Search Results (12,195)

Background: Hospital-acquired influenza remains a persistent threat that amplifies morbidity, mortality, length of stay, and operational strain, particularly among older and immunocompromised inpatients. The COVID-19 era reshaped control norms—normalizing N95 use during surges, ventilation improvements, and routine multiplex PCR—creating an opportunity to strengthen hospital outbreak management. Methods: We conducted a targeted narrative review of WHO/CDC/Infectious Diseases Society of America (IDSA) guidance and peer-reviewed studies (January 2015–August 2025), emphasizing adult inpatient care. This narrative review synthesizes recent evidence and discusses theoretical implications for practice, rather than establishing formal guidelines. Evidence was synthesized into pragmatic practice statements on detection, diagnostics, isolation/cohorting, antivirals, chemoprophylaxis, vaccination, surveillance, and communication. Results: Early recognition and test-based confirmation are pivotal. For inpatients, nucleic-acid amplification tests are preferred; negative antigen tests warrant PCR confirmation, and lower-respiratory specimens improve yield in severe disease. A practical outbreak threshold is ≥2 epidemiologically linked, laboratory-confirmed cases within 72 h on the same ward. Effective control may require immediate isolation or cohorting with dedicated staff, strict droplet/respiratory protection, and daily active surveillance. Early oseltamivir (≤48 h from onset or on admission) reduces mortality and length of stay; short-course post-exposure prophylaxis for exposed patients or staff lowers secondary attack rates. Integrated vaccination efforts for healthcare personnel and high-risk patients reinforce workforce resilience and reduce transmission. Conclusions: A standardized, clinician-led bundle—early molecular testing, do-not-delay antivirals, decisive cohorting and Personal protective equipment (PPE), targeted chemoprophylaxis, vaccination, and disciplined communication— could help curb transmission, protect vulnerable patients and staff, and preserve capacity. Hospitals should codify COVID-era layered controls for seasonal influenza and rehearse unit-level outbreak playbooks to accelerate response and recovery. These recommendations target clinicians and infection-prevention leaders in acute-care hospitals. Full article

When tunnels pass through high-stress, weak, and fractured rock layers, conventional rigid supports often struggle to resist the significant loosening pressure and deformation pressure from the surrounding rock, leading to various large deformation disasters. To address the limitations of support control in high in situ stress soft-rock tunnels, this study proposed a stress-relief concept for the surrounding rock based on the convergence–confinement method. An analytical elastoplastic model and a parameter selection approach for support design were developed accordingly. Guided by the mechanical behavior of tunnel supports under this concept, a novel circumferential yielding element with friction reduction and energy-dissipation capabilities was designed and validated through laboratory tests. Unlike previous reinforced or yielding support approaches, the proposed method provides a synchronized reduction in support resistance with surrounding-rock stress release, offering a fundamentally different and more adaptive deformation-control mechanism for high-stress soft-rock tunnels. Field applications were conducted in the asymmetric large-deformation section of the Qiaojia Tunnel, where full-face monitoring determined the design parameters of the energy-dissipating support (EDS) system. Field test data show that, compared with conventional rigid supports, the proposed system can effectively control asymmetric deformation, reducing the surrounding rock pressure difference between the left and right tunnel shoulders from 0.84 MPa to 0.23 MPa, highlighting its advantages for stabilizing high-stress soft-rock tunnels. The results provide a practical framework for designing adaptive support systems that combine controlled yielding and energy dissipation. Full article

The reliable integration of high-performance noble metal interfaces with flexible substrates is a key requirement for wearable electronics. However, achieving uniform, mechanically robust and functionally active coatings on fabric surfaces remains highly challenging. This study reports the atomic-layered-deposition (ALD) growth of platinum (Pt) on textile at low temperatures. Through ozone plasma-assisted activation technology, Pt nucleation can be achieved at 100 °C, forming a dense and defect-suppressed Pt layer that substantially increases the surface oxygen functional groups and enhances binding affinity. The resulting Pt layer also significantly enhances the adsorption behavior and sensing performance of Ti₃C₂T_x MXene gel inks on textile. At the atomic scale, the engineered Pt–MXene interface promotes stronger adsorption of MXene sheets and establishes efficient electron/ion transport pathways within the gel network. Ultimately, the conductive textile treated with Pt functionalized layers (MXene/Pt@textile) exhibits significantly enhanced sensing sensitivity and signal stability, enabling precise detection of human motions, pressure, and subtle physiological vibrations. The synergistic effect of ALD Pt layers and MXene gel inks creates a textile platform combining robustness, breathability, and high responsiveness. Full article

Biosourced materials made of a combination of raw earth and fibers are attracting increasing interest for low-carbon construction due to their reduced environmental impact and good thermal and hygric performance. This study investigates several soil–fiber composites selected and formulated at different densities to assess their thermal conductivity, enabling the selection of two complementary materials: a structural earthen mix and a lightweight insulating mix. Experimental measurements were taken under controlled conditions and used to characterize heat and moisture fluxes, and numerical calculations were carried out to evaluate the performance of single and double-layer wall configurations. The results showed that an increase in thermal gradients accelerates vapor migration and alters the internal distribution of moisture. The evaluation of wall configurations demonstrated that placing the earthen insulating layer externally optimizes thermal fluxes and eliminates condensation risks at the interface between materials, while internal insulation can be sensitive to hygrothermal gradients and prone to moisture accumulation. The combined experimental–numerical approach provides new insights into high-performance designs of bio-based earthen envelopes, establishing guidelines for minimizing moisture-related risks in low-carbon building systems. Full article

Modeling the water cycle in the critical zone requires understanding interactions between the soil–vegetation–atmosphere compartments. Mechanistic modeling of soil water flow relies on the accurate determination of hydrodynamic parameters that control hydraulic conductivity and water retention curves. These parameters can be derived either using pedotransfer functions (PTFs), using soil properties obtained from field samples, or through inverse modeling, which allows the parameters to be adjusted to minimize differences between simulations and observations. While PTFs are widely used due to their simplicity, inverse modeling requires specific instrumentation and advanced numerical tools. This study, conducted at the Hydro-Geochemical Environmental Observatory (Strengbach forested catchment) in France, aims to determine the optimal hydrodynamic parameters for two contrasting forest plots, one dominated by spruce and the other by beech. The methodology integrates granulometric data across multiple soil layers to estimate soil parameters using PTFs (Rosetta). Water content and conductivity data were then corrected to account for soil stoniness, improving the KGE and NSE metrics. Finally, inverse parameter estimation based on water content measurements allowed for refinement of the evaluation of α, Ks, and n. This framework to estimate soil parameter was applied on different time periods to investigate the influence of the calibration chronicles on the estimated parameters. Results indicate that our methodology is efficient and that the optimal calibration period does not correspond to one with the most severe drought conditions; instead, a balanced time series including both wet and dry phases is preferable. Our findings also emphasize that KGE and NSE must be interpreted with caution, and that long simulation periods are essential for evaluating parameter robustness. Full article

This study investigates the energy performance of anaerobic digesters in a municipal wastewater treatment plant by integrating empirical data from two tanks located at different distances from the heat source with simulation results. The analysis of measurements enabled the determination of heat transferred to the raw sludge, total heat losses of both systems, and provided input data for an hourly simulation of the thermal balance of the digester envelope. An analytical model was developed, including separate equations for the sludge and biogas phases, considering heat losses caused by mass transfer, conduction, convection, and radiation, as well as solar heat gains. The results show that the temperature difference between sludge and biogas exhibits seasonal variation, with a maximum value of 10.5 K, while the desired operational temperature of sludge fermentation is maintained at 38 °C. The total annual heat balance of the anaerobic digester in 2024 was estimated at 202.8 MWh, with the following structure: aboveground walls 46%, ground-contact partitions 30%, and dome 24%. Model validation using data from one of the digesters indicated a total system energy demand of 1812.0 MWh, distributed as follows: heat transferred to raw sludge 88.6%, heat transfer losses 0.2%, and digester envelope balance 11.2%. Replacing the thermal insulation of the aboveground section could reduce heat losses by 70.7 MWh, decreasing the total energy demand of the system by 3.9%. Comparison with the second digester revealed an energy gap of 166.3 MWh, which may be attributed to higher transmission losses or degradation of the insulation layer. Full article

With the rapid advancement of artificial intelligence technology, multi-agent systems are being widely applied in fields such as autonomous driving and robotic collaboration. However, existing methods often suffer from the disconnection between intention recognition and strategy optimization, leading to inefficiencies in group collaboration. This paper proposes a multi-agent cooperative group game model based on Intention-Strategy Optimization (ISO-MAGCG). The model establishes a two-layer optimization framework encompassing intention and strategy, enabling dynamic adaptation through the co-evolution of upper-layer intention recognition and lower-layer strategy optimization. A Group Attention-based Intention Recognition Network (GAIN) is designed to efficiently capture complex interactions among agents. Furthermore, an Adaptive Group Evolution Algorithm (AGEA) is proposed to ensure the stability of large-scale cooperative endeavors. Experiments conducted in navigation, resource collection, and defense collaboration scenarios validate the effectiveness of the proposed method. Compared with mainstream algorithms such as QMIX, MADDPG, and MAPPO, ISO-MAGCG demonstrates significant superiority in metrics including task success rate and cooperative efficiency, achieving an average improvement of 8.4% in task success rate, a 12% enhancement in cooperative efficiency, and an intention recognition accuracy of 94.3%. The results indicate notable performance advantages and favorable scalability. Full article

Iris recognition continues to pose challenges for deep learning models, despite its status as one of the most reliable biometric authentication techniques. These challenges become more pronounced when training data is limited, as subtle, high-dimensional patterns are easily missed. To address this issue and strengthen both feature extraction and recognition accuracy, this study introduces IRIS-QResNet, a customized ResNet-18 architecture augmented with a quanvolutional layer. The quanvolutional layer simulates quantum effects such as entanglement and superposition and incorporates sinusoidal feature encoding, enabling more discriminative multilayer representations. To evaluate the model, we conducted 14 experiments on the CASIA-Thousands, IITD, MMU, and UBIris datasets, comparing the performance of the proposed IRIS-QResNet with that of the IResNet baseline. While IResNet occasionally yielded subpar accuracy, ranging from 50.00% to 98.66%, and higher loss values ranging from 0.1060 to 2.0640, comparative analyses showed that IRIS-QResNet consistently outperformed it. IRIS-QResNet achieved lower loss (ranging from 0.0570 to 1.8130), higher accuracy (ranging from 66.67% to 99.55%), and demon-started improvement margins spanning from 0.1870% in the CASIA End-to-End subject recognition with eye-side to 16.67% in the MMU End-to-End subject recognition with eye-side. Loss reductions ranged from 0.0360 in the CASIA End-to-End subject recognition without eye-side to 1.0280 in the UBIris Non-End-to-End subject recognition. Overall, the model exhibited robust generalization across recognition tasks despite the absence of data augmentation. These findings indicate that quantum-inspired modifications provide a practical and scalable approach for enhancing the discriminative capacity of residual networks, offering a promising bridge between classical deep learning and emerging quantum machine learning paradigms. Full article

Hydrogen energy is vital for a clean, low-carbon society, and proton exchange membrane fuel cells (PEMFCs) represent a core technology for the conversion of hydrogen chemical energy into electrical energy. When PEMFC single cells are stacked under assembly force for high power output, their porous electrodes (gas diffusion layers, GDLs; catalyst layers, CLs) undergo compressive deformation, altering internal transport processes and affecting cell performance. However, existing microscale studies on PEMFC porous electrodes insufficiently consider compression (especially in CLs) and have limitations in obtaining compressed microstructures. This study proposes a combined framework from a microstructure perspective. It integrates the finite element method (FEM) with computational fluid dynamics (CFD). It reconstructs microstructures of GDL, CL, and GDL-bipolar plate (BP) interface. FEM simulates elastic compressive deformation, and CFD calculates transport properties (solid zone: heat/charge conduction via Laplace equation; fluid zone: gas diffusion/liquid permeation via Fick’s/Darcy’s law). Validation shows simulated stress–strain curves and transport coefficients match experimental data. Under 2.5 MPa, GDL’s gas diffusivity drops 16.5%, permeability 58.8%, while conductivity rises 2.9-fold; CL compaction increases gas resistance but facilitates electron/proton conduction. This framework effectively investigates compression-induced transport property changes in PEMFC porous electrodes. Full article

In this study, Ni/Mn-doped cobalt–reduced graphene oxide (Co-RGO) composites were successfully synthesized as advanced electrode materials for supercapacitors. The structural and morphological properties of the composites were characterized using FTIR, XRD, SEM, TEM, and UV–Vis spectroscopy. Their electrochemical performance was evaluated through electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and galvanostatic charge–discharge (GCD). Among the prepared samples, Co-RGO doped with Ni/Mn at a 40:10 ratio exhibited the most outstanding capacitive behavior, achieving a specific capacitance of 7414 F g⁻¹ at a current density of 10 A g⁻¹, along with a high energy density of 565 Wh kg⁻¹ and a power density of 4998 W kg⁻¹. The high capacitance arises from faradaic pseudocapacitive reactions rather than electric double-layer capacitance, eliminating the need for a large surface area. These results confirm that Ni doping significantly enhances pseudocapacitance and conductivity in the Co-RGO matrix, making Ni/Mn (40:10)–Co-RGO a potential material for advanced energy storage systems. Full article

At present, composite phase change materials are widely studied for battery thermal management. However, to ensure the battery’s thermal safety, it is necessary not only to control the temperature during regular operation, but also to prevent sudden thermal runaway. This basic function depends on the flame-retardant properties of the composite phase change materials. In this study, a hexagonal double-layer hollow nanocage S2 with defect orientation was prepared and combined with carbon nanotubes (PNT) derived from polypyrrole (PPy) tubes to form a high adsorption mixture. Multifunctional composite phase change material PNT/S2@PEG/TEP was prepared by adsorbing and coating polyethylene glycol 8000 (PEG-8000) and triethyl phosphate (TEP) with microfibrillated cellulose nanofibers (CNF) as the skeleton. The characterization shows that its thermal conductivity is 0.65 W/m·K and its phase transition enthalpy is 146.1 J/g, demonstrating its excellent thermal regulation. Microcalorimetric testing (MCC) confirmed its flame-retardant ability, attributed to the strong adsorption of PNT/S2 on PEG-8000 and TEP, the improvement in PNT’s thermal conductivity, and the contribution of CNF to flexibility. This composite phase change material, with excellent comprehensive properties, has broad application prospects in thermal safety for electronic equipment, significantly expanding its practical scope. Full article

Lignocellulosic residues are increasingly explored as alternatives to wood in particleboard production, fostering sustainability within the circular economy. Beyond these, non-lignocellulosic wastes such as plastics are gaining attention for enhancing panel durability and performance. This study evaluates waste high-density polyethylene (HDPE) as a partial substitute for adhesive resin in sugarcane bagasse-based medium-density particleboards. The objective was to valorize agricultural and plastic residues while reducing reliance on petroleum-based resins and associated environmental impacts. Panels (750 kg/m³) were produced with two face layers of sugarcane bagasse and a core layer combining bagasse and HDPE, bonded with castor oil-based polyurethane resin at 8% and 12% contents. Physical and mechanical performance was assessed against national and international standards, complemented by natural and accelerated weathering tests. A comparative life cycle assessment (LCA) was conducted to benchmark hybrid panels against conventional particleboards. Results showed that incorporating HDPE allows for resin reduction without compromising performance, meeting standard requirements for several applications. The LCA indicated lower environmental burdens in 8 of 10 impact categories for hybrid panels relative to conventional ones, underscoring their potential to reduce fossil resource use and emissions. The findings demonstrate that integrating waste plastics into particleboard production not only improves resource efficiency but also delivers tangible environmental benefits. This approach offers a scalable pathway for advancing sustainable materials, closing waste loops, and supporting circular economy practices in the wood-based panel industry. Full article

The electric power grid constitutes a foundational pillar of modern critical infrastructure (CI), underpinning societal functionality and global economic stability. Yet, the increasing convergence of Information Technology (IT) and Operational Technology (OT), particularly through the integration of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS), has amplified the sector’s exposure to sophisticated cyber threats. This study conducts a comparative analysis of five major cyber incidents targeting electric power systems: the 2015 and 2016 Ukrainian power grid disruptions, the 2022 Industroyer2 event, the 2010 Stuxnet attack, and the 2012 Shamoon incident. Each case is examined with respect to its objectives, methodologies, operational impacts, and mitigation efforts. Building on these analyses, the research evaluates the extent to which such attacks could have been prevented or mitigated through the systematic adoption of leading cybersecurity maturity frameworks. The NIST Cybersecurity Framework (CSF) 2.0, the ENISA NIS2 Directive Risk Management Measures, the U.S. Department of Energy’s Cybersecurity Capability Maturity Model (C2M2), and the Cybersecurity Risk Foundation (CRF) Maturity Model alongside complementary technical standards such as NIST SP 800-82 and IEC 62443 have been thoroughly examined. The findings suggest that a proactive, layered defense architecture grounded in the principles of these frameworks could have significantly reduced both the likelihood and the operational impact of the reviewed incidents. Moreover, the paper identifies critical gaps in the existing maturity models, particularly in their ability to capture hybrid, cross-domain, and human-centric threat dynamics. The study concludes by proposing directions for evolving from compliance-driven to resilience-oriented cybersecurity ecosystems, offering actionable recommendations for policymakers and power system operators to strengthen the cyber-physical resilience of electric generation and distribution infrastructures worldwide. Full article

This article proposes a novel transparent conductive film structure to solve the problem of electrostatic accumulation in traditional nanoimprint lithography processes. This structure is formed by spin-coating a layer of silver nanowire (AgNWs) transparent conductive films on a graphic substrate, followed by coating a layer of polydimethylsiloxane (PDMS) on the surface of the film. After the PDMS is cured, it is peeled off from the substrate to form a free-standing elastic three-dimensional structured surface. These transparent conductive films are not only designed to mitigate static electricity generated during the nanoimprint lithography process, but also have excellent UV transparency, with a 325 nm UV transmittance of up to 90%. At the same time, it exhibits good conductivity with a sheet resistance of 20 Ω/sq. In addition, the films have excellent elasticity and can maintain stable conductivity during repeated stretching, providing a novel solution for flexible optoelectronic devices and nanoimprint technology. Full article

In this study, regression-based machine learning models were developed to predict the melt pool width and depth formed during the Laser Powder Bed Fusion (LPBF) process for Fe-3.4Si and Fe-6Si alloys. Based on experimentally obtained melt pool width and depth data, a total of 11 regression models were trained and evaluated, and hyperparameters were optimized via Bayesian optimization. Key process parameters were identified through data preprocessing and feature engineering, and SHAP analysis confirmed that the input energy had the strongest influence on both melt pool width and depth. The comparison of prediction performance revealed that the support vector regressor with a linear kernel (SVR_lin) exhibited the best performance for predicting melt pool width, while the multilayer perceptron (MLP) model achieved the best results for predicting melt pool depth. Based on these trained models, a power–velocity (P-V) process map was constructed, incorporating boundary conditions such as the overlap ratio and the melt pool morphology. The optimal input energy range was derived as 0.45 to 0.60 J/mm, ensuring stable melt pool formation. Specimens manufactured under the derived conditions were analyzed using 3D X-ray CT, revealing porosity levels ranging from 0.29% to 2.89%. In particular, the lowest porosity was observed under conduction mode conditions when the melt pool depth was approximately 1.0 to 1.5 times the layer thickness. Conversely, porosity tended to increase in the transition mode and lack of fusion regions, consistent with the model predictions. Therefore, this study demonstrated that a machine learning-based regression model can reliably predict melt pool characteristics in the LPBF process of Fe-Si alloys, contributing to the development of process maps and optimization strategies. Full article