Shiv Nadar University

The growing demand for sustainable energy solutions has intensified research into lithium‐sulfur batteries (LSBs) due to their potential for high energy density, though their commercialization is primarily hindered by challenges in achieving satisfactory cycle stability and energy density, with fast‐charging capabilities also requiring significant improvement. This review explores strategies to enhance the fast‐charging capabilities of LSBs, addressing critical issues such as lithium polysulfide (LiPSs) shuttling, sluggish reaction kinetics, and lithium dendrite formation. The review discusses the mechanisms of fast‐charging, followed by a comprehensive analysis of key techniques and the crucial role of various battery components that can revolutionize fast‐charging capabilities. It covers challenges related to electrical and ionic conductivity, optimizing Li‐ion mobility, immobilizing LiPS, designing suitable electrolytes, integrating catalysts to accelerate LiPSs conversion, and engineering cathodes and separator modifications. Additionally, the review discusses scaling challenges for fast‐charging LSBs, including electrolyte design, sulfur loading, polysulfide shuttle mitigation, and electrode stability. The review concludes by providing future perspectives on developing next‐generation LSBs that could transform the energy storage landscape, with a sustainable, high‐capacity, and rapid‐charging alternative to current battery technologies, with significant implications for electric vehicles and portable electronics.

Skin cancer, one of the most prevalent and lethal cancer types, poses significant challenges for early diagnosis due to the diversity in lesion size, shape, color, and surface reflections. The Internet of Things (IoT) has revolutionized healthcare by enabling real-time data exchange and supporting advancements in automated diagnosis through deep learning (DL) techniques such as convolutional neural networks (CNNs). However, CNNs often require large, labeled datasets, which are costly and time-consuming to compile. To address these challenges, we propose an innovative active learning (AL) framework driven by deep reinforcement learning (DRL) and a novel scope loss function. This framework optimizes classification while reducing reliance on extensive labeled data. Unlike traditional active learning techniques that rely on static selection methods, our model dynamically incorporates deep reinforcement learning (DRL) for strategic sample selection during training. The scope loss function balances the exploitation of labeled data with the exploration of new, unlabeled data, enabling efficient training. Additionally, an enhanced artificial bee colony (ABC) algorithm with a mutual learning strategy optimizes hyperparameter tuning, boosting model performance. Evaluated on the International Skin Imaging Collaboration (ISIC) and human against machines 10000 images (HAM10000) datasets, the proposed framework achieved high accuracy, with F-measures of 92.791% and 91.984%, respectively. This novel approach demonstrates significant potential to advance early skin cancer detection, offering a reliable and efficient tool for healthcare professionals.

This paper investigates poverty transitions and household predictors associated with different states of poverty transitions in India during the pandemic's first year (2020). Following the unexpected shock brought on by COVID, economic activity slowed down, presumably pushing many households into poverty. For poverty alleviation programs, it is important to identify households that fell into poverty during COVID and remained in poverty subsequently. Our analysis finds that about 19% of individuals in urban areas who were not poor in the pre‐COVID period (2019) qualified as poor in the COVID period. Estimating a multinomial logit model for urban households, our analysis finds that vulnerable castes relative to upper castes had a higher probability of falling into poverty and remaining there. Moreover, households at the lower end of the expenditure distribution and those with household members with lower levels of education were more prone to falling back into poverty and more likely to remain in poverty. These findings suggest that those who were most vulnerable sustainably suffered the most; as a result, the pandemic worsened socioeconomic gaps that already existed across households.

para‐Terphenoquinone (pTPQ) in which a benzenoid ring has been replaced with a heterocycle is known as heteroterphenoquinone (HTPQ). While (H)TPQs are typically non‐emissive and potentially open‐shell species, it is recently reported that fully fused polycyclic HTPQs (or PHTPQs) bearing a sulfur heteroatom can either exhibit fluorescence or antiaromaticity depending on the π‐conjugation arrangement in the core. Since the synthesis of PHTPQs is rare, until now limited to the reported compound containing a sulfur atom, a selenium‐containing fluorescent PHTPQ is now synthesized while attempting the isolation of elusive dinaphtho‐dicyclopenta[b,d]selenophene. The fluorescent PHTPQ is further dehydrogenated to synthesize the first PHTPQ with an embedded selenophenoradialene (selena‐hybrid [5]radialene), which is found to exhibit antiaromatic properties. The narrow optical highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) gap, short intermolecular interactions in the solid state, and redox amphoterism prompt the investigation of its ambipolar charge transport properties alongside the thia‐analogue. The electron mobilities for PHTPQs comprising selenaradialene (1.96 × 10⁻³ cm² V⁻¹ s⁻¹) and thiaradialene (1.58 × 10⁻³ cm² V⁻¹ s⁻¹) cores are found to be one order of magnitude higher than that of the holes due to low‐lying LUMO energy levels.

In this study, we model the Indian stock market as heterogenous scale free network, which is then embedded in a two dimensional hyperbolic space through a machine learning based technique called as coalescent embedding. This allows us to apply the hyperbolic kmeans algorithm on the Poincare disc and the clusters so obtained resemble the original network communities more closely than the clusters obtained via Euclidean kmeans on the basis of well-known measures normalised mutual information and adjusted mutual information. Through this, we are able to clearly distinguish between periods of market stability and volatility by applying non-parametric statistical tests with a significance level of 0.05 to geometric measures namely hyperbolic distance and hyperbolic shortest path distance. After that, we are able to spot significant market change early by leveraging the Bollinger Band analysis on the time series of modularity in the embedded networks of each window. Finally, the radial distance and the Equidistance Angular coordinates help in visualizing the embedded network in the Poincare disc and it is seen that specific market sectors cluster together.

The liver’s regenerative ability depends on injury extent. Minor injuries are repaired by hepatocyte self-duplication, while severe damage triggers cholangiocyte involvement in hepatocyte recovery. This paradigm is well-documented for adult animals but is less explored during rapid growth. We design two partial liver injury models in zebrafish, which were investigated during growth spurts: 1) partial ablation, killing half the hepatocytes; and 2) partial hepatectomy, removing half a liver lobe. In both injuries, de novo hepatocytes emerged alongside existing ones. Single-cell transcriptomics and lineage tracing with Cre-driver lines generated by genome editing identified cholangiocytes as the source of de novo hepatocytes. We further identify active mTORC1 signalling in the uninjured liver of growing animal to be a regulator of the enhanced plasticity of cholangiocytes. Our study suggests cholangiocyte-to-hepatocyte transdifferentiation as the primary mechanism of liver regeneration during periods of rapid growth.

Herein, we present a photochromic compound that reversibly switches emission color by adjusting its charge transfer pathway. Its reversible photoisomerization toggles charge transfer through bonds or space, controlling white light...

The circular economy (CE) aims to reduce, reuse, recycle, remanufacture, redesign and recover products for resource optimization, leading to long-term sustainability. The electronics sector (ES), characterised by increasing e-waste, resource scarcity and operational risks, is transitioning towards CE. The existing studies on CE implementation remain fragmented and lack a comprehensive framework guiding circularity initiatives in the ES. The current study addresses this gap by developing an integrated conceptual framework that synthesises existing knowledge on models, perceived benefits and challenges of CE implementation in the ES. It included a systematic literature review of relevant journal articles, white papers and industry reports identified using keywords shortlisted in consultation with industry experts. The methodological approach used in this study included descriptive, content and thematic analyses of the literature to create an integrated conceptual framework. Findings reveal that while CE research in the ES is expanding, it remains predominantly qualitative and exploratory, lacking confirmatory empirical studies. The proposed framework integrates antecedents, mediators, moderators and consequences of CE implementation, offering a structured approach for scaling circularity. Further, the study identifies future research directions using the theory, context, characteristics and methodology (TCCM) framework, providing valuable insights for both theoretical development and empirical research in the domain.

Electric vehicles (EVs) are widely used in the transportation sector, which majorly attracts due to their characteristics of greenhouse gas emissions and environmental friendliness. However, for the effective deployment of EVs, Electric Vehicle Charging Stations (EVCS) must be built quickly and with careful planning. Moreover, mismatch of generation demand, active power losses, compromised voltage profile and voltage stability reduction issues are faced due to the rapid raise of electric load penetration. To address these concerns, a well-planned integration of EVCS with Distributed Generations (DGs) at strategic locations with appropriate capacity becomes essential. Load Flow Analysis is utilized to find power losses for each branch and voltage magnitudes for all buses. The IEEE 69 bus is employed with the Backward-Forward Sweep method for Radial Distribution Systems, and the IEEE 118 bus is a non-radial DS solved with the Newton–Raphson (NR) method. In this research, the accurate determination of optimal locations and capacities for placing EVCS in conjunction with DGs is achieved by utilizing a hybridized optimization technique called Hybrid Moth Flame Optimization (HMFO) which is formed by hybridizing the characteristics of moth and firefly optimization algorithms. The utilization of HMFO results in improved outcomes, including rapid convergence, robustness, and enhanced identification of EVCS deployment locations. Consequently, this optimization approach leads to reduced real and reactive power losses, diminished voltage deviations, increased voltage stability, cost-effectiveness, and accurate determination of EVCS location, and size. The research work illustrates the efficient result values in terms of real power, reactive power loss, AVDI, and AVSI as 55.37 kW, 22.39 kVAR, 0.02 p.u, and 0.87 p.u respectively.

Sphingosine kinase 1 (SphK1) is an essential enzyme in sphingolipid metabolism, catalyzing the phosphorylation of sphin-gosine to produce sphingosine-1-phosphate (S1P), a bioactive lipid with diverse roles in cell proliferation, survival, and migration. Dysregulation of the SphK1/S1P axis is implicated in a variety of pathological conditions, including inflamma-tory, metabolic, and neurodegenerative diseases. Targeting SphK1 represents a promising therapeutic strategy, particularly in oncology and inflammation-related pathologies. In this study, we investigated the potential of three natural compounds, Baicalin (BA), Naringenin (NR), and Noscapine (NS) as SphK1 inhibitors. Through combined molecular docking, molecular dynamics simulations, binding studies and enzyme inhibition assays, we identified these compounds as effective SphK1 inhibitors. BA, NR, and NS exhibited binding affinities characterized by IC50 values of 26.542, 32.157, and 28.134 μM, respectively. These molecules bind to the active site of SphK1 with favorable binding energies with strong non-covalent interactions. This study provides structural and functional insights into potential of BA, NR, and NS to target SphK1 selectively, which can function as lead compounds for developing novel anti-cancer therapy with minimal off-target effects, offering avenues for developing drugs with enhanced specificity and affinity for this enzyme.

Epoxy resins are a class of versatile, thermosetting polymers that have gained significant attention due to their exceptional properties and diverse applications. This chapter explores the chemistry and types of epoxy resins, highlighting their synthesis, curing mechanisms, and structure–property relationships. The chapter begins by discussing the fundamental chemical principles underlying the formation of epoxy resins, including the ring-opening polymerization of epoxide monomers and the role of curing agents in the crosslinking process. Subsequently, the review categorizes epoxy resins based on their chemical composition, such as bisphenol A, bisphenol F, novolac, and aliphatic epoxy resins. Each type’s unique characteristics and advantages are examined, emphasizing their mechanical, thermal, and chemical resistance properties. Furthermore, the article delves into the various curing agents employed in epoxy resin systems, including amines, anhydrides, and catalytic curing agents, and their influence on the final properties of the cured material. The review also addresses the recent advancements in epoxy resin technology, such as developing bio-based and sustainable epoxy resins and incorporating nanofillers and reinforcements to enhance their performance. Finally, the article presents a comprehensive overview of the diverse applications of epoxy resins across industries, including coatings, adhesives, composites, electronics, and construction. This typical chapter serves as a valuable resource for researchers, engineers, and industry professionals seeking to understand the fundamental aspects of epoxy resins and their potential for innovation in various fields.

Design patterns are integral to software development, offering solutions to recurring design challenges. Detecting these patterns within code enhances comprehension, documentation, and maintenance. We review reported design pattern detection techniques based on supervised learning, feature-based analysis, clustering, graph-based methods, dynamic approaches, and ontology integration. We investigate the effectiveness of each technique and their strengths and limitations.

The tetrasubstituted imidazoles, TSIm-1 & TSIm-2 with flexible and rigid core respectively, displayed the remarkable photophysical properties as well as aggregation-induced emission. TSIm-1 exhibited the trace water detection in THF...

This research explores luminescent properties of Eu³⁺-doped 0.42Pb(Mg0.335Nb0.665)O3–0.26Pb(In0.5Nb0.5)O3–0.32PbTiO3 (PMINT:Eu³⁺) phosphors for red emission in lighting applications. PMINT: Eu³⁺ was synthesized via solid-state-route and characterized by XRD, confirming pure tetragonal phase. Excitation spectra at 615 nm emission showed broad charge-transfer-band at 323 nm and sharp f-f transitions of Eu³⁺. Emission spectra at 465 nm excitation revealed orange and red bands (591-650 nm). Judd–Ofelt analysis offered detailed insights into optical properties, predicting radiative transition probabilities, branching ratios, and lifetimes of excited Eu³⁺ states. High branching ratio for ⁵D0 → ⁷F2 transition (red emission-615 nm) indicates efficient conversion of absorbed energy into red light. This makes these phosphors highly suitable for applications requiring pure-red emission. The phosphors achieved > 90% color purity and exhibited high activation energy of 0.30 eV, suggesting excellent thermal stability. The intense blue excitation and narrow red emission make PMINT: Eu³⁺ phosphors suitable for LEDs, enhancing color accuracy in white LEDs, offering high-fidelity white light for various applications.

Diabetic wounds lead to substantial challenges in healthcare systems due to prolonged healing and susceptibility to debilitating bacterial infections. Traditional wound dressings, designed to regenerate wound voids and aid healing, often lack antibacterial properties. In this study, we present a syringe-injectable hydrogel (HG) infused with rose-petal-derived extracellular vesicles (REVs) and fluorescent carbon dots (CDs), which exhibit intrinsic antibacterial activity. The HG matrix was created by combining oxidized sodium alginate (OA) with branched polyethylenimine (PEI). This formulation selectively targets Gram-negative bacteria through strong physical and mechanical interactions while preserving human erythrocytes, as confirmed by hemolytic assays. Using a Wistar rat type 1 diabetic model, we demonstrated that the HG effectively eradicates E. coli at the application site, ensuring slow release and retention of REVs and CDs at the wound site, causing minimal inflammation. REV-CD-HG represents a scalable, cost-efficient, and innovative wound dressing with promising clinical applications.

Heterogeneous wireless networks (HWNs) present a challenge in selecting the optimal network for user devices due to the overlapping availability of multiple networks. In order to help users choose the best HWN connection, this research is trying to build a decision-making framework that takes user preferences and network performance characteristics into account. Using a multi-attribute decision-making (MADM) method that incorporates fuzzy logic and the Fuzzy Analytic Hierarchy Process (FAHP), our goal is to improve the decision-making process for network selection. The suggested system takes into account a number of network metrics, including latency, jitter, bandwidth, and cost, and uses user preferences to determine the relative importance of each to guarantee a tailored and adaptable recommendation. Our results demonstrate that the algorithm greatly enhances the efficiency of network selection and the level of user happiness, with UMTS being the best option for conversational services, WiMAX being the best for streaming, and LTE being the best for interactive services. Through the incorporation of user-centric decision-making into the network selection process, this research enhances adaptive wireless communication systems, leading to better user experience and network efficiency.