Microplastic predictive modelling with the integration of Artificial Neural Networks and Hidden Markov Models (ANN-HMM).

Microplastic pollution poses a significant threat to our environment, necessitating effective predictive modelling approaches for better management and mitigation. In this study, we introduce a pioneering methodology that fuses the power of Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) for microplastic predictive modelling. Leveraging a comprehensive dataset, our integrated model exhibits exceptional performance, with an Accuracy of 0.

Multi-Scale and Spatial Information Extraction for Kidney Tumor Segmentation: A Contextual Deformable Attention and Edge-Enhanced U-Net.

Kidney tumor segmentation is a difficult task because of the complex spatial and volumetric information present in medical images. Recent advances in deep convolutional neural networks (DCNNs) have improved tumor segmentation accuracy. However, the practical usability of current CNN-based networks is constrained by their high computational complexity.

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data.

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages.

Classification of membrane protein using Tetra Peptide Pattern.

Membrane proteins play an important role in the life activities of organisms. The mechanism of cell structures and biological activities can be identified only by knowing the functional types of membrane proteins which accelerate the process. Therefore, it is greatly necessary to build up computational approaches for timely and accurate prediction of the functional types of membrane protein.

A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment.

Cancer is caused by the un-controlled division of abnormal cells in a body part. Various cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) threatens the lives of people and today, it is the secondary prime cause of death in women.

Dispersion aided tenside disintegration of seagrass Syringodium isoetifolium: Towards biomethanation, kinetics, energy exploration and evaluation.

In this study, an attempt was made to enhance the biomethanation potential of seagrass (Syringodium isoetifolium) by the aid of disperser-tenside (polysorbate 80) disintegration for the first time in literature. A disperser rpm of 10,000 for 20 min and PS 80 dose of 0.000864 g/g TS were selected as ideal parameters for effectual seagrass biomass disintegration.

Improving the performance of empirical mode decomposition via Tsallis entropy: Application to Alzheimer EEG analysis.

Alzheimer is a degenerative disorder that attacks neurons, resulting in loss of memory, thinking, language skills, and behavioral changes. Computer-aided detection methods can uncover crucial information recorded by electroencephalograms. A systematic literature search presents the wavelet transform as a frequently used technique in Alzheimer's detection.

Enhancing biomethanation from dairy waste activated biomass using a novel EGTA mediated microwave disintegration.

A novel approach to explore the impact of calcium specific chelant - Ethylene glycol tetra acetic acid (EGTA) on deflocculation followed by biomass disintegration using microwave (MW) was investigated. In the first phase of the study, the EGTA dosage of 0.012 g/g suspended solids (SS) was found to be optimal for disassociating the biomass.

Synergistic impact of sonic-tenside on biomass disintegration potential: Acidogenic and methane potential studies, kinetics and cost analytics.

An exploration into the symbiotic impact of sonic-tenside (SDBS - sodium dodecyl benzene sulfonate) on biomass disintegration potential and to reduce the energy consumption was studied. At optimized condition (specific energy input 9600 kJ/kg TS; SDBS dosage 0.07 g/g SS), higher percentage of biomass lysis and solids reduction (23.

Biological disintegration of microalgae for biomethane recovery-prediction of biodegradability and computation of energy balance.

The present study investigates the synergistic effect of combined bacterial disintegration on mixed microalgal biomass for energy efficient biomethane generation. The rate of microalgal biomass lysis, enhanced biodegradability, and methane generation were used as indices to assess efficiency of the disintegration. A maximal dissolvable organics release and algal biomass lysis rate of about 1100, 950 and 800mg/L and 26, 23 and 18% was achieved in PA+C (protease, amylase+cellulase secreting bacteria), C (cellulase alone) and PA (protease, amylase) microalgal disintegration.

Immobilized and MgSO induced cost effective bacterial disintegration of waste activated sludge for effective anaerobic digestion.

In this study, an attempt was made to disintegrate waste activated sludge (WAS) in a cost-effective way. During the first phase of this study, effective break down of extracellular polymeric substance (EPS) was performed by deflocculating WAS with 0.1 g/g SS of MgSO.

Fenton mediated ultrasonic disintegration of sludge biomass: Biodegradability studies, energetic assessment, and its economic viability.

Mechanical disintegration of sludge through ultrasonication demands high energy and cost. Therefore, in the present study, a comprehensive investigation was performed to analyze the potential of a novel method, fenton mediated sonic disintegration (FSD). In FSD process, extracellular polymeric substance (EPS) of sludge was first removed via fenton treatment.

Enhancement of sludge anaerobic biodegradability by combined microwave-H2O2 pretreatment in acidic conditions.

The aim of this study was to increase the sludge disintegration and reduce the cost of microwave (MW) pretreatment. Thermodynamic analysis of MW hydrolysis revealed the best fit with a first-order kinetic model at a specific energy of 18,600 kJ/kg total solids (TS). Combining H2O2 with MW resulted in a significant increment in solubilization from 30 to 50 % at 18,600 kJ/kg TS.

Brain tissue segmentation using fuzzy clustering techniques.

Background: Medical image segmentation is an essential step for most consequent image analysis tasks. Medical images can be segmented manually, but the accuracy of image segmentation using the automated segmentation algorithms is more when compared with the manual calculations. In this paper, an automated segmentation and classification of tissues are proposed for MR brain images.

Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids.

One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption.

Biosorption of lead by Kluyveromyces marxianus immobilized in alginate beads.

The uptake and recovery of Pb (II) ions were investigated by using sodium alginate beads. Biosorption experiments are carried out in batch mode. The experimental results showed that the beads were effective in removing Pb (II) ions from solution.

Remarks on Pre-ℐ-Regular Pre-ℐ-Open Sets.

We deal with the new class of pre-ℐ-regular pre-ℐ-open sets in which the notion of pre-ℐ-open set is involved. We characterize these sets and study some of their fundamental properties. We also present other notions called extremally pre-ℐ-disconnectedness, locally pre-ℐ-indiscreetness, and pre-ℐ-regular sets by utilizing the notion of pre-ℐ-open and pre-ℐ-closed sets by which we obtain some equivalence relation for pre-ℐ-regular pre-ℐ-open sets.

Enhancing the anaerobic digestion potential of dairy waste activated sludge by two step sono-alkalization pretreatment.

High efficiency resource recovery from dairy waste activated sludge (WAS) has been a focus of attention. An investigation into the influence of two step sono-alkalization pretreatment (using different alkaline agents, pH and sonic reaction times) on sludge reduction potential in a semi-continuous anaerobic reactor was performed for the first time in literature. Firstly, effect of sludge pretreatment was evaluated by COD solubilization, suspended solids reduction and biogas production.