Search Results (233)

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Sugarcane is a high-value crop in Egypt, yet weed communities in the understudied Upper Egypt region have not been systematically characterized. This study provides a comprehensive analysis of weed floristic composition, phytogeographical affinities, and the edaphic and canopy light factors governing vegetation structure across contrasting Nile Valley clay and reclaimed desert lands in Qena Governorate. Fourteen stands were surveyed during the 2024/2025 sugarcane growing season, recording 110 species from 33 families (68 annuals and 42 perennials), which were dominated by Poaceae, Asteraceae, Fabaceae, Euphorbiaceae, and Amaranthaceae (54.6% of the flora recorded). Therophytes were the most abundant life form (60.9%), and 51.8% of species belonged to Neotropical, Palaeotropical, Cosmopolitan, and Pantropical chorotypes. Diversity indices showed high and balanced species diversity, with no dominance by any single species. Seasonal variation showed that species richness peaked in spring, decreased through summer and autumn, and correlated with light intensity under the canopy. TWINSPAN identified four vegetation groups, which were merged into three primary vegetation groups (A, B, and C) via DCA and CCA ordinations and linked to microhabitats shaped by elevation and soil physicochemical properties. CCA revealed that Group C (stands in the Nile Riverbank lands) had the highest diversity, which was associated with organic matter, clay, and field capacity. In contrast, Group A (stands of reclaimed desert land) had low richness linked to high levels of Total Dissolved Solids (TDS), Electrical Conductivity (EC), Na, K, Mg, CaCO₃, and sandy soils. Group B (stands of Nile clay lands) was an intermediate transitional community between groups A and C. These findings establish edaphic factors as the primary determinant of weed community structure, with salinity as the critical constraint in reclaimed lands and seasonal light variation as a secondary diversity filter. Full article

This study presents a proof-of-concept investigation into the use of proximal policy optimization (PPO), a deep reinforcement learning (DRL) algorithm, for estimating the remaining useful life (RUL) of rolling element bearings. Although DRL has shown growing promise in prognostics, existing applications have predominantly relied on off-policy deterministic actor–critic methods such as deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3); the suitability of on-policy clipped-objective methods such as PPO for this task remains comparatively unexplored. To address this gap, statistical time-domain features are extracted from raw vibration signals and used as input to train a PPO agent with an actor–critic architecture, in which the actor network predicts RUL values and the critic network evaluates prediction quality through state-value estimation. A preprocessing pipeline comprising feature extraction, normalization, and sliding-window segmentation is developed, and the PPO framework incorporates generalized advantage estimation (GAE), a custom-designed reward function, and a policy-clipping mechanism to support stable training. The method is evaluated on a representative bearing (Bearing 2_1) from the XJTU-SY run-to-failure dataset using a chronological train/test split, and benchmarked against long short-term memory (LSTM) networks, multilayer perceptrons (MLPs), and a naive linear regression baseline. Performance is assessed using root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), and a domain-specific asymmetric scoring function that penalizes late predictions more heavily than early ones. Experimental results show that the PPO-based model produces more stable and operationally favourable RUL estimates than the supervised baselines on the unseen late-degradation segment, particularly in the critical end-of-life region. The findings support PPO as a viable on-policy DRL formulation for bearing RUL prediction and motivate further validation across multiple bearings and operating conditions, identified here as essential future work. Full article

Groundwater resources in the Kingdom of Saudi Arabia (KSA) are important for meeting the needs of human communities, agriculture, and industry. In Western KSA, groundwater from coastal aquifers is an essential resource that complements desalinated seawater. Therefore, ensuring the quality and contamination of groundwater has emerged as a critical priority for preserving water security. The aim of this research is to evaluate the groundwater quality and its vulnerability to contamination within the Wadi Marawani Basin. To achieve this aim, water quality indices (WQIs), the DRASTIC model, and machine learning (ML) algorithms were employed alongside a Geographic Information System (GIS). The results of the chemical analysis of 64 water samples were used in these assessments. Furthermore, several input parameters were evaluated using the DRASTIC model to estimate the DRASTIC index (DI) and generate a groundwater vulnerability map. Three ML algorithms—specifically, a Multilayer Perceptron (MLP), a Random Forest (RF), and a Decision Tree (DT)—were utilized to forecast WQIs such as the total dissolved solids (TDS) and sodium adsorption ratio (SAR), in addition to the DRASTIC index (DI). The results revealed that around 36% of the samples were classified as fresh water (<1000 mg/L). The SAR ranged from 1.10 to 32.50, indicating that most samples were suitable for irrigation. Approximately 22% of the basin was classified as demonstrating high vulnerability, whereas about 78% demonstrated low-to-moderate vulnerability. Assessment of the ML models showed high predictive accuracy for the TDS, SAR, and DI. The MLP-Vul. model attained an R² value of 1.00 and RMSE value of 0.01, the RF-Vul. model achieved an R² of 0.94 and RMSE of 3.17, and the DT-Vul. model attained an R² of 0.92 and RMSE of 3.57. Although there was a minor increase in RMSE across all models during the testing phase, their predictive performance remained clear. Full article

The dynamic spreading behavior of droplets impacting surfaces with different wettability is a critical hydrodynamic issue in industrial applications such as inkjet printing, spray cooling, and pesticide spraying. The maximum spreading diameter coefficient (${\beta }_{max}$) is the key parameter characterizing this process. Existing theoretical models often overlook the gravitational potential energy of droplets, resulting in significant discrepancies between the calculated viscous dissipation times and experimental results, which compromises the prediction accuracy. In this study, we incorporated gravitational potential energy into the energy balance system based on the principle of system energy conservation. We introduced the Bond number (Bo) to characterize the coupling effect of gravity and surface tension. By fitting experimental data, we corrected the viscous dissipation time, obtaining $t_c$ = 3.17$d_0/v_0$, which improves the reliability of dissipated energy calculation. Using Young’s equation and the Cassie model, we derived a fourth-order ${\beta }_{max}$ prediction model that includes the Weber number (We), Reynolds number (Re), contact angle (${\theta }_c$), and Bo number. The results show that regulating the impact height and droplet diameter will affect the trend of the maximum spreading coefficient model curve: the crossover Weber numbers are 41.519 and 41.530 for different liquid viscosities under the specific experimental and modeling conditions of this study. Below these thresholds, the maximum spreading diameter coefficients are more sensitive to impact height (inertial and kinetic-energy) than to droplet diameter (volume, mass, surface energy, gravitational potential energy, Bond number). Above the critical value, the influence of droplet diameter on the maximum spreading diameter coefficient becomes more pronounced. These intersections reflect the balance between size-dependent effects and impact-inertia-related effects under specific conditions, rather than universal physical thresholds. Compared with selected classical models, the proposed model shows better consistency with experimental data and provides improved prediction for the maximum spreading coefficient of water droplets on surfaces with different wettability. This study supplements the perspective of energy analysis for the modeling of droplet impact dynamics, and can provide a basis for the theoretical optimization of spray systems and interfacial fluid control. Full article

Steel pipelines for oil and gas transportation serve as the lifeline of energy conveyance, and their long-term safe operation constitutes a crucial safeguard for energy security. Nevertheless, in complex service environments, local defects formed on the inner pipe wall due to medium corrosion have emerged as a prominent hidden danger endangering pipeline integrity. Accurate evaluation of the residual strength of pipelines with corrosion defects is not only the technical foundation for ensuring the safe operation of pipelines, but also the key basis for formulating scientific maintenance strategies and prolonging the service life of pipelines. Taking three grades of steel pipelines (X52, X65 and X80), which represent the typical strength grades commonly used in long-distance oil and gas transmission pipelines, as the research objects, this paper establishes a three-dimensional finite element model of single-point uniform corrosion defects considering the nonlinear material behavior, and systematically investigates the influence laws of geometric parameters (depth, length and width) of corrosion defects on the failure pressure of pipelines under the action of monotonic internal pressure load. The accuracy of the proposed finite element model is verified by comparison with the test data from thirteen groups of full-scale burst experiments. On the basis of parametric analysis results, an explicit and high-precision predictive model for failure pressure is developed. The research findings reveal that corrosion depth acts as the dominant factor affecting pipeline failure pressure with a distinctly nonlinear influence characteristic: the load-bearing capacity of pipelines drops drastically when the relative depth d/t exceeds 0.6, where d is the corrosion depth and t is the pipe wall thickness. There exists a critical value for the impact of corrosion length, beyond which its weakening effect on failure pressure tends to level off. Within the commonly encountered engineering range (20~100°), corrosion width exerts a negligible influence on pipeline failure pressure and thus can be overlooked in engineering evaluation. In comparison with conventional industry assessment methods such as ASME B31G, DNV RP-F101, PCORRC and SY/T 6151, the newly established predictive model features higher prediction accuracy and broader applicability, which provides on-site engineers with a powerful theoretical tool and practical formula for the rapid and accurate evaluation of the residual strength of corroded pipelines. Full article

The mixing valve is a key component of an ultra-high-pressure premixed abrasive waterjet system, in which the abrasive–water mixing uniformity plays a decisive role in determining the erosion and cutting performance of the jet. The geometric parameters of the mixing chamber inside the valve are therefore critical factors affecting this uniformity. In this study, the liquid–solid two-phase flow within the mixing chamber was numerically investigated using the Eulerian k–ε turbulence model coupled with the Fluent–Rocky DEM approach. Single-factor simulations were first conducted to identify the effective ranges of key structural parameters influencing the mixing performance. Subsequently, a response surface model was established to describe the relationship between the mixing efficiency (ME) and four critical chamber parameters, namely the throat diameter (TD), throat length (TL), abrasive inlet pipe diameter (AD), and the distance between the throat exit and the abrasive inlet pipe center (TE). Based on this model, the optimal structural parameters of the mixing chamber were determined. The results indicate that when TD = 4 mm, TL = 12 mm, AD = 10 mm, and TE = 7 mm, the simulated ME reaches 34.40% ± 0.49%, which is in close agreement with the predicted value of 34.57%. Experimental validation conducted on a premixed abrasive waterjet test rig shows that the mean absolute relative error between the simulated and measured ME values is 7.54%, which is below the 10% threshold, confirming the reliability and accuracy of the numerical model. Full article

The ionosphere is the ionized region of the atmosphere, extending roughly from 60 km to 1000 km in altitude. During flares, the near-Earth space is subjected to high-energy X-ray and EUV (extreme ultraviolet radiation) radiation, which also impacts the ionosphere. The changes in the ionospheric parameters measured by ionosondes, namely the f_min (minimum frequency) and foF2 (F2-layer ordinary-mode critical frequency) values, were examined during solar flares that occurred in geomagnetically quiet conditions (Dst (Disturbance Storm Time index) > −40 nT, Kp (planetary K-index) < 4). The necessary data were obtained by manually evaluating ionograms recorded by the Czech DPS4D ionosonde at Pruhonice (PQ052). The degree of variation was compared to quiet reference days, allowing for the determination of the deviations in the required values (df_min, dfoF2). The time series of the deviations were investigated. Furthermore, the relationship between the deviations and a “geoeffectiveness” parameter of the solar flare was also examined. The X-ray flux, the solar zenith angle of the station at the time of the event, and the position of the flare on the solar disk were also taken into account for the determination of the “geoeffectiveness” parameter. A positive correlation was observed between df_min and the geoeffectiveness parameter of the flare, which was more significant than the correlation between the dfoF2 and the geoeffectiveness parameter. Full article

This study investigated the association between serum hypoxia-inducible factor 1-alpha (HIF-1α) levels and clinical severity in patients with coronavirus disease 2019 (COVID-19). This prospective case–control study included 91 patients with confirmed COVID-19, of whom 51 had severe-critical disease with pneumonia and 40 had mild disease without pneumonia, as well as 39 healthy controls. Vital signs, including body temperature, pulse rate, respiratory rate, oxygen saturation, and blood pressure, were recorded. Biochemical parameters such as complete blood count, D-dimer, ferritin, creatinine, urea, and high-sensitivity cardiac troponin T were analyzed. Serum HIF-1α levels were measured using ELISA. Median HIF-1α levels were 132.9 pg/mL (IQR: 131.7–138.0) in the severe-critical disease group, 137.35 pg/mL (IQR: 131.65–152.75) in the mild disease group, and 136.6 pg/mL (IQR: 132.2–162.2) in controls. Significant differences were observed between groups (p = 0.012). ROC analysis showed a discriminatory performance for HIF-1α, with a sensitivity of 89.01% and specificity of 35.90% at a cut-off value of ≤154 pg/mL for distinguishing mild disease from controls, and a sensitivity of 86.3% and specificity of 42.5% at a cut-off value of ≤141.1 pg/mL for distinguishing severe-critical disease from mild disease. HIF-1α levels decreased with increasing disease severity. HIF-1α levels were found to be associated with disease severity; however, the low AUC values indicate that this parameter has limited discriminative ability for clinical use when used alone. Full article

To address the issue of traffic accidents caused by livestock crossing roads on grassland highways, this paper proposes an adaptive cruise control method for unmanned aerial vehicles (UAVs) based on an ensemble Q-network and a Soft Actor-Critic (SAC) with delayed policy updates, namely the ED-SAC algorithm. Building upon the standard SAC framework, this method introduces multiple independent Critic networks to form an ensemble Q-network, and employs a random subset minimization strategy during the calculation of target Q-values to mitigate policy bias resulting from overestimated values; simultaneously, a delayed policy update mechanism decouples the optimization processes of the Actor and Critic networks, thereby enhancing training stability and control robustness. Using the PyBullet simulation platform, this paper constructs a UAV inspection scenario on grassland roads and designs three typical test tasks: infinite loop, grid scan and spiral trajectories, to conduct comparative validation of the PPO, TD3, SAC and ED-SAC algorithms. Experimental results demonstrate that, under disturbance-free conditions, ED-SAC achieves the highest mission success rate and the lowest tracking error across all three trajectory scenarios, with an average tracking error as low as 0.27 m and a mission success rate as high as 98.7%. Under continuous random external disturbances, ED-SAC still maintains high trajectory tracking accuracy and attitude control stability, with a mission success rate reaching up to 96.2%. The results demonstrate that the proposed ED-SAC algorithm can effectively enhance the trajectory tracking accuracy, training stability and anti-disturbance capability of UAVs in complex grassland road inspection scenarios, providing a reliable intelligent control method for active grassland road inspection and traffic safety early warning. Full article

Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. Full article

Coastal zones are sensitive areas where marine and terrestrial systems interact. Seawater intrusion, a typical coastal geological hazard, poses a serious threat to groundwater resources. This study takes the northeastern coastal zone of Laizhou Bay, a representative area affected by seawater intrusion in China and relying on groundwater for agricultural irrigation, as the research area and integrates hydrochemical analysis, irrigation hazards assessment, and a hybrid-weighted Irrigation Water Quality Index (IRWQI) to reveal seasonal changes in groundwater irrigation suitability. Results show that (1) groundwater hydrochemical facies exhibits a shift from HCO₃-Ca type in the rainy season to Cl-Ca·Mg type in the dry season, with TDS and Cl⁻ increasing coastward. The Huangshui River estuary displays a striking seasonal reversal: minimally affected during the rainy season, it becomes moderately severely intruded in the dry season, owing to the contrast between the perennial Huangshui River and adjacent ephemeral streams. (2) Salinity hazard (EC, PS) is the most immediate seawater intrusion consequence, with dry-season PS expanding inland and rendering estuarine groundwater unsuitable for irrigation. Although sodium and magnesium hazards remain below critical thresholds, strong Cl⁻–Na⁺ and Cl⁻–Mg²⁺ correlations in the dry season signal emerging risks. Bicarbonate hazard declines via conservative mixing with Ca·Mg-rich seawater, masking other hazards. Permeability hazard exhibits moderate seasonal deterioration. (3) Spatially, the IRWQI values are systematically lower during the dry season, with contiguous severe-restriction zones emerging along the Huangshui, Yongwen, and Jiehe River estuaries. These findings indicate that under reduced recharge, seawater intrusion dominates groundwater irrigation quality, triggering a seasonal tipping point. The study provides a scientific basis for adaptive coastal groundwater management. Full article

Deep reinforcement learning (DRL) for autonomous mobile robot navigation faces several inherent limitations. The stochastic nature of actions generated by DRL policies can undermine performance consistency, while inefficient exploration frequently delays the learning process or prevents the discovery of optimal solutions. This research aims to enhance the robustness of path planning by addressing these challenges. To achieve this goal, we propose a hybrid approach that integrates the flexible decision-making capabilities of deep reinforcement learning with the stability of traditional path planning. The proposed model adopts the Twin Delayed Deep Deterministic Policy Gradient (TD3) network as its base. Notably, we pre-process LiDAR point cloud data to extract only essential features for the state representation, thereby preventing performance degradation from high-dimensional inputs and improving computational efficiency. Our model optimizes the learning process through two core strategies. First, it prioritizes experience data generated during training based on negative rewards, guiding the model to learn more frequently from critical failures rather than redundant successes. Second, it dynamically compares the action proposed by the TD3 network with a goal-oriented action from a classical path-planning algorithm in real time. By selecting the action with the higher estimated value, the model guides the policy toward a stable and effective trajectory from the earliest stages of training. To validate the efficacy of our approach, we conducted simulation-based experiments comparing the performance of the proposed model with existing reinforcement learning networks. To ensure statistical significance and mitigate the impact of random initialization, all reported results are averaged over 10 independent runs with different random seeds. The results quantitatively demonstrate that our model achieves significantly higher and more stable reward values, confirming a robust improvement in the path-planning process. Full article

Background/Objectives: The clinical management of bladder cancer is severely impeded by high recurrence rates and the rapid emergence of chemoresistance, necessitating the discovery of novel therapeutic agents with distinct mechanisms of action. Dehydrodiisoeugenol (DHE), a bioactive neolignan, exhibits potent anti-tumor efficacy, yet its direct molecular targets and mode of action remain elusive. Methods: To deconvolute the mechanism of DHE, we integrated a phenotypic screening approach using 2D cell lines and 3D patient-derived organoids with a chemoproteomics-based activity-based protein profiling (ABPP) strategy. We synthesized a functionalized photoaffinity probe to capture the specific interactome of DHE under physiological conditions and validated targets via cellular thermal shift assays (CETSA), quantitative mass spectrometry, and 100 ns molecular dynamics (MD) simulations. Results: DHE exhibited potent dose-dependent cytotoxicity in bladder cancer cells, with IC50 values of 39.23 μM in T24 and 34.58 μM in 5637 cells. In 3D patient-derived organoids, DHE significantly reduced viability (p < 0.0001). Using a dual-filtering ABPP strategy, we identified 65 high-confidence candidate targets, prioritizing PTPN1 (PTP1B) as the primary functional interactor. Comparative molecular docking and 100 ns MD analyses showed that multiple stereoisomers of DHE could adopt plausible PTPN1-binding modes. Mechanistically, organoid proteomics indicated that DHE engagement with PTPN1 disrupts ER membrane homeostasis, thereby modulating the PI3K-Akt signaling axes. Conclusions: These findings establish PTPN1 as a critical druggable vulnerability in bladder cancer and define the molecular basis for the therapeutic potential of DHE. This study highlights the power of combining chemoproteomics with physiological 3D models to accelerate the translation of natural products into precision cancer therapies. Full article

Background and Objectives: Prostate cancer is the second most common cancer in men worldwide, with 1,466,680 new cases and 396,792 deaths reported in 2022. Accurate preoperative grading is critical, as the grade assessed on biopsy cores may be underestimated compared to radical prostatectomy specimens. The aim of this study was to assess the ability of quantitative diffusion parameters derived by the standard monoexponential model (ADC—apparent diffusion coefficient) and kurtosis model (Dapp—apparent diffusion coefficient corrected for non-Gausion behavior and K-kurtosis) to predict Gleason Grade Group (GG) upgrading from transrectal ultrasound-guided (TRUS) biopsy to radical prostatectomy within each GG. Materials and Methods: This retrospective study included 128 patients with prostate cancer who underwent systematic TRUS biopsies and multiparametric magnetic resonance imaging (mpMRI) at 3T before prostatectomies between 2017 and 2021. Mean values of quantitative diffusion parameters (ADC, Dapp, K) were compared between upgraded and non-upgraded cohorts within each Grade Group obtained at biopsy. Results: Significant differences in ADC and K values were found between upgraded and non-upgraded lesions in GG1 and GG2 cohorts at biopsy, with lower ADCs and higher K values indicating a higher likelihood of upgrading. In GG1, ADC demonstrated an AUC of 0.762 (p < 0.05) and K an AUC of 0.846 (p < 0.05). In GG2, ADC showed an AUC of 0.814 (p < 0.001) and K an AUC of 0.755 (p < 0.001). No significant differences were observed in GG3 and GG4 cohorts. Conclusions: Quantitative diffusion parameters—particularly ADC and kurtosis (K)—demonstrated significant predictive value for Grade Group upgrading in patients with biopsy-proven GG1 (AUC: K = 0.846, ADC = 0.762) and GG2 (AUC: ADC = 0.814, K = 0.755, D = 0.810) prostate cancer. These findings suggest that incorporating quantitative DWI parameters into preoperative assessments may improve risk stratification and support clinical decision-making, particularly regarding the selection of patients for active surveillance. Validation in larger, multicenter cohorts is warranted. Full article