Modular Design Approach Research Articles

Sustainable Future-Proof Healthcare Facilities: Modular and Adaptable Design Approach

Sustainable Future-Proof Healthcare Facilities: Modular and Adaptable Design Approach

Design and Development of a Wearable Device for Multi-Parametric Human Health Monitoring

INTRODUCTION: Wearable technology has emerged as a promising tool for continuous health monitoring, offering potential disease prevention and management benefits. However, existing devices often provide limited physiological data, hindering comprehensive health assessments.OBJECTIVES: This study aims to design and develop a wearable device capable of multi-parametric human health monitoring, focusing on vital indicators such as heart rate, body temperature, and physical activity.METHODS: A modular design approach was employed to integrate multiple biosensors into a compact wearable device. The collected data was processed by a microcontroller and transmitted wirelessly to a server.RESULTS: The developed wearable device demonstrated accurate and reliable measurement of target health parameters, with results comparable to existing prototypes in terms of functional capabilities.CONCLUSION: The proposed wearable device holds significant potential for enhancing personal health management by providing comprehensive and real-time health data. Future research will focus on expanding the device's capabilities, improving battery life, and conducting long-term user trials.

The design and implementation of a smart greenhouse control system

Abstract Against the dual backdrop of continuously improving agricultural production levels and the global scarcity of water resources, countries worldwide are actively exploring the topic of water conservation. The development of an intelligent irrigation control system could make a significant contribution to water conservation efforts, while also helping to increase crop yields. This project adopts a modular design approach, utilizing sensors to collect real-time environmental data surrounding the crops. If the data falls outside the predefined optimal range for crop growth, the system automatically initiates irrigation, cooling, and supplemental lighting. The hardware design primarily consists of a data perception module and a control terminal module. After testing, the system runs stably and can accurately obtain information such as soil moisture, light intensity and ambient temperature, and basically realize the digital intelligent control of crop growing environment.

Computationally effective machine learning approach for modular thermal energy storage design

This research presents an innovative approach that integrates computational fluid dynamics (CFD) and machine learning (ML) for the design and optimization of thermal energy storage (TES) systems. Heat discharging parametric analyses conducted using CFD serve as the basis for training ML models, including linear regression, K-nearest neighbor (KNN) regression, gradient boost regression (GBR), XGBoost, LightGBM, and neural network (NN). NN emerges as the most suitable for predicting time-dependent variations of concrete and heat transfer fluid (HTF) temperatures. The trained ML models offer an efficient alternative to traditional CFD simulations, enabling the prediction of temperatures in concrete thermal energy storage (CTES) modules under varying inlet conditions, velocities, and time. Leveraging these ML models, the research demonstrates the design of modular CTES cascaded systems with multiple modules in series and parallel configurations, significantly reducing computational cost and time by over 99% compared to full-scale CFD simulations. For instance, in predicting 4-hour time-dependent thermal behavior, CFD takes 97 s per data point and 238,500 s for a single module, compared to ML models’ 16-20 ms per data point and around 290 s per module, indicating their efficiency and scalability in predicting thermal discharge, especially for modular CTES system design and optimization. ML models also demonstrate computational efficiency for designing CTES systems involving multiple modules, taking approximately 765 s - 1047 s for various CTES system configurations, indicating their effectiveness over CFD in predicting thermal discharge for modular CTES systems. The integration of CFD and ML provides a streamlined workflow for designing and optimizing CTES systems, reducing computational efforts, cost, and time. Moreover, this workflow can be updated with additional training data to implement it for unique modular designs with different conditions. Such a generalization of this ML-based approach makes it applicable to a wide range of thermal energy storage designs and geometries, offering a promising avenue for future research and development in the field of thermal energy storage.

Capacity configuration of fuel cell hybrid vehicles using enhanced multi-objective particle swarm optimization with competitive mechanism

A well-designed hybrid powertrain is crucial for ensuring the safe, efficient, and durable operation of fuel cell hybrid vehicles. This paper introduces a modular design approach for powertrains, utilizing a Competitive mechanism-based Multi-Objective Particle Swarm Optimization (CMOPSO) algorithm integrated with nested dynamic programming. In this approach, the upper layer employs the CMOPSO algorithm to design the powertrain system, while the lower layer optimizes power coordination for each proposed design. This two-layer optimization framework considers factors such as vehicle economy and durability. Under WLTP conditions, the capacity configuration results are a fuel cell with a rated power of 22 kW, 100 batteries in series, and 7 batteries in parallel. Furthermore, the modular approach outperforms three other algorithms in terms of solution count, diversity, and overall performance metrics. The study also highlights that the vehicle’s power demand characteristics, influenced by different driving cycles, significantly affect capacity configuration results. Sensitivity analysis reveals that both the total operating cost and manufacturing cost of the vehicle are most sensitive to variations in the fuel cell rated power.

Data acquisition system for a newly-built hard x-ray/soft gamma-ray spectrometer imaging system on experimental advanced superconducting tokamak

A portable hard X-ray and soft gamma-ray spectrometer imaging system (HXS) has been constructed to gather physical information about fast electrons confined in the Experimental Advanced Superconducting Tokamak (EAST). The system is installed on the low field side of the mid-plane and provides a viewing field tangential to the toroidal field. The system utilizes a two-dimensional Cadmium Zinc Telluride (CdZnTe) semiconductor detector with 128 channels, and a data acquisition (DAQ) system has been designed for it. The DAQ system features a highly integrated signal processing system with the capability of high-speed processing and digital transmission of signals from 128 channels. In addition, a related DAQ software has been developed using a modular design approach, facilitating tasks such as data reception, storage, and preliminary processing. HXS, which has been applied during the recent EAST campaign, directly obtains the digital energy spectrum of incident photons. The DAQ system is described in detail in this paper. The hardware components and energy calibration have also been described. Experimental data have been successfully obtained and briefly discussed. More physical research will be reported in future publications.

Tailor-Made Doses of Pharmaceuticals by Tunable Modular Design: A Case Study on Tapering Antidepressant Medication.

An abrupt cessation of antidepressant medication can be challenging due to the appearance of withdrawal symptoms. A slow hyperbolic tapering of an antidepressant, such as citalopram hydrobromide (CHB), can mitigate the withdrawal syndrome. However, there are no viable dosage forms on the market to implement the tapering scheme. A solution using a tunable modular design (TMD) approach to produce flexible and accurate doses of CHB is proposed. This design consists of two parts: 1) a module with a fixed amount of preloaded CHB in a freeze-dried polymer matrix, and 2) fine-tuning the CHB dose by inkjet printing. A noncontact food-grade printer, used for the first time for printing pharmaceuticals, is modified to allow for accurate printing of the highly concentrated CHB ink on the porous CHB-free or CHB-preloaded modules. The produced modules with submilligram precision are bench-marked with commercially available CHB tablets that are manually divided. The TMD covers the entire range of doses needed for the tapering (0.5-23.8mg). The greatest variance is 13% and 88% when comparing the TMD and self-tapering, respectively. Self-tapering is proven inaccurate and showcases the need for the TMD to make available accurate and personalized doses to wean off treatment with CHB.

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2× to 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

Impact of an Artificial Intelligence in Language Learning - A survey

Bully Scan, an artificial intelligence system for identifying offensive language on social media, is proposed in "A Natural Language Processing and Machine Learning-Based Framework to Automatically Identify Cyberbullying. This paradigm, which aims to reduce the negative impacts of cyberbullying and encourage healthy online interactions, is a critical step in using AI for social well-being. The paper, "Research and Practice of Hybrid Teaching Based on AI technology for Foreign Language Translation," offers a novel strategy for teaching foreign languages through the incorporation of AI. The project investigates a hybrid teaching approach that combines AI-powered language translation tools with conventional classroom training. This method seeks to improve accuracy and efficiency of language learning by providing real-time translation support. Through the use of AI technologies, such as machine learning and natural language processing, the system offers students helpful translation assistance, enhancing their educational experience. The study demonstrates encouraging outcomes in terms of raising students' proficiency and effectiveness in translation in a blended learning setting.
 The paper "Modular Design of English Pronunciation Level Evaluation System Based on Deep Learning Algorithm" offers a novel method for determining pronunciation levels in English by utilizing deep learning algorithms. The study uses techniques like support vector machines and BP neural networks to address the problem of computational intensity in language teaching technologies. Through the application of machine deep learning, the system seeks to improve the precision and efficacy of pronunciation level assessments, providing insightful information for the development of theories for foreign language instruction in the rapidly changing field of artificial intelligence. The study's modular design approach offers a viable foundation for enhancing pronunciation assessment in language instruction.

Design of morphing wing for aerodynamic performance considering the wing flexibility effects

This paper provides an insight into ongoing research aimed at designing a morphing wing with the ability to continuously adapt its aerodynamic shape. The wing is targeted at a general purpose unmanned aerial vehicle. The morphing wing concept outlined in the paper is based on continuous camber changes of the wing leading and trailing edges, allowing optimal performance in different flight regimes. The aeroelastic tailoring method is used to design the load carrying structure of the wing in order to define the optimal stiffness and strength of the structure, which are considered as fixed in subsequent design steps. The research proposes a novel modular design approach that combines aerodynamic shape optimisation and aeroelastic considerations for designing morphing wing surfaces.

Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor

In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based real-time CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at the signalized intersections of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.

Modular Power Electronics Approach for High-Power Dynamic Wireless Charging System

Dynamic wireless power transfer (DWPT) can provide energy to EVs in motion and extend the drive range. Upscaling the charging power to 200 kW (High Power DWPT) reduces the percentage of electrified roadway required, and the solution becomes cost-effective. To smooth the power at the battery and grid, a secondary regulation stage must be added. The DWPT system therefore relies on power electronics to interface with the coils and regulate the power flow. Designing this high power system using wide bandgap devices makes ensuring high efficiency, small size, and reliable operation very challenging, and significant engineering effort is required to build such complicated systems for large-scale installation and deployment. This paper describes a modular design approach for the power electronics to achieve the 200 kW wireless power transfer. As described, the SiC power electronics building block is designed, simulated, and characterized. The approach is validated in the DWPT system to build the inverter, the rectifier, and the DC/DC converter, which demonstrated high performance and reliable operation with 188 kW power.

Context-attentive robot reconfiguration for human-machine space missions

With humankind’s aspiration of extraterrestrial inhabitation, the need for robotic support is eminent. To master the emerging challenge of providing astronauts with assistive space robots possessing the required adaptability to new situations and tasks under the constraints of severely limited supply chains in extraterrestrial missions, the utilization of existing hardware to its fullest is required. We propose the combination of a concept with an architecture to jointly support context-aware collaboration between humans and robotic systems for implementing effective resource utilization in space missions. The underlying framework is an implementation of a flexible architecture that enables context awareness for robotic systems. It supports processing nodes for retrieval of context information from the raw sensor values as well as further inferences using the output of nodes. Context information spans across three entities and describes the current state of the environment, the astronaut, and the robot itself. This information then can be used to infer the human’s current intention as well as influence the behaviour to be executed next. Additionally, dynamic changes in the data processing chains are handled by the framework to facilitate an adequate adaptation of the system to situational events. We combine this architecture with the concept of space robots as a composition from building blocks, which are supported and made accessible by a software toolkit. The modular design approach enables online self-reconfiguration of robotic hardware and software components. In combination with dynamic mission planning, based on ontological descriptions of available resources and functionalities, robots are able to adapt their physical and computational appearance dynamically during a mission according to different tasks and goals. By incorporating these two developments in a joint deployment we envision raising the efficiency of robotic systems in human-machine interaction through the usage of self-reconfiguration as a reactive behavior in order to adapt to a specific task, recognized or derived from a human’s intention. Transfer of the proposed idea back to Earth may help to abate resource dissipation caused by deploying specialized monolithic systems with a narrow range of capabilities through utilizing the adaptivity of reconfigurable robots.

Eco-Design in Manufacturing or Remanufacturing? The Sustainable Options in a Closed-Loop Supply Chain with Outsourcing

Here, the authors focused on determining the increase in consumers’ preferences for sustainable products, including that of OEMs, who are responsible for the drop in new product sales. We also assessed the effects of remanufacturing by outsourcing to third-party remanufacturers. However, the outsourcing of remanufacturing not only enables the producer to offer a remanufactured product at the same time as manufacturing new units; it also provides OEMs with the flexibility required to peruse eco-design in manufacturing and/or remanufacturing. For instance, Gree and Midea have heavily invested in eco-design to reduce energy costs in manufacturing, while Xerox has adopted a modular design approach to divert materials away from landfills and towards remanufacturing. Following observations of real-world applications, the authors developed two theoretical models in which remanufacturing can be outsourced to an independent remanufacturer, endowing the OEM with the flexibility required to enact eco-design in manufacturing or remanufacturing. This analysis reveals that OEMs tend to put more effort into eco-design in manufacturing than in remanufacturing. Furthermore, such efforts in the name of eco-design may be beneficial for both the OEM and the third-party remanufacturer. As such, this paper encourages governments to provide more incentives that will encourage OEMs to invest in eco-design in manufacturing and establish a possible “win–win” scenario in terms of both environmental and economic performances.

Development of two types of in-plane motion actuators with modular design and application to morphing wings

This study explored the design, performance evaluation, and application of two types of in-plane motion actuators that were scalable to larger sizes—that is, the extension-type electrothermal actuator (ExACT) and retraction-type electrothermal actuator (ReACT). These actuators were based on the displacement amplification mechanism (DAM) principles and regularly arranged structures in mechanical metamaterials. A modular design approach allowed these actuators to be easily configured to the desired size and scaled to create large-scale in-plane motion actuators. Studies were conducted to assess the performance of the ExACT and ReACT, focusing on the actuator displacement and blocking force. The actuation displacements for the 3 × 1 ExACT and ReACT matrices were measured at 2.0 and 0.8 mm, respectively. Similarly, the blocking forces achieved by the actuator matrices were 2.24 and 3.78 N for the ExACT and ReACT, respectively. Subsequently, the ExACT and ReACT were applied to implement large deformable morphing wings, referred to as the ExMOW and ReMOW, respectively. These wings incorporated an additional thickness amplification mechanism resembling an elliptical bridge-type DAM. The wing thickness of the ExMOW increased by approximately one-third of its initial value, whereas that of the ReMOW decreased to approximately one-quarter of its original thickness.

Research on a Data-Driven Modeling Method for Precast Concrete Balcony Components

In this paper, a data-driven modeling method for precast concrete (PC) balcony components was proposed to solve the problems of low informatization and the difficult modeling of components at the design stage. Through the analysis of the characteristics of PC balcony components and the combination of modular design methods, the paper designed a data structure for the components and developed a data-driven modeling tool for PC balcony components that can realize the input of structural design data, automatically generating component models. First, this paper introduced the data-driven modeling concept and the modeling process. Second, the PC balcony components in common prefabricated residential projects were analyzed to identify their characteristics. By using a modular design approach, these components were divided and a module dataset was created based on the split modules. Consequently, a data structure for the prefabricated balcony component model was established, wherein both conventional parameters and adaptive parameters between modules were interrelated. Finally, the function of data-driven modeling was achieved by developing a modular design tool on the Revit platform using the C# programming language. The application conducted on a prefabricated building project demonstrated that the software tool and modeling method in this paper effectively improve the level of informatization and modeling efficiency of PC balcony components. The modular design approach was satisfied with the standardization and diversification requirements of balcony components, thereby offering insights for modeling other complex components.

Design and manufacturing of a hip joint motion simulator with a novel modular design approach

Design and manufacturing of a hip joint motion simulator with a novel modular design approach

A modular design approach for porous green sound-absorbing concrete for the noise barrier on high-speed railway

Recycled aggregate porous concrete is a new type of concrete that saves natural aggregates, realizes the utilization of waste, and has the advantages of environmental protection and sustainability. However, the poor sound absorption coefficient (SAC) and the noise distribution of high-speed railway is not concentrated hinders its application to sound barriers. This study introduces a method of modular design, which includes adjusting the thickness, surface holes design, and modular combination, and the combination strategy of the modules is investigated. In this study, the porous concrete was mainly comprised of tiny and middle ceramsite recovered from waste as aggregate and cement as cementitious material. In order to investigate the effect of holes and modular combination, specimens were drilled holes on the surface, and the diameter of the holes was divided 10 mm and 5 mm. The depth of the holes is varied. The SAC of the sample in the range from 50 Hz to 1700 Hz is tested by the impedance tube and the average SAC of the sample is calculated. In addition, this study applies artificial neural networks to predict the result of the combination strategy. The results show that increasing thickness can improve the SAC of the material at low frequencies, the single module can improve the sound absorption coefficient at specific frequencies by 75%, and the combination of modules can achieve an average SAC of 0.73. The combinations with continuous holes connecting the surface have the best effect. Artificial neural networks can predict this effect accurately. The modular design method in this study provides a solution to the limited application of porous concrete with recycled aggregates.

The effects of app-related factors on app stickiness: The role of cognitive and emotional app relationship quality

Prior research tends to examine the effect of a narrow set of app-related factors on the construct of app relationship quality holistically. However, it is well-established that app relationship quality consists of cognitive and emotional aspects. To address these two issues, our study takes a more nuanced approach by examining a comprehensive set of app-related factors, including functional, offerings, experiential and relational factors, and their differential impacts on two distinct components of app relationship quality, namely cognitive app relationship quality (CARQ) and emotional app relationship quality (EARQ). It also examines the differential impact of CARQ and EARQ on app stickiness, which has long been considered as a managerial challenge. A ‘modular design approach’ was used to collect the data from an online UK consumer panel. 605 responses were analyzed using AMOS 24. The results indicated that functional and offering factors produced greater effects on CARQ than on EARQ. In contrast, experiential and relational factors were found to have stronger effects on EARQ than on CARQ. Further, CARQ was found to have a stronger effect on app stickiness compared to EARQ. The current study offers a detailed understanding of the factors that contribute to each component of app relationship quality and empirically demonstrates the differential effects of the cognitive and emotional aspects on app sickness. Such results are expected to help e-retailers to develop a strong digital presence, build future readiness for their brands, and design app-focused strategies using a consumer-centric approach.

Leveraging the modular design approach to enhance variation of SME LORI jewelry

LORI SME (Small and Medium Enterprise), operating as a producer of fashion accessories characterized by local craftsmanship, encounters challenges in accommodating diverse customer preferences due to resource constraints. To unleash its full potential, there exists a pressing need to adopt innovative design methodologies. This research endeavors to investigate the application of a modular design approach as a strategic solution for augmenting the variety of LORI SME accessories, thereby facilitating the creation of a range of jewelry products that can be easily tailored and adapted to meet individual customer requirements. The study utilizes a qualitative research methodology, encompassing literature reviews, interviews with the owner, exploration of visual motifs and materials, and practice-based research. The findings of this research illuminate that the adoption of a modular design approach significantly enhances the diversity of LORI SME's jewelry offerings, particularly in the case of its necklace products. This modular approach provides SMEs with a broader array of customizable necklace options characterized by distinctive local motifs and high craftsmanship value. These findings furnish valuable insights for SMEs within the LORI SME sector aiming to expand their product portfolios. Additionally, they offer guidance to designers and artisans regarding the potential advantages and challenges associated with the incorporation of modular design strategies in the broader context of small business innovation and product development.