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.

Virtual influencers are increasingly used in social media marketing, yet their ability to drive consumer behavior remains theoretically ambiguous. Drawing on parasocial relationship theory and human–AI interaction research, this study examines how perceived virtual influencer likeness and perceived agent autonomy shape purchase intention through synthetic parasocial relationships (S-PSR). We further account for ambivalent affective responses, coolness and eeriness, and test the moderating roles of product type and authenticity skepticism. Survey data from 372 U.S. Instagram users exposed to virtual and human influencer endorsements across technology products and experiential services were analyzed using PLS-SEM. Results show that S-PSR is the strongest predictor of purchase intention. Perceived agent autonomy significantly strengthens S-PSR, while perceived virtual likeness simultaneously increases coolness and eeriness, revealing an ambivalent pathway. The impact of S-PSR on purchase intention is stronger for technology products and weaker under high authenticity skepticism. Overall, the findings demonstrate that virtual influencers are most persuasive when perceived as autonomous, cool, rather than eerie, and contextually aligned with technology-oriented products.

As semiconductor devices become smaller, FinFETs have replaced traditional planar MOSFETs. Planar devices face issues like weak electrostatic control and high leakage current at small sizes. FinFETs solve these problems with a three-dimensional structure and multigate design. This improves gate control and reduces short-channel effects. This paper explains FinFET design, materials, and fabrication methods. It highlights how fin geometry affects current flow and device performance. Gate-source voltage (VGS) and drain-source voltage (VDS) are important parameters. These control the device operation in the lin-ear, saturation, and pinch-off regions. Performance factors such as on/off current ratio (ION /IOFF), subthreshold swing (SS), and drain-induced barrier lowering (DIBL) show that FinFETs work well for low-power and high-speed uses. Achieving uniform doping below 5 nm remains difficult. Atomic layer deposition (ALD) helps improve doping control. In summary, FinFETs play a key role in modern semiconductor design by improving scalability and efficiency.

This study addressed a persistent challenge in cloud-deployed, enterprise decision-support systems: even when machine-learning models exhibit strong predictive performance, users may under-rely or inconsistently rely on recommendations because post hoc explanations are perceived as unclear or unstable, constraining trust, defensibility, and confident action. The purpose of the study was to examine how explanation quality perceptions, specifically Perceived SHAP Interpretability (PSI) and Perceived Explanation Robustness (PER), are associated with Trust in AI decision support (TRU), Decision Confidence (DCF), and Intention to Rely or Use (INT) in high-stakes, case-based decision scenarios. A quantitative, cross-sectional, case-based design was employed using two enterprise contexts: healthcare clinical risk decision support and financial risk governance decision support. Standardized SHAP explanation artifacts were presented alongside decision vignettes, and a 5-point Likert-scale instrument measured PSI, PER, TRU, DCF, and INT, with controls for professional experience, AI familiarity, and domain group. The final sample consisted of N = 240 screened respondents (52.1% healthcare; 47.9% finance), with a mean professional experience of 7.8 years (SD = 4.6) and moderate AI familiarity (M = 3.62, SD = 0.84). The analytic approach combined descriptive statistics, reliability analysis, Pearson correlations, and a series of hierarchical multiple regression models. Mediation relationships were examined using regression-based mediation logic through sequential model estimation, assessing changes in direct effects when trust and confidence variables were introduced, rather than through causal path modeling or bootstrapped indirect effects. Reliability across constructs was strong (PSI α = .88; PER α = .86; TRU α = .90; DCF α = .87; INT α = .85). Mean scores exceeded the scale midpoint for all constructs (PSI M = 3.88; PER M = 3.61; TRU M = 3.74; DCF M = 3.69; INT M = 3.58). Correlation and regression results indicated that PSI and PER were positively associated with trust (PSI β = .41, p < .001; PER β = .34, p < .001; R² = .52). Trust accounted for substantial variance in decision confidence (β = .49, p < .001; DCF model R² = .57), and decision confidence accounted for substantial variance in intention to rely on AI recommendations (β = .43, p < .001; INT model R² = .49). When trust and confidence were included in the intention model, the direct associations of PSI and PER with intention were no longer statistically significant, indicating indirect relationships operating through trust and confidence. Overall, the findings suggest that SHAP interpretability and explanation robustness are associated with trust formation and confidence calibration, which together account for meaningful variance in reliance intentions in high-stakes healthcare and finance decision-support contexts.