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Twin deficit hypothesis: Some recent evidence from India
The purpose of this study is to examine the relationship between budget deficit and trade deficit commonly known as 'twin deficits hypotheses' in Indian economy. We used time series data for the period of 1970 to 2013. The empirical results of this study follow the autoregressive distributed lag (ARDL) cointegration technique for long run and short run estimates and error correction mechanism (ECM). In this study, we check the hypotheses that trade deficit is the determinant of budget deficit with its current values or the lag values. The results of the ARDL model confirm that there is the positive and significant relationship between trade deficit and budget deficit. So twin deficits hypothesis is valid for India. The ARDL results of the short run confirm the hypothesis that trade deficit can determine the budget deficit in the case of India. The results of the long run estimates are also significant. The error correction specification is used to find evidence of long-run causality running from budget deficit to trade deficit and vice versa. The empirical results suggest that trade deficit can determine the budget deficit in case of India. 2016 Inderscience Enterprises Ltd. -
Highly mixed high-energy d-orbital states enhance oxygen evolution reactions in spinel catalysts
Design, synthesis, and engineering of electrocatalysts for the oxygen evolution reaction (OER) are essential for attaining desirable electrocatalytic performance towards practical implementation. Emerging spinel-type OER catalysts have not reached the desirable activity and durability, thus demanding critical research to advance the field. To achieve enhanced OER performance for spinel-type OER catalysts, we present an efficient strategy of electronic structure modulation of central metal atoms. Modulation of the electronic properties of the Zn and Co atoms through the counter anionic components (O, S, and Se) regulates the adsorption of oxygen intermediates and thus enhances OER activity, which is systematically demonstrated using Density Functional Theory (DFT) calculation. Although the zinc cobalt selenide catalyst showed the less pronounced trigonal distortion, the mixing of eg orbitals with selenium accounts for the experimentally observed enhancement in OER activity. The result is, in contrast to the benchmark catalyst made of RuO2, ZnCo2Se4@rGO demonstrated lower OER overpotential (?10 = 302 mV) and Tafel slope (58 mV dec?1) as well as greater durability at 10 mA cm?2 for 50 h. The implementation of this strategy in several spinel-type catalysts could improve their electrocatalytic performance. 2023 Elsevier B.V. -
Turbulent Flow in Forced Convection Heat Transfer-Numerical Validation
Forced convective heat transfer of airflow through circular pipe with constant heat input and different free stream velocities is numerically validated. The significance of the present work is that the suction flow has been employed in the forced convection set up domain kept in the wind tunnel. From first law of thermodynamics and applying the energy balance equation, experimental heat transfer coefficient is determined. Further correlations are used to validate the experimental results. Although correlations provide reasonable estimates from the point of feasibility and accuracy, computational methods are used to estimate the convective heat transfer coefficient. Hence in this paper experimental, theoretical and computational analysis is carried out. The results reveal that the numerical validation is an effective tool from the point of feasibility and accuracy to determine the convective heat transfer coefficient. 2022. MechAero Foundation for Technical Research & Education Excellence. -
Experimental Investigations on Static, Dynamic, and Morphological Characteristics of Bamboo Fiber-Reinforced Polyester Composites
The use of natural fiber-reinforced polymer composites has increased over a period of time, majorly due to the ecosustainability and biodegradability of the composites. Among several grades of natural fibers, bamboo fibers offer numerous environmental and cost benefits and possess excellent mechanical characteristics. The superior properties of the bamboo fibers have triggered the research interests in the domain of bamboo fiber-reinforced polymer composites. Among the polymers, polyesters are long chain molecules made up of atoms arranged in various ways with other elements to form the basic building blocks of a polymeric chain. Polyester is being increasingly employed in today's industrial products due to its inherent advantages. As a result, based on the potential properties of bamboo fibers as reinforcing materials and polyester resin as matrix material, the biocomposites are synthesized by hand lay-up technique and the specimens cut as per the standard dimensions and subjected to mechanical investigations, vibration, and morphological characterization as per the ASTM test methods. The increase in fiber weight content has enhanced flexural, tensile, and impact characteristics and improved the damping characteristics of the composite specimens. The microstructural evaluations have revealed the uniform distribution of the bamboo fibers in the resin, and the morphological studies of the fractured specimens have revealed that the fracture is majorly due to the matrix cracks rather than the fiber debonding, which is a major attribute ascertaining the strong coherent strengthening mechanism brought about by the inclusion of bamboo fiber in the polyester resin. 2022 N. Santhosh et al. -
Comparative Analysis of Phytochemicals and Antioxidant Potential of Ethanol Leaf Extracts of Psidium guajava and Syzygium jambos
Background: Plant-based drugs for various human ailments are becoming very important in the current domain of therapeutics. Aim: Psidium guajava and Syzygium jambos are two such plant species known for their medicinal properties in traditional systems of medicine like Ayurveda. Methods: Phytochemical analysis including GCMS, and antioxidant studies (DPPH) was carried out for both plant extracts. Results: Comparative phytochemical analyses of ethanol extracts of both these plants have shown the existence of bioactive components like tannins, polyphenols, alkaloids, flavonoids and terpenoids. These phytochemicals were quantified and the ethanol extracts were subjected to GCMS analysis which showed the presence of cis-?-farnesene, cis-calamenene, copaene, humulene, caryophyllene, phytol, neophytadiene, n-hexadecanoic acid etc, many of which possess diverse properties like antimicrobial, antibiofilm, antioxidant and anti-inflammatory. DPPH and reducing power assays revealed the excellent radical scavenging activity of the extracts. Conclusion: Among the two plants under the current study, S. jambos extract showed better results when compared to P. guajava concerning the antioxidant potential and the quantity of flavonoids, alkaloids, polyphenols and tannins present in the plant samples. 2024, Informatics Publishing Limited. All rights reserved. -
Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data
Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Big data performance evalution of map-reduce pig and hive
Big data is nothing but unstructured and structured data which is not possible to process by our traditional system its not only have the volume of data also velocity and verity of data, Processing means ( store and analyze for knowledge information to take decision), Every living, non living and each and every device generates tremendous amount of data every fraction of seconds, Hadoop is a software frame work to process big data to get knowledge out of stored data and enhance the business and solve the societal problems, Hadoop basically have two important components HDFS and Map Reduce HDFS for store and mapreduce to process. HDFS includes name node and data nodes for storage, Map-Reduce includes frame works of Job tracker and Task tracker. Whenever client request Hadoop to store name node responds with available free memory data nodes then client will write data to respective data nodes then replication factor of hadoop copies the blocks of data with other data nodes to overcome fault tolerance Name node stores the meta of data nodes. Replication is for back-up as hadoop HDFS uses commodity hardware for storage, also name node have back-up secondary name node as only point of failure the hadoop. Whenever clients want to process the data, client request the name node Job tracker then Name node communicate to Task tracker for task done. All the above components of hadoop are frame works on-top of OS for efficient utilization and manage the system recourses for big data processing. Big data processing performance is measured with bench marks programs in our research work we compared the processing i.e. execution time of bench mark program word count with Hadoop Map-Reduce python Jar code, PIG script and Hive query with same input file big.txt. and we can say that Hive is much faster than PIG and Map-reduce Python jar code Map-reduce execution time is 1m, 29sec Pig Execution time is 57 sec Hive execution time is 31 sec. BEIESP. -
An exploration of 'pull' and 'push' motivational factors among transgender entrepreneurs
To date, studies have focused on the men and women entrepreneurs and the gender difference in motivations among cisgender entrepreneurs. The study aims to determine whether a transgender individual entrepreneur is motivated through a push motivational factor or a pull motivational factor. This study employs a qualitative approach uses face-to-face interviews and a semi-structured interview with a sample size of 16 transgender entrepreneurs in India. It was found that the participants in this study were motivated by both push and pull factors. The motivational factors, which add to the knowledge of already existing push and pull factors, were to forego begging and commercial sex work, to break stereotypes, to create a business opportunity for other transgender individuals, to earn respect from society, to prove entrepreneurship is non-binary, to be a role model for other transgender individuals and to the society. In contrast, the push motivational factors were the limited opportunities, support received from society, the hijra guru, media, government support, family, friends, landlords, NGOs and another push motivational factor was the exhibitions conducted exclusively for the transgender individual entrepreneurs. 2025 Inderscience Enterprises Ltd. -
Entrepreneurial challenges of transgender entrepreneurs in India
Social exclusion has impeded transgender individuals to enter mainstream society and curbing them to start a business venture. Sporadic transgender individuals have paved their way to start the business venture. This study aims to explore the entrepreneurial challenges faced by transgender entrepreneurs. Twenty transgender entrepreneurs who have relinquished begging and commercial sex work were interviewed. The grounded theory analysis has revealed six significant categories: financial resources, competitors, human resources, marketing issues, natural calamities, and transphobia. The participants expressed that transphobia, and financial resources were highly challenging to start a business venture. These findings extend our understanding of their challenges beyond the current knowledge of cisgender entrepreneurs. Finally, the limitation of the study is enunciated. Copyright 2025 Inderscience Enterprises Ltd. -
Modeling Consumer Price Index: A Machine Learning Approach
The change in price of a group of goods and services is reflected in terms of consumer price index (CPI), making it one of the most important economic indicators. This is also the mostly used measure of inflation. Forecasted CPI values help the Government to take corrective measures to control the economic conditions of the country. This paper implements and examines two machine learning models such as artificial neural network (ANN) and ANN model optimized with particle swarm optimization (PSO) known as ANN-PSO to assess the accuracy in predictability of CPI. The data set for four groups such as food and beverages, housing, clothing, and footwear used for the calculation of all India CPI has been taken from the official website of the Government of India. The mean absolute percentage error (MAPE) has been used as the validator for model accuracy. The MAPE calculated for all experiments are less than 10% which indicates that the ANN-PSO models used are highly accurate for prediction of CPI of India. 2022 Wiley-VCH GmbH -
Design of automatic follicle detection and ovarian classification system for ultrasound ovarian images
Polycystic Ovary Syndrome (PCOS) is a common reproductive and metabolic disorder characterized by an increased number of ovarian follicles. Accurate diagnosis of PCOS requires detailed ultrasound imaging to assess follicles size, number, and position. However, noise often needs to be improved on these images, complicating manual detection for radiologists and leading to potential misidentification. This paper introduces an automated diagnostic system for integration with ultrasound imaging equipment to enhance follicle identification accuracy. The system consists of two main stages: preprocessing and follicle segmentation. Preprocessing employs an adaptive Frost filter to reduce noise, while follicle segmentation utilizes a region-based active contour combined with a modified Otsu method. Unlike the conventional Otsu method, where the threshold value is selected manually, the modified Otsu method automatically selects initial threshold values using an iterative approach. After segmentation, features are extracted from the segmented results. An SVM classifier then categorizes the ovarian image as normal, cystic, or polycystic. Experimental results demonstrate that the proposed methods Follicle Identification Rate is 96.3% and the False Acceptance Rate is 2%, which significantly improves classification accuracy, highlighting its potential advantages for clinical application. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Optimal procurement policy for growing items under permissible delay in payment
In the last decade, growing item industries have shown an increasing trend in production and it is expected that such industries will maintain this increasing pace in the future. Existing challenges of these industries, like mortality in the production phase and deterioration in the consumption phase, make procurement decisions more complex. In this article, we established an inventory model with mortality, deterioration, and price-dependent demand. To increase the sales volume and profit, a delay in payment policy is considered. A numerical example is presented to explain the solution procedure. The concavity of the profit function is discussed analytically for decision variables. It has been observed through sensitivity analysis that selling price is the most sensitive among decision variables and parameters. 2024 Inderscience Enterprises Ltd. -
Inventory model for the growing items with price dependent demand, mortality and deterioration
Growing items like livestock, chicks, etc. gain weight in the growing phase but some of them are lost due to mortality. In the selling phase, some inventory is lost due to deterioration. Such aspects make procurement decisions quite difficult for these items. In the light of such aspects, we developed an inventory model for the growing items with price dependent demand, mortality and deterioration. Shortages are partially backlogged. Our aim is to optimise the total cost by determining the optimal ordered quantity and total cycle length. Convexity of the cost function with respect to the decision variables has been discussed analytically. Solution procedure along with numerical example at different percentage of backlogged quantity is provided to show the applicability and validity of our model. Sensitivity analysis shows that total cycle length is the most sensitive among all the decision variables and parameters. Copyright 2023 Inderscience Enterprises Ltd. -
Potential use of waste foundry sand with lateritic clayey soil in the construction of pavement sub-bases
The current study investigates the potential for use of waste foundry sand (WFS) in highway sub-base construction. Lateritic clayey soil (LCS) and waste foundry sand were combined in different proportions to examine its potential to use as construction material in sub bases. In this research, geotechnical investigation was conducted that is particle size distribution, specific gravity, Atterberg limits, OMC, density, un-soaked and soaked CBR were conducted to evaluate the properties and validity of WFS upon stabilizing with the LCS for usage in construction of sub bases. The study shows that WFS can be effectively used in highway sub-base construction upon stabilizing with the lateritic clayey soils. 2019 SERSC. -
Strength and leaching characteristics of red mud (bauxite residue) as a geomaterial in synergy with fly ash and gypsum
Red mud (Bauxite residue) comprises microscopic particles and other chemical constituents that pose a major threat to the environment. The most common solution to resolve issues related to any solid waste is its reuse in construction. This paper delves into the possibility of using red mud as a geomaterial in synergy with fly ash and gypsum. In this regard upon finding the geotechnical properties of virgin red mud, it is strengthened with fly ash by replacing 10, 20, and 30% of red mud by its dry weight and to these combinations gypsum was added by 0.5% and 1% and prepared various combinations. The impact of these material additions on the characteristics of red mud were investigated using the Unconfined compressive strength and California bearing ratio values and their environmental compatibility was further studied by conducting the leaching characteristics using Toxicity Characteristics Leaching Procudure (TCLP) method. The findings of the tests indicated that fly ash and gypsum significantly enhanced the strength qualities of red mud as compared to unstabilized red mud. The stabilization helps red mud to attain a minimum strength required to use as a subgrade material. Furthermore, leaching investigations performed on stabilised samples have revealed that the vast majority of leaching heavy metals are within the WHO's authorised threshold for toxicity. 2022 -
Android controlled robot with image transfer /
International Journals of Advanced Multidisciplinary Research, Vol.2, Issue 3, pp.70-74, ISSN No: 2393-8870. -
A mini review on recent advancements in inclined solar still
Water shortage is a global problem, and the demand for fresh water is growing at an ever-increasing rate. The only method to meet the demand for water is via water filtration. Water purification may be done in a variety of methods, including cleaning saltwater or holding rainfall and then releasing it into the environment. There are still several kinds of solar still are available, which may be utilized to improve the amount of water that is generated. The inclined solar still (ISS) is a particularly successful option because it has a large outer water surface to supplement the normal potable water production, as well as because it has a shallow depth of water to increase the overall efficacy of the inclined solar still. Increasing the water's surface area has been the subject of much investigation. As a result of this study, an evaluation was conducted on the present state of various ISS designs in order to make advanced adjustments and research to increase the productivity of the ISS in order to meet the rising need for potable water. According to this analysis, active ISS and hybrid ISS are shown to be the most successful ISS methods. 2022 The Author(s) -
Pseudocapacitive electrode performance of zinc oxide decorated reduced graphene oxide/poly(1,8-diaminonaphthalene) composite
Development of Reduced graphene oxide/Zinc oxide/poly(1,8-diaminonaphthalene) (rGO/ZnO/PDAN) composite and its supercapacitor performance has been evaluated. Functional, crystal and morphological structures along with the thermal stability of the polymer-impregnated composite were studied using analyses such as FTIR, powder XRD, Raman, SEM and TGA techniques. The SEM images showcased the random decoration of irregularly shaped ZnO in between the wrinkled rGO sheets and the PDAN. The defective structure of the hybrid composite contributes capacitive features through electron transfer kinetics. The specific capacitance value for the rGO/ZnO/PDAN composite modified NF electrode in 3 M KOH was found to be 239 F/g at the current density of 0.5 A/g. The capacitance retained is 92 % after 5000 cycles at 5 A/g current density. A solid-state symmetrical supercapacitor device based on a novel rGO/ZnO/PDAN composite was successfully developed, which exhibits the specific capacity (40 at 0.6 A/g), energy density (12.66 Wh/kg) and power density (993 W/kg). The synergistic combination of surface-active nitrogen heteroatoms, extended conjugation and ZnO particles decorated over rGO sheets of rGO/ZnO/PDAN ternary hybrid composite displayed significant structural stability and electrochemical pseudocapacitive performances. 2023 Elsevier Ltd -
Viologen appended Schiff base polymer and its symmetrical supercapacitor device performance
A new hyperbranched viologen containing Schiff base polymer (VSBP) is synthesized by condensing TRIPOD and VDA. The polymer is characterized using spectroscopic techniques (FT-IR & 13C NMR) and microscopic (SEM) analyses. The VSBP modified NF electrode renders an effectual electrochemical performance with maximum specific capacitance of 256 F/g at 0.5 A/g. From cyclic stability measurement, 87% capacitance retention is retained at 10 A/g and 95% achieved at 0.5 A/g after 3000 GCD cycles in a three-electrode system. Assembling of VSBP modified NF electrode as a symmetrical supercapacitor device exhibits a high energy density value of 17.02 Wh/kg with a power density of 816 W/kg and acceptable capacitance retention is achieved 90.6% at 1 A/g current density after 5000 cycles. 2023 Elsevier Ltd -
Zone based relative density feature extraction algorithm for unconstrained handwritten numeral recognition
The recognition of handwritten characters and numerals has been a challenging problem among the researchers for few decades. This paper proposes a relative density feature extraction algorithm for recognizing unconstrained single connected handwritten numerals independent of the languages. The proposed method consists of four phases, namely, image enhancement (dilation), representation (zone based), feature extraction (relative density) and recognition (minimum distance classifier). The handwritten numerals must be enhanced with dilation, in order to connect the broken digits. After enhancement, the dilated binary images can be represented as a mid-point aspect ratio class interval values. There can be M * N zones and subsequently there would be 2M*N relational density exist using mid-point aspect ratio class interval values. In order to minimize the number of features, a subset of W relative densities has been extracted from the binary image since the relative density is too large to be handled efficiently. The minimum distance classifier technique has been used to recognize the given numerals. The proposed algorithm would be an alternative to recognize the handwritten numerals for recognizing unconstrained single connected handwritten numerals. The method sounds promising with a recognition rate of 92.8567%. 2005 - 2014 JATIT & LLS. All rights reserved.