Case Study Of Management Research Articles

An Ontology and a Reasoning Approach for Evacuation in Flood Disaster Response

Managing flood-related data to assist in the disaster management is a critical process of high importance during a flood disaster. These data are heterogeneous and can be provided from different data sources, and integrating them is a challenging task which allows inferring new information that helps in limiting the consequences of a flood. In this paper, we propose a novel approach that manages heterogeneous flood-related data based on semantic web techniques and helps in limiting the damage caused by floods. We first propose an ontology that is used to formally describe the flood-related data, and we construct our knowledge graph through integrating heterogeneous data using the proposed ontology. Then, we propose a reasoning approach using SHACL rules to infer new information that helps manage the flood disaster or anticipate future events. The experimental evaluations of our proposed approach are conducted on a real case study in flood disaster management with the aim of generating evacuation priorities. The results show that the proposed approach succeeds in managing heterogeneous flood-related data and in generating evacuation priorities in a very short time.

Towards swarm optimization techniques for power communication systems and smart grid environments

Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions.

Mathematical modelling of waste flows and treatment based on reconstruction of historical data: Case of wastewater sludge in Czech Republic

The contribution of supporting tools developed for logistic optimization and processing infrastructure planning is highly dependent on the quality of input data. Databases containing reports on waste production and treatment are often used for waste management case studies and in the aggregated form at the national level provide sufficient information with negligible errors. On the other hand, analysis in greater detail at the smallest administrative unit usually reveals significant inconsistencies, especially in the case of mass balance control. This often results in the impossibility to track waste flows in the system and evaluate final treatment. This paper aims to present a tool for database verification in order to satisfy mass balances at all levels of the investigated system. As part of the mathematical modelling, the database is first subjected to a statistical analysis where selected inconsistencies between records can be removed. Subsequently, data reconciliation is applied with appropriate weights assigned to each entity in the system. The weights represent credibility and reflect various errors, which are identified in the previous stage of the approach. The correction of mass balances enables an evaluation of quality or composition in greater detail of individual entities and also determines the feasible interval of monitored properties. The tool is applied to the treatment evaluation of dry matter in wastewater sludge in the Czech Republic. Thanks to the developed tool, disposal and material or energy recovery can be quantified at micro-regional.

Telangana Kanti Velugu 2023: Universal Access for Eye Screening and Provision of Quality Spectacles for Millions - A Case Study in Effective Supply Chain Management, Operations, and Cost Effectiveness by TSMSIDC & CHFW, DoHMFW, Govt. of Telangana India

Telangana Kanti Velugu 2023: Universal Access for Eye Screening and Provision of Quality Spectacles for Millions - A Case Study in Effective Supply Chain Management, Operations, and Cost Effectiveness by TSMSIDC & CHFW, DoHMFW, Govt. of Telangana India

Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management

Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios.

Calibrating a process-based simulation model for the Acadian forest region

Climate change is projected to have profound impacts on Canada’s Acadian Forest Region (AFR). However, large uncertainties arising from climate change and increasing disturbance activity pose challenges for forest management decisions. Process-based (mechanistic) simulation models offer a means by which vulnerabilities and different management strategies can be tested under multiple climate and disturbance regimes. However, applying these complex models can be dauting, especially for novice modelers and forest practitioners; nonetheless, this complexity is increasingly necessary to more realistically project changes in forest growth and composition, ecosystem services, biodiversity, disturbance regimes, and the spread of forest pests. Here, we present a methodology for calibrating and validating iLand (v1.1.1), a landscape-scale, process-based forest model that offers a novel approach for assessing the feedback between individual trees and their environment (ecosystem processes, climate, and disturbance). For the first time, 18 tree species were parameterized and calibrated for the AFR and model performance was evaluated against independent field observations at the tree population and stand level. iLand was able to accurately emulate the dynamics of individual tree species populations as well as the succession of mixed-species forest stands across a range of soil conditions and is now ready to be used to simulate future forest dynamics of the AFR. We also discuss calibration method selection and the potential impacts of model and project structure in relation to our project. As the accessibility and usability of process-based forests models increases, our work provides a unique case study for forest managers looking to expand their toolbox.

Theorizing reactive policy entrepreneurship: A case study of Swedish local emergency management

AbstractIn this article, we examine public policy change at the local level of governance in the aftermath of an extraordinary event. Using the case study of a Swedish municipality after the sweeping forest fire of 2014, we contend that policy entrepreneurship, like its market counterpart, may under certain conditions take on a wider range of behaviors that are not underpinned by the proactive quest for opportunities. Rather, a sense of urgency and necessity, professional norms, and some keen technical skills make for a different kind of entrepreneurship, which we label reactive policy entrepreneurship.

Drivers of urban metabolism: Toward a framework for urban transformations

AbstractThe environmental and social crises in cities call for radical future visions that can incite transformative change. Yet, urban metabolism research typically adopts an explanatory, retrospective approach to the drivers of urban flows and stocks, resulting in conservative, business‐as‐usual future outlooks. In this study, we present the results of a narrative literature review on drivers and futures of urban metabolism, and consequently use these results to propose and apply a framework that can be used by researchers (i) to systematically identify the drivers of urban metabolism, and (ii) to critically engage with these drivers for the development of transformative future visions. The framework comprises seven thematic categories of drivers (demographic, economic, cultural, political, technological, environmental, and infrastructural) and an eighth category (power) to be used as the lens through which the interactions between drivers, activities, and flows in the city are critically examined. Applying the framework to the case study of biowaste management in Rennes, France, we found it useful for the systematic identification of often overseen drivers. The proposed framework, allowing for a combined analysis of flows and drivers, can become a useful tool toward a solution‐oriented urban metabolism research.

Handling Class Imbalance in Machine Learning-based Prediction Models: A Case Study in Asthma Management.

A data-driven prediction tool has the potential to provide early warning of an asthma attack and improve asthma management and outcomes. Most previous machine learning (ML)-based studies for asthma attack prediction have reported a severe class imbalance, with major implications for model performance. We aimed to undertake a systematic comparison of several class imbalance handling techniques in the context of risk prediction models for asthma prognosis. We used data from 9,835 asthma patients extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database and deployed five class imbalance handling methods based on synthetic minority oversampling technique (SMOTE) and cost function customisation. We then compared their performances in improving two-class classifier models developed using logistic regression (LR) and extreme gradient boosting (XGBoost) for three different prediction tasks with varying severity of class imbalance (proportion of majority class ranging from 90.86% to 98.98%). The cost function customisation technique substantially outperformed the SMOTE-based methods in all tasks. XGBoost combined with cost function customisation achieved the highest prediction performance for the outcome with the most extreme class imbalance ratio (AUC = 0.72). Our findings suggest that the cost function customisation-based approach to tackle class imbalance provides substantially better performance compared to oversampling in the context of asthma management.Clinical Relevance- This study underscores the challenge of class imbalance in the context of prediction tools to improve asthma management and outcomes and provides a methodological solution that addresses the challenge. Accurate asthma prediction tools can provide early warning and potentially prevent deterioration thereby improving the quality of life of patients with asthma.

Opportunity knocks but just once: Impact of infrastructure investment decision on climate adaptation to flood events

Extreme natural disasters caused by climate change and socio-economic development, such as floods, are likely to occur more frequently and impose an increasing threat on the safety of people‘s lives and property around the world. The infrastructure investment of adaptation projects can effectively prevent and mitigate these climate impacted disaster risks, as well as reducing the catastrophic losses. In this paper, we propose an economic evaluation framework to incorporate risk preferences and investment timing for making climate adaptation. We use an extended loss distribution approach (LDA) to estimate the expected economic loss (EEL) of flood events with different levels, in which the Bayesian inference is integrated to estimate the parameters of loss severity distributions. The modeling framework is applied to a case study of flood risk management in Nanjing City, China. We find that the appropriate delay of investment timing can significantly increase the value of the adaptation project. The optimal investment timing is earlier, and the project value under optimal investment timing is higher, when the risk aversion coefficient of insurance company or the asset growth rate becomes higher. A lower discount rate or a lower investment cost will accelerate the optimal investment timing. Interestingly, we also find that the greater the difference between the discount rate and the asset growth rate, the higher the value of the adaptation project under the optimal investment timing. These findings are expected to provide useful information on flood disasters prevention for the investment decision of adaptation infrastructure which can be economically effective to integrate the impacts of climate change and socio-economic development.

Examining Effective Management and Leadership Strategies in Conflict Resolution: A Case Study of Conflict Management in Private Secondary Schools

This study highlights private schools' management and leadership strategies in dealing with conflicts. School conflicts occur for many reasons. To address the conflicts effectively, schools need to have the ability to thoroughly examine the root cause of such conflicts. The impact on school development in terms of performance largely depends on how school leaders effectively manage and address conflicts. This qualitative research specifically choose private schools due to their autonomy, which distinguishes them from public schools in terms of decision-making and leadership approaches. Data was collected through semi-structured interviews, and analysed using manual thematic analysis methods. This study discovered that conflicts often occur within the scope of appointing new school principals, upholding the integrity of school members, and managing the resistance among teachers. The strategy predominantly used in schools in this study is a win-lose approach, where problems are resolved quickly without considering the need for negotiation. The two schools investigated in this study view future conflicts as a form of competition with other schools, placing strong emphasis on the significance of fair policies for all, and the stakeholders’ trust as the main key.

Data curation as anticipatory generification in data infrastructure

ABSTRACT Data curation is crucial for data reusability. New possibilities for digital data sharing are an urgent concern for data curators, who must keep historical datasets and present data collections always ready to meet unknown future data needs. This calls for a more nuanced understanding of the temporal horizons of data curation in Information Systems research. Based on a qualitative interpretive case study of data management in an environmental monitoring infrastructure, we characterise three data curation practices to support data reuse. These practices follow three interleaving temporal perspectives: retrospective (by upgrading historical datasets), present-oriented (by monitoring ongoing data collections), and future-looking (by disseminating data). We conceptualise this work as anticipatory generification, involving continuous and temporally oriented data curation to maintain data sufficiently open-ended to anticipate future data reusability. Anticipatory generification is essential for the sustainable evolution of environmental data infrastructures. Our study contributes to the Information Systems literature by further theorising the temporal perspectives of data infrastructures and providing additional insight into how the future is anticipated in practice.

A Case Study of Ayurvedic Management of Pakshaghata (Right Hemiplegia)

A Case Study of Ayurvedic Management of Pakshaghata (Right Hemiplegia)

Digital Management Methods and Case Studies of Enterprises Based on Emerging Information Technology

Emerging information technologies include artificial intelligence, big data, blockchain, Internet of Things, BI, etc. The gathering of emerging information technology has brought an excellent opportunity for managing enterprise digital transformation. The essence of enterprise digital transformation is to use the advantages of digital technology in data collection, network transmission and analysis and calculation to complete the optimization and upgrading of enterprise business model, management model and business model. The fundamental goal of enterprise digital management is to maximise the benefits of the enterprise. This paper examines the problems and challenges encountered by traditional enterprise management models in the digital era. It analyses classic cases of digital transformation management in pillar industries of the national economy based on emerging IT technologies. It is concluded that through the use of modern digital information technology and digital management mode, enterprises can gradually create a supervisory mechanism with fast communication, clear division of labour, responsibility, quick response, timely solution and efficient operation, and comprehensively improve their operation level to achieve the goal of digital, standardised and refined enterprise management.

It’s Turtles All the Way down: Target Fixation and the Costs of Stakeholder Engagement in Environmental Management

While stakeholder engagement can benefit managers, its implications for decision-making are not well understood. Integrating literature on stakeholder engagement and institutional theory, this paper investigates the ramifications of stakeholder involvement on organizational decision-making. It employs a qualitative comparative case study of agency managers and stakeholders involved in sea turtle management at the Georgia Department of Natural Resources to investigate the motivations and goals of different organizational actors. The results demonstrate that stakeholders can be prone to target fixation, focusing on different goals than their organization intends. Furthermore, they reveal that stakeholders’ motivations are highly resistant to attempts to align them with organizational goals, creating goal misalignment which can disrupt decision-making. These findings provide a more holistic understanding of how stakeholder engagement influences organizational management processes, formalizes how target fixation operates at the individual level, and provides critical information for administrators deciding how to utilize engagement strategies in their work.

Rules Written in Blood: A Case Study of Risk Management in the Oil and Gas Industry

Rules Written in Blood: A Case Study of Risk Management in the Oil and Gas Industry

Developing a science-based framework for the management of drifting Fish Aggregating Devices

Fish Aggregating Devices (FADs) are man-made floating objects deployed by fishers to aggregate tuna and facilitate their catch. Currently, more than half of the global tropical tuna purse-seine catches occur at FADs. The fast development of the purse-seine fisheries operating on drifting FADs (DFADs) has raised concerns regarding their impacts on tuna populations, on non-target species like sharks, as well as on the pelagic and coastal habitats. Consequently, the management of DFAD fisheries is a priority for all tuna regional fisheries management organizations. Due to the little availability of science-based advice to support management decisions, resolutions on DFADs have been mainly based on precautionary principles. In this study we propose a science-based framework for the management of DFADs, relying on indicators and operating models. A set of models and indicators that help evaluate the ecological impacts of DFADs is presented, considering the case study of DFAD fisheries management in the Indian Ocean. The objective of this framework is to assess and predict the effects of DFADs on coastal and pelagic ecosystems, in order to support and/or evaluate past, present and future management actions.

How the COVID-19 Pandemic Changed the Work of Managers: Case Study of Economic Managers

The COVID-19 pandemic has fundamentally changed the business environment in many sectors. This study analyzes how the pandemic and the resulting global economic crisis have influenced changes in management. The aim was to explore changes in the dominance of management functions through the lens of economic managers in various companies. A case study approach was adopted to achieve the research objective. The sample file consisted of 238 managers from various operational fields in the Slovak Republic. A new methodology was created to measure the overall changes. An indicator of the rate of change in the dominance of the management functions was calculated. The index consists of two factors: changes in the time devoted to each management function during the pandemic, and changes in the importance of basic activities performed within the individual management function. This study provides an overview of all industries and describes the changes in the context of a company’s revenue development during the pandemic. It was discovered that the centralization of strategic decision making was significantly underestimated. Up to 78.69% of managers working in companies whose revenues decreased during the pandemic increased the time devoted to planning, and 90.98% of them decreased the time devoted to leading people.

The effect of strategic change on Total Quality Management Case Study: Emirates Post Group (AU)

The effect of strategic change on Total Quality Management Case Study: Emirates Post Group (AU)

A case study of Ayurvedic management of Kitibha Kushtha with special reference to Palmoplantar Psoriasis

People suffering from Autoimmune diseases is increasing day by day. The exact cause of the autoimmune disease is still unknown. Psoriasis is one such disease. Dry thick raised itchy patches on the skin are the sign of Psoriasis. They are covered with silvery-white scales. It is triggered by infection stress and cold. Psoriasis is one of the most common clinical conditions encountered in clinical practice majority of the dermatological disorders have described under the roof of Kushtha in Ayurveda Kitibha is a type of Kshudra Kushtha described in different Ayurvedic classics is Vata-Kaphaaja the clinical symptoms of Kitibha described in Ayurveda resembles with symptoms of Psoriasis the current treatment modalities have their own limitations and the drugs have considerable side effects when using for longer period. According to Ayurveda Psoriasis is treated with both Shodhana and Shamana Chikitsa. lt affects almost 2% worldwide population. The high chances of recurrence and its reluctant nature makes it a matter of concern for modern medicine as they have a limited line of treatment. including Snehpana, Vamana, Virechana and Raktaamokshna etc.