Modular Design Research Articles

Courier Service System

The demand for efficient courier services is growing rapidly, and traditional systems often struggle to meet these demands. This project introduces an Enhanced Courier Service System Application, utilizing cutting-edge technologies such as ReactJS for the frontend, Java Spring Boot for the backend, and MongoDB for the database. The primary objective is to develop a robust and scalable courier service system that offers enhanced features and functionalities to meet the evolving needs of users. The system architecture facilitates seamless communication between frontend, backend, and database components, ensuring smooth data flow and efficient processing. ReactJS for the frontend enhances user experience, while Java Spring Boot provides scalability, security, and performance. MongoDB, a NoSQL database, offers flexibility and scalability for large data storage and retrieval. The modular design allows for easy maintenance and future enhancements, adapting to changing requirements and technologies. By incorporating industry-standard practices like UML diagrams, data flow diagrams, and sample test cases, the project aims to deliver a high-quality, reliable, and user-friendly courier service system application.

Modeling- and Simulation-Driven Methodology for the Deployment of an Inland Water Monitoring System

In response to the challenges introduced by global warming and increased eutrophication, this paper presents an innovative modeling and simulation (M&S)-driven model for developing an automated inland water monitoring system. This system is grounded in a layered Internet of Things (IoT) architecture and seamlessly integrates cloud, fog, and edge computing to enable sophisticated, real-time environmental surveillance and prediction of harmful algal and cyanobacterial blooms (HACBs). Utilizing autonomous boats as mobile data collection units within the edge layer, the system efficiently tracks algae and cyanobacteria proliferation and relays critical data upward through the architecture. These data feed into advanced inference models within the cloud layer, which inform predictive algorithms in the fog layer, orchestrating subsequent data-gathering missions. This paper also details a complete development environment that facilitates the system lifecycle from concept to deployment. The modular design is powered by Discrete Event System Specification (DEVS) and offers unparalleled adaptability, allowing developers to simulate, validate, and deploy modules incrementally and cutting across traditional developmental phases.

Modular Ship Design

In this paper, we introduce a modular approach to ship design, utilizing design modules to streamline the initial design phases. Ships are conceived as combinations of a primary 'ship module' and various 'design modules' tailored for specific spaces. These modules encompass predefined geometries, layouts, and equipment configurations. We introduce an optimization model that integrates decision variables for ship attributes and configuration variables for module selection. Through this framework, we aim to simplify design complexities, accelerate the production of detailed drawings, and foster innovation in ship design methodologies.

BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading

Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is usually challenging and time-consuming due to the diversity of energy markets. The lack of a market-agnostic design hampers the widespread adoption of blockchain-based peer-to-peer energy trading globally. In this paper, we propose and develop a novel unified blockchain-based peer-to-peer energy trading framework, called BPET. This framework incorporates microservices and blockchain as the infrastructures and adopts a highly modular smart contract design so that developers can easily extend it by plugging in localized energy market rules and rapidly developing a customized blockchain-based peer-to-peer energy trading system. Additionally, we have developed the price formation mechanisms, e.g., the system marginal price calculation algorithm and the pool price calculation algorithm, to demonstrate the extensibility of the BPET framework. To validate the proposed solution, we have conducted a comprehensive case study using real trading data from the Alberta Electric System Operator. The experimental results confirm the system’s capability of processing energy trading transactions efficiently and effectively within the Alberta electricity wholesale market.

An open-source research platform for mechanical ventilation based on Simulink® and STM32 nucleo

Abstract Gain in knowledge in experimental fields of research is mostly achieved using commercial hardware platforms. The linked costs however can limit a broad access by scientists and in the specific case of mechanical ventilation the pandemic demonstrated that further and collective research is essential and necessary. This contribution presents the modular design and implementation of a research platform which is based on STM32 Nucleo boards and Simulink®. The hardware required such as the pneumatic circuit are covered as well as the software architecture in which clinician related settings are processed for control references. The core functionality of mandatory volume controlled (VCV) and pressure controlled ventilation (PCV) while ensuring a positive end-expiratory pressure for the patient ( PEEP ) $(\text{PEEP})$ is evaluated and the foundational software models are published in a repository.

Design and architecture of a slender and flexible underwater robot

AbstractThis paper presents the design and analysis of a biomimetic underwater snake-like robot, addressing the main limitations of current underwater robotic systems in terms of maneuverability and adaptability in complex environments. The innovative design incorporates flexible joint modules that significantly enhance the robot’s ability to navigate through narrow and irregular terrains, which is a notable limitation in traditional rigidly connected underwater robots. These flexible joints provide increased degrees of freedom and enable the robot to absorb and release energy, ensuring stability even under external impacts, thus extending the operational lifespan of the robot. Finite element analysis demonstrates the flexible joints’ superior performance in various underwater conditions, offering a greater range of motion and workspace compared to rigid connections. The results indicate that the robot’s modular design, combined with the flexible joint module, leads to improved agility and maneuverability, allowing for precise and intentional operation. The control module, equipped with advanced sensors and a CPU, manages the complex dynamics introduced by the flexible joints, ensuring effective navigation and operation. The specific advantages of this design include the robot’s enhanced structural integrity, its ability to conform to irregular surfaces, and its adaptability to environmental variations. The paper concludes with a discussion on the implications of these findings for the future design and operation of underwater serpentine robots, emphasizing the need for a balance between the effects of elastic modulus and workspace to maximize the benefits of flexible joints.

Modular multi-degree-of-freedom soft origami robots with reprogrammable electrothermal actuation

Soft robots often draw inspiration from nature to navigate different environments. Although the inching motion and crawling motion of caterpillars have been widely studied in the design of soft robots, the steering motion with local bending control remains challenging. To address this challenge, we explore modular origami units which constitute building blocks for mimicking the segmented caterpillar body. Based on this concept, we report a modular soft Kresling origami crawling robot enabled by electrothermal actuation. A compact and lightweight Kresling structure is designed, fabricated, and characterized with integrated thermal bimorph actuators consisting of liquid crystal elastomer and polyimide layers. With the modular design and reprogrammable actuation, a multiunit caterpillar-inspired soft robot composed of both active units and passive units is developed for bidirectional locomotion and steering locomotion with precise curvature control. We demonstrate the modular design of the Kresling origami robot with an active robotic module picking up cargo and assembling with another robotic module to achieve a steering function. The concept of modular soft robots can provide insight into future soft robots that can grow, repair, and enhance functionality.

A solid-state pulse power sub-nanosecond SiC DSRD-based generator with high-voltage and high repetition frequency for pulse discharge water treatment

Water treatment is one of the most important issues for all walks of life around the world. The unique advantages of the solid-state power electronic pulses in water treatment make it attractive and promising in practical applications. The output voltage, rising time, repetition rate, and peak power of output pulses have a significant impact on the effectiveness of water treatment. Especially in pulse electric field treatment and pulse discharge treatment, the pulse with fast rising time achieves the advantage of generating plasma without corona, which can avoid water heating effect and greatly improve the efficiency of the pulse generator. High repetition rate can significantly reduce the peak power requirement of the pulse in water treatment application, making the equipment smaller and improving the power density. Therefore, the study developed a high-voltage high frequency sub-nanosecond pulse power generator (PPG) system for wastewater treatment. It adopts SiC DSRD (Drift Step Recovery Diode) solid-state switches and realize modular design, which can achieve high performance and can be flexible expanded according to the requirements of water treatment capacity. Finally, an expandable high-voltage PPG for water treatment is built. The output parameters of the PPG include output pulse voltage range from 1 to 5.28 kV, rise time <600 ps (20%–90%), repetition up to 1 MHz. The experiment results of PPG application for pulse discharge water treatment is presented. The results indicate that the proposed generator achieves high-efficiency degradation of 4-Chlorophenol (4-CP), which is one of the most common chlorophenol compounds in wastewater. From experiment, the homemade system can degrade 450 mL waste water containing 500 mg/L 4-CP in 35 min, with a degradation rate of 98%. Thereby, the requirement for electric field intensity decreased. Through the further quantitative analysis, the impact of frequency, voltage, and electrode spacing on the degradation effect of 4-CP is confirmed.

Metal Ions Trigger the Gelation of Cysteine‐Containing Peptide‐Appended Coordination Cages

We report a series of coordination cages that incorporate peptide chains at their vertices, prepared through subcomponent self‐assembly. Three distinct heterochiral tripeptide subcomponents were incorporated, each exhibiting an L–D–L stereoconfiguration. Through this approach, we prepared and characterized three tetrahedral metal‐peptide cages that incorporate thiol and methylthio groups. The gelation of these cages wasprobed through the binding of additional metal ions, with the metal‐peptide cages acting as junctions, owing to the presence of sulfur atoms on the peripheral peptides. Gels were obtained with cages bearing cysteine at the C‐terminus. Our strategy for developing functional metal‐coordinated supramolecular gels with a modular design may result in the development of materials useful for chemical separations or drug delivery.

Metal Ions Trigger the Gelation of Cysteine-Containing Peptide-Appended Coordination Cages.

We report a series of coordination cages that incorporate peptide chains at their vertices, prepared through subcomponent self-assembly. Three distinct heterochiral tripeptide subcomponents were incorporated, each exhibiting an L-D-L stereoconfiguration. Through this approach, we prepared and characterized three tetrahedral metal-peptide cages that incorporate thiol and methylthio groups. The gelation of these cages wasprobed through the binding of additional metal ions, with the metal-peptide cages acting as junctions, owing to the presence of sulfur atoms on the peripheral peptides. Gels were obtained with cages bearing cysteine at the C-terminus. Our strategy for developing functional metal-coordinated supramolecular gels with a modular design may result in the development of materials useful for chemical separations or drug delivery.

A VHH single-domain platform enabling discovery and development of monospecific antibodies and modular neutralizing bispecifics against SARS-CoV-2 variants

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve, escape coronavirus disease 2019 therapeutics and vaccines, and jeopardize public health. Methods To combat SARS-CoV-2 antigenic escape, we developed a rapid, high-throughput pipeline to discover monospecific VHH antibodies and iteratively develop VHH-Fc-VHH bispecifics capable of neutralizing emerging SARS-CoV-2 variants. Results By panning VHH single-domain phage libraries against ancestral or Beta spike proteins, we discovered high-affinity VHH antibodies with unique target epitopes. Combining two VHHs into a tetravalent bispecific construct conferred broad neutralization activity against multiple variants and was more resistant to antigenic escape than the monospecific antibody alone. Following the rise of the Omicron variant, a VHH in the original bispecific construct was replaced with another VHH discovered against Omicron BA.1 receptor binding domain—the resulting bispecific exhibited neutralization against both BA.1 and BA.5 sublineage variants. Conclusions A heavy chain-only tetravalent VHH-Fc-VHH bispecific platform derived from humanized synthetic libraries held a myriad of unique advantages: 1) synthetic preconstructed libraries minimized risk of liabilities and maximized discovery speed, 2) VHH scaffolds allowed for a modular “plug-and-play” format that could be rapidly iterated upon as variants of concern arose, 3) natural dimerization of single VHH-Fc-VHH polypeptides allowed for straightforward bispecific production and purification methods, and 4) multivalent approaches enhanced avidity boosting effects and neutralization potency, and conferred more robust resistance to antigenic escape than monovalent approaches against specific variants. This iterative platform of rapid VHH discovery combined with modular bispecific design holds promise for long-term viral control efforts.

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 modular design 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 non-contact 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 submicron precision are bench-marked with commercially available CHB tablets that are manually divided. The proposed tunable modular design (TMD) covers the entire range of doses needed for the tapering (0.5-23.8mg). The greatest variance is 13% and 88% when comparing 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. This article is protected by copyright. All rights reserved.

Refining Localized Attention Features with Multi-Scale Relationships for Enhanced Deepfake Detection in Spatial-Frequency Domain

The rapid advancement of deep learning and large-scale AI models has simplified the creation and manipulation of deepfake technologies, which generate, edit, and replace faces in images and videos. This gradual ease of use has turned the malicious application of forged faces into a significant threat, complicating the task of deepfake detection. Despite the notable success of current deepfake detection methods, which predominantly employ data-driven CNN classification models, these methods exhibit limited generalization capabilities and insufficient robustness against novel data unseen during training. To tackle these challenges, this paper introduces a novel detection framework, ReLAF-Net. This framework employs a restricted self-attention mechanism that applies self-attention to deep CNN features flexibly, facilitating the learning of local relationships and inter-regional dependencies at both fine-grained and global levels. This attention mechanism has a modular design that can be seamlessly integrated into CNN networks to improve overall detection performance. Additionally, we propose an adaptive local frequency feature extraction algorithm that decomposes RGB images into fine-grained frequency domains in a data-driven manner, effectively isolating fake indicators in the frequency space. Moreover, an attention-based channel fusion strategy is developed to amalgamate RGB and frequency information, achieving a comprehensive facial representation. Tested on the high-quality version of the FaceForensics++ dataset, our method attained a detection accuracy of 97.92%, outperforming other approaches. Cross-dataset validation on Celeb-DF, DFDC, and DFD confirms the robust generalizability, offering a new solution for detecting high-quality deepfake videos.

A workflow management system for reproducible and interoperable high-throughput self-driving experiments

To foster self-driving experimentation and address the reproducibility crisis in bioprocess development in a collaborative environment, a modular Workflow Management System (WMS) is required. In this work, a WMS based on Directed Acyclic Graphs that offers a modular and flexible design for plug-and-play integration of computational tools is presented. A case study is used to demonstrate that the implementation of a computational WMS in robotic experimental facilities promotes the application of Findable, Accessible, Interoperable and Re-usable principles, allowing researchers to readily share protocols, models, methods and data. As a proof of concept, we integrated three different computational workflows for online re-design of feeding rates in 24 parallel E. coli fed-batch cultivations producing elastin-like proteins. This approach provides a solid foundation for increasing scientific data generation in robotic experimental facilities, fostering open collaboration, and addressing the challenges of reproducibility in research.

X 2-VLM: All-in-One Pre-Trained Model for Vision-Language Tasks.

Vision language pre-training aims to learn alignments between vision and language from a large amount of data. Most existing methods only learn image-text alignments. Some others utilize pre-trained object detectors to leverage vision language alignments at the object level. In this paper, we propose to learn multi-grained vision language alignments by a unified pre-training framework that learns multi-grained aligning and multi-grained localization simultaneously. Based on it, we present X 2-VLM, an all-in-one model with a flexible modular architecture, in which we further unify image-text pre-training and video-text pre-training in one model. X 2-VLM is able to learn unlimited visual concepts associated with diverse text descriptions. Experiment results show that X 2-VLM performs the best on base and large scale for both image-text and video-text tasks, making a good trade-off between performance and model scale. Moreover, we show that the modular design of X 2-VLM results in high transferability for it to be utilized in any language or domain. For example, by simply replacing the text encoder with XLM-R, X 2-VLM outperforms state-of-the-art multilingual multi-modal pre-trained models without any multilingual pre-training.

Smart Automated Liquid Sprinkler Robot Integrating IoT and Deep Learning for Precision Crop Management

Abstract: The Smart Automated Liquid Sprinkler (SALS) is a groundbreaking solution aimed at revolutionizing traditional watering systems through the integration of IoT technology. Its development addresses pressing challenges such as water scarcity and labor-intensive maintenance in outdoor environments. SALS combines advanced sensors to monitor humidity and weather conditions in real-time, ensuring precise watering tailored to the specific needs of plants while minimizing manual intervention. Furthermore, it incorporates obstacle detection and avoidance features, enhancing its efficiency and durability by mitigating the risk of accidents. By integrating deep learning models for rainfall prediction, SALS optimizes water usage preemptively, adjusting watering schedules to environmental conditions. Offering independent operation and a modular design, SALS provides flexibility to users, catering to gardens of all sizes and promoting greener and healthier environments. The longterm savings from reduced water consumption and labor costs make SALS a wise investment for homeowners, businesses, and municipalities, heralding a new era of efficiency and sustainability in outdoor maintenance

Concept of a Peripheral-Free Electrified Monorail System (PEMS) for Flexible Material Handling in Intralogistics

With the emergence of Industry 4.0 in intralogistics, the need for flexible material handling solutions is increasing. While conventional conveyor systems are often too inflexible to meet changing requirements. Automated guided vehicles offer an answer, additional solutions are required for companies relying on already busy and crowded shop floors. This paper presents a concept for a periphery-free electrified monorail system (PEMS) that enables flexible material transport with minimal floor requirements. The PEMS is based on classic electrified monorail technology, and requires no additional peripheral devices within the rail system. Installation and maintenance costs are kept to a minimum through simplified branching elements and a battery-powered energy supply for the vehicles. The modular design of the rail elements further allows transport on standardized Euro-pallets. Moreover, a taxonomy for evaluating the passivity of branching elements of electrified monorail systems is introduced. The functionality of the PEMS was validated by conducting real experiments using a prototype, The results show that the PEMS provides high flexibility in terms of layout design and usage, allowing for fast adaption to the changing requirements of intralogistics.

Aquaculture Monitoring and Control System Based On IOT

- This project offers a comprehensive solution for monitoring and regulating aquaculture conditions using cutting-edge Internet of Things (IoT) technology. By leveraging ESP8266 NodeMCU and Arduino UNO, alongside a range of sensors including the DS18B20 waterproof temperature sensor, water level sensor, and turbidity sensor, it ensures real-time monitoring of vital parameters in aquatic environments. Precise control of water circulation and management is made possible through the integration of motor drivers with submersible pumps. The Blynk App serves as the user-friendly control interface, granting remote access and management of water pumps. Meanwhile, sensor data is continuously collected and displayed on ThingSpeak, offering consumers valuable insights into the current state of their aquaculture setup. This project harnesses IoT technology to enhance aquaculture management efficiency and productivity, while also ensuring optimal environmental conditions for aquatic life. Its modular design enables scalability and customization, catering to various aquaculture configurations and settings. Key Words: Arduino UNO, Sensors, Motor Driver, Wifi Module, ThingSpeak, IOT, Aquaculture, Blynk App.

Mechanistic interactions as the origin of modularity in biological networks.

Biological networks are often modular. Explanations for this peculiarity either assume an adaptive advantage of a modular design such as higher robustness, or attribute it to neutral factors such as constraints underlying network assembly. Interestingly, most insights on the origin of modularity stem from models in which interactions are either determined by highly simplistic mechanisms, or have no mechanistic basis at all. Yet, empirical knowledge suggests that biological interactions are often mediated by complex structural or behavioural traits. Here, we investigate the origins of modularity using a model in which interactions are determined by potentially complex traits. Specifically, we model system elements-such as the species in an ecosystem-as finite-state machines (FSMs), and determine their interactions by means of communication between the corresponding FSMs. Using this model, we show that modularity probably emerges for free. We further find that the more modular an interaction network is, the less complex are the traits that mediate the interactions. Altogether, our results suggest that the conditions for modularity to evolve may be much broader than previously thought.

Client-centered detached modular housing: natural language processing-enabled design recommender system

Abstract Designing modular housing is a complex task that necessitates a thorough understanding of the diverse needs of clients in terms of both design aesthetics and floor plan layout. Furthermore, adhering to design for manufacture and assembly (DfMA) principles adds to the complexity, as these are essential modular home requirements. Traditional construction methods frequently fail to meet the specific needs of both clients and DfMA, potentially resulting in suboptimal design solutions. Incorporating client requirements during the design phase necessitates the use of an effective system and framework to reduce changes in subsequent project stages. Existing literature lacks a suitable approach, particularly in the context of modular housing. To address this gap, this paper introduces an artificial intelligence–building information modeling recommender system (RS) for detached modular housing design. The system processes client requirements entered as text utilizing the Word2vec algorithm with the GloVe dataset, refined through transfer learning using surveyed client data of housing needs. The system recommends three distinct modular building design alternatives sourced from a building information modeling models database using cosine and Euclidean similarity functions. A sensitivity analysis ensures that client needs are considered fairly, increasing the robustness of the RS. By incorporating natural language processing, this system transforms the construction industry by making initial designs more client-centric compared with traditional methods. Furthermore, it promotes improved collaboration among clients, design, and construction teams, reducing modifications to design in later stages of construction.