Growth and Economic Assessment of Wheat under Tillage and Nitrogen Levels in Rice-Wheat System

Mechanically post-harvest puddled rice field has stubbles that often delay timely planting of winter wheat crop. Zero tillage increased the net return by decreasing the unwise tillage operations and labor charges. Keep in view, a randomized complete block design experiment in a split plot arrangement was conducted with four tillage system [conventional tillage, CT;deep tillage, DT;zero tillage with zone disc tiller, ZDT;and happy seeder, HS] in main plots and five nitrogen levels [0, 75, 100, 125, and 150 kg&middot;ha-1] in subplots during 2009 to 2010 and 2010 to 2011 cropping seasons. Results showed that in 2009-10 and 2010-11 grain yield (4.6 Mg&middot;ha-1 and 5.7 Mg&middot;ha-1) in DT and (4.5 Mg&middot;ha-1 and 5.8 Mg&middot;ha-1) in HS were significantly higher compared with CT and ZDT. Significantly, maximum leaf area index (5.18 and 5.24) and crop growth rate (12.14 g&middot;m-2&middot;d-1 and 13.15 g&middot;m-2&middot;d-1) were noted in DT. Grain protein (11.78%) was significantly higher in DT compared with CT, ZDT, and HS during 2009-10 and 2010-11. Total yield (12.4 Mg&middot;ha-1 and 16.4 Mg&middot;ha-1) and grain yield (4.9 Mg&middot;ha-1 and 6.5 Mg&middot;ha-1) at N125 kg&middot;ha-1 while grain protein (13.52%) at N150 kg&middot;ha-1 was significantly higher than other nitrogen levels. Maximum LAI (5.08 and 5.51) and crop growth rate (14.68 g m-2&middot;d-1 and 15.77 g<span style='font-family:Verdana, Helvetica, Arial;white-space:normal;

Agronomy and Economy: Impact of Tillage and Poultry Manure on Mazie (<i>Zea mays</i>L.)

Seedling emergence and seedling establishment are two important phases for the good crop stand and final maize crop harvest. A field study was conducted to explore the effects of different tillage practices and poultry manure levels on the seedling emergence, growth, development, yield, and economics of the spring planted maize during 2010 and 2011. Experimental treatments include four tillage treatments (zero, minimum, conventional and deep tillage) and three poultry manure amendments (control (no manure), 5 Mg·ha-1 and 10 Mg·ha-1). Seedling emergence was linearly affected as the tillage intensity was increased. Significant relationship of tillage with leaf area index, leaf area duration, crop growth rate, net assimilation rate and total dry matter was recorded during the both years. Poultry manure at the rate of 10 Mg·ha-1 produced the higher leaf area index, leaf area duration, crop growth rate, total dry matter and grain yield as compared to 5 Mg·ha-1 and control. Moreover, experimental results concluded that the deep tillage practice has taken less time to start emergence. Similarly, higher values trend of leaf area index, leaf area duration, crop growth rate, total dry matter accumulation and grain yield was shifted from deep tillage to conventional, minimum and zero tillage practices during both years. Economically, the minimum tillage with poultry manure at rate of 10 Mg·ha-1 gave the better benefit to cost ratio and crop productivity as compared to conventional, deep and zero tillage. The experiment suggested the minimum tillage with poultry manure at the rate of 10 Mg·ha-1 may ensure the maize grain yield sustainability.

Numerical analysis of SiO_(2)-SDS surfactant effect on oil recovery in sandstone reservoirs

Currently,the demand for using nanoparticles(NPs)in Enhanced Oil Recovery(EOR)is very high.The use of NPs can drastically benefit EOR by changing the rock wettability,improving the mobility of oil phase and decreasing the interfacial tension(IFT)between oil/water.Most of the previous studies were experimentally conducted and limited to specific operating conditions.This study contributes to the understanding of nanoparticle behaviour at altered operating conditions.In this investigation,a 3-D numerical model of silica nanofluid proposed for EOR is presented.A numerical model of core-scale sandstone was developed using Computer Modelling Group(CMG)-STARS simulator.The developed model was well validated against an experimental data.Then Silicon Dioxide(SiO_(2))and Sodium Dodecyl Sulphate(SDS)nano-surfactant mixture were injected with different concentrations.The optimum concentration of SiO_(2)eSDS solution was obtained at 0.15 wt%with the highest recovery factor of 71.86%achieved.Moreover,SiO_(2)eSDS solution injection rate and temperature were investigated and it was found that the optimum operating conditions of injection rate and temperature were 2 cc/min and 60C with the highest recovery achieved.In addition,a heavy crude oil showed a significant increment of oil recovery compared with a light crude with 3.13%and 0.6%,respectively.The intermediate viscosity of used crude oil had the highest oil recovery increment.

Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

Machine Learning(ML)has changed clinical diagnostic procedures drastically.Especially in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human errors.Enormous studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical procedures.Multi-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical diagnostics.DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets.This paper proposed a novel method that deals with the issue of less data dimensionality.Inspired by the regression analysis,the proposed method classifies the data by going through three different stages.In the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final classifications.Extensive experiments were carried out on the Cleveland heart disease dataset.The results show significant improvement in classification accuracy.It is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

Study of Intelligent Approaches to Identify Impact of Environmental Temperature on Ultrasonic GWs Based SHM:A Review

Structural health monitoring(SHM)is considered an effective approach to analyze the efficient working of several mechanical components.For this purpose,ultrasonic guided waves can cover long-distance and assess large infrastructures in just a single test using a small number of transducers.However,the working of the SHM mechanism can be affected by some sources of variations(i.e.,environmental).To improve the final results of ultrasonic guided wave inspections,it is necessary to highlight and attenuate these environmental variations.The loading parameters,temperature and humidity have been recognized as the core environmental sources of variations that affect the SHM sensing mechanism.Environmental temperature has the most significant influence on SHM results.There is still a need for extensive research to develop such a damage inspection approach that should be insensitive to environmental temperature variations.In this framework,the current research study will not only illuminate the effect of environmental temperature through different intelligent approaches but also suggest the standard mechanism to attenuate it in actual ultrasonic guided wave based SHM.Hence,the work presented in this article addresses one of the open research challenges that are the identification of the effect of environmental and operating conditions in practical applications of ultrasonic guided waves and impedance-based SHM.

OILCROP-SUN Model Relevance for Evaluation of Nitrogen Management of Sunflower Hybrids in Sargodha, Punjab

The experiments were conducted to evaluate the performance of crop system (DSSAT) OILCROP-SUN model simulating growth & development and achene yield of sunflower hybrids in response to nitrogen under irrigated conditions in semi arid environment, Sargodha, Punjab. The model was evaluated with observed data collected in trials which were conducted during spring season in 2010 and 2011 in Sargodha, Punjab, Pakistan. Split plot design was used in layout of experiment with three replications. The hybrids (Hysun-33 & S-278) and N levels (0, 75, 150 and 225 kg.ha-1) were allotted in main and sub plots, respectively. The OILCROP-SUN model showed that the model was able to simulate growth and yield of sunflower with an average of 10.44 error% between observed and simulated achene yield (AY). The results of simulation analysis indicated that nitrogen rate of 150 kg.N.ha-1 (N3) produced the highest yield as compared to other treatments. Furthermore, the economic analysis through mean Gini Dominance also showed the dominance of this treatment compared to other treatment combinations. Thus management strategy consisting?of treatment 150 kg.N.ha-1 was the best for high yield of sunflower hybrids.

Experimental Study of Effect of Temperature Variations on the Impedance Signature of PZT Sensors for Fatigue Crack Detection

Structural health monitoring(SHM)is recognized as an efficient tool to interpret the reliability of a wide variety of infrastructures.To identify the structural abnormality by utilizing the electromechanical coupling property of piezoelectric transducers,the electromechanical impedance(EMI)approach is preferred.However,in real-time SHM applications,the monitored structure is exposed to several varying environmental and operating conditions(EOCs).The previous study has recognized the temperature variations as one of the serious EOCs that affect the optimal performance of the damage inspection process.In this framework,an experimental setup is developed in current research to identify the presence of fatigue crack in stainless steel(304)beam using EMI approach and estimate the effect of temperature variations on the electrical impedance of the piezoelectric sensors.A regular series of experiments are executed in a controlled temperature environment(25°C–160°C)using 202 V1 Constant Temperature Drying Oven Chamber(Q/TBXR20-2005).It has been observed that the dielectric constantð"33 TÞwhich is recognized as the temperature-dependent constant of PZT sensor has sufficiently influenced the electrical impedance signature.Moreover,the effective frequency shift(EFS)approach is optimized in term of significant temperature compensation for the current impedance signature of PZT sensor relative to the reference signature at the extended frequency bandwidth of the developed measurement system with better outcomes as compared to the previous literature work.Hence,the current study also deals efficiently with the critical issue of the width of the frequency band for temperature compensation based on the frequency shift in SHM.The results of the experimental study demonstrate that the proposed methodology is qualified for the damage inspection in real-time monitoring applications under the temperature variations.It is capable to exclude one of the major reasons of false fault diagnosis by compensating the consequence of elevated temperature at extended frequency bandwidth in SHM.

Experimental Investigation of Performance Characteristics of PZT-5A with Application to Fault Diagnosis

In the previous couple of decades,techniques to reap energy and empower low voltage electronic devices have received outstanding attention.Most of the methods based on the piezoelectric effect to harvest the energy from ambient vibrations have been revolutionized.There’s an absence of experiment-based investigation which incorporates the microstructure analysis and crystal morphology of those energy harvest home materials.Moreover,the impact of variable mechanical and thermal load conditions has seldom been studied within the previous literature to utilize the effectiveness of those materials in several practical applications like structural health monitoring(SHM),etc.In the proposed research work,scanning electron microscope(SEM)and energy dispersive x-ray(EDX)analysis are performed to examine the inside crystal morphology of PZT-5A and ensure the quality of the piezoelectric ceramic.Further,the performance of piezoelectric vibration-based energy harvester has been investigated in the second phase of current research work under the variable mechanical and thermal load conditions through a regular series of experiments.It’s been found that the output voltage of piezoelectric sensors will increase by increasing the applied load,whereas a decreasing trend in output voltage is noticed by increasing the applied temperature,resistance and frequency.Within the third part,a measuring setup is developed in the laboratory to further investigate the effectiveness of PZT-5A in practical applications such as electromechanical impedance(EMI)based structural health monitoring under the controlled heating environment.Therefore,this analysis not only evaluates the performance of PZT sensors under the variable operating conditions but also encourages developing a temperature compensation approach in EMI-based SHM.

Mineral analysis of sandstone formation using chelating agents during sandstone matrix acidizing

For many years,the most common acid practice for sandstone acidizing is based on mud acid and dolomite formations using hydrochloric acid.During various stages of sandstone acidizing,different acids react with different minerals,and interactions of minerals with acids are an origin for precipitation reactions,which can be possibly deleterious as they may reduce reservoir permeability.During this research,the effects of chelates on pore size distribution,mineralogy,and grain size distribution have been investigated.Various chelates(GLDA,HEDTA,EDTA)were examined to react with different Berea Sandstone samples at a temperature of 180F and under 1000 psi confining pressure.Experimental techniques and analysis like Tescan Integrated Mineral Analysis(TIMA),were implemented in this research to understand the effect of chelates on Berea Sandstone.These results are related to elemental mass,element deportment,mineral mass,mineral locking,grain size distribution,and particle size distribution of the core samples reacted with different chelating agents.It has been found that all the chelating agents are effective in increasing the porosity and dissolving the cations from the Berea sandstone core sample.HEDTA proved to be more effective in dissolving quartz as compared to other chelates.GLDA proved to be more effective in the dissolution of rutile and zircon minerals.The significance of this research is the application of environment-friendly chelating agents to sandstone formation.Moreover,the detailed mineral analysis revealed that the most number of particles were dissolved by HEDTA.

Numerical investigation of modal and fatigue performance of a horizontal axis tidal current turbine using fluid-structure interaction

The tidal power has the potential to play a vital role in a sustainable energy future.The main objective of this paper is to investigate the performance and fatigue life of tidal current turbine(TCT)using fluid structure interaction(FSI)modeling.The performance of TCT was predicted using Ansys CFX.The performance curve,pressure distribution on the blade,and velocity streamline were visualized for eight repetitive analyses at different tip speed ratio.The hydrodynamic load calculated from CFD analysis was transferred to FEA model for investigation of the structural response of TCT.Modal analysis was performed to examine the mode shapes and natural frequencies of TCT.The fatigue analysis were performed and number of cycles and safety factor at different equivalent alternating stresses were investigated.The results of the simulation confirm that the turbine has a maximum value of the coefficient of performance atλ=5,the turbine operating frequency is not close to its natural frequency,and it is safe under the applied fatigue loads with a high factor of safety.

Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

The COVID-19 virus exhibits pneumonia-like symptoms,including fever,cough,and shortness of breath,and may be fatal.Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities.Clinical studies help to make a correct diagnosis of COVID-19,where the disease has already spread to the organs in most cases.Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread.Therefore,clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold,which are worth investigating.Current supervised Machine Learning(ML)based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography(CT)for disease detection.This strategy relies on a larger clinical dataset and does not focus on early symptom identification.Towards this end,an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms,fever,and cough.Specifically,various metrics,including body temperature,breathing and cough patterns,and physical activity,were considered in this study.Finally,a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 individuals.The proposed technique shows an outlier detection with an accuracy of 89%,on average.

A Machine Learning Approach for Early COVID-19 Symptoms Identification

Symptom identification and early detection are the first steps towards a health condition diagnosis.TheCOVID-19 virus causes pneumonialike symptoms such as fever,cough,and shortness of breath.ManyCOVID-19 contraction tests necessitate extensive clinical protocols in medical settings.Clinical studies help with the accurate analysis of COVID-19,where the virus has already spread to the lungs in most patients.The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography.This is heavily reliant on a larger clinical study and does not emphasize early symptom detection.The aim of this study is to investigate anomalies in patient physiological data for early COVID-19 symptoms identification.In this context,two of the most prevalent symptoms,fever and cough,were examined in a two-fold manner utilizing an unsupervised machine learningmodel.To examine disease progression,physiological features from a chest-worn device were analyzed.First,a Single Vector Activity Index(SVAI)parameter is proposed to monitor the breathing and cough patterns.Second,the dataset’s variance is examined using the DBSCAN method for clustering and outlier detection.Finally,the model accuracy is evaluated to identify outliers on real-time data based on feature dissimilarities,yielding an overall detection accuracy of 90.34%.

Comparative elemental,mineral and microscopic investigation of sandstone matrix acidizing at HPHT conditions

Matrix acidizing is usually conducted on sandstone reservoirs to increase hydrocarbon production.Mineralogy plays an important role in designing acidizing treatments to enhance the production of oil and gas from sandstone reservoirs.The most common acid in practice to acidize sandstone formation is mud acid after the pre-flush acid stage.The main aim of the pre-flush acid stage is to dissolve positive ions like calcium,potassium,sodium,etc,which can cause precipitation reactions during the mud acid stage.This research is aimed to investigate the reaction mechanism of a new pre-flush acid combination(hydrochloric acid and acetic acid)with sandstone formation.Core flooding experiments were performed to react acid with the core samples(6 in×1.5 in)under high pressure and temperature(HPHT)condi-tions(80℃and 1000 psia).Analytical techniques such as Tescan Integrated Mineral Analysis(TIMA)have been used to illustrate the effect of these acids on sandstone formation.TIMA analysis showed that the new acid combination was effective in dissolving various minerals(ankerite,magnetite)and cations(calcium,magnesium,sodium,and iron).15%HCl proved to be more effective in the dissolution of different particles from sandstone core samples.It dissolved 32.8%of the initial number of particles present inside the sample while the dissolving power of 5%CH_(3)COOH:10%HCl is 26.7%.Subsequently,the number of pores increased by the application of 15%HCl is 22%while that by 5%CH_(3)COOH:10%HCl is 21.8%.Density analysis showed that both acid combinations removed heavy particles effectively,whereas 5%CH_(3)COOH:10%HCl proved more efficient in dissolving calcium ions which is very important for pre-flush acidizing.

A Study on Technological Dynamics in Structural Health Monitoring Using Intelligent Fault Diagnosis Techniques:A Patent-Based Approach

The performance and reliability of structural components are greatly influenced by the presence of any abnormality in them.To this purpose,structural health monitoring(SHM)is recognized as a necessary tool to ensure the safety precautions and efficiency of both mechanical and civil infrastructures.Till now,most of the previous work has emphasized the functioning of several SHM techniques and systematic changes in SHM execution.However,there exist insufficient data in the literature regarding the patent-based technological developments in the SHM research domain which might be a useful source of detailed information for worldwide research institutes.To address this research gap,a method based on the Co-Operative Patent Classification(CPC)codes is proposed in the current study.The proposed method includes the patent analysis of SHM in terms of its global publication trend and technology-based applications.This analysis is performed using patent database search tools,namely,IncoPat and Espacenet.The period ranging from 2005 to 2019 is selected to retrieve the required patent documents.A new approach termed as Patents’value is utilized to investigate the technological impact of a patent in the form of forward citations,technical stability,and scope of protection.The identification of emerging SHM techniques and forecasting of vacant technology is also part of current research work.The research results have revealed the increasing trend in the number of published patents each year related to various SHM technologies.In this regard,China,the United States,and South Korea are notified as to the major depositor countries,respectively.Hence,mapping of patent data in this research is an effort to illustrate the effectiveness of the proposed method to demonstrate the development trends and dynamic inventions over the time in SHM research domain to achieve the optimal damage inspections of various mechanical components.