The eruptions of Örӕfajökull 1362 (Iceland) and Lanzarote 1730-36 (Canary Islands) : sulphur emissions and volcanology

This thesis presents new data on the volcanology, erupted volumes, and sulphur emissions of the AD 1362 Örӕfajökull eruption and the AD 1730-36 Lanzarote eruption, and relates these findings to their atmospheric and environmental impacts. The Örӕfajökull eruption was an explosive Plinian event with high eruption columns (~30 Ian) that produced an extensive tephra-fall and a small pyroclastic flow deposit (~2 km3 total volume). In contrast, the Lanzarote eruption was a long-lasting basaltic fissure eruption involving Hawaiian and Strombolian fire fountain activity (eruption plumes 8-16 km high) generating a scoria fall deposit and lava flows (~5 km3 total volume). Chapter 1 introduces the effects of volcanic gas release, and highlights the importance of SO2. In chapter 2, I provide a critical assessment of the petrologic method used to estimate the sulphur release from a volcanic eruption. The petrologic method uses the difference in sulphur concentrations between melt inclusions and matrix glasses, measured by electron microprobe, scaled to the mass of erupted magma, and corrected for the magma crystal content. I show that it provides estimates for sulphur degassing from non-arc, basaltic, reduced magmas that are similar to independent satellite measurements (TOMS). Using this technique, the AD 1362 Örӕfajökulll eruption is estimated to have released only ~0.6 Mt of SO2 into the stratosphere - supported by lack of an ice core acidity peak. The major environmental hazard resulting from this eruption was large volumes of pumice and ash injected into the upper atmosphere and its subsequent fallout over a wide area (chapter 3). The Lanzarote eruption released at least 45 Mt of SO2. This was determined using a new technique based on the correlation between S and incompatible element (K, P, Ti) ratios (S/I); knowing this ratio the original S content of a degassed liquid can be calculated from its concentration of I. The release of SO2 from the Lanzarote eruption is shown to have caused significant climatic perturbations - as demonstrated in the climate proxy record (chapter 4)

Knowledge Management and its Impact on Organisations: An Assessment of Initiatives in the Industry

Knowledge management (KM) has gained a lot of importance due to the value, which it has offered to the organizations. It has been observed that Information Technology (IT) has further made this task easier. KM efficiency of an organization varies due to its KM capabilities. KM initiatives in the industry are numerous and IT is an important tool to get these implemented. This paper helps to understand the organizational impact of KM initiatives and its assessment. The paper has two parts. The first part, which talks about organizational impact of KM is exploratory in nature. The second part is based on primary data collected from listed BSE companies. Data is analyzed to check whether organizations, which are practicing KM, are aware that they are doing KM. The paper concludes that better the KM capabilities, better will be the KM implementation results. The benefits of KM are both tangible and intangible. The paper would be helpful to the industry and to the researchers and would facilitate future research in the area to assess the impact on performance by organisations applying KM

A Review and a Proposal for Reducing the Symptoms of Attention-Deficit/Hyperactivity Disorder in Adolescents by Combining Mindfulness-Based Stress Reduction Training and Methylphenidate Medication as a Treatment

A Review and a Proposal for Reducing the Symptoms of Attention-Deficit/Hyperactivity Disorder in Adolescents by Combining Mindfulness-Based Stress Reduction Training and Methylphenidate Medication as a Treatment Kirti Sharma, Dept. of Biology, with Prof. Mary Boyes, VCU Honors College Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder among adolescents that may lead to impaired executive functioning and poor mental development. In 2018, the National Health Interview Survey reported that from 1997 to 2016, the prevalence of ADHD significantly increased from 6.1% to 10.2% in children and adolescents (age 4 to 17 years). Medication, such as methylphenidate, is considered as first-line therapy for reducing symptoms of ADHD. However, medication may produce adverse side-effects such as insomnia, loss of appetite, abdominal pain, and stress. Also, due to its lack of long-term effectiveness, medication may inflict an extra financial burden on the families of adolescents with ADHD. To balance the challenges of medication-based therapy, extant psychological research has shown that mindfulness-based practices are also efficacious in managing symptoms of ADHD. A review of two bodies of scientific research was conducted: (a) the use of medication for treating ADHD, and (b) the use of mindfulness-based practices as a therapy for ADHD in children and adolescents. The research analysis revealed that in most cases, higher doses of methylphenidate is needed to effectively counter ADHD symptoms, leading to a significant cost burden for the families. The review of the literature related to mindfulness-based practices for treating ADHD indicated its effectiveness in attention-regulation, cost-effectiveness, and long-term effects. Based on the analysis, it is proposed that a treatment combining methylphenidate medication and the Mindfulness-Based Stress Reduction (MBSR) program, a mindfulness-based practice, could be an effective therapy for reducing the symptoms of ADHD in adolescents. The findings from this study may add to the conventional medication-based methods for treating ADHD in adolescents by combining with mindfulness-based practices.https://scholarscompass.vcu.edu/uresposters/1386/thumbnail.jp

Balanced co-existence of de jure and de facto independence in the public service broadcasting sector

With directives to deliver impartial news, current affairs and programmes, the social responsibility of media, mainly public service broadcasters (PSBs), is viewed as providing resources for serving democracy and full citizenship. Through these resources, public service broadcasting (PSB) builds the trust of the public in its public service values. However, the continuance of this public trust requires evidence of independence and adherence to institutional norms beyond the reach of vested interests – corporate and political party. This paper aims to investigate critical challenges facing the independence of PSBs to uncover the significance of balanced co-existence of two aspects of independence –de jure and de fact– in the PSB sector. The main argument of the paper is that the disparity between the two elements of independence is widening due to vested interests. And narrowing of such gaps is vital for PSBs to serve the public interest

A Diaspora of Humans to Technology: VEDA Net for Sentiments and their Technical Analysis

Background: Human sentiments are the representation of one’s soul. Visual media has emerged as one of the most potent instruments for communicating thoughts and feelings in today's world. The area of visible emotion analysis is abstract due to the considerable amount of bias in the human cognitive process. Machines need to apprehend better and segment these for future AI advancements. A broad range of prior research has investigated only the emotion class identifier part of the whole process. In this work, we focus on proposing a better architecture to assess an emotion identifier and finding a better strategy to extract and process an input image for the architecture. Objective: We investigate the subject of visual emotion detection and analysis using a connected Dense Blocked Network to propose an architecture VEDANet. We show that the proposed architecture performed extremely effectively across different datasets. Method: Using CNN based pre-trained architectures, we would like to highlight the spatial hierarchies of visual features. Because the image's spatial regions communicate substantial feelings, we utilize a dense block-based model VEDANet that focuses on the image's relevant sentiment-rich regions for effective emotion extraction. This work makes a substantial addition by providing an in-depth investigation of the proposed architecture by carrying out extensive trials on popular benchmark datasets to assess accuracy gains over the comparable state-of-the-art. In terms of emotion detection, the outcomes of the study show that the proposed VED system outperforms the existing ones (accuracy). Further, we explore over the top optimization i.e. OTO layer to achieve higher efficiency. Results: When compared to the recent past research works, the proposed model performs admirably and obtains accuracy of 87.30% on the AffectNet dataset, 92.76% on Google FEC, 95.23% on Yale Dataset, and 97.63% on FER2013 dataset. We successfully merged the model with a face detector to obtain 98.34 percent accuracy on Real-Time live frames, further encouraging real-time applications. In comparison to existing approaches, we achieve real-time performance with a minimum TAT (Turn-around-Time) trade-off by using an appropriate network size and fewer parameters

Three cases of Actinomyces isolation from the eye lesions of patients with a chronic and recurrent ophthalmic infection

Actinomycosis is an indolent, slowly progressive infection caused by Gram-positive facultative anaerobic bacteria from the genus Actinomyces. These bacteria have been reported as a cause of ophthalmic infections such as endophthalmitis, keratitis, and canaliculitis. The objective of the present study was to investigate the pattern and antibiotic sensitivity profile of anaerobes isolated from lesions in the medial canthus of the eye.Three pus aspirate samples (from eye) were delivered to a microbiology laboratory in a strict anaerobic condition in Robertson Cooked Meat media (RCM). The samples were inoculated on Blood Agar and incubated anaerobically in a Gas Pack Jar incubator at 5%-10% CO2 and in aerobic condition at 37°C. Pure colonies isolated on anaerobically incubated plates were identified by the VITEK® 2 COMPACT system. Antibiotic sensitivity testing was conducted using an Epsilometer-strip test.Three isolates identified and confirmed with the help of VITEK® 2 were Actinomyces israelii, Actinomyces odontolyticus, and Actinomyces meyeri. All three species of Actinomyces were sensitive to Vancomycin, Moxifloxacin, and Imipenem, but they were resistant to Metronidazole.Since there have been several cases of anaerobic ophthalmic infections reported to date, samples from patients with chronic eye infections should be analyzed for anaerobic culture for correct diagnosis and proper treatment. Moxifloxacin but not Metroni­dazole is a suitable drug for the treatment of anaerobic eye infection. Actinomycosis is an indolent, slowly progressive infection caused by Gram-positive facultative anaerobic bacteria from the genus Actinomyces. These bacteria have been reported as a cause of ophthalmic infections such as endophthalmitis, keratitis, and canaliculitis. The objective of the present study was to investigate the pattern and antibiotic sensitivity profile of anaerobes isolated from lesions in the medial canthus of the eye.Three pus aspirate samples (from eye) were delivered to a microbiology laboratory in a strict anaerobic condition in Robertson Cooked Meat media (RCM). The samples were inoculated on Blood Agar and incubated anaerobically in a Gas Pack Jar incubator at 5%-10% CO2 and in aerobic condition at 37°C. Pure colonies isolated on anaerobically incubated plates were identified by the VITEK® 2 COMPACT system. Antibiotic sensitivity testing was conducted using an Epsilometer-strip test.Three isolates identified and confirmed with the help of VITEK® 2 were Actinomyces israelii, Actinomyces odontolyticus, and Actinomyces meyeri. All three species of Actinomyces were sensitive to Vancomycin, Moxifloxacin, and Imipenem, but they were resistant to Metronidazole.Since there have been several cases of anaerobic ophthalmic infections reported to date, samples from patients with chronic eye infections should be analyzed for anaerobic culture for correct diagnosis and proper treatment. Moxifloxacin but not Metroni­dazole is a suitable drug for the treatment of anaerobic eye infection.

Leveraging a Hybrid Deep Learning Architecture for Efficient Emotion Recognition in Audio Processing

This paper presents a novel hybrid deep learning architecture for emotion recognition from speech signals, which has garnered significant interest in recent years due to its potential applications in various fields such as healthcare, psychology, and entertainment. The proposed architecture combines modified ResNet-34 and RoBERTa models to extract meaningful features from speech signals and classify them into different emotion categories. The model is evaluated on five standard emotion recognition datasets, including RAVDESS, EmoDB, SAVEE, CREMA-D, and TESS, and achieves state-of-the-art performance on all datasets. The experimental results show that the proposed hybrid architecture outperforms existing emotion recognition models, achieving high accuracy and F1 scores for emotion classification. The proposed architecture is promising for real-time emotion recognition applications and can be applied in various domains such as speech-based emotion recognition systems, human-computer interaction, and virtual assistants

Decision Support Machine - A Hybrid Model for Sentiment Analysis of News Headlines of Stock Market

Forecasting and making speculations about the financial market is intriguing and enticing for many of us. Predicting sentiments in the field of finance is a difficult thing as there is a special language that is used in financial markets and the data is unlabeled. Generalized models are not sufficient because the words that are used in financial markets have a completely different meaning when compared to their regular use. This paper represents the study of the stock price fluctuations and forecasting of the future stock prices using financial news about the big IT giants. NLP techniques should be applied to extract the correct sentiments out of the statements. This paper proposes a hybrid Machine Learning model DSM i.e. Decision Support Machine based on Support Vector Machine and Decision Tree. In this study news headlines dataset is preprocessed and then used for making predictions. The results show that the proposed model DSM got an accuracy of 79.75%. Results are then compared with the real-time stock market data for the same time duration, thus giving us a better picture of the actual changes. DSM is also compared with BERT, TextBlob, Decision Tree, Naïve Bayes, NLTK-Vader, SVM and KNN. The proposed model can further be extended if more datasets associated with investors’ sentiments can be used for training

A Detailed Overview of Life Cycle Enhancing Approaches for WSN

The major target of a wireless sensor network (WSNs) is to amass related data in the form of packets from the physical world. Transmission of these packets towards lengthier route consumes extra battery, and amplification and causes more intervening. As a result, these variables limit the lifespan of the network and operational ability. Numerous techniques exist in the past to augment the lifespan of the WSN. In this paper we have analyzed state of art techniques which enhance the lifecycle of a WSN