The important function of a sensor network is to collect and forward data to destination. It is very important to know about the location of collected data. This kind of information can be obtained using localization technique in wireless sensor networks (WSNs). Localization is a way to determine the location of sensor nodes. Localization of sensor nodes is an interesting research area, and many works have been done so far. It is highly desirable to design low-cost, scalable, and efficient localization mechanisms for WSNs. In this paper, we discuss sensor node architecture and its applications, different localization techniques, and few possible future research directions.

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High quality relevance judgments are essential for the evaluation of information retrieval systems. Traditional methods of collecting relevance judgments are based on collecting binary or graded nominal judgments, but such judgments are limited by factors such as inter-assessor disagreement and the arbitrariness of grades. Previous research has shown that it is easier for assessors to make pairwise preference judgments. However, unless the preferences collected are largely transitive, it is not clear how to combine them in order to obtain document relevance scores. Another difficulty is that the number of pairs that need to be assessed is quadratic in the number of documents. In this work, we consider the problem of inferring document relevance scores from pairwise preference judgments by analogy to tournaments using the Elo rating system. We show how to combine a linear number of pairwise preference judgments from multiple assessors to compute relevance scores for every document.

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The urban road networks and vehicles generate exponential amount of spatio-temporal big-data, which invites researchers from diverse fields of interest. Global positioning system devices may transceive data every second thus producing huge amount of trajectory data. Subsequently, it requires optimized computing for various operations such as visualization and mining hidden patterns. This sporadically stored big-data contains invaluable information, which is useful for a number of real-time applications. Compression is a highly important, but knotty task. Optimized compression enables us achieve the desired results in efficient and effective manner by using minimum energy and computational resources without compromising on important information. We present two versions of a compression technique based on the points of intersections (PoI) of urban roads networks. Based on intelligent mining paradigm, we created a compressed lookup lexicon to store the PoIs of dynamically selected region of interests (ROI). An important feature of our lexicon is the key pattern, which is intelligently computed based on the relative geographic position of a spatial geodetic vertex with respect to Euclidean space origin in a given ROI. This compresses trajectories in linear time, making it feasible for mission critical real world applications. Our experimental dataset contained 959 547, 517 436, and 231 740 trajectories for Bikes, Cars, and Taxis, respectively. The Compr 10 reduced these trajectories to 17 428, 11 084, and 6565, respectively. Results of Compr 15 and Compr 20 show promising results. We define the quality of the compression in context of the considered problem. The results show that the proposed technique achieved satisfactory quality of the compression. INTRODUCTIONThe urban roads networks and many types of vehicles running over them generate exponential amount of spatio-temporal big-data, which attracts the keen interest of researchers from diverse fields of research such as power and energy, geographical information systems, data mining, artificial intelligence, and so on. This big-data contains invaluable This is a companion to [10.1002/ett.3886].

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BackgroundPolycystic ovary syndrome (PCOS) is mainstream gynaecological problems in adult women. Vitamin D has been linked to propensity of developing PCO. As this hormones has certain effects on insulin sensitivity and restoration of reproductive capacity in women with PCOs. So, a study was planned to establish correlation of deficiency of vitamin D and PCOs.ObjectiveTo determine the correlation of deficiency of vitamin D with PCOs in patients reporting to a teaching hospital.Material & MethodsCase control study carried out at institute of Shaikh Zayed Hospital, Lahore, Gynae-Obst Department for six months. If serum level of vitamin D is < 25nmol/L then deficiency was labeled. Odds ratio was calculated between PCOs and vitamin D deficiency, ResultsThe mean age of the patients was 26.91±6.09 years. In this study, 70% females were married. The mean BMI of patients was 22.59±2.48kg/m2, and 76(76%) patients had normal BMI and 24(24%) patients were overweight. Vitamin D deficiency was observed in 47 cases in which 31 were from case group and 16 were from control group with significant association i.e. OR=3.47. ConclusionOur study results concluded that there is more risk of vitamin D deficiency in females with PCOS as compared to females without PCOS.