Thin-film solar cells (TFSCs) remain a cornerstone of the global transition toward renewable energy, characterized by consistent reductions in manufacturing costs and steady gains in power conversion efficiency. In addition to electricity generation, TFSCs play an important role in advanced solar thermal cooling, heating, and energy storage systems, where their tunable optical absorption, low thermal mass, and flexibility enable integration with photovoltaic–thermal (PV/T) collectors, thermally driven cooling cycles, and hybrid thermal–electrical storage architectures. This paper provides a comprehensive review of prominent TFSC technologies, including copper indium gallium selenide (CIGS), cadmium telluride (CdTe/CdS), amorphous silicon (a-Si), copper zinc tin sulfide (CZTS), organic photovoltaics (OPVs), and metal halide perovskite solar cells (PSCs), with a focus on their material structures, performance specifications, and current efficiency benchmarks. Compared to state-of-the-art reviews, this article distinguishes itself by addressing next-generation innovations, cross-domain solar thermal–photovoltaic applications, and economic analysis. Specifically, the integration of machine learning and simulation-based material dynamics is examined to accelerate material discovery, process optimization, and the characterization of novel TFPV components relevant to coupled thermal–electrical energy systems. Furthermore, the study explores how additive manufacturing is transforming the industry through the development of high-efficiency electrodes, electrohydrodynamic atomization for thin-film deposition, and the fabrication of flexible solar arrays suitable for thermally integrated and building-scale energy systems, including space applications. By integrating advancements in module efficiency, scalable manufacturing approaches, and techno-economic analysis, this paper positions TFSCs as sustainable, resource-abundant technologies essential for next-generation solar thermal cooling, heating, and energy storage infrastructures.

This paper presents an approach for rail line detection and the identification of human beings in proximity to the track, utilizing the YOLOv5 deep learning model to mitigate potential accidents. The technique incorporates real-time video data to identify railway tracks with impressive accuracy and recognizes nearby moving objects within a one-meter range, specifically targeting the identification of humans. This system aims to enhance safety measures in railway environments by providing real-time alerts for any detected human presence close to the track. The integration of a functionality to identify objects at a longer distance further fortifies the preventative capabilities of the system. With a precise focus on real-time object detection, this method is poised to deliver significant contributions to the existing technologies in railway safety. The effectiveness of the proposed method is demonstrated through a comprehensive evaluation, yielding a remarkable improvement in accuracy over existing methods. These results underscore the potential of this approach to revolutionize safety measures in railway environments, providing a substantial contribution to accident prevention strategies.

There are a variety of features of the human voice that can be classified as pitch, timbre, loudness, and vocal tone. It is observed in numerous incidents that human expresses their feelings using different vocal qualities when they are speaking. The primary objective of this research is to recognize different emotions of human beings such as anger, sadness, fear, neutrality, disgust, pleasant surprise, and happiness by using several MATLAB functions namely, spectral descriptors, periodicity, and harmonicity. To accomplish the work, we analyze the CREMA-D (Crowd-sourced Emotional Multimodal Actors Data) & TESS (Toronto Emotional Speech Set) datasets of human speech. The audio file contains data that have various characteristics (e.g., noisy, speedy, slow) thereby the efficiency of the ML (Machine Learning) models increases significantly. The EMD (Empirical Mode Decomposition) is utilized for the process of signal decomposition. Then, the features are extracted through the use of several techniques such as the MFCC, GTCC, spectral centroid, roll-off point, entropy, spread, flux, harmonic ratio, energy, skewness, flatness, and audio delta. The data is trained using some renowned ML models namely, Support Vector Machine, Neural Network, Ensemble, and KNN. The algorithms show an accuracy of 67.7%, 63.3%, 61.6%, and 59.0% respectively for the test data and 77.7%, 76.1%, 99.1%, and 61.2% for the training data. We have conducted experiments using Matlab and the result shows that our model is very prominent and flexible than existing similar works.