Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Research on Product Digital Collaborative Design System

Network economy had changed manufacturing environme nt at all. Open global market offer more choice to customer, and it become changea ble and unpredictable as consumers’ needs become more and more characteristic an d diversified. Various new technology coming forth and application accelerate th e rapid change of the market. The manufacturing enterprises were compelled t o change their strategy by the variability of the market, and time has been put to the all-important place. There is a need driven by the market to set up a ne twork design and manufacturing mode which have rapid market responsiveness. In order to meet the need for network manufacturing, the organization and manage ment of manufacturing enterprise need a completely innovation, next generation o f manufacturing system must have the character such as digitization, flexibility , agility, customization and globalization and so on. As for an enterprise in au to industry, how to gather together the orders through the distribution, and rap id produce the product which can meet the customer’s need, it is the key that th e contemporary enterprises succeed in the competitive market. The competitive market requires rapid product development. Close cooperation amo ng the designers will accelerate the product development by shortening the devel opment cycle, improving the product quality and reducing the investment. It has been emphasized in the methodology of concurrent engineering (CE). But sometimes those partners are distributed in the world, so there is a need for an importan t technology contribution to collaborative engineering, and supporting distribut ed designers for rapid product development. This paper focuses on a collaborative design system: Product Digit Collaborative Design System (PDCDS). The solution of PDCDS can make it more efficient and rel iable to visit teledata as well as we can get it from local database. It will be ease to get the newest design process information aided by PDCDS, and it will h ave higher efficiency by collaborative work. Comparing with other traditional Pr oduct Data Management (PDM) software system, PDCDS have some new characters such as group, dynamicness, synchronization or asynchronism working mode, and the hi story recorder is needed, and it also surport Webservice.

Horn of plenty:Value of the international registry for pediatric chronic myeloid leukemia

Chronic myeloid leukemia(CML)in minors is a rare disease which can be effectively treated by tyrosine kinase inhibitors(TKIs)since the year 2000.A majority of pediatricians will encounter one or two CML patients in the course of their careers and will typically have to rely on written information along with their own intuition to provide care.Knowledge of response to TKIs and of agespecific side effects has an impact on the design of pediatric CML trials in many ways aiming to contribute toward greater predictability of clinical improvements.Information from a registry on a rare disease like CML offers the enormous benefit of enabling treating physicians to interact and share their collective experience.The International Registry on Pediatric CML(IR-PCML)was founded at Poitiers/France almost 10 years ago.Since then,the number of collaboration centers and in parallel of registered patients continuously increased(>550 patients as of December 2019).Ideally,from a given treatment center in a country data are transferred to a national coordinator who interacts with the IR-PCML.In the sense of quality assurance,the registry can offer dissemination of knowledge on state-of-the-art diagnostics(including reference appraisal),optimal treatment approaches,and follow-up procedures within a network that is exerting its strength via participation.With continuous growth during the recent years,very rare subgroups of patients could be identified(e.g.,CML diagnosed at age<3 years,children presenting with specific problems at diagnosis or during course of treatment)which had not been described before.Publications coming from the IR-PCML disseminated this useful information derived from patients who robustly participate and share information about their disease,among themselves and with their caregivers and clinicians.Patient input driving the collection of data on this rare leukemia is the basis for the considerable success of bringing new therapeutics into clinical use.

Databases and Web Tools for Cancer Genomics Study

Publicly-accessible resources have promoted the advance of scientific discovery. The era of genomics and big data has brought the need for collaboration and data sharing in order to make effective use of this new knowledge. Here, we describe the web resources for cancer genomics research and rate them on the basis of the diversity of cancer types, sample size, omics data comprehensiveness, and user experience. The resources reviewed include data repository and analysis tools; and we hope such introduction will promote the awareness and facilitate the usage of these resources in the cancer research community.

Preference transfer model in collaborative filtering for implicit data

Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.

Blockchain for public safety:A survey of techniques and applications

As an emerging technology,blockchain provides a range of advantages,such as decentralized and transparent data storage,secure access control,and enhanced data traceability.However,it is rarely applied in the field of public safety.This paper presents an in-depth survey of blockchain technology,focusing on its potential applications and implications within the field of public safety research.We explore the practical needs of multi-party data collaboration in emergency management and discusses the applicability and value of blockchain technology in this context.Additionally,this paper introduces and compares several popular blockchain platforms.By providing a comprehensive examination of blockchain technology and its potential benefits for public safety,this paper seeks to enhance understanding of the technology’s capabilities,encourage further research,and inspire innovation in this domain.