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There are still alarming gaps among organisations and individuals regarding' cyber-attacks in cyber space and measures to create awareness about cyber insurance. In the realm of cyber space, cyber-attacks can occur, impacting both individuals and businesses. For individuals, this often involves crimes such as identity theft and personal data breaches. For businesses, cyber-attacks can include the theft of intellectual property and other sensitive information. The threat posed by cyber-attacks has grown considerably in recent years. Globally, the survey revealed diverse reasons for not obtaining cyber insurance. It was found that people lacked a clear understanding of cyber insurance products or perceived it as too expensive. [ABSTRACT FROM AUTHOR]

This paper presents a new model, the Weighted Extreme Learning Machine optimized Diagonal-Kernels Convolution (WELM-ODKC), for automatic skin cancer detection that addresses imbalanced data and eliminates inter-operator variability. The model combines the WELM and the Enhanced Remora Optimization Algorithm (EROA) with the Diagonal-Kernels Convolution Neural Network (DKCNN) to enhance the weight function and accurately predict skin lesion class. The model was evaluated on the MNIST HAM10000 and PAD-UFES-20 datasets and outperformed other existing skin cancer classification methods such as SVM BWO, CNN, DGC-NB, and GWO-CNN. The accuracy, recall, precision, and F1-score were used to evaluate the performance of WEL-ODKC, and the results show a high accuracy during training and validation. The proposed model efficiently classifies different types of skin cancer images from the two baseline datasets and provides promising results for automatic skin cancer detection. [ABSTRACT FROM AUTHOR]

Post flowering stalk rot (PFSR) of maize caused by the Fusarium species complex is a serious threat to maize production worldwide. The identification of Fusarium species causing PFSR based on morphology traditionally relies on a small set of phenomic characteristics with only minor morphological variations among distinct Fusarium species. Seventy-one isolates were collected from 40 sites in five agro-climatic zones of India to assess the diversity of Fusarium spp. associated with maize crops showing symptoms of PFSR in the field. To investigate the pathogenicity of Fusarium spp. causing PFSR sixty isolates were toothpick inoculated between the first and second node at 55 days after sowing during the tassel formation stage of the crop in Kharif (Rainy season), and Rabi (Winter season) season field trials. Ten most virulent Fusarium isolates, based on the highest observed disease index, were identified by homology and phylogenetic analyses of partial sequences of the translation elongation factor 1 α (Tef-1α). Based on morphological traits such as mycelial growth patterns and mycelial pigmentation, Fusarium isolates were divided into nine clusters. The isolates were judged to be virulent based on their ability to decrease seedling vigour in in-vivo situations and high disease severity in field experiments. Pathogenicity test during the Kharif season showed 12 isolates with virulent disease symptoms with a mean severity ranging between 50 to 67 percent disease index (PDI) whereas in Rabi season, only five isolates were considered virulent, and the mean severity ranged between 52 to 67 PDI. Based on pathological characterization and molecular identification, 10 strains of Fusarium species namely, Fusarium acutatum (2/10), Fusarium verticillioides (Syn. Gibberella fujikuroi var. moniliformis) (7/10), Fusarium andiyazi (2/10) recorded the highest diseases index. All these species are part of the Fusarium fujikuroi species complex (FFSC). The distribution of virulent isolates is specific to a geographical location with a hot humid climate. Increased knowledge regarding the variability of Fusarium spp. responsible for PFSR of maize occurring across wide geographical locations of India will enable more informed decisions to be made to support the management of the disease, including screening for resistance in maize-inbred lines. [ABSTRACT FROM AUTHOR]

The Diabetes-Mellitus (DM) disease is considered a persistent ailment that is triggered by excessive sugar levels in the blood of a person. It gives rise to severe health complications when left untreated and can also give rise to related diseases such as cardiac attack, nervous damage, foot problems, liver and kidney damage, and eye problems. These problems are caused by a series of factors interrelated to one another such as age, gender, family history, BMI, and Blood Glucose. Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. The Diabetes prediction process can be further improvised by identifying the type a person is being affected by and the probability of the occurrence of the related diseases. In order to perform the mentioned task, two types of the dataset are used in the study, namely, PIMA and a clinical survey dataset. Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. The performance metrics used are accuracy, precision, recall, specificity, and sensitivity. Techniques such as Data Augmentation and Sampling are used. In comparison with the research conducted previously, the paper focuses on improvisation of the accuracy with a percentage of 95.20 using the LGBM Classifier, and Diabetes is also classified as Prediabetes or Diabetes using many Classification mechanisms. [ABSTRACT FROM AUTHOR]

The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease. [ABSTRACT FROM AUTHOR]

To minimize losses likely to occur due to Corynespora leaf spot, systemic and contact fungicides and formulation of bacterial antagonists like Bacillus subtilis DTBS-5, B. amyloliquefaciens DSBA-11, and Pseudomonas fluorescens DTPF-3 talc-based formulations were evaluated to manage the disease. Though mancozeb has the lowest EC50 value of 1250.54 ppm it was not efficient in the field experiment. In addition, dual culture assay to determine the efficacy of bacterial antagonists against C. cassiicola, B. amyloliquefaciens DSBA-11 effectiovely reduced growth of C. cassiicola. In the field experiments conducted during Kharif 2019 and 2020, two sprays with Azoxystrobin 23 SC@ 0.5 ml lit−1 were found most effective in reducing disease severity by 57.7% and 67.7% respectively during Kharif 2019 and 2020. Azoxystrobin 23 SC treatment resulted in a maximum increase of 281.5 kg in grain yield followed by Pseudomonas fluorescens DTPF-3 264.7 kg and Bacillus subtilis DTBS-5 248.9 kg yield. [ABSTRACT FROM AUTHOR]

We present detailed studies on spinel structured Ni0.5CoxZn0.5−xFe2O4 (x = 0.1, 0.2 and 0.3), synthesized by sol–gel combustion method. In addition to the structural properties, we present optical dielectric and magnetic response of this system as a function of x. From x-ray diffraction analysis, a single-phase formation in cubic spinel structure was confirmed with an average crystallite size (Davg) variation of 46–61 nm. X-ray diffractograms were analyzed to evaluate the lattice constant (a), x-ray density (ρx) and porosity (P) in percentage. Energy dispersive x-ray spectroscopy was used to delimit the compositional mass ratios. Terahertz (THz) time-domain spectroscopy measurements were performed to investigate the refractive index () of the samples at room temperature in a frequency range from 0.2 to 2.5 THz. Real and imaginary components of complex optical dielectric constant () with the increase of Co2+ content were deduced using THz spectroscopy data. The complex optical dielectric constant for the substitution of Co2+ content at x = 0.1, 0.2 and 0.3, was determined within 5% error ∼9.6 + i0.07, ∼10.9 + i0.2 and ∼11.7 + i0.5, respectively. The magnetic measurements were performed at a temperature range of 300–50 K using a superconducting quantum interference device vibrating sample magnetometer under an applied magnetic field of ±5 tesla. We found the highest saturation magnetization (Ms) of about 86.7 emu g−1, 83.5 emu g−1 and 80.4 emu g−1 for the substitution of Co2+ content at x = 0.1, 0.2 and 0.3, respectively at room temperature. [ABSTRACT FROM AUTHOR]

We present detailed studies on M-type barium ferrite (BaFe12O19, BaM) synthesized by the sol–gel combustion method that is calcined at 1000 °C, 1100 °C, and 1200 °C. In addition to the structural properties, we present the THz optical dielectric constant and conductivity response of this system as a function of calcination temperature. From x-ray diffraction (XRD) studies, a single-phase of the hexagonal structure is established, and the crystallite size (Dhkl) was calculated to be in the range of 26 nm–28.54 nm. The XRD patterns were analyzed to evaluate lattice parameters (a, c, V) and x-ray density (ρx). Home built terahertz time-domain spectroscopy was performed to investigate the complex refractive index ( n ̂ s ) of the samples at room temperature in a frequency range of 0.2 THz–1.2 THz. The complex dielectric constant ( ε ̂ s ) and conductivity ( σ ̂ s ) as a function of calcination temperature were deduced using THz spectroscopy data. The complex dielectric constant and conductivity of BaM were determined for the calcination temperatures of 1000 °C, 1100 °C, and 1200 °C. [ABSTRACT FROM AUTHOR]

Nickel-cobalt-zinc ferrite, Ni0.5Co0.2Zn0.3Fe2O4 is interesting as it has the low coercivity and highest magnetization. We prepared Ni0.5Co0.2Zn0.3Fe2O4 using the autocombustion route. X-ray diffraction and Field Emission Gun Transmission Electron Microscope (FEG-TEM) studies were carried out for checking the crystal structure and morphology. Magnetic properties of the ferrite were studied by using SQUID Vibrating Sample Magnetometer (SVSM) under an applied magnetic field of ±5 Tesla. An examination of M-H loops revealed highest saturation magnetization of 82 emu/g with soft magnetic nature of the sample at different temperatures. [ABSTRACT FROM AUTHOR]

Using gamma-ray-induced mutagenesis, we have developed a mutant (named G2) of Trichoderma virens that produced two- to three-fold excesses of secondary metabolites, including viridin, viridiol, and some yet-to-be identified compounds. Consequently, this mutant had improved antibiosis against the oomycete test pathogen Pythium aphanidermatum. A transcriptome analysis of the mutant vis-à-vis the wild-type strain showed upregulation of several secondary-metabolism-related genes. In addition, many genes predicted to be involved in mycoparasitism and plant interactions were also upregulated. We used tamarind seeds as a mass multiplication medium in solid-state fermentation and, using talcum powder as a carrier, developed a novel seed dressing formulation. A comparative evaluation of the wild type and the mutant in greenhouse under high disease pressure (using the test pathogen Sclerotium rolfsii) revealed superiority of the mutant over wild type in protecting chickpea (Cicer arietinum) seeds and seedlings from infection. We then undertook extensive field evaluation (replicated micro-plot trials, on-farm demonstration trials, and large-scale trials in farmers' fields) of our mutant-based formulation (named TrichoBARC) for management of collar rot (S. rolfsii) in chickpea and lentil (Lens culinaris) over multiple locations in India. In certain experiments, other available formulations were included for comparison. This formulation consistently, over multiple locations and years, improved seed germination, reduced seedling mortality, and improved plant growth and yield. We also noticed growth promotion, improved pod bearing, and early flowering (7–10 days) in TrichoBARC-treated chickpea and lentil plants under field conditions. In toxicological studies in animal models, this formulation exhibited no toxicity to mammals, birds, or fish. [ABSTRACT FROM AUTHOR]

Mixing or combining different elements for getting enhanced version, is practiced across various areas in real life. Pan-sharpening is a similar technique used in the digital world; a process to combine two images into a fused image that comprises more detailed information. Images referred herein are Panchromatic (PAN) and Multispectral (MS) images. This paper presents a pansharpening algorithm which integrates multispectral and panchromatic images to generate an improved multispectral image. This technique merges the Discrete wavelet transform (WT) and Intensity-Hue-Saturation (IHS) through separate fusing criterion for choosing an approximate and detail sub-images. Whereas the maximal local extrema are used for merging detail sub-images and finally merged high-resolution image is reconstructed through inverse transform of wavelet and IHS. The proposed fusion approach enhances the superiority of the resultant fused image is demonstrated by quality measures like CORR, RMSE, PFE, SSIM, SNR and PSNR with the help of satellite Worldview-II images. The proposed algorithm is correlated with the other fusion techniques through empirical outcomes proves the superiority of the final merged image in terms of resolutions than the others. [ABSTRACT FROM AUTHOR]

During the recent times, Software in computers use an external layer of protection to provide an extra blanket of security against crackers and reverse engineers. This is done using specialized software commonly termed as packers. Mostly all packers use the approach for packing that is easily unpack-able by crackers; also many unpacking methods are available over the Internet to deal with approach . The paper proposes a novel packer algorithm that uses encryption and an anti-debugging technique to keep away reverse engineers. The proposed algorithm can pack multiple software at the same time. The experimental results demonstrate the effective packing ability of packer on the object files. [ABSTRACT FROM AUTHOR]

With the intrusion of internet into the lives of every household and terabytes of data being transmitted over the internet on daily basis, the protection of content being transmitted over the internet has become an extremely serious concern. Various measures and methods are being researched and devised everyday to ensure content protection of digital media. To address this issue of content protection, this paper proposes an RGB image steganography based on sixteen-pixel differencing with n-bit Least Significant Bit (LSB) substitution. The proposed technique provides higher embedding capacity without sacrificing the imperceptibility of the host data. The image is divided into 4×4 non overlapping blocks and in each block the average difference value is calculated. Based on this value the block is classified to fall into one of four levels such as, lower, lower-middle, higher-middle and higher. If block belongs to lower level then 2-bit LSB substitution is used in it. Similarly, for lower-middle, higher-middle and higher level blocks 3, 4, and 5 bit LSB substitution is used. In our proposed method there is no need of pixel value readjustment for minimizing distortion. The experimental results show that stego-images are imperceptible and have huge hiding capacity. [ABSTRACT FROM AUTHOR]

Sugarcane bacilliform viruses ( SCBV; genus Badnavirus) cause leaf fleck disease in sugarcane worldwide. SCBV was detected in 28 sugarcane cultivars originating from eight states of India. Eight representative SCBV isolates from five different states showed sequence variability up to 27% in the reverse transcriptase and RNase H ( RT/RNase H) genetic region. Five isolates [SCBV-AP (): , SCBV-Assam (CoBIN94063): , SCBV-Bihar (Bo141): , SCBV-UP (Co1424): and SCBV-UP (CoSe6460): ] showed sequence identity of 86-88% with Sugarcane bacilliform IM virus ( SCBIMV), but the other three isolates [ SCBV-Kerala (Co7219): , SCBV- TN (): and SCBV- UP (CoSe92423): ] showed maximum sequence identity of 75-79% with Sugarcane bacilliform MO virus ( SCBMOV). In phylogenetic analysis, the SCBV isolates segregated into two new subclades and were distinct from the known SCBV genotypes. The rates of non-synonymous and synonymous ( dN/ dS) substitution indicated the signs of purifying selection with strong functional constraints for RT/RNase H region in SCBV population. A variant ( SCBV-UP, CoSe92423) was identified to be a recombinant isolate having two other Indian SCBV isolates as parents. Although RT/RNase H region is a recombination cold spot, a strong recombination might have played a key role in the evolution of this new variant of SCBV. Our study provides an insight into the diverse genetic structure of SCBV population and presence of a novel recombinant SCBV species/variant infecting sugarcane cultivars in India. Our results will be helpful in devising a robust detection procedure for quarantine and field testing of sugarcane germplasm. [ABSTRACT FROM AUTHOR]

Coffee is one of the most popular beverages in the world. It is consumed for its refreshing and stimulating properties. Carbohydrates are the major constituents of coffee beans and serve various functions like binding of aroma, stabilization of foam, formation of sedimentation, and increased viscosity of the extract. The principal low molecular weight carbohydrate is sucrose and no evidence of other simple oligosaccharides has been found. Polysaccharide fraction from green coffee is dominated by arabinogalactan, galactomannan, and cellulose. The polysaccharide content is reduced during roasting due to degradation to low molecular weight carbohydrates (viz., mono and oligosaccharide) and become more extractable. Various methods that can be employed to extract the carbohydrate from roasted coffee are sequential extraction, acid hydroloysis, hot water extraction, enzymatic extraction etc. Carbohydrates from coffee can be quantitatively determined by liquid chromatography, high performance anion exchange chromatography, size exclusion chromatography, and high performance liquid chromatography. Besides improving the organoleptic quality of the coffee beverage, carbohydrates also possess various biological activities such as lowering colon cancer risk. Besides their sheer mass, a variety of evidences testify to the important contribution that the polysaccharide content makes to the character of the final brew. Although a number of chemical and enzymatic methods have been devised to isolate and quantify the carbohydrates of R&G coffee, till date hot water extraction is the only method which can be accepted as a most feasible process and hence, there is wide scope of further research for the efficient and economically viable technology for extraction of carbohydrates from coffee. [ABSTRACT FROM AUTHOR]