In this paper, (NH4K)MII(SO4)2(H2O)6 (MII = Co or Ni) mixed Tutton salts and their corresponding K2MII2(SO4)3 Langbeinite phases are synthesized and investigated as thermochemical heat storage (TCHS) ...materials. Individual precursor Tutton salts have also been considered for comparison proposals. Elemental composition, thermal and vibrational properties, and phase transformations/transitions are features examined besides thermochemical application parameters. The mixed Tutton salts exhibit charging temperatures between 342 and 367 K, high energy storage densities (∼ 2.3–2.6 GJ/m3), and complete structural reversibilities (full dehydrated samples) after 18 h under open-system conditions (299 K, atmospheric H2O, and 58 % RH). Theoretical thermal efficiencies reach 48–53 %. Putting a mixture of NH4+ and K+ in a Tutton-type framework produces intermediate thermal behavior that may be valuable for tailoring thermochemical parameters, giving mixed Tutton salts promising prospects that deserve further attention. Langbeinites, derived from mixed cation Tuttons through thermal annealing, also show H2O sorptions that are incorporated into the structure during rehydration tests. Only a partial reversibility was demonstrated after 24 h to predominantly form K2MII(SO4)2(H2O)6 Tutton salts and metallic sulfate hydrates since the ammonia was lost during the heating.
•Mixed Tutton salts (NH4K)MII(SO4)2(H2O)6 (MII = Co or Ni) exhibit high energy storage densities ∼ 2.3–2.6 GJ/m3•Introducing different monovalent cations in a Tutton-type structure returns intermediate thermal behavior•The mixed Tuttons show complete structural reversibilities after 18 h under 299 K, atmospheric H2O, and 58 % RH conditions•K2MII2(SO4)3 Langbeinites, derived from mixed Tuttons, also show H2O sorptions during rehydration tests over time•After 24 h of rehydration, Langbeinites transform into K2MII(SO4)2(H2O)6 Tuttons and MII(SO4)(H2O)6 metallic sulfate hydrates
In this paper, novel mixed Tutton salts with the chemical formulas Ksub.2Mnsub.0.03Nisub.0.97(SOsub.4)sub.2(Hsub.2O)sub.6 and Ksub.2Mnsub.0.18Cusub.0.82(SOsub.4)sub.2(Hsub.2O)sub.6 were synthesized ...and studied as compounds for thermochemical heat storage potential. The crystallographic structures of single crystals were determined by X-ray diffraction. Additionally, a comprehensive computational study, based on density functional theory (DFT) calculations and Hirshfeld surface analysis, was performed to calculate structural, electronic, and thermodynamic properties of the coordination complexes Msup.II(Hsub.2O)sub.6sup.2+ (Msup.II = Mn, Ni, and Cu), as well as to investigate intermolecular interactions and voids in the framework. The axial compressions relative to octahedral coordination geometry observed in the crystal structures were correlated and elucidated using DFT investigations regarding Jahn–Teller effects arising from complexes with different spin multiplicities. The spatial distributions of the frontier molecular orbital and spin densities, as well as energy gaps, provided further insights into the stability of these complexes. Thermogravimetry, differential thermal analysis, and differential scanning calorimetry techniques were also applied to identify the thermal stability and physicochemical properties of the mixed crystals. Values of dehydration enthalpy and storage energy density per volume were also estimated. The two mixed sulfate hydrates reported here have low dehydration temperatures and high energy densities. Both have promising thermal properties for residential heat storage systems, superior to the Tutton salts previously reported.
•The increase in soil macroporosity caused by chiseling was of short duration only.•After a transition period, soil physical properties were equal in cover crops and fallow treatment.•Sunn hemp cover ...crop resulted in higher soybean yield on average across 10 years.
The introduction of cover crops in agricultural systems under no-till is important in soil structuring and remediation. However, there is a lack of studies exploring the effects of cover crops compared with other soil compaction control tools, such as chiseling, in the long term, mainly under tropical climates. This study aimed to evaluate soil physical properties by cover crops and chiseling in a compacted soil, as well as its effects on soybean yields. The experiment was conducted in Botucatu, Brazil, under no-till. Three crops were grown per year. Soybean Glycine max (L.) Merrill was cropped as summer crop in rotation with triticale (X Triticosecale Wittmack) or sunflower Helianthus annuus (L.) as fall/winter crop. In spring, three different cover crops were grown, pearl millet Pennisetum glaucum (L.) R. Brown, forage sorghum Sorghum bicolor (L.) Moench and sunn hemp Crotalaria juncea (L.), compared to a fallow treatment, which was chiseled in 2003, 2009 and 2013 only, always in October and down to 0.60m depth. The first chiseling increased soil macroporosity and soybean yields in the immediate cropping season (2003/2004). However, these benefits were short-lived and in two years the use of cover crops resulted in higher yields. In the long-term, cover crops improve soil structure, with equal or better results than those obtained by occasional chiseling, as an increase in soil macroporosity by sunn hemp up to 0.20m depth and a decrease in soil bulk density by sunn hemp and pearl millet in the 0.40–0.60m layer. Among the cover crops, sunn hemp is particularly interesting, because it increases macroporosity in clay soils otherwise with limited aeration and increases the soybean yield.
In this paper, novel mixed Tutton salts with the chemical formulas K
Mn
Ni
(SO
)
(H
O)
and K
Mn
Cu
(SO
)
(H
O)
were synthesized and studied as compounds for thermochemical heat storage potential. The ...crystallographic structures of single crystals were determined by X-ray diffraction. Additionally, a comprehensive computational study, based on density functional theory (DFT) calculations and Hirshfeld surface analysis, was performed to calculate structural, electronic, and thermodynamic properties of the coordination complexes M
(H
O)
(M
= Mn, Ni, and Cu), as well as to investigate intermolecular interactions and voids in the framework. The axial compressions relative to octahedral coordination geometry observed in the crystal structures were correlated and elucidated using DFT investigations regarding Jahn-Teller effects arising from complexes with different spin multiplicities. The spatial distributions of the frontier molecular orbital and spin densities, as well as energy gaps, provided further insights into the stability of these complexes. Thermogravimetry, differential thermal analysis, and differential scanning calorimetry techniques were also applied to identify the thermal stability and physicochemical properties of the mixed crystals. Values of dehydration enthalpy and storage energy density per volume were also estimated. The two mixed sulfate hydrates reported here have low dehydration temperatures and high energy densities. Both have promising thermal properties for residential heat storage systems, superior to the Tutton salts previously reported.
The functional L-asparaginase from Escherichia coli is a homotetramer with a molecular weight of about 142 kDa. The X-ray structure of the enzyme, crystallized in a new form (space group C2) and ...refined to 1.95 A resolution, is compared with that of the previously determined crystal form (space group P2(1)). The asymmetric unit of the new crystal form contains an L-asparaginase dimer instead of the tetramer found in the previous crystal form. It is found that crystal contacts practically do not affect the conformation of the protein. It is shown that subunit C of the tetrameric form is in a conformation which is systematically different from that of all other subunits in both crystal forms. Major conformational differences are confined to the lid loop (residues 14-27). In addition, the stability of this globular protein is analyzed in terms of the interactions between hydrophobic parts of the subunits.
The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable ...asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature.
The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the ...planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.
Interleukin-7 receptor α (encoded by IL7R) is essential for lymphoid development. Whether acute lymphoblastic leukemia (ALL)-related IL7R gain-of-function mutations can trigger leukemogenesis remains ...unclear. Here, we demonstrate that lymphoid-restricted mutant IL7R, expressed at physiological levels in conditional knock-in mice, establishes a pre-leukemic stage in which B-cell precursors display self-renewal ability, initiating leukemia resembling PAX5 P80R or Ph-like human B-ALL. Full transformation associates with transcriptional upregulation of oncogenes such as Myc or Bcl2, downregulation of tumor suppressors such as Ikzf1 or Arid2, and major IL-7R signaling upregulation (involving JAK/STAT5 and PI3K/mTOR), required for leukemia cell viability. Accordingly, maximal signaling drives full penetrance and early leukemia onset in homozygous IL7R mutant animals. Notably, we identify 2 transcriptional subgroups in mouse and human Ph-like ALL, and show that dactolisib and sphingosine-kinase inhibitors are potential treatment avenues for IL-7R-related cases. Our model, a resource to explore the pathophysiology and therapeutic vulnerabilities of B-ALL, demonstrates that IL7R can initiate this malignancy.
The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the ...presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS.
Hybrid systems, which combine statistical and machine learning (ML) techniques using residual (error forecasting) modeling, have been highlighted in the literature due to their accuracy and ability ...to forecast time series with different characteristics. In these architectures, a crucial task is the proper modeling of the residuals since they may present random fluctuations, complex nonlinear patterns, and heteroscedastic behavior. Hence, the selection, specification, and training of one ML model to forecast the residuals are costly and challenging tasks since issues, such as underfitting, overfitting, and misspecification, can lead to a system with low accuracy or even deteriorate the linear forecast of the time series. This article proposes a hybrid system, named dynamic residual forecasting (DReF), that employs a modified dynamic selection (DS) algorithm to decide: the most suitable ML model to forecast a pattern of the residual series and if it is a promising candidate to increase the accuracy of the time series forecast from the linear combination. Thus, the DReF aims to reduce the uncertainty of the ML model selection and avoid the deterioration of the time series forecast. Furthermore, the proposed system searches for the most suitable parameters of the DS algorithm for each data set. In this article, the proposed method uses a pool of five ML models widely adopted in the literature: multilayer perceptron, support vector regression, radial basis function, long short-term memory, and convolutional neural network. An experimental evaluation was conducted using ten well-known time series. The results show that the DReF obtains superior results for the majority of the data sets compared with single and hybrid models of the literature.
PDF
