Glycolysis is a universal pathway in the living cells. The complete pathway of glycolysis was elucidated in 1940. This pathway is often referred to as Embden–Meyerhof pathway in honor of the two ...biochemists that made a major contribution to the knowledge of glycolysis. The objective of the study was to review the published literature on glycolysis and relation to cancer. The material for this review was taken mostly from up-to-date biochemistry textbooks and electronic journals. To collect publications, PubMed and the Cochrane database of systematic reviews were used. Some other relevant references were collected from personal database of papers on glycolysis and cancer. Several glycolytic inhibitors are currently in preclinical and clinical development. Inhibition of glycolysis in cancer cells is a novel strategy to overcome drug resistance associated with mitochondrial respiratory defect and hypoxia. This article is an important topic to be considered by cancer researchers and those who treat cancers.
•Cut cell and full cell PV modules are studied for manufacturing & hotspot formation.•Transitory and permanent hotspots with different temperature ends are applied.•The effect of interconnector ...thickness on stress levels in half cut cell is studied.•Strain energy accumulation in interconnectors due to thermal loading is studied.•The solutions to avoid or reduce hotspot formation are also outlined.
The photovoltaic cell pattern and geometry affect the origination, level and distribution of stresses in the modules. The cut cell patterns can be potential alternative of conventional full cell patterns in terms of enhanced stability, reduced hotspot effects, etc. Here, we study the thermo-mechanical behavior of cut cell (half cut and one by three cut cell) and full cell modules during manufacturing and hotspot formation. The 3D geometrical models of modules with maximum details are simulated for these processes. Modules with cut cells are found more resistant to thermo-mechanical loads. The transitory and permanent hotspot cycles with different temperature ends are applied; and it is found that the stress level increases with the increase in temperature and dwell time of hotspots. Due to permanent hotspots with upper extreme end of 110 °C, the busbars experience compressive stresses of −137 MPa, −122 MPa, and −106 MPa in full, half cut, and one by three cut cell modules respectively; and cells experience compressive stresses of −76 MPa, −80 MPa, −72 MPa in full, half cut, and one by three cut cell modules respectively. With the use of cut cells, interconnectors thickness can be reduced and parametric analysis shows that the thinner interconnectors lead to lower stresses in cells. During thermal loading, strain energy is stored in the interconnectors due to deformation and it increases with the increase in thermal loading i.e. for each degree rise in temperature, strain energy increases. In addition, solutions to avoid or reduce hotspot formation are also outlined.
•H2 impact on combustion and emission characteristics of n-dodecane was studied.•Small linear hydrocarbons produced the first benzene aromatic rings initially.•H2 mitigated small hydrocarbons, and ...later small hydrocarbons impeded benzene.•CO2 retarded by the diminution in the formation of CH3O and CH2O when adding H2.•H2 and gravity mitigated peak soot mass concentration and particle number density.
Soot and CO2 emission are the major challenges when using fossil fuels in internal combustion engines. To mitigate the exhaust effect on the environment and reduce the dependency on fossil fuels, it is a better idea to utilize zero-carbon fuel, H2, with hydrocarbon fuels. A numerical approach was executed to elucidate the combustion and emission characteristics of n-dodecane under hydrogen enrichment. It was found that H and OH made a huge contribution to the formation of benzene to pyrene aromatic rings, while C2H2, C3H3 and C5H4CH2 linear hydrocarbons produced the first benzene aromatic rings of PAH (polycyclic aromatic hydrocarbons). The trend of peak molar fraction of benzene and pyrene was observed correlated to that of small and linear HC (hydrocarbons) at various H2 ratios. Hydrogen mitigated the production of acetylene, C3H3, C5H4CH2, C2H3, ethylene and C3H2, and later these species impeded the formation of benzene and pyrene. Besides, the diminution in the production rate of CH3O and CH2O species indicated a reduction in CO2 emission when adding H2. H2O and CO2 encouraged NH3 and NO2 production, respectively. H2 mitigated the production of peak soot mass concentration and soot particle number density, whereas gravity impact only reduced the peak soot particle number density in the flame.
Hydrogen impacts on lean flammability limits and the burning characteristics of n-decane, a kerosene surrogate, were studied using a spherical combustion chamber and Chemkin software at 460 K and ...100 kPa. Laminar flame propagated spherically at λ = 0.8–1.3 by using 50 mJ IE, whereas further leaner mixture (λ ≥ 1.4) could be ignited at 1000 mJ. However, the wrinkles appeared on flame morphology thanks to higher IE. The effect of IE on flame morphology reduced with increasing the value of λ. In contrast, the flame distortion enhanced as lifting IE, 1000–3000 mJ. Near lean limit, the spherical flame appeared initially from 0 ms to 20 ms. When time increased from 20 ms, it buoyed due to slow flame speed and rapid radiation losses. Eventually, it disappeared at t ≈ 200 ms, and the mixture could not burn completely. Lean limits of n-decane were found λ = 1.6, λ = 1.7, and λ = 1.8 at 1000 mJ, 2000 mJ, and 3000 mJ, respectively. It linearly extended by 0.5 λ with 70% H2 addition (0–70%) and enormously enlarged by 1.3 λ with 20% H2 addition (70–90%). IE, 1000–3000 mJ, extended the lean limit by 0.2 λ. H radical produced greatly from H2 and CO by consuming OH, whereas it consumed by translating formaldehyde and oxygen into aldehyde, O and OH. OH produced significantly from the consumption of H and hydroperoxyl radicals. By lifting hydrogen, H and OH increased rapidly, which enhanced the reaction rates of dominant intermediates. Consequently, the lean limit improved.
•Hydrogen impact on lean limit of n-decane was studied using schlieren technique.•Lean mixture (λ ≥ 1.4) could only be ignited with turbulence at higher IE, 1000 mJ.•Buoyance appeared near lean limit, which caused incomplete combustion issues.•λ decreased wrinkles on flame morphology, whereas ignition energy increased them.•H2 lifted lean limit 2.3 λ thru H radical, while ignition energy increased 0.2 λ.
Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is ...the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.
Functionalized iron oxide nanoparticles (IONPs) are of great interest due to wide range applications, especially in nanomedicine. However, they face challenges preventing their further applications ...such as rapid agglomeration, oxidation, etc. Appropriate surface modification of IONPs can conquer these barriers with improved physicochemical properties. This review summarizes recent advances in the surface modification of IONPs with small organic molecules, polymers and inorganic materials. The preparation methods, mechanisms and applications of surface-modified IONPs with different materials are discussed. Finally, the technical barriers of IONPs and their limitations in practical applications are pointed out, and the development trends and prospects are discussed.
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•State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and defective PV modules is collected.•Isolated and ...develop-model transfer deep learning frameworks are proposed.•Isolated & transfer learned methods give 98.67% and 99.23% accuracy respectively.•These frameworks are qualitatively evaluated with experimental testing.
With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.
The crisis of antibiotic resistance necessitates creative and innovative approaches, from chemical identification and analysis to the assessment of bioactivity. Plant natural products (NPs) represent ...a promising source of antibacterial lead compounds that could help fill the drug discovery pipeline in response to the growing antibiotic resistance crisis. The major strength of plant NPs lies in their rich and unique chemodiversity, their worldwide distribution and ease of access, their various antibacterial modes of action, and the proven clinical effectiveness of plant extracts from which they are isolated. While many studies have tried to summarize NPs with antibacterial activities, a comprehensive review with rigorous selection criteria has never been performed. In this work, the literature from 2012 to 2019 was systematically reviewed to highlight plant-derived compounds with antibacterial activity by focusing on their growth inhibitory activity. A total of 459 compounds are included in this Review, of which 50.8% are phenolic derivatives, 26.6% are terpenoids, 5.7% are alkaloids, and 17% are classified as other metabolites. A selection of 183 compounds is further discussed regarding their antibacterial activity, biosynthesis, structure–activity relationship, mechanism of action, and potential as antibiotics. Emerging trends in the field of antibacterial drug discovery from plants are also discussed. This Review brings to the forefront key findings on the antibacterial potential of plant NPs for consideration in future antibiotic discovery and development efforts.
•A novel hydrogen production approach by full spectrum solar energy was proposed.•It combined photothermal synergistic reaction with photovoltaic electrolysis water.•Mathematic model of the approach ...was developed.•Hydrogen production efficiency of the approach at various conditions was analyzed.•It improved energy conversion efficiency and the efficiency can reach 21.05%.
For the development of hydrogen energy, it is very important to find a green and efficient hydrogen production approach. By comparing the existing hydrogen production methods, it can be found that using clean and inexhaustible solar energy to produce hydrogen is very promising. Therefore, for efficient hydrogen production from solar energy, a novel hydrogen production approach using full spectrum solar energy by combining photothermal synergistic reaction with photovoltaic power generation electrolysis water is proposed in the paper. And the relevant hybrid hydrogen production model is also established for calculation and discussion. The simulation results show that the efficiency of the proposed hydrogen production approach can reach 21.05% when the elementary reaction time is 1 ns. However, under the same solar radiation conditions and parameters, if the photothermal synergistic reaction and photovoltaic power generation electrolytic water are simulated separately, the hydrogen production efficiency using only the photothermal synergistic reaction is 7.9% and the hydrogen production efficiency using only photovoltaic power generation electrolytic water is 19.19%. Compared with these two methods, the hydrogen production efficiency of this hybrid hydrogen production approach has been greatly improved. And the hydrogen production efficiency curves of this approach under different conditions (including different separation wavelengths, different photovoltaic panel materials, and different electrolysis temperatures) have been also studied in the paper. Therefore, this study can provide a new train of thought for solar hydrogen production approaches, which may provide guidance for realizing green and efficient hydrogen energy production.
Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or ...two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The independent components analysis (ICA) technique is used to remove the artifacts from EEG dataset. Then, the EEG dataset is segmented and filtered to remove noise and interference using an elliptic band-pass filter. Next, the EEG signal features are extracted from the filtered signal using a discrete wavelet transform (DWT) to decompose the filtered signal to its sub-bands delta, theta, alpha, beta and gamma. Subsequently, five statistical methods are used to extract features from the EEG sub-bands: the logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE). Further, the features are fed into four different classifiers, linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural networks (ANNs), to classify the features corresponding to their classes. The combination of DWT with SE and LBP produces the highest accuracy among all the classifiers. The overall classification accuracy approaches 99.9% using SVM and 97% using ANN for the three-class single-channel and multi-channel modes, respectively.
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