How Green Public Procurement Contributes to Sustainable Development in China: Evidence from the IISD Green Public Procurement Model

The People's Republic of China spent more than CNY 1.6 trillion (USD 252 billion) on procurement in 2013, accounting for 11.7 per cent of all national spending (Ministry of Finance of the People's Republic of China, 2014). In light of these numbers, the potential environmental, social and economic multipliers of greening government purchases become evident. The benefits of a comprehensive and efficient green public procurement (GPP) policy are not limited to the green products and services the public sector buys, but will have a ripple effect that encourages green consumption nationwide. The significant purchasing power of the government will provide the much-needed incentives in order for businesses to invest and innovate in green products and services to meet the government's guaranteed long-term and high-volume demand. Additionally, GPP is in line with China's national plans to pioneer "eco-civilisation" and with the upcoming 13th Five-Year Plan (FYP), which underlines the importance of GPP.This paper is the second and final component of IISD's contribution to greening public procurement in China. Our discussion paper Green Public Procurement in China: Quantifying the Benefits, published in April 2015, analyzed China's GPP landscape, taking a closer look at current practices, actors at different levels of government and the underlying legal framework. In addition, the paper introduced the IISD GPP Model, discussing its potential for quantifying and communicating the benefits of GPP, while providing a high-level overview of the modelling approach used and of the scope of the model envisioned. Building on the results of the IISD GPP Model, consultations with stakeholders and an extensive literature review, this paper provides targeted recommendations addressing the development areas identified to improve GPP in China. The recommendations follow a multiphase approach offering more immediate solutions as well as more ambitious, larger-scale overhauls of the GPP framework for the long term. The results of the IISD GPP Model will be shared for the first time as part of this paper, making the case for green procurement through analyzing five product categories: air conditioners, lighting, cars, paper and cement. These categories were selected because they represent significant financial flows in procurement, have notable environmental impacts and domestic production, and have sufficient data available to facilitate their analysis. A detailed overview of the key elements of the modelling approach will be provided, in addition to an explanation of the model setup and the range of externalities monetised for each product category. Finally, we will look at how to use the model at the different levels of government as well as how its scope can be extended and customised in order to leverage its potential under a wider range of circumstances and areas of procurement

Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision

Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems. These achievements, however, typically necessitate digital neural networks with heavy computational requirements, which are limited by high energy consumption and further hinder real-time decision-making when computation resources are not accessible. Here, we demonstrate an intelligent meta-imager that is designed to work in concert with a digital back-end to off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positive and negatively valued convolution operations in a single shot. The meta-imager is employed for object classification, experimentally achieving 98.6% accurate classification of handwritten digits and 88.8% accuracy in classifying fashion images. With compactness, high speed, and low power consumption, this approach could find a wide range of applications in artificial intelligence and machine vision applications.Comment: 15 pages, 5 figure

Characterization of the denaturation and renaturation of human plasma vitronectin II. Investigation into the mechanism of formation of multimers

Unfolding and refolding of plasma vitronectin appear irreversible under near physiological conditions, with rearrangements of disulfides and self-association to a multimeric form observed as prominent structural alterations which accompany denaturation. A mechanism for the folding reactions of vitronectin has been proposed (Zhuang, P., Blackburn, M. NPeterson, C. B. (1996) J. Biol. Chem. 270, 14323-14332) in which vitronectin acquires a partially folded intermediate structure which is highly prone to oligomerize into a multimeric form. Strongly oxidizing conditions adopted for refolding from urea were effective at preventing disulfide rearrangement which disrupts distal disulfides near the C terminus of the protein. Prohibiting disulfide rearrangement under these conditions, however, was not sufficient to achieve reversibility in folding. In contrast, variations in the ionic strength of the refolding medium affect the partitioning of species so that refolded monomers are obtained at high ionic strength, and self-association is precluded. The effects of ionic strength on the partially folded intermediate in the vitronectin folding pathway appear to favor intramolecular hydrophobic collapse to form a stable hydrophobic core for the monomer versus intermolecular hydrophobic interactions which stabilize multimeric vitronectin. Although both ionic and hydrophobic interactions presumably contribute to subunit interfaces within the multimer, the basic heparin-binding region near the C terminus of the protein does not provide binding interactions which are important for self-association of vitronectin

Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3x3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI

INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis

Synthesizing information from multiple data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus on single-modality data due to a lack of publicly available, multimodal medical datasets. To address this limitation, we introduce INSPECT, which contains de-identified longitudinal records from a large cohort of patients at risk for pulmonary embolism (PE), along with ground truth labels for multiple outcomes. INSPECT contains data from 19,402 patients, including CT images, radiology report impression sections, and structured electronic health record (EHR) data (i.e. demographics, diagnoses, procedures, vitals, and medications). Using INSPECT, we develop and release a benchmark for evaluating several baseline modeling approaches on a variety of important PE related tasks. We evaluate image-only, EHR-only, and multimodal fusion models. Trained models and the de-identified dataset are made available for non-commercial use under a data use agreement. To the best of our knowledge, INSPECT is the largest multimodal dataset integrating 3D medical imaging and EHR for reproducible methods evaluation and research

Breaking the Correlation between Energy Costs and Kinetic Barriers in Hydrogen Evolution via a Cobalt Pyridine-Diimine-Dioxime Catalyst

A central challenge in the development of inorganic hydrogen evolution catalysts is to avoid deleterious coupling between the energetics of metal site reduction and the kinetics of metal hydride formation. In this work, we combine theoretical and experimental methods to investigate cobalt diimine-dioxime catalysts that show promise for achieving this aim by introducing an intramolecular proton shuttle via a pyridyl pendant group. Using over 200 coupled-cluster-level electronic structure calculations of the Co-based catalyst with a variety of pyridyl substituents, the energetic and kinetic barriers to hydrogen formation are investigated, revealing nearly complete decoupling of the energetics of Co reduction and the kinetics of intramolecular Co hydride formation. These calculations employ recently developed quantum embedding methods that allow for local regions of a molecule to be described using high-accuracy wavefunction methods (such as CCSD(T)), thus overcoming significant errors in the DFT-level description of transition-metal complexes. Experimental synthesis and cyclic voltammetry of the methyl-substituted form of the catalyst indicate that protonation of the pendant group leaves the Co reduction potential unchanged, which is consistent with the theoretical prediction that these catalysts can successfully decouple the electronic structures of the transition-metal and ligand-protonation sites. Additional computational analysis indicates that introduction of the pyridyl pendant group enhances the favorability of intramolecular proton shuttling in these catalysts by significantly reducing the energetic barrier for metal hydride formation relative to previously studied cobalt diimine-dioxime catalysts. These results demonstrate a promising proof of principle for achieving uncoupled and locally tunable intramolecular charge-transfer events in the context of homogeneous transition-metal catalysts

Design Considerations for 3D Printed, Soft, Multimaterial Resistive Sensors for Soft Robotics

Sensor design for soft robots is a challenging problem because of the wide range of design parameters (e.g., geometry, material, actuation type, etc.) critical to their function. While conventional rigid sensors work effectively for soft robotics in specific situations, sensors that are directly integrated into the bodies of soft robots could help improve both their exteroceptive and interoceptive capabilities. To address this challenge, we designed sensors that can be co-fabricated with soft robot bodies using commercial 3D printers, without additional modification. We describe an approach to the design and fabrication of compliant, resistive soft sensors using a Connex3 Objet350 multimaterial printer and investigated an analytical comparison to sensors of similar geometries. The sensors consist of layers of commercial photopolymers with varying conductivities. We characterized the conductivity of TangoPlus, TangoBlackPlus, VeroClear, and Support705 materials under various conditions and demonstrate applications in which we can take advantage of these embedded sensors

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes

Black women across the African diaspora experience more aggressive breast cancer with higher mortality rates than white women of European ancestry. Although inter-ethnic germline variation is known, differential somatic evolution has not been investigated in detail. Analysis of deep whole genomes of 97 breast cancers, with RNA-seq in a subset, from women in Nigeria in comparison with The Cancer Genome Atlas (n = 76) reveal a higher rate of genomic instability and increased intra-tumoral heterogeneity as well as a unique genomic subtype defined by early clonal GATA3 mutations with a 10.5-year younger age at diagnosis. We also find non-coding mutations in bona fide drivers (ZNF217 and SYPL1) and a previously unreported INDEL signature strongly associated with African ancestry proportion, underscoring the need to expand inclusion of diverse populations in biomedical research. Finally, we demonstrate that characterizing tumors for homologous recombination deficiency has significant clinical relevance in stratifying patients for potentially life-saving therapies