Thermophysical Characterization of Local Materials from a Locality in Chad for Use in Eco-Building

With the aim of enhancing the value of local building materials, the subject of this paper is a thermophysical study of natural Chadian clay from the eastern region of Chad, “Abeche”. Samples were taken from a brickwork in Abeche from a depth of 1 m, then using a clay brick-making press, 4 × 5 × 8 cm3 clay test tubes were made with 2%, 4%, 6% and 8% cow dung, and a 100% clay sample was used as a control. These samples underwent thermophysical characterization using the hot-wire method with a hot-plane option, yielding results that could improve thermophysical performance. The thermal conductivity of the test sample is in the order of 0.715 to 0.420 W/m. K, at 8% for cow dung, so the addition of cow dung as a percentage in the clay matrix enabled us to obtain various satisfactory thermal parameters.

Loop Closure Detection via Locality Preserving Matching With Global Consensus

A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited area.Concretely,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D space.In this work,a novel appearance-based LCD system is proposed.Specifically,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task.It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance.To dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive structures.Meanwhile,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds.The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets.Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks.We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.

SELF-DEPENDENT LOCALITY PRESERVING PROJECTION WITH TRANSFORMED SPACE-ORIENTED NEIGHBORHOOD GRAPH

Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.

Locality Preserving Discriminant Projection for Speaker Verification

In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.

Violation of Locality Beyond Bell＇s Theorem for Multiparticle Perfect Correlations

We present the analogous inequalities of Bell's inequality for N-qubit system predicted respectively by realistic theory, quantum mechanics, local theory, local realistic theory, and local quantum theory on the same Belltype joint experiment. It is shown that quantum mechanics can be interpreted by hidden-variable theories while being incompatible to any local theory. A necessary condition for the separability of N-qubit system is derived.

Face recognition using illuminant locality preserving projections

A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.

Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

A supervised multimanifold method with locality preserving for face recognition using single sample per person

Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.

An Efficient Grid Service Discovery Mechanism Based on the Locality Principle

With the explosion of services in grid environment, it＇s necessary to develop a mechanism which has the ability of discovering suitable grid services efficiently. This paper attempts to establish a layered resource management model based on the locality principle which classifies services into different domains and virtual organizations （VOs） according to their shared purposes. We propose an ontologybased search method applying the ontology theory for characterizing semantic information. In addition, we extend the UD- D1 in querying, storing, and so on. Simulation experiments have shown that our mechanism achieves higher performance in precision, recall and query response time.

Regional difference and dynamic mechanism of locality of the Chinese farming-pastoral ecotone based on geotagged photos from Panoramio

Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-pastoral ecotone is a typical large-scale region but few studies were conducted. This research contributed to the understanding of cross-regional locality of the Chinese farming-pastoral ecotone from different scales, including national, sectional, and provincial administrative units by utilizing geotagged photos（GTPs） obtained from the Panoramio website. The major results were as follows：（1） the locality elements of the Chinese farming-pastoral ecotone included 52 free nodes classified into 8 types of scene attributes;（2） there were huge differences between locality elements of different regions, and there was a negative correlation between the similarity degree of elements of different provinces and their spatial distances;（3） the Chinese farming-pastoral ecotone could be divided into the northern, central and southern sections, whose localities had differences in element constitution, association structure and the strength of elements, system stability and the anti-interference capability; and（4） the evolution of the localities of the northern and central sections was mainly influenced by human activities, while the locality of southern section retained more natural features. On a theoretical level, this research aimed to establish the research methodology of locality from the perspective of open data on the web with strong operability and replicability. On a practical level, this research could enrich the structuring recognition of the locality of the Chinese farming-pastoral ecotone and the comprehension of its dynamic mechanism. The results provide a reference for locality differentiation protection and the development of a cross-regional scale.

Performance Study of Locality and Its Impact on Peer-to-Peer Systems

This paper presents the measurement study of locality-aware Peer-to-Peer solutions on Internet Autonomous System (AS) topology by reducing AS hop count and increase nearby source nodes in P2P applications. We evaluate the performance of topology-aware BT system called TopBT with BitTorrent (BT) by constructing AS graph and measure the hops between nodes to observe the impact of quality of service in P2P applications.

Comprehensiveness and Locality: Website Marketing of World City Governments in the Era of Globalisation

In the era of the Internet and globalisation, more and more international academics focus their attention on how city governments compete for talent, capital, and technology through website marketing to promote their economy and global status. However, 1) present research generally overlooks the importance of different types of elements in different marketing themes, 2) the combinations of marketing themes are still unknown, and 3) the presumption that the emphasised elements and specific combination of marketing themes on official websites differentiates cities requires more cases to be understood. In light of this background, this study collects homepage elements of 49 Alpha world cities' official websites and quantitatively analyses the frequency of different types of elements, the marketing content themes, and the dissimilarity of content of Chinese Alpha world cities. The results indicate that comprehensiveness and locality appear in the process of city marketing throughout official city websites. Overall, we make the following conclusions. 1) The importance of different kinds of elements significantly differs between 49 Alpha world cities. 2) Based on various combinations of elements, the marketing contents of Alpha world cities through official websites can be categorised into six themes of history and culture, government and information, construction and environment, government and living, construction and living, and general compound. 3) The marketing elements of five Chinese Alpha world cities, including Hong Kong, Beijing, Shanghai, Taipei and Guangzhou, are different than the other 44 Alpha world cities, and Chinese cities prefer to advertise their history and culture but rarely market citizens' activities. Moreover, Chinese cities' marketing mostly targets natives while the other 44 Alpha cities target external groups, and the locality of world cities' website marketing is reinforced especially on a native language edition website. This study ultimately finds that the Chinese edition websites of five Chinese cities place more focus on introducing local historical buildings, administrative services, and internal business information than the English edition websites do.

Early Jurassic Coleopterans from the Mintaja Insect Locality,Western Australia

Beetles （Coleoptera） are the most common insects recovered from the Lower Jurassic Mintaja insect locality of Western Australia, with over half of the fossils recorded from this site being isolated coleopteran elytra. A range of partial beetle bodies and other isolated beetle sclerites have also been recovered from the locality; much of this material is taxonomically unidenitifiable due to its disarticulation and poor preservation. A number of the Mintaja coleopterans are assigned to the archostematan family Ommatidae, including Zygadenia westraliensis （Riek, 1968） comb. nov, previously placed in the morphogenus Mesothoris, and an unnamed species of Tetraphalerus. Also recorded is a new species of elaterid, Lithomerus wunda sp. nov., along with other fragments likely attributable to the same family. The remaining material is assigned into morphospecies, separated primarily on preserved body parts - specifically, there are three morphospecies based on partially articulated coleopteran bodies, two morphospecies based on isolated head capsules, three morphospecies based on isolated thoracic sclerites, three morphospecies based on isolated abdominal sclerites, and 13 morphospecies based on isolated elytra. Overall, the ecology of these fossils is difficult to interpret due to poor preservation, although some of the beetles were likely aquatic, and the Ommatidae and Elateridae were both likely xylophilous. There is a strong similarity between the Mintaja coleopterans and those from the Late Triassic Denmark Hill locality of Queensland, though many of these similarities are based on morphotaxa and may be superficial in nature. Of the species that have been assigned to named taxa, all are generally typical of the Late Mesozoic worldwide, with Zygadenia, Tetraphalerus and Lithomerus all long-ranging, cosmopolitan genera.

Importance of genetic data from type specimens:The questionable type locality of southern white-cheeked gibbon,Nomascus siki(Delacour,1951)

DEAR EDITOR,Many historical descriptions of new species have included vague or inaccurate information about the type locality.This can lead to confusion in the study of the species and their geographic distributions,thereby hindering conservation efforts.One such example is the southern white-cheeked gibbon(Nomascus siki(Delacour,1951)).The type locality is given as Thua Luu in central Vietnam,which is located within the range of another crested gibbon species(N.annamensis)and more than 200 km south of the known range of N.siki.To investigate the problematic type locality of N.siki,we sequenced the mitochondrial genome of the holotype and compared its cytochrome b gene sequence to those of other georeferenced crested gibbon samples.

A scalable, locality aware, fault-tolerant, and decentralized location scheme for distributed networks

This paper presents Isotope, an efficient, locality aware, fault-tolerant, and decentralized scheme for data location in distributed networks. This scheme is designed based on the mathematical model of decentralized location services and thus has provable correctness and performance. In Isotope, each node needs to only maintain linkage information with about O(log n) other nodes and any node can be reached within O(log n) routing hops. Compared with other related schemes, Isotope’s average locating path length is only half that of Chord, and its locating performance and locality-awareness are similar to that of Pastry and Tapestry. In addition, Isotope is more suitable for constantly changing networks because it needs to exchange only O(log n) O(log n) messages to update the routing information for nodes arrival, departure and failure.

Factors Influencing Hepatitis C Viral Infections in the Population of Algamosi Locality, Gezira State, Central Sudan

Hepatitis C (HCV) remains a global health challenge and is transmitted via contact with contaminated blood and body fluids. This study aimed To find the factors influencing hepatitis c viral infections and to identify its prevalence among populations of the Algamosi area, Gezira state central Sudan. This cross-sectional community-based study was conducted. A total of 492 participants were randomly selected from among 4 villages and were interviewed to find out the factors that lead to transmission of infection and tested for markers of hepatitis C infection. ELISA confirmed all the positive card tests. The results showed that the individuals within the age groups > 45 and 31 - 45 years are more affected by HCV the percentage was 3.37% and 0.6% respectively, while the age groups of 15 - 30 and p < 0.05). Among the possible cause (1.4%) had a previous history of tattooing and were significantly reactive for anti-HCV (X2 = 14.588 and p value = 0.001) The study concluded that the prevalence of hepatitis C viruses was only detected in three areas. This study explored many possible causes associated with viral hepatitis, tattooing is the common cause of hepatitis C virus. Based on the finding the study recommends, the crucial intervention program to screen most of the Algamosi population who have been not screened for HCV, proper treatment for HCV, and the health education program is strongly recommended to create awareness among the general population.

An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing

In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collected and their class labels are given by a crawler-type software to analyze the website maliciousness called SPIKE. In general, since spammers use botnets to spread numerous malicious emails within a short time, such distributed spam emails often have the same or similar contents. Therefore, it is not necessary for all spam emails to be learned. To adapt to new malicious campaigns quickly, only new types of spam emails should be selected for learning and this can be realized by introducing an active learning scheme into a classifier model. For this purpose, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with a data selection function. In RAN-LSH, the same or similar spam emails that have already been learned are quickly searched for a hash table in Locally Sensitive Hashing (LSH), in which the matched similar emails located in “well-learned” are discarded without being used as training data. To analyze email contents, we adopt the Bag of Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency (TF-IDF). We use a data set of double-bounce spam emails collected at National Institute of Information and Communications Technology (NICT) in Japan from March 1st, 2013 until May 10th, 2013 to evaluate the performance of the proposed system. The results confirm that the proposed spam email detection system has capability of detecting with high detection rate.