Model Constraints Research Articles

A speed optimization model for connected and autonomous vehicles at expressway tunnel entrance under mixed traffic environment.

Rear-end collisions frequently occurred in the entrance zone of expressway tunnel, necessitating enhanced traffic safety through speed guidance. However, existing speed optimization models mainly focus on urban signal-controlled intersections or expressway weaving zones, neglecting research on speed optimization in expressway tunnel entrances. This paper addresses this gap by proposing a framework for a speed guidance model in the entrance zone of expressway tunnels under a mixed traffic environment, comprising both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs). Firstly, a CAV speed optimization model is established based on a shooting heuristic algorithm. The model targets the minimization of the weighted sum of the speed difference between adjacent vehicles and the time taken to reach the tunnel entrance. The model's constraints incorporate safe following distances, speed, and acceleration limits. For HVs, speed trajectories are determined using the Intelligent Driver Model (IDM). The CAV speed optimization model, represented as a mixed-integer nonlinear optimization problem, is solved using A Mathematical Programming Language (AMPL) and the BONMIN solver. Safety performance is evaluated using Time-to-Collision (TTC) and speed standard deviation (SD) metrics. Case study results show a significant decrease in SD as the CAV penetration rate increases, with a 58.38% reduction from 0% to 100%. The impact on SD and mean TTC is most pronounced when the CAV penetration rate is between 0% and 40%, compared to rates above 40%. The minimum TTC values at different CAV penetration rates consistently exceed the safety threshold TTC*, confirming the effectiveness of the proposed control method in enhanced safety. Sensitivity analysis further supports these findings.

Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands

The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniques were unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP).

Improving Performance of Uncombined PPP-AR Model with Ambiguity Constraints

With the advancement of multi-frequency and multi-constellation GNSS signals and the introduction of observable-specific bias (OSB) products, the uncombined precise point positioning (PPP) model has grown more prevalent. However, this model faces challenges due to the large number of estimated parameters, resulting in strong correlations between state parameters, such as clock errors, ionospheric delays, and hardware biases. This can slow down the convergence time and impede ambiguity resolution. We propose two methods to improve the triple-frequency uncombined PPP-AR model by integrating ambiguity constraints. The first approach makes use of the resolved ambiguities from dual-frequency ionosphere-free combined PPP-AR processing and incorporates them as constraints into triple-frequency uncombined PPP-AR processing. While this approach requires the implementation of two filters, increasing computational demands and thereby limiting its feasibility for real-time applications, it effectively reduces parameter correlations and facilitates ambiguity resolution in post-processing. The second approach incorporates fixed extra-wide-lane (EWL) and wide-lane (WL) ambiguities directly, allowing for rapid convergence, and is well suited for real-time processing. Results show that, compared to the uncombined PPP-AR model, integrating N1 and N2 constraints reduces averaged convergence time from 8.2 to 6.4 min horizontally and 13.9 to 10.7 min vertically in the float solution. On the other hand, integrating EWL and WL ambiguity constraints reduces the horizontal convergence to 5.9 min in the float solution and to 4.6 min for horizontal and 9.7 min for vertical convergence in the fixed solution. Both methods significantly enhance the ambiguity resolution in the uncombined triple-frequency PPP model, increasing the validated fixing rate from approximately 80% to 89%.

Out of Practice: Justice as Telos of Politics

Abstract: Those advocating practice-dependent approaches to political philosophy have tended to conceptualize justice as a constraint on politics—a position the author calls the "constraint model." This article instead argues that justice is a distinctive purpose or telos of politics—the "internalist model." If the internalist model is sound, justice is no more a constraint on politics than beauty is a constraint on art. The author argues that the constraint model cannot meet two challenges: It cannot distinguish the principles of justice from the requirements of political legitimacy, and it cannot explain how we can gain critical purchase on our practices without collapsing into practice-independent theory. By contrast, the internalist model is able to meet these challenges.

The Star-forming Main Sequence in JADES and CEERS at z > 1.4: Investigating the Burstiness of Star Formation

We have used public JWST/NIRSpec and JWST/NIRCam observations from the CEERS and JADES surveys in order to analyze the star-forming main sequence (SFMS) over the redshift range 1.4 ≤ z < 7. We calculate the star formation rates (SFRs) of the galaxy sample using three approaches: Balmer line luminosity, spectral energy distribution (SED) fitting, and UV luminosity. We find a larger degree of scatter about the SFMS using the Balmer-based SFRs compared to the UV-based SFRs. Because these SFR indicators are sensitive to star formation on different timescales, the difference in scatter may be evidence of bursty star formation histories in the early Universe. We additionally compare the Hα-to-UV luminosity ratio (L(Hα)/ν L ν,1600) for individual galaxies in the sample and find that 29%–52% of the ratios across the sample are poorly described by predictions from a smooth star formation history. Measuring the burstiness of star formation in the early Universe has multiple significant implications, such as deriving accurate physical parameters from SED fitting, explaining the evolution of the UV luminosity function, and providing constraints for subgrid models of feedback in simulations of galaxy formation and evolution.

An IGDT-WDRCC based optimal bidding strategy of VPP aggregators in new energy market considering multiple uncertainties

This study addresses the volatility and uncertainty challenges in managing renewable energy within electricity markets, particularly focusing on the role of Virtual Power Plant (VPP) aggregators. Recognizing the risks these uncertainties pose to the revenue and stability of power systems, the paper presents a novel information gap decision theory (IGDT)-Wasserstein metric based distributionally robust chance constraint (WDRCC) approach to devise an optimal bidding strategy for VPP operators. It involves a data-driven distributionally robust optimization framework, leveraging the worst-case scenario from the distributed resource uncertainties, guided by an ambiguity set rooted in the Wasserstein metric. Furthermore, the distributionally robust chance constraint modeling is introduced ensuring that uncertainty constraints of distributed resources meet a predefined risk level. Although this method shows promising out-of-sample performance, it relies on forecasted energy prices, a notable limitation given the price volatility and information inadequacy in the newly-opened market. To address this, the risk-averse bidding strategy, grounded in IGDT, is proposed simulataneously to safeguard the operator’s expected returns against price uncertainties, implementing an advanced piecewise linear approximation technique, ”nf4l,” for linearizing the bi-linear term from IGDT. The effectiveness of this approach is empirically validated through a comprehensive case study and sensitivity analysis.

Application of hybrid strategies of complex network attack and defense games

Critical infrastructure networks serve as the backbone of modern society’s operations, and the application of game theory to safeguard these networks against deliberate attacks under resource constraints is of paramount importance. Regrettably, the existing body of research on hybrid attack and defense strategies for nodes and edges is notably scarce, despite the ubiquity of such strategies in practical contexts. Current understanding regarding the establishment of a cost constraint model within the framework of hybrid attack and defense strategies, the strategic inclinations of both adversarial and defensive parties under varying cost constraint coefficients, and the influence of resource allocation ratios on equilibrium outcomes remains limited. Consequently, this study constructs a game-theoretic model for the engagement of critical infrastructure networks under non-uniform cost constraints, incorporating hybrid attack and defense strategies for nodes and edges, and conducts an equilibrium analysis across a range of cost constraint coefficients and resource allocation ratios. The findings reveal that when resource availability is limited, both attackers and defenders are inclined towards a strategy of concentrated resource allocation; in contrast, under most circumstances, defenders exhibit a preference for an equitable distribution of resources.

Fantasy Sports Participation in Young Adults

This study aimed to understand comprehensively the motivations and constraints underlying fantasy sports participation among young adults (18-25) in India. The study applied a motivation and constraint model in the Indian landscape, wherein youth groups who played and did not play fantasy sports were surveyed. Given the issue of the legality of fantasy games in India and its linkage with gambling, two additional factors of addiction and morality were added. The findings of the multiple regression show that participation in fantasy sports among the youth is influenced by intrinsic motivation instead of monetary benefits, pride, or social interaction. Constraints affecting the potential participation of non-players include addiction, consuming valuable time, causing loss of money, and perceived capitalistic exploitation for stakeholders' profit. Producers of fantasy games need to enhance engagement levels to foster intrinsic motivation instead of external among the youth and safeguard them from addiction and gambling.

Blockchain-backed resilient strategies in a stochastic supply chain sourcing and distribution environment under disruption: implications for B2B sector

Purpose With the changing landscape of the globalised business world, business-to-business supply chains face a turbulent ocean of disruptions. Such is the effect that supply chains are disrupted to the point of failure, supply is halted and its adverse effect is seen on the consumer. While previous literature has extensively studied risk and resilience through mathematical modelling, this study aims to envision a novel supply chain model that integrates blockchain to support visibility and recovery resilience strategies. Design/methodology/approach The stochastic bi-objective (cost and shortage utility) optimisation-based mixed-integer linear programming model integrates blockchain through a binary variable, which activates at a particular threshold risk-averse level of the decision-maker. Findings Firstly, visibility is improved, as identified by the average reduction of penalties by 36% over the different scenarios. Secondly, the average sum of shortages over different scenarios is consequently reduced by 36% as the recovery of primary suppliers improves. Thirdly, the feeling of shortage unfairness between distributors is significantly reduced by applying blockchain. Fourthly, unreliable direct suppliers resume their supply due to the availability of timely information through blockchain. Lastly, reliance on backup suppliers is reduced as direct suppliers recover conveniently. Research limitations/implications The findings indicate that blockchain can enhance visibility and recovery even under high-impact disruption conditions. Furthermore, the study introduces a unique metric for measuring visibility, i.e. penalty costs (lower penalty costs indicate higher visibility and vice versa). The study also improves upon shortages and recoveries reported in prior literature by 6%. Finally, blockchain application caters to the literature on shortage unfairness by significantly reducing the feeling of shortage unfairness among distributors. Practical implications This study establishes blockchain as a pro-resilience technology. It advocates that organisations focus on investing in blockchain to enhance their visibility and recovery, as it effectively reduces absolute shortages and feelings of shortage unfairness while improving recovery and visibility. Originality/value To the best of the authors’ knowledge, this is a unique supply chain model study that integrates a technology such as blockchain directly as a binary variable in the model constraint equations while also focusing on resilience strategies, costs, risk aversion and shortage unfairness.

Model Selection for Ordinary Differential Equations: A Statistical Testing Approach.

Ordinary differential equations(ODEs) are foundational tools in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nuances in internal mechanisms or abstraction levels, presents a model selection challenge. This study introduces a testing-based approach for ODE model selection amidst statistical noise. Rooted in the model misspecification framework, we adapt classical statistical paradigms (Vuong and Hotelling) to the ODE context, allowing for the comparison and ranking of diverse causal explanations without the constraints of nested models. Our simulation studies numerically investigate the statistical properties of the test, demonstrating its attainment of the nominal size and power across various settings. Real-world data examples further underscore the algorithm's applicability in practice. To foster accessibility and encourage real-world applications, we provide a user-friendly Python implementation of our model selection algorithm, bridging theoretical advancements with hands-on tools for the scientificcommunity.

Simulation and evaluation of lateral/directional dynamics in an aircraft autopilot control system

The objective of the research was to design and simulate the lateral/directional dynamics control of an aircraft’s autopilot system to automate the landing approach execution, complying with the requirements of the Instrument Landing System (CAT III C). The design methodology involved integrating a Linear Quadratic Regulator (LQR) with Affine Parameterization techniques to create a robust control system. The prototype was developed using Matlab and simulated in Simulink. Through various simulations, adjustments were made to the Q and R matrices of the LQR controller based on Bryson’s rule, allowing the system to adapt to the nonlinearities and dynamic constraints of the aircraft model. These adjustments included modifying the lateral attitude control parameters to achieve the desired damping factors and time constants, ensuring flight quality standards according to MIL-8785C. Validation under real conditions through a flight simulator confirmed the control system’s effectiveness under various operational conditions. The controllers are able to maintain the aircraft’s alignment with the runway centerline, even in the presence of external disturbances, thus demonstrating the system’s robustness and reliability. The methodologies and results provide a solid foundation for future improvements and comparative analyses of autopilot systems within CAT III C requirements.

Explicating the Role of Agricultural Socialized Services on Chemical Fertilizer Use Reduction: Evidence from China Using a Double Machine Learning Model

Reducing chemical usage, particularly chemical fertilizers, is a crucial measure for advancing sustainable agricultural development. This study utilized field survey data from 894 maize farmers across three northeastern provinces of China. A double machine learning modeling framework was established to empirically examine the impact and mechanism of agricultural socialized services on chemical fertilizer use of farm households. The model addresses numerous stringent constraints of conventional causal inference models and effectively mitigates the “curse of dimensionality” issue. Current research indicates that agricultural socialized services can substantially decrease chemical fertilizer use among farmers. Further investigation reveals that these services facilitate this reduction by enhancing the mechanization level, promoting the use of organic fertilizers, and providing a labor substitution effect. The region heterogeneity test indicates that the impact of agricultural socialized services is more pronounced in Liaoning and Heilongjiang provinces geographically. Regarding the heterogeneity analysis of food crop income levels, agricultural socialized services can decrease chemical fertilizer use among farmers more effectively with elevated food crop income levels. Consequently, the findings imply that the socialization of agricultural services has substantial potential to be an effective chemical fertilizer reduction strategy to support the agricultural green transition, which can be enhanced through promoting the degree of mechanization, organic fertilizer application, and labor division and specialization.

Resting metabolic rate and energy efficiency in response to an intensive 84-day combat-swimmer training in the German Armed Forces

Abstract Purpose According to the ‘constrained model’, there are compensations in resting metabolic rate (RMR) at high levels of physical activity (PA). Here, we have used a standardized combat-swimmer training protocol (CST) to investigate whether changes in RMR (i) confirm the ‘constraint model’, and (ii) differ between successful participants and dropouts. Methods Controlled 84d CST in 44 male soldiers with 13 finally successful. Fat mass (FM) and fat-free mass (FFM) were measured using Quantitative Magnetic Resonance. RMR was assessed by indirect calorimetry, VO2max, and work efficiency by treadmill spiroergometry. Plasma levels of thyroid hormones, testosterone, and cortisol were analysed by standard laboratory methods. Results CST increased VO2max (+ 6.9%) and exercise efficiency at low workloads of 10 and 12 km/h (+ 8.7 and + 6.5%; both p &lt; 0.05). As energy balance was moderately negative (−356 ± 383 kcal/d), FFM and FM decreased (−2 and −16%; both p &lt; 0.05). There was a considerable inter-individual variance but no change in in the mean values of RMR and RMRadjFFM. RMRadjFFM before CST had a negative association with its decrease with CST (p &lt; 0.005). Concomitantly, plasma hormone levels were unchanged. When compared with dropouts, successful participants had a higher VO2max at baseline (5.2 ± 0.6 vs. 4.9 ± 04 l/min; p &lt; 0.05) that increased with CST (+ 4.4 vs. −0.4%; p &lt; 0.05) at similar changes in body composition and energy balance. Conclusion While CST increased VO2max and exercise efficiency as a compensation, there was an inter-individual variance in exercise-related compensation of RMR with no differences between ‘completers’ and ‘non-completers’. Trial registration DRKS00018850, November 27, 2019.

Mechanism and Control Strategies for Current Sharing in Multi-Chip Parallel Automotive Power Modules

Multi-chip parallel power modules are highly favored in applications requiring high capacity and high switching frequency. However, the dynamic current imbalance between parallel chips caused by asymmetric layouts limits the available capacity. This paper presents a method to optimize dynamic current distribution by adjusting the lengths and connection points of bond wires. For the first time, a response surface model and nonlinear constraint optimization algorithm are introduced, along with parameter analysis based on finite element methods, to establish the response surface models for the parasitic inductance of bond wires and DBC (direct bonded copper). By leveraging the optimization goals for parasitic inductance and the analytical expressions of all response surfaces, the dynamic current sharing issue was transformed into a nonlinear constrained optimization problem. The solution to this optimization problem identified the optimal connection points for the bond wires, enhancing dynamic current sharing performance. Simulations and experiments were conducted, revealing that the optimized automotive-grade module exhibited a significant reduction in current differences between parallel branches, from 41.7% to 5.03% compared with the original design. This indicated that the proposed optimization scheme for adjusting bond wire connection points could significantly mitigate current disparities, thereby markedly improving current distribution uniformity.

Auxiliary relaxation method to derive thermodynamically consistent phase field models with constraints and structure preserving numerical approximations

In this paper, we introduce a novel approach for formulating phase field models with constraints. The main idea is to introduce auxiliary variables that regularize and gradually dissipate constraint deviations of the phase variables, which we name the auxiliary relaxation method. It integrates seamlessly with the energy variational framework to ensure thermodynamic consistency in the resulting phase field models. Unlike traditional penalty methods, which introduce high stiffness due to large penalty parameters to enforce constraints in phase field models, our approach reduces system stiffness, allowing larger time step sizes when solving phase field models with constraints numerically, thus improving numerical accuracy and efficiency. We demonstrate the effectiveness and robustness of the proposed auxiliary relaxation method by applying it across several scenarios to derive thermodynamically consistent phase field models with constraints. Furthermore, we introduce a general second-order implicit-explicit Crank-Nicolson scheme, combining the relaxed scalar auxiliary variable method with a stabilization technique to solve these models. Through extensive numerical tests, we validate the capability of our modeling and numerical framework to reliably simulate complex dynamics governed by phase field equations with constraints.

Predicting Monthly Runoff: A BO-CNN-LSTM Approach for Enhanced Water Resource Management

Monthly runoff prediction is critical for sustainable water resource management, flood control, and drought preparedness. Accurate forecasts assist decision-making and management of water resources, which are critical for human and environmental demands. Previous prediction algorithms frequently lack accuracy due to human-defined hyperparameters. Autonomous hyperparameter tuning is required for improved predictions. Objectives: To improve the accuracy of monthly runoff predictions, this research proposes a BO-CNN-LSTM model that integrates Bayesian optimization (BO) with a convolutional neural network-long short-term memory neural network (CNN-LSTM). Methods/Analysis: The BO algorithm is utilized to enhance the CNN-LSTM model's hyperparameters, which helps to overcome the limits of human-set parameters. Model training and validation were carried out using monthly runoff data from 1956 to 2019 from Lanxi Station in the Hulan River Basin. The model's prediction performance was assessed using root mean square error (RMSE), mean absolute error (MAE), fitting coefficient (R²), and mean relative error (MAPE), and compared with other previous models. Findings: The results show that the BO-CNN-LSTM model outperforms conventional models in prediction accuracy and reduction of errors. The BO method efficiently enhances hyperparameters, resulting in higher performance metrics. This model gives more precise and dependable monthly runoff forecasts, which are critical for handling the Hulan River Basin's water resources. Novelty/Improvement: By combining BO and CNN-LSTM, the proposed model solves the constraints of previous models, improving prediction accuracy and reducing errors. This innovative technique presents a promising novel approach for monthly runoff prediction, with possible applications in larger hydrological prediction.

Quantum-inspired gravitational search algorithm-based low-price binary task offloading for multi-users in unmanned aerial vehicle-assisted edge computing systems

Unmanned aerial vehicles (UAVs)-assisted edge computing (EC) brings computing resources closer to smart mobile devices (SMDs). Users of SMDs with limited resources can experience the flavour of computation and data-intensive applications. Users conserve energy by offloading resource-intensive tasks to edge servers (ESs) or UAVs. While offloading may introduce communication delays, leveraging ample wireless bandwidth can mitigate this issue by facilitating faster data transmission rates. However, mobile service providers (MSPs), facilitating offloading infrastructure, impose charges on users for bandwidth usage. Efficient utilization of limited computation units (CUs) is crucial. This paper introduces a binary task offloading (BTO) model for multi-user in multi-UAV-assisted EC systems using a quantum-inspired gravitational search algorithm (QGSA). Quantum encoding and decoding of the agents are provided. The decoding phase uses a novel hashing. The fitness function considers energy, delay, price, and resource utilization. Two penalties are incorporated with the fitness to discard the decoded agent that violates the constraints of the BTO model. The proposed QGSA ensures polynomial time execution. The proposed work is extensively simulated in multiple scenarios and compared with existing approaches. It is observed that the QGSA outperformed other algorithms in terms of delay, energy, resource utilization, and price. Analyses of convergence and statistics are performed.

Imaginary latent variables: Empirical testing for detecting deficiency in reflective measures

Imaginary latent variables are variables with negative variances and have been used to implement constraints in measurement models. This article aimed to advance this practice and rationalize the imaginary latent variables as a method to detect possible latent deficiencies in measurement models. This rationale is based on the theory of complex numbers used in the measurement process of common factor model–based structural equation modeling. Modeling an imaginary latent variable produces a potential deficiency within its relative reflective measures through a considerable reduction in common variance indicating the most affected indicator(s).

Interferometric phase reconstruction with temporal decorrelation model constraints for time-series InSAR surface deformation monitoring

ABSTRACT The time series interferometric synthetic aperture radar (InSAR) (TSInSAR) is an effective technique for monitoring surface deformation with wide coverage and millimeter accuracy. There are two types of scatterers in the SAR field of view, including the permanent scatterer (PS) and the distributed scatterer (DS). Compared to PS with temporarily stable scattering behavior, the filtered interferometric phases are not consistent for DS susceptible to the decorrelation effect. To overcome the phase inconsistency degradation problem of the decorrelated DSs, the phase linking (PL) theory is introduced to recover the equivalent single-reference (ESR) phases from all interferometric combinations. However, the optimization performance of the PL theory is affected by the uncontrollable estimation error of coherence magnitude. Here, we present a novel enhanced PL (EPL) by incorporating with a priori temporal decorrelation model (TDM) to enhance the weight of the short-term interferograms and suppress the abrupt coherence jump with the long temporal baseline. Real experiment selects the Daguangbao landslide triggered by the ‘5.12’ Wenchuan Mw 7.9 earthquake as the study area to verify the algorithm’s effectiveness. Compared to traditional PL methods, the new EPL method can recover the interferometric phase fringe more clearly and obtain a smoother deformation velocity spatially with higher estimation accuracy.

Continuous sign language recognition algorithm based on object detection and variable-length coding sequence

Currently, continuous sign language recognition faces challenges such as difficulty in acquiring skeletal data, long training time for Three-Dimensional convolutional neural networks, and easy occlusion and blurring of hands. To address these problems, this paper proposes a continuous sign language recognition method based on target detection and coding sequence. The algorithm uses Dual-branch Shuffle Attention Mechanis-You Only Look Once version X (DSA-YOLOX) detection network to detect the head and hands, and encodes the sign language video according to the partition to achieve the transformation from Three-Dimensional to One-Dimensional; and then uses the proposed Bi-directional Long Short-Term Memory (BiLSTM) hand coding sequence classification model with jointly weighted Fast Dynamic Time Warping (FastDTW) to extract hand coding similarity and features while reducing the number of parameters to achieve the classification and recognition of unequal-length hand coding sequences. From the results of ablation experiments and comparison experiments, all parts of the improvement perform well. The word error rate (WER) of this paper’s method is reduced by 21.26% compared to Dynamic Time Warping-Hidden Markov Model (DTW-HMM) and 11.53% compared to Long Short-Term Memory-A(LSTM-A); the Giga Floating-point Operations Per Second(GFLOPs) of the algorithm are reduced dramatically, which is about 1/13 of the Visual Alignment Constraint(VAC) model and 1/57 of the Spatial-Temporal Multi-Cue(STMC) model; and the algorithm takes better account of the speed and accuracy of sign language recognition.