The Power of Transfer Learning in Agricultural Applications: AgriNet

Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we propose AgriNet dataset, a collection of 160k agricultural images from more than 19 geographical locations, several images captioning devices, and more than 423 classes of plant species and diseases. We also introduce AgriNet models, a set of pretrained models on five ImageNet architectures: VGG16, VGG19, Inception-v3, InceptionResNet-v2, and Xception. AgriNet-VGG19 achieved the highest classification accuracy of 94 % and the highest F1-score of 92%. Additionally, all proposed models were found to accurately classify the 423 classes of plant species, diseases, pests, and weeds with a minimum accuracy of 87% for the Inception-v3 model.Finally, experiments to evaluate of superiority of AgriNet models compared to ImageNet models were conducted on two external datasets: pest and plant diseases dataset from Bangladesh and a plant diseases dataset from Kashmir

Transferability of Graph Neural Networks for Time Series Applications

Transfer learning enabled machine learning tasks with scarce data to achieve superhuman performance in multiple domains like computer vision and natural language processing. However, knowledge transfer's success was mostly on grid structured data and using convolutional neural networks that assume local, hierarchical, and stationary data. Time series data in several applications, specifically doesn't meet these assumptions. This renders traditional transfer learning irrelevant with the potential leading to negative transfer. After achieving superior performance on high-dimensional data like social networks and recommender systems, graph neural networks are currently applied to time series data. In this thesis, we investigate the transferability of graph neural networks on time series data compared to traditional time series algorithms. We also explore a new graph similarity approach and compare its effect on time series algorithms pretraining and negative transfer for pandemic time series forecasting

Determining the Impact of the ASP Health Club’s Sports Initiative Awareness Campaign on Elementary Students’ Physical Activity Habits

Ever increasing sedentary lifestyles have brought on many issues in the health and wellness of school aged children, particularly those in the third grade through fifth grade level. According to a literature review by the Dominican University of California, awareness is directly associated with an increase in fitness levels; the relation between them a positive one. The purpose of this study was to determine if the awareness program at the American School of Palestine had any tangible effects on its students. It sought to measure the correlation between the school’s activities in the awareness program conducted by members of the ASP Heart Health Club and two of its students’ daily habits: time spent on electronic devices, and time spent on physical activities. The data was gathered from the subjects by means of a questionnaire and clinical charts recording the students’ height and weight in order to calculate their individual BMIs. Participants in this study included all students of the third grade through the fifth grade level, featuring both genders, and amounting to approximately two hundred individuals. The data was collected thrice, recorded and analyzed for the presence of outliers which were removed. Our data indicated that we were able to decrease the amount of time students spent on electronic devices and increase the time spent on physical activity at the fourth grade level. There seemed to be a minimal or negative effect with the third and fifth grade students on average. We believe that a longer study duration and a more effective campaign program may lead to better awareness of the importance of maintaining a healthy and fit lifestyle

Demand-responsive Users' Travel Behavior and Satisfaction Analysis in Small Cities: Case Study of the Public Transportation System in Palestine

This study examines the differences in travel behavior between regular and occasional demand-responsive transport users (public transport users), determines the level of service satisfaction, and identifies the key factors of commuters' preferences of using the demand-responsive transport regularly or occasionally for a small-sized urban area (<50 km2). Data were supplemented through field surveys and by focus group discussions. Binary logistic regression and correlation models were used. It is found that probabilities of irregularity are higher for rural areas, male commuters, short trips, educational trips, low-income groups, and non-direct trips. All users are generally satisfied with the service. The most important factors for occasional users are waiting time, trip cost, and trip duration. On the other hand, regular users pay more attention to cleanliness, safety, and comfort. Scheduling of public transportation lines that serve educational zones and provide accessibility to rural areas are needed to improve the quality and attractiveness of the services

Mathematical Creativity: The Unexpected Links

Creativity in mathematics is identified in many forms or we can say is made up of many components. One of these components is The Unexpected Links where one tries to solve a mathematical problem in a nontraditional manner that requires the formation of hidden bridges between distinct mathematical domains or even between seemingly far ideas within the same domain. In this article, we design problems that express unexpected links in mathematics and suit students of intermediate and secondary levels. We prove their feasibility through teachers’ testimonies and through introducing them in classrooms and collecting students’ attitudes with respect to understanding and interest. Results confirm that students can sense such component and that designed problems had caught teachers’ and students’ interest

The power of transfer learning in agricultural applications: AgriNet

Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we propose AgriNet dataset, a collection of 160k agricultural images from more than 19 geographical locations, several images captioning devices, and more than 423 classes of plant species and diseases. We also introduce AgriNet models, a set of pretrained models on five ImageNet architectures: VGG16, VGG19, Inception-v3, InceptionResNet-v2, and Xception. AgriNet-VGG19 achieved the highest classification accuracy of 94% and the highest F1-score of 92%. Additionally, all proposed models were found to accurately classify the 423 classes of plant species, diseases, pests, and weeds with a minimum accuracy of 87% for the Inception-v3 model. Finally, experiments to evaluate of superiority of AgriNet models compared to ImageNet models were conducted on two external datasets: pest and plant diseases dataset from Bangladesh and a plant diseases dataset from Kashmir

Environmental Impact Assessment of the Transportation Sector and Hybrid Vehicle Implications in Palestine

During the last two decades, the development of sustainable transportation systems has been highlighted as a key element in solving environmental problems related to climate change and impacts on greenhouse gases. Globally, the transportation sector has become one of the main contributors to these environmental problems. Thus, the environmental impact assessment of this sector and the implications of new vehicle technologies have begun to be considered as first steps for any long-term future strategies in this sector. In Palestine, the lack of environmental data related to the transportation sector and the absence of studies that address the new vehicle technologies (such as hybrid vehicles) and their future implications make it difficult to set up any future strategies or plans. In this study, the current and the future environmental impacts of the transportation sector have been assessed, and the future implications of hybrid vehicles have been determined. The gross domestic product (GDP), population, and the number of vehicles for the period 1994–2018 have been used to develop an auto regressive integrated moving average (ARIMA) prediction model for the future number of vehicles. Then, the total traveled kilometers and the total consumed fuels (by diesel and gasoline vehicles) have been predicted. After that, the current and future (2020 and 2030) greenhouse gas (GHG) emissions, including CO"sub"2"/sub", N"sub"2"/sub"O, and CH"sub"4"/sub", have been estimated. Finally, the future implications of hybrid vehicles, based on two scenarios (10% and 20% hybrid vehicles) have been estimated. The results have showed that the estimated CO"sub"2"/sub", N"sub"2"/sub"O, and CH"sub"4"/sub" emissions from the transportation sector in 2020 are 4,842,164.5, 213.8, and 445.8 tons, which are very high, and even much higher than the total national emissions of 2014 (the only officially available data). Moreover, in 2030, replacing 20% of internal combustion engine vehicles (ICEVs) by hybrid vehicles would lead to 4.66% and 13.31% reductions in CO"sub"2"/sub" and N"sub"2"/sub"O, respectively, as compared to 100% ICEVs, while the CH"sub"4 "/sub"emissions will increase. However, the overall CO"sub"2"/sub"-equivalent will decrease by 5%; therefore, a more sustainable transport system will be achieved. Document type: Articl

Access and utilisation of primary health care services comparing urban and rural areas of Riyadh Providence, Kingdom of Saudi Arabia

The Kingdom of Saudi Arabia (KSA) has seen an increase in chronic diseases. International evidence suggests that early intervention is the best approach to reduce the burden of chronic disease. However, the limited research available suggests that health care access remains unequal, with rural populations having the poorest access to and utilisation of primary health care centres and, consequently, the poorest health outcomes. This study aimed to examine the factors influencing the access to and utilisation of primary health care centres in urban and rural areas of Riyadh province of the KSA

Assessment of an International Virtual Exchange Project with Civil Engineering Students from the US and Palestine: Global Competencies, Perceived Value, and Teamwork

This paper presents the results of a study conducted to assess the value of two iterations of an international virtual exchange (IVE) experience between universities in the US (Clemson University and Bucknell University) and Palestine (An-Najah National University) in 2021 and 2022. The focus of this study was a five-week collaborative project where civil engineering students enrolled in pavement design or environmental engineering courses at three universities were tasked to develop innovative solutions to a pavement related problem in one of five general areas. Based on the course enrollments at each institution (i.e., 50 US and 19 Palestinian students in 2021 and 35 US and 51 Palestinian students in 2022), there were two treatment groups: IVE and non-IVE. In 2021 there were nine bi-national IVE teams and eight non-IVE teams composed only of students from Clemson University (US). In 2022, there were nine bi-national IVE teams, five US non-IVE teams from Clemson, and seven Palestinian non-IVE teams from An-Najah. The evaluation in this study focused on (1) global competencies, (2) value of the experience, and (3) team dynamics. The influence of the experience on the global competencies of the students in IVE and non-IVE teams was assessed quantitatively and qualitatively using pre- and post-program surveys based on the Stevens Initiative and RTI International’s Common Survey Items as well as survey items developed for this IVE to measure whether the program promotes gender equity. The value of the project experience for all students (i.e., IVE and non-IVE) was evaluated using a mixed methods assessment based on the “value-creation framework” of Wenger-Trayner et al. Four cycles of the value-creation framework were included in this assessment: (1) immediate value, (2) potential value, (3) applied value, and (4) realized value. Finally, teamwork was evaluated using the Individual and Team Performance (ITP) Metrics Peer Feedback and Team Dynamics survey. Results showed that modifications made between the first and second project iterations, specifically cross-cultural dialogue modules, had positive impacts on the overall outcomes. The IVE teams exhibited greater improvement in team dynamics measures over the project duration compared to the non-IVE teams. The students on IVE teams also showed greater gains in all aspects of the global competencies assessment than their non-IVE peers. Finally, all students expressed that they found value in the experience. However, there were no differences in perceived value between the IVE and non-IVE teams. The differences came from students from different countries as the Palestinian students perceived greater value in the experience than their US peers regardless of whether they were on an IVE team or not

Towards deployment-centric multimodal AI beyond vision and language

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact