Forest fire risk assessment in parts of Northeast India using geospatial tools

Forest fire is a major cause of changes in forest structure and function. Among various floristic regions, the northeast region of India suffers maximum from the fires due to age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modeling is required. The study results demonstrate the potential use of remote sensing and Geographic Information System （GIS） in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover （LULC）, vegetation type, Digital elevation model （DEM）, slope, aspect and proximity to roads and settlements, factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modeling and ISODATA clustering was used to classify the fire zones. TO validate the results, Along Track Scanning Radiometer （ATSR）, the historical fire hotspots data was used to check the occurrence points and modeled forest fire locations. The forest risk zone map has 55-63% of agreement with ATSR dataset.

Discrete Software Reliability Growth Modeling for Errors of Different Severity Incorporating Change-point Concept

Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures, it is considered that a similar testing effort is required on each debugging effort. However, in practice, different types of faults may require different amounts of testing efforts for their detection and removal. Consequently, faults are classified into three categories on the basis of severity： simple, hard and complex. This categorization may be extended to r type of faults on the basis of severity. Although some existing research in the literatures has incorporated this concept that fault removal rate （FRR） is different for different types of faults, they assume that the FRR remains constant during the overall testing period. On the contrary, it has been observed that as testing progresses, FRR changes due to changing testing strategy, skill, environment and personnel resources. In this paper, a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept. Then, the models are formulated for two particular environments. The models were validated on two real-life data sets. The results show better fit and wider applicability of the proposed models as to different types of failure datasets.

Considering the Fault Dependency Concept with Debugging Time Lag in Software Reliability Growth Modeling Using a Power Function of Testing Time

Since the early 1970s tremendous growth has been seen in the research of software reliability growth modeling. In general, software reliability growth models （SRGMs） are applicable to the late stages of testing in software development and they can provide useful information about how to improve the reliability of software products. A number of SRGMs have been proposed in the literature to represent time-dependent fault identification / removal phenomenon; still new models are being proposed that could fit a greater number of reliability growth curves. Often, it is assumed that detected faults are immediately corrected when mathematical models are developed. This assumption may not be realistic in practice because the time to remove a detected fault depends on the complexity of the fault, the skill and experience of the personnel, the size of the debugging team, the technique, and so on. Thus, the detected fault need not be immediately removed, and it may lag the fault detection process by a delay effect factor. In this paper, we first review how different software reliability growth models have been developed, where fault detection process is dependent not only on the number of residual fault content but also on the testing time, and see how these models can be reinterpreted as the delayed fault detection model by using a delay effect factor. Based on the power function of the testing time concept, we propose four new SRGMs that assume the presence of two types of faults in the software： leading and dependent faults. Leading faults are those that can be removed upon a failure being observed. However, dependent faults are masked by leading faults and can only be removed after the corresponding leading fault has been removed with a debugging time lag. These models have been tested on real software error data to show its goodness of fit, predictive validity and applicability.

siRNA-mediated downregulation of TC21 sensitizes esophageal cancer cells to cisplatin

AIM： To determine the functional significance of TC21 in esophageal squamous cell carcinoma （ESCC）. METHODS： TC21 siRNA transfection was carried out using Hyperfectamine to knock down TC21, and tran- scripts were analyzed by reverse transcription-poly- merase chain reaction and protein by Western blotting.We demonstrated the effect of TC21 downregulation of cell signaling in esophageal cancer cells by assess- ing the phosphorylation status of its downstream tar- gets, phosphoinositide 3-kinase （PI3K）, phosphatase and tensin homolog （PTEN）, protein kinase B （pAl〈t）, nuclear factor-KB （NF-~B） and cyclinD1 using specific antibodies. Cell survival analysis after cisplatin treat- ment was carried out by cell viability assay and cell cycle analysis using flow cytometry. RESULTS： TC21 knockdown in human ESCC cell line TEl3 cells, showed only a marginal increase （14.2%） in cell death compared with control cells. The expres- sions of the signaling proteins PI3K and pAkt, transcrip- tion factor NF-KB, and cell cycle protein cyclin D1 were markedly decreased in response to TC21 downregula- tion, whereas the level of pPTEN, an antagonist of PI3K, was increased. In addition, we evaluated the potential of TC21 as a putative target for sensitizing ESCC cells to the chemotherapeutic agent cisplatin. Increased cell death （38.4%） was observed in cells treated with cis- platin after TC21 knockdown compared with cells which were treated with cisplatin alone （20% cell death）. CONCLUSION： Results suggest that TC21 mediates its effects via the PI3K-Akt pathway, NF-KB and cyclin D1, and enhances chemoresistance in esophageal cancer cells.

Nonlinear Time Series Analysis of Pathogenesis of COVID-19 Pandemic Spreadin Saudi Arabia

This article discusses short–term forecasting of the novel Corona Virus(COVID-19)data for infected and recovered cases using the ARIMA method for Saudi Arabia.The COVID-19 data was obtained from the Worldometer and MOH(Ministry of Health,Saudi Arabia).The data was analyzed for the period from March 2,2020(the first case reported)to June 15,2020.Using ARIMA(2,1,0),we obtained the short forecast up to July 02,2020.Several statistical parameters were tested for the goodness of fit to evaluate the forecasting methods.The results show that ARIMA(2,1,0)gave a better forecast for the data system.COVID 19 data followed quadratic behavior,and in the long run,it spreads with a high peak.It is concluded that COVID-19 will follow secondary shock waves,and it is strongly advisable to maintain social distancing with all safety measures as the pandemic situation is not in control.

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare.Despite the phenomenal advancement in the present healthcare services,the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’data.A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders.Various approaches and methods have been practiced by researchers to secure healthcare digital services.However,there are very few from the Machine learning(ML)domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records(EHRs).The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches.B.A.Kitchenham guidelines have been practiced as a manual to conduct this work.Seven well-known digital libraries namely IEEE Xplore,Science Direct,Springer Link,ACM Digital Library,Willey Online Library,PubMed(Medical and Bio-Science),and MDPI have been included to performan exhaustive search for the existing pertinent studies.Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion,yet the specified area has not been fully explored by the researchers.K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems’log data.Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies.This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively.Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.

Assessing forest fragmentation in north-western Himalaya:a case study from Ranikhet forest range,Uttarakhand,India

The northwestern Himalaya harbors high levels of biodiversity due to its unique topography, climatic conditions and heterogeneity. Forest fragmentation is one of the major threats causing a decline in biodiversity in the Himalayan region. We assesses forest fragmentation and changes in land use land cover（LULC） patterns using multi-temporal satellite data over a time span of four decades（1976–2013）. Fragmentation analysis using the Landscape Fragmentation Tool（LFT） reveals a decrease in core and edge areas by 14 and 2.3 %, respectively; while an increase in non-forest, patch area and perforation area by 2.1, 0.4, and 14 %, respectively. The LULC dynamics show that the areas under dense forest and scrub forest have decreased by 2.8 % and 1.9 %, respectively; and there is an increase in open forest, crop land and fallow land area by 2.6, 1.7 and 2.1 %, respectively. The quantification of landscape heterogeneity is undertaken with the help of landscape metrics computed using FRAGSTATS at class and landscape level, showing signs of increased fragmentation. Our study provides baseline database that can support the future biodiversity conservation and sustainable forest management initiatives.

Biosorption of Ni(Ⅱ) ions from aqueous solution using modified Aloe barbadensis Miller leaf powder

This study aimed to investigate the biosorption potential of Na2CO3-modified Aloe barbadensis Miller (Aloe vera) leaf (MABL) powder for removal of Ni(II) ions from a synthetic aqueous solution. Effects of various process parameters (pH, equilibrium time, and temperature) were investigated in order to optimize the biosorptive removal. The maximum biosorption capacity of MABL was observed to be 28.986 mg/g at a temperature of 303 K, a biosorbent dose of 0.6 g, a contact time of 90 min, and a pH value of 7. Different kinetic models (the pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion models) were evaluated. The pseudo-second-order kinetic model was found to be the best fitted model in this study, with a coefficient of determination of R^2 =0.974. Five different isotherm models (the Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, and Brunauer-Emmett-Teller (BET) models) were investigated to identify the best-suited isotherm model for the present system. Based on the minimum chi-square value (X^2 =0.027) and the maximum coefficient of determination (R^2 =0.996), the Langmuir isotherm model was found to represent the system well, indicating the possibility of monolayer biosorption. The sticking probability (S*) was found to be 0.41, suggesting a physisorption mechanism for biosorption of Ni(II) on MABL. The biosorbent was characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential, and BET surface area, in order to understand its morphological and functional characteristics.

Acclimatization of microalgae Arthrospira platensis for treatment of heavy metals in Yamuna River

Bioaccumulation and biosorption in microalgae are effective approaches for the removal of heavy metals(HMs)from river water.The objective of this study was to investigate the potential for use of acclimatized microalgae in the removal of HMs from the Yamuna River water as an acclimatizing medium.An active culture of Arthrospira platensis(A.platensis)was acclimatized to HMs up to a concentration of 100 mg/L.It was gradually exposed to increasing concentrations of HMs in five subsequent batches with a step increase of 20 mg/L to acclimatize live cells in the simulated Yamuna River water.The presence of high levels of HMs in the Yamuna River water caused growth inhibition.An empirical growth inhibition model was developed,and it predicted high threshold concentrations of HMs(210.7e424.5 mg/L),producing a positive specific growth rate of A.platensis.A.platensis also showed high average removal efficiencies of HMs,including 74.0%for Cu,77.0%for Cd,50.5%for Ni,76.0%for Cr,76.5%for Pb,and 63.5%for Co,from HMs-enriched Yamuna River water.The findings demonstrated that the maximum specific removal amounts of Cu,Cd,Ni,Cr,Pb,and Co were 54.0,58.0,39.0,62.8,58.9,and 45.3 mg/g,respectively.The maximum yields of the value-added products chlorophyll and phycocyanin were 2.5 mg/g(in a batch of 40 mg/L for Cd)and 1054 mg/g(in a batch of 20 mg/L for Cu),respectively.Therefore,acclimatized A.platensis was proven to be a potential microalga not only for sequestration of HMs but also for production of valuable pigments.

Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index.

Assessing the effectiveness of community managed forests for plant diversity conservation in Meghalaya,Northeast India

In the state Meghalaya,northeast India,>80%of the forest lands are owned by local communities and managed by traditional institutions.These forests are under severe threats due to a number of human disturbances.The present study was conducted to assess the plant diversity and identify the community forests for priority conservation in Khasi Hills of Meghalaya.Floristic explorations carried out in the 87 forests reveals the presence of 1300 plant species of which 400 are either rare,endemic or threatened.Of the different forest categories,reserve forests had the highest number of species(1190),followed by sacred forests(987 species)and village forests(786 species).Majority of the forests(56)had high-species richness,irreplaceability level(42 forests)and vulnerability level(54).In terms of area,13.8%(1666.8 ha)fall under low risk while 1855 ha under high risk zone.High risk zone was mostly represented by village forests.An area of 7661.56 ha of community forests falls under high priority category and hence calls for immediate conservation actions.The conservation priority map generated in the present study will help to concentrate the protection strategy to the demarcated and adjoining areas and help conservationists and planners to evolve effective strategies for conservation of the community forests.

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.

Tandem repetitions in transcriptomes of some Solanaceae species

Characterization of occurrence, density and motif sequence of tandem repeats in the transcribed regions is helpful in understanding the functional significance of these repeats in the modern genomes. We analyzed tandem repeats present in expressed sequences of thirteen species belonging to genera Capsicum, Nicotiana, Petunia and Solanum of family Solanaceae and the genus Coffea of Rubiaceae to investigate the propagation and evolutionary sustenance of these repeats. Tandem repeat containing sequences constituted 1.58% to 7.46% of sequences analyzed. Tandem repetitions of size 2, 15, 18 and 21 bp motifs were more frequent. Repeats with unit sizes 21 and 22 bp were also abundant in genomic sequences of potato and tomato. While mutations occurring in these repeats may alter the repeat number, genomes adjust to these changes by keeping the translated products unaffected. Surprisingly, in majority of the species under study, tandem repeat motif length did not exceed 228 bp. Conserved tandem repeat motifs of sizes 180, 192 and 204 bp were also abundant in the genomic sequences. Our observations lead us to propose that these tandem repeats are actually remnants of ancestral megasatellite repeats, which have split into multiple repeats due to frequent insertions over the course of evolution.

Nature of Volatility Patterns of Futures and Options on Nifty Index

Derivatives were introduced in Indian financial market to reduce volatility in the spot market.The present study attempts to study the impact of derivatives on stock market volatility.In the present study,data have been taken for Nifty Index for a period from 01-01-1996 to 05-02-2016.For analyzing the impact of introduction of derivatives on Nifty Index Volatility,we have taken proxy variable of Nifty Junior Index and Standard&Poor’s 500(S&P 500)Index returns.The data have also been classified into pre-futures(introduced on 12-06-2000)and post-futures and pre-options(introduced on 04-06-2001)and post-options period.The results show that volatility has reduced after introduction of futures and options.

Spline in Compression Methods for Singularly Perturbed 1D Parabolic Equations with Singular Coefficients

In this article, we discuss three difference schemes;for the numerical solution of singularity perturbed 1-D parabolic equations with singular coefficients using spline in compression. The proposed methods are of accurate and applicable to problems in both cases singular and non-singular. Stability theory of a proposed method has been discussed and numerical examples have been given in support of the theoretical results.

Factors Associated with Under-Five Mortality: A Comparison between Empowered Action Group (EAG) and Non-EAG States of India

Background: In India under-five mortality (U5MR) has declined by 71% from 126 to 37 deaths per 1000 live births between 1990 and 2018. The Empowered Action Group (EAG) states accounts for 74% of the under-five deaths as compared to 26% among Non-EAG states. Method: National Family Health Survey round fourth (NFHS-4), 2015-16 was used for this study. A life table method and Cox Proportional Hazard (PH) model was used to examine the various factors associated with U5MR in EAG and Non-EAG states of India. Result: Overall, it was observed that U5MR is much higher in EAG compared to Non-EAG states. Absolute difference varies according to background characteristics. The highest difference was among mothers who had never breastfed (316 vs 150 U5MR per 1000 live births in EAG & Non-EAG states respectively). Factors—total children ever born to mother, household members, children never breastfed and size of the baby were found to be statistically significantly associated with under-five mortality after controlling for other factors in both EAG and Non-EAG states. Hazard of U5MR was two and half-times higher among birth order 4+ (AHR = 2.5, 95% CI = 1.8 - 3.3) compared to birth order ≤2 after controlling for other factors in EAG states. The risk of under-five mortality was found three times higher among mother having up to primary or no education (AHR = 2.9, 95% CI = 1.4 - 5.9) compared to mother having higher education in non-EAG states. Conclusion: The study revealed that both groups of states need health program interventions focused on high risk mothers, TT immunization and promoting basic health services and breastfeeding practices for the reduction U5MR.

Economic Analysis of a Large UASB Reactor Producing Biogas from Baggase Wash Water

This paper concerns with the treatment of bagasse wash water, which is generated after washing the stored bagasse before its use in the paper manufacture. The bagasse wash water, treated earlier in open lagoons, is now treated by the anaerobic process using UASB reactor. This study, based upon an operating unit, shows that the UASB reactor reduces COD of wash water by 85% - 90%, and results in significant emission reductions. Economic analysis carried out by using financial indicators such as DSCR, Payback period and IRR reveals very attractive rate of returns and thus, greatly reduces the risks in financing such projects by the financial institutions.

Energy-growth nexus for‘Renewable Energy Country Attractiveness Index’countries:Evidence from new econometric methods

This study explores the connections between renewable energy consumption(REC),non-renewable energy consumption(NREC),gross fixed capital formation(GFCF),the labor force(LF),and economic growth(GDP)in Renewable Energy Country Attractiveness Index(RECAI)countries for 1991-2016.We quantify the nexus between REC,NREC,and GDP while utilizing a production model framework and including the measures of labor and capital,for suggesting a phase-wise strategy to attain the sustainable development goals.We use robust methodologies including Lagrange Multiplier(LM)panel unit root tests with trend shifts,Westerlund cointegration test,LM bootstrap technique for cointegration with breaks,continuously updated fully modified(CUP-FM)and continuously updated bias-corrected(CUPBC)estimators,Augmented Mean Group(AMG)approach,fully modified ordinary least squares,dynamic ordinary least squares,Canonical Cointegrating Regression(CCR),and panel causality test proposed by Canning&Pedroni.We compute non-parametric time-varying coefficients with fixed effects for seeing the impact of GFCF,LF,REC,and NREC on GDP.Our results press upon policymakers to shift toward clean energy and REC for attaining the environmental goals(SDGs 6,7,13,and 15)and the economic goals(SDGs 1,2,8,and 10).While this shift would help developed economies,which have already attained the economic goals,to progress on the front of environmental goals,it would enable developing countries to progress on both fronts in a balanced manner.

Do natural resources impact economic growth:An investigation of P5+1 countries under sustainable management

Natural resources represent the base of our living and the entire economic activity.Their depletion is a major challenge for the economic development of both developed and developing economies.Their effi-cient use is an indispensable requirement and must be the aim of the public policies designed by the authorities worldwide.In this research,we have investigated the impact of the natural resources rent on the economic growth in some major wealthy economies of the world(P5+1 countries namely:US,UK,France,China,Russia,and Germany).We have applied a quantile-on-quantile regression to analyse this impact on different quantiles and a cross-sectional autoregressive distributed lag(CS-ARDL)approach for the panel of these six countries.The Dumitrescu-Hurlin panel causality test was also used to check the causality between natural resource rents and economic growth in these countries.Results show a negative relationship between natural resources rent and economic growth for the panel but a different impact on quantiles in each country.Only for China and the US,a positive effect can be noticed for both lower and higher quantiles of natural resources and economic growth.The Dumitrescu-Hurlin causality test shows that natural resources can predict economic growth only in China,the U.S.,and the panel.In contrast,no causality was found for the other four countries included in the panel.We suggest that nations invest in wind and solar projects,use biofuels and nuclear energy,introduce a temporary profit tax to protect consumers from escalating energy prices,and increase energy efficiency in buildings and industry.Businesses would benefit from a regulatory framework that is uniform and exhaustive,as well as easier to traverse and more receptive to innovation and creativity.Public-private partnership investments in innovation,innovation incentives,and environmental sector opportunities may foster long-term economic growth。

A review of TiO_2 nanoparticles

Climate change and the consumption of non-renewable resources are considered as the greatest problems facing humankind.Because of this,photocatalysis research has been rapidly expanding.TiO2 nanoparticles have been extensively investigated for photocatalytic applications including the decomposition of organic compounds and production of H2 as a fuel using solar energy. This article reviews the structure and electronic properties of TiO2,compares TiO2 with other common semiconductors used for photocatalytic applications and clarifies the advantages of using TiO2 nanoparticles.TiO2 is considered close to an ideal semi- conductor for photocatalysis but possesses certain limitations such as poor absorption of visible radiation and rapid recombination of photogenerated electron/hole pairs.In this review article,various methods used to enhance the photocatalytic characteristics of TiO2 including dye sensitization,doping,coupling and capping are discussed.Environmental and energy applications of TiO2, including photocatalytic treatment of wastewater,pesticide degradation and water splitting to produce hydrogen have been summarized.