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Large Language Models (LLMs) exhibit remarkable generative capabilities but remain vulnerable to hallucinations—outputs that are fluent yet inaccurate, ungrounded, or inconsistent with source material. To address the lack of methodologically grounded surveys, this paper introduces a novel method-oriented taxonomy of hallucination mitigation strategies in text-based LLMs. The taxonomy organizes over 300 studies into six principled categories: Training and Learning Approaches, Architectural Modifications, Input/Prompt Optimization, Post-Generation Quality Control, Interpretability and Diagnostic Methods, and Agent-Based Orchestration. Beyond mapping the field, we identify persistent challenges such as the absence of standardized evaluation benchmarks, attribution difficulties in multi-method systems, and the fragility of retrieval-based methods when sources are noisy or outdated. We also highlight emerging directions, including knowledge-grounded fine-tuning and hybrid retrieval–generation pipelines integrated with self-reflective reasoning agents. This taxonomy provides a methodological framework for advancing reliable, context-sensitive LLM deployment in high-stakes domains such as healthcare, law, and defense. Full article

(This article belongs to the Section AI Systems: Theory and Applications)

23 pages, 5798 KB

Open AccessArticle

Application of Generative AI in Financial Risk Prediction: Enhancing Model Accuracy and Interpretability

by Kai-Chao Yao, Hsiu-Chu Hung, Ching-Hsin Wang, Wei-Lun Huang, Hui-Ting Liang, Tzu-Hsin Chu, Bo-Siang Chen and Wei-Sho Ho

Information 2025, 16(10), 857; https://doi.org/10.3390/info16100857 - 3 Oct 2025

Abstract

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article

(This article belongs to the Special Issue Modeling in the Era of Generative AI)

58 pages, 4299 KB

Open AccessArticle

Optimisation of Cryptocurrency Trading Using the Fractal Market Hypothesis with Symbolic Regression

by Jonathan Blackledge and Anton Blackledge

Commodities 2025, 4(4), 22; https://doi.org/10.3390/commodities4040022 - 3 Oct 2025

Abstract

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

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19 pages, 6432 KB

Open AccessArticle

Storage and Production Aspects of Reservoir Fluids in Sedimentary Core Rocks

by Jumana Sharanik, Ernestos Sarris and Constantinos Hadjistassou

Geosciences 2025, 15(10), 386; https://doi.org/10.3390/geosciences15100386 - 3 Oct 2025

Abstract

Understanding the fluid storage and production mechanisms in sedimentary rocks is vital for optimising natural gas extraction and subsurface resource management. This study applies high-resolution X-ray computed tomography (≈15 μm) to digitise rock samples from onshore Cyprus, producing digital rock models from DICOM images. The workflow, including digitisation, numerical simulation of natural gas flow, and experimental validation, demonstrates strong agreement between digital and laboratory-measured porosity, confirming the methods’ reliability. Synthetic sand packs generated via particle-based modelling provide further insight into the gas storage mechanisms. A linear porosity–permeability relationship was observed, with porosity increasing from 0 to 35% and permeability from 0 to 3.34 mD. Permeability proved critical for production, as a rise from 1.5 to 3 mD nearly doubled the gas flow rate (14 to 30 fm³/s). Grain morphology also influenced gas storage. Increasing roundness enhanced porosity from 0.30 to 0.41, boosting stored gas volume by 47.6% to 42 fm³. Although based on Cyprus retrieved samples, the methodology is applicable to sedimentary formations elsewhere. The findings have implications for enhanced oil recovery, CO₂ sequestration, hydrogen storage, and groundwater extraction. This work highlights digital rock physics as a scalable technology for investigating transport behaviour in porous media and improving characterisation of complex sedimentary reservoirs. Full article

(This article belongs to the Special Issue Advancements in Geological Fluid Flow and Mechanical Properties)

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18 pages, 668 KB

Open AccessArticle

Factors Affecting Human-Generated AI Collaboration: Trust and Perceived Usefulness as Mediators

by Hee-Sung Chae and Cheolho Yoon

Information 2025, 16(10), 856; https://doi.org/10.3390/info16100856 - 3 Oct 2025

Abstract

With the development of generative artificial intelligence (AI) technology, collaboration between humans and AI is expected to improve productivity, efficiency, and safety in various industries. This study presents and empirically analyzes the factors affecting collaboration between humans and AI. This study presents and empirically analyzes a research model based on the antecedents of calculative-based, cognition-based, knowledge-based, and social influence-based trust. A total of 305 valid data points were collected through questionnaires completed by experts, office workers, and graduate students, and were analyzed using structural equation modeling. The analysis showed that all antecedents except familiarity, an antecedent of knowledge-based trust, significantly affected trust. Full article

(This article belongs to the Section Artificial Intelligence)

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15 pages, 1026 KB

Open AccessArticle

Flexible, Stretchable, and Self-Healing MXene-Based Conductive Hydrogels for Human Health Monitoring

by Ruirui Li, Sijia Chang, Jiaheng Bi, Haotian Guo, Jianya Yi and Chengqun Chu

Polymers 2025, 17(19), 2683; https://doi.org/10.3390/polym17192683 - 3 Oct 2025

Abstract

Conductive hydrogels (CHs) have attracted significant attention in the fields of flexible electronics, human–machine interaction, and electronic skin (e-skin) due to their self-adhesiveness, environmental stability, and multi-stimuli responsiveness. However, integrating these diverse functionalities into a single conductive hydrogel system remains a challenge. In this study, polyvinyl alcohol (PVA) and polyacrylamide (PAM) were used as the dual-network matrix, lithium chloride and MXene were added, and a simple immersion strategy was adopted to synthesize a multifunctional MXene-based conductive hydrogel in a glycerol/water (1:1) binary solvent system. A subsequent investigation was then conducted on the hydrogel. The prepared PVA/PAM/LiCl/MXene hydrogel exhibits excellent tensile properties (~1700%), high electrical conductivity (1.6 S/m), and good self-healing ability. Furthermore, it possesses multimodal sensing performance, including humidity sensitivity (sensitivity of −1.09/% RH), temperature responsiveness (heating sensitivity of 2.2 and cooling sensitivity of 1.5), and fast pressure response/recovery times (220 ms/230 ms). In addition, the hydrogel has successfully achieved real-time monitoring of human joint movements (elbow and knee bending) and physiological signals (pulse, breathing), as well as enabled monitoring of spatial pressure distribution via a 3 × 3 sensor array. The performance and versatility of this hydrogel make it a promising candidate for next-generation flexible sensors, which can be applied in the fields of human health monitoring, electronic skin, and human–machine interaction. Full article

(This article belongs to the Special Issue Semiflexible Polymers, 3rd Edition)

24 pages, 1828 KB

Open AccessReview

New Insight into Bone Immunity in Marrow Cavity and Cancellous Bone Microenvironments and Their Regulation

by Hongxu Pu, Lanping Ding, Pinhui Jiang, Guanghao Li, Kai Wang, Jiawei Jiang and Xin Gan

Biomedicines 2025, 13(10), 2426; https://doi.org/10.3390/biomedicines13102426 - 3 Oct 2025

Abstract

Bone immunity represents a dynamic interface where skeletal homeostasis intersects with systemic immune regulation. We synthesize emerging paradigms by contrasting two functionally distinct microenvironments: the marrow cavity, a hematopoietic and immune cell reservoir, and cancellous bone, a metabolically active hub orchestrating osteoimmune interactions. The marrow cavity not only generates innate and adaptive immune cells but also preserves long-term immune memory through stromal-derived chemokines and survival factors, while cancellous bone regulates bone remodeling via macrophage-osteoclast crosstalk and cytokine gradients. Breakthroughs in lymphatic vasculature identification challenge traditional views, revealing cortical and lymphatic networks in cancellous bone that mediate immune surveillance and pathological processes such as cancer metastasis. Central to bone immunity is the neuro–immune–endocrine axis, where sympathetic and parasympathetic signaling bidirectionally modulate osteoclastogenesis and macrophage polarization. Gut microbiota-derived metabolites, including short-chain fatty acids and polyamines, reshape bone immunity through epigenetic and receptor-mediated pathways, bridging systemic metabolism with local immune responses. In disease contexts, dysregulated immune dynamics drive osteoporosis via RANKL/IL-17 hyperactivity and promote leukemic evasion through microenvironmental immunosuppression. We further propose the “brain–gut–bone axis” as a systemic regulatory framework, wherein vagus nerve-mediated gut signaling enhances osteogenic pathways, while leptin and adipokine circuits link marrow adiposity to inflammatory bone loss. These insights redefine bone as a multidimensional immunometabolic organ, integrating neural, endocrine, and microbial inputs to maintain homeostasis. By elucidating the mechanisms of immune-driven bone pathologies, this work highlights therapeutic opportunities through biomaterial-mediated immunomodulation and microbiota-targeted interventions, paving the way for next-generation treatments in osteoimmune disorders. Full article

(This article belongs to the Section Immunology and Immunotherapy)

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24 pages, 2442 KB

Open AccessArticle

Development of a Novel Weighted Maximum Likelihood-Based Parameter Estimation Technique for Improved Annual Energy Production Estimation of Wind Turbines

by Woobeom Han, Kanghee Lee, Jonghwa Kim and Seungjae Lee

Energies 2025, 18(19), 5265; https://doi.org/10.3390/en18195265 - 3 Oct 2025

Abstract

Conventional statistical models consider all wind speed ranges as equally important, causing significant prediction errors, particularly in wind speed intervals that contribute the most to wind turbine power generation. To overcome this limitation, this study proposes a novel parameter estimation method—Weighted Maximum Likelihood Estimation (WMLE)—to improve the accuracy of annual energy production (AEP) predictions for wind turbine systems. The proposed WMLE incorporates wind-speed-specific weights based on power generation contribution, along with a weighting amplification factor (β), to construct a power-oriented wind distribution model. WMLE performance was validated by comparing four offshore wind farm candidate sites in Korea—each exhibiting distinct wind characteristics. Goodness-of-fit evaluations against conventional wind statistical models demonstrated the improved distribution fitting performance of WMLE. Furthermore, WMLE consistently achieved relative AEP errors within ±2% compared to those of time-series-based methods. A sensitivity analysis identified the optimal β value, which narrowed the distribution fit around high-energy-contributing wind speeds, thereby enhancing the reliability of AEP predictions. In conclusion, WMLE provides a practical and robust statistical framework that bridges the gap between statistical distribution fitting and time-series-based methods for AEP. Moreover, the improved accuracy of AEP predictions enhances the reliability of wind farm feasibility assessments, reduces investment risk, and strengthens financial bankability. Full article

(This article belongs to the Section B: Energy and Environment)

15 pages, 1348 KB

Open AccessArticle

Carbon Emission Accounting and Emission Reduction Path of Container Terminal Under Low-Carbon Perspective

by Bingbing Li, Long Cheng, Huangqin Wang, Jiaren Li, Zhenyi Xu and Chengrong Pan

Atmosphere 2025, 16(10), 1158; https://doi.org/10.3390/atmos16101158 - 3 Oct 2025

Abstract

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy. Full article

(This article belongs to the Special Issue Anthropogenic Pollutants in Environmental Geochemistry (2nd Edition))

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19 pages, 827 KB

Open AccessArticle

Optimized Hybrid Ensemble Intrusion Detection for VANET-Based Autonomous Vehicle Security

by Ahmad Aloqaily, Emad E. Abdallah, Aladdin Baarah, Mohammad Alnabhan, Esra’a Alshdaifat and Hind Milhem

Network 2025, 5(4), 43; https://doi.org/10.3390/network5040043 - 3 Oct 2025

Abstract

Connected and Autonomous Vehicles are promising for advancing traffic safety and efficiency. However, the increased connectivity makes these vehicles vulnerable to a broad array of cyber threats. This paper presents a novel hybrid approach for intrusion detection in in-vehicle networks, specifically focusing on the Controller Area Network bus. Ensemble learning techniques are combined with sophisticated optimization techniques and dynamic adaptation mechanisms to develop a robust, accurate, and computationally efficient intrusion detection system. The proposed system is evaluated on real-world automotive network datasets that include various attack types (e.g., Denial of Service, fuzzy, and spoofing attacks). With these results, the proposed hybrid adaptive system achieves an unprecedented accuracy of 99.995% with a 0.00001% false positive rate, which is significantly more accurate than traditional methods. In addition, the system is very robust to novel attack patterns and is tolerant to varying computational constraints and is suitable for deployment on a real-time basis in various automotive platforms. As this research represents a significant advancement in automotive cybersecurity, a scalable and proactive defense mechanism is necessary to safely operate next-generation vehicles. Full article

(This article belongs to the Special Issue Emerging Trends and Applications in Vehicular Ad Hoc Networks)

17 pages, 672 KB

Open AccessReview

Saying “Yes” to NONO: A Therapeutic Target for Neuroblastoma and Beyond

by Sofya S. Pogodaeva, Olga O. Miletina, Nadezhda V. Antipova, Alexander A. Shtil and Oleg A. Kuchur

Cancers 2025, 17(19), 3228; https://doi.org/10.3390/cancers17193228 - 3 Oct 2025

Abstract

Pediatric tumors such as neuroblastoma are characterized by a genome-wide ‘transcriptional burden’, surmising the involvement of multiple alterations of gene expression. Search for master regulators of transcription whose inactivation is lethal for tumor cells identified the non-POU domain-containing octamer-binding protein (NONO), a member of the Drosophila Behavior/Human Splicing family known for the ability to form complexes with macromolecules. NONO emerges as an essential mechanism in normal neurogenesis as well as in tumor biology. In particular, NONO interactions with RNAs, largely with long non-coding MYCN transcripts, have been attributed to the aggressiveness of neuroblastoma. Broadening its significance beyond MYCN regulation, NONO guards a subset of transcription factors that comprise a core regulatory circuit, a self-sustained loop that maintains transcription. As a component of protein–protein complexes, NONO has been implicated in the control of cell cycle progression, double-strand DNA repair, and, generally, in cell survival. Altogether, the pro-oncogenic roles of NONO justify the need for its inactivation as a therapeutic strategy. However, considering NONO as a therapeutic target, its druggability is a challenge. Recent advances in the inactivation of NONO and downstream signaling with small molecular weight compounds make promising the development of pharmacological antagonists of NONO pathway(s) for neuroblastoma treatment. Full article

(This article belongs to the Special Issue Precision Medicine and Targeted Therapies in Neuroblastoma)

24 pages, 637 KB

Open AccessArticle

ZDBERTa: Advancing Zero-Day Cyberattack Detection in Internet of Vehicle with Zero-Shot Learning

by Amal Mirza, Sobia Arshad, Muhammad Haroon Yousaf and Muhammad Awais Azam

Computers 2025, 14(10), 424; https://doi.org/10.3390/computers14100424 - 3 Oct 2025

Abstract

The Internet of Vehicles (IoV) is becoming increasingly vulnerable to zero-day (ZD) cyberattacks, which often bypass conventional intrusion detection systems. To mitigate this challenge, this study proposes Zero-Day Bidirectional Encoder Representations from Transformers approach (ZDBERTa), a zero-shot learning (ZSL)-based framework for ZD attack detection, evaluated on the CICIoV2024 dataset. Unlike conventional AI models, ZSL enables the classification of attack types not previously encountered during the training phase. Two dataset variants are formed: Variant 1, created through synthetic traffic generation using a mixture of pattern-based, crossover, and mutation techniques, and Variant 2, augmented with a Generative Adversarial Network (GAN). To replicate realistic zero-day conditions, denial-of-service (DoS) attacks were omitted during training and introduced only at testing. The proposed ZDBERTa incorporates a Byte-Pair Encoding (BPE) tokenizer, a multi-layer transformer encoder, and a classification head for prediction, enabling the model to capture semantic patterns and identify previously unseen threats. The experimental results demonstrate that ZDBERTa achieves 86.677% accuracy on Variant 1, highlighting the complexity of zero-day detection, while performance significantly improves to 99.315% on Variant 2, underscoring the effectiveness of GAN-based augmentation. To the best of our knowledge, this is the first research to explore ZD detection within CICIoV2024, contributing a novel direction toward resilient IoV cybersecurity. Full article

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18 pages, 1508 KB

Open AccessArticle

Familial Molecular Burden in Autism Spectrum Disorder: A Next-Generation Sequencing Study of Polish Affected Families

by Monika Wawszczak-Kasza, Jarosław Rachuna, Łukasz Madej, Wojciech Lewitowicz, Piotr Lewitowicz and Agata Horecka-Lewitowicz

Int. J. Mol. Sci. 2025, 26(19), 9672; https://doi.org/10.3390/ijms26199672 - 3 Oct 2025

Abstract

Autism spectrum disorder (ASD) is a heritable neurodevelopmental condition with a complex genetic architecture. Dissecting the interplay between inherited variants and high-impact de novo variants is critical for understanding its etiology. We conducted a family-based study involving 42 families with ASD (139 individuals). Using a targeted next-generation sequencing (NGS) panel of 236 genes, we identified and characterized rare inherited and de novo variants in affected probands, parents, and unaffected siblings. Our analysis revealed a complex genetic landscape marked by diverse inheritance patterns. De novo variants were predominantly observed in individuals with atypical autism, while biparental (homozygous) inheritance was more common in Asperger syndrome. Maternally inherited variants showed significant enrichment in intronic regions, pointing to a potential regulatory role. We also detected variants in several high-confidence ASD risk genes, including SHANK3, MYT1L, MCPH1, NIPBL, and TSC2, converging on pathways central to synaptic function and neurogenesis. Across the cohort, five variants of uncertain significance (VUS) were identified, comprising two inherited variants in ABCC8 and additional variants in CUL23, TSC2, and MCPH1. Our findings underscore the profound genetic heterogeneity of ASD and suggest that distinct genetic mechanisms and inheritance patterns may contribute to different clinical presentations within the spectrum. This highlights the power of family-based genomic analyses in elucidating the complex interplay of inherited and de novo variants that underlies ASD. Full article

(This article belongs to the Section Molecular Genetics and Genomics)

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18 pages, 2493 KB

Open AccessArticle

Assessment of Radiological Dispersal Devices in Densely Populated Areas: Simulation and Emergency Response Planning

by Yassine El Khadiri, Ouadie Kabach, El Mahjoub Chakir and Mohamed Gouighri

Instruments 2025, 9(4), 22; https://doi.org/10.3390/instruments9040022 - 3 Oct 2025

Abstract

The increasing threat of terrorism involving Radiological Dispersal Devices (RDDs) necessitates comprehensive evaluation and preparedness strategies, especially in densely populated public areas. This study aims to assess the potential consequences of RDD detonation, focusing on the effective doses received by individuals and the ground deposition of radioactive materials in a hypothetical urban environment. Utilizing the HotSpot code, simulations were performed to model the dispersion patterns of ¹³⁷Cs and ²⁴¹Am under varying meteorological conditions, mirroring the complexities of real-world scenarios as outlined in recent literature. The results demonstrate that ¹³⁷Cs dispersal produces a wider contamination footprint, with effective doses exceeding the public exposure limit of 1 mSv at distances up to 1 km, necessitating broad protective actions. In contrast, ²⁴¹Am generates higher localized contamination, with deposition levels surpassing cleanup thresholds near the release point, creating long-term remediation challenges. Dose estimates for first responders highlight the importance of adhering to operational dose limits, with scenarios approaching 100 mSv under urgent rescue conditions. Overall, the findings underscore the need for rapid dose assessment, early shelter-in-place orders, and targeted decontamination to reduce population exposure. These insights provide actionable guidance for emergency planners and first responders, enhancing preparedness protocols for RDD incidents in major urban centers. Full article

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22 pages, 13067 KB

Open AccessArticle

Numerical Modeling of Photovoltaic Cells with the Meshless Global Radial Basis Function Collocation Method

by Murat Ispir and Tayfun Tanbay

Energies 2025, 18(19), 5267; https://doi.org/10.3390/en18195267 - 3 Oct 2025

Abstract

Accurate prediction of photovoltaic performance hinges on resolving the electron density in the P-region and the hole density in the N-region. Motivated by this need, we present a comprehensive assessment of a meshless global radial basis function (RBF) collocation strategy for the steady current continuity equation, covering a one-dimensional two-region P–N junction and a two-dimensional single-region problem. The study employs Gaussian (GA) and generalized multiquadric (GMQ) bases, systematically varying shape parameter and node density, and presents a detailed performance analysis of the meshless method. Results map the accuracy–stability–computation-time landscape: GA achieves faster convergence but over a narrower stability window, whereas GMQ exhibits greater robustness to shape-parameter variation. We identify stability plateaus that preserve accuracy without severe ill-conditioning and quantify the runtime growth inherent to dense global collocation. A utopia-point multi-objective optimization balances error and computation time to yield practical node-count guidance; for the two-dimensional case with equal weighting, an optimum of 19 intervals per side emerges, largely insensitive to the RBF choice. Collectively, the results establish global RBF collocation as a meshless, accurate, and systematically optimizable alternative to conventional mesh-based solvers for high-fidelity carrier-density prediction in P-N junctions, thereby enabling more reliable performance analysis and design of photovoltaic devices. Full article

(This article belongs to the Topic Numerical Methods and Computer Simulations in Energy Analysis, 3rd Edition)

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