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Electric vehicles (EV) are a promising alternative for the current fossil-fuel-based vehicles. However, as of 2020, the share of EV sales was only 4.6 % globally, and 1 % in Australia. It is important to identify factors that promote or hinder consumer intentions of EV adoption. In addition, there are a few types of EVs, each with different advantages and disadvantages creating consumer segmentation. This study considered battery-powered EV and hydrogen fuel cell EV and sought to understand which factors influence the preferences for one of the two types of vehicles. We designed a survey on individual perceptions toward EV and collected data of 1735 consumers in Australia. Participants had a mean age of 44.9 years (SD = 16.71) and 41 % of them were males. The median daily traveling distances was 8.7 km, and 74 % of them reported using a personal car for commuting. The results show that the safety concern has a stronger impact on adoption intention than the purchase cost and perceived benefits. Age and consumers' current mode of transportation play a significant role in EV adoption intentions. In addition, the results indicate that preference for BEV is significantly affected by BEV range sufficiency, tolerability in battery charging time, and fear of hydrogen explosion, whereas the key preference factors for FCEVs is their longer driving range, and fear of battery explosion. Besides, the results reveal that part-time employees are more likely to prefer BEV than full-time workers. On the other hand, apartment residents tend to prefer FCEV more than people living in a house. Furthermore, female is more likely to still undecided than males whether they prefer BEV or FCEV.

Improving the ways we acquire, store, distribute, and utilise data within and across research fields strengthens Australia's resilience, enabling our society to prepare for growing global concerns as a stronger and more agile nation. The Australian Research Data Commons (ARDC) is well-positioned to address these data challenges as it has a unique depth of expertise in facilitating cross-organizational collaboration to establish national data capabilities. This poster will showcase the current expertise that ARDC offers to projects and partnerships and its engagement in national digital research infrastructure initiatives.

A holy grail for military, diplomatic, and intelligence analysis is a valid set of software agent models that act as the desired ethno-political factions so that one can test the effects of alternative courses of action in different countries. This article explains StateSim, a country modeling approach that synthesizes best-of-breed theories from across the social sciences and that has helped numerous organizations over 20 years to study insurgents, gray zone actors, and other societal instabilities. The country modeling literature is summarized (Sect. 1.1) and synthetic inquiry is contrasted with scientific inquiry (Sects. 1.2 and 2). Section 2 also explains many fielded StateSim applications and 100s of past acceptability tests and validity assessments. Section 3 then describes how users now construct and run ‘first pass’ country models within hours due to the StateSim Generator, while Sect. 4 offers two country analyses that illustrate this approach. The conclusions explain lessons learned.

The philosophy of building energy management is going through a paradigm change from traditional, often inefficient, user-controlled systems to one that is centrally automated with the aid of IoT-enabled technologies. In this context, occupants’ perceived control and building automation may seem to be in conflict. The inquiry of this study is rooted in a proposition that while building automation and centralized control systems are assumed to provide indoor comfort and conserve energy use, limiting occupants’ control over their work environment may result in dissatisfaction, and in turn decrease productivity. For assessing this hypothesis, data from the post-occupancy evaluation survey of a smart building in a university in Australia was used to analyze the relationships between perceived control, satisfaction, and perceived productivity. Using structural equation modeling, we have found a positive direct effect of occupants’ perceived control on overall satisfaction with their working area. Meanwhile, perceived control exerts an influence on perceived productivity through satisfaction. Furthermore, a field experiment conducted in the same building revealed the potential impact that occupant controllability can have on energy saving. We changed the default light settings from automatic on-and-off to manual-on and automatic-off, letting occupants choose themselves whether to switch the light on or not. Interestingly, about half of the participants usually kept the lights off, preferring daylight in their rooms. This also resulted in a reduction in lighting electricity use by 17.8% without any upfront investment and major technical modification. These findings emphasize the important role of perceived control on occupant satisfaction and productivity, as well as on the energy-saving potential of the user-in-the-loop automation of buildings.

Alzheimer's Disease (AD) is one of the most common forms of neuropsychological disorder in elderly people. It is a slow progressive disease affecting the brain cells. This affects the cognitive abilities of people and their daily activities. During the course of the disease, memory gets brutally affected too. Working as well as long-term declarative memory deteriorates in AD patients. Due to this deterioration of the memory, AD patients tend to show a decline in their communicative skills as well. This decline is reflected in their speech. AD patients usually have poor grammar along with very low coherent ideas. Also, they tend to repeat the words very often and hence become unclear on the message they are trying to convey. As the disease progresses, the speech is completely impaired, and the patients are left to sing or utter words that are totally out of context. Stopwords are the words that are most commonly used in language and it is often hypothesized that AD patients use them much often as compared to Control Normal (CN) subjects. It is seen that due to the degeneration of brain cells in AD patients, they have a tendency to use a lot of stopwords to fill their perplexities in their statements. In this paper, the usefulness of the stopwords in capturing the linguistic information of the patients suffering from AD are discussed. Learning algorithms are evaluated by including stopwords and dropping stopwords at preprocessing to draw comparisons.

Occupant behaviors are one of the dominant factors that influence building energy use. Traditional building energy modeling programs use typical occupant schedules that often do not reflect actual situations. Robust occupant behavior modeling that seamlessly integrates with building energy models will not only improve simulation performance, but also provide a deeper understanding of occupant behaviors in buildings. This paper presents a development and validation approach to a novel occupant behavior model in commercial buildings. A robust agent-based modeling (ABM) tool, namely Performance Moderator Functions server (PMFserv), is used as the basis of the occupant behavior model. The ABM considers various occupant perceptions and interactions with window, door, and window-blinds based on the environmental conditions. An elaborate agent-based model that represents an office space in an existing building is developed. This is followed by a validation study of the ABM through the use of embedded sensors that capture the indoor ambient conditions and a survey to record actual occupant behaviors. By comparing the recorded behavior data with ABM output, this paper discusses the proposed ABM's prediction ability, limitations, and extensibility. Finally, the paper concludes with the potential of integrating the occupant behavior model with building energy simulation programs.

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy- efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent-based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in-depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human-in-the-loop system optimization.

The extreme workloads on the fog layer caused a misalignment in some fog nodes that affect its efficiency and degenerate fog technology’s primary goal. Therefore, creating a balanced computing environment via the offloading process is the key. However, there are many obstacles to balance computing nodes in the fog environment, such as offloading strategy and its consequences due to the extreme offloading processes, and when need to offload and where. These obstacles are vital concerns among researchers. Thus, several studies have been conducted to enhance the fog system performance to increase the entire system’s throughput. This paper explores the recent articles to determine the possible research gaps and opportunities to implement an efficient solution for load balancing in fog environments after analyzing and assessing the existing solutions. While most of the proposed solutions involve short-term solution, this literature review reveals the need to find out a sustainable resolution for load balancing to avoid listed obstacles by using the offloading technique with maintaining the bandwidth of the network.

© 2020, Springer Nature Switzerland AG. Alzheimer’s disease (AD) is the most common form of neurodegenerating disorder accounting for 60–80% of all dementia cases. The lack of effective clinical treatment options to completely cure or even slow the progression of disease makes it even more serious. Treatment options are available to treat the milder stage of the disease to provide symptomatic short-term relief and improve quality of life. Early diagnosis is key in the treatment and management of AD as advanced stages of disease cause severe cognitive decline and permanent brain damage. This has prompted researchers to explore innovative ways to detect AD early on. Changes in speech are one of the main signs of AD patients. As the brain deteriorates the language processing ability of the patients deteriorates too. Previous research has been done in the English language using Natural Language Processing (NLP) techniques for early detection of AD. However, research using local languages and low resourced language like Nepali still lag behind. NLP is an important tool in Artificial Intelligence to decipher the human language and perform various tasks. In this paper, various classifiers have been discussed for the early detection of Alzheimer’s in the Nepali language. The proposed study makes a convincing conclusion that the difficulty in processing information in AD patients reflects in their speech while describing a picture. The study incorporates the speech decline of AD patients to classify them as control subjects or AD patients using various classifiers and NLP techniques. Furthermore, in this experiment a new dataset consisting of transcripts of AD patients and Control normal (CN) subjects in the Nepali language. In addition, this paper sets a baseline for the early detection of AD using NLP in the Nepali language.

Inhabitants of sub-Saharan Africa suffer disproportionately from lack of access to improved drinking water sources than the rest of the world. Previous studies have established capacity-related success factors (or enablers) of infrastructure development projects, including drinking water supply. However, these enablers when not well established can also be indicators of risks to the sustainability and overall success of such water projects. The riskiness for success of a water project in Kassala, Sudan was assessed using the eight enablers, resulting in explicit documentation of capacity deficiencies, which may be considered as risk factors for the sustainability, and ultimately, the overall success of the water project. These capacity deficiencies may also serve as risk checklist for project managers and systems engineers involved in projects in similar environments.

We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each.

Video analytics framework detection performance is worked at cloud. Object detection and classification are the basic tasks in video analytics and become the initial point for other complex submissions. Old fashioned video analytics approaches are manual and time consuming. These are particular due to the very participation of human factor. This paper present a cloud based video analytics framework for accessible and robust analysis of video streams. The framework enables an operative by programing the object detection and classification process from recorded video streams. An operative only specifies an analysis criteria and period of video streams to analyze. The streams are then realized from cloud storage, cracked and analyzed on the cloud. The framework performs compute severe parts of the analysis to CPU powered servers in the cloud. Vehicle and face finding are accessible as two case studies for assessing the framework, with one month of data and a 15 node cloud. The framework consistently performed object detection and classification on the data, comprising of 21,600 video streams and 175 GB in size, in 6.52 hours. The GPU enabled placement of the framework took 3 hours to perform analysis on the same number of video streams, thus making it at least double as fast than the cloud deployment Without GPUs. The analysis framework is high. R. Gnana bharathy "Framework of Object Detection and Classification High Performance Using Video Analytics in Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18906.pdf

Enterprise risk management (ERM) has emerged as the new paradigm in risk management with the goal of holistically managing all risks facing an enterprise. Yet organizations still manage risks in a piece-meal fashion and struggle to effectively implement ERM and manage complex strategic risks. This article proposes a solution to this problem: ERM implementation using a system dynamics approach, which enables integrating risks in a causal modeling environment that includes feedback and delays. The methodology is then described using the ISO 31000 Risk Management Standard and illustrated using an example.

In solving variational, differential and optimisation equations subject to constraints, the penalty method is a well known procedure. Recent publications show that with a combination of positive and negative penalty parameters, constraint violations can be determined and controlled. This relies on the monotonic nature of convergence of the penalised model towards the constrained system, which has been proven and demonstrated for systems with discrete constraints. This paper investigates the use of this approach for systems with continuous constraints. It addresses the questions of whether the number of critical penalty parameters for continuous constraints is finite, and whether the positive and negative penalty method result in monotonic and bounded convergence towards the constrained solution, and compares the use of penalty functions in integral form and discrete forms to represent continuous constraints.

Many simulators today contain traditional opponents and lack an asymmetric insurgent style adversary. InsurgiSim prototypes an embeddable testbed containing a threat network of agents that one can easily configure and deploy for training and analysis purposes. The insurgent network was constructed inside a socio-cognitive agent framework (FactionSim-PMFserv) that includes: (a) a synthesis of best-of-breed models of personality, culture, values, emotions, stress, social relations, mobilization, as well as (b) an IDE for authoring and managing reusable archetypes and their task-sets (Section 2). Agents and markups in this library are not scripted, and act to follow their values and fulfill their needs. So it's desirable to profile the agents (eg, faction leaders, cell logisticians, followers, bomb maker, financier, recruiter, etc.) as faithfully to the real world as possible. Doing this will improve the utility of InsurgiSim for studying what may be driving the insurgent agents in a given area of operation as Section 3 explains. InsurgiSim's bridge is an HLA federate and can be embedded to drive all or some of the insurgent agents in a 3rd party simulator. Three such examples are summarized in Section 4. The paper closes with next steps to improve InsurgiSim's capabilities and utility.

Military, diplomatic, and intelligence analysts are increasingly interested in having a valid system of models that span the social sciences and interoperate so that one can determine the effects that may arise from alternative operations (courses of action) in different lands. Part I of this article concentrated on internal validity of the components of such a synthetic framework--a world diplomacy game as well as the agent architecture for modeling leaders and followers in different conflicts. But how valid are such model collections once they are integrated together and used out-of-sample (see Sect. 1)? Section 2 compares these realistic, descriptive agents to normative rational actor theory and offers equilibria insights for conflict games. Sections 3 and 4 offer two real world cases (Iraq and SE Asia) where the agent models are subjected to validity tests and an effects based operations (EBO, as in Smith, Effects based operations: applying network-centric warfare in peace, crisis, and war, 2002) experiment is then run for each case. We conclude by arguing that substantial effort on game realism, best-of-breed social science models, and agent validation efforts is essential if analytic experiments are to effectively explore conflicts and alternative ways to influence outcomes. Such efforts are likely to improve behavioral game theory as well.

This paper presents a theory for role-playing simulation games intended to support analysts (and trainees) with generating and testing alternative competing hypotheses on how to influence world conflict situations. Simulated leaders and followers capable of playing these games are implemented in a cognitive modeling framework, called the Performance Moderator Function Server (PMFserv), which covers value systems, personality and cultural factors, emotions, relationships, perception, stress/coping style, and decision making. Of direct interest, as Section I-A explains, is codification and synthesis of best-of-breed social-science models within PMFserv to improve the internal validity of agent implementations. Sections II and III present this for leader profiling instruments and group-membership decision making, respectively. Section IV then offers two real-world case studies (The Third Crusade and SE Asia Today) where agent models are subjected to Turing and correspondence tests under each case study. In sum, substantial effort on game realism, best-of-breed social-science models, and agent validation efforts is essential if analysis and training tools are to help explore cultural issues and alternative ways to influence outcomes. Such exercises, in turn, are likely to improve the state of the science as well.

This paper presents a synthetic approach for generating role playing simulation games intended to support analysts (and trainees) interested in testing alternative competing courses of action (operations) and discovering what effects they are likely to precipitate in potential ethno-political conflict situations. Simulated leaders and followers capable of playing these games are implemented in a cognitive modeling framework, called PMFserv, which covers value systems, personality and cultural factors, emotions, relationships, perception, stress/coping style and decision making. Of direct interest, as Sect. 1.1 explains, is mathematical representation and synthesis of best-of-breed behavioral science models within this framework to reduce dimensionality and to improve the realism and internal validity of the agent implementations. Sections 2 and 3 present this for leader profiling instruments and group membership decision-making, respectively. Section 4 serves as an existence proof that the framework has generated several training and analysis tools, and Sect. 5 concludes with lessons learned. Part II turns to the question of assessment of the synthesis and its usage in course of action studies.

Many producers and consumers of legacy training simulator and game environments are beginning to envision a new era where psycho-socio-physiologic models could be interoperated to enhance their environments' simulation of human agents. This paper explores whether we could embed our behavior modeling framework (described in the companion paper, Part 1) behind a legacy first person shooter 3D game environment to recreate portions of the Black Hawk Down scenario. Section 1 amplifies the interoperability needs and challenges confronting the field, presents the questions that are examined, and describes the test scenario. Sections 2 and 3 review the software and knowledge engineering methodology, respectively, needed to create the system and populate it with bots. Results (Section 4) and discussion (Section 5) reveal that we were able to generate plausible and adaptive recreations of Somalian crowds, militia, women acting as shields, suicide bombers, and more. Also, there are specific lessons learned about ways to advance the field so that such interoperabilities will become more affordable and widespread.

Existing cognitive agent modeling approach is feasible to use 'mixed modeling' (individuals, organizations, societal) of wellness.Validated against Philadelphia mental health system data from 2010.Useful to help isolate interventions that fail as well as those that succeed. PurposeExplore whether agent-based modeling and simulation can help healthcare administrators discover interventions that increase population wellness and quality of care while, simultaneously, decreasing costs. Since important dynamics often lie in the social determinants outside the health facilities that provide services, this study thus models the problem at three levels (individuals, organizations, and society). MethodsThe study explores the utility of translating an existing (prize winning) software for modeling complex societal systems and agent's daily life activities (like a Sim City style of software), into a desired decision support system. A case study tests if the 3 levels of system modeling approach is feasible, valid, and useful. The case study involves an urban population with serious mental health and Philadelphia's Medicaid population (n=527,056), in particular. ResultsSection 3 explains the models using data from the case study and thereby establishes feasibility of the approach for modeling a real system. The models were trained and tuned using national epidemiologic datasets and various domain expert inputs. To avoid co-mingling of training and testing data, the simulations were then run and compared (Section 4.1) to an analysis of 250,000 Philadelphia patient hospital admissions for the year 2010 in terms of re-hospitalization rate, number of doctor visits, and days in hospital. Based on the Student t-test, deviations between simulated vs. real world outcomes are not statistically significant. Validity is thus established for the 2008-2010 timeframe. We computed models of various types of interventions that were ineffective as well as 4 categories of interventions (e.g., reduced per-nurse caseload, increased check-ins and stays, etc.) that result in improvement in well-being and cost. ConclusionsThe 3 level approach appears to be useful to help health administrators sort through system complexities to find effective interventions at lower costs.

Over the last decade remote laboratories have emerged as valuable educational resources, providing the potential for improved educational outcomes, student flexibility, richer laboratory experiences, and cross-institutional resource sharing. While there has been increasing attention given to the pedagogy that underpins the use of remote labs, the focus on the design of the lessons that take advantage of remote laboratories has been much more limited. In this paper it is argued that remote laboratories provide an exciting opportunity to provide system-supported adaptation. In particular, because the interactions with the equipment are now mediated through software, it becomes possible to monitor these interactions, as well as the state of the apparatus, and use this to provide guidance to the student. The paper discusses the opportunities that are created, the requirements this places on the design of remote laboratory systems, and preliminary work on implementing such a system that has highlighted potential challenges. © 2012 IEEE.

Socio-cultural problems have special challenges that complicate training design. Problems in these domains have been called “wicked problems” due to their intractability [4]. Such problems are ill-defined: characterized by conflicting stakeholder values, disagreements over solutions, and interconnectedness between problems. Simulation-based learning can be used to explore these problems, but assessment is a bottleneck for training ill-defined domains. Problems in ill-defined domains are heterogeneous: some problems have clear right and wrong answers, but others are subjective, context-dependent, or emergent. A possible solution is hybrid tutoring, which combines multiple tutoring approaches [2]. A hybrid tutor could match different pedagogical interventions for different types of problems. However, hybrid tutoring lacks established design principles for matching domain problems with suitable interventions. The Complex Environment Assessment and Tutoring System (CEATS) follows two principles to support hybrid tutoring. First, semantic interfaces are used to decouple components, transforming the simulation environment into meaningful metrics. Assessments use metrics as evidence to calculate measures about domain concept qualities. The second principle is to support families of assessments. Together, this design decouples assessments from the simulation and embeds meta-data to make them meaningful for reporting and tutoring modules. The Complex Environment Assessment and Tutoring System uses metrics as a semantic API for the learning environment. This allows different environments (e.g. simulation vs database) to share the same metric specifications, but implement their own function and query implementations. A metrics engine currently exists for use with a real-time simulation (described below) and a second metrics engine is being added to support metrics on a database of simulation runs.

Risk is inexplicably linked to complex inter-related structural and behavioral factors. Of these, human factors tend to be overarching and predominant. Any model that would assist with exploring the inter-relationships among structural and human factors would be immensely valuable to risk management. A social system model constructed with a valid set of software agent framework and complete with factions, institutions and other organizational descriptors, all based on best-of-breed social science theories, can act as the desired testbed to evaluate effects that may arise from alternative courses of action. Through our past case studies, we describe in this article, how social systems modeling and associated intelligent system tools could be applied to assess and manage political risk (and by extension other social systems based risks). We also enumerate the challenges of such a testbed and describes best-of-breed theories drawn from across the social sciences and synthesized and implemented in an agent-based framework. These predictions are examined in a real world cases (Bangladesh) where the agent models are subjected to a validity check and the political risks are estimated.