Bis(μ-phenyl­tellurido-κ2 Te:Te)bis­[tetra­carbonyl­rhenium(I)]

The title compound, [Re2(C6H5Te)2(CO)8], crystallizes with two mol­ecules in the asymmetric unit, in which two Re atoms are coordinated in a slightly distorted octa­hedral environment and are bridged by two Te atoms, which show a distorted trigonal-pyramidal geometry. The torsion angles for the Te—Re—Te—Re sequence of atoms are 19.29 (18) and 16.54 (16)° in the two mol­ecules. Thus, the Re—Te four-membered rings in the two mol­ecules deviate significantly from planarity. Two intra­molecular C—H⋯O inter­actions occur in one of the mol­ecules. Te—Te [4.0551 (10) Å] inter­actions between the two mol­ecules and weak inter­molecular C—H⋯O inter­actions stabilize the crystal packing

4-[4-(Diethyl­amino)­phen­yl]-N-methyl-3-nitro-4H-chromen-2-amine

In the title compound, C20H23N3O3, the dihydro­pyran ring adopts half-chair conformation. The chromene system makes a dihedral angle of 87.35 (5)° with the adjacent benzene ring. An intra­molecular N—H⋯O hydrogen bond generates an S(6) motif, which stabilizes the mol­ecular conformation. In the crystal, weak inter­molecular C—H⋯O hydrogen bonds contribute to the stabilization of the packing

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

6,8-Dichloro-N-methyl-3-nitro-4-nitro­methyl-4H-chromen-2-amine

In the title compound, C11H9Cl2N3O5, the dihydro­pyran ring adopts a near-half-chair conformation. The benzene ring makes a torsion angle of 5.02 (5)° with the dihydro­pyran ring. Adjacent mol­ecules are inter­linked through inter­molecular C—H⋯O, N—H⋯O and C—Cl⋯π [3.4743 (9) Å] inter­actions. The inter­molecular N—H⋯O hydrogen bond generates an R 2 2(12) motif, which is observed to contribute to the crystal packing stability. Moreover, the mol­ecular structure displays an S(6) motif formed by intra­molecular N—H⋯O hydrogen bonding

6-Meth­oxy-N-methyl-3-nitro-4-nitro­methyl-4H-chromen-2-amine

In the title compound, C12H13N3O6, the dihydro­pyran ring adopts a near screw-boat conformation. The dihedral angle between the mean planes of the benzene and dihydro­pyran rings is 6.35 (5)°. An intra­molecular N—H⋯O hydrogen bond generates an S(6) motif, which stabilizes the mol­ecular conformation. In the crystal, weak inter­molecular C—H⋯O, N—H⋯O and C—H⋯π hydrogen bonds contribute to the stabilization of the packing

Effectiveness of vegetated patches as Green Infrastructure in mitigating Urban Heat Island effects during a heatwave event in the city of Melbourne

The city of Melbourne in southeast Australia experiences frequent heatwaves and their frequency, intensity and duration are expected to increase in the future. In addition, Melbourne is the fastest growing city in Australia and experiencing rapid urban expansion. Heatwaves and urbanization contribute in intensifying the Urban Heat Island (UHI) effect, i.e., higher temperatures in urban areas as compared to surrounding rural areas. The combined effects of UHI and heatwaves have substantial impacts on the urban environment, meteorology and human health, and there is, therefore, a pressing need to investigate the effectiveness of different mitigation options. This study evaluates the effectiveness of urban vegetation patches such as mixed forest (MF), combination of mixed forest and grasslands (MFAG), and combination of mixed shrublands and grasslands (MSAG) in reducing UHI effects in the city of Melbourne during one of the most severe heatwave events. Simulations are carried out by using the Weather Research and Forecasting (WRF) model coupled with the Single Layer Urban Canopy Model (SLUCM). The fractions of vegetated patches per grid cell are increased by 20%, 30%, 40% and 50% using the mosaic method of the WRF model. Results show that by increasing fractions from 20 to 50%, MF reduces near surface (2 m) UHI (UHI2) by 0.6–3.4 °C, MSAG by 0.4–3.0 °C, and MFAG by 0.6–3.7 °C during the night, but there was no cooling effect for near surface temperature during the hottest part of the day. The night-time cooling was driven by a reduction in storage heat. Vegetated patches partitioned more net radiation into latent heat flux via evapotranspiration, with little to no change in sensible heat flux, but rather, a reduction in the storage heat flux during the day. Since the UHI is driven by the release of stored heat during the night, the reduced storage heat flux results in reductions in the UHI. The reductions of the UHI2 varied non-linearly with the increasing vegetated fractions, with lager fractions of up to 50% resulting in substantially larger reductions. MF and MFAG were more effective in reducing UHI2 as compared to MSAG. Vegetated patches were not effective in improving HTC during the day, but a substantial improvement of HTC was obtained between the evening and early morning particularly at 2100 local time, when the thermal stress changes from strong to moderate. Although limited to a single heatwave event and city, this study highlights the maximum potential benefits of using vegetated patches in mitigating the UHI during heatwaves and the overall principles are applicable elsewhere

Green infrastructure as an urban heat island mitigation strategy—a review

202109 bchyVersion of RecordPublishe