Author：Zhan, ZH;Zhang, J;Li, Y;Shi, YH

Abstract:Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood's best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSOL algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness.

Author：Hashem, IAT;Chang, V;Anuar, NB;Adewole, K;Yaqoob, I;Gani, A;Ahmed, E;Chiroma, H

Abstract:The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data. (C) 2016 Elsevier Ltd. All rights reserved.

Author：Yin, XC;Yin, XW;Huang, KZ;Hao, HW

Abstract:Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%%, much better than the state-of-the-art performance of 71%%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method. Finally, an online demo of our proposed scene text detection system has been set up at http://prir.ustb.edu.cn/TexStar/scene-text-detection/.

Author：Farahani, B;Firouzi, F;Chang, V;Badaroglu, M;Constant, N;Mankodiya, K

Abstract:Internet of Things (loT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of loT in healthcare and medicine by presenting a holistic architecture of loT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven loT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device-network-human interfaces, security, and privacy. (C) 2017 Elsevier B.V. All rights reserved.

Author：Zhang, YG;Zhang, BL;Coenen, F;Xiao, JM;Lu, WJ

Abstract:Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

Author：Zhan, ZH;Li, JJ;Cao, JN;Zhang, J;Chung, HSH;Shi, YH

Abstract:Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.

Author：Wen, Z;Yang, YQ;Sun, N;Li, GF;Liu, YN;Chen, C;Shi, JH;Xie, LJ;Jiang, HX;Bao, DQ;Zhuo, QQ;Sun, XH

Abstract:The functionalized conductive polymer is a promising choice for flexible triboelectric nanogenerators (TENGs) for harvesting human motion energy still poses challenges. In this work, a transparent and stretchable wrinkled poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) (PEDOT:PSS) electrode based TENG (WP-TENG) is fabricated. The optimum conductivity and transparency of PEDOT:PSS electrode can reach 0.14 k Omega square(-1) and 90%%, respectively, with maximum strain of approximate to 100%%. Operating in single-electrode mode at 2.5 Hz, the WP-TENG with an area of 6 x 3 cm(2) produces an open-circuit voltage of 180 V, short-circuit current of 22.6 mu A, and average power density of 4.06 mW m(-2). It can be worn on the wrist to harvest hand tapping energy and charge the capacitor to 2 V in approximate to 3.5 min, and then drive an electronic watch. Furthermore, the WP-TENG as the human motion monitoring sensor could inspect the bending angle of the elbow and joint by analyzing the peak value of voltage and monitor the motion frequency by counting the peak number. The triboelectric mechanism also enables the WP-TENG to realize high-performance active tactile sensing. The assembled 3 pixel x 3 pixel tactile sensor array is fabricated for mapping the touch location or recording the shape of object contacted with the sensor array.

Author：Wang, FH;Qiao, M;Chen, Z;Su, JQ;Zhu, YG

Abstract:Lettuce and endive, which can be eaten raw, were planted on the manure-amended soil in order to explore the influence of plants on the abundance of antibiotic resistance genes (ARGs) in bulk soil and rhizosphere soil, and the occurrence of ARGs on harvested vegetables. Twelve ARGs and one integrase gene Until) were detected in all soil samples. Five ARGs (sulI, tetG, tetC, tetA, and tetM) showed lower abundance in the soil with plants than those without. ARGs and intI1 gene were also detected on harvested vegetables grown in manure-amended soil, including endophytes and phyllosphere microorganisms. The results demonstrated that planting had an effect on the distribution of ARGs in manure-amended soil, and ARGs were detected on harvested vegetables after growing in manure-amended soil, which had potential threat to human health. (C) 2015 Elsevier B.V. All rights reserved.

Author：Mustafa, AM;Poulsen, TG;Sheng, KC

Abstract:Rice straw was subjected to fungal pretreatment using Pleurotus ostreatus and Trichoderma reesei to improve its biodegradability and methane production via solid-state anaerobic digestion (SS-AD). Effects of moisture content (65%%, 75%% and 85%%), and incubation time (10, 20 and 30 d) on lignin, cellulose, and hemicellulose degradation during fungal pretreatment and methane yield during anaerobic digestion were assessed via comparison to untreated rice straw. Pretreatment with P. ostreatus was most effective at 75%% moisture content and 20 d incubation resulting in 33.4%% lignin removal and a lignin/cellulose removal ratio (selectivity) of 4.2. In comparison Trichoderma reesei was most effective at 75%% moisture content and 20 d incubation resulting in 23.6%% lignin removal and a lignin/cellulose removal ratio (selectivity) of 2.88. The corresponding methane yields were 263 and 214 L/kg volatile solids (VS), which were 120%% and 78.3%% higher than for the untreated rice straw, respectively. (C) 2016 Elsevier Ltd. All rights reserved.

Author：Larson, D;Chang, V

Abstract:Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile Methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions. (C) 2016 Elsevier Ltd. All rights reserved.

Author：Zhang, P;Qian, KJ;Zhou, CK;Stewart, BG;Hepburn, DM

Abstract:This paper presents a methodology of optimizing power systems demand due to electric vehicle (EV) charging load. Following a brief introduction to the charging characteristics of EV batteries, a statistical model is presented for predicting the EV charging load. The optimization problem is then described, and the solution is provided based on the model. An example study is carried out with error and sensitivity analysis to validate the proposed method. Four scenarios of various combinations of EV penetration levels and charging modes are considered in the study. A series of numerical solutions to the optimization problem in these scenarios are obtained by serial quadratic programming. The results show that EV charging load has significant potential to improve the daily load profile of power systems if the charging loads are optimally distributed. It is demonstrated that flattened load profiles may be achieved at all EV penetration levels if the EVs are charged through a fast charging mode. In addition, the implementation of the proposed optimization is discussed with analyses on the impact of travel pattern and the willingness of customers.

Author：Yang, Y;Zheng, XH;Guo, WZ;Liu, XM;Chang, V

Abstract:In this paper, a privacy-preserving smart IoT-based healthcare big data storage system with self-adaptive access control is proposed. The aim is to ensure the security of patients' healthcare data, realize access control for normal and emergency scenarios, and support smart deduplication to save the storage space in big data storage system. The medical files generated by the healthcare IoT network are encrypted and transferred to the storage system, which can be securely shared among the healthcare staff from different medical domains leveraging a cross-domain access control policy. The traditional access control technology allows the authorized data users to decrypt patient's sensitive medical data, but also hampers the first-aid treatment when the patient's life is threatened because the on site first-aid personnel are not permitted to get patient's historical medical data. To deal with this dilemma, we propose a secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations. In normal application, the healthcare staff with proper attribute secret keys can have the data access privilege; in emergency application, patient's historical medical data can be recovered using a password-based break-glass access mechanism. To save the storage overhead in the big data storage system, a secure deduplication method is designed to eliminate the duplicate medical files with identical data, which may be encrypted with different access policies. A highlight of this smart secure deduplication method is that the remaining medical file after the deduplication can be accessed by all the data users authorized by the different original access policies. This smart healthcare big data storage system is formally proved secure, and extensive comparison and simulations demonstrate its efficiency. (C) 2018 Elsevier Inc. All rights reserved.

Author：Amin, R;Kumar, N;Biswas, GP;Iqbal, R;Chang, V

Abstract:With the widespread popularity and usage of Internet-enabled devices, Internet of things has become one of the most popular techniques of the modern era. However, data generated from various smart devices in loT environment is one of the biggest concerns. To process such a large database repository generated from different types of devices in IoT environment, Cloud Computing (CC) has emerged as a key technology. But, the private information from IoT devices is stored in distributed private cloud server so that only legitimate users are allowed to access the sensitive information from the cloud server. Keeping focus on all these points, this article first shows security vulnerabilities of the multi-server cloud environment of the protocols proposed by Xue et al. and Chuang et al. Then, we propose an architecture which is applicable for distributed cloud environment and based on it, an authentication protocol using smartcard has been proposed, where the registered user can access all private information securely from all the private cloud servers. To strengthen the proposed protocol, we have used AVISPA tool and BAN logic model in this article. Moreover, an informal cryptanalysis confirms that the protocol is protected against all possible security threats. The performance analysis and comparison confirm that the proposed protocol is superior than its counterparts with respect to various parameters. (C) 2016 Elsevier B.V. All rights reserved.

Author：Douterelo, I;Boxall, JB;Deines, P;Sekar, R;Fish, KE;Biggs, CA

Abstract:The study of the microbial ecology of drinking water distribution systems (DWDS) has traditionally been based on culturing organisms from bulk water samples. The development and application of molecular methods has supplied new tools for examining the microbial diversity and activity of environmental samples, yielding new insights into the microbial community and its diversity within these engineered ecosystems. In this review, the currently available methods and emerging approaches for characterising microbial communities, including both planktonic and biofilm ways of life, are critically evaluated. The study of biofilms is considered particularly important as it plays a critical role in the processes and interactions occurring at the pipe wall and bulk water interface. The advantages, limitations and usefulness of methods that can be used to detect and assess microbial abundance, community composition and function are discussed in a DWDS context. This review will assist hydraulic engineers and microbial ecologists in choosing the most appropriate tools to assess drinking water microbiology and related aspects. (C) 2014 The Authors. Published by Elsevier Ltd.

Author：Rehman, MHU;Chang, V;Batool, A;Teh, YW

Abstract:Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as (a) lowering the service utilization cost, (b) enhancing the trust between customers and enterprises, (c) preserving privacy of customers, (d) enabling secure data sharing, and (e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-to-end data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications. (C) 2016 Elsevier Ltd. All rights reserved.

Author：Abdel-Baset, M;Chang, V;Gamal, A;Smarandache, F

Abstract:Sustainable supplier selections have been improved by an increased number of multi criteria group decision making (MCGDM) methods and techniques. This paper provides a multi criteria group decision making (MCGDM) proposed technique of the ANP (analytical network process) method and the VIKOR (ViseKriterijumska Optimizacija I Kompromisno Resenje) method under neutrosophic environment for dealing with incomplete information and high order imprecision. This is done by using of the triangular neutrosophic numbers (TriNs) to present the linguistic variables based on opinions of experts and decision makers. The aim is to solve the problem of supplier selection in sustainable supplier chain management (SSCM). The suggested technique consists of two phases. First, we use the ANP method to calculate the weights of criteria and sub criteria. Second, with the aid of VIKOR method and with obtained weights of the criteria and sub criteria from step one, we can find the solution. A case study is used to present the decision process in detail. Our proposed method is compared directly with the entropy method to justify our approach. We also use genetic algorithm to compute predicted values for five selected cities while varying economic, environmental and social criteria. Explanations of forecasted outputs and limitation for research have been presented. Our objective is to demonstrate that our proposal can calculate key measurement for major import and export cities, as well as to provide fair and reliable forecasted outcomes. (C) 2018 Elsevier B.V. All rights reserved.

Author：Sun, J;Zhou, WB;Yan, LK;Huang, DJ;Lin, LY

Abstract:Differentiated from food extrusion cooking, extrusion-based food printing is a digitally-controlled extrusion process to build up complex 3D food products layer by layer. It is the most popular method in food printing, which provides an engineering solution for digitalized food design and nutrition control. The objective of this study was to collate, analyse and categorize published work pertaining to the extrusion-based food printing technique in order to determine its impact on food texture design and identify any related technical bottlenecks. Currently, for food printing, the applied printing stages include Cartesian, Delta, Polar and Scara configurations, and three extrusion mechanisms, namely syringe, air pressure, and screw, are utilized. This paper provides a detailed discussion regarding these factors and the parameters associated with the extrusion and printing processes. A comparison between specialized food printers and 3D printers with food printing functions is reported in terms of food safety and system design complexity. Temperature control plays an important role in both food extrusion and post-deposition cooking, and the commercial food printers are reviewed based on temperature control methods, printable materials, extrusion mechanism and final products. Then, a comprehensive analysis to innovate food design is reported, based on layer structure, unique appearance, post deposition cooking, recipe reformulation and digitalized nutrition control. Finally, market challenges, technical difficulties and possible strategies along the pathway of commercialization are discussed. (C) 2017 Elsevier Ltd. All rights reserved.

Author：Abdel-Basset, M;Mohamed, M;Chang, V

Abstract:Many organizations are currently seeking to contract services from cloud computing rather than owing the possessions to supply those services. Due to the fast expansion of cloud computing, many cloud services have been developed. Any organization that tries to achieve the best flexibility and quick response to market requests, they have the options to use cloud services. Due to the diversity of cloud service providers, it is a very significant defy for organizations to select the appropriate cloud services which can fulfill their requirements, as numerous criteria should be counted in the selection process of cloud services. Therefore, the selection process of cloud services can be considered as a type of multi-criteria decision analysis problems. In this research paper, we present how to aid a decision maker to estimate different cloud services by providing a neutrosophic multi-criteria decision analysis (NMCDA) approach for estimating the quality of cloud services. Triangular neutrosophic numbers are used to deal with ambiguous and incompatible information which exist usually in the performance estimation process. An efficacious model is evolved depending on neutrosophic analytic hierarchy process (NAHP). The aim is to solve the performance estimation problem and improve the quality of services by creating a strong competition between cloud providers. To demonstrate the pertinence of the proposed model for disbanding the multi-criteria decision analysis, a case study is presented. (C) 2018 Elsevier B.V. All rights reserved.

Author：Abdel-Baset, M;Chang, V;Gamal, A

Abstract:To attain competitive advantages and promote environmental performance, a proactive approach called green supply chain management (GSCM), has been extensively employed. In this paper, we use the robust ranking technique with neutrosophic set to handle practices and performances in GSCM. We evaluate GSCM practices using the robust ranking technique in order to detect practices leading to better economic and environmental performances. We employ the neutrosophic set theory to handle vague data, imprecise knowledge, incomplete information and linguistic imprecision. The efficiency of the proposed method is evaluated by using the first case study from petroleum industry in Egypt and the second case study from manufacturing firm in China. The results display that "reverse logistics", "supplier environmental collaboration", "carbon management" are the significant in GSCM practices. Both case studies verified that our proposal could be adopted for effectiveness and improvement. Our work could help managers and decision makers to become more environmentally responsible. (C) 2019 Elsevier B.V. All rights reserved.

Author：Ikram, M;Zhou, P;Shah, SAA;Liu, GQ

Abstract:This study assessed whether the adoption of an environmental management system (EMS) as a part of an integrated management system (IMS) helps improve corporate sustainability based on the data for 211 manufacturing companies in Pakistan. A stakeholder-weighted CSR index and an equal-weighted CSR index were used to measure CSR performance of the selected companies. Descriptive t-test and exploratory factor analysis were employed to test the hypothesis and internal consistency. Structural equation modelling was used to measure various useful links between latent and observed variables. Results revealed that the mean CSR performance of EMS adopters was significantly higher than that of non-EMS adopters. Further, EMS adopters had positive and significant effect on soundness, environmental protection, fairness and contribution to society, whereas, the non-EMS companies had positive and substantial impact on employee satisfaction and economic contribution. In conclusion, EMS adoption can be an effective tool for organisations to address economic, social and environmental issues. Moreover, EMS adoption appears to be a viable means to develop business goals and improve CSR activities. (C) 2019 Published by Elsevier Ltd.