Design Methodology Research Articles

Parametric simulation of piezoelectric transducer based on finite element method

Purpose Finite element analysis of underwater transducers typically requires a high level of expertise, and the iterative process of testing various sizes, material parameters and other factors is often inefficient. To address this challenge, this paper aims to introduce underwater transducer parametric simulation (UTPS) software to streamline the design and optimization process. Design/methodology/approach The design methodology integrates the strengths of ANSYS Parametric Design Language (APDL) for parametric design with the Qt Creator framework for developing a visual interface. C++ is used to encapsulate complex, hard-to-master APDL macros and interact with ANSYS software to execute the relevant APDL macros, performing finite element analysis on the underwater transducer in the background. The results are then processed and displayed on the visual interface. Findings UTPS enables parametric modeling, modal analysis, harmonic response analysis and directivity analysis of underwater transducers. Users only need to input parameters into the software interface to obtain the transducer’s performance, significantly improving work efficiency and lowering the professional threshold. A prototype transducer was fabricated and tested based on UTPS results, which confirmed the accuracy of the software. Originality/value This paper presents an innovative parametric simulation tool for underwater transducers, combining finite element analysis and APDL to simplify and expedite the design process. UTPS reduces the need for specialized knowledge, cutting down on training costs, while its parametric design capabilities accelerate the design process, saving resources.

Modelling, optimization and characterization of carbon black derived from waste tyre pyrolysis

This study explores how to maximise the yield of carbon black from waste tyre pyrolysis, paying particular attention to important process parameters including feedstock mass, residence time, and temperature. The study employed the use of the central composite design of the response surface methodology to investigate the relationship between the process parameters (feedstock mass, residence time, and temperature) and the carbon black yield. With an R2 and adjusted R2 of 0.99, and a highly significant F-value of 389.62, the model demonstrated excellent accuracy. Analysis of perturbation plots revealed that feedstock mass had the most significant influence on carbon black yield. The interplay of process parameters was illustrated by the 3D surface plots, which showed a positive correlation between the combined effects of feedstock mass and temperature and carbon black yield. The following parameters were found to be optimal for obtaining a maximum carbon black yield: 40 g of feedstock mass, 350 °C temperature, and 60 min of residence time. This produced a carbon black yield of 25.25 wt.%. This was remarkably comparable to the experimental yield of 25 wt.%, confirming the validity of the response surface methodology model. The characterisation of carbon black generated from waste tyre pyrolysis using FT-IR and SEM reveals a compact structure with little porosity and different functional groups. These characteristics increase the material's reliability, thermal stability, and resilience to environmental degradation, making it ideal for long-term applications like rubber reinforcement and composite manufacturing. The findings indicate the optimal production of carbon black at lower temperatures and shorter residence times. The present study establishes the foundation for further investigation into the optimisation of the pyrolysis process, suggesting that different process conditions be explored.

Multivariate Approaches Boosting Lithium-Mediated Ammonia Electrosynthesis in Different Electrolytes.

Ammonia electrosynthesis through the lithium-mediated approach has recently reached promising results towards high activity and selectivity in aprotic media, reaching high Faradaic efficiency (FE) values and NH3 production rates. To fasten the comprehension and optimization of the complex lithium-mediated nitrogen reduction system, for the first time a multivariate approach is proposed as a powerful tool to reduce the number of experiments in comparison with the classical one-factor-at-a-time approach. Doehlert design and surface response methodology are employed to optimize the electrolyte composition for a batch autoclaved cell. The method is validated with the common LiBF4 salt, and the correlations between the FE and the amount of lithium salt and ethanol as proton donor are elucidated, also discussing their impact on the solid electrolyte interphase (SEI) layer. Moreover, a new fluorinated salt is proposed (i.e., lithium difluoro(oxalate) borate (LiFOB)), taking inspiration from lithium batteries. This salt is chosen to tailor the SEI layer, with the aim of obtaining a bifunctional interfacial layer, both stable and permeable to N2, the latter being an essential characteristic for batch systems. The SEI layer composition is confirmed strategic and its tailoring with LiFOB boosts FE values.

The Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review.

To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biologicalrisk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search for relevant studies on four distinct databases (Scopus, Web of Science, PubMed, and Cochrane Library). A total of 1536 relevant studies were retrieved to the EndNote library(Clarivate, Philadelphia, USA) where duplicates were removed and the rest of the studies were assessed for eligibility based on titles, abstracts, and the availability of full-text articles. After assessing the studies for eligibility, we found 42 studies eligible for inclusion in this systematic review. We found nine studies on liquid tumors, 13 on solid tumors, and 20 on central nervous system (CNS) tumors. ML goals included classification, treatment response prediction, and dose optimization. Neural networks, k-nearest neighbors, random forests, support vector machines, and naive Bayeswere among the techniques employed. The identified studies' strengths included treatment response prediction and automated analysis that matched or outperformed physician comparators. Significant variation in clinical applicability,criteria for reporting, limited sample numbers, and the absence of external validation cohorts were among the common issues. We found places where MLcan improve clinical care in manners that would not be possible otherwise. Even though MLhas great promise for enhancing pediatric cancer diagnosis, decision-making, and monitoring, the discipline is still in its infancy, and standards and recommendations will support future research to guarantee robust methodologic design and maximize therapeutic applicability.

Improving Electrical Conductivity of Commercially Pure Aluminium: The Synergistic Effect of AlB8 Master Alloy and Heat Treatment

This study aims to enhance the electrical conductivity of commercially pure aluminium by minimizing impurities and grain boundaries in its microstructure, ultimately improving the efficiency of electric motors constructed from rotors with squirrel cages made from this material. For this purpose, an aluminium–boron (AlB8) master alloy was added to aluminium with a purity of 99.7%, followed by the application of a grain-coarsening heat treatment to the rotors. To obtain commercially pure aluminium with boron additions of 0.05% and 0.1% by weight, specific amounts of the AlB8 master alloy were added into aluminium with a purity of 99.7%. Using these materials, squirrel cage components of rotors were produced via the high-pressure die-casting method. Subsequently, a grain-coarsening heat treatment of the rotors was performed at temperatures of 450 °C, 500 °C, and 550 °C, with holding times of 2, 6, and 10 h. The Box–Behnken design, which is based on statistical experimental design and response surface methodology, was employed to investigate the effects of adding boron and varying the heat treatment temperature and holding time on the electrical conductivity of commercially pure aluminium. The results showed that the synergistic effect of adding boron at 0.05 wt.% and applying the grain-coarsening heat treatment at a temperature of 550 °C for a holding time of 10 h significantly enhanced the electrical conductivity of commercially pure aluminium, increasing it from 60.62% IACS to 63.1% IACS. Correspondingly, the efficiency of the electric motor increased from 90.35% to 91.53%. These findings suggest that this hybrid method not only enhances the electrical conductivity of commercially pure aluminium but also has strong potential to improve its other properties, such as thermal conductivity. This will lead to products composed of components manufactured from the materials exhibiting better performance characteristics, such as increased efficiency and extended service life. Consequently, this innovative method will contribute economically and environmentally by facilitating the manufacture of high-performance products.

The Road to Commercializing Optical Metasurfaces: Current Challenges and Future Directions.

Optical metasurfaces, components composed of artificial nanostructures, are recognized for pushing boundaries of wavefront manipulation while maintaining a lightweight, compact design that surpasses conventional optics. Such advantages align with the current trends in optical systems, which demand compact communication devices and immersive holographic projectors, driving significant investment from the industry. Although interest in commercialization of optical metasurfaces has steadily grown since the initial breakthrough with diffraction-limited focusing, their practical applications have remained limited by challenges such as, massive-production yield, absence of standardized evaluation methods, and constrained design methodology. Here, this Perspective addresses the challenges in commercialization of optical metasurfaces, particularly focused on mass production, fabrication tolerance, performance evaluation, and integration into commercial systems. Additionally, we select the fields where metasurfaces may soon play significant roles and provide a perspective on their potentials. By addressing the challenges and exploring the solutions, this Perspective aims to foster discussions that will accelerate the utilization of optical metasurfaces and further build near-future metaphotonics platforms.

The Development and Application of Neuromorphic Silicon Neural Circuits

Silicon-based neuronal circuits, inspired by biomimetic principles, have garnered increasing scholarly interest and investigation. Building on the field of neuromorphic engineering, which focuses on designing and emulating circuits and systems that mimic the neural architecture of biological nervous systems, this study addresses high-speed modeling of large-scale neural systems and real-time applications of bio-inspired neural systems. The manuscript delineates the prevalent architectural components and methodologies for the fabrication of such circuits, offering a comprehensive survey of various neuromorphic silicon neurons (SiNs) that instantiate diverse computational paradigms. Additionally, the paper delves into the fundamental principles of competitive circuits, discussing and explaining their operational mechanisms and derivative applications. By comparing different design methodologies for basic circuits, it demonstrates how different fundamental circuits can be applied to implement spiking silicon neurons in various research fields. The study ultimately focuses on the sub-threshold operating region of these circuits and showcases the practical value of silicon neuron circuits through integrated circuits and vary-large-scale neural networks (VLSI) chip implementations. This approach highlights the potential of neuromorphic systems in achieving efficient and biologically plausible neural emulation

Design and Validation of a Testing Device for Sediment-Induced Erosion Based on Similarity Theory

Sediment-induced erosion is a primary cause of failure in the flow-passage components of Francis turbine units. This study adopted the Realizable k–ε turbulence model to numerically simulate the effects of sediment-induced erosion on the guide components of Francis turbines. Specifically, using flow similarity theory, a testing device suitable for studying the sediment-induced erosion behavior of turbine vanes was designed, and the similarity between the flow fields of actual vanes and testing device vanes was validated. The results revealed a high degree of consistency between the near-wall flow velocities and sediment volume fractions experienced by both vanes at 0.5 vane height. For instance, the midsection of the suction side of the stay and guide vanes exhibited relatively stable velocities of 12.5 m/s and 42 m/s, respectively. Further, sediment volume fractions at the leading edge of the stay and guide vanes reached 0.015, respectively, owing to the impact of sediment-laden flow. Overall, the proposed testing device design methodology can predict the operational lifespan of actual vanes and assess the wear resistance of various coating materials. These findings provide valuable scientific guidance for optimizing the design and operation of hydropower plants.

Design of Resonant Inverters with Energy Dosing, Based on Optimization with Reference Curve

This paper presents an optimization-based approach for the design of energy-dosing resonant inverters (RI) using a reference curve. RI are widely used in areas such as wireless charging, induction heating, and high-frequency power supplies due to their high efficiency and soft-switching capability. The proposed methodology uses a reference current curve in the alternating current (AC) circuit that describes the ideal behavior of the inverter during the start-up transient in order to operate in energy metering mode and minimize switching losses. This approach analyzes and optimizes the values of the circuit elements whose initial values are determined by applying a rational design methodology. The results of the study demonstrate increased dynamics of the power circuit and better stability compared to applying traditional design methods. The presented simulation results confirm the effectiveness of the proposed optimization and the improvement of the characteristics of the studied device.

Prevalence and risk factors for long COVID among cancer patients: a systematic review and meta-analysis

ObjectiveThe prevalence of long COVID among cancer patients remains unknown. This study aimed to determine the prevalence of long COVID and explore potential risk factors among cancer patients.MethodsA systematic search was performed on PubMed, Web of Science, and Embase from database inception until 21 March 2024, to identify studies that reported long COVID in cancer patients. Two investigators independently screened the studies and extracted all information about long COVID in cancer patients for subsequent analysis. Methodological quality was assessed using the “Joannagen Briggs Institute (JBI) Critical Appraisal Checklist for Studies Reporting Prevalence Data”.ResultsA total of 13 studies involving 6,653 patients were included. The pooled prevalence of long COVID was 23.52% [95% confidence interval (CI), 12.14% to 40.64%] among cancer patients reported experiencing long COVID after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The pooled prevalence of any long COVID in cancer patients was 20.51% (95% CI, 15.91% to 26.03%), 15.79% (95% CI, 11.39% to 21.47%), and 12.54% (95% CI, 6.38% to 23.18%) in 3, 6, and 12 months follow-up duration. Fatigue was the most common symptom, followed by respiratory symptoms, myalgia, and sleep disturbance. Patients with comorbidities had a significantly higher risk of experiencing long COVID [odds ratio (OR) = 1.72; 95% CI, 1.09 to 2.70; p = 0.019]. No statistically significant differences in sex, primary tumor, or tumor stage were detected.ConclusionNearly a quarter of cancer patients will experience long COVID after surviving from SARS-CoV-2 infection, and this would even last for 1 year or longer. Fatigue, respiratory symptoms, myalgia, and sleep disturbance need to be more addressed and managed to reduce symptom burden on cancer patients and improve quality of life. Patients with comorbidities are at a high risk of developing long COVID. Further randomized controlled trials with rigorous methodological designs and large sample sizes are needed for future validation.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023456665.

Structural reliability assessment of a historical reinforced concrete building using numerical analysis and NDT techniques

AbstractThis paper presents a case study of a methodology for assessing the structural reliability of an early reinforced concrete structure using a combination of primarily non‐destructive diagnostic and advanced numerical analysis methods. The case study examines the historical Csíky Gergely Theater in Kaposvár, Hungary, built in 1911. At that time, the design methodology and construction process were carried out using a relatively rudimentary standard framework. This study demonstrates that a multi‐phase assessment procedure integrating numerical analysis and primarily non‐destructive testing techniques can significantly reduce structural analysis uncertainty. This approach allows for an optimal selection of the reliability index (βtarget), even when deviating from design standards. Based on this modified reliability index and considering the diagnostic and material test results, the partial factors used for the assessment can be optimally adjusted, and in most cases reduced, compared to those in design codes for new structures. Consequently, the extent of structural interventions needed to increase the load‐bearing capacity can be minimized.

FATORES PREDITORES NA SÍNDROME DO DESCONFORTO RESPIRATÓRIO AGUDO EM PACIENTES COM A DOENÇA PULMONAR OBSTRUTIVA CRÔNICA: UMA REVISÃO INTEGRATIVA DE LITERATURA

Acute Respiratory Distress Syndrome (ARDS) is characterized by severe respiratory failure and represents a critical complication, often associated with the need for mechanical ventilation (MV). Chronic Obstructive Pulmonary Disease (COPD) is defined as a progressive condition that causes airway obstruction and injury to the lung parenchyma, often associated with smoking, and is one of the main causes of morbidity and mortality worldwide. Thus, patients with COPD are particularly susceptible to ARDS due to chronic impairment of the components of the respiratory system. This study addresses the relationship between COPD and ARDS, and aimed to identify the main predictors of ARDS in this population. Thus, this integrative literature review followed a methodological design that involved searching the PubMed, BVS and Scopus databases, with the selection of articles published in the last ten years. The research identified five relevant studies, which indicated factors such as COPD exacerbations, use of MV, advanced age, sepsis and environmental factors as predictors of ARDS in patients with COPD. Although the studies showed an association between these conditions, the scarcity of specific research on predictors of ARDS in patients with COPD was an important limitation, but among the studies of the qualitative analysis, 8,431 patients and 26 reviews were evaluated that comprised the 2024 meta-analysis evaluated, which strengthens the evidence on the subject.

Adaptive Energy Management Strategy for Sustainable xEV Charging Stations in Hybrid Microgrid Architecture

Integrating electric vehicles (EVs) into power grids presents critical energy management challenges, especially in microgrid systems powered by renewable energy sources. This study introduces a novel energy management strategy for EV charging stations utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. The system dynamically optimizes the coordination of renewable energy sources solar photovoltaic (PV) panels and wind turbines energy storage, and EV chargers. By leveraging real-time data and predictive algorithms, the ANFIS controller adapts to fluctuations in energy supply and demand, ensuring optimal performance. The innovation of this work lies in combining fuzzy logic with neural network-based learning to enhance decision-making under uncertain and variable renewable energy conditions. The proposed approach employs a robust design methodology, integrating neural network training with fuzzy logic system development, to create an adaptive and intelligent control system. Simulation results using MATLAB/Simulink demonstrate a 92% increase in energy efficiency and an 89% enhancement in load-handling capacity compared to conventional methods. The system effectively manages renewable energy variability, battery state-of-charge, and load demand, maintaining stable electrical characteristics even under dynamic wind and solar conditions. This work underscores the importance of advanced AI-driven control strategies in enabling sustainable EV charging infrastructure within microgrid environments.

Harnessing hardware acceleration in high-energy physics through high-level synthesis techniques

At the Large Hadron Collider, the vast amount of data from experiments demands not only sophisticated algorithms but also substantial computational power for efficient processing. This paper introduces hardware acceleration as an essential advancement for high-energy physics data analysis, focusing specifically on the application of High-Level Synthesis (HLS) to bridge the gap between complex software algorithms and their hardware implementation. We will explore how HLS facilitates the direct implementation of software algorithms into hardware platforms such as FPGAs, enhancing processing speeds and enabling real-time data analysis. This will be highlighted through the case study of a track-finding algorithm for muon reconstruction with the CMS experiment, demonstrating HLS’s role in translating computational tasks into high-speed, low-latency hardware solutions for particle detection and reconstruction. Key techniques in HLS, including parallel processing, pipelining, and memory optimization, will be discussed, illustrating how they contribute to the efficient acceleration of algorithms in high-energy physics. We will also cover design methodologies and iterative processes in HLS to optimize performance and resource utilization, alongside a brief mention of additional techniques like algorithm approximation and hardware/software co-design. In short, this paper will underscore the potential of hardware acceleration in high-energy physics research, emphasizing HLS as a powerful tool for physicists to enhance computational efficiency and foster groundbreaking discoveries.

Mobility Challenges and Inclusive Solutions for People with Physical Disabilities in India

This study examines the complex mobility challenges encountered by individuals with physical disabilities in India and seeks to identify inclusive solutions that can improve their accessibility and autonomy. Significant barriers to their full societal participation include inadequate infrastructure, limited transportation options, societal stigma, economic constraints, and gaps in policy implementation. Utilizing a mixed-methods approach, this research incorporates surveys, interviews, and field observations to analyze these issues. The findings underscore the critical need for enhanced accessibility standards and their enforcement, increased investment in accessible transportation, greater affordability and availability of assistive devices, as well as inclusive educational and employment opportunities. Additionally, public awareness campaigns are essential to address social stigma. The research advocates for a comprehensive framework of inclusive solutions, which encompasses universal design principles, accessible transportation systems, assistive technologies, support services, policy advocacy, and participatory design methodologies. By adopting these strategies, India can foster a more inclusive and equitable society, enabling persons with physical disabilities to fully exercise their right to mobility and engage meaningfully in all facets of life. The researchers are collaborating with a prominent NGO active in the field of disability in Bengaluru, and a study involving beneficiaries of CSR Program driven in association of NGO partner is planned.

Financial inclusion of vulnerable sectors with a gender perspective: risk analysis model with artificial intelligence based on complex thinking

The objective is to present a proposal for a gender-sensitive risk analysis model using artificial intelligence (AI) within the framework of complex thinking that provides access to opportunities, specifically for vulnerable populations such as women from underprivileged sections. This international non-parametric study highlights the vulnerability of this population in Mexico through a sample of 2787 women. The methodological design included data analysis, the postulation of a proposed model, and a validation method for the credit risk analysis model. There is a correlation between the level of schooling of impoverished and vulnerable women with the possibility of self-employment and selling a product or service. In the framework of complex thinking, the perception of innovative thinking is related to the level of education and innovative decision-making in professional projects. Women with a higher level of schooling tend to think about their professional projects systematically. Promoting complex thinking involves innovative educational practices to encourage critical, systemic, scientific, and innovative thinking in entrepreneurship and sustainable development. Integrating reasoning for complexity benefits women and contributes to economic and social growth in vulnerable regions. In contrast to other models, our credit risk analysis model uses AI and variables for gender, vulnerability, and complex thinking to detect patterns in women's behaviors and attitudes in the venture start-up process. Our proposal is the starting point of many analyses to develop further about artificial intelligence based on complex thinking.

Risk prediction models for feeding intolerance in patients with enteral nutrition: a systematic review and meta-analysis

BackgroundAlthough more risk prediction models are available for feeding intolerance in enteral-nourishment patients, it is still unclear how well these models will work in clinical settings. Future research faces challenges in validating model accuracy across populations, enhancing interpretability for clinical use, and overcoming dataset limitations.ObjectiveTo thoroughly examine studies that have been published on feeding intolerance risk prediction models for enteral nutrition patients.DesignConducted a systematic review and meta-analysis of observational studies.MethodsA comprehensive search of the literature was conducted using a range of databases, including China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Embase. The search scope was confined to articles within the database from its inception until August 12th, 2024. The data from the selected studies should be extracted, including study design, subjects, duration of follow-up, data sources, outcome measures, sample size, handling of missing data, continuous variable handling methods, variable selection, final predictors, model development and performance, and form of model presentation. The applicability and bias risk were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist.ResultsA total of 1,472 studies were retrieved. Following the selection criteria, 18 prediction models sourced from 14 studies were incorporated into this review. In the field of model construction, only one study employed the use of multiple machine-learning techniques for the development of a model. In contrast, the remaining studies used logistic regression to construct FI risk prediction models. The incidence of FI in enteral nutrition was 32.4–63.1%. The top five predictors included in the model were APACHE II, age, albumin levels, intra-abdominal pressure, and mechanical ventilation. The reported AUC, or area under the curve, exhibited a range of values between 0.70 and 0.921. All studies were identified as having a high risk of bias, primarily due to the use of inappropriate data sources and inadequate reporting within the analysis domain.ConclusionAlthough the included studies reported a certain degree of discriminatory power in their predictive models to identify feeding intolerance in patients undergoing enteral nutrition, the PROBAST assessment tool deemed all the included studies to carry a significant risk of bias. Future research should emphasize the development of innovative predictive models. These endeavors should incorporate more extensive and diverse sample sizes, adhere to stringent methodological designs, and undergo rigorous multicenter external validation to ensure robustness and generalizability.Systematic review registrationIdentifier CRD42024585099, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=585099.

Experimental investigation and optimization of process parameter of abrasive water jet machining (AWJM) on D2 steel

Abrasive water jet machining (AWJM) is the most significant hybrid unconventional machining technique for its exceptional material removal rate (MRR) and surface quality for any type of brittle, ductile, and composite materials with a higher level of precision and accuracy. It has great flexibility in machining for producing complex-shaped parts that can be used in automobile, marine, aerospace, and chemical industries. This study focuses on the application of AWJM for cutting of D2 steel components using silicon carbide (SiC) abrasives. The experiments are conducted following the Box-Behnken Design (BBD) of response surface methodology (RSM) as the design of experiment (DOE) by considering pressure (P), traverse speed (TS), and abrasive flow rate (AFR) as the input factors each with three different levels in obtaining the corresponding outputs, namely MRR and surface roughness (Ra). Finally, the multicriteria decision-making (MCDM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, is applied to optimize the output values as MRR of 2.757 mm3/sec, and Ra of 1.014 µm.

Elastic robotic structures: a multidisciplinary framework for the design and control of shape-morphing elastic system for architectural and design applications

The design of adaptive structures and objects takes place at the intersection of design, architecture, robotics and engineering. Evolving from 1960s cybernetics to today’s interactive projects, technological advancements shape new visions for adaptive systems. A key challenge in this field is developing human-scale, shape-morphing structures. Elastic materials offer a promising solution for creating lightweight systems capable of large transformations with minimal components and energy, unlike conventional rigid systems. This approach requires methodologies for designing and controlling complex material deformations. While architectural and structural design methods focus on large-scale but static elastic structures, soft robotics explores dynamic behaviors. However these approaches are limited for complex shapes and large-scale, as their focus is on specialized applications. To address these issues, this research introduces a multidisciplinary framework for the design and control of shape-morphing elastic system for architectural and design applications. It also presents the concept of elastic robotic structures (ERS), which refers to a body of work developed with the framework. ERS are defined as large-scale elastic systems that are robotically actuated and can achieve multiple geometrical states, interacting with humans and adapting to diverse conditions. The multidisciplinary framework is presented for ERS design, characterization and control, showing how it leverages the integration of architecture, engineering and robotics to overcome the limitations of discipline-specific traditional approaches. The framework is applied in the realization of different types of ERS, which are presented and categorized. Combining the flexibility and interactivity of design methodologies with the reliability of robotic solutions will enable designers and engineers to develop innovative elastic shape-changing systems and program their behaviors.

High-Frequency High-Magnetic Flux Variation Foil Winding AC Inductor Design with the Vertical Penetrating Magnetic Field Elimination

In traditional inductor design with planar windings, the magnetic field distribution may not be well-organized, leading to significant winding loss, particularly at high switching frequencies. This study explores the relationship between current distribution and magnetic field distribution in the winding region. Unlike conventional magnetic flux distribution, which directs a large portion of the magnetic field vertically through the windings in the winding region, this work introduces a structure that maintains most of the magnetic flux parallel to the foil windings through the application of quasi-distributed air gaps. This paper presents a design methodology for a high-frequency foil winding inductor with high flux variation. Building on this concept, a high-power density, low loss inductor with foil windings is designed based on the four-switch buck-boost (FSBB) converter. Experimental results demonstrate that the proposed inductor design significantly reduces winding loss in inductors with planar windings.