TyG index and insulin resistance in beta-thalassemia

Insulin resistance (IR) underlies some glucose metabolism abnormalities in thalassemia major. Recently, triglyceride glucose index (TyG) has been proposed for evaluating insulin resistance as a simple, low cost, and accessible tool. In this study, the TyG index were studied for IR monitoring in beta-thalassemia major (βTM) patients. The participants were 90 βTM patients on chronic regular transfusion therapy. The TyG index was computed based on fasting plasma glucose (FPG) and triglyceride (TG). The time gap between the first and the second TyG index survey (TyG.1 and TyG.2) was 2 years. The agreement between TyG and HOMA-IR were studied with the extension of limit of agreement (LOA). We included 90 patients 53.3 % men (n = 48). Among them, 14.4 % (14.6 % male, 14.3 % female) had impaired fasting glucose level (e.g., 100–125 mg/dl) at first test. It rose to 37.8 % (27.1 % male, 50 % female) during 2 years. Based on TyG.1, the 34.4 % of patients was detected as IR cases. After 2 years, the percent of IR based on TyG.2 was 82.2 %. The mean differences between TyG.1 and TyG.2 and their differences from the considered cutoff values were significant (P < 0.001). The prediction limits between TyG and HOMA-IR had good agreement. These data may suggest the use of TyG index for detection/monitoring of IR in βTM patients. © 2015, Research Society for Study of Diabetes in India

Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks

A mobile ad hoc network (MANET) is a group of nodes constituting a network of mobile nodes without predefined and pre-established architecture where mobile nodes can communicate without any dedicated access points or base stations. In MANETs, a node may act as a host as well as a router. Nodes in the network can send and receive packets through intermediate nodes. However, the existence of malicious and selfish nodes in MANETs severely degrades network performance. The identification of such nodes in the network and their isolation from the network is a challenging problem. Therefore, in this paper, a simple reputation-based scheme is proposed which uses the consumption and contribution information for selfish node detection and cooperation enforcement. Nodes failing to cooperate are detached from the network to save resources of other nodes with good reputation. The simulation results show that our proposed scheme outperforms the benchmark scheme in terms of NRL (normalized routing load), PDF (packet delivery fraction), and packet drop in the presence of malicious and selfish attacks. Furthermore, our scheme identifies the selfish nodes quickly and accurately as compared to the benchmark scheme

Temperature Dependent Piezoelectric Properties of Lead-Free (1-x)K0.6Na0.4NbO3–xBiFeO3 Ceramics

(1-x)K0.4Na0.6NbO3–xBiFeO3 lead-free piezoelectric ceramics were successfully prepared in a single perovskite phase using the conventional solid-state synthesis. Relative permittivity (εr) as a function of temperature indicated that small additions of BiFeO3 not only broadened and lowered the cubic to tetragonal phase transition (TC) but also shifted the tetragonal to orthorhombic phase transition (TO–T) toward room temperature (RT). Ceramics with x = 1 mol.% showed optimum properties with small and large signal piezoelectric coefficient, d33 = 182 pC/N and d∗33 = 250 pm/V, respectively, electromechanical coupling coefficient, kp = 50%, and TC = 355°C. kp varied by ∼5% from RT to 90°C, while d∗33 showed a variation of ∼15% from RT to 75°C, indicating that piezoelectric properties were stable with temperature in the orthorhombic phase field. However, above the onset of TO–T, the properties monotonically degraded in the tetragonal phase field as TC was approached

A recurrent-neural-network-based generalized ground-motion model for the Chilean subduction seismic environment

This paper proposes a deep learning-based generalized ground motion model (GGMM) for interface and intraslab subduction earthquakes recorded in Chile. A total of ∼7000 ground-motion records from ∼1700 events are used to train the proposed GGMM. Unlike common ground-motion models (GMMs), which generally consider individual ground-motion intensity measures such as peak ground acceleration and spectral accelerations at given structural periods, the proposed GGMM is based on a data-driven framework that coherently uses recurrent neural networks (RNNs) and hierarchical mixed-effects regression to output a cross-dependent vector of 35 ground-motion intensity measures (denoted as IM). The IM vector includes geometric mean of Arias intensity, peak ground velocity, peak ground acceleration, and significant duration (denoted as Iageom, PGVgeom, PGAgeom, and D5-95geom, respectively), and RotD50 spectral accelerations at 31 periods between 0.05 and 5 s for a 5 % damped oscillator (denoted as Sa(T)). The inputs to the GGMM include six causal seismic source and site parameters, including fault slab mechanism, moment magnitude, closest rupture distance, Joyne-Boore distance, soil shear-wave velocity, and hypocentral depth. The statistical evaluation of the proposed GGMM shows high prediction power with R2 > 0.7 for most IMs while maintaining the cross-IM dependencies. Furthermore, the GGMM is carefully compared against two state-of-the-art Chilean GMMs, showing that the proposed GGMM leads to better goodness of fit for all periods of Sa(T) compared to the two considered GMMs (on average 0.2 higher R2). Finally, the GGMM is implemented to select hazard-consistent ground motions for nonlinear time history analysis of a sophisticated finite-element model of a 20-story steel special moment-resisting frame. Results of this analysis are statistically compared against those for hazard-consistent ground motions selected based on the conditional mean spectrum (CMS) approach. In general, it is observed that the drift demands computed using the two approaches cannot be considered statistically similar and the GGMM leads to higher demands

A Deep Learning based Generalized Ground Motion Model for the Chilean Subduction Seismic Environment

This paper proposes a deep learning-based generalized ground motion model (GGMM) for interface and inslab subduction earthquakes recorded in Chile. A total of ~7000 ground-motion records from ~1700 events are used to train the GGMM. Unlike common ground-motion models (GMM), which generally consider individual ground-motion intensity measures such as spectral acceleration at a given period, the proposed GGMM is a data-driven framework that coherently uses recurrent neural networks (RNN) and hierarchical mixed-effects regression to output a cross-dependent vector of 35 ground-motion intensity measures (IM). The IM vector includes geomean of Arias intensity, peak ground velocity, peak ground acceleration, and significant duration, and RotD50 spectral accelerations at 32 periods between 0.05 to 5 seconds (denoted as Sa(T)). The inputs to the GMM include six causal seismic source and site parameters. The statistical evaluation of the proposed GGMM shows that the proposed framework results in high prediction power with coefficient of determination R2 > 0.7 for most IMs while maintaining the cross-IM dependencies. Furthermore, it is observed that the proposed GGMM leads to better goodness of fit for all periods of Sa(T) compared to two state-of-the-art Chilean GMMs (on average 0.2 higher R2)

ANALISIS MODEL BISNIS DAN PERANCANGAN PENYEWAAN MOBIL E-RENTAL DI PAKAR HOLIDAY BANDUNG

Di era modern ini masih banyak ditemukan perusahaan rental mobil yang tidak memanfaatkan teknologi informasi dan internet untuk sebagai alat bantu dalam mempercepat aktifitas, dan memberi ruang untuk seseorang dalam memenuhi kebutuhan sehari-hari dalam kemajuan bisnisnya. Untuk mengatasi masalah tersebut, penulis melakukan penelitan dan memberikan solusi dalam perancangan penyewaan mobil e-rental di pakar holiday bandung berbasasis web yang bertujuan untuk mempermudah pelanggan dan rental mobil dalam bertukar informasi secara cepat, tepat dan akurat pada rental mobil Pakar Holiday Bandung. Penelitian ini menggunakan metode Unified Modeling Language (UML), Work System Framework (WSF), dan Work System Participant (WSP). Berdasarkan penelitian, dapat diketahui bahwa analisis dari sistem yang berjalan ditemukan kurangnya pemanfaatkan teknologi informasi secara optimal. Walaupun terdapat website mengenai rental mobiL, pelanggan harus tetap datang langsung ke tempat rental mobil untuk pemrosesan penyewaan lebih lanjut, hal ini terjadi karena website tersebut hanya berfungsi sebagai media informasi mengenai rental mobil dan harga sewa saja. Sebagai rekomendasi, perusahaan rental mobil membutuhkan perangkat lunak yang dapat mengelola data rental mobil, pemesanan dan pembayaran yang terkomputasi melalui website. Rancangan aplikasi yang sudah di lakukan menggambarkan hubungan aktor dengan sistem dan prototype aplikasi. Kata Kunci : E-Rental Mobil, Perancangan Penyewaan Mobil, Website, Work System Framework, Work System Participan

Community engagement as a tool to help deliver smart city innovation: a case study of Nottingham, United Kingdom

Cities are complex urban conurbations and facing many challenges. The majority of the world’s population now live in cities and consume 80% of the resources. 'Smart City' innovation is emerging as a major response to the challenges cities are facing. Much of the focus remains on technological interventions, but technology alone may not be sufficient to reach smart and sustainable city goals. Cities are made up of people who have influence and are therefore key stakeholders in the development of smart city innovation and cannot be ignored. This paper aims to explore community engagement in Nottingham to help deliver smart city innovation and the way Nottingham City Council is engaging local communities in its smart projects. The paper analyses the community engagement strategy of Nottingham developed as part of the EU funded smart city project, REMOURBAN (REgeneration MOdel for accelerating the smart URBAN transformation). The main drivers and barriers to effective community engagement are identified in the smart city context. This exploratory study adopted a case study strategy and qualitative research methods. The data was collected through thirteen semi-structured interviews with middle and senior managers in Nottingham City Council and other stakeholder organisations in the city and a focus group of five community leaders from three local community groups. The content analysis of the REMOURBAN documents related to citizen engagement and the council’s energy strategies and policies was carried out. The key results are discussed with recommendations to nurture effective community engagement as a smart city tool and conclusions are drawn

p-type/n-type behaviour and functional properties of KxNa (1-x)NbO3 (0.49 <= x <= 0.51) sintered in air and N2

Abstract Potassium sodium niobate (KNN) is a potential candidate to replace lead zirconate titanate in sensor and actuator applications but there are many fundamental science and materials processing issues to be understood before it can be used commercially, including the influence of composition and processing atmosphere on the conduction mechanisms and functional properties. Consequently, KNN pellets with different K/Na ratios were sintered to 95% relative density in air and N2 using a conventional mixed oxide route. Oxygen vacancies (VO..) played a major role in the semi-conduction mechanism in low p(O2) for all compositions. Impedance spectroscopy and thermo-power data confirmed KNN to be n-type in low p(O2) in contradiction to previous reports of p-type behaviour. The best piezoelectric properties were observed for air- rather than N2-sintered samples with d33=125 pC/N and kp=0.38 obtained for K0.51Na0.49NbO3

Tripartite symbiosis of Lentil (Lense culinaris L.), Mycorrhiza and Azospirillum brasilense under Rainfed Condition

A field experiment was conducted aiming to determine the possibility of improving the lentil performance when co-inoculated with Vesicular Arbuscular Mycorrhiza (VAM) fungi and Azospirillum under natural rain-fed conditions, in Iran. Results showed the substantial impact of VAM fungi on grain protein, root colonization and shoot dry weight. Highest value for shoot dry weight recorded in plants which inoculated with G. intraradices and highest values for root colonization and grain protein content was observed in plants which inoculated with G. mosseae. Also, Azospirillum had a significant effect on shoot dry weight and root colonization. A significant differences on grain protein content observed when combination of both microorganisms have been used

Impact of high pressure homogenization on physical properties, extraction yield and biopolymer structure of soybean okara

The effect of high pressure homogenization (HPH) on soy okara was studied. To this purpose, okara dispersions (10 g/100 g) were subjected to 1 pass at 50, 100 and 150 MPa and to 5 passes at 150 MPa. Samples were analyzed for stability, particle size, microstructure, and viscosity. Results highlighted that the increase of HPH intensity was associated with the structural disruption of okara particles, leading to physically stable homogenates having increasing viscosity. This was mainly attributed to an increase in okara solubility, due to fibre and protein release. The latter resulted almost complete, reaching values up to 90% of the protein originally entrapped in okara matrix. Absorbance at 280 nm, SH groups and dimension of proteins revealed that HPH treatments favoured the extraction of the main protein fractions even if, at the higher intensity level, extracted proteins probably underwent conformational changes and reassembling phenomena