157082 research outputs found
Sort by
A Deep Implicit-Explicit Minimizing Movement Method for Partial Integro-Differential Equations, with application to Option Pricing in Jump-Diffusion Models
No full textWe develop a novel deep learning approach for solving partial integro-differential equations (PIDEs) in high dimensions, involving diffusion and drift terms. To showcase its practicality and versatility, the methodology is presented for the specific challenge of pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a partial integro-differential equation, which is approximated via a new implicit-explicit minimizing movement time-stepping approach, involving approximation by deep, residual-type Artificial Neural Networks (ANNs) for each time step. The integral operator is discretized via two different approaches: (a) a sparse-grid Gauss–Hermite approximation following localised coordinate axes arising from singular value decompositions, and (b) an ANN-based high-dimensional special-purpose quadrature rule. Crucially, the proposed ANN is constructed to ensure the appropriate asymptotic behavior of the solution for large values of the underlyings and also leads to consistent outputs with respect to a priori known qualitative properties of the solution. The performance and robustness with respect to the dimension of these methods are assessed in a series of numerical experiments involving the Merton jump-diffusion model, while a comparison with the deep Galerkin method and the deep BSDE solver with jumps further supports the merits of the proposed approach
Spatiotemporal estimation of construction and demolition waste generation using novel integrated machine learning and remote sensing approaches: A study of 83 Chinese cities
No full textA widely recognized challenge hindering effective waste management planning worldwide is a lack of systematic and continuous data on construction and demolition waste (CDW) generation. To address these critical data limitations, this study presents its first systematic effort to integrate remote sensing (RS) data into a machine learning (ML)-based framework for estimating CDW generation. Utilizing all currently available data collected from selected Chinese cities, four widely used ML algorithms were trained and evaluated: Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost) using three feature groups: statistical variables, RS variables, and a combination of both. The best-performing models were applied to an interpolation dataset covering 83 Chinese cities over a 10-year period to evaluate their spatiotemporal extrapolation capabilities and generalizability. The results consistently indicate that models based solely on RS features outperform those using only statistical data or combined feature sets, with R² values ranging from 0.77 to 0.81 across all four algorithms. Among these, the RF model exhibited the highest overall performance, while LightGBM and XGBoost also delivered competitive results. Analysis of CDW generation across 83 Chinese cities revealed distinct spatial hotspots in regions such as the Yangtze River Delta and the Pearl River Delta, with a noticeable inland expansion trend over time, reflecting China's national strategies for promoting balanced regional development. This study offers a novel, scalable, and transferable approach that expands the methodological boundary of CDW estimation beyond conventional statistical data, offering new insights and practical implications for global CDW management, particularly in rapidly urbanizing or data-scarce regions
Exploring Disciplinary Perspectives on Community Resilience
No full textThroughout human history, communities have responded to challenges in urban and rural contexts by engaging multiple agents and actors, including individuals, institutions, and governments. Disciplinary expertise, including deep knowledge and practice, has contributed to economic, social, technological, and political change. Yet, it is increasingly apparent that the complex global, systems‐level challenges facing twenty‐first century communities require responses that transcend traditional disciplinary boundaries. The ability of communities to respond to challenges faced, from natural and anthropogenic hazards to the systemic threat of climate change, is often referred to as ‘community resilience’. Despite increasing scholarly interest, there appears to be, however, a lack of consistency in understanding and applying community resilience among cross‐disciplinary practitioners. This ambiguity can limit the potential of collaborative action and impact at the community level. This study explores cross‐disciplinary perspectives of community resilience to better understand how the term is described and applied in practice. Drawing on the experiences of more than 100 international respondents to an online survey, this study analyses the emerging themes to gauge the potential of transdisciplinary community resilience in realising the possible value of collective action
Ordering groups and the Identity Problem
No full textThe Identity Problem — deciding if the subsemigroup generated by a given finite set of elements of a group contains the identity element — is shown in this paper to correspond, for certain classes, to decision problems about ordering groups. Notably, the Identity Problem for a torsion-free nilpotent group corresponds both to the problem of deciding if a given finite set of elements extends to the positive cone of a left-order on the group, and to the Word Problem for a related lattice-ordered group.A new (independent) proof is given of the decidability of the Identity and Subgroup Problems for every finitely presented nilpotent group (initially proved by Shafrir in 2024), establishing also the decidability of the Word Problem for a family of lattice-ordered groups. In contrast, it is shown that the related Fixed-Target Submonoid Membership Problem is undecidable in nilpotent groups.Decidability of the Normal Identity Problem (with ‘subsemigroup’ replaced by ‘normal subsemigroup’) for free nilpotent groups is established using the (known) decidability of the Word Problem for certain lattice-ordered groups. Connections between orderability and the Identity Problem for a class of torsion-free metabelian groups are also explored
Experimental and theoretical analysis of PVC-CFRP confined steel reinforced concrete columns under axial compressive load
No full textThe PVC-CFRP confined steel reinforced concrete (PCSRC) is a composite structure formed by embedding steel inside PVC-FRP pipes and pouring concrete. It boasts advantages such as high bearing capacity, corrosion resistance, and the ability to withstand substantial plastic deformation. The PVC-FRP pipe is created by intermittently winding CFRP strips on the surface of a PVC pipe. To investigate the mechanical properties of PCSRC in short columns, 10 specimens under axial compression, varying different parameters including section diameter, steel content, yield strength of H-shaped steel, CFRP strip spacing, and concrete strength are designed. The test results revealed that the primary damage modes observed are PVC tube bursting and CFRP strips rupturing. The ultimate bearing capacity of the specimens increased with larger section diameters, higher concrete strength, increased yield strength of H-shaped steel, greater steel content, and narrower CFRP strip spacing. Moreover, as the concrete strength increases, the rate of degradation in axial compressive rigidity also accelerates, while the other parameters have relatively minor influences. Based on these experimental findings, a theoretical formula to predict the strength of PCSRC under axial compression is developed, and it aligns well with the experimental results.</p
Influence Of Heritage Design Elements On Sustainable Interior Environments: A Case Study Of Al Seef Dubai
No full textHeritage design elements over the years in the UAE have undergone an evolutionary process, drawing inspiration from local heritage, cultural wisdom, and accumulated knowledge. These traditional design features contribute to shaping spaces that promote a sense of belonging and local culture within the UAE's built environment. The aim of the study is to identify these heritage design elements and reassure their crucial role in interior architecture to ultimately enhance the sustainability of interior-built environments. This research paper critically examines a recent urban development in Dubai, with a specific focus on the Al Seef heritage area, characterized by historic architecture, and a contrasting space featuring contemporary structures. The investigation identifies elements of local heritage seamlessly integrated into contemporary planning and design. The research will offer a crisp overview of the design strategy, emphasizing solutions for temperature control, humidity regulation, ventilation, and maximizing natural light to achieve sustainability goals of a built environment. By employing a mixed qualitative case study research method, the findings will be systematically organized into visual tables, providing detailed descriptions evaluation summary. Through a case study analysis, this research paper imparts valuable insights gathered from local heritage design elements. It underscores how contemporary interior architecture can effectively enhance the sustainability of any interior-built environments in modern settings. The findings contribute to a deeper understanding of the harmonious integration of heritage and sustainability in interior design, offering lessons applicable to future architectural endeavors in similar contexts
A Dynamics-Based Method for Determining the Local Finite Mobility of Single-loop Spatial Mechanisms
No full textThis paper proposes a method for calculating the local finite mobility of single-loop spatial mechanisms based on modal analysis. Using spanning tree-based multibody dynamics, the single-loop spatial mechanism is modeled as a tree-like kinematic chain with serial chains closed by the constraints represented by a spring force model. The dynamic model is linearized using Taylor expansion. The stiffness matrix is then yield. The correspondence between vibrating/non-vibrating generalized coordinates and the nonzero/zero eigenvalues in the stiffness matrix is clarified. The mobility of the single loop spatial mechanism is then determined by the number of zero eigenvalues in the stiffness matrix. The method is then validated and analyzed by calculating the DOF of Sarrus mechanism, Bennett mechanism, 3-mode 7R mechanism, a mechanism with special parameters and a variable-DOF 8R mechanism. One contribution is that this work enhances spanning-tree-based dynamic modeling by analyzing joint selection strategies and introducing spring forces to replace kinematic constraints. The Other contribution is that based on the linearized model, a modal analysis framework is established to determine the mobility of single-loop spatial mechanisms.</p
A Lightweight Model LGCSPNet for Sitting Posture Risk Management Applications
No full textCurrent methods for sitting posture recognition typically follow a pipeline involving keypoint extraction and skeleton graph construction, followed by pose classification using Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs). However, CNNs struggle to model long-range dependencies among keypoints, whereas ViTs suffer from high computational costs. Moreover, both approaches tend to introduce redundancy during feature modeling. To improve efficiency, some studies have explored direct classification using keypoint coordinates, but these methods often fail to balance high accuracy with computational efficiency. To this end, this paper proposes a new model LGCSPNet with lightweight graph convolution modules (LGC) and a contrastive learning module. Firstly, LGC enables efficient full-keypoint communication by shifting features across keypoint channels, allowing each keypoint to access global context at minimal computational cost. Building on this, LGC enhances sitting posture detection by computing 3D attention weights via a parameter-free energy function with a closed-form solution, enhancing feature learning for posturally significant keypoints. The contrastive learning module enhances differentiation between similar postures in different categories by strategically selecting feature samples. Experiments on public human posture datasets and our custom sitting posture dataset show that LGCSPNet has only 0.097M parameters while achieving a 99% recognition rate. It surpasses existing models in terms of parameter quantity and accuracy. Guided by ergonomic metrics, our model enables posture correction and mitigates long-term sitting-related injuries
Kinetic modelling and steady-state optimization of cooling crystallization by continuous oscillatory baffled crystallizer
No full textTo address an open issue of sufficiently describing crystallization kinetics in a continuous oscillatory baffled crystallizer (COBC), a comprehensive kinetic modelling method is proposed in this paper, along with a steady-state optimization approach (SOA) for operating the COBC. Taking into account the axial dispersion of crystal quantity (ADCQ), velocity dispersion of crystal population (VDCP), and growth rate dispersion (GRD), a non-ideal plug flow micro-distribution model (NPF-MDM) is firstly established, which could be used to predict the crystal size distribution (CSD) and mean crystal size (MCS) in each zone of COBC. The model parameters are estimated by heterogeneous tracer experiments and continuous cooling crystallization (CCC) experiments in a real COBC named DN15. Based on the established NPF-MDM, an SOA is provided for operating the COBC. The tube length distribution across different temperature zones of COBC is optimized to determine the maximum attainable region of product MCS. By introducing an objective function related to the target crystal size and the CSD width of product crystals, a sensitivity analysis (SA) is presented to identify the critical operating conditions (COCs), including the seed recipe and net flow rate. Subsequently, the SA-based SOA is carried out. A growth optimizer algorithm is offered to solve the related nonconvex optimization problems. Experiments on the CCC of L-glutamic acid (LGA) via DN15 are performed to validate the proposed modelling and SOA
Electronic and structural properties of magnesium-doped platinum clusters: superatomic features of the MgPt<sub>9</sub> complex
No full textWe address herein a theoretical study of gas phase magnesium-doped platinum clusters (MgPtn, n = 2–12) using density functional theory, genetic algorithms and the quantum theory of atoms in molecules method of wavefunction analyses. The Mg atom consistently donates electron density to the Pt framework. This electronic charge depletion increases with size before it reaches an asymptotic limit. Among the series, MgPt9 exhibits enhanced stability, a large HOMO–LUMO gap (1.30 eV), a high adiabatic ionisation potential (6.94 eV) and a filled 1S2 1P6 shell, features which indicate a superatomic character of this species. Structural analysis reveals that MgPt9 forms gradually from MgPt6 and persists as a core in larger capped clusters. Spin multiplicities vary irregularly, reflecting changes in coordination and electronic degeneracy. Electrostatic potential analysis reveals the presence of σ-holes at low-coordinated Pt sites and at the Mg centre, and thereby a potential catalytic activity. These findings identify MgPt9 as a candidate superatomic cluster and suggest broader design strategies for bimetallic nanostructures with tunable electronic and chemical properties
Access Repository Dashboard
Do you manage Heriot Watt Pure? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!