Frequent Inspection Research Articles

First record of Hololepta (Leionota) reichii (Marseul, 1853) (Coleoptera: Histeridae) in meliponaries of the state of Ceará (Brazil) and notes on the methods used to control this pest

Meliponaries are an alternative for the preservation of stingless bee species, in addition to producing raw material for the pharmaceutical and cosmetic industry. The presence of invaders that can cause damage to the integrity of productive areas is something that is frequently reported. The same happens in meliponaries, where bees can have many natural enemies, whether vertebrates or invertebrates. Updating new records and management methods are crucial to avoid major losses in production. The aim of this work is to report the first occurrence of the clown beetles, Hololepta (Leionota) reichii (Marseul, 1853) (Insecta: Coleoptera: Histeridae) in meliponaries of the state of Ceará, Brazil, and provide notes on the methods used to control it. A total of 109 individuals (40 females; 69 males) were collected between February and July 2022, placed in alcohol 70%, and subsequently identified. Measures such as the removal of decaying organic materials from nearby the colonies, added to frequent inspection, internal and external cleaning of the hive boxes after infestations, in addition to the elimination of beetles found in the hives, had a positive effect in reducing the records of attacks.

Direct energy deposition for smart micro reactor

ABSTRACT Nuclear microreactors (MRs) have attracted significant attention for their versatility in installation locations, especially with the growing demand for sustainable green energy in large data processing facilities. However, ensuring their safe operation necessitates frequent inspections, which can be challenging for practical efficient management. This study proposes a novel approach using direct energy deposition to incorporate an optical fiber sensor into MR components, enabling real-time monitoring with artificial intelligence. The embedded optical fiber generates real-time data that allows for AI-driven in-vivo thermal deformation analysis. Our smart MR component can detect structural anomalies, identify abnormal operations, and assess operational conditions through augmented reality interfaces and AI technology.

Lure Monitoring for Mediterranean Fruit Fly Traps Using Air Quality Sensors.

Effective pest population monitoring is crucial in precision agriculture, which integrates various technologies and data analysis techniques for enhanced decision-making. This study introduces a novel approach for monitoring lures in traps targeting the Mediterranean fruit fly, utilizing air quality sensors to detect total volatile organic compounds (TVOC) and equivalent carbon dioxide (eCO2). Our results indicate that air quality sensors, specifically the SGP30 and ENS160 models, can reliably detect the presence of lures, reducing the need for frequent physical trap inspections and associated maintenance costs. The ENS160 sensor demonstrated superior performance, with stable detection capabilities at a predefined distance from the lure, suggesting its potential for integration into smart trap designs. This is the first study to apply TVOC and eCO2 sensors in this context, paving the way for more efficient and cost-effective pest monitoring solutions in smart agriculture environments.

Deep Learning for Pavement Condition Evaluation Using Satellite Imagery

Civil infrastructure systems cover large land areas and need frequent inspections to maintain their public service capabilities. Conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in the ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technological advancements, this research evaluated pavement conditions using deep learning models for analyzing satellite images. We gathered over 3000 satellite images of pavement sections, together with pavement evaluation ratings from the TxDOT’s PMIS database. The results of our study show an accuracy rate exceeding 90%. This research paves the way for a rapid and cost-effective approach for evaluating the pavement network in the future.

Analysis of user acceptance of information and communications technology for electrical safety inspection based on a choice experiment and hierarchical Bayesian model

The development of information and communications technology has resulted in changes in electrical safety inspection. Electrical safety inspectors plan to introduce smart devices that detect causes of extensive electrical fires in real time without problems of power disconnections and on-site visits. However, smart inspection is expected to encounter acceptance issues because users are unaccustomed to it and obligated to pay additional costs for device installation and data communications. This study aims to investigate smart inspection acceptance using survey data. A hierarchical Bayesian model is employed to explore the effects of users' characteristics on its acceptance. The respondents prefer attributes of smart inspection to those of the prevailing method of on-site inspection, excluding monthly inspection costs. They prefer more frequent and extensive inspections, and prefer to avoid power disconnections and physical interaction. Accordingly, the government should inform users that smart inspection is convenient and accurate. It is also attractive to users who wish to avoid on-site visits because of privacy issues. The acceptance rate can increase if the government reduces inspection costs using the existing smart devices and communications infrastructure, and offers real-time electrical safety information using smart phones and in-home displays.

建筑工程中施工安全管理问题和对策解析

Construction safety management is a critical component in ensuring the safety of personnel and property during the construction process. In practice, there are numerous issues in construction safety management, such as the lack of a comprehensive management system, insufficient safety awareness, improper use of protective equipment by workers, safety hazards caused by construction materials and equipment, inadequate financial investment, and an incomplete supervision and management model. To address these problems, a series of countermeasures need to be implemented. First, a comprehensive construction safety management system should be established with clear delineation of responsibilities and management procedures. Second, safety awareness among all personnel should be enhanced through strengthened safety education and training. Additionally, safety management efforts must be intensified by increasing on-site supervision and inspection. Regular maintenance and updating of construction equipment should be carried out to ensure safety and reliability. Moreover, proper management of materials should be ensured to guarantee that construction materials meet safety standards. Lastly, frequent inspections of on-site electrical and fire safety conditions should be conducted to promptly eliminate potential hazards.

UAV Inspections of Power Transmission Networks with AI Technology: A Case Study of Lesvos Island in Greece

The inspection of overhead power transmission lines is of the utmost importance to ensure the power network’s uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project “ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)” has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically.

Deep-Learning-based damage detection of an experimentally tested full-scale wind turbine blade.

Reducing the Operation and Maintenance (O&M) costs of Wind Farms can substantially contribute towards adopting environmentally sustainable and cost effective energy sources. O&M costs in Wind Turbines can account up to 50% of the Turbine’s whole life cycle, with the majority of it being the frequent manual inspection needed for Wind Turbine Blades (WTB). Vibration-based structural health monitoring (SHM) approaches can be used for detecting WTB damages in an early state, before they lead to catastrophic failure, while they can be used to perform inspection on an informed basis, reducing O&M costs. In the present study, a data-driven damage detection framework is developed for WTBs. A Finite Element Model (FEM) of a full-scale WTB (the Aventa AV-7 “Light Wind Turbine”, sold under the Leichtwindanlagen© name) is developed in ABAQUS FEA using sparce material properties and dimensions and subsequently modified to match a physical WTB. Damage is modelled in the form of a Trailing Edge (TE) longitudinal crack propagated in a stepwise manner. The FE model is updated based on the geometry altering and material property degrading effects of the TE crack. Numerical simulations are performed to obtain the dynamic responses of the blade, under white noise and chirp excitation. A damage detection framework is then developed using Auto-Regressive, with exogenous excitation (ARX) or without (AR), models and its Nonlinear counterparts NARX and NAR models. In addition, model dimensionality reduction is performed using Principal Component Analysis (PCA) and Deep-Autoencoders (D-AE). The results are presented in terms of Receiver Operating Characteristics (ROC) curves.

Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework

Fibre ropes with the latest technology have emerged as an appealing alternative to steel ropes for offshore industries due to their lightweight and high tensile strength. At the same time, frequent inspection of these ropes is essential to ensure the proper functioning and safety of the entire system. The development of deep learning (DL) models in condition monitoring (CM) applications offers a simpler and more effective approach for defect detection in synthetic fibre ropes (SFRs). The present paper investigates the performance of Detectron2, a state-of-the-art library for defect detection andinstance segmentation. Detectron2 with Mask R-CNN architecture is used for segmenting defects in SFRs. Mask R-CNN with various backbone configurations has been trained and tested on an experimentally obtained dataset comprising 1,803 high-dimensional images containing seven damage classes (placking high, placking medium, placking low, compression, core out, chafing, and normal respectively) for SFRs. By leveraging the capabilities of Detectron2, this study aims to develop an automated and efficient method for detecting defects in SFRs, enhancing the inspection process, and ensuring the safety of the fibre ropes.

Vehicle Self-Position Estimation Using Lighting Recognition in Expressway Tunnel for Visual Inspection Flow

In this study, a stable and high-speed vision-based self-position estimation method was proposed that improves upon the existing method of lane detection by recognizing the lighting facilities that are installed in tunnels on Japanese expressways where GNSS cannot be used. In addition, we proposed a method for inspecting multiple cracks at once by estimating the self-position with the successful rate 75% in the traveling direction by counting the lighting with the successful rate 99.85%. The effectiveness of the method was verified by capturing images of cracks in an actual tunnel. The proposed method will enable more frequent inspections for tunnel cracks that lead to flaking while maintaining infrastructure safety, reducing costs, and improving tunnel visual inspection flow efficiency.

Automatic Detection and Intensity Classification of Pitch Misalignment of Wind Turbine Blades: a Learning-based Approach

In recent years, monitoring wind turbine operations, especially in offshore and remote installations, has become crucial. System failures, challenging assessments, and costly maintenance operations are notable concerns. Blade pitch misalignment poses one of the most significant threats, being a leading cause of downtime and energy production reduction. Traditional assessments through frequent inspections are resource-intensive and time-consuming; still they are frequently required as turbines lack routine implementation of effective automatic detection systems.To address this, our study introduces a novel machine learning-based method for autonomous pitch misalignment recognition, which relies on signals measured by a limited set of sensors, already integrated into modern wind turbines. From those signals, frequency domain features derived by harmonic analysis are extracted, ensuring robust detection also in turbulent wind conditions.This innovative method lays the groundwork for early and precise wind turbine diagnostics, promoting the shift from time-based to condition-based inspections. This transition aims to reduce maintenance costs and unexpected downtime, ultimately enhancing energy production.

Analysis in Terms of Environmental Awareness of Farmers’ Decisions and Attitudes: Reducing Pesticide Use and Risks

Pesticide residues are a significant problem affecting the quality and safety of agricultural products in Turkey. This study aims to investigate farmers’ risk perception regarding pesticide residues, including the primary factors that influence their behavior from the farmers’ perspective. In addition, the main characteristics of pesticide residues encountered in the production of agricultural products against the current policy background is investigated to provide decision support to the Turkish government for improving the safe production of agricultural products. This paper uses a binary logistic model to analyze farmers’ perceptions of pesticide residues using a cross-sectional dataset of 323 vegetable growers in Bursa, Turkey. Farmer characteristics and pesticide application behavior were more effective in reducing residual risk perceptions than farm characteristics. The effects of membership in a cooperative, average amount of pesticide used, location where the pesticide was purchased, application timing, and adherence to last spraying and harvest timing on the perception of residues were the largest. Farmers receive helpful but limited information from pesticide distributors. However, the primary source of information is not the advisors, which is why these grievances exist. The study results show that policymakers should support cooperatives, expand pesticide training, and make public extension services more effective and that pesticide dealers should conduct more frequent inspections.

Mosaic crack mapping of footings by convolutional neural networks

Cracks are the primary indicator informing the structural health of concrete structures. Frequent inspection is essential for maintenance, and automatic crack inspection offers a significant advantage, given its efficiency and accuracy. Previously, image-based crack detection systems have been utilized for individual images, yet these systems are not effective for large inspection areas. This paper thereby proposes an image-based crack detection system using a Deep Convolution Neural Network (DCNN) to identify cracks in mosaic images composed from UAV photos of concrete footings. UAV images are transformed into 3D footing models, from which the composite images are created. The CNN model is trained on 224 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 224 pixel patches, and training samples are augmented by various image transformation techniques. The proposed method is applied to localize cracks on composite images through the sliding window technique. The proposed VGG16 CNN detection system, with 95% detection accuracy, indicates superior performance to feature-based detection systems.

Livestock Management System

Abstract: Currently, advancements in farm automation and technological techniques contribute significantly to boosting farm productivity, with a notable focus on cattle health monitoring systems in research. This system encompasses infrastructure, hardware, and software components aimed at enhancing cattle health, crucial for overall farm productivity. Given the challenges posed by managing numerous cattle in dairy farms, continuous monitoring of individual health becomes imperative. Leveraging sensor technology to monitor vital signs such as temperature and heart rate allows for early detection of health issues, reducing the need for frequent health inspections and long-term healthcare costs for the animals.

Protection of Child Victims of Trafficking in Vietnam

Human trafficking rose as a particularly concerning problem for the international community in the 1990s and early 2000. It quickly became an outbreak when facing the global pressure of illegal immigration, loose borders, security danger, and economic and territorial integrity. Women and children are two typical vulnerable objects, hence targeted by culprits in kidnapping, blackmail, trafficking, sexual abuse, exploited labor, or taking organs. With a complicated diverse terrain, child trafficking in Vietnam has been an unsolved issue, forcing the Vietnamese government to work harder to protect the future generation. Therefore, this research focuses on two main objectives, including investigating the protection of child victims of trafficking in Vietnam and proposing potential solutions in the future. The article suggests increasing the number and quality of research (conducted by Vietnamese organizations/experts) about child trafficking. Experiences learned from the Vietnamese context showed that frequent inspection, evaluation, and summarization in implementing international rules are necessary as well as remuneration for border sites or the police army. In the end, this paper might help other developing countries face difficulties from academic studies on child trafficking, new trafficking techniques, and victim assistance measures in the COVID-19 pandemic.

Fifteen years of elasmobranchs trade unveiled by DNA tools: Lessons for enhanced monitoring and conservation actions

The trade of elasmobranchs (sharks and rays) in Brazil threatens the country's rich endemic biodiversity. The present study explored the use of DNA-based tools to monitor the Brazilian elasmobranch trade, focusing on their role in identifying processed products and supporting conservation efforts. A systematic search of literature was conducted and included 35 peer-reviewed papers published between 2008 and 2023. A shift from the development of DNA-based tools to direct trade applications has been observed since 2015. Molecular identification challenges, including costly sequencing and limited resources in national databases, were identified along with proposed solutions, such as protocol optimization and exploration of cost-effective alternatives. Biases in trade analysis papers, particularly the lack of research in the Northeast Region of Brazil, and issues with sample sizes were evident. Species identified using DNA-based tools included the critically endangered Scalloped Hammerhead Shark (Sphyrna lewini), which appeared in 46 % of the evaluated papers, followed by the Blue Shark (Prionace glauca), and several others threatened species, such as the critically endangered and endemic Brazilian Guitarfish (Pseudobatos horkelii) and the recently categorized as vulnerable Sharpnose Shark (Rhizoprionodon porosus). Other species were reassessed by IUCN, including previously non-threatened species that are now at risk, emphasizing the need for fisheries management, trade monitoring and conservation measures. Our findings highlight the importance of continued genetic monitoring to analyze market trends and adjust legislation, encouraging compliance with frequent inspections to enhance wildlife conservation. We also identified gaps in research and recommended strategies for accurate species identification, broader investigation, and effective management.

Effective augmentation of front opening unified pod filter images

The front opening unified pods (FOUPs) are popular carriers in semi-conductor industry, and their automatic optical inspection relies on the sufficiency of images. Filter papers at the bottom of FOUP require frequent inspection, while obtaining their images is time-consuming. This work thus investigates augmenting images of filter papers using conventional interpolation and a recent generative adversarial network with variations, including numbers of epochs, usage of progressive learning, loss functions, and cross frame calibration. Drawbacks of interpolated images are exhibited. Results of sixteen models are quantitatively evaluated using the Fréchet inception distance (FID). The best FID among all models is fid = 0.102, and the distribution of generated images is close to that of real images. A user study is also conducted to investigate the authenticity of generated images. Our experiments reveal that given a limited number of training images, after proper geometric calibration, images synthesized by a generative adversarial network are qualified for data augmentation and substantially benefit industrial image classification tasks.

Age Estimation of Transmission Line Using Statistical Health Index and Failure Probability Curve-Fitting Method

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines.

ECONOMIC IMPACT OF COMMUTERS AND DRIVERS’ PERCEPTION OF MINIBUS (KOROPE) AS A MEANS OF PUBLIC TRANSPORTATION IN OSOGBO, OSUN STATE, NIGERIA

The Osogbo metropolitan has seen several transportation-related problems due to its dense population, limited road space, land-use characteristics, absence of mass transit, poorly executed development plans, and encroachments on private property. The specific objectives are to identify the socioeconomic characteristics of the participants in Osogbo, Osun state; investigate commuters' and drivers' perceptions of Korope transport services in Osogbo, Nigeria; and evaluate the function of Korope transport services in the study area concerning public transportation. Data was gathered using a structured survey design questionnaire. The purposive sampling method was employed. In Osogbo, Osun state, Nigeria, commuters, passengers, and Korope transport operators and owners make up the study's population. However, 1,500 of those commuters, passengers, and Korope transport operators reside in Osogbo, Osun state, Nigeria. The study's sample frame consists of passengers in Osogbo, Osun state, Nigeria, as well as Korope transport providers and owners. A sample of 177 respondents was chosen at random. Both inferential and descriptive statistics were used in the data analysis. The findings show that the F test yields asymptotic significance values of 0.000 (less than p value of 0.05) and 54.534 (more than the tabulated f-value of 2.32). Consequently, the null hypothesis is disproved and the alternative is approved. There is evidence to support the effects of connectivity, affordability, dependability, accessibility, and ease of use on intraurban mobility in Osogbo. Korope, or minibuses, are a crucial component of Osogbo, Osun State's public transportation network. Despite their increased security and overall quality, minibuses as a mode of transportation nevertheless have several obvious drawbacks. However, the report recommended that stricter safety regulations be implemented and upheld for minibuses. Frequent car inspections, driver education programs, strict adherence to speed limits and other traffic rules should all be part of these laws.

Spatial Distribution of Petrol Filling Stations in Yakurr Lga of Cross River State-Nigeria

Petrol filling stations are vital but their location can also pose safety risks hence, there is need to adhere to regulatory guidelines. This study utilizes Geographic Information System (GIS) tools to analyze the placement of fuel stations in Yakurr local government areas of Cross River State. Quantitative research method was employed for the study. This involved acquiring primary and secondary data. Petrol filling stations coordinates were captured using Garmin 76CSX Handheld GPS, the attributes data such as name of business/roads were recorded, distance between filling stations and setback were measured directly on the ArcGIS 10.3. The results of the analysis showed that the stations are concentrated along Calabar-Ikom Highway. Furthermore, (54.17%) petrol filling stations complied with the 400M spacing rule, while 46.83% did not. 41.67% kept the minimum setback of 15M, while 58.33% of the filling stations have a setback of less than 15M. Many lacked essential firefighting equipment: fire extinguishers, sand buckets and water. In conclusion, the study suggest that regulatory bodies should be more proactive and effective by conducting frequent inspections of petrol filling stations to ensure compliance with recommended regulatory guidelines.