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A Quantitative Assessment of Supplier Service Quality Using AHP and SERVQUAL in
No full textThe selection and assessment of suppliers are essential for improving supply chain efficiency and fostering competitiveness. This study investigates the application of the Analytical Hierarchy Process (AHP) as a proficient method for assessing supplier service quality. AHP, a multi-criteria decision-making tool, provides a systematic and structured framework for evaluating the critical factors influencing supplier performance. The study employed the AHP model to evaluate supplier service quality in small and medium-sized enterprises (SMEs) to achieve this objective. The SERVQUAL framework consists of five dimensions—reliability, responsiveness, assurance, tangibles, and empathy accompanied by 20 sub-criteria, developed using the AHP technique. The analysis focused on evaluating the service quality of three distinct vendors, meticulously assessing their overall performance. The findings suggested that providers should emphasize reliability, responsiveness, certainty, and tangibles, while placing relatively less significance on empathy. Among the sub-criteria, delivering accurate services on the first attempt emerged as a significant issue for vendors. The AHP analysis indicates that Supplier A attained the highest performance ranking, followed by Supplier C and Supplier B. This study's findings offer actionable insights for decision-makers to effectively manage and enhance the aviation sector. By focusing on delivering exceptional services, suppliers can significantly improve customer satisfaction, aligning with the fundamental goal of supplier service excellence
Analysis of Delamination Behavior in Mode I under Thermal Environment Exposure in Adhesive Joints of Carbon-Epoxy Composite Materials
No full textThis study analyzes the delamination behavior in mode I, under static and fatigue loading, in adhesive joints on a composite material with an epoxy matrix and unidirectional carbon fiber reinforcement (CFRP). The samples were exposed in a climate chamber at 60°C and 70% relative humidity for different periods (no exposure, 1, 2, and 4 weeks). Subsequently, standardized DCB tests were performed to evaluate the effect of environmental aging on interlaminar fracture toughness and adhesive strength. After an initial static characterization, reference parameters for fatigue tests were defined, obtaining initiation (ΔG–N) and crack growth (G–da/dN) curves. The initiation data were analyzed using a Weibull probabilistic model. The results show a change in the epoxy adhesive behavior with exposure time, evidenced by a reduction in the fatigue limit and an increase in crack propagation rates
Mechanical performance of continuous fiber-reinforced thermoplastic composites for structural applications
No full textContinuous fiber-reinforced thermoplastic composites are emerging as an efficient structural alternative to traditional thermoset materials, thanks to their recyclability, good impact resistance, and suitability for continuous processing. In this work, a manufacturing system was developed based on the thermoplastic pultrusion of unidirectional polypropylene (PP) and carbon fiber (CF) tapes, followed by hot compression molding. This approach enables the production of structural profiles with high fiber alignment and good consolidation. Experimental characterization included tensile, flexural, and impact tests to assess the structural applicability of the manufactured profiles. The results demonstrate a balanced combination of stiffness, mechanical strength, and impact performance, confirming the potential of this system for functional applications in sectors such as mobility or infrastructure, where lightweight, efficient processing, and sustainability are key requirements
Fractional-Order Resilient Control for UAV–USV Cooperation under Actuator Constraints, Signal Attacks, and Wind Gusts
No full textThe paper presents a resilient dynamic adaptive event-triggered sliding mode control (DAET–SMC) framework for fractional-order delayed multi-agent systems under actuator saturation, stochastic disturbances, and cyber-attacks. Existing methods often fail to ensure containment and formation stability when multiple practical constraints coexist. The proposed approach leverages Riemann–Liouville fractional dynamics to capture system memory effects and integrates adaptive compensation to mitigate actuator faults, measurement attacks, and communication delays. Numerical simulations on a 16-agent network with one leader and fifteen followers show that all followers achieve containment within 20 s, with formation errors below 10−2m, while maintaining bounded control effort. Compared with conventional non-adaptive controllers, the proposed method demonstrates faster convergence, superior robustness, and resilience under combined disturbances, achieving up to 35% faster error convergence and maintaining control input within saturation limits. These results confirm the effectiveness of the DAET–SMC strategy for practical multi-agent coordination in uncertain and constrained environments.OPEN ACCESS Received: 30/10/2025 Accepted: 26/11/2025 Published: 23/01/202
A New Scalable Hybrid Model Approach of High-Dimensional Time Series Forecasting Applications
No full textThis study introduces a Lasso–Prophet hybrid framework developed to deal with the limitations and gap of Facebook’s Prophet model. The approach begins with Prophet’s decomposition of a time series into its fundamental components trend, seasonality, and holiday effects, and then applies Lasso regression to the residuals to capture additional structural patterns that fail to capture by the base model. This layered methodology boosts predictive accuracy by enabling the model to learn both systematic and irregular variations within temporal data by incorporating Lasso’s feature selection capability, the framework efficiently handles highdimensional datasets, retaining only the most informative predictors. The outcome is a hybrid model which achieves an optimal balance among interpretability, scalability, sparsity, and forecasting precision. Validation on simulated high-feature datasets and real-world electricity consumption data demonstrates that the Lasso–Prophet hybrid consistently outperforms the Prophet and other baseline models.OPEN ACCESS Received: 31/08/2025 Accepted: 17/11/2025 Published: 23/01/202
Comparative Study of Fuzzy Logic, P&O, Incremental Conductance, and Artificial Neural Network MPPT Methods in Fluctuating Irradiance
No full textPhotovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extraction under dynamic irradiance and temperature conditions, improving the efficiency of PV systems requires advanced Maximum Power Point Tracking (MPPT) techniques. To identify the most suitable technique that can be implemented practically, we conduct a comparative study in this paper between MPPT algorithms, namely Incremental Conductance (INC), Perturb and Observe (P&O), Fuzzy Logic (FL), and Artificial Neural Network (ANN). Using MATLAB/Simulink, our study was conducted under the same operating conditions, with a focus on efficiency, statistical analysis of robustness, and computational complexity. Our results show that the FL controller delivered the best overall performance, whose effectiveness depends on the accuracy of the rule base and scaling factors. It is characterized by a mean efficiency of 97.17%, a rapid response of 0.0585 s, minimal steady-state oscillations, and strong adaptability to environmental variations. The ANN-based approach achieves a mean efficiency of 94.91% and exhibits high performance at medium to high irradiance levels. However, its efficiency decreases significantly at low irradiance, resulting in reduced stability and increased deviation. INC and P&O achieve mean efficiencies of 95.20% and 95.15%, respectively. Moreover, due to their low computational cost, both techniques can be easily implemented. However, under rapidly changing conditions, they exhibit slower dynamics and more pronounced oscillations around the maximum power point, resulting in less stability.OPEN ACCESS Received: 01/08/2025 Accepted: 14/10/2025 Published: 23/01/202
Drill Bit Optimization Method Using Grey Clustering and Grey Correlation Analysis
No full textThe conventional approach to drill bit selection primarily relies on the performance records of bits used in adjacent wells, where the bestperforming bit in each formation is selected for the corresponding zone to be drilled. However, this method does not take into account the lithology and rock mechanical properties of all relevant wells, nor can it evaluate the adaptability of a particular bit type to different intervals. As a result, it fails to fully ensure an optimal match between the bit and the formation, thus exhibiting significant limitations. To address these issues, this paper proposes a bit optimization method based on grey clustering and grey correlation analysis. This method comprehensively considers the influence of rock mechanics parameters on formation drillability and quantitatively evaluates the similarity in drilling resistance between the target formation and previously drilled intervals using grey clustering. This approach breaks away from the traditional constraint of limited bit options for a specific formation grade. Instead, it screens all previously used bit types to construct a candidate bit library for the target zone. Subsequently, the grey correlation method is applied to assess the candidate bits using multiple indicators that reflect bit performance. This enables the optimization of bit types for various target zones. Field applications demonstrate that the new bit selection method effectively improves upon the conventional practices by enhancing the flexibility and scientific basis of bit selection, and has yielded favorable results in actual drilling operations.OPEN ACCESS Received: 28/07/2025 Accepted: 16/10/2025 Published: 23/01/202
