Flexible Web Research Articles

Improving and re-developing the Clerical Resolution Online Widget (CROW): open-source software for clerical review

ObjectivesClerical review is an integral part of linkage and is fundamental to quality assurance. CROW is a low dependency, adaptable, clerical matching tool that has been deployed across 40 linkage projects. Due to limitations of the interface, with the initial version (including the limited ability to view large clusters); this project sought to re-develop the tool as a flask application. ApproachOur approach was to transform the existing CROW desktop application into a Flask (web based) application. Flask was chosen because it is a flexible web framework that enabled the development of a more dynamic interface, as well as better integration with the infrastructure of secure environments. We also engaged with accessibility experts to ensure the inclusivity of our product. Once the development was completed, testing was carried out by a range of users and stakeholders, to identify further issues and bugs for resolution. ResultsThe successful redevelopment of CROW into a Flask application improved functionality, usability, parametrisation, and accessibility of the tool. Feedback from users indicates that because of using CROW, the clerical matching experience has improved vastly. Users reported that CROW increased accessibility, reducing eye strain and fatigue. CROW also increases efficiency and leads to better decision making. ConclusionThe successful redevelopment of CROW is a significant improvement to the clerical review process for data linkage projects. ImplicationsCROW is open-source and low dependency so could be adapted to work in most environments. Therefore, it could be adapted and used by the wider data linkage community.

Flexible nanofiber web electrode with high surface area and capacitance for electrochemical capacitor

Flexible nanofiber web electrode with high surface area and capacitance for electrochemical capacitor

The Sound of Data: Designing a Framework for Parameter Mapping Sonification

Abstract Parameter mapping sonification is a powerful tool for data display and analysis with uses in a myriad of fields of study. Despite the wide range of applications, parameter mapping sonification remains abstruse for people to utilize effectively. This article outlines an original design in the domain of parameter mapping sonification applications for a flexible, extensible and intuitive web app that democratizes this data display method. Sonification Interface for REmapping Nature (SIREN) accomplishes these goals through an accessible interface for sonification that demonstrates various web technologies, including Web Audio API. Existing frameworks are compared in five different areas—technology, design, functionality, accessibility, and extensibility—to determine the optimal gap for SIREN.

Bionic design and mechanical performance of spiderweb-inspired mesh fabrics

The mesh structure is widely applied due to its unique structure and exceptional performance, especially spider web structural materials, which have attracted widespread interest. However, the existing flexible spider web structural materials face problems such as low production efficiency and structural defects, constraining their practical application. This study proposed two types of no-knot mesh fabrics according to the topological structure of spider web and explored their mechanical properties. Both knitted loop divergent and ring spiral mesh fabrics showed excellent mechanical performance, with the ring spiral fabric demonstrating higher maximum failure forces (2217 N in tensile and 5738 N in bursting tests) and superior impact performance in terms of maximum impact force and energy absorption. Both fabrics exhibited similar creep behaviors and could withstand 70 J impact energy with an energy absorption rate around 90 %. This work may provide new strategies and inspiration for the preparation of high-performance flexible mesh materials and further helps in developing applications of spiderweb-inspired mesh fabrics in engineering fields.

IMPatienT: An Integrated Web Application to Digitize, Process and Explore Multimodal PATIENt daTa.

Medical acts, such as imaging, lead to the production of various medical text reports that describe the relevant findings. This induces multimodality in patient data by combining image data with free-text and consequently, multimodal data have become central to drive research and improve diagnoses. However, the exploitation of patient data is problematic as the ecosystem of analysis tools is fragmented according to the type of data (images, text, genetics), the task (processing, exploration) and domain of interest (clinical phenotype, histology). To address the challenges, we developed IMPatienT (Integrated digital Multimodal PATIENt daTa), a simple, flexible and open-source web application to digitize, process and explore multimodal patient data. IMPatienT has a modular architecture allowing to: (i) create a standard vocabulary for a domain, (ii) digitize and process free-text data, (iii) annotate images and perform image segmentation, (iv) generate a visualization dashboard and provide diagnosis decision support. To demonstrate the advantages of IMPatienT, we present a use case on a corpus of 40 simulated muscle biopsy reports of congenital myopathy patients. As IMPatienT provides users with the ability to design their own vocabulary, it can be adapted to any research domain and can be used as a patient registry for exploratory data analysis. A demo instance of the application is available at https://impatient.lbgi.fr/.

HEALTHCURE-Disease Diagnosis using NLP

HealthCure is an innovative medical project designed to provide an all-in-one solution for the detection of seven critical diseases using advanced machine learning, computer vision, and deep learning technologies. The project aims to revolutionize healthcare by enabling users to obtain immediate diagnostic results from the comfort of their homes, making medical testing more accessible and efficient. The backend of HealthCure is powered by Flask, a lightweight and flexible web framework that facilitates rapid development and seamless integration of various machine learning models. The core of the diagnostic capabilities relies on Convolutional Neural Networks (CNNs), particularly for image-based disease detection tasks. Custom CNN architectures and pre-trained models like VGG-16 are utilized to achieve high accuracy in detecting conditions such as Covid-19, brain tumors, and pneumonia. For numerical data-based diseases, such as diabetes and heart disease, algorithms like Random Forest and XGBoost are implemented, leveraging their robustness and precision in handling structured data. Data storage and management are efficiently handled using SQLite, ensuring quick access and retrieval of user data and diagnostic results. The backend also employs RESTful API design, enabling smooth and efficient communication between the front-end and back-end, providing real-time data processing and immediate feedback. On the front-end, technologies like HTML, CSS, and JavaScript are used to create a responsive and user-friendly interface. Frameworks such as React.js and Bootstrap ensure a dynamic, interactive, and visually appealing user experience. Users can easily input data and receive diagnostic results with just a few clicks, making the application highly accessible across various devices. HealthCure also emphasizes future scalability and improvement. As more data becomes available, the models will be continually refined to enhance accuracy and include additional disease detection capabilities. Plans for integrating features such as personalized health advice and real-time monitoring are also underway. This comprehensive approach makes HealthCure a powerful, reliable, and scalable platform for real-time medical diagnostics, significantly contributing to improved health outcomes and patient care.

Enhancing quality assessment of live Pacific oysters (Crassostrea gigas) using flexible sensors real‐time monitoring physiological signals

AbstractShell‐closing strength (SCS) is commonly used to assess oyster vigor and quality as an important physiological stress indicator of the oyster organism. In this study, a quality decision support system based on flexible wireless sensor network and web services was designed and developed to explore the application of SCS in the rapid assessment of vitality and quality. Based on physiological and environmental information collected by flexible wireless sensor networks, this study proposes a novel quantitative vitality assessment method to identify oyster life decline cycles and processes. Meanwhile, TVB‐N, TVC, and pH were selected as quality indicators, and a back‐propagation artificial neural network (BP‐ANN) was used to predict and evaluate oyster quality indicators. The results showed that the root mean square errors (RMSE) for TVB‐N, TVC, and pH were 0.4624, 0.8827, and 0.1941; the coefficients of determination (R2) were 0.8919, 0.8578, and 0.6249. Therefore, the rapid assessment of oyster vitality and quality based on a flexible wireless sensor network is a reliable and effective means.Practical applicationsThis study aims to explore the application of live oyster SCS for rapid quality evaluation. In this article, a quality decision support system based on a flexible wireless sensor network (WSN) and web services is developed to improve the quality and health of live oysters in the supply chain. The use of the multi‐sensor system enables the monitoring and collection of environmental and physiological signals of live oysters throughout the supply chain process. The evaluation of the quality of live oysters using physiological and environmental information collected by flexible wireless sensor networks is a reliable and efficient method. This will provide an effective and reliable quality evaluation and management for the oyster industry.

The Future of Responsive Web Design: Challenges and Opportunities in Multi-Device Compatibility

Abstract: The creation and use of websites has been completely transformed by responsive web design, which makes it possible for users to engage with websites seamlessly on a variety of devices, including desktop computers, Smartphone’s, and tablets. But as technology advances and new gadgets with different screen sizes, resolutions, and functionalities appear, responsive web design will face new possibilities as well as obstacles in the future. The present abstract delves into the dynamic field of responsive web design, emphasizing the difficulties and possibilities involved in accomplishing compatibility across several devices. An array of devices with distinct features and user experiences has resulted from the swift spread of mobile devices and the introduction of novel form factors, like smart TVs and wearable technology. Because of this, the task of designing and developing websites that are not only responsive to varied screen sizes but also offer the best possible user experience across a range of devices and operating systems falls to designers and developers. The future of flexible web design offers fascinating chances for imagination and creativity, despite these difficulties.

An Intelligent Web-based Energy Management System for Distributed Energy Resources Integration and Optimization

The integration of renewable energy sources into power distribution systems frequently presents challenges for conventional energy management systems (EMS) due to the unpredictable and unstable characteristics of such energy sources. As a result, novel and cutting-edge solutions are required. This paper presents an intelligent web-based energy management system (iW-EMS) specifically designed to address the integration and optimization of distributed energy resources, as outlined in the proposed approach. The system incorporates a hybrid novel optimization approach that integrates simulated annealing and cone programming to effectively manage the distribution of energy resources and attain optimal outcomes from the proposed EMS. Additionally, it leverages generative AI services to create optimal scenarios based on historical data and real-time information, ensuring adaptability to the dynamic nature of renewable energy generation, providing a user-friendly and flexible web environment for scenario planning. The proposed framework facilitates seamless communication and collaboration among stakeholders involved in renewable energy integration, while also enabling the incorporation of real-world data sources such as weather forecasts and energy consumption patterns into the planning process.

The new digital archive at the Neanderthal Museum

The management and publication of digital data on online repositories constitutes a task that is becoming more and more relevant for museums and cultural heritage institutions alike. Not only is it important to archive the growing volume of data produced on a daily basis, but also to make this data available and reach out to old and new audience targets, such as fellow researchers, students, enthusiasts, in new and meaningful ways. The Neanderthal Museum, since two decades engaged in the development and establishment of a diversified digital offer, profiting from the recent completion of the DISAPALE project and its hundreds of newly scanned 3D models, has created a brand new online portal, planned as the core of its digital activity. The history of development (specifically the NESPOS portal), the issues encountered in the planning and realization phases (e.g. requirements for efficiency and long-term maintainability, freedom from previous dependencies on proprietary software), as well as the design decisions and technical solutions are critically discussed, highlighting strengths, weaknesses and possible future improvements. An introduction to the client-server architecture is provided, detailing the development of the back-end using a lightweight Java server component with integrated PostgreSQL relational database and the user-facing front-end built with modern web technologies to create a tailor-made, flexible and clutter-free web application. Finally a short look at the current status of scientific enterprises in digital archaeology is given, with a specific focus on the entanglements between museums, cultural heritage institutions and government entities.

"MERN Stack Unveiled: A Research Study on the Technology's Architecture and Benefits"

The MERN stack, is a modern web development paradigm that has gained great popularity for its adaptability, efficiency, and versatility. Express.js, a web application framework based on Node.js, and MongoDB, a NoSQL database, combine to create a dependable and responsive backend infrastructure. React, a dynamic JavaScript library, on the other hand, enables programmers to create interesting and interactive user interfaces. These elements are combined by Node.js, which serves as the runtime environment for JavaScript, enabling fluid communication and data flow. This research paper offers a thorough examination of the architectural underpinnings of the MERN stack, illuminating its advantages and practical applications. Because of its performance, agility, and usability, we emphasize how appealing it is to developers. The MERN stack's crucial role in the creation of various platforms, including e-commerce websites, content management systems, and social media networks, is further illuminated by our examination of its real-world applications. Through demonstrating case studies and project examples, we show how businesses have streamlined their web development efforts using the MERN stack, producing better user experiences and scalable solutions. The MERN stack is positioned as a crucial and adaptable option for companies and developers looking to develop contemporary, strong, and flexible web solutions by carefully examining the larger context of related projects.

Expanding PROTACtable genome universe of E3 ligases

Proteolysis-targeting chimera (PROTAC) and other targeted protein degradation (TPD) molecules that induce degradation by the ubiquitin-proteasome system (UPS) offer new opportunities to engage targets that remain challenging to be inhibited by conventional small molecules. One fundamental element in the degradation process is the E3 ligase. However, less than 2% amongst hundreds of E3 ligases in the human genome have been engaged in current studies in the TPD field, calling for the recruiting of additional ones to further enhance the therapeutic potential of TPD. To accelerate the development of PROTACs utilizing under-explored E3 ligases, we systematically characterize E3 ligases from seven different aspects, including chemical ligandability, expression patterns, protein-protein interactions (PPI), structure availability, functional essentiality, cellular location, and PPI interface by analyzing 30 large-scale data sets. Our analysis uncovers several E3 ligases as promising extant PROTACs. In total, combining confidence score, ligandability, expression pattern, and PPI, we identified 76 E3 ligases as PROTAC-interacting candidates. We develop a user-friendly and flexible web portal (https://hanlaboratory.com/E3Atlas/) aimed at assisting researchers to rapidly identify E3 ligases with promising TPD activities against specifically desired targets, facilitating the development of these therapies in cancer and beyond.

The spectral underpinnings of pathogen spread on animal networks.

Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge. Here we draw on advances in spectral graph theory and machine learning to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. We show that the spectral features of an animal network are powerful predictors of pathogen spread for a variety of hosts and pathogens and can be a valuable proxy for the vulnerability of animal networks to pathogen spread. We validate our findings using interpretable machine learning techniques and provide a flexible web application for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.

Reliability coefficients for nonlinear models of load-bearing capacity of beams with flexible web

Introduction. A special class of thin-walled steel elements can be singled out, for which characteristic failure forms are accompanied by loss of local web stability with subsequent development of the post-critical stage, the so-called beams with flexible web. The effectiveness of application, including practical approbation at pilot sites, of such types of structures was confirmed as early as the 1970s. However, the widespread dissemination is constrained by the complexity and uncertainty of the calculated dependences. The development of analytical and empirical resistance models is considerably complicated because of the stage process of deformation and the change in the compartment operation scheme. Due to the development of a more universal approach for determining the bearing capacity based on numerical models, in which conservatism of the results is practically eliminated, the format of checking the safety of elements with pronounced nonlinear behavior becomes critical. To verify the safety of rafter structures, as a rule, the method of limit states in the formulation of the method of reliability factors is used. However, the methods for determining and calibrating reliability coefficient values are focused and most developed for linear models.
 
 Materials and methods. The paper presents proposals on the development of the method of reliability factors on the basis of analytical dependence with justification of required quantile for calculated values of load-carrying capacity on the basis of first-order reliability theory methods with subsequent verification by means of simulation methods.
 
 Results. The paper presents probabilistic models of the bearing capacity of a beam with a flexible web and substantiates probabilistic models of basic variables. The values of reliability factors for calculating the calculated values of bearing capa-city have been obtained.
 
 Conclusions. The analytical dependence for determining design values for the regulated level of reliability has been substantiated. Numerical values of coefficients of variation of bearing capacity for beams with flexible wall for different kinds of failure have been obtained.

A Flexible Web Traffic Generator for the dimensioning of a 5G backhaul in NPN

Web-based services have nowadays aggregated the majority of applications, ranging from multimedia delivery to instant messaging, embracing requirements from different classes of users (i.e., gamers, streamers, professionals, etc.). The common factor is the need for connectivity everywhere and every time at high performance and quality, while the services traffic profiles and the users’ behaviour can be drastically different. This need is fully satisfied by the recent 5G mobile networks, where the coexistence of heterogeneous applications with different Quality of Service (QoS) levels is possible, defining new vertical markets and cutting-edge applications. In this context, we propose a Flexible Web Traffic Generator (FWTG) tool to model the dynamics of recent applications and user interactions in a realistic way, allowing the generation of real-time HTTP traffic and the possibility to inject it into real networks. FTWG represents a valid in-laboratory solution for the rapid configuration and applications performance evaluation of network slices in the context of 5G Non-Public Networks (NPN), pursuing an effective emulation approach. The advantage of this approach is that it allows the evaluation of real traffic response to different network configurations (and vice-versa), using components, equipment and network functions of the target network under test. After the description of the rationale and the methodology for defining FWTG models and their implementation, we defined a set of Key Performance Indicators (KPI) generated by the tool (e.g., number of requested objects and size, request rate, throughput, etc.). Then, we present initial validation tests on a Linux-based testbed representing the 5G-based scenarios of interest, emulating also a satellite backhaul as representative of a use-case requiring cost-effective dimensioning. Numerical outcomes of running FWTG in different configurations are then provided, considering a variation of the number of users sharing the same resources, their composition and traffic type, showing with concrete examples the usefulness and flexibility of FWTG in supporting 5G network configuration optimizations. Definitively, the tool is released as Open Source software on Gitlab.

CPPA: A Web Tool for Exploring Proteomic and Phosphoproteomic Data in Cancer.

A tremendous amount of proteomic and phosphoproteomic data has been produced over the years with the development of mass spectrometry techniques, providing us with new opportunities to explore and understand the proteome and phosphoproteome as well as the function of proteins and protein phosphorylation sites. However, a lack of powerful tools that we can utilize to explore these valuable data limits our understanding of the proteome and phosphoproteome, particularly in diseases such as cancer. To address these unmet needs, we established CPPA (Cancer Proteome and Phosphoproteome Atlas), a web tool to mine abnormalities of the proteome and phosphoproteome in cancer based on published data sets. All analysis results are presented in CPPA with a flexible web interface to provide key customization utilities, including general analysis, differential expression profiling, statistical analysis of protein phosphorylation sites, correlation analysis, similarity analysis, survival analysis, pathological stage analysis, etc. CPPA greatly facilitates the process of data mining and therapeutic target discovery by providing a comprehensive analysis of proteomic and phosphoproteomic data in normal and tumor tissues with a simple click, which helps to unlock the precious value of mass spectrometry data by bridging the gap between raw data and experimental biologists. CPPA is currently available at https://cppa.site/cppa.

Fast Multipole Method for Gravitational Lensing: Application to High-magnification Quasar Microlensing

We introduce the use of the fast multipole method (FMM) to speed up gravitational lensing ray tracing calculations. The method allows very fast calculation of ray deflections when a large number of deflectors, N *, are involved, while keeping rigorous control on the errors. In particular, we apply this method, in combination with the inverse polygon mapping (IPM) technique, to quasar microlensing to generate microlensing magnification maps with very high workloads (high magnification, large size, and/or high resolution) that require a very large number of deflectors. Using FMM-IPM, the computation time can be reduced by a factor of ∼105 with respect to standard inverse ray shooting (IRS), making the use of this algorithm on a personal computer comparable to the use of standard IRS on GPUs. We also provide a flexible web interface for easy calculation of microlensing magnification maps using FMM-IPM (see https://gloton.ugr.es/microlensing/). We exemplify the power of this new method by applying it to some challenging interesting astrophysical scenarios, including clustered primordial black holes and extremely magnified stars close to the giant arcs of galaxy clusters. We also show the performance/use of FMM to calculate ray deflection for a halo resulting from cosmological simulations composed of a large number (N ≳ 107) of elements.

Neural-Network-Based Adaptive Model Predictive Control for a Flexure-Based Roll-to-Roll Contact Printing System

High-precision contact force control is essential for continuous roll-to-roll contact printing via mechanical contact on flexible web substrates using stamps. Nonuniformly controlled stamp contact force will cause failures during printing, especially for large-area printing processes. Due to their high precision in positioning and force control, flexure mechanisms have been applied in roll-to-roll contact printing systems; however, conventional physical model-based control systems cannot manage the nonlinear effects that exist in flexure-based roll-to-roll contact printing systems. To achieve precise contact force control, we propose a neural-network-based adaptive model predictive control for a flexure-based roll-to-roll contact printing system. The nonlinearity of the flexure mechanism is learned and modeled by an artificial neural network. To eliminate the steady-state error caused by model mismatches and external disturbances, an online adaptive mechanism is designed via updating the biases of the output layer of the neural network model. Experimental results show that the root-mean-square error of the contact force can be controlled in the range of 0–0.075 N with balances on two ends of the print roller, outperforming a proportional–integral–derivative controller, a neural-network-based standard model predictive control (MPC) controller, and a neural-network-based robust MPC controller. The proposed control algorithm is implemented in a microcontact printing process that prints 45- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m width gold patterns and achieves a variation of 0.3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m in the average gold line width at different locations on an 88.9-mm width flexible substrate. The uniform microscale printing results have shown the effectiveness of the proposed neural-network-based adaptive model predictive control in the applied printing process.

CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data.

CellMarker 2.0 (http://bio-bigdata.hrbmu.edu.cn/CellMarker or http://117.50.127.228/CellMarker/) is an updated database that provides a manually curated collection of experimentally supported markers of various cell types in different tissues of human and mouse. In addition, web tools for analyzing single cell sequencing data are described. We have updated CellMarker 2.0 with more data and several new features, including (i) Appending 36300 tissue-cell type-maker entries, 474 tissues, 1901 cell types and 4566 markers over the previous version. The current release recruits 26 915 cell markers, 2578 cell types and 656 tissues, resulting in a total of 83 361 tissue-cell type-maker entries. (ii) There is new marker information from 48 sequencing technology sources, including 10X Chromium, Smart-Seq2 and Drop-seq, etc. (iii) Adding 29 types of cell markers, including protein-coding gene lncRNA and processed pseudogene, etc. Additionally, six flexible web tools, including cell annotation, cell clustering, cell malignancy, cell differentiation, cell feature and cell communication, were developed to analysis and visualization of single cell sequencing data. CellMarker 2.0 is a valuable resource for exploring markers of various cell types in different tissues of human and mouse.

SODA: a TypeScript/JavaScript library for visualizing biological sequence annotation.

We present SODA, a lightweight and open-source visualization library for biological sequence annotations that enables straightforward development of flexible, dynamic and interactive web graphics. SODA is implemented in TypeScript and can be used as a library within TypeScript and JavaScript.