Search results

Awarding contracts based solely on the lowest price is unsuitable for every project. Consequently, most procurement systems in developed countries have progressed to the multicriteria selection practices (MSPs) for tender evaluation. MSPs consider a range of quality measures, such as completion time, life cycle cost, functional characteristics, environmental impact and innovation, alongside bid price. This study examines the prevailing MSPs in Sri Lankan public tender evaluations to enhance the effectiveness of the local tender evaluation process.

A desk study approach was employed to collect bidding documents, resulting in the identification of 66 documents. A systematic screening process was then applied to identify those bidding documents that incorporated MSPs. Subsequently, content analysis was conducted to determine the common features of the functions used in MSPs.

The study identified six primary functions related to MSPs incorporated in the bidding documents to procure building and substation projects. Three functions follow the price-to-quality method, while the remaining three follow the quality-to-price method. Among these identified functions, four functions employ objective evaluation criteria, such as thickness, capacity and operational loss. The other two functions utilize subjective evaluation criteria, such as the project’s design and technical specifications. Contract awarding will be based on either the highest score or the lowest bid, depending on the function type.

This study’s originality lies in exploring MSPs in the Sri Lankan public tender evaluation process and in disclosing their characteristics to promote the MSPs in Sri Lanka and developing countries.

The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system. Through voiceprint technology, the sounds emitted by the transformer can be monitored in real-time, thereby achieving real-time monitoring of the transformer’s operational status. However, the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer, severely impacting the accuracy and reliability of voiceprint identification. Therefore, effective preprocessing steps are required to identify and separate the sound signals of transformer operation, which is a prerequisite for subsequent analysis.

This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique (REPET) algorithm to separate and denoise the transformer operation sound signals. By analyzing the Short-Time Fourier Transform (STFT) amplitude spectrum, the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold, effectively distinguishing and extracting stable background signals from transient foreground events. The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period, then constructs a repeating segment model. Through comparison with the amplitude spectrum of the original signal, repeating patterns are extracted and a soft time-frequency mask is generated.

After adaptive thresholding processing, the target signal is separated. Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.

A REPET method with adaptive threshold is proposed, which adopts the dynamic threshold adjustment mechanism, adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal. It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

This paper aims to investigate the impact of combining grain-oriented electrical steel (GOES) grades on specific iron losses and the flux density distribution within a single-phase magnetic core.

This paper presents the results of finite-element method (FEM) simulations investigating the impact of mixing two different GOES grades on losses of a single-phase magnetic core. The authors used different models: a 3D model with a highly detailed geometry including both saturation and anisotropy, as well as a simplified 2D model to save computation time. The behavior of the flux distribution in the mixed magnetic core is analyzed. Finally, the results from the numerical simulations are compared with experimental results.

The specific iron losses of a mixed magnetic core exhibit a nonlinear decrease with respect to the GOES grade with the lowest losses. Analyzing the magnetic core behavior using 2D and 3D FEM shows that the rolling direction of the GOES grades plays a critical role on the nonlinearity variation of the specific losses.

The novelty of this research lies in achieving an optimum trade-off between the manufacturing cost and the core efficiency by combining conventional and high-performance GOES grade in a single-phase magnetic core.

The impact of COVID-19 on the lives of people and businesses across the globe was devastating. While governments across the world had undertaken a slew of measures to control the spread of the COVID-19 virus within their geography, many of these measures had long and unintended consequences. The restrictions imposed by the governments on the movement of people and goods across the world brought supply chains to a grinding halt. This study identifies the cascading effects of supply chain disruptions (SCDs) on the energy sector and thereby on the security of supply of energy from a European Union perspective. Since these systems are closely integrated and the impact of COVID-19 needs to be analysed at a much broader level, this study uses a systems-thinking approach to study the effect of SCDs on energy services. The study develops a causal loop model to gain further insight into how SCDs caused by COVID-19 affected the coping capabilities of society and how critical services were affected. Furthermore, the study puts forth certain policy recommendations for both businesses and governments to prepare for and protect against a similar situation in the future.

This paper aims to compare the aging behavior of water-based coatings and solvent-based coatings in sulfuric acid environments and to discuss the related mechanism.

A sulfuric acid solution with a concentration of 5 Wt.% was selected for immersion test at 23°C. The failure behavior of the coating was studied by combining the transformation rules of the macroscopic morphology and basic properties with the results of electrochemical impedance spectrum analysis.

The results showed that the surface smoothness of the water-based coating was lower than that of the solvent-based coating. The glossiness, thickness and hardness of the water-based coating exhibited more significant changes. The electrochemical test also indicated that the water-based coating was infiltrated by a large number of corrosive media, which may have induced corrosion under the coating. In contrast, the solvent-based coating showed good shielding properties, but the adhesion was seriously affected by the corrosive medium.

This work clarified the difference of failure behavior and mechanism between water-based coatings and solvent-based coatings in acidic environment and provided a theoretical basis for the selection and mechanism research of anticorrosive coatings.

This study investigates the potential effectiveness of ChatGPT in enhancing the academic writing skills of Saudi EFL undergraduate students. It also examines the challenges associated with its use and suggests effective ways to address them in the education sector.

The study employed a sequential mixed-methods approach, which involved distributing questionnaires to gather data from students, followed by conducting semi-structured interviews with a purposeful selection of eight students and six teachers.

The findings revealed that students were generally satisfied with the effectiveness of ChatGPT in enhancing their academic writing skills. However, they also pinpointed some challenges associated with using ChatGPT, including plagiarism, overreliance, inadequate documentation, threats to academic integrity, and inaccurate information. To alleviate these challenges, effective strategies include deploying detection tools, equipping students and educators with training sessions, and revisiting academic policies and assessment methods. It is recommended that ChatGPT be used responsibly as an assistant tool, in conjunction with students' ideas and teachers' feedback. This approach can significantly enhance students' writing skills and facilitate completing their research projects and assignments.

ChatGPT can be a valuable tool in the educational landscape, but it is essential to use it judiciously. Therefore, teachers' effective integration of ChatGPT into their classrooms can significantly enhance students' writing abilities and streamline their research process.

This study contributes to recent AI-based research and provides practical insights on the responsible integration of ChatGPT into education while addressing potential challenges.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.

The paper's goal is to examine and illustrate the useful uses of submodeling in finite element modeling for topology optimization and stress analysis. The goal of the study is to demonstrate how submodeling – more especially, a 1D approach – can reliably and effectively produce ideal solutions for challenging structural issues. The paper aims to demonstrate the usefulness of submodeling in obtaining converged solutions for stress analysis and optimized geometry for improved fatigue life by studying a cantilever beam case and using beam formulations. In order to guarantee the precision and dependability of the optimization process, the developed approach will also be validated through experimental testing, such as 3-point bending tests and 3D printing. Using 3D finite element models, the 1D submodeling approach is further validated in the final step, showing a strong correlation with experimental data for deflection calculations.

The authors conducted a literature review to understand the existing research on submodeling and its practical applications in finite element modeling. They selected a cantilever beam case as a test subject to demonstrate stress analysis and topology optimization through submodeling. They developed a 1D submodeling approach to streamline the optimization process and ensure result validity. The authors utilized beam formulations to optimize and validate the outcomes of the submodeling approach. They 3D-printed the optimized models and subjected them to a 3-point bending test to confirm the accuracy of the developed approach. They employed 3D finite element models for submodeling to validate the 1D approach, focusing on specific finite elements for deflection calculations and analyzed the results to demonstrate a strong correlation between the theoretical models and experimental data, showcasing the effectiveness of the submodeling methodology in achieving optimal solutions efficiently and accurately.

The findings of the paper are as follows: 1. The use of submodeling, specifically a 1D submodeling approach, proved to be effective in achieving optimal solutions more efficiently and accurately in finite element modeling. 2. The study conducted on a cantilever beam case demonstrated successful stress analysis and topology optimization through submodeling, resulting in optimized geometry for enhanced fatigue life. 3. Beam formulations were utilized to optimize and validate the outcomes of the submodeling approach, leading to the successful 3D printing and testing of the optimized models through a 3-point bending test. 4. Experimental results confirmed the accuracy and validity of the developed submodeling approach in streamlining the optimization process. 5. The use of 3D finite element models for submodeling further validated the 1D approach, with specific finite elements showing a strong correlation with experimental data in deflection calculations. Overall, the findings highlight the effectiveness of submodeling techniques in achieving optimal solutions and validating results in finite element modeling, stress analysis and optimization processes.

The originality and value of the paper lie in its innovative approach to utilizing submodeling techniques in finite element modeling for structural analysis and optimization. By focusing on the reduction of finite element models and the creation of smaller, more manageable models through submodeling, the paper offers designers a more efficient and accurate way to achieve optimal solutions for complex problems. The study's use of a cantilever beam case to demonstrate stress analysis and topology optimization showcases the practical applications of submodeling in real-world scenarios. The development of a 1D submodeling approach, along with the utilization of beam formulations and 3D printing for experimental validation, adds a novel dimension to the research. Furthermore, the paper's integration of 1D and 3D submodeling techniques for deflection calculations and validation highlights the thoroughness and rigor of the study. The strong correlation between the finite element models and experimental data underscores the reliability and accuracy of the developed approach. Overall, the originality and value of this paper lie in its comprehensive exploration of submodeling techniques, its practical applications in structural analysis and optimization and its successful validation through experimental testing.

This paper aims to obtain a symmetrical step-down topology with lower equivalent capacity and wider step-down range under the condition of the same output. Two new symmetrical step-down topologies of star-connected autotransformers are proposed in this paper. Taking the equivalent capacity as the main parameter, the obtained topologies are modeled and analyzed in detail.

This paper adopts the research methods of design, modeling, analysis and simulation verification. First, the star-connected autotransformer is redesigned according to the design objective of symmetrical step-down topology. In addition, the mathematical model of two topologies is established and a detailed theoretical analysis is carried out. Finally, the theoretical results are verified by simulation.

Two symmetrical star-connected autotransformer step-down topologies are designed, the winding configurations of the corresponding topology are presented, the step-down ranges of these three topologies are calculated and the influence of step-down ratio on the equivalent capacity of autotransformer are analyzed. Through analysis, the target step-down topologies are obtained when the step-down ratio is [1.1, 5.4] and [1.1, 1.9] respectively.

Because the selected research object is only a star-connected autotransformer, the research results may lack generality. Therefore, researchers are encouraged to further study the topologies of other autotransformers.

This paper includes the implications of the step-down ratio on the equivalent capacity of autotransformers and the configuration of transformer windings.

The topologies designed in this paper enable star-connected autotransformer in the 12-pulse rectifier to be applied in step-down circumstances rather than situations of harmonic reduction only. At the same time, this paper provides a way that can be used to redesign the autotransformer in other multi-pulse rectifier systems, so that those transformers can be used in voltage regulation.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.