Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

Compartmentalized regulation of organelle integrity in neurodegenerative diseases:lessons from the Drosophila motor neuron

Neurons are highly polarized,morphologically asymmetric,and functionally compartmentalized cells that contain long axons extending from the cell body.For this reason,their maintenance relies on spatiotemporal regulation of organelle distribution between the somatodendritic and axonal domains.Although some organelles,such as mitochondria and smooth endoplasmic reticulum,are widely distributed throughout the neuron,others are segregated to either the somatodendritic or axonal compartment.For example,Golgi outposts and acidified lysosomes are predominantly present in the somatodendritic domain and rarely distributed along the axon,whereas newly formed autophagosomes and synaptic vesicles are mainly distributed in the distal axon(Britt et al.,2016).

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

Method for secondary spring load equalization of railway vehicles with two-stage spring suspension: Modeling and optimal regulation

To ensure running safety,the secondary spring loads of railway vehicles must be well equalized.Due to the coupling interactive effects of these hyper static suspended structures,the equalization adjustment through shimming procedure is quite complex.Therefore,an effective and reliable method in application is developed in this paper.Firstly,the best regulation of spring load is solved based on a mechanical model of the secondary suspension system,providing a target for actual adjustment.To reveal the relationship between secondary spring load distribution and shim quantity sequence,a forecasting model is constructed and then modified experimentally with consideration of car body’s elastic deformation.Further,a gradient-based algorithm with a momentum operation is proposed for the load optimization.Effectiveness of the whole method has been verified on a test rig.It is experimentally confirmed that this research provides an important basis for achieving an optimal regulation of spring load distribution for multiple types of railway vehicles.

Advanced Policy Learning Near-Optimal Regulation

Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.

Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

In this paper, a data-driven control approach is developed by reinforcement learning (RL) to solve the global robust optimal output regulation problem (GROORP) of partially linear systems with both static uncertainties and nonlinear dynamic uncertainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncertainties. The closed-loop system is ensured to be input-to-output stable regarding the static uncertainty as the external input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-output stability for the closed-loop system and thus solves the original GROORP. Simulation results validates the efficacy of the proposed methodology.

基于D-optimal法优化香菇菌种培养基质配方的研究

为了筛选和优化香菇原种及栽培种的培养基质配方,采用D-optimal设计方法,以麦粒和木屑不同配比为原料优化香菇原种培养基质,以木屑、玉米芯、麸皮不同配比为原料优化香菇栽培种培养基质,以香菇品种L808作为供试菌种,分别以其菌丝萌发期、菌丝长速、满袋期为评价指标,通过对各评价指标的测量,建立了各配比基质与香菇培养基质配方响应值之间的回归模型,从而科学的优化出香菇原种及栽培种栽培基质的配方。试验结果表明,香菇原种栽培基质最优配方为50%麦粒+50%木屑;香菇栽培种栽培基质最优配方为37.69%玉米芯+23.33%麸皮+38.98%木屑。在以上2个配方的栽培基质接种后,香菇菌丝的生长旺盛,萌发期短、满袋期短,且理化性质较优,说明优化得到的栽培基质配方具有较高的可行性,该设计方法也在优化培养料配比上是科学并且可行的。

Role of optimal transmission switching in accommodating renewable energy in deep peak regulation-enabled power systems

Due to the shortage of fossil energy and the pollution caused by combustion of fossil fuels,the proportion of renewable energy in power systems is gradually increasing across the world.Accordingly,the capacity of power systems to accommodate renewable energy must be improved.However,integration of a large amount of renewable energy into power grids may result in network congestion.Hence,in this study,optimal transmission switching(OTS)is considered as an important method of accommodating renewable energy.It is incorporated into the operation of a power grid along with deep peak regulation of thermal power units,forming an interactive mode of coordinated operation of source and network.A stochastic unit commitment model consider!ng deep peak regulation and OTS is established,and the role of OTS in promoting the accommodation of renewable energy is analyzed quantitatively.The results of case studies involving the IEEE 30-bus system demonstrate that OTS can enable utilization of the potential of deep peak regulation and facilitate the accommodation of renewable energy.

Incentive Mechanism Design for Public Goods Provision:Price Cap Regulation and Optimal Regulation

This paper studies the mechanism design that induces firms to provide public goods under two regulatory means： price cap regulation and optimal regulation, respectively. We first outline two models of monopoly regulation with unobservable marginal costs and effort, which can be regard as an optimal problem with dual restrictions. By solving this problem, we get the two optimal regulatory mechanisms to induce the provision of public goods. Further, by comparative statics, the conclusion is drawn that the welfare loss as sociated with price cap regulation, with respective to optimal regulation, increases more with increase of the expense of public goods.

Optimal Design of Deforming Regulation for Compact Continuous Rolling Mill with Balanced Load

If the draught of each mill stand is limited by forced bite condition for compact continuous mill,the rolling load difference between one mill stand and another is very big.If deforming regulation of relative load for each mill stand is approximate to the same,the productive capacity of compact continuous mill can be brought into full play,and also the safety running and the smooth rolling of mill can be ensured.

AN OPTIMAL CONTROL PROBLEM FOR A LOTKA-VOLTERRA COMPETITION MODEL WITH CHEMO-REPULSION

In this paper we study a bilinear optimal control problem for a diffusive Lotka-Volterra competition model with chemo-repulsion in a bounded domain of ℝ^(ℕ),N=2,3.This model describes the competition of two species in which one of them avoid encounters with rivals through a chemo-repulsion mechanism.We prove the existence and uniqueness of weak-strong solutions,and then we analyze the existence of a global optimal solution for a related bilinear optimal control problem,where the control is acting on the chemical signal.Posteriorly,we derive first-order optimality conditions for local optimal solutions using the Lagrange multipliers theory.Finally,we propose a discrete approximation scheme of the optimality system based on the gradient method,which is validated with some computational experiments.

Distributed Optimal Formation Control for Unmanned Surface Vessels by a Regularized Game-Based Approach

Dear Editor,This letter explores optimal formation control for a network of unmanned surface vessels(USVs).By designing an individual objective function for each USV,the optimal formation problem is transformed into a noncooperative game.Under this game theoretic framework,the optimal formation is achieved by seeking the Nash equilibrium of the regularized game.A modular structure consisting of a distributed Nash equilibrium seeker and a regulator is proposed.

Phase separation and transcriptional regulation in cancer development

Liquid-liquid phase separation,a novel biochemical phenomenon,has been increasingly studied for its medical applications.It underlies the formation of membrane-less organelles and is involved in many cellular and biological processes.During transcriptional regulation,dynamic condensates are formed through interactions between transcriptional elements,such as transcription factors,coactivators,and mediators.Cancer is a disease characterized by uncontrolled cell proliferation,but the precise mechanisms underlying tumorigenesis often remain to be elucidated.Emerging evidence has linked abnormal transcriptional condensates to several diseases,especially cancer,implying that phase separation plays an important role in tumorigenesis.Condensates formed by phase separation may have an effect on gene transcription in tumors.In the present review,we focus on the correlation between phase separation and transcriptional regulation,as well as how this phenomenon contributes to cancer development.

A short overview of the lead iodide residue impact and regulation strategies in perovskite solar cells

Lead iodide(PbI2) is a vital raw material for preparing perovskite solar cells(PSCs),and it not only takes part in forming the light absorption layer but also remains in the grain boundary as a passivator.In other words,the PbI2 content in the precursor and as formed film will affect the efficiency and stability of the PSCs.With moderate residual PbI2,it passivates the bulk/surface defects of perovskite,reduces the interfacial recombination,promotes the perovskite stability,minimizes the device hysteresis,and so on.Deficient PbI2 residue will reduce the interfacial passivation effect and device performance.In addition to facilitating the non-radiative recombination,over PbI2 residue can also lead to electronic insulation in the grain boundary and deteriorate the device performance.However,the impact and regulation of PbI2 residue on the device performance and stability is still not fully understood.Herein,a comprehensive and detailed review is presented by discussing the PbI2 residue impact and its regulation strategies(i.e., elimination,facilitation and conversion of the residue PbI2) to manipulate the PbI2 content,distribution and forms.Finally,we also show future outlooks in this field,with an aim to help further the progression of high-efficiency and stable PSCs.

Flexible bidirectional pulse charging regulation achieving long-life lithium-ion batteries

Typical application scenarios,such as vehicle to grid(V2G)and frequency regulation,have imposed significant long-life demands on lithium-ion batteries.Herein,we propose an advanced battery life-extension method employing bidirectional pulse charging(BPC)strategy.Unlike traditional constant current charging methods,BPC strategy not only achieves comparable charging speeds but also facilitates V2G frequency regulation simultaneously.It significantly enhances battery cycle ampere-hour throughput and demonstrates remarkable life extension capabilities.For this interesting conclusion,adopting model identification and postmortem characterization to reveal the life regulation mechanism of BPC:it mitigates battery capacity loss attributed to loss of lithium-ion inventory(LLI)in graphite anodes by intermittently regulating the overall battery voltage and anode potential using a negative charging current.Then,from the perspective of internal side reaction,the life extension mechanism is further revealed as inhibition of solid electrolyte interphase(SEI)and lithium dendrite growth by regulating voltage with a bidirectional pulse current,and a semi-empirical life degradation model combining SEI and lithium dendrite growth is developed for BPC scenarios health management,the model parameters are identified by genetic algorithm with the life simulation exhibiting an accuracy exceeding 99%.This finding indicates that under typical rate conditions,adaptable BPC strategies can extend the service life of LFP battery by approximately 123%.Consequently,the developed advanced BPC strategy offers innovative perspectives and insights for the development of long-life battery applications in the future.

Regulation of Lithium-Ion Flux by Nanotopology Lithiophilic Boron-Oxygen Dipole in Solid Polymer Electrolytes for Lithium-Metal Batteries

Inhomogeneous lithium-ion(Li^(+))deposition is one of the most crucial problems,which severely deteriorates the performance of solid-state lithium metal batteries(LMBs).Herein,we discovered that covalent organic framework(COF-1)with periodically arranged boron-oxygen dipole lithiophilic sites could directionally guide Li^(+)even deposition in asymmetric solid polymer electrolytes.This in situ prepared 3D cross-linked network Poly(ACMO-MBA)hybrid electrolyte simultaneously delivers outstanding ionic conductivity(1.02×10^(-3)S cm^(-1)at 30°C)and excellent mechanical property(3.5 MPa).The defined nanosized channel in COF-1 selectively conducts Li^(+)increasing Li^(+)transference number to 0.67.Besides,The COF-1 layer and Poly(ACMO-MBA)also participate in forming a boron-rich and nitrogen-rich solid electrolyte interface to further improve the interfacial stability.The Li‖Li symmetric cell exhibits remarkable cyclic stability over 1000 h.The Li‖NCM523 full cell also delivers an outstanding lifespan over 400 cycles.Moreover,the Li‖LiFePO_(4)full cell stably cycles with a capacity retention of 85%after 500 cycles.the Li‖LiFePO_(4)pouch full exhibits excellent safety performance under pierced and cut conditions.This work thereby further broadens and complements the application of COF materials in polymer electrolyte for dendrite-free and high-energy-density solid-state LMBs.

Optimal synthesis of heat-integrated distillation configurations using the two-column superstructure

In the realm of the synthesis of heat-integrated distillation configurations,the conventional approach for exploring more heat integration possibilities typically entails the splitting of a single column into a twocolumn configuration.However,this approach frequently necessitates tedious enumeration procedures,resulting in a considerable computational burden.To surmount this formidable challenge,the present study introduces an innovative remedy:The proposition of a superstructure that encompasses both single-column and multiple two-column configurations.Additionally,a simultaneous optimization algorithm is applied to optimize both the process parameters and heat integration structures of the twocolumn configurations.The effectiveness of this approach is demonstrated through a case study focusing on industrial organosilicon separation.The results underscore that the superstructure methodology not only substantially mitigates computational time compared to exhaustive enumeration but also furnishes solutions that exhibit comparable performance.

Transcriptional regulation in the development and dysfunction of neocortical projection neurons

Glutamatergic projection neurons generate sophisticated excitatory circuits to integrate and transmit information among different cortical areas,and between the neocortex and other regions of the brain and spinal cord.Appropriate development of cortical projection neurons is regulated by certain essential events such as neural fate determination,proliferation,specification,differentiation,migration,survival,axonogenesis,and synaptogenesis.These processes are precisely regulated in a tempo-spatial manner by intrinsic factors,extrinsic signals,and neural activities.The generation of correct subtypes and precise connections of projection neurons is imperative not only to support the basic cortical functions(such as sensory information integration,motor coordination,and cognition)but also to prevent the onset and progression of neurodevelopmental disorders(such as intellectual disability,autism spectrum disorders,anxiety,and depression).This review mainly focuses on the recent progress of transcriptional regulations on the development and diversity of neocortical projection neurons and the clinical relevance of the failure of transcriptional modulations.

Mixture-optimal法优化红枣姜茶饮料配方工艺

目的:优化红枣姜茶饮料的配方,为凉茶饮料的品质提升提供理论依据和技术指导。方法:使用Mixture–optimal对红枣姜茶饮料配方工艺进行优化,通过方差分析、显著性检验等方法验证模型的合理性,得到回归方程,推测出红枣姜茶的最优配方。结果:最优配方为干姜6 g、红枣40 g、肉豆蔻4 g、肉桂3.557 4 g、山药28.442 5 g、百合24 g和红糖30 g。结论:减少干姜和肉豆蔻的添加量,增加红枣、百合和红糖的添加量,会使红枣姜茶的口感更好。