Determination of Aerodynamic Forces and Control Requirement during Ground Effect

The current work delivers the effect of ground proximity on longitudinal aerodynamic forces and the elevator deflection required for trimming the aircraft. The academic understanding states that the with the increasing proximity to the ground,downwash progressively reduces.The methodology adopts the approximation of linear decrement for the estimation of the residual downwash at any height above ground within the unit wingspan. The proposed method utilizes non-dimensional height above ground for the approximation of effective downwash angle in the presence of ground. The approximately predicted effective downwash angle is subsequently utilized to estimate the effect of ground proximity on longitudinal forces, trim angle of attack, and elevator deflection required for trimming the aircraft. The behavior of estimated aerodynamic forces,i.e., lift and drag with the dimensionless height above ground is presented for displaying the variation of forces between outside and inside ground effect regime. The conventional method of estimating longitudinal aerodynamic forces during any flight phase employ equations of motion in terms of recorded flight data. A comparative analysis of longitudinal aerodynamic forces estimated by both methods is presented for assessing the efficacy of the proposed methodology

Determination of Parameters during Quasi-Steady Stall Maneuver Using Genetic Algorithm

The current work offers the determination of longitudinal aerodynamic derivatives during flight manoeuver at angles of attack near the stall. The flight manoeuver near stall is highly non-linear in nature due to separated flow at such elevated angles of attack. Kirchoff’s model for Quasi-Steady Stall Modelling (QSSM) is employed to represent the non-linear nature of aerodynamics during flight manoeuver at elevated angles of attack close to the stall. The Genetic Algorithm (GA) optimized output error method is utilized for estimating the parameters specific to stall characteristics and longitudinal aerodynamics of the ATTAS aircraft. The comparative evaluation of the parameter estimates with the estimates obtained by using Maximum Likelihood technique is employed to assess the efficacy of the proposed method for highly non-linear applications. The comparative assessment of the estimates along with robust statistical analysis evidence that the proposed method can be a suitable parameter estimation alternative method for non-linear application

Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model

According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model’s training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods

A comparative numerical analysis on the effect of welding consumables on the ballistic resistance of SMAW joints of armor steel

In the present investigation, a comparative study of ballistic impact behavior of Armox 500T (base metal) and its weldments prepared by low hydrogen ferrite (weldment-1) and austenitic stainless steel (weldment-2) consumables against 7.62 AP bullet has been performed with the help of finite element analysis code Abaqus 2017. Further, the result is validated with the experimental results. The experiment has been performed on the base metal, weldment-1, and weldment-2 against 7.62 AP bullet. Further, a two-dimensional explicit model has been developed for given purpose to simulate the bullet penetration at such high strain rate (103 s−1). Both bullet and plate are considered as deformable. Experimental results revealed that the depth of penetration in the base metal, weldment-1, and weldment-2 is 10.93, 13.65, and 15.20 mm respectively. Further computational results revealed that the depth of penetration of base metal, weldment-1, and weldment-2 is 10.11, 12.87, and 14.60 mm, respectively. Furthermore, weldment-1 shows more resistance against 7.62 AP bullet than weldment-2 in experimentation as well as FEA results. The percentage difference between experimental and FEA results are less than 10% which shows the prediction capability of FEA models. A feasibility analysis has been presented for using the welding consumables to weld the Armox 500T plate. Finally, in terms of ballistic resistance, the low hydrogen ferrite consumables are more appropriate than austenitic stainless-steel electrodes

Global network of computational biology communities: ISCB's regional student groups breaking barriers [version 1; peer review: Not peer reviewed]

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”.Fil: Shome, Sayane. University of Iowa; Estados UnidosFil: Parra, Rodrigo Gonzalo. European Molecular Biology Laboratory; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fatima, Nazeefa. Uppsala Universitet; SueciaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Cuypers, Bart. Universiteit Antwerp; BélgicaFil: Moosa, Yumna. University of KwaZulu Natal; SudáfricaFil: Da Rocha Coimbra, Nilson. Universidade Federal de Minas Gerais; BrasilFil: Assis, Juliana. Universidade Federal de Minas Gerais; BrasilFil: Giner Delgado, Carla. Universitat Autònoma de Barcelona; EspañaFil: Dönertaş, Handan Melike. European Molecular Biology Laboratory. European Bioinformatics Institute; Reino UnidoFil: Cuesta Astroz, Yesid. Universidad de Antioquia; Colombia. Universidad Ces. Facultad de Medicina.; ColombiaFil: Saarunya, Geetha. University of South Carolina; Estados UnidosFil: Allali, Imane. Universite Mohammed V. Rabat; Otros paises de África. University of Cape Town; SudáfricaFil: Gupta, Shruti. Jawaharlal Nehru University; IndiaFil: Srivastava, Ambuj. Indian Institute of Technology Madras; IndiaFil: Kalsan, Manisha. Jawaharlal Nehru University; IndiaFil: Valdivia, Catalina. Universidad Andrés Bello; ChileFil: Olguín Orellana, Gabriel José. Universidad de Talca; ChileFil: Papadimitriou, Sofia. Vrije Unviversiteit Brussel; Bélgica. Université Libre de Bruxelles; BélgicaFil: Parisi, Daniele. Katholikie Universiteit Leuven; BélgicaFil: Kristensen, Nikolaj Pagh. Technical University of Denmark; DinamarcaFil: Rib, Leonor. Universidad de Copenhagen; DinamarcaFil: Guebila, Marouen Ben. University of Luxembourg; LuxemburgoFil: Bauer, Eugen. University of Luxembourg; LuxemburgoFil: Zaffaroni, Gaia. University of Luxembourg; LuxemburgoFil: Bekkar, Amel. Universite de Lausanne; SuizaFil: Ashano, Efejiro. APIN Public Health Initiatives; NigeriaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Moreyra, Nicolás Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

Deciphering RNA-Recognition Patterns of Intrinsically Disordered Proteins

Intrinsically disordered regions (IDRs) and protein (IDPs) are highly flexible owing to their lack of well-defined structures. A subset of such proteins interacts with various substrates; including RNA; frequently adopting regular structures in the final complex. In this work; we have analysed a dataset of protein–RNA complexes undergoing disorder-to-order transition (DOT) upon binding. We found that DOT regions are generally small in size (less than 3 residues) for RNA binding proteins. Like structured proteins; positively charged residues are found to interact with RNA molecules; indicating the dominance of electrostatic and cation-π interactions. However, a comparison of binding frequency shows that interface hydrophobic and aromatic residues have more interactions in only DOT regions than in a protein. Further; DOT regions have significantly higher exposure to water than their structured counterparts. Interactions of DOT regions with RNA increase the sheet formation with minor changes in helix forming residues. We have computed the interaction energy for amino acids–nucleotide pairs; which showed the preference of His–G; Asn–U and Ser–U at for the interface of DOT regions. This study provides insights to understand protein–RNA interactions and the results could also be used for developing a tool for identifying DOT regions in RNA binding proteins

Deciphering RNA-Recognition Patterns of Intrinsically Disordered Proteins

Intrinsically disordered regions (IDRs) and protein (IDPs) are highly flexible owing to their lack of well-defined structures. A subset of such proteins interacts with various substrates; including RNA; frequently adopting regular structures in the final complex. In this work; we have analysed a dataset of protein–RNA complexes undergoing disorder-to-order transition (DOT) upon binding. We found that DOT regions are generally small in size (less than 3 residues) for RNA binding proteins. Like structured proteins; positively charged residues are found to interact with RNA molecules; indicating the dominance of electrostatic and cation-π interactions. However, a comparison of binding frequency shows that interface hydrophobic and aromatic residues have more interactions in only DOT regions than in a protein. Further; DOT regions have significantly higher exposure to water than their structured counterparts. Interactions of DOT regions with RNA increase the sheet formation with minor changes in helix forming residues. We have computed the interaction energy for amino acids–nucleotide pairs; which showed the preference of His–G; Asn–U and Ser–U at for the interface of DOT regions. This study provides insights to understand protein–RNA interactions and the results could also be used for developing a tool for identifying DOT regions in RNA binding proteins