CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things

The Internet of Things (IoT) aims at interconnecting everyday objects (including both things and users) and then using this connection information to provide customized user services. However, IoT does not work in its initial stages without adequate acquisition of user preferences. This is caused by cold-start problem that is a situation where only few users are interconnected. To this end, we propose CRUC scheme - Cold-start Recommendations Using Collaborative Filtering in IoT, involving formulation, filtering and prediction steps. Extensive experiments over real cases and simulation have been performed to evaluate the performance of CRUC scheme. Experimental results show that CRUC efficiently solves the cold-start problem in IoT.Comment: Elsevier ESEP 2011: 9-10 December 2011, Singapore, Elsevier Energy Procedia, http://www.elsevier.com/locate/procedia/, 201

ILAPF: Incremental Learning Assisted Particle Filtering

This paper is concerned with dynamic system state estimation based on a series of noisy measurement with the presence of outliers. An incremental learning assisted particle filtering (ILAPF) method is presented, which can learn the value range of outliers incrementally during the process of particle filtering. The learned range of outliers is then used to improve subsequent filtering of the future state. Convergence of the outlier range estimation procedure is indicated by extensive empirical simulations using a set of differing outlier distribution models. The validity of the ILAPF algorithm is evaluated by illustrative simulations, and the result shows that ILAPF is more accurate and faster than a recently published state-ofthe-art robust particle filter. It also shows that the incremental learning property of the ILAPF algorithm provides an efficient way to implement transfer learning among related state filtering tasks.Comment: 5 pages, 4 figures, conferenc

Manipulation of the precursor supply for high-level production of longifolene by metabolically engineered \u3ci\u3eEscherichia coli\u3c/i\u3e

Longifolene is a naturally occurring tricyclic sesquiterpene widely used in many different fields. Up to now, this valuable terpene was mainly manufactured from the high-boiling fraction of certain pine resins. Microbial production can be a promising alternative to the extraction from natural plant sources. Here, we present the metabolic engineering strategy to assemble biosynthetic pathway for longifolene production in Escherichia coli. E. coli was rendered to produce longifolene by heterologously expressing a codon optimized longifolene synthase from Picea abies. Augmentation of the metabolic flux to farnesyl pyrophosphate (FPP) by different FPP synthases conferred a 1.8-fold increase in longifolene production. An additional enhancement of longifolene production (up to 2.64 mg/L) was achieved by introducing an exogenous mevalonate pathway. Under fed-batch conditions, the best-performing strain was able to produce 382 mg/L of longifolene in a 5 L bioreactor. These results demonstrated the feasibility of producing longifolene by microbial fermentation and could serve as the basis for the construction of more robust strains in the future

Analisis Logam Berat (Pb dan Cu) dalam Total Suspended Particulate (TSP) terhadap Kesehatan Siswa dan Guru di Sekolah Dasar (Studi Kasus: SDN Pandean Lamper 01 dan SDN Srondol Wetan 03

ABSTRACT Heavy Metal Risk Analysis (Pb and Cu) in Total Suspended Particulate (TSP) on Student and Teacher Health in Elementary Schools (Case Study: SDN Pandean Lamper 01 and SDN Srondol Wetan 03) *)Vinda Agita Ediputri,**)Pertiwi Andarani, **)Irawan Wisnu Wardhana Increasing population causes the increasing demand for transportation equipment that has the potential to cause air pollution, especially motor vehicles. Emissions generated by motor vehicles can be either gas or particulates that can be inhaled through the respiratory tract so as to affect human health risks. The type of pollutant that is often the current problem is particulate exposure in the air, especially Total Suspended Particulate (TSP) measuring ≤ 100 μm. Total Suspended Particulate (TSP) contains a variety of heavy metal elements such as Pb and Cu which are harmful to the health of the surrounding community if exposed for a long time. TSP and heavy metals in Pb and Cu were analyzed to find out the amount of pollutant concentration and risk value to the respondents studied were the students of grade 1, grade 6, the teacher of SDN Pandean Lamper 01 and SDN Srondol Wetan 03, and compared the risk analysis result between the two primary The. The tools used in sampling TSP are High Volume Air Sampler (HVAS) and for measurement of Pb and Cu pollutants using ICP (Inductively Coupled Plasma). Carcinogenic risk level Cancer Risk Ingestion (CRing) The highest Pb in SDN Pandean Lamper of 1.85 x 10-6 is carcinogenic risk because it is within the limit of cancer risk tolerance that is 10-6-10-4, while the highest CRB Pb of SDN Srondol Wetan 03 is 7.05 x 10-7 is not at risk of cancer because it is below the cancer risk tolerance limit of 10-6-10-4. Cancer Risk Inhalation (CRinh) The highest Pb in SDN Pandean Lamper and SDN Srondol Wetan 03 of 5,002 x 10-10 and 1.9 x 10-10 are not at risk of cancer because it is below the cancer risk tolerance limit of 10-6-10-4. Highest Hazard Index (HI) in SDN Pandean Lamper and hazard Index (HI) highest Pb in SDN Srondol Wetan 0,971 and 0,289, no risk of non carcinogen because HI value 0,971 and 0,289 below non-carcinogen risk tolerance that is 1. Hazard Index HI) The highest Cu in SDN Pandean Lamper and SDN Srondol Wetan of 0.106 and 0.098, are not at risk of non-carcinogen because HI values of 0.106 and 0.098 below the non-carcinogen risk tolerance limits are 1. Keywords: Total suspended particulate (TSP), Pb metal, Cu metal, carcinogenic risk (CR), non- carcinogenic ris

Analyzing eventual leader election protocols for dynamic systems by probabilistic model checking

Leader election protocols have been intensively studied in distributed computing, mostly in the static setting. However, it remains a challenge to design and analyze these protocols in the dynamic setting, due to its high uncertainty, where typical properties include the average steps of electing a leader eventually, the scalability etc. In this paper, we propose a novel model-based approach for analyzing leader election protocols of dynamic systems based on probabilistic model checking. In particular, we employ a leading probabilistic model checker, PRISM, to simulate representative protocol executions. We also relax the assumptions of the original model to cover unreliable channels which requires the introduction of probability to our model. The experiments confirm the feasibility of our approach

Metabolic engineering of Escherichia coli for the biosynthesis of alpha-pinene

Background: alpha-Pinene is an important natural product that is widely used in flavorings, fragrances, medicines, fine chemicals and high-density renewable fuels. Currently, alpha-Pinene used in industry is mainly produced either by tapping trees (gum turpentine) or as a byproduct of paper pulping (crude sulfate turpentine, CST). However, the extraction of it from trees is tedious and inefficient and requires substantial expenditure of natural resources. Therefore, it is necessary to seek sustainable technologies for alpha-pinene production

Identification and validation of candidate genes associated with domesticated and improved traits in soybean

Soybean, an important source of vegetable oils and proteins for humans, has undergone significant phenotypic changes during domestication and improvement. However, there is limited knowledge about genes related to these domesticated and improved traits, such as flowering time, seed development, alkaline-salt tolerance, and seed oil content (SOC). In this study, more than 106,000 single nucleotide polymorphisms (SNPs) were identified by restriction site associated DNA sequencing of 14 wild, 153 landrace, and 119 bred soybean accessions, and 198 candidate domestication regions (CDRs) were identified via multiple genetic diversity analyses. Of the 1489 candidate domestication genes (CDGs) within these CDRs, a total of 330 CDGs were related to the above four traits in the domestication, gene ontology (GO) enrichment, gene expression, and pathway analyses. Eighteen, 60, 66, and 10 of the 330 CDGs were significantly associated with the above four traits, respectively. Of 134 traitassociated CDGs, 29 overlapped with previous CDGs, 11 were consistent with candidate genes in previous trait association studies, and 66 were covered by the domesticated and improved quantitative trait loci or their adjacent regions, having six common CDGs, such as one functionally characterized gene Glyma15 g17480 (GmZTL3). Of the 68 seed size (SS) and SOC CDGs, 37 were further confirmed by gene expression analysis. In addition, eight genes were found to be related to artificial selection during modern breeding. Therefore, this study provides an integrated method for efficiently identifying CDGs and valuable information for domestication and genetic research

Location Privacy and Its Applications: A Systematic Study

© 2013 IEEE. This paper surveys the current research status of location privacy issues in mobile applications. The survey spans five aspects of study: the definition of location privacy, attacks and adversaries, mechanisms to preserve the privacy of locations, location privacy metrics, and the current status of location-based applications. Through this comprehensive review, all the interrelated aspects of location privacy are integrated into a unified framework. Additionally, the current research progress in each area is reviewed individually, and the links between existing academic research and its practical applications are identified. This in-depth analysis of the current state-of-play in location privacy is designed to provide a solid foundation for future studies in the field