Search Results (5,218)

Geostructures, such as foundations, embankments, retaining structures, bridge abutments, and both natural and engineered slopes, interact with the ground to ensure structural safety and functionality. One significant factor influencing these systems is climate, which continuously affects soil conditions through dynamic processes. Over the past century, climate change has intensified, increasing uncertainties regarding the safety of both existing and planned geostructures. While the impacts of climate change on geostructures are evident, effective methods to address them remain uncertain. This paper presents an approach for mitigating and adapting to climate change impacts through a stepwise geomechanical analysis and geotechnical design framework that incorporates expected climatic conditions. A novel framework is introduced that systematically integrates projected climate scenarios into geomechanical modeling, enabling climate-resilient design of geostructures. The concept establishes an interface between climate effects and geomechanical data, capturing the causal chain of climate hazards, their effects, and potential consequences. The proposed interface provides a practical tool for integrating climate considerations into geotechnical design, supporting adaptive and resilient infrastructure planning. The approach is demonstrated across different geostructure types, with a detailed slope stability analysis illustrating its implementation. Results show that the interface, reflecting processes such as water infiltration, soil hydraulic conductivity, and groundwater flow, is often critical to slope stability outcomes. Furthermore, slope stability can often be maintained through simple, timely implemented nature-based solutions (NbS), whereas delayed actions typically require more complex and costly interventions. Full article

The digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments. Full article

The shift toward sustainable packaging requires biodegradable, active alternatives. This study developed ternary nanohybrids by loading carvacrol (CV) or trans-cinnamaldehyde (tCN) onto zinc oxide/natural zeolite (ZnO/NZ) hybrids, which were incorporated into a poly-L-lactide/tri-ethyl citrate (PLA/TEC) matrix via melt extrusion to produce active films. A key finding was the distinct interaction mechanism: tCN underwent strong chemisorption with ZnO, creating a sustained-release reservoir, while CV was predominantly physisorbed, leading to rapid release. This interfacial divergence dictated functional performance. Antibacterial assessment of nanohybrids revealed that tCN@ZnO/NZ0.25 exhibited the highest inhibition zones against pathogens, correlating with its chemisorbed reservoir. In films, however, CV-based formulations (especially CV@ZnO/NZ0.25) showed superior immediate antioxidant activity (EC₅₀, ~DPPH~ = 34.43 mg/mL) and an 82% reduction in oxygen permeability. In contrast, tCN-based films (especially tCN@ZnO/NZ1.0) demonstrated superior, sustained antibacterial efficacy. In a minced pork preservation study, both films delayed lipid oxidation and preserved heme iron, while the tCN-based film provided better long-term microbial control. This work demonstrates that engineering the nanocarrier–active compound interface enables precise tailoring of release kinetics, which can be optimized for either high immediate antioxidant power or long-term antimicrobial action, depending on specific food preservation requirements. Full article

Silica spicules provide a natural transdermal conduit but require a linker that binds strongly under physiological conditions and releases payloads selectively in response to biological cues. Existing silane chemistries or polydopamine coatings lack enzyme responsiveness and show limited control over release. We created a 180-member peptide library with the motif L–X1–X2–[Y–F–Y]–A–L–G–P–H–C and screened for silica binding. Biophysical assays (circular dichroism, ζ-potential, quartz crystal microbalance, atomic force microscopy) and molecular dynamics identified high-affinity binders. The lead, P176, was tested for matrix metalloprotease (MMP)-responsive cleavage. Conjugation and release of Vitamin C and Stigmasterol were analyzed by HPLC and Franz diffusion cells. P176 showed high silica affinity (~55 µg mg⁻¹), robust biophysical signals (Δf −35 to −38 Hz; rupture force ~154 pN; ζ shift −22 to−11.5 mV), and favorable adsorption energy (−48.5 kcal mol⁻¹, contact 4.5 nm², 8.5 H-bonds). The MMP gate displayed efficient kinetics (Vmax 117.9 RFU·min⁻¹, Km 5.0 µM) with >90% cleavage at 60 min, reduced to 26% by inhibitor. Conjugation yields reached 87% (Vitamin C) and 77% (Stigmasterol). Franz diffusion showed MMP-dependent release (24 h: Vitamin C 90–96%, Stigmasterol 80–85%) with minimal basal leakage. Released Vitamin C enhanced collagen I to ~250% in fibroblasts, while Stigmasterol attenuated LPS-induced macrophage morphology; keratinocytes retained normal marker expression. This study demonstrates that a single amphipathic, sequence-programmed peptide can couple strong silica anchoring with protease-responsive release and broad payload compatibility, establishing a versatile platform for spicule-based transdermal and regenerative delivery. Full article

This study presents DeepVisionAnalytics, an integrated framework that combines eye tracking, OpenCV-based computer vision (CV), and machine learning (ML) to support objective analysis of consumer behaviour in visually driven tasks. Unlike conventional self-reported surveys, which are prone to cognitive bias, recall errors, and social desirability effects, the proposed approach relies on direct behavioural measurements of visual attention. The system captures gaze distribution and fixation dynamics during interaction with products or interfaces. It uses AOI-level eye tracking metrics as the sole behavioural signal to infer candidate choice under constrained experimental conditions. In parallel, OpenCV and ML perform facial analysis to estimate demographic attributes (age, gender, and ethnicity). These attributes are collected independently and linked post hoc to gaze-derived outcomes. Demographics are not used as predictive features for choice inference. Instead, they are used as contextual metadata to support stratified, segment-level interpretation. Empirical results show that gaze-based inference closely reproduces observed choice distributions in short-horizon, visually driven tasks. Demographic estimates enable meaningful post hoc segmentation without affecting the decision mechanism. Together, these results show that multimodal integration can move beyond descriptive heatmaps. The platform produces reproducible decision-support artefacts, including AOI rankings, heatmaps, and segment-level summaries, grounded in objective behavioural data. By separating the decision signal (gaze) from contextual descriptors (demographics), this work contributes a reusable end-to-end platform for marketing and UX research. It supports choice inference under constrained conditions and segment-level interpretation without demographic priors in the decision mechanism. Full article

Aiming at the unclear mechanisms of fluid migration in nanopore-throat systems within tight oil reservoirs, this study focuses on the microscopic interactions at the oil–water interface in nanoconfined spaces. Based on molecular dynamics simulation, water-flooding models within nanopores of tight oil reservoirs under varying salinity conditions were constructed. The microscopic flow behaviors of oil and water in the pores were investigated, and the mechanism by which interfacial hydrogen bonding influences displacement efficiency under nanoconfinement was elucidated. The results demonstrate that due to the strong hydrogen bonding interactions between acetic acid and water, it is impossible to establish an effective displacement process or form stable displacement pathways within the pores. The extensive hydrogen-bonding network formed by acetic acid molecules at the oil–water interface severely restricts the transport capacity of water. Salinity exerts a nonlinear regulatory effect on hydrogen bonding. High-salinity (246.5 g/L) waterflooding shortens hydrogen bond lengths, enhances local bonding strength, and restricts the expansion of water channels; low-salinity (21.9 g/L) waterflooding mitigates ionic interference, resulting in the highest diffusion capacity of alkanes. The diffusion coefficient increases by 1.4 times compared to that under high-salinity conditions, leading to the highest degree of crude oil mobility. The research findings provide important guidance for enhanced oil recovery in tight oil reservoirs. Full article

In this study, a three-dimensional Inverse Conjugate Heat Transfer Problem (ICHTP) is numerically and experimentally investigated to estimate the heat-source strength of multiple chips mounted on a printed circuit board (PCB) using the Conjugate Gradient Method (CGM) and infrared thermography. The interfaces between the PCB and the surrounding air domain are assumed to exhibit perfect thermal contact, establishing a fully coupled conjugate heat transfer framework for the inverse analysis. Unlike the conventional Inverse Heat Conduction Problem (IHCP), which typically only accounts for conduction within solid domains, the present ICHTP formulation requires the simultaneous solution of the governing continuity, momentum, and energy equations in the air domain, along with the heat conduction equation in the chips and PCB. This coupling introduces substantial computational complexity due to the nonlinear interaction between convective and conductive heat transfer mechanisms, as well as the sensitivity of the inverse solution to measurement uncertainties. The numerical simulations are conducted first with error-free measurement data and an inlet velocity of u_in = 4 m/s; the recovered heat-sources exhibit excellent agreement with the true values. The computed average errors for the estimated temperatures ERR1 and estimated heat sources ERR2 are as low as 0.0031% and 1.87%, respectively. The accuracy of the estimated heat sources is then experimentally validated under various prescribed inlet air velocities. During experimental verification at an inlet velocity of 4 m/s, the corresponding ERR1 and ERR2 values are obtained as 0.91% and 3.34%, while at 6 m/s, the values are 0.86% and 2.81%, respectively. Compared with the numerical results, the accuracy of the experimental estimations decreases noticeably. This discrepancy arises because the numerical simulations are free from measurement noise, whereas experimental data inherently include uncertainties due to thermal picture resolutions, environmental fluctuations, and other uncontrollable factors. These results highlight the inherent challenges associated with inverse problems and underscore the critical importance of obtaining precise and reliable temperature measurements to ensure accurate heat source estimation. Full article

The reliable joining of dissimilar stainless steel and carbon steel remains a critical challenge in Metal Inert Gas (MIG) welding due to complex thermal–metallurgical interactions and the formation of brittle phases at the weld interface. In this study, a Taguchi-based design of experiments was employed to systematically optimize MIG welding parameters for SUS201/S20C dissimilar joints using a SUS201 filler wire, with particular attention to the welding current, voltage, travel speed, and electrode stick-out. The welding process was performed using an automatic welding robot. Tensile specimens were tested on a universal testing machine. Microstructural analysis was performed using a metallurgical microscope. The microstructure reveals that the development of the carbon side’s large ferrite and the stainless steel side’s δ-ferrite both significantly degrade joint quality. Among all process parameters, electrode stick-out is identified as the most influential parameter governing both tensile and bending performance, highlighting a critical process sensitivity that has received limited attention in prior studies. Optimized parameter combinations are required to maximize tensile and flexural responses. The highest tensile strength is 450.96 MPa. These findings advance the understanding of parameter–microstructure–property relationships in dissimilar MIG welding. Future work applying numerical welding simulations and advanced evaluation techniques is recommended. Full article

Automated optical inspection (AOI) technologies are widely used in PCB and semiconductor manufacturing to improve accuracy and reduce human error during quality inspection. While existing AOI systems can perform defect detection, they often rely on pre-defined camera positions and lack flexibility for interactive inspection, especially when the operator needs to visually verify solder pad conditions or examine specific layout regions. This study focuses on the front-end optical positioning and inspection stage of the AOI workflow, providing an automated mechanism to link digitally generated layout reports from EDA layout tools with real PCB inspection tasks. The proposed system operates on component-placement reports exported by EDA layout environments and uses them to automatically guide the camera to the corresponding PCB coordinates. Since PCB design reports may vary in format and structure across EDA tools, this study proposes a vision-based extraction approach that employs Hough transform-based region detection and a CNN-based digit recognizer to recover component coordinates from visually rendered design data. A dual-axis sliding platform is driven through a hierarchical control architecture, where coarse positioning is performed via TB6600 stepper control and Bluetooth-based communication, while fine alignment is achieved through a non-contact, gesture-based interface designed for clean-room operation. A high-resolution autofocus camera subsequently displays the magnified solder pads on a large screen for operator verification. Experimental results show that the proposed platform provides accurate, repeatable, and intuitive optical positioning, improving inspection efficiency while maintaining operator ergonomics and system modularity. Rather than replacing defect-classification AOI systems, this work complements them by serving as a positioning-assisted inspection module for interactive and semi-automated PCB quality evaluation. Full article

Pile foundations are critical load-bearing components in bridge structures, particularly in soft, high-moisture soils susceptible to external disturbances. This study investigated the impact of large-scale soil excavation on the stability of adjacent pile foundations through comprehensive field monitoring of a newly constructed bridge during both the bridge construction and channel excavation phases. The close proximity of the excavation site to the pile caps facilitated a detailed assessment of soil–structure interaction. The results indicate that the pile axial force peaked at the pile head and decreased progressively with depth, consistent with the load transfer mechanism of friction piles. Notably, a distinct variation in axial force was observed at the bedrock interface, attributed to reduced relative displacement between the pile and the surrounding soil. Furthermore, channel water filling raised the local groundwater table, which increased the buoyancy and reduced negative skin friction, thereby decreasing the pile axial force. The study also highlighted the sensitivity of pile deformation in soft soil to unbalanced earth pressure. Asymmetric excavation and surface surcharge loading were identified as critical factors compromising pile stability and overall structural safety. These findings provide valuable insights for construction practices and offer effective strategies to mitigate adverse excavation effects, ensuring long-term structural stability. Full article

This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article

Chatbots have emerged as a promising interface for facilitating access to complex datasets, allowing users to pose questions in natural language rather than relying on specialized technical workflows. At the same time, remote sensing has transformed archaeological practice by producing vast amounts of imagery from LiDAR, drones, and satellites. While these advances have created unprecedented opportunities for discovery, they also pose significant challenges due to the scale, heterogeneity, and interpretative demands of the data. In related scientific domains, multimodal conversational systems capable of integrating natural language interaction with image-based analysis have advanced rapidly, supported by a growing body of survey and review literature documenting their architectures, datasets, and applications across multiple fields. By contrast, archaeological applications of chatbots remain limited to text-based prototypes, primarily focused on education, cultural heritage mediation or archival search. This review synthesizes the historical development of chatbots, examines their current use in remote sensing, and evaluates the barriers to adapting such systems for archaeology. Four major challenges are identified: data scale and heterogeneity, scarcity of training datasets, computational costs, and uncertainties around usability and adoption. By comparing experiences across domains, this review highlights both the opportunities and the limitations of integrating conversational AI into archaeological workflows. The central conclusion is that domain-specific adaptation is essential if multimodal chatbots are to become effective analytical partners in archaeology. Full article

Plasmonics and metasurfaces (MSs) have emerged as two of the most influential platforms for manipulating light at the nanoscale, each offering complementary strengths that challenge the limits of conventional optical design. Plasmonics enables extreme subwavelength field confinement, ultrafast light–matter interaction, and strong optical nonlinearities, while MSs provide versatile and compact control over phase, amplitude, polarization, and dispersion through planar, nanostructured interfaces. Recent advances in materials, nanofabrication, and device engineering are increasingly enabling these technologies to be combined within unified planar and hybrid optical platforms. This review surveys the physical principles, material strategies, and device architectures that underpin plasmonic, MS, and hybrid plasmonic–dielectric systems, with an emphasis on interface-mediated optical functionality rather than long-range guided-wave propagation. Key developments in modulators, detectors, nanolasers, metalenses, beam steering devices, and programmable optical surfaces are discussed, highlighting how hybrid designs can leverage strong field localization alongside low-loss wavefront control. System-level challenges including optical loss, thermal management, dispersion engineering, and large-area fabrication are critically examined. Looking forward, plasmonic and MS technologies are poised to define a new generation of flat, multifunctional, and programmable optical systems. Applications spanning imaging, sensing, communications, augmented and virtual reality, and optical information processing illustrate the transformative potential of these platforms. By consolidating recent progress and outlining future directions, this review provides a coherent perspective on how plasmonics and MSs are reshaping the design space of next-generation planar optical hardware. Full article

Reinforced concrete (RC) beams strengthened with carbon-fabric-reinforced cementitious matrix (CFRCM) systems have shown potential for restoring flexural performance, yet their effectiveness under different corrosion levels remains insufficiently understood. This study presents a numerical investigation of the flexural behaviour of simply supported RC beams externally strengthened with CFRCM plates. Refined finite element models (FEMs) were developed by explicitly incorporating the steel–concrete bond-slip behaviour, the carbon fabric (CF) mesh–cementitious matrix (CM) interface, and the CFRCM–concrete substrate interaction and were validated against experimental results in terms of failure modes, load–deflection responses, and flexural capacities. A parametric study was then conducted to examine the effects of CFRCM layer number, steel corrosion level, and longitudinal reinforcement ratio. The results indicate that the baseline flexural capacity can be fully restored only when the corrosion level remains below approximately 15%; beyond this threshold, none of the CFRCM configurations achieved full recovery. The influence of the reinforcement ratio was found to depend on corrosion severity, while increasing CFRCM layers enhanced flexural performance but exhibited saturation effects for thicker configurations. In addition, corrosion level and CFRCM thickness jointly influenced the failure mode. Comparisons with design predictions show that bilinear CFRCM constitutive models are conservative, whereas existing FRP-based design codes provide closer agreement with numerical and experimental results. Full article

A growing challenge in mobile robotics is the reliance on complex graphical interfaces and rigid control pipelines, which limit accessibility for non-expert users. This work introduces a latency-aware benchmarking framework that enables natural-language robot navigation by integrating multiple Large Language Models (LLMs) with the Robot Operating System 2 (ROS 2) Navigation 2 (Nav2) stack. The system allows robots to interpret and act upon free-form text instructions, replacing traditional Human–Machine Interfaces (HMIs) with conversational interaction. Using a simulated TurtleBot4 platform in Gazebo Fortress, we benchmarked a diverse set of contemporary LLMs, including GPT-3.5, GPT-4, GPT-5, Claude 3.7, Gemini 2.5, Mistral-7B Instruct, DeepSeek-R1, and LLaMA-3.3-70B, across three local planners, namely Dynamic Window Approach (DWB), Timed Elastic Band (TEB), and Regulated Pure Pursuit (RPP). The framework measures end-to-end response latency, instruction-parsing accuracy, path quality, and task success rate in standardised indoor scenarios. The results show that there are clear trade-offs between latency and accuracy, where smaller models respond quickly but have less spatial reasoning, while larger models have more consistent navigation intent but take longer to respond. The proposed framework is the first reproducible multi-LLM system with multi-planner evaluations within ROS 2, supporting the development of intuitive and latency-efficient natural-language interfaces for robot navigation. Full article