Excessive iron ions in cancer cells can catalyze H2O2 into highly toxic •OH and then promote the generation of reactive oxygen species (ROS), inducing ferroptosis. However, efficacy ferroptosis catalyst is still insufficient because low Fe(II) release, which severely limited its application clinic. Herein, we developed a novel magnetic nanocatalyst for MRI-guided chemo- synergistic therapies through iRGD-PEG-ss-PEG-modified gadolinium engineering oxide-loaded Dox (ipGdIO-Dox). The...

This work finds that Fe3O4 nanoclusters can rearrange by Gd doping and then self-assemble to a hollow magnetic nanocluster (HMNC), providing larger moments obtain an excellent MRI capability increasing the number of oxygen vacancies in HMNC. The structure makes platinum(IV) prodrugs effectively load into Second, plenty vacancy defects capture molecules, enhance catalytic activity HMNC, promote intracellular ROS generation. On basis this, targeting iRGD-labeled HMNC nanosystem (iHMNCPt-O2) is...

Black-box artificial intelligence (AI) induction methods such as deep reinforcement learning (DRL) are increasingly being used to find optimal policies for a given control task. Although represented using black-box AI capable of efficiently executing the underlying task and achieving closed-loop performance-controlling agent from initial time step until successful termination an episode, developed rules often complex neither interpretable nor explainable. In this article, we use recently...

Recent automotive technologicaladvancements mainly focus on improving fuel economy with satisfactory emissions, leading to significant increment of engine system complexity, especially for diesel engines. This results in a large number calibration parameters control features, making the process challenge and time consuming using conventional map-based approach. article proposes methodology perform surrogate assisted optimization reduce effort A high fidelity GT-Power model is used current...

Most designs in practice go through a number of different loading or operating conditions. Therefore, meaningful and resilient design must be such that it performs well under all scenarios. Despite its practical importance, multi-scenario consideration has not been paid much attention multi-objective optimization literature. In this paper, we address challenging issue by suggesting an aggregate based handling multiple scenarios contrasts the proposed approach against recently suggested which...

In this paper, we address solution methodologies of an optimization problem under multiple scenarios. Often in practice, a needs to be considered for different scenarios, such as evaluating loading conditions, blocks data, multi-stage operations, etc. After reviewing various single-objective aggregate methods handling objectives and constraints then suggest multi-objective approach solving multi-scenario problems. On Byzantine agreement problem, demonstrate the usefulness proposed explain...

Typhoons can have disastrous effects on power systems. They may lead to a large number of outages for distribution network users. Therefore, this paper establishes model predict the outage quantity users under typhoon disaster. Firstly, twenty-six explanatory variables (called global variables) covering meteorological factors, geographical and grid factors are considered as input variables. On basis, correlation between each variable response is analyzed. Secondly, we established based...

Abstract This paper proposes a wireless network traffic prediction model based on Bayesian Gaussian tensor decomposition and recurrent neural with rectified linear unit (BGCP-RNN-ReLU model), which can effectively predict the changes in upstream downstream short period of time future. The research is divided into two parts: (i) missing observations are imputed by an algorithm decomposition. (ii) used to forecast true only rather than both estimated observations. results show that, compared...

Most surrogate-assisted evolutionary algorithms save expensive evaluations by approximating fitness functions. However, many real-world applications are high-dimensional multi-objective optimization problems, and it is difficult to approximate their functions accurately using a very limited number of evaluations. This paper proposes domination-based ordinal regression surrogate, in which Kriging model employed learn the domination relationship values landscape Coupling with hybrid surrogate...

Abstract A surrogate assisted optimization approach is an attractive way to reduce the total computational budget for obtaining optimal solutions. This makes it special its application practical problems requiring a large number of expensive evaluations. Unfortunately, all applications are affected by measurement noises, and not much work has been done address issue handling stochastic with multiple objectives constraints. tries bridge gap demonstrating three different frameworks performing...

Engine calibration is an important step to achieve optimal engine performance with satisfactory emissions and it expensive process in general. In recent years, a new called Bayesian optimization has come into picture for reducing function evaluations. It efficiently performs exploration-exploitation design space identify region. But the work mostly focused on deterministic case. Unfortunately, practical system measurements almost always contain random noises. Therefore, this research work,...

Engine calibration problems are black-box optimization which evaluation costly and most of them constrained in the objective space. In these problems, decision variables may have different impacts on objectives constraints, could be detected by sensitivity analysis. Most existing surrogate-assisted evolutionary algorithms do not analyze variable sensitivity, thus, useless effort made some less sensitive variables. This article proposes a bilevel algorithm to solve real-world engine problem....

The ultimate goal of ridesharing systems is to match travelers who do not have a vehicle with those want share their vehicle. A good can be found among similar itineraries and time schedules. In this way each rider served without any delay also driver earn as much possible having too deviation from original route. We propose an algorithm that leverages biogeography-based optimization solve multiobjective problem for online ridesharing. It necessary the multi-objective since there are some...

For power system disaster prevention and mitigation, preparing for the deployment of response crew in advance have important scientific significance engineering value. This paper proposes an optimization model emergency repair path under typhoon disaster. It aims to deploy team based on prediction outage. First, random forest algorithm is used predict number outage users. Then, genetic optimize route according damage degree. Finally, taking Xuwen County, Guangdong Province, China as example....

Engine calibration is a process of optimizing its control variables, which crucial step to achieve the desired performance from engine system. However, complexity has increased dramatically due constant evolution architecture with multiple variables interacting each other, making it difficult calibrate significantly experimental costs. System also affects physics-based modeling as makes system model highly non-linear and capture dynamics under all operating conditions. To overcome these...

Abstract Diesel engines are becoming increasingly complex to control and calibrate with the desire of improving fuel economy reducing emissions (NOx Soot) due global warming energy usage. With ever increased features, it is more difficult engine parameters using traditional mapping based methods unreasonable calibration time required. Therefore, this research focuses on problem performing within a limited budget by efficiently optimizing three parameters: namely variable geometry...

Typhoons have substantial impacts on power systems and may result in major outages for distribution network users. Developing prediction models the number of users going through typhoon is a high priority to support restoration planning. This study proposes data‐driven model predict that experience when passes by. To improve accuracy model, twenty six explanatory variables from meteorological factors, geographical factors grid are considered. In addition, authors compared application effect...