Search Results (337)

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

Droplet microfluidics has been widely used in biological, chemical, and medical research owing to its advantages of miniaturization, high throughput, and low reagent consumption. However, limited sensitivity and optical path length in on-chip absorbance detection remain major challenges for droplet-based microfluidic analysis. Traditional absorbance detection suffers from low sensitivity due to the extremely short optical path in microfluidic channels, while existing optical path extension methods have drawbacks such as complex fabrication, easy droplet rupture, or strict incident angle requirements. To address these issues, this study developed a droplet microfluidic absorbance detection platform integrating optical fibers, on-chip micromirrors, external fluidic actuation, and an absorbance detection module. Microchannel sidewalls filled with low-melting-point metal act as mirrors; the multi-reflection optical path, combined with optical fibers and micromirrors, compensates for insufficient light manipulation and effectively extends the absorption path length, improving sensitivity and accuracy. Using this method, the detection limit for methylene blue solution was 20 μM, and the sensitivity for Escherichia coli (E. coli) suspension was doubled compared with traditional Nanodrop OD600 measurement. This device features low fabrication difficulty and cost and stable detection, providing a proof-of-concept strategy for enhanced absorbance detection in droplet microfluidic systems. Full article

Magnetic drug targeting (MDT) is commonly evaluated by peak accumulation at the target site. Under pulsatile flow, however, initial deposition does not predict sustained localisation. We introduce the Magnetic Targeting Optimisation Concept (M-TOC), a safety-constrained framework that restructures MDT evaluation by separating geometric deposition from retention stability and embedding both within a defined hemodynamic safety window. Deposition (D) was quantified by using obstruction degree at the injection end, OD(T0), and restricted by a structural admissibility limit (OD_max = 40%). Retention stability (R) was quantified using early washout at T0 + 30 s and an apparent half-life (τ_1/2) derived from coverage decay under controlled pulsatile washout. These descriptors were integrated into a Unified Targeting Score (UTS), applied only within the admissible domain, thereby enforcing feasibility before optimisation. Three PEG-functionalised magnetoresponsive nanocluster formulations were evaluated under identical magnetic and flow conditions. D–R mapping identified distinct operating regimes and showed that no tested configuration simultaneously achieved admissible deposition and robust pulsatile stability. By formalising MDT as a constrained multi-objective problem, M-TOC provides an objective method for regime discrimination and a transferable design principle for stability-guided targeting under physiological flow. Full article

We analyze the spherically symmetric complex diffusion and special type of the complex reaction–diffusion equations. These equations are form invariant to the free Schrödinger equations and to the Schrödinger equations with power-law space-dependent potentials. Our new type of solutions are important because we found a new realm of solutions which lie between the solutions of the classical regular diffusion equation and the usual quantum mechanical solutions of the Schrödinger equation. As the solution method, we applied the the self-similar Ansatz, which reduces the original partial differential equation (PDE) to an ordinary differential equation (ODE) which can be solved analytically. The self-similar Ansatz couples the spatial and temporal variables together instead of the usual separation which has to be used in ordinary quantum mechanics for time-independent Hamiltonian. For the complex diffusion equation—without any additional source term—the solutions are the Kummer’s M and Kummer’s U functions. For some parameter values we found $L^2$ integrability, as in the Cartesian case. We interpret that this property can be a “quantum mechanical heritage” and can be a far relation to ordinary quantum mechanics. Therefore, in this sense, our solutions might have quantum mechanical interest in the future. For the complex reaction–diffusion-type equation we derived the Whittaker M and Whittaker W functions as solutions. These solutions have no $L^2$ integrability at all. All derived solutions have complex quadratic arguments. These kind of analytic solutions are new and cannot be found in the existing scientific literature. Finally, the role of the complex angular momentum was investigated as well. Full article

Defect detection in the hot rolling process is closely linked to the quality of the final product. Among these defects, slab camber during the intermediate rolling stage is one of the primary manifestations of asymmetry, which significantly impairs both the quality of the finished strip and the stability of subsequent rolling processes. Conventional image-based edge detection methods for slab camber are prone to detection deviations in complex industrial environments, mainly due to their weak noise robustness. To address the scientific challenge of low accuracy and poor robustness in feature extraction for hot-rolled intermediate slab camber detection, which is induced by environmental interference in complex industrial settings, we break through the technical bottlenecks of traditional edge detection methods and existing deep learning models in terms of channel–spatial feature collaborative optimization and anti-interference fusion of multi-scale features. We establish a dense perception network model integrated with a channel–spatial attention mechanism, realize robust feature recognition of slab edges under complex working conditions, and provide theoretical and technical support for the real-time quantitative detection of slab shape defects in the hot rolling process. The proposed model significantly improves detection accuracy and robustness through multi-scale feature enhancement and noise suppression, effectively meeting the requirements for real-time quantitative detection of slab camber in the roughing rolling stage. Field experiments verify that the method increases detection accuracy by 36.55% and achieves favorable performance on evaluation metrics, including ODS and OIS. Full article

Non-motorized mobility networks increasingly serve as critical infrastructure for sustainable regional development that integrates recreational, environmental, and transportation functions across diverse geographical contexts. To enhance the spatial planning efficiency and support evidence-based policy development, this study develops a Geographic Information Systems (GIS)-based analytical framework to evaluate the connectivity and accessibility of Korea’s integrated non-motorized mobility system. The model systematically maps 606 walking courses, 60 cycling routes, and 66 water activity sites nationwide, and examines their spatial relationships with major transportation hubs, including Korea Train e-Xpress (KTX) stations and airports within 20–30 km buffer zones. Using proximity analysis, connectivity mapping, and origin–destination (OD) cost matrix modeling, the framework identifies intermodal distance structures and spatial integration patterns. The analysis reveals a hybrid network configuration characterized by localized multimodal clustering alongside regional accessibility gaps, with urban–coastal regions demonstrating stronger connectivity than inland–rural areas. This study proposes a data-driven Korean mobility network framework that integrates walking, cycling, and water routes with the existing transportation infrastructure. These findings demonstrate how GIS-based tools can support evidence-based sustainable mobility policies and regional tourism planning on a national scale. Full article

Background/Objectives: Staphylococcus epidermidis (RP62A) and Staphylococcus aureus (UAMS-1) are clinically relevant pathogens frequently implicated in implant-associated infections due to their ability to form biofilms. RP62A is typically linked to persistent, chronic, low-grade infections, whereas UAMS-1 is associated with acute, invasive disease. Both strains serve as representative models for chronic and acute periprosthetic joint infections (PJIs). The objective of this study was to examine and compare in vitro biofilm formation by RP62A and UAMS-1 on orthopaedic materials/disc surfaces of defined composition. Methods: In vitro biofilm formation assays were performed using orthopaedic disc surfaces composed of cobalt–chromium alloy (CoCr), titanium alloy (Ti), and polyethylene (PE) after 72 h of incubation. Biofilm biomass was quantified using crystal violet staining, with absorbance measured at OD₅₇₀. A polystyrene (PS) surface served as a control. Additionally, retrieved orthopaedic explant components were used as substrates for in vitro biofilm assays, in which RP62A was incubated for 72 h on the explanted surfaces. Supporting assays on glass slides were conducted to examine strain-specific biofilm-related architecture. Results: In vitro biofilm mass quantification assays showed strong biofilm formation by RP62A across all tested surfaces, with the highest absorbance on CoCr (OD₅₇₀ = 5.80 ± 0.19). Notably, biofilm formation on CoCr was 76% higher compared to PS (p < 0.0001). No significant differences were observed among all three surface discs (p > 0.1). Biofilm formation was highest on PE for UAMS-1 (OD₅₇₀ = 1.29 ± 0.09) and was significantly greater than on Ti (178%, p < 0.001) and CoCr (196%, p < 0.0001). In the in vitro assays performed on retrieved explant components, RP62A showed pronounced biofilm accumulation on polyethylene tibial inserts, particularly in regions of mechanical wear and friction. Supporting assays on glass slides were performed to examine strain-specific surface microstructural, revealing dense network-like structures for RP62A and thinner, discontinuous layers for UAMS-1. Conclusions: RP62A formed dense biofilms in vitro on multiple orthopaedic implant materials and retrieved explant components, consistent with its association with chronic periprosthetic joint infections. Increased biofilm accumulation was observed on mechanically worn polyethylene surfaces. In contrast, UAMS-1 showed lower biofilm formation on metallic disc surfaces, indicating strain- and material-dependent differences. These findings highlight the relevance of implant material selection and surface integrity for strategies targeting biofilm-associated implant infections. Full article

University mobility in medium-sized cities faces increasing challenges arising from traffic congestion, urban sprawl, and the limited availability of sustainable transport options. In this context, the bicycle represents an efficient and environmentally low-impact alternative, provided that safe and connected infrastructure exists to facilitate its adoption. This study assesses the potential for bicycle use in the Andean city of Loja, Ecuador, taking as a case study the university community of the Universidad Técnica Particular de Loja (UTPL). Geographic Information Systems (GIS) tools, origin–destination (OD) matrices, and logistic models were integrated to analyze the relationship between three key variables: terrain slope, minimum travel time, and the percentage of protected cycling infrastructure. The results show that protected cycling infrastructure shows the strongest positive association with the modeled probability of use, while slopes greater than 15% and trips longer than twenty minutes are associated with lower modeled probabilities. The geospatial analysis identified priority corridors where improvements in cycling protection would yield higher modeled modal returns. It is concluded that strengthening cycling connectivity and the continuity of protected routes may inform scenario-based planning to support active university mobility, offering a replicable framework for medium-sized cities with similar topographic conditions. Full article

The integration of truck platooning and electrification presents a promising avenue for improving operational efficiency and environmental sustainability in freight transportation. Realizing the energy and cost saving as well as emission reduction benefits requires a holistic design of truck routing, scheduling, and platooning strategies that account for practical operational constraints. This study investigates the integrated planning problem of routing, scheduling, and platooning for a mixed fleet of conventional trucks (CTs) and electric trucks (ETs), referred to as mixed fleet truck platooning (MFTP) problem. The MFTP incorporates charging scheduling and key operational factors, such as platooning leader–follower positioning under the battery constraints of ETs, charging station availability and capacity, and the positional configuration of trucks within a platoon. The objective is to minimize the total operation cost of the MFTP system, including charging cost, fuel cost, travel labor cost, charging labor cost, and platoon formation labor cost, while ensuring timely arrivals across multiple origin–destination (OD) pairs. The proposed MFTP is formulated as a novel mixed-integer linear program (MILP). Extensive numerical experiments on the simplified Illinois interstate highway network are conducted to examine the effectiveness and efficiency of the proposed model. Numerical results show that incorporating platooning reduces the total operational cost by 7.6% relative to the non-platooning scenario. The findings also shed some light on planning mixed fleets of CTs and ETs with platooning, offering valuable managerial insights for decision-makers. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

This paper introduces a new explicit integration method for second-order ordinary differential equations (ODEs) commonly encountered in engineering applications. Traditionally, these problems are solved either by reformulating them as first-order systems to apply one-step methods such as Runge–Kutta schemes, or by using direct second-order approaches widely adopted in linear dynamics, including the generalized-α, central difference, and Newmark methods. The proposed method is derived from a Taylor series expansion truncated at the third derivative, resulting in a fully explicit algorithm that requires only one function evaluation per time step. Similar to Newmark’s formulation, it includes adjustable parameters that allow the user to balance accuracy and stability. For a specific parameter choice, the method exhibits convergence and stability properties comparable to those of the central difference scheme. An important advantage is that it remains explicit even when nonlinearities depend on first-derivative terms. The paper presents a theoretical analysis covering stability, local truncation error, spectral properties, numerical damping, and period elongation. The method is validated through four test cases from multibody dynamics, including linear and nonlinear problems. Results demonstrate that the Explicit Integration Grade 3 (EIG-3) method achieves accuracy comparable to existing explicit second-order integrators while significantly reducing computational cost, particularly in nonlinear applications. Full article

This study investigates the internal antecedents of Strategic Learning Capability (SLC) within volatile business environments. Specifically, it explores the tripartite relationship between occupational subculture, the cognitive process of sensemaking, and the multi-dimensional facets of SLC (external focus, strategic dialogue, engagement, etc.). The research aims to bridge the empirical gap regarding how bottom-up subcultural values influence a firm’s capacity to pivot and execute new strategies. The research adopts a multi-dimensional framework of SLC, integrating theories of occupational context with sensemaking theory. By distinguishing between top-down organizational culture and bottom-up occupational subcultures, the study utilizes a conceptual (or empirical—adjust if you have specific data) model to examine how localized rules and practices within specific functions (e.g., R&D vs. Operations) lead to varied strategic outcomes through the generation of meaning. The paper proposes that sensemaking serves as a critical “bridge” or mediating mechanism that translates localized subcultural values into systemic innovative behaviors. While organizational culture sets the general tone, the findings suggest that the specific occupational environment determines the depth of strategic engagement and reflective responsiveness. The results indicate that SLC is not a monolithic construct but is lived and enacted differently across various occupational silos within the same firm. Unlike previous studies that focus on top-down leadership as the primary driver of culture, this research highlights the “bottom-up” influence of occupational subcultures on strategic agility. By introducing sensemaking as a pre-decisional activity that connects subcultural identity to Strategic Learning Capability, the study provides a more nuanced, multi-level understanding of organizational learning that accounts for internal diversity rather than assuming cultural homogeneity. Managers and OD practitioners are provided with a framework to identify subcultural barriers to learning. The study suggests that to enhance SLC, leaders must move beyond uniform cultural initiatives and instead facilitate sensemaking processes that align diverse occupational identities with the broader strategic vision. Full article

Background: Polysaccharide-based dynamic hydrogels are promising for wound management due to their biocompatibility, injectability, and tunable biofunctionality. The integration of therapeutic gasotransmitter donors offers a strategy to modulate the wound microenvironment. Objectives: This study aimed to develop an injectable, self-healing carbohydrate hydrogel capable of sustained hydrogen sulfide (H₂S) release for burn wound therapy, and to evaluate its physicochemical properties, in vivo efficacy, and mechanism of action. Methods: A dynamic hydrogel (ACMOD) was fabricated via Schiff-base crosslinking between oxidized dextran (OD) and carboxymethyl chitosan (CMCS), incorporating the H₂S donor 5-(4-hydroxyphenyl)-3H-1,2-dithiole-3-thione (ADT-OH). Rheological and recovery tests characterized its mechanical and self-healing properties. Efficacy and mechanisms were assessed in a rat full-thickness burn model, analyzing wound closure, histology, oxidative stress, macrophage polarization, angiogenesis, and collagen deposition. Results: ACMOD exhibited shear-thinning, rapid self-healing, and strong tissue adherence. Sustained H₂S release from ACMOD significantly accelerated wound closure and improved tissue regeneration compared to controls. Mechanistically, H₂S attenuated oxidative stress, promoted a pro-regenerative M2 macrophage phenotype, enhanced angiogenesis via VEGF upregulation, and fostered organized collagen deposition and extracellular matrix remodeling. Conclusions: This work demonstrates a versatile, carbohydrate-based dynamic hydrogel platform that synergizes polymer network dynamics with bioactive H₂S delivery to effectively promote burn wound healing. The findings underscore the potential of polysaccharide hydrogels with integrated gasotransmitter release for regenerative therapy and biomaterials applications. Full article

Modeling ship maneuvering dynamics presents numerous challenges, including long-term multi-step recursive error accumulation, insufficient generalization under distributed control rates, and high-frequency disturbance amplification effects. Traditional analytical models heavily rely on vessel-specific trials to characterize strongly nonlinear coupling terms and perform parameter identification, making it difficult to balance efficiency and accuracy under complex operating conditions. This paper presents a ship maneuvering-oriented integration of an invertible Koopman representation and a NeuralODE-based continuous-time predictor. The IKN reconstructs strongly coupled state spaces while enhancing representational invertibility, whereas NeuralODE directly fits the control differential equations governing ship maneuvering dynamics and supports continuous-time prediction. Experiments validate multi-rate control performance under ideal and disturbed data conditions, assessing error accumulation and extrapolation stability through long-term multi-step propagation. Evaluations utilize the KVLCC2-type L7 ship model with a 0.25 s sampling interval and a 200 s prediction horizon, validated against a multi-rate control test set. The results indicate that, compared to the baseline neural ODEs model without IKN, the normalized root mean square error (NRMSE) of state quantities decreased by 12.68% on average. In typical operational scenarios such as constant-speed emergency turns and variable-speed sine sweep maneuvers, the average state NRMSE was 7.96% lower than the LSTM model and 53.85% lower than the IKN–Koopman operator network. Noise experiments demonstrated that when introducing simulated sensor noise at 5%, 10%, and 20% into the dataset, the average state NRMSE remained at 5.98%, 8.24%, and 10.06%, respectively. This confirms the method’s stable prediction performance under varying noise intensities. Full article

This study addresses the problem of optimal resource allocation in nonlinear multi-module dynamic systems arising in complex computational and techno-economic processes, where numerical stability and strict enforcement of structural constraints are critical. The objective is to develop a computationally efficient optimal control algorithm capable of handling bilateral control constraints and external balance conditions without resorting to large-scale nonlinear programming or boundary-value shooting. The proposed method is based on a modified Lagrangian formulation, in which bilateral Karush–Kuhn–Tucker (KKT) conditions are analytically embedded into the optimality system. The resulting computational scheme consists of a coupled system of matrix and vector differential equations solved through a non-iterative backward–forward integration procedure. Numerical experiments conducted on a nonlinear model with Cobb–Douglas-type operators demonstrate the stable convergence of the trajectories toward a stationary regime, strict satisfaction of bilateral constraints, and consistent enforcement of balance relations throughout the planning horizon. Empirical scalability analysis indicates approximately cubic computational complexity with respect to the state dimension, while sensitivity tests confirm the numerical robustness across different integration tolerances and ODE solvers. These results demonstrate that the proposed structure-preserving framework provides a computationally stable and practically implementable approach to constrained optimal control in nonlinear multi-module systems. Full article