Explore the vast range of titles available.

This abstract describes the smart grid management system is an emerging technology that utilizes machine learning algorithms for efficient energy distribution. The paper presents an overview of the architecture, benefits, and challenges of smart grid management systems. The paper also discusses various machine learning algorithms used in smart grid management systems such as neural networks, decision trees, and Support Vector Machines (SVM). The advantages of using machine learning algorithms in smart grid management systems include increased energy efficiency, reduced energy wastage, improved reliability, and reduced costs. The challenges in implementing machine learning algorithms in smart grid management systems include data security, privacy, and scalability. The paper concludes by discussing future research directions in smart grid management systems based on machine learning algorithms.

The knowledge of heat transfer of MHD flows across a stretched surface plays a crucial role in transportation, heat exchangers, fibre coating, magnetic drug treatment, etc. The present research article delivers a numerical examination of 2D magnetohydrodynamic nonlinear radiative flow of micropolar fluid towards a stretching surface. The fluid motion is steady and laminar. The impacts of chemical reaction, cross-diffusion and thermal and solutal Biot numbers are deemed. Combined influence of heat and mass transfer attributes is investigated. For effective heat transfer, Cattaneo–Christov heat flux term is added to the energy equation. The appropriate transmutations are adopted for rehabilitating the flow governing PDEs into dimensionless ordinary ones. Further, these ODEs are resolved by R.K.F-4 with a shooting system. The graphs are plotted to picture the flow fields for the flow regulating parameters. The friction factor, couple stress and mass and thermal transport rates are presented for the flow relevant variables. From the results, we spotted that there are an enhancement in velocity but a decrement in temperature and concentration fields with the swelling in the values of primary slip parameter. Also the temperature and concentration fields are enhanced with the boosting values of Dufour and Soret numbers, respectively.

The synthesis of supramolecular polymers with controlled architecture is a grand challenge in supramolecular chemistry. Although living supramolecular polymerization via primary nucleation has been extensively studied for controlling the supramolecular polymerization of small molecules, the resulting supramolecular polymers have typically exhibited one-dimensional morphology. In this report, we present the synthesis of intriguing supramolecular polymer architectures through a secondary nucleation event, a mechanism well-established in protein aggregation and the crystallization of small molecules. To achieve this, we choose perylene diimide with 2-ethylhexyl chains at the imide position as they are capable of forming dormant monomers in solution. Activating these dormant monomers via mechanical stimuli and hetero-seeding using propoxyethyl perylene diimide seeds, secondary nucleation event takes over, leading to the formation of three-dimensional spherical spherulites and scarf-like supramolecular polymer heterostructures, respectively. Therefore, the results presented in this study propose a simple molecular design for synthesizing well-defined supramolecular polymer architectures via secondary nucleation. Synthesis of supramolecular polymers with controlled architecture is desirable but challenging. Here, the authors use a secondary nucleation event to prepare a range of supramolecular polymer architectures.

Purpose The purpose of this paper is to scrutinize the heat and mass transfer attributes of three-dimensional bio convective flow of nanofluid across a slendering surface with slip effects. The analysis is carried out subject to irregular heat sink/source, thermophoresis and Brownian motion of nanoparticles. Design/methodology/approach At first, proper transmutations are pondered to metamorphose the basic flow equations as ODEs. The solution of these ODEs is procured by the consecutive application of Shooting and Runge-Kutta fourth order numerical procedures. Findings The usual flow fields along with density of motile microorganisms for sundry physical parameters are divulged via plots and scrutinized. Further, the authors analyzed the impact of same parameters on skin friction, heat and mass transfer coefficients and presented in tables. It is discovered that the variable heat sink/source parameters play a decisive role in nature of the heat and mass transfer rates. The density of motile microorganisms will improve if we add Al-Cu alloy particles in regular fluids instead of Al particles solely. A change in thermophoresis and Brownian motion parameters dominates heat and mass transfer performance. Originality/value To the best of the knowledge, no author made an attempt to investigate the flow of nanofluids over a variable thickness surface with bio-convection, Brownian motion and slip effects.

Anxiety and depression are major chronic mood disorders, and the etiopathology for each appears to be repeated exposure to diverse unpredictable stress factors. Most of the studies on anxiety and related mood disorders are performed in rodents, and a good model is chronic unpredictable stress (CUS). In this study, we have attempted to understand the molecular basis of the neuroglial and behavioral changes underlying CUS-induced mood disorders in the simplest vertebrate model, the zebrafish, Danio rerio. Zebrafish were subjected to a CUS paradigm in which two different stressors were used daily for 15 days, and thorough behavioral analyses were performed to assess anxiety and related mood disorder phenotypes using the novel tank test, shoal cohesion and scototaxis. Fifteen days of exposure to chronic stressors appears to induce an anxiety and related mood disorder phenotype. Decreased neurogenesis, another hallmark of anxiety and related disorders in rodents, was also observed in this zebrafish model. The common molecular markers of rodent anxiety and related disorders, corticotropin-releasing factor (CRF), calcineurin (ppp3r1a) and phospho cyclic AMP response element binding protein (pCREB), were also replicated in the fish model. Finally, using 2DE FTMS/ITMSMS proteomics analyses, 18 proteins were found to be deregulated in zebrafish anxiety and related disorders. The most affected process was mitochondrial function, 4 of the 18 differentially regulated proteins were mitochondrial proteins: PHB2, SLC25A5, VDAC3 and IDH2, as reported in rodent and clinical samples. Thus, the zebrafish CUS model and proteomics can facilitate not only uncovering new molecular targets of anxiety and related mood disorders but also the routine screening of compounds for drug development.

Cryptocurrencies have gained immense popularity in recent years as an emerging asset class, and their prices are known to be highly volatile. Predicting cryptocurrency prices is a difficult task due to their complex nature and the absence of a central authority. In this paper, our proposal is to employ Long Short-Term Memory (LSTM) networks, a type of deep learning technique to forecast the prices of cryptocurrencies. We use historical price data and technical indicators as inputs to the LSTM model, which learns the underlying patterns and trends in the data. To improve the accuracy of the predictions, we also incorporate a Change Point Detection (CPD) technique using the Pruned Exact Linear Time (PELT) algorithm. This method allows us to detect significant changes in cryptocurrency prices and adjust the LSTM model accordingly, leading to better predictions. We evaluate our approach predominantly on Bitcoin cryptocurrency, but the model can be implemented on other cryptocurrencies provided there are valid historical price data. Our experimental results show that our proposed model outperforms the baseline LSTM algorithm, achieving higher accuracy and better performance in terms of Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Our research findings suggest that combining deep learning techniques such as LSTM with change point detection techniques such as PELT can improve cryptocurrency price prediction accuracy and have practical implications for investors, traders, and financial analysts.

Glioblastoma (GBM) is a stage 4 malignant tumor in which a large portion of tumor cells are reproducing and dividing at any moment. These tumors are life threatening and may result in partial or complete mental and physical disability. In this study, we have proposed a classification model using hybrid deep belief networks (DBN) to classify magnetic resonance imaging (MRI) for GBM tumor. DBN is composed of stacked restricted Boltzmann machines (RBM). DBN often requires a large number of hidden layers that consists of large number of neurons to learn the best features from the raw image data. Hence, computational and space complexity is high and requires a lot of training time. The proposed approach combines DTW with DBN to improve the efficiency of existing DBN model. The results are validated using several statistical parameters. Statistical validation verifies that the combination of DTW and DBN outperformed the other classifiers in terms of training time, space complexity and classification accuracy.

Purpose The purpose of this paper is to propose the knowledge of thermal transport of magneto hydrodynamic non-Newtonian fluid flow over a melting sheet in the presence of exponential heat source. Design/methodology/approach The group of PDE is mutated as dimension free with the assistance of similarity transformations and these are highly nonlinear and coupled. The authors solved the coupled ODE’s with the help of fourth-order Runge–Kutta based shooting technique. The impact of dimensionless sundry parameters on three usual distributions of the flow was analyzed and bestowed graphically. Along with them friction factor, heat and mass transfer rates have been assessed and represented with the aid of table. Findings Results exhibited that all the flow fields (velocity, concentration and temperature) are decreasing functions of melting parameter. Also the presence of cross-diffusion highly affects the heat and mass transfer performance. Originality/value Present paper deals with the heat and mass transfer characteristics of magnetohydrodynamics flow of non-Newtonian fluids past a melting surface. The effect of exponential heat source is also considered. Moreover this is a new work in the field of heat transfer in non-Newtonian fluid flows.

Neurodevelopmental disorders (NDDs) compromise the development and attainment of full social and economic potential at individual, family, community, and country levels. Paucity of data on NDDs slows down policy and programmatic action in most developing countries despite perceived high burden. We assessed 3,964 children (with almost equal number of boys and girls distributed in 2-<6 and 6-9 year age categories) identified from five geographically diverse populations in India using cluster sampling technique (probability proportionate to population size). These were from the North-Central, i.e., Palwal (N = 998; all rural, 16.4% non-Hindu, 25.3% from scheduled caste/tribe [SC-ST] [these are considered underserved communities who are eligible for affirmative action]); North, i.e., Kangra (N = 997; 91.6% rural, 3.7% non-Hindu, 25.3% SC-ST); East, i.e., Dhenkanal (N = 981; 89.8% rural, 1.2% non-Hindu, 38.0% SC-ST); South, i.e., Hyderabad (N = 495; all urban, 25.7% non-Hindu, 27.3% SC-ST) and West, i.e., North Goa (N = 493; 68.0% rural, 11.4% non-Hindu, 18.5% SC-ST). All children were assessed for vision impairment (VI), epilepsy (Epi), neuromotor impairments including cerebral palsy (NMI-CP), hearing impairment (HI), speech and language disorders, autism spectrum disorders (ASDs), and intellectual disability (ID). Furthermore, 6-9-year-old children were also assessed for attention deficit hyperactivity disorder (ADHD) and learning disorders (LDs). We standardized sample characteristics as per Census of India 2011 to arrive at district level and all-sites-pooled estimates. Site-specific prevalence of any of seven NDDs in 2-<6 year olds ranged from 2.9% (95% CI 1.6-5.5) to 18.7% (95% CI 14.7-23.6), and for any of nine NDDs in the 6-9-year-old children, from 6.5% (95% CI 4.6-9.1) to 18.5% (95% CI 15.3-22.3). Two or more NDDs were present in 0.4% (95% CI 0.1-1.7) to 4.3% (95% CI 2.2-8.2) in the younger age category and 0.7% (95% CI 0.2-2.0) to 5.3% (95% CI 3.3-8.2) in the older age category. All-site-pooled estimates for NDDs were 9.2% (95% CI 7.5-11.2) and 13.6% (95% CI 11.3-16.2) in children of 2-<6 and 6-9 year age categories, respectively, without significant difference according to gender, rural/urban residence, or religion; almost one-fifth of these children had more than one NDD. The pooled estimates for prevalence increased by up to three percentage points when these were adjusted for national rates of stunting or low birth weight (LBW). HI, ID, speech and language disorders, Epi, and LDs were the common NDDs across sites. Upon risk modelling, noninstitutional delivery, history of perinatal asphyxia, neonatal illness, postnatal neurological/brain infections, stunting, LBW/prematurity, and older age category (6-9 year) were significantly associated with NDDs. The study sample was underrepresentative of stunting and LBW and had a 15.6% refusal. These factors could be contributing to underestimation of the true NDD burden in our population. The study identifies NDDs in children aged 2-9 years as a significant public health burden for India. HI was higher than and ASD prevalence comparable to the published global literature. Most risk factors of NDDs were modifiable and amenable to public health interventions.

This paper presents the mechanism and chemistry behind the geo-polymerization and its application in development of Geo-polymer concrete. In this paper, guidelines to develop a geo-polymer concrete is discussed along with the factors affecting the geopolymerization process in concrete. It is concluded that curing temperature, ratio of alkaline liquids , chemical ratio of silicate and sodium in sodium silicate, alkaline liquids / Si-Al source materials ratio, sodium silicate/ hydroxyl ions ratio, presence of calcium, presence of excess water and Si/Al ratio in source materials have significant effect on the development of geopolymer concrete and its performance.