Search Results (314)

Background: Selecting enterprise resource planning (ERP) systems for complex port environments is a significant challenge. This study addresses a key research gap by identifying and prioritizing the critical factors for ERP selection within the strategic context of United Arab Emirates (UAE) ports, which function as vital hubs in global trade. Methods: A hybrid methodology was employed, first using the Fuzzy Delphi Method (FDM) to validate thirteen challenges with five industry experts. Subsequently, the Relative Importance Index (RII) was used to rank these challenges based on survey data from 48 UAE port professionals. Results: The analysis revealed “Cybersecurity concerns” as the highest-ranked challenge (RII = 0.896), followed by “Engagement with external stakeholders” (RII = 0.842), and both “Process optimization” and “Technical capabilities” (RII = 0.808). Notably, factors traditionally seen as critical in other sectors, such as “Organizational readiness” (RII = 0.746), were ranked significantly lower. Conclusions: The findings indicate a strategic shift in ERP selection priorities toward digital resilience and external integration rather than internal organizational factors. This research provides a sector-specific decision-support framework and offers actionable insights for port authorities, vendors, and policymakers to enhance ERP implementation in the maritime industry. Full article

With the rapid development of the tourism industry, scientific evaluation of tourism resources is crucial to realize sustainable development. Especially how to quantify resource advantages in international tourism cities has become an important basis for tourism planning and policy making. However, the limitations of traditional evaluation methods in the allocation of indicator weights and nonlinear data processing make it difficult to meet the development needs of international tourism cities. Therefore, this study takes Guilin, an international tourist city, as the research object and proposes a hybrid framework integrating fuzzy neural network (FNN) and analytic hierarchy process-fuzzy comprehensive evaluation (AHP-FCE). Based on 800 questionnaire data covering tourists, practitioners, and local residents, the study constructed a multilevel evaluation system (containing 12 specific indexes in the three dimensions of nature, service, and culture) using the Delphi method of expert interviews. It is found that AHP-FCE can effectively analyze the hierarchical relationship of evaluation indexes, but it is easily affected by the subjective judgment of experts. In contrast, FNN can effectively improve evaluation accuracy through the adaptive learning mechanism, and it especially shows significant advantages in dealing with tourists’ perception data. The empirical analysis shows that Guilin has obvious room for improvement in “environmental friendliness” and “cultural communication effectiveness”. The integration framework proposed in this study aims to enhance the scientific validity and accuracy of the assessment results, and provides reference and inspiration for the sustainable development of Guilin international tourism destination. Full article

Brent oil holds a significant position in the global energy market, as oil prices in many regions are indexed to it. Therefore, forecasting the future price of Brent oil is of great importance. In recent years, artificial intelligence techniques have been widely applied in modeling and prediction tasks. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS), a well-established AI approach, was employed for the time-series forecasting of Brent oil prices. To ensure effective learning and improve prediction accuracy, ANFIS was trained using nine different metaheuristic algorithms: Artificial Bee Colony (ABC), Selfish Herd Optimizer (SHO), Biogeography-Based Optimization (BBO), Multi-Verse Optimizer (MVO), Teaching–Learning-Based Optimization (TLBO), Cuckoo Search (CS), Moth Flame Optimization (MFO), Marine Predator Algorithm (MPA), and Flower Pollination Algorithm (FPA). Symmetric training procedures were applied across all algorithms to ensure fair and consistent evaluation. The analyses were conducted on the lowest and highest daily, weekly, and monthly Brent oil prices. Mean squared error (MSE) was used as the primary performance metric. The results showed that all algorithms achieved effective prediction performance. Among them, BBO and TLBO demonstrated superior accuracy and stability, particularly in handling the complexities of Brent oil forecasting. This study contributes to the literature by combining ANFIS and metaheuristics within a symmetric framework of experimentation and evaluation. 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

The effective segmentation of cloud and cloud shadow is an important issue in remote sensing image processing, which is of great significance for surface feature extraction, climate detection, atmospheric correction, etc. However, the characteristics of cloud and cloud shadow remote sensing images are complex. There is often noise, the cloud distribution is diverse and irregular, and the boundary information is fuzzy and vulnerable to background interference, which makes it difficult to extract and segment its features accurately. To solve the above problems, this paper proposes a semantic segmentation network, EDFF-Unet, based on the Unet model, which integrates semantic and edge features. The model comprises a semantic segmentation sub-network and an edge detection sub-network. The attention mechanism and spatial pyramid pooling module are embedded in the semantic segmentation sub-network to strengthen the acquisition of practical features, suppress noise and irrelevant information, and use the edge detection sub-network to obtain more accurate contour features. Finally, the final result is obtained by fusing the two features through the feature fusion module. The model achieved superior performance on the GF1_WHU dataset, leading the suboptimal model by 0.67% and reaching 92.87% on the MIoU index. Full article

The concept of green supply chain management (GSCM) describes how to reduce the negative impact of the supply chain on the environment while balancing the economic and social benefits of a company being in the supply chain. Selecting the optimal multi-dimensional GSCM scheme, a typical multi-criteria decision-making (MCDM) problem, is a crucial step in implementing the GSCM concept. Therefore, this paper constructs a multi-dimensional GSCM index system for the comprehensive analysis of the important influencing factors of GSCM. Then, cross-entropy combining the interval type-2 trapezoidal fuzzy set (IT2TFS) is adopted to determine the weight distribution of GSCM indices, and a hybrid MCDM method integrating the IT2TFS prospect–regret method is proposed to analyze the psychological behaviors of decision makers who are selecting the best GSCM scheme. Moreover, the case study, comparative analysis, and sensitivity analysis are presented to verify the effectiveness and reasonableness of the proposed MCDM method. The results affirm the validity of the proposed MCDM method, with A₄ identified as the optimal GSCM scheme, demonstrating its effectiveness and applicability in MCDM problems. Full article

To effectively determine the influences of fuzzy uncertainties on structural systems in engineering, according to the fuzzy failure probability (FFP) model, which is based on the probabilistic perspective, the importance measure (IM) technique is extended to fuzzy uncertain structural systems. A novel IM framework, i.e., the fuzzy-failure-probability-based IM (FFP-IM), is established. By transforming the fuzzy failure probability into the expected value of the function for the failure domain, the proposed FFP-IM index can be represented as the variance-based IM of that index function. Then, an efficient solution algorithm for the proposed FFP-IM index is established based on the state-dependent parameter method. Ultimately, the Ishigami function, alongside three practical engineering examples, validates the proposed FFP-IM’s rationality and applicability. Furthermore, these examples illustrate the solution algorithm’s superior computational efficiency and accuracy. Full article

The Takagi-Sugeno (T-S) fuzzy control is a nonlinear method that uses a combination of linear controllers as its control law. This method has been applied in various fields of scientific research: buck converters, biomedicine, civil engineering, etc. To the best of the authors’ knowledge, although works on traditional fuzzy control and optical interferometry have already been published, this is the first time that T-S fuzzy (specifically) is applied to demodulate interferometry signals. Through a proof-of-concept experiment, the paper describes the fusion of an open-loop interferometer with an external closed-loop digital observer based on T-S fuzzy (both simple and inexpensive), which actuates like a closed-loop interferometer (but without its drawbacks). The observer design is based on stability conditions using linear matrix inequalities (LMIs) solutions. The system is maintained at the optimal ${90}^{\circ }$ operation point (compensating for environmental drifts) and enables the demodulation of optical phase signals with low modulation index. Simulations and measurements were performed by using a Michelson interferometer, verifying that the method demodulates signals up to $\pi /2$ rad amplitudes and higher than 100 Hz frequencies (with maximum error of 0.45%). When compared to the important arc tangent method, both presented the same frequency response for the test PZT actuator. Full article

In recent years, the disruption of the prefabricated building supply chain has led to increased construction period delays and cost overruns, limiting the development and popularization of prefabricated buildings in China. Therefore, this study established a vulnerability evaluation index system for the prefabricated building supply chain using the driving force–pressure–state–impact–response (DPSIR) framework. We employed the intuitionistic fuzzy analytic hierarchy process (IFAHP), the projection pursuit (PP) model, and variable weight theory to determine the indicator weights. The IFAHP was utilized to reduce the subjectivity in weight assignment and to obtain the degree of membership, non-membership, and hesitation of experts in evaluating the importance of indicators. The PP model was used to determine objective weights based on the structure of the evaluation data, and variable weight theory was applied to integrate subjective and objective weights according to management needs. We utilized Set Pair Analysis (SPA) to establish a vulnerability evaluation model for the building supply chain, treating evaluation data and evaluation levels as a set pair. By analyzing the degree of identity, difference, and opposition of the set pair, we assessed and predicted the vulnerability of the building supply chain. Taking the Taohua Shantytown project in Nanchang as a case study, the results showed that the primary index with the greatest influence on the vulnerability of the prefabricated building supply chain was the driving force, with a weight of 0.2692, followed by the secondary indices of market demand and policy support, with weights of 0.0753 and 0.0719, respectively. The project’s average vulnerability rating was moderate (Level III), and it showed an improvement trend. During the project’s implementation, the total cost overrun of the prefabricated building supply chain was controlled within 5% of the budget, the construction period delay did not exceed 7% of the plan, and the rate of production safety accidents was below the industry average. The results demonstrated that the vulnerability assessment method for the prefabricated building supply chain based on SPA comprehensively and objectively reflected the vulnerability of the supply chain. It is suggested to improve the transparency and flexibility of the supply chain, strengthen daily management within the supply chain, and enhance collaboration with supply chain partners to reduce vulnerability. Full article

Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) holds significant importance in vehicle-to-everything (V2X) communications, with its main advantages being outstanding spectral efficiency and strong interference resistance. However, the existing OFDM-IM systems in vehicular networks overlook actual vehicular network channels and the impact of scatterers, thus failing to accurately reflect the system performance. Moreover, these systems focus solely on the bit error rate (BER) and ignore user requirements for low energy consumption and high spectral efficiency. To address these issues, we propose a user demand- and scenario-adaptive OFDM-IM method that optimizes the OFDM-IM index parameter by considering the spectral efficiency, BER, and energy consumption. Firstly, considering non-line-of-sight components and roadside reflectors, we establish a vehicle-to-vehicle (V2V) communication channel model for straight road scenarios. Then, we construct a transmission framework for vehicular network communication using OFDM-IM. Specifically, we develop an energy efficiency maximization formula, in which fuzzy logic is used to adjust the weights of the three performance indicators to meet various environmental and user requirements. In detail, we discuss the minimum signal-to-noise ratio (SNR) required for OFDM-IM to achieve a lower BER than traditional OFDM in various vehicular communication scenarios. Thus, we can make appropriate choices based on the robustness of the simulation results. The simulation results presented in this paper indicate our method’s effectiveness in enhancing the system’s reliability, efficiency, and flexibility. Full article

Hospital projects or healthcare projects (HPs) are major contributors of greenhouse gas emissions, high energy consumption, and environmental pollution. These problems serve as a clarion call for the development of a standardized list of metrics that define the triple bottom line of sustainability performance, track sustainability progress, and allow for essential comparisons or benchmarking of HPs. Through a comprehensive literature review, a Delphi survey with experts, and a fuzzy synthetic evaluation, the ten most suitable key performance indicators (KPIs) were identified, categorized, and modeled into a normalized HP success index (HPSI). The HPSI comprises relatively weighted (in brackets) KPI categories, namely, ‘project prosecution performance’ (0.287), ‘project purpose performance’ (0.353), and ‘project people performance’ (0.360), for evaluating and comparing success levels of HPs. The HPSI provides understanding on the relative contribution levels of the standardized KPIs to achieve predictable life cycle success levels of HPs. Ultimately, it can be used by policymakers and practitioners to inform life cycle decision-making (e.g., resource/effort allocation toward important contributors to success) in HPs. Future studies should seek to develop a computerized HPSI system, by adding quantitative indicators and ranges of KPIs to current findings, to objectively and practically assess, monitor, benchmark, and improve HP success across the life cycle. Full article

In recent years, policymakers have increasingly focused on environmental quality and economic growth. While various factors influence green growth, two important factors that have been overlooked in research are the global innovation index and the fragile states index. This study employs novel methods, such as necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA), to analyze green growth across 90 countries in 2019, surpassing traditional regression techniques. The NCA model identifies essential variables for green growth, revealing that global innovation, institutional quality, human development, and globalization are crucial conditions. Conversely, the fsQCA model offers intricate solutions by combining key variables for green growth. It presents five solutions for achieving high green growth, each tailored to specific groups of countries. For instance, Solution 1, with a consistency of 0.96%, suggests that increased consumption of renewable energy, greater trade openness, and reduced fragility in states lead to higher green growth in countries like Denmark and Austria. Thus, policymakers can foster both economic growth and environmental improvement by promoting renewable energy adoption, enhancing global trade management, and strengthening institutional quality and political stability. Full article

There is a close connection between university student entrepreneurship programs and sustainable development, which are mutually reinforcing: university student entrepreneurship programs provide innovation and vitality for sustainable development, while concepts related to sustainable development can guide the development direction of university student entrepreneurship programs. College students are the driving force of innovation and entrepreneurship. In view of the problems of the failure rate of college students’ entrepreneurial projects, this article constructs a fuzzy comprehensive evaluation model based on the fuzzy comprehensive evaluation method. Then, this study uses the hierarchical analysis method to clarify the comprehensive evaluation indexes affecting the maturity of college students’ entrepreneurial projects and takes a student entrepreneurial project of Qingdao University of Technology as an example. Ultimately, specific suggestions are offered to optimize the maturity of college students’ entrepreneurial projects based on the evaluation results so as to improve the probability of success of college students’ entrepreneurship. Great importance is placed on the quality of university entrepreneurship projects and the sustainability of society in this study. Full article

Mass building projects play a key role in the economic prosperity of any country. Furthermore, these projects are among the main drivers of environmental and social problems. In recent years, with the spread of the concept of sustainable development in the life cycle of construction projects and the dynamic and eventful nature of these projects, the issue of risk management in the sustainable construction industry has received more and more attention among researchers. The construction industry, like other industries, faces various risks. Therefore, it is crucial to identify and evaluate risks in mass construction projects due to the high volume of work. In this study, an integrated model based on fuzzy Shannon entropy and fuzzy EDAS is proposed for risk assessment in large-scale building projects. Initially, by reviewing related articles, 66 effective sub-indicators are identified and classified into 18 risk categories, including 6 external risks and 12 internal risks. Subsequently, a questionnaire is designed to assess the three factors of detection, probability of occurrence, and severity risks for each risk index. This questionnaire distributes to 15 mass production companies in the construction field in Isfahan. The fuzzy Shannon entropy method is then applied to determine the weight of risk factors. The weights of each factor, detection, probability of occurrence, and severity, are calculated as 0.386, 0.342, and 0.273, respectively. These weights are used in the fuzzy EDAS method to prioritize the identified risks in mass-building projects. The results of the fuzzy EDAS method determined the three most critical risks: “inflation rate volatility”, “import/export restrictions”, and “unforeseen climatic conditions”. Additionally, three low-risk sub-indicators are obtained: “limitation on working hours”, “collapse of the structure”, and “unpredictable fire”. Full article

The increase in tourism economic benefits is the most common purpose along the Mediterranean coastal regions but, very often, conflicts of interest arise between short-term benefits and long-term conservation goals. This is particularly the case of Albania, a very popular emerging “Sun, Sea and Sand” (3S) destination characterized by massive fluxes of national/international visitors during the summer period. Among beach users’ preferences, global studies show that five parameters of greater importance stand out from the rest, i.e., safety, facilities, water quality, no litter, and scenery, and the latter is the main concern of this study. Albania is well known for its outstanding natural coastal beauty which was assessed at 40 sites by using the Coastal Scenic Evaluation System (CSES) method. Based on the evaluation of 26 physical/human parameters and using weighting matrix parameters and fuzzy logic mathematics, the technique enables one to obtain an Evaluation Index (D) that allows one to classify each investigated site into five scenic classes, from Class I (extremely attractive natural sites; D ≥ 0.85) to Class V (very unattractive developed urban/industrial sites; D < 0.00). Pragmatically, the higher the “D” value is, the better the site scenery is. After a long process of field testing along the whole Albanian coastline (ca. 523 km in length), selected sites were chosen in rural/remote environments (22), villages (6), and urban (4) and resort areas (8) to reflect the Albanian coastal typicity and characterize the scenic impact of human activities. Most sites belonged to Class III (14), Class IV (13), Class II (8), and Class I (1). Several sites could be upgraded to Class I or Class II with slight management efforts, e.g., by carrying out cleaning operations or by reducing intrusive beach facilities. Full article