Search Results (618)

Background/Objectives: This technical report introduces an innovative hybrid digital workflow that integrates diagnostic plaster-cast scanning with intraoral scanning to produce an accurate 3D-printed model for fabricating distal-extension removable dental prostheses (RDPs). Methods: The technique aims to overcome the challenges of reproducing the mobile mucosa in free-end saddles, a critical factor for denture base accuracy and stability. The workflow began with conventional clinical procedures, including clinical examination, impression-making, and cast surveying. After performing the required mouth preparations according to the prosthetic design, the diagnostic cast was digitized and selectively modified to allow intraoral rescanning. The prepared teeth were then scanned intraorally and merged with the digitalized cast, producing a refined virtual model for CAD-based metal framework design. The framework was digitally designed, 3D-printed to verify adaptation, and cast in cobalt–chromium. Standard RDP fabrication steps were followed, including intraoral framework try-in, fabrication of acrylic bases, occlusal registration, tooth arrangement, and functional and esthetic try-in. The final prosthesis was installed and adjusted without the need for an additional impression. Results: This hybrid workflow enabled a highly accurate reproduction of the distal extension region, outperforming models derived solely from direct intraoral scanning. By digitally capturing the physiological morphology of the mobile mucosa, the method eliminates the need for the traditional altered-cast technique, reducing clinical time, technical sensitivity, and material costs. Conclusions: The proposed approach enhances denture base accuracy, improves adaptation, and promotes more uniform occlusal load distribution in free-end RDPs. This streamlined and reproducible digital protocol offers a clinically relevant advancement, with potential to improve prosthesis stability and long-term outcomes. Full article

This paper investigates the optimal installation positions of two coherent motors by analyzing the structure-borne sound power transmission to a simply supported rectangular concrete floor. The free velocity and source mobility of the motors were measured experimentally, while the receiver mobility of the floor was obtained via the modal summation method. Based on these parameters, the study examined how installation positions and inter-point interactions influence the transmitted sound power. The results showed that the difference in structure-borne sound power level between the optimal and worst-case installations was 20.44 dB in the 1/3-octave band centered at 50 Hz. Crucially, the optimal positions remained unchanged even when inter-point interactions were neglected in the power calculations, providing actionable guidance for practical vibration isolation design in building applications. Full article

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with Global Positioning System (GPS) tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system’s effectiveness in detecting elevated pollutant levels in rural areas with solid fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m³ in forested areas to as high as 160 μg/m³ in industrial zones, while CO₂ levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement. Full article

Accurate detection and positioning of road users are essential for vehicle-to-infrastructure (V2I)-assisted autonomous driving. For this purpose, the road user’s ground contact point is usually detected in a monocular camera image. Then, a homography-based method is used to convert this detected point into its corresponding map position. However, the homography-based method assumes that the ground is planar, which leads to significant positioning errors in real-world environments. This limitation degrades the reliability of V2I-assisted autonomous driving, particularly in environments with complex road geometries. This study presents a method for accurately estimating the positions of road users using 3D point clouds generated by a Mobile Mapping System (MMS) for map construction without incurring additional costs. Moreover, since surveillance cameras are typically installed in urban areas, point clouds for these regions are often already available. The proposed method uses a pre-generated Look-Up Table (LUT), which is created by projecting MMS-based 3D point clouds onto the image coordinate system, so that each pixel in the image stores its corresponding 3D map position. Once the ground contact points of road users are detected in the image, the corresponding 3D positions on the map can be directly obtained by referencing the LUT. In the experiments, the proposed method was evaluated using surveillance camera images and MMS-based point clouds collected from various real-world environments. The results show that the proposed method reduces positioning errors of road users by an average of 61.4% compared to the conventional homography-based method. The improvement is particularly significant in environments with ground slope variations. In addition, the proposed method demonstrates real-time feasibility on an embedded camera, achieving low latency and power-efficient performance suitable for V2I edge deployment. Full article

In the modular construction of liquefied natural gas (LNG) plants and receiving terminals, transport beams are critical components that enable modular mobility. However, these beams are susceptible to large deformations due to complex loads during land and sea transportation. Traditional monitoring methods (i.e., strain gauge and deflection meters) often suffer from low efficiency and poor accuracy and may disrupt operational continuity in real-time monitoring systems. This paper presents a non-contact, real-time deformation detection system for LNG modular transport beams based on digital image technology, which integrates a high-resolution camera with a real-time software framework to remotely monitor structural integrity. An experiment was conducted on a full-scale support column-transport beam frame with specialized connection joints designed for rapid assembly. Five digital image correlation (DIC) detection regions (5 cm × 5 cm) were established on box-shaped beam sleeves, column sleeves, and the end plates of the beam–column joints. In addition, displacement gauges were installed at the same DIC locations. The experimental results demonstrate that the DIC measurements show good agreement with traditional measurement methods, verifying the applicability of the proposed system for large-scale LNG engineering structures. Full article

This paper presents a model-free Best Efficiency Point (BEP) tracking method for centrifugal pumps without head measurement or manufacturer-provided characteristic curves. The proposed approach combines a discrete finite-difference extremum-seeking control (ESC) scheme with an efficiency approximation proxy derived from measurable variables—namely, flow rate and electrical power. Under constant head conditions, the proxy function is analytically shown to be proportional to the true pump efficiency, enabling real-time BEP localization using only motor feedback signals. The ESC algorithm employs a sign-based gradient rule with adaptive step-size reduction to achieve rapid and stable convergence without mathematical models. A Python-based simulation using a Schneider SUB 15-0.5cv pump demonstrates that the method can track the BEP with negligible steady-state error (less than 0.1% efficiency deviation). The proposed framework offers a cost-effective solution for efficient optimization for mobile pumping applications in large water resources where installing head sensors is impractical. Full article

This work presents an innovative concept of a mobile micro-water turbine for energy recovery from flood-threatened rivers, combining environmental protection with renewable energy production. In response to the increasing frequency and intensity of floods caused by climate change, the authors propose active utilisation of the kinetic energy of water masses during these events through the installation of mobile water turbines along rivers. Rather than merely mitigating the consequences of floods, the energy from flowing water can be converted into electrical current, and the water can be purified and used for other purposes. The article analyses various solutions for water turbines, including the Kaplan turbine, Banki–Michell turbine, and screw turbine, taking into account their efficiency and ability to adapt to changing flow conditions. For the Biała Lądecka river, it was demonstrated that a mobile micro turbine operating for three days can generate a significant amount of energy for on-site consumption or storage. The key challenge is the development of effective water filtration and treatment systems to remove pollutants brought by floods, as well as mobile platforms enabling rapid assembly and disassembly of turbines at threatened sites. The comparative analysis of turbines conducted makes it possible to determine the optimal choice for mobile systems due to operation at low heads, simple construction facilitating installation, and tolerance for contaminants. Full article

Wireless communication systems, which rely on radio frequencies (RFs), are widely utilized in various applications, such as mobile communications, radio frequency identification, marine networks, smart farms, and smart homes. Due to their ease of installation, wireless systems offer advantages over wired alternatives. But the deployment of high-frequency radio waves for a communication system can pose potential health risks. To address these concerns, many researchers have explored the use of visible light as a safer alternative to radio frequency communication. In this context, optical camera communication has emerged as a good candidate compared to the RF system. Meanwhile, artificial intelligence (AI) is reshaping industries and human life by solving complex problems, enabling intelligent automation, and driving advancements in technologies such as smart farms, smart homes, and future internet of things systems. In this study, we recommend a Multiple-Input Multiple-Output Camera On–Off Keying (MIMO C-OOK) modulation that integrates a YOLOv11 for light source detection and tracking and a deep learning network-based decoder algorithm, optimized for long-range and mobility communication scenarios. The proposed approach enhances the conventional C-OOK system by increasing the data rate and transmission range while reducing errors at the receiver. Implementation results show that the proposed approach can achieve reliable communication up to 10 m with minimal errors, even under mobility conditions (3 m/s, equivalent to walking speed), by optimizing camera parameters and employing forward error correction (FEC). Full article

Tidal turbines have emerged as a promising alternative to fossil-fuel-based energy generation, with estuarine environments identified as potential sites for their deployment. However, estuaries are sensitive ecosystems, and understanding the impacts of turbine installation on local hydrodynamics and sediment transport is critical. While previous studies have shown the influence of turbines on seabed morphology under steady current conditions, the effects of combined wave–current loading remain insufficiently explored. In this study, we present a novel numerical modeling framework to predict seabed evolution in the vicinity of tidal turbines subjected to wave–current interactions. The approach integrates Blade Element Theory (BET) to represent turbine-induced forces, an Euler–Euler multiphase model for sediment transport, and the first-order wave theory to capture wave dynamics, all implemented within the OpenFOAM-based solver. Wave effects are incorporated as source terms in the momentum equations, and wave velocities are added to the current field at the velocity inlet boundary condition. Results demonstrate that wave–current loading induces oscillatory sediment transport, but net scouring remains significant in the vicinity of the turbine. The proposed framework is validated component-wise (wave forcing and rotor loading) and then demonstrated on mobile-bed simulations to quantify how oscillatory wave–current forcing modifies near-bed transport and early-stage scour development around a tidal turbine. While the present simulations focus on short morphodynamic times, the approach provides a physics-based basis for exploring wave effects on turbine-induced sediment dynamics. Full article

Mobile pyrolysis systems offer a practical pathway for the decentralized valorization of biomass waste, addressing the high logistical and economic burdens of transporting low-density, moisture-rich feedstocks to centralized facilities. By operating directly at the source, these systems convert diverse agricultural and forestry residues into biochar, bio-oil, pyrogas, and wood vinegar, while reducing transport volumes and associated emissions. Reported mobile reactors process between 4 kg per batch and 10 t/day, achieving biochar yields of 33–44 wt.% at 400 °C and bio-oil yields of 55–68 wt.% in fast pyrolysis at 500–550 °C, demonstrating performance comparable to stationary installations. This review synthesizes current mobile pyrolysis technologies, including reactor configurations, feedstock suitability, operational constraints, and recent advances in automation, real-time monitoring, and machine learning-based optimization. The agricultural and industrial applications of pyrolysis products are examined, with emphasis on soil health enhancement, biopesticide activity, renewable gas generation, and carbon sequestration. Emerging international projects and commercial efforts are highlighted, illustrating growing interest in flexible, low-carbon pyrolysis solutions for rural waste management and distributed bioresource utilization, while outlining the technological gaps that remain to be addressed. Full article

In greenhouse systems, secondary plants are used to attract and support the multiplication of beneficial arthropods, thereby improving biological control. Three plants were selected for this study: alyssum (Lobularia maritima (L.) Desv.), yarrow (Achillea millefolium L.), and dill (Anethum graveolens L.). This study was performed in two years, 2021 and 2025, and focused on Orius laevigatus (Fieber) (Hemiptera, Anthocoridae), one of the most important predators of Thysanoptera pests in greenhouse crops. Four ornamental crops (carnation, sweet William, statice, and gerbera daisy) were included to analyse the movement and installation of the predator. Alyssum and yarrow housed O. laevigatus in both years (total mean values per sampling date of 3.0 ± 1.3 and 2.7 ± 1.0 on alyssum and 7.0 ± 2.8 and 1.8 ± 0.8 on yarrow in 2021 and 2025, respectively), increasing its population in the greenhouse. Dill was unsuitable for sustaining predator populations and attracted additional potential pests. Its short flowering period and rapid decline further limited its usefulness. Orius laevigatus adults did not show great mobility during the study and had small populations among the ornamental crops in the greenhouse. Ornamental plant statice (Limonium sinuatum (L.) Mill.) had the highest predator population. The interest of the secondary plants is discussed, highlighting their potential for biological control in greenhouses. Full article

Owing to the low cost, small size, and convenience for installation, 2D LiDAR has been widely used in mobile robots for simultaneous positioning and mapping (SLAM). However, traditional 2D LiDAR SLAM methods have low robustness and accuracy in LiDAR-degenerated environments. To improve the robustness of the SLAM method in such environments, an innovative SLAM method is developed, which mainly includes two parts, i.e., the front-end positioning and the back-end optimization. Specifically, in the front-end part, the AKF (adaptive Kalman filter) method is applied to estimate the pose of the mobile robot, zero bias of acceleration and gyroscope, lever arm length, and the mounting angle. The adaptive factor of the AKF can dynamically adjust the variance of the process and measurement noises based on the residual. In the back-end part, a particle filter (PF) is employed to optimize the pose estimation and build the map, where the pose domain constraint from the output of the front-end is introduced in the PF to avoid mismatch and enhance positioning accuracy. To verify the performance of the method, a series of experiments is carried out in four typical environments. The experimental results show that the positioning precision has been improved by about 61.3–97.9%, 35.7–99.0%, and 43.8–93.0% compared to the Karto SLAM, Hector SLAM, and Cartographer, respectively. Full article

This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increasing acceptance among states as a compliant measurement device. A comprehensive dataset was assembled spanning more than 1000 miles of roadways, capturing diverse marking materials, colors, installation methods, pavement types, and vehicle speeds. The final dataset used for model development focused on dry condition measurements and roadway segments most relevant to state transportation agencies. A detailed synchronization process was implemented to ensure the accurate pairing of retroreflectivity and LiDAR intensity values. Using these data, several machine learning techniques were evaluated, and an ensemble of gradient boosting-based models emerged as the top performer, predicting pavement retroreflectivity with an R² of 0.94 on previously unseen data. The repeatability of the predicted retroreflectivity was tested and showed similar consistency as the MRU. The model’s accuracy was confirmed against independent field segments demonstrating the potential for LiDAR to serve as a practical, low-cost alternative for MRU measurements in routine roadway inspection and maintenance. The approach presented in this study enhances roadway safety by enabling more frequent, network-level assessments of pavement marking performance at lower cost, allowing agencies to detect and correct visibility problems sooner and helping to prevent nighttime and adverse weather crashes. Full article

Microservice deployment and request routing can help improve server efficiency and the performance of large-scale mobile edge computing (MEC). However, the joint optimization of microservice deployment and request routing is extremely challenging, as dynamic request routing easily results in asymmetric network structures and imbalanced microservice workloads. This article proposes multi-objective joint optimization for microservice deployment and request routing based on structural symmetry. Firstly, the structural symmetry of microservice deployment and request routing is defined, including spatial symmetry and temporal symmetry. A constrained nonlinear multi-objective optimization model was constructed to jointly optimize microservice deployment and request routing, where the structural symmetric metrics take into account the flow-aware routing distance, workload balancing, and request response delay. Secondly, an improved artificial plant community algorithm is designed to search for the optimal route to achieve structural symmetry, including the environment preparation and dependency installation, service packaging and image orchestration, arrangement configuration and dependency management, deployment execution and status monitoring. Thirdly, a benchmark experiment is designed to compare with baseline algorithms. Experimental results show that the proposed algorithm can effectively optimize structural symmetry and reduce the flow-aware routing distance, workload imbalance, and request response delay, while the computational overhead is small enough to be easily deployed on resource-constrained edge computing devices. Full article

At present, conventional cold storage facilities in China are poorly suited to on-farm storage demands for agricultural produce, mainly due to their large spatial requirements, complex and labor-intensive installation procedures, limited portability, and insufficient coverage in rural areas. These limitations significantly contribute to post-harvest losses of perishable crops such as cherry tomatoes. To address this challenge, the present study proposes a compact and temporary cold storage system—gas-inflated membrane cold storage (GIMCS)—which exploits the inherent safety, cost-effectiveness, ease of deployment, and adaptability of inflatable membrane structures. A series of mechanical performance tests, including tensile strength, pressure resistance, and burst tests, were conducted on PA/PE (Polyamide/Polyethylene) composite membranes. The optimal configuration was identified as a membrane thickness of 70 μm, a gas column width of 2 cm, and a PA/PE composition ratio of 35%/65%. Thermal performance evaluations further revealed that filling the inflatable structure with 100% CO₂ yielded the most effective insulation. Through structural optimization, a cotton-filled gas-inflated membrane cold storage system (CF-GIMCS) incorporating a dual insulation strategy—combining intra-membrane and extra-membrane insulation—was developed. This multilayer configuration significantly reduced conductive and convective heat transfer, resulting in enhanced thermal performance. A comparative evaluation between GIMCS and a conventional cold storage system of equivalent capacity was conducted over a 15-day storage period, considering construction cost, temperature uniformity, and fruit preservation quality. The results showed that the construction cost of GIMCS was only 38% of that of conventional cold storage. The internal temperature distribution of GIMCS was highly uniform, with a maximum horizontal temperature difference of 1.4 °C, demonstrating thermal stability comparable to conventional systems. No statistically significant differences were observed between the two systems in key post-harvest quality indicators, including weight loss and respiration rate. Notably, GIMCS exhibited superior performance in maintaining fruit firmness, with a hardness of 1.30 kg·cm⁻² compared to 1.26 kg·cm⁻² in conventional storage, indicating a potential advantage in shelf-life extension. Overall, these findings demonstrate that GIMCS represents an affordable, technically robust, and portable cold storage solution capable of delivering preservation performance comparable to—or exceeding—that of conventional cold storage. Its modularity, mobility, and ease of relocation make it particularly well suited to the operational and economic constraints of smallholder farming systems, offering a practical and scalable pathway for improving on-farm cold chain infrastructure. Full article