Search Results (103)

The increasing penetration of photovoltaic (PV) generation in power systems is progressively displacing traditional synchronous generators, leading to a significant reduction in the system’s equivalent inertia. This decline undermines the system’s ability to withstand rapid frequency variations, adversely affecting its dynamic stability. In this context, battery energy storage systems (BESS) have emerged as a viable alternative for providing synthetic inertia and enhancing the system’s response to frequency disturbances. This paper proposes the design and implementation of an adaptive fuzzy logic controller aimed at frequency regulation in PV-BESS systems. The controller uses frequency deviation (Δf), rate of change of frequency (ROCOF), and battery state of charge (SOC) as input variables, with the objective of improving the system’s response to frequency variations. The controller’s performance was evaluated through simulations conducted in the MATLAB environment, considering various operating conditions and disturbance scenarios. The results demonstrate that the proposed controller achieves the lowest maximum frequency deviation across all analyzed scenarios when the initial SOC is 50%, outperforming other comparative methods. Finally, compliance with primary frequency regulation (PFR) was verified in accordance with the Technical Procedure PR-21 related to spinning reserve, issued by the Peruvian Committee for Economic Operation of the System. Full article

In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal scales, are subjective, often incomplete, and influenced by opinion dynamics within social networks. To effectively deal with these complexities and extract meaningful insights, this study proposes an information-driven decision-making framework that integrates Fuzzy Belief Structures with the TOPSIS method. To handle the uncertainty and imprecision of linguistic ratings, user opinions are modeled as fuzzy belief distributions over satisfaction levels. Rankings are then derived using TOPSIS by comparing each airline’s aggregated profile to ideal satisfaction benchmarks via a belief-based distance measure. This framework presents a novel solution for measuring synthetic satisfaction in complex social feedback systems, thereby contributing to the understanding of information flow, belief aggregation, and emergent order in digital opinion networks. The methodology is demonstrated using a real-world dataset of TripAdvisor airline reviews, providing a robust and interpretable benchmark for service quality. Moreover, this study applies Shannon entropy to classify and interpret the consistency of customer satisfaction ratings among Star Alliance airlines. The results confirm the stability of the Airline Satisfaction Index (ASI), with extremely high correlations among the five rankings generated using different fuzzy utility function models. The methodology reveals that airlines such as Singapore Airlines, ANA, EVA Air, and Air New Zealand consistently achieve high satisfaction scores across all fuzzy model configurations, highlighting their strong and stable performance regardless of model variation. These airlines also show both low entropy and high average scores, confirming their consistent excellence. Full article

Public–Private Partnership (PPP) procurement is a relatively new approach in Nigeria’s housing sector. This study introduces a Fuzzy Risk Allocation Decision Model (FRADM) designed to address the complex and subjective nature of risk allocation in PPP-procured Mass Housing Projects (MHPs). A structured quantitative approach involving 40 purposively selected PPP housing experts was employed. Using a fuzzy synthetic evaluation (FSE) technique, critical risk factors were assessed based on partners’ risk management capabilities and allocation criteria. Constants (Ci) normalized the risk-carrying capacity indices (RCCIs) of both public and private sectors. Results show that risk attitude ranks highest among nine allocation criteria (MIS = 6.21), with the private sector demonstrating higher overall risk management capability. For instance, the availability of finance risk is optimally shared 53.48% to the private and 46.52% to the public sector. The FRADM was validated as reliable, practical, and replicable. Implications point to enhanced transparency, equitable risk-sharing, and support for SDG 11. The model is a strategic tool for decision-makers in PPP housing delivery in Nigeria and can inform similar efforts in other emerging economies. Further research should examine applications across other infrastructure sectors. Full article

There is a growing global interest in making existing buildings more energy-efficient. However, stakeholders seem to have differing views on the matter, especially in developing countries, thus raising the issue of awareness amongst key stakeholders at the operational stage of existing buildings. This study aimed to examine stakeholders’ awareness of the benefits of passive retrofit in residential buildings using a convergent mixed-methods approach. Quantitative data were collected from 118 property managers and 163 owners of residential buildings, and qualitative data were collected from six government officials in Lagos State, Nigeria. The quantitative data collected were analysed using fuzzy synthetic evaluation, which addresses the fuzziness in judgement-making on multi-criteria phenomena. The results revealed that property managers and owners had a moderately high level of awareness of the environmental, economic, and social benefits of the passive retrofitting of residential buildings. However, while property managers generally had a higher level of awareness than owners, a significant gap was found in their awareness of environmental benefits. Conversely, the qualitative analysis results showed that government officials demonstrated a strong awareness of environmental benefits (energy reduction, air quality, and natural lighting) and economic advantages (cost savings and lower implementation costs). In contrast, their awareness of social benefits was limited to health improvements. The findings have practical implications for policy development and awareness campaigns. Building agencies need to further reinforce their targeted awareness programmes for owners, who demonstrated fair awareness of environmental benefits while leveraging the intermediary role of property managers in promoting home retrofit practices. Economic benefits should also be an integral part of policy frameworks to drive wider adoption across all stakeholder groups. Full article

This paper addresses a fundamental challenge in statistical radar detection systems: optimizing the trade-off between the probability of a false alarm ($P_{FA}$) and the probability of a miss ($P_M$). These two metrics are inversely related and critical for performance evaluation. Traditional detection approaches often enhance one aspect at the expense of the other, limiting their practical applicability. To overcome this limitation, a fuzzy hypothesis testing framework is introduced that improves decision making under uncertainty by incorporating both crisp and fuzzy data representations. The methodology is divided into three phases. In the first phase, we reduce the probability of false alarm $P_{FA}$ while maintaining a constant probability of miss $P_M$ using crisp data characterized by deterministic values and classical statistical thresholds. In the second phase, the inverse scenario is considered: minimizing $P_M$ while keeping $P_{FA}$ fixed. This is achieved through parameter tuning and refined threshold calibration. In the third phase, a strategy is developed to simultaneously enhance both $P_{FA}$ and $P_M$, despite their inverse correlation, by adopting adaptive decision rules. To further strengthen system adaptability, fuzzy data are introduced, which effectively model imprecision and ambiguity. This enhances robustness, particularly in scenarios where rapid and accurate classification is essential. The proposed methods are validated through both real and synthetic simulations of radar measurements, demonstrating their ability to enhance detection reliability across diverse conditions. The findings confirm the applicability of fuzzy hypothesis testing for modern radar systems in both civilian and military contexts, providing a statistically sound and operationally applicable approach for reducing detection errors and optimizing system performance. Full article

A new Integrated Spatial Geophysical and Geotechnical Evaluation (I.S.G.E) methodology has been developed to estimate the spatial distribution of geotechnical parameters using high-resolution geophysical methods. The proposed algorithm is based on fuzzy logic, and the final output is the prediction of the 2D or 3D distribution of a geotechnical parameter within a survey area. The main advantage of the developed I.S.G.E tool is that it can propagate sparse geotechnical or point information from 1D to 2D or even 3D space through a fully automatic, unbiased statistical procedure. In this study, I.S.G.E. is implemented and evaluated first using synthetic data and, afterwards, in field condition applications. The automatically derived 3D models, depicting the spatial distribution of specific geotechnical parameters, provide engineers with an additional interpretation tool for better understanding the subsurface conditions of a survey area. Full article

Lithology identification during drilling plays a vital role in geological and geotechnical exploration, as it facilitates the early detection of formation-related hazards and supports the development of optimized mining strategies. Traditional lithology identification research involves problems such as fuzzy indicator characteristics and unbalanced sample quantities, which affect the accuracy and interpretability of model identification. In order to solve these problems, the Shanxi Guoqiang Coal Mine was taken as the research object, and a combined machine learning model was used to conduct a study on lithology identification during drilling. First, the least absolute shrinkage and selection operator (LASSO) algorithm was used to screen the independent variables and retain the parameters that contributed the most to lithology identification. Then, the synthetic minority oversampling technique (SMOTE) algorithm was used to expand the data samples, increase the amounts of minority sample data, and keep the ratios of various lithology data at 1:1. Then, the Bayesian optimization (BO) algorithm was used to optimize the penalty factor C and kernel function hyperparameter γ—two important parameters of the support vector machine (SVM) model—and the BO-SVM lithology identification model was established. Finally, the data samples were processed, and the results were compared with those of single models and unbalanced sample processing to evaluate their effect. The results showed the following: during the drilling process, the four indicators of drilling speed, mud pressure, slurry flow rate, and torque are strongly correlated with the lithology and can be used for lithology identification and classification research. After the data set was oversampled using the SMOTE algorithm, each model had better robustness and generalization ability; the classification result evaluation indicators were also greatly improved, especially for the random forest model, which had a poor original evaluation effect. The BO algorithm was used to optimize the parameters of the SVM model and establish a combined model that correctly identified 95 groups of data out of 96 groups of test samples with an identification accuracy rate of 99%, which was better than that of the traditional machine learning model. The evaluation results were compared with measured data, which confirmed the reliability of the combined model classification method and its potential to be extended to lithology identification and classification work. Full article

The vulnerabilities of critical infrastructure and other disruptive events expose urban communities to severe risks. Public–private partnership (PPP) is an intensive cooperation between public and private actors with enhanced and more innovative services and policy outputs that can be achieved in building urban community resilience. Considering the potential of building urban community resilience through PPP, there is a need to assess the performance of using PPP in urban community resilience building. This study aims to develop a model for assessing the performance outcome for building urban community resilience through PPP. A questionnaire survey was conducted with experienced practitioners globally. The fuzzy synthetic evaluation method was used to develop an evaluation tool that could be used to objectively assess performance outcomes of PPP in urban community resilience building. The tool consists of five critical assessment indicators with defined coefficients: “Resilient urban community physical capital (0.270)”, “Well-developed community stakeholder engagement and training policies” (0.215), “Strong urban community disaster resilience PPP policy” (0.202), “Restriction and preservation” (0.197), “Existence of effective urban disaster risks database and PPP communication plan” (0.116). This performance assessment model can be used as a baseline for measuring the performance of PPP in urban community resilience building. Full article

Aging underground cables pose a threatening issue in distribution systems. Replacing all cables at once is economically unfeasible, making it crucial to prioritize replacements. Traditionally, age-based strategies have been used, but they are likely to fail to depict the real condition of cables. Insulation faults are influenced by electrical, mechanical, thermal, and chemical stresses, and partial discharges (PDs) often serve as early indicators and accelerators of insulation aging. The trends in PD activity provide valuable information about insulation condition, although they do not directly reveal the cable’s real age. Due to the absence of an established ranking methodology for such condition-based data, this paper proposes a fuzzy logic and fuzzy analytic hierarchy process (FAHP)-based cable vulnerability ranking framework that effectively manages uncertainty and expert-based conditions. The proposed framework requires only basic and readily accessible data inputs, specifically cable age, which utilities commonly maintain, and PD measurements, such as peak values and event counts, which can be acquired through cost-effective, noninvasive sensing methods. To systematically evaluate the method’s performance and robustness, particularly given the inherent uncertainties in cable age and PD characteristics, this study employs Monte Carlo simulations coupled with a Spearman correlation analysis. The effectiveness of the developed framework is demonstrated using real operational cable data from a Danish distribution network, meteorological information from the Danish Meteorological Institute (DMI), and synthetically generated PD data. The results confirm that the FAHP-based ranking approach delivers robust and consistent outcomes under uncertainty, thereby supporting utilities in making more informed and economical maintenance decisions. Full article

Over the past few decades, numerous efforts have been made to increase the proportion of women in the construction industry, coupled with various calls for legislation and rules to prohibit gender discrimination. Despite these efforts, minimal progress has been noticed in the construction industry. While recruitment remains crucial, the current culture in construction reveals a knowledge gap in recruitment and retention in employment—a concept known as a ‘leaky pipeline’. Lack of awareness of career options and the challenges of working in a male-dominated, occasionally discriminatory workplace are some of the significant barriers to attracting and keeping women in the construction industry. Much of the research in South Africa shows that most construction companies employed few women but only in lower secretarial and administrative positions. Therefore, this study investigated the barriers facing women’s entry and retention in construction-related employment in South Africa using fuzzy synthetic evaluation (FSE) to understand and prioritise the barriers. Data were collected through the administration of online and paper-based questionnaires. The results of the analysis show that the barriers in the order of criticality include support and empowerment issues (SEs), educational/academic-related barriers (ABs), barriers from professional conditions and work attributes (BPs), social perception and gender stereotype barriers (SPs), professional perceptions and gender bias (PP), and individual confidence/interest/awareness/circumstance-related barriers (IBs), respectively. Based on the findings of the study, several recommendations, including on-the-job tutoring and flexible work arrangements, amongst others, were provided. Full article

Image segmentation is a fundamental challenge in computer vision, transforming complex image representations into meaningful, analyzable components. While entropy-based multilevel thresholding techniques, including Otsu, Shannon, fuzzy, Tsallis, Renyi, and Kapur approaches, have shown potential in image segmentation, they encounter significant limitations when processing thermal images, such as poor spatial resolution, low contrast, lack of color and texture information, and susceptibility to noise and background clutter. This paper introduces a novel adaptive unsupervised entropy algorithm (A-Entropy) to enhance multilevel thresholding for thermal image segmentation. Our key contributions include (i) an image-dependent thermal enhancement technique specifically designed for thermal images to improve visibility and contrast in regions of interest, (ii) a so-called A-Entropy concept for unsupervised thermal image thresholding, and (iii) a comprehensive evaluation using the Benchmarking IR Dataset for Surveillance with Aerial Intelligence (BIRDSAI). Experimental results demonstrate the superiority of our proposal compared to other state-of-the-art methods on the BIRDSAI dataset, which comprises both real and synthetic thermal images with substantial variations in scale, contrast, background clutter, and noise. Comparative analysis indicates improved segmentation accuracy and robustness compared to traditional entropy-based methods. The framework’s versatility suggests promising applications in brain tumor detection, optical character recognition, thermal energy leakage detection, and face recognition. Full article

Prefabricated construction is an effective method for reducing project time and waste and improving quality and safety compared to traditional construction. However, its widespread adoption faces risks and challenges, having detrimental impacts on project performance. This research aims to assess prefabricated construction risks in Iraq using fuzzy synthetic evaluation (FSE). After determining the mean importance score for the likelihood and impact of risks extracted from comprehensive theoretical reviews, significant risks were selected using normalization, followed by FSE. The theoretical review results yielded 79 risks across 11 categories. After normalization, 34 significant risks across 10 categories were identified. The results showed that all risk categories had a medium probability and impact, except for investment and political risks, while experience risks had a high probability and high impact, respectively. FSE results showed that the highest risk importance index was for experience (12.075), followed by political (11.753), capital investment (11.362), safety (11.242), and design risks (10.902). Through its detailed and integrated methodology, the study contributes to formulating an accurate roadmap for FSE of prefabricated construction risks and provides accurate results that add a deeper understanding of risks, helping project managers identify significant risks and formulate the necessary policies to mitigate and control them. Full article

Despite the potential of digital transformation to enhance resource efficiency and waste reduction, numerous barriers hinder its adoption. This study examines the critical barriers to digital technology adoption for circular economy implementation in the construction industry in developing countries, using Ghana as a case study. A structured quantitative approach was employed, integrating mean score ranking, exploratory factor analysis, and fuzzy synthetic evaluation to assess the severity of identified barriers. Data were collected from construction professionals through structured surveys, and statistical analyses were performed using SPSS, Excel, and RStudio to determine the criticality of the barriers. The fuzzy synthetic evaluation revealed that financial and adoption constraints emerged as the most critical barrier group, followed closely by institutional and knowledge barriers, while technological and market limitations and regulatory and organizational challenges also exhibited significant impediments. In response, this study develops a strategic framework comprising targeted solutions such as financial incentives, capacity building, regulatory reforms, and technological infrastructure development. This framework addresses not only the barriers but also the associated risks, including financial uncertainty, data security threats, and regulatory gaps. This study contributes to the theoretical understanding of digital technology adoption in CE practices and offers practical recommendations for policymakers, industry stakeholders, and academics seeking to foster sustainable construction practices in the construction industry. Full article

Efficient and sustainable human resources are crucial for promoting development in emerging nations. Brazil’s education policy provides its citizens with widespread educational opportunities, resulting in high literacy rates. However, women with academic qualifications and skills often encounter significant barriers when transitioning from higher education to positions of empowerment, leading to an underutilisation of human capital. This study, conducted in 2024, gathered data from female students and staff at three Brazilian universities (the State University of Maranhãoo, the Federal University of Rio de Janeiro, and the University of São Paulo) using a survey methodology to ascertain the barriers impeding women’s transition from higher education to empowerment. The data were analysed using Fuzzy Synthetic Evaluation (FSE), a soft computing technique, and it was identified that the most significant barriers revolve around women’s freedom and mobility. Additional challenges include gender norms, family responsibilities, violence and harassment, socio-cultural constraints, and financial limitations. The study offers practical recommendations such as organising awareness programmes and integrating digital technology to enhance workplace safety, thereby addressing these barriers. The findings contribute both practically and theoretically to the more effective utilisation of human resources in Brazil. These insights are particularly valuable for stakeholders, including government bodies, managers, and academic institutions, in fostering gender equality and empowering women in the workforce. Full article

Since 1990, approximately 420 million hectares of forest have been lost worldwide due to land conversion for various uses, including agriculture, infrastructure development, urbanization, and other human activities. This study aims to investigate the critical drivers contributing to deforestation and forest degradation (DFD) in Ondo State, Nigeria, thereby identifying areas where REDD+ (Reducing Emissions from Deforestation and Forest Degradation) interventions could be most effective in reducing greenhouse gas emissions, particularly carbon dioxide (CO₂), which is released through forest loss and degradation. A questionnaire survey was used to obtain data from construction professionals such as architects, engineers, builders, quantity surveyors, and project managers. Collected data were analyzed using frequencies and percentages to report the background information of professionals, Mean Item Scores (MIS) to rank critical drivers of DFD, and Fuzzy Synthetic Evaluation (FSE) to identify the most critical drivers. FSE analysis revealed that DFD is primarily motivated by agricultural expansion (including cattle ranching and shifting cultivation) and infrastructure extension (particularly transportation networks and market and service infrastructure) among the proximate drivers. The analysis also identified demographic, economic, and policy and institutional factors as the most significant underlying drivers. The emphasis on agricultural expansion and infrastructure extension suggests that targeted interventions in these areas could significantly mitigate DFD in the study site under consideration. This may involve implementing stricter regulations and incentives to promote sustainable land use practices among farmers and landowners. Additionally, integrating environmental impact assessments into infrastructure projects can help minimize forest loss associated with road construction and urban expansion. This study introduces an innovative approach by applying the Geist and Lambin conceptual framework of ‘proximate causes and underlying driving forces’. It is among the pioneering studies conducted in the study area to comprehensively analyze the drivers contributing to DFD using these frameworks. Although conducted in Ondo State, Nigeria, the findings can be extrapolated to similar regions facing similar challenges of DFD worldwide. Full article