Leak location procedure based on the complex-valued FastICA blind deconvolution algorithm for water-filled branch pipe

Water pipe networks have a large number of branch joints. Branch joint shunting generates vortices in the fluid, which excite the pipe wall to produce a type of branch noise. The branch noise is coupled with the leak source signal through the pipe. Here, a novel leak location protocol based on the complex-valued FastICA method (C-FastICA) is proposed to address the leak location problem under the branch noise interference. The C-FastICA, a complex-value domain blind deconvolution algorithm, effectively extended the cost function, constraint function, and iteration rules of the instantaneous linear FastICA into the complex-valued domain. The C-FastICA method was used to realize the separation of branch noise and leak source signal. The experimental results showed that the separation efficiency of the C-FastICA was higher than that of time-domain blind convolution separation (T-BCS). Furthermore, the relative location error of the C-FastICA method to the leak point was less than 14.238%, which was significantly lower than in traditional T-BCS and direct cross-correlation (DCC) technology.

Electrochemically Driven Cross-Electrophile Coupling of Alkyl Halides

Recent research in medicinal chemistry suggests a correlation between an increase in the fraction of sp3 carbons in drug candidates with their improved success rate in clinical trials. As such, the development of robust and selective methods for the construction of C(sp3)-C(sp3) bonds remains a critical problem in modern organic chemistry. Owing to the broad availability and synthetic accessibility of alkyl halides, their direct cross coupling—commonly known as cross-electrophile coupling (XEC)—provides a promising route toward this objective. However, achieving high selectivity in C(sp3)-C(sp3) XEC remains a largely unmet challenge. Herein, we employ electrochemistry to achieve the differential activation of alkyl halides by exploiting their disparate electronic and steric properties. Specifically, the selective cathodic reduction of a more substituted alkyl halide gives rise to a carbanion, which undergoes preferential coupling with a less substituted alkyl halide via bimolecular nucleophilic substitution (SN2) to forge a new C–C bond. This transition-metal free protocol enables the efficient XEC of a variety of functionalized and unactivated alkyl electrophiles and exhibits substantially improved chemoselectivity versus existing methodologies.

Electrochemically Driven Cross-Electrophile Coupling of Alkyl Halides

Recent research in medicinal chemistry suggests a correlation between an increase in the fraction of sp3 carbons in drug candidates with their improved success rate in clinical trials. As such, the development of robust and selective methods for the construction of C(sp3)-C(sp3) bonds remains a critical problem in modern organic chemistry. Owing to the broad availability and synthetic accessibility of alkyl halides, their direct cross coupling—commonly known as cross-electrophile coupling (XEC)—provides a promising route toward this objective. However, achieving high selectivity in C(sp3)-C(sp3) XEC remains a largely unmet challenge. Herein, we employ electrochemistry to achieve the differential activation of alkyl halides by exploiting their disparate electronic and steric properties. Specifically, the selective cathodic reduction of a more substituted alkyl halide gives rise to a carbanion, which undergoes preferential coupling with a less substituted alkyl halide via bimolecular nucleophilic substitution (SN2) to forge a new C–C bond. This transition-metal free protocol enables the efficient XEC of a variety of functionalized and unactivated alkyl electrophiles and exhibits substantially improved chemoselectivity versus existing methodologies.

Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery

In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of the energy sector. Automatic information extraction based on deep learning requires high-quality labeled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PVs. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8, 0.3, and 0.1 m, which focus on concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs, respectively. The dataset contains 3716 samples of PVs installed on shrub land, grassland, cropland, saline–alkali land, and water surfaces, as well as flat concrete, steel tile, and brick roofs. The dataset is used to examine the model performance of different deep networks on PV segmentation. On average, an intersection over union (IoU) greater than 85 % is achieved. In addition, our experiments show that direct cross application between samples with different resolutions is not feasible and that fine-tuning of the pre-trained deep networks using target samples is necessary. The dataset can support more work on PV technology for greater value, such as developing a PV detection algorithm, simulating PV conversion efficiency, and estimating regional PV potential. The dataset is available from Zenodo on the following website: https://doi.org/10.5281/zenodo.5171712 (Jiang et al., 2021).

Population biology of accessory gland-expressed de novo genes in Drosophila melanogaster

Early work on de novo gene discovery in Drosophila was consistent with the idea that many such genes have male-biased patterns of expression, including a large number expressed in the testis. However, there has been little formal analysis of variation in the abundance and properties of de novo genes expressed in different tissues. Here we investigate the population biology of recently evolved de novo genes expressed in the D. melanogaster accessory gland, a somatic male tissue that plays an important role in male and female fertility and the post mating response of females, using the same collection of inbred lines used previously to identify testis-expressed de novo genes, thus allowing for direct cross tissue comparisons of these genes in two tissues of male reproduction. Using RNA-seq data we identify candidate de novo genes located in annotated intergenic and intronic sequence and determine the properties of these genes including chromosomal location, expression, abundance, and coding capacity. Generally, we find major differences between the tissues in terms of gene abundance and expression, though other properties such as transcript length and chromosomal distribution are more similar. We also explore differences between regulatory mechanisms of de novo genes in the two tissues and how such differences may interact with selection to produce differences in D. melanogaster de novo genes expressed in the two tissues.

Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery

In the context of global carbon emission reduction, solar photovoltaics (PV) is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of energy sector. Automatic information extraction based on deep learning requires high-quality labelled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PV. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8 m, 0.3 m and 0.1 m, which focus on concentrated PV, distributed ground PV and fine-grained rooftop PV, respectively. The dataset contains 3716 samples of PVs installed on shrub land, grassland, cropland, saline-alkali, and water surface, as well as flat concrete, steel tile, and brick roofs. We used this dataset to examine the model performance of different deep networks on PV segmentation, and on average an intersection over union (IoU) greater than 85 % was achieved. In addition, our experiments show that direct cross application between samples with different resolutions is not feasible, and fine-tuning of the pre-trained deep networks using target samples is necessary. The dataset can support more works on PVs for greater value, such as, developing PV detection algorithm, simulating PV conversion efficiency, and estimating regional PV potential. The dataset is available from Zenodo on the following website: https://doi.org/10.5281/zenodo.5171712 (Jiang et al. 2021).