An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.

A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions

Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.

From total quality management to Quality 4.0:A systematic literature review and future research agenda

Quality 4.0 is an emerging concept that has been increasingly appreciated because of the intensification of competition,continually changing customer requirements and technological evolution.It deals with aligning quality management practices with the emergent capabilities of Industry 4.0 to improve cost,time,and efficiency and increase product quality.This article aims to comprehensively review extant studies related to Quality 4.0 to uncover current research trends,distil key research topics,and identify areas for future research.Thus,46 journal articles extracted from the Scopus database from 2017 to 2022 were collected and reviewed.A descriptive analysis was first performed according to the year-wise publication,sources of publication,and research methods.Then,the selected articles were analyzed and classified according to four research themes:Quality 4.0 concept,Quality 4.0 implementation,quality management in Quality 4.0,and Quality 4.0 model and application.By extracting the literature review findings,we identify the Quality 4.0 definitions and features,develop the quality curve theory,and highlight future research opportunities.This study supports practitioners,managers,and academicians in effectively recognizing and applying Quality 4.0 to enhance customer satisfaction,achieve innovation enterprise efficiency,and increase organizational competitiveness in the era of Industry 4.0.

Simplified thermal and hygric building models： A literature review

This paper provides a systematic literature review on simplified building modelso Questions are answered like： What kind of modelling approaches are applied？ What are their （dis）advantages？ What are important modelling aspects？ The review showed that simplified building models can be classified into neural network models （black box）, linear parametric models （black box or grey box） and lumped capacitance models （white box）. Research has mainly dealt with network topology, but more research is needed on the influence of input parameters. The review showed that particularly the modelling of the influence of sun irradiation and thermal capacitance is not performed consistently amongst researchers. Furthermore, a model with physical meaning, dealing with both temperature and relative humidity, is still lacking. Inverse modelling has been widely applied to determine models parameters. Different optimization algorithms have been used, but mainly the conventional Gaus-Newton and the newer genetic algorithms. However, the combination of algorithms to combine their strengths has not been researched. Despite all the attention for state of the art building performance simulation tools, simplified building models should not be forgotten since they have many useful applications. Further research is needed to develop a simplified hygric and thermal building model with physical meaning.

Machine learning applied to road safety modeling:A systematic literature review

Road safety modeling is a valuable strategy for promoting safe mobility,enabling the development of crash prediction models(CPM)and the investigation of factors contributing to crash occurrence.This modeling has traditionally used statistical techniques despite acknowledging the limitations of this kind of approach(specific assumptions and prior definition of the link functions),which provides an opportunity to explore alternatives such as the use of machine learning(ML)techniques.This study reviews papers that used ML techniques for the development of CPM.A systematic literature review protocol was conducted,that resulted in the analysis of papers and their systematization.Three types of models were identified:crash frequency,crash classification by severity,and crash frequency and severity.The first is a regression problem,the second,a classificatory one and the third can be approached either as a combination of the preceding two or as a regression model for the expected number of crashes by severity levels.The main groups of techniques used for these purposes are nearest neighbor classification,decision trees,evolutionary algorithms,support-vector machine,and artificial neural networks.The last one is used in many kinds of approaches given the ability to deal with both regression and classification problems,and also multivariate response models.This paper also presents the main performance metrics used to evaluate the models and compares the results,showing the clear superiority of the ML-based models over the statistical ones.In addition,it identifies the main explanatory variables used in the models,which shows the predominance of road-environmental aspects as the most important factors contributing to crash occurrence.The review fulfilled its objective,identifying the various approaches and the main research characteristics,limitations,and opportunities,and also highlighting the potential of the usage of ML in crash analyses.

Prediction modelling in the early detection of neonatal sepsis

Background Prediction modelling can greatly assist the health-care professionals in the management of diseases,thus sparking interest in neonatal sepsis diagnosis.The main objective of the study was to provide a complete picture of performance of prediction models for early detection of neonatal sepsis.Methods PubMed,Scopus,CINAHL databases were searched and articles which used various prediction modelling measures for the early detection of neonatal sepsis were comprehended.Data extraction was carried out based on Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist.Extricate data consisted of objective,study design,patient characteristics,type of statistical model,predictors,outcome,sample size and location.Prediction model Risk of Bias Assessment Tool was applied to gauge the risk of bias of the articles.Results An aggregate of ten studies were included in the review among which eight studies had applied logistic regression to build a prediction model,while the remaining two had applied artificial intelligence.Potential predictors like neonatal fever,birth weight,foetal morbidity and gender,cervicovaginitis and maternal age were identified for the early detection of neonatal sepsis.Moreover,birth weight,endotracheal intubation,thyroid hypofunction and umbilical venous catheter were promising factors for predicting late-onset sepsis;while gestational age,intrapartum temperature and antibiotics treatment were utilised as budding prognosticators for early-onset sepsis detection.Conclusion Prediction modelling approaches were able to recognise promising maternal,neonatal and laboratory predictors in the rapid detection of early and late neonatal sepsis and thus,can be considered as a novel way for clinician decisionmaking towards the disease diagnosis if not used alone,in the years to come.

Barriers and Drivers of Sustainable Business Model Innovation:Present and Future Research Perspectiv

Sustainable business model innovation(SBMI)introduced a unique frontier in current business operations and innovation management.Despite the numerous advantages of SBMI to contemporary business strategy,most established firms face challenges in its successful implementation.Through a systematic review process(SRP),the paper attempted to critically evaluate and analyze the previous outcome on the barriers and drivers to SBMI.The research explored 42 prior studies to identify the thematic study areas,highlight the research gaps,and outline future propositions and agendas.The research thoroughly evaluates the state-of-the-art regarding barriers and drivers to implement SBMI.The SRP approach utilized in the study sheds light on the intricacies of SBMI by highlighting six critical barriers:institutional,organizational,strategic,resource allocation,technological,and financial barriers that hinder the successful deployment of SBMI.In addition,the study’s findings indicated that organizational learning,knowledge management,dynamic capabilities resource mobilization,innovative business activities,and human resource development could be a catalyst to the successful implementation of SBMI.Furthermore,the study highlighted some critical gaps and agendas for future research on SBMI.This study contributes to the literature on business model innovation and offers a practical outlook that can facilitate firms and policymakers in developing strategies to improve their business model.

血栓形成综合征风险预测模型的研究进展

对血栓形成综合征风险预测模型研究现状进行综述,综合分析比较各预测模型的特点,以期为我国临床医护人员精准选择深静脉血栓形成综合征风险预测模型提供参考。

ICU患者再喂养综合征风险预测模型的研究进展

再喂养综合征(refeeding syndrome,RFS)在重症患者中发生率较高,严重影响患者的康复和预后。通过对再喂养综合征的风险因素和风险预测模型进行综述,发现其风险因素包括患者相关因素、治疗相关因素和疾病相关因素三个方面;风险预测模型包括风险分层模型、风险评分模型和Logistic回归模型。预防RFS的发生重点在于早期评估,但目前尚缺乏预测效能良好的RFS风险预测模型。关注营养和血清学指标等多方面因素对RFS的预防有着重要意义,未来需开展前瞻性、多中心研究,以构建预测效能良好的ICU患者RFS风险预测模型,为RFS高危人群的早期评估和早期干预提供参考。

国外高校教师教学发展项目实施成效评估研究

采用系统性文献综述法筛选国外近二十年高校教师教学发展项目评估研究的42篇文献进行系统分析,结果发现:已有研究基于评估目的运用多元化研究方法;评估对象包括参与教师、教师同事、学生及管理人员等教师发展利益相关者;评估框架采用有效教师专业发展标准、专业发展评价模型等多项理论与模型;评估内容高度关注教师课程教学设计能力提升与教师发展共同体。基于柯氏评估模型对样本文献的实施成效进行评估,发现大多数项目能有效促进教师知识技能提升与实践行为改变。未来中国高校教师教学发展项目评估研究需强化实证研究设计,拓展教师发展实施成效的评估视域,运用多元评估方法为展现教师教学发展项目的实施成效提供证据,重视项目实施效果的跟踪评估与长效反馈等。

关键核心技术及其突破:文献评述与研究展望

中国既有相关研究对关键核心技术的理论解构尚不够清晰。为形成关键核心技术的系统观,运用文献计量分析法与文献回顾法,基于中国知网数据库中2000—2023年的205篇发表在中文社会科学引文索引(CSSCI)来源期刊的相关论文,从关键核心技术的内涵和关键核心技术突破的影响因素、组织模式和路径等方面系统梳理国内关于关键核心技术的研究现状。结果发现:相关文献在2017年至2023年迅速增长,在一定程度上是美国对中国高科技领域实施全面封锁所导致的,未来一段时间内相关研究的热点聚焦于新型举国体制、关键核心技术、人工智能、技术突破、技术识别等方面;对关键核心技术主要从知识论、技术体系、产业链和国家战略等4种视角进行研究,但尚未形成统一的解释,主要从关键核心技术本身及其重要性两个方面进行类型划分,分为驱动和制约两大类影响因素,并主要形成政府驱动、领军企业驱动和多主体协同的创新组织模式,以及基于知识重组理论、基于技术创新发展阶段和基于特定产业/企业及技术范式的路径研究。最后提出,未来相关研究可从研究问题、研究对象以及研究方法三方面进行拓展,需要更精准地界定和识别关键核心技术,拓展关键核心技术突破程度的测度方法,开展更多跨学科的实证研究,以揭示关键核心技术发展同社会、经济及政策的互动关系。

KANO模型在产品设计领域中的应用现状研究综述

对现阶段KANO模型在产品设计中的发展及应用现状进行梳理与分析,探讨KANO模型在未来产品设计中的发展方向。本文采用文献研究法,从KANO模型的理论基础、KANO模型的定量化改进研究、KANO模型在产品设计中的应用现状3个方面对KANO模型的文献进行了综述。目前,国内外文献主要集中在KANO模型的定量化研究和KANO模型的应用研究上;尽管KANO模型在实践中存在问卷设计难度及数据处理限制等问题,但该模型仍成功应用于多种行业,助力企业提升了产品性能和用户体验。KANO模型在产品设计中的应用将不断深化,实现对用户需求更精准的识别与满足,并通过精细化、个性化和动态化的改进策略提升模型效能,以适应复杂多变的市场环境。

癌症相关认知功能障碍风险预测模型的研究进展

随着癌症患者生存率的逐渐提升,癌症自身及其治疗相关后遗症与不良反应逐渐显现。部分癌症患者出现记忆力、注意力及推理执行能力下降,信息加工和反应速度减慢等认知功能损害症状[1]。这种由非中枢神经系统恶性肿瘤及其相关治疗引起的一系列与大脑结构和功能改变相关的认知障碍症状,称为“癌症相关认知功能障碍”(cancer-related cognitive impairment,CRCI)[2]。CRCI可出现在癌症病程的各个阶段。研究显示[3],30%~40%的癌症患者接受治疗前即已出现CRCI,治疗过程中CRCI的发生率高达75%,并可能持续多年,也有某些患者在治疗结束几个月甚至几年内才出现CRCI。CRCI的发展会降低癌症患者的职业功能和社会功能,对患者的生活质量产生负面影响[4]。此外,认知能力的下降,会影响患者治疗依从性,从而影响治疗策略与治疗效果[5]。因此,早期识别CRCI患者并及时进行干预十分重要。近年来,相关学者逐渐对CRCI的危险因素展开研究,并建立风险预测模型筛查癌症患者的认知功能。癌症相关认知功能障碍预测模型作为评估和筛查高风险人群的工具。

机器学习在腰椎间盘突出症诊治中的优势和应用策略

背景:基于机器学习的不同算法,如何借助各种算法模型开展腰椎间盘突出症的临床研究已成为目前智能化医学发展的趋势和热点。目的:综述机器学习不同算法模型在腰椎间盘突出症诊治中的特点,归纳相同用途的算法模型各自优势和应用策略。方法:计算机检索PubMed、Web of Science、EMBASE、中国知网、万方数据、维普及中国生物医学数据库中与机器学习在腰椎间盘突出症诊治中的相关应用文献,按入组标准筛选后最终纳入96篇文献进行综述。结果与结论:①机器学习的不同算法模型为腰椎间盘突出症的临床诊治提供了智能化、精准化的应用策略。②监督学习中的传统统计学方法和决策树在探究危险因素,制定诊断、预后模型方面简单高效;支持向量机适用于高维特征的小数据集,作为非线性分类器可应用于正常或退变椎间盘的识别、分割、分类,制定诊断、预后模型;集成学习可相互弥补单一模型的不足,具有处理高维数据的能力,提高临床预测模型的精度和准确性;人工神经网络提高了模型的学习能力,可应用于椎间盘识别和分类,制作临床预测模型;深度学习在具有以上用途的基础上,还能优化图像,辅助手术操作,是目前腰椎间盘突出症诊治中应用最广泛、性能最佳的模型;无监督学习中的聚类算法主要用于椎间盘分割和不同突出节段的分类;而半监督学习方式临床应用相对较少。③目前,机器学习在腰椎间盘的识别、分割,退变椎间盘的分类和分级,自动化临床诊断和分类,构建临床预测模型以及辅助术中操作方面具有一定临床优势。④近年来,机器学习的研究策略已向神经网络和深度学习方向转变,具有更强学习能力的深度学习算法将会是未来实现智能化医疗的关键。

缺血性脑卒中后患者认知障碍风险预测模型的系统评价

目的系统评价缺血性脑卒中后认知障碍风险预测模型性能,为预测缺血性脑卒中后患者的认知功能和相关模型的构建及应用提供参考。方法计算机检索知网、万方、维普、PubMed、EMbase、The Cochrane Library等数据库中缺血性脑卒中后认知障碍风险预测模型相关的研究,检索时限均从建库至2023年5月。由2位评价员筛选文献、提取资料并评价纳入研究的偏倚风险和适用性。结果共纳入16个缺血性脑卒中后患者的认知障碍风险预测模型;预测模型整体偏倚风险较高;其中5个模型适用性较好,11个模型适用性较差,受试者工作特征曲线下面积为0.708~0.913。常见的预测因子为年龄、受教育水平、NIHSS评分、梗死分区等。纳入模型主要的高风险偏倚在研究对象和统计分析方面。结论现有的缺血性脑卒中后认知障碍风险预测模型的整体预测性能较差,模型的临床适用价值需要进一步验证。

腹腔镜手术病人术中低体温影响因素和预测模型的研究进展

对腹腔镜术中低体温的危险因素及风险预测模型进行综述,以期为临床手术室护理人员提前预估腹腔镜手术病人术中低体温风险、制定有效的预防干预方案、改善病人的生存质量提供依据。

人工智能算法在癌症相关微RNA研究中的应用进展

微RNA(miRNA)是一类通过不完全碱基互补配对实现后转录调控作用的小分子非编码RNA,其往往在癌症患者的病灶和外周血中表达失调。近年来,基于人工智能算法如机器学习和深度学习的模型逐渐应用于miRNA生物信息学研究。与传统的生物信息学工具比较,基于人工智能算法的miRNA靶点预测工具准确度更高,并实现了miRNA亚细胞定位和亚细胞重分布的预测,进一步深化了科研人员对miRNA的认识。此外,人工智能算法在临床模型构建的应用也显著提升了miRNA生物标志物的挖掘效率。本文总结了近年来人工智能算法在miRNA靶点预测、亚细胞定位和生物标志物挖掘的应用,并探讨了机器学习和深度学习对癌症相关miRNA研究的潜在价值。

生物标志物用于压力性损伤风险预测研究进展

综述生物标志物的概念、种类,以及血生化指标、炎症标志物、表皮下水分3类生物标志物在压力性损伤风险预测中的应用研究,提出生物标志物用于压力性损伤风险预测的优势、不足及启示。旨在为医护人员早期识别压力性损伤风险、开展精准干预提供参考。

患者旅程地图在慢性病照护中的研究进展

阐述患者旅程地图的概念、起源与发展、基本原则、应用目的、构建方法。提出患者旅程地图是医护人员、慢性病患者和家属之间信息共享的重要工具。未来应结合我国国情及相关政策针对不同类型的慢性病患者展开患者旅程地图的探索,以优化慢性病患者就医体验及长期照护质量。

大语言模型在护理领域的应用进展

简要介绍大语言模型技术并总结其在护理领域(如临床实践、护理教育和护理研究方面)的应用及面临的挑战,旨在促进护理人员对大语言模型的认识,激发大语言模型在护理领域的研究和探索,为进一步优化患者护理,提高工作效率,提升护理教育赋能助力。