Galgotias University

The transformation of a normal cell state to malignant cell is a multifactorial process, as cancer is a complex network of diseases. There are various types of cancer, each with its own underlying mechanisms and symptoms. The rise in modernization has significantly limited the mobility and has also altered the diets, leading to an increased rate of cancer mortality worldwide. The treatment options in cancer therapy, such as chemotherapy and radiotherapy, result in compromising the well-being of patients with cancer. Nanoformulation has emerged as convincing tools in cancer drug delivery. In order to gather pertinent information on Nanoformulations in Cancer Patents and Clinical Trials from the year 2000 to 2026, the majority of the electronic databases including Google Scholar, Embase, Web of Science, Scopus, and PubMed/MEDLINE were searched. The key benefits of nanoformulation lie in their ability to enhance the water solubility of drugs, thereby overcoming the limitations of traditional drug formulations. The nanoformulation facilitates efficiency and enhances therapeutic efficacy. The perfection portrayed by nanoformulation in carrying the therapeutics with better performance. This article provides comprehensive details on nanoformulation-based patents and clinical trials of various cancer conditions and draws attention to novel formulation of drugs that has been designed to target specific diseases. The article will likely discuss the outcomes of the clinical trials that evaluated the effectiveness of the formulations. The current nanoformulation-based therapies can be a potential source to address the unmet medical needs that advance the patient outcomes.

This study presents a reliability analysis of an ice cream manufacturing facility based on operational failure data. The research develops a ten-component system model that captures plant dynamics and organizes it into three subsystems to reduce computational complexity. The study derives key reliability parameters and constructs a state transition diagram to represent system behavior across multiple operating states. The analysis applies an Artificial Neural Network (ANN) to address the limitations of conventional analytical methods in modeling complex nonlinear systems. The ANN provides strong predictive performance and manages uncertainty within the operational environment. The proposed framework estimates system reliability with high precision and supports maintenance planning and cost optimization. Numerical simulations validate the effectiveness of the ANN-based model. The results demonstrate improved prediction accuracy and greater computational efficiency compared to traditional approaches. State probability deviations are evaluated over a 24-hour period. The up-state probability increases from 95% to 96.02% during the useful life period and from 95% to 96.03% during the wear-out period. The findings confirm that the proposed method enhances reliability prediction, improves maintenance scheduling, and supports cost control and equipment design optimization in ice cream production systems.

This paper deals with the numerical performance evaluation of solar cells based on ZnMgO/CZTS with and without a back-surface field (BSF) layer. ZnMgO is used as a buffer layer due to its non-toxicity and strain-balancing properties at i-ZnO/ZnMgO and ZnMgO/CZTS interfaces. A multilayer structure of ZnO: Al/i-ZnO/ZnMgO/CZTS is considered for the initial analysis. Subsequently, a BSF layer of CZTS material with different optical and electronic properties, called CZTS2, is inserted between the back-contact layer and the existing CZTS layer, called CZTS1, to form a new structure as ZnO: Al/i-ZnO/ZnMgO/CZTS1/CZTS2 to enhance the performance. The performance of the structure with and without the BSF layer was evaluated in terms of various layer parameters such as thickness, band gap, carrier concentration, defect densities, and work function. Then, the effects of operating temperature and the combination of shunt and series resistance on the overall performance were investigated. The simulated results were calibrated with existing experimental data from the literature to validate the work. In the optimized structure with the BSF layer, a maximum efficiency of 23.67% is achieved, which is 4% higher than that of without the BSF layer. The generation and recombination rates of the structures with and without the BSF layer were investigated to understand the reason for the improved efficiency. The results of this work are very promising for the development of high-efficiency, low-cost, and non-toxic CZTS solar cells.

Catastrophic forgetting remains a major challenge in continual transfer learning, where performance on earlier tasks degrades after sequential adaptation. While full fine-tuning updates all parameters and achieves strong performance on new tasks, it is computationally expensive and prone to forgetting. This study compares parameter-efficient fine-tuning (PEFT) methods—adapters, additive learning, side-tuning, LoRA, and zero-initialized layers—against full fine-tuning on CIFAR-100 using a two-stage protocol: task-A (classes 0–49) followed by task-B (classes 50–99), evaluated on ResNet-18 and ResNet-50. Results are reported as mean ± standard deviation over three runs (n = 3), with retention measured using a Swapback-based recall method that distinguishes true forgetting (Δ). Across both architectures, all PEFT methods maintain task-A knowledge (Δ = 0.00), while full fine-tuning exhibits forgetting (Δ = 0.31 on ResNet-18; Δ = 0.20 on ResNet-50). PEFT methods achieve competitive task-B performance while updating only 0.22–4.49% of parameters. Notably, LoRA on ResNet-50 achieves the highest task-B accuracy (0.82) with only 0.93% parameter updates and no forgetting, slightly outperforming full fine-tuning (0.81). These findings highlight PEFT as an efficient and stable alternative for scalable continual transfer learning.

Purpose The association between entrepreneurial intentions (EI) and individuals’ psychological capital (PsyCap) is one of the evolving constructs in the entrepreneurial research domain. The main aim of this analysis is to statistically aggregate the findings of previous empirical studies on the impact of PsyCap on EI using a meta-analysis method. Design/methodology/approach This study employed a systematic literature review approach for qualitative analysis and a Meta-analysis technique for quantitative assessment of empirical studies on EI and PsyCap through the correlation coefficient. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol is applied to gather the final 30 empirical studies (N = 16,078) on EI and PsyCap. Further, to perform the meta-analysis, the Meta Essential tool is utilized. Findings The study findings revealed that PsyCap strongly associated with EI. Consequently, there is a mediation effect between EI and other factors. In addition, certain other factors mediate the relationship between EI and PsyCap. Moreover, findings revealed that PsyCap functions as an antecedent to the theory of planned behaviour constructs. Research limitations/implications The meta-analytical results reveal strong Asian and emerging-economy bias among included studies. Practical implications The article provided critical insights to college administrators, educators and entrepreneurial policy-makers to design entrepreneurship programs and formulate better policies for start-ups. Originality/value This study is an initial extension of the prior meta-analysis exploration of EI and PsyCap empirical studies within an Asia- and emerging-economy–focused corpus. The study’s novelty lies in the complementary but conceptually distinct synthesis and theoretical consolidation of prior studies.