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The purpose of this paper is to develop a new method for classification of power quality (PQ) disturbances such as the sag, interruption, swell, harmonic, notch, oscillatory transient and impulsive transient.

A PQ disturbances classification system based on wavelet packet energy and multiclass support vector machines (MSVM) is proposed to discriminate seven types of PQ disturbances. The PQ disturbance signals are first decomposed into components in different subbands using discrete wavelet packet transform (DWPT). Statistical features of the decomposed signals are required to characterize the PQ disturbances. A MSVM classifier follows to classify the PQ disturbances.

The proposed method could effectively detect information from disturbance waveforms using DWPT and MSVM techniques, which is verified on over 700 samples.

The classification stage of the proposed method does not differentiate the disturbances occurred simultaneously.

The proposed method possesses high recognition rate, so it is suitable for the PQ monitoring system for detection and classification of disturbances.

The paper describes a new and efficient way of classification of PQ disturbances. In this paper, an attempt has been made to extract efficient features of the PQ disturbances using DWPT. It is observed that these features can help correctly classify the PQ disturbances, even under noisy conditions. The MSVM is compared with artificial neural network (ANN) and it is found that the MSVM classifier gives the better result.

This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.

The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.

This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.

Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

Based on the role theory, this study examines whether workplace age discrimination indirectly relates to older workers' bridge employment intentions through work meaningfulness.

Study 1 used two-wave time-lagged survey data from one hundred and seventy nurses (≥45 years old) from the Midwestern United States. Study 2 used three-wave time-lagged survey data from one hundred and eighty-six employees from a wide range of occupations in the United States. The online survey contains various self-reports on workplace age discrimination, work meaningfulness, affective commitment, and bridge employment intentions.

Results in Study 1 found that workplace age discrimination was negatively and indirectly related to older nurses' bridge employment intentions through their experiences of work meaningfulness. Results in Study 2 further confirmed the mediating role of work meaningfulness in the relationship between age discrimination and bridge employment intentions, above and beyond the role of affective commitment.

This study contributes to the research by testing the indirect relationship between workplace age discrimination and older workers' bridge employment intentions through work meaningfulness, further raising our awareness of the importance of social and interpersonal experiences in older workers' preretirement jobs to their late-career development.

The silicone modifications of two-component epoxy resin coatings are commonly built on epoxy resins rather than on epoxy curing agents. The silicone-modified epoxy curing agent system is rarely reported yet. This study aims to prepare the polysiloxane (PS)-modified waterborne epoxy coatings based on aqueous curing agents technology.

Waterborne epoxy curing agents with different contents of terminal epoxy PS were synthesized by reacting with triethylenetetramine, followed by incorporating of epoxy resin (NPEL-128) and polyethylene glycol diglycidyl ether. The waterborne epoxy coatings were prepared with the above curing agents, and their performance was investigated through thermogravimetric analysis, scanning electron microscopy, mechanical characterization, gloss measurement, chemical resistance test and ultraviolet (UV) aging experiment.

The results showed that the epoxy coating prepared by silicon-modified curing agent has higher gloss, better chemical resistance and UV resistance than the coating from unmodified curing agent with terminal epoxy PS and commercially available waterborne epoxy curing agent (Aradur 3986), as well as the competitive mechanical properties and heat resistance. Reduced water absorption on fibrous paper was also obtained with the help of silicon-modified curing agent.

These findings will be valuable for resin researchers in addressing the modification issues about waterborne epoxy resin and curing agent.

The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.

Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.

This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.

The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

The purpose of this paper is to investigate factors that influence the patients’ intentions to visit doctors face-to-face for consultations from the perspective of online doctor–patient interaction. Justice theory, SERVQUAL and the halo effect are integrated to develop a research model based on the performance-evaluation-outcome framework. The authors hypothesize that perceived justice and service quality are the significant factors in reflecting the performance of online doctor–patient interaction, which influences patient satisfaction evaluation and online and offline behavioral intentions.

The study conducted an online survey to collect data. Patients on a healthcare consulting website were invited to participate in the survey. The research model and hypotheses were tested with 254 collected data from patients and analyzed using the partial least squares method.

The results show that perceived justice and service quality have a positive effect on patient satisfaction, and satisfaction and the intention of online consultation have a positive effect on the intention of face-to-face consultation.

This study offers suggestions on how doctors interact with patients and build their brand image. The findings also offer effective insights into improving doctors’ online services to retain patients and even encourage patients to go to clinics.

Online health consultation is one of the most popular online health services and is growing quickly. After patients consult online doctors, they are able to visit their doctors in person for further diagnosis and treatment if they have the need. This study investigates how patients’ online interactive experience influences their offline behavioral intentions, which are different from most of the past literature on eHealth.

This study aims to describe the general picture of the competition in multichannel expert services in duopoly market and discuss how the quality difference may affects the competition between service providers with different quality levels, where both providers offer face-to-face channel and one of providers offers online channel additionally and service quality that consumers have heterogeneous preferences for is vertically differentiated. These results can be used to determine which service providers should offer online expert services and understand the competition in multichannel expert services in duopoly.

This paper uses the stylized vertical differentiation model to investigate the role of quality in expert services market, assuming that two services providers offer the same services with different quality levels and one of them having additional online services. Taking into account the differences of services from products and the particularity of online service, this paper extends the vertical differentiation model to expert services market.

The quality difference is the key factor in the competition of expert services. Service prices and the profits of providers, independent of the quality levels, are positively related to the quality difference, whereas the demand of online services is in the opposite direction regardless of which provider offers online channel. It demonstrates that provider with low-quality level should open online channel from the point of view of social welfare if it is closely related to the expert services, even though any provider can make more profits by opening online channel.

This extended vertical differentiation model, taking into account the importance of vertical differentiation in expert service, ignores the horizontal differentiation. More accurate strategies for multichannel expert services providers with what level of the quality a provider should offer is needed in future work. Moreover, this paper does not consider the different waiting costs of consumers in face-to-face channel and assumes that their problem will be solved eventually.

To the best of the author’s knowledge, no study has focused on the quality difference in multichannel expert services market or discussed how to offer online expert services in the duopoly market. This study extends the vertical differentiation model to the multichannel expert service market. Therefore, it fills this research gap and extends research to expert services market in the new network environment, aiming to help understand the competition in multichannel expert services.

The purpose of this paper is to explore the extent of corruption in India, Fiji and Ethiopia and survey citizen perception of how e‐governance could fight corruption. The main objective is to investigate and explore the potential of e‐governance applications in three countries representing three different regions of Asia, Africa, and Oceania.

A survey was conducted over 918 citizens in India, Ethiopia and Fiji using convenience random sampling. A structured questionnaire was used. The main emphasis of the survey was on citizen perception about corruption and poor service. It further asked respondents on how e‐governance can cut corruption.

Benefits of e‐governance in developing countries are the same as those in developed countries but there are many potential benefits that remain unreaped by developing countries as a consequence of their unlimited use of e‐governance. Based on these assertions, the researchers tried to evaluate and assess the potential of e‐governance initiatives in India, Ethiopia and Fiji. By exploring the role of e‐governance for reducing corruption that has afflicted the entire public sector in these countries, the main finding is that e‐governance is positively related to government, “citizen relationship and corruption reduction”.

This study is highly empirical and does not provide case studies to further extend on the findings.

The implications of the research are that information communication technology (ICT) needs to be effectively integrated in the development agenda of government plans in Ethiopia and Fiji. Government agencies in Ethiopia and Fiji do not seem to be much motivated to build sound government‐citizen partnerships. Citizens can see little of the internal workings of government. However, for India, where there are many e‐governance projects underway, and which is normally considered to be awakening to the challenges of e‐governance and which has to date many success stories relating to e‐governance, it is surprising to see that citizens find various existent formats of corruption and non‐transparent service delivery activities. It is quite evident that bureaucracy is more or less opaque and very little attention has been paid to improving transparency, including through the use of e‐governance processes. Time, cost and red‐tape procedures are major constraints in public service delivery.

The paper explores a problem that is of practical importance using principal‐agent theory, which is very applicable to the public sector context.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

The purpose of this paper is to investigate the optimal geometry parameters in a dimple/protrusion-pin finned channel with high thermal performance.

The BSL turbulence model is used to calculate the flow structure and heat transfer in a dimple/protrusion-pin finned channel. The optimization algorithm is set as Non-dominated Sorting Genetic Algorithm II (NSGA-II). The high Nusselt number and low friction factor are chosen as the optimization objectives. The pin fin diameter, dimple/protrusion diameter, dimple/protrusion location and dimple/protrusion depth are applied as the optimization variables. An in-house code is used to generate the geometry model and mesh. The commercial software Isight is used to perform the optimization process.

The results show that the Nusselt number and friction factor are sensitive to the geometry parameters. In a pin finned channel with a dimple, the Nusselt number is high at the rear part of the dimple, while it is low at the upstream of the dimple. A high dissipative function is found near the pin fin. In the protrusion channel, the Nusselt number is high at the leading edge of the protrusion. In addition, the protrusion induces a high pressure drop compared to the dimpled channel.

The originality of this paper is to optimize the geometry parameters in a pin finned channel with dimple/protrusion. This is good application for the heat transfer enhancement at the trailing side for the gas turbine.