Poly[penta­kis­(μ-cyanido-κ2 N:C)tris­(5-phenyl-2,2′-bipyridine-κ2 N,N′)penta­copper(I)]

The hydro­thermal reaction of Cu(acetate)2 and K3[Fe(CN)6] with 5-phenyl-2,2′-bipyridine (5-ph-2,2′-bpy) in water yields the polymeric title complex, [Cu5(CN)5(C16H12N2)3]n, which consists of ribbons along the a axis, constructed from 26-membered {Cu10(CN)8} rings. In these rings, the metal atoms are bridged by cyanide groups, except for one close Cu⋯Cu contact [2.7535 (12) Å], which can be considered as ligand-unsupported. Within the rings, one Cu atom has a distorted tetra­hedral geometry through the coordination to two N atoms from 5-ph-2,2′-bpy and two N/C atoms from two cyanide groups. Two Cu atoms have a trigonal planar environment being coordinated by three cyanide groups and two other Cu atoms have a distorted square planar geometry through coordination to two N atoms from 5-ph-2,2′-bpy and two N/C atoms from two cyanide groups

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

Prediction of Compressive Strength Loss of Normal Concrete after Exposure to High Temperature

In recent years, there has been an increasing number of fires in buildings. The methods for detecting residual properties of buildings after fires are commonly destructive and subjective. In this context, property prediction based on mathematical modeling has exhibited its potential. Backpropagation (BP), particle swarm algorithms optimized-BP (PSO-BP) and random forest (RF) models were established in this paper using 1803 sets of data from the literature. Material and relevant heating parameters, as well as compressive strength loss percentage, were used as input and output parameters, respectively. Experimental work was also carried out to evaluate the feasibility of the models for prediction. The accuracy of all the models was sufficiently high, and they were also much more feasible for prediction. Moreover, based on the RF model, the importance of the inputting parameters was ranked as well. Such prediction has provided a new perspective to non-destructively and objectively assess the post-fire properties of concrete. Additionally, this model could be used to guide performance-based design for fire-resistant concrete

Prediction of Compressive Strength Loss of Normal Concrete after Exposure to High Temperature

In recent years, there has been an increasing number of fires in buildings. The methods for detecting residual properties of buildings after fires are commonly destructive and subjective. In this context, property prediction based on mathematical modeling has exhibited its potential. Backpropagation (BP), particle swarm algorithms optimized-BP (PSO-BP) and random forest (RF) models were established in this paper using 1803 sets of data from the literature. Material and relevant heating parameters, as well as compressive strength loss percentage, were used as input and output parameters, respectively. Experimental work was also carried out to evaluate the feasibility of the models for prediction. The accuracy of all the models was sufficiently high, and they were also much more feasible for prediction. Moreover, based on the RF model, the importance of the inputting parameters was ranked as well. Such prediction has provided a new perspective to non-destructively and objectively assess the post-fire properties of concrete. Additionally, this model could be used to guide performance-based design for fire-resistant concrete

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

In order to accurately evaluate the health condition of a shearer, a hybrid prediction method was proposed based on the integration of a fuzzy neural network (FNN) and improved particle swarm optimization (IPSO). The parameters of FNN were optimized by the use of PSO, which was coupled with a premature judgment and mutation mechanism to increase the convergence speed and enhance the generalization ability. The key technologies are elaborated and the flowchart of the proposed approach was designed. Furthermore, an experiment example was carried out and the comparison results indicated that the proposed approach was feasible and outperforms others. Finally, a field application example in coal mining face was demonstrated to specify the effect of the proposed system

Balancing stakeholder benefits: A many-objective optimal dispatch framework for home energy systems inspired by Maslow's Hierarchy of needs

The optimal scheduling of home energy systems is influenced by the benefits of different stakeholders, with the hierarchical nature of user's needs being particularly significant. However, previous studies have largely neglected these factors. To bridge the research gaps, a many-objective optimal dispatch framework for home energy systems, which was inspired by Maslow's hierarchy of needs, was proposed. In the framework, user's needs for the optimal dispatch of home energy systems were categorized into various hierarchies referring to the Maslow's theory, which were fulfilled in a specific sequence during the scheduling optimization. In addition to the user's needs, the benefits of grid operators and policymakers were considered in the developed many-objective nonlinear optimal model, which includes six objective functions that capture the interests of end-users, grid operators, and policymakers. Simulation results obtained across the home energy systems with various configurations verified the effectiveness of the proposed framework. Results indicate that user's needs can be fully satisfied and a tradeoff among the benefits of end-users, grid operators, and policymakers was achieved. For various home energy systems, the optimal scheduling demonstrated reductions of 22.33 %-81.05 % in daily operation costs, 14.39 %-25.68 % in CO2 emissions, and 15.58 %-17.49 % in peak-valley differences, associated with increment of 5.37 %-15.51 % in self-consumption rate and 8.91 %-27.29 % in self-sufficiency rate, compared with the benchmark. The proposed framework provides valuable guidance for the optimal scheduling of various home energy systems in practical applications

Image Enhancement for Surveillance Video of Coal Mining Face Based on Single-Scale Retinex Algorithm Combined with Bilateral Filtering

Surveillance videos of coal mining faces have close relation to the safety of coal miners and mining efficiency. However, surveillance videos are always disturbed by some severe conditions such as atomization, low illumination, glare, and so on. Therefore, this paper proposed a hybrid algorithm (SSR-BF) based on the integration of single-scale Retinex (SSR) and bilateral filtering (BF) to enhance the image quality of surveillance videos. BF was coupled with SSR to reduce the noises and perfect the edge information in the image. The schematic diagram and pseudocode of SSR-BF was designed, and the parameters were set rationally to ensure the enhancement effects through some simulations. Finally, some comparisons with other methods were carried out, and the simulation results demonstrated that the proposed algorithm was superior to others and could be applied to image enhancement for poormonochrome images, especially the surveillance video of a coal mining face

Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data

Lung cancer remains the most commonly diagnosed cancer and the leading cause of death from cancer. Recent research shows that the human eye can provide useful information about one’s health status, but few studies have revealed that the eye’s features are associated with the risk of cancer. The aims of this paper are to explore the association between scleral features and lung neoplasms and develop a non-invasive artificial intelligence (AI) method for detecting lung neoplasms based on scleral images. A novel instrument was specially developed to take the reflection-free scleral images. Then, various algorithms and different strategies were applied to find the most effective deep learning algorithm. Ultimately, the detection method based on scleral images and the multi-instance learning (MIL) model was developed to predict benign or malignant lung neoplasms. From March 2017 to January 2019, 3923 subjects were recruited for the experiment. Using the pathological diagnosis of bronchoscopy as the gold standard, 95 participants were enrolled to take scleral image screens, and 950 scleral images were fed to AI analysis. Our non-invasive AI method had an AUC of 0.897 ± 0.041(95% CI), a sensitivity of 0.836 ± 0.048 (95% CI), and a specificity of 0.828 ± 0.095 (95% CI) for distinguishing between benign and malignant lung nodules. This study suggested that scleral features such as blood vessels may be associated with lung cancer, and the non-invasive AI method based on scleral images can assist in lung neoplasm detection. This technique may hold promise for evaluating the risk of lung cancer in an asymptomatic population in areas with a shortage of medical resources and as a cost-effective adjunctive tool for LDCT screening at hospitals