Search Results (113)

Intelligent vehicles represent a frontier in technological innovation. Effectively identifying their key core technologies and primary competitors is a crucial prerequisite for overcoming industrial technological bottlenecks, playing a pivotal role in promoting sustainable industrial development and enhancing global market competitiveness. This study is based on 46,373 authorized invention patents in the field of intelligent vehicles from 1950 to 2024 and based on four core characteristics of key core technologies: technological centrality, technological value, economic value, and competitive monopoly. Combining the entropy weight method and gray correlation analysis method, it effectively identifies 15 key core technologies in the field of intelligent vehicles, including G05D1, B60W30, G08G1, etc. These technologies cover four core domains: autonomous driving and vehicle control, intelligent transportation and vehicle–road coordination, onboard computing and data processing, and powertrain system integration and optimization. Building on this foundation, the study analyzes the technological competitive landscape from both national and corporate perspectives. The results show that the United States and Japan, with their profound technological accumulation, demonstrate strong competitive strength. China leads globally with 25.56% of worldwide patents, exhibiting rapid growth in R&D scale. However, the technological influence of key core technology patents held by major Chinese enterprises still lags significantly behind that of the United States and Japan, indicating room for improvement in R&D quality. By precisely identifying core R&D directions for intelligent vehicles, this study provides strategic guidance and practical references for optimizing green innovation resource allocation within the industry. It aims to overcome key technological bottlenecks in low-carbon intelligent vehicles, thereby achieving breakthroughs in key core technologies and enabling high-quality, sustainable industrial development. Full article

This study provides a comprehensive analysis of peanut shell (PnS) combustion behavior using combined physicochemical characterization and non-isothermal thermogravimetric kinetics. To evaluate its potential as a sustainable solid biofuel, PnS was characterized for its proximate and ultimate composition, with its fiber structure analyzed via Van Soest methods and functional groups identified via FTIR spectroscopy. Thermogravimetric analysis (TGA) was performed at high heating rates ($20,40,60$, and $80 \mathrm{K}{ \mathrm{m}\mathrm{i}\mathrm{n}}^{-1}$) to investigate combustion stages under oxidative conditions. The results established PnS as a high-potential energy source, revealing a significant volatile matter content ($65.30 \mathrm{w}\mathrm{t}\%$) and an exceptionally high heating value ($20.87 \mathrm{M}\mathrm{J} {\mathrm{k}\mathrm{g}}^{-1}$), which surpasses many standard agricultural residues. The proximate analysis also indicated a moisture content of $9.61\%$ and an ash content of $6.59\%$. TGA profiles displayed distinct decomposition stages, with the primary devolatilization occurring between 500 and 700 K. Kinetic analysis was conducted using six model-free methods: Friedman (FR), Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), Starink (STK), Kissinger (K), and Vyazovkin (VY) and the Coats-Redfern model-fitting method. The apparent activation energy $\left({\mathrm{E}}_{\mathrm{a}}\right)$ was found to vary with conversion $\left(\mathsf{\alpha }\right)$, reflecting the complex degradation of the lignocellulosic matrix (47.86% cellulose, $28.4\%$ lignin). The activation energy values ranged from approximately $23 \mathrm{k}\mathrm{J}{ \mathrm{m}\mathrm{o}\mathrm{l}}^{-1}$ (VY method at low conversion) to $187 \mathrm{k}\mathrm{J}{ \mathrm{m}\mathrm{o}\mathrm{l}}^{-1}$ (FR method at $\mathsf{\alpha }=0.5$). Model-fitting analysis identified the three-dimensional diffusion (D3) model as the governing reaction mechanism. Thermodynamic analysis indicated positive enthalpy ($\Delta \mathrm{H}:70.7-181.8 \mathrm{k}\mathrm{J}{ \mathrm{m}\mathrm{o}\mathrm{l}}^{-1}$) and Gibbs free energy ($\Delta \mathrm{G}$: $116.2-216.7 \mathrm{k}\mathrm{J}{ \mathrm{m}\mathrm{o}\mathrm{l}}^{-1}$), with predominately negative entropy ($\Delta \mathrm{S}$), confirming the endothermic and non-spontaneous nature of the reaction activation. Full article

Our work develops the financial engineering module of the REGENerative Agriculture (REGENA) Production Function, with Soil Organic Carbon (SOC) as ecosystem service and contract underlying index, contributing to the global literature and business practices. Specifically, we design and engineer a 30-year Net Present Value (NPV) intergenerational ecological bond instrument tailored for carbon farming (CF) as a part of regenerative practices. With SOC constituting a fundamental soil health indicator for the European Union Soil Observatory (EUSO), we model the flow of value from atmospheric CO₂ removal and its metabolism into SOC within a stochastic SOC Value at Risk (VaR) framework. We assess the SOC VaR in five experimental plots in five Mediterranean countries in South Europe and North Africa for three different treatments in each plot. In turn, the SOC VaR is incorporated into an adjusted Shannon entropy index (H(X)_ADJ) to estimate the coefficient of a positive, net-zero, or negative carbon balance and further assess the risk-adjusted discount rate. The monetary value per gram of carbon per kilogram of soil (g C/kg Soil) signifies a clear advantage of combined regenerative treatments. Finally, three selected extensions of our work are discussed, such as the application of the framework to other nutrients, the establishment full cost–benefit accounting methods for monetizing the environmental benefits of CF to upscale investments and the lifecycle accounting of ecosystem services. Full article

Detecting precipitation patterns remains a central challenge in hydrological sciences due to the non-linear nature of atmospheric dynamics and the growing influence of climatic variability. This study investigates the evolution of a 64-year daily precipitation series (1961–2024) at the Tulcea meteorological station (Dobrogea, Romania) and introduces a novel adaptation of the Most Probable Precipitation Method (AMPPM), shifting its application from a regional spatial framework to a temporal one. Shannon Entropy is used as a measure of “climatic disorder.” Model evaluation incorporates Mean Error (ME), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), which here measure structural divergence rather than predictive accuracy. Results demonstrate that the Synthetic Representative Series (SRS) isolates the stable climatic signal, reducing the global coefficient of variation (cv (%)) to 70.96% and mitigating extreme skewness typical of coastal convective activity. Seasonal entropy analysis reveals divergence: winter entropy decreases through signal stabilization (minimum 2.00 bits in March), whereas July–October entropy increases, highlighting previously hidden high-frequency daily oscillations. The aggregated Tot_64 series achieves a final entropy of 2.75 bits, confirming a complex, multi-state daily precipitation process. MAE and RMSE values for the SRS (e.g., October: MAE = 1.20, RMSE = 4.53; Tot_64: MAE = 1.40, RMSE = 4.58) indicate that the SRS captures dominant precipitation patterns with minimal deviation, comparable to or better than the moving average approaches. Full article

According to existing procedures for defining the velocity distribution across cross profile sections of watercourses (e.g., Entropy theory and Power Law theory), surface velocity is a key input parameter, together with cross-sectional bathymetry. Field measurements to obtain velocity values and their distributions are often difficult due to limited equipment, unreliable data, missing data, or hazardous conditions such as flooding and inaccessible locations. This creates a strong need for alternative approaches to measuring surface velocities in rivers. The application of unmanned aerial vehicles (UAVs), mobile phones, and traditional field instruments such as the Propeller Current Meter (PCM) can significantly improve measurement efficiency, especially in situations where conventional methods are not feasible. This paper presents an algorithm for comparing these measurement approaches and quantifying their differences. The methodology is demonstrated using a real case study on the Bednja River in Croatia, which flows through alluvial deposits. The results show that video-based surface velocity estimation using UAV and mobile phone imagery is feasible under real river conditions. Still, its accuracy depends strongly on flow conditions and surface characteristics. While UAV recordings provide reliable results in fast and turbulent flows, mobile phone videos yield more stable performance in smoother flow conditions, where additional surface texture is available from natural tracers. Full article

Accurate Short-Term Load Forecasting (STLF) is paramount for the stable and economical operation of power systems, particularly in the context of high renewable energy penetration, which exacerbates load volatility and non-stationarity. The prevailing advanced “decomposition–ensemble” paradigm, however, faces two significant challenges when processing non-stationary signals: (1) The performance of Variational Mode Decomposition (VMD) is highly dependent on its hyperparameters (K, α), and traditional meta-heuristic algorithms (e.g., GA, GWO, PSO) are prone to converging to local optima during the optimization process; (2) Deep learning predictors struggle to dynamically weigh the importance of multi-dimensional, heterogeneous features (such as the decomposed Intrinsic Mode Functions (IMFs) and external climatic factors). To address these issues, this paper proposes a novel, adaptive hybrid forecasting framework, namely IRIME-VMD-CDA-LSTNet. Firstly, an Improved Rime Optimization Algorithm (IRIME) integrated with a Gaussian Mutation strategy is proposed. This algorithm adaptively optimizes the VMD hyperparameters by targeting the minimization of average sample entropy, enabling it to effectively escape local optima. Secondly, the optimally decomposed IMFs are combined with climatic features to construct a multi-dimensional information matrix. Finally, this matrix is fed into an innovative Cross-Dimensional Attention (CDA) LSTNet model, which dynamically allocates weights to each feature dimension. Ablation experiments conducted on a real-world dataset from a distribution substation demonstrate that, compared to GA-VMD, GWO-VMD, and PSO-VMD, the proposed IRIME-VMD method achieves a reduction in Root Mean Square Error (RMSE) of up to 18.9%. More importantly, the proposed model effectively mitigates the “prediction lag” phenomenon commonly observed in baseline models, especially during peak load periods. This framework provides a robust and high-accuracy solution for non-stationary load forecasting, holding significant practical value for the operation of modern power systems. Full article

Microwave-Induced Hydrogen Plasma (MIHP) is introduced as a novel synthesis route for producing high-entropy carbides (HECs), offering an alternative to conventional mechanical alloying and/or sintering techniques. In this study, a representative HEC composition, ${\mathrm{MoNbTaVWC}}_5$, was successfully synthesized using MIHP processing at 200 Torr. The process employs microwave energy to generate hydrogen plasma to facilitate carbothermal reduction of metal oxide precursors. The plasma environment generates abundant reactive atomic hydrogen species, which enhance reaction spontaneity and promote efficient HEC formation. X-ray diffraction confirmed the formation of a single-phase rocksalt-type face-centered cubic structure. Scanning electron microscopy combined with energy-dispersive X-ray spectroscopy confirmed uniform elemental distribution within the synthesized microstructure. Nanoindentation measurements yielded hardness and elastic modulus values consistent with literature reports for similar compositions. X-ray photoelectron spectroscopy confirmed the chemical state of carbon to be primarily bonded with metals as carbides, with only minor oxygen present as metal-oxides. Raman spectroscopy performed over the 750–1900 ${\mathrm{cm}}^{-1}$ range yielded a featureless spectrum with no detectable D or G bands often observed for sp²-hybridized disordered carbon, graphite, or graphene materials. These results validate the structural and chemical purity of the synthesized HECs. This work aims to demonstrate the feasibility and reproducibility of MIHP as a synthesis method for HECs. Full article

Laser Speckle Imaging (LSI) is a non-invasive optical technique used to assess biological activity by detecting dynamic variations in speckle patterns. These patterns exhibit statistical symmetry in static regions, while biological activity induces symmetry breaking that can be captured through the Graphic Absolute Value of Differences (GAVD), producing the activity map $I_{GAVD}$. This work evaluates the effect of four contrast enhancement algorithms: Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Multiscale Morphological Contrast Enhancement (MMCE), and Multiscale Top-Hat Transform with an Open-Close Close-Open (OCCO) filter, applied to intermediate LSI images, with the final activity map used for quantitative evaluation. Each method represents a distinct enhancement paradigm: HE and CLAHE are histogram-based techniques for global and local contrast adjustment, whereas MMCE and OCCO-MTH are morphological approaches that emphasize structural preservation and local detail enhancement. The dataset consisted of images of Phaseolus vulgaris (SP) and Lactuca sativa (SL) seeds. Evaluation was conducted through expert visual inspection and quantitative analysis using contrast, entropy, spatial frequency (SF), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and contrast improvement ratio (CIR). All metrics were computed on $I_{GAVD}$ activity maps, which reflect bioactivity through the disruption of statistical symmetry. Non-parametric statistical tests (Friedman, aligned Friedman, and Quade) revealed that CLAHE and MMCE significantly improved image quality compared to the original images ($p<0.05$). Among the evaluated algorithms, CLAHE increased global contrast by approximately 25% and entropy by 6% relative to the original speckle frames, enhancing the visibility of bioactive regions. MMCE achieved the highest bioactivity contrast ratio (CIR = 0.64), while OCCO-MTH provided the best structural fidelity (SSIM = 0.91) and noise suppression (PSNR = 30.7 dB). These results demonstrate that suitable contrast enhancement can substantially improve the interpretability of LSI activity maps without altering acquisition hardware. This finding is particularly relevant for experimental applications aiming to maximize information quality without modifying acquisition hardware. Full article

In the context of growing data availability and increasing complexity of demand patterns in retail fuel distribution, selecting effective forecasting models for large collections of time series is becoming a key operational challenge. This study investigates the effectiveness of a hybrid forecasting approach combining ARIMA models with dynamically updated Markov Chains. Unlike many existing studies that focus on isolated or small-scale experiments, this research evaluates the hybrid model across a full set of approximately 150 time series collected from multiple petrol stations, without pre-clustering or manual selection. A comprehensive set of statistical and structural features is extracted from each time series to analyze their relation to forecast performance. The results show that the hybrid ARIMA–Markov approach significantly outperforms both individual statistical models and commonly applied machine learning methods in many cases, particularly for non-stationary or regime-shifting series. In 100% of cases, the hybrid model reduced the error compared to both baseline models—the median RMSE improvement over ARIMA was 13.03%, and 15.64% over the Markov model, with statistical significance confirmed by the Wilcoxon signed-rank test. The analysis also highlights specific time series features—such as entropy, regime shift frequency, and autocorrelation structure—as strong indicators of whether hybrid modeling yields performance gains. Feature-conditioning analyses (e.g., lag-1 autocorrelation, volatility, entropy) explain when hybridization helps, enabling a feature-aware workflow that selectively deploys model components and narrows parameter searches. The greatest benefits of applying the hybrid model were observed for time series characterized by high variability, moderate entropy of differences, and a well-defined temporal dependency structure—the correlation values between these features and the improvement in hybrid performance relative to ARIMA and Markov models reached 0.55–0.58, ensuring adequate statistical significance. Such approaches are particularly valuable in enterprise environments dealing with thousands of time series, where automated model configuration becomes essential. The findings position interpretable, adaptive hybrids as a practical default for short-horizon demand forecasting in fuel supply chains and, more broadly, in energy-use applications characterized by heterogeneous profiles and evolving regimes. Full article

Gas disasters in coal mines are the principal constraint on safe operations; accordingly, accurate gas time-series forecasting and real-time fluctuation monitoring are essential for prevention and early warning. A method termed Decomposition-Enhanced Cross-Graph Forecasting and Anomaly Finding is proposed. The Multi-Variate Variational Mode Decomposition (MVMD) algorithm is refined by integrating wavelet denoising with an Entropy Weight Method (EWM) multi-index scheme (seven indicators, including SNR and PSNR; weight-solver error ≤ 0.001, defined as the maximum absolute change between successive weight vectors in the entropy-weight iteration). Through this optimisation, the decomposition parameters are selected as K = 4 (modes) and $\alpha$ = 1000, yielding effective noise reduction on 83,970 multi-channel records from longwall faces; after joint denoising, SSIM reaches 0.9849, representing an improvement of 0.5%–18.7% over standalone wavelet denoising. An interpretable Cross Interaction Refinement Graph Neural Network (CrossGNN) is then constructed. Shapley analysis is employed to quantify feature contributions; the m1t2 gas component attains a SHAP value of 0.025, which is 5.8× that of the wind-speed sensor. For multi-timestep prediction (T0–T2), the model achieves MAE = 0.008705754 and MSE = 0.000242083, which are 8.7% and 12.7% lower, respectively, than those of STGNN and MTGNN. For fluctuation detection, Pruned Exact Linear Time (PELT) with minimum segment length L_min = 58 is combined with a circular block bootstrap test to identify sudden-growth and high-fluctuation segments while controlling FDR = 0.10. Hasse diagrams are further used to elucidate dominance relations among components (e.g., m3t3, the third decomposed component of the T2 gas sensor). Field data analyses substantiate the effectiveness of the approach and provide technical guidance for the intellectualisation of coal-mine safety management. Full article

Assessing Mine Emergency Rescue Capability (MERC) is critical for ensuring mining safety and advancing sustainable development. However, existing MERC assessments often lack a holistic sustainability perspective. To bridge this gap, this study develops a MERC assessment model grounded in the Triple Bottom Line (TBL) framework, integrating the relative difference function (RDF) to address the fuzziness and subjectivity in evaluation processes. A hierarchical indicator system is constructed, comprising 5 primary factors and 25 sub-indicators across environmental, economic, and social dimensions, reflecting both immediate rescue effectiveness and long-term sustainability performance. Indicator weights are derived from a hybrid approach that combines the subjective G1 method with the objective entropy weight method. RDF is employed to compute membership degrees, and the final MERC level is determined by level characteristic values. The model is validated through an empirical study of six green mines in China. Results demonstrate robust performance and consistency with alternative methods and reveal the environmental dimension as the dominant driver within the TBL framework. This finding supports the ecology-first principle of green mining and underscores the alignment of high-level emergency preparedness with sustainable development objectives. By explicitly embedding sustainability principles into safety assessment, the proposed model provides a scientifically grounded tool to guide the green transformation of the mining industry. Future work will adapt the model to diverse mining contexts and refine the indicators to better support global sustainability goals. Full article

The stability and mechanical properties of (TiZrHf)_1−x(AB)_x (AB = NbTa, NbMo, MoTa) refractory high-entropy alloys have been investigated by combining the first-principles with special quasi-random structure (SQS) method. It is found that with the increase in solute concentration x, the ΔH_mix of (TiZrHf)_1−x(AB)_x (AB = NbMo, MoTa) linearly decreases, whereas both ΔH_mix and ΔS_mix of (TiZrHf)_1−x(NbTa)_x increase initially and subsequently decrease, with the crossover occurring at x = 0.56. The ΔH_mix of (TiZrHf)_1−x(NbTa)_x and (TiZrHf)_1−x(AB)_x (AB = NbMo, MoTa) alloys are larger and lower than that of TiZrHf, respectively, while the ΔS_mix of all (TiZrHf)_1−x(AB)_x is larger than that of TiZrHf. The formation possibility parameter Ω of all (TiZrHf)_1−x(AB)_x (AB = NbMo, MoTa) first decreases sharply, followed by a gradual decrease. And the local lattice distortion (LLD) parameter δ remains relatively stable around x = 0.56 for all cases, after which it decreases sharply until x = 0.89. The δ value of (TiZrHf)_1−x(AB)_x is higher than that of TiZrHf for x < 0.56 but becomes lower beyond this composition. The valence electron concentration (VEC), a possible indicator for a single-phase solution, of (TiZrHf)_1−x(AB)_x increases nearly linearly, while the formation energy ΔH_f of (TiZrHf)_1−x(AB)_x shows the opposite tendency, except for (TiZrHf)_0.67(NbTa)_0.33. Furthermore, the VEC of all (TiZrHf)_1−x(AB)_x alloys increases, whereas their ΔH_f decreases compared to that of TiZrHf. The ideal strength σ_p of (TiZrHf)_1−x(AB)_x increases linearly, reaching approximately 2.12 GPa. The bulk modulus (B), elastic modulus (E), and shear modulus (G) also exhibit linear increases, and their values in all (TiZrHf)_1−x(AB)_x alloys are higher than those of TiZrHf, with some exceptions. The Cauchy pressure (C₁₂–C₄₄) and Pugh’s ratio G/B of all (TiZrHf)_1−x(AB)_x alloys increase, whereas the Poisson’s ratio ν exhibits the opposite trend. Moreover, the C₁₂–C₄₄ and G/B ratio of TiZrHf are lower and higher, respectively, than those of (TiZrHf)_1−x(AB)_x, and the ν of TiZrHf is lower than that of (TiZrHf)_1−x(AB)_x. This study provides valuable insights for the design of high-performance TiZrHf-based refractory high-entropy alloys. Full article

This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation. Full article

This study presents a comprehensive grayscale texture analysis framework for investigating the microstructural evolution of cement-based materials during hydration. High-resolution X-ray computed tomography (X-CT) slice images were analyzed across five hydration ages (12 h, 1 d, 3 d, 7 d, and 31 d) using three complementary methods: grayscale histogram statistics, fractal dimension calculation via differential box-counting, and texture feature extraction based on the gray-level co-occurrence matrix (GLCM). The average value of the mean grayscale value of slice (Mean_{G_AVE}) shows a trend of increasing and then decreasing. Average fractal dimension values (D_{B_AVE}) decreased logarithmically from 2.48 (12 h) to 2.41 (31 d), quantifying progressive microstructural homogenization. The trend reflects pore refinement and gel network consolidation. GLCM texture parameters—including energy, entropy, contrast, and correlation—captured the directional statistical patterns and phase transitions during hydration. Energy increased with hydration time, reflecting greater spatial homogeneity and phase continuity, while entropy and contrast declined, signaling reduced structural complexity and interfacial sharpness. A quantitative evaluation of parameter performance based on intra-sample stability, inter-sample discrimination, and signal-to-noise ratio (SNR) revealed energy, entropy, and contrast as the most effective descriptors for tracking hydration-induced microstructural evolution. This work demonstrates a novel, integrative, and segmentation-free methodology for texture quantification, offering robust insights into the microstructural mechanisms of cement hydration. The findings provide a scalable basis for performance prediction, material optimization, and intelligent cementitious design. Full article

Objective: The accurate visualization of root canal systems on periapical radiographs is critical for successful endodontic treatment. This study aimed to evaluate and compare the effectiveness of several image enhancement algorithms—including a novel Total Variation–Contrast-Limited Adaptive Histogram Equalization (TV-CLAHE) technique—in improving the detectability of root canal configurations in mandibular incisors, using cone-beam computed tomography (CBCT) as the gold standard. A null hypothesis was tested, assuming that enhancement methods would not significantly improve root canal detection compared to original radiographs. Method: A retrospective analysis was conducted on 60 periapical radiographs of mandibular incisors, resulting in 420 images after applying seven enhancement techniques: Histogram Equalization (HE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), CLAHE optimized with Pelican Optimization Algorithm (CLAHE-POA), Global CLAHE (G-CLAHE), k-Caputo Fractional Differential Operator (KCFDO), and the proposed TV-CLAHE. Four experienced observers (two radiologists and two dentists) independently assessed root canal visibility. Subjective evaluation was performed using an own scale inspired by a 5-point Likert scale, and the detection accuracy was compared to the CBCT findings. Quantitative metrics including Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), image entropy, and Structural Similarity Index Measure (SSIM) were calculated to objectively assess image quality. Results: Root canal detection accuracy improved across all enhancement methods, with the proposed TV-CLAHE algorithm achieving the highest performance (93–98% accuracy), closely approaching CBCT-level visualization. G-CLAHE also showed substantial improvement (up to 92%). Statistical analysis confirmed significant inter-method differences (p < 0.001). TV-CLAHE outperformed all other techniques in subjective quality ratings and yielded superior SNR and entropy values. Conclusions: Advanced image enhancement methods, particularly TV-CLAHE, significantly improve root canal visibility in 2D radiographs and offer a practical, low-cost alternative to CBCT in routine dental diagnostics. These findings support the integration of optimized contrast enhancement techniques into endodontic imaging workflows to reduce the risk of missed canals and improve treatment outcomes. Full article