Search Results (1,123)

Background: Cancer poses a significant health risk to humans, with prostate cancer (PCa) being the second most common and deadly form among men, following lung cancer. Each year, it affects over a million individuals and presents substantial diagnostic challenges due to variations in tissue appearance and imaging quality. In recent decades, various techniques utilizing Magnetic Resonance Imaging (MRI) have been developed for identifying and classifying PCa. Accurate classification in MRI typically requires the integration of complementary feature types, such as deep semantic representations from Convolutional Neural Networks (CNNs) and handcrafted descriptors like Histogram of Oriented Gradients (HOG). Therefore, a more robust and discriminative feature integration strategy is crucial for enhancing computer-aided diagnosis performance. Objectives: This study aims to develop a multi-stage hybrid learning model that combines deep and handcrafted features, investigates various feature reduction and classification techniques, and improves diagnostic accuracy for prostate cancer using magnetic resonance imaging. Methods: The proposed framework integrates deep features extracted from convolutional architectures with handcrafted texture descriptors to capture both semantic and structural information. Multiple dimensionality reduction methods, including singular value decomposition (SVD), were evaluated to optimize the fused feature space. Several machine learning (ML) classifiers were benchmarked to identify the most effective diagnostic configuration. The overall framework was validated using k-fold cross-validation to ensure reliability and minimize evaluation bias. Results: Experimental results on the Transverse Plane Prostate (TPP) dataset for binary classification tasks showed that the hybrid model significantly outperformed individual deep or handcrafted approaches, achieving superior accuracy of 99.74%, specificity of 99.87%, precision of 99.87%, sensitivity of 99.61%, and F1-score of 99.74%. Conclusions: By combining complementary feature extraction, dimensionality reduction, and optimized classification, the proposed model offers a reliable and generalizable solution for prostate cancer diagnosis and demonstrates strong potential for integration into intelligent clinical decision-support systems. Full article

This study presents results on a thermoplastic polymer composite containing 95 wt.% steel powder, processed into samples using Fused Deposition Modeling (FDM) technology. Such a high filler loading exceeds the values commonly reported in the literature. Samples were printed with different build orientations (0° and 90°) to evaluate the influence of printing direction on the tensile behavior of the material. Tensile tests were conducted to determine the effect of printing orientation on the composite’s strength. Additionally, surface structure analysis was performed using a contact profilometer, and wettability was evaluated by measuring the contact angle with a tensiometer. Dimensional measurements were also carried out using a digital caliper. The obtained results allowed determination of the relationship between printing orientation and the tensile and surface-related properties of the analyzed composite material. Full article

Coordinate measuring techniques are essential for determining the diameter and roundness of circular features, yet measurements based on short arc segments remain highly sensitive to form deviations, sampling strategy, and instrument error. With the increasing demands placed on metrology, the choice of suitable data calculation and analysis methods becomes crucial for reliable interpretation of results. This study presents a simulation-based analysis of diameter evaluation for an oval-shaped profile, considering different levels of form deviation, three orientations of the contour peak, and the presence of random measurement error. The analysis includes both complete contours and partial arc segments and evaluates four reference-circle-fitting methods (LSCI, MZCI, MICI, MCCI). The results show that shortening the measured arc increases the influence of local geometric irregularities and random error on the obtained diameter values. The fitting methods behave differently under these conditions: LSCI is strongly affected by the orientation of the deformation peak, while MICI and MCCI provide reliable results only for sufficiently long arcs. MZCI consistently delivers the most stable performance when only fragmentary data are available. These findings indicate that both the choice of reference method and the selection of an adequate arc length are crucial for ensuring reliable and meaningful diameter assessment. Full article

Background and Clinical Significance: Adults suffering from cerebellar metastases are often at high risk for rapid deterioration of their neurological status because the posterior fossa has limited compliance and the location of these metastases are close to the brain stem and important cerebrospinal fluid (CSF) pathways. In this paper, we present a longitudinal, patient-centered report on the history of an elderly individual who suffered from cognitive comorbidities and experienced a sudden loss of function in her cerebellum. Our goal in reporting this case is to provide a comparison between the patient’s pre-operative and post-operative neurological examinations; the imaging studies she had before and after surgery; the surgical techniques utilized during her operation; and the outcome of her post-operative course in a way that will be helpful to other patients who have experienced a similar situation. Case Presentation: We report the case of an 80-year-old woman who initially presented with progressive ipsilateral limb-trunk ataxia, impaired smooth pursuit eye movement, and rebound nystagmus, but preserved pyramidal and sensory functions. Her quantitative bedside assessments included some of the components of the Scale for the Assessment and Rating of Ataxia (SARA), and a National Institute of Health Stroke Scale (NIHSS) score of 3. These findings indicated dysfunction of the left neocerebellar hemisphere and possible dentate nucleus involvement. The patient’s magnetic resonance imaging (MRI) results demonstrated an expansive mass with surrounding vasogenic edema and marked compression and narrowing of the exits of the fourth ventricle which placed the patient’s CSF pathways at significant risk of occlusion, while the aqueduct and inlets were patent. She then underwent a left lateral suboccipital craniectomy with controlled arachnoidal CSF release, preservation of venous drainage routes, subpial corticotomy oriented along the lines of the folia, stepwise internal debulking, and careful protection of the cerebellar peduncles and dentate nucleus. Dural reconstruction utilized a watertight pericranial graft to restore the cisternal compartments. Her post-operative intensive care unit (ICU) management emphasized optimal venous outflow, normoventilation, and early mobilization. Histopathology confirmed the presence of metastatic carcinoma, and staging suggested that the most likely source of the primary tumor was the lungs. Immediately post-operation, computed tomography (CT) imaging revealed a smooth resection cavity with open foramina of Magendie and Luschka, intact contours of the brain stem, and no evidence of bleeding or hydrocephalus. The patient’s neurological deficits, including dysmetria, scanning dysarthria, and ataxic gait, improved gradually during the first 48 h post-operatively. Upon discharge, the patient demonstrated an improvement in her limb-kinetic subscore on the International Cooperative Ataxia Rating Scale (ICARS) and demonstrated independent ambulation. At two weeks post-operation, CT imaging revealed decreasing edema and stable cavity size, and the patient’s modified Rankin scale had improved from 3 upon admission to 1. There were no episodes of CSF leakage, wound complications, or new cranial nerve deficits. A transient post-operative psychotic episode that was likely secondary to her underlying Alzheimer’s disease was managed successfully with short-course pharmacotherapy. Conclusions: The current case study demonstrates the value of anatomy-based microsurgical planning, preservation of venous and CSF pathways, and targeted peri-operative management to facilitate rapid recovery of function in older adults who suffer from cerebellar metastasis and cognitive comorbidities. The case also demonstrates the importance of early multidisciplinary collaboration to allow for timely initiation of both adjuvant stereotactic radiosurgery and molecularly informed systemic therapy. Full article

This study investigates the heat transfer coefficient (HTC) during flow condensation inside smooth inclined tubes, analyzing the combined effects of flow orientation, fluid properties and flow characteristics on the thermal performance. The literature review indicates that the channel inclination effect on the HTC remains insufficiently understood, highlighting the need for further investigation. Thus, a comprehensive experimental database comprising 4944 data points was compiled from 24 studies, including all flow directions, from upward, to horizontal, downward, and intermediate orientations. The study reveals that the influence of flow inclination on the HTC can be ruled by a criterion based on the liquid film thickness Froude number, ${\mathrm{Fr}}_{\delta }$. At ${\mathrm{Fr}}_{\delta }$ > 4.75, the effect of flow inclination becomes negligible, while under ${\mathrm{Fr}}_{\delta }$ < 4.75, the inclination can have a considerable effect on the HTC. The experimental data show that at low Froude numbers, upward flow typically exhibits higher HTC compared to downward flow, attributed to enhanced interfacial turbulence caused by opposing gravitational and shear forces. In contrast, under vertical downward flow, the annular pattern is more prominent, with reduced interfacial disturbances, limiting HTC performance. The compiled experimental database for inclined channels was compared against an update list of prediction methods, including seven correlations incorporating the inclination angle as an input parameter. Additionally, a new simple correction factor including the effect of inclined tubes was proposed based on the flow inclination angle and on the liquid film thickness Froude number. The proposed correction factor improved the prediction of well-ranked correlations in the literature by over 20% for stratified flow pattern conditions and by more than 5% for low Froude number values. These findings present new insights into how tube inclination can affect heat transfer in a two-phase flow. Full article

To address the limitations of existing fault diagnosis methods for excitation dry-type transformers, such as inadequate utilization of multi-axis vibration data, low recognition accuracy under complex operational conditions, and limited computational efficiency, this paper presents a lightweight fault diagnosis approach based on the fusion of multi-channel vibration signals and visual features. Initially, a multi-physics field coupling simulation model of the excitation dry-type transformer is developed. Vibration data collected from field-installed three-axis sensors are combined to generate typical fault samples, including normal operation, winding looseness, core looseness, and winding eccentricity. Due to the high dimensionality of vibration signals, the Symmetrized Dot Pattern (ISDP) method is extended to aggregate and map time- and frequency-domain information from the x-, y-, and z-axes into a two-dimensional feature map. To optimize the inter-class separability and intra-class consistency of the map, Particle Swarm Optimization (PSO) is employed to adaptively adjust the angle gain factor (η) and time delay coefficient (t). Keypoint descriptors are then extracted from the map using the Oriented FAST and Rotated BRIEF (ORB) feature extraction operator, which improves computational efficiency while maintaining sensitivity to local details. Finally, an efficient fault classification model is constructed using an Adaptive Boosting Support Vector Machine (Adaboost-SVM) to achieve robust fault mode recognition across multiple operating conditions. Experimental results demonstrate that the proposed method achieves a fault diagnosis accuracy of 94.00%, outperforming signal-to-image techniques such as Gramian Angular Field (GAF), Recurrence Plot (RP), and Markov Transition Field (MTF), as well as deep learning models based on Convolutional Neural Networks (CNN) in both training and testing time. Additionally, the method exhibits superior stability and robustness in repeated trials. This approach is well-suited for online monitoring and rapid diagnosis in resource-constrained environments, offering significant engineering value in enhancing the operational safety and reliability of excitation dry-type transformers. Full article

In this paper, the conceptual hydrogeological circulation model of natural mineral waters from Ribeirinho and Fazenda do Arco hydromineral concession (Castelo de Vide) is updated. These waters are exploited by the Super Bock Group, as bottled waters, and are commercially labeled as Água Vitalis. The physico-chemical data (2004–2024) of these waters were processed regarding their joint interpretation with recent isotopic (δ²H and δ¹⁸O) data. The study region is dominated by the Castelo de Vide syncline, which develops along the southern limit of the Central Iberian Zone. These natural mineral waters have low electrical conductivity (EC) mean values (42.80 < EC_mean < 54.45 μS/cm) and a slightly acidic pH (5.14 < pH_mean < 5.46), making them hyposaline waters. The recharge area of this aquifer system coincides fundamentally with the outcrops of Lower Ordovician quartzites. The updated conceptual circulation model presented in this work is essentially developed on the basis of the chloride–sodium signatures of these waters, explained by the preferential recharge of meteoric waters (δ²H and δ¹⁸O) and low water–rock interaction temperature. Such isotopic results seem to indicate the non-existence of a flow continuity between the two blocks (NW and SE) of the quartzite ridges, separated by a fault with a local orientation approximately N-S, as indicated by the most enriched isotopic values of the waters from borehole AC22 (δ¹⁸O = −5.90‰ vs. V-SMOW) located in the SE block, compared to the average isotopic value of the waters from the other boreholes (Vitalis I, II, III, IV, V and VI) located in the NW block (δ¹⁸O_mean = −6.30‰ vs. V-SMOW). This study enhances the understanding of the hydrogeological and geochemical processes controlling low-mineralized (hyposaline) natural mineral waters, widely used for therapeutic and commercial purposes. Despite their global importance, detailed hydrogeological and isotopic studies of such systems are still scarce, making this conceptual model a valuable reference for their sustainable management. Full article

This paper presents a neuro-symbolic approach for constructing distributed knowledge graphs to facilitate cooperation through communication among spatially proximate agents. We develop a graph autoencoder (GAE) that learns rich representations from heterogeneous modalities. The method employs density-adaptive k-nearest neighbor (k-NN) construction with Gabriel pruning to build the proximity graphs that balance local density awareness with geometric consistency. When the agents enter the bridging zone, their individual knowledge graphs are aggregated into hypergraphs using a construction algorithm, for which we derive the theoretical bounds on the minimum number of hyperedges required for connectivity under arity and locality constraints. We evaluate the approach in PettingZoo’s communication-oriented environment, observing improvements of approximately 10% in episode rewards and up to 40% in individual agent rewards compared to Deep Q-Network (DQN) baselines, while maintaining comparable policy loss values. The explicit graph structures may offer interpretability benefits for applications requiring auditability. This work explores how structured knowledge representations can support cooperation in distributed multi-agent systems with heterogeneous observations. Full article

This work presents the development and characterization of laminated composite panels produced from açaí residues and fibers, incorporated into a castor oil-based vegetable polyurethane matrix. The study aimed to evaluate the potential of these Amazonian agro-industrial residues as lignocellulosic reinforcement in sustainable materials. The manufacturing process was carried out by manual lamination and cold pressing, following the recommendations of ABNT NBR 14810-2:2018. The physical (moisture, density, and swelling) and mechanical (perpendicular tensile and static flexural) properties of the resulting panels were analyzed. The results revealed an average moisture content of 6.23% and a 24 h swelling of 2.76%, which are values within and well below the regulatory limits, respectively. The perpendicular tensile strength (0.49 N/mm²) exceeded the minimum required value, indicating good interfacial adhesion and internal cohesion. However, the flexural strength and modulus of elasticity (2.4 N/mm² and 1323 N/mm²) were below the standards due to the absence of oriented fibers and density heterogeneity. It is concluded that the composite has high potential for indoor applications with low structural stress, standing out for its lightness, dimensional stability and environmental viability in the use of açaí residues. Full article

The efficient pre-incubation selection of duck eggs is essential to ensuring stable hatchability, but most existing optoelectronic and machine vision systems have been calibrated for chicken eggs and cannot be directly used for duck eggs because of their larger size, stronger reflectivity and wider morphological variability. This study proposes an optoelectronic method specifically adapted to Adigel duck eggs that combines load cell weighing, infrared distance sensing and dual-projection image processing in a single stationary setup. A total of 300 eggs were measured manually and automatically, and the results were statistically compared. The developed algorithm uses adaptive Gaussian thresholding (51 × 51, C = 2) and a median 5 × 5 filter to stabilize contour extraction on glossy and spotted shells, followed by ellipsoid-based volume estimation with a breed-specific correction factor (K_v = 0.637). The automatic system showed high agreement with manual measurements (r > 0.95 for mass and linear dimensions) and a mean relative error below 2%. Density, the shape index (If) and the shape coefficient (K₁) were computed to classify eggs into “suitable”, “borderline” and “unsuitable” categories. A preliminary incubation trial (n = 60) of eggs classified as “suitable” resulted in 92% hatchability, thus confirming the predictive value of the proposed criteria. Unlike chicken-oriented systems, the presented solution provides breed-specific calibration and can be implemented in small and medium hatcheries for the reproducible, non-destructive sorting of hatching duck eggs. Full article

Efficient utilization of solar energy in equatorial regions depends on accurately determining the optimal tilt and azimuth angles of fixed photovoltaic (PV) systems. This study presents a three-dimensional vector-based methodology that employs Direct Normal Irradiance (DNI) to estimate the mean direction of incident solar flux. Hourly DNI data from 2020–2024 for the city of Guayaquil, Ecuador, were transformed into spatial vectors and integrated to obtain a resultant vector representing the average orientation and elevation of direct solar radiation. The analysis yielded an optimal tilt angle of 5.73° and an azimuth of 59.15°, values consistent with Guayaquil’s equatorial latitude and previous studies conducted in tropical environments. The low tilt angle reflects the persistently high solar elevation typical of equatorial zones, while the slight northeastward orientation deviation corresponds to the asymmetric diurnal distribution of solar irradiance. The main contribution of this work lies in providing a geometrically rigorous and computationally efficient approach capable of synthesizing the directional behavior of solar flux without relying on complex transposition models. The proposed method enhances the optimization of PV system design, urban energy planning, and renewable microgrid modeling in data-scarce contexts, supporting the sustainable development of solar energy in equatorial regions. Full article

This article presents the results of a comprehensive study aimed at developing automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI). The research aimed to create an effective plant disease detection system, including at the early stages, which is critically important for ensuring food security in regions where wheat plays a key role in the agro-industrial sector. The study analyses the spectral characteristics of major wheat diseases, including powdery mildew, fusarium head blight, septoria glume blotch, root rots, various types of leaf spots, brown rust, and loose smut. Healthy plants differ from diseased ones in that they show a mostly uniform tone without distinct spots or patches on hyperspectral images, and their spectra have a consistent shape without sharp fluctuations. In contrast, disease spectra, differ sharply from those of healthy areas and can take diverse forms. Wheat diseases with a light coating (powdery mildew, fusarium head blight) exhibit high reflectance; chlorosis in the early stages of diseases (rust, leaf spot, septoria leaf blotch) exhibits curves with medium reflectance, and diseases with dark colouration (loose smut, root rot) have low reflectance values. These differences in reflectance among fungal diseases are caused by pigments produced by the pathogens, which either strongly absorb light or reflect most of it. The presence or absence of pigment production is determined by adaptive mechanisms. Based on these patterns in the spectral characteristics and optical properties of the diseases, a classification model was developed with 94% overall accuracy. Random Forest proved to be the most effective method for the automated detection of wheat phytopathogens using hyperspectral data. The practical significance of this research lies in the potential integration of the developed phytopathology detection approach into precision agriculture systems and the use of UAV platforms, enabling rapid large-scale crop monitoring for the timely detection. The study’s results confirm the promising potential of combining hyperspectral technologies and machine learning methods for monitoring the phytosanitary condition of crops. Our findings contribute to the advancement of digital agriculture and are particularly valuable for the agro-industrial sector of Central Asia, where adopting precision farming technologies is a strategic priority given the climatic risks and export-oriented nature of grain production. Full article

Ensuring the structural integrity of prestressed (PS) concrete is essential for the safety and longevity of infrastructure. Magnetic Flux Leakage (MFL) testing is a widely used non-destructive testing (NDT) method for detecting fractures in prestressing steel. This study explores the application of quantum sensors based on nitrogen vacancy (NV) centers in artificial diamonds for MFL testing and presents a novel method for processing continuous-wave optically detected magnetic resonance (CW-ODMR) data into vectorized magnetic field measurements. These sensors offer high sensitivity, low hysteresis, and multi-directional magnetic field detection, making them a promising alternative for advanced NDT applications. A data processing framework was developed to transform CW-ODMR measurements into vectorized magnetic flux density values in the x, y, and z directions. This process enables the conversion of crystallographic sensor orientations into calibrated field directions, ensuring precise magnetic field reconstruction. The method was validated through 121 fracture measurements and 19 open-bar-end measurements, demonstrating its effectiveness in extracting high-resolution vectorized magnetic field data. A subsequent statistical evaluation quantified the influence of sensor displacement, magnetization direction, magnetization distance, and measurement distance. These findings establish a foundation for integrating quantum sensors into MFL-based NDT, with potential applications extending beyond building inspections to a wide range of advanced sensing technologies in scientific and industrial fields. Full article

This work presents a comprehensive literature review of the coefficient of linear thermal expansion (CLTE) of polymers and polymer composite materials (PCMs). It systematizes CLTE measurement methods for isotropic and anisotropic materials, including contact techniques such as dilatometry and thermomechanical analysis and non-contact methods such as digital image correlation, laser interferometry, diffraction-based techniques, and strain-gauge methods, with attention to their accuracy and fields of applicability. Furthermore, the review describes the principal mathemaical modeling approaches used to predict the CLTE of polymers and PCMs. The review also provides a comparative analysis of CLTE values for a broad range of thermoplastics (commodity, engineering, and high-performance grades) and thermosets, identifying the key factors that govern CLTE, such as the transition from the glassy to the viscous-flow state, the presence and anisotropy of a crystalline phase, and related structure–property effects. Special consideration is given to the factors determining the CLTE of polymer composites, including the properties of the polymer matrix, the nature, size, orientation and surface treatment of the filler, the architecture and reinforcement scheme of the composite, and the manufacturing process. The review also outlines application areas in which PCMs with controlled or reduced CLTE are required and illustrates these with specific examples. Thus, the article provides integrated view of the CLTE of polymers and PCMs, compiles reference data for CLTE values of various polymers and common composite fillers and offers practical recommendations for selecting polymer materials for fabricating goods that require high thermal dimensional stability. Full article

As land use performance undergoes abrupt shifts due to the transition from growth-centric to ecology-focused development, traditional evaluation methods often overlook the catastrophe characteristics of urban complex functions in the process of system evolution, resulting in land governance strategies being unable to adjust rapidly to adapt to regional transformation. To address this limitation, this study develops an ecological priority-oriented performance evaluation system for land use Production–Living–Ecological (PLE) Functions and introduces the Entropy–Catastrophe Progression model to conduct comprehensive measurement and obstacle diagnosis of land use PLE function performance in the Yangtze River Economic Belt of Hubei Province, a typical region, thereby proposing differentiated control strategies. The results show the following: (1) The Entropy–Catastrophe Progression Model can accurately measure the spatiotemporal evolution of land use PLE function performance during the development transition period. (2) The average value of land use PLE function performance presents a fluctuating upward trend, increasing from 0.812 (Poor level) in 2014 to 0.924 (Good level) in 2023. (3) Significant spatial disparities are observed, exhibiting a gradient decrease from provincial capital centers, provincial sub-centers, and ecological economic belts to metropolitan areas. (4) The key obstacles restricting performance improvement include a weak foundation for high-quality tertiary industries, insufficient intensity in environmental purification, and an inadequate supply of high-level living services. These can be addressed by dividing high-quality service optimization zones, green industry enhancement zones, and ecology–economy synergy zones, and establishing differentiated governance mechanisms to improve land use PLE function performance. This study provides theoretical guidance and empirical support for optimizing pathways for urban–rural land use and management. Full article