Design Approach Research Articles

Product innovation design approach driven by implicit relationship completion via patent knowledge graph

Product innovation design process involves a great deal of discrete engineering knowledge, limiting the ability of designers to quickly utilize this knowledge to support design innovation. Nowadays, innovation design based on knowledge graphs has enhanced the ability to explore design knowledge, improving the efficiency of knowledge retrieval. Previous studies have focused on mining more design knowledge to enrich the knowledge graph overlooks the implicit relationships with potential value among design knowledge, wasting design resources. To address these issues, an approach for product innovation design based on implicit knowledge relationship completion in the patent knowledge graph is proposed, which explores the implicit relationships between design knowledge to provide new knowledge satisfying design preferences and enhance the innovativeness of solutions. First, a requirements-function-structure-benefit (RFSB) knowledge ontology is constructed and extracted from the benefit knowledge of patents to build the knowledge graph. Second, an implicit relationship completion model based on the similarity of function or benefit entities explores the implicit relationships, replacing structure entities directly connected to similar function or benefit entities to generate new relationships and outputs novel ideas. Third, a scheme improvement process based on the co-occurrence frequency of requirement and structure knowledge supplements neglected design preferences. Final, a pipeline inspection robot case study is further employed to verify the proposed approach, and a patent knowledge graph assisted design solution prototype system is developed to assist in the utilization of innovative design knowledge. Evaluation results show the significant design potential of the proposed approach in inspiring innovative thinking and knowledge reuse.

LRB System Performance Validation and Its New Design Approach for Conventional Buildings in Algeria

LRB System Performance Validation and Its New Design Approach for Conventional Buildings in Algeria

Responses analysis and structural optimization of a semisubmersible platform based on the Taguchi experiment method

Responses analysis and structural optimization of a semisubmersible platform based on the Taguchi experiment method

Revolutionizing Drug Discovery: A Comprehensive Review of Computer-Aided Drug Design Approaches

Abstract: Computer-Aided Drug Design (CADD) has significantly advanced the drug discovery process, offering tools to enhance efficiency and reduce costs. This review explores essential CADD methodologies, including molecular docking, virtual screening, ADMET profiling, homology modeling, and Quantitative Structure-Activity Relationship (QSAR) models. Molecular docking predicts interactions between drugs and targets, while virtual screening evaluates large compound libraries to identify promising candidates. ADMET profiling assesses pharmacokinetic and toxicological properties early in development. Homology modeling constructs three-dimensional protein models to aid target identification, and QSAR models predict biological activities based on chemical structures. These integrated approaches streamline drug development, providing a robust framework for modern pharmaceutical research.

Design Approach for an Advanced Multi-Channel Pyrometer for Bulk Oven Processes

Industrial processes such as smelting and sintering require stable and precise temperature control of furnaces. To achieve this, accurate temperature measurements are required. Pyrometry allows for contact- less measurement of bulk materials and is particularly suitable for high temperature applications. One of the main influences on the accuracy of pyrometric measurements is the knowledge of the emissivity in the spectral measurement range. To reduce this dependence, two-color pyrometers or multi-color pyrometers can be used. With this in mind, the IRS is further developing their existing pyrometer technology by designing an advanced multi-channel pyrometer for bulk oven processes in a joint venture with Stange Elektronik GmbH and New Generation Kilns Grün GmbH. The design approach is explained here and the considered methods of achieving emissivity independent temperature measurements are examined.

Engineered Two-Dimensional Transition Metal Dichalcogenides for Energy Conversion and Storage.

Designing efficient and cost-effective materials is pivotal to solving the key scientific and technological challenges at the interface of energy, environment, and sustainability for achieving NetZero. Two-dimensional transition metal dichalcogenides (2D TMDs) represent a unique class of materials that have catered to a myriad of energy conversion and storage (ECS) applications. Their uniqueness arises from their ultra-thin nature, high fractions of atoms residing on surfaces, rich chemical compositions featuring diverse metals and chalcogens, and remarkable tunability across multiple length scales. Specifically, the rich electronic/electrical, optical, and thermal properties of 2D TMDs have been widely exploited for electrochemical energy conversion (e.g., electrocatalytic water splitting), and storage (e.g., anodes in alkali ion batteries and supercapacitors), photocatalysis, photovoltaic devices, and thermoelectric applications. Furthermore, their properties and performances can be greatly boosted by judicious structural and chemical tuning through phase, size, composition, defect, dopant, topological, and heterostructure engineering. The challenge, however, is to design and control such engineering levers, optimally and specifically, to maximize performance outcomes for targeted applications. In this review we discuss, highlight, and provide insights on the significant advancements and ongoing research directions in the design and engineering approaches of 2D TMDs for improving their performance and potential in ECS applications.

Occupational stress as a mediator between organisational intelligence traits and digital government service quality: a triangulation design approach

Occupational stress as a mediator between organisational intelligence traits and digital government service quality: a triangulation design approach

Reconfigurable inverse design of phononic crystal sensor based on a deep learning accelerated evolution strategy

Phononic crystals offer valuable sensing capabilities due to their high sensitivity to changes in sound velocity of analytes. In this work, a reconfigurable inverse design approach of phononic crystal sensors is achieved using a deep learning accelerated evolution strategy. The training data is acquired through finite element method (FEM). Two multilayer perceptrons (MLP) are constructed and trained to predict the center frequency and bandwidth of a passband in the dispersion relation. Utilizing a two-step training approach enables rapid accuracy enhancements and swift reconstruction of network targets from NaCl to KCl solutions. The trained networks accelerate the optimization process, yielding a phononic crystal with good detection ability. Compared to FEM, invoking the trained networks can reduce optimization time by a factor of 105. The optimized and initial structures are both fabricated and experimentally tested. The robust linear relation between the resonant peak and the solution concentration indicates significant sensing value. The experimental results are in good agreement with the FEM simulations. In the detection of NaCl solution, the optimized phononic crystal sensor has a sensitivity increase of 375% and a Q-factor of 999%. Our research demonstrates that the data-driven deep learning network is a very powerful tool for the design and optimization of phononic crystal devices.

Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively.

Enhancing Ionic Selectivity and Osmotic Energy by Using an Ultrathin Zr‐MOF‐Based Heterogeneous Membrane with Trilayered Continuous Porous Structure

AbstractDesigning a nanofluidic membrane with high selectivity and fast ion transport property is the key towards high‐performance osmotic energy conversion. However, most of reported membranes can produce power density less than commercial benchmark (5 W/m2), due to the imbalance between ion selectivity and permeability. Here, we report a novel nanoarchitectured design of a heterogeneous membrane with an ultrathin and dense zirconium‐based UiO‐66‐NH2 metal–organic framework (MOF) layer and a highly aligned and interconnected branched alumina nanochannel membrane. The design leads to a continuous trilayered pore structure of large geometry gradient in the sequence from angstrom‐scale to nano‐scale to sub‐microscale, which enables the enhanced directional ion transport, and the angstrom‐sized (~6.6–7 Å) UiO‐66‐NH2 windows render the membrane with high ion selectivity. Consequently, the novel heterogeneous membrane can achieve a high‐performance power of ~8 W/m2 by mixing synthetic seawater and river water. The power density can be largely upgraded to an ultrahigh ~17.1 W/m2 along with ~48.5 % conversion efficiency at a 50‐fold KCl gradient. This work not only presents a new membrane design approach but also showcases the great potential of employing the zirconium‐based MOF channels as ion‐channel‐mimetic membranes for highly efficient blue energy harvesting.

Quantifying the effect of pulse-like ground motions on the seismic design of SMA restrained isolation bridges using probabilistic repair cost method

The shape memory alloy (SMA) restrainer serves as an effective but expensive bridge restraining device. However, the impact of pulse effect on SMA design approach under pulse-like ground motions (PLGM) has not been fully quantified in previous studies, which hinders its application in near-fault regions. Therefore, based on the risk probability assessment method throughout the entire life cycle, by accounting for the comprehensive repair cost of each component damage, this paper introduces a parameter design method for SMA restrainer of near-fault bridges that considers structural parameters, pulse parameters, and economic indicators. Firstly, the repair cost ratio (RCR) of bridge system, which means the expenses for repairs expressed as a proportion of bridge replacement costs, was regarded as the life-time optimization goal and overall performance indicator. Secondly, by accounting for near-fault effects, the relationship between RCR and SMA design parameters was established by convolution algorithm of probabilistic seismic hazard analysis, demand analysis and capacity analysis. A novel probabilistic seismic demand model was utilized to quickly determine the RCR of the bridge system under PLGM. Finally, the influence of pulse period on the rational design parameter of SMA restrainer was comprehensively investigated by RCR-based method. A seismic isolation arch bridge was selected as the illustrated case in this article. The results indicate that the rational design parameters of SMA exhibit a pattern of initially rising, then falling as the pulse period increases, reaching the peak value when the pulse period approaches bridge fundamental period. Moreover, the price parameter of SMA significantly affect the optimal design parameters, and the effective range is also recommended.

Considering radial basis function neural network for effective solution generation in metaheuristic algorithms

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates.

A simple recipe for designing multicomponent ultra-high temperature ceramic classes by using structure maps coupled with machine learning

Successful synthesis of novel high entropy ceramic (HEC) for ultra-high temperature application classes, namely, borides, carbides, and nitrides, has been experiencing a bottleneck in having a suitable design and successful synthesis strategy. Producing high-entropy ultra-high-temperature ceramics from their oxides offers a major processing benefit, while employing a design approach using machine learning enhances the efficiency of the formation of single-phase HECs. In this regard, we propose a generalized strategy to generate a semi-synthetic database for each of these classes using literature data and atomic environment mapping-based structure plots, which can further be used to build machine learning models. The imbalance of the dataset was addressed using adaptive synthetic sampling and the edited nearest neighbors technique. The trained models are able to accurately predict over 90% of the single-phase chemistry for each of the classes. Furthermore, a few compositions representing these classes were successfully synthesized from the corresponding oxide mixture to validate the effectiveness of the proposed strategy.

Enhancing Ionic Selectivity and Osmotic Energy by Using an Ultrathin Zr-MOF-Based Heterogeneous Membrane with Trilayered Continuous Porous Structure.

Designing a nanofluidic membrane with high selectivity and fast ion transport property is the key towards high-performance osmotic energy conversion. However, most of reported membranes can produce power density less than commercial benchmark (5 W/m2), due to the imbalance between ion selectivity and permeability. Here, we report a novel nanoarchitectured design of a heterogeneous membrane with an ultrathin and dense zirconium-based UiO-66-NH2 metal-organic framework (MOF) layer and a highly aligned and interconnected branched alumina nanochannel membrane. The design leads to a continuous trilayered pore structure of large geometry gradient in the sequence from angstrom-scale to nano-scale to sub-microscale, which enables the enhanced directional ion transport, and the angstrom-sized (~6.6-7 Å) UiO-66-NH2 windows render the membrane with high ion selectivity. Consequently, the novel heterogeneous membrane can achieve a high-performance power of ~8 W/m2 by mixing synthetic seawater and river water. The power density can be largely upgraded to an ultrahigh ~17.1 W/m2 along with ~48.5 % conversion efficiency at a 50-fold KCl gradient. This work not only presents a new membrane design approach but also showcases the great potential of employing the zirconium-based MOF channels as ion-channel-mimetic membranes for highly efficient blue energy harvesting.

Biosilica-coated carbonic anhydrase displayed on Escherichia coli: A novel design approach for efficient and stable biocatalyst for CO2 sequestration

A robust and stable carbonic anhydrase (CA) system is indispensable for effectively sequestering carbon dioxide to mitigate climate change. While microbial surface display technology has been employed to construct an economically promising cell-displayed CO2-capturing biocatalyst, the displayed CA enzymes were prone to inactivation due to their low stability in harsh conditions. Herein, drawing inspiration from biomineralized diatom frustules, we artificially introduced biosilica shell materials to the CA macromolecules displayed on Escherichia coli surfaces. Specifically, we displayed a fusion of CA and the diatom-derived silica-forming Sil3K peptide (CA-Sil3K) on the E. coli surface using the membrane anchor protein Lpp-OmpA linker. The displayed CA-Sil3K (dCA-Sil3K) fusion protein underwent a biosilicification reaction under mild conditions, resulting in nanoscale self-encapsulation of the displayed enzyme in biosilica. The biosilicified dCA-Sil3K (BS-dCA-Sil3K) exhibited improved thermal, pH, and protease stability and retained 63 % of its initial activity after ten reuses. Additionally, the BS-dCA-Sil3K biocatalyst significantly accelerated the CaCO3 precipitation rate, reducing the time required for the onset of CaCO3 formation by 92 % compared to an uncatalyzed reaction. Sedimentation of BS-dCA-Sil3K on a membrane filter demonstrated a reliable CO2 hydration application with superior long-term stability under desiccation conditions. This study may open new avenues for the nanoscale-encapsulation of enzymes with biosilica, offering effective strategies to provide efficient, stable, and economic cell-displayed biocatalysts for practical applications.

An improved Udwadia–Kalaba approach for controller design in underactuated mechanical systems

An improved Udwadia–Kalaba approach for controller design in underactuated mechanical systems

An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design.

Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, they have now become a standard part of care. EMRs have the potential to be further optimized with the introduction of artificial intelligence (AI). There has yet to be a widespread exploration of the use of AI in primary health care and how clinicians envision AI use to encourage further uptake. The primary objective of this research is to understand if the user-centered design approach, rooted in contextual design, can lead to an increased likelihood of adoption of an AI-enabled encounter module embedded in a primary care EMR. In this study, we use human factor models and the technology acceptance model to understand the results. To accomplish this, a partnership has been established with an industry partner, TELUS Health, to use their EMR, the collaborative health record. The overall intention is to understand how to improve the user experience by using user-centered design to inform how AI should be embedded in an EMR encounter. Given this intention, a user-centered approach will be used to accomplish it. The approach of user-centered design requires qualitative interviewing to gain a clear understanding of users' approaches, intentions, and other key insights to inform the design process. A total of 5 phases have been designed for this study. As of March 2024, a total of 14 primary care clinician participants have been recruited and interviewed. First-cycle coding of all qualitative data results is being conducted to inform redesign considerations. Some limitations need to be acknowledged related to the approach of this study. There is a lack of market maturity of AI-enabled EMR encounters in primary care, requiring research to take place through scenario-based interviews. However, this participant group will still help inform design considerations for this tool. This study is targeted for completion in the late fall of 2024. DERR1-10.2196/54365.

Flavonoid as a Potent Antioxidant: Quantitative Structure-Activity Relationship Analysis, Mechanism Study, and Molecular Design by Synergizing Molecular Simulation and Machine Learning.

In this work, a quantitative structure-antioxidant activity relationship of flavonoids was performed using a machine learning (ML) method. To achieve lipid-soluble, highly antioxidant flavonoids, 398 molecular structures with various substitute groups were designed based on the flavonoid skeleton. The hydrogen dissociation energies (ΔG1, ΔG2, and ΔG3) related to multiple hydrogen atom transfer processes and the solubility parameter (δ) of flavonoids were calculated using molecular simulation. The group decomposition results and the calculated antioxidant parameters constituted the ML data set. The artificial neural network and random forest models were constructed to predict and analyze the contribution of the substitute groups and positions to the antioxidant activity. The results showed the hydroxyl group at positions B4', B5', and B6' and the branched alkyl group at position C3 in the flavonoid skeleton were the optimal choice for improving antioxidant activity and compatibility with apolar organic materials. Compared to the pyrogallol group-grafted flavonoid, the designed potent flavonoid decreased ΔG1 and δ by 2.2 and 15.1%, respectively, while ΔG2 and ΔG3 kept the favorable lower values. These findings suggest that an efficient flavonoid prefers multiple ortho-phenolic hydroxyl groups and suitable sites with hydrophobic groups. The combination of molecular simulation and the ML method may offer a new research approach for the molecular design of novel antioxidants.

Characteristics, consent patterns, and challenges of randomized trials using the Trials within Cohorts (TwiCs) design A scoping review

ObjectiveTrials within Cohorts (TwiCs) is a pragmatic design approach that may overcome frequent challenges of traditional randomized trials such as slow recruitment, burdensome consent procedures, or limited external validity. This scoping review aims to identify all randomized controlled trials using the TwiCs design and to summarize their design characteristics, ways to obtain informed consent, output, reported challenges and mitigation strategies. Study design and settingSystematic search of Medline, Embase, Cochrane, trial registries and citation tracking up to December 2022. TwiCs were defined as randomized trials embedded in a cohort with post-randomization consent for the intervention group and no specific post-randomization consent for the usual care control group. Information from identified TwiCs were extracted in duplicate from protocols, publications, and registry entries. We analyzed the information descriptively and qualitatively to highlight methodological challenges and solutions related to non-uptake of interventions and informed consent procedure. ResultsWe identified a total of 46 TwiCs conducted between 2005 and 2022 in 14 different countries by a handful of research groups. The most common medical fields were oncology (11/46; 24%), infectious diseases (8/46;17%), and mental health (7/46; 15%). A typical TwiCs was investigator-initiated (46/46;100%), publicly funded (36/46; 78%), and recruited outpatients (27/46; 59%). Excluding eight pilot trials, only 16/38 (42%) TwiCs adjusted their calculated sample size for non-uptake of the intervention, anticipating a median non-uptake of 25% (interquartile range 10%-32%) in the experimental arm. Seventeen TwiCs (45%) planned analyses to adjust effect estimates for non-uptake. Regarding informed consent, we observed three patterns: 1) three separate consents for cohort participation, randomization, and intervention (17/46; 37%); 2) combined consent for cohort participation and randomization and a separate intervention consent (10/46; 22%); and 3) consent only for cohort participation and intervention (randomization consent not mentioned; 19/46; 41%). ConclusionsExisting TwiCs are globally scattered across a few research groups covering a wide range of medical fields and interventions. Despite the potential advantages, the number of TwiCs remains small. The variability in consent procedures and the possibility of substantial non-uptake of the intervention warrants further research to guide the planning, implementation, and analysis of TwiCs.

Biomass and Transparent Supramolecular Elastomers for Green Electronics Enabled by the Controlled Growth and Self-Assembly of Dynamic Polymer Networks.

Determining the optimal method for preparing supramolecular materials remains a profound challenge. This process requires a combination of renewable raw materials to create supramolecular materials with multiple functions and properties, including simple fabrication, sustainability, a dynamic nature, good toughness, and transparency. In this work, a strategy is presented for toughening supramolecular networks based on solid-phase chain extension. This toughening strategy is simple and environmentally friendly. In addition, a series of biobased elastomers are designed and prepared with adjustable performance characteristics. This strategy can significantly improve the transparency, tensile strength, and toughness of the synthesized elastomer. The synthesized biobased elastomers have great ductility, repairability, and recyclability, and they show good adhesion and dielectric properties. A biobased ionic skin is assembled from these biobased elastomers. Assembled ionic skin can sensitively detect external stimuli (such as stretching, bending, compression, or temperature changes) and monitor human movement. The conductive and dielectric layers of the biobased ionic skin are both obtained from renewable raw materials. This research provides novel molecular design approaches and material selection methods for promoting the development of green electronic devices and biobased elastomers.