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A Conceptual Framework for Agile as HR Operational Strategy
Purpose: This paper examined Agile human resource (HR) as an operational strategy, emphasizing the relationships between operational, HR, and organizational strategies. It develops a collaborative culture, establishes learning organizations, supports agile team design, and improves agile strategic behavior. Agile HR has been underutilized in academic literature despite its potential, highlighting the disconnect between practitioner objectives and HR research. Methodology: A conceptual framework for Agile HR was developed using qualitative secondary research methods. Secondary sources included books, journal articles, research papers, reports, and whitepapers. A thematic analysis was used to code the data and identify themes relevant to Agile HR, and concept mapping was used to illustrate the relationships between the key concepts. Findings: A conceptual framework for Agile HR strategies was developed to foster an agile organizational culture and equip employees with agile strategic behaviors. Organizations will be able to establish and preserve a durable competitive edge in quickly changing marketplaces by using these tactics. Practical Implications: This paper provided insights into implementing agile HR operational strategies. Continuous iteration was used to enhance processes, boost employee experiences, and improve organizational agility to implement these strategies. Originality: While existing literature explored the relationship between organizational agility and dynamic capabilities, it largely overlooked the concept of agile behavior. This research addressed this gap by proposing a framework for flexible adjustments to human and organizational capabilities. It was a targeted approach for agile management aligned with organizational, HR, and agile strategies, emphasizing scalability. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
A concise and effectual method for neutral pitch identification in stuttered speech
Researchers have studied that human-computer interactions (HCIs) can be more effective only when machines understand the emotions conveyed in speech. Speech emotion recognition has seen growing interest in research due to its usefulness in different applications. Building a neutral speech model becomes an important and challenging task as it can help in identifying different emotions from stuttered speech. This paper suggests two different approaches for identifying neutral pitch from stuttered speech. The implementation has proved through its accuracy the best model that can be adopted for neutral speech pitch identification. 2017 Walter de Gruyter GmbH, Berlin/Boston. -
A concise route to fused tetrazolo scaffolds through 10-camphor sulfonic acid auto-tandem homogeneous catalysis and mechanistic investigation
10-Camphor sulfonic acid (10-CSA) as an organo-catalyst has gained interest due to its versatile solubility and easiness of handling. This work reports a simple synthetic method through non-classical Biginelli for the construction of tetrazolo pyrimidine (4a-m) and quinazolines (4a?-o?). Azolopyrimidines and quinazolines are of great pharmaceutical importance. Numerous compounds are currently in use for the treatment of different diseases. Therefore their synthesis is industrially inevitable. Employing aldehydes, 1,3-dicarbonyls, and 5-Aminotetrazole, we report eco-friendly, cost-effective catalysis through a tandem reaction catalyzed by the 10-CSA that gave excellent yields, 7095 % for tetrazolo quinazoline and 4576 % tetrazolo pyrimidines respectively. The homonuclear NOESY analysis confirms the selective formation of one isomer. All the compounds are characterised by 1H NMR, 13C NMR, and MS. Investigation of the reaction mechanism by both experimental and theoretical studies provides evidence. Mechanism of the reaction was also explained utilizing the information from mass spectrometry monitoring. DFT calculation carried out at PBEPBE (Perdew-Burke-Ernzerhof) functional and 6-31G (d,p) basis set level of theory of the various intermediates observed supports the experimental evidence. 2023 Elsevier B.V. -
A Congruent Approach to Normal Wiggly Interval-Valued Hesitant Pythagorean Fuzzy Set for Thermal Energy Storage Technique Selection Applications
Thermal energy is the energy from a substance in which molecules and atoms vibrate faster because of an increase in temperature. Thermal energy storage (TES) is an available energy resource for renewable energy platforms that enables them to meet sustainable technical requirements. The TES technique is divided into three categories; sensible TES, latent-heat TES, and thermo-chemical TES. The best of these techniques is selected in this research paper. Here the Interval-Valued Hesitant Pythagorean Fuzzy Set (IVPHFS) under the Normal Wiggly Mathematical Methodology is proposed and described for application to multi-criteria decision making (MCDM) technology. The MCDM methods, the Step-wise Weight Assessment Ratio Analysis (SWARA) method for determining weight values, and the Weighted Aggregated Sum Product Assessment (WASPAS) method for ranking alternative values are used employed here. The alternative values are selected based on the following criteria: capacity, efficiency, storage period, charging and discharging times, and cost 2021, Taiwan Fuzzy Systems Association. -
A constrained multi-period portfolio optimization model based on quantum-inspired optimization
Multi-period portfolio optimization (MPO) is one of the most important problems to be solved to help investors select optimal portfolios for investment plans. The portfolios are influenced by the risk factors in the market and it is important to select optimal portfolios that can maximize the returns with minimum risk values. Other than the risk factor, there are several other influential factors that reduce the optimality of the portfolios. Therefore, by considering all possible constraints, this study proposes a multi-constraint MPO model that selects the optimal portfolio based on the asset returns. To solve the multi-constrained problem, a novel quantum-inspired whale optimization algorithm (QWOA) is introduced in this paper. The proposed algorithm enhances the traditional optimization model to work in a multi-constrained scenario. Here, quantum entanglement is adapted to reduce the slow convergence issue of whale optimization. Apart from considering only the risk factors, this paper also considers certain higher-order moments (HOM), such as skewness, kurtosis, transaction cost, diversification, boundary and budget constraints. These factors affect the portfolios as the market is dynamic, and timely changes are always seen. Thus, optimizing the mentioned factors aids in attaining an optimal portfolio. Empirical evaluations are performed, and the results suggested that the proposed model provided beneficial outcomes as compared with other algorithms like whale optimization algorithm (WOA), gray wolf optimization (GWO), fruitfly optimization algorithm (FOA), particle swarm optimization (PSO) and fruitfly algorithm (FA). The overall net return rate of the proposed model is always above 0.85% for different values of upper bounds, and the obtained Sharpe ratio, Sortino ratio, STARR ratio, information ratio, Shannon entropy, and downside deviation values of the proposed algorithm are 5.016254, 0.89327, ? 0.01987, 0.103826, 3.04452 and 0.2854. Hence, the proposed approach is highly effective for optimizing the constrained MPO. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
A continuous protocol for the epoxidation of olefins, monocyclic terpenes, and Alpha Beta Unsaturated Carbonyl Synthons using eco-friendly Flow Reactor Conditions
Herein, we report a simple synthetic protocol for selective epoxidation of olefins, monocyclic terpenes, and chalcones using a continuous semi-batch process in good to excellent yields. Mainly, industrial semi-batch epoxidation is an extremely risky process that includes very high safety measures to avoid the accumulation of peroxide species in the reactor during the process, which leads to accidents. To avoid the same, we have established a constant flow reactor protocol for the epoxidation of fore mentioned key synthons using a cyanamide-potassium carbonate catalytic system which helps to reduce the accumulation of the peroxide species, and also yields moderate to high yields of the desired products. The developed methodology was successfully utilized for the epoxidation of a range of aliphatic to aromatic olefins to generate corresponding epoxides. All the products and their structures were examined using 1HNMR, and 13NMR spectroscopy. More importantly, this proposed protocol is recyclable and reproducible where in using similar research conditions. 2022 The Authors -
A critical assessment of technical advances in pharmaceutical removal from wastewater A critical review
Use of pharmaceutical products has seen a tremendous increase in the recent decades. It has been observed that more than thirty million tons of pharmaceuticals are consumed worldwide. The used pharmaceutical products are not completely metabolized in human and animal body. Therefore, they are excreted to the environment and remain there as persistent organic chemicals. These compounds emerge as toxic contaminants in water and affect the human metabolism directly or indirectly. This literature review is an endeavour to understand the origin, applications and current advancement in the removal of pharmaceuticals from the environment. It discusses about the pharmaceuticals used in medical applications such diagnosis and disease treatment. In addition, it discusses about the recent approaches applied in pharmaceutical removal including microbial fuel cells, biofiltration, and bio nanotechnology approaches. Moreover, the challenges associated with pharmaceutical removal are presented considering biological and environmental factors. The review suggest the potential recommendations on pharmaceutical removal. 2023 The Authors -
A CRITICAL STUDY IN UNDERSTANDING THE POTENTIAL BENEFITS OF IMPLEMENTING DIGITAL FINANCIAL APPLICATION IN ENHANCING THE ACCOUNTING PERFORMANCE IN ORGANISATIONS
Failure to innovate in this era of rapid IT growth is a significant obstacle to the modernization and growth of industries and increases the competitiveness of such organizations in the market. Strong innovation and competence are more than necessary to turn innovative ideas into reality, gain new competitive advantages and achieve sustainable long-term growth. Innovation is not only an important tool for companies to increase their competitiveness, it is also an important driver of long-term economic growth for a country. Regular engagement in high-quality innovation activities should be mandatory for organizations that intend to successfully adapt to today's fast-paced digital economy. If companies want to improve their chances of survival in the coming years and continue to grow, they need to invest in their innovation capabilities. Many companies now operate under the assumption that updating their accounting systems with advanced software will provide better results than relying on old, time-honored methods. Concrete steps are needed, such as developing powerful data-driven tools to improve how individuals, organizations and governments spend their money. Beginners still have to put in the effort to learn new skills because they often have trouble imagining using a device they've never used before to accomplish a task. Due to the increased automation of this financial system, the risk of error has increased; So it is very important. In fact, you can manage your needs exactly with this tool. 2024 Published by Faculty of Engineering. -
A cross-country analysis of the relationship between human capital and foreign direct investment
Purpose: The ZhangMarkusen (Z-M) inverse U-shape theory uses education as a human capital variable to investigate the impact of educational attainment on foreign direct investment (FDI) inflows to a country. The objective of this research is to empirically test this theory in a cross-country framework. Design/methodology/approach: Fixed effect panel regression has been used to test the Z-M hypothesis for 172 countries for the period 19902015. For the purpose of this study, countries were divided into four groups as per the World Bank classification: Low-income economies, lower middle-income countries, upper middle-income economies and high-income economies. Findings: The findings of this study reinforce the proposition that macroeconomic factors are the major determinants of FDI inflows into various countries. The authors find that the size of the market measured by gross domestic product (GDP), the growth potential of the market measured by real GDP growth rate and the availability of infrastructure are the major factors that enhance the attractiveness of a country as an FDI destination. Originality/value: Though the Z-M theory has been empirically tested in cross-country frameworks, no consensus has been reached. Thus, it is interesting to look again at the validity of the Z-M hypothesis using data covering longer and more recent periods. The study includes both macroeconomic and human capital determinants of FDI, so as to arrive at a comprehensive model explaining the FDI flows into various countries. 2021, Emerald Publishing Limited. -
A Cross-sectional Study for Examining Catastrophic Healthcare Expenditure Across Socio-demographic Variables among Employees in a Sedentary Occupation
Health expenditure above a certain threshold level can result in a financial catastrophe by reducing the expenses on necessities. Certain socio-demographic variables have been observed to play a role in influencing catastrophic healthcare expenditure, guiding the present study to examine this scenario for employees in sedentary occupations. A cross-sectional study has been conducted among 370 employees recruited through a random sampling technique. Multinomial logistic regression was used to test the main objective of the study. The factors associated with a higher probability of catastrophic healthcare expenditure were males with increasing age. Years of work experience tend to be associated with a lower likelihood of catastrophic healthcare expenditure. No conclusive evidence could be drawn for BMI, income, marital status and education. 2024 Indian Journal of Community Medicine. -
A Cross-Sectional Study on Mental Health of School Students during the COVID-19 Pandemic in India
The broad objective of the present study is to assess the levels of anxiety and depression of school students during the COVID-19 lockdown phase and their association with students background, stress, concerns and social support. In this regard, the present study follows a novel two stage approach. In the first phase, an empirical survey was carried out, based on multivariate statistical analysis, wherein a group of 273 school students participated in the study voluntarily. In the second phase, a novel Picture Fuzzy FFA (PF-FFA) method was applied for understanding the dynamics of facilitating and prohibiting factors for three categories of focus groups (FG), formulated on the basis of attendance in online classes. Findings revealed a significant impact of anxiety and depression on mental health. Further, PF-FFA examinedthe impact of the driving forces that steered children to attend class as contrasted to the the impact of the restricting forces. 2022 by the authors. -
A decade of climate change concern in India: Determinants of personal and societal climate concern
Scientists have called for a culturally relevant investigation of factors impacting public climate concern to devise relevant behavioural and policy interventions. Although India will be adversely affected by climate change, there is a shortage of models that track changes in Indian climate concern across time. The study tracked the growth of climate concern from 2006 to 2020 and identifies determinants of personal and societal climate concern. Secondary analyses of survey data from the International Science Survey and World Values Survey (2006-2020, N = 9254), were conducted to predict climate concern across the year, environmental protection versus economic growth preferences, and socio-demographic variables. Within responses from 2020 (N = 3176), the predictive role of anthropogenic climate change beliefs, trust in scientists, adequate government action, collective efficacy, environmental protection preferences, and sociodemographic variables were evaluated to understand personal and societal climate concern. Binary logistic regression found that climate concern increased significantly from 2006 (2.6%) to 2020 (89.5%) and was predicted by education and preferences for environmental protection. Multiple regression results identified personal climate concern as predicted by education, anthropogenic climate change beliefs, trust in scientists, and environmental protection preferences; while government action beliefs and favouring left-wing affiliation predicted societal climate concern. There was mixed support for the political polarization of climate concern. The study shows an increase in Indian climate change concern over the past decade, with personal and societal climate concern being influenced by different psychological characteristics. Important implications for future climate communication research and social policy development are discussed. 2024 by author(s). -
A Deep Ensemble Framework for DDoS Attack Recognition and Mitigation in Cloud SDN Environment
Much research has been done in the recent past on the absolute shift of Internet infrastructure in order to make it more significantly programmable, configurable and make it more conveniently feasible. Software Defined Networking (SDN) forms the basis for this absolute shift in Internet infrastructure. When you look at the benefits of an SDN-based cloud environment they are monumental. Namely, network traffic control and elastic resource management. The SDN-based cloud environment becomes susceptible to cyber threats, especially like that of Distributed Denial of Service (DDoS) attacks and other cyber-attacks that perturb the SDN-based cloud environment. Hence, automated Machine Learning (ML) models are an efficient way to protect against these cyber-attacks. This research will develop a deep learning-based ensemble model for DDoS attack detection and classification (DLEM-DDoS) in a cloud environment. Long Short-Term Memory (LSTM), 1-D Convolutional Neural Networks (1D-CNN) and Gated Recurrent Unit (GRU) are the three DL models integrated into an ensemble model that classifies the incoming packet by majority voting classifiers. Network traffic data including source and destination IP addresses, packet and byte counts, packet and byte rates, flow duration, protocol types and port numbers are fed into the DLEM-DDoS model. This model preprocesses this data by converting categorical values (like protocol types) into numerical values and removing any missing values. Once collected and preprocessed, the data is fed into deep learning models (LSTM, 1D-CNN, GRU) within the framework for analysis. Finally, in this research using the DLEM-DDoS technique an efficient DDoS attack mitigation scheme in an SDN-based cloud environment is demonstrated. The report shows comprehensive stimulations as well as a superiority into the current approaches in terms of several measures. 2024 S. Annie Christila and R. Sivakumar. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
A Deep Learning Model for Information Loss Prevention from Multi-Page Digital Documents
World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers' sensitive information of a reputed TI company securing it's one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization's information security monitoring team. 2013 IEEE. -
A Discrete Kumaraswamy Marshall-Olkin Exponential Distribution
Finding new families of distributions has become a popular tool in statistical research. In this article, we introduce a new flexible four-parameter discrete model based on the Marshall-Olkin approach, namely, the discrete Kumaraswamy MarshallOlkin exponential distribution. The proposed distribution can be viewed as another generalization of the geometric distribution and enfolds some important distributions as special cases. Some properties of the new distribution are derived. The model parameters are estimated by the maximum likelihood method, with validation through a complete simulation study. The usefulness of the new model is illustrated via counttype real data sets. 2022. Journal of the Iranian Statistical Society. All Rights Reserved. -
A distinctive symmetric analyzation of improving air quality using multi-criteria decision making method under uncertainty conditions
This world has a wide range of technologies and possibilities that are available to control air pollution. Still, finding the best solution to control the contamination of the air without having any impact on humans is a complicated task. This proposal helps to improve the air quality using the multi-criteria decision making method. The decision to improve air quality is a challenging problem with todays technology and environmental development level. The multi-criteria decision making method is quite often faced with conditions of uncertainty, which can be tackled by employing fuzzy set theory. In this paper, based on an objective weighting method (CCSD), we explore the improved fuzzy MULTIMOORA approach. We use the classical Interval-Valued Triangular Fuzzy Numbers (IVTFNs), viz. the symmetric lower and upper triangular numbers, as the basis. The triangular fuzzy number is identified by the triplets; the lowest, the most promising, and the highest possible values, symmetric with respect to the most promising value. When the lower and upper membership functions are equated to one, we get the normalized interval-valued triangular fuzzy numbers, which consist of symmetric intervals. We evaluate five alternatives among the four criteria using an improved MULTIMOORA method and select the best method for improving air quality in Tamil Nadu, India. Finally, a numerical example is illustrated to show the efficiency of the proposed method. 2020, MDPI AG. All rights reserved. -
A dual-functional rhodamine B and azo-salicylaldehyde derivative for the simultaneous detection of copper and hypochlorite: synthesis, biological applications and theoretical insights
A multifunctional rhodamine derivative containing azo-salicylaldehyde (BBS) was designed and synthesized as a colorimetric and fluorescence turn-on probe for the selective detection of copper cations (Cu2+) and hypochlorite anions (OCl?) in aqueous media. In the presence of Cu2+, the probe BBS exhibited turn-on absorption and fluorescence change at 554 nm and 585 nm, respectively. The binding mechanism of BBS with Cu2+ induces the opening of a spirolactam ring in the rhodamine moiety by the formation of a metal-ligand complex, achieving 10-fold enhancement in fluorescence and quantum yield, along with a binding constant of 1 104 M?1 and a detection limit of 2.61 ?M. Addition of OCl? enhanced the absorbance and fluorescence intensities at 520 nm and 575 nm, respectively. The probe BBS underwent hypochlorite-mediated oxidation, followed by hydrolysis, resulting in the formation of rhodamine B itself, which is detectable by the naked eye via the color and fluorescence enhancement by 11-fold with a high quantum yield and a detection limit of 1.96 ?M. For practical applications, sensor BBS can be used to detect Cu2+ in water samples and on cotton swabs. For biological applications, the interaction of the BBS-Cu(ii) complex with transport proteins such as bovine serum albumin (BSA) and ct-DNA was investigated through UV-vis and fluorescence titration experiments. Additionally, the structural optimization of BBS and the BBS-Cu(ii) complex was demonstrated using DFT, and the interactions of the BBS-Cu(ii) complex with BSA and ct-DNA were analysed through theoretical docking studies. Bioimaging studies were conducted by capturing fluorescence images of BBS with Cu2+ and OCl? in a physiological medium containing living plant tissue using green gram seeds. 2024 The Royal Society of Chemistry. -
A facile and economic electrochemical sensor for methylmalonic acid: A potential biomarker for vitamin B12 deficiency
A facile and cost-effective method based on a modified pencil graphite electrode (PGE) has been developed for the sensing of methylmalonic acid (MMA). The electrode (Ag-PEDOT/PGE) was designed by the electrodeposition of Ag nanoparticles (NPs) on carbon fibre paper (CFP) coated with poly(3,4-ethylenedioxythiophene) (PEDOT). Field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), and other electroanalytical techniques were used to characterize the modified electrodes. The fabricated sensor showcased a wide linear dynamic range (0.50 pM-55 nM) and a low detection limit (0.16 pM). A sharp increase in anodic peak current shows the excellent rate of electron transfer arising from Ag-PEDOT and PGE. The developed electrode was effectively utilized towards electrochemical MMA determination in urine and human blood serum samples. The results obtained certainly indicate that the sensor has high selectivity, ensures rapid detection, is reproducible, and has high stability towards the quantification of MMA in real samples. This journal is The Royal Society of Chemistry and the Centre National de la Recherche Scientifique. -
A Facile One-Pot Solvent-Free Synthesis, in Vitro and in Silico Studies of a Series of Tetrahydropyridine Derivatives as Breast Cancer Inhibitors
Ammonium trifluoroacetate (ATA) catalysed synthesis of 1,2,5,6-tetrahydropyridine (THP) derivatives, under eco-friendly conditions via a facile one-pot strategy. We have synthesized fifteen THP derivatives, and docked into the crystal structure of Phosphatase and Tensin Homolog deleted on Chromosome 10 (PTEN) tumour suppressor protein (PDB ID: 1D5R) based on drug-likeness prediction and pharmacokinetic properties. Molecular docking simulation studies reveal that four of our synthesised compounds are potential hit candidates because they bound to the receptor through 57 conventional hydrogen bonds with ?9.7 to ?8.6 kcal/mol of binding energy. The compounds were evaluated using the in vitro inhibitory activity of MCF-7 breast cancer cell lines. Identified hit compounds showed moderate inhibition at (160320 ?g/mL) and inhibitory concentration IC50 values in the low micromolar range of 171.062, 189.803, 195.469 and 181.272 ?g/mL respectively. The results obtained are very promising; therefore fine-tuning the substituents of hit molecules with appropriate bioisosteres can lead to the development of potential leads. 2023 Wiley-VCH GmbH. -
A facile one-step microwave synthesis of Pt deposited on N & P co-doped graphene intercalated carbon black - An efficient cathode electrocatalyst for PEM fuel cell
A facile, single step microwave assisted polyol route for simultaneously depositing platinum as well as co-doping graphene oxide, is herein proposed. However, low durability and full cell performance of Pt/NPG (platinum deposited on nitrogen phosphorous co-doped graphene) was observed due to restacking of graphene layers. This issue was addressed by intercalating CB into the graphene layers as spacers during the synthesis (in-situ addition of spacers - Pt/(NPG + S)). Moreover, to study the influence of spacers, external addition of spacers (ex-situ - Pt/(NPG) + S) were also examined. Results from our study indicate that in-situ addition of spacers- Pt/(NPG + S) enhanced the full cell performance (405 mW cm?2) and exhibited <40% ECSA loss (37.47%), thereby attaining DoE target. Thus, emerging as a durable cathode electrocatalyst (Pt/(NPG + S)) for PEM fuel cells. 2022 Hydrogen Energy Publications LLC