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Background: Public health departments in Canada are currently facing the challenging task of planning and implementing coronavirus disease 2019 (COVID-19) vaccination programs. Objective: To collect and synthesize information regarding COVID-19 vaccination program planning in each province and territory of Canada, including logistic considerations, priority groups, and vaccine safety and effectiveness monitoring. Methods: Provincial/territorial public health leaders were interviewed via teleconference during the early planning stage of COVID-19 vaccination programs (August–October 2020) to collect information on the following topics: unique factors for COVID-19 vaccination, intention to adopt National Advisory Committee on Immunization (NACI) recommendations, priority groups for early vaccination, and vaccine safety and effectiveness monitoring. Data were grouped according to common responses and descriptive analysis was performed. Results: Eighteen interviews occurred with 25 participants from 11 of 13 provinces/territories (P/Ts). Factors unique to COVID-19 vaccination included prioritizing groups for early vaccination (n=7), public perception of vaccines (n=6), and differing eligibility criteria (n=5). Almost all P/Ts (n=10) reported reliance on NACI recommendations. Long-term care residents (n=10) and healthcare workers (n=10) were most frequently prioritized for early vaccination, followed by people with chronic medical conditions (n=9) and seniors (n=8). Most P/Ts (n=9) are planning routine adverse event monitoring to assess vaccine safety. Evaluation of effectiveness was anticipated to occur within public health departments (n=3), by researchers (n=3), or based on national guidance (n=4). Conclusion: Plans for COVID-19 vaccination programs in the P/Ts exhibit some similarities and are largely consistent with NACI guidelines, with some discrepancies. Further research is needed to evaluate COVID-19 vaccination programs once implemented.

The ability of predicting solar radiation strength is particularly important for the best administration of power grids involving photovoltaic solar energy. The production-consumption balance of energy is one of the most important characteristics that should be maintained in energy grids. A qualified solar energy short-term can help in mitigating such type of problems, and therefore helps in improving the reliability of the overall energy system. The contribution of the present research consists of proposing and validating multi-stacking model that consists of a committee of recurrent long short-term memory (LSTM) models to predict the strength of solar radiation one day ahead. The individual models of the committee have been generated by modifying the internal structure and parameters of a base recurrent LSTM model. A multi-level structure that consists of two layers of stacking meta-models have been examined and validated through a cross validation strategy. The proposed models have been trained on time series dataset of solar radiation records, and then validated and tested with on subset of data records over one year using correlation and statistical metrics.

In the last two decades, solar energy sources have gained a remarkable place among others and have proven that it is possible to utilize green energy to satisfy a part of the global demands of electrical power. In Lebanon, solar Photovoltaic (PV) systems are being used progressively. However, soiling and dust accumulation, on the surface of these panels, minimizes their efficiency. As a first of its kind in Lebanon, this paper presents the results from a study conducted on the effectiveness of wet cleaning using a robot. This study is carried out on a part of a 1 MWp solar power plant station in Zahrani, a coastal region in Lebanon. The robot was designed, manufactured, implemented, and tested to clean the abovementioned station. The panels were cleaned in a period starting from June 15th to September 30th, 2018. The generated power was collected on a daily basis and compared to the generated power over the same period of 2017 without cleaning. The results showed that the cleaning procedure was able to effectively minimize the effect of dust on the solar panel’s power output. Thus, an increase average of 32.27% in the power generation is obtained.

Background: An outbreak of an unknown respiratory illness caused by a novel coronavirus, SARS-CoV-2, emerged in the city of Wuhan in Hubei Province, China, in December 2019 and was referred to as coronavirus disease-2019 (COVID-19). Soon after, it was declared as a global pandemic by the World Health Organization (WHO) in March 2020. SARS-CoV-2 mainly infects the respiratory tract with different outcomes ranging from asymptomatic infection to severe critical illness leading to death. Different SARS-CoV-2 variants are emerging of which three have raised concerns worldwide due to their high transmissibility among populations. Objective: To study the prevalence of COVID-19 in the region of Nabatieh-South Lebanon during the past year and assess the presence of SARS-CoV-2 variants and their effect on the spread of infection during times of lockdown. Methods: In our study, 37,474 nasopharyngeal swab samples were collected and analyzed for the detection of SARS-CoV-2 virus in suspected patients attending a tertiary health care center in South Lebanon during the period between 16 March 2020 and 21 February 2021. Results: The results demonstrated a variation in the prevalence rates ranging from less than 1% during full lockdown of the country to 8.4% upon easing lockdown restrictions and reaching 27.5% after the holidays and 2021 New Year celebrations. Interestingly, a new variant(s) appeared starting January 2021 with a significant positive association between the prevalence of positive tests and the percentage of the variant(s). Conclusions: Our results indicate that the lockdown implemented by the Lebanese officials was an effective intervention to contain COVID-19 spread. Our study also showed that lifting lockdown measures during the holidays, which allowed indoor crowded gatherings to occur, caused a surge in COVID-19 cases and rise in the mortality rates nationwide. More importantly, we confirmed the presence of a highly transmissible SARS-CoV-2 variant(s) circulating in the Lebanese community from at least January 2021 onwards.

BackgroundAn outbreak of an unknown respiratory illness caused by a novel corona-virus, SARS-CoV-2, emerged in the city of Wuhan in Hubei province, China, in December 2019 and was referred to as coronavirus disease-2019 (COVID-19). Soon after, it was declared as a global pandemic by the World Health Organization (WHO) in March 2020. SARS-CoV-2 mainly infects the respiratory tract with different outcomes ranging from asymptomatic infection to severe critical illness leading to death. Different SARS-CoV-2 variants are emerging of which three have raised concerns worldwide due to their high transmissibility among populations.ObjectiveTo study the prevalence of COVID-19 in the region of Nabatieh - South Lebanon during the past year and assess the presence of SARS-CoV-2 variants and their effect on the spread of infection during times of lock-down. Methods: In our study, 37,474 nasopharyngeal swab samples were collected and analyzed for the detection of SARS-CoV-2 virus in suspected patients attending a tertiary health care center in South Lebanon during the period between March 16, 2020 and February 21, 2021.ResultsResults demonstrated a variation in the prevalence rates ranging from less than 1% during full lockdown of the country to 8.4% upon easing lockdown restrictions and reaching 27.5% after the holidays and 2021 New Year celebrations. Interestingly, a new variant(s) appeared starting January 2021 with a significant positive association between the prevalence of positive tests and the percentage of the variant(s).ConclusionOur results indicate that the lockdown implemented by the Lebanese officials presented an effective intervention to contain COVID-19 spread. Our study also showed that lifting lockdown measures during the holidays, which allowed indoor crowded gatherings to occur, caused a surge in COVID-19 cases and rise in the mortality rates nationwide. More importantly, we confirmed the presence of a highly transmissible SARS-CoV-2 variant(s) circulating in the Lebanese community, at least since January 2021 onwards.

The ability to predict solar radiation one-day-ahead is critical for the best management of renewable energy tied-grids. Several machine learning ensemble techniques have been proposed to enhance the short-term prediction of solar radiation strength. In general, finding an optimal ensemble model that consists of combining individual predictors is not trivial due to the need for tuning and other related issues. Few comparative studies have been presented to obtain optimal structures of machine learning ensemble that deal with predicting solar radiation. The contribution of the present research consists of a comparative study of various structures of stacking-based ensembles of data-driven machine learning predictors that are widely used nowadays to conclude the best stacking strategies in terms of performance to combine predictors of solar radiation. The base individual predictors are arranged to predict solar radiation intensity using historical weather and solar radiation records. Three stacking techniques, namely, feed-forward neural networks, support vector regressors, and k-nearest neighbor regressors, have been examined and compared to combine the prediction outputs of base learners. Most of the examined stacking models have been found capable to predict the solar radiation, but those related to combining heterogeneous models using neural meta-models have shown superior performance. Furthermore, we have compared the performance of combined models against recurrent models. The solar radiation predictions of the surveyed models have been evaluated and compared over an entire year. The performance enhancements provided by each alternative ensemble have been discussed.

BackgroundPublic health departments in Canada are currently facing the challenging task of planning and implementing COVID-19 vaccination programs.ObjectiveTo collect and synthesize information regarding COVID-19 vaccination programs in each of the provinces and territories (P/Ts).MethodsProvincial/territorial public health leaders were interviewed via teleconference between August-October 2020 to collect information on the following topics, drawn from scientific literature and media: unique factors for COVID-19 vaccination, adoption of National Advisory Committee on Immunization (NACI) recommendations, priority groups for early vaccination, and vaccine safety and effectiveness monitoring. Data were grouped according to common responses and descriptive analysis was performed.ResultsEighteen interviews occurred with 25 participants from 11 of 13 P/Ts. Factors unique to COVID-19 vaccination included prioritizing groups for early vaccination (n=7), public perception of vaccines (n=6), and differing eligibility criteria (n=5). Almost all P/Ts (n=10) reported reliance on NACI recommendations. Long-term care residents (n=10) and health care workers (n=10) were most frequently prioritized for early vaccination, followed by people with chronic medical conditions (n=9) and seniors (n=8). Most P/Ts (n=9) are planning routine adverse event monitoring to assess vaccine safety. Evaluation of effectiveness was anticipated to occur within public health departments (n=3), by researchers (n=3), or based on national guidance (n=4).ConclusionPlans for COVID-19 vaccination programs in the P/Ts exhibit some similarities and are largely consistent with NACI guidelines, with some discrepancies. Further research is needed to evaluate the success of COVID-19 vaccination programs once implemented.

The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency.

Solar irradiance is one of the most important parameters that need to be estimated and modeled before engaging in any solar energy project. This article describes a non-linear regression model based on genetic programming technique for estimating solar irradiance in a specific region in the United Arab Emirates. The genetic programming is an evolutionary computing technique that enables automatic search for complex solutions. The best nonlinear modeling function that can estimate the global solar radiation on horizontal will be developed taking into account measured meteorological data. A reference approach to model the solar radiation is first presented. An enhanced approach is then presented which consists of multi nonlinear functions of regression in a parallel structure where each function is designed to estimate the global solar irradiance in a specific seasonal period of the year. Statistical analysis measures have been used to evaluate the performance of the proposed approaches. The obtained results are comparable with the outcomes of models developed by other researchers in the field.

Increasingly, public health research standards call for engaging communities who live with health issues under investigation to help ensure that results translate into effective public health measures. Here, we share guiding principles for community-driven epidemiologic research developed over a decade conducting research sought and controlled by participating communities. These principles provide a roadmap for epidemiologic research that effectively addresses community priorities while meeting academic standards: research questions are developed collaboratively in community-university partnerships; knowledge takes shape from information donated by participants through methods that turn information into scientifically useful data and analysis that reveals data patterns that address research questions; thus, knowledge is generated collaboratively by academic researchers and community partners; academic researchers are bound by ethical, professional, scientific, contractual, and other legal standards to be responsible stewards of information donated by participants; community review protocols ensure that interpretation and presentation of research results reflect the voices of all partners; meaningful community review creates the trust needed for open access to research results as required of academic researchers; all partners share credit for achievements. Lacking models for operationalizing these principles, we developed specific guidelines for research project initiation, data use, authorship, acknowledgment, and data dissemination. Conventions in academia present formidable challenges to effective community engagement and are often at odds with calls from community organizations and funding agencies to conduct research driven by community priorities and values. We hope others can benefit from time and effort we have spent crafting solutions that bridge this divide.

The integration of Solar Energy in smart grids and many utilities is continuously increasing due to its environmental and economical benefits. However, the uncertainty of available solar energy provides challenges regarding stability in power generation and therefore consistency in production-consumption balance. The availability of an efficient solar energy short-term predictor is found adequate to remedy such type of problems, and therefore permits to enhance the overall system reliability and power generation scheduling. In this paper we survey several ensemble methods based on machine learning named stacking for one-day-ahead predicting of solar radiation intensity. The support vectors regressors are used as base forecasters in an ensemble, and the Multi-layer perceptrons as well as the Decision Trees and K-Nearest Neighbor Regressors are used as meta-learners to combine the predictions of the ensemble in a stacking mode. The sliding window technique is used in a preprocessing phase to reframe the time series predicting problem as a supervised learning problem. The combiners provide a smart weighted averaging of the forecasters' outputs. The solar radiation prediction of the surveyed models has been evaluated and analyzed over an entire year. The stacking based ensemble models outperformed all individual models as well as other combining techniques.

Instance matching is one of the processes that facilitate the integration of independently designed knowledge bases. It aims to link co-referent instances with an owl:sameAs connection to allow knowledge bases to complement each other. In this work, we present VDLS, an approach for automatic alignment of instances in RDF knowledge base graphs. VDLS generates for each instance a virtual document from its local description (i.e., data-type properties) and instances related to it through object-type properties (i.e., neighbors). We transform the instance matching problem into a document matching problem and solve it by a vector space embedding technique. We consider the pre-trained word embeddings to assess words similarities at both the lexical and semantic levels. We evaluate our approach on multiple knowledge bases from the instance track of OAEI. The experiments show that VDLS gets prominent results compared to several state-of-the-art existing approaches.

The current status of the Lebanese electricity system necessitates an urgent solution. Lebanon’s power shortage, manifested in the daily power outages (load shedding), and whose main cause is the insufficient power generation capacity, has become an unbearable event in Lebanon. In view of that, renewable energy technologies emerge as the suitable solution, since Lebanon proves to have a notable renewable energy potential including an important average wind speed, active water resources, high daily solar irradiance, and outstanding geothermal resources. In this paper, a new innovative solution has been proposed to improve the power generation capacity of an existing Lebanese thermal power plant (Deir Ammar). Which is located in northern Lebanon, it is to be supported with an offshore wind farm. This proposal has good potential to be feasible since it utilizes the unused area next to the power plant, exploits the high average wind speed, and provides a notable boost in MW rating of the existing thermal power plant.

To enhance the forecasting of solar radiation strength on horizontals, an ensemble learning approach is proposed. Two types of machine learning models are arranged to predict solar radiation, namely the recurrent neural networks, and the support vector regressors. A multi-layer perceptron model acting as an ensemble learning technique is deployed to combine the forecasts of ensemble models through a stacking technique. The combiner provides an automatic weighted averaging of the forecasters’ outputs. The suggested approach helps improving the accuracy of a one-day-ahead solar energy forecasting. The performance of the combining technique is evaluated over an entire year using meteorological data. The experiments showed the superiority of the learning-based combinatory model compared to those of single models as well as to other combining techniques.

This paper investigates the application of model free control (MFC) strategy to optimally control the oscillation of single body heaving waves energy converter (WEC). The aim of the proposed controller is to maximize the electrical energy output of the device. The MFC is an off-line fixed structure control approach that combines between simplicity and insensitivity to system uncertainties and/or operational conditions. The proposed controller is based on a classical linear compensator, which is designed and tuned using only the well-known dynamics of the WEC. Therefore, any extra unknown or partially known dynamics are taken care of using the ultra-local model (ULM). Simulation results show that the MFC performance is superior to that of the base linear compensator, in terms of reference tracking capability and robustness towards uncertainties. Also other well-established control strategies are used to further validate the proposed controller.

As the temperature of electronic devices increases, their failure rate increases. That's why electrical devices should be cooled. One of the promising cooling techniques is using Phase Change Materials (PCMs). PCMs are potential temperature regulators that store and release thermal energy during the process of melting and freezing respectively. PCM-based heat sinks can effectively store the heat dissipated from the electronic components to delay the peak temperature of the electronic devices as much as possible and then release the stored energy during the off period. This paper compares the temperature distribution on a heat sink with and without PCM with different heat flux. Also, two different PCMs with different densities, namely salt-hydrate and wax have been investigated in cooling electronic devices.

Ages are passing and the world is consuming nonrenewable energy sources at an exponential rate. Luckily, humans have started to raise the alert bit by bit in the world of adults, officials, and even universities. However, the world of teenagers seems to be off the alerting map. This is a huge mistake considering that young minds are more capable than any adult to understand and raise a certain cause. This is why aiming out for these youngsters should be the primary concern on the agenda of any “Go Green” movement or organization. There is primordial need to integrate the renewable energy concept in a very simplified way to elementary school students who, with the correct education, could turn into a powerful green weapon for the crude black future. Hence, in this work, several alteration propositions are made to update and reshape the book chapters on electricity given to students by adding the components of renewable energy as energy sources for a better future. The end product of this research is a toolbox, a “Go Green” box, that contains a booklet consisting of a wide spectrum of renewable energy experiments. This box can be used at schools for a pair of partners and would guide them in the protocol step by step to reach the conclusion. It would therefore make them understand by themselves the benefits of the investigated renewable energy source.

This paper discusses the potential of implementing solar panels in two campuses of the Lebanese International University (LIU) to harvest solar energy. The design is conducted using HelioScope simulation tool, specifying all the mechanical parameters and the electrical components used in the proposed PV system. The expected system power is estimated to be at least 5 times the campus energy demand. The payback time of the project is calculated, showing the economic benefits of such project.

High temperature can result in the degradation of any device (solar, electrical, electronics, etc.). For example, in the case of a silicon photovoltaic (PV) system, approximately 25% of the incoming concentrated solar energy will be converted into electrical energy and the remaining is converted into waste heat which contributes to elevate the cell temperature and so decreases its efficiency. The main objective of this project is to emphasize the Phase Change Material (PCM) cooling technique. This kind of material is known for its high storing capacity of the temperature which will enhance the cycle lifetime of any device. In this project, a direct application on a PV panel was carried out, an organic PCM is tested in order to study its properties and capacity of absorption. The results shows that around 13% of the energy can be saved. Another objective of this project consists of a numerical validation of the modeling used to determine the temperature of a photovoltaic panel. This validation allows us to use this model in order to decide on the efficiency of any PCM to be used.

Current solar panels can be improved using several techniques that boost the efficiency and cell performance. Looking in the latest studies currently on-going in the laboratories, two main fields were mostly taking a considerable success: PERC technology and passive thermal management. PERC technology involves the addition of a dielectric passivation layer to the already existing layers in the p-type crystalline wafers. This helps trap more sunlight and generate more electricity, thus improving the crystalline silicon panel. In addition, proper thermal management of photovoltaics leads to better heat dissipation, which improves the conversion of sunlight to electricity and maintains a stable panel performance in the long run.

As the temperature of electronic devices increases, their failure rate increases. That's why electrical devices should be cooled. One of the promising cooling techniques is using Phase Change Materials (PCMs). PCMs are potential temperature regulators that store and release thermal energy during the process of melting and freezing respectively. PCM-based heat sinks can effectively store the heat dissipated from the electronic components to delay the peak temperature of the electronic devices as much as possible and then release the stored energy during the off period. This paper compares the temperature distribution on a heat sink with and without PCM with different heat flux.

The presents study was conducted to evaluate the DNA damage markers, antioxidants makers and lipid peroxidation in type 2 diabetes mellitus patients, the results of presents study was showed the DNA damage markers such as comet length, tail length, %DNA in tail and tail moments were significantly increased at p

The geographical location (latitude: 24° 16′ N and longitude: 55° 36′ E) of Al Ain city in the southwest of United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for predicting monthly global solar radiation (MGSR) on a horizontal surface in Al Ain. The ANN models are presented and implemented on 13-year measured meteorological data for Al Ain such as maximum temperature, mean wind speed, sunshine, and mean relative humidity between 1995 and 2007. The meteorological data between 1995 and 2004 are used for training the ANN and data between 2004 and 2007 are used for testing the predicted values. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks are used for the modeling. Models for the MGSR were obtained using eleven combinations of data sets based on the above mentioned measured data for Al Ain city. Forecasting performance parameters such as root mean square error (RMSE), mean bias error...

Electricity in Lebanon is facing many problems leading to serious implications on the economic, social, and industrial levels of this country. The big deficit in electricity generation and not meeting the demand is by far the most important problem that needs an immediate. This paper suggests a new idea to reinforce the capacity of existing thermal power plants in Lebanon with the use of photovoltaic (PV) panels. Two thermal power plants will be investigated as case studies; Zouk and Jiyeh power plants. The added PV elements will produce additional energy at a lower cost compared to traditional PV systems. The low cost of the additional PV system in the thermal power plant is obtained through the use of low cost lands and avoiding the use of batteries for energy storage.

The Lebanese electricity system system's deteriorating situation (mainly characterized by the daily long rationing hours), has led to the wide use of diesel generators by Lebanese citizens to compensate for the outage hours. Unfortunately, this has led to lower energy efficiency, higher cost of power production, and notably higher greenhouse gas emissions. In this paper, a renewable-based micro-grid system is proposed, which provides the needed backup power at a lower cost and carbon footprint. In order to increase the flexibility of the system and decrease the payback time, the concept of sharing between houses is applied. Moreover, the extra generated power is injected into the national grid through the net-metering protocol. This feasibility study is applied to 3 types of houses in Lebanon (small, medium, and large). This system could pave the way towards the wide-scale implementation of micro grids and smart grids in Lebanon. In general, it has 3 times less payback period and 5–23 tons of annual CO2 reduction per house.

A tool, “MY PV TOOL”, has been developed in this work to study the behavior of series and shunt resistances in PV modules. Experimental as well as theoretical equations have been used to determine the series and shunt resistances for a given commercial PV module.

It is well-known that the bottleneck of Photovoltaic (PV) systems is the use of storage batteries and the area needed to install the system. The main idea in this work is to design a large PV system with almost zero cost of land needed for the installation, and without the need of batteries. The proposed PV system makes use of special areas of the Lebanese lands; the middle of high-ways which are usually left unoccupied without being exploited by any other business. This way, the traditional high cost of PV systems residing in the land and storage is eliminated. The fact that we do not need batteries in the proposed PV system results from the ability to connect the output power directly to the public utility grid. The design consists of 20,476 PV panels that cover the middle of the high-way extending from Tyre to Saida, with a distance of 27,300 meters and 2 meters wide. The resulting PV system has a capacity of 6.2 MW, and the total initial cost is ∼8.845 M$, however, after 25 years (which is the proposed lifetime of the PV system) the project would generate a total of ∼54.7 M$. Moreover, the PV system would provide net reduction of CO2 and GHG emissions of ∼7,388 tCO2/year.

We propose in this paper a programmable band-pass filter based on an array of fully differential transconductance circuits that controls the filter parameters. The signal path does not contain any switch, and fine-tuning of the filter parameters is implemented using programmable capacitors. A digital building block is implemented in the proposed band-pass filter to tune its central frequency as well as its bandwidth. The filter circuit is based on a biquad topology, which is designed and implemented with CMOS 0.18 μm technology. Experimental results show a programming ability of the center frequency between 5.9 and 58 MHz, and the quality factor can be tuned from 0.36 to 10. These features are obtained for a total power consumption of less than 10.5 mW from a single 1.8 V power supply.

This study suggests a simple and robust control strategy for improving captured power in heaving wave energy converters (WEC). The controller aims at optimizing the captured power using a hierarchical control strategy (HCS), which consists of a higher and lower control levels. The top level control is responsible of producing the reference velocity that is used by the lower level controller to regulate the actual velocity of the WEC buoy. The reference is generated by solving an offline optimization problem for maximizing the absorbed energy subject to the buoy motion constraints. As an attempt to avoid computational complexity in real-time operation, a simple look-up table is produced for various sea-states conditions. In order to track this reference, sliding mode controller (SMC) is deployed. The design parameters of the SMC is tuned offline using a well-known model of WEC. Any perturbations, nonlinearities, and disturbances not covered by the used model will be handled by the robustness property of the SMC. Moreover, the used SMC does not add any further complexity to the overall control strategy. The proposed method is validated in both regular and irregular sea-states, as well as in both nominal and perturbed scenarios. Other control strategies were also used to further assess the performance of the proposed control strategy. Based on the simulation results, the proposed hierarchical controller outperformed the other controllers in both nominal and perturbed scenarios.

This paper describes a new mathematical model for the estimation of reduced ordered binary decision diagram's (ROBDD's) complexity, for any Boolean function with different degrees of variables complexity. The model is capable of predicting the maximum possible ROBDD complexity for Boolean functions with given number of variables. The proposed model is also capable of predicting the number of product terms in the Boolean function that will correspond to the maximum complexity of the ROBDD. This mathematical model works for any type of variable reordering method, and will enable the system performance to be analyzed without building the ROBDD. Since ROBDD complexity can be predicted without building it, a great reduction in terms of time complexity for VLSI CAD designs can be achieved and very useful clues to tackle ROBDD optimization problems in the design of digital circuits can also be obtained.

We propose in this paper a tunable second order band-pass filter based on two CMOS current feedback operational amplifiers (CFOAs). The CFOA includes a novel offset compensation technique. A digital building block is implemented in the proposed band-pass filter to tune its central frequency. An important feature of the adopted tuning procedure is the ability to tune the filter without affecting other characteristics such as gain, phase and quality factor. The band-pass filter topology is validated with a configuration where the central frequency is tuned from 60 MHz to 95 MHz with frequency steps of 5 MHz. Measurements of the offset-compensated CFOA are promising, and simulation results of the CFOA-based band-pass filter using the 0.18 ? m CMOS process confirm our theoretical analysis.

Modern wireless communication standards that support high rates of voice and video streaming need high-speed Analog-to-Digital Converters (ADCs) with wide Spurious-Free Dynamic Range (SFDR). Conventional time-interleaved ADCs suffer from spurious components that seriously affect the SFDR. In this paper, we present the mathematical background describing the effect of randomizing the samples among the interleaved ADCs and we propose a digitally oriented method based on this analysis to randomize the mismatches among the ADC channels. Analyses and simulations show the effectiveness of the proposed approach in multi-channel ADCs with arbitrary bit resolution, channel's number and sampling rate. For a 10-bit 500 MS/s ADC, the SFDR achieved using the proposed randomizing method can be as wide as 75 dB, which is an enhancement of more than 26 dB comparing to the conventional time interleaved ADC.

International audience; This article presents an intelligent fuzzy logic controller (FLC) for controlling single-body heaving wave energy converter (WEC) or what is widely known as “Point Absorber”. The controller aims at maximizing the energy captured from the sea waves. The power take-off (PTO) limitations are addressed implicitly in the fuzzy inference system (FIS) framework. In order to enhance the WEC power capturing bandwidth and make it less susceptible to wave environment irregularities and the system parametric uncertainties, the controller is built to have a self-configurable capability. This also eliminates the need to repeatedly run in-situ tuning procedure of the fuzzy controller or switch between several controllers based on the operating conditions. The fuzzy membership functions (MFs) are optimally tuned using particle swarm optimization (PSO) algorithm. To alleviate the computational burden associated with performing on-line optimization, the fuzzy controller is tuned at a rate significantly lower than the system sampling time. The suggested PSO-FLC has shown promising results compared with the fixed structure fuzzy logic controller (FS-FLC) and other passive control strategies. Several computer simulations were carried out to evaluate the controller effectiveness by applying different sea-states and analyzing the resultant WEC dynamics.

This paper investigates the application of function based model predictive control (MPC) approach to control the movement of single-body heaving wave energy converter (WEC). The proposed controller is designed based on the principle of maximum power transfer, at which maximum power absorption is attained. In addition, the suggested MPC controller respects the physical and thermal limitations of the WEC. Also, the generalized architecture of the proposed controller allows for longer prediction horizons and permits to effectively tune the controller based on the surrounding wave environment. Simulations have been carried out to assess the performance of the proposed controller using realistic sea states. Results show that the proposed MPC controller has achieved satisfactory performance allowing the WEC to optimize energy absorption while respecting the system limitations.

This paper describes a CMOS building block dedicated to high performance mixed analog-digital circuits and systems. The circuit consists of six MOS transistors realizing a new wideband and tunable transconductance. The theory of operation of this device is presented and the effects of transistor nonidealities on the global performances are investigated. Use of the proposed circuit to realize tunable functions (Gm-C filter and current opamp) is illustrated. HSPICE simulations show a wide tuning range of the transconductance value from 40 μS to 950 μS (500 μS) for ±2.5 V (±1.5 V) supply voltages. The transconductance value remains constant up to frequencies beyond 500 MHz. The bandpass filter built with few transconductance blocks and capacitances was simulated with ±2.5 V supply voltage, the center frequency is tunable in the range of 30 MHz to 110 MHz. However, the opamp, which is designed with a transresistance-transconductance architecture, was simulated with ±1.5 V supply voltage. The gain of the opamp can be tuned between 70 dB and 96 dB and high gain-bandwidth product of 145 MHz has been achieved at power consumption of less than 0.5 mW. Experimental results on a fabricated transconductor chip are provided.

This work describes a new CMOS current-feedback operational amplifier (CFOA) with an on-chip continuous-time current-mode input offset voltage compensation circuit. The proposed compensation method is based on a combination of two techniques: the error integration and the current feedback. In addition, this method is irrespective of process and temperature parameters because of its fully symmetrical architecture. HSPICE simulations of the designed CMOS CFOA layout show that the input offset voltage could be reduced to lass than 1 mV, and a gain of around 112 dB and a power consumption of less than 3 mW are achievable.

Many articles on the status of renewable energy in Lebanon have been published recently and this paper comes as a modest contribution to what has been said till now emphasizing the importance of keeping the renewable energy strategy adopted by the current and previous Lebanese governments. This paper sheds light on the current status of renewable energy in Lebanon, problems opposing its implementation, and suggestions to improve this unavoidable choice.

This paper proposes two control strategies for controlling the heaving wave energy converter (WEC): sliding mode control (SMC) and fuzzy logic control (FLC). The two methods are implemented to maximize the absorbed energy of the heaving WEC. This can be achieved by designing the control laws so that the buoy's velocity follows its reference. The two control strategies are tested using actual polychromatic ocean wave. The control strategies consider the physical limitation of the control force and the buoy's elevation. Finally, the performances of the two control methods are tested and compared in the nominal and perturbation scenarios.

This paper presents an artificial neural network (ANN) model for predication global solar radiation (GSR) for main cities in the UAE namely, Abu Dhabi, Al-Ain and Dubai. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) techniques with comprehensive training algorithms, architectures, and different combinations of inputs are used to develop these models. The measured data include the maximum temperature (°C), mean wind speed (knot), sunshine hours, mean relative humidity (%) and mean daily global solar radiation on a horizontal surface (kWh/m2). This data was provided by the National Center of Meteorology and Seismology (NCMS) of Abu Dhabi. The results show the generalization capability of ANN approach and its ability to generate accurate prediction of GSR in UAE.

This paper aims at presenting the experience of the renewable energy group at the United Arab Emirates University (UAEU) in the organization of the “Engineering Students Renewable Energy Competition (ESREC)” for undergraduate engineering and technical college students in the United Arab Emirates (UAE). The benefits of such competition to students are that it allows them to use and apply the technical knowledge gained during their engineering formation in the design of a renewable energy (RE) system. The paper presents the evolution of the ESREC competition over four cycles, its judging process and the results.

This paper considers sliding mode controller (SMC) for heaving wave energy converter (WEC). The proposed SMC is implemented to maximize the absorbed energy of the heaving WEC. The problem of chattering control force is solved via the approximation method using the saturation function. The proposed method is tested using polychromatic sea state to simulate the real sea environment. A large difference in energy absorption is resulted using the proposed method compare to a freely motion of heaving WEC. The proposed method maintains the excursion of the buoy within the acceptable limit. The proposed method can maintain the maximum energy absorption despite the presence of uncertainty in its model parameter.

This paper proposes a new reactive control technique for controlling single body heaving wave energy converter (WEC). A function-based continuous model predictive controller (MPC) is implemented to maximize the absorbed energy while respecting the physical limitations of the device. Laguerre orthonormal functions are deployed to approximate the input variable trajectory, which reduces the computational burden associated with MPC. The developed control strategy is evaluated using both monochromatic and polychromatic sea states. The function-based mode predictive controller showed decent reference signal tracking capabilities, optimizing the control effort to achieve maximum power transfer, while respecting the actuators mechanical and electrical limitations. Also the controller provides means to alleviate the problem of excessive real/reactive power flows.

The geographical location of the United Arab Emirates (UAE) (latitude between 26° and 32° North and longitude between 51° and 56° East) favors the development and utilization of solar energy. This chapter presents estimation models for the global solar radiation (GSR) in Dubai, UAE. It compares between six empirical regression models and the best of 11 different configurations of artificial neural network (ANN) models. The models have been developed using measured average daily GSR data for 7 years (2002–2008) while the measured data for the years 2009–2010 are used for testing the models. Results of monthly average daily GSR data of all the empirical models for the test period 2009–2010 yield low statistical error parameters and coefficients of determination (R2) better than 96 %. Comparison with ANN models and Solar Radiation (SoDa) Web site data shows that the optimal multilayer perceptron (MLP) ANN model is the best with R2 = 98 %, and with the lowest statistical error parameters. The results also confirm that a simple linear regression model provides a very good estimation for monthly and daily average GSR data.

Shading and overheating of photovoltaic cells can result in a significant energy reduction of PV systems. Tilting and natural ventilation allows the buildup of fine sand to be blown off from the PV array's surface. However, dust particles tend to gradually accumulate on the PV surface making the cleaning task more difficult. The accumulated dust reduces the overall PV panel efficiency due to the combined effect of shading and heating. In this research work an original method to reduce effect of windblown sand and dust and increased PV temperature on photovoltaic arrays is described. The proposed method consists of removing air blown dust from photovoltaics using forced air flow of cooled return air from existing air conditioning systems.

This paper presents a new control strategy based on fuzzy logic for maximizing the absorbed energy of heaving wave energy converters (WEC). The fuzzy logic controller is responsible of minimizing the discrepancy between the optimal reference buoy velocity and the actual buoy velocity, hence maximizing the captured power for irregular sea environment. The proposed controller performed well compared to a conventional fixed reactive control strategy.

Multicrystalline silicon (mc-Si) material is a promising alternative to monocrystalline CZ silicon because of its lower cost. Solar cell industries are investing in the improvement of electrical performance of mc-Si to make it competitive in the solar cell market. To improve the absorption of incident light, an isotropic texture using nitric acid (HNO 3 ), hydrofluoric acid (HF) and demineralized (i.e. dionized) water (DI H 2 O) is widely used but leads to etch pits at the grain boundary. In this research work, the formation of etch pits and its impact on the electrical performance of the solar cell has been analyzed. In addition, phosphorus diffusion temperature, phosphorus concentration, coating thickness (CT), refractive index (RI) of the anti reflection coating (ARC), and sintering speed of metal electrodes have been investigated. A batch of 156 mm2(SQ) is fabricated with 16.54% average cell efficiency which is 0.42%absolute higher and the shunt resistance (R sh ) is increased by two fold compared to the standard process. The surface morphology, reflectance factor (RF), open circuit voltage (V oc ), short circuit current (I sc ), fill factor (FF), and cell efficiency (η) have been analyzed and compared also with the standard process.

This paper proposes a new suboptimal reactive control technique for controlling single body heaving wave energy converter (WEC). A self-tunable fuzzy logic controller is implemented to maximize the absorbed energy while respecting the physical limitations of the device. Particle swarm optimization (PSO) algorithm is utilized to carry out an on-line optimization process of the fuzzy membership functions (MFs) embedded in the fuzzy controller. The proposed PSO-FLC out-performed the conventional fuzzy logic controller (C-FLC) in terms of improving reference signal tracking accuracy, widening the device wave absorption bandwidth, and preserving a degree of robustness to model uncertainties.

The prediction of the global solar radiation is of great importance for several engineering applications. Just to name few, solar radiation estimates are fundamental during the design phase of several technologies such as: flat plates and concentrating collectors, solar energy storage devices, solar heaters, and photovoltaic systems. The geographical location of the United Arab Emirates (UAE) (between 26° and 32° North Latitude and between 51° and 56° 25 East Latitude) favors the development and utilization of solar energy. A graphical user interface (GUI) was designed to assist solar energy planners, engineers, architectural scientists, and researchers to predict global solar radiation in different regions of the UAE. The predicted models are developed using Artificial Neural Network techniques.

Information about the availability of solar radiation on horizontal surface is essential for the optimum design and study of solar energy systems. For a country like UAE, the efficient application of solar energy seems inevitable because of abundant sunshine available throughout the year. The traditional way of knowing the amount of global solar radiation (GSR) in a particular region is to install pyranometers at as many locations as possible in this region thus requiring daily maintenance and data recording, and consequently increasing cost of GSR data collection. Therefore, it is rather more economical to develop methods to estimate the GSR using climatological parameters (Akhlaque et al., 2009; Kassem et al., 2009; Falayi et al., 2008; El-Sebaii & Trabea, 2005). Many researchers estimated global solar radiation by using artificial neural networks. (Mohandes et al, 1998) applied ANN techniques to predict GSR using weather data from 41 stations in Saudi Arabia. Data from 31 stations was used in training the NN and the remaining data was used for testing. Input variables to the NN included 4 parameters: latitude, longitude, altitude and sunshine duration. Their sample data was not large enough to allow a credible comparison between the ANN models used and empirical regression models. (Lam et al., 2008) have used Artificial Neural networks ANNs to develop predication models for daily global solar radiation using measured sunshine duration for 40 cities covering 9 major thermal climatic zones and sub-zones in China. (Alam et al., 2009) used ANNs to estimate monthly mean hourly and daily diffuse solar radiation based on weather data from 10 Indian stations which have different climatic conditions. (Alawi & Hinai, 1998) applied ANNs to predict solar radiation in areas not covered by direct measurement instrumentation. The input data that was used for building the network were the location, month, mean pressure, mean temperature, mean vapor pressure, mean relative humidity, mean wind speed and mean duration of sunshine. (Tasadduq et al., 2002) have used neural networks for the prediction of hourly mean values of ambient temperature 24 hours in advance. Full year hourly values of ambient temperature are used to train a neural network model for a coastal location — Jeddah, Saudi Arabia. (Elminir et al., 2005) applied ANN modeling techniques to predict solar radiation data in different spectrum bands from data of meteorology for Helwan (Egypt) meteorology monitoring station. (Rehman & Mohandes, 2008) developed ANN-based estimation GSR for Abha city in Saudi Arabia; by