EFFECT OF INM PRACTICES ON THE MORPHOLOGICAL, PHYSIOLOGICAL AND BIOCHEMICAL PARAMETER OF ARJUN LEAF PRIMARY HOST PLANT OF Antheraea mylitta D.

The present study was conducted to investigate the effect of Integrated Nutrient Management practices on the different  parameter of Arjun leaf the primary host plant of Antheraea mylitta D. Eleven different combination with three replication were laid out in Randomized Complete Block Design at the field of Research Extension Centre, Kapistha. The obtained results showed that morphological, Physiological and Biochemical parameter of Arjun leaf showed significant difference. The Arjun leaf length was recorded highest in K11 (17cm) was on par with K9 (17cm). Highest leaf breadth was recorded in K10 (6.2cm) was applied with 75%RDF+Poultry manure+ AB+PSB. Leaf weight was recorded highest in K7 (2.84g), lowest in K1(1.21g). Number of leaves was recorded highest in K6 (1816) over the control. The leaf yield was recorded highest in K11 (3735). Leaves dry matter production was highest in K8 (469.56g) over the control. Relative water content was highest in K5 (87.3%). The initial Electrical Conductivity was recorded highest in K9 (0.037dSm-1) and after 10 min EC was found to highest in K9 (0.111dSm-1). The Chlorophyll ’a’ was recorded highest in K9 (3.39), Chl’b’(2.36)and total chlorophyll in K9(5.75) was recorded highest. The result were found significant due to effect of INM practices which provided the nutrients element needed by plants. View Article DOI: 10.47856/ijaast.2022.v09i01.00

Low-No code Platforms for Predictive Analytics

In the data-driven landscape of modern business, predictive analytics plays a pivotal role inanticipating and mitigating customer churn—a critical challenge for organizations. However, thetraditional complexities of machine learning hinder accessibility for decision-makers. EnterMachine Learning as a Service (MLaaS), offering a gateway to predictive modeling without theneed for extensive coding or infrastructure.This thesis presents a comprehensive evaluation of cloud-based and cloud-agonostic AutoML(Automated Machine Learning) platforms for customer churn prediction. The study focuses onfour prominent platforms: Azure ML, AWS SageMaker, GCP Vertex AI, and Databricks. Theevaluation encompasses various performance metrics including accuracy, AUC-ROC, precision,recall to assess the predictive capabilities of each platform. Furthermore, the ease of use andlearning curve for model development are compared, considering factors such as data preparation,training steps, and coding requirements. Additionally, model training times are analyzed toidentify platform efficiencies. Preliminary results indicate that AWS SageMaker exhibits thehighest accuracy, suggesting strong predictive capabilities. GCP Vertex AI excels in AUC,indicating robust discriminatory power. Azure ML demonstrates a balanced performance,achieving notable accuracy and AUC scores. Databricks being platform independent is a winnerand has also shown good metrics. Its capability to generate notebook is an added advantage whichcan be modified by experts to fine tune the results more. This research provides valuable insightsfor organizations seeking to implement different AutoML solutions for customer churnprediction

Low-No code Platforms for Predictive Analytics

In the data-driven landscape of modern business, predictive analytics plays a pivotal role inanticipating and mitigating customer churn—a critical challenge for organizations. However, thetraditional complexities of machine learning hinder accessibility for decision-makers. EnterMachine Learning as a Service (MLaaS), offering a gateway to predictive modeling without theneed for extensive coding or infrastructure.This thesis presents a comprehensive evaluation of cloud-based and cloud-agonostic AutoML(Automated Machine Learning) platforms for customer churn prediction. The study focuses onfour prominent platforms: Azure ML, AWS SageMaker, GCP Vertex AI, and Databricks. Theevaluation encompasses various performance metrics including accuracy, AUC-ROC, precision,recall to assess the predictive capabilities of each platform. Furthermore, the ease of use andlearning curve for model development are compared, considering factors such as data preparation,training steps, and coding requirements. Additionally, model training times are analyzed toidentify platform efficiencies. Preliminary results indicate that AWS SageMaker exhibits thehighest accuracy, suggesting strong predictive capabilities. GCP Vertex AI excels in AUC,indicating robust discriminatory power. Azure ML demonstrates a balanced performance,achieving notable accuracy and AUC scores. Databricks being platform independent is a winnerand has also shown good metrics. Its capability to generate notebook is an added advantage whichcan be modified by experts to fine tune the results more. This research provides valuable insightsfor organizations seeking to implement different AutoML solutions for customer churnprediction

Synthesis and structural studies of cobalt complexes of tridentate ligands incorporating azo, oxime and carboxylate functions

335-338The tridentate ligands H2ArL ( where Ar = Ph, p-tolyl, -naphthyl) react with cobalt(II) acetate tetrahydrate, affording the dark green Et4N[CoIII(ArL)2] complex. The two quasireversible couples (in the range -0.65 to -1.20 V) in the cyclic voltammogram represent azo reduction. The X-ray structure of Et4N[Co(PhL)2] has been determined, revealing the meridional binding of the two ligands affording cis-CoN4O2 <span style="font-size:14.0pt;font-family:&quot;Times New Roman&quot;; mso-fareast-font-family:&quot;Times New Roman&quot;;mso-ansi-language:EN-US;mso-fareast-language: EN-US;mso-bidi-language:AR-SA">geometry.</span

A low-spin carboxyl-bonded iron(III) complex

85-86The ligand HON =C(Ph)N =N - C6H4CO2H (H2L) has afforded the low-spin (S = 1/2) iron(lll) complex, Et4N[Fe(PhL)2] which has been structurally characterised revealing the presence of cis-FeN4O2 coordination sphere. Carboxyl-bonded low-spin iron(lll) species are very rare. Spin-pairing contracts metal radius, the Fe - N and Fe - O lengths being significantly shorter than those in representative high-spin complexes

First examples of carboxyl-bonded low-spin manganese(III) complexes

This article does not have an abstract

Synthesis, structure and reactivity of palladated azo-oxime-carboxylates

90-94The bidentate ligands H2L(1) react with sodium tetrachloropalladate to form a presumably dimeric species [PdL]2 which on further treatment with triphenyl phosphine or n-butyl amine furnishes adducts of types [PdL(PPh3)] or [PdL(nBuNH2)] respectively. The X-ray structure of [PdL2(PPh3)] has been determined, revealing the square planar PdN2OP geo metry

Dynamically controlling exterior and interior window coverings through IoT for environmental friendly smart homes

Energy saving using smart home is of paramount importance to reduce heating and cooling energy consumption, and promote sustainable environment. Awnings and blinds have exhibited their effectiveness to reduce heating gain in summer and cooling loss in winter, respectively. Awnings are more effective to reduce heat gain in summer than blinds, while the opposite is true in winter. There exist many approaches in the current literature to remotely control flat curtains and blinds. However, up to our knowledge, no automatic technique is available in the literature, which can dynamically control the orientation of an exterior covering so that it can act like a blind in winter and an awning in summer. In this paper, we propose an automatic on-demand system to control the orientation and size of such exterior covering, and the turning air conditioners, heaters and lights on and off considering the rate of change of room temperature, and its lighting condition. We also discuss the properties and design of such exterior covering. A simulation model was developed to analysis the performance of our approach in terms of energy savings both in summer and winter. © 2017 IEEE.Proceedings - 2017 IEEE International Conference on Mechatronics, ICM 201