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The purpose of this paper is to investigate the performance of coke oven gas (COG)-combined cooling, heating and power (CCHP) system and to mainly focus on studying the influence of the environmental conditions, operating conditions and gas conditions on the performance of the system and on quantifying the distribution of useful energy loss and the saving potential of the integrated system changing with different parameters.

The working process of COG-CCHP was simulated through the establishment of system flow and thermal analysis mathematical model. Using exergy analysis method, the COG-CCHP system’s energy consumption status and the performance changing rules were analyzed.

The results showed that the combustion chamber has the largest exergy loss among the thermal equipments. Reducing the environmental temperature and pressure can improve the entire system’s reasonable degree of energy. Higher temperature and pressure improved the system’s perfection degree of energy use. Relatively high level of hydrogen and low content of water in COG and an optimal range of CH₄ volume fraction between 35 per cent and 46 per cent are required to ensure high exergy efficiency of this integration system.

This paper proposed a CCHP system with the utilization of coke oven gas (COG) and quantified the distribution of useful energy loss and the saving potential of the integrated system under different environmental, operating and gas conditions. The weak links of energy consumption within the system were analyzed, and the characteristics of COG in this way of using were illustrated. This study can provide certain guiding basis for further research and development of the CCHP system performance.

This paper aims to analyze energy and exergy analysis of solar-based intercooled and reheated gas turbine (GT) trigeneration cycle using parabolic trough solar collectors (PTC) with the use of MATLAB 2018.

In the first section of this paper, the solar-based GT is validated with the reference paper. According to the reference paper, the solar field is comprising 30 modules in series and 35 modules in parallel series, where a total of 1,050 modules of PTC are taken into consideration. In the second part of this paper, the hybridization of the solar, GT trigeneration cycle is analyzed and optimized. In the last section of this paper, the hybridization of solar, intercooled and reheated GT trigeneration systems is examined and compared.

The results examined the first section, the power produced by the cycle will be 37.34 MW at 0.5270 kg/s mass flow rate of the natural gas consumption and the efficiencies of energy and exergy will be 38.34% and 39.76%, respectively. The results examined in the second section, the power produced by the cycle will be 38.4 MW at 0.5270 kg/s mass flow rate of the natural gas consumption and accordingly the efficiency of energy and exergy is found to be 40.011% and 41.763%. Where in the last section, the power produced by the cycle will be 41.43 MW at 0.5270 kg/s mass flow rate of the natural gas consumption and the energy and exergy efficiencies will be 39.76% and 40.924%, respectively.

The author confirms that this study is original and has neither been published elsewhere nor it is currently under consideration for publication elsewhere.

The purpose of this paper is to target on designing a short-term load prediction framework that can accurately predict the cooling load of office buildings.

A feature selection scheme and stacking ensemble model to fulfill cooling load prediction task was proposed. Firstly, the abnormal data were identified by the data density estimation algorithm. Secondly, the crucial input features were clarified from three aspects (i.e. historical load information, time information and meteorological information). Thirdly, the stacking ensemble model combined long short-term memory network and light gradient boosting machine was utilized to predict the cooling load. Finally, the proposed framework performances by predicting cooling load of office buildings were verified with indicators.

The identified input features can improve the prediction performance. The prediction accuracy of the proposed model is preferable to the existing ones. The stacking ensemble model is robust to weather forecasting errors.

The stacking ensemble model was used to fulfill cooling load prediction task which can overcome the shortcomings of deep learning models. The input features of the model, which are less focused on in most studies, are taken as an important step in this paper.

The purpose of this paper is to examine relationships of self-care behavior, illness representation and diabetes knowledge with A1C (level of glycemic control) in 124 incarcerated persons.

Using a cross-sectional design, summary scores and items from the self-care inventory revised, brief illness perception questionnaire and the spoken knowledge for low literacy in diabetes were evaluated using linear regression to assess their relationship to A1C.

Metabolic control was suboptimal for the majority of inmates with diabetes. The final regression model was statistically significant (F (3, 120)=9.51, p=0.001, R²=19.2 percent). Higher log10 HbA1C (A1C) was associated with lower personal control beliefs (B=−0.007, t (122)=−2.42, p=<0.02), higher self-report of diabetes understanding (B=0.009, t (122)=3.12, p=0.00) and using insulin (B=0.062, t (122)=2.45, p=0.02).

Similar to findings with community dwelling participants, enhancing diabetes personal control beliefs among inmates may lead to lower A1C.

Highly structured environments with limited options for self-care, personal choices and readily available health care may give some incarcerated persons with diabetes no motivation to improve diabetes control even if they have an understanding of what to do.

While there is abundant research in the community describing how these factors influence A1C levels, research of this nature with incarcerated persons with diabetes is limited. Findings will inform diabetes programming during incarceration to better prepare inmates for reentry.

Smart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.

This study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.

The results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.

The users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.

This study aims to solve optimal pricing and power bidding strategy problem for integrated combined heat and power (CHP) system by using a modified heuristic optimization algorithm.

In electricity markets, generation companies compete according to their bidding parameters; therefore, optimal pricing and bidding strategy are solved. Recently, CHP units are significantly operated by generation companies to meet power and heat, simultaneously.

For validation, it is shown that profit is improved by 0.04%–48.02% for single and 0.02%–31.30% for double-sided auctions. As heat price curve is extracted, the simulation results show that when CHP system is integrated with other units results in profit increase and emission decrease by 3.04%–3.18% and 2.23%–4.13%, respectively. Also, CHP units significantly affect bidding parameters.

The novelties are pricing and bidding strategy of integrated CHP system is solved; local heat selling is considered in pricing and bidding strategy problem and heat price curve is extracted; the effects of CHP utilization on bidding parameters are investigated; a modified heuristic and deterministic optimization algorithm is presented.

The spiraling cost of health care is emerging as one of the country's most urgent problems and a major domestic political issue. In the 1940s, prepaid medical care provided by groups of health‐care professionals began to take hold and finally emerged as a serious prospect for cost‐effective health care with the passage of the Health Maintenance Organization Act of 1973 (42 U.S.C. 300e). Although still not widespread, interest in HMOs is growing and government incentives to private investment in such organizations should prompt inquiries to libraries from citizens groups, businesspeople, and potential customers of these services. Here is a sampling of items on the subject.