Transformer-based language models have demonstrated remarkable success in few-shot text classification; however, their effectiveness is often constrained by challenges such as high intraclass diversity and interclass similarity, which hinder the extraction of discriminative features. To address these limitations, a novel framework, Adaptive Masking Bidirectional Encoder Representations from Transformers with Dynamic Weighted Prototype Module (AMBERT-DWPM), is introduced, incorporating adaptive masking and dynamic weighted prototypical learning to enhance feature representation and classification performance. The standard BERT architecture is refined by integrating an adaptive masking mechanism based on Layered Integrated Gradients (LIG), enabling the model to dynamically emphasize salient text segments and improve feature discrimination. Additionally, a DWPM is designed to assign adaptive weights to support samples, mitigating inaccuracies in prototype construction caused by intraclass variability. Extensive evaluations conducted on six publicly available benchmark datasets demonstrate the superiority of AMBERT-DWPM over existing few-shot classification approaches. Notably, under the 5-shot setting on the DBpedia14 dataset, an accuracy of 0.978±0.004 is achieved, highlighting significant advancements in feature discrimination and generalization capabilities. These findings suggest that AMBERT-DWPM provides an efficient and robust solution for few-shot text classification, particularly in scenarios characterized by limited and complex textual data.

South Africa has been severely impacted by several high-profile corporate scandals, with significant financial manipulation involving both the content of financial statements and the tone set by top executives. Notably, CEOs such as Markus Jooste from Steinhoff have been accused of misleading investors through both earnings management and the use of an authoritative management tone. This study investigates the market's reaction to the interactive effect of top management tone and earnings management, employing a short-window event study methodology. The tones of two distinct management styles—autocratic and pragmatic—are examined by analysing CEO statements using the DICTION textual analysis software. The sample comprises 944 firm-year observations spanning from 2011 to 2018. The results indicate that the market did not respond to earnings management in isolation. However, a significant negative market reaction was observed when earnings management occurred in conjunction with an autocratic tone. This suggests that South African investors are particularly attuned to multiple signals of potential fraud and will adjust their valuations accordingly. The findings underline the importance of considering not only financial disclosures but also the behavioral cues given by top management in assessing firm performance and risk. Investors, regulators, and analysts must therefore remain vigilant to the combined risks posed by earnings manipulation and the tone of management communications. The study contributes to the limited literature on the stock market's response to the interplay of earnings management and management tone, particularly in the context of South Africa, and is the first to explore the combined effects of these two forms of manipulation.

Sentiment analysis in legal documents presents significant challenges due to the intricate structure, domain-specific terminology, and strong contextual dependencies inherent in legal texts. In this study, a novel hybrid framework is proposed, integrating Graph Attention Networks (GATs) with domain-specific embeddings, i.e., Legal Bidirectional Encoder Representations from Transformers (LegalBERT) and an aspect-oriented sentiment classification approach to improve both predictive accuracy and interpretability. Unlike conventional deep learning models, the proposed method explicitly captures hierarchical relationships within legal texts through GATs while leveraging LegalBERT to enhance domain-specific semantic representation. Additionally, auxiliary features, including positional information and topic relevance, were incorporated to refine sentiment predictions. A comprehensive evaluation conducted on diverse legal datasets demonstrates that the proposed model achieves state-of-the-art performance, attaining an accuracy of 93.1% and surpassing existing benchmarks by a significant margin. Model interpretability was further enhanced through SHapley Additive exPlanations (SHAP) and Legal Context Attribution Score (LCAS) techniques, which provide transparency into decision-making processes. An ablation study confirms the critical contribution of each model component, while scalability experiments validate the model’s efficiency across datasets ranging from 10,000 to 200,000 sentences. Despite increased computational demands, strong robustness and scalability are exhibited, making this framework suitable for large-scale legal applications. Future research will focus on multilingual adaptation, computational optimization, and broader applications within the field of legal analytics.

The restoration of blurred images remains a critical challenge in computational image processing, necessitating advanced methodologies capable of reconstructing fine details while mitigating structural degradation. In this study, an innovative image restoration framework was introduced, employing Complex Interval Pythagorean Fuzzy Sets (CIPFSs) integrated with mathematically structured transformations to achieve enhanced deblurring performance. The proposed methodology initiates with the geometric correction of pixel-level distortions induced by blurring. A key innovation lies in the incorporation of CIPFS-based entropy, which is synergistically combined with local statistical energy to enable robust blur estimation and adaptive correction. Unlike traditional fuzzy logic-based approaches, CIPFS facilitates a more expressive modeling of uncertainty by leveraging complex interval-valued membership functions, thereby enabling nuanced differentiation of blur intensity across image regions. A fuzzy inference mechanism was utilized to guide the refinement process, ensuring that localized corrections are adaptively applied to degraded regions while leaving undistorted areas unaffected. To preserve edge integrity, a geometric step function was applied to reinforce structural boundaries and suppress over-smoothing artifacts. In the final restoration phase, structural consistency is enforced through normalization and regularization techniques to ensure coherence with the original image context. Experimental validations demonstrate that the proposed model delivers superior image clarity, improved edge sharpness, and reduced visual artifacts compared to state-of-the-art deblurring methods. Enhanced robustness against varying blur patterns and noise intensities was also confirmed, indicating strong generalization potential. By unifying the expressive power of CIPFS with analytically driven restoration strategies, this approach contributes a significant advancement to the domain of image deblurring and restoration under uncertainty.

The environmental performance and service quality of liquefied petroleum gas (LPG) vehicles were evaluated through a dual-phase analytical approach. In the first phase, exhaust emissions from LPG and petrol-powered vehicles were quantified using the CAPELEC 3010 gas analyzer, with concentrations of carbon monoxide (CO), carbon dioxide (CO$_2$), nitrogen oxides (NOx), and hydrocarbons being measured. The results demonstrated that LPG vehicles emitted significantly lower CO levels (0.09% on average) compared to petrol vehicles (0.18%), with corrected CO values also reduced (0.08% vs. 0.19%). These findings reinforce the environmental advantages of LPG as a cleaner fuel alternative. In the second phase, the SERVQUAL model was employed to assess user perceptions of service quality, focusing on five dimensions: reliability, responsiveness, assurance, empathy, and overall service quality. A negative overall SERVQUAL gap (-0.806) was identified, with the most pronounced discrepancies observed in reliability (-1.061) and responsiveness (-0.933), indicating unmet expectations in key service aspects. Despite these gaps, LPG vehicles were perceived as cost-effective and environmentally sustainable. The findings underscore the necessity for technical refinements in LPG vehicle systems and improvements in service infrastructure to enhance user satisfaction. The insights derived from this study offer valuable guidance for policymakers and industry stakeholders seeking to promote LPG as a viable component of sustainable transportation strategies.

In recent decades, the strategic placement of capacitors for compensating inductive reactive power has been extensively investigated by network operators and researchers globally, owing to its profound impact on minimizing power losses, improving voltage regulation, and enhancing overall voltage stability. The installation of shunt capacitors has been demonstrated to significantly improve the efficiency and performance of power systems by regulating voltage levels at load points, as well as at distribution and transmission system buses. This approach not only reduces inductive reactive power but also corrects the system’s power factor, thereby optimizing energy utilization. In this study, the optimal sizing and placement of capacitor banks within a specific section of the Duhok city distribution network were systematically analyzed. The Electrical Transient Analyzer Program (ETAP) software was employed to simulate and evaluate power losses and voltage drops both before and after capacitor installation. The findings reveal a marked improvement in the voltage profile across the network, accompanied by a substantial reduction in power losses. These results underscore the critical role of capacitor banks in enhancing the operational efficiency of distribution networks, providing a robust framework for future implementations in similar systems. The methodology and outcomes presented herein offer valuable insights for network operators seeking to optimize power system performance through reactive power compensation.