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The purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.

The authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.

The results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.

The proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This study aims to contribute to social identity theory in tourism by exploring the reflections of Chinese tourists visiting North Korea and how they negotiate their intergroup identity in this similar-others destination.

Data were collected via online platforms and in-depth interviews. A qualitative approach, i.e. thematic analysis, was used to analyse the two sets of data.

Results showed that when encountering North Koreans perceived as similar others amid the social, economic and political environments in which they were embedded, Chinese tourists often categorised themselves as ordinary tourists, preferentially treated tourists and vicarious tourists based on intergroup similarities to North Koreans. They also performed intergroup comparison to boost their self-esteem at group and collective levels and developed corresponding strategies to generate distinct emotional group commitments.

It is recommended that destinations reinforce the friendly and unique relationship with their similar-others source markets to improve their attractiveness. Destinations should also strive to enhance the experience of tourists from their similar-others markets by arousing their positive collective-level affection, emotional resonance and nostalgic memories and avoiding negative emotions.

This study offers a theoretical framework analysing the features of tourists’ social identities while visiting a similar-others destination, contributing to our understanding of the interactive and contingency nature of social identity in tourism, and responding to the call for addressing the broader social contexts in which tourists’ group identity is embedded.

本文致力于探索赴朝中国游客的反思以及他们在这个“类似他者”目的地如何协商群际身份, 以对旅游领域的身份认同理论做出贡献。

本文运用主题分析方法; 资料来源于网络文本和深度访谈。

当中国游客面对被视为“类似他者”的朝鲜人时（具有相似的社会、经济和政治环境）, 他们会根据与朝鲜人的群际相似性, 将自我分类为“普通游客”、“受优待者”和“过来人”三类。此外, 他们还会通过群际比较来提高群体层面和集体层面的自尊, 并且采取对应策略以产生不同的群体承诺。

本文提出了一个分析游客访问“类似他者”目的地时的社会认同的理论框架, 对理解旅游中的社会认同的互动性和偶然性做出了贡献, 并响应了在考察游客群体认同时关注其所处的更广泛的社会背景的呼吁。

目的地应该强化它们与“类似他者”客源市场之间友好且独特的关系, 以提升自身吸引力。目的地还应该唤醒游客积极的集体情感和怀旧, 产生主客情感共鸣, 并避免负面情绪, 以尽力提升来自“类似他者”客源地的游客的体验。

Este estudio pretende contribuir a la teoría de la identidad social en turismo mediante la exploración de las reflexiones de los turistas chinos que visitan Corea del Norte y cómo negocian su identidad intergrupal en este destino similar a otros.

Los datos se recogieron mediante plataformas en línea y entrevistas en profundidad. Se utilizó un enfoque cualitativo a través de análisis temático para analizar los dos conjuntos de datos.

Los resultados mostraron que, al encontrarse con norcoreanos percibidos como otros similares en el entorno social, económico y político en el que estaban integrados, los turistas chinos a menudo se categorizaban a sí mismos como turistas ordinarios, turistas con trato preferente y turistas vicarios basándose en las similitudes intergrupales con los norcoreanos. También, realizaban comparaciones intergrupales para aumentar su autoestima a nivel grupal y colectivo, y desarrollaban las estrategias correspondientes para generar distintos compromisos emocionales grupales.

Se presenta un marco teórico en el que se analizan las características de las identidades sociales de los turistas que visitan un destino similar a otro, lo que contribuye a nuestra comprensión de la naturaleza interactiva y contingente de la identidad social en turismo y responde a la necesidad de abordar los contextos sociales más amplios en los que se inserta la identidad de grupo de los turistas.

Se recomienda que los destinos refuercen las relaciones amistosas y únicas con sus mercados emisores similares para mejorar su atractividad. Los destinos deberían esforzarse por mejorar la experiencia de los turistas de sus mercados emisores similares, estimulando su afecto positivo a nivel colectivo, su resonancia emocional y sus recuerdos nostálgicos, y evitando las emociones negativas.

According to regulations, aircraft must be in an airworthy condition before they can be operated. To ensure airworthiness, they must be maintained by an approved component maintenance organisation. This study is aimed to identify potential errors that may arise during the final inspection and certification process of aircraft components, categorise them, determine their consequences and quantify the associated risks. Any removed aircraft components must be sent to an approved aircraft component maintenance organisation for further maintenance and issuance of European Union Aviation Safety Agency (EASA) Form 1. Thereafter, a final inspection and certification process must be conducted by certifying staff to receive an EASA Form 1. This process is crucial because any errors during this stage can result in the installation of unsafe components in an aircraft.

The Systematic Human Error Reduction and Prediction Approach (SHERPA) method was used to identify potential errors. This method involved a review of the procedures of three maintenance organisations, individual interviews with ten subject matter experts and a consensus group of 14 certifying staff from different maintenance organisations to achieve the desired results.

In this study, 39 potential errors were identified during the final inspection and certification process. Furthermore, analysis revealed that 48.7% of these issues were attributed to checking errors, making it the most common type of error observed.

This study pinpoints the potential errors in the final inspection and certification of aircraft components. It offers maintenance organisations a roadmap to assess procedures, implement preventive measures and reduce the likelihood of these errors.

Global building failures, such as the Grenfell Tower fire in London, UK, emphasised the need for trustworthy building handover information for safety. However, a notable gap remains in understanding how reliable handover information can ensure the safety of occupants. This study aims to investigate the use and essential quality of handover information to understand the effects of the quality of information on the management of commercial buildings.

Ninety-four participants from nine organisations who regularly use handover information to manage multiple commercial buildings participated in the semi-structured interviews. Qualitative thematic coding using interview transcripts was performed to identify the utilisation of handover information and its quality requirements.

This study reveals that as-built drawings and product information are predominately used to fulfil statutory obligations, comply with the organisation’s internal policies, evaluate asset valuation and make informed decisions about capital investment and operating expenses. The quality dimensions of “accuracy”, “timeliness”, and “completeness” are preferred in combination to achieve desired outcomes.

This study focused on using handover information in the management of commercial buildings. However, its results can offer valuable perspectives for improving its application across various sectors in the built environment.

The findings affirm the need for quality handover information for safety, compliance and efficient management in commercial buildings.

This research significantly contributes to the current knowledge of handover information in the building sector. Given the study findings, building owners are equipped to define specific handover information requirements and quality requisites.

The purpose of this paper is to highlight the regulatory barriers to achieving sustainable buildings in Kosovo. The present paper focuses on regulatory barriers viewed from the perspective of construction industry experts in achieving sustainable buildings.

The present study uses a qualitative research method and semi-structured interviews as a research instrument. The present study interviews around 20 experts in construction and property management, property development, spatial planning and energy management.

The study finds that Kosovo building laws and regulations provide for the materials assessment criteria, but the materials assessment criteria are only for mechanic strength. The study further finds that the sustainability concept is not included and incorporated in Kosovo's urban planning laws and regulations. The study also finds that despite specific clauses mentioning energy performance certificates in the Law on Energy Performance of Buildings in Kosovo, energy performance certificates appears to be not enforced and the nature of the barrier is more organizational rather than regulatory. Finally, the study finds that Kosovo laws are silent as far as green labeling of building materials is concerned.

The implication of the present finding is that policymakers in Kosovo not only should include clear sustainable materials assessment criteria in the law, but also enforce those criteria through testing and inspection mechanisms included in the law and implemented in practice through funding and organizational support. Nonetheless, policymakers in Kosovo should contemplate amending the urban planning laws in Kosovo and include both the term of sustainability at the planning level and conformity guidelines for sustainable design that can be done at the administrative directive level. Further, the clauses in the law do not suffice if the clauses are not accompanied by specific systemic and organizational support in the issuance of energy performance certificates. Policymakers in Kosovo should be proactive in designing clauses that specify green labeling standards for materials; however, these labeling standards should not adversely affect the cost of construction and reduce the demand for real estate.

The study is the first qualitative study about the perception of construction professionals in Kosovo, regarding the regulatory barriers of sustainable buildings in Kosovo.

This study aims to investigate the possible defects and their root causes in a soft-termination multilayered ceramic capacitor (MLCC) when subjected to a thermal reflow process.

Specimens of the capacitor assembly were subjected to JEDEC level 1 preconditioning (85 °C/85%RH/168 h) with 5× reflow at 270°C peak temperature. Then, they were inspected using a 2 µm scanning electron microscope to investigate the evidence of defects. The reliability test was also numerically simulated and analyzed using the extended finite element method implemented in ABAQUS.

Excellent agreements were observed between the SEM inspections and the simulation results. The findings showed evidence of discontinuities along the Cu and the Cu-epoxy layers and interfacial delamination crack at the Cu/Cu-epoxy interface. The possible root causes are thermal mismatch between the Cu and Cu-epoxy layers, moisture contamination and weak Cu/Cu-epoxy interface. The maximum crack length observed in the experimentally reflowed capacitor was measured as 75 µm, a 2.59% difference compared to the numerical prediction of 77.2 µm.

This work's contribution is expected to reduce the additional manufacturing cost and lead time in investigating reliability issues in MLCCs.

Despite the significant number of works on the reliability assessment of surface mount capacitors, work on crack growth in soft-termination MLCC is limited. Also, the combined experimental and numerical investigation of reflow-induced reliability issues in soft-termination MLCC is limited. These cited gaps are the novelties of this study.