Real-life Processes Research Articles

The Value of Case Study Research in Practice: A Methodological Review with Practical Insights from Organisational Studies

This research provides an in-depth review of the literature on case studies as a strategic qualitative research methodology. While case studies have faced criticism for lacking scientific rigor and failing to address generalizability, this research underscores their value when examining complex, real-life processes or activities in great detail. Case studies have been widely used in social sciences, such as sociology, industrial relations, and anthropology, though they are sometimes considered an underutilized approach. This research explores the general concepts, strengths, and limitations of the case study method, noting its theoretical richness and ability to generate data that is contextually and empirically rich. It emphasises the versatility of case studies in creating a more holistic understanding of the subject matter. Based on a study involving four organisations and the researcher’s practical experience, the article details how case studies were conducted, how access to organisations was gained, and how data collection was systematically managed. The research also highlights the use of triangulation employing multiple data collection techniques, including observations and document analysis, which enhances the reliability and validity of findings.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Developing new methods for patient care pathway assessment for breast cancer patients in Hungary

Abstract Issue The idea of progressive healthcare models is to treat patients preferably in one location as close to their homes as possible. This contradicts the demands of efficiency: to centralise the availability of expensive procedures and highly specialised staff. Healthcare systems need to navigate between these challenges, especially in public health issues such as cancer care. Description of the problem Achieving the best possible health outcomes requires evidence-based policies that find balance between the demands of accessibility and efficiency. Therefore, we aimed to set up a methodology to review the healthcare model and assess patient movements across the system. We analysed the roaming paths of breast cancer patients diagnosed in Hungary in 2017. Inter-hospital roaming was represented as a graph: hospitals were the nodes, patient movements from one hospital to another were directed edges. This allowed us to apply network analysis: modularisation, weighted degrees calculation, etc. We then introduced the measure of terminality. A hospital is terminal in a given pathway if it is the last new one, i.e. all hospitals visited later had already been visited before. Terminality is defined as the proportion of terminal events among all pathways that cross a given hospital. The rationale of this is that the last hospital in cancer pathways is usually not the highest level one: after advanced treatments, patients are often referred back to local hospitals. Results Testing the method on Hungarian breast cancer patients we observed that results fit our prior expectations. The official ranking of hospitals on the progressivity scale (reflecting the availability of advanced treatments) shows sufficient agreement with terminality. Minor differences may point to situations where the healthcare system does not work fully according to regulations. Lessons Our methodology is recommended for system level patient pathway analysis in order to develop evidence-based healthcare policies. Key messages • Graph representation of inter-hospital patient roaming supports evidence-based policy developments for more efficient patient pathways in cancer care, regardless of the healthcare system design. • The applied new network analysis methodology can help assessing the real life processes of cancer care in a healthcare system through identifying the specifics of inter-hospital patient movements.

Estimating the Confidence Intervals for the Coefficients of Variation Delta-Gamma Distributions

A common application of the coefficient of variation (CV), which is the ratio of the population standard deviation to the population mean, is frequently used to assess quality control and economic processes, among others. The fiducial quantity approach, Bayesian confidence intervals (CIs) using the Jeffreys, uniform, or normal-gamma-beta (NGB) priors, and highest posterior density (HPD) intervals using the Jeffreys, uniform, or NGB priors were used to provide estimators for the CI for the ratio of CV of two delta-gamma distributions. An evaluation of their performance in terms of average length and coverage probability was carried out using Monte Carlo simulations. The results of this study indicate that the HPD using the Jeffreys prior and fiducial quantity methods are the best for estimating the CI for the ratio of the CV of two delta-gamma distributions. Rainfall data from Mae Hong Son province in Thailand was used to illustrate their practicability when analyzing real-life processes.

Exploring the Landscape of Fractional-Order Models in Epidemiology: A Comparative Simulation Study

Mathematical models play a crucial role in evaluating real-life processes qualitatively and quantitatively. They have been extensively employed to study the spread of diseases such as hepatitis B, COVID-19, influenza, and other epidemics. Many researchers have discussed various types of epidemiological models, including deterministic, stochastic, and fractional order models, for this purpose. This article presents a comprehensive review and comparative study of the transmission dynamics of fractional order in epidemiological modeling. A significant portion of the paper is dedicated to the graphical simulation of these models, providing a visual representation of their behavior and characteristics. The article further embarks on a comparative analysis of fractional-order models with their integer-order counterparts. This comparison sheds light on the nuances and subtleties that differentiate these models, thereby offering valuable insights into their respective strengths and limitations. The paper also explores time delay models, non-linear incidence rate models, and stochastic models, explaining their use and significance in epidemiology. It includes studies and models that focus on the transmission dynamics of diseases using fractional order models, as well as comparisons with integer-order models. The findings from this study contribute to the broader understanding of epidemiological modeling, paving the way for more accurate and effective strategies in disease control and prevention.

Groundwater pollution equation: Lie’s symmetry analysis and numerical consideration

The current study modeled groundwater pollution through the utilization of the advection–diffusion equation - a versatile differential equation that is capable of modeling a variety of real-life processes. Indeed, various methods of solutions were then proposed to examine the governing model after being transformed, starting with Lie’s symmetry, semi-analytical, and numerical methods, including the explicit and implicit finite difference method and the finite element method. Further, the proposed methods were demonstrated on some test models; featuring forced and unforced scenarios of the forcing function. Analytically, Lie’s symmetry method failed to unswervingly reveal the required solution to the problem; however, with the imposition of certain restrictions, a generalized closed-form solution for the forced model was acquired. This fact indeed triggered the quest for the deployment of more methods. Thus, semi-analytically, the adopted decomposition method swiftly gave the resultant closed-form solutions. Numerically, the efficiency of the sought methods was assessed using the L2−norm and CPU time, upon which the implicit schemes were found to win the race. All-in-all, the beseeched semi-analytical method is highly recommended for such investigation; at the same time advocating the effectiveness of the implicit finite difference schemes on advection–diffusion-related equations.

Unveiling the causes of waiting time in business processes from event logs

Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times in activity transitions can help analysts identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose observed waiting times in each activity transition into multiple direct causes and to analyze the impact of each identified cause on the process cycle time efficiency. The approach is implemented as a software tool called Kronos that process analysts can use to upload event logs and obtain analysis results of waiting time causes. The proposed approach was empirically evaluated using synthetic event logs to verify its ability to discover different direct causes of waiting times. The applicability of the approach is demonstrated in a real-life process. Interviews with process mining experts confirm that Kronos is useful and easy to use for identifying improvement opportunities related to waiting times.

Cloud Enabled e-Banking Payment Security Implementation using Blockchain Technology

Cloud computing has revolutionized the banking industry by offering flexible and flexible products for the delivery of electronic banking services. However, due to the sensitivity of the central authority, ensuring the security of online transaction is still a critical issue. In this context, blockchain technology is promising by offering decentralization, transferability and cryptographic security. This article presents a new way to increase the security of electronic banking payments by combining blockchain technology with cloud computing. The system plans to use the cloud to host business applications and services, and use blockchain to securely record and verify transactions. Smart contracts are used to streamline the payment process and manage transactions, providing transparency and trust. Additionally, encryption technology is used to encrypt sensitive information and protect the user's identity. Using this solution provides many benefits, including improving the security, transparency and efficiency of electronic banking. Research articles and experimental results demonstrate the effectiveness and applicability of the proposed process in real life. Overall, the integration of blockchain technology with cloud banking provides a solution to solve security issues and increase the reliability of online payments.

Random walk simulation by population dynamics P systems

PDP systems have been widely used for real-life applications, such as systems biology, ecosystems, physics or economy, among others. Complex systems related with these areas are simulated in the framework of Membrane Computing using objects and membranes that can represent entities or places in the real-life process. In physics, the study of a particle in different fluids, depending on their composition, is really interesting for several applications. A first approximation to this field is to think that particles move randomly in the available space, without any force that constrains their movements. This behavior is known as random walk, and it is used not only in physics but in economics, genetics, and ecology among other areas. In this paper, we introduce generic PDP systems for simulating the behavior of particles, both for one-dimensional spaces and for two-dimensional spaces, using different simulators to analyze the computational resources consumed.

The spindle approximation of network epidemiological modeling

Understanding the dynamics of spreading and diffusion on networks is of critical importance for a variety of processes in real life. However, predicting the temporal evolution of diffusion on networks remains challenging as the process is shaped by network topology, spreading non-linearities, and heterogeneous adaptation behavior. In this study, we propose the ‘spindle vector’, a new network topological feature, which shapes nodes according to the distance from the root node. The spindle vector captures the relative order of nodes in diffusion propagation, thus allowing us to approximate the spatiotemporal evolution of diffusion dynamics on networks. The approximation simplifies the detailed connections of node pairs by only focusing on the nodal count within individual layers and the interlayer connections, seeking a compromise between efficiency and complexity. Through experiments on various networks, we show that our method outperforms the state-of-the-art on BA networks with an average improvement of 38.6% on the mean absolute error. Additionally, the predictive accuracy of our method exhibits a notable convergence with the pairwise approximation approach with the increasing presence of quadrangles and pentagons in WS networks. The new metric provides a general and computationally efficient approach to predict network diffusion problems and is of potential for a large range of network applications.

Measure Twice, Change Once: Using Simulation to Support Change Management in Rural Healthcare Delivery.

Barriers to adequate healthcare in rural areas remain a grand challenge for local healthcare systems. In addition to patients' travel burdens, lack of health insurance, and lower health literacy, rural healthcare systems also experience significant resource shortages, as well as issues with recruitment and retention of healthcare providers, particularly specialists. These factors combined result in complex change management-focused challenges for rural healthcare systems. Change management initiatives are often resource intensive, and in rural health organizations already strapped for resources, it may be particularly risky to embark on change initiatives. One way to address these change management concerns is by leveraging socio-technical simulation models to estimate techno-economic feasibility (e.g., is it technologically feasible, and is it economical?) as well as socio-utility feasibility (e.g., how will the changes be utilized?). We present a framework for how healthcare systems can integrate modeling and simulation techniques from systems engineering into a change management process. Modeling and simulation are particularly useful for investigating the amount of uncertainty about potential outcomes, guiding decision-making that considers different scenarios, and validating theories to determine if they accurately reflect real-life processes. The results of these simulations can be integrated into critical change management recommendations related to developing readiness for change and addressing resistance to change. As part of our integration, we present a case study showcasing how simulation modeling has been used to determine feasibility and potential resistance to change considerations for implementing a mobile radiation oncology unit. Recommendations and implications are discussed.

Development of Risk-Based Approaches to Determining the Damageability and Useful Life of Railway Transportation Assets

Modern problems of railway transport in Russia, such as the general technical condition of transportation assets, and relevant issues of their safe operation, substitution of imported equipment and its components, implementation of the programs of technological development give rise to the need to create new equipment and technologies, constructing schemes for long-term development of transport based on new safety requirements and strategic planning. Current level of developments does not allow to completely eliminate damage, failures and accidents regarding railway transportation assets manufactured in accordance with the current regulatory framework. Solving these problems is possible by identifying the actual state of an object regarding cyclic, corrosion, wear and other damage accumulated during operation, which determines the onset of limit states of the basic elements of an asset, taking into account resource recovery technologies at various stages of the life cycle, both in standard (design) operating conditions and beyond them.The article refers to the problems associated with ensuring safe operation of railway transport, with the aim of substantiating based on existing regulatory and technical documents of new approaches to the analysis of damageability, vulnerability of objects and assessment of the risks of accidents and disasters. The development of interaction through a system of new problem statements and regulatory and technical documents, as well as more active involvement in the use and development of the fundamental laws of physics, chemistry, and mechanics of real-life processes in the environment, make it possible to implement new concepts, strategies, and developments of advanced research for technical objects with the use of digital and intelligent technologies.

Why Markov Switching Models Work Well: An Explanation

In general, the probability of the next state depends not only on the current state, but also on the whole pre-history. However, in practice, many real-life processes — including economic processes — can be well described by so-called Markov models, i.e., models in which the probability of the next state depends only on the current state. In this paper, we explain the empirical ubiquity of such models.

Комбинированная модель тепломассообмена в башенных градирнях

The priority directions of the scientific and technological policy of the Russian Federation include issues on research in the field of energy and resource saving technologies while generating heat and electric energy. One of the possible ways to implement it at thermal and nuclear power plants is to increase the efficiency of the operation of circulating cooling systems (COO). Modeling and optimization of circulating cooling systems with tower cooling towers is of particular relevance under modern conditions of a limited amount of low-mineralized water for charging circulating cooling systems. To determine the parameters of air flow in the tower cooling tower, a three-dimensional simulation model is developed. To describe heat and mass transfer, considering the phase transitions in coolants, a matrix model is used. The model is developed based on the mass and energy balance equations. A combined model of a circulating cooling system with cooling towers has been developed. It describes air flow within the framework of a three-dimensional simulation model and the process of heat and mass transfer, considering a possible phase transition in coolants within the framework of a matrix model. Comparison of modeling results and normative data is carried out. Adequate description of the real-life process using the combined model is presented. The developed combined model allows setting and solving problems of choosing the optimal design and operating parameters of the COO equipment, as well as solving problems of diagnosing the state of the COO according to the readings of standard instruments. In the future, the proposed calculation method will allow optimizing the operating modes of combined water supply systems, which combine water cooling in a direct-flow scheme and water cooling in a reverse scheme with cooling towers.

On Solving Stochastic Optimization Problems

Many optimization mathematical models, associated with the technical-economic processes of real-world problems, have elements of uncertainty in their structure, which places them in stochastic optimization programming. Their diversity and complexity, due to the large uncertainty space, require special methods of solving, because there is no general solution method. Within this context, in this paper we consider the category of optimization models that can contain random variable type coefficients and/or imposed probability levels on the constraints. The purpose of the paper is to propose a methodology dedicated to these studied models. Applying the methodology leads to developing a deterministic linear programming model, associated with the initial stochastic model. In fact, the proposed methodology reduces the stochastic formulation to a deterministic formulation. The methodology is illustrated with a numerical case study based on a manufacturing problem. Solving the obtained deterministic model is carried out in the version assisted by a specialized software product (WinQSB Version 2.0). It allows for the performing of a sensitivity analysis of the optimal solution, and/or a parametric analysis relative to certain model coefficients, both also presented in the paper. The main result of the study in this paper is the proposed methodology, which is applicable on a large scale, for any mathematical model of stochastic optimization of the mentioned type, regardless of complexity, dimensions and the domain of the process to which it is associated. The numerical results obtained when applying this methodology indicate its efficiency and effectiveness in finding the solution for the studied models. The approach to this issue in the present paper is determined by the wide range of stochastic optimization problems in the various studied real-life processes and by the imperative need to adopt the best decisions in conditions of uncertainty.

SIRMOD and WinSRFR model simulation comparisons for furrow irrigation in a sugarcane field at Kasinthula Cane Growers Scheme, Malawi

Models play a key role towards understanding real-life processes in various fields including surface irrigation. A better understanding of the performance of a particular model in simulating the processes in question is of prime importance. Against this background, simulation capabilities of the surface irrigation simulation, evaluation and design (SIRMOD) and WinSRFR models were compared through field studies conducted at Kasinthula Cane Growers Scheme (KCGS) in Malawi. The objectives of the study were to assess performance of each model in simulating water application and distribution efficiencies, and conduct a comparison in the resulting efficiencies and user-friendliness of each model. The study was conducted in sugarcane field furrows using volume balance method. Each of the two models estimated irrigation application efficiency, distribution efficiency, and deep percolation values that were very close to 51%, 79% and 49%, respectively, implying that the two were representative of each other. Both models showed the need for improving the current operation variables at KCGS to achieve optimal performance. It was further established that the simulation procedure in SIRMOD was more tedious than the one employed in WinSRFR. Furthermore, WinSRFR had a platform for conducting sensitivity analyses while SIRMOD did not. However, SIRMOD simulations had lower RMSE values for advance and recession than WinSRFR implying that SIRMOD model more accurately simulated these parameters than WinSRFR. The study also established that WinSRFR was a more appropriate model for practical application by irrigation frontline staff and/or farmers because it is more user-friendly than SIRMOD. The study concluded that SIRMOD's simulation accuracy presented it as a better research model than WINSRFR hence it would be ideal for calibrating and validating other surface irrigation models.

Beyond polarization towards dynamic balance: harmony as the core of mental health.

Worldviews are culturally derived assumptions that influence individual and collective behaviors, values, and representations of reality. The study of mental functions is not exempt from this influence, as reflected in scientific theories, methodological approaches, and empirical studies. Despite acknowledging the interplay of mental processes with developmental, environmental, and cultural dimensions, psychological research is still primarily based on quantitative methods, and on the conceptualization of mental phenomena as unfolding along polarized continua. A lively epistemological debate surrounds this approach, especially underscoring the risk of blurring the distinction between constructs derived from statistical models and real-life processes and experiences. Based on this debate and on recent empirical evidence derived from the positive psychology literature, this paper is aimed at proposing an integrated view of mental health, as a holistically patterned, contextually imbedded, and dynamic phenomenon changing over time and across life events, with harmony, harmonization and dynamic balance as core qualities. The heuristic potential of investigating the qualitative configuration patterns of mental health dimensions across individuals and groups, beyond their position along a quantitative continuum, is outlined. The development of more integrated approaches and methodologies to investigate mental health as a harmonization process, taking into account personal, contextual and developmental features, would be aligned with evidence derived from the integration of traditional nomothetic and ideographic approaches, and other life sciences. However, the development of a transdisciplinary line of research requires further inputs from different epistemological views, as well as higher attention to the potential contribution of different philosophical traditions.

Sosialisasi Upaya Menagani Penyalahgunaan Media Sosial di Kalangan Masyarakat Di Desa Sidorejo Kecamatan Pondok Kelapa Kabupaten Bengkulu Tengah

Social media has an influence on the larger community that is both beneficial and harmful. They help the process in real life by providing quick and comprehensive information. Additionally, owing to technical advancements that affect how simple it is to utilize social media as a communication tool, their negative consequence will be to abuse social media through crimes, fraud, and defamation. In order to help the community utilize social media responsibly, this community service aims to contribute perspective, knowledge, and understanding. This community service project is conducted in the form of socialization utilizing the lecture-based approach of information exchange, which is followed by a Q&A session or conversation between presenters and participants.The outcomes of this community service demonstrate that the community's intense use of social media must be consistent with knowledge of safe and responsible use. Through this community service project, it is intended that more individuals will learn how to use social media responsibly, enabling them to make the most of it while minimizing its negative consequences on real-world interactions.

Development of models of current relays and power direction relays in the MATLAB software

During pandemics and military aggressions, the continuation of education is ensured by the active implementation of distance learning technologies. A significant number of real-life processes can be described using mathematical models. Their further implementation in the program code and the finished software product allows them to be effectively used in virtual studies of relay protection schemes. For the correct functionality of any virtual relay or protection complex, it is necessary to build a model that takes into account the real characteristics of its constituent elements. In case of absence of the latter, they should be determined empirically. To create a virtual laboratory work on the study of the RT-40 current relay, based on the parameters specified in the passport for this device, an algorithm was created that allows the user to interactively determine the main characteristics of the relay: tripping current, release current, and response time depending on the current through the relay using various circuit elements (sliders, regulators, etc.). The algorithm of the program “Research of characteristics of power direction relay RBM-171” uses the angular characteristics and sensitivity characteristics of the real relay RBM-171, which were converted into digital form. For the RBM-178 relay, which has a different connection scheme and maximum sensitivity angle from the previous test stand, a separate test stand “Study of the RBM-178 power direction relay” was created. The logic of its operation is based on the detection of zero-sequence currents and voltages using the method of symmetrical components. All laboratory circuits have visualization and indication of the states of the circuit elements present on the stands. This allows the user to achieve more accurate results and get closer to testing relays in real laboratory conditions.

Advances in large amplitude oscillatory shear Rheology of food materials

Molecular interactions determine the microstructure of food, as well as its response to deformation and flow. In order to design efficient processing equipment, to produce high-quality, stable end products, to predict textural and sensory properties, and to ensure consumer acceptance, the characterization of food rheology is essential. Deformations are rapid and large during the processing of foods and during consumption. In food studies, large amplitude oscillatory shear (LAOS) has become increasingly popular due to its ability to mimic real-life processes. When food is subjected to dynamic oscillatory shear tests, a sinusoidal deformation is applied, the mechanical stress (or strain) is probed, and the response is recorded. This chapter summarize main methods to extract meaningful rheological parameters from complex LAOS response of selected food materials. A time-resolved nonlinear rheology method, sequence of physical processes (SPP), gave detailed interpretations of transient microstructures, whereas the Fourier Transform coupled with Chebyshev decomposition (FTC) method provide static measurements at specific strains. LAOS behavior and its relationship to food microstructures and texture still needed to be studied in depth. By constructing more accurate mechanical models of complex food systems, the fundamental knowledge can be applied to evaluate the nonlinear rheology of food for consumer acceptance and efficient processing.

Learning business process simulation models: A Hybrid process mining and deep learning approach

Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn business process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. The proposed approach allows us to simulate different types of changes, including the addition of new activity types to a process. This latter capability is achieved by encoding the activities by means of embeddings, rather than representing them as one-hot-encoded categories. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches. The evaluation also sheds light into the relative performance of multiple embedding approaches to represent the activities.