Search Results (8,173)

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics. Full article

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

Accurate postharvest quality evaluation of soybeans is essential for preserving product value and meeting industry standards. Traditional inspection methods are often inconsistent, labor-intensive, and unsuitable for high-throughput operations. This study presents a non-destructive soybean classification approach using a simplified reflectance-mode hyperspectral imaging system equipped with a single light source, eliminating the complexity and maintenance demands of dual-light configurations used in prior studies. A spectral–spatial data fusion strategy was developed to classify harvested soybeans into four categories: normal, split, diseased, and foreign materials such as stems and pods. The dataset consisted of 1140 soybean samples distributed across these four categories, with spectral reflectance features and spatial texture attributes extracted from each sample. These features were combined to form a unified feature representation for use in classification. Among multiple machine learning classifiers evaluated, Linear Discriminant Analysis (LDA) achieved the highest performance, with approximately 99% accuracy, 99.05% precision, 99.03% recall and 99.03% F1-score. When evaluated independently, spectral features alone resulted in 98.93% accuracy, while spatial features achieved 78.81%, highlighting the benefit of the fusion strategy. Overall, this study demonstrates that a single-illumination HSI system, combined with spectral–spatial fusion and machine learning, offers a practical and potentially scalable approach for non-destructive soybean quality evaluation, with applicability in automated industrial processing environments. Full article

External lighting is essential for autonomous inspections of concrete structures in low-light environments. However, previous studies have primarily relied on uniformly diffused lighting to illuminate images and faced challenges in detecting complex crack patterns. This paper proposes two novel algorithms that use directional lighting to classify concrete defects. The first method, named fused neural network, uses the maximum intensity pixel-level image fusion technique and selects the maximum intensity pixel values from all directional images for each pixel to generate a fused image. The second proposed method, named multi-channel neural network, generates a five-channel image, with each channel representing the grayscale version of images captured in the Right (R), Down (D), Left (L), Up (U), and Diffused (A) directions, respectively. The proposed multi-channel neural network model achieved the best performance, with accuracy, precision, recall, and F1 score of 96.6%, 96.3%, 97%, and 96.6%, respectively. It also outperformed the FusedNet and other models found in the literature, with no significant change in evaluation time. The results from this work have the potential to improve concrete crack classification in environments where external illumination is required. Future research focuses on extending the concepts of multi-channel and image fusion to white-box techniques. Full article

Background: The fabrication of orthodontic aligners directly via three-dimensional (3D) printing presents potential to increase the efficiency of aligner production relative to traditional workflows; however, several aspects of the 3D printing process might affect the dimensional fidelity of the fabricated appliances. The aim of this study is to measure the forces expressed by a 3D printed aligner made with TC-85 DAC resin (Grapy Inc., Seoul, Republic of Korea) when an expansion movement of the entire upper dental arch is programmed, comparing the measured forces with those obtained by a common thermoformed aligner (Smart Track^®, Align Technology, Santa Clara, CA, USA). Materials and methods: A patient in transitional mixed dentition was selected, with the presence of all the first molars and permanent upper and lower incisors, and the canines and premolars have not started the exchange. From this patient, a virtual set up of the upper arch has been planned with an expansion of 0.2 mm and 0.4 mm per side; 3 mm horizontal rectangular attachments were added to the set up on the vestibular surface of the permanent molars, deciduous premolars, and deciduous canines. On this set up, 10 Smart Track aligners and 10 3D printed aligners with TC-85 DAC resin were produced. The fabricated aligners were mounted on the machinery used for the test (ElectroForce^® Test Bench; TA Instruments, New Castle, DE, USA) by means of specific supports that simulate the upper arch of the patient (divided into two sides: right and left). To simulate the intraoral environment, the measurements were carried out in a thermostatic bath at a temperature of 37 °C. Results: The key results of this paper showed differences between Smart Track^® and TC-85 DAC. In particular, the expanding force exerted by the 0.2 mm per side expanded Smart Track^® aligners was on average +0.2162 N with a D.S. of ±0.0051 N during the 8 h; meanwhile, the force exerted by the 0.2 mm per side expanded TC-85 DAC 3D printed aligners was on average −0.0034 N with a D.S. of ±0.0036 N during the 8 h. The force exerted by the 0.4 mm per side expanded Smart Track^® aligners was on average +0.7159 N with a D.S. of ±0.0543 N during the 8 h; meanwhile, the force exerted by the 0.4 mm per side expanded TC-85 DAC 3D printed aligners was on average +0.0141 N with a D.S. of ±0.004 N during the 8 h. Conclusions: Smart Track^® aligners express a quantitatively measurable force in Newtons during the programmed movements to obtain a posterior expansion of the dental arches; on the contrary, aligners made with TC-85 DAC resin, in light of the results obtained from this study, express forces close to 0 during the realization of the movements programmed to obtain a posterior expansion of the dental arches. Full article

Despite increasing attention to inclusive education, the spatial and environmental requirements of individuals with Down syndrome remain insufficiently addressed within architectural research. This study investigates how educational environments can be redesigned to betteraccommodate the developmental, sensory, and behavioral needs of this user group, utilizing the interdisciplinary lens of building biology that emphasizes occupant health, well-being, and environmental quality. Employing a case study methodology, this study focuses on Gülseren Özdemir Special Education Practice School in Turkey. Fieldwork was conducted through structured qualitative spatial analysis based on principles derived from building biology and universal design. While the facility meets several baseline accessibility criteria, qualitative observations indicate areas for improvement, particularly in lighting quality, acoustic conditions, tactile stimuli, and spatial adaptability. These findings demonstrate the potential of building biology to serve as a comprehensive, health-centered design approach for inclusive educational settings. This study concludes by proposing spatial strategies applicable to both new construction and retrofit projects, offering a knowledge base that may inform future architectural practices aimed at fostering inclusive and supportive learning environments. Full article

Industrial environments frequently rely on analog control instruments due to their reliability and robustness; however, automating the interpretation of these controls remains challenging due to variability in design, lighting conditions, and scale precision requirements. This research investigates the effectiveness of Vision–Language Models (VLMs) for automated interpretation of industrial controls through analysis of three distinct approaches: general-purpose VLMs, fine-tuned specialized models, and lightweight models optimized for edge computing. Each approach was evaluated using two prompting strategies, Holistic-Thought Protocol (HTP) and sequential Chain-of-Thought (CoT), across a representative dataset of continuous and discrete industrial controls. The results demonstrate that the fine-tuned Generative Pre-trained Transformer 4 omni (GPT-4o) significantly outperformed other approaches, achieving low Mean Absolute Error (MAE) for continuous controls and the highest accuracy and Matthews Correlation Coefficient (MCC) for discrete controls. Fine-tuned models demonstrated less sensitivity to prompt variations, enhancing their reliability. In contrast, although general-purpose VLMs showed acceptable zero-shot performance, edge-optimized models exhibited severe limitations. This work highlights the capability of fine-tuned VLMs for practical deployment in industrial scenarios, balancing precision, computational efficiency, and data annotation requirements. Full article

The persistence of pharmaceutical pollutants such as ciprofloxacin (CIP) in aquatic environments represents a critical environmental threat due to their potential to induce antimicrobial resistance. Photocatalysis using TiO₂-based materials offers a promising solution for their mineralization; however, the limited visible-light response of TiO₂ and charge carrier recombination restricts its overall efficiency. In this study, Nb-doped TiO₂ nanoparticles were synthesized via the sol–gel method, incorporating Nb⁵⁺, ions into the TiO₂ lattice to modulate the structural and electronic properties of TiO₂ to enhance its photocatalytic performance for CIP degradation under UV and visible irradiation. Comprehensive structural, morphological, and optical analyses revealed that Nb incorporation stabilizes the anatase phase, reduces particle size (from 21.42 nm to 10.29 nm), and induces a slight band gap widening (from 2.85 to 2.87 eV) due to the Burstein–Moss effect. Despite this blue shift, Nb-TiO₂ exhibited significantly improved photocatalytic activity under visible light, achieving 86% CIP degradation with a reaction rate 16 times higher than that of undoped TiO₂. This enhancement was attributed to improved charge separation and higher hydroxyl radical (^•OH) generation, driven by excess conduction band electrons introduced by Nb doping. Density Functional Theory (DFT) calculations further elucidated the electronic structure modifications responsible for this behavior, offering molecular-level insights into Nb dopant-induced property tuning. These findings demonstrate how targeted doping strategies can engineer multifunctional nanomaterials with superior photocatalytic efficiencies, especially under visible light, highlighting the synergy between experimental design and theoretical modeling for environmental applications. Full article

With the rapid development of advanced machining technologies such as high-speed cutting, dry cutting, and ultra-precision cutting, as well as the widespread application of various difficult-to-machine materials, the surface degradation problems such as wear, oxidation, and delamination faced by tools in the service process have become increasingly prominent, seriously restricting the performance and service life of tools. Nanocoatings, with their distinct nano-effects, provide superior hardness, thermal stability, and tribological properties, making them an effective solution for cutting tools in increasingly demanding working environments. For example, the hardness of the CrAlN/TiSiN nano-multilayer coating can reach 41.59 GPa, which is much higher than that of a single CrAlN coating (34.5–35.8 GPa). This paper summarizes the most common nanocoating material design, coating deposition technologies, performance evaluation indicators, and characterization methods currently used in cutting tools. It also discusses how to improve nanocoating performance using modulation analysis of element content, coating composition, geometric structure, and coating thickness. Finally, this paper considers the future development of nanocoatings for cutting tools in light of recent research hotspots. Full article

In this perspective article, we introduce Ecocebo as a novel concept describing the modulatory effects of physical environments, whether natural or built, on drug effect. Positioned as a spatial component of the placebo effect, Ecocebo is grounded in evidence-based design principles and proposes that environmental features such as natural light, greenery, spatial geometry, and calming esthetics can significantly influence sensory, emotional, and cognitive processes. These environmental factors may enhance or modify pharmacological responses, especially for analgesics, anxiolytics, and antidepressants. We highlighted how exposure to restorative spaces can reduce pain perception, stress, and the need for medication, paralleling findings in placebo research where contextual and sensory cues influence brain regions linked to emotion and pain regulation. We propose virtual reality (VR) as the most suitable methodological tool to study Ecocebo in controlled and ecologically valid settings. VR allows for the precise manipulation of spatial features and real-time monitoring of physiological and psychological responses. We also propose integrating VR with neuromodulation techniques to investigate brain–environment–drug interactions. Finally, we addressed key methodological challenges such as defining control conditions and standardizing the measurement of presence. This perspective opens new directions for the integration of non-pharmacological and pharmacological interventions and personalized therapeutic environments to optimize clinical outcomes. Full article

This paper is a result of the search for design assumptions for a sensor to detect oil dispersed in the sea waters (oil-in-water emulsions). Our approach is based on analyzing changes in the underwater solar radiance (L) field caused by the presence of oil droplets in the water column. This method would enable the sensor to respond to the presence of oil contaminants dispersed in the surrounding environment, even if they are not located directly at the measurement point. This study draws on both literature sources and the results of current numerical modeling of the spread of solar light in the water column to account for both downward and upward radiance (Es). The core principle of the analysis involves simulating the paths of a large number of virtual solar photons in a seawater model defined by spatially distributed Inherent Optical Properties (IOPs). The IOPs data were taken from the literature and pertain to the waters of the southern Baltic Sea. The optical properties of the oil used in the model correspond to crude oil extracted from the Baltic shelf. The obtained results were compared with previously published spectral analyses of an analogous polluted sea model, considering vertical downward radiance, vertical upward radiance, and downward and upward irradiance. It was found that the optimal wavelength ratio of 555/412, identified for these quantities, is also applicable to scalar irradiance. The findings indicate that the most effective way to determine this index is by measuring it using a sensor with its window oriented in the direction of upward-traveling light. Full article

This study evaluates the feasibility and benefits of adopting the IEC 62034:2012 standard for Automatic Testing Systems (ATS) for emergency and escape lighting on the BorWin5 High Voltage Direct Current (HVDC) offshore converter platform. The system comprises approximately 1800 luminaires from multiple manufacturers that are integrated into an open-architecture 220 VDC emergency network. Life-cycle cost analysis (LCCA) and multi-criteria decision-making (MCDM) approaches were employed to evaluate four configurations, ranging from manual testing to fully automated, centrally powered systems, based on technical, economic, operational, and environmental criteria. The chosen solution, which combines centralized power with automated testing and real-time monitoring, represents a significant advancement in offshore safety infrastructure. Implementing this solution on BorWin5 enhances reliability and maintainability while ensuring compliance with international standards, supporting a projected service life of over 30 years for an emergency and escape lighting system in an extreme marine environment. The findings offer a scalable model for future offshore platforms operating in similarly challenging conditions. Full article

As global food demand continues to rise, conventional agricultural practices face increasing difficulty in sustainably meeting production requirements. In response, deep learning-driven automated systems have emerged as promising solutions for enhancing precision farming. Nevertheless, accurate fruit segmentation remains a significant challenge in orchard environments due to factors such as occlusion, background clutter, and varying lighting conditions. This study proposes the Depthwise Asymmetric Bottleneck with Attention Mechanism Network (DABAMNet), an advanced convolutional neural network (CNN) architecture composed of multiple Depthwise Asymmetric Bottleneck Units (DABou), specifically designed to improve apple segmentation in RGB imagery. The model incorporates the Convolutional Block Attention Module (CBAM), a dual attention mechanism that enhances channel and spatial feature discrimination by adaptively emphasizing salient information while suppressing irrelevant content. Furthermore, the CBAM attention module employs multiple global pooling strategies to enrich feature representation across varying spatial resolutions. Through comprehensive ablation studies, the optimal configuration was identified as early CBAM placement after DABou unit 5, using a reduction ratio of 2 and combined global max-min pooling, which significantly improved segmentation accuracy. DABAMNet achieved an accuracy of 0.9813 and an Intersection over Union (IoU) of 0.7291, outperforming four state-of-the-art CNN benchmarks. These results demonstrate the model’s robustness in complex agricultural scenes and its potential for real-time deployment in fruit detection and harvesting systems. Overall, these findings underscore the value of attention-based architectures for agricultural image segmentation and pave the way for broader applications in sustainable crop monitoring systems. Full article

The objective of this study is to explore the use of emerging technologies such as the Metaverse and Digital Twin to highlight how these can be used to analyse and improve the perception of security in urban parks. Through the proposed methodological approach, which combines real data collection, 3D modelling, immersive simulations, and user feedback, a virtual environment representative of the Quartieri Spagnoli Park in Naples, chosen as a case study, was developed and tested. The experimentation involved a heterogeneous group of users and consisted of two phases of questionnaire administration, one in person and one in a virtual environment, to compare the individual and collective perceptions of users in relation to issues such as disorientation, lighting, and maintenance. The results obtained made it possible to identify a correspondence between the data collected in the two environments, and to highlight any critical issues that emerged. Undoubtedly, the virtual experience proved to be useful, accessible, and immersive, demonstrating the potential of these tools not only in identifying issues but especially in supporting participatory design and urban planning with a view to a smart city. In urban design, as in many other fields, being able to intervene and test changes in a virtual environment before actually implementing them is a valuable opportunity, as it allows the feasibility to be assessed without compromising the real space. It is precisely this aspect that makes this type of approach extremely interesting and important. The distinctive feature of the proposed approach lies in the implementation of digital twins in the metaverse, which can perform a dual function: simulation and verification. In the first case, simulations within the virtual environment allow project planning to be tested in order to predict the outcome; in the second case, it is possible to investigate the state of affairs, thus assessing whether the planning put in place has achieved the desired results. Full article

The tension in the relationship between water and energy seriously restricts the harmonious coexistence between man and the ecological environment. The solar-powered interface evaporation technology emerging in recent years has shown good application prospects in high-salt wastewater treatment for achieving the zero-discharge treatment of wastewater. In this review, advanced solar-driven interfacial evaporation is primarily focused on its mechanisms, photothermal materials optimization, and the structure of solar evaporators for salt removal. The high wide-spectrum solar absorption rate of photothermal materials determines the total energy that can be utilized in the evaporation system. The light-to-heat conversion capacity of photothermal materials directly affects the efficiency and performance of solar interface evaporators. We highlight the microstructures enabled by the nanophotonic designs of photothermal material-based solar absorbers, which can achieve highly efficient light harvesting across the entire solar irradiance spectral range with weighted solar absorptivity. Finally, based on current research, existing problems, and future development directions for high-salt wastewater evaporation research are proposed. The review provides insights into the effective treatment of high-salt wastewater. Full article