Flooding disaster causes huge impacts on the socio-economic world. In the inundated area, some geo-referenced images are shared through some media posts, which assist in providing alertness to the critical volunteers and managing the financial loss crisis. In this work, the Adaptive Billiards-Inspired Optimization (A-BIO) and Optimized Ensemble-learning-based detection (OED) with map reduce framework is proposed for flood disaster detection. Initially, the big data is gathered and processed for detection. During the map phase, data preprocessing is performed to enhance the performance of the data, which helps in removing the noise or unwanted attributes. Furthermore, the reduction phase can be done through weighted feature selection, where the features to be selected and the weight is optimized through A-BIO, which assists in getting the most significant features for improving the performance and reducing the complexity of the designed model. Finally, OED is performed by a set of classifiers like Convolutional Neural Networks, Adaboost, XGBoost, Long Short-Term Memory, and Deep Neural Networks, where the parameters of ensemble learning classifiers are optimized by A-BIO algorithm. Finally, through the performance analysis, this detection model can provide high accuracy and better detection performance to avoid the huge impacts of flood disasters.
Using artificial intelligence to anticipate weather conditions, according to prior research, can provide positive results. Forecasts of meteorological time series can aid disaster-prevention personnel in making more informed judgments. Deep learning has recently been shown to be a viable technique for solving complicated issues and analyzing large amounts of data. Statistical learning theory is a type of machine learning that combines statistics and functional analysis. To answer the problem of rainfall forecasting, this study employs a statistically-based machine learning technique. The benchmark meteorological data is first pre-processed using data augmentation and data normalization. The machine learning is then given statistical characteristics such as "first order and second order statistical information" for prediction. The Adaptive Searched Scaling factor-based Elephant Herding Optimization (ASS-EHO)is used to optimize the Cascaded Convolutional Neural Network (CNN) for rainfall prediction as an improved prediction model, with parameter tuning such as cascaded CNN count, hidden neuron count, and activation function optimized. The new prediction model is a statistical-based machine learning model in which the aim function is the reduction of the cross entropy loss function. The results are compared to established statistical methodologies, demonstrating that the model may be used to estimate daily rainfall quickly and accurately.
Black Carbon (BC) is an important constituent of both aquatic and terrestrial environment, but also has several adverse effects on human health, aquatic life, and contributes to the global climate change. Thus, to understand the fate and transport of BC nanoparticles (NPs) in the environment, it's important to understand the colloidal stability or aggregation behaviour and factors affecting it, under various environmental conditions, including both aquatic and atmospheric. This study investigated the individual influence of ionic strengths, valence (Na+, Ca2+ and Mg2+), metals (Zn2+, Cu2+, Ni2+ and Cd2+), and organic substances (PO43- and Humic Acid: HA) on the effective diameter or hydrodynamic diameter and zeta potential of BC-NPs in aquatic systems. A dynamic light scattering (DLS) principle-based 90 Plus Particle Size Analyzer was used for measurements of BC particle size and zeta potential at varying ionic chemistry. The results showed that strong ionic strength promotes aggregation of BC-NPs till the repulsion forces become dominant due to more negative zeta potential. The Aggregation of BC-NPs was observed to be significantly dependent on the ionic valence, where divalent ions caused more aggregation than monovalent ions. Metal ions at higher concentration (around 1 mM) promoted the aggregation rate of BC-NPs, and Cu+2 dominated among all selected metals. Conversely, organic matter (PO43- and HA) tends to promote stabilisation of BC-NPs instead of aggregation. Though this study investigated individual effect of substances, influence of possible environmental combination of substances will help to get more clear idea.
Various studies have been conducted on the perfluorochemicals (PFCs) family over the years. These compounds have been sought in various industrial aspects involving the synthesis of everyday utilities due to their broad range of applications. As a result, PFCs have built up in the environment, causing concern. The presence of PFCs in various environmental media, such as terrestrial and marine settings, as well as the mechanisms of transport, bioaccumulation, and physio-chemical interactions of PFCs within plants, aquatic organisms, microplastics, and, ultimately, the human body, are discussed in this review, which draws on a variety of research publications. The interaction of PFCs with proteins, translocation, and adsorption by hydrophobic interactions were observed, and this had an impact on the natural functioning of biological processes, resulting in events such as phylogenic clustering, competitive inhibition, and many others, posing potential hazards to human health and other relevant organisms in the ecosystem. However, further research is needed to have a better knowledge of PFCs and their interactions so that low-cost treatments can be developed to eliminate them. It is therefore, future research should focus on the role of soil matrix as a defensive mechanism for PFCs, as well as the impact of PFC chain length rejection.
The Internet of Things (IoT) system is composed of several numbers of sensor nodes and systems, which are wirelessly interlinked to the internet. Generally, big data is the storage of a huge amount of information, which causes the classification process to be very challenging. Numerous big data classification approaches are implemented, but the computational time and secure handling of information are the major problems. The aim of the study is the development of big data approach in Internet of Things (IoT) healthcare application. Hence, this paper presents the proposed Dragonfly Rider Competitive Swarm Optimization-based Deep Residual Network (DRCSO-based DRN) for big data classification in IoT. First, the IoT nodes are simulated, and the heart disease patient data are collected through sensors. The routing is done using the Multi-objective Fractional Gravitational Search Algorithm (Multi-objective FGSA). In the Base Station (BS), the big data classification is done. Here, the classification is done using MapReduce (MR) framework, which includes two phases, like mapper and the reducer phase. The input data is initially fed to the mapper phase in the map-reduce (MR) framework. In the mapper phase, feature selection is carried out based on Dragonfly Rider Optimization Algorithm (DROA) in order to select the appropriate features for further processing. The DROA is modeled through merging Dragonfly Algorithm (DA) and Rider Optimization Algorithm (ROA). In the reducer phase, the classification is performed using DRN, which is trained by the developed DRCSO algorithm. The DRCSO is modeled by incorporating DA, ROA and Competitive Swarm Optimization (CSO). In addition, the performance of the developed method is outperformed than the existing approaches such as Linguistic Fuzzy Rules with Canopy Mapreduce (LFR-CM) + Fuzzy classifier, Machine learning-dependent k-nearest neighbors (FML-KNN), MapReduce-Fuzzy Integral-dependent Ensemble Learning Model+Single hidden layer feedforward neural network (MR-FI-ELM + SLFN) and DROA-recurrent neural network (RNN) based on the accuracy, average residual energy and throughput with the value of 0.929, 0.086[Formula: see text]J and 86.585. The proposed method is used to manage and derive meaningful information from the patient’s medical records, medical examinations results and hospital records.
The global production of PPCPs have increased by multiple folds promoting excessive exposure of its metabolites to humans via different aquatic systems. The higher residence time of toxic precursors of these metabolites pose direct human health risk. Among the different aquatic systems, the contamination of groundwater by PPCPs is the most concerning threat. This threat is especially critical considering the lesser oxidizing potential of the groundwater as compared to freshwater/river water. A major challenge also arises due to excessive dependency of the world's population on groundwater, which is exponentially increasing with time. This makes the identification and characterization of spatial contamination hotspots highly probabilistic as compared to other freshwater systems. The situation is more vulnerable in developing countries where there is a reported inadequacy of wastewater treatment facilities, thereby forcing the groundwater to behave as the only available sequestrating sink for all these contaminants. With increased consumption of antibiotics and other pharmaceuticals compounds, these wastes have proven capability in terms of enhancing the resistance among the biotic community of the soil systems, which ultimately can become catastrophic and carcinogenic in near future. Recent studies are supporting the aforementioned concern where compounds like diclofenac (analgesic) have attained a concentration of 1.3 mgL⁻¹ in the aquifer systems of Delhi, India. The situation is far worse for developed nations where prolonged and indiscriminate usage of antidepressants and antibiotics have life threating consequences. It has been confirmed that certain compounds like ofloxacin (antibiotics) and bis-(2-ethylhexyl)phthalate are present in some of the most sensitive wells/springs of the United States and Mexico. The current trend of the situation has been demonstrated by integrating a comparative approach of the published literatures in last three years. This review provides first-hand information report for formulating a directive policy framework for tackling PPCPs issues in the groundwater system.
Magnesium oxide nanoparticles (MgO-NPs) have received considerable attention from researchers these days because of their wide-ranging applications in areas such as pharmaceuticals, manufacturing, and dermatology. Therefore, in the present study, we have synthesized MgO-NPs with a green approach. The MgO-NPs from aqueous leaf extract of Mariposa christia vespertilionis have been successfully synthesized and checked its resistance to antimicrobial activity. The green-synthesized MgO-NPs samples were calcined at three different calcination temperatures (400 °C, 600 °C, and 900 °C) and subjected to characterization. The antimicrobial activity of the MgO-NPs against gram-positive (S. aureus) bacteria and gram-negative (E. coli) bacteria was also established. UV–Vis spectroscopy was used to confirm the formation of the nanoparticles. Based on Fourier transform infrared spectroscopy, the Mg–O stretching is found in the range of 400 cm−1 to 500 cm−1. The scanning electron microscopy showed the spherical shape of the MgO-NPs and transmission electron microscopy revealed that the smallest crystallite size of MgO-NPs calcined at 600 °C was found to be around 17 nm which is less than the size of the MgO-NPs calcined at 400 °C (90 nm) and 900 °C (158 nm). X-ray diffractometer diffractograms show highly crystalline with hexagonal wurtzite structure. Finally, the antimicrobial activities of MgO-NPs showed an efficient effect against gram-positive bacteria, but a negative effect against gram-negative bacteria. The study revealed that the prepared MgO-NPs have shown a promising result as antibacterial agents.
This paper takes the degree of debt concentration and product market competitive advantage as intermediary variables to explore the internal mechanism of the impact of CSR fulfilment on firm debt risk. It is found that the fulfilment of CSR can reduce the debt risk of firms by dispersing the degree of debt concentration and enhancing the competitive advantage of product market. The mediating effect of the degree of debt concentration has a direct impact on the competitive advantage of a product market and is particularly obvious in private firms and firms in the eastern region of China.
In this paper, we mainly study the action of Aut( G ) on the set Ω of all maximal subgroups of G , and we use P to denote the action of Aut( G ) on Ω. When G is a finite non-cyclic abelian p -group, P is transitive. When G is minimal non-abelian, there are three cases. If G is quaternion group, P is transitive; if G is a metacyclic group, P is non-transitive; if G is not a metacyclic group, P is transitive.
The influence of model deformation on the transmission characteristics in unilateral finline, antipodal finline and bilateral finline is discussed using edge-based finite element method (FEM). The deformation is considered with respect to the eight boundaries of the model, and their amplitude is set to 0.01–0.05; the transmission characteristics include the cutoff wavelength characteristics of the dominant mode and the single-mode bandwidth characteristics. The results show a decreasing trend when the vacuum area is deformed, while they show a variety of possibilities when the loading region is deformed. These numerical results have strong guiding significance for the influence of the deformation of finlines on the overall device.
As the primary killer of health, the class of infectious diseases is the greatest threat to humanity. At present, international methods of studying the large-scale spatial transmission of sudden infectious diseases from the perspective of dynamics can be divided into two categories. On the one hand, top international biomedical and medical teams discuss the restraining effects of some prevention and control strategies on infectious diseases, such as smallpox, malaria, hand, foot and mouth disease and pandemic influenza, from the perspective of pragmatism. On the other hand, researchers in theoretical physics and network science tend to use compound population network models to explore the internal dynamic mechanism of spatial transmission of infectious diseases. This paper establishes a Lotka–Volterra dispersal predator–prey system in a patchy environment. It shows the existence of model boundary equilibria and asymptotic stability under an appropriate condition. This paper adopts the method of global Lyapunov function and the results of graph theory. We also consider a predator–prey dynamical model in a patchy environment, where the prey and predator individuals in each compartment can travel among n patches.
Arthritis occurs when the bones and joints have focal or degenerative diseases. This can lead to impaired performance and quality of life of the patient. Surgical treatment is used when the bones and joints are worn out or tumors, but often due to incomplete surgery, repeated attacks will occur. Bioceramic scaffold materials can assist in repairing cartilage tissue defects and, at the same time, contribute to arthritis rehabilitation. Therefore, this article will take this as the starting point of the research and use the new porous nanoceramic scaffold material to study its effect on joint repair in patients with osteoarthritis. The research results confirmed that the porous nanoceramic scaffold material has good biocompatibility in the treatment.
This paper examines the aspect pertaining to the returns connectedness between renewable energy tokens, namely, Powerledger-POWR and WePower-WPR, and the fossil fuel markets, namely, WTI oil, Brent oil, and Natural gas. For this purpose, we employed a quantile-based regression approach, in order to explore the dependence structures that exist under diverse market conditions. The results of the analysis show that the element of connectedness in the renewable energy tokens-fossil fuel market nexus is characterized by asymmetry and heterogeneity in the tails that are compared to the respective mean and the median. Under normal market conditions, the WTI oil market emerges as the main net transmitter of return spillover to the renewable energy tokens. Whereas, Brent oil and natural gas markets are the net receivers of the return spillover from the digital assets. However, under periods of extreme negative returns, the Brent oil market behaves as the main net transmitter of return spillover to the renewable energy digital markets. Whereas, under period of extreme positive returns, the natural gas market appears to be the main net transmitter of return spillover to the renewable energy digital markets. Therefore, it can be fathomed that on aggregate, renewable energy digital tokens are weakly connected with fossil fuel markets, thus suggesting the addition of renewable energy tokens in the portfolio of fossil fuel markets.
With the development of artificial intelligence technology, machine learning has achieved very good results in the field of stock selection. This paper mainly studies the application of linear model, clustering, support vector machine, random forest, neural network and deep learning methods in the field of stock selection. The main contribution of this paper is to provide a new idea for traditional quantitative investors, so that they can build a more efficient stock selection model in practical application. The experimental results show that the stock selection model constructed by these six machine learning methods can obtain higher return and stability.
The fraction of hydrogen (H 2 ) in the blast furnace (BF) shaft gas containing a notable portion of nitrogen (N 2 ) is expected to increase. For more efficient control of the BF, it is therefore desirable to conduct more rigorous studies on gaseous reduction of iron ores especially in H 2 -N 2 atmosphere. In this paper, an unreacted shrinking core model (USCM) with multicomponent gas diffusion for iron ore reduction in H 2 -N 2 atmosphere is developed. The resultant nonlinear equations are solved using the 4th order Runge-Kutta method. The present model and the original USCM are compared based on a series of pertinent experimental data.
With globalization and the rapid evolution of internet, the channels of music communication have become diversified. The communication speed of online music has become faster, and the music-related information is enriched on the internet. However, these positive effects on music communication also increase the complexity of music copyright issues. In face of the great challenges on music copyright issues, this paper takes the online original music works trading platform, namely music trading network as the research object, and uses some mathematical methods, such as statistical theory, power function law and long tail theory, to discuss the copyright protection of the music trading network. Our motivation is to find a way to protect the copyright of original music product so as to stimulate the enthusiasm of musicians, as well as to help find a way to create a healthy original music ecosystem.
In the process of urbanisation in China, many cities expect to promote the development of new towns through the construction of sports centres. However, due to the unreasonable design of the scale, accessibility and vitality of the sports centre, the catalyst effect of the sports centre is difficult to play an effective role. Using cluster analysis algorithm and spatial syntax, this study analyses the algorithm of sports centres with sustainable development characteristics, and puts forward the conclusions of sustainable development planning strategies such as venue merger design under the principle of intensive, maintaining high accessibility of sports centres, ensuring the compound function of surrounding plots and grid space to improve regional vitality, in order to provide some inspiration for the planning and construction of Sports Centre in the future.
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