Kiilerichbloom3533

Very first thing, I hope this finds each of you, your families, and friends healthy. These are difficult times for all of us and there are priorities much greater than our professions at the moment. For those of you facing hardship, we extend our heartfelt sympathies and wish your circumstances to improve soon.This paper presents the formulation and application of a novel agent-based integrated assessment approach to model the attributes, objectives and decision-making process of investors in a long-term energy transition in India's iron and steel sector. It takes empirical data from an on-site survey of 108 operating plants in Maharashtra to formulate objectives and decision-making metrics for the agent-based model and simulates possible future portfolio mixes. The studied decision drivers were capital costs, operating costs (including fuel consumption), a combination of capital and operating costs, and net present value. Where investors used a weighted combination of capital cost and operating costs, a natural gas uptake of ~12PJ was obtained and the highest cumulative emissions reduction was obtained, 2 Mt CO2 in the period from 2020 to 2050. Conversely if net present value alone is used, cumulative emissions reduction in the same period was lower, 1.6 Mt CO2, and the cumulative uptake of natural gas was equal to 15PJ. Results show how the differing upfront investment cost of the technology options could cause prevalence of high-carbon fuels, particularly heavy fuel oil, in the final mix. Results also represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. The perception of high capital expenditures for decarbonisation represents a significant barrier to the energy transition in industry and should be addressed via effective policy making (e.g. carbon policy/price).Carbon mitigation strategies are an urgent and overdue tourism industry imperative. The tourism response to climate action has been to engage businesses in technology adoption, and to encourage more sustainable visitor behaviour. These strategies however are insufficient to mitigate the soaring carbon footprint of tourism. Building upon the concepts of optimization and eco-efficiency, we put forward a novel carbon mitigation approach, which seeks to pro-actively determine, foster, and develop a long-term tourist market portfolio. This can be achieved through intervening and reconfiguring the demand mix with the fundamental aim of promoting low carbon travel markets. The concept and the analytical framework that quantitatively inform optimization of the desired market mix are presented. Combining the "de-growth" and "optimization" strategies, it is demonstrated that in the case study of Taiwan, great potential exists to reduce emissions and sustain economic yields. The implications for tourism destination managers and wider industry stakeholders are discussed.The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges we face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, SARS, MERS, and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility, where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies based on the real-world data in South Korea and Northern Ireland.Artificial intelligence (AI) has proven to be superior to human decision-making in certain areas. Crenolanib nmr This is particularly the case whenever there is a need for advanced strategic reasoning and analysis of vast amounts of data in order to solve complex problems. Few human activities fit this description better than politics. In politics we deal with some of the most complex issues humans face, short-term and long-term consequences have to be balanced, and we make decisions knowing that we do not fully understand their consequences. I examine an extreme case of the application of AI in the domain of government, and use this case to examine a subset of the potential harms associated with algorithmic governance. I focus on five objections based on political theoretical considerations and the potential political harms of an AI technocracy. These are objections based on the ideas of 'political man' and participation as a prerequisite for legitimacy, the non-morality of machines and the value of transparency and accountability. I conclude that these objections do not successfully derail AI technocracy, if we make sure that mechanisms for control and backup are in place, and if we design a system in which humans have control over the direction and fundamental goals of society. Such a technocracy, if the AI capabilities of policy formation here assumed becomes reality, may, in theory, provide us with better means of participation, legitimacy, and more efficient government.The COVID-19 outbreak is a sharp reminder that pandemics, like other rarely occurring catastrophes, have happened in the past and will continue to happen in the future. Even if we cannot prevent dangerous viruses from emerging, we should prepare to dampen their effects on society. The current outbreak has had severe economic consequences across the globe, and it does not look like any country will be unaffected. This not only has consequences for the economy; all of society is affected, which has led to dramatic changes in how businesses act and consumers behave. This special issue is a global effort to address some of the pandemic-related issues affecting society. In total, there are 13 papers that cover different industry sectors (e.g., tourism, retail, higher education), changes in consumer behavior and businesses, ethical issues, and aspects related to employees and leadership.The COVID-19 pandemic and the lockdown and social distancing mandates have disrupted the consumer habits of buying as well as shopping. Consumers are learning to improvise and learn new habits. For example, consumers cannot go to the store, so the store comes to home. While consumers go back to old habits, it is likely that they will be modified by new regulations and procedures in the way consumers shop and buy products and services. New habits will also emerge by technology advances, changing demographics and innovative ways consumers have learned to cope with blurring the work, leisure, and education boundaries.This essay applies the "ultimate broadening of the concept of marketing" for designing and implementing interventions in public laws and policy, national and local regulations, and everyday lives of individuals. The ultimate broadening of the concept of marketing Marketing is any activity, message, emotion, or behavior by someone, firm, organization, government, community, or brand executed consciously or nonconsciously that may stimulate an observable or non-observable activity, emotion, attitude, belief, or thought by someone else, group, organization, firm or community. The broadening definition applies to the current interventions by national and state/provincial governments as well as healthcare facilities, medical science facilities, firms, and individuals to mitigate and eliminate the impact of the COVID-19 pandemic. Framing interventions as experiments is helpful in improving the quality of their designs, implementing them successfully, and validly interpreting their effectiveness. In January and February 2020, a few nations were exemplars for accurately forecasting the coming disaster of COVID-19 as a cause of illness and death and in designing/implementing effective mitigating strategies Denmark, Finland, Republic of Korea, New Zealand, Norway, and Vietnam. While the COVID-19 prevention intervention tests now being run for several promising vaccines are true experiments, the researchers analyzing the data from these interventions may need prompting to examine the efficacy of each vaccine tested by modeling demographic subgroups for the members in the treatment and placebo groups in the randomized control trials.InDisc is an R Package that implements procedures for estimating and fitting unidimensional Item Response Theory (IRT) Dual Models (DMs). DMs are intended for personality and attitude measures and are, essentially, extended standard IRT models with an extra person parameter that models the discriminating power of the individual. The package consists of a main function, which calls subfunctions for fitting binary, graded, and continuous responses. The program, a detailed user's guide, and an empirical example are available at no cost to the interested practitioner.Accurate item calibration in models of item response theory (IRT) requires rather large samples. link2 For instance, N > 500 respondents are typically recommended for the two-parameter logistic (2PL) model. link3 Hence, this model is considered a large-scale application, and its use in small-sample contexts is limited. Hierarchical Bayesian approaches are frequently proposed to reduce the sample size requirements of the 2PL. This study compared the small-sample performance of an optimized Bayesian hierarchical 2PL (H2PL) model to its standard inverse Wishart specification, its nonhierarchical counterpart, and both unweighted and weighted least squares estimators (ULSMV and WLSMV) in terms of sampling efficiency and accuracy of estimation of the item parameters and their variance components. To alleviate shortcomings of hierarchical models, the optimized H2PL (a) was reparametrized to simplify the sampling process, (b) a strategy was used to separate item parameter covariances and their variance components, and (c) the variance components were given Cauchy and exponential hyperprior distributions. Results show that when combining these elements in the optimized H2PL, accurate item parameter estimates and trait scores are obtained even in sample sizes as small as N = 100 . This indicates that the 2PL can also be applied to smaller sample sizes encountered in practice. The results of this study are discussed in the context of a recently proposed multiple imputation method to account for item calibration error in trait estimation.