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This New Articles Channel contents the latest articles published in Inderscience's distinguished academic, scientific and professional journals.



 



Text-mining based localisation of player-specific events from a game-log of cricket
Analysis and visualisation of sports data pertaining to a particular player provides an avenue to study the game of a player (or group of players) in detail, to identify strengths and weaknesses to predict the outcome of a match or select players for a fantasy league. In this paper, we propose to detect and localise events (such as shots) specific to a player or a team and present visualisations that are easy to interpret. For this, we use a textual log of the match (or the detailed ball-by-ball commentary) of a game of cricket as input. We perform hierarchical n-gram matching to detect events of interest for the player(s) specified and match them with the location (on the field). We propose the disambiguation of action and intention verbs and resolve location through auxiliary information to improve the accuracy of detection and localisation of events. The results obtained on testing the proposed system on multiple game logs demonstrate that the accuracy of detection and localisation is comparable with that of manual detection and localisation.



Analysis improvement of standing surface acoustic wave microfluidic devices for bio-particles separation
In this paper, the study and analysis to improve the Standing Surface Acoustic Wave (SSAW) microfluidic devices for particle separation are described. This study provides a theoretical analysis towards highly accurate particles separation compared to other published papers. To decrease the power consumption, the calculation of minimum AC signal power required to produce SSAW force for the separation process is briefly described and analysed. The horizontal component ratio of SSAW force and its attenuation in the liquid medium are analysed and represented. To estimate the effective width of collecting microchannels, the relations between the difference in volumes or densities of particles and the difference in the displacement of particles from the centre of the microchannel of SSAW microfluidic at the end of the SSAW force working area are represented. The separation of three different volumes of polystyrene particles and three different densities of particles in distilled water are simulated.



Usability assessment of keystroke dynamics systems
Keystroke dynamics systems are considered as a promising solution that would be used in several applications such as e-commerce and banking applications. Such systems suffer from several limitations that prevent their deployment in these applications. This paper aims to contribute to the use of keystroke dynamics solution as an authentication manner by presenting a usability assessment methodology. A comparison between physical and touchscreen keyboards is also covered in this paper. This comparison shows that the Press-Release (PR) feature collected from a physical keyboard differs from the one collected using a touchscreen keyboard.



A sensor-based approach to study sound perception in children
In the present paper we describe an instrumented toy to study auditory preferences in young children. After brief considerations on the theoretical framework, the design of the mechanical, electronic and software components of the system is presented. The system allows for: (a) producing audio stimuli according to how children play with the toy; (b) assessing children's motor behaviour during the interaction. The device is provided with a sensor core enabling the assessment of manipulation in terms of angular displacement with errors lower than 1°. The laboratory validation is presented and discussed in details. Moreover, a pilot trial on two children aged 34 and 35 months is described and discussed. Results show the appropriateness of the technology to the experimental aims, and encourage research on methods based on the interaction between perception and action to investigate music preferences in young listeners.



Binary tree multi-class SVM based on OVA approach and variable neighbourhood search algorithm
In this paper, we propose and examine the performance of an approach multiclass SVM, based on binary tree and VNS algorithm. In order to solve real multiclass problems, a mapping of the original problem to several sub-problems is used to improve the performance of multiclass SVM. The proposed paradigm is composed of two steps; in the first, we start with the construction of binary tree, using a partitioning technique, where each node is a partition of two classes. In the second step, we calculate the optimal binary tree by VNS algorithm, with the aim to explore the search space and to avoid the problem of local minima, the search process of optimisation is guided by one-versus-all strategy. In the subject to improve the temporal complexity of multiclass SVM by reducing the support vectors number and decrease the recognition time. This combination leads to decrease the test time and improve the convergence of classifier.



Development of an ontology-based generic optimisation tool for the design of hybrid energy systems
The reliability and cost of energy systems and diversity of weather are the two main concerns for Hybrid Energy Systems (HES) optimisation in the face of energy scarcity. Many techniques for sizing and optimisation of such systems have been developed. However, these techniques are developed independently of each other and require end-users to know a priori the techniques to use as well as data inputs of each. There is no sharing of data between the various techniques although they can use the same data. The novelty of this study is to develop an ontology-based system that offers the end-user an optimal design solution without having to know in advance the optimisation techniques. To that effect, we present an ontology-based generic tool that includes most used energy sources for optimising HES. The proposed tool has been applied and tested on an isolated wind-photovoltaic hybrid power system with battery storage.



On enhancing genetic algorithms using new crossovers
This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and the other randomly selects any operator. Several experiments on some travelling salesman problems have been conducted to evaluate the proposed methods, which are compared to the well-known modified crossover operator and partially mapped crossover. The results show the importance of some of the proposed methods, such as the collision crossover, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one crossover operator.



Robust control strategy for a wind energy conversion system
The Linear Parameter Varying (LPV) control of Wind Energy Conversion Systems (WECS) is a highly motivating subject. The paper deals with the robust control of a Wind Turbine (WT) driven by a Doubly Fed Induction Generator (DFIG). The WT model is enhanced and LPV representation is used for the DFIG, with uncertainties taken in an affine structure, after which, a control strategy is introduced in order to improve the performance of the WT and to assure an optimum energy capture. Also, the purpose is to ensure the stability of the considered system. Therefore, a scenario of a wind speed that varies between 5 m/s and 25 m/s is simulated, where controllers are designed for the generator and turbine and a feedback control strategy is implemented. Also, to prevent saturation, Anti-Windup (AW) structure is considered for the pitch control. The results and details for the regulators are discussed in the last section of this paper.



Relearning the practice of managing an undergraduate program: a learning lesson from a Thai institution
The purpose of managing an undergraduate education program should be on finding ways to make students learn and to keep on learning, so they develop themselves naturally to their fullest capacity and on becoming attractive to that particular industry that is looking for someone with the right qualifications and to help that organisation become competitive in the future. If the desired results are minimally gained due to obstacles within the working environment, then the practice of managing higher education has to be relearned in Thailand. To present an argument for relearning the practice of managing an undergraduate program, the authors reflected on the students' feedback from their opinions on the faculty's program mission, objectives, strategy, administration and curriculum, and their viewpoints of the techniques in teaching and learning, and suggestions for improvement. Relearning can be considered as a practical idea for the profession of managing an undergraduate program.



Human resource outsourcing and organisational learning: an industry-level investigation from Ghana
To understand the influence of human resource (HR) outsourcing on organisational learning (OL), it is particularly important to incorporate two almost forgotten perspectives: human resource outsourcing (HRO) as symptomatic of inter-organisational learning and social capital as the basis for the connection between HRO and organisational learning. This paper sought to assess the influence of HRO on organisational learning. The reasons for HRO were also determined. The findings from this paper confirmed the assumption about the link between HRO and organisational learning, showing that indeed HRO positively influences organisational learning. Thus, contrarily arguments were weakened. The findings presented in this paper have a wider use as this issue has received many doubts among practitioners, academics and industries. From our empirical findings, HRO should be considered as a mode of organisational learning where knowledge is best exchanged.



Development and evaluation of game-based learning system for supporting entity relationship diagramming skills
Educational experts are trying to connect learning strategies with computer games that students are familiar with. This paper introduces a comprehensive work for designing, developing and evaluation of an educational computer game that supports the learner in acquiring the skills necessary to construct entity relationship diagram (ERD). This topic is essential in the teaching and learning of database design course. This study also, takes advantage of registering player behaviour from a player's interaction history, while they are playing the game. Learner behaviour is important to improve both the game-based and traditional in class teaching in the future. Finally, to investigate the effect of the developed game-based learning (GBL) system on students' motivation and achievement, ARCS motivation model (attention, relevance, confidence and satisfaction) and the pre-/post tests are employed. After implementation of these measures, results showed that GBL system outperformed traditional in-class learning system in terms of students' achievement and motivation.



Study on brain plasticity through the learning model for improvement of biology learning motivation
The purpose of this study was to confirm brain plasticity through a learning model for improving learning motive based on the brain reward system in biology education. The Midbrain-Orbitofrontal-cortex-Striatum (MOSt) model is a learning model for improving learning motivation based on the brain reward system. Through the MOSt model, researchers confirmed whether learning motivation improved and brain plasticity occurred. Twenty-seven ninth grade students participated in this research. The MOSt model was applied to the experimental group and the 5E learning cycle model was applied to the control group. The degree of network connection in the brain reward system measured learning motivation before and after applying the learning model for learning biology. According to results of the study, the MOSt model improved learning motivation and increased the degree of brain reward system network connection in biology class. The learning model for improving learning motive based on the brain reward system brought about student brain plasticity.



Computer-based systems for automating instructional design of collaborative learning scenarios: a systematic literature review
Background: In computer-supported collaborative learning (CSCL), the implementation of instructional design (ID) of collaborative learning (CL) scenarios is a complex task that requires practice and experience. To solve this problem, many computer-based systems have been proposed for automating the ID of these scenarios. Aims: This study identifies, classifies and compares the current computer-based systems for automating ID of CL scenarios. To perform this classification and comparison, we also propose a framework based on the specialisation of existing ID models for the case of CL scenarios. Method: The standard systematic literature review (SLR) method was used with searches in three electronic scientific databases. Results: Of 39 selected studies, we identify 18 computer-based systems and 2 plugins, where the two most evaluated, adaptable and with high coverage of authoring activities that are automated are the systems WebCollage (including the plugins AP and MAPIS3) with GluePS and CHOCOLATO.



A cooperative assessment and evaluation scheme of ABET student outcomes in ECE program at Sultan Qaboos University: a case study
The development and implementation of a systematic process for the assessment and evaluation of student outcomes (SOs) in the electrical and computer engineering (ECE) program at Sultan Qaboos University (SQU) in Oman is described in this case study. Unlike in other programs aiming for ABET accreditation, the process is based on a cooperative and acquiescent approach that involves most of the faculty members, while a majority of faculty members take part in the evaluation process. To achieve this, SO focus groups of faculty members have been formed. Each focus group is chaired by a faculty member who coordinated a SO focus group. An SO focus group is composed of three to four members who carry out the evaluation of the SO. Moreover, for each course, course focus groups were formed to take the responsibility of implementing the recommendations on the respective courses. The proposed strategy led to a systematic continuous improvement process of the ECE program at SQU.



The analytics and applications on supporting big data framework in wireless surveillance networks
Nowadays, wireless sensor networks (WSNs) are based on techniques more and more oriented towards image, video and sound processing, hence the recent need of wireless multimedia sensor networks (WMSNs). One of the important challenges for real-time surveillance system is end-to end delay QoS for packet deliveries. Providing end-to-end QoS is difficult due to two reasons. As wireless sensor nodes may require multichip transmissions to reach the sink and some of the wireless transmissions may be not successful. Multimedia data are characterised by their large volume, and have strict requirements in terms of quality of service (QoS) such as bandwidth, delay, packet loss, delay jitter, etc. In this paper, we are interested in routing protocols based on clusters that aim to reduce congestion in order to have reliable data transmission and a reduced loss rate. This is achieved by balancing the traffic load, which results into a balanced energy consumption within the network.



The implementation of an automatic web-driven data analysis framework
Containing a huge amount of data, the web is undoubtedly a very good source of information. However, performing analysis against data fetched from the web is not an easy task. First, the web is designed to be document-centric rather than data-centric. The former refers to websites that are designed for presenting documents only while the latter refers to websites that are designed for rendering datasets. As a result, reading data shown on web pages is comfortable but collecting data is difficult. Imagine repeating the copy-paste procedure for thousands of web pages. Second, the diversity of the presentation style of web pages makes data normalisation essential but difficult. Last but not the least, data analysis itself demands high statistics skill and sometimes may even require domain expertise. In this research, the researchers would like to address these issues by designing a data analysis tool for the web.



Evaluation of life extension by migrating the legacy TTS to be under HLA architecture
Numerous military existing simulation systems are faced with the phenomenon of aging, resulting in high maintenance costs and system instability issues. It would be expensive and risk in their retirement life extension and interoperability. The aim of this study was to analyse the aging tactical training simulation (TTS) system, to seek a way to transfer it under the HLA architecture to achieve, not only prolong life, but also gives interoperability. TTS assessment process including the restoration of the original structure and operation of the process, introduce the concept of HLA in the analysis of defects and ways to improve their TTS architecture such as replacing TTS_MANAGER module. We must complete upgrades as the main body of the TTS with HLA trunk: internal system of communication and APP modules. This study provides military traditional simulation systems solutions, and paved the way for the creation of synthetic battlefield environment, can benefit military training, planning, procurement and resource allocation scheme.



Using case-based reasoning and knowledge mapping to solve multiple-condition problems
Most current case-based reasoning (CBR) systems are designed for solving single-condition problems. However, there are multiple-condition problems (i.e., those involving several different problem conditions, such as personal computer [PC] problems) that also need specific solutions. Thus, this paper attempts to integrate CBR concepts with knowledge mapping in a CBR system designed to solve multiple-condition problems. This study makes three critical contributions: 1) it adopts a user-oriented approach to measuring case similarity and allows the user to select from a list of category features to characterise the new problem; 2) it presents a knowledge mapping algorithm suitable for applying the knowledge of experts to the proposed problems; 3) it uses a prototype to demonstrate how smoothly the proposed approach can solve the target PC problems. Our results indicate that a CBR system with a knowledge mapping approach is suitable for solving multiple-condition problems.



Sharing economy and its effect on human behaiour changes in accommodation: a survey on Airbnb
Advances in computer science prompt the creation of new economic models. One of them in urban life is sharing economy. This new economic model receives high attentions and has been applied to a wide range of fields in recent years. We argue that sharing economy, and its applications, may be taken as a way for researchers to understand human behaviour, and thus this paper targets a growing issue on the relationship between sharing economy and its impacts on human behaviour. It especially looks at the change of behaviours while the sharing economy comes into our daily lives. The Airbnb, one of the most significant examples of sharing economy, is taken as a starting point of this survey, and results are revealed that young generation tends to accept this new economic experience as well as willing to be involved, not only as traveller but host, in this new phenomenon.



Female players' decision-making processes in MMORPGs: a mixed method
The potential of online games to become a major global business and entertainment in the virtual world is huge. However, few studies have investigated online players' decision-making processes, especially female players. This study applies ethnographic decision tree modelling (EDTM) which integrates qualitative and quantitative paradigms to model the decisionmaking process whether or not to play massively multiplayer online roleplating games (MMORPG) by female players. The model is built based on qualitative data through in-depth interviews of 29 respondents, where 18 criteria are elicited. The model is tested using qualitative and quantitative data from interviews, and a structured questionnaire involving 246 respondents. Armed with the results, this study provides online game industries an understanding of the decision-making process of female players, and reveals different standpoints for the researchers with similar interest.



A corpus-based computational analysis of philosophical texts: comparing analytic and continental philosophy
This is the first modern quantitative study of philosophical texts using corpus linguistics. We compared two sets of texts consisting of ten texts of analytic philosophy and ten texts of continental philosophy by using the Burrows delta consensus tree, principal components analysis, as well as multidimensional scaling (MDS) and cluster analysis. We used a supplementary corpus of ten analytical texts pertaining to film studies. The analysis shows that analytic and continental texts are clearly distinct in stylistic terms though there are divisions within the continental corpus. The results lead to the conclusion that philosophical thought is dependent on language. The introduction of an analytic philosophy of film corpus shows that the focus on a certain topic fractures the linguistic coherence of analytic philosophy. Our focus on two major philosophical schools makes it possible to generalise findings to other philosophical schools.



Taking the weather with you: user acceptance, trust and value of weather apps on smartphones
With the start of the 21st century came the rapid growth and popularity in location-based service (LBS) apps, with a particular focus on smartphone applications. As a result of such growth characteristics, a number of studies concerning user acceptance of mobile services have been undertaken within the extant literature. These studies have employed various adaptations and modifications of one of the most widely recognised of information systems success models, the technology acceptance model (TAM). One such adaptation, the technology acceptance model for mobile services (TAMM), seeks to provide a more robust framework for research into this area of mobile technology development. In view of the increasing global use of LBS apps, coupled with the widespread adoption of smartphones, further research into this emergent research field appears warranted. This current paper has sought to explore aspects of user acceptance with respect to a popular set of LBS smartphone applications; namely weather apps. The research has provided a predictive model for future research, and may have implications for authors and app developers. The findings from this research suggest that four distinct factors, ease of adoption, ease of use, trust and value; all of which influence user acceptance of a weather app on smartphones.



Electronic commerce marketing-based social networks in evaluating competitive advantages using SORM
Electronic commerce (e-commerce) base on social networks marketing in the highly competitive environment that must improve service quality and customer loyalty to impact on their competitive advantage, which is most important. Therefore, this study will explore the different dimensions of service quality that affect the antecedents of customer loyalty and behaviour in electronic store (e-store). The framework of study is constructed based on the stimulus-organism-response (S-O-R) theory, wherein dimensions of service quality correspond to stimulus, antecedents of customer loyalty to an operation, and customer loyalty to response that explore the influence of the dimensions between. Our study found that: 1) the result of the DEMATEL-based ANP (DANP) research method shows that the service quality dimension of the quality of electronic services (QES) is a driving factor that affects the service quality dimensions of SERVQUAL, as well as the antecedents of customer loyalty and behaviour intention; 2) a customer's affective commitment can influence their satisfaction with a service.



Determinants of full IFRS adoption
This paper aimed to identify the factors that influence the adoption of IASB standards. We develop a model of environmental factors that lead countries to migrate towards a full adoption strategy. This model is tested empirically using a sample of 101 countries ranked according to different standardisation strategies. Using multinomial logistic regression, the findings show that Anglo-Saxon colonisation, Christianity, the presence of the Big 4 auditing firms in the countries and the belongingness to the European continent or to North America promote the full adoption of these standards, whereas the degree of economic providers' fulfilment with IFRS standards, as well as the level of economic development lead to the opposite effect on IFRS adoption.



Social entrepreneurs as change catalysts: evidences from case studies
The contribution attempts to explicit the role of the social entrepreneur. There are countless examples of social entrepreneurs that can be cited from around the world. Social entrepreneurs' personality, temperament and characteristics have been studied by a great number of researchers. The focus has generally been "who social entrepreneur is". However, few researches have stressed "what social entrepreneur does", what are his actions and the effects of his actions. This is what the present paper tries to address and explore. The use of qualitative case study approach offers rich social entrepreneurial discovery data. However, due to the scant number of interviews, the present analysis can be considered an exploratory study. Future researches can gather data on a large scale, using variegate samples, to generalise results to a large population.



Corporate governance and segmental disclosure: evidence from Canada
The purpose of this study is to examine the association between a segmental disclosure policy and corporate governance structure within Canadian companies reporting under the Canadian accounting standard Section 1701, Segment Disclosure. Canadian firms subject to this standard are found to be reluctant to provide detailed disclosures of their activities and thus are complying with the minimum requirements using the grouping provision offered by the standard. We also found that except for the duality effect, the structure of the board of directors does not have a large effect on the chosen level of segmental disclosure. Furthermore, the ownership structure was found to influence the choice of the level of disclosure regarding the line of businesses data. This study is primarily motivated by the lack of Canadian evidence on the quality of segment disclosure. Moreover, this research is timely because of IASB's current re-evaluation of segment reporting requirements. Therefore, the expected findings of this study may be of interest to standard-setting bodies in countries lacking effective regulation on segmental reporting, to accounting practitioners, and to users of financial statements.



A descriptive framework for an excellent social accountability
Despite the creation of several guidelines for social accountability, there is not a model able to describe how a social accountability document should be. This paper aims at proposing a framework of characteristics for excellent social reports, useful for those organisations that choose the path of social accountability. After a review of literature on the typologies and features of this kind of accountability, the paper analyses the Italian social accountability awarded in the period 2012-2015. Research findings show how the benchmark documents are all characterised by high standards of communication while a more diversified scenario can be emphasised for the standards of content. Despite this paper is only an explorative study, it could represent a good guideline towards excellence in social reporting and could provide suggestions for their development.



Determinants of capital structure: evidence from Sub-Saharan Africa
This study utilised the comprehensive database of non-financial firms listed on the stock exchanges of 12 Sub-Saharan Africa countries in the period 2005-2009 to examine the determinants of capital structure of these firms. The findings indicate that capital structure is negatively associated with profitability and tangible assets, which is supported by the pecking-order theory and the trade-off theory, respectively. Furthermore, the findings show that capital structure is positively associated with free cash and firms' growth, which is consistent with the agency theory and the pecking-order theory, respectively. The results have implications for practitioners, such as business managers, bankers and lenders as well as for capital structure research. Furthermore, the study confirms that association between capital structure and some specific characteristics of firms is inconclusive as some results differ from the results of previous studies. This indicates that there is a need for scholars to conduct more studies to unravel the capital structure puzzle.



A multi-agent-based negotiation system for web service selection
The web service selection is the process of finding matches between the service descriptions and the client's specific needs including the QoS parameters. Actually, these parameters are dynamic and considering them static affects negatively the system's reliability. To make dynamic the QoS parameters, negotiation appears a relevant tool as it positively influences the success rate of the web service selection process. However, existing works do not consider important characteristics of the real humans' interactions including dependencies existing between the concurrent negotiation processes, and the hybrid as well as the dynamic negotiation aspects. In this paper, we show that omitting these aspects while implementing a selection system incurs degradation on its performance. We propose a web service selection agent system based on a hybrid negotiation that solves these drawbacks. The experimental results show that the proposed approach outperforms the existing approaches in terms of the outputs quality and the CPU time.



WSOLINK: web structure outlier detection algorithm
In this world of specialisation where everything is getting specialised, data warehouses and web mining techniques are also getting specialised. Web usage mining, web content mining, and web structure mining are various categories of web mining techniques depending upon the data to be mined. Apriori algorithm, FP growth algorithm, and average linear time algorithm are available to analyse the general access patterns in web server logs whereas WCOND-mine and signed with weight technique are web content outlier mining algorithms. However, no such algorithm is available to check the authenticity and availability of hyperlinks in the resultant web pages given by web search engines. The present research work aims at detection of outliers from the results of queries over web pages through web search engines.



CWrap: web wrapping using context variables
A procedure that extracts data from a data source is called wrapper. In some applications identifying the desired data is better served using a wrapping language rather than an unsupervised method. In this paper, we propose a novel wrapping language, called CWrap. In this language, various types of features (syntactical, semantic, visual and densitometric) can be employed in the extraction rules to identify the items of interest. Moreover, the context in which the desired items appear is specified using variables called context variables. Context variables enable the user to express different types of contextual dependencies (structural, visual and semantical) in a consistent way. They are set under certain conditions by one rule and are used later to form the contextual conditions for another extraction rule. This allows the user to organise the extraction rules in a hierarchical structure, from general to more specific rules. We also present a visual development toolkit which enables the user to develop and debug a wrapper visually and assembling it in an incremental manner.



Design of an energy management system for research institutes and organisations in Iran
Research and development centres, as an initial chain of industrial sectors, are amongst large energy consumers worldwide. Thus, implementing energy management system(s) in such centres would be beneficiary to both economy and livelihood of environment. Additionally, harbouring renewable energy sources play a key and strategic role in policies circumventing both energy production/consumption and environment. Accordingly, design of a unique and comprehensive management system, with the focus on research institutes and organisations, for both energy efficiency improvement and renewable energy production seems to be inevitable. Here and for the first time, designing of an energy management system for such Iranian organisations has been proposed. The evaluation method of our proposed management system was classified in three phases with two approaches: internal and external evaluation. This method allows compare different organisations with each other.



Fuzzy logic-based charging strategy for electric vehicles plugged into a smart grid
The smart grid allows its consumers to participate in producing cost effective, sustainable, and environmentally friendly electricity. The consumers in a smart grid, for example, can plug their electric vehicles (EVs) into the grid to charge and discharge their vehicles' batteries. However, charging of the electric vehicles, especially during the peak periods, can adversely impact the grid performance. Thus, in this paper, the coordinated charging of the electric vehicles problem is tackled. A fuzzy logic-based approach is developed to coordinate the electric vehicle charging such that the system minimum voltage is within the allowable limits. The inputs to the fuzzy charging controller (FCC) include the states of charge (SOC) of the electric vehicles and the grid parameters represented in the system minimum voltage. The output of the FCC is the charging levels of the electric vehicles' batteries. The developed fuzzy logic-based charging strategy was validated on the 69-bus test system. The fuzzy charging (FC) was compared with three modes of uncoordinated charging, namely slow charging (SC), medium charging (MC), and fast charging (FC). The results of the comparative study prove the superiority of the developed fuzzy charging approach over uncoordinated charging schemes.



Supervisory control of a resilient DC microgrid for commercial buildings
This paper presents a supervisory controller for DC microgrid consists of a solar photovoltaic (PV) system, fuel cell, a supercapacitor and battery bank. The DC microgrid is proposed for more efficient resilient electricity distribution in a commercial building. The operation strategy of energy storage systems (ESS) is proposed to solve the power changes from PV array and building loads fluctuations locally, instead of reflecting those disturbances into the utility grid. Furthermore, the ESS energy management scheme will help to achieve the peak reduction of the building daily electrical load demand. The DC microgrid studied in this paper is interfaced with the battery bank by using a bidirectional DC-DC converter, whilst the building electrical AC load is interfaced to grid using DC-AC inverter. The control of the studied microgrid is designed as a method to improve microgrid resilience and incorporate renewable power generation and storage into the grid.



Effect of treatment temperature on microstructure and properties of nickel-zirconia anode materials for solid oxide fuel cells
In this work, the structural, mechanical and electrical properties of YSZ-NiO anode ceramics before and after reduction in pure H2 and Ar-5 vol.%H2 mixture were studied. The NiO to Ni phase transformations that occur in the anode under reducing and reduction-oxidation (redox) cycling conditions and the impact on anode microstructure, strength and electrical conductivity have been examined. The results show that the redox treatment of the YSZ−NiO ceramic specimens influences on their properties controversially. At the treatment temperature 600° a structure providing improved physical and mechanical properties of the material is formed. However, at the treatment temperature 800° an anode structure with an array of microcracks is formed that significantly reduces the strength and electrical conductivity of the material. The results of this investigation show that reduction condition of YSZ-NiO is a powerful tool for influence on properties of the anode substrate.



Energy management strategy for AC/DC microgrid
This paper proposes a grid connected AC/DC microgrid to reduce the processes of multiple conversions in an individual AC or DC microgrid. The hybrid grid consists of both AC and DC networks connected by a bidirectional AC/DC converter. Wind generator, AC loads and utility are connected to the AC bus whereas PV, energy storage system (ESS) and DC loads are tied to the DC bus. The coordination control algorithms of supervisor controller are proposed for smooth power management between AC and DC links and for stable system operation under various generation and load conditions. In this paper, a flexible supervisor controller is developed for a grid connected AC/DC microgrid, where the power flow in the microgrid is supervised based on demanded power with maximum utilisation of renewable resources and ESS. So, the objective of this paper is to use supervisory controller to control the transferred power from AC bus to DC bus and vice versa and control the charging/discharging power of the ESS to reduce the purchased power from the grid or to increase the sold power to the grid with respect to the load demand. The microgrid has been modelled and simulated using MATLAB Simulink. The simulation results show that the system can maintain stable under load variations.



Status and opportunity for distributed energy resources and microgrids in meeting the New York State energy vision
The Reforming the Energy Vision (REV) program of the New York State (NYS) outlines a strategy to reach the goal of having 50% of the electric power by renewable energy resources by the year 2030. Now realising that this date is not that far away, it seemed appropriate that an interim evaluation of the progress that has been made should be done based on the available information. The review provides a clear picture of the advancement of inverter-based PV generation in the state of New York, how the state might use inverter-based solar to power residences, and the interconnection of microgrids. In this paper we present a summary of the findings that include lists of possible challenges in areas of the program. Our review indicates that a number of distributed generation resources is growing. But a lot more work needs to be done to include new technologies to reach the goal of non-carbon-based electric power system that would be able to meet the future electrical energy needs of the New York State safely and efficiently.



Analysis of non-sinusoidal wave generation during electric vehicle charging and their impacts on the power system
Interconnection of EVs could impact adversely the power system operation including power quality and safety of the power grid since the present electric power distribution network may not be designed to support the expected proliferation of EV along with other nonlinear loads and resources that are expected to occur in the future. Numerous studies on the effects of charging EVs using the existing local distribution grid has been reported in the literature, but most works on EVs impact on the distribution network are focused on the fundamental current and voltage waveforms (i.e., 60 Hz) to derive the associated impacts to the electric grid delivery system. In this paper we present our initial work that broadens the above analysis to include current harmonic distortion (iTHD) and the associated voltage harmonic distortion (vTHD) injected into the grid from EV battery charger.



Analysis of elevator drives energy consumptions with permanent magnet machines
Vertical transportations in a facility is one of the sources, which consume electrical energy. In this script, it examines the energy consumption with permanent magnet machines considering the actual system performance integrated with variable speed drive. Most common solution in the current trend of elevator technology is permanent magnet synchronous motor with variable frequency drive with direct current voltage bus intermediate. Energy consumption has been proposed to be understood with actual measurements and dynamic behaviour on various loads on the system is studied. Further, the proposal is to understand the regimes that would be higher efficient, which is analysed and presented.



A method of designing an access mechanism for social networks
The rapid development of communication and networking has lessened geographical boundaries among actors in social networks. In social networks, actors often want to access databases depending upon their access rights, privacy, context, privileges, etc. Managing and handling knowledge-based access of actors is complex and hard for which broad range of technologies need to be called. Access based on dynamic access rights and circumstances of actors impose major tasks on access systems. In this paper, we present an access mechanism for social networks (AMSN) to render access to actors over databases taking privacy and status of actors into consideration. The designed AMSN is tested over an agriculture social network (ASN) which utilised distinct access rights and privileges of actors related to the agriculture occupation, and provided access to actors over databases. The results obtained are quite encouraging for the access of actors over databases in social networks.



A hybrid framework for social tag recommendation using context driven social information
Tagging images uploaded on the web is important as tags serve as the primary entities for future retrieval of these images. The numbers of images uploaded to the web via social networking sites is increasing in the era of smart phone technology and the internet. These images need to be tagged correctly which would ease its future retrieval. With the emergence of the Web 3.0 which is a standard for semantic web, a semantic tag recommender is desirable. In this paper, a context aware social tagger is proposed which recommends high quality tags by using varied contexts of social information. A semantic collaborative filtering strategy is proposed to make the social tagger semantics compliant. The social tagger also encompasses an intelligent agent, driven by second order co-occurrence pointwise mutual information strategy to increase the relevance and quality of the recommended tags. The proposed social tagger yields an average accuracy of 84.04%.



Opinion mining for digital India scheme using fuzzy sets
In this paper, we describe the development of opinion mining for digital India (OMDI) scheme using fuzzy sets. According to people's opinions and reviews, the sentiment classifier will classify the emotion and polarity levels of the review. In this modern world, majority of the people will provide their feedback or opinions on the product that has been increased. The opinion mining results will be useful for the users to make better decision. For the classification of sentiment Naive Bayes and fuzzy logic (intuitionistic fuzzy sets) is utilised. By using these algorithms, we defined the polarity levels of the opinions such as positive, negative and neutral. In general, the sentiment classification will be done by utilising NLP, machine learning, statistical approach and classification methods. By mining powerful reasoning potential of fuzzy logics, we have accredited the polarities to the people's reviews according to their usage. Fuzzy logic deals with the vagueness by accrediting the continuous membership values to opinion words according to their usage in substance.



Malware detection techniques and tools for Android
Smartphone, with its powerful capabilities, has been wildly used in every area of our day-to-day life. The enormous kinds of applications installed in these smartphones like WhatsApp and Viber have changed the way people live and communicate. An estimate by Gartner indicates that 90% of the phones by the end of 2018 will be smartphones (MobileStorm, 2014). The most popular mobile operating system today in the industry is Android. However, with the prevalence of Android smartphone, malware authors have started to target it. Although mobile anti-malware solutions could be installed to scan malicious apps before they are made available for download, existing mobile anti-malware software relies exclusively upon a prior knowledge of malware samples in order to extract and deploy signatures for subsequent detection. Moreover, malware writers may update existing malware samples to dodge detection. Hence, this imprudent nature makes them inadequate in identifying new or mutated malware. This paper explores different techniques and tools available to analyse and detect Android malware. It also highlights the features and limitations of these techniques and tools.



Big data characteristics, challenges, architectures, analytics and applications: a review
Big data has evolved as a most challenging area in scientific study and research. It has drawn much attention during the last few years. It influences our modern society, business, government, healthcare, research and almost every discipline. In this data-driven era, where data is continuously acquired from a verity of sources for different purposes, the ability to make timely decisions based on available data is a very critical task. The massive data size, variety, velocity, accuracy and high dimensionality presents new challenges to big data. This paper attempts to present some challenges of big data. In addition, a study on the conceptual design of big data architecture presented on specific big data applications. In this paper, we presented research work on big data analytics techniques in the areas of text, sentiment, video, social media and predictive analytics. A comparative analysis on selected big data applications has been presented in detail.



An optimal policy for a deteriorating item with generalised deterioration rate and time-dependent demand under permissible delay in payment
The main objective of this paper is to develop an economic order quantity (EOQ) inventory model with the following characteristics: 1) deteriorating items follow a generalised Weibull distribution deterioration rate; 2) no shortage is allowed and (3) delay in payment is permitted. The results have been validated with the help of a numerical example. Sensitivity analysis of the optimal solution with respect to the parameters of the model is performed.



Improved real time A*-fuzzy controller for improving multi-robot navigation and its performance analysis
In this paper, we proposed a new hybrid technique for path planning of multi-robot using Improved real time A* algorithm and fuzzy logic controller (IAFLC). The proposed approach used fuzzy logic controller (FLC) to navigate mobile robots through different developed membership function and find the trajectory path for each robot by minimising time, energy, and distance as the cost function. The path planning of multi- robot is carried out in a grid-map or virtual grid environment using modified real time A* algorithm and fuzzy controller. Finally, the analytical and experimental results of the multi-robot path planning were compared to those obtained by FLC and improved A* in a similar environment. The Simulation and the Khepera environment result show outperforms of IAFLC as compared with FLC and improved A* with respect to cost matrix.



Integrated framework for semantic text mining and ontology construction using inference engine
Traditional clustering algorithms are generally either keyword or index based but not semantic based. These algorithms are facing difficulties in identifying synonymies or polysemies due to high dimensionality of text data. Ontologies are identified to overcome these difficulties. In this paper, we propose a framework which automates the extraction of concepts or terms with support of: a) our proposed metric called term rank identifier (TRI), it measures the frequent terms; b) semantically enriched terms (SETs) clustering algorithm, it calculates the semantic relation between the terms with Word net; c) Ontology Building can be done automatically for the concepts extracted from SET Clustering using inference engines. The experimental results show that our proposed metric TRI and SET clustering algorithm performed significantly.



A modified fruit fly optimisation for classification of financial distress using FLANN
Current financial market has become proficient enough to cater to the needs of a large customer base. But, at the same time, the number of market catastrophes is on the rise leading to the increase in the demand of precise and potential classifier models. In this work, a hybrid model of clustering and neural network-based classifier has been proposed, i.e., FCM-FLANN-IFFO. Three financial credit risk datasets were applied in the experiment and the model is evaluated using 12 different performance metrics. This novel metaheuristic uses an improved version of fruit fly algorithm which is inspired by the foraging behaviour of the fruit flies to locate their food. The experimental results illustrates that proposed model outperforms other models. The proposed model provides brilliant results with 94.91% of classification accuracy.



Investigating the effect of brand personality on customers' loyalty with moderating role of customers' commitment (case study: Tuka Transportation Co. (PLC), Esfahan, Iran)
This study is chiefly concerned with examining the likely associations between the variables brand personality, customer commitment, and customer loyalty in Tuka Company (PLC) from Iran's transportation industry. Based on the assumed relationships in the research model, the required data was collected from the questionnaire distributed among customers. To examine the likely associations between variables and for test of the hypotheses, multiple regression and structural equation modelling (path analysis) were performed. The results indicated that at a 95% confidence interval there was a positive and significant relationship between brand personality and customer commitment. Brand personality was also found to be positively and significantly associated with customer loyalty. In addition, customer commitment had a positive and significant relationship with customer loyalty. And lastly, the results supported significant effect of customer commitment on the relationship of brand personality with customer loyalty.



Storytelling as a managerial tool in tourism destinations: actors, processes and relations
The rise of new business models has required tourism destinations to adopt appropriate tools for the construction and promotion of their identity based on sociality, emotions, interaction and connectivity. The objective of this paper is to analyse actors, actions, processes and relations that relate to the development of storytelling practices in the management of tourism destinations, analysing critical aspects linked to the generation of content and the narration of territories. The study was conducted following the qualitative methodology of multiple case studies.