Secure Modules in TinyOS

© ASEE 2015In this paper, we discuss TinyOS as a flexible operating system that is suitable for wireless sensor networks. It is a powerful tool that is capable of forming a strong component of intelligent systems. Similarly, sensor networks are composed of accurate, low levels of power nodes that carry out simultaneous, reactive programs that operate within the limitations of power and memory. As a solution, we integrate components of the TinyOS with TinyHash or modules for better operations. We also present more data about four components based on our proposed protocol, which includes hash function, module hash table, base station, and algorithm chart

A Cyclic Universe With Varying Cosmological Constant in f(R,T)f(R,T) gravity

A new kind of evolution for cyclic models in which the Hubble parameter oscillates and keeps positive has been explored in a specific $f(R,T)$ gravity reconstruction. A singularity-free cyclic universe with negative varying cosmological constant has been obtained which supports the role suggested for negative $\Lambda$ in stopping the eternal acceleration. The cosmological solutions have been obtained for the case of a flat universe, supported by observations. The cosmic pressure grows without singular values, it is positive during the early-time decelerated expansion and negative during the late-time accelerating epoch. The time varying EoS parameter $\omega(t)$ shows a quintom behavior and is restricted to the range $-2.25 \leq \omega(t) \lesssim \frac{1}{3}$. The validity of the classical linear energy conditions and the sound speed causality condition has been studied. The non-conventional mechanism of negative cosmological constant that are expected to address the late-time acceleration has been discussed.Comment: 15 pages, 10 figure

Predicting Certification in MOOCs based on Students’ Weekly Activities

Massive Open Online Courses (MOOCs) have been growing rapidly, offering low-cost knowledge for both learners and content providers. However, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). This can impact seriously the business model of MOOCs. Nevertheless, MOOC research on learners’ purchasing behaviour on MOOCs remains limited. Thus, the umbrella question that this work tackles is if learner’s data can predict their purchasing decision (certification). Our fine-grained analysis attempts to uncover the latent correlation between learner activities and their decision to purchase. We used a relatively large dataset of 5 courses of 23 runs obtained from the less studied MOOC platform of FutureLearn to: (1) statistically compare the activities of non-paying learners with course purchasers, (2) predict course certification using different classifiers, optimising for this naturally strongly imbalanced dataset. Our results show that learner activities are good predictors of course purchasibility; still, the main challenge was that of early prediction. Using only student number of step accesses, attempts, correct and wrong answers, our model achieve promising accuracies, ranging between 0.81 and 0.95 across the five courses. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenu

Intervention Prediction in MOOCs Based on Learners’ Comments: A Temporal Multi-input Approach Using Deep Learning and Transformer Models

High learner dropout rates in MOOC-based education contexts have encouraged researchers to explore and propose different intervention models. In discussion forums, intervention is critical, not only to identify comments that require replies but also to consider learners who may require intervention in the form of staff support. There is a lack of research on the role of intervention based on learner comments to prevent learner dropout in MOOC-based settings. To fill this research gap, we propose an intervention model that detects when staff intervention is required to prevent learner dropout using a dataset from FutureLearn. Our proposed model was based on learners’ comments history by integrating the most-recent sequence of comments written by learners to identify if an intervention was necessary to prevent dropout. We aimed to find both the proper classifier and the number of comments representing the appropriate most recent sequence of comments. We developed several intervention models by utilising two forms of supervised multi-input machine learning (ML) classification models (deep learning and transformer). For the transformer model, specifically, we propose the siamese and dual temporal multi-input, which we term the multi-siamese BERT and multiple BERT. We further experimented with clustering learners based on their respective number of comments to analyse if grouping as a pre-processing step improved the results. The results show that, whilst multi-input for deep learning can be useful, a better overall effect is achieved by using the transformer model, which has better performance in detecting learners who require intervention. Contrary to our expectations, however, clustering before prediction can have negative consequences on prediction outcomes, especially in the underrepresented class

Do Termitaria Indicate the Presence of Groundwater? A Case Study of Hydrogeophysical Investigation on a Land Parcel with Termite Activity.

Termite nests have long been suggested to be good indicators of groundwater but only a few studies are available to demonstrate the relationship between the two. This study therefore aims at investigating the most favourable spots for locating groundwater structures on a small parcel of land with conspicuous termite activity. To achieve this, geophysical soundings using the renowned vertical electrical sounding (VES) technique was carried out on the gridded study area. A total of nine VESs with one at the foot of a termitarium were conducted. The VES results were interpreted and assessed via two different techniques: (1) physical evaluation as performed by drillers in the field and (2) integration of primary and secondary geoelectrical parameters in a geographic information system (GIS). The result of the physical evaluation indicated a clear case of subjectivity in the interpretation but was consistent with the choice of VES points 1 and 6 (termitarium location) as being the most prospective points to be considered for drilling. Similarly, the integration of the geoelectrical parameters led to the mapping of the most prospective groundwater portion of the study area with the termitarium chiefly in the center of the most suitable region. This shows that termitaria are valuable landscape features that can be employed as biomarkers in the search of groundwater

Drought Vulnerability Assessment Using Geospatial Techniques in Southern Queensland, Australia.

In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies

ROLE OF STRATEGIC LEADERSHIP IN APPLYING TOTAL QUALITY MANAGEMENT A FIELD STUDY IN PRIVATE HOSPITALS IN THE CAPITAL, SANA'A

Abstract Total quality management has gained extensive prominence over the last few decades and continues to play a significant role in modern healthcare delivery. This study aimed to measure the role of strategic leadership in applying TQM in private hospitals in the capital Sana'a. Cross-sectional study was adopted. The research sample included (230) employees in senior administrative positions at six hospitals. To achieve the aim of the research, a questionnaire was used to collect data related to the research variables. The data was analyzed using statistical software (SPSS) to obtain the results. The current study found a positive correlation and role between the practice of strategic leadership in the application of total quality management, and it was found that there is a varying positive role for the dimensions of strategic leadership in the application of total quality management, there was the most role the strategic orientation and the least role is the human capital development. The current study contributes to the literature by showing the concepts and dimensions of strategic leadership and total quality management. This study is also the first to analyze the role of strategic leadership in the application of total quality management in the health sector in Yemen. The study shows the important role of strategic leadership in the application of total quality management, which is reflected in the success and development of private hospitals

Minimally invasive approach for the management of right atrial angiosarcoma; A case report

Cardiac angiosarcoma is a rare primary cardiac tumor. Outcomes of minimally invasive resection of cardiac angiosarcoma are rarely reported in the literature A male patient aged 28 years old presented with a right atrial mass compressing the superior vena cava and associated with pericardial effusion. Pericardiocentesis was done, and a preoperative workup revealed no distant metastasis. We planned excision of the mass through a right mini-thoracotomy approach. Intraoperatively, we found the mass invading the entire atrial wall thickness, and excision of the mass with a reconstruction of the right atrial wall was performed. Minimally invasive resection of atrial angiosarcoma could be feasible. Atrial angiosarcoma could present with vague signs and symptom