Search Results (62)

A wall-climbing robot is an electromechanical device capable of autonomous or semi-autonomous movement on intricate vertical surfaces (e.g., walls, glass facades, pipelines, ceilings, etc.), typically incorporating sensing and adaptive control systems to enhance task performance. It is designed to perform tasks such as inspection, cleaning, maintenance, and rescue while maintaining stable adhesion to the surface. Its applications span various sectors, including industrial maintenance, marine engineering, and aerospace manufacturing. This paper provides a systematic review of the physical principles and scalability of various attachment methods used in wall-climbing robots, with a focus on the applicability and limitations of different attachment mechanisms in relation to robot size and structural design. For specific attachment methods, the design and compatibility of motion and attachment mechanisms are analyzed to offer design guidance for wall-climbing robots tailored to different operational tasks. Additionally, this paper reviews localization and path planning methods for wall-climbing robots, comparing graph search, sampling-based, and feedback-based algorithms to guide strategy selection across varying environments and tasks. Finally, this paper outlines future development trends in wall-climbing robots, including the diversification of locomotion mechanisms, hybridization of attachment systems, and advancements in intelligent localization and path planning. This work provides a comprehensive theoretical foundation and practical reference for the design and application of wall-climbing robots. Full article

Quality inspection is critical for ensuring efficiency and compliance in assembly lines. The increasing adoption of AI-driven technologies, such as machine vision systems, offers significant potential to enhance detection performance and reduce inspection costs. However, these technologies are often integrated with human operators in hybrid inspection systems, posing complex design challenges. Motivated by gaps in the existing research, this paper proposes a novel MILP model that introduces several previously unaddressed capabilities in inspection planning. Specifically, it simultaneously optimizes the inspection method selection, sampling rate, and detection rate across multi-product systems featuring multiple inspection technologies with varying costs and accuracies. This unified formulation represents a substantive advancement over existing models, which typically address only isolated aspects of the problem. The model minimizes the total quality-related costs—comprising investment, inspection, penalty, and rework costs—while considering operational constraints such as workforce availability, inspection and rework time limits, and equipment capacity. Key modeling assumptions include heterogeneous inspection accuracies, product-specific defect probabilities, and the feasibility of partial inspections. The approach is validated on both synthetic datasets and a real-world automotive case study, demonstrating its ability to significantly reduce costs and to highlight the benefits of effectively combining human and machine-based inspections. Full article

This study aimed to evaluate the effects of no tail docking on the performance, health, and behavior of piglets raised under commercial conditions in Brazil. The study included 768 weaned piglets from the Pietrain synthetic line, randomly divided into two groups: DT = the final third part of the tail-docked (n = 384) and NTD = non-tail-docked (n = 384). Tail docking was performed on day two using an electrocautery clipper for piglets from the DT group, and both groups were subjected to standard environmental enrichment with branched chains. In cases of tail biting, a contingency plan was adopted to mitigate this problem by enriching the pen with a sisal rope. Behavioral measurements were performed using scan sampling. Tail biting, reactivity to humans, and health were assessed using a methodology adapted from the Welfare Quality Protocol^®. The piglets were weighed at 140 days of age and inspected according to the parameters established by the Pig Genealogical Registration Service to be used as reproduction animals. The off-test rate was calculated based on the total number of piglets approved for animal use relative to the total number evaluated. During the nursery stage, the NDT piglets showed a trend toward significance (p = 0.07) toward a higher occurrence of tail biting than the DT piglets and exhibited a higher incidence of severe lesions. They also engaged more frequently (p < 0.05) in exploratory behavior, interacting with branched chains and sisal rope, than the DT piglets. During the finishing phase, tail biting was observed only in the NDT piglets (p = 0.001). The NDT piglets that did not require the contingency plan exhibited lower fear responses (p = 0.02) during human interactions in the nursery phase than the DT piglets. Conversely, the NDT piglets that required a contingency plan showed higher fear levels (p < 0.001). Productivity performance was not affected (p > 0.05), and new cases of tail biting ceased after the contingency plan was implemented. The number of animals that died or were removed did not differ between the treatments (p > 0.05). In conclusion, managing piglets with intact tails on commercial farms presents a significant welfare challenge. By contrast, docking the final third of the tail, in accordance with regulations, was associated with fewer negative welfare outcomes, even when best management practices were applied. Full article

In response to the issues of unreachable targets, local minima, and insufficient real-time performance in drone path planning in urban low-altitude complex scenarios, this paper proposes a fusion algorithm based on digital twin, integrating LSTM (long short-term memory), DQN (Deep Q-Network), and APF (artificial potential field). The algorithm relies on a twin system, integrating multi-sensor fusion technology and Kalman filtering to input obstacle information and UAV trajectory predictions into the DQN, which outputs action decisions for intelligent obstacle avoidance. Additionally, to address the blind search problem in trajectory planning, the algorithm introduces exploration rewards and heuristic reward components, as well as adding velocity and acceleration compensation terms to the attraction and repulsion functions, reducing the path deviation of UAVs during dynamic obstacle avoidance. Finally, to tackle the issues of insufficient training sample size and simulation accuracy, this paper leverages a digital twin platform, utilizing a dual feedback mechanism from virtual and physical environments to generate a large number of complex urban scenario samples. This approach effectively enhances the diversity and accuracy of training samples while significantly reducing the experimental costs of the algorithm. The results demonstrate that the LSTM-DQN-APF algorithm, combined with the digital twin platform, can significantly improve the issues of unreachable goals, local optimality, and real-time performance in UAV operations in complex environments. Compared to traditional algorithms, it notably enhances path planning speed and obstacle avoidance success rates. After thorough training, the proposed improved algorithm can be applied to real-world UAV systems, providing reliable technical support for applications such as smart city inspections and emergency rescue operations. Full article

Path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in power line inspection. In complex inspection environments characterized by densely distributed and dynamic obstacles, traditional path-planning algorithms struggle to ensure both efficiency and safety. To address these challenges, this study proposes a dynamic path-planning method that integrates an improved Rapidly exploring Random Tree Star (RRT*) algorithm with the Dynamic Window Approach (DWA). The proposed method includes key components such as sampling-point search, random tree growth, global path-node optimization, and local dynamic obstacle avoidance. In the sampling-point search, a target-biased search strategy is introduced to guide the random tree growth toward the target point, while an attractive function is added to enhance search efficiency. Based on a breadth-first search strategy, the path obtained is optimized to reduce path complexity. To address the RRT* algorithm’s limitation in dynamic obstacle avoidance, a local path-planning method combining the improved DWA algorithm is proposed, improving efficiency in areas with dense obstacles. Simulation results show that, compared to traditional algorithms, the proposed method achieves an 8% to 12% optimization in path length, more than 50% in node optimization, and over 95% in planning time optimization. Furthermore, in dynamic obstacle avoidance across different motion directions, the proposed method ensures effective local dynamic obstacle avoidance while minimizing global path fluctuations. Full article

With the development of robotics and vision measurement technology, the use of robots with line laser scanners for 3D scanning and measurement of parts has become a mainstream trend in the field of industrial inspection. Traditional scanning and measuring robots mainly use the teach-in scanning method, which has unstable scanning quality and low scanning efficiency. In this paper, the adaptive sampling method for a free-form surface, which can realize the adaptive distribution of surface measurement points according to the curvature features of free-form surfaces, is proposed first. Then, integrated with the proposed adaptive sampling method, the automatic path planning method is proposed. This method consists of adaptive sampling, scanning attitude calculation based on a quaternion, scanning viewpoint planning based on viewable cones, and scan path generation based on bi-directional scanning. Based on the proposed automatic path planning method, the scanning and measuring robot can obtain complete 3D information of the surface to be measured with high measurement accuracy and efficiency. The performance index of the laser scanner can be fully reached. Full article

In terms of environmental sustainability, the barriers—and interactions between these barriers—to the use of solar energy for active and passive heating in residential buildings stem from location-specific housing production patterns and the actors involved in these patterns. A clear definition of hierarchies and priorities between barriers helps managers set strategic priorities and action plans to find solutions. After the earthquake in Van in 2011, 6000 hectares of land were opened for new development, and research using the sampling method discovered that the most common type of housing production in the city is the build-to-sell housing production method. The actors involved in build-to-sell housing production are technical staff, local–central administrations, entrepreneurs, end users, landowners, financial companies, non-governmental organizations, and building inspection institutions. This article examines the barriers to the use of solar energy for active and passive heating purposes, the interactions between these barriers using ISM and MICMAC methods, and the build-to-sell housing production method and actors. Barriers were identified through a literature review and semi-structured interviews. The barriers were further categorized under eight main headings according to their subject matter. The hierarchies of barriers in creating problems and solutions were determined using ISM and MICMAC methods and the findings were interpreted. In the City of Van, with regard to the houses produced via the build-to-sell production method, the barriers against the use of solar energy for heating purposes in houses considering active and passive methods are ranked in order of priority in creating the problem and the solution. Barriers caused by political and administrative issues are ranked first; barriers caused by social awareness and end users are ranked second; barriers caused by social and sociological events are ranked third; barriers caused by laws and regulations are ranked fourth; barriers caused by the knowledge, skills, and awareness of designers are ranked fifth; barriers caused by deficiencies in technical issues are ranked sixth; and barriers caused by economic and financial issues are ranked seventh. Even though the barrier caused by the working mode of build-to-sell productions is the largest in creating the problem, it is the least effective barrier to solving the problem in the ISM hierarchical and MICMAC schemes. The research process is presented in the Methods section. Full article

An acceptance sampling plan is a method used to make a decision about acceptance or rejection of a product based on adherence to a standard. Meanwhile, prior information, such as the process capability index (PCI), has been applied in different manufacturing industries to improve the quality of manufacturing processes and the quality inspection of products. In this paper, an attribute sampling plan is developed for submitted lots based on prior information and Bayesian approach. The new attribute sampling plans adjust sample sizes to prior information based on the status of the inspection target. To be specific, the sampling plans in this paper are indexed by the parameter trust with levels of low, medium, and high, where increasing trust level reduces sample size or risk. PCIs are an important basis for the choice of the trust level. In addition, multiple comparisons have been performed, including producer’s risk and consumer’s risk under different prior information parameters and different sample sizes. Full article

Changing environmental conditions, disturbances, and escalating demands for forest ecosystem services require foresters to restore present forestlands with new forest generations that will exhibit ecological integrity, diversity, high adaptive capacity, and the ability to provide essential ecosystem services. Establishing such forests requires careful consideration of the forest landscape and site dynamics. In pursuit of these requirements, we developed a novel framework that enables the restoration of forest sites and promotes the desired features of the forest complex at the same time. This framework was designed with the methods of system engineering and was organized in the same way as the forest planning process. It was tested in the habitat type of Illyrian Fagus sylvatica forests belonging to the Natura 2000 network. The environmental, vegetation, and site conditions were investigated via field inspections, available forest management plans, and simple GIS analyses. Additionally, we established a seminatural stand composed of European beech, sessile oak, sycamore maple, silver fir, and some wild fruit tree species. The survival of planted species was assessed using census and simple random sampling, the performance of provenances by Student’s test, while microhabitat factors were explored by a one-way ANOVA. The survival rate of key species was estimated to be 55.6%, while that of fruit species was estimated to be 94.5%. Our framework demonstrated satisfactory performance and contained sufficient benchmarks to facilitate consistent decision-making. In the discussion, we elucidate the framework’s primary features and attributes of the mixed stand, where we also expose some open issues to be addressed in the future. Full article

As the robust maximum likelihood ${\chi }^2$ goodness-of-fit test had been found to yield inflated type-I error rates for certain two-level confirmatory factor analysis (CFA) models, a new correction for the test was implemented in Mplus version 8.7. In this simulation study, we inspected whether the corrected test statistics follow the expected ${\chi }^2$ distributions when applying more complex two-level models for multitrait-multimethod data with varying sample sizes and correlations within trait factors. Investigating rejection rates and probability-probability plots, we found that the new correction markedly and sufficiently reduced previously inflated rejection rates in conditions with within-trait correlations equal to 1, 100 between-level units, and 10 or 20 within-level units. In other conditions, rejection rates were hardly affected or not sufficiently reduced by the new correction. While in most conditions, 2 within-level units did not suffice, 5 within-level units and 250 between-level units were enough to yield correct rejection rates given within-trait correlations did not exceed 0.80. Correlations above 0.80 required larger sample sizes. In planning studies with multilevel CFA models, researchers should be aware that sample size requirements for likelihood-based model fit evaluations can depend on several different factors and might consider conducting Monte Carlo simulations tailored to their specific modeling conditions. Full article

UAV transmission tower inspection is the use of UAV technology for regular inspection and troubleshooting of towers on transmission lines, which helps to improve the safety and reliability of transmission lines and ensures the stability of the power supply. From the traditional manual tower boarding to the current way of manually selecting target camera shooting points from 3D point clouds to plan the inspection path of the UAV, operational efficiency has drastically improved. However, indoor planning work is still labor-consuming and expensive. In this paper, a deep learning-based point cloud transmission tower segmentation (PCTTS) model combined with the corresponding target point localization algorithm is proposed for automatic segmentation of transmission tower point cloud data and automatically localizing the key inspection component as the target point for UAV inspection. First, we utilize octree sampling with unit ball normalization to simplify the data and ensure translation invariance before putting the data into the model. In the feature extraction stage, we encode the point set information and combine Euclidean distance and cosine similarity features to ensure rotational invariance. On this basis, we adopt multi-scale feature extraction, construct a local coordinate system, and introduce the offset-attention mechanism to enhance model performance further. Then, after the feature propagation module, gradual up-sampling is used to obtain the features of each point to complete the point cloud segmentation. Finally, combining the segmentation results with the target point localization algorithm completes the automatic extraction of UAV inspection target points. The method has been applied to six kinds of transmission tower point cloud data of part segmentation results and three kinds of transmission tower point cloud data of instance segmentation results. The experimental results show that the model achieves mIOU of 94.1% on the self-built part segmentation dataset and 86.9% on the self-built instance segmentation dataset, and the segmentation accuracy outperforms that of the methods for point cloud segmentation, such as PointNet++, DGCNN, Point Transformer, and PointMLP. Meanwhile, the experimental results of UAV inspection target point localization also verify the method’s effectiveness in this paper. Full article

Background: In August 2022, the Hellenic National Public Health Organisation was notified about a gastroenteritis outbreak in town A in Southern Greece. Investigations aimed to identify the source and implement control measures. Methods: Case definition categories were used in a 1:3 case–control study. Cases and controls were interviewed about various exposures. Cases’ stool samples were cultured on agar plates and characterised by serotyping, antimicrobial susceptibility testing and Pulse Field Gel Electrophoresis (PFGE). Environmental investigations included tap water sampling for microbiological and chemical analysis in town A and inspection of the water supply system. Results: We identified 33 cases (median age: 17 years). Tap water consumption was the only significant risk factor for gastroenteritis (OR = 5.46, 95% CI = 1.02–53.95). Salmonella (S.) Bovismorbificans isolated from eight stool and one tap water samples had identical PFGE profiles. No resistant isolates were identified. Residual chlorine levels were lower than the acceptable limits before and during the outbreak. We advised consumption of bottled water and adherence to strict hand hygiene rules until tap water was declared suitable for drinking. Conclusions: Epidemiological and molecular data revealed a waterborne S. Bovismorbificans outbreak in town A. We recommend local water safety authorities to ensure that residual chlorine levels comply with the legislation towards water safety planning, to mitigate risks. Full article

The number of buildings experiencing humidity problems and fungal growth appears to be increasing as energy-saving measures and changes in construction practices and climate become more common. Determining the cause of the problem and documenting the type and extent of fungal growth are complex processes involving both building physics and indoor mycology. New detection and identification methods have been introduced, and new fungal species have been added to the list of building-related fungi. However, the lack of standardised procedures and general knowledge hampers the effort to resolve the problems and advocate for an effective renovation plan. This review provides a framework for building inspections on current sampling methods and detection techniques for building-related fungi. The review also contains tables with fungal species that have been identified on commonly used building materials in Europe and North America (e.g., gypsum wallboard, oriented strand board (OSB), concrete and mineral wool). The most reported building-associated fungi across all materials are Penicillium chrysogenum and Aspergillus versicolor. Chaetomium globosum is common on all organic materials, whereas Aspergillus niger is common on all inorganic materials. Full article

Closed-loop manufacturing is crucial in Industry 4.0, since it provides an online detection–correction cycle to optimize the production line by using the live data provided from the product being manufactured. By integrating the inspection system and manufacturing processes, the production line achieves a new level of accuracy and savings on costs. This is far more crucial than only inspecting the finished product as an accepted or rejected part. Modeling the actual surface of the workpiece in production, including the manufacturing errors, enables the potential to process the provided live data and give feedback to production planning. Recently introduced “skin imaging” methodology can generate 2D images as a comprehensive digital twin for geometric deviations on any scanned 3D surface including analytical geometries and sculptured surfaces. Skin-Image has been addressed as a novel methodology for continuous representation of unorganized discrete 3D points, by which the geometric deviation on the surface is shown using image intensity. Skin-Image can be readily used in online surface inspection for automatic and precise 3D defect segmentation and characterization. It also facilitates search-guided sampling strategies. This paper presents the implementation of skin imaging for primary engineering surfaces. The results, supported by several industrial case studies, show high efficiency of skin imaging in providing models of the real manufactured surfaces. Full article

Background and objectives: Journal articles describing randomised controlled trials (RCTs) are not optimally reported, often missing crucial details that ensure transparency and reproducibility. We aimed to estimate the frequency of reproducible and transparent research practices in a sample of recently published nutrition- or diet-related RCTs. Methods: We conducted a meta-research of nutrition- or diet-related RCTs published in journals indexed on Pubmed. We used a validated search strategy to identify eligible RCTs published between June 2021 and June 2022. Two reviewers independently screened the records retrieved by reading the titles and abstracts. Eligibility was confirmed after full text inspection. The data on the general aspects of the included RCT publications (participants, intervention, controls, outcomes, and study design) were extracted into RedCap. Research transparency and reproducibility practices included study registration, statistical analysis plan, data and analyses code sharing, conflicts of interest (CoI), authorship statements (CreditT), and whether the publication was open access. The relative frequencies for all variables were calculated, and comparisons were made between nutritional supplementation RCTs and those with other nutrition- or diet-related interventions. This meta-research is registered in the Open Science Framework (doi: 10.17605/OSF.IO/BF47G). Results: Most RCTs were single-centered (88%) with a parallel design (71%). The participants were most frequently adults (46%) or adults and elderly (23%), with some clinical condition (61%), receiving nutritional supplementation (55%) compared to other active intervention (44%) or placebo (40%). The outcomes most frequently studied were clinical (44%) or nutritional status (7%) and the frequency or severity of diseases (11%). Most RCTs were registered (81%) but did not present a statistical analysis plan (97%), data (51%), or code (97%) sharing statement. CoI statements were present in 98% RCTs, with 75% declaring no CoI. CrediT was identified in 61% RCTs and 85% were not open access publications. The practices of research transparency and reproducibility did not differ between supplementation and other nutrition- or diet-related RCTs. Conclusions: In a random sample of recently published nutrition- or diet-related RCTs, the frequency of practices of transparency and reproducibility related to statistical analysis plan and the availability of materials and code sharing are not satisfactory, while study registration and CoI were reported in most publications. Full article