Unified Design Research Articles

The design space of E(3)-equivariant atom-centred interatomic potentials

Abstract Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.

A Unified Design Methodology for Front-End RF/mmWave Receivers

A Unified Design Methodology for Front-End RF/mmWave Receivers

Settlement of piles with negative skin friction: design approaches according to part 3 of Eurocode 7

Piles offer an effective solution for supporting structures in challenging ground conditions. However, ground settlement around deep foundations can induce negative skin friction, adding complexity to the geotechnical analysis and requiring careful consideration in both the pile settlement evaluation and structural integrity design. The article discusses the general recommendations provided by the informative annex of the revised Eurocode 7 version for designing piles subjected to negative skin friction. Using the Fellenius unified pile design concept, we provide a practical design framework for piles subjected to negative skin friction that extends the load-settlement curve methods proposed by Masopust, representing the standard pile design approaches in the Czech Republic. For examples presented, the introduced practical approach, also suitable for manual calculations, is in very good agreement with a refined calculation providing a nonlinear load-settlement curve.

Information System Concept Implementing Single Design and Production Space Between Vessel Life Cycle Participants

The disadvantages in the enterprises interaction and the transition of documentation between the vessel (offshore object) life cycle stages within existing systems in electronic form has been analyzed. Based on this the authors developed the concept of an unified information system implementation algorithm in a general form. The investigation object is the data movement process, which includes: design documentation at the design stage, working design documentation at the development stage, technological processes at the stage of manufacturing components and building marine equipment samples, as well as operational documentation. The investigation subject is a system being developed that provides all the necessary functions for the data movement during the participants (enterprises) interaction at all life cycle stages. The investigation purpose is to develop a new unified information system with the exception of existing disadvantages in the implementation and use of analogues at Russian enterprises. This system will implement a unified design and production space between life cycle participants, using organizational and economic mechanisms, which will significantly reduce the project implementation time, carry out open monitoring of everything process for the designer, the construction plant and the customer himself, thanks to the integration of data from all product life cycle stages on a unified information platform. As a result of the research, it was revealed that the creation of such a system will eliminate the proliferation of data records copies, as is the case with a “paper” management organization. Simultaneous access of many users to the same data will simplify maintaining the data relevance. Unification of data collection and storage formats will allow the formation data suitable for automated processing using a specific algorithm. The presence of a unified database will reduce the time of information analysis. Automation of routine operations will reduce the labor costs of performers. Ways are proposed for the formation of organizational and economic mechanisms that contribute to the selected methods implementation effectiveness in the implementation of design and production activities.

Prescribed-time observer-based output feedback stabilisation of switched uncertain planar systems with asymmetric output constraints

This paper presents an observer-based output feedback control strategy to solve the prescribed-time stabilisation of uncertain planar switched nonlinear systems with asymmetric output constraints. Based on adding a power integrator technique, prescribed-time switched state feedback controllers and a common fractional-type barrier Lyapunov function are developed. Then, deliberately constructed prescribed-time switched reduced-order observers are presented to estimate the unmeasurable state. Combing the switched state feedback controllers and reduced-order observers proposed in this note, the closed-loop switched planar systems achieve prescribed-time stability. Meanwhile, the violation of asymmetric output constraints is prevented implicitly by the developed barrier Lyapunov function. The novelty of this approach lies in the ingenious construction of prescribed-time switched observers and the unified design characteristics of the method, which enable the accomplishment of prescribed-time output feedback stabilisation for switched systems with/without output constraints without necessitating any structure modifications to the switched controllers and observers.

Curved Geometric Networks for Visual Anomaly Recognition.

Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature. However, the success of the geometry constraints, posed in the embedding space, indicates that curved spaces might encode more structural information, leading to better discriminative power and hence richer representations. In this work, we investigate the benefits of the curved space for analyzing anomalous, open-set, or out-of-distribution (OOD) objects in data. This is achieved by considering embeddings via three geometry constraints, namely, spherical geometry (with positive curvature), hyperbolic geometry (with negative curvature), or mixed geometry (with both positive and negative curvatures). Three geometric constraints can be chosen interchangeably in a unified design, given the task at hand. Tailored for the embeddings in the curved space, we also formulate functions to compute the anomaly score. Two types of geometric modules (i.e., geometric-in-one (GiO) and geometric-in-two (GiT) models) are proposed to plug in the original Euclidean classifier, and anomaly scores are computed from the curved embeddings. We evaluate the resulting designs under a diverse set of visual recognition scenarios, including image detection (multiclass OOD detection and one-class anomaly detection) and segmentation (multiclass anomaly segmentation and one-class anomaly segmentation). The empirical results show the effectiveness of our proposal through consistent improvement over various scenarios. The code is made available at https://github.com/JHome1/GiO-GiT.

Nickel–Iron Oxyhydroxide Catalyst-Integrated Unified Electrode for High-Performance and Durable Anion-Exchange Membrane Water Electrolysis

One critical aspect in enhancing the performance and longevity of anion-exchange membrane water electrolyzers (AEMWE) is the oxygen evolution reaction (OER). In our study, we present a novel three-dimensional unified electrode architecture, where nickel and iron are integrated within a gas diffusion layer (GDL) in oxyhydroxide form, serving as the anode in AEMWE. Traditional electrodes in water electrolysis typically consist of separate catalyst and GDL layers, whereas our unified electrode combines these elements into a single layer. Compared to a conventional electrode utilizing IrO2, our unified electrode demonstrates superior catalytic activity and maintains stability for over 500 hours. Electrochemical analysis of the AEMWE employing the unified electrode reveals a performance of 3600 mA cm-2 at 1.9 V, surpassing many other AEMWE studies. Furthermore, our unified AEMWE exhibits excellent performance even at ultra-high current densities. Thus, the unified electrode design represents a promising advancement in enhancing AEMWE performance and reducing hydrogen production costs.

Optimising Instructional Design Strategies to Mitigate Cognitive Overload

A lack of unified instructional design strategies to mitigate cognitive overload in higher education necessitated this study to explore how instructional design could mitigate cognitive overload in first-year commerce students. Grounded in the Cognitive Load Theory (CLT), which posits that excessive information impedes conducive student learning experiences and metacognition. The research is situated within a South African private higher education institution (PHEI) and explores a qualitative analysis using a three-pronged approach. Firstly, a literature review on CLT and its application in instructional design impacting students' learning experiences was conducted. Secondly, the researchers evaluated the institution’s 2023 instructional design of modules and their presentation through the learning management system (LMS) using multimedia learning. The purposive sample consisted of eight first-year modules within the Bachelor of Commerce degree program to assess the learning design elements against the principles of CLT. The researchers, being three instructional designers used secondary data and peer-reviewed the module evaluations to validate the findings and verify the impact of CLT on students' learning based on the institution's existing instructional design strategy. By evaluating the PHEI’s current instructional design practices against CLT principles, this study aimed to identify effective strategies to manage cognitive load and enhance the student learning experience within the higher education context. The research findings of this study indicated that the integration of CLT into instructional design could mitigate cognitive overload, thereby improving the student learning experience, and metacognition and providing guidelines for the refinement of instructional design strategies. The value of the research outcomes was anticipated to contribute to the development of improved instructional design strategies using multi-media learning to address cognitive load. The value of the research outcomes was anticipated to contribute to the development of improved instructional design frameworks and strategies that addressed cognitive load, enabling effective and conducive learning environments in higher education. These insights aimed to guide future research in curriculum design and teaching and learning practices in higher education and to recommend instructional design strategies to manage cognitive load thus mitigating the challenges of cognitive overload experienced by students and enhancing the student learning experience.

When multiple route map designs are used by the same bus company

Abstract This paper reports three separate studies. Study 1 consisted of route map observations; study 2 looked at map creator’s perspective, and study 3 considered user research and route map usage. It was observed that the route maps issued by one bus service provider in Fukuoka, Japan, have as many as 24 types of design variations. According to the route map creators, limited resources was the main reason for this practice. Route map usability testing, keyword sorting and interviews were conducted as part of a user-focused research. Map-reading tests revealed several practices that have a negative impact on user’s ability to read route maps, and the user research revealed that users prefer a unified design.

Determining doses for backfill cohorts based on patient-reported outcome

BackgroundIncorporating backfill cohorts in phase I oncology trials is a recently developed strategy for dose optimization. However, the efficacy assessment window is long in general, causing a lag in identifying ineffective doses and more patients being backfilled to those doses. There is necessity to investigate how to use patient-reported outcomes (PRO) to determine doses for backfill cohorts.MethodsWe propose a unified Bayesian design framework, called ‘Backfill-QoL’, to utilize patient-reported quality of life (QoL) data into phase I oncology trials with backfill cohorts, including methods for trial monitoring, algorithm for dose-finding, and criteria for dose selection. Simulation studies and sensitivity analyses are conducted to evaluate the proposed Backfill-QoL design.ResultsThe simulation studies demonstrate that the Backfill-QoL design is more efficient than traditional dose-expansion strategy, and fewer patients would be allocated to doses with unacceptable QoL profiles. A user-friendly Windows desktop application is developed and freely available for implementing the proposed design.ConclusionsThe Backfill-QoL design enables continuous monitoring of safety, efficacy and QoL outcomes, and the recommended phase II dose (RP2D) can be identified in a more patient-centered perspective.

A Matrix-Based Approach to Unified Synthesis of Planar Four-Bar Mechanisms for Motion Generation With Position, Velocity, and Acceleration Constraints

Abstract This paper introduces a novel matrix-based approach for the simultaneous type and dimensional synthesis of planar four-bar linkage mechanisms, accommodating various practical constraints, including position, velocity, acceleration, and joint placements. Traditional design processes segregate type synthesis, the determination of joint and link configurations, from dimensional synthesis, which involves specifying link sizes and pivot locations. This segregation often leads to complexities in addressing the complete design challenge. The novel methodology proposed in this paper departs from the conventional sequential design approach by concurrently evaluating type and dimensional parameters using a data-driven matrix formulation. The crux of the paper’s methodology involves formulating a singular design equation through a transformation matrix, parameterized by the Cartesian parameters of the mechanism’s dyads. This formulation linearly expresses a broad range of constraints, facilitating the identification of viable solutions through singular value decomposition and null space analysis. This integrated approach not only simplifies the synthesis process but also provides direct insights into the mechanism’s parameters, encompassing both type and dimensions, thereby obviating the need for further interpretative steps common to the use of quaternions and kinematic mapping. In essence, the paper presents two main contributions: the development of a unified design equation capable of encompassing a wide array of constraints within the mechanism synthesis process, and the introduction of an algorithm that effectively identifies all potential planar four-bar linkage mechanisms by accurately satisfying up to five constraints. This approach promises to enhance the design and optimization of mechanical systems by offering a more holistic and efficient pathway to mechanism synthesis.

A unified cross-series marine propeller design method based on machine learning

Propeller design diagrams, like Bp-δ diagram, are widely applied in ship propeller design. However, different propeller series use various selection approaches, so comparisons between designs are only possible after individual candidates are chosen.This paper proposes a unified approach based on machine learning to allow efficient comparison and facilitate the selection of the optimal propeller amongst the available propeller series. The process starts by compiling propeller series data to generate a comprehensive dataset on propeller performance. This dataset is then used to train an Artificial Neural Network (ANN) model, which accurately predicts open-water propeller performance. Optimization techniques are applied to maximize propeller efficiency based on the specific needs of the vessel, while ensuring compliance with cavitation and noise constraints for safety. The model's accuracy is validated using data from the KRISO Container Ship (KCS), demonstrating the prediction's reliability. The method is then applied to select both open and ducted propellers for a variety of ship types to meet specific operational requirements. Ultimately, the optimized results are ranked by efficiency, offering an organized set of options for selecting the most suitable propeller. This approach eliminates the need for manual dataset correlation, significantly improving the efficacy of generating an outperforming initial design.

Multifunctional Smart Fabrics with Integration of Self-Cleaning, Energy Harvesting, and Thermal Management Properties.

Due to their good wearability, smart fabrics have gradually developed into one of the important components of multifunctional flexible electronics. Nevertheless, function integration is typically accomplished through the intricate stacking of diverse modules, which inevitably compromises comfort and elevates processing complexities. The integration of these discrete functional modules into a unified design for smart fabrics represents a superior solution. Here, we put forward a rational approach to functional integration for the typical challenges of thermal management, energy supply, and surface contamination in smart fabrics. This sandwich-structured multilayer fabric (MLF) is obtained by continuous electrospinning of two layer P(VDF-HFP) fabric and one layer P(VDF-HFP) fabric functionalized with core-shell SiO2/ZnO/ZIF-8 (SZZ) nanoparticles. Specifically, MLFs achieve effective and stable energy harvesting in triboelectric nanogenerators (TENGs) with hydrophobicity and antibacterial properties. Meanwhile, MLFs also have high mid-infrared emissivity and sunlight reflectivity, successfully realizing radiative cooling under different climates, and have been applied in wearing clothing, roof shading, and car covers. This work may contribute to the design and manufacturing of next-generation thermal comfort smart fabrics and wearable electronics, particularly in terms of the rational design of multifunctional devices.

Lifecycle DoE-The Companion for a Holistic Development Process.

Within process development, numerous experimental studies are undertaken to establish, optimize and characterize individual bioprocess unit operations. These studies pursue diverse objectives such as enhancing titer or minimizing impurities. Consequently, Design of Experiment (DoE) studies are planned and analyzed independently from each other, making it challenging to interlink individual data sets to form a comprehensive overview at the conclusion of the development process. This paper elucidates the methodology for constructing a Life-Cycle-DoE (LDoE), which integrates data-driven process knowledge through design augmentations. It delves into the strategy, highlights the challenges encountered and provides solutions for overcoming them. The LDoE approach facilitates the augmentation of an existing model with new experiments in a unified design. It allows for flexible design adaptations as per the requirements of subject matter experts (SME) during process development, concurrently enhancing model predictions by utilizing all available data. The LDoE boasts a broad application spectrum as it consolidates all data generated within bioprocess development into a single file and model. The study demonstrates that the LDoE approach enables a process characterization study (PCS) to be performed solely with development data. Furthermore, it identifies potentially critical process parameters (pCPPs) early, allowing for timely adaptations in process development to address these challenges.

Exploring the Performance Impact of Neural Network Optimization on Energy Analysis of Biosensor

With the popularization of new energy vehicles, lithium battery systems, as the main components of new energy vehicles, have the characteristics of short life cycles and harmful substances inside. The green treatment of lithium battery systems has become a research hotspot. Disassembly and recycling are essential means of reusing waste in lithium battery systems. Due to the wide variety of lithium battery systems, the lack of unified design standards, and the high flexibility requirements for disassembly, manual disassembly is currently the primary method used. However, this method can cause health hazards to oneself when dismantling some harmful components. The optimization of the dismantling process route for lithium batteries is a crucial step before dismantling, which directly determines the economic benefits of dismantling. However, unlike general electromechanical products, lithium batteries have prominent safety issues during the dismantling process, so the safety requirements for their dismantling process route are relatively high. Given the substantial absence of parametric evaluation and modification in prior research, this work investigates the influence of the most significant factors on the power density of biosensors. A conduction-based framework was employed to ascertain these variables, and the calculations were performed utilizing a neural network. The neural network was developed with Particle Swarm Optimization (PSO). Based on this, this article considers studying the optimization method of the lithium battery safety disassembly process to maximize safety and economic benefits comprehensively. Based on the essential characteristics of lithium-ion battery systems, an analysis is conducted on the allocation method of difficulty level for human-machine cooperation tasks and the impact indicators of task allocation. Then, a product disassembly hybrid diagram is established, and on this basis, multiple sets of human-machine cooperation disassembly sequences are generated. Finally, a multi-objective optimization model for disassembly cost, difficulty, and time is established. Finally, taking the Tesla Model 1sPBS waste lithium battery as an example, the safety prediction model for dismantling the waste lithium battery and the optimization model for the safety dismantling process route were solved to verify the effectiveness of the above optimization method.

A comprehensive control paradigm for prescribed performance attainment in complex nonlinear systems

This paper presents a comprehensive control framework tailored for achieving Prescribed Performance Control (PPC) in the context of complex nonlinear systems, addressing multifaceted challenges prevalent in control design. The focus is on a control scenario characterized by the simultaneous presence of nonlinearity, external disturbances, time delay, and unknown control direction—issues that pose considerable obstacles for existing solutions. To surmount these challenges, our proposed approach integrates well-established techniques, including neural networks, Nussbaum-type gains, and adaptive control strategies within a unified control design framework. The specific technical challenge addressed in this work involves the effective management of these intricate complexities in Single-Input Single-Output (SISO) systems. Our contributions extend the theoretical foundations, presenting an ideal PPC control design and introducing two adaptive neural network-based control methods capable of accommodating both known and unknown control directions. Utilizing Lyapunov–Krasovskii functionals, we showcase a unique integration that surpasses a mere combination of individual techniques. This work advances the theoretical underpinnings of control engineering tailored for real-world scenarios. The proposed controller’s efficacy is validated through rigorous simulations and compared with recent results and benchmark PPC controllers, establishing its superiority in addressing the intricacies of complex control scenarios.

A unified confinement-based direct design approach for novel double-skin composite columns

Concrete-filled stiffened steel tubular short columns, with cold-formed stiffened steel tubes for the outer section, have been widely used in Asia in large commercial facilities and warehouses. This paper focuses on the response of concrete-filled double-skin stiffened steel tubular (CFDSST) short columns and concrete-filled dual stiffened steel tubular (CFDST) short columns, with either square or circular inner tubes. A detailed database of existing information is assembled and presented, as well as a thorough analysis of existing design expressions. Additional examination of the concrete confinement has been undertaken which highlight the differences in behaviour between CFDSST and CFDST cross-sections. The major aim in this work is to provide a unified design approach, which accounts for concrete confinement and accounts for the material and geometric properties of the section, and offers improved accuracy over the existing approaches given in the European, American, British and Chinese design codes. The newly proposed unified confinement-based direct design approach for axially-loaded CFDSST and CFDST short columns is discussed, including the consideration given to the lateral confining pressure provided by the steel tubes. This method is shown to provide more accurate depictions of the load resistances of CFDSST and CFDST short columns relative to the international specifications, with lower scatter and greater range of applicability. It has also been shown that the CFDSST sections with a stiffened outer steel tube provides greater confinement and additional strength compared with equivalent unstiffened circular tubes of high relative tube slenderness. Given the easier fabrication and installation of beam-to-square column joints compared with circular columns, this is likely to extend their range of application in the future.

An intelligent native network slicing security architecture empowered by federated learning

Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial Internet of Things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture-intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machine learning, providing intra-slice and architectural operation security for the Slicing Future Internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated-learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving an average accuracy of approximately 95.60% in the network slicing architecture and 99.99% for the deployed slice – intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures.

Comparison of Configurable Modular Two-Level and Three-Level Isolated Bidirectional DC–DC Converters for Super-Capacitor Charging in DC Shore Power Systems

Compared to the AC counterpart, the DC shore power system provides a significant advantage of efficient power supply from renewable sources to ships and onshore loads. Super-capacitors serve as key energy storage units in such a system to buffer the power fluctuations and collect the regenerative energy. However, the ultra-wide voltage range of super-capacitors imposes a significant challenge in the topology selection and efficiency optimization of the interfacing isolated bidirectional DC–DC converter. To tackle this challenge, this paper analyzes and compares two promising converter topologies, which are a configurable modular two-level dual-active bridge (CM-2L-DAB) and a three-level dual-active bridge (3L-DAB). To facilitate an ultra-wide voltage range, extended phase-shift (EPS) modulation in conjunction with the topology reconfiguration is analyzed for the CM-2L-DAB, while a hybrid modulation scheme is proposed for the 3L-DAB. A unified design approach is provided for both topologies, which also yields to the power loss modeling. On this basis, the CM-2L-DAB and 3L-DAB are thoroughly compared in terms of the modulation schemes, current stress, soft-switching operation, power conversion efficiency, material usage, closed-loop control scheme, and reliability. A prominent conclusion can be drawn that the CM-2L-DAB provides a higher efficiency than the 3L-DAB over the whole voltage range, but it relies on additional relays to reconfigure its topology which results in lower reliability and dynamic performance than the 3L-DAB.

Moment Capacity of Bolted-Side Plate for the Eaves Joint of Nested Tapered Box Beam Portal Frame

The joints of cold-formed steel (CFS) portal frames with box sections are primarily formed either through stiffened welded connections or by bolted end plates, which typically involve costly full penetration butt welds. In New Zealand, CFS portal frames with nested tapered box beam (NTBB) sections are popular, and the joints of these portal frames are also formed through a bolted end plate system. In literature, it was found that welds in these joints are prone to cracking in the heat-affected zone (HAZ) under bearing opening moments (tension). Therefore, this paper focuses on proposing an alternative connection system (bolted-side plate) for eaves joints without any weld, aiming to reduce the overall cost of construction and increase construction speed. The performance (strength and stiffness) of these joints is mainly dependent on the geometric details of the bolted-side plate, which is limited in Australia/New Zealand standards (AS/NZ 4600). Herein, a non-linear finite element (FE) model was developed for the NTBB portal frame and validated against the experimental test results from the literature. Firstly, the validated FE model was used to investigate the feasibility of using bolted-side plates for NTBB portal frames and found that the bolted-side plate can increase the ultimate capacity of NTBB portal frame by 9%. A parametric study was then conducted by considering different geometric details of the bolted-side plates under both closing (gravity) and opening (wind uplift) moments. Finally, unified design equations for the moment capacity of bolted-side plates were proposed based on the results of the parametric study, and their accuracy was assessed through reliability analysis.