Effectiveness of a Pilot Community Physical Activity and Nutrition Intervention in American Samoa

Background: The addition of Western foods to the Samoan diet has greatly affected the health of the American Samoan people. The purpose of this study was to test the effectiveness of culturally tailored exercise and nutrition interventions for adults living in Tutuila, American Samoa. Method: Villages in the eastern, central, and western parts of the island of Tutuila were recruited to participate in this study. Villages were randomly assigned to one of three culturally tailored interventions: 46 individuals in one village participated in an exercise intervention, 27 individuals in another village participated in a nutrition-education intervention, and 22 individuals in a third village participated in a combined exercise and nutrition-education intervention. Participants’ nutrition knowledge was measured at pre- and post-intervention stages through a questionnaire. Body Mass Index (BMI), height, and weight were assessed at baseline and again at weeks 4, 8, and 12. Differences in mean BMI over time by group, were assessed using repeated measures ANOVA with baseline BMI as a covariate. To test for differences in nutrition knowledge over time by group, pair-wise comparisons were used for the percent of correct answers at baseline and at week 12. Results: All three groups realized a significant decrease in BMI, from 1.35 in the nutrition only group to 2.27 in the exercise and nutrition group. The exercise and nutrition group also showed significant gains in ability to identify foods high in fiber and fat. Implications: This study demonstrates that decreases in BMI and increases in nutritional knowledge can be obtained through a culturally-tailored intervention, especially one that combines nutrition education and exercise

Blood profile of Red Sokoto goats fed baobab seed meal fermented at different durations

The utilization of baobab seed meals in the diet of ruminants has continued to address the problem of search for non-conventional feed resources which may be available even in the dry season. The study was designed to assess the blood profile of Red Sokoto goats fed a 20 % level of inclusion of baobab seed meal fermented at different duration (24, 48, and 72 hours). Sixteen (16) Red Sokoto bucks with an average weight of 6.96±1.44 kg were used for the study. Four experimental diets were formulated (T1 – T4). T1 was the control diet, while T2, T3, and T4 were 24WFBSM (24 hours water fermented baobab seed meal), 48WFBSM (48 hours water fermented baobab seed meal), and 72WFBSM (72 hours water fermented baobab seed meal), respectively. The experiment lasted for 84 days and was laid in a completely randomized design with four replicates per treatment. The results obtained indicated that the duration of the fermenting period on baobab seed significantly (p<0.05) reduced serum total protein, globulin, zinc, and sodium. Other blood metabolites measured were however not influenced (p>0.05) by the duration period of fermentation. The findings of this study revealed that fermenting baobab seed meal beyond 24 hours is likely to impair some physiological activities of Red Sokoto goats. Keywords: baobab seed, blood profile, fermentation, Red Sokoto goa

International Issues and New Technologies for Learning

The Teaching in the Community Colleges TCC 2000 Online Conference originally commissioned a report by its Writing Team on International Issues and New Technologies for Learning that was edited by the Writing Team Chair. In this new abridgement, the Editor selects representative excerpts from his original report of the online conference discussions, which took place in asynchronous (Web Board and mailing lists) and synchronous (Web chat rooms) media across various time zones

Arabic sentiment analysis using GCL-based architectures and a customized regularization function

Sentiment analysis aims to extract emotions from textual data; with the proliferation of various social media platforms and the flow of data, particularly in the Arabic language, significant challenges have arisen, necessitating the development of various frameworks to handle issues. In this paper, we firstly design an architecture called Gated Convolution Long (GCL) to perform Arabic Sentiment Analysis. GCL can overcome difficulties with lengthy sequence training samples, extracting the optimal features that help improve Arabic sentiment analysis performance for binary and multiple classifications. The proposed method trains and tests in various Arabic datasets; The results are better than the baselines in all cases. GCL includes a Custom Regularization Function (CRF), which improves the performance and optimizes the validation loss. We carry out an ablation study and investigate the effect of removing CRF. CRF is shown to make a difference of up to 5.10% (2C) and 4.12% (3C). Furthermore, we study the relationship between Modern Standard Arabic and five Arabic dialects via a cross-dialect training study. Finally, we apply GCL through standard regularization (GCL+L1, GCL+L2, and GCL+LElasticNet) and our Lnew on two big Arabic sentiment datasets; GCL+Lnew gave the highest results (92.53%) with less performance time

COVID-19, agriculture and food security in Ghana; the way forward

Food production, its availability, and accessibility will continue to be key contributors to human existence. The world was hit by the COVID-19 pandemic in the year 2020 and its effect trickled into reduced production of goods and services in many industries across the globe. Understanding the effects of the pandemic in Ghana necessitated the writeup of this paper. The study took the form of a desk review where current studies across the globe on the effect of the pandemic on agriculture and food security were reviewed, after which it was supported by data from self-placed questionnaire administration. Across the globe, agricultural production experienced a reduction that phased into food insecurity. This was not limited only to the extremely affected countries, but also, in countries where COVID-19 infections were low. One key limiting factor that spiked the challenge in the agricultural sector was a reduction in the availability of labour for production. In many leading food-producing countries, the challenge became acute when perishable food crops began to get damaged. In Ghana, the virus similarly led to restrictions in movements in and between epicenters. It was reported by the Ghana Statistical Service that, 77.4% of Ghanaians were negatively affected by the increased prices in food sold in the country. Without immediate and effective management as well as policy interventions from the Ghanaian government, it is highly possible for most farmers and agricultural businesses to completely collapse. This communication is to highlight some ongoing and disturbing effects of the pandemic to policymakers as well as individual and governmental strategies that have been put in place to curb adverse effects on food production. This will help enhance Ghanaians’ standards of living amidst economic challenges

Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages

In a conventional Speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language does not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German and URDU. For Amharic, we use our own publicly-available Amharic Speech Emotion Dataset (ASED). For English, German and Urdu we use the existing RAVDESS, EMO-DB and URDU datasets. We followed previous research in mapping labels for all datasets to just two classes, positive and negative. Thus we can compare performance on different languages directly, and combine languages for training and testing. In Experiment 1, monolingual SER trials were carried out using three classifiers, AlexNet, VGGE (a proposed variant of VGG), and ResNet50. Results averaged for the three models were very similar for ASED and RAVDESS, suggesting that Amharic and English SER are equally difficult. Similarly, German SER is more difficult, and Urdu SER is easier. In Experiment 2, we trained on one language and tested on another, in both directions for each pair: AmharicGerman, AmharicEnglish, and AmharicUrdu. Results with Amharic as target suggested that using English or German as source will give the best result. In Experiment 3, we trained on several non-Amharic languages and then tested on Amharic. The best accuracy obtained was several percent greater than the best accuracy in Experiment 2, suggesting that a better result can be obtained when using two or three non-Amharic languages for training than when using just one non-Amharic language. Overall, the results suggest that cross-lingual and multilingual training can be an effective strategy for training a SER classifier when resources for a language are scarce.Comment: 16 pages, 9 tables, 5 figure

Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text Preprocessing.

Sentiment analysis is an essential process which is important to many natural language applications. In this paper, we apply two models for Arabic sentiment analysis to the ASTD and ATDFS datasets, in both 2-class and multiclass forms. Model MC1 is a 2-layer CNN with global average pooling, followed by a dense layer. MC2 is a 2-layer CNN with max pooling, followed by a BiGRU and a dense layer. On the difficult ASTD 4-class task, we achieve 73.17%, compared to 65.58% reported by Attia et al., 2018. For the easier 2-class task, we achieve 90.06% with MC1 compared to 85.58% reported by Kwaik et al., 2019. We carry out experiments on various data splits, to match those used by other researchers. We also pay close attention to Arabic preprocessing and include novel steps not reported in other works. In an ablation study, we investigate the effect of two steps in particular, the processing of emoticons and the use of a custom stoplist. On the 4-class task, these can make a difference of up to 4.27% and 5.48%, respectively. On the 2-class task, the maximum improvements are 2.95% and 3.87%

Application of the WHO Keys of Safer Food to Improve Food Handling Practices of Food Vendors in a Poor Resource Community in Ghana

Data was collected from food vendors in a poor resource community in Ghana, which showed that the vendors constituted an important source of oro-faecal transmission. Following this, the WHO five keys of safer food were utilized in an evidence based training programme for the vendors to improve their food handling practices. Impact assessment of the food safety training showed that 67.6% of the vendors had acquired some knowledge from the workshop and were putting it into practice. Lack of food safety equipment was a major hinderance to behavioral change among the vendors as far food handling practices are concerned

A deep CNN architecture with novel pooling layer applied to two Sudanese Arabic sentiment data sets

Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of data sets have been created to support such work. However, up until now, no work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this article, two new public data sets are introduced, the two-class Sudanese Sentiment Data set (SudSenti2) and the three-class Sudanese Sentiment Data set (SudSenti3). In the preparation phase, we establish a Sudanese stopword list. Furthermore, a convolutional neural network (CNN) architecture, Sentiment Convolutional MMA (SCM), is proposed, comprising five CNN layers together with a novel Mean Max Average (MMA) pooling layer, to extract the best features. This SCM model is applied to SudSenti2 and SudSenti3 and shown to be superior to the baseline models, with accuracies of 92.25% and 85.23% (Experiments 1 and 2). The performance of MMA is compared with Max, Avg and Min and shown to be better on SudSenti2, the Saudi Sentiment Data set and the MSA Hotel Arabic Review Data set by 1.00%, 0.83% and 0.74%, respectively (Experiment 3). Next, we conduct an ablation study to determine the contribution to performance of text normalisation and the Sudanese stopword list (Experiment 4). For normalisation, this makes a difference of 0.43% on two-class and 0.45% on three-class. For the custom stoplist, the differences are 0.82% and 0.72%, respectively. Finally, the model is compared with other deep learning classifiers, including transformer-based language models for Arabic, and shown to be comparable for SudSenti2 (Experiment 5)

Assessing the efficiency of Moringa oleifera leaf meal on the growth performance of broiler chicken

High cost of poultry feed and limited fishmeal are currently the major challenges in poultry production. To reduce cost while maximizing production, there is the need to use cheap but high nutritional feed sources like Moringa oleifera. The aim of the study was to assess the effects of Moringa oleifera on the growth performance of broiler chicken by measuring their live weight, rate of mortality, feed conversion ratio and benefit cost (b/c) ratio. Field experiment was carried out at the Animal Science Department farm, located in the Kwame Nkrumah University of Science and Technology, Kumasi-Ghana. A total of 30- day old chicks were raised for eight weeks under the required conditions, with all vaccines administered appropriately. The experiment was laid in a Complete Randomized Design with five treatments namely T1= 100% conventional feed only (as control), T2= 50% MoLM (Moringa oleifera Leaf Meal) + 50% conventional, T3= 75% MoLM + 25% conventional, T4= 25% MoLM + 75% conventional, T5= 80% MoLM and each treatment replicated six times. The result showed no significant differences between the various treatment for the feed conversion ratio and live weight at different growth periods. The benefit/cost ratio of T1 was more than one while the other treatments were less than one. T4 (25% MoLM) had a b/c ratio close to one. In conclusion, Moringa oleifera leaf meal at different levels can be used to supplement the fishmeal component in the poultry diet of broiler chicken to produce similar results as that of the conventional feed. The study recommends that farmers can adopt Moringa oleifera based poultry feed for their bird production when they cannot afford the conventional feed (fish meal-based feed) to cut down cost economically while increasing productivity