Search

This study investigates the impact of various pre-drying treatments and drying methods on the physicochemical, phytochemical, and sensorial quality of dried bananas. To achieve this goal, four pretreatment solutions were used before drying to enhance the quality characteristics of banana slices: citric acid 1% and ascorbic acid 1%, sucrose 10%, sodium bisulphate 0.5%, and lemon juice 5% and acetic acid 0.5%. Two drying methods were applied, sun drying (SD) and tunnel drying (TD). The dried slices were evaluated for moisture, total acidity, water activity (aw), browning index, rehydration ratio (RR), reducing sugar, ascorbic acid, total phenol, and DPPH %, as well as sensory attributes (color, odor, taste, texture, and overall acceptability). The results showed that the retention of acid content, ascorbic acid, total phenolics, and DPPH in slice samples dried in TD was higher than that in SD. The browning index values in the TD samples were lower than those in the SD samples. Moreover, the TD samples showed a higher RR than the SD samples. It is noted that the pretreatment solutions used have an important role in improving the final quality characteristics of dried banana slices. The results of the sensory evaluation revealed a significant difference in the degrees of color and texture for the slices pretreated with sodium bisulphate. Meanwhile, the rates of taste and odor were significantly higher for the sucrose-pretreated slices. TD can improve the drying kinetics, shorten the drying time, and maintain the quality of the dried slices. [ABSTRACT FROM AUTHOR]

Pomegranate is one of the most important and widely distributed trees. Boron and zinc are important nutrients for plant growth and fruit quality. Nanotechnology has emerged as one of the most innovative scientific fields in agriculture. This study was conducted to describe the changes in the physiochemical characteristics (weight, diameter, length, firmness and color), as well as the phytochemicals attributes (total phenolics, total flavonoids, ascorbic acid, anthocyanin and antioxidant %) and minerals contents, of pomegranates fruits of the 'Wonderful' cultivar as a result of spraying pomegranate trees using nanomaterials (zinc oxide (ZnONPs) and boron oxide (B2O3NPs)). In three successive developmental stages (full bloom, 6 weeks after full bloom and one month before harvest time), the trees were sprayed with 0.25, 0.5 and 1 g/L ZnONPs, as well as 0.25, 0.5 and 1 g/L B2O3NPs during the 2021 and 2022 seasons. The application of ZnONPs and B2O3NPs influenced the qualitative characteristics of the fruits in the studied seasons. The highest marketable % was observed for the 0.50 and 1 g/L ZnONPs and 1 g/L B2O3NPs compared to the other treatments. Also, a positive effect was recorded for the ZnONPs and B2O3NPs on the fruits' physical properties. All of the ZnONP and B2O3NP treatments resulted in increasing the total phenolic, flavonoid, anthocyanin and ascorbic acid contents and the antioxidant activity in the pomegranate juices. In conclusion, our results suggest that spraying pomegranate trees with ZnONPs and B2O3NPs improves the marketable fruit, enhances the fruit quality and increases the bioactive components and antioxidant activity. [ABSTRACT FROM AUTHOR]

The transformer health index (HI) concept has been used as an important part of management resources and is implemented for the state assessment and ranking of Power transformers. The HI state is estimated based on many power transformer oil parameters. However, the main problem in the HI procedure as a diagnostic method is the presence of routine measurements and accurate test results. The power transformer HI prediction is carried out in this work using 1361 dataset samples collected from two different utilities. The proposed model is used to predict and diagnose the HI state of the power transformer by using a convolutional neural network (CNN) approach. The imbalance between the training dataset sample classes produces a good prediction of the class with a major number of samples while a low detection of the class has a minor number of samples. The oversampling approach is used to balance the training samples to enhance the prediction accuracy of the classification methods. The proposed CNN model predicts the HI of the power transformers after applying the oversampling approach to the training dataset samples. The results obtained with the proposed CNN model are compared with those obtained with the optimized machine learning (ML) classification methods with the superiority of the CNN results. Feature reductions are applied to minimize testing time, effort, and costs. Finally, the proposed CNN model is checked with uncertain noise in full and reduced features of up to ±25% with a good prediction diagnosis of the power transformer HI. [ABSTRACT FROM AUTHOR]

Introduction. Shwachman-Diamond Syndrome (SDS) is an autosomal-recessive disorder characterized by neutropenia, pancreatic exocrine insufficiency, skeletal dysplasia, and an increased risk for leukemic transformation. Biallelic mutations in the SBDS gene have been found in about 90% of patients. The clinical spectrum of SDS in patients is wide, and variability has been noticed between different patients, siblings, and even within the same patient over time. Herein, we present two SDS siblings (UPN42 and UPN43) carrying the same SBDS mutations and showing relevant differences in their phenotypic presentation. Study aim. We attempted to understand whether other germline variants, in addition to SBDS, could explain some of the clinical variability noticed between the siblings. Methods. Whole-exome sequencing (WES) was performed. Human Phenotype Ontology (HPO) terms were defined for each patient, and the WES data were analyzed using the eVai and DIVAs platforms. Results. In UPN43, we found and confirmed, using Sanger sequencing, a novel de novo variant (c.10663G > A, p.Gly3555Ser) in the KMT2A gene that is associated with autosomal-dominant Wiedemann–Steiner Syndrome. The variant is classified as pathogenic according to different in silico prediction tools. Interestingly, it was found to be related to some of the HPO terms that describe UPN43. Conclusions. We postulate that the KMT2A variant found in UPN43 has a concomitant and co-occurring clinical effect, in addition to SBDS mutation. This dual molecular effect, supported by in silico prediction, could help to understand some of the clinical variations found among the siblings. In the future, these new data are likely to be useful for personalized medicine and therapy for selected cases. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in the elderly population. AMD is characterized in its late form by neovascularization (wet type) or geographic atrophy of the retinal pigment epithelium cell layer (dry type). Regarding the latter type, there is growing evidence supporting an association between the pathophysiology of dry AMD and key proteins in the complement cascade. The complement cascade works as a central part of the innate immune system by defending against foreign pathogens and modified self-tissues. Through three distinct pathways, a series of plasma and membrane-associated serum proteins are activated upon identification of a foreign entity. Several of these proteins have been implicated in the development and progression of dry AMD. Potential therapeutic targets include C1q, C3, C5, complement factors (B, D, H, I), membrane attack complex, and properdin. In this review, we provide an understanding of the role of the complement system in dry AMD and discuss the emerging therapies in early phase clinical trials. The tentative hope is that these drugs may offer the potential to intervene at earlier stages in dry AMD pathogenesis, thereby preventing progression to late disease. [ABSTRACT FROM AUTHOR]

Background: Diabetic retinopathy (DR) is a complication associated with uncontrolled DM. It is a leading preventable cause of visual impairment in the world and a cause of blindness in those under 75 years old in developing countries. We aimed to explore the prevalence and associated risk factors of DR among diabetic patients in the West Bank. Materials and Methods:A quantitative multicenter cross-sectional study was conducted in all West Bank cities. Nearly, 385 patients underwent a comprehensive eye examination in addition to blood and urine tests. A previously validated questionnaire for ocular examination classification was used together with a socio-demographic and past medical history information sheet. Results: The prevalence of all DR in the West Bank was 41.8%. The prevalence of non-proliferative diabetic retinopathy (NPDR) was 50.3% (38.5% for mild NPDR, 10.6% for moderate NPDR and 1.2% for severe NPDR). The prevalence of proliferative diabetic retinopathy (PDR) was 9.9% and 39.7% for diabetic macular edema (DME) (17.4% for mild, 15.5% for moderate and 6.8% for severe DME). The prevalence of vision-threatening PDR and DME was 49.7% for both. In a univariate analysis, DR was significantly associated with body mass index; BMI (p = 0.035), DM duration (p = 0.002), Low-density lipoprotein (LDL) (p = 0.034), glutamic-oxaloacetic transaminase (GOT) level (p = 0.016) andblood urea (BU) (p = 0.044). A multivariate analysis showed a strong significant association between DR andpatients who had DM for 10-19years (adjusted odds ratio; AOR (95%CI); 1.843 (1.05–3.22)), abnormal levels of LDL (AOR (95%CI); 0.50 (0.30–0.83)), abnormal levels of GOT (AOR (95%CI); 0.49 (0.27–0.89)), and overweight (AOR (95%CI); 0.39 (0.19–0.80)). Conclusions: We found that the prevalence of DR in Palestine was higher than the global prevalence. Referral coordination between ophthalmologists and internal physicians is necessary to better follow up with DR patients. An interventional educational program by clinicians and public health professionals is recommended. [ABSTRACT FROM AUTHOR]

Nowadays, water pumping systems based on photovoltaics as a source of electricity have widely increased. System cost and efficiency still require enhancement in order to spread their application. Perovskite solar cells (PSCs) are the most hopeful third-generation photovoltaic for replacing the silicon-based photovoltaic thanks to their high power conversion efficiency, reaching 25.8%; tunable band-gap; long diffusion length; low fabrication temperature; and low cost. In this work, for the first time, we proposed a high-power-density hybrid perovskite solar cell thermoelectric generator (TEG) array for feeding a synchronous reluctance motor (SynRM) driving a water pump for use in an irrigation system. A control technique was used to achieve two functions. The first function was driving the motor to obtain the maximum torque/ampere. The second was harvesting the maximum perovskite solar cell array output power on the basis of the maximum power point tracking (MPPT) algorithm using the perturbation and observation approach. Thus, the proposed hybrid perovskite solar cell–thermoelectric generator feeds the motor via an inverter without DC–DC converters or batteries. Accordingly, the short life problems and the high replacement cost are avoided. The proposed complete system was simulated via the MATLAB package. Moreover, a complete laboratory infrastructure was constructed for testing the proposed high-power-density hybrid perovskite solar cell–TEG array for the water pumping system. The results revealed that using the high-power-density hybrid perovskite solar cell–TEG array, both the motor's output power and the pump's flow rate were improved by 11% and 14%, respectively, compared to only using the perovskite solar cell array. Finally, both the simulation and experimental results proved the high-performance efficiency of the system in addition to showing its system complexity and cost reduction. [ABSTRACT FROM AUTHOR]

Apricots are a fragile fruit that rots quickly after harvest. Therefore, they have a short shelf-life. The purpose of this work is to determine the effect of coatings containing chitosan (CH) as well as its nanoparticles (CHNPs) as thin films on the quality and shelf-life of apricots stored at room (25 ± 3 °C) and cold (5 ± 1 °C) temperatures. The physical, chemical, and sensorial changes that occurred during storage were assessed, and the shelf-life was estimated. Transmission electron microscopy was used to examine the size and shape of the nanoparticle. The nanoparticles had a spherical shape with an average diameter of 16.4 nm. During the storage of the apricots, those treated with CHNPs showed an obvious decrease in weight loss, decay percent, total soluble solids, and lipid peroxidation, whereas total acidity, ascorbic acid, and carotenoid content were higher than those in the fruits treated with CH and the untreated fruits (control). The findings of the sensory evaluation revealed a significant difference in the overall acceptability scores between the samples treated with CHNPs and the other samples. Finally, it was found that CHNP coatings improved the qualitative features of the apricots and extended their shelf-life for up to 9 days at room temperature storage and for 30 days in cold storage. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) is the most promising renewable energy source available on Earth. Three topologies based on a switched-inductor capacitor and non-isolated high-step-up Cuk converter have been proposed for solar PV. These topologies of the Cuk converter have higher boosting ability than conventional Cuk and boost converters and can reduce the voltage stress of the main switch. A small voltage rating and on-state resistance can give higher efficiency of the converter. The voltage boosting ability of all three topologies was compared to each other and with a conventional Cuk converter. The boosting capability of the third converter was 11 times at 0.75 duty cycle with a solar PV source. These converters do not use a coupled inductor and transformer, which leads to less volume, reducing coupling/core saturation loss, and thus the cost of the converter. A solar PV system of 12 volts was used for boosting with these converters for analysis of the feasibility of use with renewables. The three topologies of the switched-inductor and switched-capacitor (SLSC) Cuk converter were designed and simulated in MATLAB/Simulink to evaluate their effectiveness. [ABSTRACT FROM AUTHOR]

The strawberry has a very short postharvest life due to its fast softening and decomposition. The goal of this research is to see how well a starch-silver nanoparticle (St-AgNPs) coating affects the physical, chemical, and microbiological qualities of strawberries during postharvest life. Additionally, the effect of washing with running water on silver concentration in coated strawberry fruit was studied by an inductively coupled plasma-optical emission spectrometer (ICP-OES). Furthermore, the shelf-life period was calculated in relation to the temperature of storage. Fourier transform infrared-attenuated total reflectance (FTIR-ATR), UV-Visible, and Transmission Electron Microscopic (TEM) were used to investigate the structure of starch-silver materials, the size and shape of AgNPs, respectively. The AgNPs were spherical, with an average size range of 12.7 nm. The coated samples had the lowest weight loss, decay, and microbial counts as compared to the uncoated sample. They had higher total acidity and anthocyanin contents as well. The washing process led to the almost complete removal of silver particles by rates ranging from 98.86 to 99.10%. Finally, the coating maintained strawberry qualities and lengthened their shelf-life from 2 to 6 days at room storage and from 8 to 16 days in cold storage. [ABSTRACT FROM AUTHOR]

This paper proposes an effective algorithm; this algorithm is updating the phase-locked loop (PLL) gains according to the value of the short circuit ratio that will decrease the weak grid effect in the PLL controllers to reduce problems related to renewable energy sources and connect to the point of common coupling (PCC). Based on system parameters estimation, the proposed algorithm enhances the system stability for the distribution networks. The standard IEEE 33-bus test system is used to investigate the proposed algorithm's effectiveness and superiority. An extensive mathematical effort is exerted to get the system's small-signal stability (SSS) model. The tuning of the phase-locked loop and the current control loop are choosing to dynamically change according to the system variables to ensure the same system stability margin, whatever the change in the loading conditions. The MATLAB/Simulink model is used to validate the algorithm and simulate the test system. [ABSTRACT FROM AUTHOR]

This paper presents the results of research and development of capacitive-based sensors of rotating shaft vibration for fault diagnostic systems of powerful turbines and hydro generators. It showed that diagnostic systems with special sensors are the key to increasing the reliability of powerful turbines and hydro generators. The application of sensors in monitoring systems was considered, and the requirements for the sensors used were analyzed. Structures of concentric capacitive-based sensors of rotating shaft vibration based on the measurement of the capacitance value from the distance to the metal surface were proposed. The design scheme was created for determining electrode dimensions of the rotating shaft vibration capacitive-based sensors with concentric electrodes, and analytical dependences were obtained. The calculation results allow the selection of optimal parameters of the active and guard electrodes. Analytical and computer simulation methods determined the response functions of the capacitive sensors. Analytical calculations and simulation results using 3D FEM were used to find the response functions of the sensors. The calculation of the characteristics of the capacitive-based sensors of rotating shaft vibration is presented. The study of the influence of fringe effects was carried out using the obtained results of the modeling and analytical calculations. [ABSTRACT FROM AUTHOR]

The need to meet the world's growing demand for energy in an environmentally sustainable manner has led to the exploration of various renewable energy (RE) resources for power generation. The objective of this study is to examine the techno-economic potential of concentrated solar power plants (i.e., linear Fresnel reflector (LFR) and central receiver system (CRS) for electricity generation in Eastern African countries with a case study on Ethiopia. The study was conducted using the System Advisor Model (SAM). In order to estimate the economics of the two power plants, the Levelized cost of energy (LCOE) and the net present value (NPV) metrics were used. According to results obtained from the simulations, the LFR produced annual energy of 528 TWh at a capacity factor (CF) of 60.3%. The CRS also produced a total of 540 TWh at a CF of 61.9%. The LCOE (real) for the CRS is found to be 9.44 cent/kWh against 10.35 cent/kWh for the LFR. The NPV for both technologies is found to be positive for inflation rates of 2% and below. An inflation rate above 2% renders the two power plants financially impracticable. A real discount rate above 9% also renders both projects economically unviable. Based on the obtained results, the CRS system is identified as the best technology for electricity generation under the Jijiga climatic condition in Ethiopia. [ABSTRACT FROM AUTHOR]

In recent studies, the competitiveness of the Newton-S-Iteration-Process (Newton-SIP) techniques to efficiently solve the Power Flow (PF) problems in both well and ill-conditioned systems has been highlighted, concluding that these methods may be suitable for industrial applications. This paper aims to tackle some of the open topics brought for this kind of techniques. Different PF techniques are proposed based on the most recently developed Newton-SIP methods. In addition, convergence analysis and a comparative study of four different Newton-SIP methods PF techniques are presented. To check the features of considered PF techniques, several numerical experiments are carried out. Results show that the considered Newton-SIP techniques can achieve up to an eighth order of convergence and typically are more efficient and robust than the Newton–Raphson (NR) technique. Finally, it is shown that the overall performance of the considered PF techniques is strongly influenced by the values of parameters involved in the iterative procedure. [ABSTRACT FROM AUTHOR]

Clean and renewable energy sources are the preferable power system generations for the overall world. This research aims to present a very highly integrated, economic, professional, and simple construction, clean and natural resources usage of the renewable hybrid generation system. This research performs analysis, systematic representation, evaluation, and design of the hybrid proposed system—pico-hydraulic from home usage water and photovoltaic (PV)—to generate an optimal renewable generation system using a new professional control system. Applying this proposed technique in Egypt shows that the hybrid system successfully overcame Egypt's energy crisis. Renewable energy will rise to 8.782% by increasing 7.323% (14,408.83 GWh/Y). Besides, this system increases the power supply reliability; it gives an additional emergency supply and reduces the exhausts from other generation stations (e.g., CO2). The saving from this hybrid system is very effective for; the residential sector (subscribes), which will be ranged from 9599.298 million E£/10Ys up to 86,393.68 million E£/10Ys that equals 5399.6 million $, government to use this extra generation energy to reduce the maximum loads from various stations. A practical model has been presented with results to verify the high efficiency of the proposed system that illustrates the effective performance of the used hybrid system. [ABSTRACT FROM AUTHOR]

This article proposes an intelligent control strategy to enhance the frequency dynamic performance of interconnected multi-source power systems composing of thermal, hydro, and gas power plants and the high penetration level of wind energy. The proposed control strategy is based on a combination of fuzzy logic control with a proportional-integral-derivative (PID) controller to overcome the PID limitations during abnormal conditions. Moreover, a newly adopted optimization technique namely Arithmetic optimization algorithm (AOA) is proposed to fine-tune the proposed fuzzy-PID controller to overcome the disadvantages of conventional and heuristic optimization techniques (i.e., long time in estimating controller parameters-slow convergence curves). Furthermore, the effect of the high voltage direct current link is taken into account in the studied interconnected power system to eliminate the AC transmission disadvantages (i.e., frequent tripping during oscillations in large power systems–high level of fault current). The dynamic performance analysis confirms the superiority of the proposed fuzzy-PID controller based on the AOA compared to the fuzzy-PID controller based on a hybrid local unimodal sampling and teaching learning-based optimization (TLBO) in terms of minimum objective function value and overshoots and undershoots oscillation measurement. Also, the AOA's proficiency has been verified over several other powerful optimization techniques; differential evolution, TLBO using the PID controller. Moreover, the simulation results ensure the effectiveness and robustness of the proposed fuzzy-PID controller using the AOA in achieving better performance under several contingencies; different load variations, the high penetration level of the wind power, and system uncertainties compared to other literature controllers adjusting by various optimization techniques. [ABSTRACT FROM AUTHOR]

The hybrid microgrid system is considered one of the best solution methods for many problems, such as the electricity problem in regions without electricity, to minimize pollution and the depletion of fossil sources. This study aims to propose and implement a new algorithm called improved heap-based optimizer (IHBO). The objective of minimizing the microgrid system cost is to reduce the net present cost while respecting the reliability, power availability, and renewable fraction factors of the microgrid system. The results show that the PV/diesel/battery hybrid renewable energy system (HRES) gives the best solution, with a net present cost of MAD 120463, equivalent to the energy cost of MAD 0.1384/kWh. The reliability is about 3.89%, the renewable fraction is about 95%, and the power availability is near to 99%. The optimal size considered is represented as 167.3864 m2 of PV area, which is equivalent to 44.2582 kW and 3.8860 kW of diesel capacity. The study results show that the proposed optimization algorithm of IHBO is better than the artificial electric field algorithm, the grey wolf optimizer, Harris hawks optimization, and the original HBO algorithm. A comparison of the net present cost with a different fuel price is carried out, in which it is observed that the net present cost is reduced even though its quantity used is mediocre. [ABSTRACT FROM AUTHOR]

The increase in industrial and commercial applications of photovoltaic systems (PV) has a significant impact on the increase in interest in studying the improvement of the efficiency of these systems. Estimating the efficiency of PV is considered one of the most important problems facing those in charge of manufacturing these systems, which makes it interesting to many researchers. The difficulty in estimating the efficiency of PV is due to the high non-linear current–voltage characteristics and power–voltage characteristics. In addition, the absence of ample efficiency information in the manufacturers' datasheets has led to the development of an effective electrical mathematical equivalent model necessary to simulate the PV module. In this paper, an application for an optimization algorithm named Wild Horse Optimizer (WHO) is proposed to extract the parameters of a double-diode PV model (DDM), modified double-diode PV model (MDDM), triple-diode PV model (TDM), and modified triple-diode PV model (MTDM). This study focuses on two main objectives. The first concerns comparing the original models (DDM and TDM) and their modification (MDDM and MTDM). The second concerns the algorithm behavior with the optimization problem and comparing this behavior with other recent algorithms. The evaluation process uses different methods, such as Root Mean Square Error (RMSE) for accuracy and statistical analysis for robustness. Based on the results obtained by the WHO, the estimated parameters using the WHO are more accurate than those obtained by the other studied optimization algorithms; furthermore, the MDDM and MTDM modifications enhanced the original DDM and TDM efficiencies. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is one of the regular routine tests accepted by worldwide utilities to detect power transformer incipient faults. While the DGA measurement has fully matured since the development of offline and online sensors, interpretation of the DGA results still calls for advanced approaches to automate and standardize the process. Current industry practice relies on various interpretation techniques that are reported to be inconsistent and, in some cases, unreliable. This paper presents a new application for the advanced logistic regression algorithm to improve the reliability of the DGA interpretation process. In this regard, regularized logistic regression is used to improve the accuracy of the DGA interpretation process. Results reveal the superior features of the proposed logistic regression approach over the conventional and artificial intelligence techniques presented in the literature. [ABSTRACT FROM AUTHOR]

The use of corrugated webs increases web shear stability and eliminates the need for transverse stiffeners in steel beams. Optimised regression learner techniques (ORLTs) are rarely used for calculating shear capacity in steel beam research. This study proposes a new approach for calculating the maximum shear capacity of steel beams with trapezoidal corrugated webs (SBCWs) by using ORLTs. A new shear model is proposed using ORLTs in accordance with plate buckling theory and previously developed formulas for predicting the shear strength of SBCWs. The proposed ORLT models are implemented using the regression learner toolbox of MATLAB software (2020b). The available data of more than 125 test results from different specimens prepared by previous researchers are used to create the model. In this study, web geometry and relevant web steel grades determine the shear capacity of SBCWs. Four regression methods are adopted. Results are compared with those of an artificial neural network model. The model output factor represents the ratio of the web vertical shear stress to the normalised shear stress. Shear capacity can be estimated on the basis of the resulting factor from the model. The proposed model is verified using two methods. In the first method, a series of tests are performed by the authors. In the second method, the results of the model are compared with the shear values obtained experimentally by other researchers. On the basis of the test results of previous studies and the current work, the proposed model provides an acceptable degree of accuracy for predicting the shear capacity of SBCWs. The results obtained using Gaussian process regression are the most appropriate because its recoded mean square error is 0.07%. The proposed model can predict the shear capacity of SBCWs with an acceptable percentage of error. The recoded percentage of error is less than 5% for 93% of the total specimens. By contrast, the maximum differential obtained is ±10%, which is recorded for 3 out of 125 specimens. [ABSTRACT FROM AUTHOR]