Search Results (3)

Under the constraints of carbon peaking and carbon neutrality, in order to facilitate the realization of SDGs in China’s cities, place-based policy needs to strike a balance between “economic growth” and “carbon reduction”. This paper considers the establishment of NNZs as a quasi-natural experiment and constructs an asymptotic DID model based on the data of 283 Chinese cities in 2006~2021 to assess the causal effects of place-based policy on urban carbon productivity. It is found that the establishment of NNZs significantly enhances urban carbon productivity, and this conclusion still holds after considering the validity, endogeneity, and robustness of the model. Mechanism analysis shows that the policy preferences of tax incentive and improving transport and other infrastructural facilities, the policy supervision and the industrial coagglomeration are the positive moderating mechanisms of the establishment of NNZs to enhance urban carbon productivity, but in addition to the policy preference of financial subsidy. Moreover, under the moderating effect of industrial coagglomeration, the establishment of NNZs enhances urban carbon productivity through three mechanisms: deepening of the specialized division of labor, optimizing of industrial structure, and innovating synergistically. Heterogeneity analysis showed that the moderating effect of industrial coagglomeration on urban carbon productivity is heterogeneous depending on the spatial layout of NNZs, in terms of planning area, the effective range is 1000~2000 km² and the optimal range is 1500~2000 km², and as far as the layout pattern is concerned, the optimal pattern is the dual-city layout. The conclusions provide a realistic basis and direction of thinking for optimizing the policy design of NNZs and promoting the green transformation of place-based policy. Full article

Rett syndrome (RTT) is a rare disability causing female-oriented pediatric neurodevelopmental unmet medical need. RTT was recognized in 1966. However, over the past 56 years, the United States Food and Drug Administration (USFDA) has authorized no effective treatment for RTT. Recently, Trofinetide was approved by the USFDA on 10 March 2023 as the first RTT treatment. This article underlines the pharmaceutical advancement, patent literature, and prospects of Trofinetide. The data for this study were gathered from the PubMed database, authentic websites (Acadia Pharmaceuticals, Neuren Pharmaceuticals, and USFDA), and free patent databases. Trofinetide was first disclosed by Neuren Pharmaceuticals in 2000 as a methyl group containing analog of the naturally occurring neuroprotective tripeptide called glycine-proline-glutamate (GPE). The joint efforts of Acadia Pharmaceuticals and Neuren Pharmaceuticals have developed Trofinetide. The mechanism of action of Trofinetide is not yet well established. However, it is supposed to improve neuronal morphology and synaptic functioning. The patent literature revealed a handful of inventions related to Trofinetide, providing excellent and unexplored broad research possibilities with Trofinetide. The development of innovative Trofinetide-based molecules, combinations of Trofinetide, patient-compliant drug formulations, and precise MECP2-mutation-related personalized medicines are foreseeable. Trofinetide is in clinical trials for some neurodevelopmental disorders (NDDs), including treating Fragile X syndrome (FXS). It is expected that Trofinetide may be approved for treating FXS in the future. The USFDA-approval of Trofinetide is one of the important milestones for RTT therapy and is the beginning of a new era for the therapy of RTT, FXS, autism spectrum disorder (ASD), brain injury, stroke, and other NDDs. Full article

Graphics processing units (GPUs) have delivered a remarkable performance for a variety of high performance computing (HPC) applications through massive parallelism. One such application is sparse matrix-vector (SpMV) computations, which is central to many scientific, engineering, and other applications including machine learning. No single SpMV storage or computation scheme provides consistent and sufficiently high performance for all matrices due to their varying sparsity patterns. An extensive literature review reveals that the performance of SpMV techniques on GPUs has not been studied in sufficient detail. In this paper, we provide a detailed performance analysis of SpMV performance on GPUs using four notable sparse matrix storage schemes (compressed sparse row (CSR), ELLAPCK (ELL), hybrid ELL/COO (HYB), and compressed sparse row 5 (CSR5)), five performance metrics (execution time, giga floating point operations per second (GFLOPS), achieved occupancy, instructions per warp, and warp execution efficiency), five matrix sparsity features ($\mathit{nnz}$, $\mathit{anpr}$, $\mathit{npr}\mathit{variance}$, $\mathit{maxnpr}$, and $\mathit{distavg}$), and 17 sparse matrices from 10 application domains (chemical simulations, computational fluid dynamics (CFD), electromagnetics, linear programming, economics, etc.). Subsequently, based on the deeper insights gained through the detailed performance analysis, we propose a technique called the heterogeneous CPU–GPU Hybrid (HCGHYB) scheme. It utilizes both the CPU and GPU in parallel and provides better performance over the HYB format by an average speedup of 1.7x. Heterogeneous computing is an important direction for SpMV and other application areas. Moreover, to the best of our knowledge, this is the first work where the SpMV performance on GPUs has been discussed in such depth. We believe that this work on SpMV performance analysis and the heterogeneous scheme will open up many new directions and improvements for the SpMV computing field in the future. Full article