Search Results (14,692)

Over the last few decades, due to improvement in imaging techniques, the increased detection of adrenal incidentalomas is observed. Non-aldosterone producing adrenal adenomas (NAPACAs) often co-exist with second benign or malignant lesions. In the present study, we aimed to assess the presence of second neoplasms, both benign and malignant, in patients with NAPACAs, and to investigate possible correlations with clinical parameters, hormonal characteristics and the emergence of comorbidities. A total of 130 NAPACA patients were included in this single-center retrospective study. In this cohort, 35.4% of NAPACA patients carried any second neoplasm (either benign or malignant) whereas, 26.9% had a second malignant neoplasm. Cortisol levels after 1 mg overnight dexamethasone suppression test (F-ODS) were significantly higher in patients without a second neoplasm (p = 0.02), and this finding was consistent even when categorizing patients with and without malignancies (p = 0.02). In line with this observation, ACTH/F-ODS levels were significantly higher in patients with second malignancies (p < 0.05). Interestingly, the presence of mild autonomous cortisol secretion tended to be lower in patients with second malignancies (p = 0.08). No remarkable differences in the comorbidities of NAPACA patients with and without a second neoplasm were documented. Further prospective studies will be needed to elucidate the role of mild hypercortisolemia on the development of these second tumors in NAPACA patients. Full article

In response to the limitations of traditional single-signal approaches, which fail to comprehensively reflect fault conditions, and the difficulties of existing feature extraction methods in capturing subtle fault patterns in transformer fault diagnosis, this paper proposes an innovative fault diagnosis methodology. Initially, to address common severe faults in excitation transformers, Principal Component Analysis (PCA) is applied to reduce the dimensionality of multi-source feature data, effectively eliminating redundant information. Subsequently, to mitigate the impact of non-stationary noise interference in voiceprint signals, a Deep Belief Network (DBN) optimized using the Hunter–Prey Optimization (HPO) algorithm is employed to automatically extract deep features highly correlated with faults, thus enabling the detection of complex, subtle fault patterns. For temperature and electrical parameter signals, which contain abundant time-domain information, the Random Forest algorithm is utilized to evaluate and select the most relevant time-domain statistics. Nonlinear dimensionality reduction is then performed using an autoencoder to further reduce redundant features. Finally, a multi-classifier model based on Adaptive Boosting with Support Vector Machine (Adaboost-SVM) is constructed to fuse multi-source heterogeneous information. By incorporating a pseudo-label self-training strategy and integrating a working condition awareness mechanism, the model effectively analyzes feature distribution differences across varying operational conditions, selecting potential unseen condition samples for training. This approach enhances the model’s adaptability and stability, enabling real-time fault diagnosis. Experimental results demonstrate that the proposed method achieves an overall accuracy of 96.89% in excitation transformer fault diagnosis, outperforming traditional models such as SVM, Extreme Gradient Boosting with Support Vector Machine (XGBoost-SVM), and Convolutional Neural Network (CNN). The method proves to be highly practical and generalizable, significantly improving fault diagnosis accuracy. Full article

Rose processing into essentials oil is one of the major sectors providing inputs for cosmetics and health/food supplements industry, generating significant amount of wastewater if applying the steam distillation approach. Rose distillation wastewater (RDW), the major by-product of rose processing, still contains a significant load of polyphenolic compounds. This organic burden poses a significant environmental threat for RDW disposal, while, on the other hand, it still contains valuable compounds that could be valorized in the circular economy framework. This study has investigated the possibility of utilizing RDW in various concentrations (10%, 25%, 100% v/v) as a circular tomato growth biostimulant, addressing the existing research gap in the field of circular RDW valorization and its effects on plant growth modulation. LC-MS/MS and antioxidant assays have confirmed a rich antioxidant profile of RDW samples, with gallic acid, quinic acid, quercetin, kaempferol and their glycosides as the most abundant compounds. Tomato germination assays have resulted in significantly improved germination and initial seedling growth parameters when 10% RDW samples PA (‘Pure Aroma’), MA (‘Magic Aroma’) and NA (‘Natural Aroma) had been applied as seed treatment (10 seeds per treatment with each RDW), indicating varying plant growth-promoting potential depending on the RDW chemical composition. The increase in tomato growth parameters compared to the control varied in range 34% (MA)—60% (PA) for root length, 70% (MA)—109% (PA) for shoot length and 43% (MA)—72% (PA) for total seedling length, as well as 43% (MA)—72% (PA) for SVI-I and 40% (NA)—49% (MA) for SVI-II (seedling vigor indices I and II, respectively). Contrarily, the increase in RDW concentration of up to 25% and 100% (v/v) has resulted in inhibition of tomato germination and growth compared to the control (e.g., in range 10–50% (RDW 25%) and 45–87% (RDW 100%) for root length), suggesting the necessity for further optimization of RDW dosage in biostimulant applications. The results of this study open the field of possibilities for further development of circular plant biostimulants based on rose processing by-products, as value-added enrichment of the bio-based solutions portfolio for sustainable agriculture. Full article

Open-weight generative large language models (LLMs) can be freely downloaded and modified. Yet, little empirical evidence exists on how these models are systematically altered and redistributed. This study provides a large-scale empirical analysis of safety-modified open-weight LLMs, drawing on 8608 model repositories and evaluating 20 representative modified models on unsafe prompts designed to elicit, for example, election disinformation, criminal instruction, and regulatory evasion. This study demonstrates that modified models exhibit substantially higher compliance: while an average of unmodified models complied with only 19.2% of unsafe requests, modified variants complied at an average rate of 80.0%. Modification effectiveness was independent of model size, with smaller, 14-billion-parameter variants sometimes matching or exceeding the compliance levels of 70B parameter versions. The ecosystem is highly concentrated yet structurally decentralized; for example, the top 5% of providers account for over 60% of downloads and the top 20 for nearly 86%. Moreover, more than half of the identified models use GGUF packaging, optimized for consumer hardware, and 4-bit quantization methods proliferate widely, though full-precision and lossless 16-bit models remain the most downloaded. These findings demonstrate how locally deployable, modified LLMs represent a paradigm shift for Internet safety governance, calling for new regulatory approaches suited to decentralized AI. Full article

Photon-counting computed tomography (PCCT) represents a major technological innovation compared to conventional CT, offering improved spatial resolution, reduced electronic noise, and intrinsic spectral capabilities. These advances open new perspectives for synergy with radiomics, a field that extracts quantitative features from medical images. The ability of PCCT to generate multiple types of datasets, including high-resolution conventional images, iodine maps, and virtual monoenergetic reconstructions, increases the richness of extractable features and potentially enhances radiomics performance. This narrative review investigates the current evidence on the interplay between PCCT and radiomics in cardiothoracic imaging. Phantom studies demonstrate reduced reproducibility between PCCT and conventional CT systems, while intra-scanner repeatability remains high. Nonetheless, PCCT introduces additional complexity, as reconstruction parameters and acquisition settings significantly may affect feature stability. In chest imaging, early studies suggest that PCCT-derived features may improve nodule characterization, but existing machine learning models, such as those applied to interstitial lung disease, may require recalibration to accommodate the new imaging paradigm. In cardiac imaging, PCCT has shown particular promise: radiomic features extracted from myocardial and epicardial tissues can provide additional diagnostic insights, while spectral reconstructions improve plaque characterization. Proof-of-concept studies already suggest that PCCT radiomics can capture myocardial aging patterns and discriminate high-risk coronary plaques. In conclusion, evidence supports a growing synergy between PCCT and radiomics, with applications already emerging in both lung and cardiac imaging. By enhancing the reproducibility and richness of quantitative features, PCCT may significantly broaden the clinical potential of radiomics in computed tomography. Full article

Accurate pedestrian detection from a robotic perspective has become increasingly critical, especially in complex environments such as crowded and high-density populations. Existing methods have low accuracy due to multi-scale pedestrians and dense occlusion in complex environments. To address the above drawbacks, a dense pedestrian detection network architecture based on YOLOv8n (DPDN-YOLOv8) was introduced for complex environments. The network aims to improve robots’ pedestrian detection in complex environments. Firstly, the C2f modules in the backbone network are replaced with C2f_ODConv modules integrating omni-dimensional dynamic convolution (ODConv) to enable the model’s multi-dimensional feature focusing on detected targets. Secondly, the up-sampling operator Content-Aware Reassembly of Features (CARAFE) is presented to replace the Up-Sample module to reduce the loss of the up-sampling information. Then, the Adaptive Spatial Feature Fusion detector head with four detector heads (ASFF-4) was introduced to enhance the system’s ability to detect small targets. Finally, to accelerate the convergence of the network, the Focaler-Shape-IoU is utilized to become the bounding box regression loss function. The experimental results show that, compared with YOLOv8n, the mAP@0.5 of DPDN-YOLOv8 increases from 80.5% to 85.6%. Although model parameters increase from $3\times {10}^6$ to $5.2\times {10}^6$, it can still meet requirements for deployment on mobile devices. Full article

This paper investigates the existence and multiplicity of positive solutions for a system of generalized Laplacian problems. By analyzing the asymptotic behavior of nonlinearity, we establish conditions for the existence of positive solutions and the presence of multiple positive solutions. Our main results reveal how the norm of the positive solutions behaves as the parameter $\lambda$ approaches 0 or ∞, specifically that the norm tends to either 0 or ∞. Full article

The discharge of strongly acidic industrial wastewater containing high concentration of chloride ions (Cl⁻) has become one of the major environmental challenges faced globally. For the removal of extremely stable Cl⁻ in acidic aqueous conditions, precipitation method possesses major advantages of strong adaptability and simple operation. This study proposed a novel cuprous oxide (Cu₂O) method assisted by ascorbic acid (VC) for the removal of Cl⁻ from strongly acidic wastewater. First, liquid-phase reduction was chosen as the optimal Cu₂O synthesis method based on product purity and composition. Then, parameter optimization results show that increased reagent dosage and acidity significantly enhanced Cl⁻ removal efficiency, while other factors had negligible impacts. After treatment with the sole addition of Cu₂O, the dosed Cu₂O existed in four forms, including cuprous chloride (CuCl), copper ion (Cu²⁺), elemental copper (Cu⁰), and Cu₂O, among which the generation of Cu²⁺ and Cu⁰, through the oxidation and disproportionation of cuprous ion (Cu⁺), served as the main reason for the unsatisfactory efficiency in the removal of Cl⁻. Fortunately, VC is precisely capable of inhibiting the side reactions of Cu⁺, and under the assistance of 0.10 g VC, the removal of Cl⁻ by Cu₂O was greatly improved with the multiple of theoretical reagent dosage decreasing from 12 to 3, the residual concentration of Cu²⁺ decreasing from 1197 to 18.4 mg/L and the residual concentration of Cl⁻ decreasing from 88.4 to 53.8 mg/L, thus validating the feasibility of this method. Full article

Diabetic Macular Edema (DME) poses a significant threat to vision, often leading to permanent blindness if not detected and addressed swiftly. Existing manual diagnostic methods are arduous and inconsistent, highlighting the pressing necessity for automated, accurate, and personalized solutions. This study presents a novel methodology for diagnosing DME and categorizing choroidal neovascularization (CNV), drusen, and normal conditions from fundus images through the application of transfer learning models and bio-inspired optimization methodologies. The methodology utilizes advanced transfer learning architectures, including VGG16, VGG19, ResNet50, EfficientNetB7, EfficientNetV2-S, InceptionV3, and InceptionResNetV2, for analyzing both binary and multi-class Optical Coherence Tomography (OCT) datasets. We combined the OCT datasets OCT2017 and OCTC8 to create a new dataset for our study. The parameters, including learning rate, batch size, and dropout layer of the fully connected network, are further adjusted using the bio-inspired Particle Swarm Optimization (PSO) method, in conjunction with thorough preprocessing. Explainable AI approaches, especially Shapley additive explanations (SHAP), provide transparent insights into the model’s decision-making processes. Experimental findings demonstrate that our bio-inspired optimized transfer learning Inception V3 significantly surpasses conventional deep learning techniques for DME classification, as evidenced by enhanced metrics including the accuracy, precision, recall, F1-score, misclassification rate, Matthew’s correlation coefficient, intersection over union, and kappa coefficient for both binary and multi-class scenarios. The accuracy achieved is approximately 98% in binary classification and roughly 90% in multi-class classification with the Inception V3 model. The integration of contemporary transfer learning architectures with nature-inspired PSO enhances diagnostic precision to approximately 95% in multi-class classification, while also improving interpretability and reliability, which are crucial for clinical implementation. This research promotes the advancement of more precise, personalized, and timely diagnostic and therapeutic strategies for Diabetic Macular Edema, aiming to avert vision loss and improve patient outcomes. Full article

Repurposing end-of-life hydrocarbon wells for geothermal energy generation offers a cost-effective and sustainable strategy to expand low-carbon energy deployment while utilizing existing infrastructure. Fracture-connected horizontal oil and gas well pairs present a promising configuration for enhancing heat transfer in low-permeability reservoirs. Existing modeling approaches, however, lack the ability to simulate transient heat conduction from rock to fluid in such complex fracture pathways. This work develops a mathematical model that couples time-dependent heat conduction in the reservoir rock with convective heat transport within the fractures. This model enables prediction of heat energy productivity of converted well pairs by accounting for realistic boundary conditions and operational parameters. In applying the model to a representative shale gas field in Louisiana, key factors affecting fluid temperature and thermal power output, including fracture geometry, fluid flow rate, and wellbore insulation, were considered. The results demonstrate the feasibility and sensitivity of converting hydrocarbon wells into geothermal energy production, providing critical insight for optimizing such conversions to support the increased demand for clean, sustainable energy. Full article

Glioblastoma is the most aggressive and treatment-resistant form of primary brain tumors, characterized by rapid invasion and a poor prognosis. Its complex behavior continues to challenge both clinical interventions and research efforts. Mathematical modeling provides a valuable approach to unraveling a tumor’s spatiotemporal dynamics and supporting the development of more effective therapies. In this study, we built on the existing literature by refining and adapting mathematical models to better capture glioblastoma infiltration, using a partial differential equation (PDE) framework to simulate how cancer cell density evolves across both time and space. In particular, the role of cell diffusion and growth in tumor progression and their limitations due to cell crowding and competition were investigated. Experimental data of glioblastoma taken from the literature were exploited for the identification of the model parameters. The improved data reproduction when the limitations of cell diffusion and growth were taken into account proves the relevant impact of the considered mechanisms on the spread of the tumor population, which underscores the potential of the proposed framework. Full article

Critical details in both bright and dark regions are frequently lost in high dynamic range (HDR) images when they are displayed on low dynamic range (LDR) devices. To mitigate this issue, tone mapping operators (TMOs) have been developed to convert HDR images into LDR representations while maintaining perceptual quality. However, it is challenging to effectively balance various key visual attributes, such as naturalness and structural fidelity. To overcome this limitation, a two-stage Bayesian optimization approach was proposed in this work to enhance the perceptual quality of tone-mapped images across multiple evaluation metrics. The first stage adaptively optimizes TMQI parameters to capture image-specific perceptual characteristics, while the second stage refines the tone mapping function to further improve detail preservation and visual realism. Extensive experiments using three distinct HDR benchmark datasets were conducted, indicating that the proposed method generally performs better than the existing tone mapping techniques across most evaluated metrics, including TMQI, Naturalness, and Structural Fidelity. Our adaptive approach offers a robust and effective solution for optimizing HDR image conversion, resulting in a significantly improved perceptual quality compared to traditional methods. Full article

The eco-toxicological impacts caused by organic pollutants in aquatic environments have emerged as a global concern in recent decades, resulting from the potential hazards they present to ecosystem integrity and human health. Decorating active components on mesoporous silica is considered a popular approach by which to obtain synergistic effects in persulfate activation for sustainable water decontamination. However, at present there has been no review focusing solely, specifically and comprehensively on this field. Therefore, this paper places an emphasis on the latest research progress on the synthesis and physicochemical properties of functionalized mesoporous silica materials as well as their catalytic performance. The preparation methods included co-condensation, impregnation, grinding–calcination, hydrothermal synthesis and chemical precipitation, and their synthesis parameters played a major role in the characterization of materials, thereby affecting pollutant elimination. Metal redox cycles, nonmetallic activation and confinement effects contributed to persulfate activation. Targeted pollutants were degraded via radical pathways, non-radical pathways, or a combination of the two. The effects and causes of operational conditions (catalyst and persulfate dosage, initial pollutant concentration, temperature, initial pH, co-existing anions, and natural organic matter) varied across the degradation systems, and they were categorized and summarized in detail. Furthermore, functionalized mesoporous silica presented excellent reusability, stability and applicability in practical application. Finally, current potential directions for further research and sustainable development in this field were also prospected. This critical analysis aims to fuel the evolution of functionalized mesoporous silica catalyst-driven persulfate system application in water treatment and to bridge prevailing knowledge gaps. Full article

An MC (minor component) is usually referred to as the noise part of a time-varying signal. Extracting multiple MCs from an input signal is very useful in many practical applications. Compared with single MC estimating algorithms and subspace tracking algorithms, multiple MC extraction algorithms have a wider range of applications and greater research significance. To address the existing issues with multiple MC extraction algorithms, such as the need to estimate parameters in advance and with numerous constraints, this paper proposes a novel multiple MC extraction algorithm by adding a diagonal weighted matrix to the Möller algorithm. The proposed algorithm’s fixed points are analyzed using ODE (ordinary differential equation) methods, and it is demonstrated that the algorithm achieves stable convergence only when the weight matrix converges to the desired MCs of the signal. The simulation results illustrate the effectiveness of the proposed algorithm. Full article

We study stochastic production planning in capacity-constrained manufacturing systems, where feasible operating states are restricted to a convex safe-operating region. The objective is to minimize the total cost that combines a quadratic production effort with an inventory holding cost, while automatically halting production when the state leaves the safe region. We derive the associated Hamilton–Jacobi–Bellman (HJB) equation, establish the existence and uniqueness of the value function under broad conditions, and prove a concavity property of the transformed value function that yields a robust gradient-based optimal feedback policy. From an operations perspective, the stopping mechanism encodes hard capacity and safety limits, ensuring bounded risk and finite expected costs. We complement the analysis with numerical methods based on finite differences and illustrate how the resulting policies inform real-time decisions through two application-inspired examples: a single-product case calibrated with typical process-industry parameters and a two-dimensional example motivated by semiconductor fabrication, where interacting production variables must satisfy joint safety constraints. The results bridge rigorous stochastic control with practical production planning and provide actionable guidance for operating under uncertainty and capacity limits. Full article