Design Decision Support Research Articles

Accurate Dosing Weight: When the 10% Really Matters.

Children are at increased risk of medication-associated adverse events, often due to weight-based dosing errors. We aimed to reduce the proportion of medications that were administered where the dosing weight was ≥ 10% different from the recorded weight. We adopted in-situ usability testing to iteratively improve design of clinical decision support that would enable accurate dosing weight documentation by prompting clinicians to update weight if recorded weight was > 10% different and it had been at least 7 days since the last dosing weight update. The proportion of medication administrations with difference >10% between their recorded weight and dosing weight decreased from 13.1% (56,256/ 429,006) in the baseline period to 9.5% (35,560 / 372,443) in the intervention period (P < 0.001). User-centered design of an interruptive alert improved the accuracy of dosing weights during medication administrations without substantial alert burden. In-situ usability testing is an effective approach to rapidly obtain feedback from frontline users and iterate on the design to effect desired behavior changes.

Application of Exponential Comparison Method in Designing Decision Support Systems For Performance and Promotions Assessment in Banking Industry

This research aims to find out how to design a decision support system for evaluating employee performance as a promotion. Data collection in this research used observation, interviews, and a literature study. The population in this study were employees. Rapid Application Development (RAD) is the system development method that the author used to create this application. The tools that the author uses in system design are UML (Unified Model Language) diagrams to show the process and data flow to be designed; Adobe Dreamweaver CS6 is used to design applications; Wamp Server is used as a webserver; and Diaportable is used to illustrate UML diagrams. Based on the results of the discussion, the following were obtained: The decision support system with MPE analysis (Exponential Comparison Method) aims to find employees who are worthy of promotion. The assessment system consists of seven, including education, work performance, discipline, communicativeness, cooperation, reasoning, encouragement of achievement, and responsibility for self-assessment, which are carried out by HRD staff and supervisors, while the HRD manager is only responsible for the decision-making process. The results of this research are in the form of graphs and accumulated MPE assessments, which show the ranking order of each candidate employee.

DEXPI process: Standardizing interoperable information for process design and analysis

DEXPI Process is a proposed standard for modelling information about process design, as it is presented on block flow and process flow diagrams. It was developed by the DEXPI+ working group and builds upon the DEXPI (Data Exchange in the Process Industry) standard for piping and instrumentation diagrams. Digitalization is making increasing demands on the exchange of information in the process facility lifecycle. Industry 4.0 methods require shared terminology and knowledge models to exchange information. Standards, such as ISO15926, CFIHOS and DEXPI, try to address this need. All these focus on the physical plant items, as shown on a PID or 3D model. There is a lack of standards for early-phase, top-down process design. DEXPI Process fills this gap. This paper presents the development of DEXPI Process in the context of knowledge modelling of process systems and previews how the model is a foundation for applications of automated reasoning and decision support for design and operations.

Supporting early-stage design decisions with building performance optimisation: Findings from a design experiment

Building performance optimisation (BPO) has been intensively explored due to its potential in improving building performance and design efficiency. However, studies on its application in supporting early-stage design decisions are rather limited, causing its actual effectiveness to remain questionable. This paper addresses the problem through a design experiment, where participants designed an office building for energy efficiency, using first the conventional method and then BPO. They also evaluated others' design outcomes and completed a questionnaire about the experiment afterwards. The results showed that BPO is effective in supporting early-stage architectural design even for novices. It turned manual designs that are often averagely energy-efficient in the solution space into top designs without significantly affecting other design merits. The average energy use intensity (EUI) decreased by 8.5 % in this experiment. However, it showed a narrow advantage (0.5 % on average) over evaluating a series of design samples and no clear superiority over less-automated supporting methods with more involvement of designers, as its effectiveness is limited by technical difficulties in implementing certain variables strongly correlated with energy efficiency. In addition, while the majority of participants acknowledge the effectiveness of BPO and the acceptance of its design outcomes, manual designs were still preferred by more, mostly for better aesthetics and better alignment of design concepts, implying the necessity of designer's interventions before using BPO outcomes in design practice. The useability of BPO also needs to be improved by shortening the calculation time and lowering the difficulties in building desirable parametric models.

STUDY ON AUTOMATIC REGISTRATION METHOD OF SOURCE DATA FOR CIM BUILDING MODEL CONSTRUCTION

Abstract. The City Information Model (CIM) is a digital model designed to describe, analyze, and manage urban spatial information. It integrates various urban elements, providing decision support for urban planning, design, and management. This study focuses on the construction of building models in CIM, particularly the issue of automatic registration of source data. We propose an automatic registration method based on DWG-format architectural design drawings and SHAPE-format remote sensing building outline information. This method extracts the axis line information of the building from the plan design drawings, indirectly obtains feature points, and selects registration base points, thereby achieving the registration of real estate subdivision data. Meanwhile, by automatically fitting the outer bounding rectangle of the building and dynamically adjusting the angle, this study realizes the automatic registration of the aligned multilayer real estate subdivision data with the building outline data obtained from remote sensing images. This research provides an effective method for the automatic and batch accurate alignment of data for the construction of CIM building models, thereby enhancing the precision and reliability of the modeling.

Impact of epistemic uncertainty on tradeoff in model-based decision support for methane hydrate development system design

Global decarbonization trends encouraged the consideration of a methane hydrate (MH) development system design combined with carbon capture and storage (CCS) in the Japanese national MH research program. Previous studies have also proposed decarbonized MH development systems, but the conclusions vary due to epistemic uncertainty. Epistemic uncertainty arises from the knowledge and understanding of the subject, and each expert has different assumptions. Therefore, we assume that developing a model encompassing epistemic uncertainty as a platform allows for discussion toward system design decisions. This study builds the evaluation model, considering epistemic uncertainty to support the decision-making on the system design and reveal the tradeoff. It identifies the epistemic uncertainty affecting the tradeoff by changing the conditions based on previous studies. The evaluation model consists of technological options as modules, and all system concepts are evaluated by combining them. Epistemic uncertainty was subdivided and implemented within the evaluation model. As a result, the evaluation model output the tradeoff with bands of uncertainty. While the development field significantly impacts the results, hydrogen-related systems and offshore power plant systems score well owing to synergies between options. The epistemic uncertainty analysis regarding the tradeoff suggests that system design uncertainty impacts the conclusions. This finding indicates the importance of the model development as a platform for the evaluation of various system designs. This study contributed to an approach to model-based decision-making with epistemic uncertainty.

Design of Expert Decision Support and Learning Console to Assist Farmers in Cultivation

Design of Expert Decision Support and Learning Console to Assist Farmers in Cultivation

Impact of indoor environmental quality weighting schemes on office architectural design decisions

The importance of indoor environmental quality (IEQ) cannot be overstated, as it impacts the health and satisfaction of occupants. To evaluate overall indoor comfort, all four IEQ domains must be considered together. However, there is a lack of guidance on how to prioritize each domain and corresponding metrics, particularly during the early design stage. Therefore, this study investigates the effect of different weighting schemes on commercial building design elements to have high overall comfort. First, a parametric modeling approach for a shoe-box office building is developed to quantify IEQ domains using various design parameters. Subsequently, multiple weighting options are defined, resulting in an aggregated IEQ score. Next, high-performance designs are identified and compared based on each weighting scheme for two different climates. Results show that, as long as all four comfort domains are considered, the choice of weighting scheme does not significantly affect the selection of high-performance designs. Therefore, an equal weighting scheme is suggested for the early stage. This paper presents an original approach to support design decisions and the mutual weightings of IEQ factors.

Passive Energy Conservation Strategies for Mitigating Energy Consumption and Reducing CO2 Emissions in Traditional Dwellings of Peking Area, China

Within China, brick dwellings stand as archetypal relics of traditional habitation, embodying a “living fossil” status. The sustainability of these dwellings is contingent upon the integration of energy-conservation strategies. This study scrutinized and empirically assessed a representative dwelling in the Peking area. Using numerical simulations, the impact on energy consumption of factors such as insulation and glazing type, external wall thickness, insulation thickness, and solar energy utilization was evaluated. The outcomes reveal that introducing external thermal insulation—specifically, expanded polystyrene panels with a thickness of 60 mm and 40 mm for the roof and exterior walls, respectively—along with a sunspace of depth 1.5 m yielded superior energy efficiency. Additionally, substituting conventional roofing with solar tiles exhibited a potential annual electricity generation coupled with an annual solar radiation conversion efficiency of 17%. Collectively, these strategies induced a substantial reduction in annual energy consumption. This study presents tailored energy-conservation measures and provides design decision support for architects’ practical recommendations on thermal environment control of passive traditional dwellings in the Peking area.

APPLICATIONS OF KNOWLEDGE DISCOVERY AND DATABASE (KDD) IN CLINICAL DECISION SUPPORT SYSTEM

Applications of Knowledge Discovery and Database (KDD) in Clinical Decision Support Systems) is a research work that aims to develop a medical system that has the ability to detect and suggest cure for an ailment with minimal effort. Healthcare system has been a major source of worry across the globe in recent time due to emergence of different types of diseases and epidemics. The number of health care personnel in various hospitals falls short of the number required most especially in terms of specialists. The medical system in Nigeria today suffers from lack of fast, accurate, reliable and intelligent software solutions that can help healthcare practitioners make decisions that would solve urgent, and in some cases, complex medical problems in real-time. Also, the cost of processing and analyzing large volumes of data in a medical environment is high most especially in terms of time consumption. So in this research design and implement clinical decision support system using knowledge discovery in database (KDD) was the major focus. The design required a computerized database for storing medical records as well as software for improved intelligence-based medical procedures that were going to be simpler to use, versatile, adaptable, savvy, and effortless at combining and evaluating medical data, allowing medical practitioners at all levels to make realistic, intelligent, and real-time decisions on critical health issues. The system was designed using the Object-Oriented Design and Analysis Methodology (OOADM).

Control intervention design for preclinical and clinical trials: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable.

Control comparator selection is a critical trial design issue. Preclinical and clinical investigators who are doing trials of stroke recovery and rehabilitation interventions must carefully consider the appropriateness and relevance of their chosen control comparator as the benefit of an experimental intervention is established relative to a comparator. Establishing a strong rationale for a selected comparator improves the integrity of the trial and validity of its findings. This Stroke Recovery and Rehabilitation Roundtable (SRRR) taskforce used a graph theory voting system to rank the importance and ease of addressing challenges during control comparator design. "Identifying appropriate type of control" was ranked easy to address and very important, "variability in usual care" was ranked hard to address and of low importance, and "understanding the content of the control and how it differs from the experimental intervention" was ranked very important but not easy to address. The CONtrol DeSIGN (CONSIGN) decision support tool was developed to address the identified challenges and enhance comparator selection, description, and reporting. CONSIGN is a web-based tool inclusive of seven steps that guide the user through control comparator design. The tool was refined through multiple rounds of pilot testing that included more than 130 people working in neurorehabilitation research. Four hypothetical exemplar trials, which span preclinical, mood, aphasia, and motor recovery, demonstrate how the tool can be applied in practice. Six consensus recommendations are defined that span research domains, professional disciplines, and international borders.

Control intervention design for preclinical and clinical trials: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable.

Control comparator selection is a critical trial design issue. Preclinical and clinical investigators who are doing trials of stroke recovery and rehabilitation interventions must carefully consider the appropriateness and relevance of their chosen control comparator as the benefit of an experimental intervention is established relative to a comparator. Establishing a strong rationale for a selected comparator improves the integrity of the trial and validity of its findings. This Stroke Recovery and Rehabilitation Roundtable (SRRR) taskforce used a graph theory voting system to rank the importance and ease of addressing challenges during control comparator design. "Identifying appropriate type of control" was ranked easy to address and very important, "variability in usual care" was ranked hard to address and of low importance, and "understanding the content of the control and how it differs from the experimental intervention" was ranked very important but not easy to address. The CONtrol DeSIGN (CONSIGN) decision support tool was developed to address the identified challenges and enhance comparator selection, description, and reporting. CONSIGN is a web-based tool inclusive of seven steps that guide the user through control comparator design. The tool was refined through multiple rounds of pilot testing that included more than 130 people working in neurorehabilitation research. Four hypothetical exemplar trials, which span preclinical, mood, aphasia, and motor recovery, demonstrate how the tool can be applied in practice. Six consensus recommendations are defined that span research domains, professional disciplines, and international borders.

Manufacturing system reconfiguration towards sustainable production: a novel hybrid optimization methodology.

Developing a sustainable manufacturing system is a progressively challenging issue as governments across the world have been enforcing increasingly severe regulations by promoting the reduction of environmental waste for manufacturing and energy-saving production activities. Thus, there is a need for developing a sustainable manufacturing system that can be fully examined by incorporating ecological aspects (e.g., consumed energy) for related operations of a manufacturing system using computer-based discrete event simulation tools. In this study, a combined framework of a novel hybrid fuzzy multi-objective optimization and discrete event simulation approach is presented. We combine ecological and economic data and optimization techniques that are aimed at minimizing economic and ecological objectives in a manufacturing system at an early design phase. Hence, the fuzzy multi-objective optimization model is formulated by incorporating economic and ecological parameters. Again, the discrete event simulation model is established based on a comprehensive performance evaluation of the production system. This study also supports design decisions in determining optimum machine numbers, lighting, and cooling equipment required for the production processes within the sustainable manufacturing in conjunction with the most effective level of material flows. In addition, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-epsilon constraint approach is applied to handle the multiple-objective optimization problem towards a set of trade-offs among the optimization objectives. A real-life application is carried out for investigating the applicability of the created hybrid framework. The findings of this study demonstrate that this framework is useful as a decision-making tool since it can develop a sustainable manufacturing system design considering an optimal solution associated with amounts of energy usage and CO2 emissions under economic constraints.

Modeling of Airport Check-in Units Architectural Design and Processing Time Standards with Fuzzy Approach

There is a number of architectural problems that airports must meet such as ensuring the circulation of passengers in terminal building quickly and shortening the time between flights. This is related to the concept of level of service (LOS) which aims to keep passengers in circulation at an affordable cost with minimum delay and maximum comfort without being exposed to congestion. LOS standards have been developed by the International Air Transport Association (IATA) for density analysis of airport terminal buildings. However, with the increase in capacities and user density, there are gradual decreases in standards with sharp boundaries, and this situation is true in the context of strict limits but does not fully reflect reality. Besides, a fuzzy assumption has been made to the issue, both due to the fact that LOS are set with different values in standards published in different years, and to allow rapid and efficient analysis of density changes in unexpected situations. Within the scope of the study, the LOS standards of the check-in areas and sub-functions which are used extensively and host a large number of passengers are modeled in matlab program by means of fuzzy logic. Four independent mathematical models were created: a design decision support model for the architectural design related to the check-in section, and three models for the check-in sub-functions, processing times and areas for business performance. In the models, output data were obtained for each input data and LOS could be determined by interpreting these outputs degree of representation.

Analysis of Community Outdoor Public Spaces Based on Computer Vision Behavior Detection Algorithm

Community outdoor public spaces are indispensable to urban residents’ daily lives. Analyzing community outdoor public spaces from a behavioral perspective is crucial and an effective way to support human-centered development in urban areas. Traditional behavioral analysis often relies on manually collected behavioral data, which is time-consuming, labor-intensive, and lacks data breadth. With the use of sensors, the breadth of behavioral data has greatly increased, but its accuracy is still insufficient, especially in the fine-grained differentiation of populations and behaviors. Computer vision is more efficient in distinguishing populations and recognizing behaviors. However, most existing computer vision applications face some challenges. For example, behavior recognition is limited to pedestrian trajectory recognition, and there are few that recognize the diverse behaviors of crowds. In view of these gaps, this paper proposes a more efficient approach that employs computer vision tools to examine different populations and different behaviors, obtain important statistical measures of spatial behavior, taking the Bajiao Cultural Square in Beijing as a test bed. This population and behavior recognition model presents several improvement strategies: Firstly, by leveraging an attention mechanism, which emulates the human selective cognitive mechanism, it is capable of accentuating pertinent information while disregarding extraneous data, and the ResNet backbone network can be refined by integrating channel attention. This enables the amplification of critical feature channels or the suppression of irrelevant feature channels, thereby enhancing the efficacy of population and behavior recognition. Secondly, it uses public datasets and self-made data to construct the dataset required by this model to improve the robustness of the detection model in specific scenarios. This model can distinguish five types of people and six kinds of behaviors, with an identification accuracy of 83%, achieving fine-grained behavior detection for different populations. To a certain extent, it solves the problem that traditional data face of large-scale behavioral data being difficult to refine. The population and behavior recognition model was adapted and applied in conjunction with spatial typology analysis, and we can conclude that different crowds have different behavioral preferences. There is inconsistency in the use of space by different crowds, there is inconsistency between behavioral and spatial function, and behavior is concentrated over time. This provides more comprehensive and reliable decision support for fine-grained planning and design.

Co-production opportunities seized and missed in decision-support frameworks for climate-change adaptation in agriculture – How do we practice the “best practice”?

CONTEXTTo contribute to building sustainable and effective climate change adaptation solutions avoiding usability gap, it is largely recommended to engage in the process of co-production, integrating expertise and knowledge from various academic and non-academic actors. OBJECTIVEWe want to learn if and how co-production, believed to effectively link knowledge and decision-making, and thus suggested as the best practice in building decision-support frameworks, is really applied in the frameworks that are being implemented on the ground. METHODSA literature review allowed us to identify integrated decision-support frameworks for climate-change adaptation in agriculture developed and used over the period of the last 10 years and involving non-academic stakeholders. To analyse them, we chose as an assessment tool the four co-production principles proposed by Norström and colleagues: context-based, pluralistic, goal-oriented and interactive. RESULTS AND CONCLUSIONSThe useful entry points for incorporating co-production in the design of decision-support that we found in the reviewed frameworks include among the others adequate participants selection strategy, building on existing interaction spaces, developing a theory of change with the participants, and involving participants in the design of different elements of the method. The architectures of the analyzed frameworks contained more elements that responded to pluralistic and interactive principles than to context-based and goal oriented principles, we have also identified gaps in the design, such as taking into account the personal characteristics of researchers that could strengthen a framework's implementation and its impact, or attempts at bridging different levels of decision making, to cover the triad of science, policy and practice. A detailed look at the decision-frameworks that are actually being applied allows for a critical reflection whether and how we as researchers use what we preach as an effective way of responding to sustainability challenges in agriculture. Co-production principles turn out to be a useful tool for analysis and we suggest they can be used as a check-list when designing decision-support frameworks for climate-change adaptation. SIGNIFICANCEThis papers offers useful examples of how to shift the research-led processes of decision-support towards more co-production with non-academic actors, to increase chances of bridging the gaps between science, policy and practice.

Sustained Effect of Clinical Decision Support for Heart Failure: A Natural Experiment Using Implementation Science.

In a randomized controlled trial, we found that applying implementation science (IS) methods and best practices in clinical decision support (CDS) design to create a locally customized, "enhanced" CDS significantly improved evidence-based prescribing of β blockers (BB) for heart failure compared with an unmodified commercially available CDS. At trial conclusion, the enhanced CDS was expanded to all sites. The purpose of this study was to evaluate the real-world sustained effect of the enhanced CDS compared with the commercial CDS. In this natural experiment of 28 primary care clinics, we compared clinics exposed to the commercial CDS (preperiod) to clinics exposed to the enhanced CDS (both periods). The primary effectiveness outcome was the proportion of alerts resulting in a BB prescription. Secondary outcomes included patient reach and clinician adoption (dismissals). There were 367 alerts for 183 unique patients and 171 unique clinicians (pre: March 2019-August 2019; post: October 2019-March 2020). The enhanced CDS increased prescribing by 26.1% compared with the commercial (95% confidence interval [CI]: 17.0-35.1%), which is consistent with the 24% increase in the previous study. The odds of adopting the enhanced CDS was 81% compared with 29% with the commercial (odds ratio: 4.17, 95% CI: 1.96-8.85). The enhanced CDS adoption and effectiveness rates were 62 and 14% in the preperiod and 92 and 10% in the postperiod. Applying IS methods with CDS best practices was associated with improved and sustained clinician adoption and effectiveness compared with a commercially available CDS tool.

An Exploration of Public Open Spaces with Data Driven Approaches: A Case Study of Beyazıt Square

Data-driven approaches are widely used to gain insight in urban dynamics and support urban decisions with pervasive adoption of information technologies. In the presented study, the students adopt data data-driven approaches to observe, and analyze public spaces, and make conceptual decisions for urban furniture in the context of the workshop. This workshop is developed within the scope of the Environmental Computing course. It is conducted with 27 students in Beyazıt Square as a case study area. In the scope of the study, open public spaces were observed and analyzed using data-driven approaches. Based on the analysis results, the students were expected to develop urban furniture design that would enhance user experience and activities in the area. This study questions how data-driven approaches aid in exploring public spaces and support design decisions. The objective of the study was to explore user-generated urban dynamics using multiple data and make decisions for urban furniture that augments urban dynamics. The conceptual design process of urban furniture is shaped as results of data-driven approach. The students were introduced to the Public Life Tools developed by the Gehl Institute for site observation. They were divided into particular groups and used relevant digital tracking applications to measure user activities, user profiles, and live traffic in the area. They evaluated the quality of place based on predetermined criteria by Gehl Institute. The phases of the study involve (1) the exploration of digital observation methods, (2) mapping observational, data, urban data, and locative media data in Geographic Information System (GIS), and (3) defining the relationships between the parameters affecting urban dynamics. (4) This was followed by making conceptual design decisions and (5) developing the design of urban furniture considering data analysis results. According to the findings, the use of data-driven observation and analysis methods has been effective in developing user scenarios, determining user profiles, identifying needs, and taking functional decisions in urban furniture design. Based on the students’ evaluation, the data-driven decision-making process was effective in identifying needs, problems, and potentials in the area. As the limitations of the study, the students stated that the use of digital observation methods and the learning process of GIS software were challenging. This study contributes to the field of urban computing through its conducted fieldwork.

Beyond the override: Using evidence of previous drug tolerance to suppress drug allergy alerts; a retrospective study of opioid alerts

ObjectiveDespite the extensive literature exploring alert fatigue, most studies have focused on describing the phenomenon, but not on fixing it. The authors aimed to identify data useful to avert clinically irrelevant alerts to inform future research on clinical decision support (CDS) design. MethodsWe conducted a retrospective observational study of opioid drug allergy alert (DAA) overrides for the calendar year of 2019 at a large academic medical center, to identify data elements useful to find irrelevant alerts to be averted. ResultsOverall, 227,815 DAAs were fired in 2019, with an override rate of 91 % (n = 208196). Opioids represented nearly two-thirds of these overrides (n = 129063; 62 %) and were the drug class with the highest override rate (96 %). On average, 29 opioid DAAs were overridden per patient. While most opioid alerts (97.1 %) are fired for a possible match (the drug class of the allergen matches the drug class of the prescribed drug), they are overridden significantly less frequently for definite match (exact match between allergen and prescribed drug) (88 % vs. 95.9 %, p < 0.001). When comparing the triggering drug with previously administered drugs, override rates were equally high for both definite match (95.9 %), no match (95.5 %), and possible match (95.1 %). Likewise, when comparing to home medications, overrides were excessively high for possible match (96.3 %), no match (96 %), and definite match (94.4 %). ConclusionWe estimate that 74.5% of opioid DAAs (46.4% of all DAAs) at our institution could be relatively safely averted, since they either have a definite match for previous inpatient administrations suggesting drug tolerance or are fired as possible match with low risk of cross-sensitivity. Future research should focus on identifying other relevant data elements ideally with automated methods and use of emerging standards to empower CDS systems to suppress false-positive alerts while avoiding safety hazards.

The aerodynamic force estimation of a swept-wing UAV using ANFIS based on metaheuristic algorithms

AbstractIn this paper, a new approach to modeling and controlling the problems associated with a morphing unmanned aerial vehicle (UAV) is proposed. Within the scope of the study, a dataset was created by obtaining a wide range of aerodynamic parameters for the UAV with Ansys Fluent under variable conditions using the computational fluid dynamics approach. For this, a large dataset was created that considered 5 different angles of attack, 14 different swept angles, and 5 different velocities. While creating the dataset, the analyses were verified by considering studies that have been experimentally validated in the literature. Then, an artificial intelligence-based model was created using the dataset obtained. Metaheuristic algorithms such as the artificial bee colony algorithm, ant colony algorithm and genetic algorithms are used to increase the modeling success of the adaptive neuro-fuzzy inference system (ANFIS) approach. A novel modeling approach is proposed that constitutes a new decision support system for real-time flight. According to the results obtained, all the ANFIS models based on metaheuristic algorithms were more successful than the traditional approach, the multilinear regression model. The swept angle that meets the minimum lift needed by the UAV for different flight conditions was estimated with the help of the designed decision support system. Thus, the drag force is minimised while obtaining the required lift force. The performance of the UAV was compared with the nonmorphing configuration, and the results are presented in tables and graphs.