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As the last link of product remanufacturing, reassembly process is of great importance in increasing the utilization of remanufactured parts as well as decreasing the production cost for remanufacturing enterprises. It is a common problem that a large amount of remanufactured part/reused part which past the dimension standard have been scrapped, which have increased the production cost of remanufacturing enterprises to a large extent. With the aim to improve the utilization of remanufacturing parts with qualified quality attributes but exceed dimension, the purpose of this paper is to put forward a reassembly classification selection method based on the Markov Chain.

To begin with, a classification standard of reassembly parts is proposed. With the thinking of traditional ABC analysis, a classification management method of reassembly parts for remanufactured engine is proposed. Then, a homogeneous Markov Chain of reassembly process is built after grading the matching dimension of reassembly parts with different variety. And the reassembly parts selection model is constructed based on the Markov Chain. Besides, the reassembly classification selection model and its flow chart are proposed by combining the researches above. Finally, the assembly process of remanufactured crankshaft is adopted as a representative example for illustrating the feasibility and the effectiveness of the method proposed.

The reassembly classification selection method based on the Markov Chain is an effective method in improving the utilization of remanufacturing parts/reused parts. The average utilization of remanufactured crankcase has increased from 35.7 to 80.1 per cent and the average utilization of reused crankcase has increased from 4.2 to 14 per cent as shown in the representative example.

The reassembly classification selection method based on the Markov Chain is of great importance in enhancing the economic benefit for remanufacturing enterprises by improving the utilization of remanufactured parts/reused parts.

There has been an increased interest in the use of semantic description and matching techniques, to support service discovery and to overcome the limitations in the traditional syntactic approaches. However, the existing semantic matching approaches lack certain desirable properties that must be present in an effective solution to support service discovery. The purpose of this paper is to present a solution to facilitate the effective semantic matching of resource requests and advertisements in pervasive environments.

The paper presents a semantic description and matching approach to facilitate resource discovery in pervasive environments; the approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request.

The solution has been evaluated for its effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. The solution was also evaluated for its efficiency/scalability and from the experimental results obtained, it can be observed that for most practical situations, matching time can be considered acceptable for reasonable numbers of advertisements and request sizes.

The proposed approach improves existing semantic matching solutions in several key aspects. Specifically; it presents an effective approximate matching and ranking criterion and incorporates priority consideration in the matching process. As shown in the evaluation experiments, these features significantly improve the effectiveness of semantic matching.

A research project is reported in which techniques for the automatic classification of book material were investigated. Attention was focussed on three fundamental issues, namely: the computer‐based surrogation of monographic material, the clustering of book surrogates on the basis of content association, and the evaluation of the resultant classifications. A test collection of 250 books, which was assembled on behalf of the project, is described together with its surrogation by means of the complete back‐of‐the‐book index, table of contents, title and Dewey classification code(s) of each volume. Some properties of hierarchic and non‐hierarchic automatic classifications of the test collection are discussed, followed by their evaluation with reference to a small set of queries and relevance judgements. Finally, a less formal evaluation of the classifications in terms of the logical appeal of the cluster membership is reported. The work has shown that, on a small experimental scale and in the context of the test data used, automatic classifications of book material represented by index list can be produced which are superior, on the basis of a generalized measure of effectiveness, to a conventional library classification of the same material.

The existing authentication procedures (pin, pattern, password) are not very secure. Therefore, the Gait pattern authentication scheme is introduced to verify the own user. The current research proposes a running Gaussian grey wolf boosting (RGGWB) model to recognize the owner.

The biometrics system plays an important role in smartphones in securing confidential data stored in them. Moreover, the authentication schemes such as passwords and patterns are widely used in smartphones.

To validate this research model, the unauthenticated user's Gait was trained and tested simultaneously with owner gaits. Furthermore, if the gait matches, the smartphone unlocks automatically; otherwise, it rejects it.

Finally, the effectiveness of the proposed model is proved by attaining better accuracy and less error rate.

The identification (listing) and classification (grading) of urban heritage buildings for conservation is a challenging task for urban planners and conservation architects. Most of the world's cities depend on the expert-based evaluation method (EBEM) for listing and grading heritage buildings. The Panaji city in India provided a unique opportunity to assess the performance of the EBEM as two independent agencies carried out the heritage listing and grading process. Considering the case of Panaji, this research aims to measure the performance of EBEM used for listing and grading heritage buildings and identify the issues associated with the existing methodology.

This research presents a comparative analysis of the building listed and graded by the two agencies. The buildings that both agencies graded were identified and analysed using a confusion matrix. The grading classification was tested for accuracy, precision, sensitivity and F-score.

The result shows a low accuracy and F-score, which reflects the level of buildings misclassified. The misclassification is the product of the lack of standardisation of methodology and the subjectivity level involved in the EBEM.

Heritage listing and grading is a time-consuming process, and no city has the time and resource to conduct studies to check the accuracy. The cities in India and across the world, which follow a similar EBEM process, should consider this study's finding and revisit their methodology and develop a more reliable methodology for listing and grading heritage buildings.

Given the centrality of data and information to the evaluation and operation of policies to reduce carbon emissions, the purpose of this paper is to investigate potential sources of data within in the UK on the commercial building stock in terms of its physical characteristics, financial performance, energy consumption and environmental performance. The research aimed to increase understanding of the potential sources of data on property attributes, financial performance and energy use or environmental performance, with a particular emphasis on evaluating their strengths and weaknesses in terms of scope, quality, availability and practicability.

This is an explorative, qualitative study that uses semi‐structured interviews conducted with 11 different data holding organisations.

Whilst public sector organisations have the potential to provide the data required for large samples, there are major barriers to obtaining and linking the different databases. Data on the three key data elements (prices, attributes and environmental performance) tend to be split between different governmental agencies. There are likely to be substantial problems (and costs) of linking the databases due, in particular, to definitional differences. There is a conflict between government's public good objective of increasing knowledge of the environmental performance of buildings and its objective of maximising revenues from the commercial exploitation of data. Since the private sector organisations that hold data are commercially motivated, the samples that they have gathered are largely client driven and, consequently, tend to be partial, particular, proprietary, private and product‐related.

Since pricing studies are central to evaluating the effectiveness of eco‐labelling in property markets, this paper improves one's understanding of the role of information and data barriers to improving the allocative efficiency of commercial property markets.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

With the signing of the North American Free Trade Agreement (NAFTA) in 1994, Mexico entered a bilateral free trade agreement which not only lowered its own tariffs on imports but also lowered tariffs on its exports to the United States. We find that women’s relative wage increased, particularly during the period of liberalization. Both between and within-industry shifts also favored female workers. With regards to between-industry shifts, tariff reductions expanded sectors that were initially female intensive. With regards to within-industry shifts, we find a positive association between reductions in export tariffs (U.S. tariffs on Mexican goods) and hiring of women in skilled blue-collar occupations. Finally, we find suggestive evidence that household bargaining power shifted in favor of women. Expenditures shifted from goods associated with male preference, such as men’s clothing and tobacco and alcohol, to those associated with female preference such as women’s clothing and education.