Forecasting Mental Stress Using Machine Learning Algorithms

Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is divided into six parts: Proposed Research Architecture (PRA), Data Pre-processing Approach (DPA), Research Hypothesis Testing (RHT), Concentrated Algorithm Pipeline (CAP), Loss Optimization Stratagem (LOS), and Model Deployment Architecture (MDA). The Null Hypothesis and Alternative Hypothesis are applied to test the RHT. In addition, Ensemble Learning Approach (ELA) and Frequent Model Retraining (FMR) have been utilized for optimizing the loss function. Besides, the Features Importance Interpretation is also delineated in this research. These forecasts could help individuals connect with expert mental health specialists more quickly and easily. According to the findings, 71% of people with depression and 80% of those who do not have depression can be appropriately diagnosed. This study obtained 91% and 92% accuracy through the Random Forest (RF) and Extra Tree Classifier. But after applying the Receiver operating characteristic (ROC) curve, 79% accuracy was found on top of RF, 81% found on Extra Tree, and 82% recorded for the eXtreme Gradient Boosting (XGBoost) algorithm. Besides, several factors are identified in terms of predicting depression through statistical data analysis. Though the additional effort is needed to develop a more accurate model, this model can be adjustable in the healthcare sector for diagnosing depression.     

Mucoadhesive elementary osmotic pump tablets of trimetazidine for controlled drug delivery and reduced variability in oral bioavailability

The objectives of this work was preparation and evaluation of the mucoadhesive elementary osmotic pump tablets of trimetazidine hydrochloride to achieve desired controlled release action and augmentation of oral drug absorption. The drug-loaded core tablets were prepared employing the suitable tableting excipients and coated with polymeric blend of ethyl cellulose and hydroxypropyl methylethylcellulose E5 (4:1). The prepared tablets were characterized for various quality control tests and in vitro drug release. Evaluation of drug release kinetics through model fitting suggested the Fickian mechanism of drug release, which was regulated by osmosis and diffusion as the predominant mechanism. Evaluation of mucoadhesion property using texture analyzer suggested good mucoadhesion potential of the developed osmotic systems. Solid state characterization using Fourier-transform infrared spectroscopy, differential scanning calorimetry and powder X-ray diffraction spectroscopy confirmed the absence of any physiochemical incompatibilities between drug and excipients. Scanning electron microscopy analysis showed the smooth surface appearance of the coated tablets with intact polymeric membrane without any fracture. In vivo pharmacokinetic studies in rabbits revealed 3.01-fold enhancement in the oral bioavailability vis-à-vis the marketed formulation (Vastarel MR®). These studies successfully demonstrate the bioavailability enhancement potential of the mucoadhesive elementary osmotic pumps as novel therapeutic systems for other drugs too.     

Modelling and motion control of a mechatronic system using BGM with intelligent controllers

In this research paper, a mechatronics system such as a pan tilt platform (PTP) has been considered for motion control under intelligent controllers. A proportional-derivative (PD) controller is considered for comparison of results obtained from fuzzy and hybrid controllers. The trajectory following performance of the mechatronics system is found against these controllers. The results of simulations show that hybrid fuzzy controller reduce the tracking error effectively in lesser settling time. The intelligent controllers require knowledge base of error and derivative of error to compensate the PTP dynamics. The intelligent controllers have similar trends as the PD controllers and compensated both electrical and mechanical dynamics. The PD controller requires position measurement. The intelligent controllers have knowledge base consisting of position and velocity data. Thus intelligent controllers have position measurement along with knowledge base for position control system. The best results were achieved with hybrid fuzzy controllers. They meet the desired specifications.     

Identification of spatiotemporally invariant systems for control adaptation

We present a distributed projection algorithm for system identification of spatiotemporally invariant systems with the ultimate purpose of utilizing it in an indirect adaptive control scheme. Each subsystem communicates only with its immediate neighbors to share its current estimate along with a cumulative improvement index. On the basis of the cumulative improvement index, the best estimate available is picked in order to carry out the next iteration. For small estimation error, the scheme switches over to a “smart” averaging routine. The proposed algorithm guarantees to bring the local estimates arbitrarily close to one another, developing a “local consensus”, which makes it amenable to control by the application of indirect distributed adaptive control schemes. It is also shown through simulations that the proposed algorithm has a clear advantage over the standard projection algorithm. Our proposed algorithm is also suitable for addressing the estimation problem in distributed networks that arise in a variety of applications, such as environment monitoring, target localization and potential sensor network problems.     

Isotope Engineered Fluorinated Single and Bilayer Graphene: Insights into Fluorination Selectivity,Stability, and Defect Passivation

Tailoring the physicochemical properties of graphene through functionalization remains a major interest for next-generation technological applications. However, defect formation due to functionalization greatly endangers the intrinsic properties of graphene, which remains a serious concern. Despite numerous attempts to address this issue, a comprehensive analysis has not been conducted. This work reports a two-step fluorination process to stabilize the fluorinated graphene and obtain control over the fluorination-induced defects in graphene layers. The structural, electronic and isotope-mass-sensitive spectroscopic characterization unveils several not-yet-resolved facts, such as fluorination sites and C F bond stability in partially-fluorinated graphene (F-SLG). The stability of fluorine has been correlated to fluorine co-shared between two graphene layers in fluorinated-bilayer-graphene (F-BLG). The desorption energy of co-shared fluorine is an order of magnitude higher than the C F bond energy in F-SLG due to the electrostatic interaction and the inhibition of defluorination in the F-BLG. Additionally, F-BLG exhibits enhanced light–matter interaction, which has been utilized to design a proof-of-concept field-effect phototransistor that produces high photocurrent response at a time <200 µs. Thus, the study paves a new avenue for the in-depth understanding and practical utilization of fluorinated graphenic carbon.     

Influence of matrix material on flexural fatigue performance of unidirectional composites

A deflection-controlled flexural fatigue study of unidirectional glass fiber reinforced epoxy and vinyl ester composites was undertaken. Damage initiation and growth for various deflection levels were evaluated. Also, quantitative assessment of damage was made by monitoring stiffness loss in the composites as a function of fatigue cycles. Results show that the glass/epoxy composite has better performance compared with the glass/vinyl ester composite, especially at low deflection amplitudes. Fatigue behavior of the composites at low deflection amplitudes is found to be primarily influenced by matric and fiber-matrix interfacial damage in the form of longitudinal splitting.     

Graphs Resemblance based Software Birthmarks through Data Mining for Piracy Control

Programming and Computer Software - The emergence of software artifacts greatly emphasizes the need for protecting intellectual property rights (IPR) hampered by software piracy requiring effective...     

Modeling the Flow Curve Characteristics of 410 Martensitic Stainless Steel Under Hot Working Condition

The hot deformation behavior of AISI 410 martensitic stainless steel was investigated by conducting hot compression tests between 1173 K (900 °C) and 1423 K (1150 °C) and between strain rates of 0.001 s⁻¹ to 1 s⁻¹. The hyperbolic sine function described the relation well between flow stress at a given strain and the Zener–Hollomon parameter (Z). The variation of flow stress with deformation temperature gave the average value of apparent activation energy as 448 kJ/mol. The strain and stress corresponding to two important points associated with flow curve (i.e., peak strain and the onset of steady-state flow) were related to the Z parameter using power-law equations. A model also was proposed based on the Johnson–Mehl–Avrami–Kolmogorov (JMAK) equation to estimate the fractional softening of dynamic recrystallization at any given strain. This model can be used readily for the prediction of flow stress. The values of n and k, material constants in the JMAK equation, were determined for the studied material. The strains regarding the peak and the onset of steady-state flow were formulated in term of applied strain rate and the constants of the JMAK equation. A good agreement was found between the predicted strains and those obtained by the experimental work.     

Liver-Tumor Detection Using CNN ResUNet

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest (ROI). This study uses ResUNet, an updated version of U-Net and ResNet Models that utilize the service of Residential blocks. We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients. The results showed the True Value Accuracy around 99% and F1 score performance around 95%. This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.     

Co‐poly(vinyl chloride‐vinyl acetate‐vinyl alcohol)‐silica nanocomposites from sol–gel process: Morphological,mechanical, and thermal investigations

Organic–inorganic nanocomposites consisting of co‐poly(vinyl chloride‐vinyl acetate‐vinyl alcohol) and silica were prepared via sol–gel process. Two types of hybrids were prepared, one in which interactions between hydroxyl group present in the copolymer chain and silanol groups of silica network were developed. In the second set, extensive chemical bonding between the phases was achieved through the reaction of hydroxyl groups on the copolymer chains with 3‐isocyanatopropyltriethoxysilane (ICTS). Hydrolysis and condensation of tetraethoxysilane and pendant ethoxy groups on the chain yielded inorganic network structure. Mechanical and thermal behaviors of the hybrid films were studied. Increase in Young's modulus, tensile strength, and toughness was observed up to 2.5 wt % silica content relative to the neat copolymer. The system in which ICTS was employed as binding agent, the tensile strength and toughness of hybrid films increased significantly as compared to the pure copolymer. Thermogravimetric analysis showed that these nanocomposite materials were stable up to 250°C. The glass transition temperature increases up to 2.5 wt % addition of silica in both the systems. Field emission scanning electron microscope results revealed uniform distribution of silica in the copolymer matrix. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012