Energy Efficient RPL Routing Protocol in Smart Buildings

Energy is an important factor that must be considered by multi-hop wireless mesh routing protocols because most sensors are powered by batteries with a limited capacity. We focus on the industry-standard RPL (Routing Protocol over Low-power and lossy networks) routing protocol that must find energy-efficient paths in low-power and lossy networks. However, the existing RPL objective functions route based on hop-count and ETX (expected transmission count) metrics alone, ignoring the energy cost of data transmission and reception. We address this issue in two ways. First, we design an objective function for RPL that finds paths that require, in expectation, the minimum amount of energy. Second, we design a probing mechanism which configures the transmission power of sensors to minimize energy consumption. The proposed approach is implemented and evaluated using simulations as well as on a small testbed with two Zolertial Z1 motes

Emergency response network design for hazardous materials transportation with uncertain demand

Transportation of hazardous materials play an essential role on keeping a friendly environment. Every day, a substantial amount of hazardous materials (hazmats), such as flammable liquids and poisonous gases, need to be transferred prior to consumption or disposal. Such transportation may result in unsuitable events for people and environment. Emergency response network is designed for this reason where specialist responding teams resolve any issue as quickly as possible. This study proposes a new multi-objective model to locate emergency response centers for transporting the hazardous materials. Since many real-world applications are faced with uncertainty in input parameters, the proposed model of this paper also assumes that reference and demand to such centre is subject to uncertainty, where demand is fuzzy random. The resulted problem formulation is modelled as nonlinear non-convex mixed integer programming and we used NSGAII method to solve the resulted problem. The performance of the proposed model is examined with several examples using various probability distribution and they are compared with the performance of other existing method

Automatic summarisation of Instagram social network posts Combining semantic and statistical approaches

The proliferation of data and text documents such as articles, web pages, books, social network posts, etc. on the Internet has created a fundamental challenge in various fields of text processing under the title of "automatic text summarisation". Manual processing and summarisation of large volumes of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text summarisation systems are divided into extractive and abstract categories. In the extractive summarisation method, the final summary of a text document is extracted from the important sentences of the same document without any modification. In this method, it is possible to repeat a series of sentences and to interfere with pronouns. However, in the abstract summarisation method, the final summary of a textual document is extracted from the meaning and significance of the sentences and words of the same document or other documents. Many of the works carried out have used extraction methods or abstracts to summarise the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this work, a crawler has been developed to extract popular text posts from the Instagram social network with appropriate preprocessing, and a set of extraction and abstraction algorithms have been combined to show how each of the abstraction algorithms can be used. Observations made on 820 popular text posts on the social network Instagram show the accuracy (80%) of the proposed system

Adsorptive Removal of Noxious Nickel Ions from Aqueous Mediums Using Titanium Dioxide Nanoparticles: A Comparative Assessment with an Eco-friendly Adsorbent as Well as Isotherm and Kinetic Modeling

In the present study, natural and synthetic adsorbents were used to remove nickel ions through the adsorption process. First, TiO2 nanoparticles (NPs) were prepared through the sol-gel method. The synthesized samples were then characterized using X-ray diffraction spectroscopy (XRD), Fourier transform-infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and N2 adsorption/desorption isotherms (BET). The influences of different operational parameters including adsorbate content, pH, adsorbent concentration, contact time, ionic strength, and stirring speed were also explored. According to the results, the pseudo-second-order kinetic model showed the best performance in evaluating the experimental data when using both adsorbents. The adsorption of nickel cations by the thin film membrane on the surface of TiO2 NPs is a rate-determining step of the removal reaction. The removal rate constants of nickel ions from aqueous solutions by TiO2 NPs and pomegranate peel were evaluated to be 0.013 and 0.018 g mg-1 min-1, respectively. The thermodynamic parameters such as Gibbs free energy, enthalpy, and entropy were also determined. Nickel removal processes in all cases were endothermic and spontaneous. The removal mechanism also followed physical adsorption. Equilibrium data were fitted with Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich models. The results showed that the adsorption of Ni2+ on TiO2 NPs and pomegranate peel followed Freundlich and Temkin isothermal models, respectively. Based on the calculated removal percentage, TiO2 is a better adsorbent for removing Ni2+ from the aqueous medium as compared to pomegranate peel

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

Determining spatial and temporal changes of surface water quality using principal component analysis

Study region: Shahr Chai River, Lake Urmia basin, Iran. Study focus: The present study investigated the ability of the Principal Component Analysis (PCA) technique in pointing the environmental effects of discharges from different activities. Major indicator parameters were extracted for water quality analysis of the Shahr Chai River located in Lake Urmia basin, Iran. The water quality parameters were measured monthly in six stream reaches and were affected by discharges from intensive recreational centers and rural and agricultural activities. New hydrological insights: The results showed that the NSFWQI and the WQImin-p could not distinguish between highly impacted stream reaches, while the calculated WQImin-c with two parameters including turbidity and fecal coliforms could meaningfully classify the sampling stations. These two parameters were selected based on results from correlation matrix. This study showed that calculation of the WQImin-c was an effective and easily applicable assessment method for different effluents’ impacts on stream water quality. The PCA technique could justifiably show different landscape effects on river water quality whereby the river downstream was found to experience decreased water quality

Ethics and its Impact on Engineering Education Effectiveness in Knowledge Workers

Today, one of the fundamental challenges in engineering education organizations to improve the effectiveness of human resources is knowledge. Undoubtedly, the effectiveness of the process of engineering education in knowledge workers results the interaction of various factors. Since the effectiveness of engineering education in human resource knowledge categories is not necessarily an abstract, but it is the applied aspects of management. Any organization plays an important role in providing a proper ground for institutionalizing and promoting knowledge. In this respect, participation of human resources with knowledge is important. This article tries to first indicates the factors affecting the effectiveness of moral education in human resource knowledge of engineering research literature identified and are properly classified, secondly, the effectiveness of engineering education in human resource knowledge using process analysis network analysis are examined and strategies and ultimately improving the effectiveness of human resource knowledge in engineering education are presented. For such knowledge development the MTN Company tested and evaluated using structural validation methods and results are discussed