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Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.

Estimating the state-of-charge of a lead-acid battery at remote seismic nodes is a key factor in managing the available power. Optimal management enables the continuous acquisition of seismic data of an area. This paper presents the management of lead-acid batteries at remote seismic nodes, using the Neural Network model's historical data to estimate the battery's state-of-charge. Powersim (PSIM) simulation tool was used to implement photovoltaic energy harvesting system with a buck mode converter and maximum power point tracking algorithm to acquire historical data. A backpropagation neural network technique for training the historical dataset of hourly points in 500 days on the Matlab platform is adopted, and a feedforward neural network is employed due to the irregularities of the input data. The neural network model's hidden layer contains the transfer function of the Tansig Function to produce the model output of state-of-charge estimations. Besides, this paper is based on the management of estimating the state-of-charge of the lead-acid battery near-realtime instead of relying on the vendor's lifecycle information. The simulated results show the simplicity and optimal estimations of state-of-charge of the lead-acid battery with RMSE of 0.023%.

Introduction: Chronic kidney disease (CKD) is the progressive and irreversible loss of kidney function. It exposes to many complications, among which, digestive complications. In Mali, we do not have data on the prevalence of digestive pathologies in people with chronic renal failure, hence the interest of this study. Objective: To determine the prevalence of digestive pathologies and to describe their manifestations during chronic renal failure. Patients and Methods: This was a prospective cross-sectional study conducted from September 2016 to August 2017, a period of 12 months. Included were patients hospitalized in our department with CKD who received digestive endoscopy and/or liver serology. Results: Seventy-one patients underwent digestive endoscopy with oesogastroduodenal fibroscopy (60 patients), rectoscopy (6 patients) and anoscopy (5 patients), i.e. 15.9% of those hospitalized. The mean age of the patients was 48 ± 14 years with extremes of 15 and 84 years. The sample consisted of 59.2% men versus 40.8% women, for a sex ratio of 1.5. The functional signs are in order of frequency: vomiting (72.4%), anorexia (51.3%) and epigastralgia (48.7%). Terminal CKD by creatinine clearance accounted for 88.2% of cases, of which 47.4% were monitored by hemodialysis. Hepatitis C virus infection was present in 21.3% of cases, hepatitis B (18%) and HIV (7.5%). Endoscopic examinations were represented by fibroscopy (84.5%), rectoscopy (8.5%) and anuscopy (7%). The fibroscopic lesions were respectively gastric (96.8%), duodenal (14.1%) and esophageal (12.5%). They were dominated by gastritis (40.5%), duodeno-gastric reflux (16.4%), pyloric gap (12.6%). Rectoscopy found 4 cases of hemorrhoids, 2 cases of rectitis and no lesions were observed at anuscopy. Conclusion: The prevalence of these digestive manifestations and the endoscopic lesions encountered indicate the importance of digestive endoscopy and the performance of hepatic serologies in chronic renal failure patients with digestive symptoms and/or treated by hemodialysis.

Remote area electrification is a crucial need in sub-Saharan Africa’s drive to attain universal electrification. In Sierra Leone, with a rural population of over 5 million, the electrification rate accounts for less than 10% of the total inhabitants. This paper presents a comparative techno-economic analysis carried out to determine the most feasible of four individual options for off-grid mini-grid power generation system utilizing sources that include: Solar Photo Voltaic (SPV), Diesel Generator (DG), and Battery Storage (BS) system, to provide electricity for a rural and remote village located in the northwestern part of Sierra Leone (longitude 9.1°W and latitude 12.6°N), with an average daily solar irradiance between 4.6 and 6 kWh/m2/day. An assessment of the total electrical load estimated an expected daily consumption of 178 kWh. Simulation, optimization, and sensitivity analyses of each one of the individual power generation systems were carried out using HOMER software. Economic parameters such as Cost of Capital (CC), Net Present Cost (NPC), Levelized Cost of Electricity (LCOE)), technical parameters (energy production characteristics), and greenhouse gases emissions were compared and analyzed. Key findings from the simulation result indicate that systems consisting of DG only ($29,750) and SPV/BS ($110,131) obtained the least and highest CC, respectively. Similarly, in a respective manner, the highest and least NPC were obtained for systems with DG only ($496,336) and PV/DG/BS ($152,491) over a 25-year project lifetime. Furthermore, the least LCOE was obtained for the system comprising PV/DG/BS ($0.336/kWh). With an expected annual generation of (75,121 kWh), PV/DG/BS was obtained to be the most optimal solution. The sensitivity analysis observed that a reduction in the discount rate consequently reduces the LCOE of such a system. Furthermore, the model accounts for a 90% renewable energy fraction, with a significant reduction in the amount of annual GHG emissions, when compared with a generation system using diesel generator only.

The authors have requested that this preprint be removed from Research Square.

In 2016, Mali reported a bacterial meningitis outbreak consisting of 39 suspected cases between epidemiologic weeks 9 and 17 with 15% case fatality ratio in the health district of Ouéléssebougou, 80 kilometers from the capital Bamako. Cerebrospinal fluid specimens from 29 cases were tested by culture and real-time polymerase chain reaction; 22 (76%) were positive for bacterial meningitis pathogens, 16 (73%) of which were Neisseria meningitidis (Nm). Of the Nm-positive specimens, 14 (88%) were N meningitidis serogroup C (NmC), 1 was NmW, and 1 was nongroupable. Eight NmC isolates recovered by culture from the outbreak were characterized using whole genome sequencing. Genomics analysis revealed that all 8 isolates belonged to a new sequence type (ST) 12446 of clonal complex 10217 that formed a distinct clade genetically similar to ST-10217, a NmC strain that recently caused large epidemics of meningitis in Niger and Nigeria. The emergence of a new ST of NmC associated with an outbreak in the African meningitis belt further highlights the need for continued molecular surveillance in the region.

Global concerns over the damage caused to the environment by fossil fuels, as well as the instability of oil prices, has led to an increase interest in alternate electric power generation from renewable sources such as the sun, wind and geothermal energy. Solar energy is produced by solar panels which are made of materials having the capacity to create a flow of electric carriers when exposed to the solar irradiance. The efficiency of these panels depends on the sun light, and they cannot work at their maximum power point independently, hence, requiring Maximum Power Point Tracker (MPPT). This MPPT permits to compensate the changes in irradiance and temperature conditions and variations in load demands which cause power imbalances. As such, a photovoltaic system with supercapacitor energy buffer is designed using Psim software. The system consists of a photovoltaic array, a MPPT, a supercapacitor, and a charge controller which is to effectively balance and regulate power in the system.

Due to human influence and its negative impacts on the world's environment, the world is changing into a cleaner and more sustainable energy system. In both private and public buildings, there is a desire to reduce electricity usage, automate appliances, and optimize the electricity usage of a building. This paper presents the design and implementation of a secured smart home switching system based on wireless communications and self-energy harvesting. The proposed secured smart home switching system integrates access control of the building's electricity, energy harvesting, and storage for the active electronic components and circuitries, and wireless communication for smart switches and sockets. The paper gives two contributions to the design of smart home systems: 1) A practical design and implementation of security (access control system) for a building's power supply which adds a locking feature such that only authorized personnel are capable of altering the power state of the smart sockets and switches in a building, and; 2) A model of energy harvesting and storage system for the active electronic components of the circuitries and wireless communication for smart switches and sockets. The access control involves four stages (a control unit, a comparator unit, a memory unit, and the switching unit). The access control system provides means of access control by having a security keypad that switches ON or OFF the building's electricity, provided the user knows the security pin code (8 coded pins). The proposed system also harvests and stores energy for all the active electronic devices using a photovoltaic system with ultracapacitor energy buffer. The designed secured smart home utilized smart power and switches, and message queuing telemetry transport for ease of controlling energy usage. The experimental results obtained from extensive testing of the prototype shows an improvement in security and energy management in a building.

Recent surveys in the energy harvesting system for seismic nodes show that, most often, a single energy source energizes the seismic system and fails most frequently. The major concern is the limited lifecycle of battery and high routine cost. Simplicity and inexperience have caused intermittent undersizing or oversizing of the system. Optimizing solar cell constraints is required. The hybridization of the lead-acid battery and supercapacitor enables the stress on the battery to lessen and increases the lifetime. An artificial neural network model is implemented to resolve the rapid input variations across the photovoltaic module. The best performance was attained at the epoch of 117 and the mean square error of 1.1176e-6 with regression values of training, test, and validation at 0.99647, 0.99724, and 0.99534, respectively. The paper presents simulations of Nsukka seismic node as a case study and to deepen the understanding of the system. The significant contributions of the study are (1) identification of the considerations of the PV system at a typical remote seismic node through energy transducer and storage modelling, (2) optimal sizing of PV module and lead-acid battery, and, lastly, (3) hybridization of the energy storage systems (the battery and supercapacitor) to enable the energy harvesting system to maximize the available ambient irradiance. The results show the neural network model delivered efficient power with duty cycles across the converter and relatively less complexities, while the supercapacitor complemented the lead-acid battery and delivered an overall efficiency of about 75 % .

The proposed system used to design a Fuzzy logiccontroller system based intelligent power quality conditioner is consist both the series and shunt active filter using a common DC link capacitors. The proposed system used to eliminate the harmonics, voltage which is developed because of fault which causes failure of the power system. Intelligent controllers are used in nonlinear control system mostly. The proposed fuzzy controller is not in the need of a mathematical model and the produce a good dynamic response. The simulations carried out using the MATLAB software. The real time implementation of the proposed system is carried out with the use of power electronic components. The proposed system simulation and hardware results are similar to each other. The power quality conditioner is able to improve the power quality on power systems in industrial and in the distribution area.

A seismometer is a relatively low duty electronic device that needs a reliable and continuous amount of electrical energy for it to perform optimally. Solar module deployed across seismic nodes have low efficiency, particularly among Nigerian National Network of Seismographic Stations (NNNSS) nodes. This brings about a disproportional plan and overkilling of installation of an energy harvesting system at the seismic node. This work intends to optimize parameters of Maximum Power Point Tracking and delivers a sizeable, cost-efficient, and reliable energy harvesting system at seismic nodes. This will avoid wastage of electrical energy and enable a frugal budget of energy harvesting system at a seismic node. The paper intends to present a solar cell model of a single diode and the simulations with NNNSS nodes environmental parameters. An algorithm of Maximum Power Point Tracking which sits in the power converter and performs iterations on the outputs from the solar energy transducer. That will recognize the appropriate duty cycles, deliver maximum power, and supply require voltage at the node. This work intends to identify the constraints of the Maximum Power Point Tracking techniques and optimize them. This will enhance the operations of seismic instrumentation to create a continuous seismic database without gaps. The results prove that the algorithm can hugely improve the efficiency of solar energy harvesting system.

Recent advancement in wireless technology enables free transmission between devices and automatic operation within the home areas. In this paper, we develop, design and implement an optical pulse transmitter and receiver systems to create the real ubiquitous communication link. To show the reality of the design, the optical pulse transceiver system is configured to allow detection of an intruder within a secured laboratory in Botswana International University of Science and Technology (BIUST) using a wireless link. In implementing the design, two sets of circuits, a transmitter circuit for transmitting information in the form of a signal from the source input and a receiver circuit for receiving the transmitted signal were designed. We achieved this by integrating analog component on two circuit boards. The system was simulated using Multisim, and simulation results were per the design specifications.

The optimization of 2D ultrasound arrays can be efficiently realized through the use of simulated annealing (SA) as reported in many recent works. The main limitation of SA resides in the amount of resources required and the optimization time. In this work, the application of the genetic algorithms (GA) in 2D arrays optimization is presented to evaluate the potential of these algorithms compared to the simulated annealing in terms of processing time and output beam profiles. GA have been used in the optimization of multivariable cost functions in many applications as power systems analysis, wireless sensor arrays in telecommunication and they provided satisfactory results in a relative short time. The 2D ultrasound arrays optimization can also be formulated in a similar way as the problems occurring in these domains, making genetic algorithms a potential candidate to solve them. Although GA are reported to not be as efficient as SA in the optimization of medium and large 2D arrays, the aim of this paper is to evaluate their real ability when compared to the SA. Preliminary results showed a fast convergence time of genetic algorithm for comparable acoustical properties for the optimized arrays compared to SA.

A hidden node problem occurs when two different cluster heads communicate with each other. The cluster head may always establish bidirectional connections to achieve another node in a different cluster from the first nongroup node. In the correspondence between two unique clusters, the occurrence of a hidden node cannot be recognized. The intercluster link management problem occurs in the system because of the hidden node. The intercluster grouping method is proposed to eliminate the hidden node problem when there is correspondence between two unique clusters. The cluster head initiates the proposed method by the grouping method. The Group Join Request (GJREQ) is sent by each cluster head to the other cluster, which is the nongroup member. The GJREQ is received by the nongroup member in the cluster, which promotes the request to other bidirectional nodes. This request is sent until it comes to the cluster head. The cluster head assigns a response in the affirmative and sends the information to the source node. The neighbor groups are points of interest that are used for sending and receiving the information between the clusters.

This article presents a feasibility of standalone energy system comprising of wind and solar energy systems have been studied in order to implement in Tuludimtu town. In doing so Tuludimtu has been selected as a case site; consequently the wind speed (at 25 m) and the estimated solar radiation data of this town has been analyzed in such a way that, it would be easy for using HOMER software. The results show that; the wind energy potential is reasonable, with average wind speeds of approximately 5.15 m/s. However; the solar energy potential of the site can be classified as excellent as in the town the daily average solar irradiation is more than 6.24 kWh/m2/day. As a result the cost of energy of the designed energy system is found to be in the range of sh.35/kWh to sh.39/kWh which makes the system competitive in the Ethiopian’s energy system.

International audience; The role of 2D matrix arrays in the realization of real time 3D ultrasound imaging is crucial as the latter permit to acquire a complete volume. The lack of control systems for these arrays containing thousands of elements and the dimension of connection cables for such a number of elements are the main obstacles to the use of these technologies. The sparse array techniques present a solid candidate to solve these technological limitations but give rise to beam pattern deteriorations in terms of energy loss and unwanted lobes apparition. The irregular positioning of the array elements permits to drastically decrease the unwanted lobes while the energy loss may be compensated by increasing the element size. However, as elements get larger, the directivity decreases. To solve this directivity reduction, we propose to combine elements of different sizes in such a manner that a broad directivity is maintained using small elements whereas the energy loss may be compensated by wide ones. Additionally, an innovative simulated annealing based algorithm is used to refine the array beam profile when the active element number is set to 256. The results compared to those obtained by the basic non-grid array show significant improvement in the beam pattern.

The non-grid sparse array technique is a promising approach to overcome the connection difficulties of 2D matrix arrays and to partially compensate the energy loss linked to the element number reduction. Being independent from the spatial sampling conditions, this method leads to a significant improvement of the beam pattern when combined to the simulated annealing algorithm. However, in the previous version of this method [1], the position of the elements cannot be changed during the optimization. In order to add a further degree of freedom and thus to improve the optimization performance, we propose a new strategy in which both element position and apodization can be modified. This new approach improves the sensitivity thanks to a better distribution of the elements on the array footprint.

In this study, the benefits of integrating a realistic acoustic simulation in the 2-D non-grid sparse array design are investigated. A wideband shape sensitive (WSS) energy function is introduced. New degrees of freedom in 2-D arrays transducer optimization are thus made available. A related consequence is the capability of distinguishing probes configurations so far considered as performing identically. To clearly illustrate this, we compare the radiated patterns of two 5×5 elements array probes having elements with the same rectangular size and position, but perpendicular orientations. Under linear assumption, an ergonomic data management, here named Fixy method, enables the radiated pattern computation during the optimization process thanks to a 4.8 times speed-up. Finally two optimized probes are compared with an equivalent size full-array. One was optimized using the omnidirectional monochromatic model and the other one using the proposed WSS energy function. To consider the same acoustic radiation of the probe during the optimization as in reality yields promising results.

In this paper a voltage quadrupler dc-dc converter with coupled inductor and π filter is presented. The use of the coupled inductor reduces the high leakage inductance which is present in a transformer enabled converter. The output ripples in the converter is reduced by providing a π filter. The interleaved voltage quadrupler is used in this system in order to boost the output voltage. The voltage multiplier improves the output voltage gain. The main advantage of this system is more voltage gain when compared with the transformer eneabled circuit and the overall efficiency of the system is improved. The circuit is simple to control. As a final point of this research, the simulation and the hardware investigational results are presented to demonstrate the effectiveness of this proposed converter.

International audience; Three-dimensional imaging with 2-D matrix probes is one of the most exciting recent ultrasound innovations. Unfortunately, the number of elements of a 2-D matrix probe is often very high, and reducing this number deteriorates the beam properties. In this paper, we propose a new sparse-array design technique with irregular element positioning, which significantly reduces the number of active elements as well as the grating-lobe level. In particular, we introduce a new cost function for optimizing the weighting coefficients of the elements and a new annealing-based algorithm to compute the lowest-cost solutions. Numerical simulations show substantial improvements over standard sparse arrays.

2D matrix probes present a real capability for three dimensional ultrasound imaging. The main difficulties to implement these probes come from the need of using a huge number of elements and from the grating lobes linked to the element periodical disposition and from the sidelobes arising when this number is reduced. In this study, a new design technique is proposed and shown capable of considerably reducing the probe element number as well as the grating lobes and sidelobes level. The grating lobes, in particular, are reduced by placing the elements without any grid consideration. The sidelobes and the number of active elements are optimized by the simulated annealing. Using the new approach we reduce the element number by 28% (from 235 to 169) and obtain a grating lobe reduction by -10 dB and -20 dB in lateral and elevation direction, respectively.

In this paper we present a preliminary study of a 2D sparse array design technique. The probe is intended to be suitable for needle tacking during hepatic biopsy and therapy applications. It can also be used in other micro-tools or internal organs operations. The probe is composed of 1024 elements (64x16). Due to this huge number of elements, the sparse array technique is used to reasonably reduce this number and to make possible its connection to the recent scanners. The simulated annealing algorithm permits to optimize elements coefficients and their positions to have a better beam pattern. The features of the probe must satisfy the geometrical constraints imposed by the targeted applications as well as some users defined imaging characteristics. Several simulations are made to know the acoustical characteristics of the array and its convenience to needle detection operations. Combining sparse array and optimization algorithm we reduce the initial element number to 267 with good imaging features: the sidelobes level is maintained to – 40 dB, the lateral main lobe width at -6 dB is 1.1 mm and the elevation main lobe width is 4.4 mm.