Rigorous Case Study Research Articles

Pelvic floor muscle training for female urinary incontinence: development of a programme theory from a longitudinal qualitative case study

BackgroundUrinary incontinence (UI) negatively affects the well-being of women globally. Pelvic Floor Muscle Training (PFMT) is a complex intervention that aims to decrease UI symptoms. Information about how the multiple complex components involved in PFMT achieve and maintain the desired effect are rarely studied as a whole. The evidence base lacks data about how women experience PFMT over time and in the longer-term. This study explored women’s experiences of biofeedback-assisted PFMT and PFMT alone, to identify and understand what influenced self-reported adherence to PFMT, and UI outcomes over time.MethodsThis rigorous longitudinal qualitative case study, nested within a randomised controlled trial, recruited forty cases (women with stress or mixed UI; 20 in biofeedback-assisted and 20 in PFMT alone group). A case included up to four semi-structured interviews with each woman (prior to starting PFMT, end of treatment [6 months], 12 months, 24 months). Analysis followed case study analytic traditions, resulting in a Programme Theory about PFMT from the perspectives of women with UI.FindingsThe theory demonstrates factors that motivated women to seek UI treatment, and how these influenced long-term adherence. Therapists who delivered PFMT played a crucial role in supporting women to know how to undertake PFMT (to have capability). Some, but not all, women developed self-efficacy for PFMT. Where women did not have PFMT self-efficacy, adherence tended to be poor. When women had PFMT self-efficacy, the conditions to support adherence were present, but contextual factors could still intercede to inhibit adherence. The intercession of contextual factors was individual to a woman and her life, meaning any particular contextual factor had inconsistent influences on PFMT adherence over time for individual women and exerted varying influences across different women.ConclusionLong term adherence to PFMT is a complex interaction between many different factors. Enquiring about an individual woman’s motivation to seek treatment and understanding the contextual factors that affect an individual woman will enable a practitioner to support longer-term adherence.

Issues of Social Media Usage by Adolescents in their Socialisation Process

This study, employing a rigorous qualitative approach and case study design, investigated the common types of social media adolescents use in their socialisation process in Somanya in the Yilo Krobo Municipality in the Eastern Region of Ghana. The sample size was twenty adolescents selected through the convenience sampling technique. One-on-one interviews and focus group discussions were conducted to collect data. The significant findings from the study were that adolescents use social networking sites such as Facebook, WhatsApp, and Twitter. It recommends that parents socialise adolescents to conform to contemporary social interaction patterns to aid the proper use of social media. It was concluded that social media usage affects the socialisation of adolescent, thereby affecting their social behaviour.

A novel intelligent stereo vision approach for blast-induced fragmentation size distribution: Case study at Golgohar open-pit mine, Iran

Rock fragmentation assessment is a critical aspect of evaluating blasting operations in the mining industry, offering insights for optimising procedures and subsequent mining processes. Traditional methods, such as sieving, while accurate, are costly and time-consuming. Digital image analysis methods have emerged as alternatives, yet they face challenges in accurately handling fragments of varying sizes. Recent advancements in computer vision, particularly deep learning techniques, offer promising solutions to automate and refine fragmentation analysis. This paper introduces a novel intelligent stereo vision approach utilising YOLOv8, an advanced deep learning model, for blast-induced fragmentation size distribution assessment at the Golgohar open-pit mine in Iran. By training YOLOv8 with an automated self-labelled dataset and evaluating its performance against real mine site data, this research aims to enhance the accuracy and efficiency of blast fragmentation analysis. The proposed method leverages stereo images and machine learning to provide precise measurements, leading to improved optimisation of blasting parameters and potential economic benefits compared to traditional methods. Through rigorous validation processes and case studies across diverse scenarios, the proposed approach demonstrates robustness and reliability in assessing blast fragmentation, offering a practical and efficient solution for the mining industry’s evolving needs.

Technological advancements in drilling: A comparative analysis of onshore and offshore applications

This paper examines the technological advancements in onshore and offshore drilling operations, emphasizing their significance, challenges, and environmental considerations. Technological innovations, including automation, data analytics, and real-time monitoring, have transformed drilling practices, enhancing efficiency, safety, and environmental sustainability. By comparing onshore and offshore applications, the paper evaluates the effectiveness of these advancements in addressing the unique challenges of each environment. Key areas of focus include efficiency improvements, safety enhancements, environmental impacts, and regulatory compliance. Through case studies and analysis, the study aims to provide valuable insights into the current state of onshore and offshore drilling operations and identify opportunities for innovation and improvement. Ultimately, the findings contribute to advancing sustainable practices, mitigating environmental risks, and fostering responsible resource extraction in the oil and gas industry. This comparative study delves into the dynamic landscape of technological advancements within onshore and offshore drilling operations. It explores the evolving role of automation, data analytics, and real-time monitoring in revolutionizing drilling practices, offering insights into their implications for efficiency, safety, and environmental stewardship. By juxtaposing the unique challenges and operational requirements of onshore and offshore environments, the study underscores the importance of a nuanced understanding of technological applications. Through rigorous analysis and case studies, it illuminates the interplay between advancements and environmental considerations, shedding light on opportunities for innovation and sustainable development. The study's comprehensive scope encompasses efficiency gains, safety protocols, environmental impact mitigation, and regulatory compliance, providing a holistic view of the transformative potential of technology in the drilling industry. Ultimately, the study serves as a catalyst for informed decision-making, fostering dialogue, and driving forward-looking strategies for the future of onshore and offshore drilling operations.

Short and sweet: multiple mini case studies as a form of rigorous case study research

Case study research is one of the most widely used research methods in Information Systems (IS). In recent years, an increasing number of publications have used case studies with few sources of evidence, such as single interviews per case. While there is much methodological guidance on rigorously conducting multiple case studies, it remains unclear how researchers can achieve an acceptable level of rigour for this emerging type of multiple case study with few sources of evidence, i.e., multiple mini case studies. In this context, we synthesise methodological guidance for multiple case study research from a cross-disciplinary perspective to develop an analytical framework. Furthermore, we calibrate this analytical framework to multiple mini case studies by reviewing previous IS publications that use multiple mini case studies to provide guidelines to conduct multiple mini case studies rigorously. We also offer a conceptual definition of multiple mini case studies, distinguish them from other research approaches, and position multiple mini case studies as a pragmatic and rigorous approach to research emerging and innovative phenomena in IS.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

EMBRACING DISRUPTION: AN EMPIRICAL STUDY ON THE ACCEPTED PRACTICES AND STRATEGIC RESPONSES OF ENTREPRENEURS TO RAPID TECHNOLOGICAL ADVANCEMENTS

This empirical research delves into the realm of entrepreneurial endeavors in the face of rapid technological advancements, exploring the accepted practices and strategic responses adopted by entrepreneurs as they navigate the disruptive landscape. In an era marked by continuous technological evolution, entrepreneurs are confronted with unprecedented challenges and opportunities. This study employs a rigorous empirical approach, integrating surveys, interviews, and case studies to shed light on the adaptive strategies and practices embraced by entrepreneurs in response to technological disruptions. The research aims to identify patterns and insights into how entrepreneurs perceive and respond to technological advancements, examining accepted industry practices and innovative strategies. By analyzing real-world case studies and gathering empirical evidence, the study seeks to contribute practical knowledge to the entrepreneurial community and inform policymakers about the evolving dynamics of technology-driven entrepreneurship.Furthermore, the research investigates the impact of these technological disruptions on various aspects of entrepreneurial ventures, including business models, market entry strategies, and organizational structures. By uncovering the accepted practices that lead to resilience and success, as well as the potential pitfalls, this study aims to provide a nuanced understanding of the interplay between entrepreneurs and rapid technological advancements. In essence, the findings of this research contribute to the ongoing discourse on entrepreneurship in the digital age, offering actionable insights for entrepreneurs, industry stakeholders, and policymakers alike. The study provides a roadmap for entrepreneurs to effectively navigate technological disruptions and seize opportunities for innovation and growth in an ever-changing business landscape.

Observation or Insight? Unraveling the Impact of Characters' Image on Interpersonal Communication in Contemporary Chinese TV Series

A salient determinant of effective interpersonal communication resides in the cultivation of a positive personal image. The intricate interplay between self-image, individual behaviors, and communication abilities highlights the far-reaching influence of personal perception on social interactions. In the contemporary landscape of Chinese television series, exemplified by the production ‘Nothing but Thirty’, a deliberate effort is made to depict characters that embody the evolving roles of women in modern society. Drawing from relevant literature, this study conducts a comprehensive analysis of characters’ image in ‘Nothing but Thirty’, examining content such as appearance, language, non-verbal cues (body language), and varied scene settings. Adopting a rigorous qualitative case study methodology and leveraging content analysis grounded in the coordinated management of meaning theory and social exchange theory, the research aims to dissect the nuanced dynamics of interpersonal communication as portrayed in the series. The findings illuminate that the dynamic characterizations in contemporary Chinese TV series function as reflective mirrors, capturing the intricate layers of modern interpersonal communication and societal intricacies. This narrative analysis extends beyond the purview of conventional storytelling, offering a scholarly exploration of the intricate fabric of human interaction.

A frequency stability assessment framework for renewable energy rich power grids

The worldwide power grid is undergoing a paradigm shift due to the rise of renewable energy sources. Traditional rotating synchronous generators are being replaced by inverter-based renewable energy sources (RESs), and this trend is expected to persist in the years to come. As a result, the grid’s inertia is decreasing gradually, which presents significant obstacles to maintaining grid frequency stability. Moreover, due to the uncertainties associated with the generation of RESs and load demand, grid inertia has become a stochastic quantity. In this paper, considering the stochastic characteristics of the power generation, load demand profile and grid inertia, a framework to assess grid frequency stability with high penetration of RESs has been presented. This framework first quantifies grid frequency characteristics at different levels of RES penetration using Monte-Carlo simulation (MCS) considering the intermittent nature of RESs and load demand. This is followed by assessment of frequency stability of the power grid using proposed matrices that quantifies the amount of deviation in grid frequency and rate of change of frequency (RoCoF) outside the boundary prescribed by the grid operator. In addition, the proposed framework evaluates the sensitivity of grid frequency stability to RES penetration levels and quantifies the resiliency against contingency. Rigorous case studies on modified IEEE 39 bus test system have been presented to demonstrate the effectiveness of the proposed framework in assessing grid frequency stability.

Event-triggered interval type-2 fuzzy control of networked systems with extended dissipative under Markovian switching

In this technical note, the problem of the extended dissipative control for interval type-2 fuzzy model with nonlinear network systems in the event-triggered framework is investigated. A novel dynamic event-triggered strategy is developed to ascertain the instants of activation. It has the advantage of reducing data transmission and optimising the utilisation of communication resources. On the basis of the proposed mechanism, the stochastic Lyapunov function including internal dynamic variables is constructed. Based on this function, coupled with the inequality scaling technique and the application of Dynkin's formula, we derive a sufficient condition for the system's stochastic asymptotic stability and extended dissipativity. The design of the dynamic event-triggered extended dissipative controller becomes challenging because the new sufficient condition is nonlinear for some matrix variables. However, utilising the Projection Lemma and the co-design approach, such a controller can be developed based on the obtained analysis result. The methodology is demonstrated through a rigorous numerical simulation case study.

A long and winding road: The hard graft of scaling social change in complex systems

Advice abounds on how to implement large-scale social change, much of which emphasizes a simplistic linear process, led by a heroic central actor. Rigorous case studies have shown that social change is far more complex: it is a reciprocal, iterative, and adaptive process, with multiple stakeholders who work backstage in networked, committed teams. Despite this, the myth of the social entrepreneur as a transformative change maker capable of scaling innovations to a societal level, still holds sway over social innovation support programmes and business school curricula. Using illustrative examples of successful efforts of large-scale social change across three of the most pressing international social challenges: access to medicines, the integration of migrant populations, and reorganizing social care models, we illustrate how conceptualizing social change as driven by iconic individuals is often counter-productive in terms of achieving impact at a societal level. Based on these analyses, we present five insights which illustrate how the mythology of social entrepreneurship and simplistic scaling concepts are often contrary to the practices employed within successful efforts to bring about social impact. Three counteracting principles for those leading, evaluating and funding innovative change efforts within complex systems are discussed and contrasted with the pervasive mythology of social entrepreneurship and linear scaling processes.

Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning Approaches in Single-Step Retrosynthesis.

Single-step retrosynthesis in organic chemistry increasingly benefits from deep learning (DL) techniques in computer-aided synthesis design. While template-free DL models are flexible and promising for retrosynthesis prediction, they often ignore vital 2D molecular information and struggle with atom alignment for node generation, resulting in lower performance compared to the template-based and semi-template-based methods. To address these issues, we introduce node-aligned graph-to-graph (NAG2G), a transformer-based template-free DL model. NAG2G combines 2D molecular graphs and 3D conformations to retain comprehensive molecular details and incorporates product-reactant atom mapping through node alignment, which determines the order of the node-by-node graph outputs process in an autoregressive manner. Through rigorous benchmarking and detailed case studies, we have demonstrated that NAG2G stands out with its remarkable predictive accuracy on the expansive data sets of USPTO-50k and USPTO-FULL. Moreover, the model's practical utility is underscored by its successful prediction of synthesis pathways for multiple drug candidate molecules. This proves not only NAG2G's robustness but also its potential to revolutionize the prediction of complex chemical synthesis processes for future synthetic route design tasks.

A semi-implicit finite volume scheme for dissipative measure-valued solutions to the barotropic Euler system

A semi-implicit in time, entropy stable finite volume scheme for the compressible barotropic Euler system is designed and analyzed and its weak convergence to a dissipative measure-valued (DMV) solution [Feireisl et al., Calc. Var. Part. Differ. Equ. 55 (2016) 141] of the Euler system is shown. The entropy stability is achieved by introducing a shifted velocity in the convective fluxes of the mass and momentum balances, provided some CFL-like condition is satisfied to ensure stability. A consistency analysis is performed in the spirit of the Lax’s equivalence theorem under some physically reasonable boundedness assumptions. The concept of Ƙ-convergence [Feireisl et al., IMA J. Numer. Anal. 40 (2020) 2227–2255] is used in order to obtain some strong convergence results, which are then illustrated via rigorous numerical case studies. The convergence of the scheme to a DMV solution, a weak solution and a strong solution of the Euler system using the weak–strong uniqueness principle and relative entropy are presented.

From Fragmentation to Integration: Developing a Coherent Insurance Regulatory Framework for AfCFTA

This study delivers a comprehensive analysis of insurance regulatory harmonization by examining the European Union’s Solvency II framework alongside the regional models of CIMA, EAC, and SADC. Employing a rigorous comparative case study approach, this research dissects the institutional frameworks, regulatory mechanisms, and policy strategies of these models to extract actionable insights for the African Continental Free Trade Area (AfCFTA). Data were meticulously gathered from policy documents, academic literature, and in-depth interviews with key stakeholders, including regulators and industry leaders. The analysis reveals that AfCFTA stands to gain significantly from a regulatory framework that marries central oversight with local adaptability, akin to the EU model but nuanced for the African context. The study underscores the strategic value of integrating digital tools and fortifying regional bodies to enhance cross-border insurance activities. This research provides a critical foundation for developing a robust and cohesive regulatory framework under AfCFTA, addressing the complexities of regulatory fragmentation and advancing the creation of a resilient and integrated African insurance market

Expanding Our Understanding of Birth Doulas’ Emotional and Informational Support Role: A Qualitative Analysis of Interventions Delivered During COVID-19

Birth doulas were classified as nonessential personnel during COVID-19 and generally excluded from providing physical, in-hospital birth support. This amplified their emotional and informational support to clients, offering a unique opportunity to examine psychological mechanisms underlying birth doulas’ impact on perinatal outcomes. We conducted a methodologically rigorous qualitative case study leveraging a social action theory framework and interviewed 15 demographically diverse birth doulas. Doulas played an important role in supporting their clients’ psychological change processes (e.g., problem solving, outcome expectancies, self-efficacy, goal structures, cognitive schemas). This study contributes to our understanding of psychosocial interventions that may drive the positive effects of doula care on health outcomes, both within and beyond the pandemic context.

Stakeholders’ involvement in an evidence-based sustainable tourism plan

Sustainable tourism planning is recommended for destination governance to ensure long-term development that balances the impacts of tourism on a community, especially in overtourism contexts. This study examines a sustainable tourism planning process focusing specifically on stakeholder involvement and the use of co-created evidence. A multi-stakeholder involvement management framework (MSIM) for sustainable tourism was applied to examine its fit to a rigorous case study. The study enhances the MSIM framework, featuring three stages from scene-setting, management of stakeholder involvement, to assessment. The new framework details objectives and tools for each step, and influential factors in managing stakeholders’ involvement. A significant enhancement is the extensive incorporation of evidence into the framework for different purposes (eg developing management policies, plan objectives and implementation tactics, evaluating stakeholders’ involvement). Multiple stakeholders participated in producing and using a variety of evidence throughout the planning process to provide a foundation for planning. This study is one of the first in sustainable tourism to examine evidence as a component of a broad framework to manage stakeholder involvement. It shows the usefulness, position, and dynamic of evidence in destination management.

Bioprocess Modelling, Intensification, and Control


 
 
 Chemical bio-process intensification, driven by the need for sustainability and cost-effectiveness, has gained significant attention. This study explores dynamic intensification (DI), a strategy to enhance efficiency by modifying system dynamics, operation, and control. Two key approaches are investigated: intensification by design, involving deliberate changes to physical and chemical aspects, and intensification by control, optimizing process parameters and feedback loops.
 The research focuses on the Optimal Periodic Control (OPC) method, dating back to the 1960s but now revisited with modern technology. To investigate the feasibility and effectiveness of dynamic intensification, a comprehensive methodology encompassing modeling, optimization, linearization, dynamic intensification, and control was designed. This methodology ensures systematic and standardized testing of DI in various bioprocesses within the industry.
 Through rigorous case studies, such as Activated Sludge Model No. 1 (ASM1) and an antibiotic perfusion bioreactor, the study demonstrates the practical benefits of dynamic intensification, including enhanced process efficiency and control. Applying model predictive control (MPC) further improves process efficiency, and the intensified systems are thoroughly compared to their respective base cases.
 The insights gained from this research contribute to advancements in bioprocessing technologies across industries, paving the way for sustainable and efficient engineering practices. Engineers can leverage this knowledge and the developed methodology to optimize chemical bioprocesses through innovative intensification strategies, ultimately promoting more eco-friendly and economical bio-processes. The study's comprehensive methodology offers valuable insights and understanding for bioprocess engineers seeking to enhance system efficiency and contribute to a greener future.
 
 

Exploring blockchain for disaster prevention and relief: A comprehensive framework based on industry case studies

AbstractBlockchain technology has been suggested as a groundbreaking solution for disaster prevention and relief. However, there is a dearth of both practical applications and systematic academic research into how those existing blockchain solutions actually help to prevent disasters and/or alleviate their negative impacts. The purpose of this paper was to extend previous research by investigating existing blockchain solutions using a rigorous multiple case study approach. Based on stakeholder theory, a structured framework is developed that examines the roles of key stakeholders in disaster situations, illustrates organizations' motivations to deploy blockchain technology, identifies various use cases, lists the relevant blockchain properties, and highlights contingency factors for successful implementations. The findings demonstrate that blockchain applications can improve both disaster prevention as well as disaster relief efforts by streamlining information flows and augmenting the capabilities of core stakeholders. Furthermore, numerous propositions can be derived from the framework that can be used as starting points for further in‐depth investigations. This study familiarizes practitioners with blockchain's potential for disaster management and provides academics with a theory‐based foundation for future research.

Fault detection and classification in DC microgrid clusters

With the rising popularity of DC microgrids, clusters of such grids are beginning to emerge as a practical and economical option. Short circuit problems in a DC microgrid clusters can cause overcurrent damage to power electronic devices. Protecting DC lines from large fault currents is essential. This paper presents a novel localized fault detection and classification technique for the protection of DC microgrid clusters. In this paper, a variational mode decomposition (VMD) and artificial neural network (ANN) based technique is proposed for accurate and effective fault detection and classification. This research aims to train an ANN that can detect and classify faults in DC microgrid clusters with multiple sources and loads by applying VMD to extract features of current signals. Different types of short circuit faults such as Pole to Pole and Pole to ground faults are considered under various grid operating conditions. The proposed method is capable of real-time fault detection and diagnosis, which can help prevent system failures and minimize downtime. The results indicate that the proposed approach is efficient and effective in detecting/classifying faults in DC microgrid clusters improving the reliability and system safety. The performance evaluation is carried out through rigorous case studies in MATLAB/Simulink environment to prove the efficacy of the proposed method. The VMD-ANN approach is shown to outperform other traditional signal processing techniques in terms of accuracy and robustness. Moreover, the proposed method is applicable to a wide range of DC microgrid clusters, making it a versatile and valuable tool for future research and development.

Impact of Generative AI Models on Neurocybernetics for Enhancing Brain-Computer Interface Adaptability in Motor Disabilities

Motor disabilities can arise from conditions such as stroke, spinal cord injuries, or neurodegenerative diseases, severely limiting an individual's movement and communication abilities. Brain-computer interfaces (BCIs) represent a promising technology aimed at establishing a direct connection between the brain and external devices, allowing individuals with motor impairments to control assistive technologies using their brain signals. However, conventional BCIs often rely on fixed signal patterns or require extensive user training, presenting challenges for some users and constraining system flexibility. Variability in brain signals and the inability to adapt further impede widespread BCI adoption among this population. This study explores the integration of generative AI models to enhance BCI adaptability for people with motor disabilities. By leveraging generative AI, our framework generates realistic brain signals tailored to each user's specific characteristics. Through rigorous experimentation and case studies, we demonstrate the efficacy of our approach in improving BCI performance and usability. Our findings underscore the transformative potential of generative artificial intelligence in neurocybernetics, illustrating its capacity to advance BCI accessibility and effectiveness in rehabilitating motor disabilities. The limitations of traditional BCIs through innovative AI-driven solutions, this research contributes to the evolving field of assistive technologies. It highlights the pivotal role of generative AI in fostering greater autonomy and quality of life for individuals with motor impairments, paving the way for more inclusive and responsive neurotechnology in the future.