Hypothetical Case Study Research Articles

On the Coordination of Charging Demand of Electric Vehicles in a Network of Dynamic Wireless Charging Systems

The utilization of dynamic wireless charging (DWC) systems to charge on-the-move EVs is currently gaining an increasing popularity, as it addresses range and charging downtime issues of EV users. To ensure optimal utilization of this charging infrastructure, coordination of EV charging demand is essential to achieve grid load balancing and prevent grid overload. In contrast to offline, day-ahead charging scheduling, this work proposes an online, mobility-aware, spatial EV allocation algorithm within a DWC coordination strategy. This strategy allocates EVs requesting charge to the most optimal DWC lanes within an EV charging network (ECN) in an Internet of EVs (IoEVs). A detailed charging request scenario is presented to highlight the required communication for authentication between the EVs and the charging infrastructure, to achieve the desired coordination. Description of the proposed EV allocation algorithm is then presented and the performance of the algorithm is evaluated using a hypothetical case study of predicted EV traffic trips in the cities of Dubai and Sharjah, UAE. Upon parameter optimization, results of the conducted analysis reveal that the proposed EV allocation algorithm achieves an almost flattened load profile across the DWC lanes that reduces the PAER by more than 44% in comparison with a shortest distance allocation algorithm, for a maximum 2× increase in trip length, and sufficient received energy to compensate for the energy consumed during the trip. This acknowledges grid supply limitations, EV traveling velocities and the maximum service capacity per DWC lane.

Decision-making in large corporations - role of big data analytics & data mining

Function - The goal of this particular paper is presenting a novel framework for strategic decision making utilizing Big Data Analytics methodology. Design/methodology/approach - In this particular research, 2 distinct machine learning algorithms, Random Forest as well as Artificial Neural Networks are used to forecast export volumes working with a considerable level of open industry information. The forecasted values are in the Boston Consulting Group Matrix to conduct strategic industry analysis. Results - The proposed technique is validated utilizing a hypothetical case study of a Chinese business exporting freezers and refrigerators. The results indicate the proposed methodology makes exact trade forecasts and helps to conduct strategic industry evaluation properly. Furthermore, the RF performs much better compared to the ANN in terminology of forecast accuracy. Investigate limitations/implications - This analysis provides just one case study to evaluate the proposed methodology. In future scientific studies, the validity of the suggested technique is further generalized in various item groups and nations. Functional implications - In present day extremely competitive business environment, a good strategic industry evaluation involves exporters or importers making much better predictions along with strategic choices. To us the proposed BDA based strategy, businesses may efficiently determine business opportunities and alter their strategic choices appropriately. Originality/value - This's the very first study to provide a holistic methodology for strategic industry evaluation using BDA. The proposed methodology effectively forecasts global trade volumes and helps with the strategic decision making practice through succeeding insights into worldwide marketplaces.

Dispatchability, Energy Security, and Reduced Capital Cost in Tidal-Wind and Tidal-Solar Energy Farms

The global tidal energy resource for electricity generation is small, and converting tidal kinetic energy to electricity is expensive compared to solar-photovoltaic or land-based wind turbine generators. However, as the renewable energy content in electricity supplies grows, the need to stabilise these supplies increases. This paper describes tidal energy’s potential to reduce intermittency and variability in electricity supplied from solar and wind power farms while lowering the capital expenditure needed to improve dispatchability. The paper provides a model and hypothetical case studies to demonstrate how sharing energy storage between tidal stream power generators and wind or solar power generators can mitigate the level, frequency, and duration of power loss from wind or solar PV farms. The improvements in dispatchability use tidal energy’s innate regularity and take account of tidal asymmetry and extended duration low-velocity neap tides. The case studies are based on a national assessment of Australian tidal energy resources carried out from 2018 to 2021.

Multi-objective optimization for sustainable road network maintenance under traffic equilibrium: Incorporating costs and environmental impacts

It is vital to consider traffic dynamics and environmental burdens when developing sustainable long-term maintenance schedules for asphalt road networks. This paper presents a multi-objective optimization model incorporating the interactions between pavement condition and traffic dynamics to identify a wide range of optimal solutions for the planning of multi-year road network maintenance. The multi-objective optimization model integrates three elements: agency costs, user costs and greenhouse gas (GHG) emissions. The modified non-dominated sorting genetic algorithm II (NSGA II) with a mixed initial population is proposed to search for the well-distributed Pareto frontier, to assist decision-makers to conduct trade-off analysis. A hypothetical road network maintenance case study was conducted to test the method. Compared with conventional NSGA II, the proposed method identifies more candidate solutions for the three-objective optimization problem. Trade-off analysis was carried out among the three objectives in both two and three dimensions. The results show that the proposed method generates effective maintenance schedules that minimize user costs and GHG emissions in an equilibrated network. In addition, the minimum total emissions resulting from maintenance and vehicles depend on the sensitivity of pavement deterioration to traffic loading.

A Conceptual Framework for Estimating Building Embodied Carbon Based on Digital Twin Technology and Life Cycle Assessment

Low-carbon building design requests an estimation of total embodied carbon as the environmental performance metric for comparison of different design options in early design stages. Due to a lack of consensus on the system boundaries in building life cycle assessment (LCA), the carbon estimation results obtained by the current methods are often disputable. In this regard, this paper proposes a method for estimating building embodied carbon based on digital twin technology and LCA. The proposed method is advantageous over others by providing (1) a cradle-to-cradle LCA and (2) an automated data communication between LCA and building information modelling (BIM) databases. Because data for the processes in the life cycle are collected via digital twin technology in a standard and consistent way, the obtained results will be considered credible. So far, a conceptual framework is developed based on a comprehensive literature review, which consists of three parts. In the first part, formulas for LCA are given. In the second part, a hybrid approach combining semantic web with a relational database for BIM and radio-frequency identification (RFID) integration is described. In the third part, how to design the LCA database and how to link LCA with BIM are described. The conceptual framework proposed is tested for its reasonableness by a small hypothetical case study.

Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials.

IntroductionAdaptive designs allow changes to an ongoing trial based on prespecified early examinations of accrued data. Opportunities are potentially being missed to incorporate health economic considerations into the design of these studies.MethodsWe describe how to estimate the expected value of sample information for group sequential design adaptive trials. We operationalize this approach in a hypothetical case study using data from a pilot trial. We report the expected value of sample information and expected net benefit of sampling results for 5 design options for the future full-scale trial including the fixed-sample-size design and the group sequential design using either the Pocock stopping rule or the O’Brien-Fleming stopping rule with 2 or 5 analyses. We considered 2 scenarios relating to 1) using the cost-effectiveness model with a traditional approach to the health economic analysis and 2) adjusting the cost-effectiveness analysis to incorporate the bias-adjusted maximum likelihood estimates of trial outcomes to account for the bias that can be generated in adaptive trials.ResultsThe case study demonstrated that the methods developed could be successfully applied in practice. The results showed that the O’Brien-Fleming stopping rule with 2 analyses was the most efficient design with the highest expected net benefit of sampling in the case study.ConclusionsCost-effectiveness considerations are unavoidable in budget-constrained, publicly funded health care systems, and adaptive designs can provide an alternative to costly fixed-sample-size designs. We recommend that when planning a clinical trial, expected value of sample information methods be used to compare possible adaptive and nonadaptive trial designs, with appropriate adjustment, to help justify the choice of design characteristics and ensure the cost-effective use of research funding.HighlightsOpportunities are potentially being missed to incorporate health economic considerations into the design of adaptive clinical trials.Existing expected value of sample information analysis methods can be extended to compare possible group sequential and nonadaptive trial designs when planning a clinical trial.We recommend that adjusted analyses be presented to control for the potential impact of the adaptive designs and to maintain the accuracy of the calculations.This approach can help to justify the choice of design characteristics and ensure the cost-effective use of limited research funding.

P-graph Model of Economic Networks with Partial Substitution

P-graph models of economic networks based on the Leontief input-output framework have been demonstrated in previous work. However, such models are subject to the limiting assumption of fixed technical coefficients that reflect the average state of technology currently in use. This limitation restricts the use of such models to incremental changes; more radical shifts in economic structure cannot be adequately represented. In this paper, a modified P-graph model of economic networks that allows partial substitution of inputs is developed. This modified formulation can represent major structural shifts, such as deep decarbonization or increased circularity, that are needed for improving the sustainability of modern economies. The new model is illustrated using a hypothetical case study. Results show that economic systems which allow for partial substitution are more resilient when disruptions occur in certain sectors of the economy.

An Operations Research-Based Approach to the Allocation of COVID-19 Vaccines

The global scientific community has been successful in their efforts to develop, test, and commercialize vaccines for COVID-19. However, the limited supply of these vaccines remains to be a widespread problem as different nations have started their respective vaccine rollouts. Policymakers continue to deal with the difficult task of determining how to allocate them. This research work will present how the use of mathematical models can provide valuable decision support under such conditions. Both a linear programming model and a nonlinear programming model have been developed to determine the optimal allocation of COVID-19 vaccines that minimize fatalities and COVID-19 transmission, respectively. These scenarios have to be dealt with when not enough vaccines are available, and the pandemic is still in progress. The model is capable of handling large scale allocation problems such as those intended for the general population of a country. It could also be scaled down for organizations such as private companies or universities. The model also considers multiple vaccines with different levels of efficacy. The distribution of vaccines reduces transmission and relative infectiousness of individuals across different age groups. A hypothetical case study is solved to illustrate the computational capability of the models. The results indicate that priority should be given to the elderly when fatalities are minimized. In contrast, the younger population should then be prioritized when the objective shifts to suppressing contagion.

Robot Selection Problem via Fuzzy TOPSIS Method Using Novel Distance and Similarity Measure for Generalized Fuzzy Numbers with Unequal Heights

Background: Mega multinational companies are highly dependent on robots to handle the maximum of their machinery workload, which significantly reduces human labor and saves valuable time as well. However, as vital as the role of robots is, a much more challenging task is its selection. Moreover, the robots need to be evaluated on the grounds of different specifications and their ease of handling, which results in a smooth and work-efficient environment. Objective: The prime objective of this paper is to devise a fruitful decision-making model for a robot selection problem, which utilizes a multi-criteria decision-making method known as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The TOPSIS method is based on the newly defined distance measure involving generalized fuzzy numbers with unequal heights (GFNUHs). Methodology/Approach: At first, we define a novel distance measure based on the “expected value” and “variance” of GFNUHs, where both the parameters are evaluated with the help of the [Formula: see text]-cut method. We then also give the expression for the distance-based similarity measure and investigate some of their properties. Both the distance and the similarity measure(s) are then validated for their effectiveness through a hypothetical case study of pattern recognition. Moreover, we consider 10 different bunches of generalized fuzzy numbers (GFNs) and present a comparative study with the already established measures to establish the efficiency and superiority of our proposed measures. Finally, the distance measure is deployed in the TOPSIS method, which facilitates suitable robot selection by an automobile company. Findings/Results: A comparison of results for the proposed distance measure and the similarity measure with the existing ones is presented which proves that the proposed measure(s) are effective and usable. Novelty/Value: The evaluation of expected value and variance of GFNUHs with the help of [Formula: see text]-cut technique is a completely original idea showcased in this paper and its improved version of TOPSIS for GFNUHs as discussed shall add a new direction in the realm of decision-making.

A sensitivity analysis applied to SPRAY and CALPUFF models when simulating dispersion from industrial fires

This paper discusses a hypothetical case study in which SPRAY and CALPUFF dispersion models are applied to the simulation of an incidental fire. For this type of accident, source features are typically not directly measurable, thus making their definition critical. The choice of some model-specific parameters is another critical issue, since clear indications are rarely available in guidelines. The aim of this work is to compare how pollutant concentrations simulated with the two models are affected by changing these two sets of data (i.e. parameters related to the emission source and model specific parameters), thus performing a sensitivity study to identify the most influential variables. The most relevant outcome is that sensitivities of the two models are generally comparable, except for the source diameter: if the SPRAY model is applied with the specific fire source option, then the concentrations result almost independent from this parameter. Conversely, when considering other source-types, the concentrations vary up to ± 60% within the selected uncertainty range.

Optimization of Water Distribution System Operation with Multiple Tanks and Pumps: Application for Asmara, Eritrea’s Water Supply System

Pump energy consumption is the major operational cost of water distribution systems (WDS). Defining better pump operation policies can reduce the cost and the enhance service provided. The nonlinear hydraulic behavior of WDS makes the pump scheduling problem complex, especially in large WDS in which several tanks and pumping stations coexist. The nonlinear hydraulic behavior of WDS is captured using numerical simulation. However, the required energy calculations were computed from the flow rate and head increase after the pumping stations. There are many optimization algorithms for the reduction of the operational cost. The majority of these algorithms are applied to hypothetical case studies with reduced feasible solution spaces. When the search space becomes large and many alternatives (pumps combinations) are possible, most of the proposed algorithms fail. Thus, the application of these algorithms in real case studies is very limited, and water utility managers still rely on human expertise to schedule pump operation rather than on automatic scheduling. This paper proposes the use of a genetic algorithm for large WDS with a many tanks and pumps. Offline hydraulic simulations allow the definition of water level bounds in the tanks. The combination of the pumps is determined a priori and the optimization algorithm is run in a reduced search space. These preparations allow the genetic algorithm to identify quickly the optimal policy and reduce the cost of energy consumption. The proposed framework was applied to the WDS of the city of Asmara, Eritrea, with 12 tanks and 9 pumping stations. The results showed that the tanks controlled by pumps worked perfectly and the water level was kept within the defined ranges. However, the constraints were not fulfilled for uncontrolled tanks. This application showed the limitations of methodologies proposed in the literature, and it is suggested that further techniques be found to schedule the pump working policies of large WDS while reducing the violations of constraints.

Utility of a next-generation framework for assessment of genomic damage: A case study using the pharmaceutical drug candidate etoposide.

We present a hypothetical case study to examine the use of a next‐generation framework developed by the Genetic Toxicology Technical Committee of the Health and Environmental Sciences Institute for assessing the potential risk of genetic damage from a pharmaceutical perspective. We used etoposide, a genotoxic carcinogen, as a representative pharmaceutical for the purposes of this case study. Using the framework as guidance, we formulated a hypothetical scenario for the use of etoposide to illustrate the application of the framework to pharmaceuticals. We collected available data on etoposide considered relevant for assessment of genetic toxicity risk. From the data collected, we conducted a quantitative analysis to estimate margins of exposure (MOEs) to characterize the risk of genetic damage that could be used for decision‐making regarding the predefined hypothetical use. We found the framework useful for guiding the selection of appropriate tests and selecting relevant endpoints that reflected the potential for genetic damage in patients. The risk characterization, presented as MOEs, allows decision makers to discern how much benefit is critical to balance any adverse effect(s) that may be induced by the pharmaceutical. Interestingly, pharmaceutical development already incorporates several aspects of the framework per regulations and health authority expectations. Moreover, we observed that quality dose response data can be obtained with carefully planned but routinely conducted genetic toxicity testing. This case study demonstrates the utility of the next‐generation framework to quantitatively model human risk based on genetic damage, as applicable to pharmaceuticals.

Artificial lift infrastructure planning of shale gas horizontal wells considering endogenous and exogenous uncertainties

AbstractArtificial lift methods (ALMs) lift the accumulated fluids from horizontal shale‐gas‐producing wells and help sustain well performance. An artificial lift infrastructure plan includes the selection of ALMs and their operating schedule. This paper presents two discrete‐time large‐scale nonconvex mixed‐integer nonlinear programming models to solve the artificial lift infrastructure planning problem. Two equivalent mixed‐integer linear programming models are formulated using the special structure of the nonlinear terms. A set of valid inequalities is defined to tighten the models and shorten solution times to two orders of magnitude, considering well production limitations. We incorporate endogenous uncertainty in ALM‐dependent production rates and exogenous uncertainty in shale gas prices into the models. For a hypothetical case study under only endogenous uncertainties, the value of the stochastic solution is 5%. For the same case study, the exogenous uncertainty in gas prices does not change the optimum solution.

Unlocking the UK Continental Shelf Electrification Potential for Offshore Oil and Gas Installations: A Power Grid Architecture Perspective

Most of the UK Continental Shelf (UKCS) oil and gas (OG) installations have traditionally adopted in situ power generation, which is not only inefficient but also generating about 70% of the offshore CO2 emissions. The offshore wind and energy storage technologies for deep water are developing at a fast pace, enabling great opportunities for the OG installations located in the North Sea. In this paper, a pathway for the UKCS offshore OG installations electrification is introduced. The aim is to provide different power architectures that facilitate the OG installations’ electrification, while benefiting from the existing and planned UK offshore wind power. Four hypothetical case studies (based on real data) were created, along the UKCS, where the corresponding power architectures were proposed. The selection of each architecture power component (e.g., transformers, converters and cables), as well as the transmission and distribution technology (e.g., AC or DC), is also provided and justified. Further, an overview cost estimation is carried out to predict the architecture capital cost. It is concluded that the four architectures can be mimicked not only along the UKCS but also worldwide, promoting the UKCS potential for a world-leading offshore energy hub and fostering the UK offshore wind-energy resources.

The impact of vertical greenery system on building thermal performance in tropical climates

The thermal benefits of Vertical Greenery System (VGS) in providing thermal comfort, reducing internal building temperature, and lowering operational energy consumption are not widely known. There is a lack of research and technical knowledge on the effects of vertical greenery systems related to thermal performance, especially in tropical climates, such as that of Malaysia. Therefore, this paper addresses this gap by investigating the effect of VGS on heat transfer and the thermal performance of hypothetical buildings. In this paper, a data prediction method is used to identify the overall thermal transfer value (OTTV) from several hypothetical case studies. A variety of combinations of variables have been used to identify the best design with the lowest OTTV reduction through VGS. From the calculation, Linear Greenery System achieved the highest OTTV reduction with an average of 6.87%, followed by Modular Green Wall (6.82%), Double-skin Green Facades (2.97%), and Direct Green Facades (1.32%). Therefore, this paper can conclude that Linear Green Wall is the best greenery system for reducing heat transfer for the tropical climate of Malaysia.

A Hybrid MCDM Approach in Third-Party Logistics (3PL) Provider Selection

Third-party logistics (3PL) is becoming more and more popular because of globalization, e-commerce development, and increasing customer demand. More and more companies are trying to move away from their own account transportation to third-party accounts. One reason for using 3PLs is that the company can focus more on its core activities, while the 3PL service provider can provide distribution activities in a more professional way, save costs and time, and increase the level of customer satisfaction. An emerging issue for companies in the logistics industry is how they can decide on the 3PL evaluation and selection process for outsourcing activities. For the first time, the entropy and the criteria importance through intercriteria correlation (CRITIC) methods were coupled in order to obtain hybrid criteria weights that are of huge importance to decide on the 3PL provider evaluation and selection process. The obtained criteria weights were further utilized within the additive ratio assessment (ARAS) method to rank the alternatives from the best to the worst. The introduced hybrid–ARAS approach can be highly beneficial, since combining two methods gives more robust solutions on one hand, while on the other hand eliminating subjectivity. Comparative and sensitivity analyses showed the high reliability of the proposed hybrid–ARAS method. A hypothetical case study is presented to illustrate the potentials and applicability of the hybrid–ARAS method. The results showed that 3PL-2 was the best possible solution for our case.

A Mixed-Methods Investigation of Diagnostician Sex/Gender-Bias and Challenges in Assessing Females for Autism Spectrum Disorder.

Despite the importance of clinical judgement in autism spectrum disorder (ASD) assessment, little is currently known about challenges faced by diagnosticians when the client is female, any sex/gender biases during the assessment process, and how these issues affect diagnostic outcomes. Forty-seven ASD diagnosticians completed a questionnaire containing two hypothetical case studies (a 'male' and 'female' ASD presentation), with sex/gender randomly assigned within each. Diagnosticians reported greater ASD symptom severity when female sex/gender pseudonyms were allocated to the case studies, but their confidence in ASD diagnosis was similar regardless ofcondition. Diagnosticians identified a large number of challenges associated with assessing females for ASD. Many of these related to sex/gender differences in ASD presentation and limitations of diagnostic instruments.

Application of Brake System Failed State Performance and Reliability Requirements to Brake System Architecting

<div class="section abstract"><div class="htmlview paragraph">The modern braking system in the field today may be controlled by over a million lines of computer code and may feature several hundred moving parts. Although modern brake systems generally deliver performance, even with partial failures present in the system, that is well above regulatory minimums, they also have a level of complexity that extends well beyond what the authors of existing regulations had envisioned. Complexity in the braking system is poised for significant increases as advanced technologies such as self-driving vehicles are introduced, and as multiple systems are linked together to provide vehicle-level “features” to the driver such as deceleration (which can invoke service braking, regenerative braking, use of the parking brake, and engine braking). Rigorous safety-case analysis is critical to bring a new brake system concept to market but may be too tedious and rely on too many assumptions to be useful in the early architecting stages of new vehicle development. A set of requirements, and a method for applying these requirements, that are reasonably accurate but that lend themselves to relatively fast analysis, is very useful early in vehicle development. Architectural decisions such as “what tire traction is needed on an Autonomous Vehicle”, or “is a redundant control module needed here”, or conversely “what reliability needs to be engineered into this component to justify a lack of redundancy” can be answered by such a methodology. Similar logic can be applied in the case of Autonomous Vehicles to help determine what actions for the Autonomous Controller to take when faced with certain failed states in the brake system. This paper introduces a general set of requirements, relating braking performance (deceleration capability) to a corresponding reliability (measured by “Failures In Time”, or “FIT” rate), and a methodology for assessing a brake system to these requirements. Hypothetical case studies are used to illustrate how the proposed requirements and methodology may be applied to architecting a brake system early in the design process, and to determining what actions an Autonomous Vehicle controller should take in response to failures in the brake system.</div></div>

Defensive cost–benefit analysis of smart grid digital functionalities

Modern smart grids offer several types of digital control and monitoring of electric power transmission and distribution that enable greater efficiency and integrative functionality than traditional power grids. These benefits, however, introduce greater complexity and greatly disrupt and expand the threat landscape. The number of vulnerabilities is increasing as grid-connected devices proliferate. The potential costs to society of these vulnerabilities are difficult to determine, as are their likelihoods of successful exploitation. In this article, we present a method for comparing the net economic benefits and costs of the various cyber-functionalities associated with smart grids from the perspective of cyberattack vulnerabilities and defending against them. The economic considerations of cyber defense spending suggest the existence of optimal levels of expenditures, which might vary among digital functionalities. We illustrate hypothetical case studies on how digital functionalities can be assessed and compared with respect to the costs of defending them from cyberattacks.

Modelling mixing and transport of radioactive effluents in water reservoirs: an application to the operation of a fusion facility

The aim of the present study is to model mixing and transport of radioactive effluents in the course of time between two water reservoirs. To test the model, a hypothetical case study is presented of liquid effluents potentially released during normal operation of a fusion neutron source devoted to radioisotopes production. A suitable example is an accelerator-driven intense D-T 14 MeV neutron source relying on T and D ion beams, with the potential to provide a neutron yield in the range \( 5{-}7 \cdot 10^{13}\) s-1. It is expected that during normal operation a number of radionuclides will be produced and managed. The present report discusses the mechanisms and parameters which affect and control the fate of radionuclides potentially released into two connected water reservoirs during normal operation of the plant. A mathematical mixing model is developed that describes groundwater flow and radioactive transport between the two basins. The aim of this study is to estimate the amount of radioactivity concentration in both water reservoirs at any time, an information that can be used for radiation protection purposes.