Model Constraints Research Articles

Facility location decisions for drone delivery with riding: A literature review

This study presents a comprehensive literature survey on facility location problems for drone (uncrewed vehicle) delivery in situations where drones can ride in or on other vehicles. This includes facilities visited by only one type of vehicle, as well as facilities visited by both drones and other vehicles. Unlike traditional facility location problems for delivery systems with one vehicle type, hybrid vehicle-drone delivery systems usually require determining locations where the two vehicle types meet and separate. The main goals of this paper are to review the large volume of drone delivery literature with riding from a facility location perspective to provide a connection between the studies from different research areas that cover similar problems, and to highlight future research directions in this area. We first review the functions of drones, including aerial and ground drones, and the different types of facilities used for hybrid vehicle-drone delivery systems. The literature is categorized based on the presence of resupply operations, the locations of drone launch and retrieval points, the types of drones (aerial or ground) and the location space (discrete or continuous). Each category is analyzed in terms of the modeling approach, decision(s), objective function(s), constraints and additional features. The paper concludes with promising future research directions.

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

To address the problem of low accuracy and efficiency in trajectory planning algorithms for interceptors facing multiple constraints during the midcourse guidance phase, an improved trajectory convex programming method based on the lateral distance domain is proposed. This algorithm can achieve fast trajectory planning, reduce the approximation error of the planned trajectory, and improve the accuracy of trajectory guidance. First, the concept of lateral distance domain is proposed, and the motion model of the midcourse guidance segment in the interceptor is converted from the time domain to the lateral distance domain. Second, the motion model and multiple constraints are convexly and discretely transformed, and the discrete trajectory convex model is established in the lateral distance domain. Third, the deep reinforcement learning algorithm is used to learn and train the initial solution of trajectory convex programming, and a high-quality initial solution trajectory is obtained. Finally, a dynamic adjustment method based on the distribution of approximate solution errors is designed to achieve efficient dynamic adjustment of grid points in iterative solving. The simulation experiments show that the improved trajectory convex programming algorithm proposed in this paper not only improves the accuracy and efficiency of the algorithm but also has good optimization performance.

An intelligent body frame structure modeling and optimization system based on conceptual design

The conceptual design stage is an indispensable and important stage in the development of the modern body. However, due to the lack of sufficient body structure parameter support, the design defects generated at this time are difficult to make up for in the subsequent design stage. Therefore, it is of great significance to develop a conceptual design software for body structure that integrates design, analysis, and optimization. This paper proposes an intelligent body frame structure modeling and optimization system based on conceptual design, S-iVCD (Intelligent System for Conceptual Design of Vehicle Body Structure). Based on deep learning methods and body design sketches, a conceptual model of body structure is quickly established. Based on the data parameters stored in the Excel table, the system is seamlessly connected to the domestic independent finite element software SIPESC to calculate the simulated working conditions. The body structure optimization module is driven by the MMA (method of moving asymptotes), and takes the body structure performance and total mass as the optimization goals or constraints of the mathematical model to achieve body structure performance improvement and lightweight design.

Strong outflows and inefficient star formation in the reionization-era ultrafaint dwarf galaxy Eridanus ii

ABSTRACT We present novel constraints on the underlying galaxy formation physics (e.g. mass-loading factor, star formation history, and metal retention) at z ≳ 7 for the low-mass (M* ∼ 105 M⊙) Local Group ultrafaint dwarf galaxy (UFD) Eridanus ii (Eri ii). Using a hierarchical Bayesian framework, we apply a one-zone chemical evolution model to Eri ii’s CaHK-based photometric metallicity distribution function (MDF; [Fe/H]) and find that the evolution of Eri ii is well characterized by a short, exponentially declining star formation history ($\tau _\text{SFH}=0.39\pm _{0.13}^{0.18}$ Gyr), a low star formation efficiency ($\tau _\text{SFE}=27.56\pm _{12.92}^{25.14}$ Gyr), and a large mass-loading factor ($\eta =194.53\pm _{42.67}^{33.37}$). Our results are consistent with Eri ii forming the majority of its stars before the end of reionization. The large mass-loading factor implies strong outflows in the early history of Eri ii and is in good agreement with theoretical predictions for the mass scaling of galactic winds. It also results in the ejection of >90 per cent of the metals produced in Eri ii. We make predictions for the distribution of [Mg/Fe]–[Fe/H] in Eri ii as well as the prevalence of ultra metal-poor stars, both of which can be tested by future chemical abundance measurements. Spectroscopic follow-up of the highest metallicity stars in Eri ii ([Fe/H] > −2) will greatly improve model constraints. Our new framework can readily be applied to all UFDs throughout the Local Group, providing new insights into the underlying physics governing the evolution of the faintest galaxies in the reionization era.

Adaptive robust control of unmanned tracked vehicles for trajectory tracking based on constraint modeling and analysis

Adaptive robust control of unmanned tracked vehicles for trajectory tracking based on constraint modeling and analysis

Research on two stage obstacle-avoidance trajectory planning and trajectory tracking control in curves

The existing obstacle-avoidance trajectory planning and trajectory tracking control algorithms have limitations such as long-time consumption, high failure rate in dynamic traffic environments, and insufficient trajectory tracking accuracy in curved roads. Based on the above problems, this paper designs a two stage obstacle-avoidance trajectory planner based on nonlinear optimization theory. In first stage Part-NLP, only considering the safety obstacle avoidance, a point mass model and linearization constraints are established to quickly solve the initial trajectory. In the second stage Full-NLP, considering smooth soft constraints comprehensively, the initial trajectory is optimized by establishing driving corridors and a lightweight iterative framework. In control module, this paper selects a linear quadratic form lateral trajectory tracking controller, and the parameters were optimized through the carnivorous plant algorithm. The joint simulation results show that in dynamic traffic environment of curved roads, the two stage planner proposed can accurately plan safe and smooth obstacle avoidance trajectories, and there is a significant reduction in time consumption compared to traditional NLP algorithms. The control strategy can accurately track the planned trajectories, with lateral error controlled within plus or minus 0.1 m, heading error controlled within plus or minus 0.15 rad, speed tracking error controlled within plus or minus 0.15 m/s, and vehicle yaw angle error controlled within plus or minus 0.04 rad; the hardware-in-loop test results indicate that the controller can achieve real-time and accurate trajectory tracking.

Impact of astrophysical effects on the dark matter mass constraint with 21 cm intensity mapping

ABSTRACT We present an innovative approach to constraining the non-cold dark matter model using a convolutional neural network (CNN). We perform a suite of hydrodynamic simulations with varying dark matter particle masses and generate mock 21 cm radio intensity maps to trace the dark matter distribution at z = 3 in the postreionization epoch. Our proposed method complements the traditional power-spectrum analysis. We compare the results of the CNN classification between the mock maps with different dark matter masses with those from the two-dimensional power spectrum of the differential brightness temperature map of 21 cm radiation. We find that the CNN outperforms the power spectrum. Moreover, we investigate the impact of baryonic physics on the dark matter model constraint, including star formation, self-shielding of H i gas, and ultraviolet background model. We find that these effects may introduce some contamination in the dark matter constraint, but they are insignificant compared to the system noise of the SKA instruments.

Topological constraint model of modified telluro‐vanadate glasses

AbstractTopological constraint theory has emerged as a rapid, predictive method to quantify the relationship between structural rigidity and glass properties. Understanding the structure of telluro‐vanadate (TeO2–V2O5) glasses has remained difficult owing to their complex mixture of structural units. Here, we propose a topological model that accurately captures the glass structure and can be used to predict properties such as glass transition temperature and hardness. The experimentally obtained properties are in agreement with predicted properties by the model suggesting that this simplified structural model accurately describes the V2O5–TeO2 structure.

Plato’s Shadows in the Digital Cave: Controlling Cultural Bias in Generative AI

Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato’s Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies.

Optimizing variable selection and neighbourhood size in the K-nearest neighbour algorithm

The K-nearest neighbour (KNN) algorithm is one of the well-known classifiers applied in various research areas. The input requirement includes a set of variables, the choice of the neighbourhood size (K) and the distance metric which are typically selected on experimenting with the data. The first two are usually decided sequentially in previous studies. This paper proposes a mixed integer linear program for simultaneous variable selection and determination of neighbourhood size which has not been expored in past research. When ties occur in the variable selection and/or the choice of K, an optimization approach helps search for the best combination of decisions for a given objective. Two distance metrics, Euclidean and Hassanat, are incorporated into the model for comparison. The proposed model adopts accuracy and recall as separate objective functions to determine the best combination of the two decisions in binary classification problems. Computational experiments are designed with ten publicly available datasets. Results from the proposed models are compared with the ensemble approach KNN using Hassanat distance. Three larger problems are also tested with the optimal K generated by the model. An effective set of variables and neighbourhood size decided by optimization can facilitate solving classification problems. Model limitations are also discussed.

CTGGAN: Controllable Text Generation with Generative Adversarial Network

Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s dissatisfaction or complaints. This imposes significant demands on controlling language model output. However, demerits exist among traditional methods. Promoting and fine-tuning language models exhibit the “hallucination” phenomenon and cannot guarantee complete adherence to constraints. Conditional language models (CLM), which map control codes into LM representations or latent space, require training the modified language models from scratch and a high amount of customized dataset is demanded. Decoding-time methods employ Bayesian Rules to modify the output of the LM or model constraints as a combination of energy functions and update the output along the low-energy direction. Both methods are confronted with the efficiency sampling problem. Moreover, there are no methods that consider the relation between constraints weights and the contexts, as is essential in actual applications such as customer service scenarios. To alleviate the problems mentioned above, we propose Controllable Text Generation with Generative Adversarial Networks (CTGGAN), which utilizes a language model with logits bias as the Generator to produce constrained text and employs the Discriminator with learnable constraint weight combinations to score and update the generation. We evaluate the method in the text completion task and Chinese customer service dialogues scenario, and our method shows superior performance in metrics such as PPL and Dist-3. In addition, CTGGAN also exhibits efficient decoding compared to other methods.

Forward and inversion approach for direct current resistivity based on an unstructured mesh and its application to tunnel engineering

AbstractThe accurate identification of water‐bearing structures is urgently required for the safe construction of tunnel engineering. Currently, the direct current resistivity method is an effective method for detecting water‐bearing structures in tunnels. In the advanced detection of the direct current resistivity based on the finite element method, the traditional hexahedron mesh performs poorly for the discretization of models of complex tunnel structure sections such as horseshoe‐shaped and round sections. Therefore, this study adopts unstructured grid generation technology combining tetrahedra and hexahedra to achieve more accurate modelling of complex structures, such as round and horseshoe‐shaped sections, and establishes a forward modelling method of the direct current resistivity in tunnels based on an unstructured mesh. The maximum error between the numerical simulation and theoretical results for an infinite tabular body in full space is less than 0.8%. It is more complicated to calculate the sensitivity matrix and model constraint term for the inversion region containing two types of grid than for one. For this purpose, the sensitivity matrix of different types of grid areas is calculated, a model constraint term based on the dual constraints of volume and distance is constructed, and finally, a partitioned domain‐weighted least‐squares inversion method based on an unstructured mesh is proposed. Synthetic examples of typical water‐bearing structures are analysed, and the results show that the proposed forward and inverse methods of the direct current resistivity in tunnels based on an unstructured mesh can effectively capture the position and morphology of the water‐bearing structure. Finally, an on‐site application was conducted in the Yellow River Diversion Project in central Shanxi. The proposed method could effectively identify the water body in front of the tunnel face and guide the on‐site construction of the project. These results can improve the interpretation of the direct current resistivity data in tunnels and play a positive role in promoting the use of the direct current resistivity method to prevent and control water‐inrush disasters in tunnels with complex structures.

Dynamic event-triggered state estimation for discrete-time delayed switched neural networks with constrained bit rate

In this paper, a class of discrete-time delayed switched neural networks with dynamic event-triggered mechanism (DETM) and constrained bit rate is considered. In order to reduce the transmission frequency and alleviate the unnecessary resource loss between sensor and estimator, a DETM is proposed. The data transmission from sensor to estimator is realized through constrained bit rate channel. Therefore, in order to reflect the bandwidth allocation rules of accessible neurone nodes, a bit rate constraint model is introduced and an encoding-decoding mechanism is developed. This paper is concerned with the strategy of average dwell time (ADT) and linear matrix inequality, then sufficient conditions for the exponential ultimate boundedness of switched neural networks with DETM and constrained bit rate are proposed. Finally, an example is given to prove the effectiveness of the results.

Arab teacher educators and the moral work of teaching in Israel

ABSTRACT This qualitative study explores how 14 Arab teacher educators perceive the moral preparation of teachers for Arab schools in Israel. The first theme highlights the contingent (institutional) factors that influence and may restrain the teacher educators’ mission of moral preparation. It highlights the importance of an organisational culture that supports student teachers’ learning to provide moral education, of empowering and unsilencing of student teachers, and of promoting morally oriented reflective practice in students’ clinical experiences. The second theme illustrates the teacher educators’ psychological assumptions about morality as represented by the possibilities and constraints of role modelling in preparing moral teachers. The third theme addresses the teacher educators’ socio-educational (universal, counter-hegemonic, and liberal) assumptions about the moral purposes of schooling. These assumptions are influenced by the asymmetrical structure of Arab-Jewish power relationships, as well as by the transitional status of Arab society in Israel.

A large role of missing volatile organic compound reactivity from anthropogenic emissions in ozone pollution regulation

Abstract. There are thousands of volatile organic compound (VOC) species in ambient air, while existing techniques can only detect a small part of them (approximately several hundred). The large number of unmeasured VOCs prevents us from understanding the photochemistry of ozone and aerosols in the atmosphere. The major sources and photochemical effects of these unmeasured VOCs in urban areas remain unclear. The missing VOC reactivity, which is defined as the total OH reactivity of the unmeasured VOCs, is a good indicator for constraining the photochemical effect of unmeasured VOCs. Here, we identified the dominant role of anthropogenic emission sources in the missing VOC reactivity (accounting for up to 70 %) by measuring missing VOC reactivity and tracer-based source analysis in a typical megacity in China. Omitting the missing VOC reactivity from anthropogenic emissions in model simulations will remarkably affect the diagnosis of sensitivity regimes for ozone formation, overestimating the degree of VOC-limited regimes by up to 46 %. Therefore, a thorough quantification of missing VOC reactivity from various anthropogenic emission sources is urgently needed for constraints of air quality models and the development of effective ozone control strategies.

A multi-objective spatio-temporal pricing method for fast-charging stations oriented to transformer load balancing

To address the challenges posed by the fast-charging demand of electric vehicles, causing feeder load and voltage imbalances during operation, this paper introduces a spatio-temporal pricing strategy tailored to enhance feeder operation equilibrium. This approach facilitates the spatio-temporal guidance of fast-charging loads for electric vehicles in operation. This paper begins by formulating a spatio-temporal distribution model for electric vehicle fast-charging loads, considering owners’ preferences. It further develops a behavioral model for the travel choices of electric vehicles, illustrating the impact of spatio-temporal electricity pricing at fast-charging stations on load distribution. Next, it proposes a multi-objective spatio-temporal pricing model and its solution method specifically designed for feeder-balance-oriented fast-charging stations. This model targets the minimization of the spatio-temporal imbalance in feeder voltage and load. It takes a comprehensive approach, considering the constraints of the spatio-temporal load distribution model and optimal power flow model. The resulting spatio-temporal pricing model for fast-charging stations is effectively solved using the extended Pareto evolutionary algorithm. To validate the effectiveness of the proposed method in achieving feeder balancing, this paper analyzes two examples: a self-built 29-node road network and a 9-node distribution network, as well as a 66-node road network and a 33-node distribution network in the Xinjiang region. The results show that the proposed method can effectively guide the charging of electric vehicles and make the load distribution more balanced.

Fuel-optimal satellite formation reconfiguration method with best passive safety parameters

This paper investigates the problem of satellite formation reconfiguration from the best passive safety parameters while ensuring the minimum fuel cost and desired formation configuration. Based on the relative eccentricity and inclination vectors, a hybrid method is proposed that combines the analytical solution and the genetic algorithm optimization method. It transforms the fuel-optimal conditions into the constraints of optimization model, which theoretically ensures the optimal fuel cost. The proposed hybrid approach is applicable to satellite formation reconfiguration problems with variable relative eccentricity and inclination vectors and passive safety concerns. Compared with the optimal three-impulse strategy and the analytical method in two different reconfiguration scenarios, the proposed method is proven to be more general and has advantages in passive safety performance.

Geochronology of the Sanbagawa Belt: Younger and Faster than Before

Recent advances in geochronological studies have helped establish the Sanbagawa belt as an important site for studying metamorphism related to subduction. Application of several dating methods yield the following important results. 1) Metamorphism and deformation related to subduction started ~120 Ma and were complete by ~50 Ma. 2) Subduction to eclogite facies, followed by return to the surface, was rapid and occurred within a few million years or less (at ~89 Ma), indicating exhumation rates of at least 1–2 cm·y−1. 3) The age of the slab during the peak eclogite facies metamorphism was ~60 My. These results help redefine the geological history of SW Japan and provide important constraints for mechanical and thermal models of subduction zones in general.

Constraining dark energy cosmologies with spatial curvature using Supernovae JWST forecasting

Recent cosmological tensions, in particular, to infer the local value of the Hubble constant H 0, have developed new independent techniques to constrain cosmological parameters in several cosmologies. Moreover, even when the concordance Cosmological Constant Cold Dark Matter (ΛCDM) model has been well constrained with local observables, its physics has shown deviations from a flat background. Therefore, to explore a possible deviation from a flat ΛCDM model that could explain the H 0 value in tension with other techniques, in this paper we study new cosmological constraints in spatial curvature dark energy models. Additionally, to standard current Supernovae Type Ia (SNIa) catalogs, we extend the empirical distance ladder method through an SNIa sample using the capabilities of the James Webb Space Telescope (JWST) to forecast SNIa up to z ∼ 6, with information on the star formation rates at high redshift. Furthermore, we found that our constraints provide an improvement in the statistics associated with Ω m when combining SNIa Pantheon and SNIa Pantheon+ catalogs with JW forecasting data.

Method and Validation of Real‐Time Global Ionosphere Modeling Constraint by Multi‐Source GNSS/LEO Data

AbstractThis study applies the zero‐differenced integer ambiguity method, named PPP‐Fixed, to extract real‐time ionospheric data and eliminate the latencies of rapid/final Global Ionosphere Maps (GIMs). The PPP‐Fixed method is also used to derive ionospheric data for post‐processed GIM generation, named SGG Post‐GIM, combined with low earth orbit satellite data. The obtained hardware delays are applied to revise real‐time ionospheric data. Meanwhile, the estimated multi‐source ionospheric model is regarded as historical data to estimate an ionospheric prediction model for constraint using the semi‐parameter model. Then, the Kalman filter is employed to estimate the parameters to generate real‐time GIM. Finally, the accuracy of estimated real‐time GIM, named SGG RT‐GIM, and SGG Post‐GIM is assessed. During the experimental period, the mean differences of SGG Post‐GIM and SGG RT‐GIM relative to GIMs provided by the international Global Navigation Satellite System service, named IGSG, are −0.46 and −0.57 Total Electron Content Unit (TECU), respectively. The corresponding Root Mean Square (RMS) values are 1.64 and 3.08 TECU. Over the test period, the mean positioning errors of the single‐frequency precise point positioning corrected by IGSG, SGG Post‐GIM, SGG RT‐GIM, and Klobuchar model are 0.14, 0.19, 0.21, and 0.25 m in the horizontal direction, respectively, while the corresponding errors are 0.36, 0.33, 0.38, and 0.64 m in the up direction. Further, the mean biases of experimental days for the self‐consistency assessment are 0.06, −0.01, and −0.07 TECU for IGSG, SGG Post‐GIM, and SGG RT‐GIM, respectively. The corresponding RMS values are 1.19, 1.15, and 1.57 TECU.