Search Results (20,166)

With the aim of achieving outstanding thermal control and corrosion resistance properties, a white MAO thermal control coating sealed by a silicon–zirconium hybrid sol–gel layer was prepared in this work. The corrosion behavior of the coating was evaluated using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) in 3.5 wt.% NaCl solution. Microstructural and compositional characterizations were conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy-dispersive spectroscopy (EDS). Results indicated that the sol–gel/MAO composite coating significantly outperformed the single-layer MAO coating in corrosion resistance, primarily due to effective sealing of micro-pores and cracks by the sol–gel layer, which prevented the penetration of corrosive agents. The post-immersion morphological observations were in good agreement with the EIS results. After immersion, the corrosion current density of the composite coating only increased from 10^−6.4 to 10^−5.1 A/cm², while the corrosion potential decreased from −1.25 V to −1.35 V. The post-immersion morphological observations were consistent with EIS results. Meanwhile, the composite coating can effectively mitigate the thermal control performance degradation caused by corrosion. Compared with the MAO coating, the absolute increase in solar absorptance of the sol–gel/MAO coating is reduced by 60%. After 168 h of accelerated corrosion tests in a simulated marine environment, the solar absorptance (α_S) of the sol–gel/MAO coating increased by only 0.05. This study demonstrates that the combination of MAO and sol–gel treatment provides a promising strategy for the development of lightweight, corrosion-resistant magnesium alloys for aerospace applications. Full article

The Chinese giant salamander (Andrias davidianus), a species of high ecological and conservation value, shows abnormal respiratory behaviors as early signs of health decline. Accurate assessment of its pulmonary respiration is crucial for improving captive breeding and post-breeding parental care—key strategies for its survival and population recovery. However, its nocturnal and cave-dwelling nature makes traditional observation extremely difficult. Manual monitoring suffers from poor visibility at night, while conventional detection methods often miss subtle respiratory movements, limiting behavioral and health research. To address these challenges, this study presents the first automated method for monitoring respiratory behaviors in this species. We propose Mamba-YOLO-SRC, a novel hybrid detection framework that combines Mamba and YOLO architectures to accurately identify four key behaviors: diving (Dive), head-raising (HeadUP), inhalation (Inhale), and exhalation (Exhale). The proposed model achieves a mean average precision (mAP@0.5) of 0.944, with per-class average precision scores of 0.975 for Dive, 0.925 for HeadUP, 0.948 for Exhale, and 0.928 for Inhale. Mamba-YOLO-SRC provides a feasible and referable technical solution for advancing research on the Chinese giant salamander in both captive and natural settings. Full article

Accurate prediction of overpressured formations in deepwater is important for drilling safety and reservoir evaluation. However, conventional two-step inversion workflows are affected by cumulative errors and parameter crosstalk, which limits their ability to characterize the sharp pressure-transition interfaces at the top of overpressured zones. In this study, we propose a direct prestack nonlinear inversion method for the formation pressure coefficient (λ), a dimensionless and drilling-relevant indicator of overpressure intensity. Unlike previous exact-Zoeppritz direct inversions that target effective stress or elastic moduli, here a single formation pressure coefficient drives the pressure-sensitive rock-physics chain—linking pore pressure, effective stress, and pore-space stiffness to the seismic response—thereby reducing the number of free inversion variables. This single-parameter mapping is then coupled with the exact Zoeppritz equation to build a nonlinear prestack forward operator, helping to reduce the parameter coupling and error propagation associated with conventional multiparameter inversion workflows. To describe the typical blocky structural features of overpressured strata, a nonconvex Lp-norm (0 < p < 1) regularization is introduced as a structural prior, and a decoupled optimization strategy combining the alternating direction method of multipliers (ADMM) and iteratively reweighted least squares (IRLS) is developed for a stable solution. In a single pseudo-well synthetic test, the proposed method achieved a higher correlation coefficient and lower root mean square error (RMSE) than the indirect workflow, indicating improved agreement with the reference formation-pressure-coefficient profile. Application to field seismic data from the Yinggehai Basin, South China Sea, shows that the method produces clearer pressure-transition boundaries and pressure-coefficient profiles more consistent with the available well constraints. These results suggest that, under the tested conditions, the proposed method can provide useful geophysical support for pressure prediction and the characterization of deepwater overpressured reservoirs. Full article

In the fabric warehouse, order picking operations face high labor intensity and rising operational costs, requiring urgent optimization. This study investigates the order scheduling and task assignment problem within an elastic staffing framework, where temporary labor recruitment and real-time task allocation need to be adjusted dynamically in response to fluctuations in order volumes. Nevertheless, conventional approaches often suffer from severe computational bottlenecks under such highly dynamic conditions, and struggle to maintain optimal solutions when demand undergoes large and frequent fluctuations. To address these challenges, this study proposes a Graph Transformer Policy Network with Proximal Policy Optimization (GTP-PPO), which combines graph structure features with a global attention mechanism. First, the return picking strategy and the S-shaped picking strategy are compared and analyzed in the fabric warehouse scenario. The results reveal that the return strategy is more suitable for the studied warehouse layout. Subsequently, a mixed-integer programming (MIP) model and a GTP-PPO model are established for optimizing order dispatching and scheduling. Finally, an empirical analysis is carried out based on the peak order day of the year in the fabric warehouse. The results demonstrate that the proposed GTP-PPO model not only achieves near-global optimal solutions (gap < 4%) comparable to the MIP model, but also exhibits robust real-time decision-making capabilities under dynamically increasing order volumes and unexpected disruptions. Compared to the MIP model, the GTP-PPO approach reduces unskilled labor hours by 84.80% and decreases operational volatility by 27.60%, with only a 3.52% increase in operational costs. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

The large-scale integration of distributed photovoltaics (DPVs) into the distribution network exacerbates voltage fluctuations and substantially increases network losses. To improve the voltage quality and economic efficiency of distribution networks, a Volt/Var optimization (VVO) model is established. Coordinating multiple heterogeneous devices, the model aims to minimize the total voltage deviation, the total network losses, and the regulation cost of discrete equipment simultaneously. Considering multi-constraint coupling characteristics, a quantitative method is proposed to evaluate the reactive power regulation potential of DPVs under intricate operating conditions. Then, the multi-strategy integrated rime optimization algorithm (MSIRIME) is utilized for the model solution. Fuch chaotic mapping generates uniformly distributed and ergodic initial populations. A dual-branch search mechanism combining the snow ablation optimizer with the rime optimization significantly enhances global exploration capabilities. The guided learning strategy balances exploration and exploitation for high-dimensional VVO, preventing local optima. Case tests on a modified IEEE 33-bus system demonstrate that the proposed model exhibits excellent effectiveness and robustness. Moreover, MSIRIME exhibits better optimization performance than some classic and recently proposed strategies, reducing the average network losses and voltage deviation over 30 independent runs by at least 5.87% and 52.22%, respectively, relative to those of the compared methods. Full article

The valorization of phosphogypsum (PG), a byproduct of phosphoric acid production, along with waste glass (WG) and silica fume (SF) into value-added materials has attracted growing attention in recent years. The present study aims to synthesize three types of porous geopolymers (GD, GDP, and GDG) using Tunisian clay and locally available mineral wastes, and to investigate their potential as low-cost adsorbents for the removal of methylene blue (MB) dye from aqueous solutions. The physicochemical characteristics of the raw precursors and the resulting porous geopolymers were analyzed using various techniques, including FTIR, XRD, BET, and SEM. Variations in Si/Al, Na/Al, and Ca/Al ratios play a critical role in the geopolymer structure. The high Ca/Al ratio in GDP (porous geopolymer from calcined clay and phosphogypsum) promotes the formation of C-A-S-H, leading to increased macroporosity, which favors adsorption capacity despite the presence of a more heterogeneous morphology. The results indicated that the maximum adsorption capacity (Qmax) for MB dye was obtained for the GDP sample, reaching 68 mg/g. Adsorption experiments revealed the successful removal of MB dye by geopolymers, with the Langmuir isotherm and pseudo-second-order kinetic models adequately describing the adsorption process. The MB uptake by geopolymers was facilitated by weak physicochemical interactions, including electrostatic attraction, hydrogen bonding, and π–π interactions. This study proposes a simple and effective alkali activation strategy that combines different industrial wastes within a single geopolymer system, resulting in improved porosity and adsorption efficiency. Overall, the findings highlight the potential of these waste-derived geopolymers as promising and sustainable adsorbents for wastewater treatment applications. Full article

The aim of this paper is to give an overview of emerging technologies for the greening of aviation, how they can be applied to different classes of aircraft, and the challenges to be overcome in achieving efficiency and environmental objectives. The following steps are part of the journey towards the greening of aviation: (i) developing and maturing new technologies, including electrification and sustainable fuels; (ii) where possible, using new technologies in the current fleet to maximize short-term benefits—i.e., EU Fit for 55; (iii) when it is not possible to retrofit new technologies to current aircraft, incorporating them into new next-generation aircraft designs from 2035; and (iv) replacing existing fleets with new, cleaner aircraft to meet the ICAO Net Zero 2050 goal. These technologies of prime importance will have to be supplemented by operational, regulatory, and economic enablers to support wide deployment. There will not be one solution that meets the requirements of all aircraft classes or mission profiles, but rather a combination of electrification, hydrogen propulsion, and sustainable aviation fuels will be required. Achievement of aviation’s environmental goals will hence not solely be a function of technological progress but also certification pathways, investment in infrastructure, and integrated policy strategies. Full article

Rapid urbanization, climate change, and biodiversity loss are intensifying environmental pressures on arid coastal cities through extreme heat, water scarcity, salinity intrusion, and increasing flood risks. Despite substantial investment in urban green spaces across the Gulf region, many public parks provide limited ecological functionality and climate adaptation benefits. This study evaluated the ecological performance of three coastal parks in Muscat, Oman Sarooj Beach Park (23,080 m²), Ghubrah Beach Park (34,818 m²), and Al Athaiba Beach Park (17,370 m²), to identify opportunities for more resilient landscape design. The assessment revealed that although green space occupied 76.8–82% of park areas, tree canopy cover remained low (8–12%), limiting thermal comfort, habitat provision, and ecological performance. Based on these findings, a Functional and Climate-Responsive Planting Strategy (FCRPS) was developed by integrating the 10–20–30 biodiversity guideline with performance-based planting criteria tailored to arid and saline environments. The framework was applied to the proposed Majis Beach Ecological Park in Sohar, Oman, to demonstrate the implementation of ecological urbanism and nature-based solutions in a hyper-arid coastal environment. The resulting design incorporates biodiversity-enhancing planting, blue–green infrastructure, wetland restoration, and climate-responsive spatial planning. The study demonstrates how multifunctional landscapes can enhance biodiversity, improve thermal comfort, strengthen stormwater management, and support community well-being while providing a transferable framework for resilient public park design in arid coastal cities. Full article

Glycine betaine transport systems are widely exploited by bacteria to survive osmotic stress and represent potential entry routes for antimicrobial delivery. Here, we investigate the bactericidal glycine betaine analog Tox-GB and its uptake, intracellular fate, and antimicrobial activity in Escherichia coli K-12 under osmotic stress. We show that the xenobiotic enters cells via a hierarchical uptake route involving the osmotically regulated compatible solute transporters ProU and ProP, ABC- and MFS-type transporters, respectively. ProU functions as the primary high-affinity transporter at low concentrations, whereas ProP provides a secondary uptake route at somewhat higher substrate levels. Loss of either transporter confers partial resistance, while simultaneous inactivation of both systems causes full resistance, underscoring their functional redundancy and the robustness of Tox-GB import. Intracellularly, Tox-GB undergoes oxygen-dependent degradation, yielding 4-nitrobenzaldehyde and dimethylglycine. While 4-nitrobenzaldehyde contributes to toxicity under aerobic conditions, Tox-GB remains bactericidal under anaerobic conditions, indicating additional oxygen-independent mechanisms involving either the parent compound or unidentified metabolites. These findings suggest a complex intracellular fate and multifactorial mode of action. Despite initial promise as a Trojan horse antimicrobial strategy, the use of Tox-GB for practical applications faces key limitations. Resistance readily emerges via transporter inactivation, and intrinsic resistance occurs in species lacking appropriate compatible solute uptake systems. Structural constraints in glycine betaine transporters further restrict design flexibility. Osmotic regulation limits activity to specific niches, and potential host toxicity stemming from reactive metabolites raises safety concerns. Collectively, these findings highlight the mechanistic complexity and translational challenges faced by glycine betaine–derived xenobiotics as antimicrobial agents. Full article

The present study evaluated the effects of the prepartum administration of immunotropic preparations on the growth performance, physiological status, and metabolic profile of calves. Sixty pregnant Holstein cows were divided into three groups (n = 20 each): the first experimental group received a single intramuscular injection of sodium nucleinate (5 mL), the second experimental group received a single intramuscular injection of Ribotan (5 mL), and the control group received saline solution. All treatments were administered 3–9 days before calving. The obtained calves were monitored until 60 days of age. Clinical, growth, hematological, and biochemical parameters were assessed at days 1, 10, 30, and 60. Calves from the treated cows showed improved neonatal adaptation, including faster development of standing posture and the suckling reflex. Body weight was significantly higher in experimental groups at 30 and 60 days (p ≤ 0.05), with consistently greater average daily gains. Blood analysis revealed increased total protein, albumin, and γ-globulin levels, indicating enhanced protein metabolism and immune status. In contrast, cortisol concentrations were lower in treated groups, reflecting reduced physiological stress. Multivariate (PCA) and correlation analyses confirmed strong associations between growth performance, metabolic activity, and immune indicators, and demonstrated clear separation between control and treated groups. Ribotan exhibited the most pronounced biological effect, while sodium nucleinate showed moderate but consistent improvements. In conclusion, prepartum immunotropic treatment of cows enhances early-life adaptation, metabolic efficiency, and growth performance of calves and may represent a practical strategy for improving calf rearing outcomes in dairy farming systems. Full article

The rapid emergence of antimicrobial resistance has intensified the need for advanced therapeutic platforms capable of improving the efficacy, stability, and targeted delivery of antimicrobial agents. Electrospun nanofibers have emerged as highly promising materials for biomedical applications due to their large surface area, high porosity, tunable morphology, and ability to incorporate a broad range of bioactive compounds. This review provides a comprehensive overview of the design, fabrication, and biomedical applications of electrospun bioactive nanofibers functionalized with antimicrobial drugs. It presents the main nanofiber fabrication techniques, with particular emphasis on electrospinning and the influence of solution, process, and environmental parameters on fiber morphology and drug-loading efficiency. Natural, synthetic, and hybrid polymer systems commonly employed in electrospun antimicrobial nanofibers are analyzed in relation to their physicochemical properties, biocompatibility, and therapeutic performance. In addition, the review highlights different drug incorporation strategies, including encapsulation, immobilization, and surface coating, as well as the mechanisms of action of antimicrobial agents. Recent advances in nanotechnology-based antimicrobial systems and their role in overcoming analytical, biopharmaceutical, and drug-delivery limitations are also examined. Furthermore, the review addresses current challenges related to scalability, reproducibility, stability, and clinical translation of electrospun nanofibers. Finally, future perspectives focusing on multifunctional, stimuli-responsive, and personalized antimicrobial nanofiber systems are discussed as promising directions for combating bacterial infections and reducing the global burden of antimicrobial resistance. Full article

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

Sea-level rise is accelerating coastal erosion and storm-driven flooding, increasing risks to estuarine ecosystems and coastal communities. Nature-based solutions (NbS), such as those including ecosystem restoration, are widely endorsed for climate change risk mitigation, yet their protective performance under rising sea levels remains poorly quantified across future scenarios. Here we combined scenario-based modelling with spatially explicit exposure mapping to assess how saltmarshes influence shoreline vulnerability under three Intergovernmental Panel on Climate Change (IPCC) Shared Socioeconomic Pathways (SSP) sea-level rise projections for 2050 and 2100. Using the InVEST Coastal Vulnerability Model and the Lima estuary (NW Portugal) as a case study, we showed that existing saltmarshes currently reduce mean shoreline exposure by approximately 5%, but this contribution declines with sea-level rise, falling to 2.6% by 2100 under SSP5-8.5, resulting in an increase in areas subject to High and Very High exposure risk. But under a saltmarsh revegetation scenario, model results indicated that this revegetation significantly increases the protection across all future scenarios, reducing the number of shoreline points in High and Very High exposure classes by up to 58% and lowering the potential coastal population exposure by up to 27% by 2100 under SSP5-8.5. However, the protective effect of saltmarshes diminished under the most extreme sea-level rise trajectories, indicating that saltmarsh revegetation alone may not be enough to fully offset accelerating coastal hazards. Our results demonstrate that saltmarsh restoration can deliver meaningful climate adaptation benefits; however, to safeguard estuarine systems and coastal communities under accelerating climate change in the long term, restoration actions must be integrated into broader adaptation strategies. Full article

Interzeolite transformation (IZT) has emerged as a versatile strategy for accessing zeolite frameworks through controlled framework reorganization under comparatively simplified synthesis conditions. Unlike traditional synthesis approaches that frequently require organic structure-directing agents (OSDAs), highly alkaline media, and prolonged thermal treatment, IZT converts pre-existing zeolite into a new topology, enabling direct reuse of crystalline matter while reducing synthesis complexity. This review examines how structural drivers, including framework density, structural memory, and building-unit compatibility, govern transformation pathways and phase selectivity across five principal transformation approaches: (i) solution-mediated, (ii) assembly–disassembly–organization–reassembly (ADOR), (iii) mechanically assisted, (iv) steam-assisted, and (v) fully solid-state systems. These approaches promote distinct transformation pathways that govern framework reconstruction, structural inheritance, and phase selectivity. Recent advances in solvent-free, mechanochemical, steam-assisted, and microwave-assisted synthesis demonstrate the potential of IZT to reduce solvent consumption, template usage, and crystallization times. Despite these advances, major challenges remain in predicting transformation outcomes, controlling transient intermediates, and establishing scalable and quantitatively validated sustainability metrics. Collectively, these developments position IZT as a promising platform for the rational and sustainable design of next-generation zeolitic materials. Full article