Design Of Platform Research Articles

Next-generation cell-penetrating antibodies for tumor targeting and RAD51 inhibition.

Monoclonal antibody therapies for cancer have demonstrated extraordinary clinical success in recent years. However, these strategies are thus far mostly limited to specific cell surface antigens, even though many disease targets are found intracellularly. Here we report studies on the humanization of a full-length, nucleic acid binding, monoclonal lupus-derived autoantibody, 3E10, which exhibits a novel mechanism of cell penetration and tumor specific targeting. Comparing humanized variants of 3E10, we demonstrate that cell uptake depends on the nucleoside transporter ENT2, and that faster cell uptake and superior in vivo tumor targeting are associated with higher affinity nucleic acid binding. We show that one human variant retains the ability of the parental 3E10 to bind RAD51, serving as a synthetically lethal inhibitor of homology-directed repair in vitro. These results provide the basis for the rational design of a novel antibody platform for therapeutic tumor targeting with high specificity following systemic administration.

Hydrodynamic analysis of a semi-submersible fish farming cage platform integrated with an oscillating water column wave energy converter

Abstract This research article describes an investigation into integrating semi-submersible farm cages with oscillating water column (OWC) wave energy converters. The objective is to optimize the structural parameters of the platform through an examination of the impacts of three float structures. The AQWA hydrodynamic simulation software is employed to develop a numerical model to analyze the hydrodynamic properties of the semi-submersible farming platform. The findings suggest that the response to the heavy degree of freedom is positively correlated with the size of the floating structure. In contrast, the response of the roll degree of freedom is negatively correlated within a specific range. The results of this study establish a theoretical framework that can be utilized to optimize the design of semi-submersible farming platforms that integrate OWC technology.

Mass customization using hybrid manufacturing and smart assembly: An optimal configuration and platform design approach

Hybrid Manufacturing (HM) and smart assembly stand as pivotal pillars in advanced smart manufacturing systems, offering manufacturers highly efficient and adaptable solutions for manufacturing. This paper delves into the configuration of a production line that integrates HM and assembly stages, each comprising multiple cells, with each cell housing one or more parallel stations. The objective is to manufacture a family of final assemblies, leveraging the platform concept to defer mass customization to later stages and thereby minimize processing costs. A mathematical programming model is proposed to identify the optimal configuration for such production lines, considering constraints such as an allowable capital cost and machine availabilities. In addition, the precedence, inclusion, and seclusion restrictions imposed on the part family are considered. The proposed mathematical programming model aims to delineate which HM features are processed in the part platform cell versus those processed in the mass customization (part variants) cells. Simultaneously, the model determines the components (variants from the HM stage) of final assemblies processed in the assembly platform cell, as well as components assembled or disassembled in the final assembly cells. Furthermore, the model seeks to determine the required number of stations in each cell to meet periodic demand. The overall objective of the model is to minimize the capital and the processing cost. A detailed case study illustrates the effectiveness of the proposed configuration approach and mathematical model. The proposed model is solvable in a few seconds by using commercial solvers.

Comparing Climate Change Content and Comments across Instagram Reels, TikTok, and YouTube Shorts and Long Videos

ABSTRACTSocial media plays a vital role as a communication channel for pertinent topics, including climate change. This paper investigates the content and comments of short and long‐format videos on Instagram, TikTok, and YouTube to compare climate change discourse on these platforms. Eighty videos and their respective 69,135 comments were collected and analyzed using content analysis, statistical analysis, data visualization, and social network analysis. Our findings show that in our dataset, videos in the short‐format (Instagram, TikTok, and YouTube Shorts) provide all sorts of content, while YouTube long videos only focus on educational and activism content. The short‐format videos also show different stances towards climate change, such as a climate change denier stance, while YouTube long videos do not. For comments, videos in the short‐format had less engagement compared to YouTube long videos. Our findings inform implications for both researchers and social media platform designers.

Automatic Modeling and Optimization Design Platform for Highway Tunnels with Application to Stress and Displacement Prediction of Surrounding Rocks during Tunnel Excavation

Automatic Modeling and Optimization Design Platform for Highway Tunnels with Application to Stress and Displacement Prediction of Surrounding Rocks during Tunnel Excavation

Data-driven bio-integrated design method encoded by biocomputational real-time feedback loop and deep semi-supervised learning (DSSL)

Today, under the imperatives of synthetic biology (Synbio), material-based design strategies are conceived as biofactories that can recapitulate the functionalities of living systems for developing building materials with controllable structural features. These strategies include biofabrication techniques such as vitro-culturing, Fabrication Information Modeling (FIM), growth parametrization, machinery systems, and genetic programming articulated bioproducts as the construction material for low-carbon buildings. However, these biophysical systems need interactive development by data-oriented models and decision-maker tools that can mine, measure, and re-configure the complexity in biological systems through monitoring and controlling in a higher integration toward systemic decision-making solutions. This research presents a real-time feedback loop as the bio-integrated design method for design and fabrication of Gluconacebacter Xylinus (GX) Bacterial Cellulose (BC) with Transfer Learning (TL) and Deep Semi-Supervised Learning (DSSL) through TensorFlow and Keras Python libraries. The loop started by mining datasets from BC growth via the VGG16 pre-trained model. In this stage, the loop can learn the material behavior over the growth process and attribute features to a measured BC thickness. Based on this attribution, the loop can drive real-time classifying of input datasets to thickness-based classes and guide the Region of Interests (ROIs) to a user-designed thickness till the captured features are fitted to the user-input class features. The training results over 8640 images show a 0.001 learning rate, an average loss of 0.18 (540 total steps), an accuracy of 0.706, and a precision of 0.752 for the feature extractor model. For the classifier, a 0.15 average loss (3375 total steps), an accuracy of 0.829, and a precision of 0.712 demonstrate the models' efficiency in pattern recognition and classification. Also, the loop operation persistently exhibits a more than 50% precision rate, displaying the loop's high performance in achieving user-designed 3D shapes. This research aims to a transformative shift in bio-integrated design based on feedback loops as the foundational step towards intelligent bio-digital design platforms. Also, this ground can break designing with biological systems based on desired tasks for the new generation of designers called biodesigners.

Application of a novel stall margin improvement indicator in optimizing casing treatment for a transonic compressor

This paper presents a multi-objective design optimization of axial slot casing treatments in a 1.5 stage transonic compressor. To perform the optimization, an in-house optimization design platform is constructed using genetic algorithm and Kriging surrogate model. The optimization objectives are the peak efficiency at the design rotating speed and the stall margin improvements at both design and off-design rotating speeds. Instead of massive unsteady simulations that are required to accurately predict the stall margin, a stall margin improvement indicator is introduced based on the time-averaged axial momentum budget analysis at the rotor tip region. With this indicator, only one steady simulation is needed to evaluate the stall margin enhancement ability of a new casing treatment design at a given rotating speed. The axial slots are parameterized with a total of eight design parameters. The meridional profiles are described with two B-spline curves to define parameters such as the size, axial position, and shape of the slots. Circumferential parameters include the open area ratio and inclination angle. When the optimization finally converges, different optimal variants are selected from the database and their effects are investigated with both experiments and numerical simulations. The detailed flow fields are analyzed in depth with results from unsteady simulations. The application of the optimal casing treatments exerts a suction and re-injection effect, pushes the interface between tip leakage flow and incoming main flow downstream and reduced flow blockage in the blade tip region. Consequently, the stability of the compressor is significantly improved.

Design and Development of an Unmanned Water Rescue Platform with Emergency Detection and Self-Inflating Life Jacket Launching Mechanism

Design and Development of an Unmanned Water Rescue Platform with Emergency Detection and Self-Inflating Life Jacket Launching Mechanism

Engineering Functional Particles to Modulate T Cell Responses.

T cells play a critical role in adaptive immune responses. They work with other immune cells such as B cells to protect our bodies when the first line of defense, the innate immune system, is overcome by certain infectious diseases or cancers. Studying and regulating the responses of T cells, such as activation, proliferation, and differentiation, helps us understand not only their behavior in vivo but also their translation and application in the field of immunotherapy, such as adoptive T cell therapy and immune checkpoint therapy, the situations in which T cells cannot fight cancer alone and require external engineering regulation to help them. Nano- to micrometer-sized particulate biomaterials have achieved great progress in the assistance of T cell-based immunomodulation. For example, various types of microparticles decorated with T cell recognition and activation signals to mimic native antigen-presenting cells have shown successful ex vivo expansion of primary T cells and have been approved for clinical use in adoptive T cell therapy. Functional particles can also serve as vehicles for transporting cargos including small molecule drugs, cytokines, and antibodies. Especially for cargos with limited bioavailability and high repeat-dose toxicity, systemic administration in their free form is difficult. By using particle-assisted systems, the delivery can be tailored on demand, of which targeting and controlled release are two typical examples, ultimately aiding in the regulation of T cell responses. Furthermore, when T cells become overactive and behave in ways that contradict our expectations, such as attacking our own cells or innocuous foreign molecules, this can lead to a breakdown of immune tolerance. In such cases, particles to help reprogram those overactive T cells or suppress their activity are appreciated in vivo. The urgent need to introduce immune stimulation into the treatment of cancers, infectious diseases, and autoimmune diseases has driven recent advances in the engineering of functional particulate biomaterials that regulate T cell responses. In this Account, we will first cover a brief overview of the process of T cell-based immunomodulation from principle to development. It then outlines critical points in the design of functional particle platforms, including materials, size, morphology, surface engineering, and delivery of cargos, to modulate the features of T cells, and introduces selected work from our and other research groups with a focus on three major therapeutic applications: adoptive T cell therapy, immune checkpoint therapy, and immune tolerance restoration. Current challenges and future opportunities are also discussed.

Fluoropolymer sorbent for efficient and selective capturing of per- and polyfluorinated compounds

Per- and poly-fluoroalkyl substances (PFAS) have gained widespread attention due to their adverse effects on health and environment. Developing efficient technology to capture PFAS from contaminated sources remains a great challenge. In this study, we introduce a type of reusable polymeric sorbent (PFPE-IEX + ) for rapid, efficient, and selective removal of multiple PFAS impurities from various contaminated water sources. The resin achieves >98% removal efficiency ([PFPE-IEX + ] = 0.5–5 mg mL−1, [PFAS]0 = 1–10 ppb in potable water and landfill leachate) and >500 mg g−1 sorption capacity for the 11 types of examined PFAS. We achieve efficient PFAS removal without breakthrough and subsequent resin regeneration and demonstrate good PFAS recovery in a proof-of-concept cartridge setup. The outcomes of this study offer valuable guidance to the design of platforms for efficient and selective PFAS capture from contaminated water, such as drinking water and landfill leachate.

Design of Blockchain and Strategic Financing Service under the Platform Economy

The capital constrains and uncertainties in supply disruptions faced by small and medium-sized suppliers pose critical challenges for the development of e-commerce platforms. In response, platforms have begun offering direct financing (PDF) to their suppliers alongside traditional bank credit financing (BCF). Within the private (consortium) blockchain, the platform and the supplier (and the bank) achieve consensus on financial smart contract, enabling the execution of flexible contracts tailored to suppliers’ risk profile. We develop a game-theoretic model to examine the terms of smart contracts in different blockchains and further investigate the platform's optimal blockchain design. We find that PDF serves to safeguard the platform's commission income from high-interest BCF. Our analysis reveals that PDF is more likely to be provided to a risky supplier within the consortium blockchain, contrasting with outcomes observed within the private blockchain. As a result, the platform and the supplier could reach win-win, or win-lose outcomes within the private blockchain; while within the consortium blockchain, they tend to result in win-win or lose-win outcomes. Such incentive conflict could potentially hinder blockchain adoption. Our findings further provide the managerial insights guiding the platform's blockchain design: adopting consortium blockchain under moderate market potential and low or high supplier's risk, and adopting private blockchain otherwise. Furthermore, we consider the supplier's endogenous effort on reducing risk, and numerically demonstrate that blockchain may encourage or discourage the supplier's effort. Our research complements previous studies by investigating the interaction between the PDF provision and blockchain structures under uncertain supply disruption risk.

Role Transformation of Teachers in Higher Vocational Colleges under Artificial Intelligence Assisted Art Creation: A Qualitative Study of Art Design Teachers

Artificial Intelligence (AI) technology has made remarkable progress in image processing, speech synthesis, intelligent creation, etc., and has shown great potential for application in artistic creation. In the teaching scenario of higher vocational colleges, teachers not only bear the responsibility of cultivating students' hands-on ability and creative thinking but also need to constantly adapt to the teaching changes brought about by technological advances. With their strong digital intuition, creative thinking and flexible adaptability, young art design teachers can make better use of AI technology to assist artistic creation, improve teaching quality, and enrich students' creative learning experience. Traditional art education usually relies on teachers to impart knowledge, guide practice, and evaluate works in the classroom; however, the widespread application of AI technologies has brought about a revolutionary transformation in art education, and new educational resources such as generative design tools and online assisted design platforms are gradually changing the traditional teaching mode and the role of teachers. Using self-efficacy theory and social cognitive career and motivation theory as a framework, this study investigated twelve in-service young art design teachers through 1) semi-structured interviews; 2) focus group discussions, and 3) member-checking interviews. The researcher analysed the data using a general inductive approach to capture the following core themes: 1) new role orientation; 2) continuing professional development and 3) support from society and educational institutions. Institutional support. The results of the study help to deeply understand the relationship between AI and art education in higher vocational colleges, clarify the self-worth positioning of young art design teachers in the new era, and provide theoretical support and practical guidance for the transformation of teachers' roles and the professional development of art education.

A 3D Printing Platform for Design and Manufacturing of Multi-Functional Cementitious Construction Components and Its Validation for a Post-Tensioned Beam.

Three-dimensional printing of cementitious materials for construction has been extensively investigated in recent years, with several demonstration projects successfully carried out. These efforts aim to leverage the printing process to achieve more efficient production of components compared to conventional concrete technologies. This includes both the process itself (eliminating the formwork stage) and the flexibility in producing complexly shaped elements. To maximize the potential of 3D printing in the construction industry, additional steps must be taken, grounded in a holistic view of the entire process. This involves integration of the production chain, including design, materials, and manufacturing of components, to create elements with optimal performance, encompassing structural, environmental, and architectural aspects. Such multi-functionality requires the viewing of 3D printing not just as a production technology but as a platform enabling the integration of all these components. To advance this approach, quantitative tools are developed to optimize the following three key components: material composition; manufacturing parameters to ensure buildability; and design tools to optimize multiple performance criteria, particularly structural and architectural shape. A demonstration component, namely a post-tensioned beam, featuring two multi-functional characteristics-structural and architectural-is designed, produced, and evaluated. The scientific concepts and research tools used to develop these quantitative design tools are multidisciplinary, including rheological characterization, control of the internal structure and composition of granular materials, simulation of the mechanical behavior of green material during printing, and the hardened properties of the components, all utilizing structural optimization to enhance performance.

Preliminary Techno-Economic Study of Optimized Floating Offshore Wind Turbine Substructure

Floating offshore wind turbines (FOWTs) are still in the pre-commercial stage and, although different concepts of FOWTs are being developed, cost is a main barrier to commercializing the FOWT system. This article aims to use a shape parameterization technique within a multidisciplinary design analysis and optimization framework to alter the shape of the FOWT platform with the objective of reducing cost. This cost reduction is then implemented in 30 MW and 60 MW floating offshore wind farms (FOWFs) designed based on the static pitch angle constraints (5 degrees, 7 degrees and 10 degrees) used within the optimization framework to estimate the reduction in the levelized cost of energy (LCOE) in comparison to a FOWT platform without any shape alteration–OC3 spar platform design. Key findings in this work show that an optimal shape alteration of the platform design that satisfies the design requirements, objectives and constraints set within the optimization framework contributes to significantly reducing the CAPEX cost and the LCOE in the floating wind farms considered. This is due to the reduction in the required platform mass for hydrostatic stability when the static pitch angle is increased. The FOWF designed with a 10 degree static pitch angle constraint provided the lowest LCOE value, while the FOWF designed with a 5 degree static pitch angle constraint provided the largest LCOE value, barring the FOWT designed with the OC3 dimension, which is considered to have no inclination.

Inspecting the role of learning platforms, gamification, and diverse teaching approaches in improving vocabulary acquisition, critical thinking, and student engagement in education

These days, learning through online platforms has become both widespread and popular, particularly among students. For English learners, mastering the language is critical, given its complex vocabulary and grammar. As a result, incorporating gamification in learning platforms could have a significant impact on vocabulary retention, though its effectiveness may vary depending on the student's engagement and the platform's design. Learning through platforms can affect students in different ways; it might diminish their knowledge in certain situations, but it can also be beneficial. For this study, data was gathered from students of different ages and different opinions about this particular case. After gathering and analysing the results, it was apparent that the majority of students use learning platforms to study English and improve their vocabulary. The study also found that students generally enjoy using these platforms, finding them more engaging and fun, and they believe these tools ominously help their learning experience from their perspective. Learning through platforms makes it easier for students to enjoy lessons and put in more effort, as some platforms incorporate competitive elements like awarding a winner, which boosts students’ pride and motivates them to continue working hard.

Design and Implementation of Digital Protection and Communication Platform for Wangjiang Cross - Stitch from the Perspective of Rural Revitalization

Based on the rural revitalization strategy, an in - depth study on the digital protection and communication of Wangjiang cross - stitch is carried out. The traditional folk handicraft art of Wangjiang cross - stitch is preserved and recorded by means of the new form of digital technology communication. The constructed digital communication platform will present the characteristics such as the centralization of literature materials, the visualization of information presentation, and the diversification of cultural activities. On the one hand, this study conforms to the characteristics of the current era, satisfies people's spiritual needs to a certain extent, and enhances cultural confidence; on the other hand, it also promotes the development of the local economy.

Design and immunogenicity of an HIV-1 clade C pediatric envelope glycoprotein stabilized by multiple platforms.

Various design platforms are available to stabilize soluble HIV-1 envelope (Env) trimers, which can be used as antigenic baits and vaccine antigens. However, stabilizing HIV-1 clade C trimers can be challenging. Here, we stabilized an HIV-1 clade C trimer based on an Env isolated from a pediatric elite-neutralizer (AIIMS_329) using multiple platforms, including SOSIP.v8.2, ferritin nanoparticles (NP) and an I53-50 two-component NP, followed by characterization of their biophysical, antigenic, and immunogenic properties. The stabilized 329 Envs showed binding affinity to trimer-specific HIV-1 broadly neutralizing antibodies (bnAbs), with negligible binding to non-neutralizing antibodies (non-nAbs). Negative-stain electron microscopy (nsEM) confirmed the native-like conformation of the Envs. Multimerization of 329 SOSIP.v8.2 on ferritin and two-component I53-50 NPs improved the overall affinity to HIV-1 bnAbs and immunogenicity in rabbits. These stabilized HIV-1 clade C 329 Envs demonstrate the potential to be used as antigenic baits and as components of multivalent vaccine candidates in future.

An Innovative New Approach to Light Pollution Measurement by Drone

The study of light pollution is a relatively new and specific field of measurement. The current literature is dominated by articles that describe the use of ground and satellite data as a source of information on light pollution. However, there is a need to study the phenomenon on a microscale, i.e., locally within small locations such as housing estates, parks, buildings, or even inside buildings. Therefore, there is an important need to measure light pollution at a lower level, at the low level of the skyline. In this paper, the authors present a new drone design for light pollution measurement. A completely new original design for an unmanned platform for light pollution measurement is presented, which is adapted to mount custom sensors (not originally designed to be mounted on a unmanned aerial vehicles) allowing registration in the nadir and zenith directions. The application and use of traditional photometric sensors in the new configuration, such as the spectrometer and the sky quality meter (SQM), is presented. A multispectral camera for nighttime measurements, a calibrated visible-light camera, is used. The results of the unmanned aerial vehicle (UAV) are generated products that allow the visualisation of multimodal photometric data together with the presence of a geographic coordinate system. This paper also presents the results from field experiments during which the light spectrum is measured with the installed sensors. As the results show, measurements at night, especially with multispectral cameras, allow the assessment of the spectrum emitted by street lamps, while the measurement of the sky quality depends on the flight height only up to a 10 m above ground level.

Design and Application of Driving Resistance Test Device for Aircraft Tire and Soil Pavement

In view of the lack of soil bins for studying the surface interaction between aircraft wheels and soil, this study designed an indoor test bench for aircraft wheels and soil, including a soil container, loading vehicle, and intelligent measurement and control system, to test key parameters such as tire speed and wheel frictional resistance. The test system is capable of achieving speed regulation ranging from 0 to 30 km/h. The vertical load adjustment range with an adjustment interval of 10 kg spans from 90 to 140 kg. The soil type, compaction degree, and other conditions can be modified as per requirements to vary multiple test conditions, thereby enabling us to explore their influence on the driving resistance of the wheels. Moreover, the test data can be collected and processed in real time. A performance test of a wheel–soil table was carried out. The results show that the wheel–soil table test system is stable and reliable and can determine the relationship between the tire and soil, and the structural design of the test system meets the use requirements. In addition, it achieves the target test speed, data acquisition frequency, and stability. In terms of functionality and operational difficulty, the data acquisition of the entire test process is automated, and the test system achieves better informationization than previous methods. The overall operation of the wheel–soil platform is stable and powerful; thus, the model test platform design goal is achieved, and the testing requirements are met.

Research on the Design of Performance Evaluation System for Physical Education Major Based on Multimedia Information Processing Technology

The Massive Open Online Course (MOOC) platform, despite its reach and potential, faces significant challenges related to learner diversity, including variations in student backgrounds, work experiences, and learning environments. By integrating the principles of ant colony clustering algorithms with MOOC platform dynamics, our study devises a strategy for grouping students based on shared characteristics, thereby fostering a tailored learning environment. A streamlined MOOC platform design framework is outlined, accompanied by a detailed blueprint for a peer-evaluation information system, aimed at accurately gauging students' academic progress within their disciplines. This platform enables students to effortlessly access and engage with courses aligned with their interests, while the reciprocal assessment mechanism heightens understanding of course materials and alleviates pressure on educators. Our research contributes a pragmatic solution to the challenge of student assignment clustering within MOOCs, enhancing both individual learning outcomes and the broader instructional quality.