Contraction Algorithm Research Articles

Optimal design and performance assessment for a solar powered electricity, heating and hydrogen integrated energy system

Incorporating solar energy technologies into integrated energy systems (IES) plays an increasingly important role to mitigate energy supply shortages and climate challenges. However, the low efficiency of solar energy utilization and land limitation for system deployment greatly restrict the development of the solar powered IESs. In this paper, a solar powered electricity, heating and hydrogen IES based on photovoltaic (PV), photothermal (PT) and photocatalysis of hydrogen production (PH) is proposed and investigated. The system comprises solar radiation spectrum splitting technology, PV, PT, PH, electric heating device, compressive heat pump, solid oxide fuel cell, absorption heat pump and energy storage components. A component sizing method based on a region contraction algorithm is employed to find the system optimal design with minimizing the cost of energy. Two scenarios of the solar powered IES based on solar spectrum splitting unit and independent energy supply unit are analyzed and compared. The results show that the solar powered IES can completely meet the electricity, heating and hydrogen energy demands and achieve cost savings for consumers. Compared to independent energy supply unit-based IES, the use of solar spectrum splitting unit can significantly improve the total energy efficiency by 21.74% and reduce the land area for solar capture by 52.5%, but increase the cost of energy by 27.3%.

A novel cuproptosis-related lncRNA nomogram to improve the prognosis prediction of gastric cancer.

BackgroundCuproptosis is a copper-triggered modality of mitochondrial cell death and cuproptosis process may play important roles in gastric cancer development. However, little is known about cuproptosis-related lncRNAs in gastric adenocarcinoma (STAD). This study is aimed to investigate the potential prognostic signatures of cuproptosis-related lncRNAs in STAD.MethodsThe Cancer Genome Atlas (TCGA) database were used to obtain gene expression profiles, clinicopathological, and OS information for STAD. Cuproptosis-related genes were collected based on previous studies and cuproptosis-related lncRNAs were screened out by co-expression analysis. The nomogram constructed by Cox regression analysis with the minimum absolute contraction and selection operator (lasso) algorithm. In addition, the potential response of ICB therapy and immune evasion incidence were estimated with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Immune checkpoint expressions associated with risk scores were also analyzed. The correlation of immune checkpoint CD209 and HAVCR2 expressions associated with risk scores were experimentally testified by RT-qPCR, Western Blot, and IHC. ResultsPatients were classified into high-risk and low-risk groups based on the risk score calculated in this model. The Kaplan–Meier survival curve analysis revealed that the high-risk group was associated with poor prognosis. Multivariate Cox regression analysis suggested that this lncRNA prediction model was an independent risk factor affecting the OS rate. Furthermore, ROC curve indicates that the nomogram was superior to traditional clinicopathological features in predicting STAD prognosis. Finally, functional enrichment analysis and immune checkpoint investigation revealed that the nomogram is notably associated with cholesterol metabolism and immune functions, RT-qPCR and Western Blotting demonstrated the co-expression relationship of LINC01150 with CD209 and HAVCR2.ConclusionA novel cuproptosis-related lncRNAs signature impacts on the prognosis and immunological features of GC.

Low Power Digital Image Compression Using Approximate Adders

Abstract: Low power is one of the arising trend in field of VLSI which can be used in multimedia bias employing colorful signal processing algorithms and armature. In utmost multimedia operation mortal beings can gather useful information from incorrect afFull Adderir. Thus we don’t need to produce an exact correct numerical afFull Adderir. So we're using different adders to apply this algorithm in signal processing. We design infrastructures for videotape and image contraction algorithms using the proposed approximate computation units and estimate them to demonstrate the efficiency of our approach. We also decide simple fine models for error and power consumption of these approximate adders. The approximate adders are reduced with complexity which are used in designing this algorithm. The proposed adders can achieve significant through afFull Adderir and total power reduction over conventional adders. Simulation results indicate up to 69% power savings using the proposed approximate adders, when compared to being executions using accurate adders.

DEALS MADE BY AN INDIVIDUAL IN THE RUSSIAN INVESTMENT PLATFORM

The purpose of this research work is to provide an answer to the question, based on the disclosure of the legal algorithm for the functioning of the investment platform, to what extent the entrepreneurial activity of the investment intermediary - the operator of the investment platform is optimally regulated by the Legislator. In addition, to what extent the current legal order facilitates, accelerates, simplifies the making of investments by investors, and by recipients of investments - attracting capital to their projects. The author substantiates that the interaction of the participants of the investment platform should be organized within the framework of a single agreement, in which the platform operator, the investor, the person attracting investments will act as parties, a single agreement should be technologically executed on the investment platform according to the smart contract algorithm. This will allow you to automatically make money transfers and keep records of data on each stage of the transaction executed using the investment platform on the depository and registration platform functioning as a special electronic service on the website of the Bank of Russia. Fixing the facts of transactions made using the investment platform should be carried out in the system of a polysubject jurisdictional blockchain. Six entities (two private and four public) operating on the territory of the Russian Federation should be combined into a polysubject blockchain scheme.

Elastoplastic Integration Method of Mohr-Coulomb Criterion

A new method for implicit integration of the Mohr-Coulomb non-smooth multisurface plasticity models is presented, and Koiter’s requirements are incorporated exactly within the proposed algorithm. Algorithmic and numerical complexities are identified and introduced by the nonsmooth intersections of the Mohr-Coulomb surfaces; then, a projection contraction algorithm is applied to solve the classical Kuhn–Tucker complementary equations which provide the only characterization of possible active yield surfaces as a special class of variational inequalities, and the actual active yield surface is further determined by iteration. The basic idea is to calculate derivatives of the yield and potential functions with the expressions in the principal stresses and perform the return manipulations in the general stress space. Based on the principal stress characteristic equation, partial derivatives of principal stresses are calculated. The proposed algorithm eliminates the error caused by smoothing the corner of Mohr-Coulomb surfaces, avoids the numerical singularity at the intersections in the general stress space, and does not require the stress transformation needed in the principal stress space method. Lastly, several numerical examples are given to verify the validity of the proposed method.

Improved Gauss Seidel based projection–contraction algorithm for the mixed complementarity problem in contact problem

AbstractThe GSCP (Gauss–Seidel based projection‐contraction) algorithm has been well established in solving elastoplastic mixed complementarity problems (MiCPs) or other problems which have similar mathematical form. However, the stability problems in the GSPC algorithm may cause nonconvergence or program crashes. Thus, the method of constructing and analyzing counterexamples is adopted to study the reasons for the instability of the GSPC algorithm. The R‐GSPC algorithm was developed by modifying the part of the GSPC algorithm that caused the crash to make the algorithm stable. In addition, this paper analyzes the convergence, compatibility, and initial value impact of R‐GSPC, and finds that the R‐GSPC algorithm greatly improves the robustness and stability without losing accuracy. This paper allows the GSPC algorithm to be better extended to more fields and to be suitabled for being introduced into commercial programs.

A new approach for evaluating node importance in complex networks via deep learning methods

The evaluation of node importance is a critical research topic in network science, widely applied in social networks, transport systems, and computer networks. Prior works addressing this topic either consider a single metric or assign weights for multiple metrics or select features by handcraft, which exist one-sidedness and subjectivity issues. In this paper, to tackle these problems, we propose a new approach named CGNN to identify influential nodes based on deep learning methods, including Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). CGNN obtains the feature matrices by the contraction algorithm and gets the labels by the Susceptible-Infected-Recovered (SIR) model, which will be leveraged for learning the hidden representations of nodes without utilizing any network metrics as features. We adopt three evaluation criteria to verify CGNN concerning effectiveness and distinguishability, including Kendall’s τ correlation coefficient, monotonicity index (MI), and ranking distribution function (RDF). Nine baselines are employed to compare with CGNN on thirty synthetic networks and twelve real-world networks from different domains. Simulation results demonstrate that CGNN manifests better performance than the baselines, in which the values of τ are large and significantly increase, the values of MI approach to 1, and the points in the RDF curves distribute more uniformly. These results may provide reference significance for controlling epidemic spreading and enhancing network robustness.

The Sustainable Supply Chain Network Competition Based on Non-Cooperative Equilibrium under Carbon Emission Permits

Under the background of a circular economy, this paper examines multi-tiered closed-loop supply chain network competition under carbon emission permits and discusses how stringent carbon regulations influence the network performance. We derive the governing equilibrium conditions for carbon-capped mathematical gaming models of each player and provide the equivalent variational inequality formulations, which are then solved by modified projection and contraction algorithms. The numerical examples empower us to investigate the effects of diverse carbon emission regulations (cap-and-trade regulation, mandatory cap policy, and cap-sharing scheme) on enterprises’ decisions. The results reveal that the cap-sharing scheme is effective in coordinating the relationship between system profit and carbon emission abatement, while cap-and-trade regulation loses efficiency compared with the cap-sharing scheme. The government should allocate caps scientifically and encourage enterprises to adopt green production technologies, especially allowing large enterprises to share carbon quotas. This study can also contribute to the enterprises’ decision-making and revenue management under different carbon emissions reduction regulations.

Making graphs compact by lossless contraction

This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete parts and regular structures into supernodes. The supernodes carry a synopsis S_mathcal {Q} for each query class mathcal {Q} in use, to abstract key features of the contracted parts for answering queries of mathcal {Q}. Moreover, for various types of graphs, we identify regular structures to contract. The contraction scheme provides a compact graph representation and prioritizes up-to-date data. Better still, it is generic and lossless. We show that the same contracted graph is able to support multiple query classes at the same time, no matter whether their queries are label based or not, local or non-local. Moreover, existing algorithms for these queries can be readily adapted to compute exact answers by using the synopses when possible and decontracting the supernodes only when necessary. As a proof of concept, we show how to adapt existing algorithms for subgraph isomorphism, triangle counting, shortest distance, connected component and clique decision to contracted graphs. We also provide a bounded incremental contraction algorithm in response to updates, such that its cost is determined by the size of areas affected by the updates alone, not by the entire graphs. We experimentally verify that on average, the contraction scheme reduces graphs by 71.9% and improves the evaluation of these queries by 1.69, 1.44, 1.47, 2.24 and 1.37 times, respectively.

Hybrid Inertial Contraction Algorithms for Solving Variational Inequalities with Fixed Point Constraints in Hilbert Spaces

Hybrid Inertial Contraction Algorithms for Solving Variational Inequalities with Fixed Point Constraints in Hilbert Spaces

Revisiting projection and contraction algorithms for solving variational inequalities and applications

Revisiting projection and contraction algorithms for solving variational inequalities and applications

A New Projection Contraction Algorithm for Semidefinite Programming

This paper studies a projective contraction algorithm for solving semidefinite programming problems. The interior point algorithm is a classic algorithm for solving semidefinite programming problems. It can be divided into feasible and infeasible algorithms according to whether the initial point is feasible or not. The feasible interior point algorithm needs to use a self-dual strategy to find strictly feasible points. Although the infeasible interior point algorithm does not require the initial point to be strictly feasible, the theoretical analysis of the algorithm is very complicated. Compared with the interior point algorithm, the projection contraction algorithm has no requirement on the initial point, and the algorithm analysis is much simpler. The optimal condition of the semidefinite programming problem can be rewritten as a projection equation, which is then solved by the projection contraction algorithm. We propose a new projection contraction algorithm based on different descent directions to solve the semidefinite programming problem. Further, under the framework of the projection contraction algorithm, an improved algorithm is proposed, that is, changing the descending direction by solving the linear equation system, thus avoiding the high-dimensional inversion operation so that the algorithm can solve the large-scale semidefinite programming problem. Numerical experiments show that the algorithm is feasible and the improved algorithm is competitive with the interior point algorithm.

A Projection and Contraction Method for P-Order Cone Constraint Stochastic Variational Inequality Problem

In this paper, we study the p-order cone constraint stochastic variational inequality problem. We first take the sample average approximation method to deal with the expectation and gain an approximation problem, further the rationality is given. When the underlying function is Lipschitz continuous, we acquire a projection and contraction algorithm to solve the approximation problem. In the end, the method is applied to some numerical experiments and the effectiveness of the algorithm is verified.

Modified inertial projection and contraction algorithms for solving variational inequality problems with non-Lipschitz continuous operators

Modified inertial projection and contraction algorithms for solving variational inequality problems with non-Lipschitz continuous operators

Emission trading innovation mechanism based on blockchain

The rationality and scientific nature of the emission trading mechanism is the key to the effective implementation of environmental and economic policies. As far as China is concerned, there are phenomena such as information asymmetry, low supervision efficiency, and alienation of government and enterprise behaviors caused by the incomplete mechanism of emission trading in the practice of different pilots. The introduction of blockchain technology can innovate the traditional transaction model and form a decentralized peer-to-peer transaction and a trusted emission trading market. To this end, based on the current emission trading mechanism and the characteristics of blockchain technology, this paper couples the core technologies of blockchain with the functional requirements of application scenarios. Then, an innovative application framework is built based on the consortium blockchain Fabric from three aspects: emission trading supervision, secondary trading market construction, as well as emission trading incentive and punishment mechanisms. Technologies such as the consensus mechanism, smart contract, Merkle tree and asymmetric encryption are comprehensively applied in this process. In the construction of the blockchain framework of the secondary market for emission trading, institutional changes and innovations brought about by the blockchain at various levels are analyzed in terms of participants, transaction processes and the transaction scope. At the same time, smart contract functions and algorithms are designed for the purchase, transfer-out and trading of emission rights, and the operation business logic of the smart contract is analyzed. On the whole, this paper explores the application framework of blockchain technology in the field of emission trading at the macro level, and analyzes the application mechanism of the corresponding technologies of blockchain at each coupling point in the framework at the micro level. The collaborative analysis at the two levels shows that blockchain technology and the requirements of emission trading mechanism can be effectively coupled, and the application of blockchain technology can promote the effective supervision of enterprises' emission behavior, making the processes of the purchase, transfer and transaction of emission rights intelligent and automated, and providing technical support for cross-regional emission trading to reduce transaction costs and management complexity. In addition, the issuance of emission credits based on smart contract will be a new incentive for companies to actively participate in transactions. Based on the above analysis, this paper believes that the innovative application of blockchain technology is of great significance in the promotion of the market-based allocation of element of emission trading and the rational allocation of environmental resources. It will lead to a major breakthrough in the traditional trading system in terms of trading modes, forming a value transmission network of environmental resources between the government and polluters.

Разработка методики и комплекса программных средств для дистанционного элек-тронного голосования на основе блокчейн-платформы Ethereum

The most promising way to increase voters’ confidence in the remote electronic voting (REV) procedure is a voting method based on Ethereum blockchain platform. However, the existing solutions using this method faced a range of problems: ensuring the secrecy of the vote and openness of the procedure for society, pressure on the voter and a guarantee of the reliability of the whole system. In this work, a method for constructing a REV is proposed that solves these problems. It is similar in structure to the traditional voting method, using the same principle and processes. The Ethereum blockchain based REV process is described in detail. It was shown that received votes are securely stored in the Ethereum blockchain network, and the correctness of the vote addressing to the selected candidate can always be checked in real time. The description of smart contract algorithm that implements the transfer of vote from voter to candidate using transactions and determines the winner who received the highest number of votes was provided. It was demonstrated that keccak256 hashing algorithm and secp256k1 elliptic curve signatures ensure transactions’ maximum protection, reliability, and non-rollability. The developed REV technique based on Ethereum blockchain platform increases the efficiency of data security and confidentiality, transparency and anonymity of the voting procedure, and solves the problem of coercion. The results of the work have been implemented programmatically and can be used not only in the electoral system, but also wherever there is need of remote voting.

Optimal Bounds for the k -cut Problem

In the k -cut problem, we want to find the lowest-weight set of edges whose deletion breaks a given (multi)graph into k connected components. Algorithms of Karger and Stein can solve this in roughly O ( n 2k ) time. However, lower bounds from conjectures about the k -clique problem imply that Ω ( n (1- o (1)) k ) time is likely needed. Recent results of Gupta, Lee, and Li have given new algorithms for general k -cut in n 1.98k + O(1) time, as well as specialized algorithms with better performance for certain classes of graphs (e.g., for small integer edge weights). In this work, we resolve the problem for general graphs. We show that the Contraction Algorithm of Karger outputs any fixed k -cut of weight α λ k with probability Ω k ( n - α k ), where λ k denotes the minimum k -cut weight. This also gives an extremal bound of O k ( n k ) on the number of minimum k -cuts and an algorithm to compute λ k with roughly n k polylog( n ) runtime. Both are tight up to lower-order factors, with the algorithmic lower bound assuming hardness of max-weight k -clique. The first main ingredient in our result is an extremal bound on the number of cuts of weight less than 2 λ k / k , using the Sunflower lemma. The second ingredient is a fine-grained analysis of how the graph shrinks—and how the average degree evolves—in the Karger process.

Blockchain-Based Trusted Property Transactions in the Built Environment: Development of an Incubation-Ready Prototype

Blockchain can be introduced to use cases in the built environment where reliability of transaction records is paramount. Blockchain facilitates decentralised, cryptographically secure, trustworthy, and immutable recordkeeping of transactions. However, more research is urgently required to understand the process and complications in implementing blockchain solutions in the built environment. This paper demonstrates a methodology for developing a blockchain system starting from problem analysis, selection of blockchain platform, system modelling, prototype development, and evaluation. The evolutionary prototyping model was selected as the software development methodology for the use case of property transactions. A systematic process protocol involving the multi-criteria decision-making method, Simple Multi Attribute Rating Technique (SMART), was used to select Hyperledger Fabric as the most suitable blockchain platform for the prototype. The system architecture facilitates a simplified, lean property transaction process implemented through chaincode (smart contract) algorithms and graphical user interfaces. System evaluation through test cases allowed iterative improvements, leading to an incubation-ready software prototype. The contribution to knowledge of this paper is in the demonstration of the process to follow to implement a blockchain solution for a specific domain. The findings provide the foundation for developing proofs of concept for other potential applications of blockchain in the built environment.

Further connections between contract-scheduling and ray-searching problems

This paper addresses two classes of different, yet interrelated optimization problems. The first class of problems involves a mobile searcher that must locate a hidden target in an environment that consists of a set of unbounded, concurrent rays. The second class pertains to the design of interruptible algorithms by means of a schedule of contract algorithms. Both types of problems capture fundamental aspects of resource allocation under uncertainty. We study several variants of these families of problems, such as searching and scheduling with probabilistic considerations, redundancy and fault-tolerance issues, randomized strategies, and trade-offs between performance and preemptions. For many of these problems, we present the first known results that apply to multi-ray and multi-problem domains. Our objective is to demonstrate that several well-motivated settings can be addressed using the same underlying approach.

Relaxed inertial methods for solving the split monotone variational inclusion problem beyond co-coerciveness

We study the split monotone variational inclusion problem in two real Hilbert spaces. Combining the inertial and relaxation techniques with the proximal contraction algorithm, we propose two new methods for solving this problem without the usual co-coerciveness assumption on the associated operators.