Dual Optimization Research Articles

Dual-frame optimization for informationally complete quantum measurements

Randomized measurement protocols such as classical shadows represent powerful resources for quantum technologies, with applications ranging from quantum state characterization and process tomography to machine learning and error mitigation. Recently, the notion of measurement dual frames, in which classical shadows are generalized to dual operators of positive operator-valued measure (POVM) effects, resurfaced in the literature. This brought attention to additional degrees of freedom in the postprocessing stage of randomized measurements that are often neglected by established techniques. In this work, we leverage dual frames to construct improved observable estimators from informationally complete measurement samples. We introduce novel classes of parametrized frame superoperators and optimization-free dual frames based on empirical frequencies, which offer advantages over their canonical counterparts while retaining computational efficiency. Remarkably, this comes at almost no quantum or classical cost, thus rendering dual frame optimization a valuable addition to the randomized measurement toolbox. Published by the American Physical Society 2024

Schottky Junction Enhanced Photosynthesis of Hydrogen Peroxide by Ultrathin Porous Carbon Nitride Supported Ni Nanoparticles.

Artificial photosynthesis for high-value hydrogen peroxide (H2O2) through a two-electron reduction reaction is a green and sustainable strategy. However, the development of highly active H2O2 photocatalysts is impeded by severe carrier recombination, ineffective active sites, and low surface reaction efficiency. We developed a dual optimization strategy to load dense Ni nanoparticles onto ultrathin porous graphitic carbon nitride (Ni-UPGCN). In the absence and presence of sacrificial agents, Ni-UPGCN achieved H2O2 production rates of 169 and 4116 μmol g-1 h-1 with AQY (apparent quantum efficiency) at 420 nm of 3.14% and 17.71%. Forming a Schottky junction, the surface-modified Ni nanoparticles broaden the light absorption boundary and facilitate charge separation, which act as active sites, promoting O2 adsorption and reducing the formation energy of *OOH (reaction intermediate). This results in a substantial improvement in both H2O2 generation activity and selectivity. The Schottky junction of dual modulation strategy provides novel insights into the advancement of highly effective photocatalytic agents for the photosynthesis of H2O2.

ARIMA-Driven Vegetable Pricing and Restocking Strategy for Dual Optimization of Freshness and Profitability in Supermarket Perishables

In the evolving landscape of perishable goods management, where the balance between minimizing waste and maximizing profitability is paramount, this work introduces an innovative approach to pricing and inventory decisions for products with limited shelf lives, focusing on vegetables in supermarkets. The contribution lies in its integration of an automated pricing and restocking decision model that leverages autoregressive integrated moving average (ARIMA) forecasting techniques alongside dynamic pricing strategies tailored to the goods’ freshness and remaining shelf life. The study uses a comprehensive sales, spoilage rates, and customer demand dataset to apply ARIMA forecasting for optimal restocking and adjusts prices dynamically based on product freshness, promoting competitive pricing and waste reduction. The results demonstrate the model’s effectiveness, reducing spoilage rates by up to 30% and increasing profitability margins by about 15%, highlighting its practical utility in real-world scenarios. The research highlights the potential for supermarkets to improve perishable goods inventory management, leading to significant economic benefits and reduced food waste. This study contributes to sustainable retail practices aligning with global responsible consumption and production initiatives, offering a scalable economic efficiency and environmental stewardship solution.

Dual optimality conditions for the difference of extended real valued increasing co-radiant functions

Dual optimality conditions for the difference of extended real valued increasing co-radiant functions

Deep Reinforcement Learning-Driven UAV Data Collection Path Planning: A Study on Minimizing AoI

As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path planning, focusing on the minimization of weighted average Age of Information (AoI) for IoT devices. To address this challenge, the multi-agent twin delayed deep deterministic policy gradient with dual experience pools and particle swarm optimization (DP-MATD3) algorithm is presented. The objective is to train multiple UAVs to autonomously search for optimal paths, minimizing the AoI. Firstly, considering the relatively slow learning speed and susceptibility to local minima of neural network algorithms, an improved particle swarm optimization (PSO) algorithm is utilized for parameter optimization of the multi-agent twin delayed deep deterministic policy gradient (MATD3) neural network. Secondly, with the introduction of the dual experience pools mechanism, the efficiency of network training is significantly improved. Experimental results show DP-MATD3 outperforms MATD3 in average weighted AoI. The weighted average AoI is reduced by 33.3% and 27.5% for UAV flight speeds of v = 5 m/s and v = 10 m/s, respectively.

Modeling approach for Ti3C2 MXene-based fluorescent aptasensor for amoxicillin biosensing in water matrices

Amoxicillin, a member of the penicillin family, is primarily utilized for the treatment of various bacterial infections affecting ears, nose, throat, urinary tract, and skin. Given its widespread application in medicine, agriculture, environment, and food industry, the precise and sensitive detection of amoxicillin is important. This study introduces a novel approach to developing a sensitive and selective fluorescent aptasensor relying on fluorescence resonance energy transfer (FRET) for the specific detection of amoxicillin. The carboxyfluorescein-labeled aptamer serves as a energy donor, while MXene functions as an energy acceptor, and acting as a quencher. To achieve optimal detection efficiency, a dual optimization strategy utilizing RSM-CCD and ANN-GA was used to fine-tune experimental conditions. The fluorescence measurements revealed an expansive linear range extending from 100 to 2400 ng mL−1, accompanied by an exceptionally low detection limit of 1.53 ng mL−1. Additionally, it shows an excellent selectivity towards amoxicillin over other antibiotics commonly found in water matrices. The aptasensor demonstrates good stability and reproducibility; effectiveness of the aptasensor was validated by testing in real water samples. This remarkable sensitivity and broad dynamic range affirm the efficacy aptasensor in accurately detecting varying concentrations of amoxicillin in wastewater bodies.

Dual multi-objective optimization design method for compliant guide mechanism

Dual multi-objective optimization design method for compliant guide mechanism

Dual Response Surface Optimization based on Skill Scores

The popular formulations of dual-response optimization are constructed on minimizing a function of bias and system variability. This study provides an opportunity to evaluate the dual response surface (DRS) problem from a different perspective by adapting two new terms such that internal and external quality forecasts. The background of the proposed approach focuses on the relationship between internal and external quality forecasts and discusses the DRS problem in regards of skill scores by defining a model quality criterion. Skill is the relative accuracy of the forecast and defines a correspondence between forecast of interest and reference forecasts. The reference forecast does not require any knowledge or modelling; thus, it is an unskilled forecast. In this context, skill score is a measure of this relative improvement and widely used in evaluating the performance of operational and experimental forecasts. An alternative version of mean square error (MSE) which is reconstructed by skill scores and model quality criterion is proposed as an objective function for the DRS problem. Integrating the relationship between internal and external quality forecasts into such a response function can improve the effectiveness and cooperation of the applied technique. The proposed approach has a flexible structure and provides decision makers alternative solutions for different values of the model quality criterion. The proposed procedure is discussed by conducted a simulation study and demonstrated in an engineering process.

Evaporated Nickel Oxide Films with Slow Annealing and Interface Modification for Perovskite Solar Cells.

Electron-beam-evaporated nickel oxide (NiOx) films are known for their high quality, precise control, and suitability for complex structures in perovskite (PVK) solar cells (PSCs). However, untreated NiOx films have inherent challenges, such as surface defects, relatively low intrinsic conductivity, and shallow valence band maximum, which seriously restrict the efficiency and stability of the devices. To address these challenges, we employ a dual coordination optimization strategy. The strategy includes low heating rate annealing of NiOx films and using an aminoguanidine nitrate spin coating process on the surfaces of NiOx films to strategically modify NiOx films itself and the interface of NiOx/PVK. Under the synergistic effect of this dual optimization method, the quality of the films is significantly improved and its p-type characteristics are enhanced. At the same time, the interface defects and energy level alignment of the films are effectively improved, and the charge extraction ability at the interface is improved. The combined treatment significantly improved the efficiency of inverted PSCs, from 17.85% to 20.31%, and enhanced device stability under various conditions. This innovative dual-coordinated optimization strategy provides a clear and effective framework for improving the performance of NiOx films and inverted PSCs.

Enhancing potassium‐ion storage of Bi2S3 through external–internal dual synergism: Ti3C2Tx compositing and Cu2+ doping

AbstractPotassium‐ion batteries (PIBs) offer a cost‐effective and resource‐abundant solution for large‐scale energy storage. However, the progress of PIBs is impeded by the lack of high‐capacity, long‐life, and fast‐kinetics anode electrode materials. Here, we propose a dual synergic optimization strategy to enhance the K+ storage stability and reaction kinetics of Bi2S3 through two‐dimensional compositing and cation doping. Externally, Bi2S3 nanoparticles are loaded onto the surface of three‐dimensional interconnected Ti3C2Tx nanosheets to stabilize the electrode structure. Internally, Cu2+ doping acts as active sites to accelerate K+ storage kinetics. Various theoretical simulations and ex situ techniques are used to elucidate the external–internal dual synergism. During discharge, Ti3C2Tx and Cu2+ collaboratively facilitate K+ intercalation. Subsequently, Cu2+ doping primarily promotes the fracture of Bi2S3 bonds, facilitating a conversion reaction. Throughout cycling, the Ti3C2Tx composite structure and Cu2+ doping sustain functionality. The resulting Cu2+‐doped Bi2S3 anchored on Ti3C2Tx (C‐BT) shows excellent rate capability (600 mAh g–1 at 0.1 A g–1; 105 mAh g–1 at 5.0 A g–1) and cycling performance (91 mAh g–1 at 5.0 A g–1 after 1000 cycles) in half cells and a high energy density (179 Wh kg–1) in full cells.

Using genetic algorithm for optimal sizing of stand-alone hybrid energy system

When planning a hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources—those that rely on fossil fuels—the primary considerations are the total cost of the system and the CO? emissions. In this paper, we will investigate the typical hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources involving a detailed simulation process that may require specific inputs, models, and data. Then, we employed dual optimization methods: genetic algorithm (GA) and particle swarm optimization (PSO). The consequences of GA and PSO execution in the bus timetabling problem depict that the GA algorithm is better at finding the optimal solution in terms of accuracy and iteration. Additionally, the GA algorithm is also superior to the straightforwardness of the techniques used. So, in this work, we employed a Genetic Algorithm Optimization (GA)–-based optimal sizing technique for HES configurations that include sustainability wind turbines (WTs), battery storage (BS), and diesel generators (DGs). HES improved power delivery to a rural community in the Wasit Province, Iraq, situated at 46° - 36° and 32° - 31° in the country's southeastern central region. Throughout the project's 25-year lifespan, the optimization primarily aims to minimize the total cost (CT) and total CO? emissions (ECO2T). The outcomes demonstrate that the GA algorithm may, with continuous electricity supply, minimize the objectives while meeting the load demand.

An approach to duality via image space analysis and applications to linear vector optimization problems

In Yao et al. [Yao CL, Wang SQ, Tammer C. Construction of vector-valued weak separation functions with applications to conjugate duality in vector optimization; 2024. Submitted.], based on the idea of image space analysis and abstract convexity, we proposed a uniform framework of conjugate duality for the constrained vector optimization problems. This paper aims to apply this framework to linear vector optimizations. With the aid of a special collection of separation functions, a set-valued dual problem, which was also derived by Heyde, Löhne, Tammer in [Heyde F, Löhne A, Tammer C. The attainment of the solution of the dual program in vertices for vectorial linear programs. In: Barichard V, Ehrgott M, Gandibleux X, T'Kindt V, editors. Multiobjective programming and goal programming. 2009. p. 13–24. (Lecture Notes in Economics and Mathematical Systems; 618).; Heyde F, Löhne A, Tammer C. Set-valued duality theory for multiple objective linear programs and application to mathematical finance. Math Meth Oper Res. 2009;69:159–179.], is obtained by this approach. Within the uniform framework, we provide a new way to construct this dual model, and to investigate the duality theory of it. In this method, the strong duality is established by the separation of sets in the image space and the generalized saddle points.

Duality in convex optimization for the hyperbolic augmented Lagrangian

Duality in convex optimization for the hyperbolic augmented Lagrangian

Revolutionizing energy practices: Unleashing the power of artificial intelligence in corporate energy transition

Corporate energy transition is crucial for long-term sustainable development. The widely discussed Artificial Intelligence (AI), as a disruptive technological innovation, is highly potential for enhancing environment performance. However, the specific impact of AI on the process of corporate energy transition and its underlying mechanisms have not been fully explored. This study focuses on A-share listed corporates in Shanghai and Shenzhen stock markets in China spanning from 2011 to 2021. Based on corporate annual report information and information from over 200,000 patent application texts, we innovatively construct indicators for corporate energy transition and AI technology application. Furthermore, we empirically investigate the impact of AI technology on corporate energy transition and its potential mechanisms through combining information asymmetry theory and institutional theory. The empirical results indicate that: 1) AI can drive corporate energy transition and the promoting effect of AI collaborative innovation on corporate energy transition should not be ignored. 2) AI can help corporates achieve energy transition through pathways such as mitigating information asymmetry, reducing financing constraints, adjusting sustainable development concepts and practices. 3) The driving effect of AI on corporate energy transition varies depending on the characteristics of different types of corporates, industries, and regions. This study provides strategic guidance and decision support for business managers and policymakers, assisting both corporates and governments in better utilizing AI technology during the social energy transition process to achieve a dual optimization of environmental and economic goals.

Non-convex feature selection based on feature correlation representation and dual manifold optimization

The existing feature selection algorithms often utilize local structure of data, but do not fully mine internal structure and ignore potential correlation information between samples. To address the problems and fully utilize manifold information of data, this paper proposes non-convex feature selection based on feature correlation representation and dual manifold optimization (FDNFS). Firstly, FDNFS constructs feature graph of original data, which can obtain feature correlation representation to represent the interconnection information. Based on the obtained interconnection information, FDNFS unifies feature correlation representation learning and feature selection through feature transformation matrix, so that the interconnection information between data guides feature selection. Secondly, to make multivariate frameworks guide feature selection, FDNFS introduces self-representation on the improved sparse regression model. Using self-representation can make basis matrix and reconstruction coefficient matrix reconstruct original data more accurately. Next, in order to preserve local structure information abundantly, FDNFS has two-part manifold regularization on the pseudo-label matrix in sparse regression model and the reconstructed coefficient matrix in self-representation framework. This can fully use the manifold information of data. In addition, FDNFS imposes the non-convex constraints. It can ensure the sparsity of feature selection matrix. In turn, this can select features with lower redundancy, and then select a better feature subset. Finally, this paper adopts an iterative optimization method. FDNFS is compared with nine algorithms on seven datasets. The clustering results reflect better performance of FDNFS.

Knowledge distillation model for Acute Lymphoblastic Leukemia Detection: Exploring the impact of nesterov-accelerated adaptive moment estimation optimizer

Acute lymphoblastic leukemia (ALL) is a malignancy that primarily affects the bone marrow and bloodstream, characterized by the rapid proliferation of immature lymphoblasts. In the realm of medical diagnosis, the integration of artificial intelligence (AI) presents an opportunity to revolutionize the detection of ALL. This integration has the potential to significantly enhance diagnostic accuracy, streamline the diagnostic process, and mitigate the risk of human error, which is particularly valuable when analyzing data collected from monitoring devices. This paper introduces two novel architectures designed for the detection of ALL. The first approach, known as Knowledge Nevestro Classification (KNC), incorporates the concept of knowledge distillation. This method leverages two teacher models, DenseNet and ResNet (50), which have undergone meticulous optimization employing both the Adam and Nadam optimizers. This dual optimization approach allows for a comprehensive evaluation of their respective outcomes. The second approach introduces the Nevestro DenseNet (50) Attention (DAN) architecture, which represents a variant of the DenseNet with Attention (NDA) architecture. This custom architecture is purpose-built for the specific task of ALL detection and has been meticulously fine-tuned using the Nadam optimizer. The results show impressive metrics, with accuracy, sensitivity, specificity, precision, and F1-Score measures reaching 0.991, 0.9945, 0.9924, 0.9945, and 0.9945, respectively.

Optimization of convolutional neural network with dual attention mechanism: Estimation of chlorophyll-a concentration in the Taiwan Strait using MODIS data

Chlorophyll-a (Chl-a) concentration plays a crucial role in monitoring marine phytoplankton, holding significant implications for marine ecosystems and human livelihoods. Remote sensing serves as a valuable method for directly estimating Chl-a concentration and facilitating marine monitoring activities. In order to accurately estimate Chl-a concentration and effectively monitor the marine ecological environment of the Taiwan Strait, this paper introduces a novel neural network model called Convolutional Neural Network with Dual Attention Mechanism Optimization (CNN-CBAM). Additionally, the spectral bands derived from MODIS 500 m imagery are subjected to various processing techniques to generate four distinct sets of feature data. These datasets are subsequently merged with in-situ measurements of Chl-a concentration obtained from buoy stations. Given the limited availability of buoy stations, the temporal continuity of data is leveraged to overcome the spatial distribution constraints. This enables the estimation of Chl-a concentration at various time intervals within the research area of the Taiwan Strait. To evaluate the performance of the model, metrics such as root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 are employed. The results demonstrate that the CNN-CBAM model, utilizing MODIS temporal data, effectively retrieves Chl-a concentration in the research area of the Taiwan Strait with high accuracy. For the CNN-CBAM model, the RMSE is 0.34 μg L−1, MAPE is 22%, and R2 is 0.86. In comparison, the traditional empirical model using the Band Ratio (BR) model has an RMSE of 0.61 μg L−1, MAPE of 57%, and R2 of 0.31. The results indicate that the CNN-CBAM model has improved R2 by 0.55 compared to the traditional BR model. Additionally, the RMSE and MAPE have decreased by 0.27 μg L−1 and 35%, respectively. The superiority of the CNN-CBAM model over the traditional BR model is evident, showcasing a substantial enhancement in the accuracy of Chl-a concentration retrieval. Given the challenges posed by the intricate and ever-changing marine environment, particularly the scarcity of in-situ buoy stations, this study offers a practical solution for monitoring Chl-a concentration. It establishes a framework for leveraging satellite-based multispectral sensors to enable large-scale and multi-temporal remote sensing retrieval of Chl-a concentration in the research area of the Taiwan Strait.

P2BPO: Permeable Penalty Barrier-Based Policy Optimization for Safe RL

Safe Reinforcement Learning (SRL) algorithms aim to learn a policy that maximizes the reward while satisfying the safety constraints. One of the challenges in SRL is that it is often difficult to balance the two objectives of reward maximization and safety constraint satisfaction. Existing algorithms utilize constraint optimization techniques like penalty-based, barrier penalty-based, and Lagrangian-based dual or primal policy optimizations methods. However, they suffer from training oscillations and approximation errors, which impact the overall learning objectives. This paper proposes the Permeable Penalty Barrier-based Policy Optimization (P2BPO) algorithm that addresses this issue by allowing a small fraction of penalty beyond the penalty barrier, and a parameter is used to control this permeability. In addition, an adaptive penalty parameter is used instead of a constant one, which is initialized with a low value and increased gradually as the agent violates the safety constraints. We have also provided a theoretical proof of the proposed method's performance guarantee bound, which ensures that P2BPO can learn a policy satisfying the safety constraints with high probability while achieving a higher expected reward. Furthermore, we compare P2BPO with other SRL algorithms on various SRL tasks and demonstrate that it achieves better rewards while adhering to the constraints.

Two-Stage Evolutionary Reinforcement Learning for Enhancing Exploration and Exploitation

The integration of Evolutionary Algorithm (EA) and Reinforcement Learning (RL) has emerged as a promising approach for tackling some challenges in RL, such as sparse rewards, lack of exploration, and brittle convergence properties. However, existing methods often employ actor networks as individuals of EA, which may constrain their exploratory capabilities, as the entire actor population will stop evolution when the critic network in RL falls into local optimal. To alleviate this issue, this paper introduces a Two-stage Evolutionary Reinforcement Learning (TERL) framework that maintains a population containing both actor and critic networks. TERL divides the learning process into two stages. In the initial stage, individuals independently learn actor-critic networks, which are optimized alternatively by RL and Particle Swarm Optimization (PSO). This dual optimization fosters greater exploration, curbing susceptibility to local optima. Shared information from a common replay buffer and PSO algorithm substantially mitigates the computational load of training multiple agents. In the subsequent stage, TERL shifts to a refined exploitation phase. Here, only the best individual undergoes further refinement, while the rest individuals continue PSO-based optimization. This allocates more computational resources to the best individual for yielding superior performance. Empirical assessments, conducted across a range of continuous control problems, validate the efficacy of the proposed TERL paradigm.

A Dual Optimization Mechanism for Energy Efficiency in Wireless Body Area Networks Based on Naive Bayesian Classifier and Hungarian Algorithm

A Dual Optimization Mechanism for Energy Efficiency in Wireless Body Area Networks Based on Naive Bayesian Classifier and Hungarian Algorithm