Design Challenges Research Articles

Satisfaction of academic medical staff with integrated medical curriculum: an exploratory multinational survey

BackgroundThe adoption of integrated curricula in medical schools has grown globally, emphasizing the need to understand academic medical staff satisfaction as a crucial factor influencing successful implementation.AimThis study aimed to assess satisfaction levels among academic medical staff with integrated medical curricula, identifying socio-demographic and work-related predictors of dissatisfaction.MethodsA cross-sectional, online multinational survey was conducted from December 2023 to April 2024 among 525 academic medical staff. A structured, self-reported questionnaire was used to assess satisfaction levels, demographic characteristics, and work-related factors. The data were analyzed using descriptive and inferential statistics, including logistic regression analysis, to identify predictors of dissatisfaction.ResultsThe study revealed a low satisfaction rate (44.2%) among medical staff, with significant dissatisfaction influenced by nationality (Jordanian: OR = 7.74, and Egyptian: OR = 4.68), male gender (OR = 3.27), over ten years of teaching experience in integrated curricula (OR = 4.25), and employment in governmental universities (OR = 4.23). Dissatisfaction was particularly high with aspects such as the integration of basic and clinical sciences, assessment methods, and implementation schedules. Faculty from private universities demonstrated significantly higher satisfaction (65.1%) compared to those in governmental institutions (41.3%). Younger staff < 45 years reported higher satisfaction rates (50.9%) compared to their older counterparts ≥ 45 years (39.1%), reflecting greater adaptability to curriculum changes.ConclusionsThe findings highlight substantial dissatisfaction among academic medical staff with the design and implementation of integrated curricula, particularly in governmental institutions and among senior faculty. Institutions must address challenges in curriculum design, resource allocation, and faculty support to enhance satisfaction. Revisiting conventional curriculum components or adopting a hybrid approach may help balance innovation with faculty preferences, fostering a more conducive educational environment.

Enhancing Interoperability in the Web of Things: A Reference Architecture Approach

The concept of the Internet of Things (IoT) has fundamentally redefined the connectivity landscape by integrating physical devices with the Internet for data exchange. The Web of Things (WoT) takes this integration a step further by embedding IoT devices within the web, thus facilitating their interoperability. However, the inherent complexity of WoT architecture requires a layer of abstraction to simplify development and integration. Patterns and reference architectures (RAs) provide such an abstraction, modeling solutions for recurrent problems within this domain. In this paper, we aim to develop an RA for the WoT ecosystem. We use architectural patterns to build the RA of the WoT. Here, we present the WoT gateway pattern, which provides a clear and adaptable framework for integrating diverse IoT devices, ensuring seamless communication and functionality within the broader web ecosystem. The purpose of this pattern is twofold: to ensure the interoperability of WoT implementations and to serve as a foundational guide for developers navigating the multifaceted challenges of WoT system design. The pattern also paves the way for building an RA for IoT environments. KEYWORDS Architectural patterns, Internet of Things, reference architecture, web development, web science, WoT

Design of pseudosymmetric protein hetero-oligomers

Pseudosymmetric hetero-oligomers with three or more unique subunits with overall structural (but not sequence) symmetry play key roles in biology, and systematic approaches for generating such proteins de novo would provide new routes to controlling cell signaling and designing complex protein materials. However, the de novo design of protein hetero-oligomers with three or more distinct chains with nearly identical structures is a challenging unsolved problem because it requires the accurate design of multiple protein-protein interfaces simultaneously. Here, we describe a divide-and-conquer approach that breaks the multiple-interface design challenge into a set of more tractable symmetric single-interface redesign tasks, followed by structural recombination of the validated homo-oligomers into pseudosymmetric hetero-oligomers. Starting from de novo designed circular homo-oligomers composed of 9 or 24 tandemly repeated units, we redesigned the inter-subunit interfaces to generate 19 new homo-oligomers and structurally recombined them to make 24 new hetero-oligomers, including ABC heterotrimers, A2B2 heterotetramers, and A3B3 and A2B2C2 heterohexamers which assemble with high structural specificity. The symmetric homo-oligomers and pseudosymmetric hetero-oligomers generated for each system have identical or nearly identical backbones, and hence are ideal building blocks for generating and functionalizing larger symmetric and pseudosymmetric assemblies.

Systematic approach to dynamic balancing using coloured petri nets for production lines in advanced manufacturing systems

The assembly line is widely used in the manufacturing of products that involve tasks with defined and repetitive sequences. Its efficiency greatly impacts the overall manufacturing process, necessitating resource optimization. Balancing activities among workstations is a design challenge, especially in mixed-product assembly lines, due to significant variations in task durations and short-term changes in product sequencing. This paper introduces a balancing system that considers product sequencing effects on resource utilization. The system identifies specific sets of sequenced activities for each product type and operator, enhancing workstation flexibility. It employs balancing strategies utilizing a precedence diagram and a heuristic algorithm based on positional weights. Modeling and simulation are performed using Coloured Petri Nets. A case study of a truck assembly line in Brazil illustrates the main features of this approach. Simulation results demonstrate that it is possible to reduce dependent occupancy and sequencing, meet cycle time requirements, and achieve a 12.5% reduction in resource utilization while maintaining acceptable average occupancy, ultimately establishing a system beneficial for assembly line managers.

Challenges in Value-Sensitive AI Design: Insights from AI Practitioner Interviews

As AI systems become increasingly prevalent in critical domains, ensuring their alignment with stakeholders’ values is essential. However, recent studies have revealed deficiencies in socio-technical design processes and design activities for AI, particularly in eliciting diverse stakeholder values and integrating them into system design and development. To investigate these challenges empirically, we conducted 30 semi-structured interviews with AI practitioners. Our findings reveal several key challenges faced during AI design and development. Firstly, practitioners struggle with identifying and involving stakeholders due to uncertainties regarding user demo-graphics, and a lack of interdisciplinary expertise. Secondly, they encounter obstacles in integrating values into technologies, citing practical complexities, unclear responsibilities, and limited support. This paper presents a concept map detailing these four primary barriers and discusses potential strategies and recommendations for overcoming them. These strategies revolve around improving value elicitation practices, facilitating more meaningful stakeholder engagement, and understanding the impact of stakeholder values By qualitatively describing the barriers to integrating stakeholder values into AI systems, this study contributes to the emerging field of value-sensitive AI.

Design principles for holistic student formation in online programs

Designing courses which enhance the holistic formation of students, integrating knowledge, skills, competencies, capacity for reflection, judgment and moral choice, brings challenges for learning design, particularly in distance and online learning contexts. In theological education these challenges are widely discussed, yet there are not yet evidence-based design principles for designing online courses to support student formation in these contexts. We used a design-based research approach to collaboratively develop, implement, and evaluate a complex design solution to support and enhance holistic student formation at an Australian theological college. Over three cycles of implementation we collected data from students and educators on the impact of 21 asynchronous online subjects on student formation, and iteratively reviewed the design conjectures. Through this iterative process we identified which design elements and pedagogies were effective in contributing to holistic student formation and developed ten design principles. These principles and the reflections offered on the process of refining design conjectures into design principles will be of interest to educators and designers in theological and other online learning contexts which aim to support holistic student formation.

Pedagogic insularities and fragmented identities: reconsidering worldly humanism in Indian Higher Education

ABSTRACT Amid the increasing bureaucratisation of Indian universities, this article examines the evolving pedagogical implications, including the challenges of syllabus design and the growing bureaucratic interference in classroom practices. Drawing on Edward Said's notion of humanism, it interrogates the concept of the ‘differentiated human’ – fragmented and discrete identities that lack common ground for meaningful dialogue, a phenomenon that is deeply embedded in the postcolonial Indian context. The article delves into the feasibility of a ‘humanist pedagogy’ by posing critical questions: Are the humanist agenda and the effort to foreground local identities inherently incompatible? Do they operate on fundamentally discrete and insular planes? Or is there a possibility of reconciling these two seemingly divergent projects? By addressing these issues, this paper explores the tensions and synergies between universalist humanist ideals and the specificity of localised identity politics.

Joyful living labs – pedagogical approach for urban design thinking soft skills

PurposeUrban design professionals face the challenge of creating responsive human settlements amidst complex, ever-evolving interactions between numerous factors, which makes each project a “wicked problem,” requiring designers with specific skills to solve them effectively. This paper explores how integrating “Living Labs” and “Learning Playing techniques” equips urban design students with the needed competencies to address these challenges.Design/methodology/approachThe research employed a literature review focusing on urban design theories related to “wicked problems,” design thinking and soft skills. Through this review, the authors developed the “Urban Design Thinking Soft Skills (UDTS) matrix” and identified pedagogical approaches using Living Labs and Learning Playing Techniques. An award-winning community design studio at Cairo University served as a case study to test and validate the UDTS matrix. Interviews were conducted to further investigate the effectiveness of incorporating these approaches.FindingsThe study found that integrating Living Labs principles and Learning Playing Techniques (termed “Joyful Living Labs” in this context) are effective pedagogical methods. Interviews provided evidence supporting their value within urban design curricula for the Egyptian context.Originality/valueThis paper proposes the UDTS matrix as a framework to equip students with the necessary soft skills for tackling complex urban design challenges. Furthermore, it introduces the concept of “Joyful Living Labs” as a potential pedagogical approach specifically suited for the Egyptian context.

Envisioning the incorporation of Generative Artificial Intelligence into future product design education: Insights from practitioners, educators, and students

The advancement of Generative Artificial Intelligence (GenAI) has introduced diverse opportunities and challenges in product design, demanding a corresponding evolution in design education. This study investigated GenAI’s roles in product design and its implications for design education through semi-structured interviews with 24 participants, comprising practitioners, educators, and students, who had hands-on experience leveraging GenAI in design. Through thematic analysis, we identified critical functions of GenAI in product design, including generating inspiration, exploring potential solutions, conducting prototyping, performing evaluations, finalizing detailed outputs, and serving as design materials, presenting both opportunities and challenges. Moreover, fully incorporating GenAI in product design requires adaptation in educational initiatives, including capability development, curriculum content, and assessment methods. This study provides insights from a multi-stakeholder perspective, revealing the roles of GenAI in product design and advancing educational curricula to prepare the next-generation designers to be proficient in effectively leveraging GenAI for innovation.

Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility.

Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.

Model predictive control based on single-phase shift modulation for triple active bridge DC-DC converter

The triple-active bridge (TAB) converter is widely used in various applications due to its high efficiency and power density. However, the high-frequency (HF) transformer coupling between the ports presents challenges for controller design. This article presents a model predictive control (MPC) approach based on single-phase shift modulation for the TAB converter. The developed MPC offers improved transient performance, control flexibility, and precision, ensuring compliance with DC voltage regulations and achieving optimal solutions for port decoupling. The MPC utilizes a cost function to provide robust voltage regulation, and an algorithm based on Karush-Kuhn-Tucker (KKT) conditions is developed to derive closed-form solutions for optimal control parameters. To validate the performance of the TAB converter with the proposed MPC control, Typhoon 602 hardware-in-loop (HIL) experimental case study is conducted. Additionally, a comparison with previous works is carried out to confirm the effectiveness of the proposed method. The results of the HIL experimental setup and the comparative analysis demonstrate that the developed method is effective, providing faster dynamic characteristics and port power decoupling operation capability.

Ultra-Large Virtual Screening: Definition, Recent Advances, and Challenges in Drug Design.

Virtual screening (VS) in drug design employs computational methodologies to systematically rank molecules from a virtual compound library based on predicted features related to their biological activities or chemical properties. The recent expansion in commercially accessible compound libraries and the advancements in artificial intelligence (AI) and computational power - including enhanced central processing units (CPUs), graphics processing units (GPUs), high-performance computing (HPC), and cloud computing - have significantly expanded our capacity to screen libraries containing over 109 molecules. Herein, we review the concept of ultra-large virtual screening (ULVS), focusing on the various algorithms and methodologies employed for virtual screening at this scale. In this context, we present the software utilized, applications, and results of different approaches, such as brute force docking, reaction-based docking approaches, machine learning (ML) strategies applied to docking or other VS methods, and similarity/pharmacophore search-based techniques. These examples represent a paradigm shift in the drug discovery process, demonstrating not only the feasibility of billion-scale compound screening but also their potential to identify hit candidates and increase the structural diversity of novel compounds with biological activities.

Developing A Risk Control System for Fintech Products Using Big Data and Real-Time Analysis Technology

Big data technology has revolutionized the financial sector, significantly impacting the design and operation of future financial products. This paper explores the use of big data technology in risk control in financial products, examining its potential benefits and challenges in product design. By briefly introducing the operation logic of big data, this paper analyzes the application of big data in financial products for risk control and the challenges it faces. The study reveals that big data technology enhances the efficiency of financial technology companies in internal management and decision-making, while significantly reducing systemic risk in their financial product design. Big data technology has exacerbated the conflict between traditional financial institutions and fintech companies, as well as the interest conflict between fintech companies, users, and regulatory authorities. Therefore, Big data financial products not only need to improve their information processing methods and information analysis methods internally, but also need to deal with the conflicts between their users, issuers, and financial regulatory authorities and with the traditional financial sector.

Addressing Infrastructure Design Challenges In Constructing At-Grade Rail Crossings Near Road Intersections

At-grade rail crossings, where railway tracks intersect roadways at the same level rather than being separated by bridges or tunnels, are crucial elements of transportation infrastructure. However, these crossings create significant challenges for rail operations, particularly regarding safety and efficiency. Locating an at-grade rail crossing near a road intersection is generally discouraged due to the increased risk of train delays, reduced visibility for train operators, and the potential for train-road conflicts. While these issues are undesirable, they are sometimes unavoidable when rail infrastructure must be near or across road intersections. This study examines the design challenges of constructing at-grade rail crossings near road intersections. It provides a comprehensive review of the safety and operational concerns, including delays, conflicts, and the potential for accidents. The study also discusses existing guidelines and design practices to mitigate these challenges, offering insights into improving the efficiency and safety of at-grade rail crossings near road intersections.

IN SILICO BASED RE-ENGINEERING OF A COMPUTATIONALLY DESIGNED BIOSENSOR WITH ALTERED SIGNALLING MODE AND IMPROVED DYNAMIC RANGE.

A current challenge in the rational design of biomolecular sensors is the ability to custom design binding affinities and detection mode in silico. To this end, we re-engineered a previously reported computationally-designed fluorescent maltooligosaccharide (MOS)-detecting biosensor to both alter its ligand-binding affinity and to analyse the underlying sensing mechanism. The dynamic range of the biosensor was expanded through the computer aided introduction of a series of amino acid substitutions in the starting protein scaffold (MalX from Streptococcus pneumoniae), which generated a biosensor set with binding affinities spanning over five orders of magnitude. The impact of the introduced substitutions on the underlying mode of signal generation was assessed in silico using our previously reported Computational Identification of Non-disruptive Conjugation sites (CINC) pipeline. CINC utilizes molecular dynamics simulations and an in-house developed algorithm to examine and exploit the structural dynamics of a protein at amino acid-level resolution. Using CINC, we demonstrate that re-engineering of the MOS-detecting biosensor set resulted in sensors with two distinct output modes which differed based on local conformational changes at the fluorescently modified reporter position. These output modes were classified as "ligand-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the ligand-bound state), and "apo-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the apo state). Together, these results demonstrate that structural dynamics at the individual amino acid residue level can be used as an engineer-able feature to rationally alter the fluorescence reporting properties of a biosensing device. Moving forward, the CINC workflow can also be adapted for the rational design of protein dynamic properties maximizing its utility as an in silico design platform for custom biomolecular tools.

Advances and challenges in drug design against dental caries: application of in silico approaches

Advances and challenges in drug design against dental caries: application of in silico approaches

Assessment of an effervescent breakup model for Lagrangian simulations of real fuel sprays

Flash boiling drastically alters the characteristics of spray atomization and plume structure, which could impose challenges for modern propulsion systems design regarding fuel/air mixture formation and emission performances. Numerical simulations serve as effective tools to understand and predict flash boiling sprays for modern engine applications. This study carried out Eulerian-Lagrangian spray simulation campaigns for two gasoline injectors, and realistic single-composite and multi-component fuel models were used instead of simple surrogates. A validated effervescent breakup model was adopted to account for the non-trivial breakup mechanism induced by bubble nucleation within the liquid droplets. Effects of key model parameters were discussed in detail to propose a consistent set of model constants, and comprehensive validation was achieved against near- and far-field spray experiments. The effervescent breakup model has demonstrated satisfactory effectiveness, and the numerical findings also highlight the necessity of employing real fuel properties for spray analysis.

Uni-to-Multi Modal Knowledge Distillation for Bidirectional LiDAR-Camera Semantic Segmentation.

Combining LiDAR points and images for robust semantic segmentation has shown great potential. However, the heterogeneity between the two modalities (e.g. the density, the field of view) poses challenges in establishing a bijective mapping between each point and pixel. This modality alignment problem introduces new challenges in network design and data processing for cross-modal methods. Specifically, 1) points that are projected outside the image planes; 2) the complexity of maintaining geometric consistency limits the deployment of many data augmentation techniques. To address these challenges, we propose a cross-modal knowledge imputation and transition approach. First, we introduce a bidirectional feature fusion strategy that imputes missing image features and performs cross-modal fusion simultaneously. This allows us to generate reliable predictions even when images are missing. Second, we propose a Uni-to-Multi modal Knowledge Distillation (U2MKD) framework, leveraging the transfer of informative features from a single-modality teacher to a cross-modality student. This overcomes the issues of augmentation misalignment and enables us to train the student effectively. Extensive experiments on the nuScenes, Waymo, and SemanticKITTI datasets demonstrate the effectiveness of our approach. Notably, our method achieves an 8.3 mIoU gain over the LiDAR-only baseline on the nuScenes validation set and achieves state-of-the-art performance on the three datasets.

A slip length analysis of microchannel flow with smooth and structured surfaces

One of the most significant challenges in microchannel design is the reduction of pressure drops within miniature fluidic pathways. A promising approach involves the manipulation of boundary conditions, mainly through the integration of structured hydrophobic surfaces. The mathematical characterization of these hydrophobic surfaces is achieved through the application of the Navier boundary condition, with the slip length identified as the critical parameter of interest. This paper delves into an in-depth exploration of how varying slip lengths impact the liquid flow dynamics within microchannels with a rectangular cross section. We consider a diverse range of microchannel hydraulic diameters 5–200 μm and aspect ratios from 1:1 to 1:20 for Reynolds numbers in the laminar range 10–1000. Three-dimensional calculations are performed on both conventional smooth microchannels and those equipped with air-filled groove-structured surfaces. The results are then compared with the analytical solution and experimental results. The application of a hydrophobic structure to a single microchannel surface is resulted in the friction factor reduction by over 30%, while the application of the hydrophobic structure to two opposing walls led to a reduction by over 60%. The maximum throughput is shown to be achieved for a bubble protrusion angle of approximately 0° for a microchannel with a single hydrophobic wall. The greatest reduction in the friction factor was achieved when the bubbles were positioned in a staggered configuration at bubble protrusion angles of approximately –25° for microchannel with two opposing hydrofobic walls.

Intrinsically Safe Thermohydraulic Designs for SMRs: Design advantages and challenges.

Intrinsically Safe Thermohydraulic Designs for SMRs: Design advantages and challenges.