Search Results (19)

The aim of this article is to study duality results for nonsmooth mathematical programs with equilibrium constraints in terms of tangential subdifferentials. We study the Wolfe-type dual problem under the convexity assumptions and a Mond–Weir-type dual problem is also formulated under convexity and generalized convexity assumptions for MPEC by using tangential subdifferentials. We establish weak duality and the two dual programs by assuming tangentially convex functions and also obtain strong duality theorems by assuming generalized standard Abadie constraint qualification. Full article

Background/Objectives: Ring chromosomes (RCs) can be rare or common depending on the chromosome involved. With interest in the historical RCs identified by our laboratory, we curated instances to provide further information to this research field. Methodology: We carried out a retrospective, single-center study of constitutional RCs identified starting in the late 1980s. Details for 40 RCs with a modal number of 46 chromosomes are featured here. Results: Mosaic and non-mosaic RCs are identified, with a preponderance of pediatric-aged females at first ascertainment. We corroborated an enrichment for acrocentric and X chromosome RCs. Six were ascertained perinatally, with peripheral blood being the most commonly studied postnatal specimen type. Notable RCs included a female fetus with an increased risk for monosomy X, whose mosaic RCY arose secondary to a translocation between the sex chromosomes. In another, a series of complex events formed three structurally aberrant chromosomes, including an RC4 with loss of 4p16.3, corresponding with the observed phenotypic expression of Wolf–Hirschhorn syndrome. In another, a mosaic RCX was co-identified with an isochromosome 21q, resulting in a dual diagnosis of trisomy 21 and Turner syndrome. In another, the atypical RC21 structure raises the possibility of a complex rearrangement. Chromosomal microarray data clarified breakpoints and gene dosage imbalances in fifteen, while low-level mosaicism for the RC escaped detection by array in another. Eight RCs were de novo, and parental exclusion was documented for six. Conclusions: This study illustrates the need for cytogenomic follow-up to improve the literature for patients with RCs. Full article

The precise assessment of image quality that matches human perception is still a major challenge in the field of digital imaging. Digital images play a crucial role in many technological and media applications. The existing deep convolutional neural network (CNN)-based image quality assessment (IQA) methods have advanced considerably, but there remains a critical need to improve the performance of existing methods while maintaining explicit tolerance to visual texture resampling and texture similarity. This paper introduces DualNetIQ, a novel full-reference IQA method that leverages the strengths of deep learning architectures to exhibit robustness against resampling effects on visual textures. DualNetIQ includes two main stages: feature extraction from the reference and distorted images, and similarity measurement based on combining global texture and structure similarity metrics. In particular, DualNetIQ takes features from input images using a group of hybrid pre-trained multi-scale feature maps carefully chosen from VGG19 and SqueezeNet pre-trained CNN models to find differences in texture and structure between the reference image and the distorted image. The Grey Wolf Optimizer (GWO) calculates the weighted combination of global texture and structure similarity metrics to assess the similarity between reference and distorted images. The unique advantage of the proposed method is that it does not require training or fine-tuning the CNN deep learning model. Comprehensive experiments and comparisons on five databases, including various distortion types, demonstrate the superiority of the proposed method over state-of-the-art models, particularly in image quality prediction and texture similarity tasks. Full article

With the advancement of dual-carbon goals and the construction of new types of power systems, the proportion of electric vehicle charging stations (EVCSs) in the coupling system of power distribution and transportation networks is gradually increasing. However, the surge in charging demand not only causes voltage fluctuations and a decline in power quality but also leads to tension in the power grid load in some areas. The complexity of urban road networks further increases the challenge of charging station planning. Although laying out charging stations in areas with high traffic flow can better meet traffic demands, it may also damage power quality due to excessive grid load. In response to this problem, this paper proposes an optimized layout plan for electric vehicle charging stations considering the coupling effects of roads and electricity. By using section power flow to extract dynamic data from the power distribution network and comparing the original daily load curves of the power grid and electric vehicles, this paper plans reasonable capacity and charging/discharging schemes for EVCSs. It considers the impact of the charging and discharging characteristics of EVCSs on the power grid while satisfying the peak-shaving and valley-filling regulation benefits. Combined with the traffic road network, the optimization objectives include optimizing the voltage deviation, transmission line margin, network loss, traffic flow, and service range of charging stations. The Gray Wolf Optimizer (GWO) algorithm is used for solving, and the optimal layout plan for electric vehicle charging stations is obtained. Finally, through road–electricity coupling network simulation verification, the proposed optimal planning scheme effectively expands the charging service range of electric vehicles, with a coverage rate of 83.33%, alleviating users’ charging anxiety and minimizing the impact on the power grid, verifying the effectiveness and feasibility of the proposed scheme. Full article

This article investigates robust optimality conditions and duality results for a class of nonsmooth multiobjective programming problems with vanishing constraints under data uncertainty (UNMPVC). Mathematical programming problems with vanishing constraints constitute a distinctive class of constrained optimization problems because of the presence of complementarity constraints. Moreover, uncertainties are inherent in various real-life problems. The aim of this article is to identify an optimal solution to an uncertain optimization problem with vanishing constraints that remains feasible in every possible future scenario. Stationary conditions are necessary conditions for optimality in mathematical programming problems with vanishing constraints. These conditions can be derived under various constraint qualifications. Employing the properties of convexificators, we introduce generalized standard Abadie constraint qualification (GS-ACQ) for the considered problem, UNMPVC. We introduce a generalized robust version of nonsmooth stationary conditions, namely a weakly stationary point, a Mordukhovich stationary point, and a strong stationary point (RS-stationary) for UNMPVC. By employing GS-ACQ, we establish the necessary conditions for a local weak Pareto solution of UNMPVC. Moreover, under generalized convexity assumptions, we derive sufficient optimality criteria for UNMPVC. Furthermore, we formulate the Wolfe-type and Mond–Weir-type robust dual models corresponding to the primal problem, UNMPVC. Full article

This article deals with mathematical programs with vanishing constraints (MPVCs) involving lower semi-continuous functions. We introduce generalized Abadie constraint qualification (ACQ) and MPVC-ACQ in terms of directional convexificators and derive necessary KKT-type optimality conditions. We also derive sufficient conditions for global optimality for the MPVC under convexity utilizing directional convexificators. Further, we introduce a Wolfe-type dual model in terms of directional convexificators and derive duality results. The results are well illustrated by examples. Full article

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. Full article

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup’s recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results. Full article

PDC (polycrystalline diamond compact) drill bits are widely employed for rock-breaking in many industries like underground engineering and building constructions. The cutter layout would directly affect the overall performance of the drill bits. Field applications show that the staggered cutter layout strategy of dual-cutter can increase the drilling efficiency of the PDC bit. In order to explore the rock breaking mechanism of this type of drill bit, a numerical model of a dual-cutter and rock breaking with damage evolution based on a hybrid finite and cohesive element method (FCEM) has been established in this work. The model is verified through Brazilian disk tests. The rock breaking processes of this type of bit have been analyzed, including crack initiation, propagation, and the formation of rock debris. Moreover, the effects of horizontal and vertical offset of the back cutter on the MSE (mechanical special energy) have been investigated. Results demonstrate that the dual-cutter can prominently reduce the MSE compared to a single-cutter. The vertical offset of the back cutter has a minor effect on the MSE, while the horizontal offset is of great significance on the MSE. On this basis, the relationships between the MSE and both the vertical and horizontal offset coefficients have been built based on the response surface methodology (RSM). Finally, an optimized layout solution, with optimal vertical and horizontal offset coefficients of 0.641 and 0.497, is determined via the Gray Wolf algorithm. Full article

This article deals with multiobjective fractional programming problems with equilibrium constraints in the setting of Hadamard manifolds (abbreviated as MFPPEC). The generalized Guignard constraint qualification (abbreviated as GGCQ) for MFPPEC is presented. Furthermore, the Karush–Kuhn–Tucker (abbreviated as KKT) type necessary criteria of Pareto efficiency for MFPPEC are derived using GGCQ. Sufficient criteria of Pareto efficiency for MFPPEC are deduced under some geodesic convexity hypotheses. Subsequently, Mond–Weir and Wolfe type dual models related to MFPPEC are formulated. The weak, strong, and strict converse duality results are derived relating MFPPEC and the respective dual models. Suitable nontrivial examples have been furnished to demonstrate the significance of the results established in this article. The results derived in the article extend and generalize several notable results previously existing in the literature. To the best of our knowledge, optimality conditions and duality for MFPPEC have not yet been studied in the framework of manifolds. Full article

This paper investigates some duality results of a mixed type for a class of multiple objective fractional optimal control problems. More precisely, by considering the Wolfe- and Mond–Weir-type dualities, we formulate a robust mixed-type dual problem and, under suitable convexity assumptions of the involved functionals, we establish some equivalence results between the solution sets of the considered models. Essentially, we investigate robust weak, robust strong, and robust strict converse-type duality results. To the best of the authors’ knowledge, robust duality results for such problems are new in the specialized literature. Full article

In this paper, we establish various results of duality for a new class of constrained robust nonlinear optimization problems. For this new class of problems, involving functionals of (path-independent) curvilinear integral type and mixed constraints governed by partial derivatives of second order and uncertain data, we formulate and study Wolfe, Mond-Weir and mixed type robust dual optimization problems. In this regard, by considering the concept of convex curvilinear integral vector functional, determined by controlled second-order Lagrangians including uncertain data, and the notion of robust weak efficient solution associated with the considered problem, we create a new mathematical context to state and prove the duality theorems. Furthermore, an illustrative application is presented. Full article

In this paper, we introduce a second-order strong subdifferential of set-valued maps, and discuss some properties, such as convexity, sum rule and so on. By the new subdifferential and its properties, we establish a necessary and sufficient optimality condition of set-based robust efficient solutions for the uncertain set-valued optimization problem. We also introduce a Wolfe type dual problem of the uncertain set-valued optimization problem. Finally, we establish the robust weak duality theorem and the robust strong duality theorem between the uncertain set-valued optimization problem and its robust dual problem. Several main results extend to the corresponding ones in the literature. Full article

This work addresses the robust counterpart of the shortest path problem (RSPP) with a correlated uncertainty set. Because this problem is difficult, a heuristic approach, based on Frank–Wolfe’s algorithm named discrete Frank–Wolfe (DFW), has recently been proposed. The aim of this paper is to propose a semi-definite programming relaxation for the RSPP that provides a lower bound to validate approaches such as the DFW algorithm. The relaxed problem is a semi-definite programming (SDP) problem that results from a bidualization that is done through a reformulation of the RSPP into a quadratic problem. Then, the relaxed problem is solved by using a sparse version of Pierra’s decomposition in a product space method. This validation method is suitable for large-size problems. The numerical experiments show that the gap between the solutions obtained with the relaxed and the heuristic approaches is relatively small. Full article

In this paper, Karush-Kuhn-Tucker type robust necessary optimality conditions for a robust nonsmooth interval-valued optimization problem (UCIVOP) are formulated using the concept of LU-optimal solution and the generalized robust Slater constraint qualification (GRSCQ). These Karush-Kuhn-Tucker type robust necessary conditions are shown to be sufficient optimality conditions under generalized convexity. The Wolfe and Mond-Weir type robust dual problems are formulated over cones using generalized convexity assumptions, and usual duality results are established. The presented results are illustrated by non-trivial examples. Full article