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.

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.

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.

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.

基于PI建模和反步滑模控制的主动波浪补偿策略

海上起重船受风、浪、涌影响会产生剧烈的船舶姿态变化,造成起重机和货物的位姿变化,对货物和人员存在安全隐患,波浪补偿平台的稳定性控制能有效减少复杂海况下船舶运动对海上作业安全性、稳定性和精准性的影响,对浮式起重船海上设备精准装载作业极其重要。针对补偿平台的迟滞非线性导致的建模困难和控制不精确问题,本文提出基于PI(Prandtle–Ishlinskii)建模和反步滑模控制的主动波浪补偿策略。首先,通过实验得到补偿系统的迟滞效应曲线,分析系统迟滞环建立PI迟滞模型,并采用递推最小二乘法辨识模型的各个参数,从而求得系统模型。然后,基于李雅普诺夫(Lyapunov)稳定性设计反步控制补偿方法,并结合滑模控制规律加快初始控制速度。最后,将反步滑模法应用于补偿系统,采用MATLAB软件仿真在规则波和不规则波下的响应来验证算法和模型的正确性,并在工控机中用C#编写控制程序,驱动运动控制卡控制伺服电机带动电缸进行补偿运动,同时通过传感器采集系统运动的实时数据,并反馈给工控机形成闭环,以期验证补偿平台在补偿规则波和不规则波下的补偿效果。实验结果表明,所建立的斯图尔特(Stewart)浮式平台中,PI迟滞模型具有良好的精度,反步终端滑模控制算法在Stewart平台的实际控制中能够很好地补偿波浪运动,相比比例–积分–微分控制(PID)、反步法、强化学习等控制方法,反步终端滑模方法能快速较好跟踪期望位移,补偿精度达到0.9729。

益智仁-乌药药对调控PI3K/Akt/mTOR通路介导细胞自噬保护肾小球足细胞的作用机制研究

目的研究益智仁-乌药药对通过调控PI3K/Akt/mTOR信号通路促进足细胞自噬治疗糖尿病肾病(Diabetic Nephropathy,DN)的作用。方法60只造模成功的C57BL/KSJ-db/db(以下简称db/db)小鼠随机分为模型组、二甲双胍组、缬沙坦组、益智仁-乌药药对(低、中、高剂量)组,每组10只;另取10只C57BL/KSJ-db/m(以下简称db/m)小鼠为正常组,正常组和模型组给予生理盐水,治疗组小鼠分别给予相应药物,给药8周后检测小鼠肾脏病理学改变,足细胞自噬体数量、结构及相关蛋白表达。结果与模型组相比,益智仁-乌药药对组可显著减轻糖尿病肾病小鼠肾小球基底膜增厚情况,增加足细胞自噬体数量,显著升高自噬相关蛋白表达(P<0.05),降低PI3K/Akt/mTOR信号通路相关蛋白的表达(P<0.05)。其中益智仁-乌药药对高剂量组各指标改善优于益智仁-乌药低、中剂量组。结论益智仁-乌药药对通过抑制PI3K/Akt/mTOR信号通路激活,提高足细胞自噬水平,减轻足细胞损伤,发挥治疗糖尿病肾病的作用。

基于Raspberry Pi的安全帽识别系统设计与实现

为了便于施工危险区域人员的自动化识别,提出了一种基于Raspberry Pi的安全帽识别算法。该算法将摄像头采集到的原始视频图像进行滤波、形态学等处理,再对图像中的安全帽进行识别。对于彩色安全帽,将原始图像转换到HSV空间,根据不同颜色色调阈值的设定同时识别红、黄、蓝三种颜色的安全帽,并结合形状特征剔除错误目标。对于白色安全帽,将原始图像转化成B通道下的灰度图像,解决了将黄色误检为白色的问题。采用V通道直方图均衡化的方法,提升了昏暗光线条件下的图像亮度。实验结果表明:在无需提前训练的情况下,算法对于单色安全帽识别准确率超过了91%,对于多色安全帽识别率超过了90%,为施工危险区域的安全隐患排查和作业管控提供了解决方案。

自拟平衡针灸通过调控PI3K-AKT信号通路及血清GABA水平对老年失眠的治疗作用

目的探讨自拟平衡针灸通过调控磷脂酰肌醇3激酶(PI3K)-蛋白激酶B(AKT)信号通路及血清氨基丁酸(GABA)水平对老年失眠的治疗作用。方法以老年失眠患者120例作为研究对象,按照随机分组原则分为研究组及对照组,各60例。两组均采取阿普唑仑进行治疗,研究组在此基础上联合采取自拟平衡针灸进行治疗,两组均治疗4 w。比较两组治疗效果、临床改善指标、PI3K-AKT信号通路及GABA、多导睡眠监测仪指标、睡眠质量之间的差异。结果研究组治疗总有效率显著高于对照组(P<0.05)。治疗后,两组睡眠潜伏期、睡眠总时间及觉醒次数均显著改善,且研究组睡眠潜伏期、觉醒次数显著低于对照组(P<0.05),睡眠总时间显著高于对照组(P<0.05)。两组PI3K、AKT及GABA均显著改善,且研究组PI3K、AKT显著低于对照组,GABA显著高于对照组(P<0.05)。两组总睡眠时间(TST)、睡眠效率(SE),第一(TS1)、二(TS2)、三(TS3)及四期(TS4)睡眠、快速眼动睡眠时间(REM)、觉醒期时间(WASO)、睡眠潜伏期时间(SL)均显著改善,且研究组以上指标改善均显著优于对照组(P<0.05)。两组日间功能障碍、睡眠质量、睡眠时间、睡眠障碍及入睡时间均显著改善,且研究组日间功能障碍、睡眠质量、睡眠时间、睡眠障碍及入睡时间显著优于对照组(P<0.05)。结论自拟平衡针灸通过调控PI3K-AKT信号通路及血清GABA水平,有效降低局部炎性反应,优化神经系统的递质传递,有效改善患者的治疗效果。

达沙替尼基于PI3K/AKT信号通路调节乳腺癌细胞生物学行为

目的:基于PI3K/AKT信号通路探讨达沙替尼(dasatinib,DAS)对乳腺癌MCF-7细胞生物学行为的影响。方法:分别使用噻唑蓝(methyl thiazolyl tetrazolium,MTT)法、Transwell法、流式细胞术和Western blotting法检测DAS不同浓度(0、2、6、10μmol/L)作用下MCF-7细胞增殖、侵袭和迁移、细胞凋亡以及PI3K/AKT信号通路相关蛋白表达情况。同时设置对照组(溶媒对照)、DAS组(DAS 10μmol/L)、PI3K抑制剂组(LY29400220μmol/L)、联合组(DAS 10μmol/L+LY29400220μmol/L),比较各组细胞增殖、侵袭和迁移、细胞凋亡以及PI3K/AKT信号通路相关蛋白表达情况。结果:随着DAS作用浓度的升高,MCF-7细胞增殖抑制率和细胞凋亡率升高(P<0.05),侵袭细胞数、迁移细胞数和PI3K、p-PI3K、p-AKT蛋白表达降低(P<0.05)。与对照组相比,DAS组、PI3K抑制剂组、联合组MCF-7细胞增殖抑制率和细胞凋亡率升高(P<0.05),PI3K、p-PI3K、p-AKT蛋白表达降低。与PI3K抑制剂组、DAS组相比,联合组MCF-7细胞增殖抑制率和细胞凋亡率升高,PI3K、p-PI3K、p-AKT蛋白表达降低(P<0.05)。结论:DAS能抑制乳腺癌MCF-7细胞增殖、侵袭和迁移能力,诱导细胞凋亡,其机制可能与调控PI3K/AKT信号通路有关。

PI3K信号过度活化引发免疫病理机制研究进展

目前少数研究发现磷脂酰肌醇3-激酶(PI3K)基因PIK3CD(编码p110δ)发生功能获得性(GOF)变异后,可导致活化的PI3Kδ综合征(APDS)。该突变可通过诱导T细胞减少/衰老/耗竭、B细胞发育受阻、NK细胞毒性降低等多种免疫系统内部缺陷机制导致机体内免疫缺陷、免疫失调甚至肿瘤发生。本文主要对PI3Kδ GOF诱导APDS发生的相关临床疾病特点及免疫缺陷分子机制展开综述,重点阐述该突变导致淋巴细胞发育、分化及功能缺陷的分子机制。

基于改进灰色预测单神经元PI的全超导托卡马克核聚变发电装置快控电源电流控制

全超导托卡马克核聚变发电装置(EAST)快控电源负载电感的电流受多种不确定环境因素的影响而难以预测,灰色预测无需精确对象模型,只需少量已知信息即可实现输出电流短期预测,已在EAST快控电源中有了一定研究应用。为解决灰色预测在EAST快控电源中对突变信号边沿预测精度低和预测延时时间长的问题,提出一种改进灰色预测算法实现输出电流预测。在一个开关周期内对输出电流进行等时长间隔采样4次作为原始序列,将滚动式采样预测改为逐周期采样预测,在实现灰色预测的过程中不必依赖过去几个周期的历史采样信息,只需本周期的4个原始采样值即可实现输出电流的预测。根据预测电流与参考电流误差自适应调整单神经元比例-积分(PI)控制的输出增益,实现输出电流的快速准确控制。仿真和实验结果表明,在EAST快控电源电流预测过程中所提出的改进灰色预测,对比传统灰色预测具有更高的预测精度和更小的预测延时,改进灰色预测变增益单神经元PI控制能够在减小电流超调的同时加快输出电流响应速度。

Peterson图和图D_(m,n)的边PI指数

利用分析法和分类讨论法,给出Peterson图和D_(m,n)图的边PI指数计算公式,丰富了图的PI指数理论.

模糊整定PI参数的双柔性机械臂振动抑制

机械臂伺服驱动系统中柔性因素的存在易导致机械臂输出转速波动,甚至引发系统谐振.为抑制系统转速波动,使机械臂传动系统性能稳定,使用模糊理论整定PI控制器参数的控制策略.根据假设模态法和拉格朗日动力学方程建立了考虑LuGre摩擦模型的双柔性机械臂传动系统动力学方程,并分析了动力学方程中耦合非线性项对系统传动特性的影响.使用极点配置策略确定PI控制器参数的取值范围,后根据模糊规则实时调整控制器参数,以减小伺服系统输出转速的波动,进而抑制系统谐振.最后,借助数值仿真分析和机械臂控制实验,与传统PI控制策略对比,发现本文所述控制策略可使电机端转角跟踪误差绝对值的平均值降低43.066%,柔性负载转角误差的标准差降低46.506%,更加验证了所提模糊规则整定PI控制器参数抑振方法的有效性.

稻瘟病抗性基因Pi2、Pita的特异KASP标记开发与应用

【目的】水稻稻瘟病抗性基因Pi2和Pita是对华南稻区稻瘟病生理小种具有广谱抗性的基因,对水稻的稻瘟病抗性育种具有重要应用价值,开发一套高效的鉴定方法有利于提高水稻抗病品种培育效率。【方法】根据高抗稻瘟病品种‘黄广油占’与高感稻瘟病品种‘广陆矮4号’在Pi2基因的第787位、第788位密码子上的变异GCA GGA/GTG TTA,以及高抗稻瘟病国际稻种质资源Tetep与高感稻瘟病地方种质资源丽江新团黑谷在Pita基因第6 640位碱基上的变异G/T,基于竞争性等位基因PCR(Kompetitive Allele Specific PCR,KASP)标记技术原理,开发抗稻瘟病基因的分子标记。【结果】开发了两个抗稻瘟病基因Pi2和Pita的功能位点KASP标记(W-Pi2、W-Pita),利用标记对广东省农业科学院水稻研究所培育的常规稻、香稻、杂交稻新品种进行检测,筛选出抗性品种19个,其中单个基因检测为抗病等位基因型的水稻品种有13个,两个基因均为抗病等位基因型的品种有6个,比较两个标记结果,Pi2抗性等位基因在育成品种中的频率高于Pita抗性等位基因频率。综合所有结果,表明2个标记可在早期(种子或苗期)检测育种材料抗稻瘟病基因Pi2和Pita的等位基因型,无需将育种材料种植到病圃鉴定即可筛选出抗病单株。【结论】利用开发的KASP功能分子标记W-Pi2、W-Pita能够较好区分不同抗性的水稻亲本品种,可清楚区分水稻育种材料间不同的等位基因型,对育种材料进行准确筛选。