基于文本挖掘和二元Logistics回归的生物医药行业上市公司财务预警研究

本研究论文主要探讨了基于文本挖掘和二元logistics回归的生物医药行业上市公司财务预警研究。通过分析生物医药行业上市公司的财务数据和管理层讨论与分析(MD&A)文本,利用二元logistics回归模型和文本挖掘技术,研究构建了一套财务预警模型,以预测企业是否可能发生财务危机。研究选取了现金比率、现金流量负债比率、资产负债率、净资产收益率、营业毛利率、息税前营业利润率、总资产增长率、营业收入增长率、资本保值增率、每股经营活动产生的净流量增长率等财务指标,以及基于MD&A文本信息构建的语调指标作为模型的关键预警指标。这些指标涵盖了企业的多个财务维度,旨在全面评估公司的财务健康状况。模型在非ST公司的判断准确率达到了87.50%,在ST公司的判断准确率为80.00%,整体准确率为85.29%,显示了该模型对于早期识别财务风险具有一定的有效性。

大学生“慢就业”现象及其影响因素——基于二元Logistic回归模型的实证研究

通过对32所高校调研分析发现,大学生“慢就业”现象呈现上升态势。研究表明:个人最高学历、职业规划清晰程度、就业主动性、父母对于子女选择“慢就业”的态度、是否为独生子女、家庭人均年收入、学校层次、学校就业指导服务质量等因素均显著影响大学生对于“慢就业”行为的认知与选择。为此,必须结合大学生“慢就业”行为选择的影响因素,开展“广谱式”的职业生涯规划教育、构建家校联合的就业指导机制、形成具有强针对性的就业指导体系。

基于二元Logistic回归模型分析机器人辅助子宫内膜癌术后并发症危险因素

目的:探索基于二元Logistics回归模型分析机器人辅助子宫内膜癌患者术后并发症的危险因素。方法:回顾性分析2021年5月—2022年5月在空军军医大学第一附属医院收治的96例子宫内膜癌患者的临床资料,根据手术方式不同将所有患者分为常规组(n=47)和机器人组(n=49),并比较两组患者并发症发生率。同时,经二元Logistic回归模型分析影响术后并发症的危险因素。结果:机器人组术后并发症发生率低于常规组(6.12%Vs 31.91%,P<0.05)。经二元Logistic回归模型分析,年龄>60岁、BMI>24 kg/m^(2)、术中出血量>200 ml、有贫血史、常规手术是影响患者术后出现并发症的独立危险因素(P<0.05)。利用Bootstrap法内验证,预测模型AUC为0.818,特异性71.8%,灵敏性77.8%,95%CI 0.720~0.917。结论:影响机器人辅助子宫内膜癌术后并发症的主要因素为贫血史、手术时间、BMI、年龄、手术方式,针对合并此类危险因素的患者需采用相应的干预措施,从而降低患者术后并发症,改善预后。

基于二元Logistic回归模型的基层农技人才培养意愿及影响因素分析

基于2022年广西基层农技推广体系人员培训班的调查问卷数据,利用二元Logistic回归模型分析了个体基本特征、培养因素、其他影响因素等对基层农技人才培养意愿的影响。结果表明:年龄、单位政策支持、对农业生产的帮助等对培养意愿有显著的影响。提出实施精准培养、加强政策支持、优化培养内容等建议,旨在增强基层农技人员培养意愿和参与积极性,实现农业和农村发展的高质量和可持续发展。

峨眉雪芽消费者购买行为影响因素分析——基于二元Logistic模型

党的二十大报告强调:“全面推进乡村振兴,坚持农业农村优先发展。”在乡村振兴的大背景下,茶产业的可持续发展是有效脱贫的途径之一,从供需两端深入打通乡村振兴茶产业链,深耕茶叶产业赋能乡村振兴。本文以峨眉雪芽品牌为例,通过设计问卷对成都市消费者进行实地调查,观察其购买行为特征,挖掘影响消费的主要因素,通过二元Logistic模型分析后得到相应结论,为峨眉雪芽可持续发展提出可行性建议。

基于logistics回归模型的山洪泥石流风险评估

山洪泥石流作为一种常见的自然灾害,其突发性和破坏性给人类社会的生产生活带来了巨大的威胁。在全球气候变暖的背景下,山洪泥石流的发生频率和破坏力呈现增加趋势。通过采用有序logistics回归模型,对四川省北川羌族自治县地区山洪泥石流危险性进行了定量化评估,选取流域切割密度、流域相对高差、主沟长度、50年一遇泥石流规模等多个关键因素,构建了logistics回归模型进行危险性评估。根据模型结果发现,主沟长度和50年一遇规模的影响最为显著。本文不仅对山洪泥石流风险进行了较为精确的定量化评估,同时也为防治措施的制定提供了科学依据。

基于二元Logistic回归模型的农户参与乡村旅游开发意愿影响因素研究——以榕江县乐里镇为例

在乡村振兴背景下,乡村旅游作为乡村经济发展新路径,在改善农村基础设施建设、提高农户收入方面具有显著作用。农户作为乡村旅游开发主体,其参与意愿对于乡村旅游开发具有关键作用。文章选取贵州省榕江县乐里镇作为研究对象,采用二元Logistic回归模型对当地农户参与乡村旅游开发意愿的影响因素进行分析。研究发现,当地农户参与意愿受4个因素影响,分别是教育程度,政府对当地乡村旅游开发政策宣传解读,政府在资金、专业技能等方面的支持,当地乡村旅游企业提供工作岗位。根据研究结果,针对性提出提升农户参与乡村旅游开发意愿的对策建议,以期助力乐里镇乡村旅游的开发。

多媒体技术运用视角下的小学生成绩影响因素研究——基于二元Logistic回归模型的实证分析

从多媒体技术运用视角出发,基于265名农村小学生的调查数据,运用二元Logistic回归模型,分析小学生学业成绩的影响因素。结果表明,多媒体技术的运用是提高小学生成绩的重要因素;此外,小学生学业成绩还受到学生个体特征、家庭社会资本特征、社会环境特征等多重因素的影响。建议通过开设多媒体课程,增加课堂多媒体技术应用时长,推广学校、家庭、社会统一教育模式等途径以提升小学生的学业成绩。

普外科术中低体温发生情况Logistics回归分析及预测模型构建

目的探讨普外科术中低体温发生的风险因素,构建并验证术中低体温发生的风险预测模型。方法选取2020年12月至2022年12月在本院接受普外科手术的117例患者作为观察对象。收集患者临床资料,根据患者术中是否发生低体温将其分为发生低体温组(A组)和未发生低体温组(B组)。Logistic回归分析影响患者普外科术中发生低体温的危险因素,并建立风险预测模型,受试者工作特征曲线(ROC)检测该模型的预测效能。结果117例患者中发生低体温31例(26.49%),未发生低体温86例(73.50%)。两组患者在年龄、体重指数、手术方式、ASA分级、术中输液量、手术室温、手术时间等方面比较,差异具有统计学意义(P<0.05)。Logistic回归分析结果显示,年龄≥65岁、术中输液量≥1000 mL、麻醉时间≥4 h、手术室温<23℃是普外科术中发生低体温的危险因素(P<0.05)。独立危险因素纳入Logistic回归分析并建立预测模型,模型预测概率P=1/[1+e(-1.020+0.874)×(年龄)+1.114×(术中输液量)+1.556×(手术室温)+1.453×(麻醉时间)]。ROC曲线检验该模型预测效能,结果显示ROC曲线下面积(AUC)=0.754(P<0.05);Hosmer-Lemeshow拟合优度检验显示,χ^(2)=1.001,P=0.998。结论患者年龄、术中输液量、麻醉时间、手术室温是普外科患者术中发生低体温的独立危险因素,Logistic风险预测模型拟合效度好,具有较高预测价值。

财经素养对大学生消费行为的影响研究——基于二元Logistic回归模型

为探讨财经素养对大学生消费行为的影响,本研究对广西某高校大学生进行问卷调查,结果发现:(1)47.7%的大学生每个月生活费为1000-1500元,92.6%的大学生生活费来自家庭供给,79.2%的大学生会把剩余的月生活费储存下来。(2)大学生中有87%对财经素养有兴趣,29%从来不主动学习财经类知识或技能,72.9%希望通过学校开设相关课程系统学习。(3)大学生消费行为的二元Logistic回归分析显示,户籍、财经素养兴趣和攒钱习惯对大学生电商节消费行为影响显著。实证分析结果表明,大学生财经素养水平还有很大的上升空间,财经素养对大学生消费行为有重要影响,加强大学生财经素养教育,有利于大学生形成科学合理的消费行为习惯。

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

Potential of Applying Artificial Intelligence to Hot Metal Logistics Management

The steel industry,known for its complexity and the need to reduce CO_(2)emissions,is adopting advanced digitalization tools to move towards a more sustainable,integrated,and agile operating model.Digital twins with artificial intelligence-based optimization and scheduling models can improve decision-making in logistics,refractory maintenance,and energy efficiency.By incorporating advanced AI algorithms into this decision support system,the hot metal route scenarios can be evaluated,resulting in minimized hot metal temperature losses and increased scrap utilization.This paper integrated digital twins with reinforcement learning algorithms to investigate the logistics of torpedoes and hot metal ladles.It considered important input parameters such as the ladles and torpedoes'thermal state and location,refractory thickness,hot metal volume and temperature,and crane availability.By incorporating advanced AI algorithms into this decision support system,energy-efficient scenarios can be evaluated,increasing scrap utilization and resulting in a possible reduction of 15°C in hot metal temperature losses.

ISO/TC 344 on innovative logistics set up in Qingdao

The 2024 International Standardization Conference on Logistics,the first of its kind in China,was held by China Federation of Logistics&Purchasing(CFLP)on May 29 in Qingdao,Shandong province,where ISO/TC 344,Innovative logistics,was officially set up.ISO President Sung Hwan Cho sent a congratulatory message via video.He Liming,Chair of CFLP,Zhao Shengcun,Deputy Mayor of Qingdao,officers from Standards Innovative Management Department of SAMR,and relevant leaders attended and addressed the meeting.Over 400 attendees participated in the conference,including representatives from the ISO Central Secretariat,members of ISO/TC 344,members of Asia-Pacific Logistics Federation(APLF),and representatives from national departments,industrial associations,research institutions,and logistics enterprises.ISO/TC 344 is responsible for the standardization of services,technological application,and management in the field of logistics.Its secretariat is held by China,the domestic counterpart of which is CFLP.

The heterogeneous impact of China-Europe railway express on the efficiency of logistics industry in node cities

Purpose-The spatiotemporal compression effect of China-Europe Railway Express(CR-Express)can reduce the filow costs of resources between China's node cities.Additionally,it can break through the limitations of low-added-value marine products,significantly impacting the logistics industry efficiency.However,there are few literature verifying and analyzing its heterogeneity.This study explores the impact of CR-Express on the efficiency of logistics industry in node cities and analyzes the heterogeneity.Design/methodology/approach-First,this study uses panel data to measure the efficiency of node city logistics industry.Secondiy,this study discusses the impact of the opening of CR-Express on the efficiency of logistics industry in node cities based on the multi-period differential model.Finally,according to the node city difference,the sample city experimental group is grouped for heterogeneity analysis.Findings-The results show that CR-Express can promote the urban logistics industry efficiency,with an average effect of 4.55%.According to the urban characteristics classification,the heterogeneity analysis shows that the efficiency improvement effect of logistics industry in inland cities is more obvious.The improvement effect of node cities and central cities in central and western China is stronger,especially in the sample of megacities and type I big cities.Compared with non-value chain industrial products,the CR-Express has significant promotion effects on the logistics efficiency of the cities where main goods are value chain products.Originality/value-Under the background of double cycle development,this paper can provide a scientific basis for the investment benefit evaluation of CR-Express construction and the follow-up route planning.

Research on Smart Logistics Development Based on Blockchain Technology

Blockchain technology has been incorporated into the field of next-generation information infrastructure in China and is experiencing rapid development.Utilizing blockchain and other emerging technologies to develop intelligent logistics is essential for logistics enterprises to reduce costs,enhance efficiency,and improve quality.By analyzing and summarizing the issues within the development of China’s logistics industry,this paper discusses its growth from three perspectives:policy,technology,and industry.

Automobile Transportation Logistics Supply Chain Management Strategy

To enhance the management level and quality of the automobile transportation logistics supply chain and promote innovation and development in automobile transportation logistics enterprises,it is essential to strengthen the construction of the automobile transportation logistics supply chain management model.This can be achieved through the gradual improvement of the automobile transportation logistics management process,ensuring that the management of the automobile transportation logistics supply chain proceeds in an orderly manner.The aim is to improve automobile transportation and logistics service levels while meeting the changing market supply needs.This will enable automobile transportation and logistics enterprises to maintain steady economic benefits and enhance their core competitiveness in the market.Therefore,this paper has conducted a comprehensive exploration and research on managing the automobile transportation logistics supply chain.Corresponding management strategies are proposed as a starting point to achieve the aforementioned goals.

基于logistics回归分析的互助养老意愿影响因素研究

人口老龄化是我国今后较长一个时期的基本国情。为探究安徽省某市城镇居民参加社区互助养老服务的意愿影响因素,本文从个人、家庭、社会三个层面出发,构建互助养老服务意愿判别模型。结果显示,性别、婚姻状况、子女融洽程度、社区邻里关系等因素通过卡方检验。由二元logistics回归模型可知,家庭因素和社会支持因素影响居民参加互助养老意愿。因此,本文提出要加强互助养老服务的宣传,形成社会、社区和家庭的三合一互助养老体系。

基于多元logistics回归法探究绿色食品消费行为影响因素研究

绿色食品消费是实现双碳目标的重要途径之一。选择环保、健康的绿色食品,减少过度包装与消费,不仅降低碳排放,更呵护我们的环境。此举契合我国绿色发展战略,助力经济社会可持续发展。因此,倡导并推广绿色食品消费,对实现2030碳达峰、2060碳中和至关重要。成都市作为中国西南经济文化重镇,绿色食品发展成绩斐然。然而,挑战依旧严峻,消费者认知度低、产品质量参差不齐。深度研究成都市绿色食品市场至关重要,不仅增进消费者和企业了解,更为行业可持续发展提供新策略。本次调研深入成都市,通过问卷和实地调查收集消费者绿色食品购买行为数据。成功回收1246份有效问卷,回收率高达95.2%。数据经检验显示问卷设计合理,为分析提供了可靠依据。采用多元logistics回归模型,深入分析了个人特征、绿色食品认知、低碳发展认知及网络舆情感知等因素对绿色食品消费行为及频率的影响。

Development status and prospect of cold chain logistics of fruits,vegetables,and agricultural products based on intelligent technology

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality,but it is also a weak link.With the application of big data technology in cold chain logistics,intelligent devices,and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas,extending the shelf life of fruits and vegetables,and reducing fruit and vegetable losses.They have many advantages in fruit and vegetable pre-cooling,sorting and packaging,testing,warehousing,transportation,and other aspects.This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years,including automated guided vehicle intelligent handling based on electromagnetic or optical technology,intelligent sorting based on sensors,electronic optics,and other technologies,intelligent detection based on computer vision technology,intelligent transportation based on perspective imaging technology,etc.It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage,sorting,detection,transportation,and other links,and improves the efficiency of fruit and vegetable cold chain logistics.However,applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems.The challenges of high cost,difficulty in technological integration,and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains.To solve the current problems,it is proposed that costs be controlled through independent research and development,technological innovation,and other means to lower the entry threshold for small enterprises.Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility.At the same time,the government should introduce relevant policies,provide necessary financial support,and establish talent training mechanisms.Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics.Through technological innovation,cost control,talent cultivation,and policy guidance,we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.