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Education plays a crucial role in eradicating unemployment because it imparts both skills and knowledge. An educated person is more likely to secure employment that offers adequate remuneration, allowing them to meet their basic needs. The specific aims of this study are: -to define the term-" Education as a Instrument to Eradicate Unemployment", to evaluate the relationship between the education and unemployment, present study is based on the secondary data, secondary data have collected from the some specific websites-www.geeksforgeeks.org and www.adda247.com. Data have analysed by the help of tables, correlation test to conduct this study by using SPSS software. [ABSTRACT FROM AUTHOR]

Abstract The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.

Depression often known as depressive disorder is described by a persistent lack of enjoyment, enthusiasm in pursuits, or melancholy feelings. Depression is not the same as normal mood swings and feelings about day-to-day living. It could affect every aspect of life, including relationships with friends, family, and the community. It could be brought on by or exacerbated by problems at work or in the classroom. This study aims to evaluate antidepressant like activity of ethanolic extract of roots of Boerhavia diffusa (Nyctaginaceae). control and cure of this diseases, a vast variety of medications are used daily. The herbal drugs are biodegradable and are natural medications hence are becoming more and more popular. Using a Soxhlet equipment and a normal extraction procedure, the ethanolic extract was produced. The mice were administered several dosages of the extract (100, 200, and 400 mg/kg) in addition to the vehicle (normal saline) for the control group & ARS (Acute Restraint Stress) Group and fluoxetine as the conventional medication. The mice were subjected to ARS and were treated with ethanolic extract of Boerhavia diffusa at a dose of 100mg/kg, 200mg/kg and 400mg/kg respectively. After that they were subjected to animal models, the tail suspension test (TST), force suspension test (FST) to assess the antidepressant potential and open field test (OFT), to assess locomotor & antidepressant potential of administered drug The TST revealed prolonged immobility in the BDEE (Boerhavia diffusa ethanolic extract) treated animals. The crossing over of squares activity in the OFT rose, indicating a decline in depression levels. The mice's FST revealed a decrease in their ability to reach the plateau stage following immobility. According to the study, BDEE demonstrates strong antidepressant effect and offer a natural alternative to pharmaceutical treatment for depression disorders. In futuristic study, further investigation is required to identify certain active molecules and have a deeper comprehension of the underlying processes of action. [ABSTRACT FROM AUTHOR]

The current study uses rodent models to explore the anxiolytic properties of Hemidesmus indicus' ethanolic extract (EEHI). Hemidesmus indicus, an evergreen plant well-known for its therapeutic characteristics, has been widely used in Ayurvedic medicine to treat a variety of conditions, including anxiety. This study seeks to give scientific validation for its anxiolytic properties. The ethanolic extract was made using normal extraction processes and given to rodents in varied doses (100,200 and 400 mg/kg). The anxiolytic activity was assessed using a variety of widely recognized behavioural assays, including the Elevated Plus Maze (EPM), Open Field Test (OFT), and Light-Dark Box (LDB) test. Diazepam (2 mg/kg) served as the standard reference drug. The results revealed that EEHI significantly reduced anxiety-like behaviours in all three tests when compared to the control group. In the EPM test, EEHI-treated rodents spent more time and entered the open arms. In the OFT, ambulation, rearing and assisted rearing increased, indicating lower levels of anxiety. The LDB test supported these findings, with a significant increase in time spent in the light compartment. The study suggests that EEHI has promising anxiolytic activity and may offer a natural treatment alternative for anxiety disorders. Further research is needed to isolate specific active molecules and better understand the underlying mechanisms of action. [ABSTRACT FROM AUTHOR]

Deep learning has been widely studied in many technical domains such as image analysis and speech recognition, with its benefits that effectively deal with complex and high-dimensional data. Our preliminary experiments show a high degree of non-linearity from the network connection data, which explains why it is hard to improve the performance of identifying network anomalies by using conventional learning methods (e.g., Adaboosting, SVM, and Random Forest). In this study, we design and examine deep learning models constructed based on Fully Connected Networks (FCNs), Variational AutoEncoder (VAE), and Sequence-to-Sequence (Seq2Seq) structures. For the extensive evaluation, we employ a broad range of the public datasets with unique characteristics. Our experimental results confirm the feasibility of deep learning-based network anomaly detection, with the improved performance compared to the conventional learning techniques. In particular, the detection model based on Seq2Seq with LSTM is highly promising, consistently yielding over 99% of accuracy to identify network anomalies from the entire datasets employed in the evaluation.

The evaluation of data breaches and cybersecurity risks has not yet been formally addressed in modern business systems. There has been a tremendous increase in the generation, usage and consumption of industrial and business data as a result of smart and computational intensive software systems. This has resulted in an increase in the attack surface of these cyber systems. Consequently, there has been a consequent increase in the associated cybersecurity risks. However, no significant studies have been conducted that examine, compare, and evaluate the approaches used by the risk calculators to investigate the data breaches. The development of an efficient cybersecurity solution allows us to mitigate the data breaches threatened by the cybersecurity risks such as cyber-attacks against database storage, processing and management. In this paper, we develop a comprehensive, formal model that estimates the two components of security risks: breach cost and the likelihood of a data breach within 12 months. The data used in this model are taken from the industrial business report, which provides the necessary information collected and the calculators developed by the major organizations in the field. This model integrated with the cybersecurity solution uses consolidated factors that have a significant impact on the data breach risk. We propose mathematical models of how the factors impact the cost and the likelihood. These models allow us to conclude that results obtained through the models mitigate the data breaches in the potential and future business system dynamically.

Abstract Background Anti-malarial drug resistance continues to be a leading threat to malaria control efforts and calls for continued monitoring of waning efficacy of artemisinin-based combination therapy (ACT). Artesunate + sulfadoxine/pyrimethamine (AS + SP) is used for the treatment of uncomplicated Plasmodium falciparum malaria in India. However, resistance against AS + SP is emerged in northeastern states. Therefore, artemether–lumefantrine (AL) is the recommended first line treatment for falciparum malaria in north eastern states. This study investigates the therapeutic efficacy and safety of AL for the treatment of uncomplicated falciparum malaria in three malaria-endemic states in India. The data generated through this study will benefit the immediate implementation of second-line ACT as and when required. Methods This was a one-arm prospective evaluation of clinical and parasitological responses for uncomplicated falciparum malaria using WHO protocol. Patients diagnosed with uncomplicated mono P. falciparum infection were administered six-dose regimen of AL over 3 days and subsequent follow-up was carried out up to 28 days. Molecular markers msp-1 and msp-2 were used to differentiate recrudescence and re-infection and K13 propeller gene was amplified and sequenced covering the codon 450–680. Results A total of 402 eligible patients were enrolled in the study from all four sites. Overall, adequate clinical and parasitological response (ACPR) was 98 % without PCR correction and 99 % with PCR correction. At three study sites, ACPR rates were 100 %, while at Bastar, cure rate was 92.5 % on day 28. No early treatment failure was found. The PCR-corrected endpoint finding confirmed that one late clinical failure (LCF) and two late parasitological failures (LPF) were recrudescences. The PCR corrected cure rate was 96.5 %. The mean fever clearance time was 27.2 h ± 8.2 (24–48 h) and the mean parasite clearance time was 30.1 h ± 11.0 (24–72 h). Additionally, no adverse event was recorded. Analysis of total 186 samples revealed a mutation in the k13 gene along with non-synonymous mutation at codon M579T in three (1.6 %) samples. Conclusion AL is an efficacious drug for the treatment of uncomplicated falciparum malaria. However, regular monitoring of AL is required in view of malaria elimination initiatives, which will be largely dependent on therapeutic interventions, regular surveillance and targeted vector control.

Purpose: To evaluate the role of fine needle aspiration (FNAC) as a diagnostic tool in cases of orbital and ocular adnexal masses. Cytological findings were correlated with histopathological diagnosis wherever possible. Methods: FNAC was performed in 29 patients of different age groups presenting with orbital and ocular adnexal masses. Patients were evaluated clinically and investigated by non-invasive techniques before fine needle aspiration of the masses. Smears were analyzed by a cytologist in all cases. Further, results of cytology were compared with the histopathological diagnosis. Results: The age of patients ranged from 1 to 68 years (mean: 29.79±19.29). There were 14 males and 15 females with a male to female ratio of 0.93:1. Out of 29 cases, 26 aspirates were cellular. Cellularity was insufficient in three (10.34%) aspirates. Out of 26 cellular aspirates, 11 were non-neoplastic while 15 were neoplastic on cytology. Subsequent histopathologic examination was done in 21/26 cases. Concordance rate of FNAC in orbital and ocular adnexal mass lesions with respect to the precise histologic diagnosis was 90%. Conclusion: When properly used in well-indicated patients (in cases where a diagnosis cannot be made by clinical and imaging findings alone), FNAC of orbital and periorbital lesions is an invaluable and suitable adjunct diagnostic technique that necessitates close cooperation between the ophthalmologist and cytologist. However, nondiagnostic aspirates may sometimes be obtained, and an inconclusive FNAC should not always be ignored.

One of the recent news headlines is that a pedestrian was killed by an autonomous vehicle because safety features in this vehicle did not detect an object on a road correctly. Due to this accident, some global automobile companies announced plans to postpone development of an autonomous vehicle. Furthermore, there is no doubt about the importance of safety features for autonomous vehicles. For this reason, our research goal is the development of a very safe and lightweight camera-based blind spot detection system, which can be applied to future autonomous vehicles. The blind spot detection system was implemented in open source software. Approximately 2000 vehicle images and 9000 non-vehicle images were adopted for training the Fully Connected Network (FCN) model. Other data processing concepts such as the Histogram of Oriented Gradients (HOG), heat map, and thresholding were also employed. We achieved 99.43% training accuracy and 98.99% testing accuracy of the FCN model, respectively. Source codes with respect to all the methodologies were then deployed to an off-the-shelf embedded board for actual testing on a road. Actual testing was conducted with consideration of various factors, and we confirmed 93.75% average detection accuracy with three false positives.

Modern infrastructure networks are often coupled together and thus could be modeled as interdependent networks. Overload and interdependent effect make interdependent networks more fragile when suffering from attacks. Existing research has primarily concentrated on the cascading failure process of interdependent networks without load, or the robustness of isolated network with load. Only limited research has been done on the cascading failure process caused by overload in interdependent networks. Redundant design is a primary approach to enhance the reliability and robustness of the system. In this paper, we propose two redundant methods, node back-up and dependency redundancy, and the experiment results indicate that two measures are effective and costless. Two detailed models about redundant design are introduced based on the non-linear load-capacity model. Based on the attributes and historical failure distribution of nodes, we introduce three static selecting strategies-Random-based, Degree-based, Initial load-based and a dynamic strategy-HFD (historical failure distribution) to identify which nodes could have a back-up with priority. In addition, we consider the cost and efficiency of different redundant proportions to determine the best proportion with maximal enhancement and minimal cost. Experiments on interdependent networks demonstrate that the combination of HFD and dependency redundancy is an effective and preferred measure to implement redundant design on interdependent networks. The results suggest that the redundant design proposed in this paper can permit construction of highly robust interactive networked systems.

The applicability of six vulnerability discovery models to four major operating systems is examined. Results for the evaluation of the risk of vulnerability exploitation show that some of the models tend to capture the discovery process better than the other models.

The educational and social life of adolescents has been transformed in the last one and a half years, due to the global pandemic COVID-19. In order, to control, the expansion of this virus during the second wave (Feb.-August 2021), as it is a severe acute respiratory syndrome with modified features, all the educational institutions had been closed and even board examinations of central as well as state boards were cancelled. Students were forced to stay at home and classes were organised through online mode. Now adolescents, spend most of their time with their smartphones for attending their online classes as well as during their leisure time for socialising with their peers. The specific objective of the study is to examine the leisure time activities of adolescents during the second wave. The study is a cross-cultural study of 460 adolescents in the age group of (16-18) years studying in senior secondary class (Grade XIIth) in South Asian countries; India, Maldives, and Bhutan. The study has used tools such as Leisure Interest Measure (LIM by Ragheb and Beard, 1992) and a structured key information schedule designed for studying smartphone usage patterns (SPUP) for collecting information. AMOS 23, NVivo 12 and other statistical packages have been used for analysing the data, to test the model designed for studying leisure time activities and their effects. The results of the study focus on digital leisure and the health problems associated with it. The findings of the study will be helpful in policy formulation related to adolescents' technology usage and the effective use of their leisure time The study concluded that excess use of technology either for educational or any other purpose has a bad impact on the psycho-physiological health of adolescents and it requires serious attention. [ABSTRACT FROM AUTHOR]

Carbon nanotubes are newer generation material broadly emerged for biomedical applications due to its tempting properties such as size and aspect ratio covering surface area to the length, electrical, mechanical and thermal properties in addition to their unique architecture. The view for CNT-based nanomedicines is attractive and promising. Carbon nanotubes compared to other nanocarriers can be easily modified for conjugation of bioactive compounds and ligands for targeting. The applications of carbon nanotubes are not only limited to drug delivery but also in diagnostic applications. Due to a variety of applications in drug delivery, it is necessary to bear in mind the toxicities of carbon nanotubes and catch the way to overcome the problems related to toxicities. Currently, many studies have explored the biodegradation mechanism of carbon nanotubes. Biodegradation shows a conspicuous pathway that enables degradation and removal of CNTs from living body and catalyzes the release of the load from the CNTs. Many articles have been published till date which evidentiary provides the promising applications of CNTs. The present review seeks to provide an overview on intracellular fate, applications and toxicities of CNTs. [ABSTRACT FROM AUTHOR]

Huntington's disease (HD) is a dominantly inherited neurodegenerative disorder caused by a trinucleotide expansion in exon 1 of the huntingtin (HTT) gene. Cell death in HD occurs primarily in striatal medium spiny neurons (MSNs), but the involvement of specific MSN subtypes and of other striatal cell types remains poorly understood. To gain insight into cell type-specific disease processes, we studied the nuclear transcriptomes of 4524 cells from the striatum of a genetically precise knock-in mouse model of the HD mutation, HttQ175/1, and from wild-type controls. We used 14- to 15-month-old male mice, a time point at which multiple behavioral, neuroanatomical, and neurophysiological changes are present but at which there is no known cell death. Thousands of differentially expressed genes (DEGs) were distributed across most striatal cell types, including transcriptional changes in glial populations that are not apparent from RNA-seq of bulk tissue. Reconstruction of cell type-specific transcriptional networks revealed a striking pattern of bidirectional dysregulation for many cell type-specific genes. Typically, these genes were repressed in their primary cell type, yet de-repressed in other striatal cell types. Integration with existing epigenomic and transcriptomic data suggest that partial loss-of-function of the polycomb repressive complex 2 (PRC2) may underlie many of these transcriptional changes, leading to deficits in the maintenance of cell identity across virtually all cell types in the adult striatum. [ABSTRACT FROM AUTHOR]

The evaluation of data breaches and cybersecurity risks has not yet been formally addressed in modern business systems. There has been a tremendous increase in the generation, usage and consumption of industrial and business data as a result of smart and computational intensive software systems. This has resulted in an increase in the attack surface of these cyber systems. Consequently, there has been a consequent increase in the associated cybersecurity risks. However, no significant studies have been conducted that examine, compare, and evaluate the approaches used by the risk calculators to investigate the data breaches. The development of an efficient cybersecurity solution allows us to mitigate the data breaches threatened by the cybersecurity risks such as cyber-attacks against database storage, processing and management. In this paper, we develop a comprehensive, formal model that estimates the two components of security risks: breach cost and the likelihood of a data breach within 12 months. The data used in this model are taken from the industrial business report, which provides the necessary information collected and the calculators developed by the major organizations in the field. This model integrated with the cybersecurity solution uses consolidated factors that have a significant impact on the data breach risk. We propose mathematical models of how the factors impact the cost and the likelihood. These models allow us to conclude that results obtained through the models mitigate the data breaches in the potential and future business system dynamically. [ABSTRACT FROM AUTHOR]

Adaptive testing (AT) is a software testing approach that uses a feedback mechanism to enhance test effectiveness. Its testing strategy can be adjusted online by using the testing data collected during the software testing process. However, it requires complex parameter estimation which results in excessive computational overhead that may hinder the applicability of AT. In this paper, we propose an approach called AT based on moment estimation (AT-ME) to address this problem. The proposed approach uses moment estimation to serve as the algorithm of parameter estimation, which reduces the complexity of AT-ME. In addition, a dynamic length for testing action is set to limit the number of decisions without influencing the test effectiveness. The proposed approach has been validated on the Siemens test suite, which includes seven real programs. The experiments show that AT-ME can reduce the computational overhead of AT without compromising overall testing efficiency. Results demonstrate that AT-ME is a feasible and effective AT strategy. [ABSTRACT FROM AUTHOR]

One of the recent news headlines is that a pedestrian was killed by an autonomous vehicle because safety features in this vehicle did not detect an object on a road correctly. Due to this accident, some global automobile companies announced plans to postpone development of an autonomous vehicle. Furthermore, there is no doubt about the importance of safety features for autonomous vehicles. For this reason, our research goal is the development of a very safe and lightweight camera-based blind spot detection system, which can be applied to future autonomous vehicles. The blind spot detection system was implemented in open source software. Approximately 2000 vehicle images and 9000 non-vehicle images were adopted for training the Fully Connected Network (FCN) model. Other data processing concepts such as the Histogram of Oriented Gradients (HOG), heat map, and thresholding were also employed. We achieved 99.43% training accuracy and 98.99% testing accuracy of the FCN model, respectively. Source codes with respect to all the methodologies were then deployed to an off-the-shelf embedded board for actual testing on a road. Actual testing was conducted with consideration of various factors, and we confirmed 93.75% average detection accuracy with three false positives. [ABSTRACT FROM AUTHOR]