Explore the vast range of titles available.

Background Dyslipidemia in children with chronic kidney disease (CKD) is identified based on lipid profile parameters; however, changes in lipoprotein quality precede quantitative changes. Methods A cross-sectional study was done from January to October 2021; overweight, obese children, known cases of diabetes mellitus, hypothyroidism or on steroid therapy, or lipid lowering drugs were excluded. Clinical details were elicited and examinations done. Besides hemogram, kidney function tests, liver function tests, total cholesterol, low density lipoproteins (LDL), triglycerides, high density lipoproteins (HDL), and apolipoproteins A-1 and B were estimated to identify dyslipidemia. Relevant tests of significance were applied, and ROC curves were drawn for apoA-1, apoB, and apoB/apoA-1 ratios. Results A total of 76 (61 M:15 F) children with median (IQR) age 7 (3.25–11) years were enrolled; cause of CKD was CAKUT in 82.3% patients. Dyslipidemia (alteration of 1 or more lipid parameters) was seen in 78.9% with a prevalence of 71.7% in early and 95.7% in later stages of CKD ( P  = 0.02); most had elevated serum triglyceride levels. The median (IQR) values of apoB, apoA-1, and apoB/apoA-1 ratio were 78 (58–110) mg/dl, 80 (63–96.75) mg/dl, and 0.88 (0.68–1.41), respectively; apoB, apoA-1, and apoB/apoA-1 ratio had a sensitivity of 26.67%, 86.67%, and 70%, respectively, and specificity of 87.5%, 62.5%, and 62.5%, respectively, for diagnosis of dyslipidemia. The ROC for apoB, apoA-1, and apoB/apoA-1 ratio showed AUC of 0.66, 0.68, and 0.74 ( P  = 0.4, 0.02, < 0.01), respectively. Conclusions The prevalence (78.9%) of dyslipidemia was high in patients with CKD especially in those with later stages. The ratio of apoB/apoA-1 was altered early and appears to be promising for early detection. Graphical Abstract A higher resolution version of the Graphical abstract is available as Supplementary information

Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000–2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted  R 2 , Mallows’ (Cp), Akaike’s (AIC), Schwarz’s (SBC), and Amemiya’s PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ( r ), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of r , MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.

The increasing level of atmospheric carbon dioxide (CO2), and the resultant global warming is a matter of growing concern among scientists, environmentalists, and climate experts across the globe over the past several decades. Numerous attempts are being undertaken today that seek solutions to mitigate this global crisis. This includes designing functional catalysts, devices and reactors to convert greenhouse gasses such as CO2 into useful products like low‐carbon fuels and chemicals, thereby reducing the amount of CO2 considerably in the atmosphere. Advancements in emerging technologies like 3D‐printing can effectively aid in the fabrication of electrodes and devices to tackle the rising CO2 concerns. Low cost, rapid prototyping ability, and printing simple and complex structure are few of the significant merits of this technology. Thus, in this perspective article, discussions on fabricating 3D‐printed (electro)catalysts, customized devices, reactors, etc., via multiple strategies are put forward with emphasis on the electrochemical reduction of CO2. Also, a detailed discussion on the post‐printing treatments, catalyst modifications, and other CO2 mitigation strategies is provided as well. Although studies in this direction are scarcely reported, observations made hitherto show promising possibilities of broadening this field for large scale CO2 reduction reaction applications, and similar catalytic applications in the near future. The rising level of CO2 in the atmosphere has resulted in alarming and unprecedented warming of the earth's surface, which requires immediate solutions. Technological advancements like 3D‐printing can effectively reduce CO2 to several value‐added products. This perspective article discusses the possibility of 3D‐printing technology in the engineering of devices, electrodes, and beyond to control the global menace.

The reliability assessment of smart grid-integrated distributed power-generating coordination is an operational measure to ensure appropriate system operational set-ups in the appearance of numerous issues, such as equipment catastrophes and variations of generation capacity and the connected load. The incorporation of seasonable time-varying renewable energy sources such as doubly fed generator-based wind turbines into the existing power grid system makes the reliability assessment procedure challenging to a significant extent. Due to the enormous number of associated states involved in a power-generating system, it is unusual to compute all possible failure conditions to determine the system’s reliability indicators. Therefore, nearly all of the artificial intelligence methodology-based search algorithms, along with their intrinsic conjunction mechanisms, encourage establishing the most significant states of the system within a reasonable time frame. This review’s finding indicates that machine learning and deep learning-based predictive analysis fields have achieved fame because of their low budget, simple setup, shorter problem-solving time, and high level of precision. The systems analyzed in this review paper can be applied and extended to the incorporated power grid framework for improving functional and accurate analytical tools to enrich the power system’s reliability and accuracy, overcome software constraints, and improve implementation strategies. An adapted IEEE Reliability Test System (IEEE-RTS) will be applied to authenticate the relevance and rationality of the proposed approach.

Background Brassica nigra (BB), also called black mustard, is grown as a condiment crop in India. B. nigra represents the B genome of U’s triangle and is one of the progenitor species of B. juncea (AABB), an important oilseed crop of the Indian subcontinent. We report the genome assembly of B. nigra variety Sangam. Results The genome assembly was carried out using Oxford Nanopore long-read sequencing and optical mapping. A total of 1549 contigs were assembled, which covered ~ 515.4 Mb of the estimated ~ 522 Mb of the genome. The final assembly consisted of 15 scaffolds that were assigned to eight pseudochromosomes using a high-density genetic map of B. nigra . Around 246 Mb of the genome consisted of the repeat elements; LTR/Gypsy types of retrotransposons being the most predominant. The B genome-specific repeats were identified in the centromeric regions of the B. nigra pseudochromosomes. A total of 57,249 protein-coding genes were identified of which 42,444 genes were found to be expressed in the transcriptome analysis. A comparison of the B genomes of B. nigra and B. juncea revealed high gene colinearity and similar gene block arrangements. A comparison of the structure of the A, B, and C genomes of U’s triangle showed the B genome to be divergent from the A and C genomes for gene block arrangements and centromeric regions. Conclusions A highly contiguous genome assembly of the B. nigra genome reported here is an improvement over the previous short-read assemblies and has allowed a comparative structural analysis of the A, B, and C genomes of the species belonging to the U’s triangle. Based on the comparison, we propose a new nomenclature for B. nigra pseudochromosomes, taking the B. rapa pseudochromosome nomenclature as the reference.

Background Microsatellites, or Simple Sequence Repeats (SSRs), are short tandem repeats of 1–6 nt motifs present in all genomes. Emerging evidence points to their role in cellular processes and gene regulation. Despite the huge resource of genomic information currently available, SSRs have been studied in a limited context and compared across relatively few species. Results We have identified ~ 685 million eukaryotic microsatellites and analyzed their genomic trends across 15 taxonomic subgroups from protists to mammals. The distribution of SSRs reveals taxon-specific variations in their exonic, intronic and intergenic densities. Our analysis reveals the differences among non-related species and novel patterns uniquely demarcating closely related species. We document several repeats common across subgroups as well as rare SSRs that are excluded almost throughout evolution. We further identify species-specific signatures in pathogens like Leishmania as well as in cereal crops, Drosophila , birds and primates. We also find that distinct SSRs preferentially exist as long repeating units in different subgroups; most unicellular organisms show no length preference for any SSR class, while many SSR motifs accumulate as long repeats in complex organisms, especially in mammals. Conclusions We present a comprehensive analysis of SSRs across taxa at an unprecedented scale. Our analysis indicates that the SSR composition of organisms with heterogeneous cell types is highly constrained, while simpler organisms such as protists, green algae and fungi show greater diversity in motif abundance, density and GC content. The microsatellite dataset generated in this work provides a large number of candidates for functional analysis and for studying their roles across the evolutionary landscape.

In this paper, a heuristic approach to doubly fed induction generator (DFIG) protection and low voltage ride through (LVRT) is carried out. DFIG-based wind systems are rapidly penetrating the power generation section. Despite their advantages, their direct coupling grid makes them highly sensitive to symmetrical faults. A well-known solution to this is the crowbar method of DFIG protection. This paper provides a method to determine the optimal crowbar resistance value, to ensure a strong trade-off between the rotor current and DC voltage transients. Further, since the crowbar method requires disconnection from the grid, the linear quadratic regulator (LQR) is applied to the system. This is to ensure fault ride through compliance with recent grid code requirements. The well-known PSO, as well as the recently developed African vultures optimization algorithm (AVOA), was applied to the problem. The first set of results show that for severe symmetrical voltage dips, the AVOA provides a good option for crowbar magnitude optimization, whereas PSO performed better for moderately severe dips. Secondly, when the LQR was optimized via the AVOA, it exhibited superiority over the conventional PSO-based PI controller. This superiority was in terms of rotor current transient magnitude, DC voltage transient magnitude, and reactive power steady-state ripple and were in the order of 67.5%, 20.35%, and 37.55%, respectively. When comparing the crowbar method and the LQR, it was observed that despite the LQR exhibiting superiority in terms of transient performance, the crowbar method offered a unanimously superior settling time.

This research introduces an enhanced fault detection approach, variational mode decomposition (VMD), for identifying open-circuit IGBT faults in the grid-side converter (GSC) of a doubly fed induction generator (DFIG) wind turbine system. VMD has many advantages over other decomposition methods, notably for non-stationary signals and noise. VMD’s robustness stems from its ability to decompose a signal into intrinsic mode functions (IMFs) with well-defined centre frequencies and bandwidths. The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. Ten operational scenarios with a wind speed range of 5–16 m/s were simulated using a comprehensive MATLAB/Simulink version R2022b model, including one healthy condition and nine unique IGBT failure conditions. The obtained current signals were decomposed via VMD to extract essential frequency components, which were normalised and utilised as input sequences for deep learning models. A comparative comparison of CNN-LSTM and CNN-only classifiers revealed that the CNN-LSTM model attained the greatest classification accuracy of 88.00%, exhibiting enhanced precision and resilience in noisy and dynamic environments. These findings emphasise the efficiency of integrating advanced signal decomposition with deep sequential learning for real-time, high-precision fault identification in wind turbine power electronic converters.

High entropy alloys are an innovative class of materials for a wide range of industrial applications due to their competitive properties such as improved mechanical properties, superior wear resistance characteristics, and excellent corrosion behavior, which are widely desired for a variety of applications considering several attributes such as economical, eco-friendly and safety. Thus, the quest for high-performance materials with exceptional properties is an unfading research topic for researchers, academia, and metallurgical scientists. HEA presents a novel alloy design idea focused on multi principal elements, a huge compositional space, and more opportunities to develop diverse alloys with exceptional properties. As universally acknowledged, the immense potential in compositions, microstructures, and properties has sparked a great interest in this field. Researchers primarily focused on equimolar HEAs, but the precedent eventually shifted to non-equimolar alloys. As the investigation over HEAs progressed, four core effects were identified as the most important aspects in enabling the distinct characteristics. Mechanical alloying (MA), followed by the sintering approach, has piqued the interest of all researchers focusing on HEA development. As a result, the main intent of this study is to examine mechanically alloyed HEAs critically for mechanical properties, tribological behavior, corrosion behavior, and functional properties. Furthermore, the predominant challenges and their conceivable prospects are also deliberated that offer novelty to this review article.