Design Challenges Research Articles

Modelling PM dipoles with longitudinal field gradient for SILA facility

Computational models are described which were built for parametric simulations of permanent magnet (PM) dipoles with a longitudinal field gradient. The dipoles will be used as bending magnets in the synchrotron facility SILA, a new project of the National Research Center “Kurchatov Institute”. The dipoles employ a Sm-Co permanent magnet material and pure iron. The need for high magnetic performance and anticipated interference between neighbouring components is a challenge for design and construction of the magnet system. For this reason the magnet designs must rely on precision 3D simulations. The dipoles are described in detail with parameterized models. Parametric simulations are to be applied at all stages of the project till the commissioning to provide realistic prediction of the magnet behaviour.

Learning-based Predictive LPV Control of Atmospheric Pressure Plasma Jets

Abstract Complexity of atmospheric pressure plasma jet dynamics poses a significant challenge for control design, and this letter presents a learning- and Scenario-based Model Predictive Control (ScMPC) method in the Linear Parameter-Varying (LPV) framework to tackle this challenge. By leveraging artificial neural networks, an LPV state-space representation of the system dynamics is first learned. The mismatch between this model and real plant is then estimated using Bayesian neural networks, enabling scenario generation for ScMPC design. Soft constraints are imposed in the control design formulation to ensure the feasibility of the underlying optimization problem. Results from extensive simulations are used to compare the proposed framework with a benchmark LTI-based ScMPC, demonstrating superior performance in both reference tracking and thermal dose delivery. The proposed approach allows for accurate control of plasma jets while reducing conservatism inherent in either LTI-based approaches or other robust control methods.

Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality

>Sampling is one of the most widely employed approximations in big data processing. Among various challenges in sampling design, sampling for join is particularly intriguing yet complex. This perplexing problem starts with a classical case where the join of two Bernoulli samples shrinks its output size quadratically and exhibits a strong dependency on the input data, presenting a unique challenge that necessitates adaptive sampling to guarantee both the quantity and quality of the sampled data. The community has made strides in achieving this goal by constructing offline samples and integrating support from indexes or key frequencies. However, when dealing with stream data, due to the need for real-time processing and high-quality analysis, methods developed for processing static data become unavailable. Consequently, a fundamental question arises: Is it possible to achieve adaptive sampling in stream data without relying on offline techniques? To address this problem, we propose FreeSam, which couples hybrid sampling with intra-window join, a key stream join operator. Our focus lies on two widely used metrics: output size, ensuring quantity, and variance, ensuring quality. FreeSam enables adaptability in both the desired quantity and quality of data sampling by offering control on the two-dimensional space spanned by these metrics. Meanwhile, adjustable trade-offs between quality and performance make FreeSam practical for use. Our experiments show that, for every 1% increase in latency limitation, FreeSam can yield a 3.83% increase in the output size while maintaining the level of the estimator's variance. Additionally, we give FreeSam a multi-core implementation and ensure predictability of its latency through both an analytic model and a neural network model. The accuracy of these models is 88.05% and 96.75% respectively.

Evolution of public policy education in the global South: The case of India

ABSTRACT This paper addresses the dearth of comparative knowledge on policy education in the Global South by highlighting the remarkable growth of the field in India and comparing it with the international discourse. We discuss four key themes: the growth trajectory of policy schools, the contemporary landscape, the challenges of curriculum design, and career prospects for graduates. An empirical comparison of 56 policy schools was conducted alongside interviews with stakeholders. The results suggest that the evolution of policy education in India aligns with the internationalization model. However, in school objectives, curriculum, and careers, there is an emergent Indian model. These findings shed light on the policy education patterns of a large country in the Global South and highlight the challenges faced by countries where policy education is emerging.

Design Project Classroom: Fostering Industry-Ready Industrial Designers through Academic-Industry Collaboration

As the demands of the job market continue to evolve, it becomes imperative for educational institutions to equip students with the necessary skills and competencies to thrive in their respective industries. This article explores the implementation of a design project classroom as an innovative approach to preparing students for the job market, specifically in industrial design. The research employs the method of participant observation combined with interviews to gain valuable insights from industry professionals and student participants. Collaboration with industries is integral to this research, where it enables students to engage directly with real-world design challenges and obtain expert guidance. By leveraging the expertise and resources of these partnerships, this project aims to bridge the gap between academic knowledge and industry expectations. The results of this study showcase several key elements that students need to master before graduating, identified through feedback from both students and mentoring companies. The findings underscore the significance of hands-on experience, interdisciplinary collaboration, and industry engagement in preparing students for successful careers as industrial designers. The positive feedback received from both students and industry partners affirms the effectiveness of this approach in grooming students and enhancing their readiness for the job market. This article contributes to the literature on design education, and the insights provided serve as a foundation for educators and institutions seeking to enhance their design programmes, ensuring that students are adequately equipped with the skills, knowledge, and practical experience to enter the job market.

Cs‐Promoted Co Particles on Yttria‐Stabilized Zirconia as Coke‐Tolerance Methane Dry Reforming Catalyst under Elevated Pressure

Methane reforming with CO2 (dry reforming) co‐converts the two green‐house gases into synthesis gas and offers a promising way to integrate CO2 utilization into the current chemical infrastructure. One major obstacle for its industrial deployment is coke deposition on catalyst surface, in particular, under industrially relevant, pressurized operation conditions. Most catalytic investigations are conducted at atmospheric pressure, but the elevation in pressure poses a grand challenge for catalyst design. In this study, we demonstrate that Cs can promote carbon‐tolerance of Co catalyst supported on Yttria‐stabilized Zirconia under 20 bar, 850 oC with stochiometric feed flow for up to 100 h, which is often regarded as accelerated deactivation testing condition. Lowered amount and mostly CO2 gasifiable residue carbons are determined in Cs‐promoted spent Co‐catalyst, with respect to pristine Co catalyst. Kinetic studies reveal that Cs slows down coke deposition, while the essential reaction mechanism on pristine Co catalyst remains unaltered. Cs+ moieties absorb CO2 to afford Cs2CO3 that can release O* on adjacent Co surface to facilitate surface C* oxidation and simultaneously suppress carbon nucleation. The disclosure of the promoting effect of Cs on Co catalyst may have implications to other reforming catalyst and process design.

Akustisch optimierte Arbeitswelten

Acoustically optimized working environments: Strategies to increase productivity, comfort and attractiveness In open office structures, noise is still a widespread source of dissatisfaction, annoyance, disruption and stress. The well-documented effects of noise in research, such as the “irrelevant speech effect”, i.e. the disruption of cognitive performance due to irrelevant background speech, can be found in practice as central challenges of acoustic design. Five aspects are presented below that should definitely be considered when planning and optimizing acoustics – for greater satisfaction, well-being and productivity in the office.

NeoDesign: a computational tool for optimal selection of polyvalent neoantigen combinations.

Tumor polyvalent neoantigen mRNA vaccines are gaining prominence in immunotherapy. The design of sequences in vaccine development is crucial for enhancing both the immunogenicity and safety of vaccines. However, a major challenge lies in selecting the optimal sequences from the large pools generated by multiple peptide combinations and synonymous codons. We introduce NeoDesign, a computational tool designed to tackle the challenge of sequence design. NeoDesign comprises four modules: Library Construction, Optimal Path Filtering, Linker Addition, and λ-Evaluation. It aims to identify the optimal protein sequence for tumor polyvalent neoantigen vaccines by minimizing linker usage, avoiding unexpected neoantigens and functional domains, and simplifying the structure. It also provides a preference scheme to balance mRNA stability and protein expression when designing mRNA sequences for the optimal protein sequence. This tool can potentially improve the sequence design of tumor polyvalent neoantigen mRNA vaccines, thereby significantly advancing immunotherapy strategies. NeoDesign is freely available on https://github.com/HuangLab-Fudan/neoDesign and https://figshare.com/projects/NeoDesign/221704.

Machine learning guided prediction of dynamic energy release in high-entropy alloys

High-entropy alloy (HEA) type energetic structural materials (ESMs) offer exceptional strength, adequate ductility and reactivity upon dynamic loading, thus demonstrating great potentials in pyrotechnic applications. However, the main factors governing their energetic performance remain elusive, primarily attributable to the intricate mechanical-thermal-chemical coupling effects and the inherent challenges of HEA design. To address this, we propose a small-data machine learning framework designed to predict the energetic performance of HEA-type ESMs, employing support vector regression, leave-one-out cross-validation, and principal component analysis (PCA) to effectively manage a small, unevenly distributed, and highly dimensional dataset. Notably, the framework achieved a coefficient of determination (R2) of 0.854 while upholding robust performance, interpretability and computational efficiency. Fracture elongation (εt) and compressive yield strength (σcys) were identified as critical features, with σcys positively influencing performance while both εt and unit theoretical heat of combustion (UTHC) demonstrated negative effect. Guided by the framework, a series of novel Ti-V-Ta-Zr alloys with the comparable UTHC, velocity (v) and weight (m) but tailored εt and σcys were designed and tested. Ti30V30Ta30Zr10 alloy exhibited a commendable balance of mechanical properties and the smallest mean particle size, aligning with the model predictions and suggesting more thorough energy release during ballistic experiments.

A Systematic Literature Review of Design Considerations, Challenges and Guidelines in Primary School Physical Learning Space Design

A Systematic Literature Review of Design Considerations, Challenges and Guidelines in Primary School Physical Learning Space Design

Non-Markovian Dynamic Models Identify Non-Canonical KRAS-VHL Encounter Complex Conformations for Novel PROTAC Design.

Targeted protein degradation (TPD) is emerging as a promising therapeutic approach for cancer and other diseases, with an increasing number of programs demonstrating its efficacy in human clinical trials. One notable method for TPD is Proteolysis Targeting Chimeras (PROTACs) that selectively degrade a protein of interest (POI) through E3-ligase induced ubiquitination followed by proteasomal degradation. PROTACs utilize a warhead-linker-ligand architecture to bring the POI (bound to the warhead) and the E3 ligase (bound to the ligand) into proximity. The resulting non-native protein-protein interactions (PPIs) formed between the POI and E3 ligase lead to the formation of a stable ternary complex, enhancing cooperativity for TPD. A significant challenge in PROTAC design is the screening of the linkers to induce favorable non-native PPIs between POI and E3 ligase. Here, we present a physics-based computational protocol to predict noncanonical and metastable PPI interfaces between an E3 ligase and a given POI, aiding in the design of linkers to stabilize the ternary complex and enhance degradation. Specifically, we build the non-Markovian dynamic model using the Integrative Generalized Master equation (IGME) method from ∼1.5 ms all-atom molecular dynamics simulations of linker-less encounter complex, to systematically explore the inherent PPIs between the oncogene homologue protein and the von Hippel-Lindau E3 ligase. Our protocol revealed six metastable states each containing a different PPI interface. We selected three of these metastable states containing promising PPIs for linker design. Our selection criterion included thermodynamic and kinetic stabilities of PPIs and the accessibility between the solvent-exposed sites on the warheads and E3 ligand. One selected PPIs closely matches a recent cocrystal PPI interface structure induced by an experimentally designed PROTAC with potent degradation efficacy. We anticipate that our protocol has significant potential for widespread application in predicting metastable POI-ligase interfaces that can enable rational design of PROTACs.

Manual steering and robust adaptive constrained control of ship autopilot

This study proposes two schemes for ship autopilot: the first is a follow-up mode, applying manual dynamics to the state-space-based underactuated large merchant ship (ULMS) model, using a steering wheel and joystick to emulate manual operation; the second is a Nav mode developed by path-following control. In this mode, a novel path-following control strategy is developed for ULMS with input saturation and delay, two novel auxiliary systems are employed to stabilize the plant with input constraints. The input delay auxiliary system (IDAS) effectively tackles the design challenges posed by input delay, while the input saturation auxiliary system (ISAS) is dedicated to generating actual control inputs under the constraint of saturation. Furthermore, the dynamic surface control (DSC) technique and the roust neural damping technique are introduced to mitigate the computational burden and simplify the controller structure, thereby enhancing the efficiency and performance of the entire control system. Lyapunov stability analysis proves the ship motion system is semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of these two schemes is verified through turning experiment, comparative simulations and a semi-physical simulation platform that integrates physical models with computer simulation technology.

Artistic Expertise and Free Viewing of Modern Art

Abstract Visual art and vision science had long been intertwined, both revealing and sharing countless insights on human vision over their respective histories. Deep artistic experiences and extensive artistic knowledge are often associated with “artistic expertise,” which in vision science traditionally echoes visual expertise from other professional domains (e.g., medicine, sports, driving). However, unlike other visual domains, artistic experiences involve more spontaneous and imaginative aspects, and no explicit success parameters. We hypothesized that viewing of artworks would lead to nuanced effects of expertise that may differ from other types of visual expertise. In our study, art experts and non-experts observed modern paintings and were free to view them within a relatively long time window. Higher expertise in art was associated with viewing modern paintings for significantly longer durations, and liking the paintings significantly more. However, contrary to other visual expertise studies, eye movement patterns exhibited little to no effect of artistic expertise. Based on these findings, we suggest a recontextualization of visual artistic expertise and discuss potential research directions, alongside methodological challenges in the experimental design.

Trends and Challenges in Medical Device Design: A Comprehensive Review of Innovations in Medical Electronics

Trends and Challenges in Medical Device Design: A Comprehensive Review of Innovations in Medical Electronics

Fluidization behavior of stirred gas–solid fluidized beds: A combined X-ray and CFD–DEM–IBM study

Stirred gas–solid fluidized bed reactors are commercially employed in polyolefin manufacturing, but the complex gas–solid contacting dynamics pose challenges in design, scale-up, and operation. In this study, the influence of agitation on the fluidization performance of Geldart B particles was investigated experimentally by X-ray imaging and pressure drop measurements and numerically by Computational Fluid Dynamics (CFD) - Discrete Element Method (DEM) - Immersed Boundary Method (IBM). The experimentally obtained minimum fluidization curve and time-averaged pressure drop show good qualitative agreement with the simulation results. Visual observations underscore that an increase in the angular velocity of the agitator results in reduced bubble size and improved bed homogeneity, as further evidenced by reduced pressure fluctuations. Furthermore, the simulations reveal that while the impeller enhances solids agitation, a proper design study is imperative, considering that static immersed bodies, such as the stirrer shaft, can adversely impact solids motion.

Exploring the challenges to development and institutionalization of E-learning content as perceived by faculty members of medical schools in Iran: A qualitative content analysis study

Electronic learning (E-learning) is a method of acquiring knowledge by using information technology. In this regard, faculty members of medical schools play an important role in the design, development and institutionalization of electronic content education and face many challenges. Hence, the present study was performed to explore the challenges to the development and institutionalization of e-learning content as perceived by faculty members of medical schools in southern Iran. The present study is a descriptive qualitative study. Purposeful sampling was performed. Based on this, 23 individual and in-depth semi-structured interviews were conducted with 23 faculty members of medical schools. Granheim and Lundman's approach was used for data analysis. Three themes and eleven subthemes were extracted based on the qualitative data analysis results. Three main themes included individual challenges, organizational management challenges, and course design challenges. Based on the results of the present study, we should keep in mind that electronic teaching methods and preparation of e-learning content by professors are different from traditional educational methods. Hence, the senior managers of educational system should make necessary arrangements and plans for methodological support, technological and technical support, and all kinds of organizational support and create continuous professional development opportunities for professors to minimize the negative effects caused by the rapid changes of electronics era and educational environments on professors, and consequently on the quality of educational contents and processes.

Design challenges and response plans for intelligent decision support systems for Korean nuclear power plants under normal and abnormal conditions

In nuclear power plants (NPPs), an intelligent decision support system (IDSS) aids the decision-making process of main control room operators. It achieves this by monitoring and diagnosing conditions, predicting progress, and providing preventive advice during both normal and abnormal operations. Despite ongoing research in Korea, few IDSS for NPPs have been effectively applied and verified. Furthermore, established designs and validation guidelines for IDSSs for Korean NPPs remain lacking. To address this gap, this study systematically identifies problems and corresponding countermeasures for applying artificial intelligence-based design and validation technologies, while complying with licensing regulatory standards. The aim is to develop a practical and effective IDSS. This study identifies design challenges in the practical application of fundamental IDSS technology, outlining necessary design and validation technologies. Additionally, it suggests technology-specific response plans for the identified design challenges.

Towards hydrogen gas turbine engines aviation: A review of production, infrastructure, storage, aircraft design and combustion technologies

This study explores the potential of hydrogen gas turbine engines for sustainable aviation, with a particular focus on their development for low subsonic to transonic flight regimes. Hydrogen offers notable advantages, including zero CO2 emissions and high energy density. However, its widespread adoption is prevented by significant challenges in production, infrastructure, storage, aircraft design, and combustion technology. The limited availability of green hydrogen – the global production totaled only 127 kt in 2023, with 76% produced in China - raises concerns about the feasibility of a rapid shift to hydrogen-powered general aviation. Despite numerous pledges for hydrogen adoption in the Western world, major aircraft manufacturers have primarily engaged in marketing activities rather than substantial product innovation. To date, the only hydrogen aircraft demonstrator from this century is the 2012 Boeing Phantom Eye, which utilized modified hydrogen internal combustion engines rather than a gas turbine engine. The only hydrogen aircraft demonstrator with a gas turbine engine was the 1988 hybrid Tupolev Tu-155 which coupled one hydrogen gas turbine engine to other two jet fuel engines. This paper evaluates the current state of hydrogen gas turbine technology, comparing its combustion characteristics with traditional jet fuels and examining various NOx emission control techniques. It also investigates the potential of micro-mix lean combustion and staged combustion to achieve efficient and low-emission hydrogen combustion in gas turbines. Given the more extensive experience with terrestrial gas turbines compared to their aeronautical counterparts, the review includes insights from terrestrial power generation applications. Additionally, it considers hythane - a blend of hydrogen and natural gas - as a transitional solution for significantly reducing CO2 emissions while progressing towards net-zero targets. The review underscores the urgent need for advancements in green hydrogen production, technology, and infrastructure to overcome existing barriers and enable a more sustainable aviation future powered by hydrogen. Importantly, the review emphasizes the need to move beyond mere virtue signaling and make tangible progress toward a hydrogen economy.

Robust position regulation of a 2-DOF experimental helicopter using higher order super twisting sliding mode control

This study presents a third-order super twisting sliding mode control algorithm for robust position regulation of a two-degree-of-freedom (2-DOF) experimental helicopter. The proposed approach explores high-order sliding mode control, offering advantages such as finite-time convergence and continuous control signals. The controller is implemented and validated on the Quanser Aero 2 platform, an inherently unstable and nonlinear system that presents significant challenges for control design including cross-coupling effects, model uncertainties, and external disturbances. Extensive simulations and experimental results are presented to validate the efficacy and robustness of the proposed control strategy. They highlight the controller's ability to achieve precise position control while significantly mitigating the chattering phenomenon, a common drawback associated with traditional sliding mode control. This study contributes to the field of nonlinear control systems by demonstrating the potential of high-order sliding mode control in managing nonlinear, coupled dynamic systems. The successful implementation on a real-world platform underscores the practical applicability of the proposed method in aerospace and robotics applications.

A Cross-Layer Approach to Analyzing Energy Consumption and Lifetime of a Wireless Sensor Node

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.