Modular Design Research Articles

Open and Extensible Benchmark for Explainable Artificial Intelligence Methods

The interpretability requirement is one of the largest obstacles when deploying machine learning models in various practical fields. Methods of eXplainable Artificial Intelligence (XAI) address those issues. However, the growing number of different solutions in this field creates a demand to assess the quality of explanations and compare them. In recent years, several attempts have been made to consolidate scattered XAI quality assessment methods into a single benchmark. Those attempts usually suffered from a focus on feature importance only, a lack of customization, and the absence of an evaluation framework. In this work, the eXplainable Artificial Intelligence Benchmark (XAIB) is proposed. Compared to existing benchmarks, XAIB is more universal, extensible, and has a complete evaluation ontology in the form of the Co-12 Framework. Due to its special modular design, it is easy to add new datasets, models, explainers, and quality metrics. Furthermore, an additional abstraction layer built with an inversion of control principle makes them easier to use. The benchmark will contribute to artificial intelligence research by providing a platform for evaluation experiments and, at the same time, will contribute to engineering by providing a way to compare explainers using custom datasets and machine learning models, which brings evaluation closer to practice.

Demystifying Infusion Pumps: Design of a Cost-Effective Platform for Education and Innovation.

This article presents a cost-effective, modular infusion platform to help diabetes specialists customize and understand infusion pump mechanics and control principles. Traditional insulin pumps are costly and inflexible, limiting accessibility, and particularly in low-resource settings. Inspired by open-source initiatives like OpenAPS, this platform engages specialists in device operation and customization, offering practical insights into infusion technology. An initial survey assessed technological literacy, customization interests, and feature preferences among Mexican diabetes specialists, followed by a hands-on engagement session with the platform's hardware. Core components are described and chosen for reliability, affordability, and integration ease. A follow-up survey evaluated specialists' confidence and interest in device customization, gathering feedback on usability and design. Survey data showed strong specialist interest in understanding device mechanics and high confidence in customization after hands-on engagement. Most specialists found the hardware layout conducive to experimentation, with significant interest in closed-loop capabilities. Key valued features included safety, affordability, ease of use, customization, and integration of diverse continuous glucose monitors, with added suggestions for potential clinical certification, cost-effective supplies, and artificial intelligence integration. This platform offers a promising educational and developmental tool in diabetes management, bridging clinical application, and customization. Its low-cost, modular design provides a feasible solution for low-resource settings, equipping specialists to tailor devices for specific patient needs. While the platform's educational potential is clear, further studies and validation are essential for a possible transition to a clinical-grade device. Continued development could democratize access to advanced diabetes technology, transforming specialist training, and patient care.

IOT Based Integrated Environmental Monitoring System

A weather monitoring system is a robust and cost-effective solution designed to monitor and record real-time environmental parameters using Arduino as the central controller. This system integrates various sensors, such as a DHT11 for temperature and humidity, an LDR for light intensity, and a soil moisture sensor, to gather precise data about environmental conditions. The data is processed by the Arduino and displayed locally on an LCD screen, ensuring ease of access for users. The system provides a versatile platform for monitoring key weather parameters in applications such as agriculture, greenhouses, and indoor climate control. Threshold-based automation is implemented to activate actuators like water pumps and fans, providing intelligent responses to environmental changes. For example, the water pump is triggered when soil moisture falls below a certain level, and the fan is activated when the temperature exceeds a predefined threshold. Manual override is also supported through push-button controls, allowing users to operate the system as needed. Designed as a standalone device, the system runs entirely on Arduino and does not rely on internet connectivity, making it ideal for remote or resource-constrained locations. Its modular design allows for easy integration of additional sensors, such as rain or wind-speed sensors, for expanded functionality. By providing accurate, real-time weather data and automating responses to environmental changes, this system reduces manual effort, optimizes resource usage, and enhances productivity. Its simplicity, affordability, and reliability make it a valuable tool for individuals and organizations in agriculture, horticulture, and environmental research. This project demonstrates the potential of Arduino-based systems to solve real-world challenges in a sustainable and scalable..[7] Keywords: IoT (Internet of Things), Environmental Monitoring, Smart Cities, Real-time Data, Sensors, Air Quality Monitoring, Water Quality Monitoring, Climate Data, Data Analytics, Cloud Computing, Wireless Sensor Networks (WSNs), Pollution Detection.

Design, development and testing of IF section for FMCW reflectometry diagnostics for tokamaks

Reflectometry diagnostics is essential for determining plasma position, density, and density fluctuations to characterize tokamak plasmas. An Ultra-fast Frequency Modulated Continuous Wave reflectometry system for measuring time evolution of density profiles is operational on ADITYA-U. This paper discusses the development of an in-house IF section, signal acquisition and processing has already been reported separately. Initially, a modular design of the IF section was implemented. However, due to the higher cost and bulkiness of the modular design, it was replaced by IF section card developed in-house to achieve a compact and cost-effective solution. This paper details the circuit simulations, printed circuit board layout, design, and testing of the developed IF electronics. Both modular and card based IF sections were used and were found to be in excellent agreement with expected values of beat frequency. Specifically, modular IF design achieves a 2.5% error, and the card-based IF design measures with a 2% error. In addition to having a lower error compared to the modular IF design, the card-based design also offers a lower cost in a more robust and compact form factor.

Reliable modular designs under time-continuous input data

Reliable modular designs under time-continuous input data

Harmonic Motion Tracing Patterns in a Rotating Field

Abstract: In this study, we present a radar detection system for real-time object localization based on ultrasonic sensor technology. The system combines ultrasonic sensors, a servo motor, and an Arduino microcontroller to send ultrasonic waves, detect their echoes, and compute distances with high accuracy. This information is processed to generate a spatial map of the environment around it, allowing for precise object detection and localization in real time. The system works by utilizing the servo motor to spin the ultrasonic sensor, taking distance readings over an array of angles. The data is processed and presented, giving a real-time visualization of the objects found in the surroundings. This method guarantees high precision with simplicity and affordability. The flexibility of the system to various environmental conditions makes it a perfect solution for a variety of applications such as robotics, security systems, surveillance, obstacle detection, and industrial automation. One of the major benefits of this system is its cost-effectiveness and ease of construction, making it affordable for hobbyists, researchers, and industry experts. The modular design also guarantees flexibility, making it possible to customize it for particular applications or integrate it with sophisticated systems. Our study emphasizes the potential of ultrasonic sensor technology in object detection systems, opening doors to novel applications in automation, navigation, and safety systems.

Towards net-zero futures: Modular Passivhaus design as a solution to meeting national carbon targets for new housing

Towards net-zero futures: Modular Passivhaus design as a solution to meeting national carbon targets for new housing

Design for manufacture and assembly (DfMA) for modular buildings: an analytical framework

ABSTRACT Design for Manufacture and Assembly (DfMA) is a proactive strategy aimed at maximizing the potential of modular buildings to address issues, including labor shortages, time overruns, and low productivity. Despite its growing adoption, research on examining existing design practices remains limited. This study therefore aims to 1) develop an analytical framework and 2) uncover underlying modular building design patterns. Drawing inspiration from space syntax analysis, the framework comprises six components: nomenclature system, space identification, spatial relations, module shape, dimensions, and convex break-up. It was then applied to analyze 39 modular building units in Hong Kong. The established framework helps visualize underlying design patterns, e.g. spatial typologies, module shapes and sizes, and standardized spatial configurations. Several design patterns are also identified, i.e. typical space requirements, common module sizes, dimension-coordinated spaces, and the repetitive use of standardized spaces and modules to form units. This innovative framework, along with a discussion on evolving professional practices, offers valuable insights for advancing DfMA and modular construction. Future research is recommended to refine the framework and enhance DfMA by integrating these patterns with expert knowledge and computational intelligence.

ISODA: A Comprehensive Tool for Integrative Omics Data Analysis in Single- and Multi-Omics Experiments.

Thanks to the plummeting costs of continuously evolving omics analytical platforms, research centers collect multiomics data more routinely. They are, however, confronted with the lack of a versatile software solution to harmoniously analyze single-omics and interpret multiomics data. We have developed iSODA, a web-based application for the analysis of single- and multiomics data. The tool emphasizes intuitive interactive visualizations designed for user-driven data exploration. Researchers can access a variety of functions ranging from simple visualization like volcano plots and PCA to advanced functional analyses like enrichment analysis and lipid saturation analysis. For integrated multiomics, iSODA incorporates multi-omics factor analysis and similarity network fusion. The ability to adapt the data on-the-fly allows for tasks, such as the removal of outlier samples or failed features, imputation, or normalization. All results are presented through interactive plots, the modular design supports extensions, and tooltips and tutorials provide comprehensive guidance for users. iSODA is accessible under http://isoda.online/.

Adding Value to Outdoor Space with Modular Bird Houses Approach

The aim of the study is to reveal the cultural values of bird houses used on the facades of buildings as a traditional approach and current design approaches for their use today. For this purpose, modular bird house designs that can be used in outdoor spaces were developed and the visual situation in the areas where they can be used was analyzed with the visuals prepared. As a result, with the use of modular bird house designs developed within the scope of the study, it will be possible to develop ecosystems for bird species in residential areas and open-green areas.

AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.

Plant viruses pose a significant threat to global agriculture and require efficient tools for their timely detection. We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. The pipeline uses Biopython to automatically retrieve different genomic sequences from the NCBI database to increase the robustness of the subsequent primer design. The design_primers_with_tuning module uses a random forest classifier that optimizes parameters and provides flexibility for different experimental conditions. Quality control measures, including the evaluation of poly-X content and melting temperature, increase primer reliability. Unique to AutoPVPrimer is the visualize_primer_dimer module, which supports the visual evaluation of primer dimers-a feature missing in other tools. Primer specificity is validated via primer BLAST, which contributes to the overall efficiency of the pipeline. AutoPVPrimer has been successfully applied to the tomato mosaic virus, proving its adaptability and efficiency. The modular design allows customization by the user and extends the applicability to different plant viruses and experimental scenarios. The pipeline represents a significant advance in primer design and provides researchers with an effective tool to accelerate molecular biology experiments. Future developments aim to extend compatibility and incorporate user feedback to consolidate AutoPVPrimer as an innovative contribution to the bioinformatics toolbox and a promising resource for the advancement of plant virology research.

Cooperative co-evolution deep echo state network for time series prediction

Abstract The deep echo state network demonstrates strong predictive performance in time series tasks, but inter-layer weight optimization and reservoir structure design are still challenging tasks. To address this, we propose the cooperative co-evolution deep echo state network (CCEDESN). Firstly, inspired by the hierarchical structure of the brain, we introduce a modular hierarchical design. Each layer of the reservoir contains multiple sub-reservoirs, with central neurons in each sub-reservoir interacting with those in other sub-reservoirs to enhance information processing. Secondly, an advanced cooperative co-evolution algorithm is proposed to simultaneously optimize the layer connection weights and (hyper)parameters of the deep echo state network. We evaluate the CCEDESN model on the Mackey-Glass system, Sunspot data, and Apple stock opening prices. The experimental results show that the CCEDESN model achieves superior prediction accuracy compared to other models.

Hospital Architecture Adaptability: A Systematic Analysis of Concepts, Implementation, and Measuring Tools

This research aims to explore the concept of adaptability in hospital architecture through a systematic literature review. The methodology used was Systematic Literature Review (SLR) with PRISMA approach, utilising Watase Uake platform for analysis for the analysis of 26 selected articles published between 2015 and 2024. The results show that the need for adaptability of hospital architecture is driven by advances in medical technology, demographic changes, and learning from the COVID-19 pandemic. Findings reveal that the implementation of modular design, separation of building systems into independent layers, and integration of information technology can facilitate building adaptability. The development of measurement tools from simple qualitative approaches to comprehensive models such as GAAT, OBAT, and OFAT designed specifically for healthcare facilities. However, their implementation still faces gaps especially in different local contexts. This research contributes to the understanding of the importance of adaptability in hospital design and highlights the need for the development of more specific measurement tools for the Indonesian context.

System Integration Failures and Their Impact on Patient Safety in Critical Care Settings

Robotic surgery systems offer precision and minimally invasive procedures, enhancing surgical outcomes in critical care settings. However, these systems' complexity, with integrated hardware and software, can lead to integration failures, compromising patient safety. This paper examines the system integration challenges in robotic surgery, explores their effects on patient safety, and suggests solutions to overcome them. Rigorous testing strategies during development and continuous monitoring of system performance are crucial in detecting failures early and ensuring reliable system operation. Keywords: Robotic Surgery Systems, System Integration, Patient Safety, Software Failures, Hardware Failures, Critical Care, Integration Testing, Failover Mechanisms, Robotic Arms, Sensor Calibration, Real-Time Data Processing, Medical Robotics, Surgical Precision, Failure Detection, Stress Testing, Fault-Tolerant Design, Error Handling, Modular Design, Quality Assurance, ISO 13485, Robotic Surgery Failures, Calibration Errors, Mako System, Da Vinci Surgical System, CyberKnife System, Automated Testing, Robotic Surgery Technology.

LES and RANS Modeling of an 84-Pin Hexagonal Rod Bundle with Spacer Grid and Experimental Validation

As part of an ongoing integrated research project (IRP), detailed investigations of the flow characteristics of an 84-pin hexagonal rod bundle representing a potential modular gas-cooled fast reactor design have been performed. The rod bundle features a pitch-to-diameter ratio of 1.5 and a large central guide tube, along with simple spacer grids and an outer enclosure. The primary focus of the current work is modeling and simulation of this geometry using multiple computational techniques. These include high-fidelity large eddy simulation (LES) and advanced Reynolds-averaged Navier-Stokes (RANS) approaches. The flow predictions are validated at a Reynolds number of 12 000 using matched index of refraction particle image velocimetry experimental data that were also generated by Texas A&M University as part of the IRP. The comparison data include velocity magnitude and turbulent kinetic energy (TKE) at different lateral locations and at multiple distances downstream of the spacer grid. Many characteristics of the experimental flow are replicated in the LES and RANS runs, and a general agreement between simulation and experiment is shown. Except for the immediate vicinity of the grid, LES is able to capture the velocity magnitude within a 10% error and the TKE to within the experimental uncertainty. The LES shows notably better agreement with the experimental data than RANS, particularly regarding the TKE decay behavior downstream of the grid. Both methods show significant departures from the experiment in their predictions close to the central guide tube. The experimental and high-fidelity data will be used to inform closures for novel multiscale methodologies. The long-term goal of the work is to use the high-fidelity data to yield improved multiscale thermal analysis techniques for solving fuel performance problems of direct relevance to industry.

HemaScope: A Tool for Analyzing Single-cell and Spatial Transcriptomics Data of Hematopoietic Cells.

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information. Here, we present HemaScope, a specialized bioinformatics toolkit featuring modular designs to analyze scRNA-seq and ST data generated from hematopoietic cells. It enables users to perform quality control, basic analysis, cell atlas construction, cellular heterogeneity exploration, and dynamical examination on scRNA-seq data. Also, it can perform spatial analysis and microenvironment analysis on ST data. Meanwhile, HemaScope takes into consideration hematopoietic cell-specific features, including lineage affiliation evaluation, cell cycle prediction, and marker gene collection. To enhance the user experience, we have deployed the toolkit in user-friendly forms: HemaScopeR (an R package), HemaScopeCloud (a web server), HemaScopeDocker (a Docker image), and HemaScopeShiny (a graphical interface). In case studies, we employed it to construct a cell atlas of human bone marrow, analyze age-related changes, and identify acute myeloid leukemia cells in mice. Moreover, we characterized the microenvironments in angioimmunoblastic T cell lymphoma and primary central nervous system lymphoma, elucidating tumor boundaries. HemaScope is freely available at https://zhenyiwangthu.github.io/HemaScope_Tutorial/.

To Review on Speech-to-Sign Language Conversion System Using Natural Language Processing

The project aims to bridge the communication gap between hearing individuals and the deaf community by developing a speech-to-sign language conversion system. This system transforms spoken language into corresponding sign language gestures, enabling real-time, inclusive communication. Leveraging advanced speech recognition algorithms and natural language processing (NLP), the spoken input is converted into text. The text is then analysed, segmented into meaningful components, and mapped to sign language gestures displayed as animations or visuals. This approach prioritizes accessibility and ease of use, ensuring that individuals with hearing impairments can participate seamlessly in conversations. The system's modular design allows adaptability for sign languages and continuous improvement through machine learning techniques. By combining cutting-edge technology with a user-centric focus, this project provides an innovative tool for enhancing inclusivity in communication. Key Words:Speech recognition , Real-Time Translation, Natural Language Processing, Sign Language Animation, Deaf Accessibility, Speech Recognition, Communication Bridge, Text preprocessing, NLP.

Technical note: A fast and reproducible autosampler for direct vapor equilibration isotope measurements

Abstract. To investigate water movement in environmental systems, stable isotope (2H and 18O) ratios of water are commonly used tracers. Analyzing the isotopic ratios of water in or adsorbed to substances like soil or plant tissue necessitates the extraction or equilibration of water prior to analysis. One such method, direct vapor equilibration, is popular due to its cost-effectiveness and straightforward sample processing. However, sample analysis requires significant manual labor, thereby limiting the number of samples that can be analyzed. This limitation is compounded by the fact that stored samples undergo evaporative isotopic changes over time. Moreover, manual measurements require many laborious procedural steps that can easily compromise reproducibility. The operator has to subjectively decide if the measurements are stable and then record the analyzer readings. To address these challenges, we have developed a system that automates the analysis process. Our autosampler for vapor samples, named VapAuSa, features a modular design that allows for up to 350 ports for direct vapor equilibration samples. These ports sequentially connect the prepared samples to a laser isotope analyzer, enabling continuous automated measurements. Within the accompanying software, measurement criteria can be specified, facilitating reproducible analysis. The developed system was tested by co-measuring 90 soil samples and 21 liquid water samples with known δ values. VapAuSa measurements have a negligible measurement bias (<1×10-13 ‰ for both δ2H and δ18O) and similar measurement repeatability compared to manual analysis of identical samples (δ2H=±4.5 ‰ and δ18O=±0.58 ‰ for VapAuSa measurements vs. δ2H=±5.7 ‰ and δ18O=±0.37 ‰ for manual analysis). However, the increased sample throughput minimizes storage-induced isotopic changes. Moreover, VapAuSa triples sample throughput per week while also reducing the direct labor time to just 10 % of that required for manual processing.

ModeHunter: A Package for the Reductionist Analysis, Animation, and Application of Elastic Biomolecular Motion.

ModeHunter is a modular Python software package for the simulation of 3D biophysical motion across spatial resolution scales using modal analysis of elastic networks. It has been curated from our in-house Python scripts over the last 15 years, with a focus on detecting similarities of elastic motion between atomic structures, coarse-grained graphs, and volumetric data obtained from biophysical or biomedical imaging origins, such as electron microscopy or tomography. With ModeHunter, normal modes of biophysical motion can be analyzed with various static visualization techniques or brought to life by dynamics animation in terms of single or multimode trajectories or decoy ensembles. Atomic structures can also be refined against volumetric densities with flexible fitting strategies. The software consists of multiple stand-alone programs for the preparation, analysis, visualization, animation, and refinement of normal modes and 3D data sets. At its core, two spatially reductionist elastic motion engines are currently supported: elastic network models (typically for a Cα level of detail and rectangular meshes) and bend-twist stretch (for trigonal or tetrahedral meshes or trees resulting from spatial clustering). The programs have recently been modernized to Python 3, requiring only the common numpy and scipy external libraries for numerical support. The main advantage of our modular design is that the tools can be combined by the end users for specific modeling applications, either standalone or with complementary tools from our C/C++-based Situs modeling package. The modular design and consistent look and feel facilitate the maintenance of individual programs and the development of novel application workflows. Here, we provide the first complete overview of the ModeHunter package as it exists today, with an emphasis on functionality and workflows supported by version 1.4.

Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence

This paper proposes a hybrid approach that combines intelligent algorithms and modular design to solve a foraging problem within the context of swarm robotics. Deep reinforcement learning (RL) and particle swarm optimization (PSO) are deployed in the proposed modular architecture. They are utilized to search for many resources that vary in size and exhibit a dynamic nature with unpredictable movements. Additionally, they transport the collected resources to the nest. The swarm comprises 8 E-Puck mobile robots, each equipped with light sensors. The proposed system is built on a 3D environment using the Webots simulator. Through a modular approach, we address complex foraging challenges characterized by a non-static environment and objectives. This architecture enhances manageability, reduces computational demands, and facilitates debugging processes. Our simulations reveal that the RL-based model outperforms PSO in terms of task completion time, efficiency in collecting resources, and adaptability to dynamic environments, including moving targets. Notably, robots equipped with RL demonstrate enhanced individual learning and decision-making abilities, enabling a level of autonomy that fosters collective swarm intelligence. In PSO, the individual behavior of the robots is more heavily influenced by the collective knowledge of the swarm. The findings highlight the effectiveness of a modular design and deep RL for advancing autonomous robotic systems in complex and unpredictable environments.