Case Study Research Articles

LEGO®-inspired decision-making model for automatic construction of precast diaphragm walls

This paper proposes using a precast diaphragm wall (PDW) for an urban low-carbon strategy. However, PDW using mechanized automatic construction still lacks the support of segmentation decision-making modeling. Therefore, this paper introduces the PDW LEGO®-inspired decision-making model (PDW-LDM) to address this problem. First, the paper establishes the parameterization, adaptive analysis, and decision-making modules. Second, it integrates these modules to create the PDW-LDM. Finally, the PDW-LDM is applied in a case study. The main conclusions include: (1) Multi-disturbance analysis revealed the most vulnerable disturbance case for PDW, indicating the need and feasibility of joints for PDW; (2) Legoized PDW exhibited both disturbance resilience and sufficient weight reduction (15%); and (3) A LEGO® frame model integrates with the hardware to build a complete set of equipment for PDW automatic construction. This paper provides a practical tool for decision-making in PDW automatic construction and promotes urban low-carbon strategy.

Incorporating hydraulic gradient and pumping rate into GALDIT framework to assess groundwater vulnerability to salinity in coastal aquifers: a case study from Urmia Plain, Iran.

The critical role of groundwater in meeting diverse needs, including drinking, industrial, and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion-groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T)-merge to shape the GALDIT vulnerability index for coastal aquifers. This study enriches the GALDIT framework by incorporating two additional hydrogeological variables: hydraulic gradient (i) and pumping rate (P). This expansion produces seven distinct vulnerability maps (GALDIT, GAiDIT, GAiDIT-P, GALDIT-i, GALDIT-iP, GALDIT-P, GAPDIT). In the Urmia Plain, the traditional GALDIT index reveals vulnerability values ranging from 2 to 8.1, categorized into six classes from negligible to very high vulnerability. However, the modified indices, GAiDIT and GAiDIT-P, yield a three-class categorization, ranging from low to high vulnerability. The introduction of the "i" and "P" parameters in GALDIT-i and GALDIT-iP enhances the precision of vulnerability mapping, altering class distribution and intensifying vulnerability ratings. The eastern, central, and coastal areas of the Urmia Plain demonstrate high to very high vulnerability levels, in contrast to the lower vulnerability observed in the western regions. Both the GALDIT-P (r = 0.82) and GALDIT-iP (r = 0.81) indices show strong correlations with Cl concentration, thereby improving mapping accuracy over the traditional GALDIT index (r = 0.72). A sensitivity analysis highlights the critical influence of the "i" parameter, suggesting its weighting should be revised. Parameter recalibration serves to amplify the significance of "G," "L," "D," and "i" parameters, while diminishing others. The integration of multiple hydrogeological variables considerably enhances the precision of groundwater vulnerability assessments.

Unlocking the potential of Naive Bayes for spatio temporal classification: a novel approach to feature expansion

Prediction processes in areas ranging from climate and disease spread to disasters and air pollution rely heavily on spatial–temporal data. Understanding and forecasting the distribution patterns of disease cases and climate change phenomena has become a focal point of researchers around the world. Machine learning models for prediction can generally be classified into 2: based on previous patterns such as LSTM and based on causal factors such as Naive Bayes and other classifiers. The main drawback of models such as Naive Bayes is that it does not have the ability to predict future trends because it only make predictionsin the present time. In this study, we propose a novel approach that makes the Naive Bayes classifier capable of predicting future classification. The process of expanding the dimension of the feature matrix based on historical data from several previous time periods is performed to obtain a long-term classification prediction model using Naive Bayes. The case studies used are the prediction of the distribution of the annual number of dengue fever cases in Bandung City and the distribution of monthly rainfall in Java Island, Indonesia. Through rigorous testing, we demonstrate the effectiveness of this Time-Based Feature Expansion approach in Naive Bayes in accurately predicting the distribution of annual dengue fever cases in 30 sub-districts in Bandung City and monthly rainfall in Java Island, Indonesia with with both accuracy and F1-score reaching more than 97%.Graphical

A Simplified Evaluation Framework for Adaptation Measures to Urban Heat Islands

Adaptation measures to urban heat islands are classified into the following three categories: measures to reduce solar radiation incident on the human body, measures to control and cool ground and wall surface temperature, and measures to control and cool the air and human body temperature. Case studies are conducted to evaluate the effects of the implementation of a cool water circulation sunshade and to examine the adverse effects of cool pavements on the human thermal environment, in addition to the effects of mist sprays on the human body. The effect of the sunshade, watering road, and mist spray, which are typical adaptation measures to urban heat islands, on the human thermal environment was estimated using Wet Bulb Globe Temperature (WBGT) as an indicator for heat stroke prevention and Standard New Effective Temperature (SET*) as an indicator for thermal comfort assessment. The effect of solar radiation shielding on improving the human thermal environment was the most significant, with a large decrease in the amount of solar radiation absorbed by the human body, resulting in a large decrease in SET* and WBGT of 2.7 °C and 1.0 °C, respectively, on fine summer days.

Examining Energy Poverty among Vulnerable Women-Led Households in Urban Housing before and after COVID-19 Lockdown: A Case Study from a Neighbourhood in Madrid, Spain

People with lower incomes often live in homes with poor thermal properties, making it difficult for them to maintain a comfortable indoor temperature. This vulnerability is closely related to the quality and maintenance of housing, which negatively affects indoor environmental comfort, especially in terms of energy usage, having an impact on health and well-being. Studying energy poverty from a qualitative perspective allows us to delve deeper into the experience of these people. A qualitative study was carried out through a case study of women in a situation of household vulnerability and energy poverty. Semi-structured interviews were conducted with five women-led households and two key informants before and after the COVID-19 confinement, in one of the most vulnerable neighbourhoods of Madrid. Unveiling the complexity of this topic, three categories were identified: household composition and economic resources, perception and proposals for household improvements, and household health and well-being. Furthermore, the results suggest that reliance on inefficient solutions such as electric radiators or butane-cylinder heaters for space heating in winter (more affordable in the short term, but unsustainable in the long one) shows evidence of energy precariousness, which, together with other poor housing conditions and users’ behaviours, impact clearly on health, generating or worsening chronic diseases. Research in vulnerable populations requires interventions beyond visibility, supported by key informants. Social workers and educators are essential to improving the living conditions of the most vulnerable people; however, they need social policies and adequate intervention plans and strategies to support and make their efforts effective.

Challenges to anticipatory coastal adaptation for transformative nature-based solutions

Much of England’s coastline is underprepared for adapting to long-term coastal change, with many coastal areas moving from ‘hold the line’ to managed realignment as this century progresses. This paper offers a unique case study of a frontage experiencing this transition. It explores the perspectives of Bacton and Walcott residents and coastal policymakers on managing the risk of coastal change for the Bacton-Walcott frontage on the Norfolk coast (UK), after the projected lifetime of a nature-based solution (NBS), known as sandscaping. Drawing upon survey and interview data, this research finds local residents have an increased sense of security of future coastal change through the perceived importance of the nearby Bacton Gas Terminal (currently supplying up to a third of the UK’s gas supply), and the protection afforded to it by sandscaping. For policymakers, sandscaping has bought time to prepare for managed realignment, whereas for residents, sandscaping has bought time to postpone it. There is a risk of maladaptation if reduced concern of future erosion affects willingness to engage in coastal adaptation in the present. This case study highlights the multiple temporal and spatial interests in coastal management, where decision-making at a local level has national-scale implications for domestic energy supply, and where novel nature-based solutions may bring additional uncertainty and complexity to building social resilience. It provides insights on the challenges of anticipatory adaptation, which is of relevance to other coastal areas looking to mitigate climate impacts and better prepare against future risk.

The Assessment of Solar Powered Green Campus vs The Conventional Approach: A Case Study at the Main Campus of Universiti Sains Malaysia, Penang, Malaysia

As a guarantee that Malaysia practices sustainable development, this matter is important to be approached in detail and proactively as it guarantees a better future change. The concern of many people including educational institutions is not only to be able to preserve the environment but also to be able to manage the institution's budget expenses and provide satisfactory well-being to the people of Universiti Sains Malaysia. Achieving stability is an important endeavor, encompassing both private and public structures, with a particular focus on educational institutions. This study aims to understand in detail about the effectiveness of solar use in energy saving efforts compared to the use of conventional approaches to meet the term green campus in educational institutions. This objective is in line with the aim of this research which is to evaluate the annual electricity bill in Ringgit Malaysia (RM) main campus of Universiti Sains Malaysia, to analyze the difference in electricity before and after the use of solar energy is practiced on buildings at Universiti Sains Malaysia and the final objective is to give recommendations for productive and efficient electricity saving measures that can be done on campus. This research uses qualitative as a data collection technique to obtain information about electricity and energy consumption at USM. The results of this study will increase the understanding of the effectiveness of solar use in reducing the cost of electricity in a building and give a larger and specific picture of the energy savings that need to be done to maintain the term of green building in the aspect of energy efficiency in educational building.

Several intuitionistic fuzzy hamy mean operators with complex interval values and their application in assessing the quality of tourism services.

In order to assess the quality of senior tourism services in vacation destinations, we examine complex interval valued intuitionistic Fuzzy Dombi Hamy Mean (CIVIFDHM) operators. These operators successfully manage imprecision and uncertainty in the preferences of senior tourists. However, the Hamy mean (HM) operator can identify the connections between various input data sets and produce excellent outcomes when combining and evaluating information. We illustrate their usefulness and efficacy through a case study, providing a strong instrument for improving service quality for senior citizens and promoting an inclusive and fulfilling travel experience. In this work, we develop the HM operator and Dombi operations with Complex interval valued intuitionistic fuzzy numbers (CIVIFNs). We recommend the CIVIFDHM operator, complex interval valued intuitionistic fuzzy weighted Dombi Hamy mean (CIVIFWDHM), complex interval valued intuitionistic fuzzy dual Dombi Hamy mean (CIVIFDDHM), and complex interval valued intuitionistic fuzzy weighted dual Dombi Hamy mean (CIVIFWDD) operators. Next, multiple attribute decision making (MADM) models are constructed with the help of CIVIFWDHM and CIVIFWDDHM operators. We provided an evaluation of an older tourism operator in a tourist area as an example to show the suggested models.

Fire hazard management in dining properties: a case study

Fire hazard management in dining properties: a case study

Natural Capping Enhancing the Resilience of Earthen Heritage Under Rainfall Impacts

ABSTRACT Soft capping has proven effective in enhancing masonry ruins in humid regions. To-date, there are limited studies on the use of soft capping for earthen heritage in arid and semi-arid regions. Considering the diverse climatic conditions and the presence of vegetation on the Northern China earthen sites, there is an urgent need to study the effect of these vegetations on earthen heritage sites. As the plant species used in the previous studies are mainly based on artificial selection, we propose to use native plant species that are typically found in the study site and define the application of native plant species as ‘natural capping’. Taking the Ming dynasty Shanxi Great Wall sections as case study, four scale-down rammed earth test walls were subjected to rainfall tests under four patterns of rainfall once. Results showed that native plants excel in Northern climates. Under moderate rainfall, the moisture at the wall head of natural capping walls is nearly constant. However, when subjected to heavy and extreme rainfall, natural capping walls reduced inner wall moisture infiltration by 5.56%–7.52% and material loss by 58.24%–75.04%. Post-rainfall monitoring demonstrated that natural capping has sustainable benefits for temperature and moisture reduction in summer.

Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function

Transmissibility function (TF) is widely applied in damage detection due to its sensitivity to damage and robustness to external excitations, but its application in online damage detection is rarely reported due to challenges in handling data streams. This study proposes a new TF-based online damage detection method that integrates a truncation-free variational inference-based full Dirichlet process Gaussian mixture model (VI-FDPGMM) within a streaming variational inference (SVI) paradigm. As an improved Bayesian nonparametric approach, the truncation-free VI-FDPGMM addresses the issue of truncating mixing components in traditional VI-DPGMM for online learning with increasing data by strategically setting the variational distributions of parameters for the components without assigned data (i.e., inactivated components) to their prior distributions based on the Bayesian viewpoint, which enables computing the probabilities to assign data points to these components and determining the creation of new components. As a result, the truncation-free VI-FDPGMM allows dynamically adding components to the mixture model, providing the flexibility to automatically adapt the number of components for arbitrary amounts of data. This characteristic enables its intuitive integration into the SVI paradigm featured as the variational posterior conditioned on the previous data as the prior when new data are observed, facilitating continuous refinement of the mixture model without repeatedly making inference of previous data. Therefore, the proposed method is highly efficient and well-suited for online damage detection. The proposed method is validated using two case studies, demonstrating its capability to dynamically generate new clusters as new data are available online to indicate the emergence of new damage patterns during the monitoring process, which enables it to perform structural anomaly detection tasks in a semi-supervised manner. Furthermore, the method outperforms some state-of-the-art methods due to its capability for continuous model refinement and robustness in interpreting and capturing uncertainties.

A need for emotionally intelligent leaders in educational institutions: A review of the effects of leadership style on burnout teachers

For the three pillars of education: teacher, student, and curriculum to function properly, leadership plays an intrinsic role. Academic leadership aims to provide the right working environment for proper teaching and learning to take place. Thus, leadership style has a paramount effect on teachers who consequently affect students and ensure proper instruction to all aspects of curriculum. This article review aims to unfold the dynamics of this relationship. In particular the researchers will attempt to explore the role of transformational leadership which also displays qualities of emotional intelligence on reducing the occurrence of the syndrome of burn out among teachers. Sample of empirical, experimental and case studies that examined the correlation between symptoms and causes of job burnout among teachers and the style of leadership practiced will be presented. Seven of which were examined to verify the impact of leadership in a school setting and three others to examine the same impact but in a university context. As a result, researchers assert that in traditional leadership where qualities of empathy, social awareness, and management of relationships are missing, job burnout cases among teachers increase. Finally, a recommendation to introduce emotional intelligence skills into academic leaders' training programs was made. The findings of this study shall serve leaders and policymakers in the teaching and learning sector in a way that would benefit them and assist their subordinates in avoiding burnout and achieve greater heights.

Dissecting Neuromyths – Bridging the Gap between Education and Neuroscience in EFL Pedagogy

This study investigates the prevalence of neuromyths among English as a Foreign Language (EFL) teachers and examines their general neuroscience knowledge (GNK). Neuromyths, often stemming from misinterpretations of neuroscience research, can lead to ineffective teaching practices. The research employs a mixed-methods approach, surveying 45 EFL teachers to assess their GNK and beliefs in common neuromyths, while considering variables such as gender, years of teaching experience, educational background, and exposure to neuroscience training. Additionally, a case study explores the practical integration of educational neuroscience concepts (ENCs) into EFL teaching. The findings reveal a moderate level of GNK among participants, but a significant struggle in identifying neuromyths, particularly those related to learning styles and brain usage. Teachers with more teaching experience tend to hold stronger neuromyth beliefs. The case study demonstrates both the challenges and benefits of applying ENCs in EFL teaching, showing improvements in teaching strategies and student outcomes. The study highlights the critical need to enhance neuroscience literacy among EFL teachers through targeted professional development programs. These programs should focus on debunking neuromyths and promoting evidence-based teaching strategies. The conclusions emphasize the importance of integrating neuroscience education into teacher training to improve teaching effectiveness and student learning. Future research should investigate the long-term impacts of such training and explore the application of neuroscience principles across various educational contexts.

Strategizing Effective Maintenance Strategies for Air Conditioning Systems in Commercial Buildings in Malaysia: A Systematic Literature Review

The use of air conditioning systems is prevalent in many locations in Malaysia due to the elevated temperatures seen in the region. The importance of air conditioning system maintenance has not been accorded due attention. Taking this issue into consideration, this study aims to enhance the maintenance of air conditioning systems by understanding their importance, identifying maintenance issues and proposing solutions to enhance system maintenance, with a specific focus on commercial buildings in Malaysia. The data used in the study was gathered from case studies, images, newspapers, journals and previous theses. This study used fully qualitative methods to obtain data and was acquired through a systematic literature review (SLR) and open-ended interview questionnaire with experts. From the findings, it was found that there were three main strategies, which are preventive maintenance, corrective maintenance and predictive maintenance that are mostly applied for the maintenance of the air conditioning system at the commercial building in Malaysia. The overall findings from the interview with experts highlighted that technical expertise, specialized tools and comprehensive maintenance strategies were important elements to be employed by HVAC technicians and facility managers in Malaysia when managing the HVAC, especially the air conditioning system. This research expands knowledge by investigating air conditioning system maintenance. Furthermore, the findings of this study are useful to the maintenance team and users since they provide the best strategies for the maintenance of the air conditioning systems of commercial buildings in Malaysia.

Short-Term Load Forecasting Method Based on Bidirectional Long Short-Term Memory Model with Stochastic Weight Averaging Algorithm

To accommodate the rapid development of the distribution network of China, it is essential to research load forecasting methods with higher accuracy and stronger generalization capabilities in order to optimize distribution system control strategies, ensure the efficient and reliable operation of the power system, and provide a stable power supply to users. In this paper, a short-term load forecasting method is proposed for low-voltage distribution substations based on the bidirectional long short-term memory (BiLSTM) model. First, principal component analysis (PCA) and the fuzzy C-means method based on a genetic algorithm (GA-FCM) are used to extract the main influencing factors and classify different types of user electricity consumption behaviors. Then, the BiLSTM forecasting model utilizing the stochastic weight averaging (SWA) algorithm to enhance generalization capability is constructed. Finally, the load data from a low-voltage distribution substation in China over recent years are selected as a case study. Compared with conventional LSTM and BiLSTM prediction models, the annual electricity load curves for various user types forecasted by the PCA-BiLSTM model are more closely aligned with actual data curves. The proposed BiLSTM forecasting model exhibits higher accuracy and can forecast user electricity consumption data that more accurately reflect real-life usage.

The Ecclesiastical Impact: Exploring the Role of Christian Church in Democracy Development in Nigeria

This research investigates the multifaceted role of Christian church in the development and consolidation of democracy in Nigeria. Through a comprehensive analysis of scholarly literature, empirical data, and case studies, the study aims to elucidate the various dimensions of the church's influence on democratic processes, institutions, and norms in the Nigerian context. Specifically, the research will examine how Christian churches contribute to promoting democratic values, civic education, moral leadership, peace-building, social welfare initiatives, and political engagement. By employing a mixed-methods approach, including qualitative interviews, surveys, and content analysis of church statements and activities, the study seeks to provide empirical evidence and theoretical insights into the mechanisms through which the church impacts democracy development in Nigeria. The findings of this research will have implications for understanding the complex dynamics of religion and politics in Nigeria and inform policy and practices aimed at strengthening democratic governance and pluralism in the country.

Burn pain management in a female patient with severe burn injuries in Nepal: a case study and review.

Burn injuries in low-resource settings like Nepal present significant public health challenges, leading to substantial morbidity, mortality and severe pain. This paper assesses burn pain management in Nepal, emphasizing the need for enhanced strategies. A case study of a female patient with severe burn injuries from a rural village in Western Nepal illustrates current challenges. Reviewing studies on burn pain management in Nepal shows limited access to specialized facilities, inadequate palliative care, medication shortages and insufficient healthcare professionals. Pharmacological interventions are impacted by financial constraints and a lack of protocols, while nonpharmacological approaches have not been explored and contextualized for the Nepalese context due to similar financial issues. Comprehensive burn pain management requires addressing resource constraints through collaborative health-aid partnerships.

Evaluation study of the urban governance of the COVID-19 crisis in the city of Antwerp

BackgroundThe COVID-19 pandemic has demanded crisis management at all governance levels. While most research has focused on responses of national governments, city-level governance had significant potential to develop tailored approaches. This study explored how the local COVID-19 response was organised and adapted to the specific city population and context in the City of Antwerp, Belgium.MethodsA case study using semi-structured interviews was set up with 20 key informants with a central role in the coordination and implementation of the city-level COVID-19 response in Antwerp. Thematic analysis of transcripts was guided by an adapted version of the OECD risk management cycle.ResultsRespondents’ accounts provide a granular understanding of pandemic preparedness, crisis management, and response and adaptation to the COVID-19 crisis in a mid-size European city. Its size was large enough to have a strong capacity within its government bodies, to utilise the expertise of strong partners in the area and to mobilise a critical mass for action, yet small enough to quickly establish connections and trust. In addition to a universal approach, they developed tailored responses to specific neighbourhoods and groups. Well-established community and organisational ties enhanced the effectiveness of grassroots initiatives. The perceived feeling of joint action contributed to a strong collective agency, but respondents noted the need for a system for monitoring and learning.ConclusionsLocal governments are important to develop tailored strategies and organise a locally relevant crisis response in cities. They need expertise and agency, with enough flexibility to experiment. To harness promising practices and avoid repeating errors in future crises, it is crucial to develop a system that better evaluates and monitors local responses, before, during and after crises.

ViCubeLab-An Integrated Platform Using VR to Visualise and Analyse Road Traffic Conditions

The main contribution of this paper is to introduce a framework for integrating Machine Learning (ML), Human, and Virtual Reality (VR) into one platform to promote a collaborative visualisation environment that can assist in better analysis and improve the human-machine teaming capability. This platform was demonstrated using a case study in ana-lysing road traffic conditions. The ‘Ab-normal Machine Learning Road Traffic Detection in VR (AbnMLRTD-VR)’ prototype system was developed to assist the human analyst. The proposed system has two main integrative components: a data-driven ML model and a 3D real-time visualisation in a VR environment. An unsupervised ML model was built using real traffic data. The AbnMLRTD-VR system highlights the outliers in the road sections in actual road contexts of a road traffic network. This gives the human analyst a 3D real-time immersive visualisation in a VR environment to evaluate road conditions. The AbnMLRTD-VR system demonstrated that it could help minimise the need for human pre-labelling of the data. It enables the visualisation of the road traffic conditions more meaningfully and to understand the context of the road traffic conditions of road sections at any given time.

A Dynamic Behavior Verification Method for Composite Smart Contracts Based on Model Checking

A composite smart contract can execute smart contracts that may belong to other owners or companies through external calls, bringing more security challenges to blockchain applications. Traditional static verification methods are inadequate for analyzing the dynamic execution of these contracts, resulting in misjudgment and omission issues. Therefore, this paper proposes a model checking approach based on dynamic behavior that verifies the security and business logic of composite smart contracts. Utilizing automata, the method models contracts, users, attackers, and extracts properties, focusing on six types of common security vulnerabilities. A thorough case study and experimental evaluation demonstrate the method’s efficiency in identifying vulnerabilities and ensuring alignment with business requirements. The UPPAAL tool is employed for comprehensive verification, proving its effectiveness in enhancing smart contract security.