Landau damping of ion-acoustic waves with simultaneous effects of non-extensivity and non-thermality in the presence of hybrid Cairns-Tsallis distributed electrons

The dispersion properties and Landau damping rate of ion-acoustic waves (IAWs) with the hybrid Cairns-Tsallis distributed (CTD) electrons and Maxwellian ions are investigated using the plasma kinetic model based on Vlasov-Poisson's equations. For both super-extensive (q < 1) and sub-extensive (q > 1) plasmas, the dielectric response function, real frequency, and Landau damping rate of IAWs are derived. By taking the effect of θ_{i, e} (ion-to-electron temperature ratio) into account, it is found that with the increase of ion temperature, the real frequency and wave dispersion effects increase as well (for both super-extensive and sub-extensive cases). Exploring the properties of the Landau damping rate of IAWs with the simultaneous presence of non-thermal parameter α and non-extensive parameter q, a comparison of numerical and analytical results is presented. It is found that in different ranges of θ_{e, i} (electron-to-ion temperature ratio), on decreasing the values of the non-extensive parameter and increasing values of the non-thermal parameter, the weak damping rate is observed (vice versa) in super-extensive or super-thermal plasma, although the trend of the damping rate in sub-thermal plasma is similar (as in the case of super-thermal plasma) but is less weak. It is further revealed that the damping rate of IAWs in thermal plasmas (Maxwellian) is stronger than the damping rate of IAWs in the case of non-thermal plasmas (CTD). The current study is applicable to provide deep insight and further allow the exploration of electrostatic plasma modes in different space and laboratory plasma environments where the hybrid CTD plasma exists.     

A real-time monitoring approach for bivariate event data

Early detection of changes in the frequency of events is an important task in many fields, such as disease surveillance, monitoring of high-quality processes, reliability monitoring, and public health. This article focuses on detecting changes in multivariate event data by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited because they only signal after an event occurred for each of the individual processes. This results in delays (i.e., long time-to-signal), especially when we are interested in detecting a change in one or a few processes with different rates. We propose a bivariate TBE chart, which can signal in real-time. We derive analytical expressions for the control limits and average time-to-signal performance, conduct a performance evaluation and compare our chart to an existing method. Our findings showed that our method is an effective approach for monitoring bivariate TBE data and has better detection ability than the existing method under transient shifts and is more generally applicable. A significant benefit of our method is that it signals in real-time and that the control limits are based on analytical expressions. The proposed method is implemented on two real-life datasets from reliability and health surveillance.     

A Functionalized Ag2S Molecular Architecture: Facile Assembly of the Atomically Precise Ferrocene‐Decorated Nanocluster [Ag74S19(dppp)6(fc(C{O}OCH2CH2S)2)18]

A ferrocene‐based dithiol 1,1′‐[fc(C{O}OCH₂CH₂SH)₂] has been prepared and treated with a Ag^I salt to form the stable dithiolate compound [fc(C{O}OCH₂CH₂SAg)₂]_n (fc=[Fe(η⁵‐C₅H₄)₂]). This is used as a reagent for the preparation of the nanocluster [Ag₇₄S₁₉(dppp)₆(fc(C{O}OCH₂CH₂S)₂)₁₈] which was obtained in good yield (dppp=1,3‐bis(diphenylphosphino)propane).     

Intercomparison of techniques for estimation of sedimentation rate in the Sabah and Sarawak coastal waters

A total of eight sediment cores with 50 cm length were taken in the Sabah and Sarawak coastal waters using a gravity corer in 2004 to estimate sedimentation rates using four mathematical models of CIC, Shukla-CIC, CRS and ADE. The average of sedimentation rate ranged from 0.24 to 0.48 cm year^?1, which is calculated based on the vertical profile of ²¹⁰Pb_ex in sediment core. The finding also showed that the sedimentation rates derived from four models were generally shown in good agreement with similar or comparable value at some stations. However, based on statistical analysis of paired sample t-test indicated that CIC model was the most accurate, reliable and suitable technique to determine the sedimentation rate in the coastal area.     

A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems

In this paper, we present a novel decision support system for order acceptance/rejection in a hybrid Make-to-Stock/Make-to-Order production environment. The proposed decision support system is comprised of five steps. At the first step, the customers are prioritized based on a fuzzy TOPSIS method. Rough-cut capacity and rough-cut inventory are calculated in the second step and in case of unavailability in capacity and materials, some undesirable orders are rejected. Also, proper decisions are made about non-rejected orders. At the next step, prices and delivery dates of the non-rejected orders are determined by running a mixed-integer mathematical programming model. At the fourth step, a set of guidelines are proposed to help the organization negotiate over price and due date with the customers. In the next step, if the customer accepts the offered price and delivery date, the order is accepted and later considered in the production schedule of the shop floor, otherwise the order is rejected. Finally, numerical experiments are conducted to show the tractability of the applied mathematical programming model.     

Catalyst-free one-pot synthesis of 1,4,5-trisubstituted pyrazoles in 2,2,2-trifluoroethanol

A simple, efficient and three component one-pot synthesis of 1,4,5-trisubstituted pyrazoles by condensation of β-dicarbonyls, N,N-dimethylformamide dimethyl acetal (DMFDMA) and hydrazine derivatives in 2,2,2-trifluoroethanol without using any catalyst and activation, is described.     

Novel method for laser focal point positioning on the cover slip for TPP-based microfabrication and detection of the cured structure under optical microscope

Detection of a single cured structure in two-photon photopolymerization (TPP) based microstructure fabrication requires the laser focal spot to be exactly positioned on the cover slip. This is due to the fact that if the laser focal position is not exactly on the cover slip, the structure may not stick to the cover slip and flow away with the liquid during the washing and developing stages. In this paper, we report a scheme of laser spot positioning for the implementation of TPP process and the detection of a single cured microstructure under an optical microscope. For this, a novel yet very simple approach is devised and an uncomplicated procedure is developed. Experimental results are also included to prove the worthiness of the devised method.     

Bioresource Polymer Composite for Energy Generation and Storage: Developments and Trends

The ever-growing demand of human society for clean and reliable energy sources spurred a substantial academic interest in exploring the potential of biological resources for developing energy generation and storage systems. As a result, alternative energy sources are needed in populous developing countries to compensate for energy deficits in an environmentally sustainable manner. This review aims to evaluate and summarize the recent progress in bio-based polymer composites (PCs) for energy generation and storage. The articulated review provides an overview of energy storage systems, e. g., supercapacitors and batteries, and discusses the future possibilities of various solar cells (SCs), using both past research progress and possible future developments as a basis for discussion. These studies examine systematic and sequential advances in different generations of SCs. Developing novel PCs that are efficient, stable, and cost-effective is of utmost importance. In addition, the current state of high-performance equipment for each of the technologies is evaluated in detail. We also discuss the prospects, future trends, and opportunities regarding using bioresources for energy generation and storage, as well as the development of low-cost and efficient PCs for SCs.     

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.     

Trident Nano-Indexing the Proteomics Table: Next-Version Clustering of Iron Carbide NPs and Protein Corona

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.