Extraction of Chitosan From Fish Scale for Food Preservation and Shelf-Life Enhancer

With the growing demand for chitosan in a variety of applications and the drawbacks associated with crustacean shell-based chitosan, there is a need to look for alternative sources of chitosan extraction and production. Chitosan was extracted from the scales of red snapper ( Lutjanus johnii ) in this study. It was discovered that the concentration of solvent (HCl and NaOH) and temperature at various stages of the extraction process influenced the yield of extracted fish scale chitosan. The characterization result revealed that the commercial crustacean-based chitosan and the extracted fish scale chitosan had similar Fourier-transform infrared spectroscopy (FTIR) spectra, indicating that the two had similar functional groups. The degree of deacetylation (DDA) of the extracted fish scale chitosan was determined to be 76.9%, with the ash value, loss on drying, and solubility being 1.28%, 3.62%, and 88.8%, respectively. The extracted and commercialised chitosan were found to be similar in all characterization results. The potential of fish scale chitosan as a food preservative and shelf-life enhancer was then investigated in this study. Strawberries coated with chitosan were stored at various temperatures, and their physical appearance and moisture loss were recorded. When used in conjunction with traditional preservation techniques such as storage in a cool environment, fish scale chitosan was found to be capable of preventing up to 50% moisture loss in strawberries.

Stroke Prediction using Machine Learning

Stroke is a drawn out inability sickness caused everywhere on the world and it is the third driving reason for demise. Early forecast of stroke gives more important to the current time. Stroke happens fundamentally due to individuals' way of life in the advanced time changing elements, for example, high glucose, coronary illness, weight, diabetes. In this examination, we analyze the Support vector machine, Decision tree, Random forest and XG Boost.we have utilized four AI calculations to recognize the sort of stroke that can happen or happened to structure an individual's actual state and clinical report information. We have gathered a decent number of sections from the clinics and use them to take care of our concern. The characterization result shows that the outcome is good and can be utilized continuously clinical report. We accept that AI calculations can help better comprehension of illnesses and can be a decent medical care buddy.

SVM: Object Detection using Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks

With the touchy development of information, the multi-see information is broadly utilized in numerous fields, for example, information mining, AI, PC vision, etc. Since such information consistently has a perplexing construction, for example numerous classes, numerous viewpoints of portrayal and high measurement, how to detail a precise and solid system for the multi-see order is an exceptionally difficult assignment. In this paper, we propose a novel multi-see characterization technique by utilizing various multi-class Backing Vector Machines (SVMs) with a novel shared system. Here each multi-class SVM installs the scaling component to renewedly change the weight assignment, everything being equal, which is useful to feature more significant and discriminative highlights. Besides, we receive the choice capacity esteems to incorporate different multi-class students and present the certainty score across various classes to decide the last characterization result. Moreover, through a progression of the numerical derivation, we connect the proposed model with the reasonable issue and address it through a rotating emphasis improvement strategy. We assess the proposed strategy on a few picture and face datasets, and the test results exhibit that our proposed technique performs better compared to other cutting edge learning calculations.

A characterization for totally real submanifolds using self-adjoint differential operator

<abstract><p>In this article, we study totally real submanifolds in Kaehler product manifold with constant scalar curvature using self-adjoint differential operator $ \Box $. Under this setup, we obtain a characterization result. Moreover, we discuss $ \delta- $invariant properties of such submanifolds and get an obstruction result as an application of the inequality derived. The results in the article are supported by non-trivial examples.</p></abstract>

Pengaruh Variasi Waktu Sintering Terhadap Pertumbuhan Fase Bahan Superkonduktor BSCCO-2212 dengan Kadar Ca=1,10 Menggunakan Metode Pencampuran Basah

This research was conducted to determine the effect of sintering time on the formation of the superconducting phase BSCCO-2212 by calculating the level of purity of the phases formed and looking at the microstructure. The variation of sintering time was 10, 20, 30 and 40 hours using the wet mixing method. The sample was calcinated with 800 °C for 10 hours and sintered with 830 °C. The XRD’s characterization result shows a decrease in phase purity with increasing the sintering time. The relative high volume fraction of the BSCCO-2212/ts10 sample is 90,48% while, the lowest volume fraction of BSCCO-2212/tc40 is 50,74%. The relative high orientation degree of BSCCO-2212/ts20 is 18,47% and the lowest orientation degree of BSCCO-2212/ts10 is 8,4%. The SEM’s characterization result shows of all samples have been oriented and have relatively little space between slabs (voids).

A Dual Measure of Uncertainty: The Deng Extropy

The extropy has recently been introduced as the dual concept of entropy. Moreover, in the context of the Dempster–Shafer evidence theory, Deng studied a new measure of discrimination, named the Deng entropy. In this paper, we define the Deng extropy and study its relation with Deng entropy, and examples are proposed in order to compare them. The behaviour of Deng extropy is studied under changes of focal elements. A characterization result is given for the maximum Deng extropy and, finally, a numerical example in pattern recognition is discussed in order to highlight the relevance of the new measure.

Dual Generalized Order Statistics from Gompertz-Verhulst Distribution and Characterization

The dual generalized order statistics is a unified scheme which contains the well known decreasingly ordered random variables such as (reversed) order statistics, lower record values and lower Pfeifer record values. In this article, characterization results on Gompertz-Verhulst distribution through the conditional expectation of dual generalized order statistics based on non-adjacent dual generalized order statistics are given. These relations are deduced for moments of reversed order statistics, order statistics and lower record values. Further a characterization result through the truncated moment is also derived.

Newforms of Half-integral Weight: The Minus Space Counterpart

We study genuine local Hecke algebras of the Iwahori type of the double cover of $\operatorname{SL}_{2}(\mathbb{Q}_{p})$ and translate the generators and relations to classical operators on the space $S_{k+1/2}(\unicode[STIX]{x1D6E4}_{0}(4M))$, $M$ odd and square-free. In [9] Manickam, Ramakrishnan, and Vasudevan defined the new space of $S_{k+1/2}(\unicode[STIX]{x1D6E4}_{0}(4M))$ that maps Hecke isomorphically onto the space of newforms of $S_{2k}(\unicode[STIX]{x1D6E4}_{0}(2M))$. We characterize this newspace as a common $-1$-eigenspace of a certain pair of conjugate operators that come from local Hecke algebras. We use the classical Hecke operators and relations that we obtain to give a new proof of the results in [9] and to prove our characterization result.

A characterization related to Schrödinger equations on Riemannian manifolds

In this paper, we consider the following problem: [Formula: see text] where [Formula: see text] is a [Formula: see text]-dimensional ([Formula: see text], non-compact Riemannian manifold with asymptotically non-negative Ricci curvature, [Formula: see text] is a real parameter, [Formula: see text] is a positive coercive potential, [Formula: see text] is a bounded function and [Formula: see text] is a suitable nonlinearity. By using variational methods, we prove a characterization result for existence of solutions for [Formula: see text].

ADMISSIBLE, SIMILAR TESTS: A CHARACTERIZATION

This article studies a classical problem in statistical decision theory: a hypothesis test of a sharp null in the presence of a nuisance parameter. The main contribution of this article is a characterization of two finite-sample properties often deemed reasonable in this environment: admissibility and similarity. Admissibility means that a test cannot be improved uniformly over the parameter space. Similarity requires the null rejection probability to be unaffected by the nuisance parameter.The characterization result has two parts. The first part—established by Chernozhukov, Hansen, and Jansson (2009, Econometric Theory 25, 806–818)—states that maximizing weighted average power (WAP) subject to a similarity constraint suffices to generate admissible, similar tests. The second part—hereby established—states that constrained WAP maximization is (essentially) a necessary condition for a test to be admissible and similar. The characterization result shows that choosing an admissible, similar test is tantamount to selecting a particular weight function to report weighted average power. This result applies to full vector inference with a nuisance parameter, not to subvector inference.The article also revisits the theory of testing in the instrumental variables model. Specifically—and in light of the relevance of the weighted average power criterion in the main theoretical result—the article suggests a weight function for the structural parameters of the homoskedastic instrumental variables model, based on the priors proposed by Chamberlain (2007). The corresponding test is, by construction, admissible and similar. In addition, the test is shown to have finite- and large-sample properties comparable to those of the conditional likelihood ratio test.