Impact Analysis Approach Research Articles

How influential are environmental hazard assessments on policy decision-making? An impact analysis approach and recommendations for environmental public health

How influential are environmental hazard assessments on policy decision-making? An impact analysis approach and recommendations for environmental public health

Investigating on thermal insulation in concrete partition wall with insulation-material-infilled Voronoi sections: Feature impact analysis and efficient assessment approach

Reducing energy consumption in building operations, occupying 27 % of total carbon emissions, is crucial for decarbonization. Concrete partition walls with insulation-filled hollow sections, offer high thermal resistance, reducing air conditioning needs. Voronoi pattern represent a space being subdivided into polygonal regions and can be incorporated as the cavity pattern of the hollow section. However, the existing studies on Voronoi sections focus on porosity and pore quantity, with limited research on other feature metrics. This research addresses this gap by quantifying additional feature metrics of Voronoi sections using Bayesian inference, determining their collective influence on insulation. Findings reveal that feature metrics like cavity distribution and standard deviation of cavity areas affect insulation. Additionally, to improve time-consuming simulations, we proposed a model using Graph Neural Networks (GNNs) for accurate and rapid thermal assessments of complex Voronoi sections. Our GNN model, which uses graph-structured data rather than design parameters as training inputs, outperforms another four widely used models, achieving over 85 % prediction accuracy. In this research, two novel approaches are introduced: a Bayesian-based statistical model to understand the relationship between multiple feature metrics and insulation, pinpointing the most promising search space affiliated with certain values ranges of various feature metrics, and a rapid and accurate GNN-based model that uses graph representations of Voronoi sections in building components, preserving detailed geometric information for insulation prediction. Both approaches collectively improve the design process for building components with material-filled Voronoi sections by narrowing the search space and enhancing evaluation efficiency, respectively.

Enhancing Prescriptive Capabilities in Electrical Substations: A Systemic Impact Factor Approach for Failure Impact Analysis

This research looks into a systemic impact factor (SIF) as a complement to enhance the prescriptive capabilities of electric substations with the objective of improving the reliability and robustness of components. As electrical networks become increasingly complex, prescriptive analytical tools are needed to ensure sustained power delivery. The purpose is to develop a framework for evaluating failure effects, including both direct and indirect effects on substation performance. The study makes use of a multi-criteria method that incorporates safety, environment protection, reliability, and network robustness among other factors like SAIDI and SAIFI to determine the actual impact of failures to make better decisions. The methodology is illustrated using a criticality model, and a case study, focusing on operational contexts of the failure. The SIF proposal provides a better understanding of different aspects and their contribution, when a failure event occurs under a given operational context. This factor improves maintenance management by identifying critical equipment and projecting the effects of failure through a specific operation network. A case study in an electrical substation estimates the SIF for each element used in daily operations to improve support services and increase substation dependability. Therefore, SIF is an essential factor to consider in electrical substations for their maintainability and reliability evaluation.

Impact Analysis and Development of Collaborative Communication Models in Public Relations Programs for Mangrove Conservation

Mangrove forests, as coastal ecosystems, play a crucial role in maintaining the sustainability of fishery resources and protecting shores from erosion and storms. However, mangrove degradation continues to escalate, primarily due to human activities such as shrimp pond expansion and uncontrolled logging. Public Relations programs can serve as effective tools to raise awareness and engage communities in mangrove conservation. Impact analysis and the development of a collaborative communication model are key to understanding the effectiveness of such programs. This research aims to analyze the impact of the Public Relations program on mangrove conservation and develop a more effective collaborative communication model. The focus is on understanding behavioral changes, community responses, and the program's contribution to the sustainability of the mangrove ecosystem. The research methodology employed is qualitative-descriptive with an impact analysis approach. Interviews with representatives from local communities, local governments, non-governmental organizations, and Public Relations practitioners were conducted to gather data. Content analysis was also performed on the program's communication materials. The research findings indicate that the Public Relations program has positively impacted the increase in community awareness and behavioral changes related to mangrove conservation. However, challenges persist concerning environmental uncertainties and the lack of participation from certain segments of the community. A collaborative communication model integrated with mangrove conservation theories, impact analysis, and community engagement proves essential for further improvement. The implications of this research reflect the importance of impact analysis and developing collaborative communication models in Public Relations programs for mangrove conservation. These findings underline that active collaboration with various stakeholders is key to ensuring the sustainability of mangrove ecosystems.

Mauritius oil spill detection using transfer learning approach for oil spill mapping and wind impact analysis using Sentinel-1 data

Mauritius oil spill detection using transfer learning approach for oil spill mapping and wind impact analysis using Sentinel-1 data

BECIA: a behaviour engineering-based approach for change impact analysis

This paper introduces Behaviour Engineering-based Change Impact Analysis (BECIA), a novel approach to Change Impact Analysis (CIA). BECIA enables visualization of change impacts from modified requirements on architecture design. It also introduces the Change Impact Indicator (CII) metric, quantifying impact by calculating the Kolmogorov Complexity (KC) of design models. Our contributions include: an algorithm to convert behavior trees to composition trees, a requirements components dependency network, and a metric for quantifying change impacts.

Exploration of an alternative reconstructed individual patient data-based approach for budget impact analysis of anticancer drugs

BackgroundThe duration of treatment (DOT) of the initial intervention and subsequent treatment is the key to determining the accuracy of anticancer-drug budget impact analysis (BIA) calculations. However, existing studies only use simple assumptions as a proxy for DOT, resulting in a high degree of bias.ObjectivesTo enhance the accuracy and reliability of anticancer-drug BIA and solve the problem regarding DOT, we propose an alternative individual patient data (IPD)-based approach that reconstructs IPD from the published Kaplan Meier survival curves to estimate DOT.MethodsWe developed a four-step methodological framework for this new approach, taking the use of pembrolizumab in treating microsatellite-instability–high (MSI-H) advanced colorectal cancer as an example: (1) reconstructing the IPD; (2) calculating the total DOT of the initial intervention and subsequent treatment for each patient; (3) assigning a randomized time and DOT; and (4) multiple replacement sampling and calculation of the mean value.ResultsUsing this approach, the average DOT for the initial intervention and subsequent treatment in each year of the BIA time horizon can be calculated and used to calculate the resources consumed and costs in each year. In our example, the average DOT for the initial intervention with pembrolizumab from the first to the fourth year was 4.90, 6.60, 5.24, and 5.06 months, respectively, while the average DOT for subsequent treatment was 0.75, 2.84, 2.99, and 2.50 months, respectively.ConclusionsThe reconstructed IPD-based approach can improve the accuracy and reliability of anticancer-drug BIA compared with conventional methods, and can be widely used, especially for anticancer drugs with excellent efficacy.

A novel MICMAC approach for cross impact analysis with application to urban water/wastewater management

Water as the core of Sustainable Development (SD) alongside a significant increase in the global demand for water causes the subject of Urban Water and Wastewater (U2W) management to be essential in exploring the future. As a means of future studies, MICMAC reveals the influence and dependence of a variable concerning all others by examining all interactions in the system. These interactions should be analyzed in two dimensions: “Impact” and “Time”. Usually, two types of impacts are used, including direct and indirect. Also, most methods have a critical weakness in that it does not include the impact of “time” in the analysis.This paper suggests a novel approach to MICMAC (named MICMAC-T), which promotes understanding indirect cross impacts by considering the influence of “time” in analysis. This helps in particular with U2W, which faces major future challenges toward SD. The practical approach developed in this paper can be quickly and flexibly initiated because of its toolbox-based design, integrating text mining, PESTEL, MICMAC, Floyd-Warshall, and interviews in a single model. Therefore, the proposed method could also be considered a new contribution to the CIA.To demonstrate the contribution of MICMAC-T, the case of the Iranian U2W pathway toward SD is investigated to find the driving forces. The results of applying this approach in finding the driving forces for Iranian U2W include new interesting findings. The results of MICMAC-T suggest that “Legal comprehensiveness,” “Urban economic development policies,” “High-level policy documents,” “Public social awareness in society,” “Geographical location (climate),” “Law enforcement guarantee,” “U2W governance”, and “Urban water resources scarcity” are driving forces for the Iranian U2W. Among these variables, “Urban economic development policies” and “Public social awareness in society” were neglected in traditional MICMAC and categorized as driving forces only using MICMAC-T, which indicates the crucial influence of time on Iranian U2W.

Implications of AI-based robo-advisory for private banking investment advisory

PurposeAI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.Design/methodology/approachThe present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.FindingsThe study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.Originality/valueThe research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.

An Automated Impact Analysis Approach for Test Cases based on Changes of Use Case based Requirement Specifications

Change Impact Analysis (CIA) is essential to the software development process that identifies the potential effects of changes during the development process. The changing of requirements always impacts on the software testing because some parts of the existing test cases may not be used to test the software. This affects new test cases to be entirely generated from the changed version of software requirements specification that causes a considerable amount of time and effort to generate new test cases to re-test the modified system. Therefore, this paper proposes a novel automatic impact analysis approach of test cases based on changes of use case based requirement specification. This approach enables a framework and CIA algorithm where the impact of test cases is analysed when the requirement specification is changed. To detect the change, two versions as before-change and after-change of the use case model are compared. Consequently, the patterns representing the cause of variable changes are classified and analysed. This results in the existing test cases to be analysed whether they are completely reused, partly updated as well as additionally generated. The new test cases are generated automatically by using the Combination of Equivalence and Classification Tree Method (CCTM). This benefits the level of testing coverage with a minimised number of test cases to be enabled and the redundant test cases to be eliminated. The automation of this approach is demonstrated with the developed prototype tool. The validation and evaluation result with two real case studies from Hospital Information System (HIS) together with perspective views of practical specialists confirms the contribution of this tool that we seek.

Mauritius oil spill detection using transfer learning approach for oil spill mapping and wind impact analysis using Sentinel-1 data

Mauritius oil spill detection using transfer learning approach for oil spill mapping and wind impact analysis using Sentinel-1 data

A Semantic Web-Enabled Approach for Dependency Management

The use of external libraries in today’s software projects allows developers to take advantage of features provided by such application programming interfaces (APIs) without having to reinvent the wheel. However, APIs have also introduced new challenges to the software engineering community (e.g. API incompatibilities, software vulnerabilities, and license violations) that extend beyond traditional project boundaries and often involve different software artifacts. One potential solution to these challenges is to provide a technology-independent representation of software dependency semantics and its integration with knowledge from other software artifacts. In our research, we take advantage of the semantic web (SW) and its technology stack to establish a unified knowledge representation of build and dependency repositories. Given this knowledge base, we can now extend and integrate other (heterogeneous) resources to allow for a flexible and comprehensive global impact analysis approach. To illustrate the applicability of our SW-enabled modeling approach, we discuss two different applications. These applications illustrate how our modeling approach can not only integrate and reuse knowledge from dependency management systems and other software artifacts, but also take advantage of inference services provided by the SW to support novel software analytics services across artifact and project boundaries.

Value Creation, Capture, and Destruction

Problem, research strategy, and findings Municipalities relying on tax increment financing (TIF) to underwrite large-scale infrastructure and development projects assume that tax revenues from the increase in property values will be sufficient in the short run to cover capital expenses (i.e., that projects are self-financing). By examining a case of TIF in action, we move beyond expectations about how TIF should work in theory. Hudson Yards on the far West Side of Manhattan is one of the largest TIF projects in the United States in recent years and the first one implemented in New York City. We refined the conventional fiscal impact analysis approach by tracking costs and revenue flows of the project between 2005 and 2020 and comparing rents, assessed values, property tax appeals, abatements, and land prices across relevant submarkets. Through this exercise and critical reading of the literature, we developed a framework for analyzing TIF that treats property values as extrinsic and determined by the political arrangements and calculative negotiations that create, capture, and destroy them. Interactions between private property markets and governing bodies cause costs to fluctuate, responsibility for different expenditures and claims on new revenues to shift, and the location and magnitude of spillover effects to change over time. The flexibility of property values in Hudson Yards allowed the taxes to be used at cross-purposes, jeopardizing the payback streams and inflating risks shouldered by the public sector. Takeaway for practice Although our findings are limited by the single case study approach, the creation–capture–destruction heuristic we developed can help planners modify their expectations of the future and spend economic development dollars more effectively in the present. We suggest that municipalities claw back subsidies upon a property’s sale through a local capital gains tax and restrict the use of appeals and abatements in TIF districts to limit the potential for value destruction.

Evaluating the Effectiveness of the COVID-19 Emergency Outbreak Prevention and Control Based on CIA-ISM.

The COVID-19 pandemic, characterized by high uncertainty and difficulty in prevention and control, has caused significant disasters in human society. In this situation, emergency management of pandemic prevention and control is essential to reduce the pandemic’s devastation and rapidly restore economic and social stability. Few studies have focused on a scenario analysis of the entire emergency response process. To fill this research gap, this paper applies a cross impact analysis (CIA) and interpretive structural modeling (ISM) approach to analyze emergency scenarios and evaluate the effectiveness of emergency management during the COVID-19 crisis for outbreak prevention and control. First, the model extracts the critical events for COVID-19 epidemic prevention and control, including source, process, and resultant events. Subsequently, we generated different emergency management scenarios according to different impact levels and conducted scenario deduction and analysis. A CIA-ISM based scenario modeling approach is applied to COVID-19 emergency management in Nanjing city, China, and the results of the scenario projection are compared with actual situations to prove the validity of the approach. The results show that CIA-ISM based scenario modeling can realize critical event identification, scenario generation, and evolutionary scenario deduction in epidemic prevention and control. This method effectively handles the complexity and uncertainty of epidemic prevention and control and provides insights that can be utilized by emergency managers to achieve effective epidemic prevention and control.

Tragic Realism: How to regulate Deepfakes in Colombia?

Recent technological advances in artificial intelligence and machine learning have made it possible to create fake videos that look authentic. As these videos—known as “DeepFakes”— leave few traces of manipulation, they can be used to spread false statements and alter elections in a more sophisticated way than both online disinformation and misinformation. Considering that online disinformation and misinformation were used to capture the electorate’s decision-making in the Colombian peace referendum, deepfakes constitute a risk to Colombia’s future elections. This article explores how other jurisdictions have responded to the emergence of deepfakes to evaluate three regulatory alternatives based on a regulatory impact analysis approach: leave the regulation unaltered; making social networks accountable for their content; and criminalizing the production and distribution of deepfakes. We conclude that, despite the criminalization of deepfakes being an alternative that could better achieve regulatory purposes in Colombia, its enforcement could also represent a threat to freedom of speech and to democracy.

Measures Driven Change Impact Analysis Approach for Prioritizing Software Requirements Changes

Measures Driven Change Impact Analysis Approach for Prioritizing Software Requirements Changes

Integrated approach analysis of the effectiveness of disaster prevention projects

The impact of national projects such as disaster prevention, should be analyzed by simultaneously considering the results of direct and indirect effect analyses. Existing research has verified the effectiveness of disaster prevention projects only through direct effect analysis. This study proposed an impact analysis approach that employed both direct and indirect effect analyses to assess their effectiveness. For the impact analysis, both direct effect and indirect effect analyses were conducted, using cost–benefit analysis (CBA) along with a safety sentiment index of residents and the efforts of the local government in implementing disaster prevention projects in 33 areas in South Korea. CBA calculates the scale of damage through a survey of properties (e.g., crops and public facilities) and the estimated damage according to the conditions of the disaster area. Matrix analysis was performed by applying the results of the CBA and safety sentiment index. The direct and indirect effect analyses identified the highly affected groups and areas where the results of the aforementioned analyses were above average, compared with the lowly affected groups and areas where the results were below average. An area belonging to the group with negative effects could be considered as part of the group with relatively good effects by supplementing its projects. The proposed method of impact analysis can be used to determine the efficiency of a disaster prevention project and comprehensively analyze its effects.

Policy Analysis of Enforcement of Halal Product Guarantee Regulations Through The Regulatory Impact Analysis (RIA) Approach

Halal products are products consumed by the community, especially Muslim communities. Given that Indonesia is a Muslim-majority country, the State handles the production and distribution of halal products. In 2019, the Ministry of Religion stipulated Regulation of the Minister of Religion of the Republic of Indonesia (PMA) Number 26 of 2019 concerning Guaranteed Halal Products. Therefore, enforcement of halal certification for products circulating in Indonesia is mandatory. However, regulation enforcement generally has many challenges to predict impacts without detailed studies and consultations with affected parties. In this regard, the purpose of this study is to analyze the impact of the application of PMA number 26 of 2019 compulsorily by using Regulatory Impact Analysis (RIA). This study concludes a gap between current conditions and ideal conditions that need to be postponed to enact the Regulation of the Minister of Religion of the Republic of Indonesia Number: 26 of 2019 by correcting the gap.

Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment

Electric vehicles (EVs) have received massive consideration in the automotive industries due to their improved performance, efficiency and capability to minimize global warming and carbon emission impacts. The utilization of EVs has several potential benefits, such as increased use of renewable energy, less dependency on fossil-fuel-based power generations and energy-storage capability. Although EVs can significantly mitigate global carbon emissions, it is challenging to maintain power balance during charging on-peak hours. Thus, it mandates a comprehensive impact analysis of high-level electric vehicle penetration in utility grids. This paper investigates the impacts of large-scale EV penetration on low voltage distribution, considering the charging time, charging method and characteristics. Several charging scenarios are considered for EVs’ integration into the utility grid regarding power demand, voltage profile, power quality and system adequacy. A lookup-table-based charging approach for EVs is proposed for impact analysis, while considering a large-scale integration. It is observed that the bus voltage and line current are affected during high-level charging and discharging of the EVs. The residential grid voltage sag increases by about 1.96% to 1.77%, 2.21%, 1.96 to 1.521% and 1.93% in four EV-charging profiles, respectively. The finding of this work can be adopted in designing optimal charging/discharging of EVs to minimize the impacts on bus voltage and line current.

An Operational Framework Model for Geospatial Business Impact Analysis in West Coast of Sabah, Malaysia

Population expansion and rapid urbanisation rate coupled with extreme changes of weather conditions have triggered more frequent disaster occurrences resulting in bigger economic and commercial losses. Like many other economic sectors that contributed significantly towards local socio-economic development, the small and medium enterprises (SMEs) also were not spared from being affected and/or disrupted by disaster events. In 2020, SMEs in Malaysia represented 98.5% of local business establishments and contributed approximately 41% to the country’s GDP. Although Malaysia can be considered a relatively safe and stable country from a political and socio-economic tussle, as a country with tropical climate, Malaysia, is highly vulnerable to the risk of flooding, landslide and mudslide which jeopardise the sustainability of local SMEs in the long run. Unfortunately, until today, there is no locallydriven comprehensive framework for promoting SMEs resiliency that has been developed particularly in disaster-prone areas. In this light, advanced geospatial technology that has been widely used in mapping for various purposes can be utilized or integrated into mapping of disaster risk profiles and providing significant inputs for establishing Business Continuity Planning (BCP) for disaster-prone areas. Using business impact analysis (BIA) approach, this study explains the operational framework model for business continuity and recovery. The case study of local SMEs in West Coast Sabah, Malaysia was adopted to explain the impacts of the 2015 earthquake and its cascading geohazards to local SMEs including severe disruptions to the main lifeline facilities e.g. telecommunication, electricity and transportation. The research design is based on the qualitative and quantitative methods including guided interviews with the affected local SMEs groups. The proposed operational framework is a result of integrated smart geospatial solutions for advancing business continuity and resiliency for SMEs in disasterprone areas.