Design Of Applications Research Articles

Conception of a mobile health application targeting early postoperative physiotherapeutic care after total knee replacement, a qualitative study

IntroductionMobile applications targeting physiotherapeutic care after total joint replacements are increasing in popularity among end-users. However, these applications were primarily conceived out of financial interest and lack an evidence-based programs tailored to the specific needs of the target population. The primary objective of this study is to describe the conception of an evidence-based mobile health application that targets early postoperative physiotherapeutic care after total knee replacement (TKR).MethodsA literature search of eHealth applications targeting physical therapy after TKR was carried out. Articles were then screened and suggestions as well as recommendations were extracted to inform the design of a new application. The beta version of the application was then passed onto experts for evaluation. Final changes were then undertaken to account for the expert's opinions.ResultsSeveral reviews with recommendations for the design of applications targeting patients after total joint replacement were identified. Primarily, mobile applications targeting rehabilitative care after TKR need to be tailored to the needs of the elderly population. Additionally, no unified rehabilitative physiotherapeutic (PT) program was found reflecting a discrepancy regarding what exercises are most useful. A comparison of different programs yielded no significant difference favoring one single exercise regimen.DiscussionAs the elderly population was shown to be less proficient regarding the use of new technologies, the application at hand was explicitly made simple. Elements of different PT programs were incorporated and quadriceps strengthening exercises were included. application was composed based on the findings of the reviewed literature and then subsequently modified to incorporate the expert's suggestions. Experts mainly expressed concerns regarding the safety of patients during unsupervised physical therapy as well as the safety of the recorded data. Thus, password protection and a split between the physician's and the patient's interface was created.

Bioconjugation of Podophyllotoxin and Nanosystems: Approaches for Boosting Its Biopharmaceutical and Antitumoral Profile

Podophyllotoxin is a natural compound belonging to the lignan family and is well-known for its great antitumor activity. However, it shows several limitations, such as severe side effects and some pharmacokinetics problems, including low water solubility, which hinders its application as an anticancer agent. Over the past few years, antitumor research has been focused on developing nanotechnology-based medicines or nanomedicines which allow researchers to improve the pharmacokinetic properties of anticancer compounds. Following this trend, podophyllotoxin nanoconjugates have been obtained to overcome its biopharmaceutical drawbacks and to enhance its antitumor properties. The objective of this review is to highlight the advances made over the past few years (2017–2023) regarding the inclusion of podophyllotoxin in different nanosystems. Among the huge variety of nanoconjugates of diverse nature, drug delivery systems bearing podophyllotoxin as cytotoxic payload are organic nanoparticles mainly based on polymer carriers, micelles, and liposomes. Along with the description of their pharmacological properties as antitumorals and the advantages compared to the free drug in terms of biocompatibility, solubility, and selectivity, we also provide insight into the synthetic procedures developed to obtain those podophyllotoxin-nanocarriers. Typical procedures in this regard are self-assembly techniques, nanoprecipitations, or ionic gelation methods among others. This comprehensive perspective aims to enlighten the medicinal chemistry community about the tendencies followed in the design of new podophyllotoxin-based drug delivery systems, their features and applications.

Advancing Microscale Electromagnetic Simulations for Liquid Crystal Terahertz Phase Shifters: A Diagnostic Framework for Higher-Order Mode Analysis in Closed-Source Simulators

This work addresses a critical challenge in microscale computational electromagnetics for liquid crystal-based reconfigurable components: the inadequate capability of current software to accurately identify and simulate higher-order modes (HoMs) in complex electromagnetic structures. Specifically, commercial simulators often fail to capture modes such as Transverse Electric (TE11) beyond the fundamental transverse electromagnetic (TEM) mode in coaxial liquid crystal phase shifters operating in the terahertz (THz) regime, leading to inaccurate performance predictions and suboptimal designs for telecommunication engineering applications. To address this limitation, we propose a novel diagnostic methodology incorporating three lossless assumptions to enhance the identification and analysis of pseudo-HoMs in full-wave simulators. Our approach theoretically eliminates losses associated with metallic conductivity, dielectric dissipation, and reflection effects, enabling precise assessment of frequency-dependent HoM power propagation alongside the primary TEM mode. We validate the methodology by applying it to a coaxially filled liquid crystal variable phase shifter device structure, underscoring its effectiveness in advancing the design and characterization of THz devices. This work provides valuable insights for researchers and engineers utilizing closed-source commercial simulators in micro- and nano-electromagnetic device development. The findings are particularly relevant for microscale engineering applications, including millimeter-wave (mmW), sub-mmW, and THz systems, with potential impacts on next-generation communication technologies.

Development of Desmos Application Design Integrated PBL Assisted Liveworksheet Based on Mathematical Literacy in AKM Preperation Efforts

This research aims to develop a learning media using Desmos application integrated with PBL (Problem-Based Learning) assisted by live worksheets based on mathematical literacy in preparation for AKM (Minimum Competency Assessment); and determine the feasibility and practicality of the learning media using Desmos application integrated with PBL assisted by live worksheets based on mathematical literacy in preparation for AKM. The research method used is Research and Development with ADDIE models. The research instrument uses a rating scale for media experts, material experts, practitioners, and students. The results of the development of learning media on quadratic equations obtained a percentage of 95.45% with a very valid category from media experts. Material experts gave a percentage of 93.33% with a very valid category. Practitioners gave a percentage of 94.37% with a very practical category. The results of small group trials of students were declared fit for use, and the results of large group trials obtained a percentage of 39.29% strongly agreed and 57.14% agreed to be interested when using Desmos application media assisted by live worksheets. From the results of the research, the learning media developed is very valid and practical to be used in mathematics learning based on expert validation.

Essential Oils as Antimicrobials against Acinetobacter baumannii: Experimental and Literature Data to Definite Predictive Quantitative Composition-Activity Relationship Models Using Machine Learning Algorithms.

Essential oils (EOs) exhibit a broad spectrum of biological activities; however, their clinical application is hindered by challenges, such as variability in chemical composition and chemical/physical instability. A critical limitation is the lack of chemical consistency across EO samples, which impedes standardization. Despite this, evidence suggests that EOs with differing chemical profiles often display similar (micro)biological activities, raising the possibility of standardizing EOs based on their biological effects rather than their chemical composition. This study explored the relationship between EO chemical composition and antibacterial activity against carbapenem-resistant Acinetobacter baumannii. A dataset comprising 82 EOs with known minimal inhibitory concentration values was compiled using both experimental results and literature data sourced from the AI4EssOil database (https://www.ai4essoil.com). Machine learning classification algorithms including Support Vector Machines, Random Forest, Gradient Boosting, Decision Trees, and K-Nearest Neighbors were employed to generate quantitative composition-activity relationship models. Model performance was assessed using internal and external prediction accuracy metrics with the Matthews correlation coefficient as the primary evaluation metrics. Features importance analysis, based on the Skater methodology, identified key chemical components influencing EO activity. The single chemical components limonene, eucalyptol, alpha-pinene, linalool, beta-caryophyllene, nerol, beta-pinene, neral, and carvacrol were highlighted as critical to biological efficacy. The predictive capacity of the ML models was validated against a test set of freshly extracted and chemically characterized EOs. The models demonstrated a 91% prediction accuracy for new EO samples, and a strong correlation was observed between predicted features importance and experimental inhibitory values for six selected pure compounds (limonene, eucalyptol, alpha-pinene, linalool, carvacrol, and thymol). Additionally, the machine learning approach was extended to cytotoxicity data from 3T3-Swiss fibroblasts for 61 EOs. The analysis revealed the potential to design EOs with both high antibacterial activity and low cytotoxicity through blending or selective enrichment with identified key components. These findings pave the way for biologically standardized EOs, enabling their rational design and optimization for clinical applications.

Study of High Performance Nanoscale Channel Length Vertical Transistors with a Self-Aligned Blocking Layer.

A transistor design employing all vertically stacked components has attracted considerable attention due to the simplicity of the fabrication process and the high conductivity easily realized by achieving nanolevel short channel lengths with two-dimensional current paths. However, fundamental issues, specifically the blocking of the gate electrical field to the semiconductive channel layer and high leakage current at the "off" state, have impeded this configuration in becoming a major transistor design. To address these issues, it has been proposed to introduce a blocking layer (BL) with embedded hole structures and source electrode with embedded hole structures, enhancing gate field penetration and carrier modulation. The hole structure embedded in the source and the BL on the drain induced a desirable combined effect of gate field penetration and carrier pathway modulation. The align accuracy and the hole size difference between BL and source electrode were confirmed as the most important design parameters for high performance of a transistor. We therefore proposed a self-aligning lithography method using a built-in mask that allows high alignment accuracy between the source hole structure and the BL hole structure on the drain over a large area without a high-resolution process system. This method also enables easy and fast fabrication of nanoscale channels with high performance. This design resulted in a transistor with an output of 28 mA/cm2 and an on-off ratio exceeding 106 at 1 mV of VDS. However, at 3 V of VDS, the off-current increased significantly due to short-channel effects in the all metal electrode design. To solve this issue, Fermi level-tunable graphene replaced metal electrodes, maintaining an off-current below 10 pA and an on-off ratio around 107 at 3 V. In addition, the device demonstrates robust electrical properties to light without any special treatment and is stable with a threshold voltage shift of less than 1 V under bias stress. This study demonstrates that the proposed vertical transistor design is a viable candidate as a new major transistor design for various applications.

Field-Programmable Gate Array (FPGA)-Based Lock-In Amplifier System with Signal Enhancement: A Comprehensive Review on the Design for Advanced Measurement Applications.

Lock-in amplifiers (LIAs) are critical tools in precision measurement, particularly for applications involving weak signals obscured by noise. Advances in signal processing algorithms and hardware synthesis have enabled accurate signal extraction, even in extremely noisy environments, making LIAs indispensable in sensor applications for healthcare, industry, and other services. For instance, the electrical impedance measurement of the human body, organs, tissues, and cells, known as bioelectrical impedance, is commonly used in biomedical and healthcare applications because it is non-invasive and relatively inexpensive. Also, due to its portability and miniaturization capabilities, it has great potential for the development of new point-of-care and portable testing devices. In this document, we highlight existing techniques for high-frequency resolution and precise phase detection in LIA reference signals from field-programmable gate array (FPGA) designs. A comprehensive review is presented under the key requirements and techniques for single- and dual-phase digital LIA architectures, where relevant insights are provided to address the LIAs' digital precision in measurement system configurations. Furthermore, the document highlights a novel method to enhance the spurious-free dynamic range (SFDR), thereby advancing the precision and effectiveness of LIAs in complex measurement environments. Finally, we summarize the diverse applications of impedance measurement, highlighting the wide range of fields that can benefit from the design of high performance in modern measurement technologies.

Characteristics and Functional Properties of Bioactive Oleogels: A Current Review.

Oleogels have been a revolutionary innovation in food science in terms of their health benefits and unique structural properties. They provide a healthier alternative to traditional solid or animal fats. They have improved oxidative stability and nutritional value to maintain the desirable sensory qualities of lipid-based foods. Moreover, oleogels offer an ideal carrier for poorly water-soluble bioactive compounds. The three-dimensional structure of oleogels can protect and deliver bioactive compounds in functional food products. Bioactive compounds also affect the crystalline behavior of oleogelators, the physical properties of oleogels, and storage stability. Generally, different incorporation techniques are applied to entrap bioactive compounds in the oleogel matrix depending on their characteristics. These approaches enhance the bioavailability, controlled release, stability of bioactive compounds, and the shelf life of oleogels. The multifunctionality of oleogels extends their applications beyond fat replacements, e.g., food preservation, nutraceutical delivery, and even novel innovations like 3D food printing. Despite their potential, challenges such as large-scale production, cost efficiency, and consumer acceptance remain areas for further exploration. This review emphasizes the understanding of the relationship between the structure of oleogels and their functional properties to optimize their design in different food applications. It also highlights the latest advancements in bioactive oleogels, focusing on how they incorporate bioactive compounds such as polyphenols, essential oils, and flavonoids into oleogels. The impact of these compounds on the gelation process, storage stability, and overall functionality of oleogels is also critically examined.

The Impact of Digital Technology, Automation, and Data Integration on Supply Chain Performance: Exploring the Moderating Role of Digital Transformation

Background: This study investigates digital transformation as a moderating variable in determining the effect of digital technologies, automation, and data integration of upstream and downstream providers on supply chain performance. By filling the existing research gap, the study reveals that more research regarding how digital transformation interventions impact the effectiveness of these technologies for industrial supply chains must be understood. Methods: A structured survey was applied to 181 supply chain managers in manufacturing firms scattered across Jordan. Results: The findings using SmartPLS for statistical analysis indicated that automation has the strongest positive effect on supply chain performance, followed by data integration. But digital technology did not have a significant direct effect, unless it was accompanied by broader digital transformation initiatives. Conclusions: Theoretically, this study reinforces digital transformation theory as a vital framework, whereas in practice, it invokes the strategic deployment of automation and integrated data application designs to underpin supply chain efficiency and competitiveness. Finally, this study offers practical guidance for practitioners who seek to employ the use of digital transformation in the current dynamic business environment.

ANALISIS SISTEM INFORMASI KOPERASI SIMPAN PINJAM MENGGUNAKAN METODE WEIGHTED PRODUCT

In the rapidly developing digital era, information systems have become crucial in improving organizational efficiency and productivity. In XYZ Cooperative, information systems also play an important role. This cooperative, which is under a government agency, has a vision to improve the welfare of its employees through savings and loan services. However, in performing its business processes, this cooperative has not had applications and digital data storage, so there is a lot of work that is conducted redundantly. The aim of the research is to develop a cooperative information system (SIKOP) using the Weighted Product method. This research explains the analysis phase. The Weighted Product method is used to calculate the eligibility of an employee when applying for a loan at the cooperative, using 5 criteria. As a case study, the calculation was carried out on 20 current cooperative members. In the analysis phase, business processes were defined by using Porter Diagrams. The details of main activities were described by using Business Process Management Notation (BPMN). Software user requirements for application design features were measured by using the Kano method by distributing questionnaires to 30 respondents. The results of measuring software user requirements are the feature categories: 8 one-dimensional features, 3 must-be features and 3 indifferent features. The outputs of the research phase of the analysis of the savings and credit cooperative information system are software analysis documents and publication as knowledge dissemination and a basis for the next research phase.

Heat transfer software for local materials in Cameroon (HTSLM 1.0): simplified building energy software based on local realities

We report in this work the overall design of a software application for calculating the indoor temperature in a room of a building constructed from local materials in Cameroon based on heat transfer modes. This software is intended to be simplified and at a lower cost for the user. We dubbed it LOBATIN/HTSLM 1.0 for the first release. We describe the physical modeling background based on the stationary aspect of the transfer of heat flow via conduction, convection and radiation from the outside to the inside of the supposedly uninhabited building. We present how we manage Cameroon’s meteorological data to implement them in LOBATIN/HTSLM 1.0 and we compare our results with pre-existing software such as EnergyPlus. The curves obtained for hourly indoor temperatures for the chosen days in the five climatic zones defined for the software, all show a realistic bell-shaped evolution, thus prefiguring a digital decision support tool for such admitted infrastructures to be made of materials from the Local Materials Promotion Authority of Cameroon.

Research on the Thermal Runaway Behavior and Flammability Limits of Sodium-Ion and Lithium-Ion Batteries

Batteries are widely used in energy storage systems (ESS), and thermal runaway in different types of batteries presents varying safety risks. Therefore, comparative research on the thermal runaway behaviors of various batteries is essential. This study investigates the thermal runaway characteristics of sodium-ion batteries (NIBs), lithium iron phosphate batteries (LFP), and lithium-ion batteries with NCM523 and NCM622 cathodes. The experiments were conducted in a nitrogen-filled constant-volume sealed chamber. The results show that the critical surface temperatures at the time of thermal runaway are as follows: LFP (346 °C) > NIBs (292 °C) > NCM523 (290 °C) > NCM622 (281 °C), with LFP batteries exhibiting the highest thermal runaway critical temperature. NIBs have the lowest thermal runaway triggering energy (158 kJ), while LFP has the highest (592.8 kJ). During the thermal runaway of all four battery types, the primary gases produced include carbon dioxide, hydrogen, carbon monoxide, methane, ethylene, propylene, and ethane. For NCM622 and NCM523, carbon monoxide is the dominant combustible gas, with volume fractions of 35% and 29%, respectively. In contrast, hydrogen is the main flammable gas for LFP and NIBs, with volume fractions of 44% and 30%, respectively. Among these, NIBs have the lowest lower flammability limit (LFL), indicating the highest explosion risk. The thermal runaway characteristics of 50 Ah batteries provide valuable insights for battery selection and design in energy storage applications.

Immersive Haptic Technology to Support English Language Learning Based on Metacognitive Strategies

One of the most widely used strategies for learning support is the use of Information and Communication Technologies (ICTs), due to the variety of applications and benefits they provide in the educational field. This article describes the design and implementation of an immersive application supported by Senso gloves and 3D environments for learning English as a second language in Ecuador. The following steps should be considered for the app design: (1) the creation of a classroom with characteristics similar to a real classroom and different buttons to navigate through the scenarios; (2) the creation of a virtual environment where text, images, examples, and audio are added according to the grammatical topic; (3) the creation of a dynamic environment for assessment in which multiple choice questions are interacted with, followed by automatic grading with direct feedback. The results showed that the interaction between the physical and virtual environment through navigation tests with the glove in different 3D environments achieved a complete activation and navigation rate. Teachers showed a clear interest in using the application in their classes as an additional teaching tool to complement the English language teaching process, given that it can increase motivation and memorization in students, as it is an easy-to-use application, and the 3D environments designed are attractive, which would make classes more dynamic. In addition, the availability of the application at any place and time represents a support for the current academic community as it adapts to the needs of today’s world.

Rancang Bangun UI/UX dengan Metode Design Thinking pada Aplikasi Laundry Quick and Clean Berbasis Mobile

The purpose of this research is to develop a mobile-based laundry application with Design Thinking method and measure the level of user satisfaction through System Usability Scale (SUS) testing. As the popularity of laundry services increases, manual management becomes a significant challenge, such as inefficiency in recording and tracking orders. Therefore, this research offers an innovative solution by designing a Quick and Clean application that focuses on improving user experience (UI/UX) to simplify the management of laundry services. This research uses a Design Thinking approach that includes five stages: empathize, define, ideate, prototype, and test. The empathize stage involved interviews with 15 laundry service users to identify key needs and problems. The define stage mapped out the functional and non-functional requirements that formed the basis of the application design. The ideate stage produced a 16-page wireframe design, while the prototype stage produced an application model with UI design using Figma. The novelty value of this research lies in the application of the Design Thinking method in the context of laundry services, which is rarely applied in similar studies, as well as the integration of UI/UX design that emphasizes ease of use and operational efficiency. SUS testing shows an average score of 85.6 which falls into the Excellent category. This result indicates that the application design not only meets user needs but also provides a satisfying experience, contributing to the development of UI/UX standards in laundry business applications. Overall, this research has made a significant impact by presenting an application model that can improve the efficiency and quality of moobile-based laundry services. And this development makes it easier for further development to develop further.

Recent Developments in the Design of Photoactivated Metal Oxide Gas Sensors and Application of Plasmonic Nanoparticles in Hydrogen‐Sensing Devices

Recent Developments in the Design of Photoactivated Metal Oxide Gas Sensors and Application of Plasmonic Nanoparticles in Hydrogen‐Sensing Devices

RSVP Keyboard with Inquiry Preview: mixed performance and user experience with an adaptive, multimodal typing interface combining EEG and switch input.

The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce Inquiry Preview, a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes. Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of Inquiry Preview and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of Inquiry Preview and two modes of switch input on typing accuracy and speed during a copy-spelling task. For participants without disabilities, Inquiry Preview and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience with Inquiry Preview and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with Inquiry Preview and switch input and gave favorable user experience ratings to those conditions, but preferred standard RSVP Keyboard. Inquiry Preview is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and user experience and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users. gov Identifier: NCT04468919.

Perancangan Website Preloved

The emergence of the trend to purchase preloved items as a cost-effective alternative to meet needs, particularly among students and young people, is driven by a sustainable lifestyle. The aim of this study is to develop a web based application that is easy to use, secure, and cost-efficient for managing and purchasing second-hand goods. To support the application design, data collection methods were employed through observations of similar platforms and literature reviews.These features include the ability for sellers to upload items, for buyers to search and purchase goods, and a review and rating system. To ensure that transaction processes run smoothly and are well-organized, a database design was developed. This application not only provides practical solutions for users but also contributes to reducing waste from the fashion industry, supporting a modern lifestyle.

Numerical Simulation of Compressive Testing of Sandwich Structures with Novel Triply Periodic Minimal Surface Cores.

Sandwich structures with triply periodic minimal surface (TPMS) cores have garnered research attention due to their potential to address challenges in lightweight solutions, high-strength designs, and energy absorption capabilities. This study focuses on performing finite element analyses (FEAs) on eight novel TPMS cores and one stochastic topology. It presents a method of analysis obtained through implicit modeling in Ansys simulations and examines whether the results obtained differ from a conventional method that uses a non-uniform rational B-spline (NURBS) approach. The study further presents a sensitivity analysis and a qualitative analysis of the meshes and four material models are evaluated to find the best candidate for polymeric parts created by additive manufacturing (AM) using a stereolithography (SLA) method. The FEA results from static and explicit simulations are compared with experimental data and while discrepancies are identified in some of the specimens, the failure mechanism of the proposed topologies can generally be estimated without the need for an empirical investigation. Results suggest that implicit modeling, while more computationally expensive, is as accurate as traditional methods. Additionally, insights into numerical simulations and optimal input parameters are provided to effectively validate structural designs for sandwich-type engineering applications.

Machine Learning-Assisted Prediction of Photothermal Metal-Phenolic Networks.

Photothermal therapy (PTT) demonstrates significant potential in cancer treatment, wound healing, and antibacterial therapy, with its efficacy largely depending on the performance of photothermal agents (PTAs). Metal-phenolic network (MPN) materials are ideal PTA candidates due to their low cost, good biocompatibility and excellent ligand-to-metal charge transfer properties. However, not all MPNs exhibit significant photothermal properties, and the vast chemical space of MPNs (over 700,000 potential combinations) complicates the screening of high-photothermal materials. To address this challenge, this study introduces machine learning (ML) methods for predicting the photothermal performance of MPNs. A database of photothermal properties of 80 modular MPNs was constructed, and the ML process was optimized through feature engineering and model training. The selected extreme gradient boosting model (XGBoost) successfully identified 1,654 high photothermal MPNs from a virtual database of 44,438. Subsequent experimental validation revealed a remarkable success rate of 70% in predicting high photothermal MPNs. Additionally, several previously unreported high photothermal MPNs were discovered, demonstrating advantages in photothermal antibacterial applications. This study offers an innovative ML-driven approach for the efficient screening of MPN materials, providing a solid foundation for PTA design in PTT and other biomedical applications.

Creating Immersive Learning Experiences With AI: An Android App Approach

This paper explores the development of AI-powered Android-based learning media to enhance immersive learning experiences. Utilizing Design-Based Research (DBR), the study involved the iterative design, development, and evaluation of an interactive Android application. AI tools, including ChatGPT, Quizziz AI, and various image generation software, were embedded to personalize and support the learning process. The app's interactive features, such as quizzes and instant feedback mechanisms, aim to increase learner engagement and motivation. Preliminary testing among trainers highlighted the app's effectiveness in improving user experience and the potential for broader adoption in educational settings. The results suggest that integrating AI into mobile learning platforms can enhance the accessibility and adaptability of educational content. Future research will focus on further app refinements and assessing its long-term impact on student learning outcomes.