Cognitive radio-enabled Internet of Vehicles (IoVs): a cooperative spectrum sensing and allocation for vehicular communication

Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75MHz spectrum in the 5.9GHz band to support vehicular communication which many studies have found insufficient. In this paper, we studied the application of Cognitive Radio (CR) technology to IoVs in order to increase the spectrum resource opportunities available for vehicular communication, especially when the officially allocated 75MHz spectrum in 5.9GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of IoTs. We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum bands on the highways. We also developed a novel co-operative three-state spectrum sensing and allocation model which makes CR vehicular secondary units (SUs) aware of additional spectrum resources opportunities on their current and future positions and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing and provide vehicles with additional spectrum opportunities without harmful interference against the Primary Users (PUs) activities

Discovering student interactions with a collaborative learning forum that predict group collaboration problems

This paper investigates the role of various student interactions with a learning forum in order to ascertain the existence of different group collaboration problems. A particular focus of interest has been learning forums, since forums have become broadly adopted tools to support online group collaboration. The types of collaboration problems were drawn from previous research that identified the main student-induced collaboration problems. A data set was collected for 87 undergraduates who participated in a web-based computer science group project. It consists of two kinds of data. The first is student interaction data which were collected from a learning forum system on which the group project was undertaken. The second is the data relating to assessment of group collaboration problems, and were gathered through a questionnaire delivered to the students who participated in the group project. Multinomial logistic regression analysis has been applied for modelling the relationship between a response variable corresponding to the existence of a group collaboration problem and several predictor variables representing various student interactions with a learning forum. A set of predictive models were produced by the regression analysis, each representing a statistically significant combination of student interactions that predict the existence of one of the collaboration problems in question. The findings reveal that indicators including the number of posts that were created and replied to by individual students, and the number of times that a student viewed a discussion on a learning forum, contribute significantly in predicting the collaboration problems which were identified. The results also demonstrate that how the existence of a problem fluctuates with the alterations in the value of an indicator variable. The goodness-of-fit of the identified predictive models was measured by the Pearson chi-square test and the results of this test indicate that the models fit the sample data well. The average rate of correct classification by the models was approximately 80%, which means the regression method performs well on the sample data set. The outcomes of this research can help teachers to assess problems in web-based collaborative group work and also can be used to develop tools for automatically diagnosing group collaboration problems in web-based collaborative learning environments

Timely and reliable packets delivery over Internet of Vehicles (IoVs) for road accidents prevention: a cross-layer approach

With the envisioned era of Internet of Things (IoTs), all aspects of Intelligent Transportation Systems (ITS) will be connected to improve transport safety, relieve traffic congestion, reduce air pollution, enhance the comfort of transportation and significantly reduce road accidents. In IoVs, regular exchange of current position, direction, velocity, etc., enables mobile vehicles to predict an upcoming accident and alert the human drivers in time or proactively take precautionary actions to avoid the accident. The actualization of this concept requires the use of channel access protocols that can guarantee reliable and timely broadcast of safety messages. This paper investigates the application of network coding concept to increase content of every transmission and achieve improved broadcast reliability with less number of retransmission. In particular, we proposed Code Aided Retransmission-based Error Recovery (CARER) scheme, introduced an RTB/CTB handshake to overcome hidden node problem and reduce packets collision rate. In order to avoid broadcast storm problem associated with the use of RTB/CTB packet in a broadcast transmission, we developed a rebroadcasting metric used to successfully select a vehicle to rebroadcast the encoded message. The performance of CARER protocol is clearly shown with detailed theoretical analysis and further validated with simulation experiments

Teaching Methods in English Language Instruction: Case of Selected English Language Teachers in General Santos City, Philippines

The aim of the Philippine education is to highlight the learners' equal opportunity in learning and holistic development to the best level possible amid insufficient learning materials and facilities. Hence, the teachers’ pedagogies, their best practices and strategies applied in the classroom are the key which leads to the questions on what are the most common teaching methods used by the teachers and how are they using these methods? The underpinning concept in this study is guided by the theory of constructivism. As such, in view of the research questions; the mixed method concurrent design was used to explore the experiences of the selected English language teachers in rendering an effective teaching strategy. The teaching methods in English language instruction were determined through a survey questionnaire. This is further reinforced by the qualitative data obtained through the semi-structured interview and classroom observations. Results showed that the lecture method and brainstorming were most often used as a method of teaching. The profile of the subjects showed that teachers are well experienced and qualified English language teachers in view of their educational background and professional development. Implications for teachers considering the results emphasized more innovative and contextualized teaching strategies in English instruction. Keywords: Teaching methods, constructivism, English language instruction DOI: 10.7176/JLLL/66-07 Publication date:March 31st 202

Incorporating learning styles in a computer-supported collaborative learning model

Collaborative learning enables individual learners to combine their own expertise, experience and ability to accomplish a mutual learning goal. The grouping of learners, and learning from social interactions with peer-learners, are two basic characteristics of collaborative learning. For individual learners to benefit from collaborative learning, individual learners with different characteristics must be grouped together. In this paper, we propose a computer-supported collaborative learning model which incorporates learning styles for improving collaborative learning. The proposed model is novel since it can provide overall support for collaborative learning. In addition, the way we have incorporated learning styles in the model is a new approach to constituting heterogeneous groups containing learners with dissimilar learning styles and detect learning styles through monitoring collaborative interactions

Design of exponential state estimators for neural networks with mixed time delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the state estimation problem is dealt with for a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. We aim at designing a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally exponentially stable in the presence of mixed time delays. By using the Laypunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of Jiangsu Education Committee of China under Grants 05KJB110154 and BK2006064, and the National Natural Science Foundation of China under Grants 10471119 and 10671172

State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov–Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01 and EP/C524586/1, an International Joint Project sponsored by the Royal Society of the UK, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, the National Natural Science Foundation of China under Grant 60774073, and the Alexander von Humboldt Foundation of Germany

A novel brain-enriched E3 ubiquitin ligase RNF182 is up regulated in the brains of Alzheimer's patients and targets ATP6V0C for degradation

<p>Abstract</p> <p>Background</p> <p>Alterations in multiple cellular pathways contribute to the development of chronic neurodegeneration such as a sporadic Alzheimer's disease (AD). These, in turn, involve changes in gene expression, amongst which are genes regulating protein processing and turnover such as the components of the ubiquitin-proteosome system. Recently, we have identified a cDNA whose expression was altered in AD brains. It contained an open reading frame of 247 amino acids and represented a novel RING finger protein, RNF182. Here we examined its biochemical properties and putative role in brain cells.</p> <p>Results</p> <p>RNF182 is a low abundance cytoplasmic protein expressed preferentially in the brain. Its expression was elevated in post-mortem AD brain tissue and the gene could be up regulated <it>in vitro </it>in cultured neurons subjected to cell death-inducing injuries. Subsequently, we have established that RNF182 protein possessed an E3 ubiquitin ligase activity and stimulated the E2-dependent polyubiquitination <it>in vitro</it>. Yeast two-hybrid screening, overexpression and co-precipitation approaches revealed, both <it>in vitro </it>and <it>in vivo</it>, an interaction between RNF182 and ATP6V0C, known for its role in the formation of gap junction complexes and neurotransmitter release channels. The data indicated that RNF182 targeted ATP6V0C for degradation by the ubiquitin-proteosome pathway. Overexpression of RNF182 reduced cell viability and it would appear that by itself the gene can disrupt cellular homeostasis.</p> <p>Conclusion</p> <p>Taken together, we have identified a novel brain-enriched RING finger E3 ligase, which was up regulated in AD brains and neuronal cells exposed to injurious insults. It interacted with ATP6V0C protein suggesting that it may play a very specific role in controlling the turnover of an essential component of neurotransmitter release machinery.</p

A Multiple Step-like Spectrum of Primordial Perturbation

We show that if the inflaton effective potential has multiple discontinuous points in its first derivative, the spectrum of primordial perturbation will be multiple step-like. We give a general analysis by applying a simple model. In principle, as long as the height of step is low enough, the result of spectrum will be consistent with observations.Comment: minor changes and Refs. added, publish in PL

Checkered Landscapes: Quantifying Dominant Control on Nitrogen Legacies and Time Lags along the River Continuum

In agricultural watersheds across the world, decades of commercial fertilizer application and intensive livestock production have led to elevated stream nutrient levels and problems of eutrophication in both inland and coastal waters. Despite widespread implementation of a range of strategies to reduce nutrient export to receiving water bodies, expected improvements in water quality have often not been observed. It is increasingly understood that long time lags to seeing reductions in stream nutrient concentrations can result from the existence of legacy nutrient stores within the landscape. However, it is less understood how spatial heterogeneity in legacy nutrient dynamics might allow us to target implementation of appropriate management practices. In this thesis, we have explored the dominant controls of legacy nitrogen accumulation in a predominantly agricultural 6000-km2 mixed-landuse watershed. First, we synthesized a 216 year (1800 – 2016) nitrogen (N) mass balance trajectory at the subbasin scale accounting for inputs from population, agriculture, and atmospheric data, and output from crop production using a combination of census data, satellite imagery data, and existing model estimates. Using these data, we calculated the N surplus, defined as the difference between inputs to the soil surface from manure application, atmospheric deposition, fertilizer application, and biological N fixation, and outputs primarily from crop production. We then used the ELEMeNT-N model, with the estimates of the N mass balance components as the model inputs, to quantify legacy accumulation in the groundwater and soil in the study basin and 13 of its subbasins. Our results showed that from 1950, N surplus across the study site rose dramatically and plateaued in 1980. Agricultural inputs from fertilizer and biological nitrogen fixation were the dominant drivers of N surplus magnitude in all areas of the watershed. Model results revealed that 40% of the N surplus to the watershed since 1940 is stored as legacy N, and that the proportion of N surplus that is stored as legacy vary across the watershed, ranging from 33% to 69%. Where legacy tends to accumulate also varies across the watershed, ranging from 49% - 72% stored in soil, and 28% - 51% stored in groundwater. Through correlation analysis, we found that soil N accumulation tends to occur where there is high agricultural N surplus, and groundwater N accumulation tends to occur where mean groundwater travel times are long. We also found that using the model calibrated mean groundwater travel times as an indication of lag times, we can identify the length of lag time in various regions in the watershed to help inform long-term management plans. Our modeling framework provides a way forward for the design of more targeted approaches to water quality management