Search results for: utility

Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Jun Won Kim

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Objective: Theta-phase gamma-amplitude coupling (TGC) was used as a novel evidence-based tool to reflect the dysfunctional cortico-thalamic interaction in patients with schizophrenia. However, to our best knowledge, no studies have reported the diagnostic utility of the TGC in the resting-state electroencephalographic (EEG) of neuroleptic-naive patients with schizophrenia compared to healthy controls. Thus, the purpose of this EEG study was to understand the underlying mechanisms in patients with schizophrenia by comparing the TGC at rest between two groups and to evaluate the diagnostic utility of TGC. Method: The subjects included 90 patients with schizophrenia and 90 healthy controls. All patients were diagnosed with schizophrenia according to the criteria of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) by two independent psychiatrists using semi-structured clinical interviews. Because patients were either drug-naïve (first episode) or had not been taking psychoactive drugs for one month before the study, we could exclude the influence of medications. Five frequency bands were defined for spectral analyses: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), beta (13.5–30 Hz), and gamma (30-80 Hz). The spectral power of the EEG data was calculated with fast Fourier Transformation using the 'spectrogram.m' function of the signal processing toolbox in Matlab. An analysis of covariance (ANCOVA) was performed to compare the TGC results between the groups, which were adjusted using a Bonferroni correction (P < 0.05/19 = 0.0026). Receiver operator characteristic (ROC) analysis was conducted to examine the discriminating ability of the TGC data for schizophrenia diagnosis. Results: The patients with schizophrenia showed a significant increase in the resting-state TGC at all electrodes. The delta, theta, slow alpha, fast alpha, and beta powers showed low accuracies of 62.2%, 58.4%, 56.9%, 60.9%, and 59.0%, respectively, in discriminating the patients with schizophrenia from the healthy controls. The ROC analysis performed on the TGC data generated the most accurate result among the EEG measures, displaying an overall classification accuracy of 92.5%. Conclusion: As TGC includes phase, which contains information about neuronal interactions from the EEG recording, TGC is expected to be useful for understanding the mechanisms the dysfunctional cortico-thalamic interaction in patients with schizophrenia. The resting-state TGC value was increased in the patients with schizophrenia compared to that in the healthy controls and had a higher discriminating ability than the other parameters. These findings may be related to the compensatory hyper-arousal patterns of the dysfunctional default-mode network (DMN) in schizophrenia. Further research exploring the association between TGC and medical or psychiatric conditions that may confound EEG signals will help clarify the potential utility of TGC.

Keywords: quantitative electroencephalography (QEEG), theta-phase gamma-amplitude coupling (TGC), schizophrenia, diagnostic utility

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Authors: Simone Tedeschi, Francesco Crespi, Paolo Liberati, Massimo Paradiso, Antonio Sciala

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This paper aims at contributing to the understanding of smokers’ responses to cigarette prices increases with a focus on heterogeneity, both across individuals and price levels. To do this, a stated preference quasi-experimental design grounded in a random utility framework is proposed to evaluate the effect on smokers’ utility of the price level and variation, along with social conditioning and health impact perception. The analysis is based on individual-level data drawn from a unique survey gathering very detailed information on Italian smokers’ habits. In particular, qualitative information on the individual reactions triggered by changes in prices of different magnitude and composition are exploited. The main findings stemming from the analysis are the following; the average price elasticity of cigarette consumption is comparable with previous estimates for advanced economies (-.32). However, the decomposition of this result across five latent-classes of smokers, reveals extreme heterogeneity in terms of price responsiveness, implying a potential price elasticity that ranges between 0.05 to almost 1. Such heterogeneity is in part explained by observable characteristics such as age, income, gender, education as well as (current and lagged) smoking intensity. Moreover, price responsiveness is far from being independent from the size of the prospected price increase. Finally, by comparing even and uneven price variations, it is shown that uniform across-brand price increases are able to limit the scope of product substitutions and downgrade. Estimated price-response heterogeneity has significant implications for tax policy. Among them, first, it provides evidence and a rationale for why the aggregate price elasticity is likely to follow a strictly increasing pattern as a function of the experienced price variation. This information is crucial for forecasting the effect of a given tax-driven price change on tax revenue. Second, it provides some guidance on how to design excise tax reforms to balance public health and revenue goals.

Keywords: smoking behaviour, preference heterogeneity, price responsiveness, cigarette taxation, random utility models

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Authors: Shanshan Zhou, Massimo Battaglia

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Identity plays a critical role when an organization faces disasters. Individuals reflect on their working identities and identify themselves with the group and the organization, which facilitate collective sensemaking under crisis situations and enable coordinated actions to respond to and recover from disasters. In addition, an organization’s identity links it to its regional community, which fosters the mobilization of resources and contributes to rapid recovery. However, identity is also problematic for disaster prevention because of its persistence. An organization’s ego-defenses system prohibits the rethink of its identity and a rigid identity obstructs disaster prevention. This research aims to tackle the ‘problem’ of identity by study in-depth a case of an Italian multi–utility which experienced the 2012 Northern Italy earthquakes. Collecting data from 11 interviews with top managers and key players in the local community and archived materials, we find that the earthquakes triggered the rethink of the organization’s identity, which got reinforced afterward. This research highlighted the importance of identity in disaster response and recovery. More importantly, it explored the solution of overcoming the barrier of ego-defense that is to transform the organization into a learning organization which constantly rethinks its identity.

Keywords: community identity, disaster, identity, organizational learning

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Authors: Yuyang Cheng, Marcos Escobar-Anel

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This paper introduces a multivariate 4/2 stochastic covariance process generalizing the one-dimensional counterparts presented in Grasselli (2017). Our construction permits stochastic correlation not only among stocks but also among volatilities, also known as co-volatility movements, both driven by more convenient 4/2 stochastic structures. The parametrization is flexible enough to separate these types of correlation, permitting their individual study. Conditions for proper changes of measure and closed-form characteristic functions under risk-neutral and historical measures are provided, allowing for applications of the model to risk management and derivative pricing. We apply the model to an expected utility theory problem in incomplete markets. Our analysis leads to closed-form solutions for the optimal allocation and value function. Conditions are provided for well-defined solutions together with a verification theorem. Our numerical analysis highlights and separates the impact of key statistics on equity portfolio decisions, in particular, volatility, correlation, and co-volatility movements, with the latter being the least important in an incomplete market.

Keywords: stochastic covariance process, 4/2 stochastic volatility model, stochastic co-volatility movements, characteristic function, expected utility theory, verication theorem

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Authors: Sanjeev Prasher, T. Sai Vijay, Chandan Parsad, Abhishek Banerjee, Sahakari Nikhil Krishna, Subham Chatterjee

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With rapid rise in internet retailing, the industry is set for a major implosion. Due to the little difference among competitors, companies find it difficult to segment and target the right shoppers. The objective of the study is to segment Indian online shoppers on the basis of the factors – website characteristics and shopping values. Together, these cover extrinsic and intrinsic factors that affect shoppers as they visit web retailers. Data were collected using questionnaire from 319 Indian online shoppers, and factor analysis was used to confirm the factors influencing the shoppers in their selection of web portals. Thereafter, cluster analysis was applied, and different segments of shoppers were identified. The relationship between income groups and online shoppers’ segments was tracked using correspondence analysis. Significant findings from the study include that web entertainment and informativeness together contribute more than fifty percent of the total influence on the web shoppers. Contrary to general perception that shoppers seek utilitarian leverages, the present study highlights the preference for fun, excitement, and entertainment during browsing of the website. Four segments namely Information Seekers, Utility Seekers, Value Seekers and Core Shoppers were identified and profiled. Value seekers emerged to be the most dominant segment with two-fifth of the respondents falling for hedonic as well as utilitarian shopping values. With overlap among the segments, utilitarian shopping value garnered prominence with more than fifty-eight percent of the total respondents. Moreover, a strong relation has been established between the income levels and the segments of Indian online shoppers. Web shoppers show different motives from being utility seekers to information seekers, core shoppers and finally value seekers as income levels increase. Companies can strategically use this information for target marketing and align their web portals accordingly. This study can further be used to develop models revolving around satisfaction, trust and customer loyalty.

Keywords: online shopping, shopping values, effectiveness of information content, web informativeness, web entertainment, information seekers, utility seekers, value seekers, core shoppers

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Authors: Gultekin Gurcay

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It is known that the most crucial influence of the psychological, social and emotional factors that affect any human behavior is to corrupt the rational decision making mechanism of the individuals and cause them to display irrational behaviors. In this regard, the social context of human beings influences the rationality of our decisions, and people tend to display different behaviors when they were alone compared to when they were surrounded by others. At this point, the interaction and interdependence of the behavioral finance and economics with the area of social psychology comes, where intentions and the behaviors of the individuals are being analyzed in the actual or implied presence of others comes into prominence. Within the context of this study, the prevalent theories of behavioral finance, which are The Prospect Theory, The Utility Theory Given Uncertainty and the Five Axioms of Choice under Uncertainty, Veblen’s Hidden Utility Theory, and the concept of ‘Overreaction’ has been examined and demonstrated; and the meaning, existence and validity of these theories together with the social context has been assessed. Finally, in this study the behavior of the individuals in financial crisis situations where the majority of the society is being affected from the same negative conditions at the same time has been analyzed, by taking into account how individual behavior will change according to the presence of the others.

Keywords: conditional variance coefficient, financial crisis, garch model, stock market

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Authors: Halima Akhtar, Abhijeet Chandra

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The paper attempts to analyze the factors that influence the Consumer Behavior to purchase apparel through the internet. The intentions to buy apparels online were based on in terms of user style, orientation, size and reputation of the merchant, social influence, perceived information utility, perceived ease of use, perceived pleasure and attractiveness and perceived trust and risk. The basic framework used was Technology acceptance model to explain apparels acceptance. A survey was conducted to gather the data from 200 people. The measures and hypotheses were analyzed using Correlation testing and would be further validated by the Structural Equation Modelling. The implications of the findings for theory and practice could be used by marketers of online apparel websites. Based on the values obtained, we can conclude that the factors such as social influence, Perceived information utility, attractiveness and trust influence the decision for a user to buy apparels online. The major factors which are found to influence an online apparel buying decision are ease of use, attractiveness that a website can offer and the trust factor which a user shares with the website.

Keywords: E-tailers, consumer behaviour, technology acceptance model, structural modelling

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Authors: Violeta Damjanovic-Behrendt

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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning

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Authors: Constant Akama, Frank Kulor, Frederick Agyemang

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All over the world, due to increasing population, electric power distribution companies are looking for more efficient ways of reading electricity meters. In Ghana, the prepaid metering system was introduced in 2007 to replace the manual system of reading which was fraught with inefficiencies. However, the prepaid system in Ghana is not capable of integration with online systems such as e-commerce platforms and remote monitoring systems. In this paper, we present a design framework for an automatic metering system that can be integrated with e-commerce platforms and remote monitoring systems. The meter was designed using ADE 7755 which reads the energy consumption and the reading is processed by a microcontroller connected to Sim900 General Packet Radio Service module containing a GSM chip provisioned with an Access Point Name. The system also has a billing server and a management server located at the premises of the utility company which communicate with the meter over a Virtual Private Network and GPRS. With this system, customers can buy credit online and the credit will be transferred securely to the meter. Also, when a fault is reported, the utility company can log into the meter remotely through the management server to troubleshoot the problem.

Keywords: access point name, general packet radio service, GSM, virtual private network

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Authors: Manuel Branco

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This paper argues that mainstream economics is one of the reasons that can explain the difficulty in fully realizing human rights because its logic is intrinsically contradictory to human rights, most especially economic, social and cultural rights. First, its utilitarianism, both in its cardinal and ordinal understanding, contradicts human rights principles. Maximizing aggregate utility along the lines of cardinal utility is a theoretical exercise that consists in ensuring as much as possible that gains outweigh losses in society. In this process an individual may get worse off, though. If mainstream logic is comfortable with this, human rights' logic does not. Indeed, universality is a key principle in human rights and for this reason the maximization exercise should aim at satisfying all citizens’ requests when goods and services necessary to secure human rights are at stake. The ordinal version of utilitarianism, in turn, contradicts the human rights principle of indivisibility. Contrary to ordinal utility theory that ranks baskets of goods, human rights do not accept ranking when these goods and services are necessary to secure human rights. Second, by relying preferably on market logic to allocate goods and services, mainstream economics contradicts human rights because the intermediation of money prices and the purpose of profit may cause exclusion, thus compromising the principle of universality. Finally, mainstream economics sees human rights mainly as constraints to the development of its logic. According to this view securing human rights would, then, be considered a cost weighing on economic efficiency and, therefore, something to be minimized. Fully realizing human rights needs, therefore, a different approach. This paper discusses a human rights-based political economy. This political economy, among other characteristics should give up mainstream economics narrow utilitarian approach, give up its belief that market logic should guide all exchanges of goods and services between human beings, and finally give up its view of human rights as constraints on rational choice and consequently on good economic performance. Giving up mainstream’s narrow utilitarian approach means, first embracing procedural utility and human rights-aimed consequentialism. Second, a more radical break can be imagined; non-utilitarian, or even anti-utilitarian, approaches may emerge, then, as alternatives, these two standpoints being not necessarily mutually exclusive, though. Giving up market exclusivity means embracing decommodification. More specifically, this means an approach that takes into consideration the value produced outside the market and an allocation process no longer necessarily centered on money prices. Giving up the view of human rights as constraints means, finally, to consider human rights as an expression of wellbeing and a manifestation of choice. This means, in turn, an approach that uses indicators of economic performance other than growth at the macro level and profit at the micro level, because what we measure affects what we do.

Keywords: economic and social rights, political economy, economic theory, markets

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Authors: Antonio V. Romana, Rizaide A. Salayo, Maria Lavinia E. Fetalino

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This study assessed the subjective task value and research culture of SLSU faculty. It used the sequential explanatory mixed-method research design. For the quantitative phase, a questionnaire on the research culture and task value were used. While in the qualitative phase, the data was coded and thematized to interpret the focus group discussion outcome. Results showed that the dimensions of the subjective task value, intrinsic, got the highest rank while the utility value got the lowest. It is worth mentioning that all subjective task values were "Agreed." From the FGD, faculty members valued research and wanted to be involved in this undertaking. However, the limited number of faculty researchers, heavy teaching workload, inadequate information on the research process, lack of self-confidence, and low incentives received from research hindered their writing and engagement with research. Thus, a policy brief was developed. It is recommended that the institution may conduct a series of research seminar workshops for the faculty members, plan regular research idea exchange activities, and revisit the university's research thrust and agenda for faculties specialization and expertise alignment. In addition, the university may also lessen the workload and hire additional faculty members so that educators may focus on their research work. Finally, cash incentives may still be considered upon knowing that the faculty members have varied experiences in doing research tasks.

Keywords: task value, interest value, attainment value, utility value, research culture

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Authors: Gordon Wong

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This paper shows how the task of disk defragmentation can be handled by modern operating systems in a transparent, automated, efficient, and confined way through user's file-access patterns. Since files tend to gradually fragment from time to time through file creation, deletion, growth, and shrinking, the problem gets even worse when a disk becomes so fragmented that file accesses cannot be made reasonably efficient without performing the operation of defragmentation for the "entire" disk, which is done manually by the user by launching the disk defragmentation utility program normally bundled with the operating system. In this paper, we argue that the disk defragmentation problem described can be solved without having to manually use the utility program to defragment the entire disk. The argument is based on the observation that system users tend to access certain files in a particular time interval like the way observed for programs exhibiting temporal locality of memory references during their execution. The task of disk defragmentation can be initiated and acted upon for those files contained in the current file-access locality detected and identified by the operating system. The paper also discusses how to use the locality of file references approach to quantitatively measure and determine the locality of user's file access patterns on which the task of disk defragmentation is based.

Keywords: operating systems, disk defragmentation, locality of file accesses, system performance

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Authors: Okay Gunes

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In this article, for the first time in the literature for this subject we propose a new method for the measuring of well-being inequality through a model composed of superimposing satisfaction waves. The displacement of households’ satisfactory state (i.e. satisfaction) is defined in a satisfaction string. The duration of the satisfactory state for a given period of time is measured in order to determine the relationship between utility and total satisfactory time, itself dependent on the density and tension of each satisfaction string. Thus, individual cardinal total satisfaction values are computed by way of a one-dimensional form for scalar sinusoidal (harmonic) moving wave function, using satisfaction waves with varying amplitudes and frequencies which allow us to measure well-being inequality. One advantage to using satisfaction waves is the ability to show that individual utility and consumption amounts would probably not commute; hence it is impossible to measure or to know simultaneously the values of these observables from the dataset. Thus, we crystallize the problem by using a Heisenberg-type uncertainty resolution for self-adjoint economic operators. We propose to eliminate any estimation bias by correlating the standard deviations of selected economic operators; this is achieved by replacing the aforementioned observed uncertainties with households’ perceived uncertainties (i.e. corrected standard deviations) obtained through the logarithmic psychophysical law proposed by Weber and Fechner.

Keywords: Heisenberg uncertainty principle, superimposing satisfaction waves, Weber–Fechner law, well-being inequality

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Authors: Kalandi Charan Pradhan

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In this study, we investigate does weather variation determine temporary labour migration using 210 sample households from six Semi-Arid Tropic (SAT) villages for the period of 2005-2014 in India. The study has made an attempt to examine how households use temporary labour migration as a coping mechanism to minimise the risk rather than maximize the utility of the households. The study employs panel Logit regression model to predict the probability of household having at least one temporary labour migrant. As per as econometrics result, it is found that along with demographic and socioeconomic factors; weather variation plays an important role to determine the decision of migration at household level. In order to capture the weather variation, the study uses mean crop yield deviation over the study periods. Based on the random effect logit regression result, the study found that there is a concave relationship between weather variation and decision of temporary labour migration. This argument supports the theory of New Economics of Labour Migration (NELM), which highlights the decision of labour migration not only maximise the households’ utility but it helps to minimise the risks.

Keywords: temporary migration, socioeconomic factors, weather variation, crop yield, logit estimation

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Authors: Judith Mwakalonge, Geophrey Mbatta, Cuthbert Ruseruka, Gurcan Comert, Saidi Siuhi

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Recent advancements in technology have fostered the development of Electric Vehicles (EVs) that provides relief from transportation dependence on natural fossil fuels as sources of energy. It is estimated that more than 50% of petroleum is used for transportation, which accounts for 28% of annual energy use. Vehicles make up about 82% of all transportation energy use. It is also estimated that about 22% of global Carbon dioxide (CO2) emissions are produced by the transportation sector, therefore, it raises environmental concerns. Governments worldwide, including the United States, are investing in developing EVs to resolve the issues related to the use of natural fossil fuels, such as air pollution due to emissions. For instance, the Bipartisan Infrastructure Law (BIL) that was signed by President Biden on November 15th, 2021, sets aside about $5 billion to be apportioned to all 50 states, the District of Columbia, and Puerto Rico for the development of EV chargers. These chargers should be placed in a way that maximizes their utility. This study aims at studying the charging behaviors of Electric Vehicle (EV) users to establish factors to be considered in the selection of charging locations. The study will focus on social-economic and land use data by studying the relationship between charging time and charging locations. Local factors affecting the charging time and the chargers’ utility will be investigated.

Keywords: electric vehicles, EV charging stations, social economic factors, charging networks

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Authors: Roberta Martino, Viviana Ventre

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Intertemporal choices are choices under conditions of uncertainty in which the consequences are distributed over time. The Discounted Utility Model is the essential reference for describing the individual in the context of intertemporal choice. The model is based on the idea that the individual selects the alternative with the highest utility, which is calculated by multiplying the cardinal utility of the outcome, as if the reception were instantaneous, by the discount function that determines a decrease in the utility value according to how the actual reception of the outcome is far away from the moment the choice is made. Initially, the discount function was assumed to have an exponential trend, whose decrease over time is constant, in line with a profile of a rational investor described by classical economics. Instead, empirical evidence called for the formulation of alternative, hyperbolic models that better represented the actual actions of the investor. Attitudes that do not comply with the principles of classical rationality are termed anomalous, i.e., difficult to rationalize and describe through normative models. The development of behavioral finance, which describes investor behavior through cognitive psychology, has shown that deviations from rationality are due to the limited rationality condition of human beings. What this means is that when a choice is made in a very difficult and information-rich environment, the brain does a compromise job between the cognitive effort required and the selection of an alternative. Moreover, the evaluation and selection phase of the alternative, the collection and processing of information, are dynamics conditioned by systematic distortions of the decision-making process that are the behavioral biases involving the individual's emotional and cognitive system. In this paper we present an original decomposition of the discount function to investigate the psychological principles of hyperbolic discounting. It is possible to decompose the curve into two components: the first component is responsible for the smaller decrease in the outcome as time increases and is related to the individual's impatience; the second component relates to the change in the direction of the tangent vector to the curve and indicates how much the individual perceives the indeterminacy of the future indicating his or her aversion to uncertainty. This decomposition allows interesting conclusions to be drawn with respect to the concept of impatience and the emotional drives involved in decision-making. The contribution that neuroscience can make to decision theory and inter-temporal choice theory is vast as it would allow the description of the decision-making process as the relationship between the individual's emotional and cognitive factors. Neurofinance is a discipline that uses a multidisciplinary approach to investigate how the brain influences decision-making. Indeed, considering that the decision-making process is linked to the activity of the prefrontal cortex and amygdala, neurofinance can help determine the extent to which abnormal attitudes respect the principles of rationality.

Keywords: impatience, intertemporal choice, neurofinance, rationality, uncertainty

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Authors: Bekir O. Bartin, Kaan Ozbay, Sandeep Mudigonda, Hong Yang

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The use of microscopic traffic simulation in evaluating the operational and safety conditions at toll plazas is demonstrated. Two toll plazas in New Jersey are selected as case studies and were developed and validated in Paramics traffic simulation software. In order to simulate drivers’ lane selection behavior in Paramics, a utility-based lane selection approach is implemented in Paramics Application Programming Interface (API). For each vehicle approaching the toll plaza, a utility value is assigned to each toll lane by taking into account the factors that are likely to impact drivers’ lane selection behavior, such as approach lane, exit lane and queue lengths. The results demonstrate that similar operational conditions, such as lane-by-lane toll plaza traffic volume can be attained using this approach. In addition, assessment of safety at toll plazas is conducted via a surrogate safety measure. In particular, the crash index (CI), an improved surrogate measure of time-to-collision (TTC), which reflects the severity of a crash is used in the simulation analyses. The results indicate that the spatial and temporal frequency of observed crashes can be simulated using the proposed methodology. Further analyses can be conducted to evaluate and compare various different operational decisions and safety measures using microscopic simulation models.

Keywords: microscopic simulation, toll plaza, surrogate safety, application programming interface

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Authors: Orla McConnell, Cormac MacMahon, Jen Harvey

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Ireland’s national professional development framework for those who teach in higher education, aims to provide guidance and leadership in the planning, developing and engaging in professional development practices. A series of pilot projects have been initiated to help explore the framework’s likely utility and acceptance by educators and their institutions. These projects require engagement with staff in the interpretation and adaption of the framework within their working contexts. The purpose of this paper is to outline the development of one such project with engineering educators at three Institutes of Technology seeking designation as a technological university. The initiative aims to gain traction in the acceptance of the framework with the engineering education community by linking core and discipline-specific teaching and learning competencies with professional development activities most valued by engineering educators. Informed by three strands of literature: professional development in higher education; engineering education; and teaching and learning training provisions, the project begins with a survey of all those involved in teaching and learning in engineering across the three institutes. Based on engagement with key stakeholders, subsequent qualitative research informs the contextualization of the national framework for discipline-specific and institutional piloting. The paper concludes by exploring engineering educator perceptions of the national framework’s utility based on their engagement with the pilot process. Feedback from the pilot indicates that there is a significant gap between the professional development needs of engineering educators and the current professional development provision in teaching and learning.

Keywords: engineering education, pilot, professional development, teaching and learning

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Authors: Pooja Verma, Sumana Ghosh

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There is a growing need for alternative way of power generation worldwide. The reason can be attributed to limited resources of fossil fuels, environmental pollution, increasing cost of conventional fuels, and lower efficiency of conversion of energy in existing systems. In this context, one of the potential alternatives for power generation is wave energy. However, it is difficult to estimate the amount of electrical energy generation in an irregular sea condition by experiment and or analytical methods. Therefore in this work, a numerical wave tank is developed using the computational fluid dynamics software Open FOAM. In this software a specific utility known as waves2Foam utility is being used to carry out the simulation work. The computational domain is a tank of dimension: 5m*1.5m*1m with a floating object of dimension: 0.5m*0.2m*0.2m. Regular waves are generated at the inlet of the wave tank according to Stokes second order theory. The main objective of the present study is to validate the numerical model against existing experimental data. It shows a good matching with the existing experimental data of floater displacement. Later the model is exploited to estimate energy extraction due to the movement of such a point absorber in real sea conditions. Scale down the wave properties like wave height, wave length, etc. are used as input parameters. Seasonal variations are also considered.

Keywords: OpenFOAM, numerical wave tank, regular waves, floating object, point absorber

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Authors: Roberta Martino, Viviana Ventre

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The Discounted Utility Model is the essential reference for calculating the utility of intertemporal prospects. According to this model, the value assigned to an outcome is the smaller the greater the distance between the moment in which the choice is made and the instant in which the outcome is perceived. This diminution determines the intertemporal preferences of the individual, the psychological significance of which is encapsulated in the discount rate. The classic model provides a discount rate of linear or exponential nature, necessary for temporally consistent preferences. Empirical evidence, however, has proven that individuals apply discount rates with a hyperbolic nature generating the phenomenon of intemporal inconsistency. What this means is that individuals have difficulty managing their money and future. Behavioral finance, which analyzes the investor's attitude through cognitive psychology, has made it possible to understand that beyond individual financial competence, there are factors that condition choices because they alter the decision-making process: behavioral bias. Since such cognitive biases are inevitable, to improve the quality of choices, research has focused on a personalized approach to strategies that combines behavioral finance with personality theory. From the considerations, it emerges the need to find a procedure to construct the personalized strategies that consider the personal characteristics of the client, such as age or gender, and his personality. The work is developed in three parts. The first part discusses and investigates the weight of the degree of impatience and impatience decrease in the anomalies of the discounted utility model. Specifically, the degree of decrease in impatience quantifies the impact that emotional factors generated by haste and financial market agitation have on decision making. The second part considers the relationship between decision making and personality theory. Specifically, four behavioral categories associated with four categories of behavioral investors are considered. This association allows us to interpret intertemporal choice as a combination of bias and temperament. The third part of the paper presents a method for constructing personalized strategies using Analytic Hierarchy Process. Briefly: the first level of the analytic hierarchy process considers the goal of the strategic plan; the second level considers the four temperaments; the third level compares the temperaments with the anomalies of the discounted utility model; and the fourth level contains the different possible alternatives to be selected. The weights of the hierarchy between level 2 and level 3 are constructed considering the degrees of decrease in impatience derived for each temperament with an experimental phase. The results obtained confirm the relationship between temperaments and anomalies through the degree of decrease in impatience and highlight that the actual impact of emotions in decision making. Moreover, it proposes an original and useful way to improve financial advice. Inclusion of additional levels in the Analytic Hierarchy Process can further improve strategic personalization.

Keywords: analytic hierarchy process, behavioral finance anomalies, intertemporal choice, personalized strategies

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Authors: Paolo Guzzini, Emad Samuel M. Ebeid

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Bridges and Railways are critical infrastructures. Ensuring safety for transports using such assets is a primary goal as it directly impacts the lives of people. By the way, improving safety could require increased investments in O&M, and therefore optimizing resource usage for asset maintenance becomes crucial. Drones4Safety (D4S), a European project funded under the H2020 Research and Innovation Action (RIA) program, aims to increase the safety of the European civil transport by building a system that relies on 3 main pillars: • Drones operating autonomously in swarm mode; • Drones able to recharge themselves using inductive phenomena produced by transmission lines in the nearby of bridges and railways assets to be inspected; • Data acquired that are analyzed with AI-empowered algorithms for defect detection This paper describes the business model behind this disruptive project. The Business Model is structured in 2 parts: • The first part is focused on the design of the business model Canvas, to explain the value provided by the Drone4safety project; • The second part aims at defining a detailed financial analysis, with the target of calculating the IRR (Internal Return rate) and the NPV (Net Present Value) of the investment in a 7 years plan (2 years to run the project + 5 years post-implementation). As to the financial analysis 2 different points of view are assumed: • Point of view of the Drones4safety company in charge of designing, producing, and selling the new system; • Point of view of the Utility company that will adopt the new system in its O&M practices; Assuming the point of view of the Drones4safety company 3 scenarios were considered: • Selling the drones > revenues will be produced by the drones’ sales; • Renting the drones > revenues will be produced by the rental of the drones (with a time-based model); • Selling the data acquisition service > revenues will be produced by the sales of pictures acquired by drones; Assuming the point of view of a utility adopting the D4S system, a 4th scenario was analyzed taking into account the decremental costs related to the change of operation and maintenance practices. The paper will show, for both companies, what are the key parameters affecting most of the business model and which are the sustainable scenarios.

Keywords: a swarm of drones, AI, bridges, railways, drones4safety company, utility companies

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Authors: Aderonke Oluseun Akinwumi, Olusola A. Komolaf

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This paper gives information about the wide array of challenges facing both the electric utilities and consumers in the distribution system in developing countries, using Obafemi Awolowo University, Ile-Ife Nigeria as a case study. It also proffers cost-effective solution through remote monitoring, diagnostic and operation of distribution networks without compromising the system reliability. As utilities move from manned and unintelligent networks to completely unmanned smart grids, switching activities at substations and feeders will be managed and controlled remotely by dedicated systems hence this design. The Substation Remote Diagnostic and Operation Solution (sRDOs) would remotely monitor the load on Medium Voltage (MV) and Low Voltage (LV) feeders as well as distribution transformers and allow the utility disconnect non-paying customers with absolutely no extra resource deployment and without interrupting supply to paying customers. The aftermath of the implementation of this design improved the lifetime of key distribution infrastructure by automatically isolating feeders during overload conditions and more importantly erring consumers. This increased the ratio of revenue generated on electricity bills to total network load.

Keywords: electric utility, consumers, remote monitoring, diagnostic, system reliability, manned and unintelligent networks, unmanned smart grids, switching activities, medium voltage, low voltage, distribution transformer

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Authors: Jonathan Iworiso

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Forecasting the equity premium out-of-sample is a major concern to researchers in finance and emerging markets. The quest for a superior model that can forecast the equity premium with significant economic gains has resulted in several controversies on the choice of variables and suitable techniques among scholars. This research focuses mainly on the application of Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regression models involving model complexity was employed. The RT models include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net, and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the RT models demonstrates significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk.

Keywords: regression training, out-of-sample forecasts, expanding window, statistical predictability, economic significance, utility gains

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Authors: Alexandra Shipley

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Background: Undergraduate paediatric teaching must overcome two major challenges: 1) balancing patient safety with active student engagement and 2) exposing students to a comprehensive range of pathologies within a relatively short clinical placement. Whilst lectures and shadowing on paediatric wards constitute the mainstay of learning, Simulation and Virtual Reality (VR) are emerging as effective teaching tools, which - immune to the unpredictability and seasonal variation of hospital presentations - could expose students to the entire syllabus more reliably, efficiently, and independently. We aim to evaluate the potential utility of Simulation and VR in addressing gaps within the traditional paediatric curriculum from the perspective of medical students. Summary of Work: Exposure to and perceived utility of various learning opportunities within the Paediatric and Emergency Medicine courses were assessed through a questionnaire completed by 5th year medical students (n=23). Summary of Results: Students reported limited exposure to several common acute paediatric presentations, such as bronchiolitis (41%), croup (32%) or pneumonia (14%), and to clinical emergencies, including cardiac/respiratory arrests or trauma calls (27%). Across all conditions, average self-reported confidence in assessment and management to the level expected of an FY1 is greater amongst those who observed at least one case (e.g. 7.6/10 compared with 3.6/10 for croup). Students rated exposure through Simulation or VR to be of similar utility to witnessing a clinical scenario on the ward. In free text responses, students unanimously favoured being ‘challenged’ through ‘hands-on’ patient interaction over passive shadowing, where it is ‘easy to zone out.’ In recognition of the fact that such independence is only appropriate in certain clinical situations, many students reported wanting more Simulation and VR teaching. Importantly, students raised the necessity of ‘proper debriefs’ after these sessions to maximise educational value. Discussion and Conclusion: Our questionnaire elicited several student-perceived challenges in paediatric education, including incomplete exposure to common pathologies and limited opportunities for active involvement in patient care. Indeed, these experiences seem to be important predictors of confidence. Quantitative and qualitative feedback suggests that VR and Simulation satisfy students’ self-reported appetite for independent engagement with authentic clinical scenarios. Take-aways: Our findings endorse further development of VR and Simulation as high-yield adjuncts to paediatric education.

Keywords: paediatric emergency education, simulation, virtual reality, medical education

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Authors: Mojtaba Mahmoodian, Joshua Phelan, Mehdi Shahparvari

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With a large percentage of countries’ total infrastructure expenditure attributed to water network maintenance, it is essential to optimise maintenance strategies to rehabilitate or replace underground pipes before failure occurs. The aim of this paper is to provide water utility managers with a maintenance management approach for underground water pipes, subject to external loading and material corrosion, to give the lowest life cycle cost over a predetermined time period. This reliability-based maintenance management methodology details the optimal years for intervention, the ideal number of maintenance activities to perform before replacement and specifies feasible renewal options and intervention prioritisation to minimise the life cycle cost. The study was then extended to include feasible renewal methods by determining the structural condition index and potential for soil loss, then obtaining the failure impact rating to assist in prioritising pipe replacement. A case study on optimisation of maintenance plans for the Melbourne water pipe network is considered in this paper to evaluate the practicality of the proposed methodology. The results confirm that the suggested methodology can provide water utility managers with a reliable systematic approach to determining optimum maintenance plans for pipe networks.

Keywords: water pipe networks, maintenance management, reliability analysis, optimum maintenance plan

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Authors: Shishir Lamichhane, Saurav Dulal, Bibek Gautam, Madan Thapa Magar, Indraman Tamrakar

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Renewable energies such as wind turbines and solar photovoltaic have gained significance as a result of global environmental pollution and energy crises. These sources of energy are converted into electrical energy and delivered to end-users through the utility system. As a result of the widespread use of power electronics-based grid-interfacing technologies to accommodate renewable sources of energy, the prevalence of converters has expanded as well. As a result, the power system's rotating inertia is decreasing, endangering the utility grid's stability. The use of Virtual Synchronous Machine (VSM) technology has been proposed to overcome the grid stability problem due to low rotating inertia. The grid-connected inverter used in VSM can be controlled to emulate inertia, which replicates the external features of a synchronous generator. As a result, the rotating inertia is increased to support the power system's stability. A power angle control strategy is proposed in this paper and its model is simulated in MATLAB/Simulink to study the effects of parameter disturbances on the active power and frequency for a VSM. The system consists of a synchronous generator, which is modeled in such a way that the frequency drops to an unacceptable region during transient conditions due to a lack of inertia when VSM is not used. Then, the suggested model incorporating VSM emulates rotating inertia, injecting a controllable amount of energy into the grid during frequency transients to enhance transient stability.

Keywords: damping constant, inertia–constant, ROCOF, transient stability, distributed sources

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Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

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Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

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Authors: Aziz Rezapour, Abdosaleh Jafari, Marziye Hadian, Elaheh Mazaheri

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Introduction: Cardiac rehabilitation is an accurate educational and sporting program designed to help heart patients to increase their physical activities and reduce the risk factors that make their health worse and help to a healthier lifestyle so that they can return to their families and society with a better spirit. The aim of this study was to examine the cost-effectiveness and cost-utility of cardiac rehabilitation programs for patients with cardiovascular diseases. Methods: In the present review study, published articles related to cost-effectiveness and cost-utility of cardiac rehabilitation programs for patients with cardiovascular diseases within the time interval between 2004 and 2019 were searched using electronic databases. The methodological quality of the structure of articles was examined by Drummond’s standard checklist. Results: The results of reviewing studies showed that most of the studies related to the economic evaluation of cardiac rehabilitation programs in patients with cardiovascular disease were flawed in Drummond’s criteria, and only one study adhered to Drummond’s criteria. The results of the present study indicated use of cardiac rehabilitation programs in patients with cardiovascular disease was cost-effective. Conclusion: The results of this review study showed that although the results of the studies were different in terms of a number of aspects, such as the study perspective, the time horizons, and the costs of rehabilitation programs, they achieved a similar conclusion, they concluded that the use of cardiac rehabilitation programs in patients with cardiovascular diseases, leading to higher quality-adjusted life years (QALYs) and lower costs.

Keywords: economic evaluation, systematic review, cardiac rehabilitation, Drummond’s checklist

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Authors: Dina Tareq Ismail, Alexandria A. Proff

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The United Arab Emirates (UAE) is a nation seeking to advance in all fields, particularly education. One area of focus for UAE 2021 agenda is to restructure UAE schools and universities by equipping them with highly developed technology. The agenda also advises educational institutions to prepare students with applicable and transferrable Information and Communication Technology (ICT) skills. Despite the emphasis on ICT and computer literacy skills, there exists limited empirical data on the use of M-Learning in the literature. This qualitative study explores the motivation of higher primary Arabic teachers in private schools toward implementing and integrating M-Learning apps in their classrooms. This research employs a phenomenological approach through the use of semistructured interviews with nine purposefully selected Arabic teachers. The data were analyzed using a content analysis via multiple stages of coding: open, axial, and thematic. Findings reveal three primary themes: (1) Arabic teachers with high levels of procedural knowledge in ICT are more motivated to implement M-Learning; (2) Arabic teachers' perceptions of self-efficacy influence their motivation toward implementation of M-Learning; (3) Arabic teachers implement M-Learning when they possess high utility and/or intrinsic value in these applications. These findings indicate a strong need for further training, equipping, and creating buy-in among Arabic teachers to enhance their ICT skills in implementing M-Learning. Further, given the limited availability of M-Learning apps designed for use in the Arabic language on the market, it is imperative that developers consider designing M-Learning tools that Arabic teachers, and Arabic-speaking students, can use and access more readily. This study contributes to closing the knowledge gap on teacher-motivation for implementing M-Learning in their classrooms in the UAE.

Keywords: ICT skills, m-learning, self-efficacy, teacher-motivation

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