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The purpose of this paper is to propose an intelligent service recommendation model. The paper formulates the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning.

The authors formulate the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning. Bayesian Network is used to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the rules for adopting the policies of implementing a service, fitness degree computation and service recommendation. In addition to this the paper proposes maximum to minimum priority based context attributes matching algorithm for rule selection based on fitness degree of rules. The context aware mobile is tested for library and class room scenario to exemplify the proposed service recommendation engine and demonstrate its effectiveness.

First, it was found that there was reduction in application searching time in different contexts. For example, if user enters into the library, the proposed mobile will be adapted to the library situation automatically by configuring its desktop and internal settings to facilitate the library services like book search, web link, silent mode and friends search. Second, the design of the recommendation engine, utilizing contextual parameters like Location (class room, college campus, house, etc.) Personal (age, name), Temporal (time, date), Physical (fall, normal), and schedule agendas, was found to be of importance.

Exploitation of hybrid fuzzy system, Bayesian Networks and the utility theory (usage history and context history) for modeling and implementation.

Law enforcement officers (LEOs) suffer from premature mortality, intentional and unintentional injury, suicide and are at an increased risk for several non-communicable disease outcomes including cardiovascular disease and several cancers, compared to those employed in other occupations. Repeated exposure to stressful and traumatic stimuli is a possible mechanism driving these adverse health outcomes among LEOs. To better identify the sources of these health problems, the purpose of this paper is to determine the feasibility of conducting a cohort study using physiological measures of stress (e.g. heart rate) with LEOs; perceptions of the FitBit device, including LEO buy-in and attitudes associated with the protocol.

Data were collected from ten recent graduates of the Dallas Police Training Academy.

Results suggest that officer buy-in and protocol compliance was high. Officers were eager to participate in this study, and completion of weekly surveys was 100 percent. Minute-level missing data from wearable devices was relatively low (25 percent), and 90 percent of participants wore the FitBit devices on more than 90 percent of study days.

Results from this study suggest that wearable physiological devices can be effectively used in law enforcement populations to measure stress.

Tourism–economy relationship in India has been studied extensively in the past literature using a single equation approach. However, the present paper diverted from this trend and examined the tourism–economy relationship using the specific to general modelling approach over the 1990–2018 time period. The study also accounts for the influence of merchandise trade, capital formation, foreign investment inflows and inflation on economic growth to achieve the robustness of the coefficient estimates.

To achieve the objective, the study utilised a specific to general modelling strategy. First, the regression equation includes only three core variables: gross domestic product (GDP), international tourist receipts and international tourist expenditures. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegration among the variables. As for the estimation technique, the authors employed autoregressive distributed lag (ARDL) approach.

The paper's findings highlight that tourism receipts and expenditures exert a positively significant impact on economic growth. Moreover, including the additional independent variables does not substantially change the tourism and economic growth relationship. The existence of one-way causality from tourism expenditures to economic growth supports the tourism-led growth hypothesis. These findings highlight the rationale for intervention by the government and policymakers to promote tourism potential and facilities to accelerate the overall growth performance of the country. While the existence of one-way causal effect from economic growth to tourism revenues supports the growth-led tourism development hypothesis, implying that economic expansion is necessary for tourism development.

This research article tried to present a comprehensive picture of India's tourism–economy relationship. However, the present study is organised as an aggregate economy-level analysis. It assumed that the aggregate tourism sector is homogenous. However, different tourism sectors exert different levels of influence on the economy. The authors expect future research can take the disaggregated analysis of the tourism–economy relationship.

This study provides valuable insights into the tourism-led growth hypothesis in India. The study highlights comprehensive intervention by the government and policymakers for accelerating tourism development to invigorate the overall growth performance of the country over the long run. The principal recommendation emerging from the present research is that the tourism growth potential can be depended upon to stimulate the economic performance of the Indian economy.

The present study diverted from the previous empirical studies by following a specific to general modelling strategy. First, the regression model includes only three core variables such as economic growth, tourism receipts and tourism expenditure. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegrating relationship among the variables. GDP growth rate is used as a dependent variable in all five specifications. The idea is to expand the model to capture every feature of the data generating process.

The set of services provided by the mobile phone platform, is becoming increasingly complex and requiring more computing power, hence higher energy consumption, and compromising the autonomy of these devices. The purpose of this paper is to identify scenarios where methods could be applied to reduce such consumption and extend the mobile autonomy.

This mobile evolution has given rise to a lot of energy saving research activities, which mainly focus on the hardware side of computational systems. However, it is tempting to suppose that only hardware dissipates power, not software. This paper characterizes several hardware and software scenarios, which could be explored to develop energy‐efficient mobile techniques.

From this analysis, the authors argue that the development of applications that consider energy saving as one of their requirements, can result in a significant final energy saving because solutions will be part of their own software and they do not depend on external resources to obtain a lower consumption.

This paper provides a broad analysis about the different research directions in energy consumption optimization and stresses the important contributions that the software engineering area could offer to this subject.