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Objectives: Parenteral drug products manufactured under GMP conditions should be visually inspected for defects and particulate contamination by trained and qualified personnel. Although personnel qualification is required, no practical protocols or formal guidelines are available for the development of qualification test sets (QTSs) used for qualification procedures. The current practice is to either procure a standardized QTS from a commercial supplier or amass sufficient manufacturing rejects during visual inspection procedures to compile in-house QTSs. However, both strategies inherently possess disadvantages and limitations. The objective of this study was to develop a manufacturing protocol for an optimal and adaptable QTS for training and qualification procedures. Methods: We combined the results of a literature search, survey of five Dutch hospital pharmacy compounding facilities, semi-structured personnel interviews, and extensive pre-GMP formulation studies to develop an optimal and adaptable QTS manufacturing protocol. Results: The literature search did not identify a manufacturing protocol for an optimal and adaptable QTS, but did identify specifications and requirements for optimal QTSs. The survey among hospital pharmacy compounding facilities revealed considerable variability in the qualification procedures and used QTSs. Semi-structured personnel interviews and pre-GMP formulation studies demonstrated that defects encountered during routine productions could be realistically simulated with pharmaceutical-grade excipients. As a proof-of-concept, we manufactured two different QTSs under GMP conditions and assessed these for formal GMP training and qualification purposes, which were considered a significant improvement compared to using manufacturing rejects. Conclusions: To the best of our knowledge, this is the first study presenting these data and our adaptable protocol, which is provided in the Supplemental Materials, may aid compounding facilities in the standardization, training, and qualification of personnel involved in visual inspection procedures. [ABSTRACT FROM AUTHOR]

Alkali-activated concrete (AAC) gained considerable attention from the beginning of its discovery as a substitute for conventional concrete (CC) due to its superior properties and powerful environmental impact. This paper investigated physical and mechanical characteristics, considering the presence of polypropylene fibers (PPFs), of ambient cured alkali-activated slag concrete (AASC) at ambient temperature, 25°C, and after exposing to elevated temperatures, 300°C, 600°C, and 900°C. For a comparison purpose, a similar CC was also investigated by observing changes in the physical and mechanical characteristics. Visual inspection, weight changes, compressive strength, flexural strength, bond strength, and ultrasonic pulse velocity tests were carried out. Also, scanning electron microscope (SEM) and thermogravimetric analysis (TGA) tests were used for microstructure analysis. Test results demonstrated that the AASC achieved higher mechanical properties than similar CC at ambient temperature. Results also showed that the AASC has a higher resistance to elevated temperatures than the CC due to its higher resistance to spalling and cracking, its higher recorded residual mechanical properties after exposure to elevated temperatures, and its higher thermal stability and resistance to degradation recorded by the microstructure analysis. The inclusion of polypropylene fibers decreased most of the studied properties for both AASC and CC. However, the reduction in mechanical properties for AASC was higher than that for CC at equivalent PPF content. [ABSTRACT FROM AUTHOR]

With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI‐VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie—Artificial Intelligence‐Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so‐called thermal turbulence patterns (TTPs) that result from such surface contamination or damage. To complement the thermographic inspections, high‐resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills. [ABSTRACT FROM AUTHOR]

The paper deals with the problems of guidance and motion control of a space robot during approach and visual checking the state of an information spacecraft, namely a geostationary communications satellite. Original methods are proposed for predicting the influence of gravitational disturbances, as well as solar pressure forces in the synthesis of the robot's guidance and motion control laws. The developed methods and algorithms for a space robot control are based on the local optimizing the fuel consumption of the plasma electric propulsion unit. Simulation results on developed algorithms are presented that demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

Background: Substandard and falsified (SF) medicines are a serious threat to public health in low- and middle-income countries (LMICs). Visual inspection of medicines and screening analysis using the Global Pharma Health Fund (GPHF)-Minilab are important in medicine quality surveillance in low-resource settings. Methods: Recently, 260 medicine samples from Nigeria had been investigated for assay and dissolution according to the United States Pharmacopeia (USP). In the present study, these results were compared to the results of the investigation of the same samples by visual inspection and by GPHF-Minilab analysis by local personnel in Nigeria. Results: Visual inspection identified many deficiencies of dosage units and packaging information in SF medicines. All four falsified medicines were readily identifiable, primarily from serious spelling errors in the labelling, and from manufacturer names which could not be verified using internet resources. In GPHF-Minilab disintegration testing, two samples did not disintegrate even after 60 min; both were found to fail USP dissolution testing with extreme deviations. Of the 20 samples which deviated in USP assay analysis by more than 20% from the declared API amount, seven (35%) were detected as non-compliant in TLC analysis. Evaluation by TLC image analysis with a recently developed smartphone application (named TLCyzer) increased sensitivity to 62.5% but led to an unacceptably low specificity (75.2%). Additional training of the local personnel improved the results of both TLC analysis and TLCyzer evaluation. Photographs of the visual deficiencies and of the TLC analysis results of the SF medicines are provided as PowerPoint and PDF slides with this publication, for future training courses of pharmacy staff and health workers in LMICs. Conclusion: Visual inspection, and screening analysis with simple, rapid and inexpensive methods, are important in the surveillance for SF medicines in LMICs. This study provides data on the potential and the limitations of such screenings. [ABSTRACT FROM AUTHOR]

The self-quenching fluorogenic probe facilitates precise identification of LAMP (loop-mediated isothermal amplification) amplicons, unaffected by non-specific products resulting from primer dimers. However, low quenching efficiency by surrounding nucleobases leads to high background signal, posing significant challenges for visual inspection with the naked eye. The present study aims to identify an oligonucleotide sequence that is complementary to the self-quenching fluorogenic probe, and to employ the fluorescence super-quenching mechanism of double-stranded DNA to establish a visualization system for the LAMP assay. The results indicated that the incorporation of a sequence fully complementary to the probe could significantly reduce the system's background fluorescence (p < 0.05). When the melting temperature exceeds room temperature, truncating the complementary sequence from the 3′ end does not compromise the probe's quenching efficiency. The LAMP visualization system, using a 10–13-base complementary sequence of the loop primer-based probe, could effectively minimize background fluorescence and yield straightforward visual results post-reaction. Applied to rainbow trout and Atlantic salmon detection, the system detected 1 pg DNA in a closed-tube format. In conclusion, a suitable complementary sequence can reduce the background fluorescence of the self-quenching fluorogenic probe. Employing this sequence alongside the self-quenching fluorogenic probe to develop a low-background fluorescence LAMP system demonstrates great potential for successful visual detection and holds considerable promotional merit. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer remains a major health concern in developing countries, primarily due to the presence of human papillomavirus (HPV) types 16 and 18. Visual Inspection with Acetic Acid (VIA) is a common and affordable screening method, yet its effectiveness is closely tied to the skills and competency of healthcare providers. This study aimed to assess the impact of clinical training on improving VIA screening outcomes for cervical cancer in a county in Kenya. Methods: Seven government health facilities equipped with cryotherapy machines were selected, with two healthcare providers sampled from each. A six-day VIA training intervention was conducted, combining theoretical instruction with hands-on clinical practice using preceptors to guide trainees. Baseline data were collected using questionnaires, abstraction tools, and observational checklists, followed by pre-and post-tests to measure knowledge and skill improvement. Results: The training program significantly improved the healthcare providers’ knowledge and VIA skills (p

Visual inspection remains key for assessing the condition of bridges and hence assisting with planning and maintenance activities. There have been many efforts to improve or supplement visual inspection processes using new sensing technologies and data capture methods to usher in an era of 'smart bridges' or 'smart infrastructure'. One method to improve data capture is a 'remote inspection' where inspectors use digital photographs of a bridge to identify and grade structural defects to the standard of a 'general inspection'. In this paper, survey data are presented to help formulate a preliminary assessment of the potential for engineers to implement this possible evolution of the visual inspection process. A potential schema for remote visual inspections is developed and presented as a conceptual web application. The focus on the development of the schema includes the need for ease of use by inspectors and integration of collected digital data into bridge management systems. The suggested platform is seen as a transitional method to aid in the long-term implementation of further automation of the inspection process. The system architecture is provided along with possible technologies that may support or enhance it, as well as a discussion of the potential barriers to implementation. [ABSTRACT FROM AUTHOR]

Nowadays, fingerprint Presentation Attack Detection systems (PADs) are primarily based on deep learning architectures subjected to massive training. However, their performance decreases to never-seen-before attacks. With the goal of contributing to explaining this issue, we hypothesized that this limited ability to generalize is due to the lack of "representativeness" of the samples available for the PAD training. "Representativeness" is treated here from a geometrical perspective: the spread of samples into the feature space, especially near the decision boundaries. In particular, we explored the possibility of adopting three-dimensionality reduction methods to make the problem affordable through visual inspection. These methods enable visual inspection and interpretation by projecting data into two-dimensional spaces, facilitating the identification of weak areas in the decision regions estimated after the training phase. Our analysis delineates the benefits and drawbacks of each dimensionality reduction method and leads us to make substantial recommendations in the crucial phase of the training design. [ABSTRACT FROM AUTHOR]

Semi-integral abutments help increase the service life of bridges by eliminating the issues associated with the use of expansion joints. However, the behavior of such abutments under service loads has complexities, especially in their connections to approach slabs. This motivated the current study to systematically investigate the in-service performance of semi-integral abutment bridges through a holistic suite of visual inspections, long-term monitoring programs, and numerical analyses. For this purpose, nine bridges were visually inspected to identify common problems associated with semi-integral abutments, including joint issues, embankment erosion, cracks in approach slabs, and water leakage. Additionally, two bridges were instrumented with various sensors at the time of construction and monitored to understand their long-term behavior under service loads. This was with a focus on top and bottom approach slab strains, longitudinal and transverse abutment displacements, and earth pressures on abutment walls. Two bridges with a high skew angle was further studied numerically in four different design configurations to evaluate the stress, strain, and displacement distributions in the approach slab and tie bars. The numerical investigations also assessed the bridge end's response to embankment settlement, given the frequent occurrence of settlement-related issues. The study's results and findings provided new insights into key structural response measures paired with relevant recommendations. The outcome facilitates the design and assessment of semi-integral abutment bridge ends, taking into account various approach slab details. [ABSTRACT FROM AUTHOR]

Objectives: Evaluating gingival thickness is an essential factor for esthetics, function, and the maintenance of soft tissue health around natural teeth and dental implants. This study aimed to evaluate the accuracy of visual inspection procedures used to identify gingival phenotype by clinicians with different expertise and level of experience. Method and materials: The gingival phenotypes of a total of 200 cases were identified as either thick or thin via visual assessment with clinical photographs by eight clinicians. One periodontist examined the gingival phenotype of all subjects based on the translucency of the periodontal probe through the gingival sulcus. The accuracy between these two methods was evaluated using percentile agreement and k statistics. Results: The mean percentage of accurately identified cases was 75% irrespective of the clinician's experience and expertise. Almost half of the thin phenotype cases were misclassified by clinicians. Inter-examiner agreement was generally fair to moderate and intra-examiner agreement was generally moderate. Conclusion: Within the limits of this study, the present findings demonstrated that the visual inspection method misclassified the cases, especially thin gingival phenotype, compared to the probe translucency method. Therefore, there is a need to identify a universally accepted, accurate method and clearclinical definitions. [ABSTRACT FROM AUTHOR]

In this study, a ratiometric fluorescent sensor CdTe QDs@ZIF-8 with butterfly spectra is successfully constructed by in situ encapsulating mercaptopropionic acid-modified CdTe quantum dots in zeolitic imidazolate framework-8 (ZIF-8) with a simple strategy, and used for the detection of tetracycline in fluorescence/smartphone colorimetry dual-mode. ZIF-8 not only reduces the agglomeration of the quantum dots but also surprisingly generates a new green fluorescence signal at 524 nm while the red fluorescence of the CdTe quantum dots at 650 nm quenches when tetracycline is added. The two opposing fluorescence signals create a butterfly-shaped fluorescence spectrum, allowing the sensor to detect tetracycline over a linear range of 0–70 μM with the detection limit (LOD) of 0.0155 μM by using a ratiometric fluorescence technique. What is more, based on the obvious color change of the fluorescent sensor gradually from red to green under UV light, a highly stable point-of-care testing sensor has been developed for on-site detection of tetracycline through color recognition by smartphones, which can be used for real-time detection of this antibiotic in the range of 0–1000 μM with the LOD of 0.0249 μM. This work provides a simple and efficient method for the on-site detection of tetracycline. [ABSTRACT FROM AUTHOR]

Composite structure is widely used in various technological fields because of its superior material properties. Composite structure detection technology has been exploring efficient and fast damage detection technology. In this paper, image-based NDT technology is proposed to detect composite damage using deep learning. A data set was established through literature, which contained images of damaged and non-damaged composite material structures. Then, five convolutional neural network models Alexnet, VGG16, ResNet-34, ResNet-50, and GoogleNet were used to automatically classify the damage. Finally, the performance of five pre-trained network architectures is evaluated, and the results show that RESNET-50 technology can successfully detect damage in a reasonable computation time with the highest accuracy and low complexity using relatively small image datasets. [ABSTRACT FROM AUTHOR]

The Japanese orange fly, Bactrocera tsuneonis (Miyake) (Diptera: Tephritidae), is a univoltine tephritid pest of citrus fruits. Its occurrence in citrus-growing regions restricts the export of citrus fruits owing to plant quarantine regulations. We investigated whether the B. tsuneonis infestation rate could meet export quarantine standards if proper preharvest management were carried out. Our approach involved using orchards unsuitable for B. tsuneonis occurrence, implementing appropriate pesticide control, and removing suspected infested fruits. Sunny, open orchards away from any thickets that could serve as hiding places for the adults were chosen as the demonstration orchards. During the peak adult emergence and larval hatching periods, pesticides were sprayed to protect the fruits from infestation. We observed a total of 724,296 fruits throughout the fruit coloring period and removed 1027 suspected infested fruits. We dissected 58,193 harvested fruits and confirmed that they were not infested. No adults were captured during trap surveys in these orchards. In contrast, poorly managed orchards in the same area consistently exhibited fruit damage by B. tsuneonis larvae, and adults were captured in trap surveys. These results prove the feasibility of establishing damage-free orchards through appropriate preharvest management, even in regions where B. tsuneonis is distributed. [ABSTRACT FROM AUTHOR]

When applied to functional analysis results, structured visual inspection criteria have resulted in improvements in the levels of agreement between raters as well as earlier identification of the function of challenging behavior. However, multistep criteria can be difficult to apply in real time, which could be a barrier to widespread adoption in practice. This study evaluated a Microsoft‐Excel‐based functional analysis decision support system (FADSS), which could aid behavior analysts with interpreting functional analysis results. Final overall agreement between the FADSS and post hoc visual inspection was high at 95%. Final overall agreement between the post hoc results generated by FADSS and ongoing results generated by FADSS was acceptable at 81%, representing a 50% increase in efficiency. These results indicate that FADSS could aid behavior analysts when interpreting functional analysis results in real time. [ABSTRACT FROM AUTHOR]

In order to meet the current expanding market demand for knitwear, high-speed automatic knitting machines with "one-line knit to shape" capability are widely used. However, the frequent emergence of floating-yarn stacking anomalies during the high-speed knitting process will seriously hinder the normal reciprocating motion of the needles and cause a catastrophic fracture of the whole machine needle plate, greatly affecting the efficiency of the knitting machines. To overcome the limitations of the existing physical-probe detection method, in this work, we propose a visual floating-yarn anomaly recognition framework based on a CNN-BiLSTM network with the knit feature sequence (CNN-BiLSTM-KFS), which is a unique sequence of knitting yarn positions depending on the knitting status. The sequence of knitting characteristics contains the head speed, the number of rows, and the head movements of the automatic knitting machine, enabling the model to achieve more accurate and efficient floating-yarn identification in complex knitting structures by utilizing contextual information from knitting programs. Compared to the traditional probe inspection method, the framework is highly versatile as it does not need to be adjusted to the specifics of the automatic knitting machine during the production process. The recognition model is trained at the design and sampling stages, and the resulting model can be applied to different automatic knitting machines to recognize floating yarns occurring in various knitting structures. The experimental results show that the improved network spends 75% less time than the probe-based detection, has a higher overall average detection accuracy of 93% compared to the original network, and responds faster to floating yarn anomalies. The as-proposed CNN-BiLSTM-KFS floating-yarn visual detection method not only enhances the reliability of floating-yarn anomaly detection, but also reduces the time and cost required for production adjustments. The results of this study will bring significant improvements in the field of automatic floating-yarn detection and have the potential to promote the application of smart technologies in the knitting industry. [ABSTRACT FROM AUTHOR]

Developments in drones and imaging hardware technology have opened up countless possibilities for enhancing structural condition assessments and visual inspections. However, processing the inspection images requires considerable work hours, leading to delays in the assessment process. This study presents a semantic segmentation architecture that integrates vision transformers with Laplacian pyramid scaling networks, enabling rapid and accurate pixel-level damage detection. Unlike conventional methods that often lose critical details through resampling or cropping high-resolution images, our approach preserves essential inspection-related information such as microcracks and edges using non-uniform image rescaling networks. This innovation allows for detailed damage identification of high-resolution images while significantly reducing the computational demands. Our main contributions in this study are: (1) proposing two rescaling networks that together allow for processing high-resolution images while significantly reducing the computational demands; and (2) proposing Dmg2Former, a low-resolution segmentation network with a Swin Transformer backbone that leverages the saved computational resources to produce detailed visual inspection masks. We validate our method through a series of experiments on publicly available visual inspection datasets, addressing various tasks such as crack detection and material identification. Finally, we examine the computational efficiency of the adaptive rescalers in terms of multiply–accumulate operations and GPU-memory requirements. [ABSTRACT FROM AUTHOR]

Visual inspection tasks often require humans to cooperate with artificial intelligence (AI)‐based image classifiers. To enhance this cooperation, explainable artificial intelligence (XAI) can highlight those image areas that have contributed to an AI decision. However, the literature on visual cueing suggests that such XAI support might come with costs of its own. To better understand how the benefits and cost of XAI depend on the accuracy of AI classifications and XAI highlights, we conducted two experiments that simulated visual quality control in a chocolate factory. Participants had to decide whether chocolate molds contained faulty bars or not, and were always informed whether the AI had classified the mold as faulty or not. In half of the experiment, they saw additional XAI highlights that justified this classification. While XAI speeded up performance, its effects on error rates were highly dependent on (X)AI accuracy. XAI benefits were observed when the system correctly detected and highlighted the fault, but XAI costs were evident for misplaced highlights that marked an intact area while the actual fault was located elsewhere. Eye movement analyses indicated that participants spent less time searching the rest of the mold and thus looked at the fault less often. However, we also observed large interindividual differences. Taken together, the results suggest that despite its potentials, XAI can discourage people from investing effort into their own information analysis. [ABSTRACT FROM AUTHOR]

AbstractThe increasing emphasis on the quality and safety of agricultural products, which are vital to global trade and consumer health, has driven the innovation of cost-effective, convenient, and rapid smart detection technologies. Smartphones, with their interdisciplinary functionalities, have become valuable tools in quantification and analysis research. Acting as portable, affordable, and user-friendly analytical devices, smartphones are equipped with high-resolution cameras, displays, memory, communication modules, sensors, and operating systems (Android or IOS), making them powerful, palm-sized remote computers. This review delves into how visual inspection technology and smartphones have enhanced the quality and safety of agricultural products over the past decade. It also evaluates the key features and limitations of existing smart rapid inspection methods for agricultural products and anticipates future advancements, offering insights into the application of smart rapid inspection technology in agriculture. [ABSTRACT FROM AUTHOR]

Traditional inspection methods often fall short in detecting defects or damage in fibre-reinforced polymer (FRP) composite structures, which can compromise their performance and safety over time. A prime example is barely visible impact damage (BVID) caused by out-of-plane loadings such as indentation and low-velocity impact that can considerably reduce the residual strength. Therefore, developing advanced visual inspection techniques is essential for early detection of defects, enabling proactive maintenance and extending the lifespan of composite structures. This study explores the viability of using novel bio-inspired hybrid composite sensors for detecting BVID in laminated FRP composite structures. Drawing inspiration from the colour-changing mechanisms found in nature, hybrid composite sensors composed of thin-ply glass and carbon layers are designed and attached to the surface of laminated FRP composites exposed to transverse loading. A comprehensive experimental characterisation, including quasi-static indentation and low-velocity impact tests alongside non-destructive evaluations such as ultrasonic C-scan and visual inspection, is conducted to assess the sensors' efficacy in detecting BVID. Moreover, a comparison between the two transverse loading types, static indentation and low-velocity impact, is presented. The results suggest that integrating sensors into composite structures has a minimal effect on mechanical properties such as structural stiffness and energy absorption, while substantially improving damage visibility. Additionally, the influence of fibre orientation of the sensing layer on sensor performance is evaluated, and correlations between internal and surface damage are demonstrated. [ABSTRACT FROM AUTHOR]

Automated industrial Visual Inspection Systems (VIS) are predominantly designed for specific use cases, resulting in constrained adaptability, high setup requirements, substantial capital investments, and significant knowledge barriers. This paper explores the business potential of recent alternative architectures proposed in the literature for the visual inspection of individual products or complex assemblies within highly variable production environments, utilizing next-generation VIS. These advanced VIS exhibit significant technical (hardware and software) enhancements, such as increased flexibility, reconfigurability, Computer Aided Design (CAD)-based integration, self-X capabilities, and autonomy, as well as economic improvements, including cost-effectiveness, non-invasiveness, and plug-and-produce capabilities. The new trends in VIS have the potential to revolutionize business models by enabling as-a-service approaches and facilitating a paradigm shift towards more sustainable manufacturing and human-centric practices. We extend the discussion to examine how these technological innovations, which reduce the need for extensive coding skills and lengthy reconfiguration activities for operators, can be implemented as a shared resource within a circular lifecycle. This analysis includes detailing the underlying business model that supports shared utilization among different stakeholders, promoting a circular economy in manufacturing by leveraging the capabilities of next-generation VIS. Such an approach not only enhances the sustainability of manufacturing processes but also democratizes access to state-of-the-art inspection technologies, thereby expanding the possibilities for autonomous manufacturing ecosystems. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effect of steam sterilization treatments on anodized titanium devices (nails) of the same length and geometry. A fixed sterilization process was employed with the parameters: temperature of 121 °C, pressure of 15 psi, and autoclaving time of 30 minutes. The objective was to examine the color-fading issue after three autoclave cycles. During sterilization, two nail samples were wrapped in sterile cloth, while the other two samples were wrapped in aluminum foil. In each cycle, a total of four samples were used. Finally, all samples were visually analyzed for any signs of color fading. This study highlighted the adverse interaction between aluminum foil and steam, leading to potential moisture retention, chemical reactions, and surface damage. Conversely, sterile cloth allowed effective sterilization while preserving the anodized coating. These findings underscore the necessity of material-specific sterilization protocols to prevent damage to anodized implants, with sterile cloth recommended to maintain both sterilization efficacy and implant aesthetics. Further research into the long-term effects of high pressure and hightemperature sterilization on anodized surfaces is suggested. [ABSTRACT FROM AUTHOR]

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In this study, a plastic infrared sorting machine was used to conduct sorting tests on the normal polyethylene terephthalate (PET) plastic bottles with blue, white, and green colors as well as the other plastic bottles made from polycarbonate (PC), [polyethylene (PE), and polypropylene under the dual-path excitation environment of visible and infrared light. Three common Retinex color enhancement algorithms were further utilize to identify the white -, blue-, and green-color PET bottles, and the recognition effect of plastic infrared sorting machines was investigated. The results indicated that there was no significant difference in the imaged images between PP, PC, and PET bottles with a white color under visible light. However, under infrared imaging, PP, PC, and PET bottles reflected green, purple, and blue light with significant differences. The HSV range value was expanded from 0-46 to 0-61, the background saturation was adjusted from 72 to 80, the missing proportion of white and blue PET bottles was reduced from 4. 58 % to 1. 26 %, and the misidentification rate of white bottles made from PP, PC, and PE was reduced from 3. 88 % to 2.69 %. There was no impact on the misidentification rate of the PE bottle, maintaining a recognition rate of 100 %. The best removal effect was obtained through the MSR color enhancement algorithm with removal rates of 94. 8 %, 95. 83 %, and 95. 83 % for the recognition of white, blue, and green PET bottles, respectively. [ABSTRACT FROM AUTHOR]

Bridges deteriorate due to different causes such as ageing and climatic conditions. As a consequence, repair actions are considered more expensive than maintenance. Therefore, the prediction of maintenance at the bridge network level is particularly complex due to the considerable level of heterogeneity spanning various bridge types and functions. Consequently, in this article an analytical deterioration model called 'two-step cluster analysis' is presented and applied to predict the results of a homogeneous data set. This model had been used in different fields including civil engineering but not for superstructure deterioration forecasting. Variables that interfere with the performance of the structure were considered, such as geometry, volume of traffic, among others. The results show that deterioration drop rapidly if repair treatments were not performed. Variations of up to 20.9% at 50 years between the degradation of the database and the analysis of groups, demonstrates the importance of grouping within the analysis of the database, since the late maintenance can affect the life of the bridge. Preventive actions could significantly mitigate the deterioration process. The applied methodology benefits from current bridge inspection practice and produces a probabilistic rating that is consistent with the Markov approach for model deterioration and grouping variables. [ABSTRACT FROM AUTHOR]

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%. [ABSTRACT FROM AUTHOR]