Search Results (2,992)

Software-defined satellite networks (SDSNs) enhance resource utilization and flexibility in space-based networks by leveraging a global view and programmability. A highly reliable control plane is essential to sustain network operations. However, the highly dynamic topology and physical failures in Low Earth Orbit (LEO) environments can cause satellite node outages or inter-satellite link disruptions, leading to control plane interruptions and local load imbalances. To address this, we propose a switch migration mechanism for failure recovery and establish a multi-objective migration model that jointly optimizes control link delay, controller load variance, and normalized migration ratio. To accommodate distinct dynamic characteristics such as frequent topology changes, failure-intensive periods, and stable periods, we design two algorithms: a robust migration algorithm, DNSGA-II, which features population diversity maintenance and environmental awareness, and an efficient migration algorithm, IHAOAVOA, which integrates strong global exploration with powerful local exploitation. Simulation results show that IHAOAVOA rapidly converges under large-scale failures, achieving millisecond-level delay recovery and low normalized migration ratio overhead during failure-intensive periods, while DNSGA-II focuses on long-term load balancing and system stability during stable periods, effectively suppressing localized controller overload. By adopting IHAOAVOA during topology fluctuations or high-failure phases to reduce delay, and switching to DNSGA-II during stable phases to optimize load distribution, the overall network robustness can be improved under the evaluated failure scenarios. This work provides effective support for achieving highly reliable control in SDSNs under failure scenarios. Full article

This study explores the role of arts-based educational interventions in fostering social mending and urban regeneration in disadvantaged urban contexts. The intervention consisted of participatory mosaic workshops designed to foster cooperation, inclusion, and civic engagement, culminating in the co-creation of public artworks installed in a degraded urban underpass and included 193 children aged 5–15 living in Tor Bella Monaca, a highly marginalized suburb of Rome (Italy). The analysis of the intervention used a mixed-methods approach, based on systematic observation, questionnaires, interviews, and document analysis. The findings indicate that the artistic process acted as a catalyst for both individual and collective transformation: at the individual level, participation enhanced relational skills, emotional expression, and sense of agency; at the community level, the intervention contributed to the reactivation of public space, increased social cohesion, and improved care for the urban environment. We interpret these outcomes through the capability approach, identifying art as a “conversion factor” linking individual capabilities to collective regeneration. The intervention aligns with SDGs 4, 10, 11, and 17 of the 2030 Agenda and supports the relevance of scalable, community-based models for inclusive and sustainable urban policies in deprived areas. Full article

Aero-propulsive interactions in flapped configurations are a critical consideration for Short Take-Off and Landing (stol) aircraft, where extreme operational requirements demand robust, optimization-ready methodologies during preliminary design. This study develops a surrogate modeling framework that predicts the section-level aerodynamic response of a propeller–airfoil–flap configuration across a multi-dimensional space of propeller positioning, flap geometry, and operational conditions. A paired powered and unpowered design of experiments isolates the propulsive contribution to lift, drag, and pitching moment, while a virtual-disk propeller model parameterized by volumetric thrust decouples the prediction from any specific blade design. The framework couples two-dimensional steady Reynolds-averaged Navier–Stokes (rans) dataset generation with a Deep Neural Network (dnn) surrogate, which achieves coefficient of determination values above 0.96 for all three coefficients and reduces evaluation cost by several orders of magnitude relative to direct cfd, a benefit that is decisive in optimization. Single- and multi-objective optimization identify Pareto-optimal configurations, and independent cfd verification confirms prediction accuracies within 5 to 10% across the operational envelope. The resulting surrogate enables rapid, optimization-ready exploration of propeller–airfoil–flap configurations, providing actionable trade-off information for the preliminary design of stol aircraft. Full article

This study constructs a buckling analysis model integrating Euler–Bernoulli beam theory and Hamiltonian formulation to clarify the buckling characteristics of graphene origami (GOri)-reinforced beams and systematically explore the structural stability of graded composite beams. Under the symplectic space framework, the thermal buckling issue of GOri composite beams is converted into a zero-eigenvalue problem, where critical thermal buckling loads and corresponding buckling modes correspond to the symplectic eigenvalues and eigenfunctions of the Hamiltonian system. Taking the through-thickness continuity of GOri fillers into consideration, analytical expressions of buckling modes and critical buckling loads are derived using bifurcation criteria and normalization operations. Afterwards, parametric investigations are conducted to reveal how GOri content, spatial distribution, ambient temperature and folding degree affect beam buckling responses. Numerical results demonstrate that GOri distribution exerts a dominant influence on the structural buckling performance; critical thermal buckling loads tend to decline with rising folding degree and temperature. Reasonable optimization of GOri layout can significantly strengthen the mechanical capacity of composite beams, which lays solid theoretical guidance for their structural design and mechanical property enhancement. Full article

Background: Early-stage elderly-care robot design requires biological analogies to be translated without turning qualitative inspiration into unvalidated numerical evidence. Methods: We audited 15 initial variables and retained a four-dimensional exploratory space: nominal shell-edge radius (V06), pre-braking trigger distance (V09), translational speed (V11), and commanded deceleration (V12). Latin hypercube samples were filtered by V09 − V11²/(2V12) ≥ 0. A derived warning margin proxy, I5 = 1 − [V11²/(2V12)]/V09, was evaluated with fixed-seed feasibility, distribution, coverage, cluster, coordinate stability, and distance-sensitivity diagnostics. Results: The pooled pre-check acceptance rate was 0.8621. I5 descriptive and distributional stability passed at N = 768→1024, but four-dimensional coverage passed in only 10/30 seeds; no sufficient N was established up to 1024. Natural cluster structure was not detected, exact representative coordinates were seed-sensitive, and selection overlap under an alternative distance definition was 0.53. Two fixed-seed points were therefore retained only as illustrative boundaries. AI-assisted images and legacy Rhino studies were used for qualitative design communication, not as validated realizations of the computation. Conclusions: The evidence-tiered workflow supports traceable exclusion of infeasible combinations and transparent product design translation while preserving explicit limits: no physical safety, usability, manufacturing, or optimality claim is made. Full article

The structural lightweighting of next-generation containerized energy storage systems requires reliable fatigue design methodologies for architected lattice materials subjected to complex multiaxial service loading. Despite extensive studies on static performance, fatigue capacity prediction of parameterized lattices remains computationally demanding and experimentally fragmented, particularly when cyclic characteristics cannot be idealized as simple periodic histories. This work develops a unified multiscale evaluation platform grounded in shakedown theory to directly predict multiaxial fatigue capacity for lattice structures without explicit cycle counting. A topology-agnostic nodal-coupling periodic boundary formulation ensures consistent homogenized response evaluation across diverse unit-cell geometries. Numerical robustness in stress computation is achieved through full-integration tetrahedral discretization (FITD), enabling stable treatment of bending-dominated lattice members. To facilitate rapid exploration of high-dimensional design spaces, an ensemble-learning surrogate is trained on multiscale shakedown datasets for capacity prediction and parameter sensitivity analysis. The framework is demonstrated on body-centered cubic and peanut-like auxetic lattices relevant to lightweight container structures. Validation studies confirm accurate FITD scheme-based shakedown fatigue loading prediction. The surrogate model achieves high predictive precision, and parametric analysis reveals topology-dependent fatigue drivers, establishing quantitative linkages between mesoscale geometric variables and shakedown-based fatigue capacity. The proposed methodology provides an efficient and scalable route for fatigue-oriented lattice design and optimization in energy storage container applications. Full article

External spaces of buildings represent a type of potential space that can supplement the urban public space system under the background of urban renewal. However, as spaces with ambiguous publicness, their design and layout often need to convey stronger welcoming attitudes to attract citizens’ use. This study aims to explore the expression elements of publicness in external spaces of museum buildings. A mixed method approach, including field observation and measurement, questionnaire surveys (with a total sample size of 277), and statistical analysis, was adopted to explore and reveal the design logic of publicness expression in the layout and form of external spaces of museum buildings. Taking nine museum building external spaces located in Beijing’s core area as empirical cases, this study analyzes the influence of spatial layout on publicness expression from six dimensions: leisure atmosphere and activity diversity, spatial quality and perceived safety, landscape creation and spatial comfort, pedestrian friendliness, spatial identity, and boundary treatment and management measures. The study aims to understand the subtle threshold characteristics between the design of external spaces of museum buildings and the expression of publicness, and to explore the benefits of external spaces of buildings in supplementing the urban public space system. This research is expected to respond to public demands for high quality public spaces and improve the refinement of urban space management and optimization. Full article

Hybrid structural materials (HSMs), such as nanoparticle-treated fabrics, have been shown to enhance acoustic and ballistic performance in multifunctional protective structures. They offer a promising means for low-frequency (<~1000 Hz) noise mitigation, which remains a critical challenge in aerospace and defense applications. The measurement and modeling of their sound transmission loss (TL) behavior using a coupled acoustic–poroelastic approach is explored in this study. A colloid-based soaking and drying process is used to impregnate nanoparticles into the fabric. Previous studies using SEM imaging have established that at low (<~20 wt.%) treatment levels, the nanoparticles agglomerate in the interstitial spaces between yarn crossover points, whereas at higher levels, they begin to coat the yarn bundle tops. TL was measured experimentally using normal-incidence impedance tube tests. Further, parameters such as static flow resistivity, porosity, flexural modulus, and density required to model the neat and treat samples as fluid-filled porous solids using the Biot–Allard model were obtained from experiments for a limited set of neat and treated cases. Static flow resistivity was measured using an air permeability tester as per ISO 9237, and a modified version of the Peirce’s cantilever beam test was used to obtain the flexural modulus for neat and treated samples. Porosity was estimated using digital image analytics. The poroelastic fabric model was implemented in finite element simulations, and the predicted TL was compared with experiments including those for uncalibrated treated cases. The model shows close alignment with measured TL at low frequencies (<~600 Hz) for all cases but deviates closer towards the theoretical mass law at higher frequencies, where flanking effects and the influence of the hierarchy of pores are expected to be dominant in experiments. Further studies are underway to incorporate such higher-order effects to improve predictions at higher frequencies. The development of this model provides a means to capture the influence of nanoparticle addition on the acoustic performance of Kevlar, enabling fast and efficient virtual design iterations. The approach helps optimize HSMs for noise mitigation in multifunctional applications for the aerospace, defense, and infrastructural sectors. Full article

Overnight programming is an effective intervention that builds social satisfaction, well-being and community; however, the mechanisms supporting these outcomes are not well understood. This qualitative study explores concepts that contribute to social benefits of overnight programs and outlines best practices. Using purposive convenience sampling of an agency that conducts overnight programming, eight adults with disabilities (ages 22–56) participated in semi-structured interviews. Researchers utilized thematic analysis to analyze the interviews. Five themes developed: (a) Experience (program activities, connection to friends, and fun), (b) Liminality (being away from everyday life, respite, and transitory spaces), (c) Opportunity (personal growth, and trying new things), (d) Social Facilitation (interactions between participants, like-mindedness and relationship building), and (e) Support (friends, and staff). Results illustrate overnight programming provides opportunities for participants to experience new things and build social connections. The findings of this study are consistent with the ideas of liminality and communitas, supported by sense of community theory. Practitioners should consider incorporating the four elements of sense of community (membership, influence, fulfillment of needs, and strong emotional connections) when designing overnight programming. Full article

Background: The transition towards a more sustainable global economy requires meaningful changes in the way organisations purchase products across industrial value chains. Whilst extensive research has explored sustainable consumption within the business-to-consumer (B2C) space, comparatively little research has examined the antecedents of sustainable purchasing intent within business-to-business (B2B) environments. Existing literature has predominantly focused on individual behavioural determinants, and has under-explored the organisational, informational and product-level antecedents that shape sustainable purchasing decisions within industrial contexts. Objectives: This research explores the antecedents of sustainable purchasing intent within the chemical and ingredients value chain, and develops a novel integrated framework, the Business-to-Business Sustainable Buying Behaviour (B2B-SBB) Model, capable of explaining sustainable purchasing intent within the B2B setting. Methods: An abductive, mixed-methods, single-case study design was adopted, set within the customer base of a global chemical and ingredient distributor. Twelve semi-structured interviews were conducted with purchasing professionals across six end-market segments, and a quantitative survey generated 57 complete responses from a global customer base. Qualitative data were analysed through thematic analysis, and quantitative data through descriptive statistics and Wilcoxon Signed Rank testing. Results: The findings indicate that organisational antecedents, including buying centre process and responsibilities, corporate sustainability commitments, and information flow, exert materially greater influence on B2B sustainable purchasing intent than individual behavioural antecedents. Product sustainability claim variables emerge as a distinct and material antecedent, with a strong and statistically significant preference for third-party assured claims over self-declared claims. A structural value–action gap is also observed between publicly stated sustainability commitments and observed purchasing behaviour. Conclusions: The findings indicate that sustainable purchasing intent within the B2B chemical value chain is best understood through an integrated framework combining individual, organisational, environmental, product sustainability and information-flow antecedents. The proposed B2B-SBB Model provides such a framework, and offers both theoretical extension of existing scholarship and practical application for organisations seeking to accelerate the adoption of more sustainable products within industrial value chains. Full article

The optimization of the serpentine belt drive system (SBDS) is characterized by conflicting objectives, strong nonlinearity and a complicated search space. Traditional multi-objective evolutionary algorithms tend to suffer from insufficient convergence, uneven solution distribution and limited local search capability when tackling problems with complex, degenerated and discontinuous Pareto fronts. To address these drawbacks, this paper proposes a decomposition-based multi-objective piranha predation optimization algorithm (MOPPOA/D). Taking MOEA/D as the basic framework, the proposed algorithm decomposes the multi-objective optimization problem into a set of scalar subproblems with diverse search directions. An evolutionary operator inspired by piranha predation behavior is introduced, which dynamically switches between global exploration and local exploitation according to population satiety. Candidate solutions are generated via straight-line search and spiral search. A total of sixteen three-objective benchmark problems from the DTLZ and WFG test suites are selected to compare MOPPOA/D with NSGA-II, MOEA/D, MOEA/D-DQN and MOEA/D-AWA. Experimental results reveal that MOPPOA/D achieves the optimal mean HV and IGD values on 11 and 8 test problems, respectively. Furthermore, MOPPOA/D is applied to parameter optimization of the tensioner in the SBDS. Engineering calculations show that the maximum ratio of dynamic tension amplitude to installation tension for belt spans decreases by 18.63%, while the maximum average pulley slip rate declines by 9.07%. Despite a 5.88% increase in the maximum tensioner swing amplitude, the value is still within the allowable engineering range. The results verify the effectiveness and engineering application potential of MOPPOA/D on complex multi-objective benchmarks and the design optimization of the SBDS. Full article

The early design courses engage and hone students’ visual, spatial, and communicative skills so that first-year design students begin to understand composition, abstraction, hierarchy, development of process, and representation through experimental, process-driven exercises. Exhibitions have traditionally presented the students’ final work; however, virtual exhibitions allow one to push past the physical studio and present student work as a sustained pedagogical experience. A case study was presented of the development and curation of a speculative gallery-style virtual exhibition for process work by first-year students undertaking Basic Design and a Design Communication course. Rather than falling into the pitfall of functioning as a “digital stand-in for the gallery” approaches were considered where virtual curation becomes an educative space for being literate in the visual, mindful learning, and observer witnessing peer students enculturating themselves into identity. Using a practice-based reflective approach, to examine curatorial decisions (visual sequencing, categorisation, representation, and navigational design), exploring how exhibition design frames beginner work and communicates thinking and process rather than simply “polished” outcomes. It is argued that virtual exhibition, by allowing for prolonged engagement with works, scaling access, and encouraging a visual dialogue among peers, may extend the foundation of design learning. The study contributes to discourse on design pedagogy, digital curation, and the shifting educational place of exhibitions in foundation-level design education. Full article

The cathode material design is a persistent challenge in the development of next-generation rechargeable batteries. The cathode performance is critically influenced by certain key parameters, i.e., composition, crystal structures, ion transport, and degradation behaviour. Moreover, techno-economic and sustainable considerations also play a pivotal role in the viable cathode material design. In recent years, the integration of static machine learning models with conventional experimental techniques has significantly enhanced the cathode material design. However, the sequential nature of cathode discovery has not been fully captured by these techniques as they do not update their decision strategy based on prior outcomes. In this review, reinforcement learning (RL) as a decision making technique for cathode material design has been evaluated. Firstly, cathode design space, including major cathode families, optimisation objectives, and key material variables have been explored. Afterwards, cathode discovery has been presented in terms of RL states, actions, rewards, policies, environments, constraints, and feedback. The key focus of this review is to analyse how RL can support the composition selection, dopant, and crystal structure optimisation. The review also discusses the current limitations of RL based cathode design including data scarcity, dataset bias, limited cathode specific benchmarks, reward function design, physical validity, and experimental validations. The future directions have been proposed for physics informed and experimentally validated RL infrastructure that integrates the density functional theory, molecular dynamics, artificial intelligence and human expertise. Full article

Current design approaches for non-circular gears are generally restricted to predefined pitch curve geometries, lacking a unified methodology for systematically exploring wider families of admissible curves. To overcome this limitation, the paper proposes a generalized parametric design methodology capable of generating and evaluating an entire family of feasible pitch curves from a single analytical formulation—the Gielis’ superformula. Numerical analysis and CAD-based implementation are employed to identify a sub-family of generalized ellipses within the infinite family of Gielis curves that meet the geometric requirements for non-circular gear centrodes, namely closed and periodic profiles, smooth curvature, the absence of angular discontinuities and undercutting during tooth generation. The proposed methodology integrates the Gielis curves into gear design by (i) introducing gear-specific parameters, (ii) generating the conjugate pitch curve through the rolling without-slip equations, (iii) determining the center distance, (iv) determining the tooth number relation associated with the revolution ratio, (v) considering the curvature-based undercutting criterion and (vi) validating the resulting gears through CAD-based tooth generation simulations. This approach extends the geometric design space while providing a unified framework for the non-circular gears synthesis. Appropriate combinations of the Gielis parameters allow the amplitude, frequency and waveform of the transmission ratio function to be tailored to the kinematic requirements of variable-speed applications. Rather than identifying universal optimal values, the proposed methodology provides a framework for systematically evaluating Gielis parameter combinations against the geometric and gear-design requirements. Full article

Blended learning has become a structural feature of contemporary higher education, yet little is known about how students experience the digital study practices that shape their everyday learning rhythms. This qualitative study explores how students navigate autonomy, competence, and relatedness within digitally mediated blended environments. Semi-structured interviews were conducted with 20 students enrolled in blended programs at a Belgian university. Using reflexive thematic analysis, four themes were identified: fostering flexibility without losing orientation, cultivating belonging in blended spaces, providing scaffolds to build competence and motivation, and navigating digital strain. Students valued the autonomy afforded by flexible study rhythms, yet this autonomy was fragile when digital structures were unclear or inconsistent. Belonging depended on visible instructor presence and intentional opportunities for interaction, while competence was shaped by clarity, coherence, and timely feedback across platforms. Digital strain accumulated through extended screen time, technical frictions, and blurred boundaries between study and personal life. These findings offer a situated understanding of how students experience technology-mediated learning as both enabling and demanding. The study highlights the importance of predictable digital routines, relational presence, and coherent design in supporting sustainable engagement in blended higher education. Full article