Automatic Road Extractions from High Resolution Satellite Imagery Using Road Intersection Model in Urban Areas

This paper proposes intersection model and strategy for road extraction from high resolution satellite images. Satellite images are rich in information. For Geographic Information System (GIS), many features require fast and reliable extraction of roads and intersections. They are also complex to analyze. Satellite image provides useful data that is extracted from satellite image of the urban area. Automatic extraction of the road intersections from the urban areas has been a challenging topic because the high resolution satellite images contain multiple layers that represent roads, buildings, and other high density objects. Our goals is to automatically separate the road layer from the other layers then extract the road intersections. Usually traditional image processing methods don't achieve satisfied performance in case of satellite images. This paper proposes a modified and a cost effective method for road extraction from high resolution satellites images. In order to find the precise road intersection of urban areas we have divided whole process into two sequential modules: first, extraction of road line using different Morphological direction filtering to automatically eliminate the other layers from road layer and finally, extraction of road intersections to determine the road orientation and interconnectivity. We applied this method to a set of randomly selected high resolution satellite image from urban and semi urban areas and the correctness of road network extraction reaches 95.71%, significantly higher than those of other existing road extraction methods

Simulation, Analysis and Performance Comparison among different Routing Protocols for Wireless Sensor Network

Wireless sensor network is a network of tiny, autonomous sensor nodes. Nodes of these networks functions as a hosts and routers which discovers and maintains the routes to other nodes in the network. In such networks, nodes are able to move and synchronize with their neighbors. Due to mobility, connections in the network can change dynamically and nodes can be added and removed at any time. In this paper, we are going to compare wireless sensor network's routing protocols AODV, DYMO and OLSR using network simulator NS-2.34. We have compared the performance of three protocols together. The performance matrix includes PDR (Packet Delivery Ratio), Throughput, End to End Delay, Normalized Routing Load. We are comparing the performance of routing protocols when number of nodes changes, when mobility of nodes changes. Here we basically emphasize to show the behavior of the protocols in different scenario, so that it becomes easier for the network designer to choose a specific protocol based on his/her needs. The comparison results suggest that different routing protocol performs well in different scenarios and good for specific performance metrics. For example, OLSR performs well in the network with strict requirement on time but doesn't perform well in high mobility environment whereas DYMO performs well in high mobility environment. AODV shows average behavior

Catalytical performance of heteroatom doped and undoped carbon-based materials

Developing cost-effective, eco-friendly, efficient, stable, and unique catalytic systems remains a crucial issue in catalysis. Due to their superior physicochemical and electrochemical properties, exceptional structural characteristics, environmental friendliness, economic productivity, minimal energy demand, and abundant supply, a significant amount of research has been devoted to the development of various doped carbon materials as efficient catalysts. In addition, carbon-based materials (CBMs) with specified doping have lately become significant members of the carbon group, showing promise for a broad range of uses (e.g., catalysis, environmental remediation, critical chemical production, and energy conversion and storage). This study will, therefore, pay attention to the function of heteroatom-based doped and undoped CBMs for catalytical applications and discuss the underlying chemistries of catalysis. According to the findings, doping CBMs may greatly improve their catalytic activity, and heteroatom-doped CBMs may be a promising option for further metal doping to attach them to an appropriate place. This paper also covers the potential applications of both doped and undoped CBMs in the future

Recent Advances in Centrifugal Spinning and Their Applications in Tissue Engineering

Over the last decade, researchers have investigated the potential of nano and microfiber scaffolds to promote wound healing, tissue regeneration, and skin protection. The centrifugal spinning technique is favored over others due to its relatively straightforward mechanism for producing large quantities of fiber. Many polymeric materials have yet to be investigated in search of those with multifunctional properties that would make them attractive in tissue applications. This literature presents the fundamental process of fiber generation, and the effects of fabrication parameters (machine, solution) on the morphologies such as fiber diameter, distribution, alignment, porous features, and mechanical properties. Additionally, a brief discussion is presented on the underlying physics of beaded morphology and continuous fiber formation. Consequently, the study provides an overview of the current advancements in centrifugally spun polymeric fiber-based materials and their morphological features, performance, and characteristics for tissue engineering applications

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression

Abstract: Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression

Abstract: Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

Background: Many patients with COVID-19 have been treated with plasma containing anti-SARS-CoV-2 antibodies. We aimed to evaluate the safety and efficacy of convalescent plasma therapy in patients admitted to hospital with COVID-19. Methods: This randomised, controlled, open-label, platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) is assessing several possible treatments in patients hospitalised with COVID-19 in the UK. The trial is underway at 177 NHS hospitals from across the UK. Eligible and consenting patients were randomly assigned (1:1) to receive either usual care alone (usual care group) or usual care plus high-titre convalescent plasma (convalescent plasma group). The primary outcome was 28-day mortality, analysed on an intention-to-treat basis. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936. Findings: Between May 28, 2020, and Jan 15, 2021, 11558 (71%) of 16287 patients enrolled in RECOVERY were eligible to receive convalescent plasma and were assigned to either the convalescent plasma group or the usual care group. There was no significant difference in 28-day mortality between the two groups: 1399 (24%) of 5795 patients in the convalescent plasma group and 1408 (24%) of 5763 patients in the usual care group died within 28 days (rate ratio 1·00, 95% CI 0·93–1·07; p=0·95). The 28-day mortality rate ratio was similar in all prespecified subgroups of patients, including in those patients without detectable SARS-CoV-2 antibodies at randomisation. Allocation to convalescent plasma had no significant effect on the proportion of patients discharged from hospital within 28 days (3832 [66%] patients in the convalescent plasma group vs 3822 [66%] patients in the usual care group; rate ratio 0·99, 95% CI 0·94–1·03; p=0·57). Among those not on invasive mechanical ventilation at randomisation, there was no significant difference in the proportion of patients meeting the composite endpoint of progression to invasive mechanical ventilation or death (1568 [29%] of 5493 patients in the convalescent plasma group vs 1568 [29%] of 5448 patients in the usual care group; rate ratio 0·99, 95% CI 0·93–1·05; p=0·79). Interpretation: In patients hospitalised with COVID-19, high-titre convalescent plasma did not improve survival or other prespecified clinical outcomes. Funding: UK Research and Innovation (Medical Research Council) and National Institute of Health Research

Mechanistic insight into antibiotics and endocrine disruptors removal by wood-derived biochar, functionalized biochar and biochar composite

University of Technology Sydney. Faculty of Engineering and Information Technology.Today water pollution has become one of the most critical issues in many regions of the world. The presence of antibiotics and endocrine disruptors (EDCs) in the aquatic environment causes critical problems to aquatic life and eventually on human. The efficacy for removing antibiotics (such as sulfamethazine (SMT), sulfamethoxazole (SMX), sulfathiazole (STZ) and chloramphenicol (CP)) and EDCs (such as estrone (E1), 17β-estradiol (E2), estriol (E3), 17α-ethynylestradiol (EE2), bisphenol A (BPA) and 4-tert-butylphenol (4tBP)) in traditional wastewater treatment processes is not satisfactory. However, adsorptive materials are cost effective and highly suitable for removal of different pollutants. Adsorptive materials such as biochar, functionalized biochar (fBC) and biochar composite with zero-valent-iron were prepared by the utilization of Australian abundantly grown woody biomasses (e.g. bamboo and eucalyptus wood). The materials were applied, for the first time, to remove antibiotics and EDCs, in both single and competitive modes, from water and wastewaters (e.g. synthetic wastewater, Lake Water and sewage sludge wastewater). Special focus was given to both single and competitive sorption performance based on experimental and theoretical findings. Sorption mechanism was determined based on pH profile, speciation, ionic strength, materials characteristics, temperature, thermodynamics, solution chemistry, and water matrixes. Different isotherm models such as the Langmuir isotherm model, Freundlich isotherm model, and Polani-Mane model were applied to analyse the sorption data. For competitive sorption, summarized isotherm models were proposed. Several kinetic models such as pseudo first order kinetic model, pseudo second order kinetic model, intra-particle diffusion model and Boyd model were also employed. Effects of different synthetic wastewater composition, ions and competitive solute were also studied. The findings suggested that antibiotics and EDCs sorption occurred mainly through pseudo-second order and external mass transfer diffusion processes, by forming different types of H-bonds along with π-π electron-donor–acceptor (EDA) interactions at different pH. More importantly specific direction of π-π EDA interaction was proposed based on 1ᴴ NMR spectroscopic study. fBC was regenerated and reused several times after treatment of different water. Therefore, fBC can be a potent sorbent to adsorb a wide varieties of organic contaminants from water and wastewater

Real time speaker identification: an approach to the implementation of real time speaker identification system by using artificial neural network

This book presents the real time speaker identification system using artificial neural network. This system takes the speech signal as its input. The input signal is then filtered to remove noise. After preprocessing, frame blocking and windowing the features are obtained using signal processing techniques. The extracted feature is then fed to the multilayer back propagation neural network as input. Thus the network is trained and creates a knowledge base for identification. The same procedures are applied for identification but the only difference is that the neural network uses the previously learned weights to calculate the output