Search Results (2,456)

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

Background: Chronic pain affects nearly one in five adults worldwide and is increasingly recognized not only as a disease but as a potential risk factor for neurocognitive decline and dementia. While some evidence supports this association, existing systematic reviews are static and rapidly outdated, and none have leveraged advanced methods for continuous updating and robust uncertainty modeling. Objective: This protocol describes a living systematic review with dose–response Bayesian meta-analysis, enhanced by artificial intelligence (AI) tools, to synthesize and maintain up-to-date evidence on the prospective association between any type of chronic pain and subsequent cognitive decline. Methods: We will systematically search PubMed, Embase, Web of Science, and preprint servers for prospective cohort studies evaluating chronic pain as an exposure and cognitive decline as an outcome. Screening will be semi-automated using natural language processing models (ASReview), with human oversight for quality control. Bayesian hierarchical meta-analysis will estimate pooled effect sizes and accommodate between-study heterogeneity. Meta-regression will explore study-level moderators such as pain type, severity, and cognitive domain assessed. If data permit, a dose–response meta-analysis will be conducted. Living updates will occur biannually using AI-enhanced workflows, with results transparently disseminated through preprints and peer-reviewed updates. Results: This is a protocol; results will be disseminated in future reports. Conclusions: This living Bayesian systematic review aims to provide continuously updated, methodologically rigorous evidence on the link between chronic pain and cognitive decline. The approach integrates innovative AI tools and advanced meta-analytic methods, offering a template for future living evidence syntheses in neurology and pain research. Full article

Static mixers are commonly used for process intensification in a wide range of industrial applications. For the design and selection of a static mixer, an accurate prediction of the hydraulic performance, particularly the pressure drop, is essential. This experimental study examines the pressure drop for turbulent single-phase and gas–liquid two-phase flows through a Komax triple-action static mixer placed on a horizontal pipeline. New values of friction factor and z-factor are reported for fully turbulent liquid single-phase flow (11,700 ≤ Re_L ≤ 18,700). For two-phase flow, the pressure drop for stratified and intermittent flows (0.07 m/s ≤ U_L ≤ 0.28 m/s and 0.46 m/s ≤ U_G ≤ 3.05 m/s) is modeled using the Lockhart–Martinelli approach, with a coefficient, C, correlated to the homogenous void fraction. Conversely, the analysis of power dissipation reveals a dependence on both liquid and gas superficial velocities. For conditions corresponding to intermittent flow upstream of the mixer, flow visualization revealed the emergence of a swirling flow in the Komax static mixer. It is interesting to note that an increase in slug frequency leads to an increase, followed by stabilization of the pressure drop. The results offer valuable insights for improving the design and optimization of Komax static mixers operating under single-phase and two-phase flow conditions. In particular, the reported correlations can serve as practical tools for predicting hydraulic losses during the design and scale-up. Moreover, the observed influence of the slug frequency on the pressure drop provides guidance for selecting operating conditions that minimize energy consumption while ensuring efficient mixing. Full article

Rainfall-induced landslide deformation is governed by both intrinsic geological conditions and external dynamic triggers. However, many existing predictive models rely primarily on rainfall inputs, which limits their interpretability and robustness. To address these shortcomings, this study introduces a group-based data augmentation method informed by displacement curve morphology and proposes a multi-slope predictive framework that integrates static geological attributes with dynamic triggering factors. Using monitoring data from 274 sites across China, the framework was implemented with a Temporal Fusion Transformer (TFT) and benchmarked against baseline models, including SVR, XGBoost, and LSTM models. The results demonstrate that group-based augmentation enhances the stability and accuracy of predictions, while the integrated dynamic–static TFT framework delivers superior accuracy and improved interpretability. Statistical significance testing further confirms consistent performance improvements across all groups. Collectively, these findings highlight the framework’s effectiveness for short-term landslide forecasting and underscore its potential to advance early warning systems. Full article

The biomechanical performance of dental implants is affected by both abutment height and bone quality, which influence stress distribution around the implant and the preservation of surrounding bone. This study used three-dimensional finite element analysis (FEA) to assess the combined effects of these factors. Two implants with abutment heights of 3 mm and 6 mm were modeled and placed in mandibular bone blocks representing class II and class IV bone, according to Lekholm and Zarb’s classification. A static load of 150 N, inclined at 6° buccolingually, was applied during the analysis. The simulation results showed that increasing the abutment height raises stress on the implant, leading to greater stress transfer to the peri-implant bone. The von Mises stress levels were higher in the crestal cortical bone of the class IV model with a 6 mm abutment (126 MPa). Notably, peak stresses exceeding 300 MPa were localized at the implant-abutment connection. These findings suggest that abutment height is a critical factor that negatively affects the biomechanical response, especially in low-density bone, although longer abutments offer biological benefits. This highlights the importance of minimizing the crown-to-implant ratio to reduce overload, preserve bone, and prevent mechanical failure complications. Full article

Dental caries, a prevalent biofilm-mediated chronic disease, causes enamel demineralization, pulp infection, and systemic complications. Dental plaque biofilm is the initiating factor for the occurrence and development of caries. Streptococcus mutans is an opportunistic pathogen linked to the structure and ecology of dental plaque biofilms. The molecular mechanism of S. mutans during biofilm ontogeny in driving cariogenesis has been extensively elucidated. Here, we observed that asiatic acid is a potent biofilm disruptor that selectively dismantles S. mutans biofilm architectures, prompting us to investigate its mechanism. The minimum biofilm inhibition concentration (MBIC) of asiatic acid on S. mutans was 62.5 μM, but the MBIC level did not substantially impede planktonic growth. Using the static active-attachment model, it was demonstrated that asiatic acid significantly reduced biofilm biomass (p < 0.001) and extracellular polysaccharides (EPS) content (p < 0.001), while concurrently diminishing acid production (p = 0.017) and metabolic activity (p = 0.014). Confocal and scanning electron microscopy further confirmed structural disintegration, including bacterial detachment and reduced biofilm thickness. Transcriptome analysis of S. mutans biofilm treated with asiatic acid revealed 454 differentially expressed genes (adjusted p < 0.05, |log₂FC| ≥ 1). Notably, genes related to the CiaRH two-component system (ciaR, ciaH), a central regulatory hub for biofilm maturation and acid tolerance. This disruption initiates a downstream cascade, causing a coordinated downregulation of critical gene clusters essential for virulence and pathogenesis, including stress response (htrA, clpP, groEL, dnaK), and the glucan-binding protein gene (gbpC) essential for biofilm structural integrity. These findings provide the first mechanistic evidence linking asiatic acid to transcriptional reprogramming in S. mutans biofilm, offering a novel ecological strategy for caries prevention by targeting key regulatory pathways. Full article

This study examines the mechanical properties of concrete in which natural aggregates are entirely replaced by modified basic oxygen furnace slag (MBOFS) and reinforced with chopped carbon fibers, under both dynamic and quasi-static loading conditions. The carbon fiber (CF) was subjected to heat treatment and pneumatic dispersion prior to mixing, and its performance was validated using thermogravimetric analysis (TGA) and single-fiber tensile tests. The experimental program included tests on workability, compressive strength, flexural strength, splitting tensile strength, impact resistance, and high strain rate behavior using the reverse split Hopkinson pressure bar (RSHPB) method. Thermogravimetric analysis (TGA) and scanning electron microscope (SEM) confirmed that heat treatment removed surface sizing from carbon fibers (CF) with minimal effect on tensile strength. Replacing natural aggregates with MBOFS reduced slump but enhanced compressive, flexural, and splitting tensile strength. Incorporating 1% chopped CF further improved mechanical performance: 6 mm CF increased compressive strength, while 12 mm CF enhanced flexural and splitting tensile strength. Impact resistance improved with CF addition, with 12 mm CF slightly outperforming 6 mm. RSHPB tests showed higher dynamic strength for 6 mm CF specimens, with both strength and dynamic increase factor rising with strain rate and gas pressure. Full article

In the context of the global transition toward carbon neutrality and the circular economy, remanufacturing has emerged as a vital strategy for enhancing resource efficiency and reducing environmental impact. However, the remanufacturing sector faces significant uncertainties—including fluctuations in market demand, variability in the quality of returned products, and dynamic policy changes. These factors collectively challenge production decision-making and system sustainability. Following the preferred peporting items for systematic reviews and meta-analyses (PRISMA) guidelines, this study conducted a systematic review and bibliometric analysis of 98 core articles published between 2015 and 2024, with a focused examination of three interdisciplinary themes: (1) decision-making and optimization under uncertainty, (2) supply chain coordination and policy mechanisms, and (3) digital transformation and the application of emerging technologies. A novel micro–meso–macro analytical framework is proposed to integrate fragmented findings. The results highlight a paradigm shift from static models to dynamic, real-time decision-making systems, facilitated by digital twins (DTs), blockchain, and intelligent algorithms. Furthermore, the study identifies the synergistic effects of carbon-financial instruments and policy incentives in aligning economic and environmental objectives. This research develops a systematic framework to understand and address uncertainties in remanufacturing, offering policymakers and industry practitioners actionable insights to enhance the resilience, sustainability, and global applicability of remanufacturing systems. Full article

This paper provides an overview of various size-dependent theories based on modified/consistent couple stress and strain gradient theories (CSTs and SGTs), highlighting the development of two-dimensional (2D) refined and advanced shear deformation theories (SDTs) and three-dimensional (3D) pure analytical and semi-analytical numerical methods, including their applications, for analyzing the static and dynamic behaviors of microscale plates and shells made from advanced materials such as fiber-reinforced composites, functionally graded (FG) materials, and carbon nanotube/graphene platelet-reinforced composite materials. The strong and weak formulations of the 3D consistent CST, along with their corresponding boundary conditions for FG microplates, are derived and presented for illustration. A comparison study is provided to show the differences in the results of a simply supported FG microplate’s central deflection, stress, and lowest natural frequency obtained using various 2D size-dependent SDTs and 3D analytical and numerical methods based on the consistent CST. A parametric study is conducted to examine how primary factors, such as the effects of dilatational and deviatoric strain gradients and couple stress, impact the static bending and free vibration behaviors of a simply supported FG microplate using a size-dependent local Petrov–Galerkin meshless method based on the consistent SGT. Influences such as the inhomogeneity index and length-to-thickness ratio are considered. It is shown that the significance of the impact of various material length-scale parameters on the central deflection and its lowest natural frequency (in the flexural mode) of the FG microplate is ranked, from greatest to least, as follows: the couple stress effect, the deviatoric strain gradient effect, and finally the dilatational strain gradient effect. Additionally, when the microplate’s thickness is less than 10⁻⁷ m, the couple stress effect on its static and dynamic behaviors becomes saturated. Conversely, the impact of the dilatational and deviatoric strain gradients consistently influences the microplate’s static and dynamic behaviors. Full article

This study evaluates China’s civil aviation total factor productivity (TFP) in 2000–2021, treating carbon emissions as undesirable output via the Global Malmquist–Luenberger (GML) index, with a modified carbon emission estimation coefficient to boost calculation accuracy. Static and dynamic time series regression models analyze short/long-term effects of influencing factors. Results show civil aviation TFP grows ~0.3% annually, stable with cyclical fluctuations, and exhibits dynamic adjustment and mean reversion. Market demand/competition significantly positively affect TFP in both models; technological innovation and capital investment have substantial lagged effects (not significant in the static model). The contributions include three aspects. First, methodological innovation through a revised carbon emission coefficient. Second, theoretical expansion by combining static and dynamic models. Third, macro policy guidance for balancing economic-environmental performance under China’s dual carbon strategy. Full article

Fuzzing has established itself as a cornerstone technique for uncovering defects in both stand-alone executables and software libraries. In the domain of library testing, prior research has predominantly concentrated on the automated generation of fuzz drivers-code harnesses that invoke individual Application Programming Interfaces (APIs) under test. While these approaches successfully orchestrate API calls in the correct sequence, they often neglect a critical factor: the semantic relevance and structural validity of the input data supplied to each API parameter. Unlike monolithic programs, where inputs are typically drawn from well-defined file or network formats, API parameters may span a broad spectrum of primitive and composite data types-ranging from integers and floating-point values to strings, containers, and user-defined aggregates—each of which demands tailored mutation strategies to exercise deep code paths and trigger latent faults. To address this gap, we introduce PointFuzz, a novel fuzzing framework that integrates type-aware input generation into existing harness generation pipelines. PointFuzz begins by statically analyzing the API’s function signatures and associated type definitions to accurately identify the data type of every parameter. It then applies a suite of specialized mutation operators. This data-type-guided mutation maximizes the likelihood of traversing previously untested execution branches. Moreover, PointFuzz incorporates an innovative feedback mechanism that dynamically adjusts mutation priorities based on real-time coverage gains. By assigning quantitative scores to parameter-specific operators, our system continuously learns which strategies yield the most valuable inputs, and reallocates computational effort accordingly. Empirical evaluation across multiple widely used C/C++ libraries demonstrates that PointFuzz achieves superior API coverage compared to generic, agnostic-type fuzzers. These results validate the efficacy of combining type-aware mutation with adaptive feedback to advance the state of library API fuzzing. Full article

This work is framed as an application of static and small exponential random graph models for complex networks in multiple layers. This document revisits the small network and exhibits its potential. Examining the bibliography reveals considerable interest in large and dynamic complex networks. This research examines the application of small networks (50,000 population) for analyzing global commerce, conducting a comparative graph structure of the tariffs, and importing multilayer networks. The authors created and described the scenario where the readers can compare the graph models visually, at a glance. The proposed methodology represents a significant contribution, providing detailed descriptions and instructions, thereby ensuring the operational effectiveness of the application. The method is organized into five distinct blocks (Bn) and an accompanying appendix containing reproduction notes. Each block encompasses a primary task and associated sub-tasks, articulated through a hierarchical series of steps. The most challenging mathematical aspects of a small network analysis pertain to modeling and sample selection (sel_p). This document describes several modeling tasks that confirm that sel_p = 10 is the best option, including modeling the edges and the convergence and covariance model parameters, modeling the node factor by vertex names, Pearson residual distributions, goodness of fit, and more. This method establishes a foundation for addressing the intricate questions derived from the established hypotheses. It provides eight model specifications and a detailed description. Given the scope of this investigation, a historical examination of the relationships between different network actors is deemed essential, providing context for the study of actors engaged in global trade. Various analytical perspectives (six), encompassing degree analyses, diameter and edges, hubs and authority, co-citation and cliques in mutual and collapse approaches, k-core, and clustering, facilitate the identification of the specific roles played by actors within the importation network in comparison to the tariff network. This study focuses on the Latin American and Caribbean region. Full article

Urban heat exposure, which intensifies with climate change, poses serious threats to public health in rapidly growing cities. Traditional assessments rely on static land surface temperature, often overlooking the role of human mobility in exposure frequency. This study introduces a travel-related heat exposure index (THEI) that combines ride-hailing trajectories and remote sensing data to capture dynamic human–environment thermal interactions. Using Chengdu, China, as a case study, the THEI is combined with local indicators of spatial association to outline high-exposure risk zones (HERZ). XGBoost with SHAP and partial dependence plot (PDP) methods is also applied to identify the nonlinear effects of built environment factors. Results showed the following: (1) distinct spatial clustering of high travel-related heat exposure in central urban districts and transit hubs; (2) city-wide exposure is primarily driven by transportation accessibility and urban form, such as intersection density and floor area ratio; (3) in contrast, HERZ are more strongly associated with demographic and socioeconomic factors, including population density, housing price and road density; and (4) vegetation, measured by the normalized difference vegetation index, demonstrates a consistent negative effect across scales, highlighting its critical role in mitigating thermal risks. These findings emphasize the necessity of incorporating mobility-based exposure metrics and spatial heterogeneity into climate-resilient urban planning, with differentiated strategies tailored for city-wide versus high-risk zones. Full article

By critically reviewing pseudo-static methods, it is demonstrated that approximating the earth pressure on a short heel’s vertical face (V-plane) using the Rankine solution for long-heel walls induces a negligible error. A finite element analysis is deployed to validate the pseudo-static results, with dynamic simulations incorporating 1–5 Hz sinusoidal seismic excitations to probe the resonance effects. The key results show that disregarding the impact of layered backfill placement on the initial stress states leads to non-conservative estimates of active earth pressure. Furthermore, the point of application of earth pressure rises significantly during strong shaking, and although the transient safety factors against sliding and overturning may fall below 1.0 during seismic events, the residual deformation analysis suggests that this does not necessarily lead to collapse. A significant amplification of bending moments and greater reductions in post-earthquake safety factors occur when the input frequency approaches the natural frequency of a wall. Finally, the paper proposes resonance prevention strategies for the seismic design of cantilever retaining walls, a methodology incorporating construction effects into the initial stress field modeling, and recommendations for selecting effective safety factors. Full article

The implementation and application of a safety factor (SF)-based numerical framework in FLAC3D-IMASS (Itasca Model for Advanced Strain Softening) is presented for the evaluation of the short-term stability of unsupported underground excavations in sedimentary rock masses during pillar recovery in bord-and-pillar mining. The stability of underground openings during the initial hours post-excavation must be ensured, as they are not accessed thereafter; therefore, short-term stability assessment is essential. The framework was specifically calibrated to field observations and applied to a case study from an Australian bord-and-pillar mine, focusing on plunge and bellout configurations commonly used during the pillar extraction stage to enhance ore recovery. The modelling approach was integrated with rock mass degradation behavior under static loading conditions and was used to calculate three-dimensional distributions of SF to identify potential failure zones. The results demonstrate that the coal (CO) roof scenario generally maintains structural stability, while the impure coal (Cox) roof scenario is observed to exhibit significant instability, particularly at greater excavation advancement. Among the tested bellout geometries, 8.0 m spans were observed to provide improved performance due to shorter tunnel lengths that enhance confinement and reduce the volume of disturbed rock. Overall, the proposed SF framework effectively captures localized failure mechanisms and is demonstrated as a practical design tool for assessing the short-term stability of unsupported structures during critical stages of underground mining operations. Full article