A COST-BENEFIT ANALYSIS OF GREEN BUILDINGS WITH RESPECT TO CONSTRUCTION WASTE MINIMIZATION USING BIG DATA IN HONG KONG

It is generally accepted that the extra construction costs involved in the construction of green buildings will result in benefits including lower operation costs,higher sale/rental prices,and better sustainability performance.However,there has been little recognition of construction waste minimization(CWM)as one of the important benefits of sustainability performance as designated in green building.is paper aims to provide a better understanding of the cost benefit of green buildings with respect to CWM by using big data in the context of Hong Kong.The study is innovative in that it conducts a cost-benefit analysis specifically on CWM of green buildings by mining large-volume datasets.A surprise finding is that Hong Kong’s green building rating system(GBRS),i.e.the BEAM Plus,has a negligible effect on CWM,while it generally increases construction costs by approximately 24%.Hence,the increased construction cost of green buildings cannot be offset by CWM if corresponding items in the BEAM Plus are not properly incentivized.is paper demonstrates the necessity of emphasizing CWM-related items in GBRSs and of taking appropriate measures to deal with them.It also provides better decision-support information on the increased construction costs and the attainable benefits of green building that developers may wish to consider when initiating a green building project.

Input-output Efficiency of Elements of Urban Construction Land in Beijing: Empirical Analysis Based on Panel Data of Jurisdiction

Based on the Data Envelopment Analysis method,and by using CCR and BCC model,Super Efficiency model and Malmquist model guided by input efficiency,the input-output efficiency of elements of urban construction land in different jurisdictions of Beijing from 2005 to 2015 was studied.The results showed that there were obvious differences between input-output efficiency of elements of urban construction land in different jurisdictions of Beijing,among which the efficiency of the core area of capital,Yanqing District,Fangshan District and Huairou District was relatively high,while the efficiency of Daxing District,Fengtai District and Miyun District was relatively low.There was no obvious correlation between efficiency differentiation and location factors,which is mainly caused by whether the land use in each jurisdiction has scale effect,whether the technology is improved,whether the input is redundant and whether the output is insufficient.For the jurisdiction of inefficient land use,we should strengthen the consciousness of intensive land use,improve the technical level,appropriately reduce the redundancy of input elements,and pay attention to the output of social and ecological benefits.

An Efficiency Assessment of Tuberculosis Treatment on Health Centers: A Data Envelopment Analysis Approach

This study utilized Data Envelopment Analysis (DEA) in assessing the efficiency of health center in tuberculosis (TB) treatment. Assessing the efficiency of health center treating TB is a vital and sensitive topic, because there is a cumulative amount of public funds devoted to healthcare. In this research, a DEA model has been correlated to evaluate and assess the efficiency of 17 health centers. The researchers selected the health budget and the number of health workers as input variables likewise, the number of people served, number of TB patients served, and TB patients treated (%) as output variables. Based on the result of the study, only five (5) health centers out of seventeen (17) have 100% efficiencies throughout the 2 years period. It is recommended that other health centers should learn from their efficient peers recognized by the DEA model so as to increase the overall performance of the healthcare system. Likewise, health centers should integrate Health Information Technology to deliver healthier care for their patients.

An integrated analysis platform merging SuperDARN data within the THEMIS tool developed by ERG-Science Center (ERG-SC)

The Energization and Radiation in Geospace （ERG） mission seeks to explore the dynamics of the radiation belts in the Earth＇s inner magnetosphere with a space-borne probe （ERG satellite） in coordination with related ground observations and simulations/modeling studies. For this mission, the Science Center of the ERG project （ERG-SC） will provide a useful data analysis platform based on the THEMIS Data Analysis software Suite （TDAS）, which has been widely used by researchers in many conjunction studies of the Time History of Events and Macroscale Interactions during Substorms （THEMIS） spacecraft and ground data. To import SuperDARN data to this highly useful platform, ERG-SC, in close collaboration with SuperDARN groups, developed the Common Data Format （CDF） design suitable for fitacf data and has prepared an open database of SuperDARN data archived in CDE ERG-SC has also been developing programs written in Interactive Data Language （IDL） to load fltacf CDF files and to generate various kinds of plots-not only range-time-intensity-type plots but also two-dimensional map plots that can be superposed with other data, such as all-sky images of THEMIS-GBO and orbital footprints of various satellites. The CDF-TDAS scheme developed by ERG-SC will make it easier for researchers who are not familiar with SuperDARN data to access and analyze SuperDARN data and thereby facilitate collaborative studies with satellite data, such as the inner magnetosphere data pro- vided by the ERG （Japan）-RBSP （USA）-THEMIS （USA） fleet.

AI-enabled organoids:Construction,analysis,and application

Organoids,miniature and simplified in vitro model systems that mimic the structure and function of organs,have attracted considerable interest due to their promising applications in disease modeling,drug screening,personalized medicine,and tissue engineering.Despite the substantial success in cultivating physiologically relevant organoids,challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis.The advent of AI-Enabled Organoids,which interfaces with artificial intelligence(AI),holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids.This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids,summarizing the prospective applications on rapid screening of construction strategies,cost-effective extraction of multiscale image features,streamlined analysis of multi-omics data,and precise preclinical evaluation and application.We also explore the challenges and limitations of interfacing organoids with AI,and discuss the future direction of the field.Taken together,the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression,ultimately laying the groundwork for clinical application.

Fast and scalable routing protocols for data center networks

Data center networks may comprise tens or hundreds of thousands of nodes,and,naturally,suffer from frequent software and hardware failures as well as link congestions.Packets are routed along the shortest paths with sufficient resources to facilitate efficient network utilization and minimize delays.In such dynamic networks,links frequently fail or get congested,making the recalculation of the shortest paths a computationally intensive problem.Various routing protocols were proposed to overcome this problem by focusing on network utilization rather than speed.Surprisingly,the design of fast shortest-path algorithms for data centers was largely neglected,though they are universal components of routing protocols.Moreover,parallelization techniques were mostly deployed for random network topologies,and not for regular topologies that are often found in data centers.The aim of this paper is to improve scalability and reduce the time required for the shortest-path calculation in data center networks by parallelization on general-purpose hardware.We propose a novel algorithm that parallelizes edge relaxations as a faster and more scalable solution for popular data center topologies.

A Multi-Scale Thermal Analysis Method for Data Centers with Application in a Ship Data Center

Thermal analysis of data centers is in urgent need to ensure that computer chips remain below the critical temperature while the energy consumption for cooling can be reduced.It is difficult to obtain detailed hotspot locations and temperatures of chips in large data centers containing hundreds of racks or more by direct measurement.In this paper,a multi-scale thermal analysis method is proposed that can predict the temperature distribution of chips and solder balls in data centers.The multi-scale model is divided into six scales:room,rack,server,Insulated-Gate Bipolar Transistor(IGBT),chip and solder ball.A concept of sub-model is proposed and the six levels are organized into four simulation sub-models.Sub-model 1 contains Room,Rack and Server(RRS);Sub-model 2 contains Server and IGBT(SI);Sub-model 3 contains IGBT and Chip(IC),and Sub-model 4 contains Chip and Solder-ball(CS).These four sub-models are one-way coupled by transmitting their results as boundary conditions between levels.The full-field simulation method is employed to verify the efficiency and accuracy of multi-scale simulation method for a single-rack data center.The two simulation results show that the highest temperature emerges in the same location.The Single-rack Full-field Model(SRFFM)costs 2.5 times more computational time than that with Single-rack Multi-scale Model(SRMSM).The deviation of the highest temperature of chips and solder balls are 1.57℃and 0.2℃between the two models which indicates that the multi-scale simulation method has good prospect in the data center thermal simulation.Finally,the multi-scale thermal analysis method is applied to a ship data center with 15 racks.

Multi-Hazard Evaluation Using Cluster Analysis—For Designated Evacuation Centers of Yokohama

Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are likely not aware of the specific types of disaster. So, first of all, we need to know what kinds of hazards are important. However, the information that integrates multiple hazards is not well maintained, and there are few such studies. On the other hand, in Japan, a lot of hazard information is being released on the Internet. So, we summarized and assessed hazard data that can be accessed online regarding shelters (where evacuees live during disasters) and their catchments (areas assigned to each shelter) in Yokohama City, Kanagawa Prefecture. Based on the results, we investigated whether a grouping by cluster analysis would allow for multi-hazard assessment. We used four natural disasters (seismic, flood, tsunami, sediment disaster) and six parameters of other population and senior population. However, since the characteristics of the population and the senior population were almost the same, only population data was used in the final examination. From the cluster analysis, it was found that it is appropriate to group the designated evacuation centers in Yokohama City into six groups. In addition, each of the six groups was found to have explainable characteristics, confirming the effectiveness of multi-hazard creation using cluster analysis. For example, we divided, all hazards are low, both flood and Seismic hazards are high, sediment hazards are high, etc. In many Japanese cities, disaster prevention measures have been constructed in consideration of ground hazards, mainly for earthquake disasters. In this paper, we confirmed the consistency between the evaluation results of the multi-hazard evaluated here and the existing ground hazard map and examined the usefulness of the designated evacuation center. Finally, the validity was confirmed by comparing this result with the ground hazard based on the actual measurement by the past research. In places where the seismic hazard is large, the two are consistent with the fact that the easiness of shaking by actual measurement is also large.

A Statistical Analysis of Point-like Sources in the Chandra Galactic Center Survey

The Chandra Galactic Center Survey detected -800 X-ray point-like sources in the 2°× 0.8° sky region around the Galactic Center. We study the spatial and luminosity distributions of these sources according to their spectral properties. Fourteen bright sources detected are used to fit jointly an absorbed power-law model, from which the power-law photon index is determined to be -2.5. Assuming that all other sources have the same power-law form, the relation between hardness ratio and HI column density NH is used to estimate the NH values for all sources. Monte Carlo simulations show that these sources are more likely concentrated in the Galactic center region, rather than distributed throughout the Galactic disk. We also find that the luminosities of the sources are positively correlated with their HI column densities, i.e., a more luminous source has a higher HI column density. From this relation, we suggest that the X-ray luminosity comes from the interaction between an isolated old neutron star and interstellar medium （mainly dense molecular clouds）. Using the standard Bondi accretion theory and the statistical information of molecular clouds in the Galactic center, we confirm this positive correlation and calculate the luminosity range in this scenario, which is consistent with the observation （10^32 - 10^35 erg s^-1）.

Non-contact monitoring and analysis system for tunnel surrounding rock deformation of underground engineering

It is very important to monitor surrounding rock deformation in tunnel construction. The principle, function, development and application of the system composed of a total station and computer for monitoring and analyzing surrounding rock deformation were discussed. The new methods of two free station of 3D measurement and its mathematic adjustment mode were presented. The development of software for total station on-board and post for computer were also described. Without centering it and measuring its height, the total station controlled by the software on-board can fulfill the whole measurements to target points. Monitoring data can be processed by the post software and results of regression analysis, forecasting information of the tunnel surrounding rock deformation can be provided in time. The practical use shows that this system is practicable, highly accurate and efficient. It satisfies the needs of safety and information construction in tunnel construction of underground engineering.

StreamTune： dynamic resource scheduling approach for workload skew in video data center

Video surveillance applications need video data center to provide elastic virtual machine （VM） provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The unknown arrival workloads easily lead to workload skew among VMs. In this paper, we study how to balance the workload skew on online video surveillance system. First, we design the system framework for online surveillance service which con- sists of video capturing and analysis tasks. Second, we propose StreamTune, an online resource scheduling approach for workload balancing, to deal with irregular video analysis workload with the minimum number of VMs. We aim at timely balancing the workload skew on video analyzers without depending on any workload prediction method. Furthermore, we evaluate the performance of the proposed approach using a traffic surveillance application. The experimental results show that our approach is well adaptive to the variation of workload and achieves workload balance with less VMs.

Evaluating the Efficiency and Determinants of Efficiency of Rural Health Centers Situated in Punjab, Pakistan

This study calculates the efficiency of Rural Health Centers (RHCs) and investigates the impact of other variables affecting the efficiency of RHCs. The study considers 29 RHCs, 13 of District Faisalabad, 9 of Toba and 7 of Jhang;a survey was conducted to collect data from each RHC for the year 2016. Data Envelopment Analysis (DEA) model was utilized to get the scores for efficiency. Thereafter, after getting the results from DEA Tobit regression was used in the second stage. Out of the 29 Rural Health Centers, only 11 (38%) are working efficiently as compare to others. Distance from the tehsil headquarter, Distance from the road with “0” probability, Distance from private hospital with “0” probability, Behavior of the staff with “0.0064” probability and laboratory equipment’s with “0” probability, have an impact on the efficiency scores. Distance from other health facilitators, Staff’s behavior, list of medicine and equipment’s used at RHCs should be improved to increase the efficiency of RHC’s.

铁路施工中的智能监测技术及其在高铁项目中的应用

随着现代铁路建设规模的不断扩大和技术要求的提高,智能监测技术在铁路施工中的应用日益广泛。文章探讨了智能监测技术的基本原理及其在高铁项目中的实际应用,通过案例分析展示了该技术在施工过程中的显著优势。通过传感器、图像与视频监控、大数据分析等多种技术手段,智能监测技术不仅提高了施工精度与效率,还显著提升了安全保障水平,降低了工程风险。文章结合多个高铁建设项目,详细分析了这些技术在实际施工中的具体实施及其产生的效果,并提出了未来在铁路建设领域智能监测技术的发展方向。

基础教育资源配置大数据分析平台的整体架构与运行保障

教育大数据是反映基础教育资源配置现状、需求和问题的数据集合,也是重塑基础教育资源配置结构、构建良好教育生态的有效工具。教育大数据能够为资源精准配置提供客观依据、助力理性决策、拓展实践路径、提供监测反馈,可从数据资源层、处理层、分析层和可视化层,整体架构基础教育资源配置大数据分析平台,实现多学段基础教育数据精准采集、多模态基础教育数据分类储存、多因素基础教育数据指标计量、多维度基础教育数据结果呈现。基础教育资源配置大数据分析平台顺利运行的保障措施主要包括:政策跟进,赋予数据驱动合法地位;强化治理,保障平台教育数据质量;注重成效,发挥数据分析平台价值;多元联动,助力数据分析平台共建。通过这些措施,努力实现数据驱动基础教育资源优化配置,促进决策科学化、供给精准化、评估动态化。

基于多模态数据的智慧课堂学生学习行为研究

新兴技术赋能下的智慧课堂为学生学习行为监测、评价、反馈、预警提供丰富的教育多模态数据,充分采集、处理和分析教育多模态数据能够为教育研究和实践提供参考。首先,梳理已有智慧课堂学习行为分析和多模态数据教学研究,借鉴学习行为分类系统,构建基于多模态数据类型的“声音数据—图像数据—文本数据”三维的多模态数据分析框架,从言语学习活动、位置移动、身体动作、技术使用四个方面呈现智慧课堂下学生学习行为变化;其次,将体现学生学习行为特征的多模态数据编码,并进行质性表征形成编码体系;最后,从情境和时序行为分析视角出发,对课例中的学习行为多模态数据进行频度变化及周期性变化分析。研究得出,在智慧课堂环境下,学生课堂学习参与性、主动性、专注度均在不断提升,课堂学习氛围朝着积极的方向发展,无效及无关学习行为逐渐减少。此外,通过对学生学习行为多模态数据的剖析,以期为学生了解自身学习行为及状态、教师进行个性化教学,以及研究者构建学习评价体系提供数据来源、科学依据。

基于深度学习的智能问答型智慧校园平台设计

目前智慧校园数据中心存在数据质量较差、数据应用困难、数据分析收益较小等问题,设计基于深度学习技术的智能问答式数据中心,可以对智慧校园复杂数据的高效应用及梳理提供平台化的支撑。相对于传统的推荐技术与数据可视化技术,问答式数据中心能够直接给出数据分析人员关心的问题答案,有效降低数据分析成本。

规制数据不正当竞争行为的司法实践检视及改进方向

数据作为新型生产要素,在数字经济高质量发展中发挥越来越重要的基础作用,成为数字经济市场竞争中经营者竞相争夺的要素。为获取和保持竞争优势,经营者围绕数据展开激烈争夺,引发一些不正当竞争行为。在司法实践中,对这类数据不正当竞争行为的规制主要是采取《反不正当竞争法》“一般条款”“互联网专条”“商业秘密条款”。虽然这三个条款均起到一定作用,但仍存在适用条件不明确问题,造成可预见性不足。为此,建议审慎适用“一般条款”,细化和优化“互联网专条”,优先适用具体条款。结合《反不正当竞争法》第三次修订,具体规定数据商业利用中的不正当竞争行为,做好例外条款的平衡设计。运用个案分析原则回应数字经济激烈动态竞争的现实,鼓励多元主体共同参与,深化跨部门协同,综合运用多制度多工具,完善“共建共治共享共赢”的数据商业利用模式及不正当竞争行为规制方案。

基于低代码平台搭建校园统一数据中台的研究

针对前期“按需、逐个、独立”的数字校园信息化建设导致学校间资源无法共享,进而形成“孤岛架构”的问题,提出将低代码平台和数据标准化嵌入融合,构建可以灵活搭建业务管理模块的数据中台,以实现数据的统一标准、统一采集、统一处理、统一分析,旨在为学校数据应用服务提供准确的数据支撑,并为高校的信息化发展降本增效。

面向兵棋推演复盘分析的机器学习数据集构建

运用机器学习进行兵棋推演复盘分析,首先要解决的是数据集构建问题。由于机器学习对数据结构的规范化要求,以及算力和存储限制,通过兵棋推演数据构建机器学习数据集,在如何描述兵棋推演状态,如何描述推演过程,如何处理高维数据,如何数据保真等方面,还面临不少问题。针对此类问题,构建了兵棋推演过程数据向机器学习数据集映射模型,在总体框架上对数据集构建的映射流程、态势描述数据范围和数据统计计算规则进行规范,并从时间关联数据、地理空间关联数据和高维数据降维3个视角设计针对性处理方法,以保证构建数据集的数据结构统一、高维数据降维需求和数据集保真要求。通过数据集构建实验进行了验证,结果表明:在时间分辨率和地理空间分辨率适中情况下,所构建数据集映射模型,既能较好对兵棋推演高维数据进行降维,又能较好防止构建的数据集失真。

公立医院绩效考核智能指标分析系统构建与应用

目的:改善传统绩效考核方式考核指标不明确、数据采集和分析不规范等问题。方法:利用人工智能、自然语言处理等技术,将各系统分散的数据进行统一集成、治理,按照上报指标的形式存储于数据中心,实现指标全局定义,构建绩效考核系统,展示、分析相关指标趋势,并动态追踪与管理。结果:该平台集展示、统计、分析、反馈为一体,实现了指标分析智能化、运营管理精细化、绩效动态监测与预警,同时提升了业务数据质量。结论:该系统的应用提高了医院绩效考核的准确性和效率,可为医院管理提供决策支持。