An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.publishedVersio

Knowledge, attitudes, and practices of the general population of Pakistan regarding typhoid conjugate vaccine: findings of a cross-sectional study

Typhoid fever, a common enteric disease in Pakistan, caused by Salmonella typhi, is becoming an extended drug-resistant organism and is preventable through the typhoid conjugate vaccine (TCV). Public adherence to preventive measures is influenced by knowledge and attitude toward the vaccine. This study investigates the knowledge, attitudes, and practices of the general population of Pakistan toward TCV. The differences in mean scores and factors associated with typhoid conjugate vaccine knowledge, attitudes, and practices were investigated. A total of 918 responses were received with a mean age of 25.9 ± 9.6, 51% were women, and 59.6% had graduation-level education. The majority of them responded that vaccines prevent illness (85.3%) and decrease mortality and disability (92.6%), and typhoid could be prevented by vaccination (86.7%). In total, 77.7 and 80.8% considered TCV safe and effective, respectively. Of 389 participants with children, 53.47% had vaccinated children, according to the extended program on immunization (EPI). Higher family income has a higher odds ratio (OR) for willingness toward booster dose of TCV [crude odds ratio (COR) = 4.920, p–value <0.01; adjusted odds ratio (aOR) = 2.853, value of p <0.001], and negative attitude regarding the protective effect of TCV has less willingness toward the booster dose with statistical significance (COR = 0.388, value of p = 0.017; aOR = 0.198, value of p = 0.011). The general population of Pakistan had a good level of knowledge about the benefits of TCV, and attitude and practices are in favor of the usage of TCV. However, a few religious misconceptions are prevalent in public requiring the efforts to overcome them to promote the usage of vaccines to prevent the disease and antibiotic resistance

A Review of Alternative Treatment Options in Diabetic Polyneuropathy

Currently there is no recognized curative treatment for diabetic polyneuropathy (DPN). Strict glucose control and symptomatic pain relief are the first line management routes. DPN is a common complication of diabetes and has a major detrimental influence on the quality of life (QOL) for many patients. Due to the scope of the problem, it is imperative that treatment options which impede DPN\u27s progression and restore sensorineural function should be researched comprehensively and made available to the masses at an economical cost. We reviewed a multitude of atypical treatment options for DPN including capsaicin, lidocaine, acupuncture, electrical nerve stimulation, alpha lipoic acid, benfotiamine, and acetyl-l-carnitine and explored the evidence to date regarding their safety and efficacy. Most of these options have been around for a long time and have promising pilot studies or small-scale trials focused on DPN treatment

Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems

We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg–Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 Ω. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 Ω and is more robust

Designing four naphthalene di-imide based small organic solar cells with 5,6-difluoro-3-oxo-2,3-dihydro-indene non-fullerene acceptors

Four new molecules namely bis (5,6-difluoro-3-oxo-2,3-dihydro-1H-indene-2,1-diylidene) di-malononitrile (NDM-1), 3-fluorothiophen-2-yl) methylene)-5, 6-difluoro-3-oxo-2, 3-dihydro-1H-inden-1-ylidene) acetate (NDM-2), 5, 6-difluoro-3-oxo-2, 3-dihydro-1H-inden-1-ylidene)-3-methyl-2-thioxothiazolidin-4-ylidene) malononitrile (NDM-3) and bis (1-methyl-2, 6-dioxo-1, 2, 5, 6-tetrahydropyridine-3-carbonitrile) (NDM-4) contains central Naphthalene Di-Imide unit with different end cap acceptors have been designed for enhance the photovoltaic efficiencies. Absorption values of designed molecules lies between 400 and 490 nm, re-organization energy values varies from 0.41 to 0.67 eV for electron and 0.49 eV to 1.25 eV for hole transfer, open circuit voltages range from 4.39 to 4.73 V which indicates their better photovoltaic properties as compared to the R (3-methyl-4-oxo-2-thioxothiazolidin-5-ylidene) methyl). Designed molecules proposed large number of electronic excitations and less charge loss at donor/acceptor interfaces due to small binding energy than reference molecule. Graphic abstract Charge transfer mechanism for adiabatic and vertical exchange

Integration of Different Individual Heating Scenarios and Energy Storages into Hybrid Energy System Model of China for 2030

Traditional energy supply infrastructures are on the brink of facing a major transformation due to energy security concerns, environment pollution, renewable energy intermittency and fossil fuel scarcity. A hybrid energy system constitutes the integration of different energy carriers like electricity, heat and fuel which play a vital role in addressing the above challenges. Various technological options like combined heat and power, heat pumps, electrolysers and energy storages ease out multiple carrier integration in an energy hub to increase system flexibility and efficiency. This work models the hybrid energy system of China for the year 2030 by using EnergyPLAN. Atmosphere decarbonization is achieved by replacing conventional coal and natural gas boilers with alternative individual heating sources like hydrogen operated micro combined heat and power natural gas micro combined heat and power and heat pumps. Moreover, rockbed storage as well as single and double penstock pumped hydro storages are added in the proposed system in order to cope with the stochastic nature of intermittent renewable energy such as wind and solar photovoltaic. The technical simulation strategy is employed to analyze the optimal combination of energy producing components by determining annual costs, fuel consumption and CO2 emissions. The results substantiate that a heat pump and double penstock pumped hydro storage addition to the individual heating and electricity network not only proves to be an economically viable option but also reduces fuel consumption and emissions

Effect of Traditional Spices on the Quality and Antioxidant Potential of Paneer Prepared from Buffalo Milk

This study aims to evaluate the effect of different spices (black pepper, cumin, clove, nigella, cardamom, and cinnamon) on the physicochemical characteristics and microbial quality, as well as antioxidant potential, of paneer during storage. Different types of spices were incorporated into the paneer at different levels (0.2 and 0.3%). In addition to paneer, the antioxidant potential of spices was also investigated. The results concerning total plate counts (TPC) or yeast and molds (Y & M) (log10 CFU/g) of all treatments were substantially (p  0.05) increased during storage. Generally, all freshly prepared spicy paneer and control had higher sensory scores for all the sensory characteristics which declined during subsequent storage. All the paneer samples having 0.3% spices showed very slight variations (nonsignificant) in sensory score of all the attributes of their relative samples containing 0.2% spice. The incorporation of spices into the paneer matrix also showed promising results concerning all the above-mentioned attributes revealing antioxidant potential. There was significant (p  0.05) effect of treatments and storage days on antioxidant potential of paneer. The freshly prepared control paneer (P0) showed the lowest total phenol (TP), total flavonoids (TF), 2, 2-diphenyl-1-picrylhydrazyl (DPPH), reducing power (RP), and total antioxidant capacity (TAC) values compared to all the spicy paneer (treatments). The freshly prepared control paneer (P0) showed the lowest antioxidant potential compared to all the spicy paneer (treatments). The maximum antioxidant potential was observed in the paneer having 0.3% clove (P6). All the spicy paneer showed increasing trend of all the attributes, showing antioxidant potential up to 6 days of storage, but afterwards, the activities were slightly decreased. It may be concluded that spicy paneer would be considered as a functional dairy product with enhanced sensory and antioxidant properties, and shelf stability

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat–top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for QFAB, CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters

Development of a hybrid mixed-mode solar dryer for product drying

Gefördert durch den Publikationsfonds der Universität Kasse