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Due to the increased quantity and variety of cyber-attacks against networks, the conventional firewalls and encrypting data technologies are no longer sufficient to meet the network security requirements. As a consequence of this, the idea of using intrusion detection systems to combat network threats has been advocated. Machine learning aids common intrusion detection methods which show poor detection rates and requires wider feature engineering. This research offers the random synthetic over-sampling (RSO) Linguistic algorithm for detecting network intrusion and to identify communication anomalies. Deep learning method for network security is combined with an attention mechanism and bidirectional long short-term memory (Bi-LSTM) network. Frequent pattern stream characteristics were retrieved using CNN network and weights in each channel are changed. Hyperparameter tuning techniques like improvised elephant herding optimization algorithm is employed to increase the Bi-LSTM's efficiency. The training process, the number of hidden layers and epochs are chosen optimally using these strategies. The RSOA-IEHO-BiLSTM model's effectiveness testing showed that it outperformed the competing techniques. With NSLKDD dataset model, an accuracy of 98.36% and an F-measure of 99.05% were achieved both of which point to an increase in the accuracy of malicious node detection. [ABSTRACT FROM AUTHOR]

With artificial intelligence technology progressing at a tremendous speed, intelligent driving has got a lot of recognition in recent years. Lane detection is one of the primary functions in self-driving cars. Traditionally, lane detection was done using image processing algorithms and computer vision techniques, which included extraction of areas which are possible lane areas, edge enhancement etc. Deep learning models with new improvements are being introduced till date. Additionally, a self-driving vehicle must be able to recognise traffic signs. In the proposed work a VGG-16 convolutional neural network is used for road segmentation. The model is trained on the KITTI road/lane detection evaluation 2013 dataset. The model performed well with an accuracy of 98.58%. For traffic sign detection, the German traffic sign recognition benchmark dataset is used. A convolutional neural network is used with ADAM optimizer, which gives an accuracy of 95%. [ABSTRACT FROM AUTHOR]

Green and sustainable synthesis of carbon dots (CD) has garnered significant interest due to their versatile applications and environmentally friendly production methods. Its primary characteristics of demonstrating superior optical properties, like high quantum yields and lengthy photoluminescence decay lifetimes, cheaper methods of preparation due to readily available raw materials and simpler preparation techniques, biocompatibility, and tunable structural functionalization have made it the material preferred in many contemporary research fields. In this study, we represent a novel approach for the synthesis of CD using date seed extract as a natural spring of carbon. The CD were fabricated via a simplistic one-pot hydrothermal method, avoiding the use of toxic chemicals and energy-intensive processes. Characterization techniques, including transmission electron microscopy (TEM), Raman spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, UV-visible spectroscopy, and photoluminescence spectroscopy, were employed to analyze the properties of the synthesized CD. The analysis using TEM revealed, that the CD exhibited a spherical morphology with an regular size of [20 up to 40 nm]. The Raman spectroscopy confirmed the presence of sp2 carbon, indicating their graphene-like structure. FTIR analysis provided insights into the functional groups existing on the surface of the CD. The UV-visible spectroscopy demonstrated strong absorbance in the visible region, indicating their potential for photoluminescence applications. The photoluminescence spectroscopy further revealed their tunable emission properties. Utilizing a green process to create CD extract offers a sustainable and renewable pathway for the production of nanomaterials, promoting environmental responsibility. [ABSTRACT FROM AUTHOR]

The common insecticide used in Cabbage and Basmati rice fields is Cartap hydrochloride (CHC). It is a low toxicity insecticide belongs to Neurotoxin. Present study aimed to synthesize a new Nano-insecticide by using polypropylene glycol (PPG) as an encapsulated agent. Nano-CHC is useful for plant disease control. Polypropylene glycol encapsulated CHC size distribution was obtained by PadeLaplace dispersion in the size range in between 60-67nm. The functional group analysis of encapsulated CHC was carried by FT-IR spectroscopy. The structural topology of encapsulated CHC was obtained by scanning electron microscope. The morphological internal structural elucidation of encapsulated CHC can be obtained by transmission electron microscope analysis. The bioactivity study was conducted and shows good efficacy results against Aspergillus flavus, Sclerotium rolfsii and Rhizoctonia solani. [ABSTRACT FROM AUTHOR]

The occurrence of botnets over the network is crucial as it shows advent effect on various applications like finance, cyber-security, and healthcare application. Botnets are refined and more dangerous in their functionality over the network model. Most of the prevailing models and flow- and rule-based models feel challenging to predict the bot functionalities in a preventive manner. Therefore, the modeling of efficient and automated botnet detection approaches is highly essential. This research concentrates on modeling a novel botnet detection approach based on the recursively analyzing the flow of features of the network nodes spatially and temporally where the attack samples are intra-dependent time-series data. The hierarchical structural design of the network helps to integrate various levels of feature information and learns the spatial and temporal information automatically among the adjacent network connection. This process is carried out by the proposed architectural model known as Recursively Learning Long Short-Term Memory over spatial and temporal ( RL 2 TM-st). Thus, the bot activities are detected by recursively analyzing the limited number of nodes. The RL 2 TM-st) model is modeled to improve the efficiency of the network by eliminating unnecessary activities. The proposed model is validated using the online accessible CTU-13 dataset and benchmarked against the prevailing classification approaches for botnet detection. The simulation is done in a MATLAB environment, and the outcomes work efficiently and evaluated with prevailing models to project the significance of the RL 2 TM-st model. [ABSTRACT FROM AUTHOR]

Bot detection is considered a crucial security issue that is extensively analysed in various existing approaches. Machine Learning is an efficient way of botnet attack detection. Bot detection is the major issue faced by the existing system. This research concentrates on adopting a graph-based feature learning process to reduce feature dimensionality. The incoming samples are correctly classified and optimised using an Adaboost classifier with an improved grey wolf optimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multi-constraint issues related to bot detection and provide better local and global solutions (to satisfy exploration and exploitation). The extensive results show that the proposed g-AGWO model outperforms existing approaches to reduce feature dimensionality, under-fitting/over-fitting and execution time. The error rate prediction shows the feasibility of the given model to work over the challenging environment. This model also works efficiently towards the unseen data to achieve better generalization. [ABSTRACT FROM AUTHOR]

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers. However, certain limitations need to be addressed efficiently. The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints. The bots' patterns or features over the network have to be analyzed in both linear and non-linear manner. The linear and non-linear features are composed of high-level and low-level features. The collected features are maintained over the Bag of Features (BoF) where the most influencing features are collected and provided into the classifier model. Here, the linearity and non-linearity of the threat are evaluated with Support Vector Machine (SVM). Next, with the collected BoF, the redundant features are eliminated as it triggers overhead towards the predictor model. Finally, a novel Incoming data Redundancy Elimination-based learning model (RedE-L) is built to classify the network features to provide robustness towards BotNets detection. The simulation is carried out in MATLAB environment, and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset (benchmark dataset). The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM, C4.5, RepTree and so on. Here, various metrics like Accuracy, detection rate, Mathews Correlation Coefficient (MCC), and some other statistical analysis are performed to show the proposed RedE-L model's reliability. The F1-measure is 99.98%, precision is 99.93%, Accuracy is 99.84%, TPR is 99.92%, TNR is 99.94%, FNR is 0.06 and FPR is 0.06 respectively. [ABSTRACT FROM AUTHOR]

The current agricultural practice is to develop novel Nano agrochemicals in the form of Nano pesticides. Many researchers in the world have foretold an upheaval in present agricultural practice. This paper aims at the application of nanotechnology for plant protection in the form of Nano pesticides. Our study endowed with a novel polymeric Nano formulation of a dithiocarbamate nonsystemic agricultural fungicide called Mannose (MCB) by using polycaprolactone as a capping agent. The encapsulated dithiocarbamate complex was characterized by using analytical techniques like UV-Visible spectroscopy, Dynamic light scattering, Scanning electron microscope, and Transmission electron microscope. The particle size distribution emerged at 17-44nm. The bioassay was conducted against Aspergillus flavus, Aspergillus niger, Sclerotium rolfsii, and Rhizoctonia solani. The bioassay illustrated better results as compared to the commercial pesticide. [ABSTRACT FROM AUTHOR]

We examine here the relative influence of natural and anthropogenic forcing on surface air temperature (SAT) variability over the Indian subcontinent (IND) through the analysis of the normalized data of maximum (Tmax), minimum (Tmin), and mean (Tmean/INDSAT) temperatures, total solar irradiance (TSI), El-Nino Southern Oscillations (ENSO), Pacific Decadal Oscillations (PDO), North Atlantic Oscillations (NAO), sea surface temperature (SST), all-India rainfall (AIR), and CO2 using artificial neural networks (ANN). Sensitivity analysis reveals the dependency of each of the natural (e.g. TSI, ocean-atmospheric circulations [OAC, e.g. ENSO, PDO, NAO], SST, AIR) and anthropogenic (CO2) parameters on SAT as follows: (i) SAT is highly sensitive to continuous emission of anthropogenic CO2, (ii) intensive influence of TSI at 11- and 22-year cycles, and (iii) intermittent influence of OAC and SST at low intensive solar radiance. These dependent parameters are used to develop a model to predict future temperatures using multi-perceptron nonlinear feed-forward and back-propagation schemes with a NARX network encoded Bayesian regularization (BR) training algorithm. We used TSI, CO2, OAC, SST, and AIR data as input and Tmean/INDSAT as target (supervised learning). In order to ensure the stable and best possible result, we trained the network successively with different numbers (1–10) of neurons and delay parameters. Optimum mean square error (MSE) and R values (minimum MSE and maximum R value) with eight neurons were found appropriate for the present analysis. We therefore, chose eight neurons to develop a model for training and testing the data and obtained MSE = 0.00039976 and R = 0.98933. This model was validated from 2001 to 2007 and predicted the temperatures from 2001 to 2019 with error ≤ 20%. [ABSTRACT FROM AUTHOR]

The theory of Finite fields plays a significant role in the theory of Galois extensions. Finite fields have many applications in Coding theory, Computing and Statistics. Therefore, this paper makes an attempt to study some finite fields and their properties. Especially it concentrates on the construction of finite fields. It is known that the characteristic of a finite field is a prime number. Here, first we construct the finite fields containing 4, 8, and 16 elements of characteristic 2, then the fields with 9 and 27 elements of characteristic 3, and finally the fields of 25 elements with characteristic 5. It is well known that the multiplicative group of a finite field is a cyclic group. Therefore, the generators of all the cyclic groups of the finite fields are also found. [ABSTRACT FROM AUTHOR]

Rabindranath Tagore also known by his pen name Bhanu Singha Thakur (Bhonita), and also known by his sobriquets Gurudev, Kabiguru, and Biswakabi, was a polymath, poet, musician, and artist from the Indian subcontinent. He reshaped Bengali literature and music, as well as Indian art with Contextual Modernism in the late 19th and early 20th centuries. Author of the "profoundly sensitive, fresh and beautiful verse" of Gitanjali, he became in 1913 the first non-European to win the Nobel Prize in Literature. The present research paper is a modest attempt to trace how far Tagore succeeds in promoting Indian culture and country in his 'The Home and The World'. [ABSTRACT FROM AUTHOR]

Context: Various methods are used for gender identification in anthropologic and forensic investigations. The canine tooth is widely used for gender determination because of its sexual dimorphism and durability in the oral cavity. Mandibular canine index (MCI) is one of the most reliable and valid predictor for gender identification.Aim: To assess the reliability of MCI in gender determination in Indian population.Settings and Design: This was an experimental study involving the preparation of plaster models of 100 individuals residing in Udaipur, Rajasthan, India. Of these 45 were males and 55 were females. Both sexes were in the age group of 20-30 years. Study casts were prepared for all individuals and the measurements of mandibular canine teeth were taken. The MCI was calculated using standardized equation. In addition, the percentage of sexual dimorphism was calculated.Statistical Analysis Used: Independent sample t-test was used. P < 0.05 was considered statistically significant.Results: There was a significant difference (P < 0.05) observed between inter-canine distance, right and left canine width and right and left MCI between males and females. Sexual dimorphism is more on left permanent mandibular canine teeth than right permanent mandibular canine teeth. The overall percentage of accuracy observed was 85.5%.Conclusions: Study findings reveal that MCI can be used a reliable predictor for gender determination in Indian population and can form the basis of anthropologic and forensic investigations. [ABSTRACT FROM AUTHOR]

We examine here a 2000-year-long record of surface air temperature from China using powerful spectral and statistical analysis techniques to assess the trend and harmonics, if any. Our analyses reveal statistically significant periodicities of order ~900 ± 50, ~480 ± 20, 340 ± 10, ~190 ± 10 and ~130 ± 5 years, which closely match with the known higher-order solar cycles. These periodicities are also similar to quasi-periodicities reported in the climate records of sedimentary cores of subarctic and subpolar regions of North America and North Pacific, thus attesting to the global signature of solar signals in temperature variability. A visual comparison of the temperature series shows that the nodes and antinodes of the underlying temperature variation also match with sunspot variations. We also compare the China temperature (CT) with temperature of northern and southern hemispheres of the past 1000 years. The study reveals strong correlation between the southern hemispheric temperatures and CT during the past 1000 years. However, the northern hemisphere temperature shows strong correlation with CT only during the past century. Interestingly, the variations in the correlation coefficient also have shown periodicities that are nearly identical to the periods observed from CT and higher-order solar cycles. We suggest that the solar irradiance induces global periodic oscillations in temperature records by transporting heat and thermal energy, possibly through the coupling of ocean-atmospheric processes and thereby reinforcing the Sun-ocean-climate link. [ABSTRACT FROM AUTHOR]

Significant fluctuations have been observed in Indian temperatures during past century. In order to identify the statistical periodicities in the maximum and minimum temperature data of different Indian zones, we have spectrally and statistically analyzed the homogeneous regional temperature series from the Western Himalayas, the Northern West, the North Central, the North East (NE), the West Coast, the East Coast, and the Interior Peninsula for the period of 107 years spanning over 1901-2007 using the multitaper method (MTM) and singular spectrum analysis (SSA) methods. The first SSA reconstructed the principal component of all the data sets representing a nonlinear trend (indicating a monotonic rise in temperature probably due to greenhouse gases and other forcing) that varies from region to region. We have reconstructed the temperature time series using the second to tenth oscillatory principal components of all the eight regions and computed their power spectral density using MTM. Our analyses indicate that there is a strong spectral power in the period range of 2-7 years and 53 years, which are matched respectively with the known El Niño-Southern oscillation (ENSO) periods and ocean circulation cycles. Further, the spectral analysis also revealed a statistically significant but riven cycle in a period range of 9.8-13 years corresponding to the Schwabe cycle in all Indiaian maximum and minimum temperature records and almost all the zonal records except in the NE data. In some of the cases, the 22 year double sunspot (Hale cycle) cycle was also identified here. Invariably the splitting of spectral peaks corresponding to solar signal indicated nonlinear characteristics of the data and; therefore, even small variations in the solar output may help in catalyzing the coupled El Niño-atmospheric ENSO cycles by altering the solar heat input to the oceans. We, therefore, conclude that the Indian temperature variability is probably driven by the nonlinear coupling of ENSO and solar activity. [ABSTRACT FROM AUTHOR]

Background: Candida and epithelial dysplasia have long been associated with oro-mucosal lesions. Aim and Objective: The present study was designed to evaluate the correlation between presence of Candida organisms and epithelial dysplasia in various oral mucosal lesions associated with areca nut and tobacco use. Materials and Methods: A total of 50 individuals were selected, between age range of 19-70 y. Three separate cytosmears were prepared for each participant. All the slides were stained with PAS stain and the best slide was viewed for candidal organisms. Statistical Tests: The data was analysed using the SPSS version1 6 Chi square test was performed. Results: Out of these, samples of 26 participants displayed presence of Candida. It was further observed that all the samples that were positive for presence of Candida displayed the organism in hyphal form. Out of 50 biopsy specimens stained for presence of Candida using PAS stain, samples of only 2 participants demonstrated presence of Candida in hyphal form, whereas the biopsy specimens stained for demonstrating dysplastic changes using H&E stain displayed various levels of cellular atypia in samples of 16 participants. Out of these 12 were mild, 3 were moderate & 1 displayed severe dysplastic changes. Conclusion: The study revealed a statistically non significant correlation between the presence of Candida and epithelial dysplasia in oral mucosal lesions. [ABSTRACT FROM AUTHOR]

Hemifacial spasm is characterized by tonic and clonic contractions of the muscles innervated by the ipsilateral facial nerve. Magnetic resonance imaging and angiography studies frequently demonstrate vascular compression of the root exit zone of the facial nerve. Importantly, an underlying space-occupying lesion needs to be excluded in patients with associated atypical features such as facial numbness and weakness. This is a case report of a dental patient with involuntary facial movements which were noted during history recording. Thorough clinical examination was carried out and magnetic resonance imaging revealed a tortuous left vertebral artery causing mild impingement of root exit zone of left facial nerve confirming the diagnosis of hemifacial spasm. The etiology, clinical features, treatment options and differential diagnosis of hemifacial spasm are discussed. [ABSTRACT FROM AUTHOR]

'Crohn's disease' is an inflammatory granulomatous disease of the gastrointestinal tract with extra-intestinal manifestations. Oral lesions may precede the intestinal disease and serve as a source for histological diagnosis. We present a case of orofacial Crohn's disease where orofacial symptoms were present for about 13 years and occasional constipation was present, since 6 months. Oral examination plays an important role in early diagnosis of Crohn's disease. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s00024-021-02724-z. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]

The original article can be found online at https://doi.org/10.1007/s00024-019-02247-8. The correct name of the first author is Padmavathi B. The original article has been corrected. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India The first author's surname was missing in the section "About this article". [Extracted from the article]

Nutrition is a fundamental pillar of human life.1 Nutrition by affecting the brain function may influence the occupational performance of an individual. This study was designed to evaluate the corelationship between the brain function and hemoglobin level of more than 200 students studying dentistry for assessing the possible impact of nutritional status on brain function. Study was conducted in two stages; first stage involved estimation of hemoglobin and the second stage involved assessment of brain function test by 3 set of tests based on self answering questionnaires. Results of our study show that the quality of brain function does depend but not limited to the hemoglobin level of the study participants. [ABSTRACT FROM AUTHOR]

In this paper, we study five different types of problems in potential and viscous flows past intersecting circles or spheres in two and three dimensions, respectively, with different angles of intersection. We observe a striking resemblance in the form of the solutions in all these cases by introducing certain operators L and M which generate a group G. By introducing a procedure called ‘closure’ which determines the order of the group G, we give a general method to discuss the problem of flow past two intersecting circles or spheres in potential and Stokes flows with different angles of intersection. [ABSTRACT FROM AUTHOR]

A general non-axisymmetric Stokes flow past a porous sphere in a viscous, incompressible fluid is considered. The flow inside the sphere is governed by Brinkman's equations. A representation for velocity and pressure for the Brinkman's equations is suggested and a method of finding the flow quantities is given. Faxén's laws for drag and torque for the flow past a porous sphere are also given. [ABSTRACT FROM AUTHOR]

The problem of a general non-axisymmetric Stokes flow of a viscous fluid past a porous sphere is considered. The expressions for the velocity and pressure, both inside and outside the sphere are given, when the flow outside satisfies the Stokes equations and the flow inside the sphere is governed by Darcy's law. The expressions for drag and torque are given. It is found that the drag is greater or smaller than the drag in the rigid case, depending on whether the undisturbed velocity is a pure biharmonic or a harmonic respectively. The torque is same as in the rigid case. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the procoagulant activity of the lung in rats after exposure to acrylonitrile. The study uses trypan blue exclusion, manual tilt method and standard human brain thromboplastin preparation to assess the effect of acrylonitrile in rat lung. Results show that lung exposure to acrylonitrile demonstrates fibrin network formation.