An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

This paper addresses the problem of predicting population density leveraging cellular station data.As wireless communication devices are commonly used,cellular station data has become integral for estimating population figures and studying their movement,thereby implying significant contributions to urban planning.However,existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction.To address this,we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data.The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift.Further,we devise a multi-view enhancement model grounded on the Transformer(MVformer),targeting the improvement of the accuracy of extended time-series population predictions.Comparative experiments,conducted on the above-mentioned population dataset using four alternate Transformer-based models,indicate that our proposedMVformer model enhances prediction accuracy by approximately 30%for both univariate and multivariate time-series prediction assignments.The performance of this model in tasks pertaining to population prediction exhibits commendable results.

Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population:NHANES database

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)is the most common chronic liver disease,affecting over 30% of the United States population.Early patient identification using a simple method is highly desirable.AIM To create machine learning models for predicting NAFLD in the general United States population.METHODS Using the NHANES 1988-1994.Thirty NAFLD-related factors were included.The dataset was divided into the training(70%)and testing(30%)datasets.Twentyfour machine learning algorithms were applied to the training dataset.The bestperforming models and another interpretable model(i.e.,coarse trees)were tested using the testing dataset.RESULTS There were 3235 participants(n=3235)that met the inclusion criteria.In the training phase,the ensemble of random undersampling(RUS)boosted trees had the highest F1(0.53).In the testing phase,we compared selective machine learning models and NAFLD indices.Based on F1,the ensemble of RUS boosted trees remained the top performer(accuracy 71.1%and F10.56)followed by the fatty liver index(accuracy 68.8% and F10.52).A simple model(coarse trees)had an accuracy of 74.9% and an F1 of 0.33.CONCLUSION Not every machine learning model is complex.Using a simpler model such as coarse trees,we can create an interpretable model for predicting NAFLD with only two predictors:fasting C-peptide and waist circumference.Although the simpler model does not have the best performance,its simplicity is useful in clinical practice.

A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability

Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.

Detection of Asymptomatic Carotid Artery Stenosis in High-Risk Individuals of Stroke Using a Machine-Learning Algorithm

Objective Asymptomatic carotid stenosis(ACS)is closely associated to the incidence of severe cerebrovascular diseases.Early identifying the individuals with ACS and its associated risk factors could be beneficial for primary prevention of stroke.This study aimed to investigate a machine-learning algorithm for the detection of ACS among high-risk population of stroke based on the associated risk factors.Methods A novel model of machine learning was utilized to screen the associated predictors of ACS based on 30 potential risk factors.The algorithm of this model adopted a random forest pattern based on the training data and then was verified using the testing data.All of the original data were retrieved from the China National Stroke Screening and Prevention Project(CNSSPP),including demographic,clinical and laboratory characteristics.The individuals with high risk of stroke were enrolled and randomly divided into a training group and a testing group at a ratio of 4:1.The identification of carotid stenosis by carotid artery duplex scans was set as the golden standard.The receiver operating characteristic(ROC)curve and the area under the curve(AUC)was used to evaluate the efficacy of the model in detecting ACS.Results Of 2841 high risk individual of stroke enrolled,326(11.6%)were diagnosed as ACS by ultrasonography.The top five risk fectors contributing to ACS in this model were identified as family history of dyslipidemia,high level of lowdensity lipoprotein cholesterol(LDL-c),low level of high-density lipoprotein cholesterol(HDL-c),aging,and low body.

Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease:An extended study

Background:Nonalcoholic fatty liver disease(NAFLD)is a public health challenge and significant cause of morbidity and mortality worldwide.Early identification is crucial for disease intervention.We recently proposed a nomogram-based NAFLD prediction model from a large population cohort.We aimed to explore machine learning tools in predicting NAFLD.Methods:A retrospective cross-sectional study was performed on 15315 Chinese subjects(10373 training and 4942 testing sets).Selected clinical and biochemical factors were evaluated by different types of machine learning algorithms to develop and validate seven predictive models.Nine evaluation indicators including area under the receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),accuracy,positive predictive value,sensitivity,F1 score,Matthews correlation coefficient(MCC),specificity and negative prognostic value were applied to compare the performance among the models.The selected clinical and biochemical factors were ranked according to the importance in prediction ability.Results:Totally 4018/10373(38.74%)and 1860/4942(37.64%)subjects had ultrasound-proven NAFLD in the training and testing sets,respectively.Seven machine learning based models were developed and demonstrated good performance in predicting NAFLD.Among these models,the XGBoost model revealed the highest AUROC(0.873),AUPRC(0.810),accuracy(0.795),positive predictive value(0.806),F1 score(0.695),MCC(0.557),specificity(0.909),demonstrating the best prediction ability among the built models.Body mass index was the most valuable indicator to predict NAFLD according to the feature ranking scores.Conclusions:The XGBoost model has the best overall prediction ability for diagnosing NAFLD.The novel machine learning tools provide considerable beneficial potential in NAFLD screening.

Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

The only known predictable aggregation of dwarf minke whales (Balaenoptera acutorostrata subsp.) occurs in the Australian offshore waters of the northern Great Barrier Reef in May-August each year. The identification of individual whales is required for research on the whales’ population characteristics and for monitoring the potential impacts of tourism activities, including commercial swims with the whales. At present, it is not cost-effective for researchers to manually process and analyze the tens of thousands of underwater images collated after each observation/tourist season, and a large data base of historical non-identified imagery exists. This study reports the first proof of concept for recognizing individual dwarf minke whales using the Deep Learning Convolutional Neural Networks (CNN).The “off-the-shelf” Image net-trained VGG16 CNN was used as the feature-encoder of the perpixel sematic segmentation Automatic Minke Whale Recognizer (AMWR). The most frequently photographed whale in a sample of 76 individual whales (MW1020) was identified in 179 images out of the total 1320 images provid-ed. Training and image augmentation procedures were developed to compen-sate for the small number of available images. The trained AMWR achieved 93% prediction accuracy on the testing subset of 36 positive/MW1020 and 228 negative/not-MW1020 images, where each negative image contained at least one of the other 75 whales. Furthermore on the test subset, AMWR achieved 74% precision, 80% recall, and 4% false-positive rate, making the presented approach comparable or better to other state-of-the-art individual animal recognition results.

Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation

In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,China.Firstly,multiple MLMs were selected and hyperparameters were optimized,and the generated 11 models were crossintegrated to select the best model to calculate landslide susceptibility;by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard.Using the town as the basic unit,the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways(SSPs)scenarios in each town were assessed,and then combined with the hazard to estimate the LPAR in 2050.The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment.The distribution of hazard classes is similar to susceptibility,and with an increase in precipitation,the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes.The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability,whereas the northern towns of Baohua and Qinglin are at the lowest risk class.The LPAR increased with the intensity of extreme precipitation.The LPAR differs significantly among the SSPs scenarios,with the lowest in the“fossil-fueled development(SSP5)”scenario and the highest in the“regional rivalry(SSP3)”scenario.In summary,the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability.The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.

“数字鸿沟”对城市低龄老年人再就业的影响研究

促进低龄老年人再就业,实现“银发红利”是实施积极应对人口老龄化国家战略的重要一环。随着数字经济的快速发展,“数字鸿沟”严重影响老年人平等享受数字红利的权利,阻碍了城市低龄老年人在退休后选择再就业。聚焦于“数字鸿沟”,选取2018年和2020年中国家庭追踪调查数据,实证检验“数字鸿沟”和其他特征变量对城市低龄老年人再就业选择的影响,并利用机器学习算法预测未来“数字鸿沟”对其再就业行为的影响程度变化。研究发现:“数字鸿沟”会抑制城市低龄老年人再就业,且数字技术使用程度加深会增加其再就业的概率。在未来更多的城市低龄老年人会选择再就业,且“数字鸿沟”对其再就业行为的影响将加深。研究结论为填平老年人“数字鸿沟”,释放老龄人口二次红利,实践积极老龄化战略提供了新思路。

基于帕累托前沿关系求解约束多目标优化问题

为解决约束多目标优化问题中的平衡约束满足与目标函数优化以及可行域复杂等挑战,提出了基于不同帕累托前沿关系的分类搜索方法。提出一种双种群双阶段框架:进化一个辅助种群Pa和一个主种群Pm,并将进化过程分为学习阶段和搜索阶段。学习阶段,种群Pa向UPF(unconstrained Pareto front)进行搜索,而种群Pm向CPF(constrained Pareto front)进行搜索,旨在探索UPF与CPF之间的关系;完成学习后,对不同问题的UPF与CPF关系进行分类,以指导后续搜索策略;在搜索阶段,根据不同的分类关系,调整种群Pa的搜索策略,旨在使种群Pa为种群Pm提供更有效的辅助信息。基于此算法框架,对不同类型约束多目标优化问题的帕累托前沿关系进行了分类,实现了对CPF更有效的搜索。实验结果表明:所提算法与其他7种先进的约束多目标优化算法相比具有更显著的性能优势。通过学习与利用UPF与CPF的关系,能够选择更合适的搜索策略去应对具有不同特性的约束多目标优化问题,以获得更具优势的最终解集。

基于机器学习和颅缝CT-MPR技术的北方成人年龄推断

目的运用CT和多平面重组(multiplanar reformation,MPR)技术获取颅缝断层图像,建立中国北方汉族成人年龄推断模型,探讨颅缝闭合规律在中国北方汉族人群年龄推断中的适用性。方法回顾性收集29~80岁健康北方汉族成人头部CT样本132例。对颅骨进行容积重组(volume reconstruction,VR)和MPR,每例样本生成160张颅缝断层图像。根据颅缝闭合分级标准对颅缝MPR图像进行评分,分别计算矢状缝、左右侧冠状缝和左右侧人字缝的平均闭合等级。以上述等级为自变量,建立北方汉族成人年龄推断的线性回归模型和梯度提升回归、支持向量回归、决策树回归和贝叶斯岭回归4种机器学习模型,并评估各模型推断年龄的准确性。结果各颅缝闭合等级均与年龄呈正相关,其中矢状缝相关性最高。4种机器学习模型年龄推断的准确性均高于线性回归模型,其中支持向量回归模型的准确性最高,平均绝对误差为9.542岁。结论机器学习模型和颅骨CT-MPR技术可联合用于中国北方汉族成人的年龄推断,但在法医学实践中仍需与其他成人年龄推断指标联合使用。

基于人群特征的阿尔兹海默症分类方法

在阿尔兹海默症(Alzheimer’s disease,AD)分类研究中,图像、生物标志物等数据集包含的样本少,获取成本高。为应对这一问题,论文提出一种基于人群特征进行建模的方法,并在CMDS数据集上进行了实验。首先,使用PAR方法分析特征与AD之间的相关性,根据分析结果进行特征选择;然后,使用ADASYN算法解决训练集样本不平衡问题;最后,使用XGBoost算法进行训练,得到最终模型。该模型的准确率和召回率达到了79.5%和77.6%,AUC达到了0.83。实验结果证明了该方法的有效性。

一种多种群进化和差分变异的鲸鱼优化算法

针对鲸鱼优化算法容易陷入局部最优,求解精度低,收敛速度慢,提出了一种多种群进化和差分变异的鲸鱼优化算法(MDWOA).首先,根据适应度值将种群划分为两个大小相等的子种群,并为每个子种群分配不同的移动策略,以平衡全局和局部搜索能力.其次,设计了一种种群进化和差分变异的策略来帮助MDWOA提高收敛速度,避免其陷入局部最优.最后,引入反向学习策略,增加种群多样性.将MDWOA与多种优化算法在13个基准函数上进行仿真测试,非参数检验的结果表明相较于其他优化算法来说改进的算法具有更高的精度和稳定性.在此基础上,建立了基于MDWOA优化BP神经网络模型,预测波士顿房价的实验结果表明所提出的预测模型具有更好的预测性能和有效性.

代际学习何以重塑多代工作场所:国际研究进展与思考

随着社会结构的变迁和劳动力老龄化的加剧,代际学习作为实践共同体和组织框架内发展起来的一个社会化过程,为多代组织减少知识流失、增强其可持续性作出了重要贡献。基于国外相关数据库的文献检索,从工作场所代际学习的概念内涵、实践形式、影响因素、过程模型、作用效果等五个维度对相关研究成果进行系统梳理。相关研究成果呈现如下特征:一是研究视角从单一学科向多学科领域的交叉与融合;二是研究场域以教育机构、医疗机构和企业三类组织为重点;三是研究方法以实证研究为主流研究范式。未来,我国代际学习研究应当强化工作场所代际学习的理论建构,拓展工作场所代际学习的研究场域并进一步凸显工作场所代际学习的中国话语。

基于机器学习的隐藏人口在线主题演化预测方法

针对隐藏人口在在线社交网络中的群体行为,首先提出隐藏人口的在线主题演化预测问题,即根据当前阶段隐藏人口的群体行为特征来预测下一阶段隐藏人口的在线主题状态;其次,基于机器学习的方法设计隐藏人口在线主题演化预测模型,以隐藏人口当前在线主题相关的社会复杂网络特征和文本特征为输入,该主题在下一阶段的状态为输出设计隐藏人口在线主题演化预测问题,使用LightGBM对该预测问题进行建模;最后,以某隐藏人口在微博上的数据来验证该方法的有效性和实用性。研究发现,基于机器学习的隐藏人口在线主题演化预测方法能够快速准确地对隐藏人口的群体演化行为进行预测,可以辅助管理者对隐藏人口的下一步动向进行准确判断。

免疫粒子群算法的测试数据生成

为有效改善粒子群算法进化后期收敛速度慢,克服易陷入局部极值的缺陷,提出一种自适应免疫粒子群算法并在面向路径的测试数据生成中得到应用。本文提出自适应的惯性权重的调整方法和学习因子的调节策略,加快算法的搜索速率;引入免疫算法中的免疫算子,提出抗体的浓度调节机制,使得粒子群的多样性更加丰富,提升算法的寻优能力;通过免疫选择操作,避免算法的早熟收敛;以分支函数叠加法构造适应度函数。实验结果表明,该算法避免了粒子群算法早熟收敛现象的发生,有效地提高了测试数据自动生成的效率。

基于改进NSGA-Ⅱ算法的柔性车间调度问题研究

研究了柔性车间调度中双目标调度优化问题,以最小化最大完工时间和最小化机器空载率为优化目标,基于生产机加车间产线建立数学模型。选取NSGA-Ⅱ(Non-dominated Sorting Genetic AlgorithmsⅡ,NSGA-Ⅱ)算法作为基础算法,在此基础上提出基于反向学习的NSGA-Ⅱ算法(简称OBL-NSGA-Ⅱ),通过引入反向种群,增加种群的多样性,保证了解的质量,能够有效避免算法迭代过程中由于种群多样性降低导致算法陷入局部最优的问题。最后通过Matlab仿真软件进行了对比实验,验证了所提算法的有效性。

基于多策略融合灰狼算法的移动机器人路径规划

针对标准灰狼算法(GWO)在解决移动机器人路径规划问题时存在初始参数依赖性强、缺乏多样性及易陷入局部极值的缺陷,提出一种基于多策略融合灰狼算法(LTGWO)。首先运用精英化思想将Logistic-Tent复合混沌映射与反向学习结合,优化灰狼种群分布序列;然后引入sigmoid函数修改收敛因子a,平衡算法全局探索与局部开发能力,并改进控制参数C以更好地拟合灰狼实际捕猎过程;最后加入随适应度值变化的比例权重,提高灰狼个体搜索能力,同时采用种群淘汰策略,淘汰适应度值差的个体,促进种群进化。选用3组不同的栅格地图进行实验,实验结果表明:由LTGWO算法生成的平均路径长度、路径长度标准差都优于对比算法。

求解约束优化问题的改进蛇优化算法

结合外点罚函数法与改进蛇优化算法求解约束优化问题,得到一种新的求解约束优化问题的算法WDFSO。算法WDFSO首先通过外点罚函数法将约束优化问题转化为一系列界约束优化问题,然后运用基于变异质心的对立学习策略与种群分类策略改进的蛇优化算法对所得界约束优化问题进行求解,进而获得所求约束优化问题的解。为验证算法WDFSO的有效性,选取CEC2006中19个标准约束优化问题进行数值实验,并使用Wilcoxon秩和检验来证明算法的显著性。实验结果表明,与对比算法相比,算法WDFSO求解约束优化问题具有更高的收敛精度和更好的稳定性。最后应用算法WDFSO求解两个工程约束优化问题,结果表明算法WDFSO求解性能更好。

新时代老年教育高质量发展:生成逻辑、创新路径与成效评价

高质量发展是新时代赋予老年教育的新主题。老年教育的高质量发展是推动中国式教育现代化以及建设学习型社会、学习型大国的重要基础性力量。新时代老年教育高质量发展具有内在要求和现实需求的双重生成逻辑,这决定了老年教育走高质量发展之路的必然性。高质量发展意味着理念体系、实践范式需要重建,发展理念和实践路径需要创新。老年教育高质量发展是一个长期而复杂的系统工程,科学全面的成效评测能够提供清晰的发展样态刻度,引导高质量发展目标的实现。

CT三维重建技术结合深度学习算法推断成人坐骨年龄

目的探索适用于中国西部汉族人群的CT三维重建图像年龄自动推断深度学习模型,评估其可行性与可靠性。方法收集20.0~80.0岁中国西部汉族人群骨盆CT回顾性影像学数据1200例(男性600例,女性600例),重建为三维虚拟骨骼模型,区分性别、左右截取坐骨结节特征区域图像建立样本库。使用ResNet34模型,随机抽取不同性别各500例样本作为训练及验证集,剩余样本作为测试集,使用初始学习及迁移学习对区分性别、左右的图像进行训练,以平均绝对误差(mean absolute error,MAE)、均方根误差(root mean square error,RMSE)作为主要指标评价模型。结果不同性别组成中预测结果存在差异,双侧模型预测结果优于左、右单侧模型,迁移模型预测结果优于初始模型。不同性别组成的双侧迁移模型预测结果中,男性MAE为7.74岁、RMSE为9.73岁,女性MAE为6.27岁、RMSE为7.82岁,混合性别MAE为6.64岁,RMSE为8.43岁。结论基于中国西部汉族人群坐骨结节图像应用ResNet34结合迁移学习算法构建的骨龄推断模型可以有效推断成人坐骨骨龄。