Display omitted
•A systematic ab-initio study has been carried out on cubic CaPd3B4O12 (B = Ti, V) quadruple perovskite via the DFT method.•Metallic and semiconducting characteristics have been ...observed for CPVO and CPTO, respectively.•CaPd3B4O12 shows mechanical and thermodynamical stability with anisotropic nature.•Phonon properties of CPBO perovskite are investigated.•The optical properties related to the electric structure of CaPd3B4O12 are investigated to justify the optoelectronic applications.•CPTO has potential candidacy in thermoelectric applications.
This study has explored numerous physical properties of CaPd3Ti4O12 (CPTO) and CaPd3V4O12 (CPVO) quadruple perovskites employing the density functional theory (DFT) method. The calculated lattice constants show inclinable compliance with the experimental results that ensure their structural stability. The mechanical permanence of these two compounds was observed by the Born stability criteria as well. The mechanical and elastic behaviors have been rationalized to investigate elastic constants, bulk, shear, and Young’s modulus, Pugh’s ratio, Poisson’s ratio, and elastic anisotropy indexes. The ductility and anisotropic indexes confirm that both materials are ductile and anisotropic in essence. The band structure of CPTO reveals a 0.88 and 0.46 eV direct narrow band gap while using TB-mBJ and GGA-PBE potentials, respectively, which is an indication of its fascinating semiconducting nature. Whereas, CPVO perovskite exhibits a metallic character. The calculated partial density of states indicates the strong hybridization between Pd-4d and O-2p orbital electrons for CPTO, whereas Pd-4d and V-3d-O-2p for CPVO. The study of the chemical bonding nature and electronic charge distribution graph reveals the coexistence of covalent O-V/Pd bonds, ionic O-Ti/Ca bonds, as well as metallic Ti/V-Ti/V bonding for both compounds. The Fermi surface of CPVO ensures a kind of hole as well as electron faces simultaneously, indicating multifarious band characteristics. The prediction of the static real dielectric function (optical property) of CPTO at zero energy implies its promising dielectric nature. The photoconductivity and absorption coefficient of CPBO display good qualitative compliance with the consequences of band structure computations. The calculated thermodynamic properties manifest the thermodynamical stability for CPBO, whereas phonon dispersions of CPVO exhibit stable phonon dispersion in contrast to slightly unstable phonon dispersion of CPTO. The predicted Debye temperature (θD) has been utilized to correlate its topical features including thermoelectric behaviors. The studied thermoelectric transport properties of CPTO yielded a higher Seebeck coefficient (186 μV/K), power factor (11.9 μWcm−1K−2), and figure of merit (ZT) value of about 0.8 at 800 K, indicating that this material could be a promising candidate for thermoelectric applications.
Distribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity ...analysis quantifies the level of power quality violation on the network due to the high penetration of the DER, such as over voltage, under voltage, transformer and feeder overloading, and protection failures. Real-time monitoring of the power quality factors such as the voltage, current, angle, frequency, harmonics and overloading that would help the distribution network operators overcome the challenges created by the high penetration of the DER. In this paper, different conventional hosting capacity analysis methods have been discussed. These methods have been compared based on the network constraints, impact factors, required input data, computational efficiency, and output accuracy. The artificial intelligence approaches of the hosting capacity analysis for the real-time monitoring of distribution network parameters have also been covered in this paper. Different artificial intelligence techniques have been analysed for sustainable integration, power system optimisation, and overcoming real-time monitoring challenges of conventional hosting capacity analysis methods. An overview of the conventional hosting capacity analysis methods, artificial intelligence techniques for overcoming the challenges of distributed energy resources integration, and different impact factors affecting the real-time hosting capacity analysis has been summarised. The distribution system operators and researchers will find the review paper as an easy reference for planning and further research. Finally, it is evident that artificial intelligence techniques could be a better alternative solution for real-time estimation and forecasting of the distribution network hosting capacity considering the intermittent nature of the DER, consumer loads, and network constraints.
Eggplant, or brinjal (
), is a popularly consumed vegetable grown throughout Asia that is prone to vicious and sustained attack by the eggplant fruit and shoot borer (EFSB) (
) throughout the growing ...season. Yield losses in Bangladesh because of EFSB infestation have been reported as high as 86%. Farmers reduce crop losses by frequent applications of insecticide. To counter the EFSB pest, Bangladesh has developed and released four Bt brinjal varieties expressing Cry1Ac (Bt brinjal). Bangladesh is the first developing country to release a commercial genetically engineered (GE) food crop. In this article, we discuss the development and adoption of Bt brinjal in Bangladesh from initial distribution to 20 farmers in 2014 to cultivation by more than 27,000 farmers in 2018. Bt brinjal provides essentially complete control of EFSB, dramatically reduces insecticide sprays, provides a sixfold increase in grower profit, and does not affect nontarget arthropod biodiversity. A major focus is to ensure its durability through stewardship. Bangladesh has shown great leadership in adopting biotechnology for the benefit of its farmers and serves as an example for other countries.
RhoA GTPase plays a variety of functions in regulation of cytoskeletal proteins, cellular morphology, and migration along with various proliferation and transcriptional activity in cells. RhoA ...activity is regulated by guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs), and the guanine nucleotide dissociation factor (GDI). The RhoA‐RhoGDI complex exists in the cytosol and the active GTP‐bound form of RhoA is located to the membrane. GDI displacement factors (GDFs) including IκB kinase γ (IKKγ) dissociate the RhoA‐GDI complex, allowing activation of RhoA through GEFs. In addition, modifications of Tyr42 phosphorylation and Cys16/20 oxidation in RhoA and Tyr156 phosphorylation and oxidation of RhoGDI promote the dissociation of the RhoA‐RhoGDI complex. The expression of RhoA is regulated through transcriptional factors such as c‐Myc, HIF‐1α/2α, Stat 6, and NF‐κB along with several reported microRNAs. As the role of RhoA in regulating actin‐filament formation and myosin‐actin interaction has been well described, in this review we focus on the transcriptional activity of RhoA and also the regulation of RhoA message itself. Of interest, in the cytosol, activated RhoA induces transcriptional changes through filamentous actin (F‐actin)‐dependent (“actin switch”) or—independent means. RhoA regulates the activity of several transcription regulators such as serum response factor (SRF)/MAL, AP‐1, NF‐κB, YAP/TAZ, β‐catenin, and hypoxia inducible factor (HIF)‐1α. Interestingly, RhoA also itself is localized to the nucleus by an as‐yet‐undiscovered mechanism.
We descibed the regulation of RhoA activity and expression levels. In addition, we described transcription factors which are regulated by RhoA.
This paper presents a new methodology to extract the unknown parameters of a single-diode photovoltaic (PV) cell model. The first contribution of this paper is the development and implementation of a ...new version of the wind-driven optimization algorithm, called an adaptive wind-driven optimization (AWDO) algorithm. The advantages of the AWDO algorithm are: 1) accurate extraction of the global values of the optimized PV parameters in changing weather conditions, which is achieved by building solutions from random operations; and 2) capability of handling the given complex multi-modal and multi-dimensional optimization problems. The second contribution is the identification of a generalization model to generalize the extracted parameters of a single-diode PV cell model. That provides an ability of the proposed methodology to work with any I-V characteristic curve of PV cells and at any weather condition on a 15-min basis. To validate the proposed methodology, it has been tested for 1307 I-V characteristic curves of a PV module at various weather conditions on a 15-min basis. Additionally, its accuracy and computational efficiency are verified and compared with five well-known existing extraction methods: Villalva's model, particle swarm optimization, biogeography-based optimization, Gang's model, and bacterial foraging optimization by both simulation and outdoor measurements. The results show that the AWDO algorithm can provide the extracted five parameters with an acceptable range of accuracy and faster than the aforementioned models. Therefore, the proposed methodology (AWDO based on Chenlo's model) can be confidently recommended as a reliable, feasible, valuable, and fast optimization algorithm for parameter extraction of a single-diode PV cell model.
This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This ...research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model’s performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications.
In this article, the impact of prediction errors on the performance of a domestic power demand management is thoroughly investigated. Initially, real-time peak power demand management system using ...battery energy storage systems (BESSs), electric vehicles (EVs), and photovoltaics (PV) systems is designed and modeled. The model uses real-time load demand of consumers and their roof-top PV power generation capability, and the charging-discharging constraints of BESSs and EVs to provide a coordinated response for peak power demand management. Afterward, this real-time power demand management system is modeled using autoregressive moving average and artificial neural networks-based prediction techniques. The predicted values are used to provide a day-ahead peak power demand management decision. However, any significant error in the prediction process results in an incorrect energy sharing by the energy management system. In this research, two different customers connected to a real-power distribution network with realistic load pattern and uncertainty are used to investigate the impact of this prediction error on the efficacy of an energy management system. The study shows that in some cases the prediction error can be more than 300%. The average capacity of energy support due to this prediction error can go up to 0.9 kWh, which increases battery charging-discharging cycles, hence reducing battery life and increasing energy cost. It also investigates a possible relationship between environmental conditions (solar insolation, temperature, and humidity) and consumers' power demand. Considering the weather conditions, a day-ahead uncertainty detection technique is proposed for providing an improved power demand management.
An enormous number of domestic and international tourists visit Saint Martin’s Island in Bangladesh annually. Unfortunately, the lack of proper planning as well as severe electricity shortages are ...hampering its development towards a smart city. This study proposes a smart city model for the remote area with a grid-independent microgrid to meet the rising load demand. It demonstrates that implementation of the Internet of things can effectively utilize the resources of Saint Martin following the smart city criteria. The distributed energy resources have been optimized to identify the best microgrid configuration that complies with Sustainable Development Goal 7. Finally, nonlinear simulations are carried out to compare the stability of the proposed systems. The study outlines the benefits of employing eco wave power and second-life batteries, as well as the advantages of using a supercapacitor as a speedy responder to disturbances. The research ultimately gives the multidisciplinary knowledge to policymakers that they require to transform a small island like Saint Martin into a tourist-intensive smart city.
Around 27,000 prokaryote genomes are presently deposited in the Genome database of GenBank at the National Center for Biotechnology Information (NCBI) and this number is exponentially growing. ...However, it is not known how many of these genomes correspond correctly to their designated taxon. The taxonomic affiliation of 44 Aeromonas genomes (only five of these are type strains) deposited at the NCBI was determined by a multilocus phylogenetic analysis (MLPA) and by pairwise average nucleotide identity (ANI). Discordant results in relation to taxa assignation were found for 14 (35.9%) of the 39 non-type strain genomes on the basis of both the MLPA and ANI results. Data presented in this study also demonstrated that if the genome of the type strain is not available, a genome of the same species correctly identified can be used as a reference for ANI calculations. Of the three ANI calculating tools compared (ANI calculator, EzGenome and JSpecies), EzGenome and JSpecies provided very similar results. However, the ANI calculator provided higher intra- and inter-species values than the other two tools (differences within the ranges 0.06-0.82% and 0.92-3.38%, respectively). Nevertheless each of these tools produced the same species classification for the studied Aeromonas genomes. To avoid possible misinterpretations with the ANI calculator, particularly when values are at the borderline of the 95% cutoff, one of the other calculation tools (EzGenome or JSpecies) should be used in combination. It is recommended that once a genome sequence is obtained the correct taxonomic affiliation is verified using ANI or a MLPA before it is submitted to the NCBI and that researchers should amend the existing taxonomic errors present in databases.
To investigate the molecular basis of the emergence of Aeromonas hydrophila responsible for an epidemic outbreak of motile aeromonad septicemia of catfish in the Southeastern United States, we ...sequenced 11 A. hydrophila isolates that includes five reference and six recent epidemic isolates. Comparative genomics revealed that recent epidemic A. hydrophila isolates are highly clonal, whereas reference isolates are greatly diverse. We identified 55 epidemic-associated genetic regions with 313 predicted genes that are present in epidemic isolates but absent from reference isolates and 35% of these regions are located within genomic islands, suggesting their acquisition through lateral gene transfer. The epidemic-associated regions encode predicted prophage elements, pathogenicity islands, metabolic islands, fitness islands and genes of unknown functions, and 34 of the genes encoded in these regions were predicted as virulence factors. We found two pilus biogenesis gene clusters encoded within predicted pathogenicity islands. A functional metabolic island that encodes a complete pathway for myo -inositol catabolism was evident by the ability of epidemic A. hydrophila isolates to use myo -inositol as a sole carbon source. Testing of A. hydrophila field isolates found a consistent correlation between myo -inositol utilization as a sole carbon source and the presence of an epidemic-specific genetic marker. All epidemic isolates and one reference isolate shared a novel O-antigen cluster. Altogether we identified four different O-antigen biosynthesis gene clusters within the 11 sequenced A. hydrophila genomes. Our study reveals new insights into the evolutionary changes that have resulted in the emergence of recent epidemic A. hydrophila strains.
PDF
