D-STEM: a Design led approach to STEM innovation

Advances in the Science, Technology, Engineering and Maths (STEM) disciplines offer opportunities for designers to propose and make products with advanced, enhanced and engineered properties and functionalities. In turn, these advanced characteristics are becoming increasingly necessary as resources become ever more strained through 21st century demands, such as ageing populations, connected communities, depleting raw materials, waste management and energy supply. We need to make things that are smarter, make our lives easier, better and simpler. The products of tomorrow need to do more with less. The issue is how to maximize the potential for exploiting opportunities offered by STEM developments and how best to enable designers to strengthen their position within the innovation ecosystem. As a society, we need designers able to navigate emerging developments from the STEM community to a level that enables understanding and knowledge of the new material properties, the skill set to facilitate absorption into the design ‘toolbox’ and the agility to identify, manage and contextualise innovation opportunities emerging from STEM developments. This paper proposes the blueprint for a new design led approach to STEM innovation that begins to redefine studio culture for the 21st Century

Decoupled Model Schedule for Deep Learning Training

Recent years have seen an increase in the development of large deep learning (DL) models, which makes training efficiency crucial. Common practice is struggling with the trade-off between usability and performance. On one hand, DL frameworks such as PyTorch use dynamic graphs to facilitate model developers at a price of sub-optimal model training performance. On the other hand, practitioners propose various approaches to improving the training efficiency by sacrificing some of the flexibility, ranging from making the graph static for more thorough optimization (e.g., XLA) to customizing optimization towards large-scale distributed training (e.g., DeepSpeed and Megatron-LM). In this paper, we aim to address the tension between usability and training efficiency through separation of concerns. Inspired by DL compilers that decouple the platform-specific optimizations of a tensor-level operator from its arithmetic definition, this paper proposes a schedule language to decouple model execution from definition. Specifically, the schedule works on a PyTorch model and uses a set of schedule primitives to convert the model for common model training optimizations such as high-performance kernels, effective 3D parallelism, and efficient activation checkpointing. Compared to existing optimization solutions, we optimize the model as-needed through high-level primitives, and thus preserving programmability and debuggability for users to a large extent. Our evaluation results show that by scheduling the existing hand-crafted optimizations in a systematic way, we are able to improve training throughput by up to 3.35x on a single machine with 8 NVIDIA V100 GPUs, and by up to 1.32x on multiple machines with up to 64 GPUs, when compared to the out-of-the-box performance of DeepSpeed and Megatron-LM

Green growth, economic development, and carbon dioxide emissions: an evaluation based on cointegration and vector error correction models

Economic development is mainly dependent on fossil fuels. The massive use of fossil fuels has led to changes in the climate environment, in which the deterioration of air quality has affected people’s daily lives. This paper introduces the green growth level as a control variable to explore the connection between carbon dioxide emissions and the level of economic growth. It uses the EKC algorithm and VEC model to analyze Nanjing city’s data from 1993 to 2018. Given the data availability, the ARIMA algorithm was used to project carbon emissions for 2019–2025. It is found that the EKC curve of Nanjing City shows an N-shape, and the growth of economic level will cause the enhancement of carbon dioxide emissions. Carbon emissions will reach 7,592,140 tons in 2025. At present, we are in an essential stage of transition from N-shape to inverted U-shape, and this paper makes several recommendations based on the findings

A Nanoparticle Approach towards Morphology Controlled Organic Photovoltaics (OPV)

polymer

slapo-artifact

<div> <div>This artifact contains scripts for setting up environments and reproducing results presented in the ASPLOS 2024 paper entitled <a href="https://arxiv.org/abs/2302.08005">Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training</a>.</div> </div> <p>Please refer to <a href="https://github.com/chhzh123/slapo-artifact">our github repo</a> for instructions on how to install and run the artifact.</p> <p> </p&gt

LATERAL LOAD RESISTANCE OF PARALLEL BAMBOO STRAND PANEL-TO-METAL SINGLE-BOLT CONNECTIONS – PART I: YIELD MODEL

The lateral load resistance behavior of single-shear unconstrained metal-to-parellel bamboo strand panel (PBSP) single-bolt connections was investigated.  The connection consisted of a PSBP main member fastened to a metal plate as a side member using a 6-mm diameter bolt without a nut or washer used.  The mechanics-based approach waas used to evaluate critical factors on the lateral load resistance performance of metal-to-PBSP single-bolt connections. Experimental results indicated that the lateral resistance loads of the metal-to-PBSP single-botl connections were significantly affected by its shear strength parallel to bamboo strand orientation, tensile strength perpendicular to bamboo strand orientation and bolt-bearing strenth in PBSPs.  Lower tensile strength perpendicular to bamboo strand orientation of PBSPs can limit its usage as connection members resisting lateral loads. The proposed mechanical model was verified experimentally as a valid means for deriving estimation equations of lateral resistance loads of unconstrained metal-to-PBSP single-bolt connections evaluated in this study.

Xue-Fu-Zhu-Yu capsule in the treatment of qi stagnation and blood stasis syndrome: a study protocol for a randomised controlled pilot and feasibility trial

Abstract Background Qi stagnation and blood stasis syndrome (QS&BSS) is one of the common Zhengs in traditional Chinese medicine (TCM), which manifests as various symptoms and signs, such as distending pain or a tingling sensation in a fixed position. In recent years, a number of clinical trials have focused on the effectiveness and safety of XFZYC in patients with a QS&BSS subtype disease, such as coronary heart disease, hyperlipidaemia, ischaemic cerebrovascular disease, gastritis, dysmenorrhoea, or arthritis, in terms of the outcomes of relevant diseases. However, there is lack of evidence of the effects of XFZYC in patients with QS&BSS with different diseases, focusing on the outcomes of Zhengs. Methods/Design A randomised, controlled, pilot and feasibility trial will be employed in this study, using a 7-week study period. Participants will be recruited from Guang’anmen Hospital, Huguosi TCM Hospital, Wangjing Hospital in China. One hundred and twenty participants will be randomised to a treatment group (Xue-Fu-Zhu-Yu Capsule (XFZYC)) and placebo group in a 1:1 ratio. Participants included in the study must be diagnosed with Qi stagnation and blood stasis syndrome criteria. The outcome measurements will include the traditional Chinese medicine patient-reported outcome (PRO) scale for QS&BSS, the single symptom and sign scale of QS&BSS, and the pain scale of QS&BSS. The clinical data management system (http://www.tcmcec.net/) will be used to collect and manage the data. Quality control will be used, according to Good Clinical Practice (GCP). Discussion Previous studies were expected to evaluate whether the addition of XFZYC to standard routine treatment would enhance the treatment effectiveness and improve the biomedical parameters pertaining to relevant disease. However, this trial is focused on the outcome of Zhengs, and we chose a range of outcome measurements to assess the improvement of relevant symptoms and signs. This trial is the first study designed to define and optimise the outcome measurements of Zhengs of XFZYC in the treatment of patients with QS&BSS. Trial registration ClinicalTrials.gov, NCT03091634. Registered on 12 August 2018. Release date 6 May 2017