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This study goes into the essential challenge of estimating potato output in order to ensure sustainable agricultural practices while also providing vital insights into global market patterns. The potato production data series compares the accuracy of two popular forecasting models, ARIMA (AutoRegressive Integrated Moving Average) and ETS (Error-Trend-Seasonality), in predicting potato production. The study assesses the efficacy of these models with a particular focus on their relevance to the agricultural markets of India, China, and the USA, three major potato-producing countries. This research builds ARIMA and ETS models and thoroughly assesses their forecasting performance using historical production data series from these important nations. The results show that the ETS model, especially when considering the chosen countries, consistently performs better in predicting potato production for the testing data set than the ARIMA model. According to the models, China and India will keep contributing more to the potato market, solidifying their positions as key players. It is anticipated that the US economy will plateau and stabilize. For the anticipated year 2027, the expected potato output for China, India, and the USA is 100,417, 61,882, and 18,229 thousand tonnes, respectively. Nonetheless, the increasing diversity of confidence intervals in extended forecasts illustrates the intricacy of agricultural productivity and the numerous factors that could impact outcomes. We believe that this research significantly advances sustainable farming methods by offering a thorough analysis of worldwide potato production projections. It also improves our comprehension of the dynamics of the potato market, providing insightful information that can guide decision-making at different levels. In the conclusions, we stated that the studies not only have consequences for the potato sector, but they also highlight how crucial it is to use cutting-edge forecasting methods in order to promote sustainable food production and guarantee future food security. [ABSTRACT FROM AUTHOR]

The fight against climate change is one of the main global challenges of our time, and the European Union (EU) seeks to achieve climate neutrality and energy transition for the continent by 2050 through various policies. This research studies the economic implications of the EU Emissions Trading System (ETS) on European ports. By analysing various maritime scenarios, the study assesses how the ETS influences shipping routes, port competitiveness, and overall economic activity. A key finding is that the ETS imposes significant additional costs on shipping companies, which could lead to adjustments in routes and a shift in cargo volumes to ports in regions with less stringent environmental regulations. This could result in job losses in European port communities and reduce the competitiveness of European ports. In addition, the potential for carbon leakage, where shipping activities are simply relocated to regions with fewer emission controls, is explored. [ABSTRACT FROM AUTHOR]

The results of the identification of sensitive areas are affected by the forecast time and verification area settings in targeted observations. Understanding this setting issue is important for improving the effectiveness of the identification of sensitive areas in real-time field campaigns. To determine this, a series of experiments were carried out based on the Ensemble Transform Sensitivity (ETS) method, and the results are as follows: (1) First, Observation System Simulation Experiments (OSSEs) were conducted to assimilate simulated dropsondes in sensitive areas (SENS) or non-sensitive areas (OTHR). The results showed that the SENS experiment improved forecasts of typhoon intensity, track, precipitation score, and RMSE of forecast elements. However, the OTHR experiment only improved the forecast in some aspects and even had negative effects on other aspects. This indicates that the sensitive areas identified by the ETS method are effective. (2) Different forecast time experiments were carried out. There were significant differences between the sensitive areas of fixed verification times and variable targeted observation times, indicating that the sensitive areas changed greatly with time. In the field campaign, it was necessary to calculate the sensitive area for multiple times in advance and to design or adjust the observation scheme according to the time. (3) Finally, comparative experiments of position deviation and size change in the verification area were carried out. It was found that for a big deviation, too large or too small a verification area will result in significant differences in the sensitive areas. Based on the study in this article, a verification area size of about 6° × 6° is recommended; this can not only accommodate the position deviation of the verification area from the typhoon center caused by forecast errors, but also does not contain too much noise unrelated to typhoons, which may affect the accuracy of identification of sensitive areas. [ABSTRACT FROM AUTHOR]

Green technology innovation is an important driving force for low-carbon development of enterprises. As a market-based environmental policy to promote greenhouse gas emission reduction, whether carbon emission trading scheme (ETS) can encourage enterprises to carry out green technology innovation under the background of "dual carbon" goal deserves further research. Taking Chinese A-share listed enterprises in the five major sectors as samples, this study constructed a modified Difference-in-Differences (DID) model to test the causal effect of ETS on green innovation in high-carbon industries. Three significant results can be summarized from the empirical study. Firstly, the ETS has a significant promoting effect on green innovation of high-carbon enterprises. And it can effectively promote high-carbon enterprises to achieve an average of a 13.24–19.56% increase in low-carbon innovation capabilities. Secondly, enterprises with different characteristics have heterogeneity in the impact of ETS implementation on green innovation. Secondly, the implementation of ETS exerts heterogeneous effects on green technology innovation across enterprises that possess diverse characteristics. Enterprises with a large capital scale (low equity concentration) have a more significant promoting effect than those with a small capital scale (high equity concentration). Thirdly, the green innovation effect of ETS exhibits significant heterogeneity across different types of industries. In the mining, manufacturing, and construction sectors, the ETS has effectively stimulated green innovation to a certain extent. There has been no significant change in green innovation in the sector of electricity, heat, gas, and water production and supply. In particular, after the implementation of the ETS, green innovation has actually been weakened in the transportation sector. As such, for policy makers, differentiated ETS policies should be implemented based on the actual situation of different industries and types of carbon-emitting enterprises. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With ambitious emission reduction targets presented in the international commitments, Vietnam has a pathway to implement an emission trading scheme (ETS) to curb its carbon emissions. However, research on the impacts of ETS in Vietnam is still unclear, especially since there is no study on ETS revenue redistribution policies. By developing a computable general equilibrium (CGE) model, this paper simulates the potential impacts of the ETS with different revenue redistribution options. The results indicate that to achieve Vietnam's target of 15.8% carbon emission reduction, the carbon price would significantly affect the economy with a GDP drop of 3.7% and a welfare loss of 2.89% of GDP. The revenue redistribution policies would lighten these negative impacts. While transferring to government activities reduces GDP loss, reducing income tax policy results in welfare improvement. At the sectoral level, the electricity sector is most negatively affected and also plays a key role in reducing carbon emissions in Vietnam. The revenue redistribution for government activities could improve outputs for the construction and some heavy industries while the revenue redistribution policy for households creates improvements for light industries and the electricity industry. [ABSTRACT FROM AUTHOR]

Using nine DNA barcode markers, we investigated genetic differences among Heracleum mantegazzianum Sommier & Levier samples from the species' native range in the Western Caucasus (Karachai-Cherkess Republic), invasive large Heracleum species from Kirovsk (Murmansk oblast) and Syktyvkar (Komi Republic), and Heracleum sosnowskyi Manden. samples from Nalchik (Kabardino-Balkar Republic). New sequence data for the markers rbcL, matK, trnL, trnH-psbA, rps16, trnQ-rps16, rps16-trnK, rpl32-trnL, ITS, and ETS were compared with GenBank data for the investigated and other Heracleum species. The ITS and ETS sequences of nuclear DNA exhibited certain polymorphism level, aligning well with the species boundaries of most Heracleum genus members analyzed, except for those identified morphologically as H. mantegazzianum and H. sosnowskyi. Molecular data indicate that all large invasive Heracleum samples collected in northern European Russia and the Western Caucasus likely belong to H. mantegazzianum. Our results show the absence of H. sosnowskyi in the Murmansk oblast and the Komi Republic. A possible explanation for these findings is the hybridization of H. mantegazzianum and H. sosnowskyi, which may have occurred spontaneously during their initial introduction at the Polar-Alpine Botanical Garden in Kirovsk, Murmansk oblast. [ABSTRACT FROM AUTHOR]

Firmly anchored on observational data, giant radio lobes from massive galaxies hosting supermassive black holes can exert a major negative feedback effect, by endowing the intergalactic gas with significant magnetic pressure hence retarding or preventing gas accretion onto less massive halos in the vicinity. Since massive galaxies that are largely responsible for producing the giant radio lobes, this effect is expected to be stronger in more overdense large-scale environments, such as protoclusters, than in underdense regions, such as voids. We show that by redshift z = 2 halos with masses up to (1011 to 12, 1012 to 13)M are significantly hindered from accreting gas due to this effect for radio bubble volume filling fraction of (1.0, 0.2), respectively. Since the vast majority of the stars in the universe at z < 2 to 3 form precisely in those halos, this negative feedback process is likely one major culprit for causing the global downturn in star formation in the universe. It also provides a natural explanation for the rather sudden flattening of the slope of the galaxy rest-frame UV luminosity function around z ~ 2. A cross-correlation between protoclusters and Faraday rotation measures may test the predicted magnetic field. Inclusion of this external feedback process in the next generation of cosmological simulations may be imperative. [ABSTRACT FROM AUTHOR]

The present study aims to reveal phylogenetic and phylogeographical patterns of the Eurasian species of Allium subg. Amerallium, covering 75% of the species in the Mediterranean region and East Asia, as well as 12 North American species representing most sections of the monophyletic American part of the subgenus. We sampled 38 species of A. subg. Amerallium out of a total of 52 species from the Old World. A molecular phylogeny of A. subg. Amerallium was constructed based on predominantly non‐coding sequences from two nuclear ribosomal RNA regions (ITS, ETS), and four chloroplast (cp) regions (trnL‐trnF, atpB‐rbcL, rpl32‐trnL, rps16 intron). Most clades are well supported in the analyses of the combined nuclear and chloroplast data. However, analyses of single chloroplast fragments produced different topologies, which can be explained by differences in the variability of the chloroplast regions studied. Analysis of all cpDNA fragments combined produced a similar topology compared to the tree based on nuclear data. Most sections in the Eurasian part of A. subg. Amerallium are monophyletic. Only the position of A. chamaemoly (monotypic A. sect. Chamaeprason) nested in A. sect. Molium makes the latter paraphyletic. Different positions of A. chamaemoly within nuclear and plastid trees indicate an ancient hybridogenic origin. Monophyly of the American species of A. subg. Amerallium suggests an origin from extinct North Asian taxa. Currently, only one species from this ancient group, A. kingdonii, still exists in the eastern Himalayas. [ABSTRACT FROM AUTHOR]

Wind speed forecasting is essential for various domains, such as renewable energy generation, aviation, agriculture, and disaster management. However, wind speed is a complex and stochastic phenomenon that exhibits multiple stochastic patterns. Therefore, forecasting methods that can account for the dynamics and uncertainty of wind speed are required. The literature on wind forecasting classifies the time-scale into different categories, such as short-term, medium-term, and long-term. The data used for these categories is usually daily or sub-daily, such as hourly. This implies that high frequency data poses more challenges for the modeling. Machine learning (ML) methods have shown great potential in forecasting wind speed. However, ML methods may not be able to capture the complex and stochastic patterns of time series data, especially when there are multiple seasonal cycles. Therefore, it is often beneficial to use a preprocessing method like time series decomposition before applying ML methods. This article proposes a novel hybrid model that integrates machine learning and time series decomposition for forecasting wind speed. A multivariate seasonal trend decomposition method based on loess (MSTL) is used to preprocess the wind speed data before applying various forecasting methods. MSTL is a generalization of the popular STL method that can split the time series data into a trend component, multiple seasonal components, and a residual component. The seasonal components are considered as deterministic and forecasted using a seasonal naive method that repeats the most recent cycle. For the other non-seasonal components, the neural network autoregression (NNAR) method is employed. The same approach is followed for two common statistical methods: autoregressive integrated moving average and exponential smoothing state space model (ETS). This enables the comparison of the performance of machine learning and statistical forecasting methods based on time series decomposition. The MSTL-NNAR model is also contrasted with the NNAR model without preprocessing to assess the effectiveness of MSTL. The proposed model is applied to two wind speed datasets with different frequencies: daily and hourly. The model is evaluated for both long-term and short-term forecasts by comparing it with other models using various accuracy and bias measures. The results show that the hybrid model, MSTL-NNAR, outperforms all other models in terms of accuracy and bias for both types of data and both time horizons. The results also show that MSTL preprocessing significantly improves the performance of NNAR by reducing its error and bias. The results demonstrate the effectiveness of the hybrid model for forecasting wind speed data that are complex, nonlinear and high-frequency. The model can provide reliable forecasts for different time horizons and different types of data. The model can be useful for various applications, such as wind power generation, weather prediction, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Xanthomonas citri pv. fuscans (Xcf) and Xanthomonas phaseoli pv. phaseoli (Xpp) are responsible for the Common Bacterial Blight (CBB), a major common bean (Phaseolus vulgaris) disease. The pathogenicity of Xcf and Xpp is known to be dependent upon a functional Type III Secretion System (T3SS) allowing the injection of numerous bacterial Type III Effectors (T3Es) into plant cells. T3Es have been described as able to disrupt plant defence and manipulate plant metabolism.In this work we described the transcriptomic response of one susceptible (Flavert) and one resistant (Vezer) cultivars of P. vulgaris to the inoculation of the virulent strain Xcf CFBP4885 or its avirulent T3SS-defective hrcV mutant (CFBP13802).Leaves of both bean cultivars were infiltrated with water or bacterial suspensions. Inoculated leaves were sampled at 24 or 48 h post inoculation (hpi). The experiment was independently repeated three times for total RNA extraction and sequencing analysis. Library construction and total RNA sequencing were performed with BGISEQ-500 at Beijing Genomics Institute (BGI, Hong-Kong), generating an average of 24M of paired-end reads of 100bp per sample. FastQC was used to check reads quality. Mapping analyses were made using a quasi-mapping alignment from Salmon (version 1.2.1) against the Phaseolus vulgaris reference genome (version 2.1), revealing the expression profiles of 36,978 transcripts in leaf tissues.Fastq raw data and count files from 36 samples are available in the Gene Expression Omnibus (GEO) repository of the National Center for Biotechnology Information (NCBI) under the accession number GSE271236.This dataset is a valuable resource to investigate the role of T3Es in subverting the cellular functions of bean.

The notochord is a defining feature of all chordates. The transcription factors Zic and ETS regulate enhancer activity within the notochord. We conduct high-throughput screens of genomic elements within developing Ciona embryos to understand how Zic and ETS sites encode notochord activity. Our screen discovers an enhancer located near Lama, a gene critical for notochord development. Reversing the orientation of an ETS site within this enhancer abolishes expression, indicating that enhancer grammar is critical for notochord activity. Similarly organized clusters of Zic and ETS sites occur within mouse and human Lama1 introns. Within a Brachyury (Bra) enhancer, FoxA and Bra, in combination with Zic and ETS binding sites, are necessary and sufficient for notochord expression. This binding site logic also occurs within other Ciona and vertebrate Bra enhancers. Collectively, this study uncovers the importance of grammar within notochord enhancers and discovers signatures of enhancer logic and grammar conserved across chordates.

The objective of study was to propose the appropriate model for forecasting the area and production of rice crops in Pakistan. The data for the rice area and production was taken for the agriculture statistics from 1947 to 2020 from the official website, Ministry of Finance, Government of Pakistan. The conventional ARIMA methodology was applied initially to forecast the rice area and production by using the proposed ARIMA(1,1,0) model. Then ARIMA, ETS, TBATS, Artificial Neural Network (ANN) and ARIMA-ETS, ARIMA-TBATS and ARIMA-ANN hybrid model were compared. It was observed that ARIMA(1,1,0) model was best for forecasting the production of rice than all other seven models with the lowest value of RMSE = 401.90 (tons) and MAE = 250.32. For forecasting the rice area, the ARIMA-ANN model was selected because it showed the lowest value of RMSE = 121.89 (Ha) and MAE = 85.82 among all the models. Rice area and production was forecast from 2021 to 2030 by the proposed models, which show that the average prediction of rice production is 7879.96 tons while the rice area would be 3010.82 hectare for the next ten years. It shows that area will be increased by 2.11 percent and rice production will be increased by 11.97 percent upto 2030. [ABSTRACT FROM AUTHOR]

Many modern statistical models are used for both insight and prediction when applied to data. When models are used for prediction one should optimise parameters through a prediction error loss function. Estimation methods based on multiple steps ahead forecast errors have been shown to lead to more robust and less biased estimates of parameters. However, a plausible explanation of why this is the case is lacking. In this paper, we provide this explanation, showing that the main benefit of these estimators is in a shrinkage effect, happening in univariate models naturally. However, this can introduce a series of limitations, due to overly aggressive shrinkage. We discuss the predictive likelihoods related to the multistep estimators and demonstrate what their usage implies to time series models. To overcome the limitations of the existing multiple steps estimators, we propose the Geometric Trace Mean Squared Error, demonstrating its advantages. We conduct a simulation experiment showing how the estimators behave with different sample sizes and forecast horizons. Finally, we carry out an empirical evaluation on real data, demonstrating the performance and advantages of the estimators. Given that the underlying process to be modelled is often unknown, we conclude that the shrinkage achieved by the GTMSE is a competitive alternative to conventional ones. [ABSTRACT FROM AUTHOR]

Copyright of Matronas Hoy is the property of Asociacion Espanola de Matronas (AEM) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The results of the identification of sensitive areas are affected by the forecast time and verification area settings in targeted observations. Understanding this setting issue is important for improving the effectiveness of the identification of sensitive areas in real-time field campaigns. To determine this, a series of experiments were carried out based on the Ensemble Transform Sensitivity (ETS) method, and the results are as follows: (1) First, Observation System Simulation Experiments (OSSEs) were conducted to assimilate simulated dropsondes in sensitive areas (SENS) or non-sensitive areas (OTHR). The results showed that the SENS experiment improved forecasts of typhoon intensity, track, precipitation score, and RMSE of forecast elements. However, the OTHR experiment only improved the forecast in some aspects and even had negative effects on other aspects. This indicates that the sensitive areas identified by the ETS method are effective. (2) Different forecast time experiments were carried out. There were significant differences between the sensitive areas of fixed verification times and variable targeted observation times, indicating that the sensitive areas changed greatly with time. In the field campaign, it was necessary to calculate the sensitive area for multiple times in advance and to design or adjust the observation scheme according to the time. (3) Finally, comparative experiments of position deviation and size change in the verification area were carried out. It was found that for a big deviation, too large or too small a verification area will result in significant differences in the sensitive areas. Based on the study in this article, a verification area size of about 6° × 6° is recommended; this can not only accommodate the position deviation of the verification area from the typhoon center caused by forecast errors, but also does not contain too much noise unrelated to typhoons, which may affect the accuracy of identification of sensitive areas.

An important unanswered question in regenerative biology is to what extent regeneration is accomplished by the reactivation of gene regulatory networks used during development versus the activation of regeneration-specific transcriptional programs. Following damage, Drosophila imaginal discs, the larval precursors of adult structures, can regenerate missing portions by localized proliferation of damage-adjacent tissue. Using single-cell transcriptomics in regenerating wing discs, we have obtained a comprehensive view of the transcriptome of regenerating discs and identified two regeneration-specific cell populations within the blastema, Blastema1 and Blastema2. Collectively, these cells upregulate multiple genes encoding secreted proteins that promote regeneration including Pvf1, upd3, asperous, Mmp1, and the maturation delaying factor Ilp8. Expression of the transcription factor Ets21C is restricted to this regenerative secretory zone; it is not expressed in undamaged discs. Ets21C expression is activated by the JNK/AP-1 pathway, and it can function in a type 1 coherent feedforward loop with AP-1 to sustain expression of downstream genes. Without Ets21C function, the blastema cells fail to maintain the expression of a number of genes, which leads to premature differentiation and severely compromised regeneration. As Ets21C is dispensable for normal development, these observations indicate that Ets21C orchestrates a regeneration-specific gene regulatory network. We have also identified cells resembling both Blastema1 and Blastema2 in scribble tumorous discs. They express the Ets21C-dependent gene regulatory network, and eliminating Ets21C function reduces tumorous growth. Thus, mechanisms that function during regeneration can be co-opted by tumors to promote aberrant growth.

This paper studies when and under what conditions the actions undertaken by the power plants involved in China's emission trading scheme (ETS) pilot became cost effective. Based on unique plant-level panel data and the difference-in-differences strategy, we identify that an insignificant initial reduction in cost efficiency occurred at the announcement stage for power plants in the pilot provinces; however, the cost efficiency of the pilot plants increased significantly following formal policy implementation. Additionally, the by-stage treatment effects differed across the pilot provinces due to localized market and non-market variations. Localized conditions of higher marketization, stricter policy enforcement, and lower carbon dependence enhanced this positive effect. The synthetic control results confirmed this variation in the policy effects. The carbon trading pilots resulted in improved efficiency in power plants in Shanghai, Guangdong, and Tianjin during the period 2013--2017, with an associated total cost saving of approximately 29.75 million RMB. To enhance the efficacy of the ETS policy, our findings suggest that the design of the policy should consider localized external factors. [ABSTRACT FROM AUTHOR]

High-impact weather (HIW) events, such as typhoons, usually have sensitive regions where additional observations can be deployed and sensitive observations assimilated, which can improve forecasting accuracy. The ensemble transform sensitivity (ETS) method was employed to estimate the sensitive regions in the "Chaba" case in order to explore the impact of observation data in sensitive areas on typhoon forecasting during the rapid intensification phase. A set of observation system simulation experiments were conducted, with assimilations of sensitive observations (SEN), randomly selected observations (RAN), whole domain observations (ALL), and no assimilation (CTRL). The results show that (1) the sensitive areas of Typhoon "Chaba" are primarily located in the southwest of the typhoon center and are associated with the distribution of the wind field structure; (2) the typhoon intensity and tracks simulated by the SEN and RAN experiments are closer to the truth than the CTRL; (3) the SEN experiment, with only 3.6% of assimilated data observations, is comparable with the ALL experiment during the rapid intensification phase of the typhoon; (4) the uncertainty of the mesoscale model can be improved by capturing large-scale vertical wind shear and vorticity features from the GEFS data and then using the data assimilation method, which makes the vertical shear and vorticity field more reasonable. [ABSTRACT FROM AUTHOR]

Higher education institutions place a lot of importance on their electronic learning systems. Educational institutions in Pakistan and other countries have adopted learning management systems (LMS) due to the coronavirus (COVID-19) pandemic scenario. The learning management system (LMS) establishes a digital learning environment where evaluation and user learning behavior must be carefully analyzed. The "quality of interaction" (QoI) of students is one of the main issues in LMS. Based on various usage matrices (such as the number of logins, clicks, total time spent on the LMS, and actions taken), a student's level of interaction with the LMS can be determined. QoI is an essential predictor of the accomplishment of students' final grades. Normally, to examine the effectiveness of LMS usage on students' learning performance, studies have relied on data gathered from users via surveys. However, the data gathered through surveys are typically associated with the risk of distortion or low quality. Therefore, in order to evaluate and predict the quality of interaction in terms of usage matrices, our proposed work analyzed data from the Moodle LMS at "Hazara University" (HU) for the law and English departments' courses. This research aims to assess and forecast the quality of student interaction within an LMS by analyzing usage metrics. Unlike traditional survey-based approaches, we explored the predictive performance of LSTM (Long Short-Term Memory), Exponential Smoothing method (ETS), and ARIMA (Autoregressive Integrated Moving Average) methods to predict the weekly LMS usage factors of students. ARIMA and ETS produce better prediction results than LSTM for weekly predictions. Moreover, LSTM model training took considerable computational time for provided datasets. [ABSTRACT FROM AUTHOR]

Freshwater ecosystems are undergoing rapid thermal shifts, making it increasingly important to understand species-specific responses to these changes. Traditional techniques for determining a species' thermal tolerance are often lethal and time consuming. Using the enzyme activity associated with the electron transport system (ETS; hereafter referred to as enzyme assay) may provide a non-lethal, rapid, and efficient alternative to traditional techniques for some species. We used largemouth bass Micropterus salmoides (Lacepede, 1802) to test the efficacy of using an enzyme assay to determine thermal tolerance and respiratory exploitation in response to variable acclimation temperatures. Three tissue types were dissected from fish acclimated to 20, 25, or 30 °C and used in ETS assays at temperatures ranging from 7.5 to 40 °C. While there were significant differences among tissue types and acclimation temperatures, maximal enzyme activity occurred from 25.23 to 31.91 °C. Fish lost equilibrium at 39–42 °C in traditional CTmax trials, significantly higher than the upper optimum range determined via enzyme assays. The ratio of enzyme activity to measured whole organism respiration rate decreased with increasing water temperature, with the largest changes occurring at the upper optimum thermal range determined by enzyme assays. Our results indicate that ETS analysis may prove useful for obtaining biologically relevant thermal tolerances. [ABSTRACT FROM AUTHOR]

Water striders are abundant in areas with high humidity and rainfall. Raindrops can weigh more than 40 times the adult water strider and some pelagic species spend their entire lives at sea, never contacting ground. Until now, researchers have not systematically investigated the survival of water striders when impacted by raindrops. In this experimental study, we use high-speed videography to film drop impacts on water striders. Drops force the insects subsurface upon direct contact. As the ensuing crater rebounds upward, the water strider is propelled airborne by a Worthington jet, herein called the first jet. We show the water strider's locomotive responses, low density, resistance to wetting when briefly submerged, and ability to regain a supersurface rest state, rendering it impervious to the initial impact. When pulled subsurface during a second crater formation caused by the collapsing first jet, water striders face the possibility of ejection above the surface or submersion below the surface, a fate determined by their position in the second crater. We identify a critical crater collapse acceleration threshold 5:7 gravities for the collapsing second crater which determines the ejection and submersion of passive water striders. Entrapment by submersion makes the water strider poised to penetrate the air-water interface from below, which appears impossible without the aid of a plastron and proper locomotive techniques. Our study is likely the first to consider second crater dynamics and our results translate to the submersion dynamics of other passively floating particles such as millimetric microplastics atop the world's oceans. [ABSTRACT FROM AUTHOR]

Noncoding mutation hotspots have been identified in melanoma and many of them occur at the binding sites of E26 transformation-specific (ETS) proteins; however, their formation mechanism and functional impacts are not fully understood. Here, we used UV (Ultraviolet) damage sequencing data and analyzed cyclobutane pyrimidine dimer (CPD) formation, DNA repair, and CPD deamination in human cells at single-nucleotide resolution. Our data show prominent CPD hotspots immediately after UV irradiation at ETS binding sites, particularly at sites with a conserved TTCCGG motif, which correlate with mutation hotspots identified in cutaneous melanoma. Additionally, CPDs are repaired slower at ETS binding sites than in flanking DNA. Cytosine deamination in CPDs to uracil is suggested as an important step for UV mutagenesis. However, we found that CPD deamination is significantly suppressed at ETS binding sites, particularly for the CPD hotspot on the 5' side of the ETS motif, arguing against a role for CPD deamination in promoting ETS-associated UV mutations. Finally, we analyzed a subset of frequently mutated promoters, including the ribosomal protein genes RPL13A and RPS20, and found that mutations in the ETS motif can significantly reduce the promoter activity. Thus, our data identify high UV damage and low repair, but not CPD deamination, as the main mechanism for ETS-associated mutations in melanoma and uncover important roles of often-overlooked mutation hotspots in perturbing gene transcription. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) play a crucial role in regulating the dynamic and precise patterns of gene expression required for the initial specification of endothelial cells (ECs), and during endothelial growth and differentiation. While sharing many core features, ECs can be highly heterogeneous. Differential gene expression between ECs is essential to pattern the hierarchical vascular network into arteries, veins and capillaries, to drive angiogenic growth of new vessels, and to direct specialization in response to local signals. Unlike many other cell types, ECs have no single master regulator, instead relying on differing combinations of a necessarily limited repertoire of TFs to achieve tight spatial and temporal activation and repression of gene expression. Here, we will discuss the cohort of TFs known to be involved in directing gene expression during different stages of mammalian vasculogenesis and angiogenesis, with a primary focus on development. Key Findings: Up‐to‐date summary of all transcription factors involved in angiogenesis and vasculogenesis Discusses the roles for each factor in the vasculature, including the consequences of transcription factor ablation, and known downstream targets of each factor Includes comprehensive tables listing consensus binding motifs, expression profiles and available ChIP‐seq datasets for all endothelial transcription factors alongside known enhancers with bound transcription factors. This provides an essential and much needed resource for all scientists researching vascular biology, particularly in the era of single cell ATAC and RNA‐seq datasets [ABSTRACT FROM AUTHOR]

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models. [ABSTRACT FROM AUTHOR]

Varicose veins are the most common venous disorder in humans and are characterized by hemodynamic instability due to valvular insufficiency and orthostatic lifestyle factors. It is unclear how changes in biomechanical signals cause aberrant remodeling of the vein wall. Our previous studies suggest that Notch signaling is implicated in varicose vein arterialization. In the arterial system, mechanoresponsive ETS1 is a transcriptional activator of the endothelial Notch, but its involvement in sensing disrupted venous flow and varicose vein formation has not been investigated. Here, we use human varicose veins and cultured human venous endothelial cells to show that disturbed venous shear stress activates ETS1-NOTCH4/DLL4 signaling. Notch components were highly expressed in the neointima, whereas ETS1 was upregulated in all histological layers of varicose veins. In vitro microfluidic flow-based studies demonstrate that even minute changes in venous flow patterns enhance ETS1-NOTCH4/DLL4 signaling. Uniform venous shear stress, albeit an inherently low-flow system, does not induce ETS1 and Notch proteins. ETS1 activation under altered flow was mediated primarily by MEK1/2 and, to a lesser extent, by MEK5 but was independent of p38 MAP kinase. Endothelial cell-specific ETS1 knockdown prevented disturbed flow-induced NOTCH4/DLL4 expression. TK216, an inhibitor of ETS-family, prevented the acquisition of arterial molecular identity and loss of endothelial integrity in cells exposed to the ensuing altered shear stress. We conclude that ETS1 senses blood flow disturbances and may promote venous remodeling by inducing endothelial dysfunction. Targeting ETS1 rather than downstream Notch proteins could be an effective and safe strategy to develop varicose vein therapies.

Abstract Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory, there is little research on using blockchain in carbon emission trading schemes (ETS). This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology. Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security. Considering the augmentation of information interaction during the auction process brought about by blockchain, the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction. In particular, implementing smart contracts can provide a swift and automatic settlement. The simulation results of the proposed system demonstrate the following: (1) fine-grained access is possible with a second delay; (2) the average annual compliance levels increase by 2% when bidders’ learning behavior is considered; and (3) the blockchain network can process more than 350 reading operations or 7 writing operations in a second.

Abstract Background ETS transcription factors, known as the E26 transformation-specific factors, assume a critical role in the regulation of various vital biological processes in animals, including cell differentiation, the cell cycle, and cell apoptosis. However, their characterization in mollusks is currently lacking. Results The current study focused on a comprehensive analysis of the ETS genes in blood clam Tegillarca granosa and other mollusk genomes. Our phylogenetic analysis revealed the absence of the SPI and ETV subfamilies in mollusks compared to humans. Additionally, several ETS genes in mollusks were found to lack the PNT domain, potentially resulting in a diminished ability of ETS proteins to bind target genes. Interestingly, the bivalve ETS1 genes exhibited significantly high expression levels during the multicellular proliferation stage and in gill tissues. Furthermore, qRT-PCR results showed that Tg-ETS-14 (ETS1) is upregulated in the high total hemocyte counts (THC) population of T. granosa, suggesting it plays a significant role in stimulating hemocyte proliferation. Conclusion Our study significantly contributes to the comprehension of the evolutionary aspects concerning the ETS gene family, while also providing valuable insights into its role in fostering hemocyte proliferation across mollusks.

Abstract Background Ajania Poljakov, an Asteraceae family member, grows mostly in Asia’s arid and semi-desert areas and is a significant commercial and decorative plant. Nevertheless, the genus’ classification has been disputed, and the evolutionary connections within the genus have not been thoroughly defined. Hence, we sequenced and analyzed Ajania’s plastid genomes and combined them with ETS data to assess their phylogenetic relationships. Results We obtained a total of six new Ajania plastid genomes and nine ETS sequences. The whole plastome lengths of the six species sampled ranged from 151,002 bp to 151,115 bp, showing conserved structures. Combined with publicly available data from GenBank, we constructed six datasets to reconstruct the phylogenetic relationships, detecting nucleoplasmic clashes. Our results reveal the affinities of Artemisia, Chrysanthemum and Stilpnolepis to Ajania and validate the early taxonomy reclassification. Some of the plastid genes with low phylogenetic information and gene trees with topological differences may have contributed to the ambiguous phylogenetic results of Ajania. There is extensive evolutionary rate heterogeneity in plastid genes. The psbH and ycf2 genes, which are involved in photosynthesis and ATP transport, are under selective pressure. Plastomes from Ajania species diverged, and structural aspects of plastomes may indicate some of the real evolutionary connections. We suggest the ycf1 gene as a viable plastid DNA barcode because it has significant nucleotide diversity and better reflects evolutionary connections. Conclusion Our findings validate the early Ajania taxonomy reclassification and show evolutionary rate heterogeneity, genetic variety, and phylogenetic heterogeneity of plastid genes. This research might provide new insights into the taxonomy and evolution of Ajania, as well as provide useful information for germplasm innovation and genetic enhancement in horticultural species.

Emission trading scheme (ETS) is popular to abate anthropogenic emissions throughout the world. Previous researchers focused on evaluating ETS policy effect, but ETS design is usually neglected because ETS is already mostly sophisticated worldwide. This is not the case in China, as the Chinese nationwide ETS (CNETS) came into effect in July 2021. Implemented for a brief period, the CNETS lacks implementation details and thus may not achieve mitigation targets cost-effectively. In this paper, we attempt to narrow the research gap by comprehensively designing the CNETS. Our research framework is based on a dynamic recursive computable general equilibrium (CGE) model. The CGE model results show that the appropriate CNETS should include the coverage of the electricity generation and manufacturing sectors, higher carbon price (175 ${CNY} / {t} {CO}_2$), quota allocation based on the carbon intensity in the previous year, higher quota decline factor (2%) and time-decreasing free quota ratio. Although we have only designed the Chinese ETS in this paper, the research framework may become a paradigm of designing appropriate ETS.

In cancer, the production of ATP depends mainly on glycolysis, usually accompanied by the dysfunction of the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS). Nicotinamide adenine dinucleotide (NAD+) is a coenzyme for various biological enzymatic reactions such as those involved in the TCA cycle. To investigate the molecular mechanisms involved in carcinogenesis, the transcription system of genes associated with mitochondrial function should be elucidated. In this study, we isolated several mitochondrial function-associated bidirectional promoters and tested whether they responded to NAD+-metabolism regulating compounds, namely, trans-resveratrol (Rsv), 2-deoxy-D-glucose (2DG), 3-amino benzamide (3AB), and olaparib (OLA), in HeLa S3 cells. Transient transfection and luciferase (Luc) reporter assay showed that the IDH1 promoter was prominently activated by these compounds. The IDH1 gene, which encodes a nicotinamide adenine dinucleotide phosphate (NADP+) dependent isocitrate dehydrogenase, is frequently mutated in glioma and leukemia cells. In this study, RT-PCR showed that IDH1 gene and protein expression was induced in response to the NAD+-regulating drugs Rsv and 3AB. However, IDH1 protein amount was rather stable at control level. The result suggested that a post-transcriptional controlling system works to keep IDH1 at a stable level.

This study examined the existing methodologies and the applicability of process modeling concerning the estimation of nitrous oxide (N2O) emissions from wastewater treatment processes. The method of extimating N2O emissions from wastewater occurring during biological nitrification and denitrification processes can be divided into those before and after legal nitrogen effluent quality enhancement for domestic wastewater treatment plant. Initial IPCC guidelines such as IPCC 1996 GL, GPG 2000, and IPCC 2006 GL, provided methods for estimating N2O emissions throughout domestic wastewater, while the revised version, IPCC 2019 RF, proposed methods for estimating each N2O emission by distinguishing wastewater treatment and effluent after the advancement of wastewater treatment plants. The National Greenhouse Gas Inventory Report (NIR) in Korea adopted the methodology of GPG 2000, and the Emissions Trading System (ETS) applies the estimation method of IPCC 2006 GL in the wastewater treatment plants. This study utilized process modeling to consider the process characteristics and operating conditions of wastewater treatment plants. IPCC, NIR, and ETS estimate N2O emissions based on nitrogen behavior activity data and emissions factors. On the other hand, process modeling estimates N2O emissions in each unit process of the wastewater treatment plant using a 4-step AOB nitrification/4-step denitrification model and a liquid/gas transfer model. The estimation results of N2O emissions from modeling method were compared with the estimation results of IPCC, NIR, and ETS to analyze the suitability of the estimation method. Through this, it was evaluated how appropriate process modeling is for estimating N2O emissions in domestic wastewater treatment plants.

Forecasting is valuable to countries because it enables them to make informed business decisions and develop data-driven strategies. Fruit production offers promising economic opportunities to reduce rural poverty and unemployment in developing countries and is a crucial component of farm diversification strategies. After vegetables, fruits are the most affordable source of essential vitamins and minerals for human health. India's fruit production strategies should be developed based on accurate predictions and the best forecasting models. This study focused on the forecasting behavior of production of apples, bananas, grapes, mangoes, guavas, and pineapples in India using data from 1961 to 2015 (modelling set) and 2016–2020 (predicting set). Two unit root tests were used, the Ng–Perron (2001) test, and the Dickey–Fuller test with bootstrapping critical values depending on the Park (2003) technique. The results show that all variables are stationary at first differences. Autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) models were used and compared based on goodness of fit. The results indicated that the ETS model was the best in all the cases, as the predictions using ETS had the smallest errors and deviations between forecasting and actual values. This result was confirmed using three tests: Diebold–Mariano, Giacomini–White, and Clark–West. According to the best models, forecasts for production during 2021–2027 were obtained. In terms of production, an increase is expected for apples, bananas, grapes, mangoes, mangosteens, guavas, and pineapples in India during this period. The current outcomes of the forecasts could enable policymakers to create an enabling environment for farmers, exporters, and other stakeholders, leading to stable markets and enhanced economic growth. Policymakers can use the insights from forecasting to design strategies that ensure a diverse and nutritious fruit supply for the population. This can include initiatives like promoting small-scale farming, improving postharvest storage and processing facilities, and establishing effective distribution networks to reach vulnerable communities.

Abstract Background Accelerated globalization especially in the late 1980s has provided opportunities for economic progress in the world of emerging economies. The BRICS nations’ economies are distinguishable from other emerging economies due to their rate of expansion and sheer size. As a result of their economic prosperity, health spending in the BRICS countries has been increasing. However, health security is still a distant dream in these countries due to low public health spending, lack of pre-paid health coverage, and heavy out-of-pocket spending. There is a need for changing the health expenditure composition to address the challenge of regressive health spending and ensure equitable access to comprehensive healthcare services. Objective Present study examined the health expenditure trend among the BRICS from 2000 to 2019 and made predictions with an emphasis on public, pre-paid, and out-of-pocket expenditures for 2035. Methods Health expenditure data for 2000–2019 were taken from the OECD iLibrary database. The exponential smoothing model in R software (ets ()) was used for forecasting. Results Except for India and Brazil, all of the BRICS countries show a long-term increase in per capita PPP health expenditure. Only India’s health expenditure is expected to decrease as a share of GDP after the completion of the SDG years. China accounts for the steepest rise in per capita expenditure until 2035, while Russia is expected to achieve the highest absolute values. Conclusion The BRICS countries have the potential to be important leaders in a variety of social policies such as health. Each BRICS country has set a national pledge to the right to health and is working on health system reforms to achieve universal health coverage (UHC). The estimations of future health expenditures by these emerging market powers should help policymakers decide how to allocate resources to achieve this goal.

Although blockchain technology has received a significant amount of cutting-edge research on constructing a novel carbon trade market in theory, there is little research on using blockchain in carbon emission trading schemes (ETS). This study intends to address existing gaps in the literature by creating and simulating an ETS system based on blockchain technology. Using the ciphertext-policy attributed-based encryption algorithm and the Fabric network to build a platform may optimize the amount of data available while maintaining privacy security. Considering the augmentation of information interaction during the auction process brought about by blockchain, the learning behavior of bidding firms is introduced to investigate the impact of blockchain on ETS auction. In particular, implementing smart contracts can provide a swift and automatic settlement. The simulation results of the proposed system demonstrate the following: (1) fine-grained access is possible with a second delay; (2) the average annual compliance levels increase by 2% when bidders' learning behavior is considered; and (3) the blockchain network can process more than 350 reading operations or 7 writing operations in a second. Highlights: Novel cooperative management of an ETS platform based on blockchain is proposed. The data access control policy based on CP-ABE is used to solve the contradiction between data privacy on the firm chain and government supervision. A learned auction strategy is proposed to suit the enhancement of information interaction caused by blockchain technology. This study provides a new method for climate change policymakers to consider the blockchain application of the carbon market. [ABSTRACT FROM AUTHOR]

Background: Endoscopic transsphenoidal surgery is the primary method used to treat pituitary adenomas (PAs) at present; however, this technique is associated with certain risks, including cerebrospinal fluid leakage (CFL) and residual tumors (RTs). In this study, we aimed to identify specific risk factors for intraoperative CFL (ioCFL) and postoperative RT in patients with pituitary adenoma and construct a corresponding nomogram for risk assessment. Methods: We collected a range of information from 782 patients who underwent endoscopic transsphenoidal PA resection in the Department of Neurosurgery at Beijing Tiantan Hospital between 2019 and 2021. Patients were then randomly assigned to training and validation groups (in a 8:2 ratio) with R software. Univariate and multivariable logistic regression models were then used to screen variables related to ioCFL and RT. These variables were then used to construct a predictive nomogram. Finally, the accuracy of the nomogram was validated by receiver operating characteristic curve (ROC) analysis, calibration plots, and decision curve analysis (DCA). Results: Univariate and multivariable logistic regression models identified four risk factors for ioCFL (Hardy grade, tumor size, position, and consistency) and five risk factors for RT (operation time, tumor size, consistency, Knosp grade, and primary/recurrence type). The area under the ROC curve (AUC) for the ioCFL risk model was 0.666 and 0.697 for the training and validation groups, respectively. For RT, the AUCs for the two groups were 0.788 and 0.754, respectively. The calibration plots for the ioCFL and RT models showed high calibration quality and DCA analysis yielded excellent efficiency with regards to clinical decision making. Conclusion: Tumor size, growth characteristics, and invasion location were identified as the main factors affecting intraoperative CFL and RT. With our novel nomogram, surgeons can identify high-risk patients according to preoperative and intraoperative tumor performance and reduce the probability of complications. [ABSTRACT FROM AUTHOR]

Today, since the renewable energy transformation has not taken place yet, economic growth brings with it the devastating effects of climate change. There are various tax applications to remove these effects and new tax types have started to be included in the economy. Emissions trading system (ETS) is currently implemented in European Union countries (EU) to prevent carbon leakage. In the near future, the Carbon Border Adjustment Mechanism (CBAM) will be implemented gradually. These regulations will be a trigger for countries exporting to EU countries in terms of reducing CO2 emissions. However, since the carbon tax application has just begun, its relationship with economic growth cannot be examined. For this reason, in this study, the enviromental tax, which is the sum of energy, transportation and pollution taxes was focused on and the causality relationship between economic growth and it was examined. Correlogram, Dickey-Fuller and Augmented Dickey-Fuller (ADF-1979) and Philips-Perron (PP-1988), Johansen and Juselins (1990) cointegration test and finally Granger (1969) Causality test were applied. As a result of the study, one-way causality from economic growth to enviromental tax was determined. It was proven that a 1 percent increase in economic growth will increase the enviromental tax by approximately 0.005. [ABSTRACT FROM AUTHOR]

Background: ETS transcription factors, known as the E26 transformation-specific factors, assume a critical role in the regulation of various vital biological processes in animals, including cell differentiation, the cell cycle, and cell apoptosis. However, their characterization in mollusks is currently lacking. Results: The current study focused on a comprehensive analysis of the ETS genes in blood clam Tegillarca granosa and other mollusk genomes. Our phylogenetic analysis revealed the absence of the SPI and ETV subfamilies in mollusks compared to humans. Additionally, several ETS genes in mollusks were found to lack the PNT domain, potentially resulting in a diminished ability of ETS proteins to bind target genes. Interestingly, the bivalve ETS1 genes exhibited significantly high expression levels during the multicellular proliferation stage and in gill tissues. Furthermore, qRT-PCR results showed that Tg-ETS-14 (ETS1) is upregulated in the high total hemocyte counts (THC) population of T. granosa, suggesting it plays a significant role in stimulating hemocyte proliferation. Conclusion: Our study significantly contributes to the comprehension of the evolutionary aspects concerning the ETS gene family, while also providing valuable insights into its role in fostering hemocyte proliferation across mollusks. [ABSTRACT FROM AUTHOR]

The Manawatu region experiences deep tremor and long‐term slow slip events (SSEs); however, tremor is adjacent to, and not co‐located with, long‐term SSEs. Observations of episodic tremor and slip (ETS) elsewhere suggest it is possible smaller short‐term SSEs below the current detection threshold occur where tremor is observed. Therefore, we sought to determine if small SSEs occurred with Manawatu tremor. We decomposed GNSS data using times of tremor to assess average surface displacements and performed a static slip inversion to model the displacement during tremor. The slip inversion suggested small slow slip partially coincided with tremor and long‐term SSEs may influence these small SSEs by increasing slip rates. We suggest that the interface below deep long‐term SSEs may slip often, in small ETS‐like SSEs that are not individually detectable geodetically. The question remains as to the nature of the strong variability in SSE behavior with depth and duration in the southern Hikurangi margin. Plain Language Summary: In between the Earth's tectonic plates, energy builds over time and can be released along faults suddenly (seconds‐minutes; i.e., earthquakes) or slowly (weeks‐years; i.e., slow slip events). Slow slip often happens with low‐frequency earthquakes (i.e., tectonic tremor). The North Island, New Zealand features two colliding tectonic plates with the potential to generate large earthquakes. The interface between these plates has both deep tectonic tremor and large, long‐lasting slow slip, but the tectonic tremor is deeper on the fault than the large slow slip. Studies have suggested small, short‐lasting slow slip, usually not able to be detected, occur where tectonic tremor is found. In this study we tried a different approach to find the small slow slip. While small slow slip are not detected by themselves, we were able to detect their cumulative effect in the tectonic tremor area. We modeled small slow slip during tectonic tremor to find the mean sliding rate on the fault that is between the tectonic plates. The large long‐lasting slow slip may drive this smaller slow slip by making them slip faster. The question remains as to the cause of the many types of slow slip in New Zealand. Key Points: GNSS data indicates newly detected short‐term slow slip in region of deep tremorLong‐term slow slip events may influence these small slow slip events by increasing slip ratesThree types of slow slip overlap along‐strike in Hikurangi margin: shallow, short‐term; deep, long‐term; deeper, short‐term with tremor [ABSTRACT FROM AUTHOR]

Introduction: Seasonal influenza is a serious public health issue in China. This study aimed to develop a new hybrid model for seasonal influenza incidence prediction and provide reference information for early warning management before outbreaks. Methodology: Data on the monthly incidence of seasonal influenza between 2004 and 2018 were obtained from the China Public Health Science Data Center website. A single seasonal autoregressive integrated moving average (SARIMA) model and a single error trend and seasonality (ETS) model were built. On this basis, we constructed SARIMA, ETS, and support vector regression (SARIMA-ETS-SVR) hybrid model. The prediction performance was determined by comparing mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE) indices. Results: The optimum SARIMA model was SARIMA (0,1,0) (0,0,1)12. Error trend and seasonality (ETS) (M,A,M) was the SARIMA optimal model. For the fitting performance, the SARIMA-ETS-SVR hybrid model achieved the lowest values of MAE, MSE, and RMSE, in addition to the MAPE. In terms of predictive performance, the SARIMA-ETS-SVR hybrid model had the lowest MAE, MSE, MAPE, and RMSE values among the three models. Conclusions: The study demonstrated that the SARIMA-ETS-SVR hybrid model provides better generalization ability than a single SARIMA model and a single ETS model, and the predictions will provide a useful tool for preventing this infectious disease. [ABSTRACT FROM AUTHOR]

JET experiments using the fuel mixture envisaged for fusion power plants, deuterium and tritium (D–T), provide a unique opportunity to validate existing D–T fusion power prediction capabilities in support of future device design and operation preparation. The 2021 JET D–T experimental campaign has achieved D–T fusion powers sustained over 5 s in ITER-relevant conditions i.e. operation with the baseline or hybrid scenario in the full metallic wall. In preparation of the 2021 JET D–T experimental campaign, extensive D–T predictive modelling was carried out with several assumptions based on D discharges. To improve the validity of ITER D–T predictive modelling in the future, it is important to use the input data measured from 2021 JET D–T discharges in the present core predictive modelling, and to specify the accuracy of the D–T fusion power prediction in comparison with the experiments. This paper reports on the validation of the core integrated modelling with TRANSP, JINTRAC, and ETS coupled with a quasilinear turbulent transport model (Trapped Gyro Landau Fluid or QualLiKiz) against the measured data in 2021 JET D–T discharges. Detailed simulation settings and the heating and transport models used are described. The D–T fusion power calculated with the interpretive TRANSP runs for 38 D–T discharges (12 baseline and 26 hybrid discharges) reproduced the measured values within 20 %. This indicates the additional uncertainties, that could result from the measurement error bars in kinetic profiles, impurity contents and neutron rates, and also from the beam-thermal fusion reaction modelling, are less than 20 % in total. The good statistical agreement confirms that we have the capability to accurately calculate the D–T fusion power if correct kinetic profiles are predicted, and indicates that any larger deviation of the D–T fusion power prediction from the measured fusion power could be attributed to the deviation of the predicted kinetic profiles from the measured kinetic profiles in these plasma scenarios. Without any posterior adjustment of the simulation settings, the ratio of predicted D–T fusion power to the measured fusion power was found as 65%–96% for the D–T baseline and 81%–97% for D–T hybrid discharge. Possible reasons for the lower D–T prediction are discussed and future works to improve the fusion power prediction capability are suggested. The D–T predictive modelling results have also been compared to the predictive modelling of the counterpart D discharges, where the key engineering parameters are similar. Features in the predicted kinetic profiles of D–T discharges such as underprediction of ne are also found in the prediction results of the counterpart D discharges, and it leads to similar levels of the normalized neutron rate prediction between the modelling results of D–T and the counterpart D discharges. This implies that the credibility of D–T fusion power prediction could be a priori estimated by the prediction quality of the preparatory D discharges, which will be attempted before actual D–T experiments. [ABSTRACT FROM AUTHOR]

Background: Ajania Poljakov, an Asteraceae family member, grows mostly in Asia's arid and semi-desert areas and is a significant commercial and decorative plant. Nevertheless, the genus' classification has been disputed, and the evolutionary connections within the genus have not been thoroughly defined. Hence, we sequenced and analyzed Ajania's plastid genomes and combined them with ETS data to assess their phylogenetic relationships. Results: We obtained a total of six new Ajania plastid genomes and nine ETS sequences. The whole plastome lengths of the six species sampled ranged from 151,002 bp to 151,115 bp, showing conserved structures. Combined with publicly available data from GenBank, we constructed six datasets to reconstruct the phylogenetic relationships, detecting nucleoplasmic clashes. Our results reveal the affinities of Artemisia, Chrysanthemum and Stilpnolepis to Ajania and validate the early taxonomy reclassification. Some of the plastid genes with low phylogenetic information and gene trees with topological differences may have contributed to the ambiguous phylogenetic results of Ajania. There is extensive evolutionary rate heterogeneity in plastid genes. The psbH and ycf2 genes, which are involved in photosynthesis and ATP transport, are under selective pressure. Plastomes from Ajania species diverged, and structural aspects of plastomes may indicate some of the real evolutionary connections. We suggest the ycf1 gene as a viable plastid DNA barcode because it has significant nucleotide diversity and better reflects evolutionary connections. Conclusion: Our findings validate the early Ajania taxonomy reclassification and show evolutionary rate heterogeneity, genetic variety, and phylogenetic heterogeneity of plastid genes. This research might provide new insights into the taxonomy and evolution of Ajania, as well as provide useful information for germplasm innovation and genetic enhancement in horticultural species. [ABSTRACT FROM AUTHOR]