Single Molecule with Dual Function on Nanogold: Biofunctionalized Construct for In Vivo Photoacoustic Imaging and SERS Biosensing

Multimodal imaging provides complimentary information that is advantageous in studying both cellular and molecular mechanisms in vivo, which has tremendous potential in pre‐clinical research and clinical translational imaging. It is desirable to design probes for multimodal imaging that can be administered minimally but provides multifaceted information. Herein, we demonstrate the complementary dual functional ability of a nanoconstruct for molecular imaging in both photoacoustic (PA) and surface‐enhanced Raman scattering (SERS) biosensing simultaneously in tandem. To realize this, a group of NIR active organic molecules are designed and synthesized that possess both SERS and PA activity. Nanoconstructs realized by anchoring such molecules onto gold nanoparticles are demonstrated for targeting cancer biomarkers in vivo while providing complimentary information about biodistribution and targeting efficiency. In future, such nanoconstructs could play a major role in identifying surgical margins and also for disease monitoring in translational medicine.     

Improving Readability through Individualized Summary Extraction,Using Interactive Genetic Algorithm

The impact of individualized summarization is high, because a summary would be difficult to understand by all if summarized in a generic manner. When sentences are important as well as readable to the learner with reading difficulties, the summary might be useful for better comprehension of the text. In this article, we propose an Interactive Genetic Algorithm-based individualized summarization to maximize the readability of selected important sentences. The individualized summarization applies to the educational domain using readability-based features to extract readable important sentences. Inclusion of features representing reading difficulty should not dilute the informative score of the summary, moreover it aids the learner who has reading difficulties to comprehend the complete text better, using the summary as supplementary to the complete text and not as a substitute for it. The experimental results derived from intrinsic evaluation shows that individual summary extraction performs better than a GA-based approach and a baseline approach. Moreover, user -based direct evaluation also supports individualized summarization for improving the readability by the target audience.     

Effect of manuring and frequency of cutting on the yield of leaf protein from some fodder grasses

A study to find out the effect of manuring and the frequency of cutting on the yield of leaf protein was conducted with four fodder grasses. The study revealed that Cenchrus glaucus was superior to the other grasses tried and it gave the maximum yield of dry matter and extractable protein. Farm yard manure at 10 tons/ha along with ammonium sulphate at 33 kg N/ha proved to be the best in giving the highest yield of dry matter and leaf protein. For all the grasses the maximum yield was found to range within the first three cuttings.     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.     

Scaling the Aspect Ratio of Nanoscale Closely Packed Silicon Vias by MacEtch: Kinetics of Carrier Generation and Mass Transport

Metal‐assisted chemical etching (MacEtch) has shown tremendous success as an anisotropic wet etching method to produce ultrahigh aspect ratio semiconductor nanowire arrays, where a metal mesh pattern serves as the catalyst. However, producing vertical via arrays using MacEtch, which requires a pattern of discrete metal disks as the catalyst, has often been challenging because of the detouring of individual catalyst disks off the vertical path while descending, especially at submicron scales. Here, the realization of ordered, vertical, and high aspect ratio silicon via arrays by MacEtch is reported, with diameters scaled from 900 all the way down to sub‐100 nm. Systematic variation of the diameter and pitch of the metal catalyst pattern and the etching solution composition allows the extraction of a physical model that, for the first time, clearly reveals the roles of the two fundamental kinetic mechanisms in MacEtch, carrier generation and mass transport. Ordered submicron diameter silicon via arrays with record aspect ratio are produced, which can directly impact the through‐silicon‐via technology, high density storage, photonic crystal membrane, and other related applications.     

High Velocity Oxy Fuel Spraying of Cold Work Tool Steels‐ A Novel Approach to Thick Coatings for Wear Protection Applications

Within this work, HVOF sprayed coatings based on X220CrVMo13‐4 cold work steel were applied to a S235JR construction steel substrate. The investigations focus on the influence of particle size and spray parameters on the coating microstructure, analyzed by means of optical microscope (OM) and scanning electron microscopy (SEM). Additional XRD measurements and micro hardness plots across the interface between substrate material and coating were carried out. Furthermore, the influence of particle size on the detected phases and coating porosity was studied. The results were compared with an X220CrMoV13‐4 reference sample produced by HIP.     

On Lagrangian twin support vector regression

In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression (TSVR) is proposed. Though at the outset the algorithm requires inverse of matrices, it has been shown that they would be obtained by performing matrix subtraction of the identity matrix by a scalar multiple of inverse of a positive semi-definite matrix that arises in the original formulation of TSVR. The algorithm can be easily implemented and does not need any optimization packages. To demonstrate its effectiveness, experiments were performed on well-known synthetic and real-world datasets. Similar or better generalization performance of the proposed method in less training time in comparison with the standard and twin support vector regression methods clearly exhibits its suitability and applicability.