Combined particle swarm optimization and heuristic fuzzy inference systems for a smart home one-step-ahead load forecasting

This article presents a hybrid approach that combines particle swarm optimization (PSO) and heuristic fuzzy inference system (HFIS) for smart home one-step-ahead load forecasting. Smart home load forecasting is an important issue in the development of smart grids. Generally, the electricity consumption of a household is inherently nonlinear and dynamic and heavily dependent on the habitual nature of power demand, activities of daily living and on holidays or weekends, so it is often difficult to construct an adequate forecasting model for this type of load. To address this problem, a hybrid model, consisting of two phases, is proposed in this article. In the first phase, the popular PSO algorithm is used to determine the locations of fuzzy membership functions. Then, the proposed HFIS technique is used to develop the one-step-ahead load forecasting model in the second phase. Because of the robust nature of the proposed HFIS technique, which does not need to retrain or re-estimate model parameters, it is very suitable for smart home load forecasting. The proposed method was verified using two different households’ load data. Simulation results indicate that the proposed method produces better forecasting accuracy than existing methods.     

Recommender systems using cluster-indexing collaborative filtering and social data analytics

As a result of the extensive variety of products available in e-commerce settings during the last decade, recommender systems have been highlighted as a means of mitigating the problem of information overload. Collaborative filtering (CF) is the most widely used algorithm to build such systems, and improving the predictive accuracy of CF-based recommender systems has been a major research challenge. This research aims to improve the prediction accuracy of CF by incorporating social network analysis (SNA) and clustering techniques. Our proposed model identifies the most influential people in an online social network by SNA and then conducts clustering analysis using these people as initial centroids (cluster centres). Finally, the model makes recommendations using cluster-indexing CF based on the clustering outcomes. In this step, our model adjusts the effect of neighbours in the same cluster as the target user to improve prediction accuracy by reflecting hidden information about his or her social community. The experimental results indicate that the proposed model outperforms other comparison models, including conventional CF, with statistical significance.     

Modelling quality dynamics,business value and firm performance in a big data analytics environment

Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance (FPER). The study identifies that system quality (i.e. system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e. completeness, accuracy, format and currency) are key to enhance business value and FPER in a big data environment. The study also proposes that the relationship between quality and FPER is mediated by business value of big data. Drawing on the resource-based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and FPER.     

A framework for decision makers to design a business analytics platform for distributed organizations

The growth of business analytics applications in decision making is becoming a significant component in today's organizations, and the powerful changes brought by such applications to both centralized and distributed organizations have led decision makers to revise the way they capture, process, and analyze both structured and unstructured data and make decisions. This study discusses how business analytics tools can supply distributed organizations with a new operating model, process, and outlet to disseminate knowledge, and provides a framework for building a business analytics platform that may be employed by decision makers and managers to realize the full potential of a comprehensive decision-making platform in a distributed organizational setting.     

Systematic literature review on smart contracts in the construction industry: Potentials,benefits, and challenges

The development of digital transformation in the construction industry has led to the increasing adoption of smart contracts. As programmable applications to automatically write, verify, and enforce transaction conditions, smart contracts can be used in different areas mainly to improve automation level, information security, and built digital environment enhancement. However, the smart contract is commonly mentioned as a blockchain appendage, while its unique connotation and value in the construction industry have not been recognized. Therefore, this study carries out a systematic review based on 81 research articles published from 2014 to 2021 on smart contract applications in construction to explore and highlight their potentials under domain-specific requirements. Results are analyzed according to research type categorization and domain codification. Eight research domains are identified, where the three most highly explored domains are contract and payment, supply chain and logistics, and information management. The integration of smart contracts with other innovative concepts and advanced technologies is analyzed. The applicability, benefits, and challenges of smart contract applications regarding different research domains are discussed.     

Environmental air pollution management system: Predicting user adoption behavior of big data analytics

This study has a dual purpose: to explore the novel phenomenon of a big data analytics-environmental air pollution (BDA-EAP) management system, and to propose a research model of factors influencing adoption of such a system. The research model is based on task-technology fit (TTF) and unified theory of acceptance and use of technology (UTAUT) concepts. A comprehensive BDA-EAP management system is proposed and the potential adoption speed of such a system evaluated by sending structured questionnaires to the employees of relevant environmental agencies, yielding 412 valid responses, using the structural equation modeling approach. The results of the study predict that factors of TTF including task characteristics and technology characteristics are strong influencers of TTF, and TTF is a strong predictor of the behavioral intention of users to adopt a BDA-EAP management system. The results demonstrated that the combination of TTF and UTAUT is a stronger predictor of behavioral intention than either TTF or UTAUT alone. Furthermore, resistance to change negatively moderates and extrinsic motivation positively moderates the significant positive relationship between behavioral intention and adoption of a BDA-EAP management system. Meanwhile, behavioral intention, resistance to change, and extrinsic motivation have a significant three-way interaction impact on adoption of a BDA-EAP management system such that an increase in users’ extrinsic motivation will decrease the negative impact of resistance to change during the process of adoption. The study findings contribute to the literature regarding the use of BDA to manage EAP, and provide a basis for future research in this area.     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.     

Manufacturing data analytics using a virtual factory representation

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.     

Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects

Big data has recently been recognised as one of the most important areas of future technology. It has attracted the attention of many industries, since it has the potential to provide companies with high business value. This paper examines the forms of business value that companies can create from big data analytics investments, the direct impacts it has on the financial performance of a firm, and the mediating effects of market performance and customer satisfaction. Drawing on the resource-based view theory, this study demonstrates that the business value achieved from investments in big data analytics leads to advantages in terms of the financial performance of a firm. The results offer evidence of the existence of a customer satisfaction mediation effect and of the absence of a market performance mediation effect. Theoretical and practical implications are discussed at the end of the paper.     

Knowledge-based analytics for massively distributed networks with noisy data

This article develops and implements an improving search algorithm that effectively and efficiently identifies pathways of interest using knowledge-based analytics for massively distributed networks with noisy data. The method developed in this article fundamentally changes how critical information is extracted from large data-sets. Many methods have been developed in the past to identify structures in large graphs. Most of these methods are computationally inefficient for large graphs and their outcome depends on the graph metrics and statistical measures. There has been limited research on using optimisation techniques for data mining in large networks with noisy data. The algorithm developed in this article converges to the optimal solution by traversing the interior of a feasible region. Experiments show that it identifies a pathway of interest from a network of 160,000 components in 10 hours using parallel computing. Future work will include customisation and implementation of the method to other large networks in a variety of applications.     

Patent data analytics for technology benchmarking: R-based implementation

We have utilized data analytics techniques to produce highly detailed, accurate, and actionable insights on patent data to enable the decision makers to take informed decisions. We have developed a unique method to help business professionals easily understand the patent landscape around a particular technology domain. The data inputs for the analyses are the patent statistics and the organization's technology priorities. We have used and implemented clustering algorithms on the patent data while considering the organization's technology priorities to identify solutions that can help organizations gain a competitive advantage, identify potential collaboration targets, technology-product alignment, business decision making, strategy planning and other strategic decisions.     

Blockchain-based smart contract for smart payment in construction: A focus on the payment freezing and disbursement cycle

Late payment, and indeed no payment, is a rampant and chronic problem that has plagued the global construction industry for too long. Recent development in blockchain technology, particularly its smart contract, seems to provide a new opportunity to improve this old problem. However, this opportunity is largely unexploited. This study aims to develop a blockchain-based smart contract (BBSC) system for smart payment in the construction industry by focusing on the fundamental cycle of payment freezing (sometimes also synonymously called payment guarantees) and disbursement application. Firstly, a BBSC framework, containing three processes of (a) initiation and configuration, (b) payment freezing, and (c) disbursement application, is developed. Next, based on the framework, the system architecture of the BBSC system, containing three layers of (1) Infrastructure as a Service (IaaS), (2) Blockchain as a Service (BaaS), and (3) Software as a Service (SaaS) is proposed and elabora-ted. Finally, based on the system architecture, a BBSC prototype system is developed using a real-life modular construction project as a case study. It was found that the prototype system can improve the certainty and efficiency of the progress payment, thereby enabling smart payment in construction transactions. Without advocating radical changes (e.g., the contractual relationships or the intermediate role of banks in modern construction projects), the prototype can be developed into a real-life BBSC system that can work compatibly with current advancements in the field. Future works are recommended to fine-tune the findings and translate and implement them in real-life applications.     

A novel smart hybrid-Trefftz finite element for smart laminated composite plates

A novel smart hybrid-Trefftz finite element ( HTFE ) has been developed for the analysis of smart laminated composite plates. The substrates of the smart plates are symmetric and antisymmetric cross-ply plates. The derivation of this HTFE is devoid of the complicated task of finding the particular solutions of simultaneous governing partial differential equations. The Trefftz functions are constructed from the finite number of free-field exact solutions of the homogeneous simultaneous governing partial differential equations of the element domain in a straightforward manner without transforming them into a single governing equation. The HTFE is validated with the exact solutions of the smart composite plates. It is observed that this HTFE is an efficient finite element and can be utilized for the analysis of active control of smart composite structures.     

非接触式IC卡在考勤管理系统中的应用

本文综述了非接触式智能射频 IC卡的工作原理及其特点 ,并介绍基于非接触式 IC卡的考勤管理系统的开发 .     

Big data analytics business value and firm performance: linking with environmental context

Previous studies, grounded on the resource based view, have already explored the relationship between the business value that Big Data Analytics (BDA) can bring to firm performance. However, the role played by the environmental characteristics in which companies operate has not been investigated in the literature. We inform the theory, in that direction, via the integration of the contingency theory to the resource based view theory of the firm. This original and integrative model examines the moderating influence of environmental features on the relationship between BDA business value and firm performance. The combination of survey data and secondary financial data on a representative sample of medium and large companies makes possible the statistical validation of our research model. The results offer evidence that BDA business value leads to higher firm performance, namely financial performance, market performance and customer satisfaction. More original is the demonstration that this relationship is stronger in munificent environments, while the dynamism of the environment does not have any moderating effect on the performance of BDA solutions. It means that managers working for firms in markets with a growing demand are in the best position to profit from BDA.     

Operations management of smart logistics: A literature review and future research

The global collaboration and integration of online and offline channels have brought new challenges to the logistics industry. Thus, smart logistics has become a promising solution for handling the increasing complexity and volume of logistics operations. Technologies, such as the Internet of Things, information communication technology, and artificial intelligence, enable more efficient functions into logistics operations. However, they also change the narrative of logistics management. Scholars in the areas of engineering, logistics, transportation, and management are attracted by this revolution. Operations management research on smart logistics mainly concerns the application of underlying technologies, business logic, operation framework, related management system, and optimization problems under specific scenarios. To explore these studies, the related literature has been systematically reviewed in this work. On the basis of the research gaps and the needs of industrial practices, future research directions in this field are also proposed.     

Exploring adoption of augmented reality smart glasses: Applications in the medical industry

This study explores the use of augmented reality smart glasses (ARSGs) by physicians and their adoption of these products in the Turkish medical industry. Google Glass was used as a demonstrative example for the introduction of ARSGs. We proposed an exploratory model based on the technology acceptance model by Davis. Exogenous factors in the model were defined by performing semi-structured in-depth interviews, along with the use of an expert panel in addition to the technology adoption literature. The framework was tested by means of a field study, data was collected via an Internet survey, and path analysis was used. The results indicate that there were a number of factors to be considered in order to understand ARSG adoption by physicians. Usefulness was influenced by ease of use, compatibility, ease of reminding, and speech recognition, while ease of use was affected by ease of learning, ease of medical education, external influence, and privacy. Privacy was the only negative factor that reduced the perceived ease of use, and was found to indirectly create a negative attitude. Compatibility emerged as the most significant external factor for usefulness. Developers of ARSGs should pay attention to healthcare-specific requirements for improved utilization and more extensive adoption of ARSGs in healthcare settings. In particular, they should focus on how to increase the compatibility of ARSGs. Further research needs to be conducted to explain the adoption intention of physicians.     

Embrace artificial intelligence technologies for advanced analytics and management of intellectual properties

This short communication describes the background, objectives, and publications of World Patent Information's special issue on Artificial Intelligence for Intellectual Property (AI for IP). The report serves as the editorial for the WPI's special issue on AI for IP. We look forward to receiving future contributions in research articles, literature/book reviews, conference reports and short communications in the subject areas.     

Automated structural optimization system for integrated topology and shape optimization

Abstract

This paper combines previously developed techniques for image‐preprocessing and characteristic image‐interpreting together with a newly proposed automated shape‐optimization modeling technique into an integrated topology‐optimization and shape‐optimization system. As a result, structure designers are provided with an efficient and reliable automated structural optimization system (ASOS). The automated shape‐optimization modeling technique, the key technique in ASOS, uses hole‐expanding strategy, interference analysis, and hole shape‐adjusting strategy to automatically define the design variables and side constraints needed for shape optimization. This technique not only eliminates the need to manually define design variables and side constraints for shape optimization, but during the process of shape optimization also prevents interference between the interior holes and the exterior boundary. The ASOS is tested in three different structural configuration design examples.     

Iot based laundry services: an application of big data analytics,intelligent logistics management,and machine learning techniques

The authors propose an innovative Internet of Things (IoT) based E-commerce business model Cloud Laundry for mass scale laundry services. The model utilises big data analytics, intelligent logistics management, and machine learning techniques. Using GPS and real-time update of big data, it calculates the best transportation path and update and re-route the logistic terminals quickly and simultaneously. Cloud laundry intelligently and dynamically provides the best laundry solutions based on the current state spaces of the laundry terminals through the user's specifications and thus offers local hotel customers with convenient, efficient, and transparent laundry services. Taking advantage of the rapid development of the big data industry, user interest modelling, and information security and privacy considerations, cloud laundry uses smartphone terminal control and big data models to maintain customers’ security needs. Different from the traditional laundry industry, cloud laundry companies have higher capital turnover, more liquidity, and stronger profitability. Therefore, this new generation of smart laundry business model could be of interest to not only academic researchers, but E-commerce entrepreneurs as well.