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This note presents a method of teaching accounting problems involving the use of the effective interest method such as bonds, notes, capital leases, and installment sales. The method is conceptually sound and simpler than the traditional method found in current textbooks and stimulates student interest by focusing on the economics of the transaction and relating it to real-life examples.

To assess its pedagogical efficacy, the method was tested in the introductory and intermediate accounting classes. In both courses, the results indicate that students' test scores are significantly higher under the new method than the traditional method. It is hoped that this evidence of the superiority of the new method in a classroom environment will spur its adoption by instructors and textbook writers.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

The use of additive manufacturing in many branches of industry is increasing significantly because of its many advantages, such as being able to produce complex parts that cannot be produced by classical methods, using fewer materials, easing the supply chain with on-site production, being able to produce with all kinds of materials and producing lighter parts. The binder jetting technique, one of the additive manufacturing methods researched within the scope of this work, is predicted to be the additive manufacturing method that will grow the most in the next decade, according to many economic reports. Although additive manufacturing methods have many advantages, they can be slower than classical manufacturing methods regarding production speed. For this reason, this study aims to increase the manufacturing speed in the binder jetting method.

Adaptive slicing and variable binder amount algorithm (VBAA) were used to increase manufacturing speed in binder jetting. Taguchi method was used to optimize the layer thickness and saturation ratio in VBAA. According to the Taguchi experimental design, 27 samples were produced in nine different conditions, three replicates each. The width of the samples in their raw form was measured. Afterward, the samples were sintered at 1,500 °C for 2 h. After sintering, surface roughness and density tests were performed. Therefore, the methods used have been proven to be successful. In addition, measurement possibilities with image processing were investigated to make surface roughness measurements more accessible and more economical.

As a result of the tests, the optimum printing condition was decided to be 180–250 µm for layer thickness and 50% for saturation. A separate test sample was then designed to implement adaptive slicing. This test sample was produced in three pieces: adaptive (180–250 µm), thin layer (180 µm) and thick layer (250 µm) with the determined parameters. The roughness values of the adaptive sliced sample and the thin layer sample were similar and better than the thick layer sample. A similar result was obtained using 12.31% fewer layers in the adaptive sample than in the thin layer sample.

The use of adaptive slicing in binder jetting has become more efficient. In this way, it will increase the use of adaptive slicing in binder jetting. In addition, a cheap and straightforward image processing method has been developed to calculate the surface roughness of the parts.

The purpose of this paper is to present a new approach to optimizing the design of 3D magnetic circuits. This approach is based on topology optimization, where derivative calculations are performed using the continuous adjoint method. Thus, the continuous adjoint method for magnetostatics has to be developed in 3D and has to be combined with penalization, filtering and homotopy approaches to provide an efficient optimization code.

To provide this new topology optimization code, this study starts from 2D magnetostatic results to perform the sensitivity analysis, and this approach is extended to 3D. From this sensitivity analysis, the continuous adjoint method is derived to compute the gradient of an objective function of a 3D topological optimization design problem. From this result, this design problem is discretized and can then be solved by finite element software. Thus, by adding the solid isotropic material with penalization (SIMP) penalization approach and developing a homotopy-based optimization algorithm, an interesting means for designing 3D magnetic circuits is provided.

In this paper, the 3D continuous adjoint method for magnetostatic problems involving an objective least-squares function is presented. Based on 2D results, new theoretical results for developing sensitivity analysis in 3D taking into account different parameters including the ferromagnetic material, the current density and the magnetization are provided. Then, by discretizing, filtering and penalizing using SIMP approaches, a topology optimization code has been derived to address only the ferromagnetic material parameters. Based on this efficient gradient computation method, a homotopy-based optimization algorithm for solving large-scale 3D design problems is developed.

In this paper, an approach based on topology optimization to solve 3D magnetostatic design problems when an objective least-squares function is involved is proposed. This approach is based on the continuous adjoint method derived for 3D magnetostatic design problems. The effectiveness of this topology optimization code is demonstrated by solving the design of a 3D magnetic circuit with up to 100,000 design variables.

This study aims to treat a novel system of Volterra integro-differential equations with multiple delays and variable bounds, constituting a generic numerical method based on the matrix equation and a combinatoric-parametric Charlier polynomials. The proposed method utilizes these polynomials for the matrix relations at the collocation points.

Thanks to the combinatorial eligibility of the method, the functional terms can be transformed into the generic matrix relations with low dimensions, and their resulting matrix equation. The obtained solutions are tested with regard to the parametric behaviour of the polynomials with $\alpha$, taking into account the condition number of an outcome matrix of the method. Residual error estimation improves those solutions without using any external method. A calculation of the residual error bound is also fulfilled.

All computations are carried out by a special programming module. The accuracy and productivity of the method are scrutinized via numerical and graphical results. Based on the discussions, one can point out that the method is very proper to solve a system in question.

This paper introduces a generic computational numerical method containing the matrix expansions of the combinatoric Charlier polynomials, in order to treat the system of Volterra integro-differential equations with multiple delays and variable bounds. Thus, the method enables to evaluate stiff differential and integral parts of the system in question. That is, these parts generates two novel components in terms of unknown terms with both differentiated and delay arguments. A rigorous error analysis is deployed via the residual function. Four benchmark problems are solved and interpreted. Their graphical and numerical results validate accuracy and efficiency of the proposed method. In fact, a generic method is, thereby, provided into the literature.

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 research investigates the impact of concrete design methods on performance, emphasizing environmental sustainability. The study compares the modified Bolomey method and Abrams’ law in designing concretes. Significant differences in cement consumption and subsequent CO2 emissions are revealed. The research advocates for a comprehensive life cycle assessment, considering factors like compressive strength, carbonation resistance, CO2 emissions, and cost. The analysis underscores the importance of evaluating concrete not solely based on strength but also environmental impact. The study concludes that a multicriteria approach, considering the entire life cycle, is essential for sustainable concrete design, addressing durability, environmental concerns, and economic factors.

The study employed a comprehensive design and methodology approach, involving the formulation and testing of 20 mixed concretes with strengths ranging from 25 MPa to 45 MPa. Two distinct design methods, the modified Bolomey method (three equations method) and Abrams’ law, were utilized to calculate concrete compositions. Laboratory experiments were conducted to validate the computational models, and subsequent analyses focused on assessing differences in cement consumption, compressive strength, CO2 emissions, and concrete resistance to carbonation. The research adopted a multidisciplinary perspective, integrating theoretical analysis, laboratory testing, and life cycle assessment to evaluate concrete performance and sustainability.

Conclusion from the study includes substantial variations (56%–112%) in cement content, depending on the calculation method. Abrams' law proves optimal for compressive strength (30 MPa–45 MPa), while the three equations method yields higher actual strength (30%–51%). Abrams' law demonstrates optimal cement use, but concrete designed with the three equations method exhibits superior resistance to aggressive environments. Cement content exceeding 450 kg/m³ is undesirable. Concrete designed with Abrams' law is economically favorable (12%–30% lower costs). The three equations method results in higher CO2 emissions (38–83%), emphasizing the need for life cycle assessment.

This study’s originality lies in its holistic evaluation of concrete design methods, considering environmental impact, compressive strength, and cost across a comprehensive life cycle. The comparison of the traditional Abrams' law and the three equations method, along with detailed laboratory tests, contributes novel insights into optimal cement use and concrete performance. The findings underscore the importance of a multicriteria approach, emphasizing sustainability and economic viability. The research provides valuable guidance for engineers and policymakers seeking environmentally conscious and economically efficient concrete design strategies, addressing a critical gap in the field of construction materials and contributing to sustainable infrastructure development.

This study explores clothing disposal in a developing economy. It focuses on how consumers dispose of clothing and what motives influence them to use a specific disposal method.

Semi-structured interviews, a qualitative research method, were conducted with a purposive sample of 27 participants from diverse demographic backgrounds within the developing economy of Ethiopia. The interviews were coded and analysed using thematic analysis to identify categories and themes.

The findings reveal various clothing disposal methods, such as bartering, donating, gifting, repurposing and reusing, and discarding. Different motives drive consumers to use these methods, including economic benefits, altruism, and convenience.

The study bridges an important knowledge gap in literature mainly on three aspects, as highlighted by previous research. Theoretically, in addition to proposing a different perspective of bartering as a disposal method, the study investigates the motives behind clothing disposal methods from diverse consumer groups and proposes a conceptual framework to illustrate the link between clothing disposal methods and motives. Methodologically, the study addresses the call for a more inclusive and diverse sample, considering gender and varied socio-economic groups. Contextually, while previous research has focused on developed economies, this study explains clothing disposal methods and motives from a developing economy context, specifically Ethiopia.

The purpose of the project is to explore the biosoftening of raw areca nut fibers using two different biological retting methods and assess their impact on fiber properties for improved spinning. The study aims to contribute to the fashion industry’s shift toward sustainability.

The project involves collecting raw brown areca shells, subjecting them to two retting methods (stagnant water retting and changing water daily retting) and then extracting and drying the fibers. Various physical and chemical properties of the fibers are measured to evaluate their suitability for spinning.

The stagnant water retting method, especially the fibers obtained on the second day, showed improved properties in terms of fiber strength, elongation, fineness and cellulose content, making them suitable for spinning applications. The method also resulted in better moisture regain.

The study focused on two retting methods and a limited timeframe. Further research could explore additional techniques and durations. The labor-intensive nature of the daily changing water retting method may have implications for scalability.

The project demonstrates a cost-effective and sustainable method for converting agricultural waste (areca nut husks) into valuable fibers suitable for various end users.

The research supports the fashion industry’s sustainability efforts by promoting the use of eco-friendly natural fibers, potentially benefiting rural farming communities.

The project highlights the innovative use of areca nut fibers and their potential to contribute to sustainable fashion. The stagnant water retting method is presented as a reliable and effective approach for improving fiber properties. Additionally, all fiber testing was exclusively conducted at the South India Textile Research Association (SITRA), with sponsorship from the industry and support from the Ministry of Textiles, Government of India.

The primary objective of this research paper was to examine the objectivity of the preselected items evaluation (PIE) method, a prevalent translation scoring method deployed by international institutions such as UAntwerpen, UGent and the University of Granada.

This research critically analyzed the scientific and theoretical bottlenecks associated with the PIE method, specifically focusing on its parameters, namely the p-value and d-index, in adherence to established statistical protocols. Proposed remedies to mitigate the identified bottlenecks and augment the efficacy of the method were grounded in practicality.

The paper provided an extensive overview of the PIE method, which served as the foundation for the subsequent analysis and discussions. This research presented potential avenues for refinement and contributed to the current debate on objective translation assessment by addressing the theoretical and practical challenges associated with the PIE method.

Translation researchers, practitioners and international institutions seeking to enhance the accuracy and reliability of translation evaluation should consider the implications of this research’s findings.

Although several publications focused on the role of the PIE method in translation evaluation, no study(ies) is available to critically analyze the scientific and theoretical bottlenecks of this translation evaluation method.