Algebraic independence of arithmetic gamma values and Carlitz zeta values

We consider the values at proper fractions of the arithmetic gamma function and the values at positive integers of the zeta function for F_q[theta] and provide complete algebraic independence results for them.Comment: 15 page

Leaning into a Critical Theory of Love to Adaptively Engage in Teaching and Learning During COVID-19 in India and the US

This article considers the experiences of teachers and learners in higher education institutions that led to the need for adapted learning modalities during COVID-19. It is critical to provide reflective faculty narratives. We position them as Street-Level Bureaucrats on the front lines, who as de facto policymakers, made adaptive decisions impacting students’ educational opportunities. Consequently, this article engages experiential reflections of two university professors in the field of education, one in the US the other in India. It examines how known ways of learning changed as universities closed and teachers and students were mandated to switch to online, remote or distance teaching and learning. Rooted in a critical theory of love, which calls for justice-centered and humanizing orientations, and the critical need for a quality education, as outlined in SDG 4, reflections are discussed regarding opportunities for supporting university policy discussions, which can enhance classroom-level student success during traumatic times

Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter

Online social networks have been widely engaged as rich potential platforms to predict election outcomes' in several countries of the world. The vast amount of readily-available data on such platforms, coupled with the emerging power of natural language processing algorithms and tools, have made it possible to mine and generate foresight into the possible directions of elections' outcome. In this paper, lexicon-based public emotion mining and sentiment analysis were conducted to predict win in the 2019 presidential election in Nigeria. 224,500 tweets, associated with the two most prominent political parties in Nigeria, People's Democratic Party (PDP) and All Progressive Congress (APC), and the two most prominent presidential candidates that represented these parties in the 2019 elections, Atiku Abubakar and Muhammadu Buhari, were collected between 9th October 2018 and 17th December 2018 via the Twitter's streaming API. tm and NRC libraries, defined in the 'R' integrated development environment, were used for data cleaning and preprocessing purposes. Botometer was introduced to detect the presence of automated bots in the preprocessed data while NRC Word Emotion Association Lexicon (EmoLex) was used to generate distributions of subjective public sentiments and emotions that surround the Nigerian 2019 presidential election. Emotions were grouped into eight categories (sadness, trust, anger, fear, joy, anticipation, disgust, surprise) while sentiments were grouped into two (negative and positive) based on Plutchik's emotion wheel. Results obtained indicate a higher positive and a lower negative sentiment for APC than was observed with PDP. Similarly, for the presidential aspirants, Atiku has a slightly higher positive and a slightly lower negative sentiment than was observed with Buhari. These results show that APC is the predicted winning party and Atiku as the most preferred winner of the 2019 presidential election. These predictions were corroborated by the actual election results as APC emerged as the winning party while Buhari and Atiku shared very close vote margin in the election. Hence, this research is an indication that twitter data can be appropriately used to predict election outcomes and other offline future events. Future research could investigate spatiotemporal dimensions of the prediction

Rab-KAMS: A reproducible knowledge management system with visualization for preserving Rabbit Farming and Production Knowledge

The sudden rise in rural-to-urban migration has been a key challenge threatening food security and most especially the survival of Rabbit Farming and Production (RFP) in Sub-Saharan Africa. Currently, significant knowledge of RFP is going into extinction as evident by the drastic fall in commercial rabbit farming and production indices. Hence, the need for a system to proactively preserve RFP knowledge for future potential farmers cannot be overemphasized. To this end, knowledge archiving and management are key concepts of ensuring long-term digital storage of conceptual blueprints and specifications of systems, methods and frameworks with capacity for future updates while making such information readily accessible to relevant stakeholders on demand. Therefore, a reproducible Rabbit production' Knowledge Archiving and Management System (Rab-KAMS) is developed in this paper. A 3-staged approach was adopted to develop the Rab-KAMS. This include a knowledge gathering and conceptualization stage; a knowledge revision stage to validate the authenticity and relevance of the gathered knowledge for its intended purpose and a prototype design stage adopting the use of unified modelling language conceptual workflows, ontology graphs and frame system. For seamless accessibility and ubiquitous purposes, the design was implemented into a mobile application having interactive end-users' interfaces developed using XML and Java in Android 3.0.2 Studio development environment while adopting the V-shaped software development model. The qualitative evaluation results obtained for Rab-KAMS based on users' rating and reviews indicate a high level of acceptability and reliability by the users. It also indicates that relevant RFP knowledge were correctly captured and provided in a user-friendly manner. The developed Rab-KAMS could offer seamless acquisition, representation, organization and mining of new and existing verified knowledge about RFP and in turn contributing to food security

Potential impact of trophy hunting on vigilance and flight behaviour in Blue Sheep (Bharal: Pseudois nayaur)

Conservation management is often integrated into a broader tourism context, and under some conditions allows wildlife trophy hunting to support its goals. In such cases it is generally acknowledged that the direct impact of hunting requires careful monitoring and regulation with respect to the size and dynamics of hunted populations. However, hunting may also affect the behaviour of local wildlife, including their reaction to the approach of humans. Thus, hunting may have broader consequences on tourism and conservation management if animals respond by changing their behaviour in a way that makes them more difficult to monitor or for tourists to observe. We examined the potential impact of trophy hunting on vigilance and flight behaviour of Blue Sheep (Bharal: Pseudois nayaur) in Nepal, by comparing their behavioural responses in conservation areas with contrasting management approaches: the Dhorpatan Hunting Reserve (DHR) where male Blue Sheep have been trophy hunted since the 1980 s, and the Annapurna Conservation Area (ACA) where hunting is forbidden. Blue Sheep in the DHR had higher levels of vigilance than sheep in the ACA (10 % versus 8 % of their time respectively). Sheep in the DHR were also much more difficult to approach on foot, with Blue Sheep groups in the DHR having an average flight initiation distance of 96 +/- 7 m versus 39 +/- 3 m for the ACA, and subsequently moving much greater distances when disturbed (flight movement distance in the DHR versus ACA: 79 +/- 3 m versus 26 +/- 2 m respectively). These results suggest that hunting impacts on tourism and conservation may extend well beyond the population dynamic consequences of trophy animal removal. These behavioural effects suggest additional consideration is required when balancing wildlife hunting and observation tourism activities in the same area. It would also be valuable to assess the impacts of hunting-induced behaviour changes on the effectiveness of wildlife monitoring in such areas

Thin film structural analysis using variable-period x-ray standing waves

Variable-period x-ray standing wave (VPXSW) studies have been carried out using 3 keV x rays and photoelectron detection. Two model surfaces have been used, a native SiO2 layer (20 Å thick) on bulk silicon, and a purpose-built multilayer surface comprising a chloroform/water marker layer (12 Å thick) on an ionic liquid spacer layer (211 Å thick) deposited on a SiO2/Si substrate at 90 K. By using photoelectron detection, both chemical and elemental sensitivity were achieved. The surfaces were modeled using dynamic x-ray scattering for x-ray intensity, and attenuation of photoelectrons transmitted through the layers, to produce simulations which accurately reproduced the experimental VPXSW measurements. VPXSW measurements made using the substrate, spacer layer, and marker layer photoelectron signatures produced consistent structural values. This work demonstrates that VPXSW can be used to determine chemically specific layer thicknesses within thick (≲300Å) surface structures composed of the light elements B, C, N, O, F, and Cl with an accuracy of 10 to 15 Å, perpendicular to the surface

Consistent Surgeon Evaluations of Three-Dimensional Rendering of PET/CT Scans of the Abdomen of a Patient with a Ductal Pancreatic Mass.

Two-dimensional (2D) positron emission tomography (PET) and computed tomography (CT) are used for diagnosis and evaluation of cancer patients, requiring surgeons to look through multiple planar images to comprehend the tumor and surrounding tissues. We hypothesized that experienced surgeons would consistently evaluate three-dimensional (3D) presentation of CT images overlaid with PET images when preparing for a procedure. We recruited six Jefferson surgeons to evaluate the accuracy, usefulness, and applicability of 3D renderings of the organs surrounding a malignant pancreas prior to surgery. PET/CT and contrast-enhanced CT abdominal scans of a patient with a ductal pancreatic mass were segmented into 3D surface renderings, followed by co-registration. Version A used only the PET/CT image, while version B used the contrast-enhanced CT scans co-registered with the PET images. The six surgeons answered 15 questions covering a) the ease of use and accuracy of models, b) how these models, with/without PET, changed their understanding of the tumor, and c) what are the best applications of the 3D visualization, on a scale of 1 to 5. The six evaluations revealed a statistically significant improvement from version A (score 3.6±0.5) to version B (score 4.4±0.4). A paired-samples t-test yielded t(14) = -8.964,

News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited. Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable. In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem. 2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes. Porter’s algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency–Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction. Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency