Location Analysis of Chain Supermarket Distribution Center Based on CFLP

The number of stores operated by chain supermarkets is large, and the distribution of goods has the characteristics of low delivery volume and multiple delivery times. Logistics transportation costs account for a large proportion of operating costs. Establishing distribution centers is an effective measure to reduce operating costs. Through large-scale distribution, the chain supermarket distribution center can centrally manage the inventory of goods and distribute them to each store, thereby improving the distribution efficiency. This paper analyzes the influencing factors of the location of chain convenience supermarket distribution center, and then qualitatively analyzes the actual situation of the location object, determines the initial location plan, and establishes the CFLP model to solve the problem of the location selection of the chain supermarket distribution center

GraphGPT: Graph Learning with Generative Pre-trained Transformers

We introduce \textit{GraphGPT}, a novel model for Graph learning by self-supervised Generative Pre-training Transformers. Our model transforms each graph or sampled subgraph into a sequence of tokens representing the node, edge and attributes reversibly using the Eulerian path first. Then we feed the tokens into a standard transformer decoder and pre-train it with the next-token-prediction (NTP) task. Lastly, we fine-tune the GraphGPT model with the supervised tasks. This intuitive, yet effective model achieves superior or close results to the state-of-the-art methods for the graph-, edge- and node-level tasks on the large scale molecular dataset PCQM4Mv2, the protein-protein association dataset ogbl-ppa and the ogbn-proteins dataset from the Open Graph Benchmark (OGB). Furthermore, the generative pre-training enables us to train GraphGPT up to 400M+ parameters with consistently increasing performance, which is beyond the capability of GNNs and previous graph transformers. The source code and pre-trained checkpoints will be released soon\footnote{\url{https://github.com/alibaba/graph-gpt}} to pave the way for the graph foundation model research, and also to assist the scientific discovery in pharmaceutical, chemistry, material and bio-informatics domains, etc.Comment: 9 page

Taiji Data Challenge for Exploring Gravitational Wave Universe

The direct observation of gravitational waves (GWs) opens a new window for exploring new physics from quanta to cosmos and provides a new tool for probing the evolution of universe. GWs detection in space covers a broad spectrum ranging over more than four orders of magnitude and enables us to study rich physical and astronomical phenomena. Taiji is a proposed space-based GW detection mission that will be launched in the 2030s. Taiji will be exposed to numerous overlapping and persistent GW signals buried in the foreground and background, posing various data analysis challenges. In order to empower potential scientific discoveries, the Mock LISA Data Challenge and the LISA Data Challenge (LDC) were developed. While LDC provides a baseline framework, the first LDC needs to be updated with more realistic simulations and adjusted detector responses for Taiji's constellation. In this paper, we review the scientific objectives and the roadmap for Taiji, as well as the technical difficulties in data analysis and the data generation strategy, and present the associated data challenges. In contrast to LDC, we utilize second-order Keplerian orbit and second-generation time delay interferometry techniques. Additionally, we employ a new model for the extreme-mass-ratio inspiral waveform and stochastic GW background spectrum, which enables us to test general relativity and measure the non-Gaussianity of curvature perturbations. Furthermore, we present a comprehensive showcase of parameter estimation using a toy dataset. This showcase not only demonstrates the scientific potential of the Taiji Data Challenge but also serves to validate the effectiveness of the pipeline. As the first data challenge for Taiji, we aim to build an open ground for data analysis related to Taiji sources and sciences. More details can be found on the official website at http://taiji-tdc.ictp-ap.org.Comment: 15 pages, 3 figure

Use of diverging apertures to minimize the edge scatter in passive scattering proton therapy

The purpose of this study was to evaluate the use of diverging-cut aperture to minimize collimator contamination in proton therapy. Two sets of apertures with nondivergent and divergent edge were fabricated to produce a 10 cm x 10 cm field at the radiation isocenter of a single-room proton therapy unit. Transverse profiles were acquired in a scanning water tank with both aperture sets. Up to 9.5% extra dose was observed from aperture scattering near the field edges with the nondivergent aperture set at 2 cm above the water surface and remained 3.0% at depth of 10 cm. For the divergent set, the contamination was reduced to less than 3.5% and 1.3%, respectively. Our study demonstrated that scattering from apertures contaminated the dose distribution near the field edge at shallow depth. A diverging-cut aperture was capable of reducing the contamination and is recommended for use in passive scattering proton therapy, especially when critical organs are lateral and proximal to the target at shallow depth

A structural and mechanistic study of π-clamp-mediated cysteine perfluoroarylation

Natural enzymes use local environments to tune the reactivity of amino acid side chains. In searching for small peptides with similar properties, we discovered a four-residue π-clamp motif (Phe-Cys-Pro-Phe) for regio- and chemoselective arylation of cysteine in ribosomally produced proteins. Here we report mutational, computational, and structural findings directed toward elucidating the molecular factors that drive π-clamp-mediated arylation. We show the significance of a trans conformation prolyl amide bond for the π-clamp reactivity. The π-clamp cysteine arylation reaction enthalpy of activation (ΔH‡) is significantly lower than a non-π-clamp cysteine. Solid-state NMR chemical shifts indicate the prolyl amide bond in the π-clamp motif adopts a 1:1 ratio of the cis and trans conformation, while in the reaction product Pro3 was exclusively in trans. In two structural models of the perfluoroarylated product, distinct interactions at 4.7 Å between Phe1 side chain and perfluoroaryl electrophile moiety are observed. Further, solution 19F NMR and isothermal titration calorimetry measurements suggest interactions between hydrophobic side chains in a π-clamp mutant and the perfluoroaryl probe. These studies led us to design a π-clamp mutant with an 85-fold rate enhancement. These findings will guide us toward the discovery of small reactive peptides to facilitate abiotic chemistry in water.National Institutes of Health (U.S.) (Grant R01GM110535)National Institutes of Health (U.S.) (Grant GM088204)National Science Foundation (U.S.) (Award CHE-1464804

Catechol functionalized hyperbranched polymers as biomedical materials

The catechol plays a variety of important roles in biological processes, prompting researchers to include them in the design of biomimetic biomedical materials. The low molecular weight and good water solubility of the catechol group (or its derivatives) make it a good candidate for functionalizing biomaterials that can be typically achieved by grafting it onto a polymer chain. To fully harness the powerful capabilities of catechols, one can think beyond grafting to linear polymer chains, towards hyperbranched polymers, wherein at least one of the branches comprises at least one catechol moiety. In recent years, a number of approaches have been developed to synthesize multifunctional hyperbranched polymers with catechol functionalities for bioadhesives and surface coatings. This review article focuses on the main synthetic approaches applied for introducing the important and versatile catechol building blocks within the (hyper)branched structure polymers. In addition, the applications of these polymers in biomedical fields are highlighted as well

Knowledge-Based Open Performance Measurement System (KBO-PMS) for a Garment Product Development Process in Big Data Environment

Globally, customers are getting increasingly demanding in terms of personalization of products and are asking for shorter product development periods with more predictable product performance, especially in fashion industry. Current market pressures drive firms to adapt new design process in product development (PD) processes. Nevertheless, choosing the effective PD process is a challenging, complex decision. There is a critical need to develop a performance measurements system (PMS) for choosing appropriate product development (PD) processes in garment design to support product mangers to effectively respond to market. This paper presents a knowledge-based open performance measurement system (KBO-PMS) in big data environment, in order to support complex industrial decision-making for new product development. Its dynamic and flexible structure enables the whole system to be more adapted to knowledge sharing of product managers and processing of various time-varying data. The proposed KBO-PMS is composed of an interactive structure, capable of both integrating new KPIs from the open resource and tracking the evolution of the KBO-PMS components with time. The proposed KBO-PMS has been validated by realizing the performance evaluation of product development (PD) in fashion industry. It can be regarded as an application of open-resource based dynamic group decision-making in fashion big data environment

Coherent deglacial changes in western Atlantic Ocean circulation

Abrupt climate changes in the past have been attributed to variations in Atlantic Meridional Overturning Circulation (AMOC) strength. However, the exact timing and magnitude of past AMOC shifts remain elusive, which continues to limit our understanding of the driving mechanisms of such climate variability. Here we show a consistent signal of the 231Pa/230Th proxy that reveals a spatially coherent picture of western Atlantic circulation changes over the last deglaciation, during abrupt millennial-scale climate transitions. At the onset of deglaciation, we observe an early slowdown of circulation in the western Atlantic from around 19 to 16.5 thousand years ago (ka), consistent with the timing of accelerated Eurasian ice melting. The subsequent weakened AMOC state persists for over a millennium (~16.5–15 ka), during which time there is substantial ice rafting from the Laurentide ice sheet. This timing indicates a role for melting ice in driving a two-step AMOC slowdown, with a positive feedback sustaining continued iceberg calving and climate change during Heinrich Stadial 1NERC | Ref. NE/K008536/