metrics were measured in terms of completion time, intervention rate, NASA TLX survey for workload, and valuable insights of practical applications were summarized through a qualitative analysis. This study highlights the potential of NUIs and agentic AI-embodied robots to overcome existing HRC barriers and contributes to improving HRC intuitiveness and efficiency. Full article

Reactive power compensation devices (RPCDs) are crucial for improving the efficiency of energy systems. Distribution systems are commonly modeled under the simplifying assumption of balanced operation, which does not accurately represent real operating conditions. Motivated by the need to develop an effective computational tool for the proper selection of RPCDs, this paper proposes the application of the experience exchange strategy (EES) to the coordinated design of RPCDs. To the best of the authors’ knowledge, this is the first study to employ EES for this purpose. The proposed methodology is validated through two case studies. In the first case, an extensive exploration of the search space is performed by repeating the optimization process, resulting in a solution with a high probability of being the global optimum. Under this scenario, a comparative analysis shows that EES outperforms the genetic algorithm by 7.4%. In the second case, EES is compared with other popular heuristic techniques, including particle swarm optimization (PSO), without performing a deep exploration of the search space, observing that EES ranks in the middle, with a difference of 11.9% relative to PSO. Overall, the results confirm that the proposed EES-based framework constitutes a reliable and efficient approach. Full article

Urban energy infrastructure in dense metropolitan regions must decarbonize essential public services while avoiding additional land take and excessive costs. This study presents a techno-economic and sustainability assessment of a grid-connected hybrid wind–solar street-lighting microgrid, created by retrofitting an existing nine-node off-grid installation at Odaiba Beach, Tokyo Bay, into an embedded distributed generation asset. The design departs from complementary sizing by independently scaling PV and wind so that each source can satisfy worst-case winter lighting demand, enabling both a reduction in battery autonomy from five to two days and the deliberate use of seasonal surpluses for grid export. Steady-state load-flow analysis in ETAP indicates annual generation of 1803.73 kWh from PV and 1192.54 kWh from wind, corresponding to approximately 1.84 MWh/year of net clean energy export after supplying 1009.15 kWh/year of lighting demand and incurring 149.09 kWh/year of distribution losses, with voltages compliant with industry standards. Sensitivity analysis under conservative solar and wind scenarios shows that the system remains export-positive in all cases, thereby supporting sustainable urban development by decarbonizing street-lighting, improving land-use efficiency through infrastructure co-location, and providing a replicable framework for similar coastal cities. Full article

The safety of waterproof joints in concrete-faced rockfill dams (CFRDs) founded on deep overburden was determined during construction, impoundment, and sedimentation periods, employing the flexible FEM-NSBPFEM coupled method. Through eleven numerical scenarios, critical deformation zones are identified, and the effects of upper soil loads (upstream weighting and sedimentation) and cutoff wall design plans on the key joint between the connecting plate and the cutoff wall (J1) are systematically evaluated. The principal findings reveal that: (1) Joint deformation is dominated by vertical shear, primarily localized at J1, with the shear deformation at J1 reaching approximately 15 cm when the height of the upper soil load reaches 40 m. (2) Upper soil loads exert a greater influence on J1 shear deformation than hydrostatic pressure. (3) Increasing sedimentation loads cause J1 shear deformation to initially mirror impoundment trends before undergoing a sharp surge, and the effect is exacerbated by higher upstream weighting loads. (4) Shear deformation varies markedly between closed and suspended cutoff walls, whereas variations among different suspended wall designs are smaller. Based on these mechanical insights, two optimization schemes for the impermeable system are proposed, effectively constraining joint shear and opening displacements to within 4 cm. These findings provide critical guidance for the reliability analysis and design optimization of CFRD impermeable systems in deep overburden environments. Full article

The energy system is transforming in clean, low-carbon, safe, and efficient directions. As a key carrier of energy consumption, the operation optimization of the integrated energy system (IES) in industrial parks has become an important lever for facilitating energy transformation. This paper focuses on the modeling of the operation optimization of the IES, pays attention to the impact of electricity–carbon–green certificate coordination, and studies the operation optimization of the IES considering hydrogen energy utilization. Firstly, the topological structure of IES is analyzed, and a model of the integrated energy system in industrial parks covering multiple energy links, such as electricity, heat, and gas, is constructed. Hydrogen energy conversion units such as electrolyzers, fuel cells, and methane reactors are introduced. Secondly, the impact of electricity, carbon, and green certificate markets on the operation of IES is analyzed, and a green certificate-carbon trading integration mechanism is designed, along with the establishment of a corresponding market trading model. Then, with the system’s energy purchase and sale costs, electricity curtailment costs, carbon market transaction costs, green certificate transaction revenues, and equipment operation and maintenance costs as the core, an IES daily optimization scheduling model is constructed to minimize the overall cost. Finally, the feasibility of the model constructed in this paper is verified through a case study in the industrial park in the north of Dezhou, Shandong Province, and the result shows that the cost of IES is 15,013.7 yuan under the optimal operation schedule. The utilization rate of new power energy reaches 89.6%, and the 2.135 green certificates are converted into the carbon market. Meanwhile, comparative analysis across multiple scenarios and sensitivity analysis of single factors are conducted to discuss the necessity and effectiveness of the factors considered in this paper, providing a decision-making basis and inspiration for managers to carry out IES operation scheduling. Full article

Building materials’ fire properties directly affect the fire risk of buildings. Ignition, the initiating event of any building fire, occurs when a heat source ignites surrounding combustible materials. Although several parameters—such as the Thermal Response Parameter (TRP), thermal inertia, ignition temperature, ignition time, critical heat flux (CHF), and heat of combustion—have been used to characterize ignition behavior, a unified metric capable of representing overall ignitability under diverse and often unknown and unintended heat source (UHS) patterns is generally lacking. To address this gap, we propose a new method to evaluate material ignitability by generalizing UHS patterns and linking them to known or readily obtainable material properties, including ignition temperature and thermal inertia. The UHS patterns are represented using lognormal distributions for both exposure duration and incident heat flux (IHF), reflecting conditions that may occur in real buildings. Monte Carlo simulations are employed to generate a large number of heat exposure events from these UHS patterns, enabling statistical determination of material ignitability. The method applies to both thermally thick and thermally thin materials, with a simple expression provided to determine the critical thickness separating these behaviors. Sensitivity analysis demonstrates that the ignitability metric is robust with respect to variations in the lognormal distribution parameters. The proposed ignitability metric provides a general measure of a material’s susceptibility to ignition under typical building fire scenarios and enables relative comparison of fire risk for buildings differing only in the materials adopted. Full article

To address the mounting environmental burden caused by solid waste from the food supply chain—specifically agricultural residues and plastic packaging—this study systematically investigated the synergistic mechanisms and product regulation pathways in the co-pyrolysis of four representative food processing by-products—rice husk, pine wood, corn stover, and chestnut shell—with polypropylene, a common food packaging material. A comprehensive methodology integrating thermogravimetric analysis, kinetic modeling, and product characterization was employed. The results demonstrate that incorporating polypropylene into co-pyrolysis systems, such as those involving waste oil, significantly reduces the average activation energy, indicating a catalytic effect that enhances reaction kinetics. Notably, the co-catalytic interaction between corn stover and PP led to a substantial 54.90% reduction in oxygen content, underscoring PP’s role as an effective hydrogen donor that promotes deoxygenation and free radical reactions, thereby increasing hydrocarbon production. At an optimal pyrolysis temperature of 600 °C, product distribution was effectively regulated: the hydrocarbon yield in the CP (corn stover/PP) system increased from 39.8% to a maximum of 65.6%, reflecting a targeted conversion of oxygenated compounds into high-value hydrocarbons. Furthermore, greenhouse gas (GHG) emission calculation and techno-economic analyses indicate that a natural gas-assisted co-pyrolysis process (Scenario C) can generate a net daily profit of 1835 RMB while reducing annual CO₂ emissions by 6515 tons, demonstrating both economic feasibility and environmental benefits. This study provides a theoretical foundation for the circular economy in the food industry, offering a viable technical pathway for the simultaneous treatment of organic food waste and packaging plastics, thereby supporting the sustainable development of the agri-food sector. Full article