bluuebunny/crossref_metadata_2025 huggingface dataset:
Financing of water transport infrastructure: foreign experience
Authors: Miloslavskaya Svetlana V. Miloslavskaya , Plotnikova Elena S. Plotnikova
Published: September 2021

In recent decades, many countries of the world are faced with the problem of congestion in land transport due to the increase in the volume of transported goods. As a result, much attention is paid to inland waterway transport, which has significant untapped potential. It is also important that inland waterway transport (IWT), in comparison with alternative modes of transport, has such advantages as low cost of transportation, reliability, and environmental friendliness. This article analyzes the experience of financing water transport infrastructure facilities in foreign countries with a developed network of inland waterways, namely, Germany and the United States. The purpose of the work is to generalize the practices of attracting funds for the development of the infrastructure of inland water transport.
Ultrasound-guided regional anaesthesia for upper limb surgery
Authors:   Universitatea de Medicină şi Farmacie „Carol Davila“, Bucureşti, UNGUREANU Raluca ,   Clinica ATI, Spitalul Clinic de Urgenţă Bucureşti, GRINŢESCU Ioana ,   Universitatea de Medicină şi Farmacie „Carol Davila“, Bucureşti,   Clinica ATI, Spitalul Clinic de Urgenţă Bucureşti, TULBURE Dan ,   Universitatea de Medicină şi Farmacie „Carol Davila“, Bucureşti,   Institutul Clinic „Fundeni“, București
Published: September 2021

Brachial plexus blockade is a cornerstone of the peripheral nerve regional anesthesia practice of most anesthesiologists. The new inovative application of ultrasound imaging may overcome the shortcomings of current techniques and increase the efficacity and safety of locoregional anesthesia. Ultrasound guidence allows the operator to see neural structures, guide the needle under real-time visualization, navigate away from sensitive anatomy, and monitor the spread of local anesthetic. This review article focuses on approaches and techniques used for ultrasound guidance of brachial plexus blocks for upper extremity surgery.
Characterization of seasonal and chemotypical variability in the essential oil from leaves of Annona neosalicifolia H. Rainer (Annonaceae)
Authors: DE SOUZA Tiago Juliano Tasso , ZANETTİ Gilberto , APEL Miriam , HENRİQUES Amelia , MANFRON Melânia
Published: September 2021

The essential oil (EO) from leaves of Annona neosalicifolia H. Rainer (Annonaceae) obtained from two chemotypes in the southern Brazilian Atlantic Rainforest was analysed both by GC-FID and GC-MS. Forty compounds were identified, accounting for 95.9 to 99.5% of the total oil in each sample. The VOs were characterized by the predominance of sesquiterpenes in all samples, being main compounds bicyclogermacrene (13.9-41.8%) and beta-caryophyllene (9.3-26.3%); germacrene D was also found in high amounts, but its concentration was more variable. Monoterpene compounds were absent in the summer and fall samples, with higher concentrations in winter samples (6.1-7.4% for α-pinene and 6.4-11.2% for β-pinene). EO from chemotype II was characterized by a high concentration of the sesquiterpene alcohol (E)-nerolidol (36.3%), which was absent or in trace amounts in chemotype I. The characterization of these seasonal and individual variations in the chemical composition of the essential oil from A. neosalicifolia may have agronomical implications and may be important in the understanding and delimitation of the metabolic plasticity of the highly variable taxon A. emarginata as well.
Counselling Patterns of Community Pharmacies when Dispensing
Antibiotics in Galle DS Division, Sri Lanka
Authors: Nayanathara R. K. A. B. , Jayasinghe B. G. V. A. N. S.
Published: September 2021

Antibiotic resistance has been identified as a significant health issueacross the world. Inappropriate antibiotics prescribing by physicians, dispensing antibiotics without prescription and counseling by pharmacists, and the misuse of antibiotics by patients make them chief parties responsible for this global problem. Community pharmacists have a major responsibility to counsel the patients before dispensing antibiotics. The objectives of this study was to investigate the counselling patterns of community
pharmacies when dispensing antibiotics and to examine whether the community pharmacists demand a prescription when dispensing antibiotics in Galle, Sri Lanka. This was a simulated patient study involving community pharmacists/ pharmacy assistants who work in community pharmacies in the Galle DS division (Divisional Secretary Division), Sri Lanka. Case scenarios of specific product requests (Erythromycin tablets, Ciprofloxacin tablets, Amoxicillin syrup) were presented by a simulated patient, and data were recorded after purchasing each product. Most of the pharmacists/ pharmacy assistants (72%) didn’t demand a prescription for antibiotic dispensing. About 82% of the visited pharmacies had dispensed antibiotics without a prescription. Only 14% of community pharmacies in the area had taken medical and lifestyle history of simulated patients (investigators) before dispensing. Advice or counselling regarding antibiotics were given only by 24% of the community pharmacies. Counselling patterns of community pharmacies in the area were unsatisfactory. Major issues of dispensing antibiotics and, not demanding a prescription were common. These issues need to be addressed by health authorities and policymakers to safeguard patients.
SELF-ASSESSMENT TO SUBJECTIVE CREATIVITY AND NEW IDEAS: DETERMINANT WITHIN RISK TAKING, AUTONOMY AND TRADITION
Authors: Ngọc Trung Nguyễn , Thị Hoai Le Nguyễn , Viết Hồi Trần , Kim Won Joong
Published: September 2021

Individuals’ creativity and new ideas today are not only essential for firms, agencies or organizations but also indispensable even for a nation. This article analyzes impacts of autonomy, risk taking and, especially, factor of tradition on self-assessment to subjective creativity and attitude to new ideas. Specifically, the article empirically analyzes how those factors affect creativity and new ideas and tests whether tradition is more meaningful than others in explaining creativity or new ideas. Most of previous research has theoretically concluded that, because of rigid and unchangeable norms and rules in tradition, individuals are not easy to generate or do new things, especially in Eastern countries. South Korea, with a long tradition within Asian and Confucian values, it is said that these values may limit creativity and new ideas. However, South Korea has achieved satisfactory outcomes in process of creative development, which could positively be affected by its tradition. Using the data from World Values Survey for South Korea at the latest wave 6th, the results show that tradition has a positive impact on subjective creativity beside significantly positive influence of risk taking, which is not considered to be important from cultural perspective.
An Examination of Pulsational Changes in the Classical Cepheid X Cygni
Authors: Hintz Eric G. , Harding Tyler B. , Hintz Maureen L.
Published: September 2021

Using an extensive archive for visual observations from the AAVSO, along with published times of maximum light, we determined a new model for the period of X Cygni. The best model is two linear fits for data before and after 1917 (JD2421512). Before that time the period is 16.38438 ± 0.00036 days. After we find a period of 16.386470 ± 0.000028 days. An examination of the O-C values for data after 1917 shows no clear evidence of a constant period change or of sinusoidal variations. The period looks to be constant. From an examination of H-alpha index measurements we find a drop in the value between data before 2013 October and data after 2014 July. This drop is not related to temperature and is likely related to mass loss in the star. Finally, we find that radial-velocity measurements match well with previously published values and show no seasonal variation over the 11 yr of data. This again seems to limit the possibility of a companion.
Probing Jets from Young Embedded Sources: Clues from HST Near-IR [Fe ii] Images
Authors: Erkal Jessica , Nisini Brunella , Coffey Deirdre , Bacciotti Francesca , Hartigan Patrick , Antoniucci Simone , Giannini Teresa , Eislöffel Jochen , Manara Carlo Felice
Published: September 2021

We present near-infrared [Fe ii] images of four Class 0/I jets (HH 1/2, HH 34, HH 111, HH 46/47) observed with the Hubble Space Telescope (HST) Wide Field Camera 3. The unprecedented angular resolution allows us to measure proper motions, jet widths and trajectories, and extinction along the jets. In all cases, we detect the counterjet, which was barely visible or invisible at shorter wavelengths. We measure tangential velocities of a few hundred kilometers per second, consistent with previous HST measurements over 10 years ago. We measure the jet width as close as a few tens of au from the star, revealing high collimations of about 2° for HH 1, HH 34, and HH 111 and about 8° for HH 46, all of which are preserved up to large distances. For HH 34, we find evidence of a larger initial opening angle of about 7°. Measurement of knot positions reveals deviations in trajectory of both the jet and counterjet of all sources. Analysis of asymmetries in the inner knot positions for HH 111 suggests the presence of a low mass stellar companion at separation 20–30 au. Finally, we find extinction values of 15–20 mag near the source, which gradually decrease moving downstream along the jet. These observations have allowed us to study the counterjet at unprecedentedly high angular resolution and will be a valuable reference for planning future JWST mid-infrared observations that will peer even closer into the jet engine.
An Exquisitely Deep View of Quenching Galaxies through the Gravitational Lens: Stellar Population, Morphology, and Ionized Gas
Authors: Man Allison W. S. , Zabl Johannes , Brammer Gabriel B. , Richard Johan , Toft Sune , Stockmann Mikkel , Gallazzi Anna R. , Zibetti Stefano , Ebeling Harald
Published: September 2021

This work presents an in-depth analysis of four gravitationally lensed red galaxies at z = 1.6–3.2. The sources are magnified by factors of 2.7–30 by foreground clusters, enabling spectral and morphological measurements that are otherwise challenging. Our sample extends below the characteristic mass of the stellar mass function and is thus more representative of the quiescent galaxy population at z > 1 than previous spectroscopic studies. We analyze deep VLT/X-SHOOTER spectra and multiband Hubble Space Telescope photometry that cover the rest-frame UV-to-optical regime. The entire sample resembles stellar disks as inferred from lensing-reconstructed images. Through stellar population synthesis analysis, we infer that the targets are young (median age = 0.1–1.2 Gyr) and formed 80% of their stellar masses within 0.07–0.47 Gyr. Mg ii
λλ 2796, 2803 absorption is detected across the sample. Blueshifted absorption and/or redshifted emission of Mg ii are found in the two youngest sources, indicative of a galactic-scale outflow of warm (T ∼ 104 K) gas. The [O iii] λ5007 luminosity is higher for the two young sources (median age less than 0.4 Gyr) than the two older ones, perhaps suggesting a decline in nuclear activity as quenching proceeds. Despite high-velocity (v ≈ 1500 km s−1) galactic-scale outflows seen in the most recently quenched galaxies, warm gas is still present to some extent long after quenching. Altogether, our results indicate that star formation quenching at high redshift must have been a rapid process (<1 Gyr) that does not synchronize with bulge formation or complete gas removal. Substantial bulge growth is required if they are to evolve into the metal-rich cores of present-day slow rotators.
X-Ray Sources in the 1.75 Ms Ultra Narrow Deep Field Observed by XMM-Newton
Authors: Elías-Chávez M. , Longinotti A. L. , Krongold Y. , Vignali C. , Nicastro F. , Rosa-González D. , Mayya Y. D. , Mathur S.
Published: September 2021

In this work, we present the results of the survey carried out on one of the deepest X-ray fields observed by the XMM-Newton satellite. The 1.75 Ms Ultra Narrow Deep Field (XMM175UNDF) survey is made by 13 observations taken over 2 yr with a total exposure time of 1.75 Ms (1.372 Ms after flare-filtered) in a field of 30′ × 30′ centered around the blazar 1ES 1553+113. We stacked the 13 observations reaching flux limits of 4.03 × 10−16, 1.3 × 10−15, and 9.8 × 10−16 erg s−1 cm−2 in the soft (0.2–2 keV), hard (2–12 keV), and full (0.2–12 keV) bands, respectively. Using a conservative threshold of Maximum Likelihood significance of ML ≥ 6, corresponding to 3σ, we detected 301 point-sources for which we derived positions, fluxes in different bands, and hardness ratios. Thanks to an optical follow-up that was carried out using the 10.4m the Gran Telescopio Canarias on the same field in the u′g′r′i′z′ bands, combined with WISE/2MASS IR data, we identified 244 optical/IR counterpart candidates for our X-ray sources and estimated their X-ray luminosities, redshift distribution, X-ray/optical–X-ray/IR flux ratios, and absolute magnitudes. Finally, we divided this subsample into 40 non-active sources and 204 active galactic nuclei, of which 139 are classified as Seyfert galaxies and 41 as Quasars.
Molecular Cloud Cores with High Deuterium Fractions: Nobeyama Mapping Survey
Authors: Tatematsu Ken’ichi , Kim Gwanjeong , Liu Tie , Evans II Neal J. , Yi Hee-Weon , Lee Jeong-Eun , Wu Yuefang , Hirano Naomi , Liu Sheng-Yuan , Dutta Somnath , Sahu Dipen , Sanhueza Patricio , Kim Kee-Tae , Juvela Mika , Tóth L. Viktor , Fehér Orsolya , He Jinhua , Ge Jixing , Feng Siyi , Choi Minho , Kang Miju , Thompson Mark A. , Fuller Gary A. , Li Di , Ristorcelli Isabelle , Wang Ke , Di Francesco James , Eden David , Ohashi Satoshi , Kandori Ryo , Vastel Charlotte , Hirota Tomoya , Sakai Takeshi , Lu Xing , Nguyên Lu’o’ng Quang , Shinnaga Hiroko , Kim Jungha , “SCOPE” collaboration JCMT Large Program
Published: September 2021

We present the results of on-the-fly mapping observations of 44 fields containing 107 SCUBA-2 cores in the emission lines of molecules N2H+, HC3N, and CCS at 82–94 GHz using the Nobeyama 45 m telescope. This study aimed at investigating the physical properties of cores that show high deuterium fractions and might be close to the onset of star formation. We found that the distributions of the N2H+ and HC3N line emissions are approximately similar to the distribution of the 850 μm dust continuum emission, whereas the CCS line emission is often undetected or is distributed in a clumpy structure surrounding the peak position of the 850 μm dust continuum emission. Occasionally (12%), we observe CCS emission, which is an early-type gas tracer toward the young stellar object, probably due to local high excitation. Evolution toward star formation does not immediately affect the nonthermal velocity dispersion.
New or Increased Cometary Activity in (2060) 95P/Chiron
Authors: Dobson Matthew M. , Schwamb Megan E. , Fitzsimmons Alan , Kelley Michael S. P. , Lister Tim , Shingles Luke J. , Denneau Larry , Heinze A. N. , Smith Ken W. , Tonry John L. , Weiland Henry , Young David. R. , Benecchi Susan D. , Verbiscer Anne J.
Published: September 2021

Asteroid Terrestrial-impact Last Alert System (ATLAS) observations of Centaur (2060) 95P/Chiron show a brightening in the apparent magnitude not due rotational light curves or phase effects. This onset or enhancement of cometary activity started after 2021 February 8 UT and continued in later observations from 2021 June 18 UT onward. Recent ATLAS observations and deeper follow-up imaging obtained using the Las Cumbres Observatory 1.0 m robotic telescope network find no confirmed signature of coma or tail-like features.
The Cauchy problem of a two-phase flow model for a mixture of non-interacting compressible fluids
Authors: Cheng Zhen , Wang Wenjun
Published: September 2021

In this paper, we consider the global existence of the Cauchy problem for a version of one velocity Baer-Nunziato model with dissipation for the mixture of two compressible fluids in \begin{document}$ \mathbb{R}^3 $\end{document}. We get the existence theory of global strong solutions by using the decaying properties of the solutions. The energy method combined with the low-high-frequency decomposition is used to derive such properties and hence the global existence. As a byproduct, the optimal time decay estimates of all-order spatial derivatives of the pressure and the velocity are obtained.
Nondegeneracy of solutions for a class of cooperative systems on $ \mathbb{R}^n $
Authors: Lucia Marcello , Sweers Guido
Published: September 2021

We consider fully coupled cooperative systems on \begin{document}$ \mathbb{R}^n $\end{document} with coefficients that decay exponentially at infinity. Expanding some results obtained previously on bounded domain, we prove that the existence of a strictly positive supersolution ensures the first eigenvalue to exist and to be nonzero. This result is applied to show that the topological solutions for a Chern-Simons model, described by a semilinear system on \begin{document}$ \mathbb{R}^2 $\end{document} with exponential nonlinearity, are nondegenerate.
Final dynamics of systems of nonlinear parabolic equations on the circle
Authors: Romanov Aleksandr V. ,   School of Applied Mathematics, National Research University Higher School of Economics, 34 Tallinskaya St., Moscow 123458, Russia
Published: September 2021

We consider the class of dissipative reaction-diffusion-convection systems on the circle and obtain conditions under which the final (at large times) phase dynamics of a system can be described by an ODE with Lipschitz vector field in $ \mathbb{R}^{N} $. Precisely in this class, the first example of a parabolic problem of mathematical physics without the indicated property was recently constructed.
Time decay rates for the coupled modified Navier-Stokes and Maxwell equations on a half space
Authors: Kim Jae-Myoung ,   Department of Mathematics Education, Andong National University, Andong 36729, Korea
Published: September 2021

This paper is concerned with time decay rates of the strong solutions of an incompressible the coupled modified Navier-Stokes and Maxwell equations in a half space $ \mathbb{R}^3_+ $. With the use of the spectral decomposition of the Stokes operator and $ L^p-L^q $ estimates developed by Borchers and Miyakawa [2], we study the $ L^2 $-decay rate of strong solutions.
Crude oil prices and volatility prediction by a hybrid model based on kernel extreme learning machine
Authors: Niu Hongli ,   School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China, Zhao Yazhi
Published: September 2021

In view of the important position of crude oil in the national economy and its contribution to various economic sectors, crude oil price and volatility prediction have become an increasingly hot issue that is concerned by practitioners and researchers. In this paper, a new hybrid forecasting model based on variational mode decomposition (VMD) and kernel extreme learning machine (KELM) is proposed to forecast the daily prices and 7-day volatility of Brent and WTI crude oil. The KELM has the advantage of less time consuming and lower parameter-sensitivity, thus showing fine prediction ability. The effectiveness of VMD-KELM model is verified by a comparative study with other hybrid models and their single models. Except various commonly used evaluation criteria, a recently-developed multi-scale composite complexity synchronization (MCCS) statistic is also utilized to evaluate the synchrony degree between the predictive and the actual values. The empirical results verify that 1) KELM model holds better performance than ELM and BP in crude oil and volatility forecasting; 2) VMD-based model outperforms the EEMD-based model; 3) The developed VMD-KELM model exhibits great superiority compared with other popular models not only for crude oil price, but also for volatility prediction.
Neural Network for Big Data Sets
Authors: Phu Vo Ngoc , Tran Vo Thi Ngoc
Published: September 2021

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).
Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns
Authors: Zhang Lei
Published: September 2021

In this research, artificial neural networks (ANN) with various architectures are trained to generate the chaotic time series patterns of the Lorenz attractor. The ANN training performance is evaluated based on the size and precision of the training data. The nonlinear Auto-Regressive (NAR) model is trained in open loop mode first. The trained model is then used with closed loop feedback to predict the chaotic time series outputs. The research goal is to use the designed NAR ANN model for the simulation and analysis of Electroencephalogram (EEG) signals in order to study brain activities. A simple ANN topology with a single hidden layer of 3 to 16 neurons and 1 to 4 input delays is used. The training performance is measured by averaged mean square error. It is found that the training performance cannot be improved by solely increasing the training data size. However, the training performance can be improved by increasing the precision of the training data. This provides useful knowledge towards reducing the number of EEG data samples and corresponding acquisition time for prediction.
Tool Condition Monitoring Using Artificial Neural Network Models
Authors: Pai Srinivasa P. , N. Nagabhushana T.
Published: September 2021

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.
An Integrated Model of Data Envelopment Analysis and Artificial Neural Networks for Improving Efficiency in the Municipal Solid Waste Management
Authors: Cavallin Antonella , Frutos Mariano , Vigier Hernán Pedro , Rossit Diego Gabriel
Published: September 2021

In the last decades, integral municipal solid waste management (IMSWM) has become one of the most challenging areas for local governmental authorities, which have struggled to lay down sustainable and financially stable policies for the sector. In this paper a model that evaluates the efficiency of IMSWMs through a combination of Data Envelopment Analysis (DEA) and an Artificial Neural Network (ANN) is presented. In a first stage, applying DEA, municipal administrations are classified according to the efficiency of their garbage processing systems. This is done in order to infer what modifications are necessary to make garbage handling more efficient. In a second stage, an ANN is used for predicting the necessary resources needed to make the waste processing system efficient. This methodology is applied on a toy model with 50 towns as well as on a real-world case of 21 cities. The results show the usefulness of the model for the evaluation of relative efficiency and for guiding the improvement of the system.
Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection
Authors: Mabu Audu Musa , Prasad Rajesh , Yadav Raghav
Published: September 2021

With the progression of bioinformatics, applications of GE profiles on cancer diagnosis along with classification have become an intriguing subject in the bioinformatics field. It holds numerous genes with few samples that make it arduous to examine and process. A novel strategy aimed at the classification of GE dataset as well as clustering-centered feature selection is proposed in the paper. The proposed technique first preprocesses the dataset using normalization, and later, feature selection was accomplished with the assistance of feature clustering support vector machine (FCSVM). It has two phases, gene clustering and gene representation. To make the chose top-positioned features worthy for classification, feature reduction is performed by utilizing SVM-recursive feature elimination (SVM-RFE) algorithm. Finally, the feature-reduced data set was classified using artificial neural network (ANN) classifier. When compared with some recent swarm intelligence feature reduction approach, FCSVM-ANN showed an elegant performance.
Analyzing Intraductal Papillary Mucinous Neoplasms Using Artificial Neural Network Methodologic Triangulation
Authors: Walczak Steven , Permuth Jennifer B. , Velanovich Vic
Published: September 2021

Intraductal papillary mucinous neoplasms (IPMN) are a type of mucinous pancreatic cyst. IPMN have been shown to be pre-malignant precursors to pancreatic cancer, which has an extremely high mortality rate with average survival less than 1 year. The purpose of this analysis is to utilize methodological triangulation using artificial neural networks and regression to examine the impact and effectiveness of a collection of variables believed to be predictive of malignant IPMN pathology. Results indicate that the triangulation is effective in both finding a new predictive variable and possibly reducing the number of variables needed for predicting if an IPMN is malignant or benign.
Disease Classification Using ECG Signals Based on R-Peak Analysis With ABC and ANN
Authors: Lata Suman , Kumar Rakesh
Published: September 2021

ECG feature extraction has an important role in identifying a number of cardiac diseases. Lots of work has been done in this field but the most important challenges faced in previous work are the selection of proper R-peaks and R-R intervals due to the lack of appropriate pre-processing steps like decomposition, smoothing, filtering, etc., and the optimization of the features for proper classification. In this article, DWT-based pre-processing and ABC is used for optimization of features which helps to achieve better classification accuracy. It is utilized for initial diagnosis of abnormalities. The signals are taken from MIT-BIH arrhythmia database for the analysis. The aim of the research is to classification of six diseases; Normal, Atrial, Paced, PVC, LBBB, RBBB with an ABC optimization algorithm and an ANN classification algorithm on the basis of the extracted features. Various parameters, like, FAR, FRR, and accuracy are measured for the execution. Comparative analysis is shown of the proposed and the existing work to depict the effectiveness of the work.
Artificial Neural Network for Markov Chaining of Rainfall Over India
Authors: Pabreja Kavita
Published: September 2021

Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.
Comparative Analysis of Proposed Artificial Neural Network (ANN) Algorithm With Other Techniques
Authors: Chatha Deepak , Aggarwal Alankrita , Kumar Rajender
Published: September 2021

The mortality rate among women is increasing progressively due to cancer. Generally, women around 45 years old are vulnerable from this disease. Early detection is hope for patients to survive otherwise it may reach to unrecoverable stage. Currently, there are numerous techniques available for diagnosis of such a disease out of which mammography is the most trustworthy method for detecting early cancer stage. The analysis of these mammogram images are difficult to analyze due to low contrast and nonuniform background. The mammogram images are scanned and digitized for processing that further reduces the contrast between Region of Interest and background. Presence of noise, glands and muscles leads to background contrast variations. Boundaries of suspected tumor area are fuzzy & improper. Aim of paper is to develop robust edge detection technique which works optimally on mammogram images to segment tumor area. Output results of proposed technique on different mammogram images of MIAS database are presented and compared with existing techniques in terms of both Qualitative & Quantitative parameters.
Developing Strategies in the Sharing Economy
Authors: Leon Ramona Diana
Published: September 2021

The sharing economy is challenging the traditional business models and strategies by encouraging collaboration, non-ownership, temporal access, and redistribution of goods and/or services. Within this framework, the current chapter aims to examine how managers influence, voluntarily or involuntarily, the reliability of a managerial early warning system, based on an artificial neural network. The analysis focuses on seven Romanian sustainable knowledge-based organizations and brings forward that managers tend to influence the results provided by a managerial early warning system based on artificial neural network, voluntarily and involuntarily. On the one hand, they are the ones who consciously decide which departments and persons are involved in establishing the structure of the managerial early warning system. On the other hand, they unconsciously influence the structure of the managerial early warning system through the authority they exercise during the managerial debate.
Wavelet Packet Analysis of ECG Signals to Understand the Effect of Cannabis Abuse on Cardiac Electrophysiology of Indian Women Working in Paddy Fields
Authors: Nayak Suraj Kumar , Pradhan Ashirbad , Khan Salman Siddique , Nayak Shikshya , Das Soumanti , Yadu Gitika , Patel Shankar Jaykishan , Bhattacharyya Champak , Pal Kunal
Published: September 2021

This chapter is aimed at identifying the variation in the cardiac electrophysiology due to the abuse of the cannabis products (bhang) in a non-invasive manner. ECG signals were acquired from 25 Indian women working in the paddy fields. Amongst them, 10 women regularly abused bhang and the rest 15 women never abused bhang. The ECG signals were preprocessed and subjected to wavelet packet decomposition (WPD) up to the level 3 using db04 wavelet. Ninety-six statistical features were extracted from the wavelet packet coefficients and analyzed using linear and non-linear statistical methods. The results suggested a variation in the cardiac electrophysiology due to the abuse of bhang. Artificial neural networks (ANNs), namely, radial basis function (RBF) and multilayer perceptron (MLP) were able to classify the ECG signals with an accuracy of ≥95%. This supported the hypothesis that abuse of bhang may alter the cardiac electrophysiology. The results of the study may be used to increase awareness among people to avoid the abuse of cannabis products.
Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate
Authors: Outanoute M. , Lachhab A. , Selmani A. , Oubehar H. , Snoussi A. , Guerbaoui M. , Ed-dahhak A. , Bouchikhi B.
Published: September 2021

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.
Financial Asset Management Using Artificial Neural Networks
Authors: Sinaki Roohollah Younes , Sadeghi Azadeh , Lynch Dustin S. , Young II William A. , Weckman Gary R.
Published: September 2021

Investors typically build portfolios for retirement. Investment portfolios are typically based on four asset classes that are commonly managed by large investment firms. The research presented in this article involves the development of an artificial neural network-based methodology that investors can use to support decisions related to determining how assets are allocated within an investment portfolio. The machine learning-based methodology was applied during a time period that included the stock market crash of 2008. Even though this time period was highly volatile, the methodology produced desirable results. Methodologies such as the one presented in this article should be considered by investors because they have produced promising results, especially within unstable markets.
Using an Artificial Neural Network to Improve Email Security
Authors: Maki Mohamed Abdulhussain Ali Madan , Subramanian Suresh
Published: September 2021

Email is one of the most widely used features of internet, and it is the most convenient method of transferring messages electronically. However, email productivity has been decreased due to phishing attacks, spam emails, and viruses. Recently, filtering the email flow is a challenging task for researchers due to techniques that spammers used to avoid spam detection. This research proposes an email spam filtering system that filters the spam emails using artificial back propagation neural network (BPNN) technique. Enron1 dataset was used, and after the preprocessing, TF-IDF algorithm was used to extract features and convert them into frequency. To select best features, mutual information technique has been applied. Performance of classifiers were measured using BoW, n-gram, and chi-squared methods. BPNN model was compared with Naïve Bayes and support vector machine based on accuracy, precision, recall, and f1-score. The results show that the proposed email spam system achieved 98.6% accuracy with cross-validation.
A New Data Hiding Scheme Combining Genetic Algorithm and Artificial Neural Network
Authors: Chatterjee Ayan , Barik Nikhilesh
Published: September 2021

Today, in the time of internet based communication, steganography is an important approach. In this approach, secret information is embedded in a cover medium with minimum distortion of it. Here, a video steganography scheme is developed in frequency domain category. Frequency domain is more effective than spatial domain due to variation data insertion domain. To change actual domain of entropy pixels of the video frames, uniform crossover of Genetic Algorithm (GA) is used. Then for data insertion in video frames, single layer perceptron of Artificial Neural Network is used. This particular concept of information security is attractive due to its high security during wireless communication. The effectiveness of the proposed technique is analyzed with the parameters PSNR (Peak Signal to Noise Ratio), IF and Payload (bpb).
Assessment and Management of ID in Childhood
Authors: Chowhan Sumita P. , Patowary Plabita
Published: September 2021

Intellectual disability is a neurodevelopment disorder that affects the intellectual and adaptive functioning. The individual fails to meet standards of personal independence and social responsibility in one or more aspects of daily life, including communication, social participation, academic or occupational functioning, and personal independence at home or in community settings. There is an immense need to understand and be aware of the signs and symptoms of the disorder. This chapter focuses on the process of the assessment which is necessary to identify intellectual deficiency and also the issues that should be taken care of during the process. The various principles and tools of assessment, which can be used to measure IQ, are mentioned. It also focuses on the various challenging behaviors and functional analysis. It also focuses on recent and efficient management strategies that can be used to help the affected person cope and acquire new skills.
Inclusion and Exclusion
Authors: Scott Jessica Armytage , Dostal Hannah M. , Ewen-Smith Tisha N.
Published: September 2021

In this chapter, the authors explore the practice of inclusion as it relates to the education of deaf and hard of hearing (d/hh) students. Using the current situation in Jamaica as a microcosm, it is argued that for this specific population of students it may be necessary to reframe and redefine the notion of inclusion more broadly. For example, the authors argue that as a result of the specific cultural, linguistic, and academic needs of d/hh students, a more traditional approach to inclusion may in fact result in isolation and less access to content and skills. Inclusion that considers how deaf education classrooms may include accessible language, the Deaf community, families of d/hh children, and Deaf role models may be more appropriate for this population.
Disability and Dominant Leadership Models Over Time
Authors: Lee Melanie
Published: September 2021

This chapter is grounded in scholarly sources and personal narrative, and it concludes with recommended best practices about fostering more socially just higher education environments for college students. Specifically, the author focuses on the development of more equitable inclusion of students with disabilities in curricular and co-curricular leadership development programs. This chapter provides a context of major models of disability over time, a chronological scaffold of dominant student leadership models, and recommendations for educators inside and outside of classroom spaces. The intersection of models of disability and leadership models has not been explored. This chapter fills that gap in the literature.
Building Theories-in-Practice on Social Innovation in Disability Nonprofit Organizations
Authors: Taylor Rachel , (Ann) Torugsa Nuttaneeya
Published: September 2021

This chapter discusses the key theoretical and empirical steps undertaken throughout the authors' previous-but-related mixed methods studies on social innovation in nonprofit organizations (NPOs) in the Australian disability sector with the aim of using the key findings of these studies to develop ‘theories-in-practice' in disability NPOs. In this chapter, the authors summarize the associated theory-building processes deployed to explain how disability NPOs develop and implement social innovations and the societal ‘system-level' impacts of such innovations. These theory-building processes involve two broad phases, and the culmination of these phases (grounded in the abductive logics of inquiry, complexity theorizing, and set-theoretic methods) leads to the development of several ‘theories-to-practice' that not only convey the interactivity of contextual causal mechanisms leading to social innovation by NPOs, but also outline change-oriented solutions for managers who are working to address complex social challenges.
Marginalization of Pupils With Disabilities in Educational System
Authors: Stepanović Saša , Milivojević Tatjana Đ. , Manić Ljiljana
Published: September 2021

The educational development history of pupils with disabilities is characterized by a very slow change in the social awareness that their specialty and importance are not obstacles for successful inclusion in education and society. The obstacles to the full integration of these pupils into the educational process, as well as other segments of social life, are the result of the community's attitude towards people with disabilities, often based on their marginalization and extradition. However, when disability is viewed as only one of the personality specificities, through the adjustment of the environment, it influences the fact that the attitude of the society towards the person with disabilities is not an obstacle in the development of the personality and its socialization. In this sense, society as a whole plays an important role in the optimal development of each member, and therefore we will deal with the problem of inclusion and education as well as the importance of involving children with disabilities in the educational system.
Teaching ELL Students in the Elementary Grades
Authors: McCalley Sue Ellen
Published: September 2021

This chapter presents information regarding teaching ELL students with high incidence disabilities in the mild to moderate range. Specific disabilities to be discussed are learning disabilities, dyslexia, cognitive impairments, and autism. Identification procedures and implications for the individual education plan are offered. Learning characteristics that are manifested with these disabilities are explored. Instructional strategies that are most effective for children with these disabilities are explained. The impact of ELL on the disability is discussed. Accommodations to instructional strategies for ELL students are suggested. The misidentification of ELL students as having a disability is examined as well as misplacement into special education.
Teaching Menstrual Care to a Student With Autism Spectrum Disorder in a School Setting
Authors: Cordova Karly
Published: September 2021

There is a lack of training for parents, school staff, and residential staff aimed at helping persons with intellectual disabilities acquire menstrual self-care skills. This may be due in part to the sensitive nature of this topic, the aversion to performing menstrual care for individuals with a disability, and the lack of empirically supported training protocols. This chapter critically reviews behavior analytic research on menstrual care that has been published in peer reviewed journals. This is followed by a case illustration using behavior analytic methods to teach menstrual self-care skills for a student with autism spectrum disorder in a public school setting. Evaluated using a multiple baseline across behaviors design, it was shown that the student increased her independent performance of selected menstrual self-case skills.
Disability, Culture, and Technology
Authors: Komabu-Pomeyie Sefakor Grateful-Miranda Ama
Published: September 2021

Ghana has many interventions or systems to eradicate poverty among vulnerable people, especially those with disabilities. Ghana's Parliament launched the Social Protection Program in conformity with the United Nations Convention on the Right of People with Disabilities (UNCRPD) as well as the Disability Law of Ghana. One of these programs is the Social Protection Program, under which rehabilitation and RLG ICT training of People with Disabilities (PWDs) have been implemented in the classroom. The main goal of this program is to educate PWDs, granting them employable skills and thereby enabling them to become independent citizens. This chapter, which is related to one of the recommended topics, “Issues and Challenges of Digital Tools and Applications in the Classroom,” draws on and employs a phenomenological approach to confirm the lack of culturally responsiveness of technology to the Ghanaian disability community. Participants indicated they were disconnected from the program because the technological devices were foreign and not connected to their indigenous culture.
The Australian National Disability Insurance Scheme and People With Disabilities From CALD Backgrounds
Authors: Adibi Hossein
Published: September 2021

The National Disability Insurance Scheme (NDIS) is considered to be the second greatest reform in healthcare in Australia after the introduction of Medicare in Australia in 1983. This reform was introduced in 2012 in two phases. The first phase as a trial took place for three years. The expectation was that the reform will be rolled out by 2019 or 2020. This article argues that the trial implementation process has achieved very positive outcomes in the lives of a great number of people with disability in Australia. At the same time, NDIS is facing many serious challenges in some areas. One of the obvious challenges is that this reform is a market approached reform. The second challenge relates to meeting the needs of minorities. People with disabilities from Culturally and Linguistically Divers (CALD) backgrounds are one of the five most venerable, underutilised users of NDIS services in Australia. They have no strong voice and negotiable abilities. The main question here is how NDIS is to meet its commitment to satisfy the needs of these vulnerable people in Australia.
Social Media and Student With Disabilities
Authors: Doran Caoimhe , Schnackenberg Heidi Lee
Published: September 2021

In the past decade, social media has become an increasingly prevalent, being the daily form of communication and entertainment for a majority of individuals. Social media and networking sites are not designed to appeal to a specific type of personality and are designed to benefit many. The students of Generation Z respond positively to teaching styles that incorporate technology-based learning, and frequent communication or feedback. Gen Z students with disabilities are entitled to the same opportunities and experiences as their peers without disabilities. This includes being exposed to various forms of social media to discover alternative ways to communicate, interact with the community, find information and access entertainment. While other members of Generation Z possess the capability to pick up a smart phone or computer, create a social media account and figure out the functions independently, students with disabilities may require explicit instruction, strategies and practice with social media usage.