Design Of Applications Research Articles

Deep-ultraviolet transparent conducting SrSnO3 via heterostructure design.

Exploration and advancements in ultrawide bandgap (UWBG) semiconductors are pivotal for next-generation high-power electronics and deep-ultraviolet (DUV) optoelectronics. Here, we used a thin heterostructure design to facilitate high conductivity due to the low electron mass and relatively weak electron-phonon coupling, while the atomically thin films ensured high transparency. We used a heterostructure comprising SrSnO3/La:SrSnO3/GdScO3 (110), and applied electrostatic gating, which allow us to effectively separate charge carriers in SrSnO3 from dopants and achieve phonon-limited transport behavior in strain-stabilized tetragonal SrSnO3. This led to a modulation of carrier density from 1018 to 1020 cm-3, with room temperature mobilities ranging from 40 to 140 cm2 V-1 s-1. The phonon-limited mobility, calculated from first principles, closely matched experimental results, suggesting that room temperature mobility could be further increased with higher electron density. In addition, the sample exhibited 85% optical transparency at a 300-nm wavelength. These findings highlight the potential of heterostructure design for transparent UWBG semiconductor applications, especially in DUV regime.

Design, development, and evaluation of a mobile application for safety engineers

Design, development, and evaluation of a mobile application for safety engineers

Lipid nanoparticle properties explored using online asymmetric flow field-flow fractionation coupled with small angle X-ray scattering: Beyond average characterisation

This study employs asymmetric flow field-flow fractionation online coupled with small angle X-ray scattering at a synchrotron beamline, along with multiple downstream detectors, including multi-angle light scattering, dual wavelength UV and dRI. This setup enables size-resolved characterization of lipid nanoparticles, allowing for a detailed comparison between empty and cargo-loaded lipid nanoparticles intended for nucleic acid delivery. Batch-mode characterization techniques, including cryogenic transmission electron microscopy and dynamic light scattering, alongside collection of fractions for offline characterization with liquid chromatography-charged aerosol detection, allowed for determination of the particle morphology, hydrodynamic radius, and the lipid composition over the size distribution. Cargo-containing and empty lipid nanoparticles show differences in density, and loaded particles exhibit a broader size distribution and a higher frequency of blebs at the surface. Both samples consist of spherical core–shell structured particles, with no distinguishable internal structure. A pivotal finding, often assumed until now, is that the mole fraction of each individual lipid component closely matches the original formulation. This work contributes to a more detailed understanding of lipid nanoparticles, supporting their continued development and rational design in medical applications.

Internal heating and distribution in electromagnetic Powell-Eyring [formula omitted]/glycerin hybridized nanomaterial with radiation and wall slip conditions

The synergistic influences of the hybridization of molybdenum disulfide (MoS2) and graphene oxide (GO) nanoparticles on electromagnetic radiation absorption and augmentation of thermal conductivity coupled with the industrial demand for an enhanced non-Newtonian working fluid spurred this study. Therefore, the study examines the radiative properties and thermal behaviour of hybridized MoS2 and GO nanoparticle systems dispersed in a glycerin Powell-Eyring fluid with wall slip conditions. A theoretical mathematical setup is modelled for the nanoparticle thermophysical characteristics with viscous dissipation and heat generation under the influence of the electromagnetic Riga plate. The model is transformed into an invariant dimensionless form and solved utilizing the pseudo-spectral collocation technique. The effect of fluid rheology, nanoparticle concentration, and fluid-wall slip conditions interface on radiative heat transfer and thermal propagation is investigated. The results reveal momentous augments in radiation absorption and thermal conductivity of about 4.13% to 10.22% due to MoS2 and GO hybridized nanoparticles. Also, it was found that thermal transport is enhanced close to the solid boundary wall slip, leading to an improve heat distribution rate. Hence, this study contributes to the interplay complexity between external stimuli, fluid dynamics, and nanoparticle morphology in thermal systems, offering insights into the advanced materials synthesis, thermal management systems, and optimization and design of nanoparticle fluid enhancement applications.

Visual Perceptual Confidence: Exploring Discrepancies Between Self-reported and Actual Distance Perception In Virtual Reality.

Virtual Reality (VR) systems are widely used, and it is essential to know if spatial perception in virtual environments (VEs) is similar to reality. Research indicates that users tend to underestimate distances in VR. Prior work suggests that actual distance judgments in VR may not always match the users self-reported preference of where they think they most accurately estimated distances. However, no explicit investigation evaluated whether user preferences match actual performance in a spatial judgment task. We used blind walking to explore potential dissimilarities between actual distance estimates and user-selected preferences of visual complexities, VE conditions, and targets. Our findings show a gap between user preferences and actual performance when visual complexities were varied, which has implications for better visual perception understanding, VR applications design, and research in spatial perception, indicating the need to calibrate and align user preferences and true spatial perception abilities in VR.

Measuring and Extracting the Complex Permittivity of Porous Ceramic Materials in the Y-Band

ABSTRACT Porous ceramics find extensive applications in aerospace and other fields, and the measurement of their electromagnetic parameters is helpful in optimization of material properties and device designs. In this paper, a free-space 8f quasi-optical measurement system consisting of a vector network analyzer, a 325–500 GHz frequency expansion module, and four off-axis parabolic mirrors have been established to measure the transmission parameter S21 of porous ceramics. The complex permittivity was extracted according to the Newton-Raphson iterative method utilizing its measured S21 parameters based on flat mode. The porous ceramics with different porosity and density were measured and the relationships between their permittivity and porosity and density was established, respectively. These results are beneficial to the quality evaluation and application design of porous ceramic materials in the aerospace field.

Designing UI and UX Design in GenApp Application Using Design Thinking Method

This research aims to create an ARBook learning application design for high school students on genetic material. Today's growing technology has affected various fields, one of which is education. Generation Z is a generation that understands and is literate in technology. High school students born in Generation Z have characteristics that tend to like technology, are smart, open, and like to express themselves. To attract students' interest in learning, there needs to be media used by utilizing technology. It is necessary to understand the characteristics of students when making learning media design. The method used in this research is the design thinking method, which has five stages: emphasize, define, ideate, prototype, and test. This research produces an ARBook GenApp application design that suits the characteristics of Generation Z students. The test results from the student response trial obtained good results of 89.17%, which were included in the very feasible criteria.

Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

Foodies Full Stack Web Application: Connecting You to Your College Canteens

The "Foodies Full Stack Web Application" is designed to revolutionize the way college students interact with their campus canteen services by providing real-time access to menus, nutritional information, and personalized food recommendations. This project aims to bridge the gap between canteen owners and students, offering seamless communication and better decision-making for daily meals. By leveraging the power of the Spoonacular API, the platform can not only display available menu items but also offer detailed nutritional data, which is crucial for students with dietary preferences or restrictions. Additionally, the application uses machine learning to suggest dishes based on individual preferences, ensuring a tailored dining experience for each user. This project has been developed using modern web development technologies, integrating both front-end and back-end functionalities to create a responsive, user-friendly platform. The application has the potential to enhance user satisfaction, improve the efficiency of canteen operations, and promote healthier eating habits among students. This paper outlines the design, implementation, and testing of the Foodies Web Application, while also discussing future improvements and scalability.

Imperial Study on Convolutional Layer in CNN for Data Mining

Convolutional Neural Networks (CNNs) have become a cornerstone in the field of data mining, particularly for tasks involving large-scale image and pattern recognition. This paper presents an imperial study on the efficacy of convolutional layers within CNN architectures in data mining applications. We analyze the impact of varying convolutional layer configurations, such as kernel size, stride, and depth, on performance metrics including accuracy, computational cost, and feature extraction quality. By conducting a series of experiments across different datasets, we explore how these configurations influence the model's ability to generalize and detect complex patterns in structured and unstructured data. Our findings indicate that optimizing the convolutional layers significantly improves the efficiency of CNNs for data mining tasks, offering insights into best practices for architectural design in such applications. This study provides valuable guidelines for leveraging CNNs in the realm of data mining, pushing the boundaries of automated data analysis and knowledge discovery

Hypersonic Flow past LD-Haack Series, Parabolic Series, Power Series Nose Cone: A Comparative Study

This research paper presents a detailed aerodynamic analysis of three rocket nose cone designs—LD-Haack, Parabolic, and Power Series—under hypersonic conditions ranging from Mach 5 to Mach 12. Using advanced Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent, the study focuses on key aerodynamic parameters, including drag force, drag coefficient, velocity, pressure, and temperature distributions. The results indicate that the Parabolic Nose Cone offers the most favorable aerodynamic performance, with the lowest drag force and drag coefficient across all Mach numbers. This superior performance can be attributed to its ability to streamline airflow and minimize boundary layer separation, effectively reducing pressure drag and limiting the onset of turbulent wake regions. In contrast, the LD-Haack Series Nose Cone, though designed to minimize wave drag at supersonic speeds, shows moderate drag at hypersonic velocities due to its less optimal control of boundary layer behavior. The Power Series Nose Cone, despite its highly tapered shape, experiences the highest drag. This is largely caused by increased flow separation and turbulent wake generation, which exacerbate pressure drag, particularly at higher Mach numbers. These findings highlight the critical influence of nose cone geometry on aerodynamic efficiency and emphasize the importance of optimizing designs for hypersonic applications. This study provides valuable insights for aerospace engineers seeking to reduce drag and improve performance in high-speed flight scenarios.

Eco-friendly wastewater treatment technologies (concept): Conceptualizing advanced, sustainable wastewater treatment designs for industrial and municipal applications

Eco-friendly wastewater treatment technologies (concept): Conceptualizing advanced, sustainable wastewater treatment designs for industrial and municipal applications

Experimental Investigations on the Comparison of Multi-Objective Design for High Thermal Energy Applications: An Insight into Response Surface Methodology

ABSTRACT This paper focuses on optimizing the thermal properties of aluminum, copper, and iron. Response Surface Methodology was employed to determine the optimal values for thermal conductivity and thermal expansion, minimizing experimental repetitions, and reducing costs by using pre-determined input parameters. These parameters include the volume percentages of aluminum, copper, and iron. The optimized output values for thermal conductivity and thermal expansion are measured using the two-probe method and buoyancy method, respectively, and inferred from ANOVA. The Response Surface Methodology is utilized for optimization, and various approaches analyzed include Central-Composite-Design, Box-Behnken-Design, Full-Factorial-Design, and Optimal-I method. For Central-Composite-Design, deviations are 1.07 for thermal conductivity and 0.98 for thermal expansion, with R2 values of 0.9898 and 0.9355 and F values of 96.910 and 29. For Full-Factorial-Design, deviations are 39.42 for thermal conductivity and 26.83 for thermal expansion, with R2 values of 0.86 and 0.89 and F values of 20.2936 and 17.3521. For BBD, deviations are 13.49 for thermal conductivity and 22.47 for thermal expansion, with R2 values of 0.9756 and 0.9768, and F values of 66.5 and 70.1. For Optimal-I, deviations are 12.67 for thermal conductivity and 22.56 for thermal expansion, with R2 values of 0.9937 and 0.9793, and F values of 87.38 and 63.02.

AI and Thinkable-Assisted learning media for physical education: a descriptive study on collaborative lecturer education

This research aims to determine the role of Artificial Intelligence (AI) and Thunkable interactive learning media application design in cerebrating practices of lecturer education in the context of physical education (PE) methods integrated training. Questions arise on how lecturers can reinforce PE lessons with technologies such as AI and Thunkable and what challenges they face in doing so. This study utilized descriptive research design and data was collected by means of semi-structured interviews with PE lecturers who were knowledgeable about and had utilized AI or Thunkable in their teaching practice. Analysis of quantitative data was also descriptive but focused on transcribing the qualitative data based on, among others, lecturers' technological competence, benefits and challenges of the implementation, student outcome, and qualitative, lecturers' responses. Results found out that despite 70% of lecturers’ awareness of AI in PE lessons, only 30% of them used the available AI tools for the lessons. Out of the few lecturers who attempted to use the Thunkable application, only 25% were able to generate apps which is the focus of the application. Lecturers suggested that students would benefit a lot from AI especially through active participation rewarded by instant feedback without possibilities of concerns being addressed properly due to insufficient training and technical infrastructures. The implications of this study will focus on educational linguistics considering practical uses of AI tools in the language of education in terms of physical education. The use of AI systems in PE lessons can enhance communication and interaction in a multilingual setting that promotes both language and movement skills. This research aims to determine the role of Artificial Intelligence (AI) and Thunkable interactive learning media application design in cerebrating practices of lecturer education in the context of physical education (PE) methods integrated training. Questions arise on how lecturers can reinforce PE lessons with technologies such as AI and Thunkable and what challenges they face in doing so. This study utilized descriptive research design and data was collected by means of semi-structured interviews with PE lecturers who were knowledgeable about and had utilized AI or Thunkable in their teaching practice. Analysis of quantitative data was also descriptive but focused on transcribing the qualitative data based on, among others, lecturers' technological competence, benefits and challenges of the implementation, student outcome, and qualitative, lecturers' responses. Results found out that despite 70% of lecturers’ awareness of AI in PE lessons, only 30% of them used the available AI tools for the lessons. Out of the few lecturers who attempted to use the Thunkable application, only 25% were able to generate apps which is the focus of the application. Lecturers suggested that students would benefit a lot from AI especially through active participation rewarded by instant feedback without possibilities of concerns being addressed properly due to insufficient training and technical infrastructures. The implications of this study will focus on educational linguistics considering practical uses of AI tools in the language of education in terms of physical education. The use of AI systems in PE lessons can enhance communication and interaction in a multilingual setting that promotes both language and movement skills.

Web-Based Nursing Care Documentation for Students to Support Online Learning

Nursing care is the most critical element in nursing services, which aims to improve the patient’s health status. Ineffective and inefficient care documentation can impact the quality of nursing services. However, the increasingly advanced development of technology provides freedom for the health world to improve the quality of patient-centered services. Educational institutions have also used information technology in learning process activities. As prospective health professionals, students must be equipped with competencies to support their performance. In this study, a web-based nursing care information system was developed to assist students in documenting care activities. The website application was designed to increase student competency in nursing documentation activities to provide high-quality nursing services even though learning is online. The waterfall model approach was used in application design. The design stage started with analyzing application requirements, followed by system design and coding. Next, using a Likert-scale questionnaire, a usability test was performed on 15 beta users to assess the functionality of the application. The results showed that the nursing care website application was easy to use and that students felt satisfied. It is hoped that the website application can be made more attractive by including a decisional support system to make it easier for students to enforce nursing diagnoses on patients.

Implementation of Augmented Reality Teaching and Learning Kit to Enhance Visualization, Motivation and Understanding among TVET Students

Augmented Reality (AR) technology is one of the increasingly preferred choices for educational development of education. This study aims to identify the level of visualization, motivation and understanding by designing and developing an AR application for the topic of Engineering Drawing. This application enhances the user's clarity and comprehension on the types of drawings and the form of the drawing to be presented. The design and development process of this AR application uses the Hannafin and Peck model as a guideline. The research instruments used are expert review forms for product development and questionnaires. The study included a total of 52 students from Sungai Buloh Vocational College, Selangor, as participants. The findings were analysed descriptively using the Statistical Package for the Social Sciences (SPSS) software version 27. The evaluation was conducted on three (3) aspects, namely the level of visualisation, motivation and comprehension of the students, as well as the significance of research to examine the gender differences between the three aspects. The findings of the study revealed that the use of the AR application produced for the three (3) components yielded a favourable outcome with a reliability value of r=0.855, which demonstrates that the instrument possesses a high level of reliability. However, no significant difference was found between the genders of the students that participated in the study. In general, the findings of the study indicate that the implementation of the AR teaching and learning kit has yielded positive outcomes in terms of enhancing students' visualisation skills, motivation levels and comprehension of the Engineering Drawings subject.

Retraction Note: A defensive design for control application based on networked systems

Retraction Note: A defensive design for control application based on networked systems

Hybrid Optimization Framework for MIMO Antenna Design in Wearable IoT Applications Using Deep Learning and Bayesian Method

Hybrid Optimization Framework for MIMO Antenna Design in Wearable IoT Applications Using Deep Learning and Bayesian Method

Design of active disturbance rejection controller for the system with unknown model using Laguerre functions

This paper concentrates on the design problem of linear active disturbance rejection control (LADRC) for uncertain systems, whose order and parameters are unknown. In general, LADRC has diversified structural forms due to different order and parameters selection. To clarify the prerequisites for constructing the LADRC strategy, a Laguerre-based LADRC (LLADRC) method is proposed, which provides controller design guidance for systems with unknown models by capturing the impulse response. Then, intensive attention is focused on establishing internal stability conditions of the feedback system and parameter tuning rules of the prescribed controller. To gain a better understanding, the internal correlation between LLADRC and standard LADRC of the same order is analyzed. It is shown that observer and controller gains of the standard LADRC can be tuned via a workable LLADRC to achieve better control performance. Finally, a numerical example and an application design of DC motor speed control are studied, both of which demonstrate the feasibility and validity of the proposed method.

Construction of Asymmetric Filler Density Mixed-Matrix MOF Membranes via Liquid-Phase Ion Activation for Advanced Oxidation Processes.

Mixed-matrix membrane (MMM) reactors incorporating metal-organic framework (MOF) fillers have significant applications in fields such as separation, catalysis, and chiral resolution. However, the traditional method of directly mixing MOF fillers with polymers results in a symmetric structure where most of the MOF fillers are uniformly distributed across the membrane reactor's cross-section. During usage, this leads to a pronounced trade-off between flux and efficiency and the waste of the MOF, as the rich pore characteristics and active sites of MOFs are not fully utilized. In this study, we propose a liquid-phase ion activation strategy that enables the in situ formation of a uniform and densely packed MOF shell layer on the membrane surface during the phase inversion process of MMM fabrication. This asymmetric density distribution of the MOF-based MMM reactor selectively induces the generation of considerable nonradical 1O2 in the peroxomonosulfate (PMS) system, demonstrating superior catalytic degradation performance against antibiotics like TC and dye molecules (with a flux of 1880 L m-2 h-1 and a Kapp value of 648 min-1), as well as outstanding cycling stability and environmental tolerance. This discovery sheds light on the design and engineering of composite membrane materials and applications for advanced oxidation processes in water purification.