High-solids saccharification and viscosity studies in a scraped surface bio-reactor.

High solids processing of biomass slurries provides the following benefits: maximized product concentration in the fermentable sugar stream, reduced water usage, and reduced reactor size. However, high solids processing poses mixing and heat transfer problems above about 15% for pretreated corn stover solids due to their high viscosities. Also, highly viscous slurries require high power consumption in conventional stirred tanks since they must be run at high rotational speeds to maintain proper mixing. An 8 liter scraped surface bio-reactor (SSBR) is employed here that is designed to efficiently handle high solids loadings for enzymatic saccharification of pretreated corn stover (PCS) while maintaining power requirements on the order of low viscous liquids in conventional stirred tanks. The determination of the rheological behavior of biomass slurries is vital for process design at industrial scale. The viscosities of biomass slurries are seen here to be a function of initial solids concentration and initial biomass particle size. An extensive study has been conducted to investigate the effect of solids loading and viscosity on the rates and extent of enzymatic hydrolysis reactions. For batch testing with 25% (highest loading studied) initial PCS solids concentration, about 10% more glucose is released in the SSBR than in the shake flask after 168 hours of the saccharification reaction. The role of the viscosity of biomass slurries in power consumption of the reactor is presented. A semi-batch approach is employed to maintain lower slurry viscosity and, therefore, improved glucose release rates and reduced power consumption when operating with higher solids content. A processing efficiency is defined as sugar released per unit energy input. The 20% semi-batch saccharification test efficiency is about 27% higher than the 20% batch saccharification test efficiency. The settling of biomass particles presents a serious problem for measuring the viscosity of the slurries. Maintaining homogeneity by uniformly suspending all the particles is necessary for accurate viscosity measurements. Therefore, a new viscosity measuring technique has been developed here that incorporates the uniform suspension speed (USS) for particles in the viscometer cup that can be applied to any type of particulate suspension. The USS has been determined experimentally and computationally by a Computational Fluid Dynamics (CFD) model developed here that is well validated by experimental results. The wet density of PCS solids, which is not reported in the literature, is determined from the CFD model to be 1100 ± 50 kg/m3 based on the volume fraction distribution of solids at 305 rpm, the USS of a 5% solids slurry

REPLACEMENT OF COARSE AGGREGATE IN CONCRETE WITH WASTE CONCRETE AGGREGATE

Concrete is the most broadly utilized as development materials on the planet. Truth be told, concrete is utilized in for all intents and purposes everything and there is still no substitutes are accessible for a large number of its application. Without concrete, the network and society today couldn't exist. Subsequently, loads of analysts and designers are doing the exploration of the total sources. All these exploration as elective hotspots for the substitution of the normal totals in delivering concrete in the different future development works. In that idea we had influenced the exploration on the fine total substitution in M20 to level cement with the development and annihilation squander on Different examples like M1,M2,M3,M4 and M5. The proportion of sand supplanted to reused fine total are 100:0% as control, 80:20%, 50:50%, 20:80% and 0:100%. The venture clarifies about the properties of materials utilized in concrete, mechanical and transport properties of the soli

Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction

It's important to note that Alzheimer's disease can also affect individuals over the age of 60, and in fact, the risk of developing Alzheimer's increases with age. Additionally, while deep learning approaches have shown promising results in detecting Alzheimer's disease, they are not the only techniques available for diagnosis and treatment. That being said, using Region-based Convolutional Neural Network (RCNN) for efficient feature extraction and classification can be a valuable tool in detecting Alzheimer's disease. This new approach to identifying Alzheimer's disease could lead to a more accurate and personalized diagnosis. It can also help in early treatment and intervention. However, it's still important to continue developing new methods and techniques for this disorder. Considering this our work proposes an innovative Region-based Convolutional Neural Network Driven Alzheimer’s Severity Prediction approach in this paper. The exhaustive experimental result carried out, which proves the efficacy of our Alzheimer prediction system

Matrix metalloproteinase-9 is elevated and related to interleukin-17 and psychological stress in male infertility: A cross-sectional study

Background: Matrix metalloproteinase-9 (MMP-9), interleukin-17 (IL-17) and psychological stress are known to play a role in the pathogenesis of male infertility. Objective: To assess the association of MMP-9 with IL-17 and psychological stress in infertile men. Materials and Methods: In this cross-sectional study, 39 men with infertility diagnosed based on semen analysis and 39 subjects with normal semen analysis were included in the study. MMP-9 and IL-17 were estimated in both groups by ELISA. Perceived stress scale was used to assess psychological stress in controls and cases. Results: In infertile cases, MMP-9 and IL-17 were significantly increased when compared with controls (p = 0.046, p = 0.041 respectively). A significant association of MMP-9 was observed with IL-17 (r = 0.335, p = 0.037) and perceived stress scale (r = 0.329, p = 0.041). Conclusion: IL-17 and stress increase MMP- 9 levels in infertile men. Key words: Infertility, Interleukins, Peptide hydrolase

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Data privacy is essential in the financial sector to protect client’s sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property. It has become a challenging task due to the increase in usage of the internet and digital transactions. In this scenario, DDoS attack is one of the major attacks that makes clients’ privacy questionable. It requires effective and robust attack detection and prevention techniques. Machine Learning (ML) is the most effective approach for employing cyber attack detection systems. It paves the way for a new era where human and scientific communities will benefit. This paper presents a hierarchical ML-based hyperparameter-optimization approach for classifying intrusions in a network. CICIDS 2017 standard dataset was considered for this work. Initially, data was preprocessed with the min-max scaling and SMOTE methods. The LASSO approach was used for feature selection, given as input to the hierarchical ML algorithms: XGboost, LGBM, CatBoost, Random Forest (RF), and Decision Tree (DT). All these algorithms are pretrained with hyperparameters to enhance the effectiveness of algorithms. Models performance was assessed in terms of recall, precision, accuracy, and F1-score metrics. Evaluated approaches have shown that the LGBM algorithm gives a proven performance in classifying DDoS attacks with 99.77% of classification accuracy

Fabrication of copper sulfide nanoparticles from b

A nanomaterial has played a major role in protecting the environment-related issues. The prime reason for that nanomaterials synthetics approach is greener pathway, without using any hazardous chemicals and solvents. A huge number of plant-mediated metal sulfide nanoparticle (especially, Copper sulfide) synthesis has been reported and is still successfully continuing, because of its cost effective manner, eco-friendly nature, simple approach, reaction was carried out room temperatur. The current reports to synthesis of Copper sulfide nanostuctured materials through the green patch way, using Boswellia Ovalifoliolata leaves extract. From the UV-Visible spectroscopy noticed nanoparticles absorbance value is around 325 nm. As identified by FT-IR spectroscopy, a variety sources of phytochemicals in the extract which are responsible for the reduction of metal ions and stabilizing of the nanoparticles. X-ray diffraction studies revealed that nanomaterials were crystalline in nature, average crystalline size around 11 nm. SEM revealed that nanoparticles are spherical in nature and average size is 38.43 nm. The current report emphasizes that the materials are an excellent catalyst activity for the reduction of environmental pollutant azo dyes, antibacterial and antioxidant activity. The current article highlights the reduction of the azo dyes, antibacterial and antioxidant activity so the nanomaterials are apromising for the reduction of polluntat dyes