Effects of nursing team communication and collaboration on treatment outcomes in intensive care unit patients with severe pneumonia

BACKGROUND Severe pneumonia is a common severe respiratory infection worldwide,and its treatment is challenging,especially for patients in the intensive care unit(ICU).AIM To explore the effect of communication and collaboration between nursing teams on the treatment outcomes of patients with severe pneumonia in ICU.METHODS We retrospectively analyzed 60 patients with severe pneumonia who were treated at the ICU of the hospital between January 1,2021 and December 31,2023.We compared and analyzed the respiratory mechanical indexes[airway resistance(Raw),mean airway pressure(mPaw),peak pressure(PIP)],blood gas analysis indexes(arterial oxygen saturation,arterial oxygen partial pressure,and oxygenation index),and serum inflammatory factor levels[C-reactive protein(CRP),procalcitonin(PCT),cortisol(COR),and high mobility group protein B1(HMGB1)]of all patients before and after treatment.RESULTS Before treatment,there was no significant difference in respiratory mechanics index and blood gas analysis index between 2 groups(P>0.05).However,after treatment,the respiratory mechanical indexes of patients in both groups were significantly improved,and the improvement of Raw,mPaw,plateau pressure,PIP and other indexes in the combined group after communication and collaboration with the nursing team was significantly better than that in the single care group(P<0.05).The serum CRP and PCT levels of patients were significantly decreased,and the difference was statistically significant compared with that of nursing group alone(P<0.05).The levels of serum COR and HMGB1 before and after treatment were also significantly decreased between the two groups.CONCLUSION The communication and collaboration of the nursing team have a significant positive impact on respiratory mechanics indicators,blood gas analysis indicators and serum inflammatory factor levels in the treatment of severe pneumonia patients in ICU.

An Evaluation of the Critical Success Factors in Sustainable Food Supply Chains in Developing Countries

Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcing and the growing technological, social, and environmental concerns. The food business faces serious sustainability and growth challenges in developing countries. A comprehensive analysis of the critical success factors (CSFs) influencing the performance outcome and the sustainable supply chain management (SSCM) process. A theoretical framework is established to explain how they are used to examine the organizational aspect of the food supply chain life cycle analysis. This study examined the CSFs and revealed the relationships between them using a methodology that included a review of literature, interpretative structural modeling (ISM), and cross-impact matrix multiplication applied in classification (MICMAC) tool analysis of soil liquefaction factors. The findings of this research demonstrate that the quality and safety of food are important factors and have a direct effect on other factors. To make sustainable food supply chain management more adequate, legislators, managers, and experts need to pay attention to this factor. In this work. It also shows that companies aiming to create a sustainable business model must make sustainability a fundamental tenet of their organization. Practitioners and managers may devise effective long-term plans for establishing a sustainable food supply chain utilizing the recommended methodology.

A Collaboration Network Model with Multiple Evolving Factors

To describe the empirical data of collaboration networks, several evolving mechanisms have been proposed, which usually introduce different dynamics factors controlling the network growth. These models can reasonably reproduce the empirical degree distributions for a number of we11-studied real-world collaboration networks. On the basis of the previous studies, in this work we propose a collaboration network model in which the network growth is simultaneously controlled by three factors, including partial preferential attachment, partial random attachment and network growth speed. By using a rate equation method, we obtain an analytical formula for the act degree distribution. We discuss the dependence of the act degree distribution on these different dynamics factors. By fitting to the empirical data of two typical collaboration networks, we can extract the respective contributions of these dynamics factors to the evolution of each networks.

Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering

Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms.

Implicit Trust Based Context-Aware Matrix Factorization for Collaborative Filtering

Matrix factorization （MF） has been proved to be a very effective technique for collaborative filtering （ CF）, and hence has been widely adopted in today＇s recommender systems, Yet due to its lack of consideration of the users＇ and items＇ local structures, the recommendation accuracy is not fully satisfied. By taking the trusts among users＇ and between items＇ effect on rating information into consideration, trust-aware recommendation systems （TARS） made a relatively good performance. In this paper, a method of incorporating trust into MF was proposed by building user-based and item-based implicit trust network under different contexts and implementing two implicit trust-based context-aware MF （]TMF） models. Experimental results proved the effectiveness of the methods.

Collaborative Supply Chain Management: A Critical Redesigning Factor

In improving the competitiveness of business organi sa tion in the 21st century, minimising cost and increasing productivity are no lon ger factors that could promise the success. The changes in customer trends whic h focusing more on product or service customising, high quality and short delive ry times are additional crucial factors that organisation should be aware of. T hese factors have direct relations on how the management could utilise the capab ility of its supply chain management (SCM). The importance of SCM in organisati on specifically to manufacturer is to play a vital role in managing the flow of material and information along the chain from suppliers to customers. In a trad itional way, SCM is mainly manage by the production department in organisation b ut with the advancement in information technology, resulted the changes in worki ng environment. One of the ways that could be implemented in decision making p rocess in order to improve the effectiveness and efficiency of the decision is t hrough collaborative environment. Due to this situation, there is a need to rede sign the existing SCM in order to optimise its capability and functionality in t he way to ensure the organisation competitiveness is sustainable. A minor or ma jor alignment to SCM business processes should be done in order to streamline th e flow of information, which could also affect the flow of materials. Factors s uch as SCM operational, structural and even its managerial are among the issues that are critical in redesigning its business process. Each of these factors has its own attributes such as lead-times, complexity, frequency and organisationa l setting that could lead in improving organisational competitiveness. In additi on, by identifying these factors, it would help the management to plan and desig n the collaborative SCM that would effectively correspond to the changes in busi ness and customer trends. The intention of the paper is to promote a list of fa ctors and attributes, which are critical in redesigning an existing SCM in order to shift its environment to become collaborative SCM. By utilising these facto rs and attributes, a model for redesigning SCM into collaborative environment is currently developed by the author and will be used in the next stage of his res earch.

黄河流域两大区域的土地生态安全动态评价及比较

[目的]探究山东半岛与黄河几字弯两大区域的土地生态安全水平差异,为实现黄河流域生态保护与高质量发展,推动“共同抓好大保护,协同推进大治理”战略提供现实依据。[方法]运用熵值法、核密度估计、马尔科夫链与障碍因子模型等方法,对黄河流域两大区域的土地生态安全进行动态评价。[结果](1)在水平变化上,两大区域的土地生态安全增速均呈“倒U”形态势,山东半岛的土地生态安全水平明显高于黄河几字弯,但黄河几字弯的增长速度更快。(2)在时间上,两大区域土地生态安全均呈现“单核”演变特征,山东半岛区域差异有扩大态势,几字弯区域差异则有缩小态势。在空间上,两大区域均呈现出“东中部高—西部低”的空间分布态势。(3)动态演化特征上,两大区域土地生态安全等级演变具有“路径依赖”与“自身锁定”效应。(4)山东半岛土地生态安全受人均耕地面积、地均化肥农药施用量等影响;黄河几字弯土地生态安全受景观破碎度、森林覆盖率等影响。[结论]未来山东半岛与黄河几字弯既要根据区域优势分类施策,也要从流域全局出发,协同提升黄河流域土地生态安全水平。

物流业和制造业融合发展对全要素生产率的影响

基于2006—2020年的省际面板数据,使用耦合协调度模型测算我国物流业和制造业的融合发展水平。研究发现:我国各省份物流业和制造业融合发展度逐步上升,总体上处于低水平向高水平过渡的协调阶段。(1)省际之间物流业与制造业融合水平存在差异,其中浙江、江苏、山东和广东的融合发展水平最高,中部和西部省份融合发展水平提高较快,吉林、黑龙江和辽宁三省则有退步。(2)使用固定效应模型检验两业融合发展对全要素生产率的影响,发现两业融合发展度的提高能够促进区域全要素生产率的提升,该结论在利用IV-GMM和IV-LIML等模型估计下依旧显著。(3)两业融合发展度的提高一方面可以推动全要素生产率边界的扩大,另一方面有利于实际效率趋近最优效率。

基于知识追踪机的多特征融合习题推荐模型

个性化习题推荐是智慧教育个性化服务领域的重要课题,然而传统的习题推荐算法对于学生特征的研究不够彻底,对于学生知识掌握与答题行为之间的关联信息挖掘未能充分,导致推荐精准度不佳。为解决上述问题,结合知识追踪机(KTM)和基于用户的协同过滤算法,提出一种基于KTM多特征融合的习题推荐模型SKT-MFER。该模型首先构造了一个融入学生学习行为和学习能力的知识追踪模型KTM-LC,精准挖掘学生的知识掌握水平;接着设置两次筛选,先利用知识点掌握矩阵初步筛选出相似的学生,再根据认知状态相似度和习题难度相似度组合而成的综合相似度进行二次筛选,双重过滤以保障习题推荐的准确度。通过广泛的实验证明,所提方法相比于一些现有的基线模型有更好的效果。

职业教育赋能县域城乡融合发展:逻辑理路、现实梗阻与实践路向

职业教育与县域城乡融合发展以人的全面发展为共同逻辑基点,职业教育依托技能人才培养、科教融汇发展、公共服务供给、教化育人实践和开放合作办学等赋能县域城乡要素流动、产业融合、社会公平、文化交融与治理协同。在加快构建新发展格局的时代背景下,发挥职业教育在县域城乡融合发展中的赋能作用,需要聚焦当前职业教育在人才培养、科技创新、资源配置、文化传播、开放合作领域的现实梗阻,以育人体系完善、科教融汇深化、资源配置优化、文化场域构建、开放格局打造为路向,推动县域城乡人才、产业、服务、文化和治理的多维融合。

结合矩阵补全的宽度协同过滤推荐算法

协同过滤是推荐系统中最经典的方法之一,能够满足人们对个性化推荐任务的需求,但许多协同过滤算法在面对评分数据稀疏性问题时推荐效果不佳。为解决此问题,提出一种结合矩阵补全的宽度协同过滤推荐算法。先使用矩阵补全技术对用户项目评分矩阵进行补全,再利用补全后的矩阵对已评分的用户和项目分别寻找其近邻项,进而构造用户与项目的评分协同向量,最后使用宽度学习系统来构建用户项目与评分之间的复杂的非线性关系。在MovieLens和filmtrust数据集上对所提出算法的有效性进行检验。试验结果表明,与当前最先进的方法相比,该方法能够有效地缓解数据稀疏性问题,具有较低的计算复杂度,在一定程度上提升了推荐系统的性能。

教学过程智能化发展评估新方向——《2023人工智能促进教育发展报告》节选三

智能技术的快速发展深刻影响着教育的理念重塑和形态变革。教学过程智能化对促进人的个性化发展,培养创新型人才,形成人才竞争力具有重要意义。通过检索国内外文献发现,目前尚未形成科学完善的教学过程智能化发展评价指标体系。该研究采用调查研究、专家访谈等方法,系统构建了教学过程智能化发展评价框架体系,并通过专家调查法和层次分析法对各项指标体系进行权重计算,最终形成包括人机协同与具体教学过程应用2个二级指标、13个3级指标的教学过程智能化发展评价指标体系,完善人工智能促进教育发展的评估体系,明确教学过程研究的未来指向,有助于推动智能技术全面融入教学全过程,为数字教学改革发展与评价提供参照。

基于净能力及二阶锥规划的分布式光储多场景协同优化策略

针对现有配电网中分布式光储调度模型存在资源协同不足、求解复杂等问题,提出了一种基于净能力及二阶锥规划的分布式光储多场景协同优化调度策略。通过引入储能接入配电网后的功率转移分布因子,提出一种基于系统净能力的储能最优选址计算方法;综合考虑储能的运行特性和分布式光伏的出力不确定性,建立以系统日综合成本和削峰填谷为目标的分布式光储多场景协同优化调度模型;利用二阶锥松弛和Big-M法对潮流约束、储能运行约束进行处理,将原规划模型转化为混合整数二阶锥规划问题。以IEEE 33节点系统和西北某实际系统为算例进行仿真分析,结果表明所提方法能在降低负荷峰谷差和日综合成本、平抑负荷波动的同时,显著提高对分布式光伏的消纳能力,验证了所提方法的有效性和可行性。

好氧堆肥的生物除臭机制及原位控制技术进展

因子调控是实现好氧堆肥臭气原位控制的最主要途径之一。总结了好氧堆肥中原位生物除臭的过程机制,从主控环境因子调控的角度综述了碳氮比、通气量、温度、含水率和pH对原位生物除臭技术中微生物群落结构和代谢活性的影响,总结了覆盖除臭法、腐熟堆肥回流法、功能菌接种法和其他辅助方法等综合环境因子调控技术。研究发现,未来应在不同臭气的协同转化路径和微生物作用机制,主控环境因子对臭气物质去除的微生物代谢影响,生物除臭技术的联用及辅助方法的使用等方面加强研究,为原位生物除臭技术的优化和应用提供理论指导。

工业互联网环境下面向生产要素需求的服务推荐方法

随着服务型制造模式的出现,工业互联网平台可配置所需的生产要素服务来完成生产任务。在工业互联网环境下的工业生产过程中,一个任务可能需要多个或多种生产要素服务的协同来完成。随着制造服务资源的种类和数量增多,服务组合变得更加困难。同时,服务协同的有效性和服务质量是整个服务组合流程执行成功的关键因素。鉴于此,本文基于工业互联网平台中服务调用历史数据,结合FP-Growth和SimRank算法来评估候选服务协作的有效性。随后,基于该协作有效性指标并结合多目标进化算法,提出一种过滤方法MOEA+CEIF,为用户推荐具有高协作有效性且服务质量优化的服务组合方案。最后,以船舶的生产组装流程为例,通过仿真数据集验证同了所提方法的有效性。

大数据试验区建设助推经济绿色发展的机制与实现路径

数字经济是推动经济结构转型、实现经济绿色发展的全新动能.综合运用EBM模型、双重差分模型等,以国家级大数据综合试验区设立为准自然实验,基于中国271个地级及以上城市面板数据,实证检验了大数据试验区建设对经济绿色发展的影响.研究结果表明:(1)大数据试验区的建设显著促进了经济绿色发展,经过一系列稳健性检验后依旧显著;(2)机制检验发现,地区创新水平与产业协同集聚在主回归中发挥中介效应;(3)异质性分析表明,大数据试验区建设对经济绿色发展的助推作用在地理区位、人力资本水平、城镇化水平不同的地区影响效果不同.通过分析得出了大数据试验区建设助推经济绿色发展的作用机制,提出了完善西部地区数字基础设施的建设、加大科研资金投入等对策建议,以期对当前研究有一定借鉴意义.

创新要素协同、产业结构升级与共同富裕

选取2011—2022年中国284个地级及以上城市样本数据,实证检验创新要素协同对共同富裕的影响,以及产业结构升级在二者间的作用机制。研究显示,创新要素协同不仅可以显著推动本地城市共同富裕,更对邻近城市共同富裕具有积极的空间溢出效应。异质性结果表明,东部地区城市创新要素协同对共同富裕的促进作用更明显。中介效应结果表明,创新要素协同可通过推动产业结构升级实现共同富裕。由此,提出合理配置创新要素,实现要素市场一体化;助力产业结构优化升级,加快构建现代产业体系;实施差异化发展战略,推动创新要素协同均衡发展,为扎实推进共同富裕提供新思路。

基于因子图的多机器人协同算法改进

为解决多机器人协同定位与建图在复杂、大规模场景下耗时久、工作效率低等问题,提出一种融合算法PO-ORB。在因子图模型中引入锚点,用于存储世界坐标系相对位置,将改进后的因子图算法与ORB-SLAM3算法融合,用于帧间优化,结合两种算法的优点,解决大规模问题下多机器人定位精度低和实时性差的问题。通过DBoW2数据库和筛选策略进行地图融合,对全局地图进行优化,提高地图精度。实验结果表明,所提算法能够有效应用于多机器人协同定位与建图。

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

基于标签挖掘的个性化推荐算法

基于标签的推荐算法中存在两个主要缺陷,缺乏用户对于标签偏好值的量化,以及不同标签在用户使用中所占权重。为此提出一种从标签角度出发的个性化推荐算法。分析用户历史行为中使用过的标签,根据用户历史行为建立用户的标签兴趣模型,利用标签兴趣模型计算用户对不同标签的偏好值;统计用户的历史评分记录,计算不同标签所占权重;将两者进行线性组合,得出用户对标签的兴趣度。利用余弦相似度,计算用户偏好相似度,将用户偏好相似度引入到矩阵分解模型中,进行项目评分预测和推荐。实验结果表明,在MovieLens数据集上,该算法相比于传统算法LFM和SVD++在RMSE上分别降低了5.00%和1.41%,在MAE上分别降低了5.07%和1.00%。