Guru Ghasidas Vishwavidyalaya

Link prediction is a field within social network studies that aims to forecast future connections based on the structure of a social network. This paper introduces a link prediction method based on the strength and prominence of seed node pairs, referred to as the strength prominence index. In this method, we get a consistent score for any pair of nodes, regardless of whether they share a common neighbour. Several key characteristics have been identified. In our experiments, we used three well-known estimators to evaluate the accuracy of link prediction algorithms: precision, area under the precision-recall curve, and area under the receiver operating characteristic curve. A comparative study with existing methods is also presented, supported by relevant graphs and tables. Validation using Facebook data sets demonstrates the effectiveness of the proposed method.

This study deals with the properties of 2‐aminopropionic acid (APPA), an amino acid, using a powerful combination of theory and experiment. We employ computational tools to predict its structure and reactivity, revealing its most stable conformation and potential reaction sites. DFT theory was employed using the software “Gaussian 09W” and “Gauss‐View 5.0′ with the ‘B3LYP/6–311++G(d,p)” basis set for DFT calculations. The optimized structure of 2‐aminopropionic acid was determined from a variety of conformations, and their associated values were obtained. Analyzing the optimized geometry allowed for identifying the parameters of the stable conformation of 2‐aminopropionic acid. Additionally, 2‐aminopropionic acid was experimentally investigated by employing FTIR and UV–visible spectroscopic methods. The experimentally gained values were correlated with the theoretical values obtained from the DFT calculations. Molecular electrostatic potential analysis was utilized to identify reactive sites and predict chemical reactivity, whereas electron localization function analysis provided insights into electron distribution. Additionally, we analyzed promising nonlinear optical properties, hinting at future applications. We also studied 2‐aminopropionic acid's potential for pharmaceutical development, revealing its drug‐like nature. This study further explores 2‐aminopropionic acid's fascinating properties and paves the way for potential drug development.

This paper introduces the design of a high‐performance Surface Plasmon Resonance (SPR) sensor using a SnSe2/Si/PbTiO3 heterostructure for detecting malaria, targeting the different stages of the plasmodium parasite lifecycle. The Tin di‐selenide (SnSe2) with high refractive index (RI) and excellent absorption property in visible and infrared regions allows efficient interaction with the evanescent field, thereby increasing sensitivity for small RI changes near the surface. The strategic integration of lead titanate (PbTiO3), known for its high RI and tunable bandgap, with SnSe2 and Silicon (Si) layers, the proposed sensor design (FK51A‐prism/Ag/SnSe2/Si/PbTiO3/Sensing‐Medium) significantly improves sensitivity to 390.41°/RIU for the ring stage of malaria. The Kretschmann configuration, in conjunction with the Transfer Matrix Method (TMM) and angular interrogation, has been utilized to optimize the performance of the proposed SPR sensor. The proposed design achieves an optimal Quality Factor (QF) of 130.92 RIU⁻¹, enabling the detection of small changes in RI. With a Detection Accuracy (DA) of 0.33 deg⁻¹ for the ring stage, the proposed SPR sensor demonstrates its potential for early and accurate malaria diagnosis. Also, the enhanced DA and QF in later stages (trophozoite and schizont stages) offers broad detection range of the proposed SPR design. The design offers a promising application across different biomedical applications.

Fungal diseases pose significant threats to agriculture, impacting crop quality, productivity, and global food security. Succinate dehydrogenase Inhibitors (SDHIs) have played a pivotal role in the agrochemical industry due to their potent fungicidal activity, with widespread use for over 60 years. According to the Fungicide Resistance Action Committee (FRAC), the first SDHI was reported in 1966, and to date, 17 inhibitors have been approved and commercialized. This chapter provides a comprehensive overview of SDHIs, focusing on their classification, case studies, mechanism of action, role in disease control, structure–activity relationships (SAR), resistance mechanisms, synthetic approaches, and future challenges. It begins by defining and describing the structure of SDHIs and their critical roles in various metabolic pathways. It highlights case studies of fungal attacks on crops and their adverse effects on global crop yields. Fungal pathogens affect different plant parts—including roots, leaves, and fruits—leading to significant reductions in productivity. Novel SDHIs, particularly those incorporating carboxamide, nicotinamide, benzothiazole, and pyrazine-carboxamide-diphenyl-ether scaffolds, are essential in enhancing crop yields. However, resistance development due to genetic mutations in fungi and the environmental and health hazards associated with prolonged SDHI use remain significant challenges. Recent research emphasizes sustainable approaches to overcome these issues, aiming to improve the future application of SDHIs in agriculture.

Using the reductive perturbation method, we obtained a nonlinear Schrödinger equation to study the modulational instability of dust ion acoustic waves propagating obliquely to the direction of the uniform static magnetic field in a magnetized five component plasma system composed of warm adiabatic ions, nonthermal positrons, static dust grains, nonthermal electrons (hot), and isothermal electrons (cold). By obtaining the nonlinear dispersion relation of the modulated dust ion acoustic wave, the effect of different species involved in the system has been studied to the instability regions in the parameter plane. The instability region grows with the isothermal to nonthermal electron number density ratio. For a certain range of increasing values of the ion cyclotron frequency, the stable region increases. The instability region also grows with the nonthermal parameter of the energetic hot electrons. As the ratio of isothermal electron to nonthermal electron number density increases, the maximum modulational growth rate of instability experiences a decline. The maximal modulational growth rate of instability’s zone of existence grows as the positrons’ nonthermal parameter rises. A rise in the ratio of positron to nonthermal electron temperature results in a maximum modulational growth rate of instability.

This study investigates a novel perovskite solar cell (PSC) architecture employing CsSn₀.₅Ge₀.₅I₃ as the active absorber layer, aiming to develop a high-efficiency, lead-free photovoltaic technology. The simulated device configuration FTO/TiO₂/CsSn₀.₅Ge₀.₅I₃/Spiro-OMeTAD/Au is modeled using SCAPS-1D and achieves an impressive power conversion efficiency (PCE) of 23.08%, along with an open-circuit voltage (V_OC) of 1.1253 V, short-circuit current density (J_SC) of 26.87 mA/cm², and a fill factor (FF) of 76.32%. The incorporation of CsSn₀.₅Ge₀.₅I₃ not only addresses the common issue of tin oxidation but also offers several key advantages, including an optimal bandgap (~ 1.5 eV), low exciton binding energy, and improved intrinsic charge transport properties. Furthermore, the study explores alternative hole transport layers (HTLs) beyond Spiro-OMeTAD, evaluating their stability and cost-effectiveness to enhance both performance and long-term operational viability. The findings position CsSn₀.₅Ge₀.₅I₃ as a promising next-generation absorber material for high-efficiency, eco-friendly PSCs, representing a significant step toward the realization of sustainable and economically viable lead-free solar energy technologies.

Abiotic stresses, including drought, salinity, extreme temperatures, and heavy metal toxicity encompassing adverse environmental conditions, poses a significant threat to plant growth, development, and productivity. These stress factors disrupt physiological and biochemical processes in plants, leading to reduce crop yields and posing a threat to global food security. To mitigate the detrimental effects of abiotic stresses, innovative agronomic practices are essential. Different methodologies have evolved over time for stress tolerance through conventional breeding and transgenesis. To enhance plant resilience against these stresses, priming strategies have emerged as a promising tool to improve plant stress tolerance. This review explores various priming approaches that precondition plants to better cope with subsequent stress exposure. Moreover, it discusses the underlying mechanisms involved in enhanced activation of antioxidant systems, improved osmotic balance, regulation of stress-responsive genes, and the strengthening of cellular defence systems. Additionally, priming induces stress memory in plants, enabling faster and more robust responses during recurrent stress events. This review also highlights recent advancements in priming techniques and their potential applications in sustainable agriculture. Integration of these strategies into crop management practices can improve plant performance and productivity in the face of increasing abiotic stresses. Overall, this review provides insights into the efficiency, sustainability, and applicability of different priming strategies as a means to mitigate abiotic stress in plants, offering a promising path toward sustainable agriculture in the face of changing environmental conditions.

People share their comments and reviews on public platforms in advanced social media systems. The customer’s perception of the product is reviewed by analysing the sentiment of product reviews, thus assisting in business decision-making. In most of the prevailing works, the sentence type of product review was not recognised to analyse the sentiment; thus, the complexity of the sentiment analysis process increased. Thus, this study performs sentence-type assessment-based product review sentiment analysis using beta divergence divide and conquer (BeDi-DC) and Log-Squish Convolutional Neural Network (Log-Squish CNN). Initially, the input product review data were preprocessed, followed by word count extraction. Next, the data were clustered with the Permutation Distribution Hierarchical Clustering (PerDHC) algorithm and classified into real and fake reviews by the proposed Log-Squish CNN approach. Subsequently, the BeDi-DC technique was used to identify the sentence types of real reviews. Word sense disambiguation is performed on the multi-target review to identify the exact target. Next, to analyse the sentiwords and their score values, the Mean-Senticircle Method (MeSM) was utilised. Finally, using the Log-Squish CNN model, the sentiment of the review was classified as positive, neutral, or negative. The accuracy, f-measure, and degree correlation attained by the proposed model are 98.99%, 98.78%, and 0.845, respectively, thus outperforming the prevailing models.

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated imaging diagnostics using a range of modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and digital pathology, highlighting the growing role of deep learning in detecting early-stage cancers. We also explore applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, AI-based clinical decision support systems, individualized treatment planning, and AI-facilitated drug discovery are transforming precision cancer therapies. The review also evaluates the effects of AI on radiation therapy, robotic surgery, and patient management, including survival predictions, remote monitoring, and AI-facilitated clinical trials. Finally, we discuss important challenges such as data privacy, interpretability, and regulatory issues, and recommend future directions that involve the use of federated learning, synthetic biology, and quantum-boosted AI. This review highlights the groundbreaking potential of AI to revolutionize cancer care by making diagnostics, treatments, and patient management more precise, efficient, and personalized. Graphical Abstract This graphical abstract schematically illustrates the progressive role of artificial intelligence in the cancer treatment continuum.

Breast cancer is a major global health problem, with the WHO estimating approximately 2.3 million new cases every year. In this study, we present a new approach to improve the early detection of breast cancer using deep learning methods and visual inspection of histopathological images. In a world where access to a doctor with specialised knowledge is limited, this study attempts to address the important limitations of current diagnostic strategies that facilitate the efficient detection of diseases. In the proposed method, we use transfer learning with a combination of classifiers, such as SVM, decision trees, and K-nearest neighbours, while implementing two different feature extraction approaches, PCA for dimensionality reduction and no PCA. The approach includes a full evaluation system using metrics such as recall, accuracy, precision, and ROC curves to evaluate the performance of the models. This yields major performance gains for almost all classifiers. The experimental results showed that the SVM classifier with PCA feature extraction obtained the best accuracy of 99.5% with 99.2% precision and 99.6% recall, indicating a significant improvement over the current approach. Even without PCA implementation, the Decision Tree classifier also performed well, scoring 99.4% accuracy. In particular, the application of PCA improved the accuracy of the Boosted Tree from 82.9% to 91.01%. The execution times of classifiers varied significantly; for example, SVM, which is the fastest one as of now, with an execution time without PCA of 38.24 s. This study suggests a potential clinical tool that combines advanced deep learning methods and subsequent classification in real healthcare systems to improve breast cancer detection capabilities. This shows that the high accuracy of this framework, coupled with its computational efficiency, makes it an invaluable tool for real-life clinical applications, which could minimise misdiagnosis and lead to better patient outcomes through earlier detection of respiratory viruses worldwide, especially in remote areas with limited health-care resources.

This study advances the design of high-performance hybrid composites by integrating experimental characterization, multi-criteria decision-making, and statistical optimization. Epoxy-based composites reinforced with jute (J), glass (G), and carbon (C) fibers were fabricated in six distinct stacking sequences and evaluated for tensile strength, flexural strength, tensile modulus, flexural modulus, and break strain. The C5 configuration, featuring carbon fibers on the outer layers and glass fibers internally, demonstrated exceptional flexural strength (227 MPa) and tensile modulus (7.79 GPa), underscoring the critical role of fiber placement in optimizing mechanical performance. While C5 exhibited a marginal reduction in tensile strength (3.7% lower than C1), its balanced properties validated the efficacy of hybrid architectures. AHP-TOPSIS analysis ranked configurations using weighted mechanical criteria, identifying CG4C as optimal for balanced performance. To validate and refine this selection, Response Surface Methodology (RSM) was employed to model nonlinear relationships between stacking parameters and mechanical responses. High predictive accuracy (R² > 0.90 for modulus and break strain) and desirability-based optimization confirmed C5’s superiority, achieving a composite desirability score of 0.57. This work establishes a novel framework bridging decision-theoretic ranking (AHP-TOPSIS) and statistical modeling (RSM), demonstrating their synergistic utility in composite design. The methodology not only identifies optimal configurations but also quantifies trade-offs between strength, stiffness, and ductility, offering a scalable pathway for developing sustainable, application-specific hybrid composites. By validating rankings against RSM-predicted performance regions, this approach enhances confidence in material selection processes for structural and high-stiffness applications in aerospace, automotive, and construction industries.

This study delves into the synergistic electrochemical advantages of a niobium‐doped molybdenum trioxide (MoO3) nanorods combined with a tantalum pentoxide (Ta2O5) catalyst to increase the efficiency of the oxygen evolution reaction (OER). In light of the increasing demand for sustainable energy solutions, the imperative to develop efficient electrocatalysts conducive to water splitting, a critical process in hydrogen production, becomes evident. This investigation involves the synthesis of a niobium‐doped MoO3/Ta2O5 composite and comprehensively evaluating its structural, electrochemical, and catalytic properties through various spectroscopic and electrochemical techniques. These findings highlight that incorporating niobium markedly enhances the electronic conductivity and availability of active sites within the catalyst, resulting in improved OER performance. Comparative analyses against conventional electrocatalysts underscore that the 8% niobium‐doped MoO3/Ta2O5 composite demonstrates lower overpotentials (238 mV ) and higher current densities, indicating its significant potential for practical applications. Furthermore, the robust metal–support interactions enabled by the Ta2O5 support stabilize the active phase and increase the catalyst's overall durability. This work yields valuable insights into the mechanisms of OER catalysis involving niobium‐doped metal oxides, thereby underscoring the potential of such innovative catalyst designs in advancing hydrogen production technologies.

This study presents and evaluates a novel double-D-shaped photonic crystal fiber (PCF) biosensor with a rectangular split core, designed for rapid cancer diagnosis in skin, adrenal gland, cervical, blood, and breast tissues by differentiating the refractive indices of healthy and malignant cells. The cladding features five rings of air holes in circular and elliptical shapes, while the central region contains three rectangular strip air holes intended for sample analyte insertion. The sensing performance is assessed by quantifying the dip wavelength shift in the transmission spectra between cancerous and normal cells. The proposed biosensor is modeled and analyzed through simulations performed using the finite element method in COMSOL multiphysics. Mathematical analysis indicates that the biosensor achieves a maximum wavelength sensitivity of 16 571.42 nm RIU⁻¹ for blood cancer cells. This high-sensitivity PCF biosensor offers a promising approach for early cancer detection.

This paper presents a theoretical analysis of the l-Lysine molecule using the DFT (density functional theory) method with a 6-311+ + G(d,p) basis set, a quantum–mechanical atomistic simulation method. The research encompasses the analysis of optimized chemical structure, vibrations, FMO, ELF, NLO, RDG, etc., to study the molecule's intensive properties, stability, and other biological activities. IR and UV spectra were analysed for the spectrochemical study, and the VEDA program was used to determine the PED values. The chemical reactivity of the molecule was identified through analysis of the Frontier molecular orbitals, Fukui, and molecular electrostatic potential. The electron localization function and reduced density gradient were determined to understand bonding and electronic structure. The temperature dependence on the properties of the molecule was estimated. The optical properties of the molecule were discussed by analyzing the non-linear optical property. The feasibility of the molecule as a therapeutic drug was examined using the drug likeness concept. Molecular docking analysis was conducted to acquire the best ligand–receptor complex and to study the molecule's biological activity. Supplementary Information The online version contains supplementary material available at 10.1186/s13065-025-01511-4.

Graphitic carbon nitrides are unique π-conjugated polymers, now known for their physiochemical stability, earth-abundance in nature, visible-light-driven, electron-hole pair conduction leading to photocatalysis, and environmentally benign materials. This review summarizes recent and emerging concepts in heterogeneous catalysis employing carbon nitrides and their fabricated materials towards heterocyclic syntheses. 1 Introduction 2 Synthesis of Heterocycles 3 Conclusion

Over the last 250 years, anthropogenic activity has increased atmospheric carbon dioxide by nearly 40%. This increase is mainly caused by human fossil fuel combustion and deforestation, which are the main causes of global warming. Phytoplankton of the world’s oceans synthesizes half of the carbon dioxide of the total Earth’s photosynthetic activity. Thus, phytoplankton plays a crucial role in controlling Earth’s climate. To study this scenario, we propose and analyze a mathematical model for the carbon-phytoplankton-zooplankton interaction dynamics. Positivity, boundedness, existence, and stability of biologically possible equilibrium points are studied. The system exhibits Hopf bifurcation with respect to the carbon capture coefficient and the criteria of Hopf bifurcation is established around the coexisting equilibrium. Complex spatiotemporal dynamics and patchy pattern formation are observed in the spatially explicit model. The proposed carbon-phytoplankton-zooplankton system incorporates the effect of global warming, and our simulation shows shifts in plankton seasonal dynamics.

The study was conducted to address the limited documentation of medicinal plant knowledge among the Gond tribe in Surguja district, which threatens the loss of valuable ethnobotanical information. This investigation aimed to explore the significance of medicinal plants used by the tribe in treating various illnesses. Semi-structured questionnaires were used to interview 46 tribe members, including 41 males and 5 females. Data were collected on plant species, their habitats, the plant parts used, and methods of preparation and administration. The collected data were analysed using metrics such as Relative Frequency Citation (RFC), Use Value (UV), Informants’ Consensus Factor, and Fidelity Level (FL) to assess reliability and consistency. Comparisons with previous studies were made using the Jaccard Index (JI). A total of 107 plant species, belonging to 96 genera and 50 families, were identified as being used by the Gond tribe for treating various human ailments. These ailments were categorized into 12 groups. The most frequently mentioned plant families were Fabaceae and Apocynaceae. The bark was the most utilized plant part, while paste was the preferred method of preparation, followed by decoction. Seven plant species exhibited a 100% FL. Use values ranged from 0.17 to 0.89, while RFC values varied from 0.11 to 0.70. The highest observed JI similarity was 54.25%. The study underscored the importance of medicinal plants and traditional knowledge in primary healthcare, highlighting the need to identify plants with high RFC and UV values for further research. The preservation and promotion of ethnobotanical heritage were deemed essential for phytochemical analysis and drug discovery.