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Women's contributions to business growth and innovation in startups: Agile business
This chapter delineates the significance of women in innovation and growth as founders and leaders in the startup ecosystem. Agile environments require female entrepreneurship and leadership, offering distinct views, a spirit of cooperation, and flexibility; therefore, these are desirable attributes for business success during its highest uncertainty and optimal high-growth periods. Case studies of womenled startups have been analyzed in consideration of their inabilities to innovate, diversify, and lead through inclusive leadership to face market uncertainties and scale effectively. It shares some of the challenges women meet while trying to access venture capital and networks and gives strategies to overcome such pitfalls. This chapter gives an account of how gender, innovation, and agile methodologies intersect, thus underpinning empowerment of women in startups to cultivate competitive advantage and sustainable growth, thus developing a diverse and resilient entrepreneurial landscape. 2025, IGI Global Scientific Publishing. All rights reserved. -
A Design of Agricultural Robotics for the use of Sowing and Planting
Agricultural robots is always getting better to deal with problems like population growth, fast urbanization, fierce competition for high-quality goods, worries about protecting the environment, and a lack of skilled workers. This in-depth study looks at the main uses of farming robotic systems, covering jobs like preparing the land, sowing, planting, treating plants, gathering, estimating yields, and phenotyping. Each robot is judged on how it moves, what it will be used for, whether it has sensors, a robotic arm, or a computer vision program, as well as its development stage and where it came from. The study finds trends, possible problems, and things that stop business growth by looking at these shared traits. It also shows which countries are putting money into studying and developing (R&D) for these products. The study points out four important areas - movement systems as a whole sensor, computer vision computer programs, and communication technologies - that need more research to make smart agriculture better. The results make it clear that spending money on farming robotic systems can pay off in the long run by helping with things like accurate yield estimates and short-term benefits like keeping an eye on the harvest. 2024 IEEE. -
Advanced Materials for Next-Generation Energy Storage Devices: A Focus on Efficiency and Cost Reduction
The increasing demand for efficient and cost-effective energy storage systems has pushed extensive research into improved materials for next-generation energy storage devices. This study discusses the crucial significance of material advances in boosting the performance and reducing the costs of storage technologies such as batteries and supercapacitors. Conventional energy storage systems face limits in energy density, charge or discharge rates, and scalability, which impede their broad implementation. Advanced materials, including nanomaterials, solid-state electrolytes, and innovative electrode compounds, offer solutions to these difficulties by enhancing energy efficiency, power output, and overall longevity. Additionally, the use of plentiful and low-cost materials, such as sodium-ion and aluminium-based compounds, presents prospects for significant cost savings. This research analyzes current trends, issues in material manufacturing, and future perspectives for energy storage systems, concentrating on balancing efficiency improvements with cost-effectiveness to enable the rising integration of renewable energy sources. The development of these materials is important to creating sustainable, scalable, and economical energy storage systems for the future. The Authors, published by EDP Sciences. -
A robust explainable machine learning pipeline for transformer health index prediction addressing data pathologies and redundancy
Power transformers are critical infrastructure assets where unexpected failures incur severe technical and economic penalties. This study proposes a robust, explainable machine-learning (ML) pipeline for predicting the transformer Health Index (HI) using routinely collected dissolved gas analysis (DGA) and dielectric measurements. To ensure model reliability, the pipeline specifically addresses data pathologiesnamely extreme skewness and heavy tailsusing YeoJohnson transformations, while mitigating multicollinearity through hierarchical correlation clustering (|r| ? 0.85) followed by a Variance Inflation Factor (VIF) screening (VIF ? 5). Four high-performance ensemblesRandom Forest, XGBoost, LightGBM, and CatBoostwere optimized via randomized cross-validation. Experimental results on a dataset of 470 records demonstrate consistent generalization across all models (RMSE ? 0.022), with Random Forest providing superior accuracy (MAPE ? 1.24%). A Taylor diagram confirmed consistent generalization (correlation ? 0.730.78 and matched variance), while residual analysis showed minimal bias. SHAP explanations indicated that dibenzyl disulfide (DBDS) and interfacial tension (Interfacial V) were the most influential positive drivers of HI; water content tended to depress HI; and several gases (e.g., methane, hydrogen, acetylene, CO) contributed positively at higher concentrations. The proposed workflow was robust to skew/heavy tails and multicollinearity, required no feature scaling, and produced transparent, practitioner-ready insights that support condition-based maintenance at fleet scale. 2026 Elsevier B.V. -
Intelligent Time Management Recommendations Using Bayesian Optimization
This paper focuses on the improvement of the intelligent time management system which employ Bayesian optimization for suggesting time management plans for each particular person. In this sense, through historical data of input-output patterns and users' preferences, the system aims at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate model in the Bayesian optimization so that the required evaluations by the algorithm to realize optimal scheduling methodologies are kept to a minimum. Implementation is done as a web application where users submit their tasks and get the recommended schedule instantly. Indicators like, the degree of task accomplishment, time, and scheduling compliance, and probably the users' satisfaction suggest that system helped enhance time management results. Lack of feedback from the users is removed through questionnaire that reveals the simplicity of the system and the quality of its recommended times, thereby supporting the idea of Bayesian optimization as a game changer in the management of time. This research significance points to the need for maintaining efficient and individualized approaches to time management strategies and agrees with others' findings, which suggest that this is an area ample fiction research needs to acknowledge and pursue. 2024 IEEE. -
Co-Existence of Union and Management is Possible
ITIHAS The Journal of Indian Management, Vol-2 (4), pp. 100-101. ISSN-2249-7803 -
Biological treatment solutions using bioreactors for environmental contaminants from industrial waste water
Human needs have led to the development of various products which are produced in the industries. These industries in turn have become a source of various environmental concerns. As industries release regulated and unregulated contaminants into the water bodies, it has become a serious concern for all living organisms. Various emerging contaminates from industries like pesticides, pharmaceuticals drugs like hormones, antibiotics, dyes, etc., along with byproducts and new complexes contaminate the water bodies. Numerous traditional approaches have been utilized for the treatment of these pollutants; however, these technologies are not efficient in most cases as the contaminants are mixed with complex structures or as new substances. Advanced technologies such as bioreactor techniques, advanced oxidation processes, and so on have been used for the treatment of industrial wastewater and have served as an alternative way for wastewater treatment. Overall, biological treatment techniques based on bioreactors provide a long-term and ecologically useful solution to industrial wastewater contamination. They play an important role in saving water resources and encouraging a greener sustainable future for mankind. The current review outlines the industrial effluents that are released into water bodies, contaminating them, as well as the numerous traditional and novel treatment procedures used for industrial wastewater treatment. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s). -
Detection of carbapenem resistance genes and cephalosporin, and quinolone resistance genes along with oqxAB gene in Escherichia coli in hospital wastewater: A matter of concern
Aims: This study was performed to detect the presence of Escherichia coli resistant to cephalosporins, carbapenems and quinolones in hospital wastewater. Methods and Results: Wastewaters from a rural (H1) and an urban (H2) hospital were tested for E.coli resistant to cephalosporins, carbapenem and quinolones. Genes coding for chromosomal and plasmid-mediated resistance and phylogenetic grouping was detected by multiplex polymerase chain reaction (PCR) and for genetic relatedness by rep-PCR. Of 190 (H1=94; H2=96) E.coli examined, 44% were resistant to both cephalosporins and quinolones and 3% to imipenem. ESBLs were detected phenotypically in 96% of the isolates, the gene blaCTX-M coding for 87% and blaTEM for 63%. Quinolone resistance was due to mutations in gyrA and parC genes in 97% and plasmid-coded aac-(6?)-Ib-cr in 89% of isolates. Only in one carbapenem-resistant E.coli, NDM-1 was detected. Nearly 67% of the isolates belonged to phylogenetic group B2. There was no genetic relatedness among the isolates. Conclusions: Hospital wastewater contains genetically diverse multidrug-resistant E.coli. Significance and Impact of the Study: This study stresses the need for efficient water treatment plants in healthcare settings as a public health measure to minimize spread of multidrug-resistant bacteria into the environment. 2014 The Society for Applied Microbiology. -
TamilEnglish Machine Translation: A Comparative Analysis of Human and AI Renderings
This chapter explores the understudied challenges of TamilEnglish machine translation (MT), which is a field where most existing scholarship has focused on Western language pairs. It performs a comparative analysis of published translations of acclaimed Tamil authors such as Perumal Murugan, Ashokamitran, Jeyamohan, and Charu Nivedita with AI- generated translations produced by systems including ChatGPT, Grok, and AI Novel Translation. It explores issues of accuracy, fidelity, style, and cultural nuance. By examining how AI tools interpret and reproduce linguistic choices, syntax, and cultural particularities, it evaluates the extent to which AI can approximate human translation and highlights the role of post- editing in bridging gaps. The analysis not only identifies recurring errors but also suggests methods for refining human and AI translations for Tamil literary texts. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Impact of IFRS on the financial statements of select IT companies in India
Globalization of economies and shift in financial environment from the traditional bank based one to a market based one necessitated a uniform financial reporting language across countries to facilitate comparisons. This resulted in the establishment of International Accounting Standard Board (IASB) which issued International Financial Reporting Standards (IFRS), a global standard for company financial statements. More than 120 countries, including European Union, Australia, Canada have already adopted IFRS. India was expected to converge with IFRS from April 2016 for listed and unlisted companies with a net worth of more than rs 500 crores. However, few Indian companies listed internationally are voluntarily reporting IFRS. The present study aimed to understand the effect of this voluntary reporting of IFRS on key financial ratios of four selected IT sector companies. The study compared 12 major financial ratios under IFRS and Indian Generally Accepted Accounting Principles (IGAAP) as reported in their financial statements for a period of 5years from 2009-10 to 2013-14. For the purpose of the study, financial ratios representing four key dimensions of companies namely liquidity, leverage, profitability, and efficiency were considered. To understand the statistical significance of the difference between the ratios, Wilcoxon signed rank test, a non parametric test was used. Of the 12 ratios analyzed, 10 were found to be statistically significant. Further, the study explained the financial statement items which cause the difference in the ratios of these companies. The results indicated current liability and shareholder's equity to be significant at the 10% level, thus explaining the difference in financial statement items under IFRS. -
Exploring REITs in Indian Context: A Modern Avenue for Real Estate Investment
This paper explores how Real Estate Investment Trusts (REITs) are conceived and are poised for growth in Indias financial domain. REITs bring the opportunity for ordinary investors to invest in real estate based income generating assets with the added advantages of liquidity, transparency and control of SEBI regulation (2019). A closer look at how Indias listed REITs have performed to date, and what impact they have had on capital markets, investor preference and real estate development (Sharma and Iyer 2021). The results suggest that REITs may contribute in attracting additional capital to the real estate industry and The results indicate that REITs can significantly facilitate capital flow into the real estate industry and democratize property investment in India. The article emphasizes future prospects and policy concerns vital for the evolution of the REIT sector in India through worldwide benchmarking and empirical analysis (World Bank 2020). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Growth and microindentation analysis of pure and doped Sb2 Se3 crystals
Pure and doped antimony selenide (Sb2 Se3, Sb 2 Se2.8 Te0.2, and Sb2 Se 2.6 Te0.4) crystals have been grown from melt by the Bridgman Stockbarger method. X-ray powder diffraction analysis was carried out to determine the lattice parameters of the grown samples. The morphology of cleavage planes was observed using SEM. Energy dispersive analysis by X-rays (EDAX) was done to find out the chemical composition of the grown samples. Correlation of microhardness with other mechanical characteristics such as toughness, brittleness, and yield strength, has been investigated. The effects of Te doping on the mechanical behaviour and energy gap were also studied on the cleavage faces. Ti?tak. -
Implementation of biological fuel cells in treating pharmaceutical effluents
Mankind suffers from a wide variety of infections, diseases, and lifestyle problems. To overcome, several industries worldwide aim to achieve their main objective as the synthesis of an enormous number of diverse drugs that neutralize problems. With the production of tones-to-tones pharmaceutical products these industries also generate extreme good amount of waste, pharmaceutical waste which is now concerned as it contains massive quantity of high organic load of toxic and non-toxic elements. However, the industrial sector adopts anaerobic wastewater treatment strategies to overcome this. As pharmaceutical waste owes highly varied and complexed recalcitrant elements in their complex drug molecules it is not ideal to treat only with anaerobic treatment. Hence, several biotreatments are becoming popularized because they employ MFCs, which are known for the generation of electricity directly from biodegradable organic compounds. The new bio electrochemical technology promises to be inexpensive in comparison to conventional ones. MFC holds the process of both oxidation and reduction permitting the degradation of a wide range of compounds to easily degradable and generates concurrent renewable energy. 2022 by Nova Science Publishers, Inc. -
Enhancing Video Surveillance for Crime Detection Using Anomaly Detection Techniques
Security cameras are widely used to detect and prevent crimes, but the number of surveillance videos has increased due to this prevalence. The process of detecting similarities or data points that significantly depart from the norm or expected behavior of a given system is known as anomaly detection. Predictive maintenance, network intrusion detection, and fraud detection are just a few of the areas where anomaly detection is applied. By processing these videos with the help of a suitable machine learning algorithm, unfavorable events can be brought to the attention of experts to manually monitor. Since these unfavorable events are of various types and few in number, this problem can be addressed in the anomaly detection structure. An anomaly detection algorithm has been developed using the UCF-Crime dataset consisting of 1900 surveillance videos of various lengths. In this context, video surveillance refers to observing the scenes of improper human behaviors which are termed as real world anomalies. Depending on the availability of data sets, anomaly detection algorithms can be supervised, unsupervised, or semi- supervised. The quality of the data and the selection of the best algorithms determine how well anomaly detection techniques work. This paper proposes the use of anomaly detection techniques to enhance video surveillance systems for crime detection. By identifying unusual activities in surveillance footage, the system can alert authorities to potential criminal activity and improve overall security measures. The effectiveness of this approach is demonstrated through experiments and analysis of real-world surveillance data. 2025 Author(s). -
Peristaltic mechanism of Ellis fluid with viscous dissipation and thermal radiation induced by cilia wave
Bioheat transfer analysis in tissue has attracted the attention of numerous researchers due to its widespread potential applications in the medical field, mainly in thermotherapy and the human thermoregulation system. Also, temperature regulation of the human body primarily occurs through bioheat transfer. Due to the widespread biomedical applications of bio-heat transfer, we aim to investigate the movement of biofluid and bioheat in human organs with the influences of thermal radiation and ciliary waves. The mathematical model for Ellis fluid flow through a tube includes the metachronal wave of cilia motion and convective conditions. The governing equations are created based on mass, momentum conservation, and energy. The current problem is displayed and exact solutions are managed under long wavelength (? < 1) and low Reynolds number (Re < 1) approximations. An analytical approach is employed to derive expressions for longitudinal velocity, temperature, pressure gradient, and stream function as a function of the parameters of the problem. The physical behavior of the peristaltic motion of the Ellis fluid is explained in detail and illustrated graphically for various parameter values. The results of the current study provide potential information for advancement in the biomedical industry, particularly in the development of biomedical devices and processes. World Scientific Publishing Europe Ltd. -
Electro-osmotic peristaltic streaming of a fractional second-grade viscoelastic nanofluid with single and multi-walled carbon nanotubes in a ciliated tube
Mathematical modeling of carbon nanotubes (CNTs) in biological fluids is essential for drug delivery, biosensing, and targeted therapy. This study explores the transport dynamics of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) based nanofluids under electro-osmotic peristaltic flow influenced by ciliary motion. A microfluidic channel lined with cilia, hair-like structures found in human airways and reproductive tracts, is considered. The coordinated beating of cilia generates a wavelike motion that propels the surrounding biological fluid. When an electric field is applied across the channel, electro-osmotic forces further modify the flow, affecting velocity and temperature distribution. A nanofluid, consisting of CNTs suspended in a base fluid, flows through this cilia-driven microchannel. The transport process is governed by electro-osmosis, heat transfer, and thermal radiation effects, with simplifications based on long-wavelength and low Reynolds number assumptions. The Caputo fractional model and DebyeHkel linearization are used to analyze the interaction between electro-osmotic forces and thermal-mechanical effects. The results reveal that the negative Helmholtz-Smoluchowski parameter (Uhs) reduces the axial velocity in the core whereas it increases in the periphery of the channel, while the opposite trend is observed for positive Uhs. Longer cilia (?) and higher electro-osmotic parameter (m) slow the core flow while accelerating peripheral transport. Thermal effects indicate that an increased heat source (B) raises temperature and axial velocity, whereas a higher nanotube volume fraction (?) enhances axial velocity but reduces temperature. Notably, MWCNTsexhibit superior axial velocity and temperature enhancement compared to SWCNTs. These outcomes provide valuable insights into electro-osmotic cilia-driven nanofluid transport, offering a theoretical foundation for optimizing microfluidic and biomedical applications. 2025 -
Electro-osmotically modulated viscoelastic SWCNT-blood flow in symmetric/nonsymmetric stenosed arteries with heat generation using a fractional second grade model
This study examines the electro-osmotically modulated viscoelastic blood flow in arteries with both symmetric and nonsymmetric stenosis, accounting for heat generation and thermal buoyancy effects. Blood is modeled as a fractional second-grade fluid to more accurately capture its viscoelastic and memory-dependent behavior. The DebyeHkel linearization is applied to analyze the electro-osmotic effects. The governing partial differential equations are reduced to a system of ordinary differential equations using appropriate scaling transformations. Analytical solutions are derived for the resulting nondimensional boundary value problem. Key flow characteristics, such as axial velocity, temperature distribution, electric potential, volumetric flow rate, and wall shear stress, are computed and illustrated graphically using the Mathematica software. The computations reveal that axial velocity decreases near the arterial walls but increases in the core region for both symmetric (n = 2) and nonsymmetric (n = 6) stenoses with rising HelmholtzSmoluchowski velocity (UHs), CNT volume fraction (?), Debye length parameter (m), and stenosis height (e). Heat generation (? > 0) further enhances both velocity and temperature. Increasing ? reduces temperature and wall shear stress (?w), while higher flow rate (Q) and stenosis height (e) are elevated (?w). Nonsymmetric stenoses yield higher temperatures than symmetric ones. Trapping boluses grow in size and number with increasing Q for both stenosis types. These findings underscore the significant role of electro-osmotic and viscoelastic effects in hemodynamic regulation, with potential biomedical applications. World Scientific Publishing Europe Ltd. -
Peristaltic mechanism of Ellis fluid with viscous dissipation and thermal radiation induced by cilia wave
Bioheat transfer analysis in tissue has attracted the attention of numerous researchers due to its widespread potential applications in the medical field, mainly in thermotherapy and the human thermoregulation system. Also, temperature regulation of the human body primarily occurs through bioheat transfer. Due to the widespread biomedical applications of bio-heat transfer, we aim to investigate the movement of biofluid and bioheat in human organs with the influences of thermal radiation and ciliary waves. The mathematical model for Ellis fluid flow through a tube includes the metachronal wave of cilia motion and convective conditions. The governing equations are created based on mass, momentum conservation, and energy. The current problem is displayed and exact solutions are managed under long wavelength (? ? 1) and low Reynolds number (Re ? 1) approximations. An analytical approach is employed to derive expressions for longitudinal velocity, temperature, pressure gradient, and stream function as a function of the parameters of the problem. The physical behavior of the peristaltic motion of the Ellis fluid is explained in detail and illustrated graphically for various parameter values. The results of the current study provide potential information for advancement in the biomedical industry, particularly in the development of biomedical devices and processes. 2025 World Scientific Publishing Europe Ltd. -
Economic, Political, and Demographic Drivers of Social Isolation: Exploring the Role of Digital Literacy and Migration in Shaping Social Isolation - A Qualitative Study
This chapter examines the impact of migration and digital literacy on social isolation amongst workers. Migration can disrupt established social networks, making it challenging for an individual to establish and build new connections. The research employed a qualitative approach, and data were collected through semi-structured interviews with migrant workers residing in Bengaluru. The findings provide contextual information on the causes of social isolation and help acquire more knowledge on how migration and digital literacy relate and influence social isolation. It prioritises individual experience over statistical data, with an increased understanding of the drivers of social isolation. Advanced digital literacy, on the other hand, can reduce social isolation by enabling migrants to maintain connections with their immediate family, access information, and develop innovative social networks. The research study's findings had a significant impact on policies and employers, highlighting the importance of social integration and mental health. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition
In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015. 2023 Institute of Advanced Engineering and Science. All rights reserved.