Since the reform and opening up, China's economy has achieved amazing growth. At the same time of industrialization transformation, the traditional high-energy consumption and external growth model has brought ser...
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Since the reform and opening up, China's economy has achieved amazing growth. At the same time of industrialization transformation, the traditional high-energy consumption and external growth model has brought serious environmental problems, and the pollution control situation is facing many difficulties. The Yellow River Valley gives birth to the Chinese civilization. With the formulation of the national strategy for ecological protection and high-quality development in the Yellow River Basin and the demand for green development, the control of industrial pollution has become the main task of environmental protection in the Yellow River Basin. However, the research on the ecotope pollution in the Yellow River Valley mainly stays in the theory and strategy. In response to the above problems, this paper would carry out scientific dataanalysis and high-dimensional research on the ecotope pollution in the Yellow River Valley from the perspective of green development and data analysis algorithms. The research results showed that the reconstruction probability of the Threshold-based Sparsity Adaptive Matching Pursuit (TSAMP) algorithm remained 100% when K was less than 50.
One of the fundamental challenges in analyzing wind turbine performance is the occurrence of torque creep under load and without load. This phenomenon significantly impacts the proper functioning of torque transducers...
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One of the fundamental challenges in analyzing wind turbine performance is the occurrence of torque creep under load and without load. This phenomenon significantly impacts the proper functioning of torque transducers, thus necessitating the utilization of appropriate measurement data analysis algorithms. In this regard, employing the least squares method appears to be a suitable approach. Linear regression can be employed to investigate the creep trend itself, while visualizing the creep in the form of a non-linear curve using a third-degree polynomial can provide further insights. Additionally, calculating deviations between the measurement data and the regression curves proves beneficial in accurately assessing the data.
The accuracy of determining the parameters of the first layer of an optical coating is of crucial importance for the accuracy of monitoring the deposition process of an entire multilayer coating. In this paper we prop...
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The accuracy of determining the parameters of the first layer of an optical coating is of crucial importance for the accuracy of monitoring the deposition process of an entire multilayer coating. In this paper we propose a nonlocal algorithm for analyzing optical monitoring data, which allows the use of a more accurate layer model. The possibilities of the new nonlocal algorithm are demonstrated based on the example of studying the parameters of the first coating layer made of niobium oxide, which has a high refractive index and is one of the main materials used for film coatings.
Electoral boundaries are an integral part of election administration. District boundaries delineate which legislative election voters are eligible to participate in, and precinct boundaries identify, in many localitie...
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Electoral boundaries are an integral part of election administration. District boundaries delineate which legislative election voters are eligible to participate in, and precinct boundaries identify, in many localities, where voters cast in-person ballots on Election Day. Election officials are tasked with resolving a tremendously large number of intersections of registered voters with overlapping electoral boundaries. Any large-scale data project is susceptible to errors, and this task is no exception. In two recent close elections, these errors were consequential to the outcome. To address this problem, we describe a method to audit the assignment of registered voters to districts. We apply the methodology to Florida's voter registration file to identify thousands of registered voters assigned to the wrong state House district, many of which local election officials have verified and rectified. We discuss how election officials can best use this technique to detect registered voters assigned to the wrong electoral boundary.
After seeking a "manageable standard" to apply to claims of partisan gerrymandering for over three decades, the Supreme Court has finally given up the chase, ruling that such claims are nonjusticiable. What ...
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After seeking a "manageable standard" to apply to claims of partisan gerrymandering for over three decades, the Supreme Court has finally given up the chase, ruling that such claims are nonjusticiable. What is to be done? An extended history of successful congressional action suggests that the legislative pathway is more practical than often believed. Statutory requirements also make it possible to consider a broader suite of districting objectives. This paper presents a flexible new software and a framework for evaluating the practical implications of explicit objectives. I apply this approach to the conditions last required by Congress, generating equipopulous, contiguous, and compact districts. Among these conditions, the formal definition of compactness has proven contentious. Does it matter? I contrast the representation of the political parties and of racial and ethnic minorities under plans optimized according to 18 different definitions of compactness. On these grounds, the definitions are markedly consistent. These methods may be extended to alternative districting objectives and criteria.
Computers hold the potential to draw legislative districts in a neutral way. Existing approaches to automated redistricting may introduce bias and encounter difficulties when drawing districts of large and even medium...
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Computers hold the potential to draw legislative districts in a neutral way. Existing approaches to automated redistricting may introduce bias and encounter difficulties when drawing districts of large and even medium-sized jurisdictions. We present a new algorithm that can neutrally generate legislative districts without indications of bias that are contiguous, balanced and relatively compact. The algorithm does not show the kinds of bias found in prior algorithms and is an advance over previously published algorithms for redistricting because it is computationally more efficient. We use the new algorithm to draw 10,000 maps of congressional districts in Mississippi, Virginia, and Texas. We find that it is unlikely that the number of majority-minority districts we observe in the Mississippi, Virginia, and Texas congressional maps of these states would happen through a neutral redistricting process.
Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common...
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ISBN:
(数字)9781118889398
ISBN:
(纸本)9781118889060
Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive dataanalysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data
A simple and efficient dataanalysis algorithm for biological oxygen consumption assays based on quenched-fluorescence oxygen sensing is presented. The algorithm allows the linearization of raw fluorescence profiles i...
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A simple and efficient dataanalysis algorithm for biological oxygen consumption assays based on quenched-fluorescence oxygen sensing is presented. The algorithm allows the linearization of raw fluorescence profiles in transformed co-ordinates thereby facilitating data processing and generating accurate results for multiple samples analysed in parallel on a fluorescence reader. This approach provides stability with respect to the uncertainty of the reaction start time, it allows on-the-flight determination of kinetic parameters by pairwise reading method, and additional filtration of raw data leading to improved accuracy. This methodology was validated with three independent sets of data from the analysis of activity and inhibition of isolated rat liver mitochondria representing a complex oxygen-dependent enzymatic system. It demonstrated good performance allowing accurate determination of enzymatic parameters from simple linear regression analysis (R-2 as high as 0.999), good correlation with simulated data and general convenience. (C) 2007 Elsevier B.V. All rights reserved.
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