Huge Scale Research Articles

Analysis of the Development Strategy of Urban Investment Companies Under the Perspective of Financial Generosity

This paper analyzes the current situation of the city investment company in terms of capital, funds and operation from the perspective of financial prosperity, based on the huge financing scale and potential risk situation of the city investment company, and takes A County City Investment Company as an example, and provides a new development mode and direction for the revitalization of the economy of the city investment company. From the perspective of "financial prosperity", it provides a new development mode and direction for revitalizing the economy of city investment company, and puts forward corresponding development strategies from the aspects of asset management, capital management and operation management.

Mathematical Approaches to Circuit Optimization in VLSI Design

Exceptionally Huge Scale Integration (VLSI) plan is continuously trying to find ways to improve circuit speed, lower control utilize, and make the finest utilize of space. A big portion of coming to these objectives is utilizing numerical strategies, which allow organized ways to form circuits work way better. This paper looks at a number of distinctive numerical methods and how they can be utilized in VLSI plan. It centers on how well these strategies can progress the value and productivity of circuits. Optimization calculations, like mimicked toughening, hereditary calculations, and molecule swarm optimization, are a prevalent way to see at math issues. Iteratively investigating plan regions is how these strategies discover combinations that grant superior execution measures, such as speed, control, and zone. Case considers appear that they are great at moving forward complicated circuits, appearing enormous picks up in plan parameters compared to old-fashioned heuristic strategies. Scientific modeling is additionally exceptionally imperative for accurately appearing how a circuit will work and anticipating execution measurements some time recently the genuine application. Direct programming and numbers programming are two strategies that offer assistance software engineers set limits and objectives, which leads to superior circuit structure and asset allotment. These models offer assistance individuals make choices by giving them numbers that appear the trade-offs between diverse plan objectives, like speed vs. power consumption or range minimization. Utilizing huge information sets and prescient analytics, machine learning procedures have moreover changed optimization strategies since they were included to VLSI plan.

Evaluating the Impact of Universal Health Coverage Policies on Population Health Outcomes Using Big Data Analytics for Comprehensive Policy Assessment

Universal Health Coverage (UHC) aims to eliminate financial barriers to care which is important for everyone’s affordability. Thus, it is helpful to study how UHC affects population health outcomes and where it may be headed in the future. Big Data Analytics provides good tools for scrutinizing those principles and their results on a huge scale. Communities differ in terms of their health outcomes; and health systems are complex. This paper tries to address these among other issues by integrating big data sets that are diverse and analysing them together. As an innovative way of examining universal health coverage initiatives’ impact on populations, researchers propose the Comprehensive Analysis of Fragmented Health Systems (CA-FHS). CA-FHS uses Big Data Analytics to compile and analyse information from many different sources thereby giving an exhaustive breakdown of health outcomes by demography as well as geographic area. This would allow trends or patterns not seen using conventional evaluation tools to be discovered. It encompasses public health, policy-making, as well as healthcare management concerns. In this way, the method may bring out the strengths and weaknesses inherent in the healthcare system such that policies are recommended for change while resource allocation is done for bettering UHC-related consequences internationally. It will enable the evaluation of long-term effects resulting from these projects so that they can meet their goals eventually. The process will involve creating hypothetical policy scenarios and then assessing how these would affect population’s historical health outcomes through use of historical data simulation techniques; thus providing input into potential policy choices at federal level concerning evidence-based recommendations towards achieving improvement in health status.

Analysis of Existing Problems in Chinese A-share Market and Relevant Solutions

China's A-shares have developed rapidly in recent years, with huge market volume and scale, and have become an important direction for corporate financing and mass investment. There are still many problems in the A-share market, which will affect the development of the A-share market. There is a problem of slow delisting in the A-share market, which will lead to bad enterprises not being cleared in time, and the frequency of market update is not enough, which needs to be alleviated by further improving the legal system and changing policies. Some sectors of the A-share market have the problem of downward market, resulting in financing difficulties for companies in these sectors and unable to develop normally. This problem can be fixed by increasing the proportion of companies that pay dividends to attract investors. Listed companies in the A-share market may also have goodwill problems, which will seriously affect investor confidence and reduce the amount of investment. It is necessary to improve the goodwill accounting policy, so that goodwill can be correctly measured and reflected. Finally, A-share market funds have the problem of homogeneity, which leads to vicious competition in a single sector and is unfriendly to small and medium-sized enterprises. The government can mitigate such problems by encouraging innovation and regulating the market, diversifying stock development. It is necessary to study the problems of the A-share market and give solutions of the existing problems, which can make the country, enterprises and investors have a more intuitive understanding of the market so they can make corresponding responses to market changes.

Exploring the Dynamics of Charge Transfer in Photocatalysis: Applications of Femtosecond Transient Absorption Spectroscopy.

Artificial photocatalytic energy conversion is a very interesting strategy to solve energy crises and environmental problems by directly collecting solar energy, but low photocatalytic conversion efficiency is a bottleneck that restricts the practical application of photocatalytic reactions. The key issue is that the photo-generated charge separation process spans a huge spatio-temporal scale from femtoseconds to seconds, and involves complex physical processes from microscopic atoms to macroscopic materials. Femtosecond transient absorption (fs-TA) spectroscopy is a powerful tool for studying electron transfer paths in photogenerated carrier dynamics of photocatalysts. By extracting the attenuation characteristics of the spectra, the quenching path and lifetimes of carriers can be simulated on femtosecond and picosecond time scales. This paper introduces the principle of transient absorption, typical dynamic processes and the application of femtosecond transient absorption spectroscopy in photocatalysis, and summarizes the bottlenecks faced by ultrafast spectroscopy in photocatalytic applications, as well as future research directions and solutions. This will provide inspiration for understanding the charge transfer mechanism of photocatalytic processes.

Isolation and characterisation of Stenotrophomonas maltophilia, a multidrug resistant opportunistic pathogen from cultured Rainbow trout (Oncorhynchus mykiss, walbaum, 1792) in India

The farming of Rainbow trout, an exotic fish has advanced on a huge scale in north western regions of India which provides the best suitable climate and topography for its culture. The advancement in farming is challenged by the infectious diseases. The isolation of Stenotrophomonas maltophilia, an opportunistic pathogen has been reported from the cultured channel catfish in china, and cultured African catfish in West Bengal, India. The present study reports isolation of Stenotrophomonas maltophilia from the kidney of Rainbow trout, Oncorhynchus mykiss along with its biochemical, 16 s rRNA sequencing and phylogenetic analysis. Antibiotic susceptibility of isolates to 10 antimicrobial drugs was investigated using Disc diffusion method. The isolates were found to be resistant to multiple drugs, characterised by resistance to all Beta-lactams (Methicillin and Ampicillin), Macrolides (Erythromycin, Azithromycin and Rifampicin), aminoglycosides (Streptomycin, Amikicin) except Gentamicin and susceptible to Tetracycline and Chloramphenicol antibiotics. The auto-aggregation capability of the isolates showed 55.02 ± 5.9 and 49.52 ± 7.6% auto-aggregation after 24 h of incubation, respectively. The isolates showed 32.77 ± 6.78 and 29.41 ± 7.71 % hydrophobicity after 2 h of extraction with xylene, respectively. The 16 s rRNA sequences of the were submitted to the GenBank database with the accession numbers OP647420 and PP077305.

EXPLOITATION AND PROCESSING OF TECHNICAL STONE FROM QUARRIES PLJEŠEVAC NEAR KISELJAK

Today, technical stone is a material that is used on a huge scale, and its annual consumption in the world is expressed in thousands of millions of tons. The demand for construction, such as houses, hospitals, hotels, schools and so on, has expanded, especially in developing countries. One of the key elements that increases the demand for such products is the numerous government infrastructure projects and maintenance programs. All levels of government in Bosnia and Herzegovina strive to implement good infrastructure, because in this way they enable trade, support industry, connect workers with jobs, and in this way bring hope for prosperity in these areas. The paper gives a brief overview of the production and processing of dolomite from the "Plješevac" quarry near Kiseljak as a positive example from the practice of "production and processing from raw material to final product". Key words: quarry, dolomite, aggregate, technical stone, exploitation, processing

Arsenic accumulation in spolic technosols in the area of a large copper smelting plant in the Middle Urals

Relevance. The necessity to study arsenic behaviour in soils as one of the main pollutants of depositing environments in areas of mining and metallurgical industry. In recent decades, arsenic emissions into the environment have reached a huge scale, so this element attracts the attention of researchers around the world. But in spite of all the studies of this problem, there are still some aspects that need to be clarified to understand the geochemistry of arsenic. This paper deals with spolic technosols with critically high arsenic content in the soil profile with alternating water-logged and dry conditions. As one of the main soil-forming elements, orthosteins contain significant amounts of iron and manganese compounds and are capable of adsorption and involvement of the elements in nodule formation. Aim. To determine the influence of orthosteins on arsenic accumulation in soil subjected to intensive anthropogenic load. Object. Ortsteins spolic technosols in the zone of operation of copper smelting plant and background soils not exposed to pollution. Methods. Arsenic was determined by inversion voltammetry method. Morphological characterisation and patterning of the orthosteins were performed by scanning electron microscopy using a Thermo Fisher Phenom XL G2 Desktop SEM unit. Results. The study of morphological features of orthosteins revealed differences in soils subjected to greater anthropogenic load. Arsenic was not detected inside the orthosteins in all studied soils. Also arsenic concentrations in the total mass of ortstein is up to 40–50% of the soil mass. This fact indicates the accumulation of arsenic by surface adsorption and the appearance of orthosteins as a geochemical barrier. The data obtained in the course of this work can contribute to the search for ways to clean up soils contaminated with arsenic, as well as provide an understanding of the basic processes of the behaviour of this element in soil formation.

ADNet: A Real-Time Floating Algae Segmentation Using Distillation Network

The segmentation of floating algae is a hot topic in the field of marine environmental research. Given the vastness of coastal areas and complex environments, algae detection models must have both higher performance and lower deployment costs. However, relying solely on a single Convolutional Neural Network (CNN) or transformer structure fails to achieve this objective. In this paper, a novel real-time floating algae segmentation method using a distillation network (ADNet) is proposed, based on the RGB images. ADNet can effectively transfer the performance of the transformer-based teacher network to the CNN-based student model while preserving its lightweight design. Faced with complex marine environments, we introduce a novel Channel Purification Module (CPM) to simultaneously strengthen algae features and purify interference responses. Importantly, the CPM achieves this operation without increasing any learnable parameters. Moreover, considering the huge scale differences among algae targets in surveillance RGB images, we propose a lightweight multi-scale feature fusion network (L-MsFFN) to improve the student’s modeling ability across various scales. Additionally, to mitigate interference from low-level noises on higher-level semantics, a novel position purification module (PPM) is proposed. The PPM can achieve more accurate weight attention calculation between different pyramid levels, thereby enhancing the effectiveness of fusion. Compared to CNNs and transformers, our ADNet strikes an optimal balance between performance and speed. Extensive experimental results demonstrate that our ADNet achieves higher application performance in the field of floating algae monitoring tasks.

Dense Sample Deep Learning.

Deep learning (DL), a variant of the neural network algorithms originally proposed in the 1980s (Rumelhart etal., 1986), has made surprising progress in artificial intelligence (AI), ranging from language translation, protein folding (Jumper et al., 2021), autonomous cars, and, more recently, human-like language models (chatbots). All that seemed intractable until very recently. Despite the growing use of DL networks, little is understood about the learning mechanisms and representations that make these networks effective across such a diverse range of applications. Part of the answer must be the huge scale of the architecture and, of course, the large scale of the data, since not much has changed since 1986. But the nature of deep learned representations remains largely unknown. Unfortunately, training sets with millions or billions of tokens have unknown combinatorics, and networks with millions or billions of hidden units can't easily be visualized and their mechanisms can't be easily revealed. In this letter, we explore these challenges with a large (1.24 million weights VGG) DL in a novel high-density sample task (five unique tokens with more than 500 exemplars per token), which allows us to more carefully follow the emergence of category structure and feature construction. We use various visualization methods for following the emergence of the classification and the development of the coupling of feature detectors and structures that provide a type of graphical bootstrapping. From these results, we harvest some basic observations of the learning dynamics of DL and propose a new theory of complex feature construction based on our results.

SHA 512 Enhanced Secure Hash Decentralized Response System

Today a very huge lack of confidence about the current EVM/ballot response system like voting has made voting response collection in any democratic country very condemnatory. People have watched their fundamental rights which are being violated. On the other hand the other digital voting systems are also in danger due to a minimal of transparency. Most of the traditional response systems are not clear enough so as to meet today's requirements; this crucial reason makes it very sweaty for the government to come up with voters' trust. The motive behind the downfall of the traditional and current cyber voting system is that it can be easily compromised due to centralized network. The primary objective here is to counter the issues of the traditional digital voting and such response systems, which has any type of mistrust or inequality during the process of accepting responses through response systems like voting. Blockchain technology can be used in such response systems like voting to have a fair response system and reduce wrongdoing. The current physical response systems like voting have many downs in it as well as the digital voting systems are not up to the mark to be implemented on a huge scale. Such all problems raises the need for a solution to secure the democratic rights of the people. This article put-ups a framework based on up-to-date blockchain algorithm technology that provides high clarity and reliability of the system to construct a healthy relationship between response systems and election authorities. The insisted software provides a framework that can be implemented in real world to conduct voting like procedures digitally through blockchain rather than involving any physical polling stations and centralized ledger. Our proposed framework provides an extendible hashing, by using the latest consensus algorithms. The Secure Hash Algorithms applied in the voting system along with Chain Security makes the response systems more secure. Distributed ledger contracts also provide a protected connection between the user and the consensus network while running a transaction in the chain. The security of the blockchain based response system has also been discussed. In Addition to this, encryption of responses using cryptographic hash functions and prevention of exploitation attacks about 51% on the blockchain has also been elaborated. Also, the procedure for carrying out blockchain transactions during the process of response collection has been elaborated using Blockchain. Finally, the throughput performance evaluation of the proposed system shows that such a system can be deployed in a large-scale real time population.

Vertical control framework with levelling method as the based for detailed mapping in UNS Campus of Pabelan

Detail maps are one of the outputs created by measuring soil and mapping the topography of the earth’s surface on a huge scale. Preliminary steps must be completed, including the creation of fundamental mapping framework points that are evenly scattered across the area to be mapped. The goal of this study is to construct a vertical control framework and determine the accuracy of vertical control frameworks basic outline points in the form of polygons, measuring, processing data, displaying in the form of different height images and verifying their accuracy, and assessing the findings comprise the implementation technique. Vertical Control Framework measurement employs the chain levelling method and a levelling measuring tool. The results of the data analysis obtained a high difference of 8 mm in the results of the point before the correction, so the calculation of the height of the point after correction so that the height of the point equals zero in order to meet the feasibility as the basic framework of detail mapping and can be used for further measurements. The implications of the vertical control framework are utilized as a guide to generate a detail map.

Segmenting large historical notarial manuscripts into multi-page deeds

Archives around the world hold vast digitized series of historical manuscript books or “bundles” containing, among others, notarial records also known as “deeds” or “acts”. One of the first steps to provide metadata which describe the contents of those bundles is to segment them into their individual deeds. Even if deeds are often page-aligned, as in the bundles considered in the present work, this is a time-consuming task, often prohibitive given the huge scale of the manuscript series involved. Unlike traditional Layout Analysis methods for page-level segmentation, our approach goes beyond the realm of a single-page image, providing consistent deed detection results on full bundles. This is achieved in two tightly integrated steps: first, we estimate the class-posterior at the page level for the “initial”, “middle”, and “final” classes; then we “decode” these posteriors applying a series of sequentiality consistency constraints to obtain a consistent book segmentation. Experiments are presented for four large historical manuscripts, varying the number of “deeds” used for training. Two metrics are introduced to assess the quality of book segmentation, one of them taking into account the loss of information entailed by segmentation errors. The problem formalization, the metrics and the empirical work significantly extend our previous works on this topic.

Taylor political optimizer‐based cluster head selection in IOT‐assisted WSN networks

SummaryIoT‐assisted WSN contains various nodes, which are placed on a huge scale that increases complications. Thus, the challenges and issues of these networks fluctuate as compared to WSNs. Hence, sensor nodes are imperative unit that runs on less energy resources. Hence, devising a robust and energy‐effective protocol for increasing network lifetime is a complex task. This paper devises a novel hybrid optimization‐driven approach for selecting cluster head (CH) in IoT‐assisted WSNs. Initially, the simulation of IoT nodes is done by configuration. Thereafter, the Cluster Head selection is done using a newly devised optimization technique, namely the Taylor‐Political optimizer (Taylor‐PO). Thus, fitness is newly developed by adapting certain attributes like energy, delay, inter and intra‐cluster distance, Link Lifetime (LLT), predicted energy, and delay. Here, the multipath routing is accomplished using Tunicate Swarm gray wolf optimization (TSGWO). Thus, the proposed Taylor‐PO is offered for effective Cluster Head selection along with multipath routing using TSGWO. The proposed Taylor‐PO offered improved performance with smallest delay of 0.006 sec, highest energy of 2.368 J, highest throughput of 494.043 kbps.

Circular olefin copolymers made de novo from ethylene and α-olefins

Ethylene/α-olefin copolymers are produced in huge scale and widely used, but their after-use disposal has caused plastic pollution problems. Their chemical inertness made chemical re/upcycling difficult. Ideally, PE materials should be made de novo to have a circular closed-loop lifecycle. However, synthesis of circular ethylene/α-olefin copolymers, including high-volume, linear low-density PE as well as high-value olefin elastomers and block copolymers, presents a particular challenge due to difficulties in introducing branches while simultaneously installing chemical recyclability and directly using industrial ethylene and α-olefin feedstocks. Here we show that coupling of industrial coordination copolymerization of ethylene and α-olefins with a designed functionalized chain-transfer agent, followed by modular assembly of the resulting AB telechelic polyolefin building blocks by polycondensation, affords a series of ester-linked PE-based copolymers. These new materials not only retain thermomechanical properties of PE-based materials but also exhibit full chemical circularity via simple transesterification and markedly enhanced adhesion to polar surfaces.

A review on network representation learning with multi-granularity perspective

Network data is ubiquitous, such as telecommunication, transport systems, online social networks, protein-protein interactions, etc. Since the huge scale and the complexity of network data, former machine learning system tried to understand network data arduously. On the other hand, thought of multi-granular cognitive computation simulates the problem-solving process of human brains. It simplifies the complex problems and solves problems from the easier to harder. Therefore, the application of multi-granularity problem-solving ideas or methods to deal with network data mining is increasingly adopted by researchers either intentionally or unintentionally. This paper looks into the domain of network representation learning (NRL). It systematically combs the research work in this field in recent years. In this paper, it is discovered that in dealing with the complexity of the network and pursuing the efficiency of computing resources, the multi-granularity solution becomes an excellent path that is hard to go around. Although there are several papers about survey of NRL, to our best knowledge, we are the first to survey the NRL from the perspective of multi-granular computing. This paper proposes the challenges that NRL meets. Furthermore, the feasibility of solving the challenges of NRL with multi-granular computing methodologies is analyzed and discussed. Some potential key scientific problems are sorted out and prospected in applying multi-granular computing for NRL research.

The Use of Solar Energy for Heating Premises in the Southern Regions of the Russian Federation in the Spring-Autumn Period

The task of restoring the housing stock in destroyed cities and towns in the south of Russia is urgent. It will be paramount after the restoration of peaceful life in the DPR, LPR, Kherson, and Zaporozhye regions of the Russian Federation. It is known that in the Krasnodar region, in the northern Caucasus, and in other regions of southern Russia there is a shortage of electricity. Restoring energy-intensive industries in Donbass will require large electricity capacities. At the same time, the climate of southern Russia allows the use of the Sun in housing construction as an inexhaustible source of renewable energy. At the same time, the huge scale of restoration of the housing stock requires the design and construction of inexpensive energy-efficient housing, which must comply with the requirements of Federal Law No. 261 of June 11, 2021 “On energy saving and increasing energy efficiency and on introducing amendments to certain legislative acts of the Russian Federation”.

Big Data in Social Media: Introduction, Methodology, Applications.

In this paper, we have discussed about how Big-Data has influenced the working, modeling and behavior of social media on a huge scale. Not only use but also storing and restructuring of data have created a new model and pattern of data on its own. Big-data is used and managed in a lot many ways by the different social media applications and platforms according to the need and methodology accepted. We also saw how we can use this available data can help us to grow our business

Adopting Text Mining for Patent Analysis to Determine the Attribute and Segment in Automotive Industries

Analysing massive patent documents in heavy industries including automotive has become important in recent years as they contain a lot of information that is extremely difficult to deal with from huge numbers and various forms. Important documents such as patent data on a huge scale has become a major concern owing to time constraints and enormous costly work. In natural language processing (NLP), text mining is used to determine several features such as segmentations and attributes of data. The important step in data mining is pre-processing data information from massive text data. In this paper, the fundamental concept of pre-processing and data analysing is examined to provide accurate and meaningful information from the chosen data set. This study focuses on two automotive companies, namely Mazda and Mitsubishi to describe the similarities, distances and frequencies between several patent documents. To demonstrate the behaviour of selected patent documents, word cloud, co-occurrence networks and correspondence analysis are also presented.

Sustainable Development of Construction Industry in China

The research aims to determine the direction of sustainable development of the construction industry in China and ensure its regular and stable development. A series of data comparisons and methods of the comprehensive analysis showed that the construction industry in China, on the one hand, is a major industry with a huge scale and a significant position on the international market. On the other hand, it is also an industry with low investment in technology, high energy consumption, relatively backward engineering technology, and management. Besides, management and operation methods are relatively traditional and associated with challenges of industry development. The comparison with advanced manufacturing industries and foreign developed countries clarified the shortcomings of China’s construction industry and set the directions for future development. These include (1) active development of green building, (2) promotion of the industrialization process, (3) improvement of the technology application level, and (4) timely improvements of the relevant regulations. China also needs to adjust the industrial form of the construction industry and increase the internationalization level. Based on the methods of data description research and literature review, we identified the conditions for sustainable development of the construction industry and proved the importance of using Integrated Project Delivery [IPD] and BIM (Building Information Modeling) technology in the development of the Chinese construction industry. The research allowed us to understand the important role of technology and organization management in developing the construction industry. When we continue to learn lessons from advanced science and technology, smart organization, and management and implement them in the construction industry, only then it will develop sustainably.