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The purpose of this paper is to examine significant steps taken by the Pennsylvania State University (Penn State) to account for both energy cost savings and greenhouse gas (GHG) emissions reduction goals through strategic investments in energy conservation measures (ECMs) in campus buildings. Through an analysis of multiple years of investment in facility upgrades across the university, the impacts of ECMs of various types are characterized by building type. The standards and criteria for ECMs investments are also evaluated with the goal to develop a predictive tool to support decision making pertaining to an annual investment in a portfolio of ECMs that will maintain a trajectory to achieve both financial return on investment as well as GHG reduction goals.

This study is comprised of three main parts: analyzing the energy costs saving and GHG emissions reduction contribution of various building types in which ECMs were conducted, analyzing costs saving and GHG emissions reduction contribution of each ECM while considering the average annual investments made in them and estimating the impact of upgrading Penn State’s steam plants from firing a mixture of coal and natural gas to natural gas only on the GHG emissions.

These analyses help identify which types of buildings and ECMs would have larger savings and emissions reduction contributions. A calculator is also created to enable forecasting of costs saving and GHG emissions reduction of investment distribution strategy among ECMs. This study demonstrates that the calculator based on data from previous years will benefit decision makers in more wisely configuring the investment portfolio.

This paper fulfills an identical need to couple energy efficiency strategies coupled with the environmental impacts associated with different fossil fuel energy sources.

Due to technological improvement and development of the vehicle-to-home (V2H) concept, electric vehicle (EV) can be considered as an active component of net-zero energy buildings (NZEBs). However, to achieve more dependable results, proper energy analysis is needed to take into consideration the stochastic behavior of renewable energy, energy consumption in the building and vehicle use pattern. This study aims to stochastically model a building integrating photovoltaic panels as a microgeneration technology and EVs to meet NZEB requirements.

First, a multiobjective nondominated sorting genetic algorithm (NSGA-II) was developed to optimize the building energy performance considering panels installed on the façade. Next, a dynamic solution is implemented in MATLAB to stochastically model electricity generation using solar panels as well as building and EV energy consumption. Besides, the Monte Carlo simulation method is used for quantifying the uncertainty of NZEB performance. To investigate the impact of weather on both energy consumption and generation, the model is tested in five different climatic zones in Iran.

The results show that the stochastic simulation provides building designers with a variety of convenient options to select the best design based on level of confidence and desired budget. Furthermore, economic evaluation signifies that investing in all studied cities is profitable.

Considering the uncertainty in building energy demand and PV power generation as well as EV mobility and the charging–discharging power profile for evaluating building energy performance is the main contribution of this study.

Genetics, a discipline of biology, is one of the most recent and rapidly advancing disciplines in science. This study aims to present a bibliometric analysis of the genetics research output of Iranian authors, map the intellectual structure of these studies and investigate the development path of this literature and the interrelationships among the main topics.

This study searched the Web of Science database for documentation of Iranian-published genetics research published up to 2020. Further, this study used HistCite software to profile and analyze the most cited articles and references and to draw their historiographies.

A database search revealed 21,329 documents that created the study population. The highest cited publications based on the Global Citation Score (GCS) and Local Citation Score (LCS) achieved scores of 602 and 47, respectively. The publication growth rate study demonstrated consistent expansion over time. The scientific maps based on LCS and GCS had five and four clusters, respectively. Furthermore, journal articles emerged as the predominant type of publication.

The significance of this study is in its contribution to understanding the genetics research position in Iran, informing policymakers and researchers, helping scientific collaboration and its impact on public attitudes and quality of life. The results of the present study, with benefits for various groups of communities, such as policymakers, academic groups and public society, can bridge the gap between theoretical research and practical implications.

The results of this study, by helping future advancement in health care, medical genetics and disease prevention, may have a direct and indirect positive influence on the quality of life. Furthermore, it may lead to more informed discussions on health care and biotechnology as well as influencing public attitudes and perceptions.

Ultimately, this study concludes that despite the proliferation of publications in terms of quantity and complexity, especially in areas such as disease diagnosis, prevention and treatment, there remains a need for more attention to other facets of genetics such as biology and biotechnology. Iranian publications are most related to population genetics, human genetics, molecular genetics, medical genetics, genomics, developmental genetics and evolutionary genetics out of 10 branches of genetics. This study reveals patterns in scientific outputs and authorship collaborations and plays an alternative and innovative role in revealing Iranian research trends in genetics.