Design Tool Research Articles

Advanced Controllers for Heat Transfer Fluid Mass Flow Rate Control in Solar Tower Receivers

Efficient control strategies for managing the mass flow rate (MFR) of heat transfer fluids (HTF) during cloud transients in Solar Tower receivers play a pivotal role in optimizing plant profitability and receiver durability. This study focuses on the performance and durability of Solar Tower receivers during cloud transients. It evaluates adaptive feedback and feedforward control methods, which adjust the flow rate of heat transfer fluids based on real-time measurements of direct normal irradiance (DNI), receiver outlet and receiver panel outlet temperatures. The effectiveness of an aggressive all-sky and conservative clear-sky control strategy is explored against a conventional PI controller, emphasizing energy efficiency and receiver longevity. Simulations using a thermal Modelica model resembling a 100 MWel Crescent Dunes-like solar tower plant reveal that both advanced controllers provide precise setpoint tracking, while the PI controller struggles. The conservative controller which has a cloud standby mode prevents overheating during cloud transients by using a clear sky mass flow rate, while the aggressive controller uses the receiver panel outlet temperatures to correct for upstream tube temperature variations allowing for fast tracking correction and disturbance rejection, albeit with slight overshoots. Furthermore, the controllers significantly decrease the creep-fatigue damage accumulated in the receiver panels during cloudy days, due to limiting the increase in wall temperature spikes when cloud events end. Overall, this study underscores the pivotal role of HTF mass flow rate control systems in influencing receiver system failure modes and longevity and offers a new tool in controller design and operation assessment.

Modified heated CGMD simulations for discovering stable docked conformations of BiTE antibody against CD3 and CD117/c-kit

ABSTRACT In cancer immunotherapy, the design and optimisation of bispecific antibodies hold great promise. Bispecific T-cell engager (BiTE) antibodies targeting CD3 and CD117/c-kit have shown significant potential in experimental settings. Nevertheless, knowledge on their stable docked conformations at molecular level is still limited. This study presents an approach of employing modified heated coarse-grained molecular dynamics (CGMD) simulations to elucidate the stable docked conformations of BiTE antibodies against CD3 and CD117/c-kit. We integrated molecular dynamics simulation with coarse-grained and temperature control to explore the conformational landscape of these complex interactions. The modified heated CGMD simulation aimed to re-assess the docked poses suggested by ClusPro webserver. Furthermore, the all-atomic trajectories unveiled the dynamic residues formed throughout the simulation process. The per-residue-energy-binding emphasised the crucial amino acids involved in binding within the complex especially between the complementarity-determining regions (CDR) of BiTEs and residues located at the N-terminal of CD117/c-kit and the C-terminal of CD3. The formation of three types of interactions, such as hydrogen bonds, salt-bridge contact and hydrophobic interactions plays a crucial role in the motion, configuration and the free energy landscape of the complexes. This method is a valuable tool for rational drug design especially in the field of cancer immunotherapy.

Characterization of natural product inhibitors of quorum sensing reveals competitive inhibition of Pseudomonas aeruginosa RhlR by ortho-vanillin.

Quorum sensing (QS) regulates many aspects of bacterial pathogenesis and has attracted much interest as a target for anti-virulence therapies over the past 30 years, for example, antagonists of the LasR and RhlR QS receptors in Pseudomonas aeruginosa. Potent and selective QS inhibitors remain relatively scarce. However, natural products have provided a bounty of chemical scaffolds with anti-QS activities, but their molecular mechanisms are poorly characterized. The current study serves to fill this void by examining the activity of an important and wide-spread class of natural product QS modulators, benzaldehydes, and related derivatives, in LasR and RhlR. We demonstrate that ortho-vanillin can act as a competitive inhibitor of RhlR, a receptor that has emerged and may supplant LasR in certain settings as a target for P. aeruginosa QS control. The results and insights provided herein will advance the design of chemical tools to study QS with improved activities and selectivities.

AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities

The project "AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities" addresses the pressing need for educational support in rural areas. With advancements in AI and natural language processing (NLP), chatbots have emerged as promising tools to bridge educational gaps. However, existing solutions often lack multilingual support and fail to cater to the unique needs of rural communities. This project aims to develop a multilingual chatbot tailored specifically for rural contexts, enabling access to educational resources and support in local languages. By leveraging AI and NLP technologies, the chatbot will provide personalized assistance to rural students, empowering them to pursue higher education aspirations. Through this project, we aim to address the challenges faced by rural communities in accessing quality education and contribute to their educational advancement. The methodology of the project involves leveraging machine learning algorithms and natural language processing techniques to develop the chatbot. Objectives include designing a user-friendly interface, implementing multilingual support, and ensuring robust data storage and retrieval. Design and experimental tools such as Python, Flask, NLTK, and scikit-learn will be utilized. Key specifications include handling diverse user queries, ensuring data security, and optimizing response generation algorithms. The sequence involves initial data gathering, followed by algorithm development, system testing, and iterative refinement based on user feedback. The key findings of the project showcase significant improvements in user interaction and system performance. Experimental data demonstrates high accuracy in query processing and response generation. The developed chatbot exhibits a working efficiency of over 90%, as indicated by user satisfaction surveys and performance metrics. These outcomes validate the effectiveness of the implemented methodologies and design choices. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)

Linking implementation science and policy: Process and tools for congressionally mandated implementation, evaluation, and reporting.

To describe a process model for assisting partners in addressing requirements of legislation and review policy analysis, planning, and evaluation design processes and tools. Throughout its 25-year history, the United States Department of Veterans Affairs (VA) Quality Enhancement Research Initiative (QUERI) program has been a forerunner in partnering with organizational leaders to improve health care. The Foundations of Evidence-based Policymaking Act of 2018 provided new opportunities for QUERI and other implementation scientists to support federal agency leaders in implementing, evaluating, and reporting on congressionally mandated programs. Although implementation scientists have the skills to support partnered implementation and evaluation, these skills must be adapted for congressionally mandated projects as many scientists have limited experience in policy analysis and the intersection of data informing organizational policy, programs, and practices (i.e., evidence-based policy). During the conduct of four congressionally mandated projects, our national VA QUERI team developed processes and tools to achieve the goals and aims of our VHA partners and to ensure our collective work and reporting met legislative requirements. Our process model, program planning, and analysis tools were informed by an iterative process of refining and adapting the tools over a period of six years, spanning the years 2017 to 2023. Work to support our partners was conducted across three phases: preparation and planning, conducting implementation and evaluation, and developing the congressionally mandated report. The processes and tools we developed within the context of mutually respectful and honest partnerships have been critical to our QUERI center's success in this area. Lessons we learned may help other scientists partnering in VA or other federal agencies to plan, conduct, and report on congressionally mandated projects.

Optimized analysis and reinforcement design model for RC/SCC box-section bridges using developed advanced design framework

In recent years, there has been a significant rise in the construction of reinforced concrete (RC) and steel-concrete composite (SCC) box-section bridges. However, design codes do not provide specific design methods for the RC plates of such bridges where the plates experience both intricate forces and moments. The current design approaches are primarily based on conventional one-dimensional beam element theory, which neglects the complex spatial force characteristics of the structural elements, potentially leading to unsafe design. Therefore, it is imperative to address this issue. To bridge this gap, a comprehensive analysis and reinforcement design procedure named as the developed advanced design framework (DADF) is proposed in this study. The spatial analysis of bridges was performed using a refined numerical model, called the spatial grid model (SGM), to consider the structural spatial effects with limited model scales. Subsequently, the internal force corresponding matrix (IFCM) was implemented through Python scripts to determine the most critical internal force combination for an RC plate element. Additionally, the three-layered sandwich model (T-LSM) was employed with limit analysis to automatically design reinforcement for all elements, serving as a practical tool for design. This research demonstrates that the implementation of the DADF can significantly reduce steel reinforcement in the RC plates of such bridges by up to 48 %, and 23 % in total, showcasing the efficiency of the developed framework. Overall, the findings emphasize that the proposed DADF offers a robust procedure for steel reinforcement design of RC/SCC box-section bridges.

A Pedagogic Approach Serving Inclusive Design

This paper presents an architecture teaching approach intended to final year architecture students, which is based on a human-centered design perspective, while respecting environment in all its compounds. The present work claims, as a hypothesis, that to design inclusive projects, it is worthy to develop in future architects’ capacity to analyze and reconcile both environment requirements and users’ needs as well. The large array of environment key elements and the complexity of users’ global apprehension, make it crucial for the future architects and urban planners to control the appropriate set of analysis methodologies and put to contribution the most efficient design tools. The pedagogical approach we deal with in this article, educates students in empathy, listening, and developing an open-mindedness that according to this study, allows them design better inclusive spaces. I am adopting such a vision for several years now, and I am experimenting pedagogical strategies and tools accordingly, either within the final year Architectural Design Studio I share with other colleagues, or during the Seminar I offer to the same audience. In trying to subscribe to a perspective of action research and reflexivity, I chose to present in this paper the analysis of four architectural dissertations addressing inclusive design concerns. This analysis aims to fathom how experienced analysis tools, such as SWOT grid, urban transect and qualitative social survey, as well as design tools, such as sequential approach of space, functional analysis and value creation tools, may help final year architecture students better analyze and design inclusive projects.

Innovating visitor research within museums: Concepts, tools and practices

Increasing demands on museums for visitor insights, theoretical shifts in the learning sciences, the embeddedness of multimodal media in museums and society, and advancements in data visualisation tools and web-based applications are all clear calls for innovating visitor research in museums. In this article, I describe how concepts from multimodal and sociocultural research help shift the analytical lens for understanding and studying connections between visitor behaviours, social interactions and engagement in exhibitions. The following question is explored: In which ways can multimodal theory and the learning sciences inform the design and use of new visitor research tools and methods that are relevant for museum practice? A case study from a university-museum research partnership illustrates how multimodal and sociocultural perspectives from the humanities and learning sciences, respectively, merged in the development of a new tool to enhance and innovate visitor research within museums.

Large-Scale Solar Potential Analysis in a 3D CAD Framework as a Use Case of Urban Digital Twins

Solar radiation impacts diverse aspects of city life, such as harvesting energy with PV panels, passive heating of buildings in winter, cooling the loads of air-conditioning systems in summer, and the urban microclimate. Urban digital twins and 3D city models can support solar studies in the process of urban planning and provide valuable insights for data-driven decision support. This study examines the calculation of solar incident radiation at the city scale in Sofia using remote sensing data for the large shading context in a mountainous region and 3D building data. It aims to explore the methods of geometry optimisation, limitations, and performance issues of a 3D computer-aided design (CAD) tool dedicated to small-scale solar analysis and employed at the city scale. Two cases were considered at the city and district scales, respectively. The total face count of meshes for the simulations constituted approximately 2,000,000 faces. A total of 64,379 roofs for the whole city and 4796 buildings for one district were selected. All calculations were performed in one batch and visualised in a 3D web platform. The use of a 3D CAD environment establishes a seamless process of updating 3D models and simulations, while preprocessing in Geographic Information System (GIS) ensures working with large-scale datasets. The proposed method showed a moderate computation time for both cases and could be extended to include reflected radiation and dense photogrammetric meshes in the future.

PyAMPA: a high-throughput prediction and optimization tool for antimicrobial peptides.

This paper introduces PyAMPA, a new bioinformatics platform designed for the discovery and optimization of antimicrobial peptides (AMPs). It addresses the urgent need for new antimicrobials due to the rise of antibiotic-resistant infections. PyAMPA, with its five predictive modules -AMPScreen, AMPValidate, AMPSolve, AMPMutate and AMPOptimize, enables high-throughput screening of proteomes to identify potential AMP motifs and optimize them for clinical use. Its unique approach, combining prediction, design, and optimization tools, makes PyAMPA a robust solution for developing new AMP-based therapies, offering a significant advance in combatting antibiotic resistance.

How to sustain a centralised approach to learning design

Innovative changes to online teaching practices are becoming increasingly important with the rise of e-learning across the higher education sector. Such innovations ideally become part of teaching repertoires rather than reverting to prior approaches. This study investigated the sustainability of a centralised approach to online learning design. Of the 74 survey respondents, more than 70% described changes to their teaching or work practices, uptake of learning design tools or development of student-facing materials, and these changes were mostly reported as sustained. Changes to system-level supports were less common and less likely to be sustained. Free-text comments highlight the importance of adaptation, collaboration and iteratively developed learning design templates. These provide guidance on how to promote sustained changes to online teaching practice. Implications for practice or policy: Outcomes for sustained changes to online teaching practice can be improved through innovations that are adaptable and collaborative and build educator capability. Learning design templates and resources can guide educators to develop their online teaching practice while allowing flexible and iterative implementation. Sustained changes to online teaching practices may be impeded by aging student-facing materials, withdrawal of support and unfeasible workloads.

Friction Stir Channeling in Heat Sink Applications: Innovative Manufacturing Approaches and Performance Evaluation

The fabrication of compact heat exchangers with precisely designed micro- and mini-channels is crucial for enhancing the efficiency of thermal management systems. Friction stir channeling (FSC) emerges as a cost-effective advanced manufacturing process to create complex integral channels, offering channel shape and size flexibility. This review article highlights the pivotal role of processing parameters in channel formation and maintaining their integrity, necessitating a comprehensive understanding of material flow dynamics. A rigorous assessment has been conducted on the channel under mechanical stresses, including tension, bending, and fatigue. The paper emphasizes the potential of FSC to revolutionize heat sink applications by exploring the fundamental concepts, governing parameters, ongoing enhancements in tool design, microstructural and mechanical properties, and heat transfer performance.

A Novel Wedge Anchor System for Double-Layer CFRP Plate Cables: Concept, Theoretical Analysis and FEA.

This study introduces an innovative wedge anchor for double-layer carbon fiber reinforced polymer (CFRP) plate cable to address the current limitation of traditional wedge anchors. By employing the design concept of "secondary force transmission path", the friction force for anchoring the CFRP plate is effectively transferred into the barrel through its contracting wedge, thus reducing the clamping pressure requirement of traditional wedge anchorage for anchoring thick or double-layer CFRP plates. Based on this conception, this study presents a theoretical analysis model for predicting the influence of parameter variations on the compressive stress of the CFRP plate, which can serve as a tool for rapid configuration preliminary design. Through finite element analysis, the internal stress distribution of the anchor is thoroughly investigated, and the theoretical analysis model for fast predicting compressive stress of CFRP plate is also validated. The results also indicate that the anchorage conception is valid and effective, providing sufficient anchorage of CFPR plates with an anchorage length of 100 mm.

Multi-class Breast Cancer Classification Using CNN Features Hybridization

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for earlier breast cancer classification. Meanwhile, the design of CAD tools using deep learning is becoming popular and robust in biomedical classification systems. However, deep learning gives inadequate performance when used for multilabel classification problems, especially if the dataset has an uneven distribution of output targets. And this problem is prevalent in publicly available breast cancer datasets. To overcome this, the paper integrates the learning and discrimination ability of multiple convolution neural networks such as VGG16, VGG19, ResNet50, and DenseNet121 architectures for breast cancer classification. Accordingly, the approach of fusion of hybrid deep features (FHDF) is proposed to capture more potential information and attain improved classification performance. This way, the research utilizes digital mammogram images for earlier breast tumor detection. The proposed approach is evaluated on three public breast cancer datasets: mammographic image analysis society (MIAS), curated breast imaging subset of digital database for screening mammography (CBIS-DDSM), and INbreast databases. The attained results are then compared with base convolutional neural networks (CNN) architectures and the late fusion approach. For MIAS, CBIS-DDSM, and INbreast datasets, the proposed FHDF approach provides maximum performance of 98.706%, 97.734%, and 98.834% of accuracy in classifying three classes of breast cancer severities.

Chat messenger use in the care of patients with Parkinson's disease

The demand for chat messaging apps for communication between physicians, therapists and patients is increasing. The expectations for this form of communication and uncertainties regarding introduction and use are heterogeneous. The implementation of chat messengers in the care of patients with Parkinson's disease should be facilitated by recommendations regarding introduction and usage. Semi-structured interviews with neurologists and physiotherapists were conducted to capture the expectations and needs regarding the use of chat messengers. From the data analysis, recommendations were derived. The expectations for technical functionality exceeded the chat messenger functions. This concerns, e.g., the connection of the chat messenger to the electronic patient file. There is agreat deal of uncertainty, particularly when it comes to the applicable General Data Protection Regulations (GDPR). The recommendations relating to the use of chat messengers, data protection aspects, the design of such tools and methodological considerations can help to implement the tool as an additional communication channel. Practical recommendations regarding functionality, the use of chat messengers in everyday life and in relation to data protection are derived from the results. By improving knowledge, physicians and therapists can contribute to the successful establishment of chat messengers as an additional communication tool.

Software cost estimation tool: A App based application, estimate the cost of software project

This paper presents the design and implementation of a software cost estimation tool integrated into a mobile application developed using Flutter. The tool incorporates various techniques for software cost estimation, including expert judgment, function point analysis, 3D point analysis, and the COCOMO model. The purpose of the program is to give software engineers and project managers a practical and effective tool for calculating the time and money needed for software development projects. The paper provides a thorough explanation of each estimation technique’s implementation, along with a discussion of the app’s main features and functionalities. Because of the app’s intuitive and user-friendly design, users can quickly enter project data and get precise cost estimates. The tool’s efficacy is assessed using case studies and contrasts with other software cost estimation methods currently in use. The outcomes show that the app can produce trustworthy and precise cost estimates, which makes it an important resource for software development projects.

Computational Optimisation of Urban Design Models: A Systematic Literature Review

The densification of urban spaces globally has contributed to a need for design tools supporting the planning of more sustainable, efficient, and liveable cities. Urban Design Optimisation (UDO) responds to this challenge by providing a means to explore many design solutions for a district, evaluate multiple objectives, and make informed selections from many Pareto-efficient solutions. UDO distinguishes itself from other forms of design optimisation by addressing the challenges of incorporating a wide range of planning goals, managing the complex interactions among various urban datasets, and considering the social–technical aspects of urban planning involving multiple stakeholders. Previous reviews focusing on specific topics within UDO do not sufficiently address these challenges. This PRISMA systematic literature review provides an overview of research on topics related to UDO from 2012 to 2022, with articles analysed across seven descriptive categories. This paper presents a discussion on the state-of-the-art and identified gaps present in each of the seven categories. Finally, this paper argues that additional research to improve the socio-technical understanding and usability of UDO would require: (i) methods of optimisation across multiple models, (ii) interfaces that address a multiplicity of stakeholders, (iii) exploration of frameworks for scenario building and backcasting, and (iv) advancing AI applications for UDO, including generalizable surrogates and user preference learning.

Controller of Pneumatic Muscles Implemented in Active Seat Suspension

In this work, we present a study on seat suspension technology that integrates pneumatic muscles, marking a significant advancement in active vibration control. This innovative approach addresses the limitations of traditional suspension systems, providing enhanced comfort and adaptability. A key achievement is the development of a mathematical model for controlling horizontal seat vibration, which serves as a valuable design tool for evaluating seat suspension under various conditions and control strategies. The creation of a custom microcontroller, benchmarked against a standard from National Instruments, highlights the practical applications of this research. Positive results suggest a promising future for this technology in industrial settings, where vibration reduction is critical. The system’s scalability and user-adjustable signal levels further enhance its potential for widespread industrial adoption.

Building Mass Optimization to Reduce Solar Radiation in High Rise Building by Using Parametric Approach

Buildings use 40% of global primary energy, therefore their design and use affect climate change. Building performance analysis can assist architects predict performance before construction with parametric design tools. Radiance can be reduced via a parametric mass, lowering cooling load and energy use. The study uses theoretical and computational research to explain, forecast, and analyze events, whereas parametric design optimizes complicated geometries using mathematical parameters and algorithms. Environmental analysis in Grasshopper with the Ladybug plugin uses Rhinoceros. This plugin provides solar radiation, and climate analysis capabilities. To determine the most energy-efficient building design, the research links independent and dependent variables such solar radiation intensity and building mass. The study uses Surabaya weather data and high rise buildings. The land is formed like a square, with a 15-degree slope to the north and is flanked by low-rise buildings. As a result, the location receives the most direct sunlight during the day. Then, solar radiation analysis. It helps optimize passive solar design solutions. According to the modelling results, solar radiation on the top and west sides are particularly large and dominant in 65.37 and 32.69 kWh/m2. Meanwhile, the north, east and south sides receive very little solar radiation. The following simulation considers the optimal direction, which is to extend west-east and face to the south. A multi-towered megastructure is a high-rise building that responds best to solar radiation. The total solar radiation value is 3,718,100 kWh. It can accommodate large spaces with large mass composition but relatively low total solar radiation values. The building towers provide shade to each other, thereby reducing direct radiation from the sun to the building. The sides of the building's podium are also shaded, so the top of the building is partially red.

Tailored detection efficiency for linear variable filter-based sensors: applying simulation models of spectral characteristics in optical design

To accurately model the specific detection characteristics of spectral sensors based on linear variable filters (LVFs) within an optical design tool, it is essential to consider crucial position-variable spectral properties, such as peak transmittance, central wavelength, half width, or slope steepness. In this context, we propose a straightforward approach, integrating a dynamic link library (DLL) containing all position-dependent spectral properties of the LVF into a commercial optical design software. Exemplary investigations are conducted for an LVF with a detection range of 450–850 nm. For ease of use, the measured position-, wavelength-, and angle-dependent transmission properties of the LVF have been described through a simple yet highly accurate model system. Moreover, to highlight the essential value of this simulation for specific applications, an efficiency-enhancing spectral module is simulated, which is an LVF-mirror arrangement characterized by a multiple-reflected beam path. The introduced optical design tool demonstrates its particular strength by enabling the optimization of the highest detection efficiency for either the short- or long-wavelength range.