Search Results (3,593)

Herein, an electrochemical sensor is reported for the first time based on an ordered macro–microporous composite derived from metal–organic frameworks (MOFs) for the highly sensitive detection of auramine O (AO), a Group 2B carcinogen. The hierarchical pore architecture, integrating an ordered macroporous network with a microporous ZIF-8 framework, enables the uniform dispersion of a high density of catalytically active sites. The interconnected macroporous channels facilitate efficient mass transport and rapid removal of reaction byproducts, effectively preventing pore blockage and ensuring stable sensing performance during repeated measurements. Owing to these structural advantages, the proposed sensor exhibits outstanding analytical performance toward AO detection, with a sensitivity of 0.4843 μA μM⁻¹, a detection limit of 0.168 μM (S/N = 3), and a wide linear range from 0.5 to 50 μM. Moreover, the sensor demonstrates excellent selectivity and reproducibility, maintaining reliable responses even in the presence of 100-fold excess common food constituents such as tartrazine and glucose. Real sample analysis further confirms its high accuracy and operational stability. Overall, the electrochemical sensor based on silver nanoparticle-decorated ordered macro–microporous ZIF-8 synthesized via in situ reduction shows great potential as a portable and on-site tool for rapid AO detection in food. More broadly, ordered macro–microporous MOF-derived materials represent a promising platform for advanced electrochemical sensor applications. Full article

The Korteweg–de Vries (KdV) equation is a foundational model in geophysical fluid dynamics (GFD), governing the propagation of long internal and surface gravity waves in stratified and shallow ocean environments where the interplay between nonlinear steepening and frequency-dependent dispersion gives rise to solitons. Although the analytical tractability of the KdV equation through inverse scattering is well established, systematic numerical exploration of multi-soliton interactions remains valuable for benchmarking solvers, probing conservation properties under varied oceanic initial conditions, and building intuition for more complex ocean wave phenomena. This article presents sangkuriang, an open-source Python library that solves the KdV equation using Fourier pseudo-spectral spatial discretization and adaptive eighth-order Runge–Kutta time integration. The implementation leverages just-in-time (JIT) compilation to achieve research-grade computational efficiency on standard hardware, making it readily accessible for coastal and ocean engineering applications, including idealized modeling of internal solitary waves on continental shelves, rapid parameter studies for solitary wave propagation in stratified basins, and pedagogical investigations of nonlinear dispersive wave dynamics. The solver is validated through four progressively complex idealized scenarios motivated by oceanic wave dynamics: isolated soliton propagation, symmetric interactions, overtaking collisions, and three-body interactions. High-fidelity conservation of mass, momentum, and energy is demonstrated, with relative errors remaining below $\mathcal{O}\left({10}^{-4}\right)$ across all test cases. Measured soliton velocities align with theoretical predictions within 5%, confirming the capture of the amplitude-dependent dispersion characteristic of oceanic solitary waves. Complementary diagnostics, including spectral entropy and recurrence quantification analysis (RQA), verify that the numerical solutions preserve the regular phase-space structure characteristic of integrable Hamiltonian systems. These results establish sangkuriang as a robust, lightweight platform for reproducible numerical investigation of idealized nonlinear dispersive wave dynamics relevant to coastal and ocean engineering applications. Full article

The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures. Full article

Vessel vibrations have serious safety risks and must be effectively mitigated. This study investigates the reduction in ship pitch–roll vibrations modeled as a two degrees of freedom of nonlinear spring–pendulum system subjected to multi-parametric excitation, using proportional–derivative controller. The main objective is to develop a rapid and efficient analytical approach to nonlinear vibration analysis. A non-perturbative approach is employed to transform weakly nonlinear oscillators of ordinary differential equations into equivalent linear ones without using Taylor expansions. He’s frequency formula plays a central role in this transformation. The resulting parametric solutions are validated using Mathematica Software (v13) and show a strong agreement with the original nonlinear model. The effects of various parameters on stability are examined. Theoretical analysis is conducted using the multiple time scales method to identify worst resonance conditions and derive frequency response equations. Stability near simultaneous sub-harmonic resonance is assessed using Routh–Hurwitz criterion. Numerical simulations based on the fourth-order Runge–Kutta method confirm the effectiveness of proportional–derivative control. Excellent agreement between analytical and numerical results demonstrates the accuracy, efficiency, and practical applicability of the proposed method. Full article

The unchecked and uncontrolled global spread of multidrug-resistant (MDR) bacteria is a serious challenge to healthcare and modern medicine, and demands diagnostic approaches that are rapid, sensitive, multiplexed, and reliable in point-of-care (POC) settings. At the interface of nanomaterials and aptamer-based biosensing, significant advances have been reported. The convergence of portable electrochemical sensing technologies, smartphone-based readout systems, and artificial intelligence (AI)- and machine learning (ML)-based data analysis is also playing a significant role in advancing this area. This perspective reflects on the most recent breakthroughs and translational developments in electrochemical nano-aptasensors for MDR bacterial detection, covering diagnostic targets and translation trends, nanomaterials advancements, aptamer engineering-integration, POC strategies and microfluidics platforms, and novel multimodal strategies that enhance accuracy, reliability, and efficiency in POC testing. Moreover, limitations and knowledge gaps in this area, as well as key considerations for sustainable development, large-scale manufacturing, and deployment of integrated electrochemical nano-aptasensors, are also highlighted. Electrochemical nano-aptasensors can pave the way for the transformation of MDR bacterial diagnosis, but need coordinated translational efforts for POC diagnostic advancements towards real-world applications. Full article

Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in Kunming by creating a well-developed evaluation framework with reference to the DPSIR (Driving Force–Pressure–State–Impact–Response) model and using the Entropy Weight TOPSIS technique to measure infrastructure performance during the years 2020–2024. The study fills an existing gap in the literature regarding the integration of intelligence and resilience evaluation, as well as the dynamic obstacle diagnosis based on causal logic. It provides a transferable analytical framework and empirical evidence for the “smart-resilience” development of similar cities. The findings suggest that there is steady progress in infrastructure smart-resilience in Kunming, whereby the composite index grew from 0.330 to 0.597, which is equivalent to an average growth rate of about 16.0 per annum. In spite of this favorable tendency, there are a number of structural issues that remain unsolved. The driving force dimension is unstable with regard to long-term mechanisms of investment, and the responding dimension is lagging behind, indicating weaknesses in the governance capacity and inter-departmental coordination. Moreover, extreme weather events have become the major threat to infrastructure systems in the city, superseding traditional social and operational risks; consequently, the city has changed its risk profile. Obstacle factor analysis shows that state and response dimensions make up almost 60% of the total constraint level, which shows the significance of enhancing the effectiveness of management. The research findings are based on the proposal of specific policy actions, such as the creation of special infrastructure resilience funds, the enhancement of mechanisms relating to cross-departmental emergency responses, the implementation of risk-based engineering standards, and the creation of an integrated infrastructure data platform to facilitate efficient, resilient, and sustainable urban governance. Full article

The development of sustainable and environmentally benign N-methylation methodologies is essential for enhancing sustainable synthetic practice in pharmaceutical manufacturing. In this study, we demonstrate that microwave heating (MWH) markedly enhanced the efficiency of cobalt-catalyzed N-methylation using methanol or methanol-d₄ as green C1 sources. Compared with conventional heating (CH), MWH enabled highly efficient syntheses of key pharmaceutical intermediates—including 6-dimethylamino-1-hexanol, imipramine hydrochloride, and butenafine hydrochloride—under milder conditions and shorter reaction times and without generating hazardous halogen-containing waste. UV–vis spectroscopic analysis revealed that MWH accelerated the transformation of Co(acac)₂ into catalytically active Co species by approximately four-fold, providing a mechanistic basis for the enhanced reactivity. We hypothesized that this effect was caused by the selective microwave heating of the catalyst, which in turn promoted the rapid generation of catalytically active species. Notably, MWH also significantly improved the N-trideuteromethylation of amines using methanol-d₄, achieving a 95% yield for imipramine-d₃ hydrochloride versus 32% under CH. Molecular dynamics simulations indicated that methanol-d₄ exhibited slower dipole relaxation and enhanced cluster fragmentation under microwave fields, improving catalyst–substrate contact, while kinetic isotope effects stabilized reactive intermediates. These synergistic effects account for the pronounced microwave promotion observed in deuterated systems. Overall, the combination of MWH and cobalt catalysis offers an energy-efficient, waste-minimizing, and environmentally benign strategy for the scalable synthesis of both methylated and deuterated amines. Full article

Supersulfated cement (SSC) is an environmentally friendly cementitious material with a low clinker content, in which industrial byproduct gypsum serves as the sulfate source, thereby enabling the valorization of solid waste. The hydration process, pore structure, microstructure, and hydration products were investigated using paste samples by means of isothermal calorimetry, X-ray diffraction (XRD), thermogravimetric analysis (TG–DTG), Fourier transform–infrared spectroscopy (FT-IR), mercury intrusion porosimetry (MIP), and scanning electron microscopy (SEM), while compressive strength was evaluated using mortar specimens. Compared with ordinary Portland cement (OPC), SSC offers clear advantages in reducing energy consumption and greenhouse gas emissions. In this study, the effects of titanium gypsum (TG) and flue gas desulfurization gypsum (FGD) on the hydration behavior, fluidity, mechanical properties, and microstructural evolution of an anhydrite (AH)–phosphogypsum (PG)-based SSC were systematically investigated. The results indicate that the incorporation of 11% TG and FGD mitigates the strong sulfate environment caused by the rapid dissolution of soluble AH, thereby regulating the hydration process. As the proportion of TG and FGD increased, the cumulative heat release within 72 h gradually decreased. When AH was completely replaced, the cumulative heat release of TG4 and FG4 decreased by approximately 19.7% and 28.6%, respectively. TG and FGD exhibited opposite effects on the fluidity of SSC while both promoting strength development. Among all mixtures, TG2 and FG2 showed the best performance, with the highest 28-day compressive strengths of 50.15 MPa and 51.95 MPa, respectively. Microstructural analysis reveals that differences in particle size distribution and dissolution kinetics among gypsums governed the sulfate release characteristics and slag activation mechanisms, thus leading to distinct hydration pathways, pore structure evolution, and microstructural densification. This study provides a theoretical basis for the efficient utilization of various industrial byproduct gypsums and offers important guidance for the controllable design of SSC performance. Full article

As a key low-expansion material for high-end equipment such as aerospace and precision instruments, the surface quality of Invar alloy directly determines the operational performance of devices. To fill the research gap in the multi-parameter synergy and mechanism of Invar alloy laser polishing, this study performs polishing experiments on Invar alloy using a burst-mode femtosecond laser, with a repetition rate of 1 MHz and four sub-pulses per burst. The results indicate that energy density plays a dominant role in the polishing effect: with the increase in energy density, the surface roughness first decreases and then increases. A stable molten pool is formed under medium energy density (0.47–0.64 J/cm²), and under the optimal parameter conditions, the surface roughness is reduced to 394 ± 50 nm, representing a 52% reduction compared to the original surface (821 nm). Scanning speed and scanning pitch affect the polishing effect by synergistically regulating energy input: increasing scanning speed under high energy density can inhibit the rise in roughness, while a small scanning pitch can lower the threshold of optimal energy density. Amplitude spectrum analysis reveals that the medium-scale surface undulations are significantly improved after polishing. A four-layer Fully Connected Neural Network (FCNN) model is established to achieve high-precision prediction of polishing effects with a coefficient of determination R² = 0.92, which enables rapid prediction of unknown polishing parameter combinations and provides a new solution path for the optimization of polishing effects. This study clarifies the interaction mechanism between a burst-mode laser and Invar alloy, proposes an efficient ultra-precision polishing method for Invar alloy, and lays a theoretical foundation for its application in the field of high-end manufacturing. Full article

Accurate and efficient analysis of embankment seepage is of vital importance for scientific assessment of embankment safety. Conventional numerical simulation techniques for embankment seepage analysis suffer from high computational cost and low efficiency. To address this issue, this paper proposes an embankment seepage response analysis method based on physical information neural network (PINN). Initially, this method considering the fluid–solid coupling and spatial variability of soil parameters of the embankment. Consequently, a numerical simulation method was developed using the finite difference method to analyze the seepage response. On this basis, a neural network loss function for the surrogate model is introduced by integrating the governing equations for fluid–solid coupling of embankments with boundary conditions. This integration incorporates physical restrictions into the seepage analysis, hence improving its interpretability. Furthermore, a feature sequence is derived from the soil parameter field via a Variational Autoencoder (VAE) to diminish input dimensionality and improve training accuracy. The feature sequence and hydraulic loading function as the model input, while the output is the piezometric head obtained from the pore water pressure. The PINN model is trained by numerical simulation results to establish the surrogate model for seepage responses analysis. A case study on the practical embankment engineering is employed to confirm the feasibility and efficacy of the proposed strategy. Comparative tests demonstrate that the PINN surrogate model markedly enhances computational accuracy relative to conventional baseline models. Overall, this approach offers a trustworthy and effective method for rapid and accurate assessment of embankment seepage characteristics. Full article

Microalgae are a promising third-generation biomass resource due to their high photosynthetic efficiency, rapid growth rates, capacity to accumulate energy-rich biochemical fractions, and efficient utilisation of carbon dioxide (CO₂). In this study, the effect of illumination colour on the growth and energetic properties of Chlorella vulgaris cultivated in laboratory-scale photobioreactors was investigated. Four independent cultivation cycles were conducted under controlled conditions using a 16 h light/8 h dark photoperiod, temperatures of 20–30 °C, and aeration with air enriched with 10% CO₂. Cultures were illuminated using six light colours: plant-specific, white, green, red, blue, and ultraviolet. Biomass productivity was quantified, and the higher heating value (HHV) of the produced biomass was determined by bomb calorimetry. In addition, proximate (technical) analysis was performed for Chlorella vulgaris and compared with Chlorella pyrenoidosa, Spirulina, and Fucus vesiculosus (bladderwrack). The results showed that white illumination promoted both the highest biomass growth and the highest HHV for Chlorella vulgaris (15.08 MJ·kg⁻¹), while ultraviolet illumination had a disruptive effect, leading to the lowest growth and calorific value (11.49 MJ·kg⁻¹). Comparative analysis revealed that Chlorella pyrenoidosa exhibited the most favourable energetic properties; however, Chlorella vulgaris remains attractive for cultivation due to its robustness and broad tolerance to operating conditions. Full article

To achieve efficient recognition of hollows in building external walls, this study adopts infrared thermography to construct a dedicated dataset and focuses on comparing the performance of three instance segmentation models: Mask R-CNN, YOLACT, and YOLOv8. Experimental results indicate that YOLACT is suitable for on-site rapid screening balancing accuracy and speed, YOLOv8 is more applicable to detection tasks requiring strict control over missed detections and accurate restoration of complex boundaries, while Mask R-CNN is better suited for non-real-time static image analysis. To further improve model performance, this paper introduces the position-sensitive attention (PSA) mechanism to YOLOv8 and trains the modified model. The improved model has achieved significant enhancements in various performance metrics. This study provides a reference scheme for the automatic detection of hollow defects and offers a basis for model selection under different application scenarios. 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

This paper proposes an innovative research algorithm “measurement—pattern—driving force—synergy” that determines the eco-efficiency of 83 Russian federal subjects (2000–2019) using the Slacks-Based Measure (SBM) model with non-desired outputs (incorporating comprehensive input indicators such as water resources and electricity input, and dual non-desired outputs of waste gas and wastewater). Combined with hot spot analysis, a gravity center model, and panel Tobit regression, we reveal the temporal-spatial evolution and driving mechanisms of eco-efficiency in resource-based economies. The research finds that the overall eco-efficiency of Russia is at a medium level and shows a dynamic correlation with the economic development stage. In the early stage of the period under review, there was a high degree of synergy, but the efficiency declined during the period of rapid economic growth. Later, it rebounded somewhat in tie with technological progress. Spatially, it presents a special pattern of low efficiency in the western European industrialized regions and high efficiency in the Arctic and Far East peripheral regions, reflecting the spatial heterogeneity of resource-dependent economies and the survival-constrained efficiency feature. The analysis of influencing factors indicates that per capita GDP has a significant positive driving effect on eco-efficiency, but the expansion of residents’ consumption, the improvement of education level and the dependence on foreign trade all have inhibitory effects, highlighting the path dependence of the current growth model on the structure of resource consumption. The research suggests that Russia should implement differentiated spatial governance in the future, promote the green transformation of consumption and trade structures, and strengthen the ecological orientation of the education and scientific research system to achieve a fundamental transformation of regional sustainable development from survival constraints to innovation-driven. Full article

To reduce the computational burden of 3D finite element analysis for hybrid-flux high-speed switched reluctance motors (HFHSRMs), a quasi-3D parameterized equivalent magnetic network (EMN) is proposed. A parameterized radial–circumferential cross-grid is used to discretize the stator, air-gap, and rotor regions, and axial coupling branches are introduced to represent key 3D flux paths. Rotor rotation and rotor dislocation are implemented through a circumferential node-shift mapping, thereby avoiding topology reconstruction at different rotor positions. Core nonlinearity is incorporated using a piecewise fit of measured B–H data, and sparse-matrix assembly is adopted to improve solution efficiency. Based on the proposed EMN, key electromagnetic quantities are evaluated, including air-gap flux density, static characteristics, and dynamic characteristics. The results are validated against 3D finite element method (FEM) and prototype experiments. In the prototype experiments, the EMN prediction errors of key quantities are within 6%. In addition, computational efficiency is significantly improved compared with the 3D FEM, enabling rapid parameter iteration and early-stage design evaluation for HFHSRMs. Full article