Set Of Constraints Research Articles

Sun-as-a-star Observations of Obscuration Dimmings Caused by Filament Eruptions

Filament eruptions often lead to coronal mass ejections (CMEs) on the Sun and are one of the most energetic eruptive phenomena in the atmospheres of other late-type stars. However, the detection of filament eruptions and CMEs on stars beyond the solar system is challenging. Here, we present six filament eruption cases on the Sun and show that filament material obscuring part of the solar disk can cause detectable dimming signatures in Sun-as-a-star flux curves of He ii 304 Å. Those filament eruptions have similar morphological features, originating from small filaments inside active regions and subsequently strongly expanding to obscure large areas of the solar disk or the bright flare regions. We have tracked the detailed evolution of six obscuration dimmings and estimated the dimming properties, such as dimming depths, dimming areas, and duration. The largest dimming depth among the six events under study is 6.2% accompanied by the largest dimming area of 5.6% of the solar disk area. Other events have maximum dimming depths in a range of around 1%–3%, with maximum areas varying between about 3%–4% of the solar disk area. The duration of the dimming spans from around 0.4–7.0 hr for the six events under study. A positive correlation was found between the dimming depth and area, which may help to set constraints on the filament sizes in stellar observations.

A Recurrent Neural Network Approach for Constrained Distributed Fuzzy Convex Optimization.

This article investigates a class of constrained distributed fuzzy convex optimization problems, where the objective function is the sum of a set of local fuzzy convex objective functions, and the constraints include partial order relation and closed convex set constraints. In undirected connected node communication network, each node only knows its own objective function and constraints, and the local objective function and partial order relation functions may be nonsmooth. To solve this problem, a recurrent neural network approach based on differential inclusion framework is proposed. The network model is constructed with the help of the idea of penalty function, and the estimation of penalty parameters in advance is eliminated. Through theoretical analysis, it is proven that the state solution of the network enters the feasible region in finite time and does not escape again, and finally reaches consensus at an optimal solution of the distributed fuzzy optimization problem. Furthermore, the stability and global convergence of the network do not depend on the selection of the initial state. A numerical example and an intelligent ship output power optimization problem are given to illustrate the feasibility and effectiveness of the proposed approach.

Exponential stabilization of linear systems with feedback optimization and its application to microgrids control

This paper proposes a feedback-optimization-based control method for linear time-invariant systems, which is aimed to exponentially stabilize the system and, meanwhile, drive the system output to an approximate solution of an optimization problem with convex set constraints and affine inequality constraints. To ensure the exponential stability of the closed-loop system, the original optimization problem is first reformulated into a counterpart that has only convex set constraints. It is shown that the optimal solution of the new optimization problem is an approximate optimal solution of the original problem. Then, based on this new optimization problem, the projected primal–dual gradient dynamics algorithm is used to design the controller. By using the singular perturbation method, sufficient conditions are provided to ensure the exponential stability of the closed-loop system. The proposed method is also applied to microgrid control.

Scheduling of Automated Wet-Etch Stations with One Robot in Semiconductor Manufacturing via Constraint Answer Set Programming

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constraint solvers is comparatively better than a decade ago, and some complex combinatorial problems can be better solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics.

IMMU-13. TUMOR INFLAMMATION-ASSOCIATED NEUROTOXICITY (TIAN): A TOXICITY SYNDROME IN PATIENTS TREATED WITH IMMUNOTHERAPY FOR CENTRAL NERVOUS SYSTEM TUMORS

Abstract BACKGROUND Immunotherapies are increasingly being explored as potential therapeutic modalities for the treatment of central nervous system (CNS) tumors and have unique toxicity profiles. Toxicity syndromes such as cytokine release syndrome (CRS) and immune effector-cell associated neurotoxicity (ICANS) were identified through experiences with chimeric antigen receptor (CAR) T-cell therapies for B cell malignancies; the creation of grading scales for these toxicities standardized the reporting and enabled management. METHODS From our collective experiences in treating patients with immunotherapies for CNS tumors, we have identified a localized neurotoxicity syndrome, distinct from the systemic toxicities of CRS and ICANS, termed tumor inflammation-associated neurotoxicity (TIAN). RESULTS TIAN develops secondary to localized inflammation at the tumor site. From a mechanistic perspective, we identified two types of TIAN: 1) type 1 TIAN arises in the setting of mechanical space constraints when peritumoral edema results in tissue shifts and increased intracranial pressure, which if untreated, can lead to a life-threatening herniation syndrome and 2) type 2 TIAN occurs when local neuronal dysfunction causes transient worsening/new neurological deficits. Whereas type 1 TIAN encompasses the concept of “pseudoprogression,” type 2 TIAN can occur in the absence of edema when neural-immune interactions disrupt local neural function. Patients can have both type 1 and type 2 TIAN simultaneously; understanding the type of TIAN can inform management. To facilitate the uniform reporting and management of TIAN, we created a TIAN grading scale that applies to both types of TIAN. Although, type 1 TIAN is generally associated with higher-grade toxicity, high-grade type 2 TIAN can occur when local inflammation compromises respiratory or autonomic functions. CONCLUSIONS Recognizing TIAN as a distinct, local neurotoxicity syndrome is critical to guiding clinical management and prognostication when treating patients with CNS tumors with immunotherapies.

Sequential optimality conditions for optimization problems with additional abstract set constraints

Sequential optimality conditions for optimization problems with additional abstract set constraints

An appraisal of the LQI as an approach to setting target reliabilities in ISO 2394:2015

The idea of coupling economics and safety into one optimization exercise, and hence deriving target reliabilities explicitly from socio-economic consequences of limit state violation, appeared in the JCSS Probabilistic Model Code around the turn of the century; the effort culminated in the form of a Life Quality Index (LQI)-based marginal lifesaving costs (MLSC) methodology in ISO 2394:2015 for determining maximum acceptable failure probabilities, pT, in life safety limit states for civil engineering structures across all nations. Unfortunately, while the methodology does yield adequate levels of safety when applied to structures in the developed countries, the recommended values of pT turn out to be one to two orders of magnitude higher, and unacceptable to every known standard in the world including its own earlier edition (ISO 2394:1998) when applied to structures in the developing world (exemplified by India) with identical functions and expected fatalities. This arises from two shortcomings: (1) an MLSC approach is generally unable to provide an independent constraint on monetary optimization, and (2) the LQI-based measure of MLSC constraint is strongly dependent on a country’s per capita GDP (g) and the resultant pT is effectively governed by the reciprocal of g. This paper recommends continuing with the absolute − not marginal − consequences of violating life safety limit states, and setting constraints on monetary optimization that are consistent with fatality risks from engineering activities more universally accepted to be safe.

Adaptive penalty-based neurodynamic approach for nonsmooth interval-valued optimization problem

The complex and diverse practical background drives this paper to explore a new neurodynamic approach (NA) to solve nonsmooth interval-valued optimization problems (IVOPs) constrained by interval partial order and more general sets. On the one hand, to deal with the uncertainty of interval-valued information, the LU-optimality condition of IVOPs is established through a deterministic form. On the other hand, according to the penalty method and adaptive controller, the interval partial order constraint and set constraint are punished by one adaptive parameter, which is a key enabler for the feasibility of states while having a lower solution space dimension and avoiding estimating exact penalty parameters. Through nonsmooth analysis and Lyapunov theory, the proposed adaptive penalty-based neurodynamic approach (APNA) is proven to converge to an LU-solution of the considered IVOPs. Finally, the feasibility of the proposed APNA is illustrated by numerical simulations and an investment decision-making problem.

Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling

Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, and optimal operations. This article focuses on developing a strategy for scheduling EV charging in a specific region, addressing waiting time, charging time, and uneven scheduling due to unevenly distributed charging stations (CS). The proposed approach optimizes CS using separate queues for different levels, reducing waiting time and costs during peak hours. Which considers trade-offs between time-aware fairness and overall waiting time, and factors like reachability, battery state of charge, depth of discharge limits, and charging rate constraints. A bi-objective formulation and online scheduling algorithm based on dynamic schedulable time, energy demand fluctuation and user’s prioritization are proposed. The aim is to allocate a charging station to each EV by considering travel needs and battery specifics, with the objective of minimizing travel time, queue time, recharging time, and energy costs. To achieve this, the scheduling system utilizes the Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration of the previously discussed metaheuristic, the Harris Hawk Optimization. Validation of the system is conducted through Vehicular Ad-hoc Network (VANET) simulation and comparison with alternative algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer and Random allocation. The outcomes demonstrate noteworthy decreases in travel time, queue time, recharging time, and energy costs, all while adhering to set constraints.

Higher-order KKT optimality conditions through contingent derivatives for constrained nonsmooth vector equilibrium problems

In this paper, we deal with some higher-order optimality conditions for local strict efficient solutions to a nonsmooth vector equilibrium problem with set, cone and equality constraints. For this aim, the concept of m−stable and m−steady functions (m≥2 and integer) for single-valued functions and some constraint qualifications of higher order in terms of contingent derivatives are proposed accordingly. We analyze the sum calculus rule of mth-order adjacent set, mth-order interior set, asymptotic mth-order tangent cone and asymptotic mth-order adjacent cone. Subsequently, we employ the obtained calculus rules to treat KKT necessary and sufficient optimality conditions of higher order in terms of contingent derivatives for the mth-order (local) strict efficient solutions to such problem. Simultaneously, we employ these rules to study KKT higher-order optimality conditions for such efficient solutions for a nonsmooth vector optimization problem with constraints. Some illustrative examples are provided to demonstrate the main results of the literature.

Distributed Discrete-Time Convex Optimization With Closed Convex Set Constraints: Linearly Convergent Algorithm Design.

The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this article, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking framework, two distributed algorithms are, respectively, designed over balanced and unbalanced graphs, where momentum terms and two time-scales are involved. Furthermore, it is demonstrated that the designed distributed algorithms attain linear speedup convergence rates provided that the momentum coefficients and the step size are appropriately selected. Finally, numerical simulations verify the effectiveness and the global accelerated effect of the designed algorithms.

Identification of Real and Complex Solution Varieties and Their Singularities Defined by Loop Constraints of Linkages Using the Kinematic Tangent Cone

Abstract The configuration space (c-space) of a mechanism is the real-solution variety of a set of loop closure constraints, which is therefore the chief object in kinematic analysis. Singularities of this variety (referred to as c-space singularities) are singular configurations of the mechanism. In addition, a mechanism may exhibit other kinematic singularities that are not visible from the differential geometry of the c-space (referred to as hidden singularities). Such situations were analyzed by investigating the local geometry of the c-space and its corank stratification. It has been shown recently that hidden singularities and shakiness can be attributed to the fact that complex solution branches intersect with the c-space, i.e., with real-solution branches. This paper employs the kinematic tangent cone to identify local solution branches. While the kinematic tangent cone is an established generally applicable concept, which gives rise to a computational (numeric and symbolic) algorithm, it has yet only been applied to analyzing the real-solution set. Application of the method is shown for several examples. Further, the algebraic aspects are briefly elaborated.

A model-based approach for self-adaptive security in CPS: Application to smart grids

Security risk assessment is an important challenge in the design of Cyber Physical Systems (CPS). Even more importantly, the intrinsically dynamical nature of these systems, due to changes in their environment, as well as evolutions in their infrastructures, makes them self-adaptive systems, where security aspects have to be considered in terms of management of detections and reactions for self-protection. In this work, we propose an approach to autonomously mitigate the threats in each reconfiguration at application or infrastructure levels of CPS. We propose and implement a framework for self-adaptive security: software architecture, design method, and integration with model-based decision. We use Attack-Defense Trees for modeling threats, and our approach involves security risk assessment, taking into account its balancing and coordination with quality-of-service aspects. We formulate and formalize the on-line decision problem to be solved at each cycle of the self-adaptation control loop in terms of Constraint Programming (CP) modeling and resolution. The CP model implements a set of constraints that allow to specify secure configurations, evaluated regarding their impact on system performance to pinpoint the most relevant one portraying a good balance between the security and quality of service. We perform validation of our approach with its application to Smart Grids, more particularly to an industrial case study from RTE (the French Energy Transmission company).

Constraints on axion-like particles from the observation of Galactic sources by the LHAASO* *Xiao-Jun Bi is supported by the National Natural Science Foundation of China (12175248). Xiaoyuan Huang is supported by the National Key Research and Development Program of China (2022YFF0503304), the National Natural Science Foundation of China (12322302), the Project for Young Scientists in Basic Research of Chinese Academy

High-energy photons may oscillate with axion-like particles (ALPs) when they propagate through the Milky Way's magnetic field, resulting in an alteration in the observed photon energy spectrum. Ultra-high energy gamma-ray spectra, measured by the Large High Altitude Air Shower Observatory (LHAASO) up to , provide a promising opportunity to investigate the ALP-photon oscillation effect. In this study, we utilize the gamma-ray spectra of four Galactic sources measured by the LHAASO, that is, the Crab Nebula, LHAASO J2226+6057, LHAASO J1908+0621, and LHAASO J1825-1326, to explore this effect. We employ the CLs method to set constraints on the ALP parameters. Our analysis of the observations of the four sources reveals that the ALP-photon coupling is constrained to be smaller than for an ALP mass of at 95% C.L. Combining the observations of the Crab Nebula from the LHAASO and other experiments, we find that the ALP-photon coupling may be set to approximately for an ALP mass of , which is similar to the CAST constraint.

Predictive fault-tolerant control for nonlinear batch processes with small time delays via iterative learning control: A Lyapunov–Razumikhin approach

For batch processes with small time delays and actuator partial fault, the existing methods based on iterative learning control still have some limitations, including the conservative and computationally burdensome of stability conditions and the limited fault-tolerant control capabilities. For this background, an iterative learning robust predictive fault-tolerant control method is developed, which integrates the Lyapunov–Razumikhin function method and derives stability conditions based on robust positive definite invariant set and terminal constraint set. With small time delays, the stability conditions of the system deduced using the Lyapunov–Razumikhin function are solved at a lower computational cost, which is due to the fact that the dimensionality of the stabilization condition is directly related to the size of the time delay, and thus the small time delay implies a lower dimensionality. Especially, the computational effort for solving the stability conditions online is reduced, allowing real-time control law gains to be obtained and combined with historical batches of control inputs, reducing the learning cycles of the system, and realizing stable tracking of the setpoints within shorter operating batches. Moreover, the robust positive invariant set and the set of terminal constraints are able to constrain the state of the system within a safe range for all possible uncertainties, bounded disturbances, and faults. This makes the proposed methods based on them more robust and fault tolerant. Finally, a nonlinear batch reactor is used as an example to demonstrate the effectiveness and feasibility of the developed method.

Optimal Consolidation of Polling Locations

Problem definition: Many logistical and financial challenges of facilitating an election lead election officials to consolidate polling locations. However, determining when it is appropriate to consolidate polling locations and how to consolidate polling locations, if necessary, is a difficult and high-stakes decision that influences voter participation. Methodology/results: We formalize the set of constraints and criteria that election officials should follow as the polling location consolidation problem (PLCP), which is formulated as an integer programming model. The PLCP simultaneously selects which polling locations will be used in the upcoming election, reassigns voter precincts to polling locations, and allocates critical resources to the selected polling locations. The PLCP minimizes the increased distance that voters must travel to their updated polling location. Because empirical research also demonstrates the importance of reducing the voters’ wait times, we require that most voters do not wait longer than a prespecified limit, such as 30 minutes, using a chance constraint. We prove that identifying a feasible solution to PLCP is NP-complete, which demonstrates the difficulty in making consolidation decisions, as well as the importance of optimization for this problem. Managerial implications: This paper introduces a structured and transparent approach to support election officials in making informed, data-driven decisions regarding how and when to consolidate polling locations that minimally impact voters and encourage voter participation. The approach could be used to develop preapproved contingency plans that could be employed if there are major disruptions to an election. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2022.0497 .

Co-optimization of power and water distribution systems for simultaneous hydropower generation and water pressure regulation

Water Distribution Systems (WDSs) are gravitational systems supplying water, mainly. Little research has been conducted on harnessing the great amount of energy that is currently dissipated during water pressure regulation, usually performed with “passive” devices like Pressure Reduction Valves (PRVs). This paper develops a co-optimization framework between Water and Power Distribution Systems (PDS) and introduces the concept of Integrated Power-Water Distribution System (IPWDS). Under this framework, PRVs are replaced by hydro-turbines, which form the interconnecting links between the two systems. They regulate pressure at the WDS while injecting the power resulting from their action to PDS. The complete WDS and PDS mathematical models are adopted and the detailed turbine design and performance model that accounts for turbines feasible operational range is considered. The suggested method optimizes the power generation of the hydro-turbines subject to the hydraulic restrictions and power flow constraints imposed by each network. Optimal Power Flow (OPF) has been extended in order to accommodate turbine’s model and constraints imposed by the varying daily flows of the WDS and PDS. Results taken under the suggested framework from a typical WDS, demonstrated that more than 393 MWh (approximately $40 K) of clean power could be produced per year. However, the violation of the unified set of constraints from the IPWDS may reduce the amount of renewable power generation by a significant 7%. This paper focuses mainly on the technical aspect of such an approach. Future work will focus on the economic assessment and viability of such joint projects, resulting from co-optimization and on social-technical aspects that aim on maximizing the penetration of turbines within existing PDS.

Dual‐mode resilient model predictive control for cyber‐physical systems against DoS attacks

AbstractIn this article, a resilient model predictive control algorithm incorporating a dual‐mode control strategy is proposed for a class of cyber‐physical systems subject to state and input constraints, additive disturbances and denial‐of‐service (DoS) attacks. An adversary aims to disrupt the controller‐to‐actuator channel by deploying adversarial jamming signals. By exploring the prediction uncertainty between a nominal model and an actual model and utilizing a constraint tightening technique, a novel set of constraints is developed, which guarantees robust constraint satisfaction in open‐loop mode caused by DoS attacks. A robust positive invariant set is designed, which allows the control mode to switch from rolling optimization to a state‐feedback control law when the state enters its interior, thus saving computational resources. We explicitly determine a critical duration for DoS attacks and, through theoretical analysis, establish that when the duration of DoS attacks falls below this critical value, the proposed algorithm ensures guaranteed recursive feasibility and robust asymptotic stability. Finally, some comparisons are provided to illustrate the effectiveness of the proposed approach.

Review on structural optimization techniques for additively manufactured implantable medical devices

Recent developments in additive manufacturing (AM) have led to significant opportunities in the design and fabrication of implantable medical devices due to the advantages that AM offers compared to conventional manufacturing, such as high customizability, the ability to fabricate highly complex shapes, good dimensional accuracy, a clean build environment, and reduced material usage. The study of structural design optimization (SDO) involves techniques such as Topology Optimization (TO), Shape Optimization (SHO), and Size Optimization (SO) that determine specific parameters to achieve the best measurable performance in a defined design space under a given set of loads and constraints. Integration of SDO techniques with AM leads to utmost benefits in designing and fabricating optimized implantable medical devices with enhanced functional performance. Research and development of various lattice structures represents a powerful method for unleashing the full potential of additive manufacturing (AM) technologies in creating medical implants with improved surface roughness, biocompatibility, and mechanical properties. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) in structural optimization has expanded opportunities to improve device performance, adaptability, and durability. The review is meticulously divided into two main sections, reflecting the predictability of the implant’s internal structure: (a) unpredictable interior topology, which explores topology-based optimization techniques, and (b) predictable inner topology, concentrating on lattice structures. The analysis of the reviewed literature highlights a common focus on addressing issues such as stress shielding, osseointegration enhancement, customization to individual needs, programmable functionalities, and weight reduction in implant designs. It emphasizes significant advances in reducing stress shielding effects, promoting osseointegration, and facilitating personalized implant creation. The review provides a detailed classification of optimization methods, with each approach scrutinized for its unique contribution to overcoming specific challenges in medical implant design, thus leading to more advanced, effective, and patient-oriented implantable devices.

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.