Solution Framework Research Articles

Extended King integral for modeling of parametric array loudspeakers with axisymmetric profiles.

Parametric array loudspeakers have been widely used in audio applications for generating directional audio beams. However, accurately calculating audio sound with a low computational load remains challenging, even for basic axisymmetric source profiles. This work addresses this challenge by extending the King integral in linear acoustics to incorporate both cumulative and local nonlinear effects, under the framework of the quasilinear solution without the paraxial approximation. The proposed method exploits the azimuthal symmetry in cylindrical coordinates to simplify modeling. To further improve computational efficacy, fast Hankel and Fourier transforms are employed for the radial and beam radiation directions, respectively. Numerical results with both uniform and focusing profiles demonstrate the advantages of the proposed approach over the traditional spherical wave expansion and direct integration methods, especially for larger aperture sizes. Specifically, for typical configurations with source aperture size of 0.2 m, we observe at least a 24-fold improvement in computational speed and a 227-fold reduction in memory requirements. These advancements allow us, for the first time, to present the sound field radiated by parametric array loudspeakers with a large aperture size of up to 0.5 m, without paraxial approximations. The implementation codes are available on https://github.com/ShaoZhe-LI/PAL_King.

Maximizing influence via link prediction in evolving networks

Influence Maximization (IM), targeting the optimal selection of k seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the k most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.

COSMO: Dynamic Uploading Scheduling in mmWave-Based Sensor Networks with Mobile Blockers

Wireless sensor networks (WSNs) leveraging millimeter wave (mmWave) communication for bandwidth-demanding applications is considered in this paper. Despite the large bandwidth, the delivery of delay-sensitive information collected by sensors may still face significant latency due to the vulnerability to intermittent link blockage. Hence, the guarantee of low age of information (AoI) in mmWave WSNs is not straightforward. In this paper, the wireless sensing and dynamic programming techniques are jointly exploited to relieve the above issue. The former tracks the human blockers and predicts the chance of link blockage; the latter optimizes the transmission of multiple sensors based on the prediction. Particularly, the long-term optimization of sampling, uplink time and power allocation policies in a sensor network can be formulated as an infinite-horizon Markov decision process (MDP) with discounted cost, where the state transition probabilities can be predicted via wireless sensing. A novel low-complexity solution framework, namely COSMO, with a guaranteed performance in the worst case, is proposed. Simulations show that compared with heuristic benchmarks, benefiting from the prediction of the link blockage, COSMO can significantly suppress the average system cost, which consists of both AoI and energy consumption.

Applications and Prospects of Smooth Particle Hydrodynamics in Tunnel and Underground Engineering

Smoothed particle hydrodynamics (SPH) is a state-of-the-art numerical simulation method in fluid mechanics. It is a novel approach for modeling and comprehending complex fluid behaviors. In contrast to traditional grid-dependent techniques like finite element and finite difference methods, SPH utilizes a meshless, purely Lagrangian approach, offering significant advantages in fluid simulations. By leveraging a set of arbitrarily distributed particles to represent the continuous fluid medium, SPH enables the precise estimation of partial differential equations. This grid-free methodology effectively addresses many challenges associated with conventional methods, providing a more adaptable and efficient solution framework. SPH’s versatility is evident across a broad spectrum of applications, ranging from advanced computational fluid dynamics (CFD) to complex computational solid mechanics (CSM), and proves effective across various scales—from micro to macro and even astronomical phenomena. Although SPH excels in tackling problems involving multiple degrees of freedom, complex boundaries, and large discontinuous deformations, it is still in its developmental phase and has not yet been widely adopted. As such, a thorough understanding and systematic analysis of SPH’s foundational theories are critical. This paper offers a comprehensive review of the defining characteristics and theoretical foundations of the SPH method, supported by practical examples derived from the Navier–Stokes (N-S) equations. It also provides a critical examination of successful SPH applications across various fields. Additionally, the paper presents case studies of SPH’s application in tunnel and underground engineering based on practical engineering experiences and long-term on-site monitoring, highlighting SPH’s alignment with real-world conditions. The theory and application of SPH have thus emerged as highly dynamic and rapidly evolving research areas. The detailed theoretical analysis and case studies presented in this paper offer valuable insights and practical guidance for scholars and practitioners alike.

Impact of urbanization and human development on ecological footprints in OECD and non-OECD countries

Ecological footprints play a crucial role in assessing how human activities impact the environment, serving as key indicators. This study investigates the influence of urbanization and human development controlling for GDP and industrialization on ecological footprints, focusing on both OECD and non-OECD countries during the period from 1990 to 2018. The investigation employs an open-access solution framework and utilizes the Generalized Method of Moments approach for analysis. The findings highlight distinct patterns between OECD and non-OECD countries. In OECD countries, ecological footprints are increasing with urbanization and GDP growth while showcasing a negative impact of the Human Development Index (HDI) and industrialization on ecological footprint. Conversely, non-OECD countries demonstrate a positive impact of GDP and HDI on ecological footprints, while there is a negative impact of industrialization and urbanization on ecological footprints. These disparities underscore the need for tailored environmental strategies based on a country's economic and developmental status. The results underscore the importance of investing in the renewable energy sector and implementing stringent environmental policies to mitigate the environmental impact of human activities. This evidence reinforces the urgency for countries, irrespective of their OECD status, to take proactive measures to safeguard the planet from further environmental hazards.

Enhanced framework for solving general energy equations based on metropolis-hasting Markov chain Monte Carlo

Due to the widespread presence of heat and mass transfer phenomena in industrial applications, numerous studies have been devoted to the accurate solution of energy equations, providing a foundation for the analysis of heat and mass transfer processes in practical applications. In this study, a Green's Function Markov Superposition Monte Carlo (GMSMC) for solving general energy equations has been developed based on probability and statistical principles owing to its advantageous features of insensitivity towards dimension and geometric complexity as well as the capability to handle multiple integrals in complex domains. The energy equation is first decomposed, and corresponding probability models are established for each component, considering their interrelationships. Subsequently, a solution framework for solving the general energy equation is constructed by integrating these probability models based on a Markov chain structure. The mathematical principles and formulae of the proposed method are derived in detail. The performance of the proposed method is validated by several heat transfer systems with different combinations of boundary conditions and features, which mainly include the distribution of the internal heat source and whether the convection or transient term is included. Results of the validation show that the temperatures obtained by the proposed method are in good agreement with the FEM based on a fine grid, no matter whether the calculation is for a single point or a distribution.

Toward a personalized autonomous transportation system: Vision, challenges, and solutions

The fragmented design of Intelligent Transportation Systems creates isolated intelligent systems. Resource competition and information gaps are fierce and widespread, worsening traffic issues and degrading overall service levels. Therefore, empowered by advanced technologies, an evolution towards Autonomous Transportation System (ATS) is observed. This evolution aims to develop a collaborative and sustainable ecosystem, prompting interoperability within the cloud-edge-device continuum. It can, accordingly, dismantle internal resource barriers and achieve a systematic balance between demand and supply with less human intervention. Despite the promising vision of ATS, it encounters three key challenges: disparate data, deficient models, and conflicting interests in supporting autonomous and personalized mobility. Hence, as an innovative solution, a trustworthy, private, and equal-serving framework called TPE is designed. It seamlessly integrates Blockchain, Federated Learning, and Large-scale Model to deploy a trustworthy operating environment, process private data for globally shareable knowledge, and develop a foundation model for personalized adaptation, respectively. Consequently, ATS empowered by TPE can serve diverse user groups both privately and equally.

A trust-region framework for iteration solution of the direct INDSCAL problem in metric multidimensional scaling

A trust-region framework for iteration solution of the direct INDSCAL problem in metric multidimensional scaling

Green and Efficient Syntheses of a Series of Imine-Connected Covalent Organic Frameworks in Acetic Acid Solution

Green and Efficient Syntheses of a Series of Imine-Connected Covalent Organic Frameworks in Acetic Acid Solution

Global solutions for a hyperbolic conservation system of three equations

In this short paper, we are concerned with a mathematical model, which is the variant of non-isentropic system of polytropic gas. The main contribution is to prove the a-priori L∞ estimates for the viscosity approximation solutions and to obtain a new application, on a large hyperbolic system of three equations, of the global existence framework of weak solutions in the paper “Existence of Global Entropy Solutions to a Nonstrictly Hyperbolic System” (Lu, 2005).

Unveiling Special Numbers Arising from Diophantine Equations: Exploring Solutions and Ramifications

This paper delves into the exploration of special numbers stemming from the resolution of Diophantine equations—mathematical challenges centered around identifying integer solutions to specific polynomial equations. Investigating various instances of Diophantine scenarios, we meticulously examine and analyze the special numbers that naturally manifest during the process of solving these equations. We scrutinize the distinctive features and implications of each special number within the framework of Diophantine solutions. The discoveries illuminate the intricate relationships between specific numerical patterns and the underlying algebraic structures inherent in Diophantine equations, presenting avenues for further inquiry and potential applications.

Mechanism of eccentricity influence on 3-DOF aerodynamic stability: New insights into instability evolution, energy harvesting, and vibration control

The inconsistency of mass center and elastic center, i.e., eccentricity, widely exists in various engineering structures and components, including iced conductors, wind turbine blades, airfoils, and cable-supported bridges, and can also be extended to the application of energy harvesting and vibration control. Although the eccentricity influence on aerodynamic stability has already been recognized as important, the underlying mechanism remains rather unclear. This study established a comprehensive framework of explicit eigenvalue solutions, offering in-depth insights into the aerodynamic stability mechanism in a vertical-horizontal-torsional 3-degree-of-freedom linear system under all possible frequency scenarios. These solutions elucidated the coupling mechanism of eccentricity, aerodynamic damping, aerodynamic stiffness, and structural frequencies. Remarkably straightforward criteria to predict the promoting/suppressing effect of eccentricity were proposed, consisting of eccentric angle and aerodynamic forces, which not only align with the traditional principles but also offer a broader application range in terms of structure types and wind speeds. Validations of the proposed solutions were performed for all frequency cases via numerical studies. For instability tendency evaluation, numerical studies on a thin plate showed that eccentricity has a significant impact due to the strong aerodynamic effect and large ratio of width/gyration radius, highlighting the importance of considering small eccentricity errors during experiments. Examination for energy harvesting revealed that a small eccentricity can dramatically enhance power generation efficiency, and the explicit solutions successfully explained the rules governing performance changes against frequency ratio, mass center position, rotation center position, etc. The analysis of a bundled conductor showed that the double pendulum can control galloping because it diminishes the eccentricity effect by shifting the eccentric angle. The proposed explicit solution framework provides a systematic view and new insights into the aerodynamic instability mechanism influenced by eccentricity, serving as a reference for the design and studies of various engineering structures, energy harvesting, and vibration control.

How Data Infrastructure Deals with Bias Problems in Medical Imaging.

The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data infrastructure initiative: the PADME (Platform for Analytics and Distributed Machine-Learning for Enterprises), which is a part of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) project. The PADME facilitates the structuring and sharing of health data while ensuring privacy and adherence to FAIR principles. The paper presents experimental results that show that generative methods can be effective in data augmentation. Complying with PADME infrastructure, this work proposes a solution framework to deal with bias in the different data stations and preserve privacy when transferring images. It highlights the importance of standardized data infrastructure in mitigating biases and promoting FAIR, reusable, and privacy-preserving research environments in healthcare.

Efficient Optimization for Time-Constrained Encounter of Spacecraft with Multirevolution Phasing

“Spacecraft encounter” refers to a close flyby or fast proximity between two or more spacecraft in space. It involves the approach of one spacecraft to another, whether for observation, communication, capture, or interception purposes. This paper presents a novel approach to planning orbital encounter missions for spacecraft within a specified time constraint. The proposed method takes advantage of a unique encounter process where the vehicle performs only two in-plane impulses and matches the target location at the encounter position using multirevolution phasing. To ensure the minimum maneuvering fuel consumption from the selection of the encounter position and analyze the optimality of the proposed encounter maneuver, the determination method of the minimal orbital intersection distance is first given by constructing constraint equations in polynomial form. Then, the algorithm framework for determining the optimal encounter maneuvers is established. This scheme allows us to derive the optimal maneuver position, direction, and impulse magnitude, thereby creating a detailed two-layer solution framework for an arbitrary target. Concurrently, the strategy accommodates orbital phase deviations by proffering a rapid correction method that factors in the perturbation environment. To expand this optimal encounter maneuver planning method to multitarget missions, we develop a reduced-order allocation procedure for optimal pairing between vehicles and targets. Through the use of numerical simulations exemplified by one single-target and two multitarget missions, it is evident that the proposed method is both highly efficient and effective for encountering any given target.

Alternative service network design for bus systems responding to time-varying road disruptions

In practice, road disruptions occur frequently, interrupting multiple bus routes at the same time and causing widespread passenger delays. Typically, these disrupted roads are repaired sequentially and then gradually put into service. In response to such time-varying road disruptions, this paper aims to assist bus operators in developing effective alternative service networks for passengers. The proposed approach involves the joint optimization of service-based route adjustments, bus timetables, and passenger assignment to minimize the total passenger cost and weighted bus operation time. Specifically, a novel service-based adjustment strategy is introduced to flexibly adapt each bus service to time-varying road disruptions. An integer programming model is built for the studied problem based on the set of passengers’ time-space itineraries. To efficiently generate these time-space itineraries and solve models for large-scale problems, this paper develops a hierarchical solution framework. The framework consists of three key parts: (1) a column generation procedure to iteratively explore passengers’ spatial paths; (2) a customized extension algorithm to extend these spatial paths to time-space itineraries; and (3) a tailored adaptive large neighbourhood search heuristic to solve the final itinerary-based model. After that, the overall methodology is tested with both an illustrative example and a real-world example in Beijing. Experimental results show that our methodology produces a high-performance solution with only 7.3% of unserved passengers. Besides, compared to the two benchmark adjustment strategies, our service-based adjustment strategy reduces the average itinerary cost for all passengers by 27.0% and 43.3%, respectively.

A unified solution framework for flexible job shop scheduling problems with multiple resource constraints

This paper examines flexible job shop scheduling problems with multiple resource constraints. A unified solution framework is presented for modelling various types of non-renewable, renewable and cumulative resources, such as limited capacity machine buffers, tools, utilities and work in progress buffers. We propose a Constraint Programming (CP) model and a CP-based Adaptive Large Neighbourhood Search (ALNS-CP) algorithm. The ALNS-CP uses long-term memory structures to store information about the assignment to machines of both individual operations and pairs of operations, as encountered in high-quality and diverse solutions during the search process. This information is used to create additional constraints for the CP solver, which guide the search towards promising regions of the solution space. Numerous experiments are conducted on well-known benchmark sets to assess the performance of ALNS-CP against the current state-of-the-art. Additional experiments are conducted on new instances of various sizes to study the impact of different resource types on the makespan. The computational results show that the proposed solution framework is highly competitive, while it was able to produce 39 new best solutions on well-known problem instances of the literature.

Optical appearance of numerical black hole solutions in higher derivative gravity

The optical appearance of the numerically black hole solutions within the higher derivative gravity illuminated by an accretion disk context is discussed. We obtain solutions for non-Schwarzschild black holes with r0=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r_0=1$$\\end{document}, r0=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r_0=2$$\\end{document}, and r0=3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r_0=3$$\\end{document}. Further analysis of spacetime trajectories reveals properties similar to Schwarzschild black holes, while the r0=2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r_0=2$$\\end{document} black hole exhibits significant differences. The results reveal the presence of a repulsive potential barrier for the black hole, allowing only particles with energies exceeding a certain threshold to approach it, providing a unique gravitational scenario for non-Schwarzschild black holes. Additionally, the optical images are derived through numerical simulations by discussing the trajectories of photons in the black hole spacetime. The distribution of radiation flux and the effects of gravitational redshift and Doppler shift on the observed radiation flux are considered. Interestingly, previous analyses of the optical appearance of black holes were conducted within the framework of analytic solutions, whereas the analysis of numerical black hole solutions first appears in our analysis.

DEALING WITH LANGUAGE VARIATION IN A SOURCE TEXT: MALAY SKETCHES AND ITS MALAY TRANSLATION

Background and Purpose: Although translation generally involves the transfer of meaning from the source language (L1) to the target language (L2), it may also involve a third language or L3, that is, any other language(s) found in either the source or target text. Language variation in a source text presents a challenge to translators, as they need to deal with both L1 and L3. This paper aims to explore the issue of language variation in a source text to see how L3 is rendered in translation. More specifically, the aim is to explore how L3 segments in French, Latin and Italian in a text in English entitled Malay Sketches are dealt with when the text is translated into Malay, and to determine the effects resulting from the solutions taken by the translators. Methodology: L3 in Malay Sketches are first identified. These L3 segments are subsequently mapped to their counterparts in the Malay translation, Gelagat & Ragam Orang Melayu. This mapping results in source language-target language pairs, which are then analysed to determine the solutions taken by the translators. These solutions are categorised based on the framework of translation solutions in L3 translation by Corrius and Zabalbeascoa (2011). Findings: It was found that L3 translation generally involves substitution in which L3 is translated into Malay. Consequently, language variation, a defining feature of the source text, becomes invisible in the Malay translation. Contributions: This study hopes to contribute to and expand current discourse on L3 in translation, especially in view of the fact that scant attention had been paid to L3 translation in Malaysia. Keywords: Malay Sketches, language variation, multilingual text, third language, translation. Cite as: Haroon, H. (2024). Dealing with language variation in a source text: Malay Sketches and its Malay translation. Journal of Nusantara Studies, 9(2), 537-556. http://dx.doi.org/10.24200/jonus.vol9iss2pp537-556

Research on Dynamic Subsidy Based on Deep Reinforcement Learning for Non-Stationary Stochastic Demand in Ride-Hailing

The ride-hailing market often experiences significant fluctuations in traffic demand, resulting in supply-demand imbalances. In this regard, the dynamic subsidy strategy is frequently employed by ride-hailing platforms to incentivize drivers to relocate to zones with high demand. However, determining the appropriate amount of subsidy at the appropriate time remains challenging. First, traffic demand exhibits high non-stationarity, characterized by multi-context patterns with time-varying statistical features. Second, high-dimensional state/action spaces contain multiple spatiotemporal dimensions and context patterns. Third, decision-making should satisfy real-time requirements. To address the above challenges, we first construct a Non-Stationary Markov Decision Process (NSMDP) based on the assumption of ride-hailing service systems dynamics. Then, we develop a solution framework for the NSMDP. A change point detection method based on feature-enhanced LSTM within the framework can identify the changepoints and time-varying context patterns of stochastic demand. Moreover, the framework also includes a deterministic policy deep reinforcement learning algorithm to optimize. Finally, through simulated experiments with real-world historical data, we demonstrate the effectiveness of the proposed approach. It performs well in improving the platform’s profits and alleviating supply-demand imbalances under the dynamic subsidy strategy. The results also prove that a scientific dynamic subsidy strategy is particularly effective in the high-demand context pattern with more drastic fluctuations. Additionally, the profitability of dynamic subsidy strategy will increase with the increase of the non-stationary level.

Mapping sustainable options in the fashion industry: A systematic literature review and a future research agenda

AbstractThe fashion industry accounts for large impacts on the environment and social welfare, both on the consumers' and companies' sides. This study systematically reviews the literature on sustainable consumption in the fashion industry, clarifying sustainable fashion's meaning. Existing sustainable fashion solutions are investigated from consumers' behavior perspective and classified into three consumption phases, that is, (pre‐)purchase, use, and post‐use. 187 articles are included, and twenty‐six sustainable solutions are identified across the consumption phases. These include techniques, features, services, and behaviors able to increase garment sustainability by mitigating negative impacts and ensuring product circularity by recycling and/or reusing at the product's end of life, representing promising strategies for fostering a transition toward sustainable fashion consumption practices. An innovative and comprehensive framework of sustainable fashion solutions is developed. Future research agenda and a sustainable offering inventory for marketers are provided.