Search Results (401)

The global construction industry faces persistent challenges of low productivity and labor shortages, positioning Off-Site Construction (OSC) as a critical solution. However, standardized industrialization indices for objectively evaluating OSC adoption remain underdeveloped, particularly in emerging markets. This study aims to identify a benchmark policy model and derive design principles for future indices. Specifically, this study focuses on ‘policy-driven markets’ where strong government intervention is essential for initial ecosystem formation, excluding mature market-driven economies where the ecosystem is already established (e.g., USA, Sweden, Japan). To identify an optimal benchmark, a comparative assessment was conducted on five institutional frameworks across four countries (UK, Malaysia, Singapore, and China). Notably, within China, Hong Kong SAR was analyzed as a distinct regulatory jurisdiction separate from Mainland China due to its unique construction governance system. This assessment was based on five key policy dimensions: Legal Mandate, Scope, Indicator Composition, Enforcement Mechanism, and Sustainability. The analysis identified Singapore’s ‘Buildability Score’ as the most comprehensive model in terms of systemic completeness and practical efficacy. A virtual project simulation demonstrated that the scoring system functions as a powerful regulatory mechanism, effectively driving the adoption of standardized, dry-process, and modularized high-productivity methods from the earliest design stages. While Singapore’s system serves as an effective policy tool for OSC proliferation, it exhibits clear limitations regarding reduced architectural design flexibility and insufficient sustainability integration. Consequently, future industrialization indices must evolve to balance productivity with architectural design diversity and integrate sustainability criteria while reflecting specific regional construction ecosystems. Full article

Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and cluster-guided local neighborhood sampling. Using a comprehensive dataset of protein kinase ligands, we examine scaffold topology, latent-space geometry, and model-driven generative trajectories. We show that chemically distinct scaffolds can converge toward overlapping latent representations, revealing intrinsic degeneracy in scaffold encoding, while specific topological motifs function as organizing anchors that constrain generative diversification. The results demonstrate that kinase scaffolds spanning 37 protein kinase families spontaneously organize into a coherent, low-dimensional manifold in latent space, with SRC-like scaffolds acting as a structural “hub” that enables rational scaffold transformation. Our local sampling approach successfully converts scaffolds from other kinase families (notably LCK) into novel SRC-like chemotypes, with LCK-derived molecules accounting for ~40% of high-similarity outputs. However, both generative strategies reveal a critical limitation: SMILES-based representations systematically fail to recover multi-ring aromatic systems—a topological hallmark of kinase chemotypes—despite ring count being a top feature in our structural similarity metric. This “representation gap” demonstrates that no amount of scoring refinement can compensate for a generative engine that cannot access topologically constrained regions. By diagnosing these constraints within a transparent pipeline and reframing scaffold-aware ligand design as a problem of molecular representation our work provides a conceptual framework for interpreting generative model behavior and for guiding the incorporation of structural priors into future molecular AI architectures. Full article

In the face of accelerating climate change and urbanization, sustainable mobility infrastructure plays a critical role in reducing greenhouse gas emissions. This article assesses the Sunglider concept—an elevated, solar-powered transport system—through the New European Bauhaus (NEB) Compass, which emphasizes sustainability, inclusion, and esthetic value. Designed by architect Peter Kuczia and collaborators, Sunglider combines photovoltaic energy generation with modular, parametrically designed wooden pylons to form a lightweight, climate-positive mobility solution. The study evaluates the system’s technological feasibility, environmental performance, and urban integration potential, drawing on existing design documentation and simulation-based estimates. While Sunglider demonstrates strong alignment with NEB principles, including zero-emission operation and material circularity, its implementation is challenged by high initial investment, political and planning complexities, and integration into dense urban environments. Mitigation strategies—such as adaptive routing, visual screening, and universal station access—are proposed to address concerns around privacy, esthetics, and accessibility. The article positions Sunglider as a scalable and replicable model for mid-sized European cities, capable of advancing inclusive, carbon-neutral mobility while enhancing the urban experience. It concludes with policy and research recommendations, highlighting the importance of embedding infrastructure innovation within broader ecological and cultural transitions. Full article

Synoptic assessment has gained prominence in higher education as a way to bridge fragmented curricula by enabling students to synthesize knowledge across modules. However, structural integration through assessment does not automatically produce authentic learning. Drawing on theoretical analysis and three reflective case studies from UK undergraduate programmes, this paper offers a critical practitioner perspective on how synoptic assessment and authentic learning intersect in practice. We argue that integration and authenticity represent distinct pedagogical imperatives that require deliberate alignment. Through comparative analysis of successful, partially successful, and unsuccessful implementations of assessment strategies, we demonstrate that authentic learning emerges not from integration per se, but from intentional design embedding real-world relevance, developmental scaffolding, clear purpose, and student agency. Our case studies reveal that without such intentionality, synoptic assessments risk becoming structurally coherent but pedagogically hollow exercises that fail to engage students meaningfully. Key challenges include inconsistent staff understanding, inadequate contextual framing, and insufficient attention to progressive capability development. We propose practical design principles grounded in practitioner experience: embedding authenticity through professional relevance, scaffolding complexity appropriately, enabling open-ended student responses, and establishing strong programme-level leadership with authority over assessment strategy. The core contribution of the paper is to articulate these design principles for embedding authenticity within synoptic assessment at programme level, particularly in increasingly modularised and flexible curricula, such as those designed to enable lifelong learning. By positioning integration as necessary but insufficient for authentic learning, we advance critical understanding of assessment reform and address emerging tensions between programme coherence and increasingly modularized curricula serving diverse learner pathways. Full article

Background: The primary concern of hip surgeons is restoring the physiological biomechanics of the hip joint through arthroplasty, thereby enabling patients with osteoarthritis to engage better in daily activities. The modularity of the femoral stem-neck head allows surgeons to better restore the hip’s native biomechanics. However, concerns have been raised regarding the potential postoperative complications. This study aims to evaluate patients’ satisfaction and functional outcomes following primary Total Hip Arthroplasty (THA) with modular stem-neck, with a mean follow-up duration of eight years. Methods: We retrospectively reviewed 208 patients who underwent primary THA with modular stem-neck between February 2012 and July 2019. The follow-up period extended from November 2024 to April 2025. Patients who died from unrelated causes were excluded. Patients’ satisfaction was assessed using the SF-36 questionnaire, while functional outcomes were evaluated using the Harris Hip Score (HHS). Intraoperative and postoperative complications were meticulously documented. Results: The average follow-up duration was 95.6 months, with a range from 67.7 to 159.7 months. The mean SF-36 score was 91.2 out of 100, indicating high patient satisfaction. The mean HHS was 90 out of 100, reflecting excellent functional outcomes. Notably, some patients achieved the maximum score of 100 in both SF-36 and HHS assessments, while the lowest recorded scores were 54 and 50, respectively. The mean age of patients at the time of surgery was 67.1 years. One case of periprosthetic fracture was reported; however, no complications related to modular necks, such as trunnionosis or implant failure, were observed. Conclusions: The present study demonstrates that modular neck primary THA could achieve excellent functional and radiological outcomes, high patient satisfaction, and outstanding long-term survivorship, provided that implant selection and surgical technique follow biomechanical principles. Full article

User Interface (UI) and User Experience (UX) design play a critical role in shaping human interaction with digital systems, particularly in professional environments where accuracy, safety, and efficiency are essential. Poor visual design increases cognitive load and the likelihood of human error, whereas ergonomically informed interfaces can substantially improve task performance. This systematic literature review analyzes 20 peer-reviewed studies published between 2020 and 2024 to examine how visual ergonomics embedded in UI/UX design contributes to error reduction across industrial and professional contexts. The reviewed studies report measurable improvements when ergonomic principles are applied, including reductions in operational errors ranging from approximately 30% to 70%, improvements in task completion time between 20% and 60%, and increased user accuracy and satisfaction in safety-critical and high-workload environments. The findings indicate that visual hierarchy, modular layouts, adaptive components, and real-time feedback are consistently associated with improved performance outcomes. Moreover, task complexity, user expertise, and working conditions were identified as moderating factors influencing ergonomic demands. Overall, the review demonstrates that visual ergonomics should be treated not merely as a usability enhancement but as a strategic design approach for minimizing human error and supporting reliable human–machine interaction in complex digital environments. Full article

This study explores the integration of Georgi Lozanov’s Suggestopedia with Alain Berthoz’s theory of simplexity as a pedagogical paradigm for inclusive and creative educational design. The research, conducted within the specialization courses for educational support at the University of Salerno, involved 230 trainee teachers engaged in a participatory action-research process aimed at translating suggestopedic principles, positive suggestion, music, and relational harmony into didactic planning. Through a combination of theoretical training, laboratory design activities, and reflective evaluation, participants produced 21 interdisciplinary educational projects assessed according to the properties and rules of simplexity. The results show a high degree of methodological coherence, aesthetic quality, and curricular inclusiveness, with music emerging as a key factor in fostering attention, cooperation, and emotional engagement. Data analysis indicates that the fusion of suggestopedic and simplex approaches promotes adaptive, modular, and meaning-oriented design processes that enhance teachers’ creativity and metacognitive awareness. Overall, the findings highlight the educational value of a pedagogy of resonance, in which body, mind, and environment interact harmoniously. The study concludes that the suggestopedic—simplex model represents a regenerative framework for contemporary didactics, capable of transforming complexity into harmony and restoring to education its aesthetic, relational, and human dimension. Full article

Digital cultural heritage platforms face a dual challenge: preserving rich historical information while engaging an audience with declining attention spans. This paper addresses that challenge by proposing a modular information architecture designed to mitigate cognitive overload in cultural heritage tourism applications. We begin by examining evidence of diminishing sustained attention in digital user experience and its specific ramifications for cultural heritage sites, where dense content can overwhelm users. Grounded in cognitive load theory and principles of user-centered design, we outline a theoretical framework linking mental models, findability, and modular information architecture. We then present a user-centric modeling methodology that elicits visitor mental models and tasks (via card sorting, contextual inquiry, etc.), informing the specification of content components and semantic metadata (leveraging standards like Dublin Core and CIDOC-CRM). A visual framework is introduced that maps user tasks to content components, clusters these into UI components with progressive disclosure, and adapts them into screen instances suited to context, illustrated through a step-by-step walkthrough. Using this framework, we comparatively evaluate personalization and information structuring strategies in three platforms—TripAdvisor, Google Arts and Culture, and Airbnb Experiences—against criteria of cognitive load mitigation and user engagement. We also discuss how this modular architecture provides a structural foundation for human-centered, explainable AI–driven personalization and recommender services in cultural heritage contexts. The analysis reveals gaps in current designs (e.g., overwhelming content or passive user roles) and highlights best practices (such as tailored recommendations and progressive reveal of details). We conclude with implications for designing cultural heritage experiences that are cognitively accessible yet richly informative, summarizing contributions and suggesting future research in cultural UX, component-based design, and adaptive content delivery. Full article

In this work, a comprehensive literature review on power-routing devices is presented, outlining their current design principles and potential uses. Additionally, a comprehensive loss model for Modular Multilevel Converters (MMCs) in the context of power routers (PRs), a promising technology for enhancing flexibility and efficiency in future smart and hybrid AC–DC grids. Despite their potential, large-scale PR deployment is still limited by the lack of accurate and validated loss models. To address this gap, a detailed analytical model based on the Marquardt approach is proposed, capturing both conduction and switching losses in converter-based PRs. The model is validated through analytical comparison and PLECS simulations, showing strong agreement with theoretical and experimental data. Four case studies are presented to assess the effect of parameters such as power factor, active and reactive power, and the number of submodules on the overall converter losses. The results demonstrate that PR efficiency improves with optimized converter design and proper parameter selection. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish National Geographic Institute creates significant challenges due to differences in formats, update frequencies, and access methods. To overcome these limitations, this paper presents a modular and automated framework for the scheduled near-real-time ingestion of global seismic data using open APIs and semi-structured web data. The system, implemented using a Docker-based architecture, automatically retrieves, harmonizes, and stores seismic information from heterogeneous sources at regular intervals using a cron-based scheduler. Data are standardized into a unified schema, validated to remove duplicates, and persisted in a relational database for downstream analytics and visualization. The proposed framework adheres to the FAIR data principles by ensuring that all seismic events are uniquely identifiable, source-traceable, and stored in interoperable formats. Its lightweight and containerized design enables deployment as a microservice within emerging data spaces and open environmental data infrastructures. Experimental validation was conducted using a two-phase evaluation. This evaluation consisted of a high-frequency 24 h stress test and a subsequent seven-day continuous deployment under steady-state conditions. The system maintained stable operation with 100% availability across all sources, successfully integrating 4533 newly published seismic events during the seven-day period and identifying 595 duplicated detections across providers. These results demonstrate that the framework provides a robust foundation for the automated integration of multi-source seismic catalogs. This integration supports the construction of more comprehensive and globally accessible earthquake datasets for research and near-real-time applications. By enabling automated and interoperable integration of seismic information from diverse providers, this approach supports the construction of more comprehensive and globally accessible earthquake catalogs, strengthening data-driven research and situational awareness across regions and institutions worldwide. Full article

This paper presents an Interoperable User-Centred Digital Twin (I-UCDT) framework for sustainable energy system management, addressing the growing complexity of energy generation, storage, demand, and grid interaction across industrial and community-scale systems. The proposed framework provides a unified environment for the visual representation and management of interconnected energy components, supporting informed decision-making among diverse stakeholder groups. The I-UCDT framework adopts a modular plug-and-play architecture based on the Functional Mock-up Interface (FMI) standard, enabling scalable and interoperable integration of heterogeneous energy models from platforms such as Modelica, MATLAB/Simulink, and EnergyPlus. A standardised data layer processes and structures raw model inputs, while an interactive visualisation layer translates complex energy flows into intuitive, user-accessible insights. By applying human–computer interaction principles, the framework reduces cognitive load and enables users with varying technical backgrounds to explore supply–demand balancing, decarbonisation pathways, and optimisation strategies. It supports the full lifecycle of energy system design, planning, and operation, offering flexibility for both industrial and community-scale applications. A case study demonstrates the framework’s potential to enhance transparency, usability, and energy efficiency. Overall, this work advances digital twin research for energy systems by combining technical interoperability with explicitly formalised user-centred design characteristics (C1–C10) to promote flexible and sustainable energy system management. Full article

Cancer immunotherapy increasingly relies on nucleic acid-based vaccines, yet achieving efficient and safe delivery remains a critical limitation. Polyplexes—electrostatic complexes of cationic polymers and nucleic acids—have emerged as versatile carriers offering greater chemical tunability and multivalent targeting capacity compared to lipid nanoparticles, with lower immunogenicity than viral vectors. This review summarizes key design principles governing polyplex performance, including polymer chemistry, architecture, and assembly route—emphasizing microfluidic fabrication for improved size control and reproducibility. Mechanistically, effective systems support stepwise delivery: tumor targeting, cellular uptake, endosomal escape (via proton-sponge, membrane fusion, or photochemical disruption), and compartment-specific cargo release. We discuss therapeutic applications spanning plasmid DNA, siRNA, miRNA, mRNA, and CRISPR-based editing, highlighting preclinical data across multiple tumor types and early clinical evidence of on-target knockdown in human cancers. Particular attention is given to physiological barriers and engineering strategies—including size-switching systems, charge-reversal polymers, and tumor-penetrating peptides—that improve intratumoral distribution. However, significant challenges persist, including cationic toxicity, protein corona formation, manufacturing variability, and limited clinical responses to date. Current evidence supports polyplexes as a modular platform complementary to lipid nanoparticles in selected oncology indications, though realizing this potential requires continued optimization alongside rigorous translational development. Full article

Microbial fuel cells (MFCs) are a promising technology for simultaneously treating wastewater and recovering energy, yet scaling them from lab prototypes to practical systems poses persistent challenges. This review addresses the scale-up gap by systematically examining recent pilot-scale MFC studies from multiple perspectives, including reactor design configurations, materials innovations, treatment performance, energy recovery, and environmental impact. The findings show that pilot MFCs reliably achieve significant chemical oxygen demand (COD) removal (often 50–90%), but power densities remain modest (typically 0.1–10 W m⁻³)—far below levels needed for major energy generation. Key engineering advances have improved performance; modular stacking maintains higher power output, low-cost electrodes and membranes reduce costs (with some efficiency trade-offs), and power-management strategies mitigate issues like cell reversal. Life cycle assessments indicate that while MFC systems can outperform conventional treatment in specific scenarios, overall sustainability gains depend on boosting energy yields and optimizing materials. The findings highlight common trade-offs and emerging strategies. By consolidating recent insights, a roadmap of design principles and research directions to advance MFC technology toward sustainable, energy-positive wastewater treatment was outlined. Full article

Wearable multi-modal body fluid monitoring enables continuous, non-invasive, and context-aware assessment of human physiology. By integrating biochemical and physical information across multiple modalities, wearable systems overcome the limitations of single-marker sensing and provide a more holistic view of dynamic health states. This review offers a system-level overview of recent advances in multi-modal body fluid monitoring, structured into three hierarchical dimensions. We first examine sensing-combination strategies such as multi-marker analysis within single fluids, coupling biochemical signals with bioelectrical, mechanical, or thermal parameters, and emerging multi-fluid acquisition to improve analytical accuracy and physiological relevance. Next, we discuss platform-integration mechanisms based on biochemical, physical, and hybrid sensing principles, along with monolithic and modular architectures enabled by flexible electronics, microfluidics, microneedles, and smart textiles. Finally, the data-processing patterns are analyzed, involving cross-modal calibration, machine learning inference, and multi-level data fusion to enhance data reliability and support personalized and predictive healthcare. Beyond summarizing technical advances, this review establishes a comprehensive framework that moves beyond isolated signal acquisition or simple metric aggregation toward holistic physiological interpretation. It guides the development of next-generation wearable multi-modal body fluid monitoring systems that overcome the challenges of high integration, miniaturization, and personalized medical applications. Full article