•Yield increases with increasing cropping interval for sugar beet.•Yield and yield stability are lowest when sugar beet is cultivated as a preceding crop.•Cropping interval of two years seems ...necessary for high yield stability.•Integrating alfalfa enables shorter cropping intervals without yield loss.
Long-term field trials constitute an essential basis for research into the effects of agricultural management practices on yield and soil properties. The long-term field trial Etzdorf (Germany) was set up in 1970 and uses various crop rotations with sugar beets (Beta vulgaris L., SB) to investigate the influence of increasing cropping concentrations (20 %–100 %) and decreasing cropping intervals (0–4 years) on the yield and quality parameters of SB. However, evaluation of the yield stability of SB in diverse crop rotations has not been conducted in this context so far. For this reason, the yield for the last 13 years of the trial (2002 until 2014) was subjected to such an evaluation. Besides cropping interval and cropping concentration, the crop rotations investigated also differed in terms of the complementary crops cultivated (winter wheat, Triticum aestivum L.; alfalfa, Medicago ssp.; potato, Solanum tuberosum L. and grain maize, Zea mays L.). Both SB root yield and white sugar yield increased with an increasing cropping interval or decreasing cropping concentration of SB in the crop rotation. In addition, a positive effect on root yield and white sugar yield was seen when integrating alfalfa, while cultivating SB after SB displayed the lowest root yield and white sugar yield. Sugar content was lowest in SB monoculture. In order to assess stability of white sugar yield, the coefficient of variation and ecovalence were calculated, and a linear regression analysis of the individual crop rotations’ annual yield was performed for the annual average of all crop rotations. When considering these three parameters, the crop rotations with a cropping interval of at least 2 years displayed higher yield stability, with simultaneously higher white sugar yield, than the crop rotations with a cropping interval of 0 and 1year. By integrating alfalfa into the crop rotation, it was also possible to achieve above-average white sugar yield with high yield stability for a cropping interval of 1year.
Kebutuhan masyarakat akan ruang untuk memenuhi kegiatannya benar-benar menyebabkan meningkatnya persaingan dalam pemerolehan dan juga meningkatnya nilai tanah. Ada banyak faktor yang mempengaruhi ...nilai tanah, salah satunya adalah jenis penggunaan lahan untuk memenuhi ruang untuk mendukung kegiatan mereka. Kondisi ini dapat diidentifikasi tidak hanya di daerah perkotaan tetapi juga di daerah pinggiran kota yang salah satu kebutuhan utama rumah tangga adalah memiliki ruang untuk tempat tinggal. Akibatnya, rumah tangga berusahan untuk memperoleh tanah, baik berasal dari lahan pertanian ataupun lahan permukiman, untuk pembangunan rumah. Tujuan dari penelitian ini adalah untuk menganalisis pola transaksi tanah dan peningkatan nilai tanah lahan pertanian dan lahan pemukiman di daerah perkotaan. Kabupaten Magelang Selatan dipilih sebagai daerah studi kasus karena ketersediaan data dan aksesibilitas untuk mengamati secara empiris faktor-faktor yang mempengaruhi nilai tanah. Analisis regresi berganda dipilih untuk memodelkan estimasi nilai tanah pertanian dan permukiman pada tahun 2016 dan 2019, sehingga peningkatan dan pola spasial nilai tanah dapat disajikan pada peta. Hasilnya menunjukkan bahwa selama 3 tahun, peningkatan nilai tanah pada lahan pertanian dan lahan permukiman tidak berbeda secara signifikan. Tercatat kenaikan lahan pertanian dan lahan permukiman sekitar Rp 360.000,- dan Rp 400.000,-. Dalam hal pola spasial, telah diidentifikasi bahwa semakin kecil ukuran tanah dan semakin dekat ke daerah permukiman, semakin tinggi estimasi nilai tanahnya.
It is unclear whether lifestyle factors affect bone mineral density (BMD) during different inflammatory states.
This study investigated the effects of coffee consumption, vitamin D (VD) intake, ...smoking, and alcohol consumption on heel BMD in adults with different inflammatory states.
The phenotypic data from 249,825 participants were analyzed using the UK Biobank cohort. The inflammatory status was evaluated using C-reactive protein (CRP) levels and the systemic immune-inflammation index. Linear regression analysis was used to examine the association between coffee consumption, VD, smoking, alcohol consumption, and heel BMD in adults with different inflammatory states. Linear regression models were used to analyze the interaction between inflammation and the four lifestyle factors with respect to their influence on heel BMD in adults.
Our findings revealed that VD was positively associated with adult heel BMD (β = 2.41 × 10−2, SE = 5.14 × 10−3, P = 2.72 × 10−6), while alcohol consumption and smoking were negatively associated with adult heel BMD. Coffee was negatively associated with adult heel BMD in low inflammatory states (β = −1.27 × 10−2, SE = 4.79 × 10−3, P = 8.00 × 10−3), while there was no association between coffee and adult heel BMD in high inflammatory states. Overall, it was found that these four lifestyle factors interacted negatively with inflammatory states.
Our study suggests that VD is positively associated with adult heel BMD and that alcohol consumption and smoking are negatively associated with adult heel BMD. Coffee may reverse the adverse effects of inflammation on BMD when the patient is in a highly inflammatory state, thus acting as a protective agent against heel BMD in adults.
Prediction of machine performance is an essential step for planning, cost estimation and selection of excavation method to assure success of tunneling operation by hard rock TBMs. Penetration rate is ...a principal measure of TBM performance and is used to evaluate the feasibility of using a machine in a given ground condition and to predict TBM advance rate. In this study, a database of TBM field performance from two hard rock tunneling projects in Iran including Zagros lot 1B and 2 for a total length of 14.3km has been used to assess applicability of various analysis methods for developing reliable predictive models. The first method used for this purpose was principal component analysis (PCA) which resulted in development of a set of new empirical equations. Also, two Soft computing techniques including adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) have been employed for this purpose. As statistical indices, root mean square error (RMSE), correlation coefficient (R2), variance account for (VAF), and mean absolute percentage error (MAPE) were used to evaluate the efficiency of the developed artificial intelligence models for TBM performance prediction. The results of the analysis show that AI based methods can effectively be implemented for prediction of TBM performance. Moreover, it was concluded that performance of the SVR model is better than the ANFIS model. A high correlation was observed between predicted and measured TBM performance for the SVR model. This study shows the feasibility of using these systems and subsequent work is underway to expand the database of TBM field performance and use the aforementioned methods to develop a more comprehensive TBM performance prediction model.
Quantitative structure-property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the ...geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.
In this study, the rheological properties of cementitious systems were investigated through modeling studies on structural build-up and breakdown area. The area values were calculated using Herschel ...Bulkley analysis and hysteresis area method. The properties were examined by varying the composition of the cementitious system (cement fineness, C4AF, C3S, C2S, C3A, equivalent alkali and metakaolin ratio) and changes made in the rheological measurement processes (applied shear rate, maximum shear rate and duration). For this purpose, cement paste mixtures were prepared by substituting metakaolin at four different ratios (3%, 6%, 9%, and 12%) into cements with varying C3A content (2.13, 3.60, 6.82, 9.05%). The modeling study of the obtained results was conducted using three different learning methods: Linear Regression Analysis (LR), AdaBoost, and K Nearest Neighbor (KNN), encompassing machine learning and ensemble learning techniques. It was determined that the most dominant parameter affecting the rheology and thixotropic properties of the mixtures is the metakaolin usage ratio. The pre-shear rate was dominant over the duration and maximum shear rate parameters. Effect of the C3A content on dynamic yield stress and viscosity becomes more pronounced with an increase in the applied shear rate. The KNN method has yielded the best results in all experimental modeling studies. Euclidean distance criterion was used in the KNN method. Although the AdaBoost method obtained results close to the KNN method, the opposite situation was observed depending on the number of data. Logcosh, MAE and RMSE metrics were used to evaluate the experimental results. When the results for 3 different metrics in all modeling studies were examined, the success order of the metrics was found to be Logcosh, MAE and RMSE.
•Increase in C3A content and metakaolin ratio leads to an increase in DYS, V, and SBU.•KNN method has yielded the best results in all experimental modeling studies.•AdaBoost requires training with a large amount of data.
•Inverse associations between chemical mixtures and child’s FIQ were observed.•Prenatal exposure to Pb and BPA were associated with decreased child’s FIQ.•Sex-stratified analyses showed stronger ...effects of chemical exposures in boys.•Integrating multiple statistical methods were beneficial to draw a more accurate conclusion.
Prenatal exposure to heavy metals, pesticides and phenols has been suggested to interfere with neurodevelopment, but the neurotoxicity of their mixtures is still unclear. We aimed to elucidate the associations of maternal urinary concentrations of selected chemical mixtures with intelligence quotient (IQ) in children.
Maternal urinary concentrations of selected heavy metals, pesticide metabolites, and phenols were quantified in pregnant women who participated in the Sheyang Mini Birth Cohort Study (SMBCS) from June 2009 to January 2010. At age 7 years, child’s IQ score was assessed using the Chinese version of Wechsler Intelligence Scale for Children (C-WISC) by trained pediatricians. Generalized linear regression models (GLM), Bayesian kernel machine regression (BKMR) models and elastic net regression (ENR) models were used to assess the associations of urinary concentrations individual chemicals and their mixtures with IQ scores of the 7-year-old children.
Of 326 mother-child pairs, single-chemical models indicated that prenatal urinary concentrations of lead (Pb) and bisphenol A (BPA) were significantly negatively associated with full intelligence quotient (FIQ) among children aged 7 years β = −2.31, 95% confidence interval (CI): −4.13, −0.48; p = 0.013, sex interaction p-value = 0.076; β = −1.18, 95% CI: −2.21, −0.15; p = 0.025; sex interaction p-value = 0.296, for Pb and BPA, respectively. Stratified analysis by sex indicated that the associations were only statistically significant in boys. In multi-chemical BKMR and ENR models, statistically significant inverse association was found between prenatal urinary Pb level and boy’s FIQ scores at 7 years. Furthermore, BKMR analysis indicated that the overall mixture was associated with decreases in boy’s IQ when all the chemicals’ concentrations were at their 75th percentiles or higher, compared to at their 50th percentiles. ENR models revealed that maternal urinary Pb levels were statistically significantly associated with lower FIQ scores (β = −2.20, 95% CI: −4.20, −0.20; p = 0.031).
Prenatal exposure to selected chemical mixtures may affect intellectual performance at 7 years of age, particularly in boys. Pb and BPA were suspected as primary chemicals associated with child neurodevelopment.
The relationships of antioxidant properties (AOPs), measured by four conventional in vitro methods, with monosaccharides and glycosyl linkages in the polysaccharide, were evaluated using multiple ...linear regression analysis with minor modifications. Polysaccharides extracted from culture broth filtrates of Lentinula edodes were used as model samples for evaluation. Results indicate that the composition of monosaccharides and the type of glycosyl linkage modulates the AOPs of the polysaccharides. The AOPs of the polysaccharides were dependent on the ratios of different monosaccharides in the composition. Among the monosaccharides, rhamnose was the most significant determinant factor associated with AOPs. The glycosyl linkages of the monosaccharides also affected the anti-oxidation characteristics of the polysaccharides. Specifically, the arabinose 1→4 and mannose 1→2 linkages of the side-chain were significantly related to the reducing power, whereas the glucose 1→6 linkage and arabinose 1→4 linkages were related to the scavenging on DPPH− radicals.
Herein, detailed mechanistic investigations into formamide-catalyzed nucleophilic substitution (S N ) of alcohols are reported. Alkoxyiminium chlorides and hexafluorophosphates were synthesized and ...characterized as a key intermediate of the catalytic cycle. The determination of reaction orders and control experiments indicated that the nucleophilic attack of the formamide catalyst onto the reagent BzCl is the rate-determining step. Linear free energy relationship revealed a correlation between the quantified Lewis basicity strength of formamides by means of 11B NMR spectroscopy and their catalytic activity in S N -transformations. The observed difference in catalytic ability was attributed to the natural bond order charge, dipole moment, and Sterimol parameter B5. Importantly, this rationalization enables the prediction of the capacity of formamides to promote S N -type transformations in general.
The theoretical study on the fixation and release of nitric oxide (NO) by N‐heterocyclic carbenes (NHCs) is explored by utilizing a multivariate linear regression approach. We utilized data‐driven ...tools to establish correlations between molecular descriptors of NHC and reaction outputs of the formation and NO release of NHC nitric oxide radicals (NHCNOs). A key electronic parameter, the singlet‐triplet energy gap (ΔEST), plays a pivotal role in the thermodynamics of NHCNO formation (ΔGf) and NO release (ΔGr). The activation energy models highlight HOMO energy level (EHOMO), C–N bond length (dC–N), and NBO charge on N atom (qNBON) as crucial factors for the formation of NHCNO (ΔG‡f), while the activation energy for the NO release (ΔG‡r) significantly correlates with free energy difference (ΔGr), resulting a strong ΔEST dependency. This study provides valuable insights into the thermodynamics and kinetics of NHCNO processes, offering predictive capabilities to design NHCs tailored for NO release.
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