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With the irresistible growth in digitization, data backup policies become essential more than ever for organizations seeking to improve reliability and availability of organizations' information systems. However, since backup operations do not come free, there is a need for a data-informed policy to decide how often and which type of backups should be taken. In this paper, the authors present a comprehensive mathematical framework to explore the design space for backup policies and to optimize backup type and interval in a given system. In the authors' framework, three separate cost factors related to the backup process are identified: backup cost, recovery cost and data loss cost. The objective function has a multi-criteria structure leading to a backup policy minimizing a weighed function of these factors. To formalize the cost and objective functions, the authors get help from renewal theory in reliability modeling. The authors' optimization framework also formulates mixed policies involving both full and incremental backups. Through numerical examples, the authors show how the authors' optimization framework could facilitate cost-saving backup policies.

The methodology starts with designing different backup policies based on system parameters. Each constructed policy is optimized in terms of backup period using renewal theory. After selecting the best back-up policy, the results are demonstrated through numerical studies.

Data backup polices that are tailored to system parameters can result in significant gains for IT (Information Technology) systems. Collecting the necessary parameters to design intelligent backup policies can also help managers understand managers' systems better. Designed policies not only provides the frequency of back up operations, but also the type of backups.

The original contribution of this study is the explicit construction and determination of the best backup policies for IT systems that are prone to failure. By applying renewal theory in reliability, the authors present a mathematical framework for the joint optimization of backup cost factors, i.e. backup cost, recovery time cost and data loss cost.

This study aims to investigate the basic public service level in the Beijing-Tianjin-Hebei region under the impact of COVID-19.

This study constructed a basic public service-level evaluation system from the five dimensions of education, culture, health, social security and infrastructure and environment, and measures the basic public service level in 13 cities in Beijing, Tianjin and Hebei using the entropy method. The spatial pattern and dynamic evolution of the public service level are analysed from the perspective of dynamic trends in time series and spatial distribution, along with the reasons for the evolution of spatial distribution.

(1) The basic public service level in the 13 cities is generally on the rise, but the trend is unstable. (2) The basic public service level in space shows a general trend of attenuation from northeast to southwest, with significant spatial imbalance and orientation. (3) The regional differences first increase and then decrease. (4) The inter-group mobility of different basic public service levels is low, and cities with lower initial levels find it difficult to achieve leapfrog development. Moreover, the health service level of the region is still at a low stage, which is not conducive to effectively preventing and controlling the epidemic.

From the perspective of this research, the spatial pattern and dynamic evolution of basic public service were adopted to analyse the coordinated development of the Beijing-Tianjin-Hebei region. Furthermore, this study discusses how to improve the basic public service level to ensure sustainable operation in the region under the impact of COVID-19.

The aims of this paper is to establish an appropriate physical-change-based renewal (PCBR) projects selection mechanism capable of selecting the combination of the PCBR projects that can make up an integrated urban renewal program in high-density cities.

The research design follows a sequential integrated methodology that combines the calculation algorithms of Fuzzy Analytic Network Process (Fuzzy-ANP) with Zero-One Goal Programming (ZOGP) to support decisions for the selection of PCBR projects. In the first phase, general criteria for assessing the sustainability performance of PCBR projects were collected from relevant literature. In the second phase, the Fuzzy-ANP was used to identify the priority weights of the candidate projects through clarifying the interdependent degree between the criteria and candidate projects. Finally, ZOGP method was selected as a predetermined number of PCBR projects among candidate projects.

The feasibility and effectiveness of this hybrid approach is then verified in a case study of Yuzhong District, Chongqing in China. The results of this study indicate that the integrated method is capable of directing the decision maker toward the best compromising solution of PCBR program that can achieve the maximization of sustainable benefits and allocate limited resources most efficiently.

The novelty of this paper consists in combining the algorithms of the Fuzzy-ANP method with those of the ZOGP model that serves as an effective analysis tool to address practical decision problems. This is the first hybrid algorithms to make PCBR projects selection decision that reach the maximization of the sustainable benefits, both in economic and socio-environmental terms.

An integrated queueing network focused on container storage/retrieval operations occurring on the yard of a transshipment hub is proposed. The purpose of the network is to support decisions related to the organization of the yard area, while also accounting for operations policies and times on the quay.

A discrete-event simulation model is used to reproduce container handling on both the quay and yard areas, along with the transfer operations between the two. The resulting times, properly estimated by the simulation output, are fed to a simpler queueing network amenable to solution via algorithms based on mean value analysis (MVA) for product-form networks.

Numerical results justify the proposed approach for getting a fast, yet accurate analytical solution that allows carrying out performance evaluation with respect to both organizational policies and operations management on the yard area.

Practically, the expected performance measures on the yard subsystem can be obtained avoiding additional time-expensive simulation experiments on the entire detailed model.

As a major takeaway, deepening the MVA for generally distributed service times has proven to produce reliable estimations on expected values for both user- and system-oriented performance metrics.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

The purpose of this paper is to develop a double-objective economic statistical design (ESD) of (X ‾) control chart under Weibull failure properties with the Linex asymmetric loss function. The authors have expressed the probability of type II error (β) as the statistical objective and the expected cost as the economic objective.

The design used in this study is based on a double-objective economic statistical design of (X ‾) control chart with Weibull shock model via applying Banerjee and Rahim's model for non-uniform and uniform schemes with Linex asymmetric loss function. The results in the least average cost and β in uniform and non-uniform schemes by Linex loss function, compared with the same schemes without loss function.

Numerical results indicate that it is not possible to reduce the second type of error and costs at the same time, which means that by reducing the second type of error, the cost increases, and by reducing the cost, the second type of error increases, both of which are very important. Obtained based on the needs of the industry and which one has more priority has the right to choose. These designs define a Pareto optimal front of solutions that increase the flexibility and adaptability of the X ‾ control chart in practice. When the authors use non-uniform schemes instead of uniform schemes, the average cost per unit time decreases by an average and when the authors apply loss function, the average cost per unit time increases by an average. Also, this quantity for double-objective schemes with loss function compared to without loss function schemes in cases uniform and non-uniform increases. The reason for this result is that the model underestimated the costs before using the loss function.

This research adds to the body of knowledge related to flexibility in process quality control. This article may be of interest to quality systems experts in factories where the choice between cost reduction and statistical factor reduction can affect the production process.

The cost functions for double-objective uniform and non-uniform sampling schemes with the Weibull shock model based on the Linex loss function are presented for the first time.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

This paper aims to review the topic of “luxury fashion consumption”, a field of recent interest for academics and practitioners. However, a literature review that can map the existing knowledge and aggregate it into relevant topics and offers a research agenda for future research is still lacking.

This paper uses a systematic review and a text mining approach to analyse 73 articles on luxury fashion consumption aiming to clarify, rationalise and critically interpret the literature on luxury fashion consumption; identify the core topic, create an integrative framework of core constructs; and offer research gaps and suggest a research agenda for future studies.

From this analysis, eight major research topics are found and analysed (brand desire, authenticity, luxury markets, value perceptions, luxury retail experience, luxury brands communication, responsible consumption and sustainability and status signalling). Based on these topics and following the TCM framework, this review offers directions for future research.

This research offers a text-mining review of luxury fashion consumption to help scholars and managers further develop this field, as there is no comprehensive review on the topic exploring the themes, theories, constructs and methods used in prior studies.

Este artículo pretende revisar el “consumo de moda de lujo”, un tema de reciente interés para académicos y profesionales. Sin embargo, sigue faltando una revisión de la literatura que pueda ordenar el conocimiento existente y aglutinarlo en temas relevantes y que ofrezca una agenda de investigación futura.

Este trabajo emplea una revisión sistémica de la literatura y la minería de textos para analizar 73 artículos sobre el consumo de moda de lujo con el objetivo de (i) aclarar, racionalizar e interpretar críticamente la literatura sobre el consumo de moda de lujo, (ii) identificar el tema central, crear un marco integrador de constructos clave y (iii) presentar las lagunas de la investigación y sugerir una agenda de investigación para futuros estudios.

A partir de este análisis, se identifican y analizan ocho temas principales de investigación (el deseo de marca, la autenticidad, los mercados de lujo, las percepciones de valor, la experiencia de la venta al por menor de lujo, la comunicación de las marcas de lujo, el consumo responsable y la sostenibilidad, y la señalización de estatus). Sobre la base de estos temas y siguiendo el marco del TCM, esta revisión propone líneas para futuras investigaciones.

Esta investigación ofrece una revisión de la minería de textos sobre el consumo de moda de lujo para ayudar a los académicos y gestores a seguir desarrollando este campo, ya que no existe una revisión exhaustiva sobre el tema que explore los conceptos, teorías, constructos y métodos utilizados en estudios previos.

Revisión de la literatura

本文旨在回顾 “奢侈时尚消费”, 这是学术界和从业人员最近关注的一个话题。然而, 目前仍然未能将现有知识分类并为未来研究提供议程的文献回顾。

本文使用系统的文献综述和文本挖掘, 分析了73篇关于奢侈时尚消费的文章。此文目的是：（1）批判性地解释关于奢侈时尚消费的文献; （2）确定中心主题, 建立综合框架; （3）提出研究缺憾, 为未来的研究提出议程。

从这个分析中, 我们发现并分析了八个主要的研究主题（品牌欲望、真实性、奢侈品市场、价值认知、奢侈品零售体验、奢侈品品牌传播、负责任的消费和可持续性、以及地位信号）。基于这些主题并遵循TCM框架, 本评论提出了未来研究的方向。

目前还没有关于该主题的全面文献回顾, 以探索以前研究中使用的概念、理论、构造和方法。本研究对奢侈时尚消费的文本挖掘进行了回顾, 以帮助学者和管理者进一步发展该领域。

文献评论

This paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings.

In this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds.

The developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested.

Future research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization.

This study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.