Organizational analysis of private nursing educational institution in Karachi,Pakistan

I would like to begin with the words of Nelson Mandela, “Education is the most powerful weapon which you can use to change the world”. I strongly believe that in order to transform the coming up generation, the vision, mission and philosophy of an educational organization significantly reflects in the standards teaching and learning culture provided to their learners. In the health sector, nursing profession have undergone through a significant process of diversification from the time of Florence Nightingale. It is extremely crucial that in order to generate quality and professional nurses, educational institutions should focus on coaching the learners based on the growing burden of diseases, and equipping them with future coming up challenges

AVaTER: Fusing Audio, Visual, and Textual Modalities Using Cross-Modal Attention for Emotion Recognition

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for audio, video and textual emotion recognition (i.e., AVaTER) that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods

Active Vision-based Attention Monitoring System for Non-Distracted Driving

Inattentive driving is a key reason of road mishaps causing more deaths than speeding or drunk driving. Research efforts have been made to monitor drivers&amp;#x2019; attentional states and provide support to drivers. Both invasive and non-invasive methods have been applied to track driver&amp;#x2019;s attentional states, but most of these methods either use exclusive equipment which are costly or use sensors that cause discomfort. In this paper, a vision-based scheme is proposed for monitoring the attentional states of the drivers. The system comprises four major modules such as cue extraction and parameter estimation, monitoring and decision making, level of attention estimation, and alert system. The system estimates the attentional level and classifies the attentional states based on the percentage of eyelid closure over time (PERCLOS), the frequency of yawning and gaze direction. Various experiments were conducted with human participants to assess the performance of the suggested scheme, which demonstrates the system&amp;#x2019;s effectiveness with 92% accuracy.</p

Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors&#x2019; classes and unique words. Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification. In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD). Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45&#x0025;, 95.02&#x0025;, and 98.67&#x0025; for the datasets BACC-18, BAAD16, and LD, respectively

Acquiring insights through a sequence-based approach to the critical Zika virus MTase domain

Background: ZIKV is one of the re-emerging arboviruses (viruses carried by arthropods), which is spread through the Aedes mosquito. It is an RNA virus with only one strand that is appropriate to the family Flaviviridae's  Flavivirus (genus) & has been linked to other Flaviviruses such as the West Nile virus, chikungunya virus, & dengue (DENV) virus. The envelope, precursor membrane, and capsid are three structural proteins, and seven nonstructural proteins are also encoded by the Zika virus genome.Methods: We conducted an in-silico analysis of the Zika virus' MTase domain protein for this publication. We predicted that methylation would play a significant role in the available Prosite, Pfam, and InterProScan tools to aid in locating the MTase domain. Along with alignment, amino acid composition, charged amino acids, atomic level studies, & molecular weight, we also make predictions for these variables, including theoretical Pi.Results: We also examine the MTase domain's simulated structure (alpha helix, beta sheet, turn) and its specifics, including secondary structure. We also pinpoint the locations where proteins, DNA, and RNA bind. Potential phosphorylation sites can be found on the Ser, Thr, and Tyr residues in the MTase domain.Conclusion: These outcomes imply a complicated interaction between different phosphorylation modifications that modulates the activity of the MTase domain. To fully appreciate the auxiliary and practical perspectives and to clarify the varied roles of PTM in the MTase domain will be a primary goal of future study.Keywords: I-TASSER; Secondary structure; Prosite; α-helix; Pfam; InterProScan; Binding sites; Posttranslational modification; SOPMA; Phyre2 

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase&nbsp;1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation&nbsp;disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age&nbsp; 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score&nbsp; 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc&nbsp;= 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N&nbsp;= 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in&nbsp;Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in&nbsp;Asia&nbsp;and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701