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Breast Cancer Classification Using Machine Learning A Study
Nowadays, breast cancer is the most common disease found in women. Although many researchers and experts have aimed to discover the solution to this widespread disease, they have not determined it. In this study, the techniques that are used to find the early signs of breast cancer with the use of machine learning (ML) are discussed. ML is an emerging technology in the field of computer science and information technology, especially in disclosing medical diagnoses. ML is also used, for example, in image recognition, speech recognition, traffic prediction, virtual personal assistants, and online fraud detection. There are plenty of algorithms and techniques that are used in ML. Some of the most popular techniques are discussed in this study. 2025 selection and editorial matter, A. Malini, Surbhi Bhatia Khan, S. Kayalvizhi, and Mohammed Saraee; individual chapters, the contributors. -
The role of social antecedents in enhancing psychological safety for women in software development organisations: a mixed-methods investigation
Purpose: This paper aims to study the social influences on psychological safety among women employed in software development organisations in India. By integrating conceptual and practical perspectives, the research aims to provide insights into developing inclusive workplace environments for women. Design/methodology/approach: A sequential mixed-methods approach was used. In the first phase, qualitative interviews were conducted with 10 senior women managers to explore enablers of psychological safety and identify key antecedents, including supervisor support, coworker support, trust and social embedding. In the second phase, a quantitative survey using a five-point Likert scale was distributed to 300 women in operational roles, yielding 250 valid responses for statistical analysis. Findings: The results indicate that social embedding mediates the impact of trust and supervisor support on psychological safety. Furthermore, trust moderates the relationship between supervisor support and social embedding. Both coworker and supervisor support influence trust, which enhances psychological safety, underscoring the significance of social relationships in shaping workplace experiences. Practical implications: The paper provides valuable insights for firms seeking to develop HR policies and leadership practices that promote psychological safety for women. By emphasising the role of social embedding and trust, companies can create inclusive and supportive environments that enhance job engagement, retention and overall well-being. Originality/value: This research is a pioneering effort to explore the role of social antecedents in psychological safety among women in an emerging economy, contributing to the wider discourse on gender, workplace culture and organisational behaviour. 2025, Emerald Publishing Limited. -
Humanizing Industry 5.0: Creating Human-centric Experiences
Industry 5.0 (I5.0) saw a paradigm shift from a human-centric automation approach that focused on well-being, sustainability, and resilience. The transformation further elevates the process of personalization in various industries. We focus on the progression of personalization from a traditional rule-based system to advanced technologies. The technological revolution in I5.0 has transformed how we view personalization. The integration of technologies like the Internet of Things (IoT), artificial intelligence (AI), and digital twin technology is allowing businesses to create highly tailored products. This includes personalized recommendations, targeted marketing campaigns, and immersive experiences in the metaverse. The chapter further analyzes various techniques and models in personalization, like collaborative filtering, content-based filtering, and hybrid approaches. Technological advancements also bring many challenges. Concerns like data privacy, hesitancy to share personal information, and irrelevant personalization can impact the content of personalization. The ethical implications of AI personalization, like potential biases and discriminatory outcomes, require careful attention. It is important to balance the benefits of personalization with individuals privacy rights and equitable access to personalized services. 2026 Aarti Saini and Vikas Garg. -
Enhancing Learning and Societal Innovation with Cyber-Physical Systems: Education and Society
Cyber-Physical Systems (CPS) are evolving rapidly and can significantly impact education and society. In this paper, we explore the various technologies and applications of CPS in the context of education and social concerns. Through the integration of computation, networking, and physical processes, CPS can enhance educational experiences, foster social inclusion, and address societal challenges. Using CPS in education allows for intelligent learning environments, personalized learning experiences, and real-time feedback systems that meet the diverse needs of students and encourage them to engage in their learning. With real-time monitoring and response systems, CPS can help develop smart communities, improve accessibility for disabled individuals, and enhance public safety. This paper provides a comprehensive analysis of CPS technologies and their applications, highlighting their transformative potential in education and society, motivating further research and policy development. The implementation of CPS can transform education and society, but enhanced security measures must accompany their implementation. Integrating CPS in real-world applications requires safeguarding sensitive data and safeguarding against cyber-threats. This collaboration ensures that diverse perspectives are considered, leading to more comprehensive solutions that address the multifaceted challenges of CPS deployment. It also enhances the effectiveness and sustainability of CPS applications by combining different expertise. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Water Sustainability and Smart Monitoring: IOT and AI for Water Use
This chapter discusses the contributions of Artificial Intelligence (AI) and the Internet of Things (IoT) to sustainable water management. It considers how traditional water management, including reservoirs, qanats, and aqueducts, has provided important lessons in climate adaptation and sustainability, while contemporary technologies effectively address challenges of scarcity, leakage, and inefficient management. AI-supported predictive analytics, IoT-enabled smart sensors, together with precision irrigation, can support leak detection and water demand forecasting, detecting soil moisture through sensors, and water quality monitoring. The chapter also touches on trends such as blockchain capabilities, circular water systems, and the use of citizen science. It offers new opportunities to innovate, focus on global partnerships, and small-scale capabilities, to help combine ancient wisdom with innovation to promote equitable, efficient, and durable approaches to water sustainability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
An investigation on structural, electrical and optical properties of GO/ZnO nanocomposite
Coupling of graphene oxide with metal oxide is an effective way to enhance the opto-electric properties of the composite. Herein, a hybrid structure of graphene oxide (GO) -Zinc oxide (ZnO) nanostructure was successfully designed and fabricated with varying concentrations of ZnO. The GO and ZnO nanoparticles were synthesized through Hummer's and simple precipitation method respectively. Structural and physiochemical properties were examined via X-ray powder diffraction, FTIR and UV-Vis spectroscopy. The XRD results of GO showed a peak at 2? of 12.02 with particles of size 6nm and inter layer spacing 0.87 nm. The XRD patterns of ZnO nanoparticles showed a hexagonal unit cell structure and the average dimension of the sample was calculated to be 15 nm. The band gap of the synthesized GO is found to be 5.1 eV and that of ZnO to be 3.07 eV with the help Tauc plot. The dependence of various concentration of ZnO on the electrical behaviour is discussed by an impedance analyzer in the frequency range 100Hz to 1MHz. The ZnO/GO composite with best results have been obtained for 20% and 60 % ratios of ZnO. The composite has high dielectric permittivity and low loss tangent values and is identified as a promising candidate for energy storage applications. 2019 The Authors. -
Cost Sensitive based Support Vector Machine for Energy Efficient Clustering and Routing
Wireless Sensor Networks (WSNs) are ideal for applications requiring rapid deployment, as they operate without the need for a pre-existing network infrastructure. WSNs have constrained capacity to manage large volumes of data, and processing, transmitting as well as receiving this data consumes substantial energy. On account of their constrained power usage and bandwidth, sensors are unable to transmit each gathered information to a Base Station (BS) or sink for analysis. Thus, this research proposes the Cost Sensitive based Support Vector Machine (CS-SVM) approach for the energy efficient clustering and routing. The proposed approach minimizes an energy consumption at data transmission and clustering, extending network lifetime through effectively selects CHs according to energy levels as well as node distribution. The CH selection considers the three important fitness functions such as distance from sink to CH, distance from CH to sensor nodes and residual energy. Then, the routing considers the two important fitness functions. The various performance indices are considered to validate the effectiveness of proposed method. The simulation outcomes proven that the proposed CS-SVM approach attains the better energy consumption of 8.32J as compared to the previous approach named Distributed Energy-efficient two-hop-based Clustering and Routing (DECR). 2025 IEEE. -
Can stochastic clocks in FLRW minisuperspace prevent dynamical singularities?
We develop a stochastic extension of the Wheeler-DeWitt equation in Friedmann-Lemare-Robertson-Walker (FLRW) minisuperspace and show that quantum backreaction can dynamically regulate the big bang singularity without imposing external boundary conditions. Using Laplace-Beltrami (LB) quantisation and an open-system treatment of coarse-grained graviton modes, we obtain a stochastic Hamiltonian evolution equation in which the diffusion coefficient takes the form ? ( a ) ? a 2 . This multiplicative noise vanishes at the origin and renders a = 0 an entrance boundary in Fellers classification, leading to super-exponential suppression of the LB weighted stationary density and zero probability flux into the singular point. At large scale factor, the global behaviour depends on the cosmological sector: de Sitter and positive potential-dominated regimes exhibit power-law stationary tails, whereas confining potentials or negative effective cosmological constant lead to an entrance boundary at infinity and a globally normalisable steady state. Taken together, these results indicate that stochastic backreaction arising from semiclassical coarse-graining provides a consistent and dynamical mechanism for singularity avoidance in minisuperspace quantum cosmology. 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Spontaneous symmetry breaking as a late-time trigger for interacting dark energy
Persistent tensions in the Hubble constant (H 0) and the matter clustering parameter (S 8) motivate late-time new physics that suppresses structure growth while remaining compatible with current background constraints of the ?CDM model. We study a new class of dark sector dynamics in which a scalar dark energy (DE) field, governed by a ?2-symmetric quartic potential, interacts with dark matter (DM) through Yukawa and portal couplings. When the matter density drops below a critical threshold, a cosmological spontaneous symmetry breaking (SSB) mechanism generates a time-dependent vacuum expectation value v(a) and activates an effective coupling ?(a). This produces a symmetric phase (a ? ac) identical to ?CDM at early times, and a broken phase (a > ac) in which ?(a) > 0 transfers energy from DM to DE, suppressing the linear growth of structure. We confront this framework with RSD, BAO, CC, and Pantheon+SH0ES supernova data, jointly with compressed Planck distance priors, comparing a fixed ?CDM background to a self-consistent coupled scalar evolution. The RSD-only analysis shows a pronounced shift: the dynamical background yields ?m ? 0.31 0.10 and ? 80 ? 0.59 0.01, with a higher matter density but a substantially lower growth amplitude compared to the fixed background case ?m ? 0.20 0.09 and ? 80 ? 0.75 0.05. In the full joint fit, conditioned on geometric distance information, the inferred parameters are ?m = 0.29 0.01, H 0 = 69.7 0.6 km s-1 Mpc-1, and ? 80 = 0.78 0.01. These results demonstrate that a late-time, SSB-activated interaction can efficiently dampen structure growth and potentially alleviate the S 8 tension. In growth-dominated analyses such as RSD-only constraints, the inferred value of H 0 is only weakly affected by the coupling. However, in joint analyses including strong external distance calibrations, the inferred H 0 becomes sensitive to dataset choice and to whether the background expansion is treated self-consistently. 2026 IOP Publishing Ltd and Sissa Medialab. All rights, including for text and data mining, AI training, and similar technologies, are reserved. This article is available under the terms of the https://publishingsupport.iopscience.iop.org/iop-standard/v1. -
Study of multilayer flow of non-Newtonian fluid sandwiched between nanofluids
This theoretical investigation examines the nonlinear convective heat transport and multilayer flow of a non-Newtonian fluid within a vertical slab, incorporating viscous heating effects. The middle layer of the slab contains a third-grade fluid, while the outer layers are filled with a water-based Ag-MgO hybrid nanoliquid. Continuity in temperature, heat flux, velocity, and shear stress is maintained at the interfaces of the fluid layers. The thermal buoyancy force is modeled using the nonlinear Boussinesq approximation. The governing system comprises conservation equations for mass, momentum (Navier-Stokes), and energy for each of the three layers. These differential equations are non-dimensionalized, and the resulting dimensionless four-point nonlinear boundary value problem is transformed into a two-point boundary value problem before being solved numerically. For limiting cases, analytical and semi-analytical solutions are computed and used as benchmark results to validate the numerical method employed. Entropy generation analysis indicates that higher third-grade fluid parameters reduce the magnitude of velocity and temperature fields, as well as entropy production across all regions. The third-grade fluid parameter shows a decreasing influence on velocity and temperature fields throughout the system. The continuity of interfacial conditions induces a dragging effect; despite the absence of third-grade fluid parameters in regions I and III, their influence is apparent in these regions. The Bejan number slightly decreases at the walls with increasing third-grade fluid parameters, exhibiting a dual effect in the third-grade fluid layer. Near the walls, the Bejan number decreases as the nanoparticle volume fraction increases. Findings of this work may have applications in polymer industries and processes involving high temperatures. 2024 -
Convective instability analysis of couple-stress dielectric fluid saturated anisotropic porous medium with radiation effect
Purpose: The effects of anisotropy and radiation cannot be considered negligible while investigating the stability of the fluid in convection. Hence, the purpose of this paper is to analyze how these effects could affect the system while considering a couple-stress dielectric fluid. Therefore, the study establishes the effect of thermal radiation in a couple-stress dielectric fluid with an anisotropic porous medium using Goody's approach (Goody, 1956). Design/methodology/approach: To analyze the effect of radiation on the onset of convection, the MilneEddington approximation is employed to convert radiative heat flux to thermal heat flux. The equations are further developed to approximate for transparent and opaque medium. Stability of the quiescent state within the framework of linear theory is performed. The principle of exchange of stabilities is shown to be valid by means of single-term Galerkin method. Large values of conductionradiation and absorptivity parameters are avoided as fluid is considered as liquid rather than gas. Findings: The radiative heat transfer effect on a couple-stress dielectric fluid saturated anisotropic porous medium is examined in terms of MilneEddington approximation. The effect of couple-stress, dielectric, anisotropy and radiation parameters are analyzed graphically for both transparent and opaque medium. It is observed that the conductionradiation parameter stabilizes the system; in addition, the critical DarcyRayleigh number also shows a stabilizing effect in the absence of couple-stress, dielectric and anisotropy parameters, for both transparent and opaque medium. Furthermore, the absorptivity parameter stabilizes the system in the transparent medium, whereas it exhibits a dual effect in the case of an opaque medium. It was also found that an increase in thermal and mechanical anisotropy parameters shows an increase in the cell size, whereas the increase in DarcyRoberts number and conductionradiation parameter decreases the cell size. The validity of principle of exchange of stability is performed and concluded that marginal stability is the preferred mode than oscillatory. Originality/value: The effects of anisotropy and radiation on RayleighBard convection by considering a couple-stress dielectric fluid has been analyzed for the first time. 2020, Emerald Publishing Limited. -
Optimization procedure for multilayer heat transfer in nanoliquid with Joule heating using response surface methodology
In this chapter, magnetohydrodynamic flow (MHD) and heat transfer in a multilayer vertical channel are studied with one phase containing pure water and the other phase containing oil-based Cu nanofluid. The effects of viscous dissipation and Joule heating are included in the energy equation. The modeled equations are coupled and nonlinear; they are solved using the regular perturbation method (RPM) and the differential transform method (DTM). The analysis examines the impact of the Hartmann number, thermal Grashof number, nanoparticle volume fraction (NVF), and Brinkman number on the Nusselt number, velocity, and temperature distributions. Furthermore, an optimization of the Nusselt number is performed for three different levels of the Hartmann number (5?M?6), the Brinkman number (0.1?Br?0.3), and the NVF (1%???3%) using the Response Surface Methodology (RSM). The Hartmann number and NVF were found to suppress flow, while the thermal Grashof number and the Brinkman number increase the flow field. Sensitivity computations reveal that the Nusselt number on the left wall is more sensitive to the Hartmann number and the NVF, while the Nusselt number of the right wall is more sensitive to the Brinkman number and NVF. 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Study of multilayer flow of non-Newtonian fluid sandwiched between nanofluids
This theoretical investigation examines the nonlinear convective heat transport and multilayer flow of a non-Newtonian fluid within a vertical slab, incorporating viscous heating effects. The middle layer of the slab contains a third-grade fluid, while the outer layers are filled with a water-based Ag-MgO hybrid nanoliquid. Continuity in temperature, heat flux, velocity, and shear stress is maintained at the interfaces of the fluid layers. The thermal buoyancy force is modeled using the nonlinear Boussinesq approximation. The governing system comprises conservation equations for mass, momentum (Navier-Stokes), and energy for each of the three layers. These differential equations are non-dimensionalized, and the resulting dimensionless four-point nonlinear boundary value problem is transformed into a two-point boundary value problem before being solved numerically. For limiting cases, analytical and semi-analytical solutions are computed and used as benchmark results to validate the numerical method employed. Entropy generation analysis indicates that higher third-grade fluid parameters reduce the magnitude of velocity and temperature fields, as well as entropy production across all regions. The third-grade fluid parameter shows a decreasing influence on velocity and temperature fields throughout the system. The continuity of interfacial conditions induces a dragging effect; despite the absence of third-grade fluid parameters in regions I and III, their influence is apparent in these regions. The Bejan number slightly decreases at the walls with increasing third-grade fluid parameters, exhibiting a dual effect in the third-grade fluid layer. Near the walls, the Bejan number decreases as the nanoparticle volume fraction increases. Findings of this work may have applications in polymer industries and processes involving high temperatures. 2024 -
Narratives on using critical approaches in teacher education
Using the approach of autoethnographic narrative, three teacher educators from a cosmopolitan city in South India discuss how they use critical approaches in preparing preservice teachers and educational psychologists in the courses that they teach at a private university. The students are sensitized about the marginalized and the privileged sections in a multicultural and multilingual nation as India and to become culturally responsive in their classrooms or with their clientele in terms of their dispositions, knowledge, and skills. The chapter also describes the integration of critical approaches in the doctoral program aimed at addressing educational disparities and promoting social justice in education. 2024, IGI Global. All rights reserved. -
Think Big with Big Data: Finding Appropriate Big Data Strategies for Corporate Cultures
The aim of this research is to learn how big data strategies (BDS) as a corporate culture might improve confidence and cooperative performance across civil and defence sectors involved in disaster relief activities. The research conceptualized a unique conceptual framework to demonstrate, employing the competitive value model (CVM), how BDS influences swift confidence (SC) and cooperative performance (CP) beneath the moderating impact of the corporate culture. The findings have four significant consequences. Initially, the BDS has a strong beneficial influence on SC and CP. Secondly, neither adaptable orientation (AO) nor regulated orientation (RO) had any effect on constructing SC. Thirdly, AO has a strong and beneficial moderating influence on the path connecting BDS and CP. As a result, RO shows a considerable negative moderating impact on the path linking BDS and CP. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Phytochemicals for neurodegeneration and neuroinflammation: medicine of the future or a mirage?
Dietary polyphenols cease to be mere nutrients but have immense health enhancing and disease modifying effects. Phytochemical-based therapeutic approaches for neurodegenerative diseases are becoming increasingly popular. This may be attributed to the lack of long-term benefits or adverse effects of current pharmacotherapy. Polyphenols target multiple pathways and their long-term use could prove beneficial for diseases involving multiple etiological factors. While polyphenols are nontoxic and oral route is the preferred mode of administration, bioavailability in the brain is limited rendering the neuroprotective efficacy questionable. Methods employing synthetic biopolymers, nanoformulations, liposomal carriers, or conjugation have been explored to enhance the bioavailability. While results have been promising in experimental models, translation to human neurodegenerative conditions is limited. It can therefore be surmised that the present knowledge on dietary polyphenols is only the tip of the iceberg and extensive translational research is warranted to fill the gap for their therapeutic use. 2023 Elsevier Inc. All rights reserved. -
Biopolymers as promising vehicles for drug delivery to the brain
The brain is a privileged organ, tightly guarded by a network of endothelial cells, pericytes, and glial cells called the blood brain barrier. This barrier facilitates tight regulation of the transport of molecules, ions, and cells from the blood to the brain. While this feature ensures protection to the brain, it also presents a challenge for drug delivery for brain diseases. It is, therefore, crucial to identify molecules and/or vehicles that carry drugs, cross the blood brain barrier, and reach targets within the central nervous system. Biopolymers are large polymeric molecules obtained from biological sources. In comparison with synthetic polymers, biopolymers are structurally more complex and their 3D architecture makes them biologically active. Researchers are therefore investigating biopolymers as safe and efficient carriers of brain-targeted therapeutic agents. In this article, we bring together various approaches toward achieving this objective with a note on the prospects for biopolymer-based neurotherapeutic/neurorestorative/neuroprotective interventions. Finally, as a representative paradigm, we discuss the potential use of nanocarrier biopolymers in targeting protein aggregation diseases. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Navigating AI and chatbot applications in education and research: a holistic approach
Purpose: This study aimed to identify factors influencing AI/chatbot usage in education and research, and to evaluate the extent of the impact of these factors. Design/methodology/approach: This study used a mixed approach of qualitative and quantitative methods. It is based on both primary and secondary data. The primary data were collected through an online survey. In total, 177 responses from teachers were included in this study. The collected data were analyzed using a statistical package for the social sciences. Findings: The study revealed that the significant factors influencing the perception of the academic and research community toward the adoption of AI/interactive tools, such as Chatbots/ChatGpt for education and research, are challenges, benefits, awareness, opportunities, risks, sustainability and ethical considerations. Practical implications: This study highlighted the importance of resolving challenges and enhancing awareness and benefits while carefully mitigating risks and ethical concerns in the integration of technology within the educational and research environment. These insights can assist policymakers in making decisions and developing strategies for the efficient adoption of AI/interactive tools in academia and research to enhance the overall quality of learning experiences. Originality/value: The present study adds value to the existing literature on AI/interactive tool adoption in academia and research by offering a quantitative analysis of the factors impacting teachers' perception of the usage of such tools. Furthermore, it also indirectly helps achieve various UNSDGs, such as 4, 9, 10 and 17. 2024, Abhishek N., Sonal Devesh, Ashoka M.L., Neethu Suraj, Parameshwara Acharya and Divyashree M.S. -
Environment and Human Rights - Interrelatedness
Shodh Prerak, Vol. 2, Issue 3, pp 69-73, ISSN No. 2231-413X -
Parametric analysis for thermally magnetized hybrid ternary (TMHT) nanofluid flow on thin film with temperature stratification
The thermophysical examination of flow field claims various applications in both scientific and industrial domains and hence it remains important to inspect especially when both the heat and mass transfer are taken simultaneously. Owning such motivation, the present study offers a response surface optimization for thermal flow field of hybrid ternary water-based aluminium, silicon and Zinc nanofluid over a stretched surface manifested with both temperature stratification and concentration stratification effects. The governing equations are formulated for mathematical model and those PDE's are reduced to ODE's by using appropriate similarity transformations. Those obtained resultant equations are solved numerically by using Runge Kutta Fehlberg fourth fifth-order (RKF 45) technique. The supremacy of essential aspects on the flow field, heat and mass transfer rates were analyzed using graphical representation. Additionally, Response surface Methodology is performed to derived the heat transfer rate as a response function for the input factors for different parameters. From the graph it is noticed that temperature profile drops as the thermal stratification parameter increases. The temperature admits the direct relation with an increase in the solid volume fraction of ternary nanofluids. From RSM it is noticed that adjusted R-squared and R-squared are obtained as 100 % accuracy of the mathematical model. 2025 The Author(s)