Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Research on Maneuver Decision-Making of Multi-Agent Adversarial Game in a Random Interference Environment

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Influence of social media on maternal decision-making and breastfeeding practices

Breastfeeding practices are influenced by multifactorial determinants including individual characteristics,external support systems,and media influences.This commentary emphasizes such complex factors influencing breastfeeding practices.Potential methodological limitations and the need for diverse sampling in studying breastfeeding practices are highlighted.Further research must explore the interplay between social influences,cultural norms,government policies,and individual factors in shaping maternal breastfeeding decisions.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

儿童重症肺炎支原体肺炎危险因素的Meta分析

背景近年来,儿童肺炎支原体肺炎的发病率持续上升,重症肺炎支原体肺炎的发病人数也相应升高,引起了临床医师的广泛关注。了解与重症肺炎支原体肺炎相关的危险因素,以判断患儿病情的严重程度、预防重症发生和减少后遗症,一直是研究的热点。虽然已经有许多关于重症肺炎支原体肺炎危险因素的研究,但这些研究在时间和地理区域上存在差异,因此需要进行系统综述及分析以对其进行全面了解。目的系统评价重症肺炎支原体肺炎的危险因素。方法计算机检索中国知网(CNKI)、万方数据知识服务平台(Wanfang Data)、维普网(VIP)、中国生物医学文献数据库(CBM)、独秀学术搜索数据库(Duxiu)、中华医学期刊全文数据库(Yiigle)、Cochrane Library、PubMed、Embase、Web of Science、Science Direct和BioMed Central,检索涉及儿童重症肺炎支原体肺炎危险因素的相关研究,检索时限均从建库至2023年8月。由2位研究者独立筛选文献、提取资料并评价纳入研究的偏倚风险后,采用Stata 14.0和RevMan 5.4软件进行Meta分析。结果共纳入22个研究,均为回顾性病例对照研究,包括4531例患儿。Meta分析结果显示,C反应蛋白(CRP)(OR=1.92,95%CI=1.72~2.15,P<0.00001)、红细胞沉降率(ESR)(OR=2.61,95%CI=2.12~3.22,P<0.00001)、降钙素原(PCT)(OR=2.60,95%CI=1.43~4.75,P=0.002)、D-二聚体(OR=4.36,95%CI=2.93~6.50,P<0.00001)、白细胞计数(WBC)(OR=1.98,95%CI=1.66~2.36,P<0.00001)、肺下叶病变(OR=5.70,95%CI=3.48~9.35,P<0.00001)、肺大片状病变(OR=6.37,95%CI=4.09~9.92,P<0.00001)、高肺炎支原体抗体滴度(OR=2.83,95%CI=1.78~4.49,P<0.0001)、乳酸脱氢酶(LDH)(OR=1.03,95%CI=1.00~1.05,P=0.05)、发热时间(OR=8.33,95%CI=3.38~20.56,P<0.00001)是儿童重症肺炎支原体肺炎的影响因素。结论炎性标志物(CRP、ESR、PCT、LDH、WBC)的升高、出现影像学特征性改变(大片状实变、下叶病变)、高肺炎支原体抗体滴度、D-二聚体升高以及发热时间延长可能为儿童重症肺炎支原体肺炎的危险因素。未来需要更高质量的研究来进一步探讨其他临床、影像学和实验室结果与儿童重症肺炎支原体肺炎之间的关系,并基于发现的危险因素建立预后模型。

丁酸甘油酯对断奶仔猪生长性能和腹泻率影响的Meta分析

研究旨在探讨丁酸甘油酯对断奶仔猪生长性能和腹泻率的影响,运用Meta分析方法进行系统评价。经检索万方、中国知网、维普、Pub Med和Web of Science数据库,搜索丁酸甘油酯对断奶仔猪生长性能影响的文献,按照纳入和排除标准筛选文献、提取资料,用Review Manager 5.4软件进行Meta分析。共纳入12个研究,样本838例。结果显示:丁酸甘油酯组的平均日增重显著高于空白组(P<0.0001,SMD=1.58,95%CI:0.80~2.36),平均日采食量与空白组差异不显著(P=0.30,SMD=-0.44,95%CI:-1.28~0.40),料重比显著低于空白组(P=0.0002,SMD=-0.85,95%CI:-1.29~-0.40),腹泻率显著低于空白组(P=0.0003,SMD=-2.78,95%CI:-4.27~-1.28)。说明饲料中添加丁酸甘油酯可显著提高断奶仔猪的平均日增重,降低料重比和腹泻率,建议丁酸甘油酯添加量为1%~2%。

自体骨移植与骨替代物手术治疗胫骨平台骨折的Meta分析

目的:通过Meta分析系统评价自体骨移植和骨替代物治疗胫骨平台骨折的临床疗效。方法:计算机检索2005年1月至2022年8月PubMed、Web of Science、中国知网、万方等数据库已发表的关于自体骨移植和骨替代物治疗胫骨平台骨折的病例对照研究文献。根据纳排标准进行文献筛选及数据提取,对入选的随机对照研究(randomized controlled trial,RCT)采用Cochrane手册中的干预性Meta分析标准进行质量评价。采用RevMan 5.3软件对两种方法的关节凹陷、关节面二次塌陷率、失血量、手术时间和感染率进行Meta分析。结果:共纳入7项RCT研究,424例患者,其中骨替代物组296例,自体骨移植组128例。两组手术时间[MD=-16.79,95%CI(-25.72,-7.85),P=0.0002],出血量[MD=-70.49,95%CI(-79.34,-61.65),P<0.00001]比较,差异有统计学意义。而关节凹陷[MD=-0.17,95%CI(-0.91,0.58),P=0.66],关节面二次塌陷率[RR=-0.74,95%CI(0.35,1.57),P=0.43],感染率[RR=1.21,95%CI(0.31,4.70),P=0.78]比较,差异无统计学意义。结论:骨替代物与自体骨移植治疗胫骨平台骨折在关节凹陷、关节面二次塌陷率和感染率方面疗效相近。但骨替代物较自体骨移植,能减少失血量,缩短手术时间。

不同镇痛方法用于髋部或股骨干骨折患者椎管内麻醉摆放体位时镇痛效果的网状Meta分析

目的采用网状Meta分析系统评价不同镇痛方法用于髋部或股骨干骨折患者椎管内麻醉摆放体位时的镇痛效果。方法计算机检索PubMed、Cochrane Library、Web of Science、EMbase、中国生物医学文献数据库(CBM)、中国知网、维普、万方,检索时间为建库至2022年8月,纳入髋部或股骨干骨折患者摆放体位和椎管内麻醉时实施镇痛的随机对照研究。由两名研究员独立进行文献筛选、资料提取和偏倚风险评价,采用Stata 17.0和RevMan 5.3软件进行统计分析。结果共纳入28篇文献,患者1773例。累计排序概率曲线下面积(SUCRA)显示,降低摆放体位时VAS疼痛评分PENG阻滞(94.4%)效果最佳,其次是FIB联合IVA(83.8%)和FIB(71.1%);降低椎管内麻醉时VAS疼痛评分PENG阻滞(98.2%)效果最佳,其次是FIB(71.1%)和FNB(55.6%);缩短椎管内麻醉操作时间PENG阻滞(84.1%)效果最佳,其次是FNB(70.7%)和FIB(68.5%);升高体位摆放质量评分PENG阻滞(99.1%)效果最佳,其次是FIB(73.1%)和FNB(52.9%)。结论神经阻滞或神经阻滞联合IVA可降低髋部或股骨干骨折患者体位摆放和椎管内麻醉时VAS疼痛评分、缩短麻醉操作时间和升高体位摆放质量评分。PENG阻滞对髋部或股骨干骨折患者摆放体位和椎管内麻醉时实施镇痛的效果最佳。

护肝片预防性治疗抗结核药物性肝损伤的Meta分析

目的:评价预防性使用护肝片对anti-TB DILI的有效性和安全性,明确护肝片对anti-TB DILI的预防作用。方法:对6个主要的中文和英文数据库进行了检索,从数据库建立之日起至2021年12月,不限制语言和发表偏倚。两名作者根据纳入和排除标准独立选择合格的研究,然后提取数据并进行Meta分析。结果:本研究共纳入18项研究的4270例患者,Meta分析结果显示预防性使用护肝片与anti-TB DILI发生率降低相关[RR=0.29,95%CI(0.24,0.36),P<0.05],敏感性分析提示结果稳健。预防性服用护肝片在4周内[RR=0.27,95%CI(0.16,0.48),P<0.001]和4周后[RR=0.34,95%CI(0.17,0.66),P=0.001]均可显著降低anti-TB DILI的发生率,且异质性较低。在乙型肝炎表面抗原阳性(HBsAg+)患者中的anti-TB DILI发生率也显著降低[R=0.25,95%CI(0.15,0.39),P<0.001]。结论:在抗结核治疗的患者中预防性使用护肝片可降低anti-TB DILI发生,尤其在HBsAg+的患者中有更多获益。由于纳入研究的方法学质量不高,尚需要更多高质量的研究来进一步验证其有效性和安全性。

不同运动对老年人骨密度影响的网状Meta分析

目的通过网状Meta分析法比较太极、八段锦、五禽戏、易筋经、核心稳定训练、振动训练、抗阻训练、健步走、广场舞、毽球运动、综合训练对老年人骨密度的干预效果。方法检索数据知识服务平台搜集应用运动对老年人骨密度影响的随机对照试验(RCT),搜集建库至2023年4月1日发表的不同运动方式对老年人骨密度影响的相关文献。收集第一作者、发表时间、总样本量、年龄、运动干预措施、运动干预时间、运动干预周期、结局指标(腰椎BMD、股骨颈BMD、Ward三角BMD)等信息。采用Revman 5.3和Stata 16软件进行统计学分析。结果共纳入了37个研究,涉及研究总样本量2922人。网状Meta分析结果显示,与对照组相比易筋经(MD=-0.13,95%CI:-0.18~-0.07,P<0.05)改善腰椎BMD和股骨颈BMD最有效,其次为核心稳定训练(MD=-0.08,95%CI:-0.12~-0.04),P<0.05);振动训练(MD=-0.10,95%CI:-0.14~-0.06),P<0.05)改善Ward三角BMD最有效,其次为太极(MD=-0.03,95%CI:-0.05~-0.02),P<0.05)。聚类分层结果显示,易筋经、核心稳定训练、振动训练是改善老年人骨密度最优运动干预。结论30~60 min易筋经、30~60 min核心稳定训练、10~30 min振动训练对老年人骨密度改善更为显著。