Cross-Listing and Valuation Effects＂ Evidence From Nairobi Securities Exchange

The purpose of this study is to analyze the valuation effects of cross-listing. The study has conducted a univariate analysis of the Tobin＇s Q and the market-to-book ratio for the period before and after the cross-listing by using paired tests. Non-cross-listed firms are then included in multivariate regressions by using pooled Time Series Cross Section （TSCS） and Panel Corrected Standard Error （PCSE） regressions for a period of 13 years to find out if there is a difference in the valuations between cross-listed firms and non-cross-listed firms. The study＇s results indicate that the Tobin＇s Q of cross-listed-firms increases two years prior to cross-listing and that it continues to increase two years after cross-listing. The market-to-book ratios also show an increase two years prior to cross-listing and up to one year after cross-listing, then decrease in the second year after cross-listing. When non-cross-listed firms are included in the analysis, results indicate that cross-listed firms are valued higher than non-cross-listed firms. When data are portioned for positive earnings per share （EPS） and dividends, results indicate that valuation is the highest when EPS is positive. Since segmentation theories cannot be ruled out, the study＇s findings are more in support of the growth opportunity hypothesis.

Individualized music for dementia:Evolution and application of evidence-based protocol

The theory-based intervention of individualized music has been evaluated clinically and empirically leading to advancement and refinement of an evidence-based protocol, currently in its 5th edition. An expanded version of the protocol was written for professional health care providers with a consumer version tailored for family caregivers. The underlying mid-range theory is presented along with a seminal study that was followed by further research in the United States, Canada, Great Britain, France, Sweden, Norway, Japan and Taiwan. Key studies are summarized. Given its efficacy when implemented by research staff, studies have advanced to testing the intervention under real-life conditions when implemented and evaluated by trained nursing assistants in long-term care facilities and visiting family members. In addition, one study evaluated the implementation of music by family members in the home setting. Initial research focused on agitation as the dependent variable with subsequent research indicating a more holistic response such as positive affect, expressed satisfaction, and meaningful interaction with others. The article advances by describing on-line programs designed to train health care professionals in the assessment, implementation and evaluation of individualized music. In addition, Gerdner has written a story for apicture book intended for children and their families(in press). The story models principles of individualized music to elicit positive memories, reduce anxiety and agitation, and promote communication. The article concludes with implications for future research.

Initial Rifting Age of the Nearly N–S Rifts in Southern Tibetan Plateau:New Evidence from the Age limit of the Early Sediments

Objective The nearly parallel N-S-trending rifts in southern Tibet represent the E-W extension of the Tibet Plateau. Most data which constrained the age of the extensional deformation come from isotopic dating of the dikes probably related to the activity of the nearly N-S faulting and micas from hydrothermal activity and the low- temperature thermochronology of plateau uplift. Previous research shows that there are at least three different ideas about the age of the rifts： （1） older than 16-12 Ma, （2） 14- 10 Ma, and （3） 8-4 Ma （Fig. la）. For the old sedimentary strata represented the beginning of the rifting, the dating of the sediments helps to better define the initial rifting age.

Tectonic Environments of the Yan-Liao Rift during Earth's Middle Age(1.7～0.75 Ga): Evidence from Mafic Dyke Swarms in Eastern Hebei, North China

During the past decade,generations of Precambrian mafic dykes/sills have been investigated and revealed in the North China Craton(NCC).Researchers identified more than 20 episodes of Precambrian dyke swarms,

Risk assessment of water security in Haihe River Basin during drought periods based on D-S evidence theory

The weights of the drought risk index （DRI）, which linearly combines the reliability, resiliency, and vulnerability, are difficult to obtain due to complexities in water security during drought periods. Therefore, drought entropy was used to determine the weights of the three critical indices. Conventional simulation results regarding the risk load of water security during drought periods were often regarded as precise. However, neither the simulation process nor the DRI gives any consideration to uncertainties in drought events. Therefore, the Dempster-Shafer （D-S） evidence theory and the evidential reasoning algorithm were introduced, and the DRI values were calculated with consideration of uncertainties of the three indices. The drought entropy and evidential reasoning algorithm were used in a case study of the Haihe River Basin to assess water security risks during drought periods. The results of the new DRI values in two scenarios were compared and analyzed. It is shown that the values of the DRI in the D-S evidence algorithm increase slightly from the original results of Zhang et al. （2005）, and the results of risk assessment of water security during drought periods are reasonable according to the situation in the study area. This study can serve as a reference for further practical application and planning in the Haihe River Basin, and other relevant or similar studies.

A Model of Federated Evidence Fusion for Real-time Urban Traffic State Estimation

In order to make full use of heterogeneous multi-sensor data to serve urban intelligent transportation systems, a real-time urban traffic state fusion model was proposed, named federated evidence fusion model. The model improves conventional D-S evidence theory in temporal domain, such that it can satisfy the requirement of real-time processing and utilize traffic detection information more efficaciously. The model frame and computational procedures are given. In addition, a generalized reliability weight matrix of evidence is also presented to increase the accuracy of estimation. After that, a simulation test is presented to explain the advantage of the proposed method in comparison with conventional D-S evidence theory. Besides, the validity of the model is proven by the use of the data of loop detectors and GPS probe vehicles collected from an urban link in Shanghai. Results of the experiment show that the proposed approach can well embody and track traffic state at character level in real-time conditions.

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

A generalized evidence distance

How to efficiently measure the distance between two basic probability assignments（BPAs） is an open issue. In this paper, a new method to measure the distance between two BPAs is proposed, based on two existing measures of evidence distance. The new proposed method is comprehensive and generalized. Numerical examples are used to illustrate the effectiveness of the proposed method.

Morphological and molecular evidence supports the occurrence of a single species of Zebrias zebrinus along the coastal waters of China

The so-called zebra sole includes a group of small flatfishes characterized by transverse band pairs on the ocular side and distributed throughout shallow waters along the coast of the Indo-West Pacific Ocean. Sev-eral species of the zebra sole have been recorded from the coastal waters of China. Morphological analysis of 1 107 specimens of the zebra sole from 15 successive localities along the China’s coast demonstrated that no significant variations among these localities were found on the basis of meristic counts and morphometric characters. Phylogenetic analysis based onCOI gene sequences of 14 individuals and D-loop of 22 indi-viduals from eight localities showed that they were indistinguishable among these localities. Therefore, both morphological and molecular evidence supported the occurrence of a single species of the zebra sole along the China’s coast. The available name for this species isZebriaszebrinus （Temminck and Schlegel, 1846） in-stead ofZ.zebra （Bloch, 1787）.Zebrias fasciatus（Basilewsky, 1855） andSolea ommatura （Richardson, 1846） are considered here as two synonyms ofZ. zebrinus.

Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

This paper presents an innovative approach for the fault isolation of Light Rail Vehicle (LRV) suspension system based on the Dempster-Shafer (D-S) evidence theory and its improvement application case. The considered LRV has three rolling stocks and each one equips three sensors for monitoring the suspension system. A Kalman filter is applied to generate the residuals for fault diagnosis. For the purpose of fault isolation, a fault feature database is built in advance. The Eros and the norm distance between the fault feature of the new occurred fault and the one in the feature database are applied to measure the similarity of the feature which is the basis for the basic belief assignment to the fault, respectively. After the basic belief assignments are obtained, they are fused by using the D-S evidence theory. The fusion of the basic belief assignments increases the isolation accuracy significantly. The efficiency of the proposed method is demonstrated by two case studies.

Metallic Reserves of Submarine Manganese Nodules to be Estimated and Harnessed Prudently: Evidence from China's Jiaolong Manned Submersible

Since the discovery of oceanic manganese nodules during the expedition of the British ocean-going ship Challenger from 1872 to 1876, research and development for seabed manganese nodules have never ceased owing to the huge economic inducements. Manganese nodules are the black or dark brown, spherical or massive Mn-bearing ores, deposits of which are found on the sea bottom. The nodules are a mixture of silicate and insoluble potassium permanganates （also with sub-Ti, Fe and Na permanganates） that contain more than 30 kinds of metallic elements, among which those of greatest economic interest are Mn （27-30%）, Ni（1.25-1.5%）, Cu（1- 1.4%）, Co（0.2- 0.25 %）, Fe, Si, and AI, with minor amounts of Ca, Na, K, Ti, B, H and O.

A Map-Matching Algorithm forGPS/DR Integrated Navigation Systems Basedon Dempster-Shafer Evidence Reasoning

GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and the unbounded DR accumulation of errors over time make the position information with undesirable position errors. The map matching can improve the position accuracy and availability of the vehicular position system. In this paper, general principle of map matching is investigated according to segmentation and feature extraction, and a map matching algorithm based on D-S (Dempster-Shafer) evidence reasoning for GPS integrated navigation system is proposed, which can find the exact road on which a car moves. For the experiments, a car navigation system is developed with some sensors and the field test demonstrates the effectiveness and applicability of the algorithm for the car location and navigation.

A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.

Study on Power Transformers Fault Diagnosis Based on Wavelet Neural Network and D-S Evidence Theory

>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.

Network Security Situation Evaluation Based on Modified D-S Evidence Theory

With the rapid development of global information and the increasing dependence on network for people, network security problems are becoming more and more serious. By analyzing the existing security assessment methods, we propose a network security situation evaluation system based on modified D-S evidence theory is proposed. Firstly, we give a modified D-S evidence theory to improve the reliability and rationality of the fusion result and apply the theory to correlation analysis. Secondly, the attack successful support is accurately calculated by matching internal factors with external threats. Multi-module evaluation is established to comprehensively evaluate the situation of network security. Finally we use an example of actual network datasets to validate the network security situation evaluation system. The simulation result shows that the system can not only reduce the rate of false positives and false alarms, but also effectively help analysts comprehensively to understand the situation of network security.

RETHINKING CHINESE EVIDENCE THEORIES AND RECONSTRUCTING SYSTEM OF EVIDENCE： ＂A THREAD FOR THE PEARLS OF CHINESE EVIDENCE＂

This article analyses Chinese traditional evidence theories that have evolved over a long period of time, to explore which theory, between objectivity and relevancy, best represents the basic attribute and logical thread of evidence. These theories are considered in the context of issues arising in evidential adjudication, including： the ＂Mirror of Evidence,＂ truth, the probability of proof standard, the choice between a notion of pursuing 100-percent certainty in adjudication and that wrongful acquittals are better than wrongful convictions, and the statutory proof doctrine comparedwith the system of free proof. Finally, the article presents the framework of and methods for drafting provisions of procedural evidence of the People＇s Court.

Using the Dempster-Shafer Theory of Evidence to Rank Documents

Multi-source information can be utilized collaboratively to improve the performance of information retrieval. To make full use of the document and collection information, this paper introduces a new informa- tion retrieval model that relies on the Dempster-Shafer theory of evidence. Each query-document pair is taken as a piece of evidence for the relevance between a document and a query. The evidence is combined using Dempster＇s rule of combination, and the belief committed to the relevance is obtained. Retrieved documents are then ranked according to the belief committed to the relevance. Several basic probability as- signments are also proposed. Extensive experiments over the Text REtrieval Conference （TREC） test col- lection ClueWeb09 show that the proposed model provides performance similar to that of the Vector Space Model （VSM）. Under certain probability assignments, the proposed model outperforms the VSM by 63% in terms of mean average precision,

Analysis of Early-Warning Threshold for Metro Construction Collapse Risk Based on D-S Evidence Theory and Rough Set

The existing early-warning system in metro construction are generally based on traditional single-sensor data and simple analytic model, which makes it difficult to deal with the complex and comprehensive environment in metro construction. In this paper, the framework of early-warning threshold for metro construction collapse risk based on D-S evidence theory and rough set is built. By combining the primary data fusion collected based on rough set with the secondary data fusion which is based on D-S evidence theory, the integration of multiple information in metro construction is realized and the risk assessment methods are optimized. A case trial based on Hangzhou metro construction collapse accident is also carried out to exemplify the framework. The empirical analysis guarantees the completeness and independence of the prediction information, and realizes the dynamic prediction of the variation trend of metro construction collapse risk.

Assessment of Power Quality Based on D-S Evidence Theory

Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer(D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed,and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally,the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.

基于D-S证据理论的岩爆预测方法研究

为了有效预测岩爆,提出基于D-S证据理论的岩爆预测方法.首先,选取与岩爆发生相关的6个指标因素作为证据体,并通过模糊物元框架和正态型隶属度函数构建证据体的基本概率分配.然后,利用K均值将证据体分类,并提出簇内证据用传统方式融合而簇间证据用权重方式融合的组合融合规则,以减轻高冲突证据融合的不利影响.最后,将模型应用在秦岭终南山公路隧道2号竖井工程,且与经验方法对比.为了分析预测过程的不确定性和估计岩爆发生概率,采用蒙特卡洛模拟进行抽样仿真,并通过Spearman秩相关系数衡量输入指标的全局敏感性.研究结果表明:输入指标在不同的岩爆案例的影响程度差异较大且方向不同;5个岩爆案例的发生概率在40.8%~70.1%之间.该模型表现出优异的预测分类性能,可为深埋地下工程岩爆预测提供参考.