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A major obstacle in investigating the relationship between the anthropometric properties of an individual's pinnæ and their corresponding head-related transfer function (HRTF) is the inability to perform controlled experiments free from other confounding factors. We propose a method for performing these experiments by manufacturing a digitally-altered replica pinna. The process involves 3D-scanning a real pinna, performing the desired alterations, manufacturing the replica, and measuring the acoustic properties of the replica. Here, we describe our design and manufacturing methodology, 3D printing a mold of the altered pinna, which is then used to make a silicone casting, as well as our acoustic measurement apparatus and procedures. To demonstrate the effectiveness of our process, we manufactured an unaltered pinna replica and show that the replica has acoustic properties comparable to the original within the range of human hearing ( ≤ 20 kHz).

The Indonesian government has now developed a QRIS EPayment for non-cash transaction between MSMEs and customers. MSMEs are the main channel for the success of the program through various transaction services provided to customers. This study aims to investigate the determine customers intention to adopt of QRIS e-Payment in Indonesia. Extended of UTAUT Theory will be used to investigate. Partial least squares structural equation modeling (PLS-SEM) was use to analyze the data. Using the purposive sampling technique, this study collected 195 respondents. We found that There are two main variables driving the customer in adopting of QRIS e-payment, namely social influence and facilitating conditions. Meanwhile, the variable performance expectancy and effort expectancy have a positive but insignificant effect. This result implies that In order to increase customer intention to use the QRIS e-payment system by consumers in Indonesia, QRIS service providers need to form a favorable opinion in society by increasing social influence through collaborating with community leaders or important people in their community and facilitating services that facilitate community adopts QRIS. This will encourage customer intention to adopt of QRIS e-payment and accelerate the migration of cash transactions to non-cash transactions in Indonesia.

User segmentation categorises a large and complex user base into manageable similar groups of users. Existing works encounter challenges when dealing with a sparse dataset and finding insights from the generated clusters. This study has two objectives: (1) to identify an optimal clustering model that can handle a sparse dataset and (2) to extract post-clustering insights via a descriptive persona for each cluster. This study deployed clustering models to handle a behavioural user-interaction dataset with a sparsity rate of 85%. The findings revealed that Density-Based Spatial Clustering of Applications with Noise that leveraged on One-hot Encoding and data representation learning via an autoencoder performed best, with a Silhouette score of 0.36. Subsequently, this study enacted techniques and tools such as classification, SHapley Additive exPlanation value, and manual analysis. Classification and SHAP values were used to identify important features that can differentiate clusters created by different clustering models. Specifically, a linear SHAP explainer object was applied to Logistic Regression had been identified to outperformed Random Forest and Light Gradient Boosting Machine, with an accuracy of 97%. A manual analysis of the central tendencies of these relatively more important features within each cluster was performed to create a descriptive persona. The findings revealed four distinctive personas, namely the \"Active User,\" \"COVID-19 Preventer,\" \"Inactive User,\" and \"Average Joe.\"

Skin cancer, a life-threatening disease, necessitates early detection and accurate classification for successful treatment. Misdiagnoses can lead to significant consequences for patients, highlighting the critical need for improved accuracy. Despite advancements in machine learning (ML) and deep learning (DL) techniques, accurate diagnosis remains challenging due to its complex nature. This comprehensive Systematic Literature Review (SLR) aims to examine the use of ML and DL models in skin cancer detection and classification, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning. Model performance is evaluated based on accuracy, sensitivity, specificity, and precision. Key findings reveal the dominance of DL models, with SVM-PSO emerging as a top-performing hybrid model with 97.50% accuracy. Tailored models, such as M-SVM and FCN-ResAlexNet, demonstrate high accuracy, emphasizing the importance of customization for dermatology tasks. Deep neural networks, such as ResNet-50, ResNet34, Inception V3, and ResNet 152, consistently exhibit strong performance, highlighting the impact of architectural depth. Traditional ML algorithms like Random Forest, KNN, and Naive Bayes face challenges compared to DL models. Furthermore, the analysis explores correlations between dataset size and accuracy, revealing varied model responses. Temporal trends and model-specific analyses uncover outliers, anomalies, and the influence of specific datasets (e.g. imbalanced classes), providing valuable insights for future research and model development. This multifaceted nature of model performance, influenced by factors beyond dataset size, underscores the need for nuanced considerations in dermatology image classification. Overall, the findings of this SLR offer valuable insights for researchers and practitioners, serving as a crucial step towards developing even more accurate and reliable tools for skin cancer diagnosis.

With the advent of the era of great intersection and integration, the development of generative artificial intelligence has caused the renewal of design methods, promoting a new paradigm of research in design fundamentals. The study seeks to investigate the research method of design form in the collaborative mode of artificial intelligence, to provide new ideas for design to conduct interdisciplinary research, and to promote design innovation under AI collaboration. This research begins with the design morphology theory, integrates interdisciplinary theories such as bionic design, and topology research, and collaborates with AIGC tools such as Midjourney, Stable Diffusion, and Chilloutmix to conduct case-specific research. To improve the accuracy of the morphological study, parametric design, bi-directional progressive topology optimization, genetic algorithm and simulation analysis, and other methods were also used in the research process to carry out a comprehensive design experiment exploration. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. AI synergy provides new ideas for complex morphology research, extends and complements design, and plays a crucial role in the phases of morphology exploration, concept generation, and solution implementation, thereby assisting in the exploration of the central content of design morphology.

Student performance can fluctuate over time due to various factors (e.g. previous assignment grades, social life and economic conditions). Temporal dynamics, such as semester-to-semester variations and changes in students' academic achievements, behaviors and engagement over time, can be critical factors in designing predictive models. It can be said that most existing work focuses on one-time forecasting of student performance in specific semesters, subjects or short online courses without considering temporal elements. In this paper, we present a student performance-based temporal dynamic approach to progressively predict semester-wise performance. Eight semesters of data representing 3,093 undergraduate Health Sciences students was collected from a public university in Ghana, analyzed, pre-processed and transformed into a time-series format. Then a dynamic experimental framework utilizing four different machine learning methods to predict student performance was created. This includes Random Forest, Support Vector Machine, Long Short-Term Memory and Bidirectional Long Short-Term Memory to predict student performance semester-wise over eight semesters. The results indicate that utilizing past students' performance records obtained over time enhances the accuracy of forecasting their performance in future semesters. Moreover, the results evident that high school grades and semester GPAs are the most powerful discriminant features influencing the models' performance, emphasizing the importance of consistent in-course performance.

TRIZ, also known as the theory of innovative problem-solving, has garnered attention from several proponents who advocate its merits as a systematic technique or toolkit that offers a rational framework for fostering creativity in pursuing innovation and creative problem-solving. The broad range of tools and procedures used in the TRIZ-based innovating enable the effective development of next-generation items while successfully enhancing existing ones. Using these tools and approaches may also facilitate the development of the necessary functionality and mitigate the costs associated with manufacturing processes, hence allowing the introduction of novel and enhanced products to be introduced into the market. This study aims to employ the TRIZ approach to develop an optimal laparoscopic chair design. The design of the laparoscopic chair was with a particular emphasis on ease and usability. The laparoscopic chair's design demonstrated adherence to ergonomic principles despite the absence of actual ergonomic testing. This design has the chair's adjustable components, which allow for height and level modifications, as well as customizable positioning to accommodate the preferences of the seated individual. The outcome of this design is comparable to laparoscopic chairs offered by medical corporations and preparing for the prototyping phase.

Various studies have been conducted to improve the tribological performance of thrust bearings. In the present study, thermohydrodynamic (THD) analysis was carried out to explore the performance of textured thrust bearings with rectangular dimples in three dimensions, considering turbulence and cavitation. Various models are available for predicting turbulence. Owing to disparities in forcing viscosity, velocity fluctuation, and parameter estimation, the performance of these models is typically distinct. Model comparison is the simplest method for identifying the strengths, limitations, and sources of uncertainty in a model. In this study, the k-ω turbulence model was of particular interest. Four k-ω models, namely, the standard k-ω model, BSL (Baseline) k-ω model, SST (shear-stress transport) k-ω model, and GEKO (Generalized k-Omega) k-ω model, were evaluated to predict the bearing characteristics. In this study, not only the tribological performance but also the acoustic characteristics were investigated. To capture more realistic phenomena, a multiphase mixture cavitation model was used to analyze the cavitation behavior of the lubricant. The results of this study show a comparison of the tribological performance between each turbulence model while considering the presence of cavitation and thermal conditions on the thrust bearing. In addition, the performance of thrust bearings under laminar flow conditions without cavitation was compared with these results. By comparing the various models, the necessity of incorporating turbulence and cavitation-coupled effects was confirmed. The main results also show that in the case of the BSL and standard k-ω turbulence models.

Mango harvesting remains predominantly manual despite technological advances in agriculture. Traditional manual harvesting methods are labour-intensive and often result in mechanical injuries, bruising, and sap-induced infections, leading to reduced fruit quality and market value. This study presents the development of a specialized end effector for robotic mango harvesting. The developed end effector is capable of simultaneously cutting and grasping the mango peduncle, maintaining fruit integrity while minimizing mechanical injuries, bruising, and sap-induced infections. Its compact and modular design enhances maneuverability and operational efficiency, providing a secure grip on mangoes during cutting and transport until release in the conveying unit, ensuring the harvested fruit remains undamaged and market-ready. Initially inspired by handheld tools, the design evolved from using a net bag to finger grippers, and finally, a compact unit with integrated pads, capable of cutting and holding the fruit simultaneously. The design process involved conceptualizing and evaluating three designs, with the most efficient one being fabricated and tested. The end effector operates using a single servo motor for both cutting and grasping the peduncle. In lab trials, it successfully picked 17 mangoes at a rotational speed of 105 rad/s. The developed end effector achieved a conveying efficiency of approximately 96.7% with only 2 out of 60 mangoes falling from it during the lab trials. The performance evaluation demonstrated the end effector's ability to consistently harvest mangoes with minimal failures, validating its practicality and robustness.

Design improvement is becoming vital for enhancing product performance, reducing costs and meeting customer needs and market demands. Recent advancements utilize clustering algorithms for supporting informed decision making by grouping similar objects based on common characteristics. Among other clustering techniques, K-Means clustering is widely used because of its simplicity and scalability which makes it ideal for a wide range of applications. The screw-down stop valve controls the fluid flow by adjusting the position of the valve seat attached to the stem within the valve body. Minimizing manufacturing costs and assembly time across diverse sectors is crucial to improve competitiveness. This study is focused on improving the design of the valve assembly without affecting its functionality. The K-Means algorithm grouped components based on their material similarities and interactions, and these were combined using an interaction based merging method. The structural integrity of the re-designed components was validated through a static structural analysis. The modified product was fabricated through additive manufacturing using polylactic acid material to verify its manufacturability and form. The findings indicated that the maximum equivalent stress obtained for both the modified design segments were found to be 51.82 MPa and 53.7 MPa which is well within the yield strengths of their respective material. Additionally, the number of components in the modified product was reduced from 14 to 5.