The Effect of the Menstrual Cycle on Cognitive Performance: Spatial Reasoning, Visual & Numerical Memory

The menstrual cycle has been a topic of interest in relation to behavior and cognition for many years, with historical beliefs associating it with cognitive impairment. However, recent research has challenged these beliefs and suggested potential positive effects of the menstrual cycle on cognitive performance. Despite these emerging findings, there is still a lack of consensus regarding the impact of the menstrual cycle on cognition, particularly in domains such as spatial reasoning, visual memory, and numerical memory. Hence, this study aimed to explore the relationship between the menstrual cycle and cognitive performance in these specific domains. Previous studies have reported mixed findings, with some suggesting no significant association and others indicating potential differences across the menstrual cycle. To contribute to this body of knowledge, we explored the research question of whether the menstrual cycles have a significant effect on cognition, particularly in the domains of spatial reasoning, visual and numerical memory in a regionally diverse sample of menstruating females. A total of 30 menstruating females from mixed geographical backgrounds participated in the study, and a repeated measures design was used to assess their cognitive performance in two phases of the menstrual cycle: follicular and luteal. The results of the study revealed that while spatial reasoning was not significantly related to the menstrual cycle (p = 0.256), both visual and numerical memory had significant positive associations (p < 0.001) with the luteal phase. However, since the effect sizes were very small, the importance of this relationship might be commonly overestimated. Future studies could thus entail designs with larger sample sizes, including neuro-biological measures of menstrual stages, and consequently inform competent interventions and support systems.

Qualitative spatial reasoning on topological relations by combining the semantic web and constraint satisfaction

Qualitative spatial reasoning on topological relations can extract hidden spatial knowledge from qualitatively described topological information,which is of significant importance for decisionmaking and query optimization in spatial analysis.Qualitative reasoning on spatial topological information based on semantic knowledge and reasoning rules is an efficient means of reducing both the known relations and the corresponding rules,which can result in enhanced reasoning performance.This paper proposes a qualitative reasoning method for spatial topological relations based on the semantic description of reasoning rules and constraint set.Combined with knowledge from the Semantic Web,the proposed method can easily extract potential spatial results consistent with both unique and non-unique rules.The Constraint-Satisfactionbased approach,describing constraint set with semantic expressions,is then used together with an improved path consistency algorithm to verify the consistency of the unique-rules-based and non-unique-rules-based reasoning results.The verification can eliminate certain reasoning results to ensure the reliability of the final results.Thus,the task of qualitative spatial reasoning on topological relations is completed.

AN OPTIMUM VEHICULAR PATH ALGORITHM FOR TRAFFIC NETWORK BASED ON HIERARCHICAL SPATIAL REASONING

Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers’ choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large_scale traffic networks.

Hierarchical Spatial Reasoning and Case of Way-Finding

Human beings use hierarchies to simplify their conceptual models of reality and to perform reasoning more efficiently. Hierarchical structures are conceptually imposed on space and allow performance of complex tasks in very large contexts easily. Hierarchical spatial reasoning is an important method for solving spatial problems. This paper briefly discusses the definition and frame of hierarchical spatial reasoning and its application to way-finding of road networks.

Evaluation of Metal Fatigue Problems Using Qualitative Reasoning Approach

Fatigue failures are often encountered in steel structures under heavy cyclic loadings. This paper presents metal fatigue problems in structural engineering using outcomes of recent advancements in numerical qualitative reasoning. Qualitative reasoning provides an effective and sound technique for solving complex and uncertain scenarios, regardless of the uncertainty or linearity of the design parameters and their constraints. This paper introduces the algorithms behind a software platform, built upon numerical qualitative reasoning for engineering applications. The software expresses the results of the analysis in variable ranges and diagrams showing a two-dimensional design space. The capability of representing design parameters and outcomes in solution spaces provides a practical way for engineers to leverage their existing knowledge and experience. Case studies in metal fatigue design are given to reflect on the capability of qualitative reasoning in engineering applications.

QUALITATIVE REASONING BY COMPUTING WITH WORDS IN HERRERA-MARTíNEZ’S LINGUISTIC MODEL

Most of modern systems for information retrieval, fusion and management have to deal with more and more qualitative information （by linguistic labels） besides information expressed quantitatively （by numbers）, since human reports are better and easier expressed in natural language than with numbers. In this paper, Herrera-Martfnez＇s 2-Tuple linguistic representation model is extended for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory （DSmT） framework, in order to overcome the limitations of current approaches, i.e., the lack of precision in the final results of linguistic information fusion according to 1-Tuple representation （ q1 ）- The linguistic information which expresses the expert＇s qualitative beliefs is expressed by means of mixed 2 Tuples （equidistant linguistic labels with a numeric biased value）. Together with the 2-Tuple representation model, some basic operators are presented to carry out the fusion operation among qualitative information sources. At last, through simple example how 2-Tuple qualitative DSmT-based （q2 DSmT） fusion rules can be used for qualitative reasoning and fusion under uncertainty, which advantage is also showed by comparing with other methods.

Orders of Magnitude Reasoning Applied in Selecting Shield

Orders of magnitude reasoning in artificial intelligence (AI) and qualitative algebra are discussed and applied in selecting shield in this paper. It includes basic quantitative calculation, qualitative constraint and principle of orders of magitude reasoning. The method has potential prospects in dealing with the engineering problems.

Hierarchical Detailed Description for Spatial Direction Relations

Direction relation is an important spatial relation. Descriptions and representations for direction relations have different levels of detail because of the varying dimensions of spatial objects and different scales of the embedding spaces. Based on a direction- relation matrix, the hierarchical frame of spatial direction relations which partitions direction relations orderly and thoroughly is built. Interior direction relations are used to perfect the representation of direction relations and the binary-encoding idea is creatively applied to construct an interior detailed matrix describing multiple interior direction relations by a uniform matrix. The model integrates topological information into the description model for direction relations, which will lay the foundations of spatial compositive reasoning.

A case-based reasoning approach for task-driven spatial-temporally aware geospatial data discovery through geoportals

There is a critical need to develop a means for fast,task-driven discovery of geospatial data found in geoportals.Existing geoportals,however,only provide metadata-based means for discovery,with little support for task-driven discovery,especially when considering spatial–temporal awareness.To address this gap,this paper presents a Case-Based Reasoning-supported Geospatial Data Discovery(CBR-GDD)method and implementation that accesses geospatial data by tasks.The advantages of the CBR-GDD approach is that it builds an analogue reasoning process that provides an internal mechanism bridging tasks and geospatial data with spatial–temporal awareness,thus providing solutions based on past tasks.The CBR-GDD approach includes a set of algorithms that were successfully implemented via three components as an extension of geoportals:ontology-enhanced knowledge base,similarity assessment model,and case retrieval nets.A set of experiments and case studies validate the CBR-GDD approach and application,and demonstrate its efficiency.

Multimorbidity and clinical reasoning through the eyes of GPs:a qualitative study

Objectives Despite the high prevalence of patients suffering from multimorbidity,the clinical reasoning processes involved during the longitudinal management are still sparse.This study aimed to investigate what are the different characteristics of the clinical reasoning process clinicians use with patients suffering from multimorbidity,and to what extent this clinical reasoning differs from diagnostic reasoning.Design Given the exploratory nature of this study and the difficulty general practitioners(GPs)have in expressing their reasoning,a qualitative methodology was therefore,chosen.The Clinical reasoning Model described by Charlin et al was used as a framework to describe the multifaceted processes of the clinical reasoning.Setting Semistructured interviews were conducted with nine GPs working in an ambulatory setting in June to September 2018,in Geneva,Switzerland.Participants Participants were GPs who came from public hospital or private practice.The interviews were transcribed verbatim and a thematic analysis was conducted.Results The results highlighted how some cognitive processes seem to be more specific to the management reasoning.Thus,the main goal is not to reach a diagnosis,but rather to consider several possibilities in order to maintain a balance between the evidence-based care options,patient’s priorities and maintaining quality of life.The initial representation of the current problem seems to be more related to the importance of establishing links between the different pre-existing diseases,identifying opportunities for actions and trying to integrate the new elements from the patient’s context,rather than identifying the signs and symptoms that can lead to generating new clinical hypotheses.The multiplicity of options to resolve problems is often perceived as difficult by GPs.Furthermore,longitudinal management does not allow them to achieve a final resolution of problems and that requires continuous review and an ongoing prioritisation process.Conclusion This study contributes to a better understanding of the clinical reasoning processes of GPs in the longitudinal management of patients suffering from multimorbidity.Through a practical and accessible model,this qualitative study offers new perspectives for identifying the components of management reasoning.These results open the path to new research projects.

基于感知推理和外部空间先验特征的图像修复

在基于深度学习的图像修复算法中,当存在大面积掩码时,由于缺乏合理的先验信息指导,修复结果往往会出现伪影和模糊纹理等现象。针对此问题,提出将先验特征与图像预测滤波相结合的图像修复算法。该算法包含两个分支:图像滤波核预测分支和特征推理与图像滤波分支。从图像滤波核预测分支的解码器部分提取特征,利用多尺度外部空间特征融合对掩码区域特征进行重建,并传递给另一分支的解码阶段作为先验特征,为图像修复提供更为丰富的语义信息。然后,在特征推理和图像滤波分支部分引入空间特征感知推理块,它能够过滤掉分散注意力的特征,同时捕捉信息丰富的远距离图像上下文进行推理。最后,使用图像预测滤波核进行过滤消除伪影。在CelebA和Places2数据集上与其他修复网络进行对比实验,证明了该方法在修复质量上的优越性。

顺平县平原区耕地土壤有机质预测精度研究——基于采样尺度和空间插值方法

为研究县域尺度土壤调查最适宜的采样尺度和插值方法,以河北省顺平县山前平原区为例,按照8点/km^(2)点位密度采集1532个土壤样品并测定土壤有机质。从样点中抽取250m×250m、500m×500m、1km×1km、2km×2km 4个采样尺度样本子集,结合地统计学和GIS对有机质空间变异特征进行分析,并对不同采样尺度和插值方法下有机质含量空间插值结果进行分析比较。结果表明:研究区有机质含量具有中等变异性,普通克里金法插值精度优于规则样条函数法和反距离权重法。为保证研究区有机质空间变异评价精度,较为适宜的采样尺度为1km×1km。

云模型在倒立摆控制中的应用

文中提出一种利用云模型有效地实现单电机控制的三级倒立摆动态平衡的方法．通过研究倒立摆系统的定性控制机理，给出了定性和定量之间转换的云模型的形式化表示，用来反映语言值中蕴涵的模糊性和随机性，解释多条定性规则同时被激活的不确定性推理过程．研究成果不仅可用于对太空飞行器以及机器人控制。

基于云模型的主观信任管理模型研究

在开放网络中,信任管理是信息安全的重要前提与基础。主观信任具有模糊性和不确定性的双重特征。将定性定量转换模型-云模型引入主观信任管理研究中,提出了一种新的主观信任的表示方法,较好地解决了信任表达中的模糊性和不确定性难题,进而提出了一种灵活直观,描述能力较强的形式化定性信任推理机制,为主观信任管理研究进行了有益的探索和尝试。

混合维定性空间查询语言MQS-SQL

定性空间推理的RCC理论能够比较全面地表达空间对象的拓扑关系.但由于不支持混合维空间对象,RCC不能直接用于空间查询.本文扩展了RCC,建立了能表达混合维空间关系,更适合于空间查询的空间关系模型MRCC.该模型采用GIS中常用的数据结构,支持混合维空间对象的全拓扑(mereotopology)关系,并根据混合维对象的特点建立了与维数无关的统一的方向、距离关系.利用该模型扩充了标准SQL语言的关系代数,实现了混合维定性空间查询语言MQS-SQL.

土壤养分空间变异及合理取样数研究

土壤养分存在明显的空间差异 ,田间土壤养分合理取样数取决于养分要素自身空间变异程度和人们对数据精度的要求。若空间变异大、精度要求高则需要较多采样数 ,反之需较少样点数。计算结果表明 ,极大多数养分要素的合理取样数较实际取样数有较大幅度减少 ,意味着可大幅降低采样及分析成本。据统计学空间自相关和半方差函数方法研究结果 ,养分要素存在一定的空间自相关性 ,但不同方向上的自相关性有明显差异 ,独立间距也因养分要素而异。研究结果可为精准农业土壤养分样点密度设定。

青藏高原近50年来气温的年代际变化

根据青藏高原及周边地区一百多个气象台站的月平均气温资料,利用统计方法,分析了近50年来气温的年代际变化。结果表明:整个高原地区温度变化可分为6个不同的区域。在时间演变上可划分出相对高温时段(1963年以前)、相对低温时段(1963—1987年)和另一个相对高温时段(1987年以后)。还从天文因素、地球系统各圈层及气候系统内各因子相互作用和相互制约出发,探讨了引起高原气候变化的可能原因。

喀斯特地区典型峰丛洼地表层土壤水分空间变异及合理取样数研究

在桂西北喀斯特洼地90m×40m地块范围内。用经典统计和地统计分析方法探讨了表层土壤（0～5，5～10，10～20，20～30cm）水分的空间变异结构及其分布格局，并给出了不同置信水平和精度条件下的合理取样数。结果表明：表层土壤水分总体上具有良好的半方差结构，呈明显的斑块状分布格局。0～5cm层具有中等的空间相关性，其余各层具有强烈的空间相关性，而且变异程度具有明显层次性；除20～30cm层土壤水分用球状模型拟合外，其余各层均符合指数模型，且拟合效果较好。等值线图结合半方差分析可以看出，地形、微地貌、降雨和植被等是研究区土壤水分空间变异的重要影响因素。在确定合理取样数的过程中，除考虑土壤水分的统计特征外还要考虑其空间结构性。

空间目标的方向关系定性推理

由于空间问题固有的复杂性和不确定性,空间关系的描述和推理普遍采用定性的方法来符合人们的认知。方向关系是一类重要的空间关系,以往对方向关系的定性推理大多在2个参照系下进行,将目标对象抽象为点或者认为目标的大小近似相等。为了更细致地推理方向关系,提出四种不同情况下基于1个参照系的定性方向关系推理方法,并就本方法存在的问题和今后的研究方向提出了一些看法和思路。

基于定性空间推理的多层空间关联规则挖掘算法

很多应用于空间数据挖掘的空间关系模型存在表达能力不强、可理解性较差、不支持不确定性等问题 针对这种情况 ,以定性空间推理的RCC理论为基础 ,结合模糊逻辑 ,提出了一种面向空间数据库的近似区域空间关系模型 ,在此基础上给出了多层空间关联规则的挖掘算法QSRSAR 该算法使用了MBR优先判定、顶点近似等手段针对大型空间数据库进行了优化处理 实验结果表明 ,QSRSAR在空间知识表达能力。