Vasco da Gama’s Voyages to India: Messianism, Mercantilism, and Sacred Exploits

The Portuguese explorer, Vasco da Gama (1460-1524), was the first European to sail from Portugal to India. Accolades for this achievement have long obscured the messianic motivation for the 1498 voyage, “to invade, capture, vanquish, and subdue all Saracens (Muslims) and pagans and other enemies of Christ; to reduce them to perpetual slavery; to convert them to Christianity; [and] to acquire great wealth by force of arms from the Infidels,” as sanctified by various Papal Bulls, together called “the Doctrine of Discovery” (Dum Diversas, 1452; Romanus Pontifex, 1455; Inter Caetera, 1493). The other key motive in this enormous undertaking was to displace Arab control of the spice trade and establish, instead, Portuguese hegemony that eventually resulted in colonialism/imperialism. The main instrument in this effort was extreme violence, sanctioned by the Church, inflicted upon the natives, and predicated on the Portuguese Inquisition and earlier crusades. The paper concludes with some cautionary remarks about the current Islam-West clash environment

Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment

The purpose of this research is to investigate the impact of social media sentiments on predicting the Bitcoin price using machine learning models, with a focus on integrating on-chain data and employing a Multi Modal Fusion Model. For conducting the experiments, the crypto market data, on-chain data, and corresponding social media data (Twitter) has been collected from 2014 to 2022 containing over 2000 samples. We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting and a Multi Modal Fusion. Next, we added Twitter sentiment data to the models, using the Twitter-roBERTa and VADAR models to analyse the sentiments expressed in social media about Bitcoin. We then compared the performance of these models with and without the Twitter sentiment data and found that the inclusion of sentiment feature resulted in consistently better performance, with Twitter-RoBERTa-based sentiment giving an average F1 scores of 0.79. The best performing model was an optimised Multi Modal Fusion classifier using Twitter-RoBERTa based sentiment, producing an F1 score of 0.85. This study represents a significant contribution to the field of financial forecasting by demonstrating the potential of social media sentiment analysis, on-chain data integration, and the application of a Multi Modal Fusion model to improve the accuracy and robustness of machine learning models for predicting market trends, providing a valuable tool for investors, brokers, and traders seeking to make informed decisions

Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark

The trend for listening to music online has greatly increased over the past decade due to the number of online musical tracks. The large music databases of music libraries that are provided by online music content distribution vendors make music streaming and downloading services more accessible to the end-user. It is essential to classify similar types of songs with an appropriate tag or index (genre) to present similar songs in a convenient way to the end-user. As the trend of online music listening continues to increase, developing multiple machine learning models to classify music genres has become a main area of research. In this research paper, a popular music dataset GTZAN which contains ten music genres is analysed to study various types of music features and audio signals. Multiple scalable machine learning algorithms supported by Apache Spark, including naïve Bayes, decision tree, logistic regression, and random forest, are investigated for the classification of music genres. The performance of these classifiers is compared, and the random forest performs as the best classifier for the classification of music genres. Apache Spark is used in this paper to reduce the computation time for machine learning predictions with no computational cost, as it focuses on parallel computation. The present work also demonstrates that the perfect combination of Apache Spark and machine learning algorithms reduces the scalability problem of the computation of machine learning predictions. Moreover, different hyperparameters of the random forest classifier are optimized to increase the performance efficiency of the classifier in the domain of music genre classification. The experimental outcome shows that the developed random forest classifier can establish a high level of performance accuracy, especially for the mislabelled, distorted GTZAN dataset. This classifier has outperformed other machine learning classifiers supported by Apache Spark in the present work. The random forest classifier manages to achieve 90% accuracy for music genre classification compared to other work in the same domain

Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermore, they cannot accommodate the incremental update of the model with the arrival of new data making them unsuitable for MOOCs dynamic environment. From this line of research, we propose a novel online recommender system, namely NoR-MOOCs, that is accurate, scales well with the data and moreover overcomes previously recorded problems with recommender systems. Through extensive experiments conducted over the COCO data-set, we have shown empirically that NoR-MOOCs significantly outperforms traditional KMeans and Collaborative Filtering algorithms in terms of predictive and classification accuracy metrics

Influence of temperature and relative humidity on the efficacy of diatomaceous earth and Metarhizium anisopliae (Metschinkoff) Sorokin (Hyphomycetes: Deuteromycotina) against Tyrophagus fatimii F. (Astigmata: Acaridae)

The combined as well as the alone effect of diatomaceous earth (DE) and entomopathogenic fungi were evaluated against Tyrophagus fatimii (Astigmata: Acaridae). Two different dose rates of DE (1 g and 1.5 g/kg of wheat) and three of the fungus Metarhizium anisopliae (Hyphomycetes: Deuteromycotina) (3.6 x 107, 3.6 x 108 and 3.6 x 109 conidia/kg of wheat) were taken and studied at 20°C and 25°C with 45% and 55% r.h. under three exposure intervals. It was found that the combined effect of DE diatomaceous earth and M. anisopliae was maximum at 25°C and 55% r.h. which gave 75% adult mortality at their highest dose rates, however, DE alone exhibited the highest mortality (61.3%) at 25°C and 45% r.h. On the other hand, M. anisopliae gave maximum mortality of mites (48.7%) at 20°C and 55% r.h. at 3.6 x 109 conidia/kg of wheat. It was concluded that the efficacy of both DE and M. anisopliae increased with the increase of the exposure interval. Moreover, the increase of dose increased the mortality. In addition, temperature and r.h. are the key factors for determining the effectiveness of both DE and M. anisopliae. Keywords: Diatomaceous earth, Tyrophagus fatimii, Metarhizium anisopliae, Stored wheat

STUDIO LONGITUDINALE MULTICENTRICO PER LA VALUTAZIONE DI FATTORI PRENATALI E POSTNATALI PRECOCI CORRELABILI AL RISCHIO DI SOVRAPPESO E OBESITÀ INFANTILE

Title: longitudinal and multicentre study and evaluation of the pre and early postnatal factors associated with the risk of childhood overweight and obesity. Location: Italy, Lombardia, centres for the monitoring of infants' body weight in Monza e Brianza ASL. Aim: evaluation of the anthropometric variations in children up to 6-7 months of life and their association with pre and postnatal factors. Study design and duration: multicentre cohort study. Subjects enrolled between March and October 2013 had been measured until the seventh month of life in four occasions. Inclusion criteria: mothers with children within 60 days of life, who attended the centres to monitor the growth of their children; the only exclusion criterion was the ignorance of the Italian language. Data collection: weight and length assessment, self-administration of a questionnaire. Statistical methods: 171 mother-child dyads. The anthropometric prenatal and parental data had been expressed as standard deviation score with the aim of removing the effects of age, sex and number of previous deliveries. The role of a set of covariates on the evolution of parental roles, breastfeeding and weaning has been analyzed with a generalised linear mixed model, based on binomial distribution and link logit. Weight growth (expressed as SDS) has been analyzed with an analogous linear mixed model, based on Gaussian distribution and identity link. Conclusions: the analysis showed that some prenatal factors and obstetrical variables, such as BMI and weight gain, have a direct role in determining the growth trends. These factors may be modified by the preventive approach that caracterises midwifery. Birthweight affects both maternal feeding choices for their children and growth trends. The study highlighted the role of father and health professionals of family care health centers, who can affect growth trend or growth related feeding choices, such as weaning timing

A novel DeepMaskNet model for face mask detection and masked facial recognition

Coronavirus disease (COVID-19) has significantly affected the daily life activities of people globally. To prevent the spread of COVID-19, the World Health Organization has recommended the people to wear face mask in public places. Manual inspection of people for wearing face masks in public places is a challenging task. Moreover, the use of face masks makes the traditional face recognition techniques ineffective, which are typically designed for unveiled faces. Thus, introduces an urgent need to develop a robust system capable of detecting the people not wearing the face masks and recognizing different persons while wearing the face mask. In this paper, we propose a novel DeepMasknet framework capable of both the face mask detection and masked facial recognition. Moreover, presently there is an absence of a unified and diverse dataset that can be used to evaluate both the face mask detection and masked facial recognition. For this purpose, we also developed a largescale and diverse unified mask detection and masked facial recognition (MDMFR) dataset to measure the performance of both the face mask detection and masked facial recognition methods. Experimental results on multiple datasets including the cross-dataset setting show the superiority of our DeepMasknet framework over the contemporary models

CSR Practices of a Company Toward Stakeholders: The Case of Pakistan Tobacco Company (PTC)

The performance of the companies in corporate sector is reliant greatly on the practices of Corporate Social Responsibility (CSR); therefore in today’s business environment companies are paying more attention to the sense of CSR. These companies also consider the aspects of socio-culture environment into business practices and compliance with other regulatory and ethical issues. However, it has been found that CSR is being practiced in Pakistani firms in tobacco industry because the concept is new for the emerging economies like Pakistan. The paper consists of brief study about the CSR practices on stakeholder dimension of Pakistan Tobacco Company (PTC). The basic aim of this paper is to examine that how companies engage their stakeholders in CSR activities and what is the role of stakeholders in CSR policies. This research was conducted by using a qualitative method and the case study of PTC.  Data has been collected from relevant scientific articles, research books, and online resources regarding CSR and stakeholders theoretical framework while empirical data was gathered through interviews and company annual reports. However, PTC products are injurious for customers’ health but their efforts for the environment and community make a good image of the company in the minds of customer and stakeholders. Keywords: Corporate Social Responsibility (CSR), Stakeholder, Health & Safety Environment (HSE), Community Involvement, Pakistan Tobacco Company (PTC)