Incremental Solution Research Articles

A novel meshless radial basis reproducing kernel particle approach for 3D thermo-mechanical analysis of mushy zone phase-change problems

This paper presents a three-dimensional thermo-elastoplastic computational model for the analysis of phase change problems involving the mushy zone. The model utilizes a novel meshless radial basis reproducing kernel particle approach, incorporating a high-order kernel that integrates Gaussian and cosine functions. An energy balance equation for the entire domain is formulated using the effective heat capacity method, based on the enthalpy formulation. The governing equations are discretized using the Galerkin weak form to compute thermal residual stresses, with essential boundary conditions enforced through the penalty method. Plastic deformation is characterized using the von Mises criterion with isotropic hardening, and the strain term resulting from temperature-dependent material properties is incorporated into the incremental solution process. Determination of the plastic strain increment is based on the Prandtl–Reuss flow rule. The accuracy and efficiency of the proposed meshless approach were rigorously evaluated through numerical examples encompassing two- and three-dimensional problems. The computational results were compared against available analytical and validated numerical solutions. This comprehensive assessment substantiated the robustness and precision of the developed technique. The case studies presented elucidate the capacity of the novel meshless model to effectively predict temperature distributions and residual stress fields within mushy zone phase change phenomena, accommodating both regular and irregular geometries under various boundary conditions. Notably, the model demonstrated consistent stability and high accuracy across the spectrum of simulated scenarios, thus underscoring its potential as a valuable tool for investigating complex phase change processes.

Deriving with Derivatives: Optimizing Incremental Fixpoints for Higher-Order Flow Analysis

At the heart of efficient program analysis implementations are incremental solutions to fixpoint problems. These solutions can be interpreted as the derivative of the underlying analysis function. Methods that describe how to systematically derive higher-order analyses from program semantics, such as Abstracting Abstract Machines, don’t shed light on how to efficiently implement those analyses. In this paper, we explore complementary techniques to optimize the derivative computation towards deriving efficient implementations. In particular, we use static specializations (by partial evaluation and rewriting) and dynamic specializations (in the form of tracking dependencies during the fixpoint), yielding efficient incremental fixpoints. We present how these optimizations apply to an example analysis of continuation-passing-style λ-calculus, and describe how they pair particularly well with tunable and optimized workset-based fixpoint methods. We demonstrate the efficacy of this approach on a flow analysis for the Standard ML language, yielding an average speed-up of 56x over an existing fixpoint method for higher-order analyses from the literature.

Nonlinear static analysis of masonry structures with mortar joints and cracking units by optimization‐based rigid block models

AbstractA rigid block model with elasto‐plastic softening interfaces is developed for nonlinear static analysis of masonry structures subject to monotonic loading. Cracking, crushing, and shear failures are taken into account at interfaces, following a simple micro‐modeling approach. An optimization‐based formulation is used for the solution of the equation systems governing the behavior of the rigid block assemblage. A simple incremental solution procedure is implemented to take into account the material softening behavior and the effects of large displacements on equilibrium conditions. The interface models are validated against tension and shear tests on bi‐block prisms from the literature. Applications to numerical and experimental out‐of‐plane loaded masonry walls as well as to circular arches with mortar joints are presented to evaluate the effects of tensile strength and the accuracy of the developed model against responses involving P‐Δ effects. Comparisons with experimental tests on shear walls also involving cracking of the units in the failure mechanisms are finally reported to discuss the potentialities and limitations of the proposed modeling approach.

Incremental Nonlinear Dynamics Inversion and Incremental Backstepping: Experimental Attitude Control of a Tail-Sitter UAV

Incremental control strategies such as Incremental Nonlinear Dynamics Inversion (INDI) and Incremental Backstepping (IBKS) provide undeniable advantages for controlling Uncrewed Aerial Vehicles (UAVs) due to their reduced model dependency and accurate tracking capacities, which is of particular relevance for tail-sitters as these perform complex, hard to model manoeuvres when transitioning to and from aerodynamic flight. In this research article, a quaternion-based form of IBKS is originally deduced and applied to the stabilization of a tail-sitter in vertical flight, which is then implemented in a flight controller and validated in a Hardware-in-the-Loop simulation, which is also made for the INDI controller. Experimental validation with indoor flight tests of both INDI and IBKS controllers follows, evaluating their performance in stabilizing the tail-sitter prototype in vertical flight. Lastly, the tracking results obtained from the experimental trials are analysed, allowing an objective comparison to be drawn between these controllers, evaluating their respective advantages and limitations. From the successfully conducted flight tests, it was found that both incremental solutions are suited to control a tail-sitter in vertical flight, providing accurate tracking capabilities with smooth actuation, and only requiring the actuation model. Furthermore, it was found that the IBKS is significantly more computationally demanding than the INDI, although having a global proof of stability that is of interest in aircraft control.

Use of Ticketing System in Freelancing Platform to Maintaining Client Trust in Product Development Process

Micro, small, and medium enterprises (MSMEs) are considered to be one of the important components in the economic development of a country, especially Indonesia. However, it has been found that MSMEs are lagging in digitalization, the adoption of information technology, and digital marketing. An information system where MSMEs can have easy access to IT and digital marketing professionals can be a solution to boost and encourage digitalization among local MSMEs. Developing such an information system requires the project to be able to quickly adapt and change based on the user’s needs and current trends. This study proposes an incremental solution to building an accessible information system catered for MSMEs by incorporating the ADDIE model into the development cycle. To understand the feasibility of the system, several group meetings are arranged to demonstrate and try out the system’s capability to the target users. The results indicate that the system is generally able to fit the needs of MSMEs and is quite effective at connecting the MSESs to IT and Digital marketing resources and experts.

A Bayesian analysis of adaptation of mountain grassland production to global change

Abstract In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: While an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified. We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2085. We built a Bayesian Belief Network (BBN) from long‐term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios. Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the conservation LULC scenario (−28% on average between 2020 and 2085), followed by the tourism development scenario (−7%) and the business‐as‐usual scenario (+3%). Climate change under representative concentration pathways (RCP) 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Synthesis and applications: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannual variability threaten the current grass‐based protected designation of origin (PDO) production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. Along with decreased total annual production due to decreasing grassland area compounded by more extreme climate change, the seasonal shift in production and increased interannual variability threaten the current grass‐based PDO production system. Further Bayesian modelling co‐developed with local stakeholders and experts could greatly contribute to adaptation planning of the regional production system.

Frequency effects in linear discriminative learning.

Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for how word frequency effects arise. The Discriminative Lexicon Model (DLM) models lexical processing with mappings between words' forms and their meanings. Comprehension and production are modeled via linear mappings between the two domains. So far, the mappings within the model can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modeling the theoretical endstate of learning (EL) where all words are learned optimally. In the present study we show how an efficient, yet frequency-informed mapping between form and meaning can be obtained (Frequency-informed learning; FIL). We find that FIL well approximates an incremental solution while being computationally much cheaper. FIL shows a relatively low type- and high token-accuracy, demonstrating that the model is able to process most word tokens encountered by speakers in daily life correctly. We use FIL to model reaction times in the Dutch Lexicon Project by means of a Gaussian Location Scale Model and find that FIL predicts well the S-shaped relationship between frequency and the mean of reaction times but underestimates the variance of reaction times for low frequency words. FIL is also better able to account for priming effects in an auditory lexical decision task in Mandarin Chinese, compared to EL. Finally, we used ordered data from CHILDES to compare mappings obtained with FIL and incremental learning. We show that the mappings are highly correlated, but that with FIL some nuances based on word ordering effects are lost. Our results show how frequency effects in a learning model can be simulated efficiently, and raise questions about how to best account for low-frequency words in cognitive models.

New procedure for soil-structure interaction problems incorporating free-field effects by 2D quadratic elements

Following several decades of tragic seismic events, current national regulations emphasise the importance of considering seismic soil-structure interaction (SSSI) in the design of foundations, particularly for deep foundations in loose soils. However, incorporating the free field into the SSSI calculation model can be challenging when high order finite or infinite elements are involved.This paper presents a new procedure for considering the effect of the free field in a seismic soil-structure interaction (SSSI) model when using 8-node finite elements bounded by compatible absorbing infinite elements.The proposal, based on a new formulation of three-node column elements paired with a modification of the Cundall method, allows the calculation of the free field effects to be applied at the level of the lateral boundaries of the SSSI model, represented here by the finite-infinite element interfaces. This modification of the dissipative system, considering the kinematic effect at each time step, allows an incremental solution of the SSSI problem, taking into account the nonlinearities of the materials constituting the geotechnical profile. The test validation of the method, performed on two cases of shear wave propagation problems in soft soils, shows good results, demonstrating the usefulness of the 2D approach for SSSI problems.

On thermodynamic extremal principles in gradient plasticity with energetic forces

Incremental energy minimization is revisited as a method of determining an incremental solution for rate-independent dissipative solids undergoing isothermal quasi-static deformation. The incremental minimization is applied to the total internal energy of the compound thermodynamic system that consists of a deforming body with internal variables, a conservative loading device, and an ambient heat reservoir. It is shown that the difference between the virtual and actual dissipation rates plays a fundamental role in this minimization, which is related to thermodynamic extremal principles of local and global type. The analysis is carried out within the gradient plasticity framework with the energetic forces derived as the variational derivative of the Helmholtz free energy depending on the spatial gradient of arbitrary internal variables. Specifications are given for existing models of gradient plasticity.

Maximal D-Truss Search in Dynamic Directed Graphs

Community search (CS) aims at personalized subgraph discovery which is the key to understanding the organisation of many real-world networks. CS in undirected networks has attracted significant attention from researchers, including many solutions for various cohesive subgraph structures and for different levels of dynamism with edge insertions and deletions, while they are much less considered for directed graphs. In this paper, we propose incremental solutions of CS based on the D-truss in dynamic directed graphs, where the D-truss is a cohesive subgraph structure defined based on two types of triangles in directed graphs. We first analyze the theoretical boundedness of D-truss given edge insertions and deletions, then we present basic single-update algorithms. To improve the efficiency, we propose an order-based D-Index, associated batch-update algorithms and a fully-dynamic query algorithm. Our extensive experiments on real-world graphs show that our proposed solution achieves a significant speedup compared to the SOTA solution, the scalability over updates is also verified.

Can Managing Climate Risks Be a Catalyst for Broader Transformative Change?

This essay addresses the long-term effectiveness of urban climate change adaptation approaches, based, inter alia, on work in the C40 city network. We argue that in most cities, the dominant framing of climate risk management almost exclusively focuses on short-term incrementalities and preventive solutions directly tackling hazards, vulnerability, and exposure. This approach has serious flaws, leading to missed opportunities for longer-term sustainable urban development. Until very recently, climate science usually provided only a marginal input to long-term urban planning and design. We argue that any analysis of urban climate risk management and the associated climate services should be broadened beyond solely climate focusing on impacts. In this context, the development of positive urban visions is a key gap for both research and practice. A change is required from negatively addressing risks to positively pursuing a positive vision of attractive, resilient, and sustainable cities. The emphasis on short-term incremental solutions should shift towards long-term transformation. This embodies a paradigm shift from “function follows system” to “system follows function”. For many cities, this also means a change in procedural practice from siloed top-down to integrated, participatory urban transformation. Our main argument in this paper is that simple, longer-term sustainable urban transformation would not only reduce climate risks but also enhance overall environmental quality, economic opportunities, and social wellbeing.

Evidence of Counterterrorism Effectiveness in Theory and Practice: Incremental Solutions for a Complex Problem

Evidence of Counterterrorism Effectiveness in Theory and Practice: Incremental Solutions for a Complex Problem

Understanding resilience, self-reliance and increasing country voice: a clash of ideologies in global health

Background‘Resilience’, ‘self-reliance’ and ‘increasing country voice’ are widely used terms in global health. However, the terms are understood in diverse ways by various global health actors. We analyse how these...

Efficient Vehicle Routing Solutions: A Metaheuristic Approach

Efficient vehicle directing is fundamental for smoothing out strategies processes, cutting costs, and raising consumer satisfaction. The current study explores the usage of metaheuristic procedures to handle multifaceted vehicle directing issues that emerge in various fields, including public transportation frameworks, transportation logistics, and delivery services. The study is explicitly worried about the application and investigation of a modified version of Ant Colony Optimization (ACO), a metaheuristic algorithm that draws motivation from ant foraging behavior. To increment solution quality, robustness, and convergence speed, the modified ACO algorithm incorporates enhancements like multi-colony systems, local search heuristics, and dynamic pheromone update instruments. This examination shows the adequacy and adaptability of the recommended approach in delivering ideal or almost ideal routing arrangements through mathematical modeling, theoretical analysis, and empirical assessments utilizing real-world datasets. The outcomes add to the corpus of information on optimization algorithms and give valuable exhortation to working on the manageability and proficiency of vehicle routing operations.

A new theoretical model incorporating geomechanical effects for hydrate dissociation in sediments under thermal stimulation

Previous analytical models for hydrate dissociation under thermal stimulation usually focused on heat and mass transfer while ignoring geomechanical effects. However, it is accepted that hydrate dissociation under thermal stimulation involves multifield coupling problems, and the effect of a stress field on hydrate dissociation cannot be ignored. Thus, in this paper, based on a classic analytical model for predicting hydrate dissociation under thermal stimulation derived by Selim and Sloan, we propose a novel model that takes the geomechanical behavior into account to simulate hydrate dissociation. Specifically, we applied an incremental solution scheme to solve this coupled system. Afterwards, we validated our derived model and compared the influence of different porosity strategies (i.e., Lagrange's porosity and true porosity) on the coupled system. The result shows that, at small strains, porosity strategies have little influence on the coupled system. Moreover, parametric analysis was conducted to study the effects of relative parameters on hydrate dissociation. Compared with previous studies, our model not only is more efficient but also reveals more mechanisms of hydrate dissociation under thermal stimulation.

A new framework for mixed-user dynamic traffic assignment considering delay and accessibility to information

We consider a transport network consisting of two classes of users, namely, selfish users whose objective is to minimize their individual travel times, and cooperative users whose objective is to maximize the aggregate throughput of their class, or equivalently, to minimize the total travel time of their class. The selfish users decide the paths from their respective origins to destinations depending on their knowledge of the traffic conditions prevailing up to the beginning of their journeys, and they do not alter their paths once their journeys start. The cooperative users, on the other hand, keep making the decisions of ‘the next link to take towards the destination’ throughout their journeys based on their current knowledge of the traffic conditions, which evolves with time and the availability of traffic information. We develop a new framework for the problem of informed mixed-user dynamic traffic assignment (IMUDTA) considering the delay and the degree of penetration of information. The proposed framework enables us to study the route choices made by the two classes of users and the resulting network performance. Due to its non-linear formulation with high complexity, we propose a novel incremental solution method to this problem where we present a transformation of this complex IMUDTA problem into a relaxed linear programming (LP) model. We show via numerical results that (i) appropriately postponing (intentionally or unintentionally) the access to information can improve the system performance, (ii) better information penetration increases the robustness of the network performance against the information delay, and (iii) the value of information (in terms of improving network performance) is reduced when the degree of information penetration is high. The applicability of our framework is demonstrated in a large-scale network example, and analyze the computational aspects and the impact of information on network performance with varying information delay and penetration.

Molecular level investigation on the dispersion of hydrophilic modified SiO2 nanoparticles in water from a bond energy viewpoint

AbstractThe dispersion characteristic of nanoparticles is of more interest in some engineering applications, including polymer filling, foam stability, chemical catalysis, and materials surface package. In this paper, the surface modification of SiO2 nanoparticles was carried out based on molecular dynamics simulation. The characteristics of aggregation and diffusion of SiO2 nanoparticles were explained by the radial distribution function (RDF), concentration profile, length distribution, mean squared displacement (MSD), and microscopic testing (MT). The results showed that the orbital provided by the three types of atoms (H, O, and Si) corresponding to the different bandwidths caused the energy alternation of state density. According to the results of RDF, the HO bond energy mainly provided by the water molecules showed the maximum bond energy with 463 kJ/mol. The results indicated that the bonds breakage and formation were accompanied by changes in total energy, kinetic energy, non‐bond energy, and potential energy. After the modification of SiO2 nanoparticles, the concentration profile of the water molecules decreased first at 1–8.5 Å and then increased at 8.5–17.2 Å, but the length distribution climbed to 15.7 at 0.975 Å. When the temperature reached 398 K, the peak value of the length distribution declined to 13.6 Å and the relative concentration profile of water molecules fluctuated around 1.0. With the increase of salinity, the peak value of length distribution reached 15.7 at 0.975 Å, but the concentration profile of water molecules at 3.1–9 Å decreased quickly and then gradually increased. The results of MSD and MT about water molecules presented the largest diffusion coefficient appeared at 398 K and had the best dispersion effect owing to the average kinetic energy among the molecules. Conversely, the diffusion coefficient decreased with the incremental solution salinity because the inhabitation of sodium for the motion of water molecules resulted in the ion bridging and hydrogen bonding.

3D concrete printing: Lattice modeling of structural failure considering damage and deformed geometry

This research studies the impact of localized damage and deformed printing geometry on the structural failure of plastic collapse for 3D concrete printing (3DCP) using the lattice model. Two different approaches are utilized for buildability quantification: the (previously developed) load-unload method, which updates and relaxes the printing system after each analysis step and repeatedly applies the gravitational loading to the undeformed structure; and the incremental method, which keeps the load after each analysis step and applies the incremental loading to the deformed printing system. The former can consider the material yielding but cannot capture accurately the structural deformation during printing process. Compared to the load-unload method, the incremental method can not only consider deformed printing geometry but can also simulate the non-proportional loading conditions and disequilibrium force occurring during 3D printing. In this study, computational uniaxial compression tests are first conducted to compare two algorithms. The numerical results indicate the consideration of nonequilibrium force and deformed geometry affects the peak load and crack information for fracture analysis. Subsequently, the incremental method is incorporated into the lattice model to quantify buildability of 3DCP. The predictions are compared with previously published numerical results obtained using the load-unload method. The lattice model based on incremental method reproduces correct failure mode; better quantitative agreement about critical printing height also can be obtained. These numerical analyses demonstrate that the incremental solution is an approximate method for buildability quantification since it can account for the nonequilibrium force induced by the deformed printing geometry and localized damage.

ML-MMAS: Self-learning ant colony optimization for multi-criteria journey planning

Ant Colony Optimization (ACO) algorithms have been widely employed for solving optimization problems. Their ability to find optimal solutions depends heavily on the parameterization of the pheromone trails. However, the pheromone parameterization mechanisms in existing ACO algorithms have two major shortcomings: 1) pheromone trails are instance-specific; hence they need to be generated for each new problem instance, 2) solution is constructed based on static pheromone trails, which ignores the impact of the evolving decisions on the final solution. In this paper, we study the personalized journey route planning problem on multimodal public transport networks (MMPTN) that considers multiple travel criteria. The problem is addressed with a weighted sum method, which provides a journey route that best matches passenger’s preference vector consisting of multiple travel criteria. We propose a Machine Learning (ML) based Max–Min Ant System (called ML-MMAS) to solve optimization problems by incorporating ML techniques into Ant Colony Optimization algorithms. ML-MMAS learns a pheromone function to directly produce prominent pheromone trails in order to construct solutions for any new instance, without the need to initialize and update the pheromone trails from scratch. We propose a self-learning framework to train the ML-MMAS using incremental solutions generated by MMAS, hence avoiding the need for pre-computed optimal solutions. Specifically, we develop a deep learning-based pheromone prediction model. We design several groups of features to train the model to characterize the evolving states of the search space during solution construction. Finally, we propose a solution component embedding (SCE) model to learn representations of solution components (transit services), which takes into account the transferability among transit services and passenger transfer preferences. The SCE model enables the extraction of high-quality features for the solution components. It can also be directly applied to solve other optimization problems with solutions that can be modeled as sequences of solution components. We evaluate the proposed ML-MMAS by comparing with exact algorithms and the underlying MMAS, using the MMPTN and passenger demands of Singapore. Results show that ML-MMAS is significantly faster than both the exact algorithm and the original MMAS, while achieving near-optimal solutions.

Closing the gap on wicked urban stream restoration problems: A framework to integrate science and community values

Restoring the health of urban streams has many of the characteristics of a wicked problem. Addressing a wicked problem requires managers, academics, practitioners, and community members to make negotiated tradeoffs and compromises to satisfy the values and perspectives of diverse stakeholders involved in setting restoration project goals and objectives. We conducted a gap analysis on 11 urban stream restoration projects to identify disconnections, underperformance issues, and missing processes in the project structures used to develop restoration project goals and objectives. We examined the gap analysis results to investigate whether managers appropriately identified problem statements and met stated objectives. Projects that aimed to restore overall stream health commonly fell short for various reasons, including limited stakeholder and community input and buy-in, revealing potential limitations in the breadth of objectives, values, and stakeholder perspectives and knowledge types. Projects that emphasized integrating community values and diverse knowledge types tended to meet the expected outcomes of restoring stream processes through incremental solutions. Managers implementing more holistic solutions and values-driven approaches are more likely to consider diverse viewpoints from a variety of community local institutions. Based on these and other results, we propose a conceptual framework that integrates diverse perspectives and knowledge to enhance social and ecological outcomes of urban stream restoration. The framework also emphasizes the importance of setting objectives that support incremental solutions to foster more realistic expectations amongst stakeholders.