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Climate change and air pollution have important societal consequences, especially in emerging economies, wherein transitions from polluting technologies to cleaner alternatives coincide with high population vulnerability to environmental threats. India is home to a fifth of the world’s population and a gamut of human activities, employing a far ranging spectrum of technologies and fuels, with consequent emissions. Atmospheric fine particles or aerosols in the region predominate in carbonaceous constituents and dust. Multi-institutional studies in the region have earlier focused on natural and anthropogenic climate forcing by aerosols and feedbacks on regional and global climate. Important gaps remain in understanding human activities influencing emissions, emission aerosol properties, and regional atmospheric processes, specifically those related to carbonaceous aerosol impacts on climate and air quality. With an aim to address these gaps, the COALESCE (Carbonaceous Aerosol Emissions, Source Apportionment and Climate Impacts) project was launched on 7 July 2017. The project adopts integration of scientific methods developed by both the climate and air quality research communities. New fundamental knowledge from the project and strong links to India’s policy framework would enable climate and clean-air action in the region. The article describes the scientific rationale, objectives, and planned activities under COALESCE to explore engagement with the international climate and air quality research communities and to enable eventual dissemination of research findings, knowledge products, and decision-support tools.

The fatigue crack growth kinetics in a 2024-T3 aluminum alloy during constant- and variable-amplitude loading a various stress ratios in the medium-amplitude segment of a fatigue crack growth rate curve is studied. Variable-amplitude loading is represented by quasi-random samples of standard random loading spectra, which were preliminarily schematized using a rainflow algorithm. Parameters are proposed to estimate the character of random loading. The influence of the character of random loading using various standard spectra on the fatigue crack growth rate is analyzed. The fatigue crack growth time is estimated with allowance for fatigue crack closure and the character of random loading using a proposed formula and a cycle-by-cycle calculation method.

The experimental data on the fatigue lives of crack growth that were obtained in tests of aluminum specimens with overloads and underloads in the region of near-threshold crack growth rates are compared. The fatigue crack growth time is predicted using various models, including those embedded in the NASGRO and FASTRAN software packages, in order to reveal the model that is the best for calculating the fatigue life. The results obtained are compared with the prediction of a proposed combined model, which takes into account crack closure and the local stresses in the crack mouth and is based on the Neuber and Ramberg–Osgood equations. The efficiencies of the existing and proposed models used to describe the growth of fatigue cracks during random loading are estimated.

D31 cluster is located in the prolific Mumbai Offshore Basin, Western part of India. B-192Afield is part of this cluster, where Bassein (Middle Eocene),Mukta and Panvel (Early Oligocene)Formations are the main reservoirs. The reservoirs are complex in terms of reservoir heterogeneity. They were deposited in a shallow marine carbonate platform.Sea level fluctuations andchange in depositional environment impacted the porosity development. The main objective of this study was to integrate spectral gamma ray signatures with seismic interpretation for demarcating significant stratigraphic surfaces and differentiating depositional environments for robust reservoir characterization. Regionally, Bassein Formation (Middle Eocene) is characterized by thick foraminiferal and algal wackestone, packstone and occasional grainstone facies.The Mukta Formation (Early Oligocene),which unconformably overlies the Bassein Formation, is characterized by presence of fossiliferous limestone with shale intercalations. In the present study, data from four exploration wells data have been analyzed, where spectral gamma ray log patterns in carbonate reservoirs appear to have a distinctive relationship to depositional facies and stratigraphic surfaces in the Bassein and Mukta Formations. Different cross plots have also been utilized for analyzing the depositional conditions (i.e. oxic or anoxic).Later, the spectral log interpretations have been integrated with seismic interpretation. This study is part of a larger effort for reservoir characterization, as a basis for seismic interpretation and integrated reservoir modelling. The spectral gamma ray signatures demarcated significant stratigraphic surfaces. In BasseinFormation, three different units have been marked as Upper, Middle and Lower Bassein. The major lithological boundary between the Bassein and Mukta Formation is also well demarcated with spectral GR signature. The carbonate strata of Bassein & Mukta Formation have also been subdivided with U-Th-K abundance.The "Low Th-Low U" units indicative of pure carbonate and deposition in oxidizing environment whereas "Low Th-High U"is indicative ofreducing environment, which gave a relative sea level fluctuation in the area.The major stratigraphic boundaries identified from these spectral GR logs has been incorporated in the seismicinterpretation and used for regional seismic mapping.As porosity development is governed by thesea level fluctuations,this study also gave an indication of the possibility of porous zonein the reservoir section. These results can be useful as a basis for applying spectral GR signature as a tool for stratigraphic interpretation in un-cored heterogenous carbonate sections. Along with the petrophysical interpretation, integration of core analysis, biostratigraphy and seismic attribute are critical for detailed carbonate reservoir characterization incorporating depositional environment.This approach can be applied to support commercial development of the complex carbonate reservoirs.

A long-overlooked aspect of fatigue crack growth is the potential contribution to it, of stable crack extension (SCE). Reduction in specimen size and increase in magnitude of cyclic loading will induce increased contribution of SCE. SCE as a load interaction effect is manifest in disproportionately high crack extension due to periodic overloads. SCE can exceed by more than an order of magnitude estimates of crack growth from the da/dN versus DK relationship. Simple equations are proposed to account for SCE in fatigue crack growth. A numerical analysis is performed to characterize the significance of SCE to constant amplitude and variable amplitude fatigue crack growth.

The results of overloading and underloading tests of aluminum specimens in near-threshold crack growth rate range are considered. A combined model is proposed to take into account crack closure and the influence of the local stresses at the crack mouth. The model is based on the Neuber and Ramberg–Osgood equations and can be used to estimate the fatigue life of crack growth in the near-threshold range. This model is grounded from a physical standpoint.

The offshore Lakshmi Field, located in the southern part of Cambay Basin in the CB/OS-2 block in western India, is a success story of transitioning from a plateauing gas field to an oil-producing asset. Hydrocarbons sourced from Hazira Shale at Lakshmi Field are trapped in inversion-related four-way dip closures. The reservoirs are within the Miocene-age Tarkeshwar and overlying Babaguru formations; both have excellent reservoir quality with porosity ranging from 25% to 30% and Darcy-scale permeability. During the initial exploration stage, presence of oil was established in the deeper lower Tarkeshwar sands. However, the full oil potential of the field was not realized in the early stage of development due to difficulty in characterizing laterally and vertically discontinuous thin sands. These sands are often not detected in conventional 3D seismic data due to severe attenuation-related masking by overlying thick gas sands. Given the uncertainty of the oil potential of the field, a revised look using advanced seismic technologies was undertaken to drive a new infill drilling campaign. Seismic reprocessing using Q-tomographic inversion was performed to address the gas-attenuation problem, resulting in an improved seismic image. The new seismic data enabled characterization of thin reservoir sands using analytical techniques such as spectral decomposition and amplitude-variation-with-offset (AVO) attribute analysis. An integrated reservoir-characterization approach using field analogs, interpretation within a sequence stratigraphic framework, concepts of seismic geomorphology, and quantitative seismic analysis helped identify optimal areas of the sand fairway for infill well placement. Several infill wells have since been drilled that encountered good pay zones, validating the integrated approach. This successful campaign doubled oil production at Lakshmi and extended the field life by increasing reserves.

Currently, among other models for crack growth prediction, crack closure models that consider the decrease in stress intensity factor (SIF) range associated with cyclic loading asymmetry are more popular. One of drawbacks of these models is impossibility of considering the loading history sequence. The theory related threshold SIF range and ΔKth and local near-tip stresses, induced by overloads, and postulated that the overload effect in the near-threshold region of growth rates is caused by residual local stresses. The proposed model applies the local stress and strain approach to estimate stress σ* in the stress concentration region for fatigue analysis. This region is characterized by local near-tip stress σ*, whose amplitude is determined by cyclic inelastic reaction at crack tip. Further development of the model is due to varying nature of the threshold SIF range ΔKth. As a basis, the Forman-Mettu formula was adopted, which describes the fatigue crack growth curve in all three regions of fatigue crack growth rate. The range ΔK was determined by the peak load ΔP of the load history. The crack closure was considered by the Schijve equation, considering the asymmetry of the half-cycle U = f(R), and effective SIF was estimated by ΔKeff = ΔK*U. Given known SIF range ΔK, the value of the local stress σ* at distance from the crack tip r* was determined for each half cycle by Neuber and Ramberg-Osgood equations, and threshold SIF was estimated from the analytical formula of Кth=f(σ). Thus, known loading history made it possible to determine ΔKeff, Kmax, and Kth on each cycle for fatigue life estimation. Mathematical modeling of fatigue crack growth life, especially in near-threshold region of its growth, according to the Sunder’s scheme, showed that investigated aluminum alloy 2024-T3 exhibited crack growth sensitivity to various types of force action, including various types of random loading.

Leaves are part of plants that interface with the climate and deal with their fundamental requirements. In our lives, healthy plants are important and used for many purposes throughout life. Plant diseases represent a significant environmental concern. A big hurdle is the detection of leaf diseases in large fields. Farmers face great problems in handling different types of leaf diseases. Plant biologist assist cultivators to recognize leaf diseases through agricultural labs or by observing visual features. These approaches cannot be suitable for all cultivators due to the cost of experts and the lack of availability of labs. High performance rate of Convolutional neural networks plays a significant role in detecting leaf diseases. This survey demonstrates various Convnets to assorts leaf syndromes. It also performs an important role in information collection to improve accuracy.

Image segmentation plays a vital role in the field of medical imaging as it renders better diagnosis and early detection of diseases. The brain is one of the most important organs in the human body as it is responsible for all the actions and reactions in our body. It is the key factor that distinguishes us from other animals. Therefore, brain imaging is important in medical imaging because it helps the doctors to examine and understand the interior of the brain which is active. Magnetic Resonance Imaging is one of the brain imaging techniques. Brain image segmentation is used for measuring and anticipating brain anatomy so that we can figure out any changes in it. The brain tumor is any abnormal or uncontrolled growth of cells in our brain. This project illustrates the application of fuzzy logic in medical imaging, mainly for image segmentation. This uses FCM clustering for providing effective segmentation of blurred boundary areas of the brain. Thus, it segments any abnormalities in the MRI images. This work also uses morphological operations to detect the presence of any brain tumor. The segmentation and analysis of brain anatomy with the help of brain MRI images is the prime objective of this work.

The unexpected outbreak of the new infectious disease called the novel coronavirus 2019 has a great impact on public health worldwide. The virus has challenged the healthcare systems globally, and it remains a mystery in front of them. Thus, there is a need to study how this deadly disease has been spread across the world in such a rapid phase. This study is beneficial to the industries that are affected by the current situation by making certain observations while considering the COVID-19 outbreak cases. It also helps these industries in taking the needed precautionary measures in the future if such a global pandemic situation arises. An interactive visual representation of this task efficiently and effectively done is more understandable and presentable. This study gives you a clear idea of COVID-19, Visual Analytics and its application in the field of public health, especially in the case of COVID-19. The main scope of this review is to create awareness among the community about the outbreak and spread of this contagious disease using the visual analytics approach.

In atmospheric fatigue, intrinsic threshold stress intensity, \(\Delta\)Kth,i, far from being a material constant, varies significantly. It is controlled by a certain computable, load-history sensitive, near-tip residual stress, σ*, that appears to moderate crack-tip diffusion kinetics and thereby determines ∆Kth,i, the incremental K required for an open crack to extend by fatigue. This demands reconsideration of the relationship between near-threshold crack growth rates and applied effective stress intensity range, ∆Keff. Its practical implementation requires considerations of Linear Hysteretic Fracture Mechanics (LHFM). LHFM is applied to translate applied K, to near-tip cyclic inelastic stress–strain response. The new relationships permit extension of fracture mechanics considerations to short cracks including defects in additive manufactured materials and naturally forming cracks in components over high-cycle and very high-cycle (HCF/VHCF) regimes. Most importantly, the new approach permits handling near-threshold fatigue response both under constant amplitude as well as under variable-amplitude loading, representative of actual service conditions. It also permits re-interpretation of test data obtained by the so-called cyclic R-curve and compression-compression pre-cracking techniques.

Elections help in finding out the true leaders and the public’s opinion. Very keen importance must be given to the act of conducting elections. But nowadays, the opinion of the public is not being obtained, since many fraudulent acts do exists while voting. Taking in consideration this problem, we developed the SECURED BALLOTING SYSTEM which helps to check the identity and verify it very clearly using the three phase biometric verification process. This helps in maintaining the democracy of the nation., {"references":["Baig, S., Ishtiaq, U., Kanwal, A., Ishtiaq, U., & Javed, M. H. (2011, January). Electronic voting system using fingerprint matching with Gabor filter. In Proc. Int. Bhurban Conf. Appl. Sci. Echnol. Islamabad, Pakistan (pp. 130-135).","Najam Syed, S., Zeb Shaikh, A., & Naqvi, S. (2018). A Novel Hybrid Biometric Electronic Voting System: Integrating Finger Print and Face Recognition. arXiv-1801.","Alausa, D. W. S., & Akingbade, L. O. (2017). Electronic Voting: Challenges and Prospects in Nigeria's Democracy. Electronic Voting: Challenges and Prospects in Nigeria's Democracy, 6(5), 67-76.","Saxena, P., Prakash, S., & Pandey, P. (2017). Design of Biometric Electronic Voting Machine. International Journal of Advance Research, Ideas and Innovations in Technology, 3(6), 211- 214.","Blanz, V., & Vetter, T. (2003). Face recognition based on fitting a 3d morphable model. IEEE Transactions on pattern analysis and machine intelligence, 25(9), 1063-1074.","M. Blumenthal. (2018). Trump Approval Poll. Accessed: Jan. 31, 2019. [Online]. Available: https://www.surveymonkey.com/curio sity/trump-approval-poll/","Murty, P. C., & Reddy, E. S. (2009). Iris recognition system using principal components of texture characteristics. TECHNIA-Int. J. Computing Science and Communication Technologies, 2(1), 343-348.","Clarkson, M. R., Chong, S., & Myers, A. C. (2008, May). Civitas: Toward a secure voting system. In 2008 IEEE Symposium on Security and Privacy (sp 2008) (pp. 354-368). IEEE."]}

Nowadays, illegal practices of voting happens, mainly the illiterate people are being cheated. Thus, affecting the democracy of the nation. We need to ensure that the rights of each and every voter have to be ensured by providing a tight security and an efficient way of voting method. To overcome this scenario, we proposed the secured balloting system. This system is implemented by verifying the voter details through three screening levels which includes face recognition, fingerprint scanning, iris scanning. Thus, helps in avoiding the scenario of illegal voting and helps in producing robust results., {"references":["Association Headquarters. (2018). What are Public Opinion Polls? Accessed: Jan. 31, 2019. [Online]. Available: https://www.historians.org/about-ahaand-membership/aha-history- andarchives/go-roundtableseries/pamphlets/em-4-are-opinionpolls-useful-(1946)/what-are-publicopinion-polls.","A. Wells. UK Polling Report. Accessed: Jan. 31, 2019. [Online]. Available: http://ukpollingreport.co.uk/.","Roy, Kaushik, Prabir Bhattacharya, and Ching Y. Suen. Towards nonideal iris recognition based on level set method, genetic algo- rhythms and adaptive asymmetrical SVMs. Engineering Applications of Artificial Intelligence. 2011.24(3):458-475p.","M. Blumenthal. Trump Approval Poll. Accessed: Jan. 31, 2019. [Online]. Available: https://www.surveymonkey.com/curio sity/trump- approval-poll/.","R. Szewczyk, K. Grabowski, M. Napieralska, W. Sankowski, M. Zubert, and A. Napieralski. A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern Recognit. Lett.2012.33(12):1019–1026p.","Murty, PSR Chandra, and E. Sreenivasa Reddy. Iris Recognition system using Principal Components of Texture Characteristics. TECHNIA International Journal of Computing Science and Communication Technologies. 2009.","M. R. Clarkson, S. Chong, and A. C. Myers. Civitas: Toward a secure voting system. Proc. IEEE Symp. Secur. Privacy (SP).2008:354–368p.","F. Zago´rski, R. T. Carback, D. Chaum, J. Clark, A. Essex, and P.L. Vora. Remotegrity: Design and use of an end-to-end verifiable remote voting system. Applied Cryptography and Network Security, M. Jacobson, M. Locasto, P. Mohassel, and R. SafaviNaini, Eds. Berlin, Germany: Springer, 2013:441–457p.","D. Chaum et al. Scantegrity II: Endto-end verifiability for optical scan election systems using invisible ink confirmation codes. Proc. Electron. Voting Technol., 2008:1–13p.","Bronstein, A., Bronstein, M., Gordon, E., And Kim- Mel, R. 3D face recognition using geometric invariants. International Conference on Audio- and Video-Based Person Authentication.2003."]}

Parkinson disease is the neurodegenerative disease caused to the central nervous system and the long term progressive disorder that is mainly depends on the movement of the body is termed as the Parkinson Disease (PD). During the early stages of these diseases the person affected by this have to phase many difficulties and mostly it is affected to the vocal system. This disease can be seen for the people above 60 ages also it does not have complete cure. The Parkinson Disease can be identified using smartphones in which each and every steps of the patients can be recognized. Hence we are able to do it with a deep multi layer perceptron classifier as it does not need to have a physical interactions with the physicians. It is possibly done with the actions through the speech and movements of the people affected by this. From the missing data using the smartphone we can also detect the Parkinson disease patients in other way.

Modern cultivators in this century have applied the latest technologies to increase the efficiency of horticultural products. Agricultural automation is the prime concern in precision farming. Artificial intelligence framework and machine learning algorithms figure out the complications in farming mechanics. Intelligent autonomous framework own farmers to monitor crops and live stalk conditions to find problem that would not be visible in ground level. The autonomous UAV's are realized using artificial intelligence and human skills for precision farming.

The results of overload and underload tests on aluminum specimens in the near-threshold crack growth rate region are discussed. These tests are performed to choose a model for predicting the fatigue life. The influence of the character and the magnitude of overload and underload on the fatigue crack growth time is analyzed. The obtained crack growth curves and the fatigue lives cannot be explained in terms of classical models, which makes it necessary to develop a new model.

About 60-70% of the drugs synthesized are poorly soluble and comes under BCS Class-II&IV. Now it is a challenging situation during the development of different dosage forms for pharmaceutical industries because solubility of the drug is the rate limiting step. Based on the solubility, the dissolution, bioavailability & therapeutic effect is dependent. To overcome this consequence a novel technique -Liquisolid compact is used by dissolving the poorly soluble drug in a non-volatile solvent that improves wettability & decreases the surface tension and ensures drug molecular dispersion in the formulation to increase the solubility of the drug. This admixture of drug loaded solution is blended with carrier adsorption & coating material (adsorption) that has free flowing and compressible powder properties.

A cloud data center consumes more energy for computation and switching servers between modes. Virtual Machine (VM) migration enhances the execution of cloud server farm in terms of energy proficiency, adaptation to internal failure, and accessibility. Cloud suppliers, be that as it may, ought to likewise enhance for amounts like energy consumption and administrations costs and in this manner, trying to have all the Virtual Machines with the least measures of physical equipment machines conceivable. The part of virtualization is critical and its execution is subjected to VM migration and machine allotment. A greater amount of the energy is caught up in the cloud; consequently, the use of various calculations is required for sparing energy and productivity upgradation in the proposed work. In the proposed work, the Naive Bayes classifier with hybrid optimization using Artificial Bee Colony–Bat Algorithm (ABC–BA) was implemented to reduce the energy consumption in VM migration. The proposed method was evaluated in CloudSim and the performances were compared using performance index such as success &failure rate, and energy consumption. It is observed from the implementation results that the proposed method reduces energy consumption compared to other existing methods. From the implementation outcomes of the proposed work, it was understood that the model was able to achieve the minimum energy consumption and failure rate i.e., 1000–1200 kWh, 0.2 with maximum success rate and accuracy of 1 and 97.77%.

Closure-free intrinsic threshold stress intensity, ΔKth,i, varies significantly as a unique function of near-tip residual stress. It does so in equal measure in 5 mm and 50 mm thick compact tension specimens. Specially designed load sequences employing marker loads are employed to fractographically characterize the effect of load history on fatigue crack growth rate under closure-free conditions in 7075-T6 Al-alloy. Observed variations correlate with ΔKth,i, confirming that near-tip residual stress operates as an independent load interaction mechanism. A certain ‘micro latency’ before crack arrest due to increased ΔKth,i is investigated under different gaseous environments through experiments involving hold time.

Digital image steganography is an emerging technique in secure communication for the modern connected world. It protects the content of the message without arousing suspicion in a passive observer. A novel steganography method is presented to hide text in digital images. A compact dictionary is designed to efficiently communicate all types of secret messages. The sorting order of pixels in image blocks are chosen as the carrier of embedded information. The high correlation in image pixel values means reordering within image blocks do not cause high distortion. The image is divided into blocks and perturbed to create non repeating sequences of intensity values. These values are then sorted according to the message. At the receiver end, the message is read from the sorting order of the pixels in image blocks. Only those image blocks with standard deviation lesser than a given threshold are chosen for embedding to alleviate visual distortion. Information Security is provided by shuffling the dictionary according to a shared key. Experimental Results and Analysis show that the method is capable of hiding text with more than 4000 words in a 512×512 grayscale image with a peak signal to noise ratio above 40 decibels.

The paper describes near-tip experimental strain measurements under simple variable-amplitude loading. Local strain was estimated by means of Digital Image Correlation (DIC) technique using optical microscope equipped with digital camera. Fatigue crack growth tests were performed on single edge crack tension specimens made of Al-Cu alloy under specially designed load sequences, which included baseline cycles, single overload or underload with measurement cycles for DIC. The main idea of the study is to compare strain response and shapes of hysteresis loops at the crack tip and crack wake. The obtained results suggest that COD values measured at the crack tip are significantly affected by both underload and overload, but the influence is different for each case. The observed behavior is attributed to abrupt changes in Kop and ΔKth and their subsequent recovery to the basic level.

Fatigue crack growth tests were performed on an Al-Cu alloy under specially designed load sequences with a periodic overload cycle. These overloads were either immediately followed by or preceded by underloads of controlled magnitude. Baseline loading magnitude was designed to induce near-threshold conditions well below the Paris Regime. The fatigue fracture surfaces obtained from the tests were subject to fractographic analysis. The study shows that irrespective of their magnitude, underloads preceding tensile overloads do not affect crack growth rate after the overload. However, if the underload follows an overload, it carries a deleterious effect on retardation, whose magnitude is determined by the extent to which the underload drops below baseline cycling level. In either case, observed effects do not seem to have anything to with crack closure, but rather, are attributable to the effect of underloads on near-tip residual stress during baseline cycling that in turn controls threshold stress intensity range. Post-overload crack closure is reduced by crack-tip blunting, but rapidly recovers.

To determine the socio-demographic and clinical profile of exotropia surgery outcomes amongst paediatric patients.This is a descriptive, retrospective, clinical study of surgeries performed between 2014 and 2016 at the Sarawak Heart Centre, Malaysia. Medical records of patients with primary and secondary exotropia were reviewed. The following factors that affected the surgical outcomes were collected: onset age of squint, age at the time of surgery, the interval between diagnosis and surgery, the type of exotropia, visual acuity, presence of amblyopia, previous patching, anisometropia, refractive error, type of surgery, preoperative and postoperative deviation, pre-existing ocular comorbidity and systemic illness.A total of 15 patients were studied with more than two thirds being females. Seven patients had primary exotropia while eight patients had secondary exotropia. Average interval between diagnosis and surgery was 1.3 years (±0.82) for primary exotropia and 1.2 years (±0.84) for secondary exotropia. Average pre-operative angle for primary exotropia was 50.57PD (±10.83) whereas secondary exotropia was 39.38PD (±8.63). Seven patients had successful surgical outcomes of within 10 prism dioptres, five for primary exotropia and two for secondary exotropia. The response to surgery was 3.0PD/mm (±0.59) for primary exotropia and 2.2PD/mm (±0.74) for secondary exotropia.In our study, primary exotropia had larger preoperative angle than secondary exotropia. The response to surgery was positively correlated with the preoperative angle of deviation. Primary exotropia showed better surgical outcome.

A number of problems related to the digital image correlation method from the standpoint of both the hardware and software development and testing, as well as solving problems in the field of fatigue fracture mechanics are described. Testing of the developed computer stereo vision system was carried out using a series of stereo pairs reflecting the change in the location of the object in space as well as plane and out-of-plane deformations. It is shown that the error in determining spatial coordinates does not exceed 0.75 units, while the error in computing the strain tensor components in case of a system with a single camera is two orders of magnitude larger than that at using the stereo machine vision system. An algorithm for automatic crack detection on optical images and calculating its tip coordinates was proposed and tested. It is shown that when the frame size is 2000×1000 pixels the coordinates of the crack tip might be determined with an average error of about 56 pixels, while the average error of the crack area determination does not exceed 1.93%. A modified incremental algorithm for calculating displacements on a series of stereo pairs is proposed, which allows one to estimate large magnitude displacements during serial processing of images. An algorithm for measuring the J-integral using the digital image correlation method has been developed. It is shown that the deviation of the calculated J-integral values from the model ones is on average 1.75 %. A quantitative characterization of the fatigue crack growth process in metal alloys was carried out using the technique based on the digital image correlation (in terms of fracture mechanics) including the calculation of the fatigue crack growth rate da / dN , maximum strain (emax) and effective cycle asymmetry ( R eff).

This paper is a review of developments of note at BISS that impact structural integrity. A test system and procedure were developed to determine J1c for the case of a through crack in the wall of a pressure vessel subject to internal pressure. More recently, an online scanner was developed and integrated with an automated test system to track damage growth in carbon fibre reinforced plastics under static and cyclic loading. By far the most significant development, from a scientific perspective, is a path-breaking discovery that the well-known mean stress (or residual stress) effect on metal fatigue may be attributed to environmental action. A variety of specially designed experiments proved that the mean stress effect becomes negligible in high vacuum. Further research involving the development of a new test procedure led to the establishment of a unique relationship between computed near-tip residual stress and a threshold stress intensity required for the onset of (atmospheric) fatigue crack extension. All this research required concomitant development of test techniques unavailable from the typical ‘black box’ test equipment associated with conventional fatigue testing.

A wireless specially appointed system is a self-sorting out, self-arranging confederation of remote frameworks. Wireless ad-hoc network gadgets (WANET) will interface and leave the system non-concu...

The close relationship between sequence-sensitive near-tip residual stress and threshold stress intensity raises questions about load interaction models currently in use to estimate fatigue crack growth under variable amplitude loading. In an attempt to address them, experiments were performed on an Al–Cu alloy under specially designed load sequences with periodic overloads. Fractographic evidence from these tests confirms that fatigue crack closure, together with sequence sensitive variation in threshold stress intensity appear to explain all observed results. The fractographic data provide quantitative inputs for improved modeling of variable-amplitude fatigue, particularly at near-threshold crack growth rates. This study appears to suggest that conventional approaches based on the Wheeler and Willenborg residual stress models can provide reasonable estimates only by coincidence. They model the wrong parameter at lower fatigue crack growth rates and may simply not be valid at other growth rates.

Cancer is enlisted as the second leading reason for death across the world wherein almost one person out of six dies of cancer. Breast cancer is one of the most common forms of cancer predominant in women having the second highest mortality rate in the world. Various scientific studies have been conducted to combat this disease, and machine learning approaches have been an extremely popular choice. Particle swarm optimization has been identified as one of the most powerful and efficient technique for the diagnosis of breast cancer guiding physicians towards timely and accurate treatment. It is also pertinent to mention that multi-modal prediction methods are used to make decisions depending upon different scenarios and aspects whereas the non-dominating sorting feature is useful to sort different objects based on differing requirements. The main novelty of this work is multi-modal prediction algorithm for breast cancer prediction is proposed. The work encompasses the use of particle swarm optimization, non-dominating sorting and multi-classifier techniques, namely, k-nearest neighbour method, fast decision tree and kernel density estimation. Finally, Bayes’ theorem is implemented for revising the results to achieve optimum accuracy in the breast cancer prediction. The proposed particle swarm optimization and non-domination sorting with classifier technique model helps to select the most significant features relevant to breast cancer predictions. The selected features design the objective of the problem model. The proposed model is implemented on the WBCD and WDBC breast cancer data sets publicly available from the UCI machine learning data repository. The metrics considered are sensitivity, specificity, accuracy and time complexity. The experimental results of the study using measures such as sensitivity, specificity, accuracy and time complexity. The experimental results of the study are evaluated against the state-of-the-art algorithms, namely, genetic algorithm kernel density estimation and particle swarm optimization kernel density estimation wherein the results justify the superiority of the proposed model.