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Between home and enterprise: the Swakruta dilemma of scaling women entrepreneurs
Learning outcomes After completing the case study, the learners will be able to: Analyze how deep-rooted socio-cultural and family expectations create systemic barriers for small women entrepreneurs in India. Apply social identity theory to design interventions that reshape self-perception and entrepreneurial identity among Swakruta women entrepreneurs. Evaluate the influence of loss aversion on womens entrepreneurial decision-making and develop the nudging strategies to encourage women to engage with large opportunities. Case overview/synopsis This case explored the challenges faced by Manik Patwardhan, founder of Swakruta Charitable Trust, which supported over 200 small and marginal women entrepreneurs in Bengaluru. Despite training and opportunities provided, many women were hesitated to accept large, profitable orders due to socio-cultural norms and financial constraints. Using social identity theory, loss aversion and nudging, the case highlighted how strong family responsibilities and societal expectations influenced their cautious approach in scaling up their businesses. The women need to balance family responsibilities with business growth, which restricted their willingness to take risks. Their concerns ranged from balancing family duties and managing time, to addressing uncertainty by hiring staff other than family members, trust issues and difficulties in arranging upfront funds. Upon reviewing their response, Manik realised that these entrepreneurs were hesitant to accept a lucrative order. At this critical point, she had to decide whether to let the order go or encourage and nudge the women to seize a career-transforming opportunity, despite the risks involved. Accepting the order could boost earnings and reputation, but failure could harm the NGOs credibility, and declining the order could jeopardise future prospects. What should she do? Complexity academic level This case is designed for undergraduate and postgraduate courses in entrepreneurship, social entrepreneurship and related business disciplines such as behavioural economics. It focuses on the challenges and barriers faced by women entrepreneurs that limit their growth and ability to scale their businesses. Subject code CSS 3: Entrepreneurship. 2026 Emerald Publishing Limited -
Dynamic linkage among crude oil, exchange rates and P/E ratio: The case of India /
International Journal of Pure And Applied Mathematics, Vol.119, Issue 18, pp.1-14, ISSN No: 1314-3395. -
SS-CNN BruiseFinder: Hyperspectral imaging and CNN-driven spatial-spectral fusion for non-destructive plum bruise analysis
Plum fruit is susceptible to damage at various stages, from growth to packaging, and such bruising is often difficult to detect visually due to its subtle surface appearance. This research seeks to develop a convolutional neural network (CNN) model that leverages 3D convolutional layers to integrate spatial and spectral features from hyperspectral data, enabling accurate bruise analysis in plum fruit. In this study, plums sourced from a Norwegian fruit store were intentionally bruised and then imaged using hyperspectral technology at various time intervals (30 min to 48 h post-bruising). A novel CNN model, dubbed SS-CNN BruiseFinder, is developed to harness the spatial and spectral characteristics of these hyperspectral images for accurate bruise detection and classification. The SS-CNN BruiseFinder model demonstrates detection accuracy ranging from 68.5% to 91.5% and categorization accuracy between 67.39% and 98.16%. To further establish the effectiveness of this approach, three additional deep learning models a custom spectral CNN, ResNet 101, and a bidirectional LSTM model are developed and evaluated on the same dataset, providing a comprehensive validation of the proposed method's superiority. Timely detection of bruising helps prevent contaminated plums from entering the supply chain during transportation or storage. By categorizing plums based on bruise age, retailers can offer consumers more accurate freshness and quality information, enabling them to make better-informed purchasing choices and ultimately enhancing the overall shopping experience. To encourage community engagement and re-implementation, our code is available at https://github.com/SS-CNN BruiseFinder. 2025 Elsevier Ltd -
NorBlueNet: Hyperspectral imaging-based hybrid CNN-transformer model for non-destructive SSC analysis in Norwegian wild blueberries
Soluble solids content (SSC) is a vital parameter in blueberries, reflecting the concentration of dissolved sugars (primarily fructose and glucose) and directly influencing the fruit's sweetness, flavour, and ripeness. As part of this study, Norwegian wild blueberries were carefully hand-picked from a forest in Norway and subsequently imaged using a hyperspectral camera to capture their detailed spectral characteristics. This study introduces NorBlueNet, a hybrid CNN-transformer architecture, for accurately predicting SSC in wild blueberries through hyperspectral imaging and deep learning. This hybrid architecture combines CNN layers for local feature extraction and spatial hierarchy representation, followed by transformer layers that capture global relationships and long-range dependencies. The hybrid approach combines the computational advantages of CNNs with the advanced attention mechanisms of transformers, achieving enhanced accuracy while maintaining computational efficiency. A comprehensive evaluation is conducted by comparing the proposed model with two additional deep learning models on the custom dataset. The results indicate that the NorBlueNet achieves the highest prediction accuracy, with an R2 = 0.98, RMSE = 0.0136, and RPD = 9.3759 thereby demonstrating its superior performance. To foster community engagement, collaboration and facilitate re-implementation of our work, we have made our code available at:https://github.com/NorBlueNet. 2025 -
Polymer-nanocarbon composites: a promising strategy for enhanced performance of organic solar cells
The exigency for sustainable and clean energy resources has led to profound research in development of various generations of solar cells, aiming to control the over-exploitation of fossil fuels and subsequently limit environmental degradation. Among the fast-emerging third-generation solar cells, polymer solar cell technology has gained much consideration due to its potential for achieving economically feasible, lightweight, flexible solar energy harvesting devices. As a predominant research area, at present, the major concerns regarding polymer solar cells include improving conversion efficiency, enhancing absorption bandgap in polymers, limiting photochemical degradation, and remediating low dielectric constant. Nanocarbon materials can be effectively blended with polymers and have been widely reported to enhance the performance of polymer solar cells owing to their desirable characteristics like high electrical conductivity, mechanical strength, thermal stability, non-toxicity, large specific surface area, flexibility, and optical transparency. In this review, we briefly discuss various conjugated polymer-nanocarbon composites, including polymer/graphene derivatives, polymer/graphene quantum dots (GQD), and polymer/carbon nanotubes (CNTs), elucidating their roles in the performance enhancement of polymer solar cells (PSCs). Graphical abstract: (Figure presented.). The Author(s) 2023. -
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Multimodal data generation and synthesis
Multimodal data generation and synthesis have become new promising directions in artificial intelligence research, making possible the combination and transformation of the different data modalities: text, images, audio, and video. In this chapter a look will be made about the principles, methodologies, applications, and challenges linked with multimodal data, bringing attention to the current trends and needs regarding multimodal systems and systems approaches to tackle complex real-world challenges across the medical and health care, autonomous systems, entertainment, and extended reality (XR) fields. The chapter introduces multimodal data and discusses how the approach differs from unimodal methods, considering the merits of working with multiple data forms. Multimodal systems present richer and more comprehensive representations that lead to better decision-making and provide a better interaction with users. The complexity due to alignment, synchronization, and representation of diverse modes is inherently difficult. This section further discusses state-of-the-art techniques in multimodal synthesis, especially focusing on generative approaches like generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. These methods are shown to facilitate cross-modal transformations, such as text-to-image or audio-to-video synthesis, driving innovation in artificial intelligence and beyond. Applications of multimodal data synthesis are discussed in detail, underscoring its transformative impact. In health care, for instance, synthesizing medical images paired with textual annotations enhances diagnostic accuracy and medical training. Autonomous vehicles benefit from the integration of LiDAR, visual, and auditory data, enabling robust decision-making in real-time environments. Similarly, in entertainment and XR, multimodal synthesis is redefining content creation, making immersive experiences more personalized and dynamic. The chapter also delves into novel applications such as multimodal translation, exemplified by systems that translate sign language into spoken text, fostering inclusivity and accessibility. Despite its potential, multimodal synthesis faces critical challenges, including bias in data and models, privacy concerns, and the ethical implications of creating hyperrealistic synthetic data, such as deepfakes. All these raise pressing concerns, and addressing these requires robust privacy-preserving techniques, bias-mitigation strategies, and stringent ethical guidelines. 2026 Elsevier Inc. All rights reserved. -
An introduction to multimodal data representation
The contemporary digital epoch is characterized by a radical transformation of data representation methodologies that imply increased intricacy as well as an enlarged bulk of data. An unimodal approach focusing on judicious data types, considered in isolation, was the earlier norm. The emphasis was on structured data, which had the advantage of being arranged systematically within relational databases and entity-relationship frameworks. This facilitated efficient data management. With the introduction of the internet and digital communication, such unstructured data as textual content, images, and audio began to be placed up front. But unimodal techniques were not adequately equipped to manage the intricate and interconnected nature of real-world phenomena. The welcome result was the development of multimodal data representation methodologies, which constitute a sophisticated paradigm that integrates data from such varied sources as text, images, audio, video, and sensor data. This results in a more holistic comprehension of complex scenarios. Distinct attributes and inherent challenges characterize each modality. To exemplify, text data need advanced natural language processing strategies to comprehend context and semantics; Image data necessitate methodologies well versed in managing spatial features and elevated dimensionality; audio data requires concentration on temporal patterns and noise; video data, on the contrary, integrates these complexities, leading to efficient processing techniques to accommodate its substantial volume and dynamic characteristics. The unsynchronous and heterogeneous sensor data complicate the integration of diverse data streams. Sophisticated fusion techniques, that is, early fusion, late fusion, and hybrid fusion, capable of integrating features from various modalities, are employed to mitigate the challenges faced by multimodal data representation. It increases interpretative insights and precision. The deep learning technologies, such as convolutional neural networks for image analysis, recurrent neural networks for sequential data processing, and attention mechanisms, have led to advancements in this domain. These models have become competent in recognizing complex patterns across modalities. Naturally, they bring about significant progress in domains such as health care, autonomous systems, multimedia processing, and natural language comprehension. This chapter explores the historical background of data representation, right from the beginnings in unimodal to its advancement in multimodal. The unique characteristics and challenges associated with each modality are scrutinized; Fusion techniques alongside contemporary deep learning models are examined; and underscore real-world applications, which are effective examples of the transformative potential of multimodal data representation. The chapter also emphasizes the necessity of escalating these methodologies in an increasingly data-centric world. It lays the foundation for advancements in the future with the goal of overcoming existing limitations and enlarging the scope of multimodal applications. 2026 Elsevier Inc. All rights reserved. -
Breaking the Glass Ceiling: Will the Role of Organizational Workplace Policies Perpetuate or Mitigate Gender Bias?
Despite significant global progress in narrowing gender gaps, inequality persists across many countries. Organizations like the Global Gender Gap Index and the European Institute for Gender Equality monitor improvements in political leadership, economic opportunities, health, and education. However, women continue to face challenges, including unequal pay, limited career advancement, and imbalanced household labor. The "glass ceiling" refers to invisible barriers that prevent women from achieving top positions despite equal qualifications. Long-term effects include temporary employment and lower retirement savings. True gender equality requires more than quotas-it demands equitable opportunities, flexible work policies, pay transparency, and mentorship programs. Tackling unconscious bias and fostering inclusive environments is essential for sustainable change and women's holistic success. 2026, IGI Global Scientific Publishing. All rights reserved. -
A Compact Super Wideband Antenna with Controllable Dual Notch Band Capability
In this paper, a novel super wideband (SWB) antenna with dual band notch capability is designed and analyzed for wide band applications. The proposed antenna consists of a pentagonal shaped radiator, beveled-shaped partial ground plane with slot and U-shaped parasitic strips. The beveled-shaped defected ground structure with rectangular slot helps to realize wideband characteristics from 2.4 to 28.2 GHz. Independent control of the notch band's center frequency and bandwidth is achieved by using U-shaped parasitic strips. This key feature is achieved in the WiMAX (3.3 to 3.7 GHz) and WLAN (5.1 to 5.9 GHz) bands. Furthermore, it exhibits a stable radiation pattern and offers acceptable gain over the entire operating bandwidth with sharp decrease in gain at the notches. The percentage bandwidth of 169% is achieved with a bandwidth dimension ratio (BDR) of 6986. Group delay is less than 1 ns in the entire operating bandwidth except at the notch bands. The measured reflection and radiation characteristics of fabricated SWB antenna are in good agreement with the simulation results. The proposed antenna has the advantage of simple design and compact size with an overall dimension of 18 x 21 x 1.6 mm3. The performance of the proposed antenna is superior compared to reported antenna designs in terms of controllable sharp notches and size for the bandwidth achieved. 2022 IAMOT -
Single Port Multimode Reconfigurable UWB-NB Antenna for Cognitive Radio Applications
In this paper, a compact, single port, multimode reconfigurable UWB-NB antenna with a novel feeding network is presented. The proposed antenna consists of a pentagonal-shaped monopole radiator, a beveled-shaped partial defected ground plane with a rectangular slot, and a reconfigurable bypass feeding network. The antenna realizes a wideband frequency range from 2.4 to 18 GHz and four narrow band frequency ranges, 5.3 to 6.8 GHz, 6.0 to 7.6 GHz, 7.2 to 8.8 GHz and 8.4 to 11.4 GHz. The antenna provides an omnidirectional radiation pattern with gain from 2.2 to 6.2 dBi maximum at 12 GHz and voltage standing wave ratio (VSWR) ranges from 1 to 2. The fabricated antenna has an overall dimension of 181.6 mm3. Sensing and tuning ranges of the fabricated antenna shows good agreement with the simulation results. The proposed antenna has an advantage of simple design, low profile, single port excitation and omnidirectional radiation pattern making it suitable for applications such as handheld mobile cognitive radio systems. 2022 SBMO/SBMag -
Relationships between Ultrasonographic Placental Thickness in the Third Trimester and Foetal Outcomes
Poor neonatal outcomes, including low birth weight (LBW), poor APGAR scores, more NICU hospitalizations, and a higher chance to develop Pre-Eclampsia, IUGR, and Oligo Hydramnios, are all linked to thin placental thickness. While both thin and thick placentae are connected to a greater prevalence of C-sections, thick placentae are linked with a greater possibility of developing GDM and an increase in NICU hospitalizations. Objective of this research was to investigate the association between placental thickness as measured by ultrasonography in the third trimester and foetal outcome, including the relationship between placental histopathology and placental thickness. investigate the link among placental thickness, foetal outcome, and placental histology. Most newborns had fibrinoid necrosis and calcifications. Babies with Macrosomia and IUGR, respectively, were more likely to develop Syncytial knots and thickening of the vessel wall. Patients with normal placenta thickness at 36 weeks' gestation experienced fewer difficulties than those with thin or thick placentas at the same time. The study emphasizes the value of evaluating placental thickness using ultrasound in the third trimester to detect high-risk pregnancies. The study also shows that aberrant foetal and neonatal events are linked to certain placental histological characteristics, like artery wall thickening and infarctions. RJPT All right reserved. -
Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis
Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 20072022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price. 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved. -
Colonial Migration and Cultural Transformation in India and Burma: Exploring the Role of Transnational Mobility in Shaping Chettinad Heritage
Migration has played an important role in the transformation of tangible and intangible cultural heritage across regions, including in the Global South, especially among postcolonial nations, due to their longstanding people-to-people contacts leading to socio-economic and historical-cultural transferences over time. In the case of India, global migratory forces have irreversibly transformed its tangible and intangible sociocultural landscape into a form of syncretism reflected in our civilizational ethos of Vasudhaiva Kutumbakam. In this context, this paper explores the role of international in-/outmigration in historical and contemporary times toward the evolution of India's regional cultural identities using a case of Chettiars' migration from the hinterlands of Chettinad in present-day Tamil Nadu, which is in the south of the Indian subcontinent, to the far-flung nation state of Burma/Myanmar in the northeast, including their subsequent return, primarily during the 19th and the 20th centuries. Using the 3i Framework (Interests, Ideas and Institutions), the study explains the role of cross-border migration in shaping the tangible and intangible heritage of Chettinad, as reflected in its architecture, cuisine and social customs. 2026 Association for the Study of Ethnicity and Nationalism and John Wiley & Sons Ltd. -
Data Science in the Medical Field
Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
AGRI 4.0 AND THE FUTURE OF CYBER-PHYSICAL AGRICULTURAL SYSTEMS
Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with a focus on technologies that enable the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery, and consumption. Specific sections explore ways to implement CPS effectively and appropriately and cover digitalization of agriculture, digital computers to assist the processes of agriculture with digitized data and allied technologies, including AI, Computer Vision, Big data, Block chain, and IoT. Other sections cover Agri 4.0 and how it can digitalize, estimate, plan, predict, and produce the optimum agricultural inputs and outputs required for commercial purposes. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices, and government policies in Agri 4.0. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Machine Learning in Investment Analysis-Enhancing or Replacing Human Judgment
Machine Learning (ML) involvement in investment analysis is quickly revolutionizing the investment-based decisions through becoming highly accurate, quick, and embracing increased data processing capabilities. This paper is to research on whether ML is complementary or a possible replacement to human financial judgment. We run experiments over 1.2 million financial transactions between 150 firms comparing old style analyst recommendations and ML-models, including XGBoost, LSTM and Random Forest. The findings indicate that ML models outperformed prediction capability by 19.6 percent and lowered the volatility of the portfolios by 14.3 percent in 5-year investment. Also, the ML-Aided decision-making was better than human (only) approaches in 78 percent of the cases in markets with high volatility or that involved trading in complicated assets. The qualitative variables like regulatory policy changes and investor sentiment however were too difficult to decipher under the leadership of ML only. Our results indicate that ML supports rather than supers the human judgement and thus demonstrates a hybrid paradigm of decision making that resolves computational exactitude with context sensitive understanding in the modern investment scenarios. 2025 IEEE. -
The Impact of AI on High-Frequency Trading: A New Paradigm in Share Market Dynamics
A fresh approach in financial trading has emerged as a result of the significant shifts in the dynamics of global share markets brought about by the incorporation of artificial intelligence (AI) into high-frequency trading (HFT). In order to analyse large datasets in real-time, perform trades in microseconds, and AI-driven Deep learning, machine learning, and natural language processing are all examples of things that HFT uses to help people make the best choices they can in markets that have to change all the time. This change will help people make better decisions, and sellers will be able to respond quickly to changes in the market. Individuals can trade faster, better, and for greater amounts of cash with it than ever before. AI does have some problems when used in HFT, though. It can make the market less stable, lead to legal problems, and make it more diligently to be fair and honest. The amount of money, how well markets work, along with the way risks are handled are all changed by AI. It also discusses about how AI changes HFT. This study talks about the pros and cons of HFT powered by AI. Along with the way shares are sold, it also hints at how it might change future rules. 2025 IEEE. -
Enhancing Investment Advisory with Machine Learning for a New Era in Financial Services
The current financial service environment, where the volatility of markets and the need to offer flexible solutions is growing, is starting to challenge the traditional investment advisory models. This paper implements a new framework, which incorporates the most advanced methods of machine learning, to make investment advising a process driven by real data. This is unlike the current models which are overly dependent on historical trends or fixed risk profiles, our system allows us to use real time behavior analytics, sentiment analysis and dynamic portfolio optimization to give hyper personalized investment recommendations. The framework feeds the ensemble learning, attention-based neural networks, explainable AI (XAI) to make sure the transparency, regulatory, and investor trust. The innovation in particular is based on the constant interaction between client and adjustment of the model in terms of a ready and sensitive advisory intervention. The study will not only improve the relevance and precision of financial advice, but will with its informed automation of advisor-client relationship led to a redefinition of the advisor-client relationship. The insights guide to a world of advisory services where ML and machine learning complement strategic decision-making with unheard levels of specificity and individuality. 2025 IEEE.