Co-digestion of domestic kitchen food waste and palm oil mill effluent for biohydrogen production

Biohydrogen production from organic waste not only provides a sustainable way to produce biofuel but it also resolves the growing environmental concerns associated with agro-industrial waste. This research study investigated the biological hydrogen production potential in batch mode through co-digestion of domestic kitchen food waste (DKFW) and palm oil mill effluent (POME) under mesophilic conditions by immobilized Bacillus anthracis bacterial strain. The results showed that hydrogen production from co-digestion of DKFW and POME with an equal proportion of the combination is pH and temperature-dependent. Where, the elevated pH from 4.0 to 5.0 increases hydrogen production significantly; however, increasing the pH > 5.0 reduces productivity. Similarly, by raising the operating temperature from 25 °C to 35 °C the hydrogen production rate (HPR) increases up to 67 mL/h. Apart from hydrogen production, a reduction in chemical oxygen demand (COD) was observed by up to 72 % in this study. The improvement observed for HPR and a significant reduction in COD, suggests that the co-digestion of POME and DKFW is an ideal substrate for hydrogen production at operational temperatures and initial pH of 35 °C and 5.0, respectively. The strategy for utilizing the different organic waste together as a substrate provides a new avenue for the complex substrate for bioenergy production

Influence of Different Spacings under Varying Fertility Levels on Protein Content, Protein Yield and Available NPK in Soil of Summer Greengram (Vigna radiata L.)

The present study highlights the influence of different spacings under varying fertility levels on protein content, protein yield and available npk in soil of summer greengram (vigna radiata l.). Due to its importance in Indian agricultural and ability to provide vital amino acids and protein, food beans are a staple of the Indian diet. In the summer of 2020, a field experiment on loamy sand soil was conducted at the Agronomy Instructional Farm, Chimanbhai Patel College of Agriculture, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar. Twelve treatment combinations comprising of four spacings S1 (30 × 10 cm), S2 (30 × 20 cm), S3 (45 × 10 cm) and S4 (45 × 20 cm) and three fertility levels viz., F1 (75% RDF), F2 (100% RDF) and F3 (125% RDF) were evaluated in factorial randomized block design with three replications. The results revealed that significantly higher protein yield (256.10 kg/ha) under 30 × 10 cm spacing as compared to other treatments. Among the fertility levels significantly higher protein content (24.17%), protein yield (260.27 kg/ha) and Higher value of available N and P2O5 status in soil after harvest was observed in treatment (F3) 125% RDF of summer greengram. Thus, from the foregoing results of one year experiment, it can be concluded that for securing higher seed yield, stover yield, protein yield and higher available N, P2O5 in soil after harvest of summer greengram should be sown at 30 cm × 10 cm spacing and fertilized with 100% RDF (20-40-00 N-P2O5-K2O kg/ha) under loamy sand soil

Deep trained features extraction and dense layer classification of sensitive and normal documents for robotic vision-based segregation

The digitization of important documents and their segregation can be a beneficial and time-saving activity as individuals will have greater access to important documents and will be able to use them in regular tasks as well as endeavours. In recent years, research into the application of deep networks in robot systems has increased as a direct consequence of the advancements made in classification algorithms over the past few decades. Robotic vision automation for the segregation of sensitive and non-sensitive documents is required for many security concerns. The methodology of this article is initially focused on the identification of a good computer vision-based technique for the classification of sensitive documents from non-sensitive documents. The authors first identified the standard parameters in terms of reliability, loss, precision, and recall by employing deep learning techniques, such as neural networks with convolutions and transfer learning (TL) algorithms. The extraction of features based on pre-trained deep learning models was referenced in numerous publications. Similarly, we applied most of the feature extraction techniques to identify feature extraction from the images. Then, these features were classified by machine and ensemble learning models. However, the pre-trained models-based feature extraction along with machine learning classification resulted better in comparison to the deep learning and TL procedures. Further, the better-identified techniques were applied as the brain behind the vision of a robotic structure to automate the segregation of sensitive documents from non-sensitive documents. This proposed robotic structure could be applied when we have to find some specific and classified document from the haystack

Panoptic View of Prognostic Models for Personalized Breast Cancer Management

The efforts to personalize treatment for patients with breast cancer have led to a focus on the deeper characterization of genotypic and phenotypic heterogeneity among breast cancers. Traditional pathology utilizes microscopy to profile the morphologic features and organizational architecture of tumor tissue for predicting the course of disease, and is the first-line set of guiding tools for customizing treatment decision-making. Currently, clinicians use this information, combined with the disease stage, to predict patient prognosis to some extent. However, tumoral heterogeneity stubbornly persists among patient subgroups delineated by these clinicopathologic characteristics, as currently used methodologies in diagnostic pathology lack the capability to discern deeper genotypic and subtler phenotypic differences among individual patients. Recent advancements in molecular pathology, however, are poised to change this by joining forces with multiple-omics technologies (genomics, transcriptomics, epigenomics, proteomics, and metabolomics) that provide a wealth of data about the precise molecular complement of each patient’s tumor. In addition, these technologies inform the drivers of disease aggressiveness, the determinants of therapeutic response, and new treatment targets in the individual patient. The tumor architecture information can be integrated with the knowledge of the detailed mutational, transcriptional, and proteomic phenotypes of cancer cells within individual tumors to derive a new level of biologic insight that enables powerful, data-driven patient stratification and customization of treatment for each patient, at each stage of the disease. This review summarizes the prognostic and predictive insights provided by commercially available gene expression-based tests and other multivariate or clinical -omics-based prognostic/predictive models currently under development, and proposes a more inclusive multiplatform approach to tackling the challenging heterogeneity of breast cancer to individualize its management. “The future is already here—it’s just not very evenly distributed.”-William Ford Gibso