Lingaya's Vidyapeeth

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.

Nanotechnology has been applied to the diagnostic and therapeutic treatment of cancer, with Carbon Nanotubes (CNTs) serving as an effective platform for these processes. In addition to their known physicochemical characteristics, such as high surface area, mechanical strength, and ease of functionali-zation, CNTs possess pharmacokinetic properties that enable their use in targeted drug-delivery and diag-nostic systems. Through functionalization, biodistribution, cellular uptake, and circulatory time can be modulated, thereby overcoming the limitations of traditional therapies, such as low bioavailability and systemic toxicity, and enabling more robust absorption, distribution, metabolism, and excretion profiles. Targeted CNT formulations can reduce off-target exposure and improve therapeutic efficiency through targeted delivery and controlled release. Besides, conjugation of CNTs to imaging or diagnostic agents enables improved assessment of biodistribution and metabolic characteristics, which justify their use as theranostic platforms. This review describes the new developments in CNT-based drug delivery systems for cancer treatment, with particular regard to their interactions with metabolism and the importance of these interactions on drug excretion. The fact that CNTs cross biological barriers and can boost drug bio- availability highlights the importance of these nanoparticles in enhancing the effectiveness of treatment procedures and minimizing toxicity. However, safety issues, including toxicity, long-term safety, and bi- ocompatibility, are also significant impediments to clinical translation. There will be a need to address such issues by systematizing pharmacokinetic and metabolic studies to assist in developing CNT-based solutions for precision oncology.

The chapter analyzes how Geographic Information Systems (GIS) and remote sensing can help fortify the early warning system (EWS) and prompt response decision support to international disaster readiness. It gives a review on the technological fundamentals, e.g., remote sensing platforms, GIS-based hazard modeling, and/or AI-enhanced geospatial analytics. Its use in applications involving floods, earthquakes, tsunamis, wildfires, and cyclones indicates how real-time information can be used to reduce risks proactively, direct evacuation, and design resilient infrastructure. Successes, challenges, and limitations in multi-hazard EWS are clear evidence of case studies at a global and regional level. The concluding chapter provides recommendations and future directions that are inclusive, reliable, and adaptive of systems and focuses on technology, policy, and community outlook on building resilient societies.

In memory execution, OS tools (for example, PowerShell, WMI), and registry manipulation are widely leveraged by fileless malware, which makes it very difficult to detect with the traditional forensic tools. This chapter studies memory forensics as a primary method to trace down attacks such as this one, concentrating on the volatile memory (RAM) analysis. They talk about attack types such as code injection, LOLBins and reflectively loaded DLLs, to name a few, and a few tools such as Cobalt Strike and Empire. Volatility, FTK Imager and YARA rules are leveraged to generate artifacts such as executable injected code, network sockets and command history. In the chapter, find out how we map the findings to the cyber kill chain, and what makes RAM analysis a must for detection, response, and proactive hunting.

Two-dimensional liquid chromatography (2D-LC) represents a significant advancement in food analysis, addressing the limitations of one-dimensional liquid chromatography (1D-LC) in separating complex food matrices. Food analysis plays a critical role in assessing the components, physicochemical properties, sensory qualities, and contamination risks of food products. Given the rising interest in identifying bioactive compounds in food due to their health benefits, the need for enhanced analytical techniques has become more pressing. Traditional 1D-LC often struggles to resolve closely related and co-eluting species in complex food samples, necessitating methods with greater resolving power. 2D-LC offers a valuable alternative by coupling two independent separation processes, thereby significantly increasing separation efficiency. This approach is particularly effective when paired with mass spectrometry (MS), which enhances detection sensitivity and specificity, allowing for more accurate structural identification. Moreover, 2D-LC has proven instrumental in environmental analysis, particularly in detecting and characterizing organic micropollutants (OMPs) at trace levels. As the demand for comprehensive food analysis and environmental monitoring grows, 2D-LC coupled with MS is poised to play an increasingly vital role in ensuring food safety, quality, and informed consumer choices. Therefore, this review briefly introduces the working principle of 2D-LC concerning its application in the field of food and environmental analysis in recent years.

Since parking allocation being one of the main challenges in modern cities, the need for a smart parking system has emerged. This paper carries out an in-depth study, comparison, and analysis of various SPS that focus on the technological approaches, sensors, networking technologies, user interfaces, computational methodologies, and the range of services they offer. In order to address the critical issue of locating available parking spaces, we present a novel automated parking management system. The system ensures seamless communication through automated WhatsApp receipts with the help of the Twilio API, supports data backup at entry, exit, and logout, and offers a “Find My Vehicle” feature as well. It features user-friendly access, real-time tracking, and billing through cloud integration, and QR code-based parking. This is a system that leverages such advancements to improve operational efficiency, better resource utilization, and better user experience in addressing parking challenges in metropolitan areas.

This paper proposes a wideband millimeter wave (mmW) antenna based on a frequency selective surface (FSS) for gain improvement for 5 G cellular communication. The presented antenna is a planar inverted cone antenna (PICA) which resonates at 28 GHz in the FR2 band and has a footprint of $11 \times 12 \times 1.6 \mathrm{~mm}^{3}$. The antenna gain is improved by integrating an FSS superstrate implemented with $6 \times 6$ unit elements antenna that used as a reflecting surface. Proposed antenna has a impedance bandwidth -10 dB at 12.6 GHz $(25.2-37.8 \mathrm{GHz})$ and a peak gain of 7.73 dB at 28 GHz. Due to FSS, the proposed antenna gain is improved by about 4.02 dB. The far-field patterns in H and E planes are at 90° to the antenna boresight axis. A prototype is fabricated to verify the simulation results. Due to compactness, wideband, and high gain, the proposed antenna may be a great choice for 5 G communication.

This research focuses on utilizing unsupervised anomaly detection methods to uncover irregularities within thyroid-related datasets, specifically employing the “An Thyroid Unsupervised Anomaly” benchmark. Three key algorithms Isolation Forest, One Class SVM, and Local Outlier Factor (LOF) are applied and assessed through precision, recall, and AUC ROC metrics to evaluate their effectiveness in detecting abnormal patterns. The results indicate a significant occurrence of anomalies linked to certain physiological and biochemical parameters, highlighting possible data inconsistencies or deviations in thyroid function measures. Furthermore, the study emphasizes that effective feature selection substantially enhances model precision and anomaly detection capability. By integrating domain specific insights into the analytical process, this work presents a more interpretable and reliable framework for anomaly detection in medical datasets, ultimately supporting more accurate diagnostics and data-driven clinical decision-making in healthcare analytics.

Movie ratings are very important for figuring out how to market films, create better content, and keep audiences happy. In today's world, movies bring people together from all sorts of backgrounds, thanks to streaming platforms and tons of online data. This project dives into the IMDB dataset, which has a lot of information like ratings, genres, cast, and runtime, to study and predict how movies score with viewers. We used machine learning to investigate what makes a movie rating high or low, looking at things such as genre, how long the film is, and other details. By cleaning the data, picking the right features, and building models, we worked to predict ratings as accurately as possible. Our findings, like a slight link between shorter movies and better ratings, can help filmmakers and studios make smarter choices about what movies to make and how to promote them.