Showing papers by "Minia University published in 2017"

A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions

Abstract: The characteristics of photovoltaic array under partial shading comprises multiple local MPPs and one global. The classical maximum power point tracking (MPPT) algorithms can’t reach to global MPP. Accordingly, this work aims to study the behavior performance of two optimization techniques. They have been developed for extracting the global MPP from the partially shaded PVPS. The two studied techniques include Particle Swarm Optimization (PSO) and Cuckoo Search (CS). A comprehensive assessment of the two techniques has been carried out against a conventional algorithm of INR−based tracker. The tracking performances of PSO and CS based trackers are evaluated for different partial shading patterns based on MATLAB software. Results confirm that PSO and CS based trackers guarantee the convergence to the global MPP. Furthermore, they have the best performance in comparison with the conventional one. Additionally; the obtained results show that the CS−based tracker has superiority compared with PSO. The tracking time in case of CS−tracker is reduced compared to PSO in all the studied cases.

A compact double-sided MIMO antenna with an improved isolation for UWB applications

Abstract: This paper presents compact size 4 × 4 cm 2 MIMO antenna for UWB applications. The proposed antenna consists of four symmetric circular elements printed on low cost FR4 substrate with partial slotted ground plane. The two sides of the substrate are symmetric and each side is consisting of two radiators with the partial ground planes associated to the two other elements mounted on the other side. The two elements of the front side are orthogonal to the two other elements of the back side in order to increase the isolation between elements. For further reduction in the mutual coupling between elements, decoupling structures are presented in the top and bottom layers of the substrate. The simulated and measured results are investigated to study the effectiveness of the MIMO-UWB antenna. The results demonstrate the satisfactory performance of MIMO-UWB antenna, which has a return loss less than −10 dB from approximately 3.1 GHz to more than 11 GHz with an insertion loss lower than −20 dB through the achieved frequency band, and a correlation less than 0.002. Moreover, the proposed MIMO model exhibits a nearly omni-directional radiation pattern with almost constant gain of average value 3.28 dBi.

Challenges and opportunities for a European HVDC grid

Abstract: In 2009 the European governments committed their countries to a reduction in green-house-gas emissions up to 80% by 2050. Consequently, the employment of renewable energies has been strongly encouraged, making the upgrade of the existing AC grid necessary. Several associations are considering whether it would be more beneficial to build an HVDC grid instead. The main objective of the current study is to investigate possibilities and challenges in relation to the installation of a European HVDC grid. Most suitable technologies and possible configurations have been identified. Applications and existing projects of HVDC have been briefly mentioned. After examining various Supergrids models, financial aspects resulted challenging, since big investments are required. From a technical point of view, further research for DC voltage control and protection strategies is required. The publication of the HVDC Grid Codes represents a first step towards a European energy union; requirements are however not fully mandatory in all nations yet. The most probable development of the future HVDC grid has been defined: the grid is not expected to be built at once, but rather to develop in an organic way. In this sense, the installation of an offshore grid would be highly beneficial. Studies of possible supergrid architectures are still controversial and in part superficial. A solution to solve those lacks and achieve an agreement would be a strict collaboration between existing associations and involved European countries.

Potential of marine natural products against drug-resistant fungal, viral, and parasitic infections

Abstract: Antibiotics have revolutionised medicine in many aspects, and their discovery is considered a turning point in human history. However, the most serious consequence of the use of antibiotics is the concomitant development of resistance against them. The marine environment has proven to be a very rich source of diverse natural products with significant antibacterial, antifungal, antiviral, antiparasitic, antitumour, anti-inflammatory, antioxidant, and immunomodulatory activities. Many marine natural products (MNPs)—for example, neoechinulin B—have been found to be promising drug candidates to alleviate the mortality and morbidity rates caused by drug-resistant infections, and several MNP-based anti-infectives have already entered phase 1, 2, and 3 clinical trials, with six approved for usage by the US Food and Drug Administration and one by the EU. In this Review, we discuss the diversity of marine natural products that have shown in-vivo efficacy or in-vitro potential against drug-resistant infections of fungal, viral, and parasitic origin, and describe their mechanism of action. We highlight the drug-like physicochemical properties of the reported natural products that have bioactivity against drug-resistant pathogens in order to assess their drug potential. Difficulty in isolation and purification procedures, toxicity associated with the active compound, ecological impacts on natural environment, and insufficient investments by pharmaceutical companies are some of the clear reasons behind market failures and a poor pipeline of MNPs available to date. However, the diverse abundance of natural products in the marine environment could serve as a ray of light for the therapy of drug-resistant infections. Development of resistance-resistant antibiotics could be achieved via the coordinated networking of clinicians, microbiologists, natural product chemists, and pharmacologists together with pharmaceutical venture capitalist companies.

Direct Acting Anti-hepatitis C Virus Drugs: Clinical Pharmacology and Future Direction

Abstract: Chronic hepatitis C virus (HCV) infection is a leading cause of chronic liver disease. The introduction of direct acting antiviral agents (DAAs) for its treatment represents a major advance in terms of sustained virologic response (SVR) rates and adverse effect profiles. Mechanistically, DAAs inhibit specific HCV non-structural proteins (NS) that are vital for its replication. Boceprevir, telaprevir, simeprevir, asunaprevir, grazoprevir and paritaprevir are NS3/4A inhibitors. Ombitasvir, ledipasvir, daclatasvir, elbasvir and velpatasvir are NS5A inhibitors. Sofosbuvir and dasabuvir are NS5B inhibitors. Currently, a combination of two or more DAAs is the corner stone for the treatment of HCV infection. However, the success of DAA therapy is facing several challenges, including the potential of drug-drug interactions and resistant variance. Moreover, the shortage of relevant clinical pharmacological data and drug interaction regarding DAA is a clinical concern. The present review discusses the clinical pharmacology of DAAs with special emphasis on drug-drug interaction.

Swarming behaviour of salps algorithm for predicting chemical compound activities

Abstract: The increasing size of chemical search space of chemical compound databases and importance of similarity measurements to drug discovery are main factors in chem.-informatics research. This paper introduces a swarming behavior of salps algorithm for predicting chemical compound activities. The salp optimization algorithm is proposed for chemical descriptor selection with three initialization (small, mixed and large). The K-nearest neighbor (KNN) was utilized for the fitness function of salps swarm optimization algorithm (SSOA) to choose a small number of features and achieve high classification accuracy. Experimental results reveal the capability of SSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. A set of assessment indicators are used to evaluate and compared with different algorithms inclduing particle swarm optimization (PSO), Grasshopper Optimization Algorithm (GOA), Grey Wolf Optimizer(GWO), Sine Cosine Algorithm (SCA), Whale Optimization algorithm (WOA) using three initialization method and a superior accuracy was obtained with our proposed approach. Also, in comparison with other algorithms that used the same data, our approach has a higher performance using less number of features. The previous algorithms (GOA, GWO, PSO, SSA, SCA, WOA) are compared and three different methods are used to initialize the different optimization algorithms to ensure capability of the different optimizers to converge from different initial positions namely mixed initialization, small initialization, and large initialization.

A survey on behavioral biometric authentication on smartphones

Abstract: Recent research has shown the possibility of using smartphones’ sensors and accessories to extract some behavioral attributes such as touch dynamics, keystroke dynamics and gait recognition. These attributes are known as behavioral biometrics and could be used to verify or identify users implicitly and continuously on smartphones. The authentication systems that have been built based on these behavioral biometric traits are known as active or continuous authentication systems. This paper provides a review of the active authentication systems. We present the components and the operating process of the active authentication systems in general, followed by an overview of the state-of-the-art behavioral biometric traits that used to develop an active authentication systems and their evaluation on smartphones. We discuss the issues, strengths and limitations that associated with each behavioral biometric trait. Also, we introduce a comparative summary between them. Finally, challenges and open research problems are presented in this research field.

IFN-λ3, not IFN-λ4, likely mediates IFNL3 – IFNL4 haplotype–dependent hepatic inflammation and fibrosis

Abstract: Genetic variation in the IFNL3-IFNL4 (interferon-λ3-interferon-λ4) region is associated with hepatic inflammation and fibrosis. Whether IFN-λ3 or IFN-λ4 protein drives this association is not known. We demonstrate that hepatic inflammation, fibrosis stage, fibrosis progression rate, hepatic infiltration of immune cells, IFN-λ3 expression, and serum sCD163 levels (a marker of activated macrophages) are greater in individuals with the IFNL3-IFNL4 risk haplotype that does not produce IFN-λ4, but produces IFN-λ3. No difference in these features was observed according to genotype at rs117648444, which encodes a substitution at position 70 of the IFN-λ4 protein and reduces IFN-λ4 activity, or between patients encoding functionally defective IFN-λ4 (IFN-λ4-Ser70) and those encoding fully active IFN-λ4-Pro70. The two proposed functional variants (rs368234815 and rs4803217) were not superior to the discovery SNP rs12979860 with respect to liver inflammation or fibrosis phenotype. IFN-λ3 rather than IFN-λ4 likely mediates IFNL3-IFNL4 haplotype-dependent hepatic inflammation and fibrosis.

A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection

Abstract: To overcome the curse of dimensionality problem, a binary variant of the whale optimization algorithm (bWOA) with V-shaped is proposed. A hyperbolic tangent function is employed as a fitness function for mapping the continuous values to binary ones. Feature selection (FS) has attracted much attention in recent years and played a critical role in dealing with high-dimensional problems and can be modeled as an optimization problem. Eleven datasets from UCI repository from various applications are used. During the experiments, the effectiveness of feature selection is tested via a different type of data and size of features in the generic dataset. Furthermore, Wilcoxons rank-sum nonparametric statistical test was carried out at 5% significance level to judge whether the results of the proposed algorithms differ from those of the other compared algorithms in a statistically significant way. The quantitative and qualitative results revealed that the proposed binary algorithm in the FS domain is capable of minimizing the number of selected features as well as maximizing the classification accuracy within an appropriate time.

Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems

Abstract: Recently, with the stringent environmental regulations and shortage fossil-fuel reserve, power generation based on renewable energy sources is seen as a promising solution for future generation systems. A combination of these sources with an optimized configuration can face the climate change obstacles, permit better reliability, and reduce the cost of the generated energy. This paper presents a proposed particle swarm optimization (PSO) algorithm for an optimized design of grid-dependent hybrid photovoltaic-wind energy systems. This algorithm uses the actual hourly data of wind speeds, solar radiation, temperature, and electricity demand in a certain location. The PSO algorithm is employed to obtain the minimum cost of the generated energy while matching the electricity supply with the local demand with particular reliability index. The algorithm has been tested by considering a real case study used the actual situation to supply the electricity demand from utility grid at electricity market prices to estimate how significant are the cost saving compared to the actual situation costs. Results showed that the proposed algorithm responds well to changes in the system parameters and variables while providing a reliable sizing solution.

Simulation of global MPPT based on teaching–learning-based optimization technique for partially shaded PV system

Abstract: The power against voltage curve for PV power system during shadow condition contains number of local maximum power points (MPPs) and only one global. The classical maximum power point tracking (MPPT) algorithms are designed to follow the global MPP, but they stuck around local MPPs such as fuzzy logic controller (FLC). Therefore, A global MPPT based on teaching–learning-based optimization (TLBO) algorithm has been presented in this paper. The performance of PV system under abnormal conditions such as partial shading has been improved. TLBO algorithm is simple computational steps and faster convergence to optimal solution. A comprehensive assessment of TLBO-based tracker is carried out against FLC and particle swarm optimization (PSO) techniques for same conditions. Six different partial shading patterns have been employed to investigate TLBO performance using MATLAB/Simulink. The parameters of comparison include the tracking speed and overall tracking efficiency. The results confirm that TLBO-based tracker exactly convergence to global MPP under different studied cases. TLBO has best performance compared to the other studied techniques. The tracking speed is increased using TLBO-based tracker; the average tracking time of global MPP is reduced by more than 23.8 % compared with PSO in all studied different partial shading patterns.

Recent advances to accelerate re-endothelialization for vascular stents:

Abstract: Cardiovascular diseases are considered as one of the serious diseases that leads to the death of millions of people all over the world. Stent implantation has been approved as an easy and promising way to treat cardiovascular diseases. However, in-stent restenosis and thrombosis remain serious problems after stent implantation. It was demonstrated in a large body of previously published literature that endothelium impairment represents a major factor for restenosis. This discovery became the driving force for many studies trying to achieve an optimized methodology for accelerated re-endothelialization to prevent restenosis. Thus, in this review, we summarize the different methodologies opted to achieve re-endothelialization, such as, but not limited to, manipulation of surface chemistry and surface topography.

Porous Inorganic Drug Delivery Systems-a Review.

Abstract: Innovative methods and materials have been developed to overcome limitations associated with current drug delivery systems. Significant developments have led to the use of a variety of materials (as excipients) such as inorganic and metallic structures, marking a transition from conventional polymers. Inorganic materials, especially those possessing significant porosity, are emerging as good candidates for the delivery of a range of drugs (antibiotics, anticancer and anti-inflammatories), providing several advantages in formulation and engineering (encapsulation of drug in amorphous form, controlled delivery and improved targeting). This review focuses on key selected developments in porous drug delivery systems. The review provides a short broad overview of porous polymeric materials for drug delivery before focusing on porous inorganic materials (e.g. Santa Barbara Amorphous (SBA) and Mobil Composition of Matter (MCM)) and their utilisation in drug dosage form development. Methods for their preparation and drug loading thereafter are detailed. Several examples of porous inorganic materials, drugs used and outcomes are discussed providing the reader with an understanding of advances in the field and realistic opportunities.

Double-blind randomized controlled trial of letrozole versus clomiphene citrate in subfertile women with polycystic ovarian syndrome.

Abstract: STUDY QUESTION: Would letrozole as a primary ovulation induction agent generate better pregnancy rates than clomiphene citrate (CC) in subfertile women with anovulatory polycystic ovarian syndrome (PCOS)? SUMMARY ANSWER: Participants receiving letrozole as a primary treatment achieved a significantly (P = 0.022) higher clinical pregnancy rate per patient (61.2%) compared to CC (43.0%). WHAT IS KNOWN ALREADY: According to a recent Cochrane systematic review (2014), letrozole appears to improve live-birth (LB) and pregnancy rates in anovulatory women with PCOS, compared to CC. However, the review concluded that the quality of evidence was low due to poor reporting of study methods and possible publication bias. STUDY DESIGN, SIZE, DURATION: This double-blind randomized controlled trial (RCT) included 159 participants between April 2007 and June 2014. Subjects were randomly allocated to either CC (n = 79) or letrozole (n = 80) in a 1:1 ratio. Both drugs were encapsulated to look identical. Randomization was performed in mixed blocks and stratified by patients’ BMI (<30 and 30–35 kg/m2). PARTICIPANTS/MATERIALS, SETTING, METHODS: The trial included subfertile women diagnosed with PCOS. Treatment started with one tablet (CC 50 mg, letrozole 2.5 mg) increasing to two in non-responders and continuing until pregnancy or for up to six ovulatory cycles. Non-responders were crossed over to the other treatment after a 6-week break. Cycles were initially monitored with ultrasound follicle tracking then mid-luteal serum progesterone measurement in subsequent cycles. MAIN RESULTS AND THE ROLE OF CHANCE: Amongst the 159 participants included in the intention-to-treat analysis, four women conceived before treatment and six were lost-to-follow-up. The remaining 149 participants (74 on CC and 75 on letrozole) completed at least the first treatment. Women receiving letrozole achieved a significantly (P = 0.022; absolute difference [95% confidence interval] 18% [3–33%]) higher pregnancy rate (61.%) than those on CC (43%). The median number of treatment cycles received until pregnancy was significantly (log rank P = 0.038) smaller with letrozole (4[3–5] cycles) compared to CC (6[4–7] cycles). LB rates were not statistically (P = 0.089) different between the two groups, although there was a trend towards higher rates on letrozole (48.8%) compared to CC (35.4%). After the crossover, pregnancy and LB rates on letrozole (n = 45; 28.9 and 24.4%, respectively) were not statistically (P = 0.539 and P = 0.601) different from CC (n = 31; 22.6 and 19.4%). LIMITATIONS, REASONS FOR CAUTION: One possible limitation of this trial may be the exclusion of PCOS women with BMI > 35 kg/m2, which would limit the applicability of the results in this subgroup of PCOS. However, this group of women are generally excluded from treatment in the majority of fertility centres, especially in Europe, due to the associated challenges and risks. WIDER IMPLICATIONS OF THE FINDINGS: The results of this trial are consistent with the recent Cochrane systematic review. However, with its robust design, the current RCT provides more valid and compelling evidence for the superiority of letrozole over CC as a primary ovulation induction agent in PCOS women with 40% increase in pregnancy rates and with a shorter time-to-pregnancy. Furthermore, the participants in this RCT are a good representation of subfertile PCOS population receiving fertility treatment in Europe and worldwide. The results are therefore globally generalizable for clinical practice. STUDY FUNDING/COMPETING INTEREST(S): This RCT was mainly funded by the R&D Funding Scheme of Derby Hospitals NHS Foundation Trust. The study also received funds from School of Medicine, University of Nottingham. The Trust R&D department was involved in the development of the protocol and the running of the trial. The trial was sponsored and monitored by the University of Nottingham. The authors have no conflicts of interest. TRIAL REGISTRATION NUMBER: www.Clinicaltrials.gov: NCT00478504. TRIAL REGISTRATION DATE: Registration was verified on 23/05/2007. DATE OF FIRST PATIENT’S ENROLMENT: 25/04/2007.

Sofosbuvir-based treatment regimens: real life results of 14 409 chronic HCV genotype 4 patients in Egypt.

Abstract: SummaryBackground Chronic hepatitis C virus infection is one of the most important health problems in Egypt. The Ministry of Health's National Treatment Programme introduced sofosbuvir-based therapy in October 2014. Aim To assess the clinical effectiveness and predictors of response to SOF-based treatment regimens, either dual therapy, with SOF/ribavirin (RBV) for 6 months or triple therapy with SOF/peg-IFN-alfa-2a/RBV for 3 months, in a cohort of patients treated in National Treatment Programme affiliated centres in Egypt. Methods Between October 2014 and end of 2014, patients who were eligible for treatment were classified according to their eligibility for interferon therapy: Group 1 (interferon eligible) were treated with triple therapy for 12 weeks and Group 2 (interferon ineligible) were treated with dual therapy for 24 weeks. Difficult to treat patients included those with F3-F4 on Metavir score, Fib-4 >3.25, albumin ≤3.5, total Bilirubin >1.2 mg/dL, INR >1.2 and platelet count <150 000 mm3. Results Twelve weeks post-treatment data were available on 14 409 patients; 8742 in group 1 and 5667 in group 2. In group 1, the sustained virological response at week 12 (SVR12) was 94% and in group 2 the SVR12 was 78.7%. Multivariate logistic regression analysis in which treatment failure is the dependent variable was done. Male gender, being a difficult to treat patient and previous interferon therapy were significant predictors of nonresponse in both treatment groups. Conclusion Results of sofosbuvir-based therapies in Egypt achieved similar rates of SVR12 as seen in phase III efficacy studies.

ZrO2 nanofibers/activated carbon composite as a novel and effective electrode material for the enhancement of capacitive deionization performance

Abstract: Among the various forms of carbon materials, activated carbon still possesses the maximum attention as an optimum commercially available, cheap, and effective electrode material for the capacitive deionization desalination process. However, the well-known hydrophobicity and low specific capacitance limit its wide application. In this study, incorporation of zirconia nanofibers with activated carbon is reported as an effective and simple strategy to overcome the abovementioned problems. Typically, zirconia nanofibers, which were synthesized by the calcination of electrospun nanofiber mats, were added to the activated carbon to fabricate novel electrodes for the capacitive deionization units. In a single-mode cell, it was observed that the addition of the proposed metal oxide nanofibers distinctly enhanced the desalination process as the electrosorption capacity and the salt removal efficiency improved from 5.42 to 16.35 mg g−1 and from 16.37% to 53.26% for the pristine and composite electrodes, respectively. However, the inorganic nanofiber content should be optimized; a composite having 10 wt% zirconia nanofibers with respect to the activated carbon showed the best performance. This distinct enhancement in the performance is attributed to the improvement in the wettability and specific capacitance of the electrode. Numerically, the water contact angle and the specific capacitance of the pristine and composite electrodes were found to be 145° and 26.5°, and 875 and 225 F g−1, respectively. Overall, the present study strongly draws attention towards zirconia nanostructures as effective, cheap, environmentally friendly, and biologically safe candidates to enhance the performance of capacitive deionization electrodes.

Quantitative Determination of Cannabinoids in Cannabis and Cannabis Products Using Ultra‐High‐Performance Supercritical Fluid Chromatography and Diode Array/Mass Spectrometric Detection

Abstract: Ultra-high-performance supercritical fluid chromatography (UHPSFC) is an efficient analytical technique and has not been fully employed for the analysis of cannabis. Here, a novel method was developed for the analysis of 30 cannabis plant extracts and preparations using UHPSFC/PDA-MS. Nine of the most abundant cannabinoids, viz. CBD, ∆8 -THC, THCV, ∆9 -THC, CBN, CBG, THCA-A, CBDA, and CBGA, were quantitatively determined (RSDs < 6.9%). Unlike GC methods, no derivatization or decarboxylation was required prior to UHPSFC analysis. The UHPSFC chromatographic separation of cannabinoids displayed an inverse elution order compared to UHPLC. Combining with PDA-MS, this orthogonality is valuable for discrimination of cannabinoids in complex matrices. The developed method was validated, and the quantification results were compared with a standard UHPLC method. The RSDs of these two methods were within ±13.0%. Finally, chemometric analysis including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to differentiate between cannabis samples.

Central and peripheral control of food intake.

Abstract: The maintenance of the body weight at a stable level is a major determinant in keeping the higher animals and mammals survive. Th e body weight depends on the balance between the energy intake and energy expenditure. Increased food intake over the energy expenditure of prolonged time period results in an obesity. Th e obesity has become an important worldwide health problem, even at low levels. The obesity has an evil effect on the health and is associated with a shorter life expectancy. A complex of central and peripheral physiological signals is involved in the control of the food intake. Centrally, the food intake is controlled by the hypothalamus, the brainstem, and endocannabinoids and peripherally by the satiety and adiposity signals. Comprehension of the signals that control food intake and energy balance may open a new therapeutic approaches directed against the obesity and its associated complications, as is the insulin resistance and others. In conclusion, the present review summarizes the current knowledge about the complex system of the peripheral and central regulatory mechanisms of food intake and their potential therapeutic implications in the treatment of obesity.

NeuroPlace: Categorizing urban places according to mental states

Abstract: Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture.