Search Results (5,280)

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

This work analyzes the aero-elastic oscillations of cantilever beams reinforced by carbon nanotubes (CNTs). Four different distributions of single-walled CNTs are assumed as the reinforcing phase, in the thickness direction of the polymeric matrix. A modified third-order piston theory is used as an accurate tool to model the supersonic air flow, rather than a first-order piston theory. The nonlinear dynamic equation governing the problem accounts for Von Kármán-type nonlinearities, and it is derived from Hamilton’s principle. Then, the Galerkin decomposition technique is adopted to discretize the nonlinear partial differential equation into a nonlinear ordinary differential equation. This is solved analytically according to a multiple time scale method. A comprehensive parametric analysis was conducted to assess the influence of CNT volume fraction, beam slenderness, Mach number, and thickness ratio on the fundamental frequency and lateral dynamic deflection. Results indicate that FG-X reinforcement yields the highest frequency response and lateral deflection, followed by UD and FG-A patterns, whereas FG-O consistently exhibits the lowest performance metrics. An increase in CNT volume fraction and a reduction in slenderness ratio enhance the system’s stiffness and frequency response up to a critical threshold, beyond which a damped beating phenomenon emerges. Moreover, higher Mach numbers and greater thickness ratios significantly amplify both frequency response and lateral deflections, although damping rates tend to decrease. These findings provide valuable insights into the optimization of CNTR composite structures for advanced aeroelastic applications under supersonic conditions, as useful for many engineering applications. Full article

Municipal wastewaters pose a severe risk to the environment and human health when discharged untreated. This is due to their high content of pathogens, such as viruses and bacteria, which can cause diseases like cholera. Herein, the research and development of porphyrin-modified polyethersulfone (PES) ultrafiltration (UF) membranes was conducted to improve bacterial inactivation in complex municipal wastewater and enhance the fouling resistance and filtration performance. The synthesis and fabrication of porphyrin nanofillers and the resultant membrane characteristics were studied. The incorporation of porphyrin-based nanofillers improved the membrane’s hydrophilicity, morphology, and flux (247 Lm⁻² h⁻¹), with the membrane contact angle (CA) decreasing from 90° to ranging between 58° and 50°. The membrane performance was monitored for its flux, antifouling properties, reusability potential, municipal wastewater, and humic acid. The modified membranes demonstrated an effective application in wastewater treatment, achieving notable antibacterial activity, particularly under light exposure. The In-BP@SW/PES membrane demonstrated effective antimicrobial photodynamic effects against both Gram-positive S. aureus and Gram-negative E. coli. It achieved at least a 3-log reduction in bacterial viability, meeting Food and Drug Administration (FDA) standards for efficient antimicrobial materials. Among the variants tested, membranes modified with In-PB@SW nanofillers exhibited superior antifouling properties with flux recovery ratios (FRRs) of 78.9% for the humic acid (HA) solution and 85% for the municipal wastewater (MWW), suggesting a strong potential for long-term filtration use. These results highlight the promise of porphyrin-functionalized membranes as multifunctional tools in advanced water treatment technologies. 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

Molecularly imprinted polymers (MIPs) have emerged as robust, cost-effective analogues of bioreceptors, offering high selectivity and stability. When applied in sensors, one key step is the integration of MIPs with the transducer, which critically affects sensor performance. Demanding challenges come when such integration involves nanoscaling processes, meaning that the transducer is nanostructured or the MIP itself is nanosized on a bulk transducer. In both cases, the integration results in the development of nanosensors, with advantages arising from the nanoscale, such as a high MIP surface-to-volume ratio, with surface-located, easily accessible binding sites, fast binding kinetics, and, thus, a rapid sensor response. Major advantages come also from nanostructured transducers, with nanoscale geometry enabling highly sensitive signal generation processes, not allowed on their bulk counterparts. In this review, we discuss advances in imprinting technologies, focusing on techniques that, enabling the nanoscale control of MIP synthesis, are conveniently applied to directly integrate MIPs with nanosensors in a one-step process. Two main approaches are reviewed, consisting in MIP nanostructuring on bulk transducers and in the direct growth of MIPs on nanotransducers, highlighting how different strategies achieve good conformity at the nanoscale and address spatial complexity to ensure stable and accurate signal acquisition. Finally, we consider future directions in MIP-based nanosensor development. Full article

Background/Objectives: The TOPAZ-1 phase III trial reported a survival benefit of using durvalumab, an anti-programmed death ligand 1 (anti-PD-L1) antibody, in combination with gemcitabine and cisplatin (GCD) treatment in patients with advanced biliary tract cancer. This retrospective study investigated the efficacy and safety of GCD treatment for advanced biliary tract cancer in real-world conditions. Methods: The study subjects were 52 patients with biliary tract cancer who received GCD therapy between January 2023 and May 2024. The observation parameters included the modified Glasgow Prognostic Score (mGPS), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), tumor markers (CEA, CA19-9), overall response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and adverse events. Results: The cohort included 36 men and 16 women, with a median age of 73.0 years. There were 36 cases of cholangiocarcinoma (distal: 10, perihilar: 19, intrahepatic: 7), 13 cases of gallbladder cancer, and 3 cases of ampullary carcinoma. The stages were locally advanced in 30 cases and metastatic in 22 cases. Biliary drainage was performed in 30 cases. There were 38 cases receiving first-line therapy and 14 cases receiving second-line or later treatments. The median values at the start of GCD therapy were ALB 3.7 g/dL, CRP 0.39 mg/dL, NLR 2.4, PLR 162.5, CEA 4.8 ng/mL, and CA19-9 255.9 U/mL. The mGPS distribution was 0:23 cases, 1:18 cases, and 2:11 cases. The treatment outcomes were ORR 25.0% (CR 2 cases, PR 11 cases), DCR 78.8% (SD 28 cases, PD 10 cases, NE 1 case), median PFS 8.6 months, and median OS 13.9 months. The PLR was suggested to be useful for predicting PFS. A decrease in CEA at six weeks after the start of treatment was a significant predictor of PFS and OS. Gallbladder cancer had a significantly poorer prognosis compared to other cancers. The immune-related adverse events included hypothyroidism in two cases, cholangitis in one case, and colitis in one case. Conclusions: The ORR, DCR, and PFS were comparable to those in the TOPAZ-1 trial. Although limited by its retrospective design and small sample size, this study suggests that GCD therapy is an effective treatment regimen for unresectable biliary tract cancer in real-world clinical practice. Full article

Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures. Full article

Complex wastewater matrices hinder the efficacy of conventional treatment methods due to the presence of various inorganic and organic pollutants, along with their intricate interactions. Leveraging the synergy between algae and bacteria, algal–bacterial symbiosis (ABS) systems offering an evolutionary and highly effective approach. The ABS system demonstrates 10–30% higher removal efficiency than conventional biological/physicochemical methods under identical conditions, especially at low C/N ratios. Recent advances in biology techniques and big data analytics have deepened our understanding of the synergistic mechanisms involved. Despite the system’s considerable promise, challenges persist concerning complex pollution scenarios and scaling it for industrial applications, particularly regarding system design, environmental adaptability, and stable operation. In this review, we explore the current forms and operational modes of ABS systems, discussing relevant mechanisms in various wastewater treatment contexts. Furthermore, we examine the advantages and limitations of ABS systems in treating complex wastewater matrices, highlighting challenges and proposing future directions. Full article

Lipid nanoparticle (LNP)-based delivery systems are promising tools for advancing RNA-based therapies. However, there are underlying challenges for the oral delivery of LNPs. In this study, we optimized an LNP formulation, which we encapsulated in a pH-sensitive Eudragit^® S 100 (Eu) coating. LNPs were prepared using the DLin-MC3-DMA ionizable lipid, cholesterol, DMG-PEG, and DSPC at a molar ratio of 50:38.5:10:1.5. LNPs were coated with 1% Eu solution via nanoprecipitation using 0.25% acetic acid to get Eu-coated LNPs (Eu-LNPs). Particle characteristics of LNPs were determined by using dynamic light scattering (DLS). Ribogreen and agarose gel retardation assays were used to evaluate nucleic acid entrapment and stability. LNPs and Eu-LNPs were ~120 nm and 4.5 μm in size, respectively. Eu-LNPs decrease to an average size of ~191 ± 22.9 nm at a pH of 8. Phosphate buffer (PB)-treated and untreated Eu-LNPs and uncoated LNPs were transfected in HEK-293 cells. PB-treated Eu-LNPs showed significant transfection capability compared to their non-PB-treated counterparts. Eu-LNPs protected their nucleic acid payloads in the presence of a simulated gastric fluid (SGF) with pepsin and maintained transfection capacity following SGF or simulated intestinal fluid. Hence, Eu coating is a potentially promising approach for the oral administration of LNPs. Full article

Although the strawberry tree (A. unedo L.) has been long considered a neglected species of the Mediterranean maquis, the valorization of its fruit production may enhance its socioeconomic value, especially in rural areas. In this study, strawberry trees from different Sicilian sites were investigated in terms of macronutrients, fatty acid (FA) composition, tocopherols, total phenols, carotenoids, and minerals. Sicilian berries were a good source of carbohydrates (mainly fructose, glucose and sucrose) and dietary fiber. They were low in fat; however, the FA composition revealed the abundance of unsaturated FAs over saturated FAs and an advantageous n-6/n-3 ratio. Additionally, Sicilian berries showed an inversed linoleic/α-linolenic acid ratio with respect to berries from other Mediterranean regions, that had previously investigated in literature. This evidence suggests that this ratio may have a chemotaxonomic relevance. Considering antioxidants, the fruits had levels of tocopherols, particularly α-tocopherol, total phenols and carotenoids similar to those of certain commercial fruits. Precious amounts of minerals, such as Ca, K, Zn and Fe were also determined. Interestingly, berries harvested near a Sicilian volcanic area had higher levels of minerals, as well as tocopherols, phenols and carotenoids, than fruits from other Sicilian sites, thereby advancing the hypothesis that fruits from volcanic areas may have a superior nutritional value. Overall, data from this study elaborated by a proper statistical analysis revealed that the geographical origin was a relevant variable to consider in the reliable study of this fruit species. Full article

This study optimizes the production of activated carbons from hydrothermally carbonized (HTC) biomass using potassium hydroxide (KOH) and phosphoric acid (H₃PO₄) as activating agents. A 2³ factorial experimental design evaluated the effects of agent-to-precursor ratio, dry impregnation time, and activation duration on mass yield and iodine adsorption capacity. KOH-activated carbons achieved superior iodine numbers (up to 1289 mg/g) but lower mass yields (18–35%), reflecting enhanced porosity at the cost of material loss. Conversely, H₃PO₄ activation yielded higher mass retention (up to 54.86%) with moderate iodine numbers (up to 1117.3 mg/g), balancing porosity and yield. HTC pretreatment at 190 °C reduced the ash content, thereby enhancing the stability of hydrochar. These findings highlight the trade-offs between adsorption performance and process efficiency, with KOH suited for high-porosity applications (e.g., water purification) and H₃PO₄ for industrial scalability. The study advances biomass waste valorization, aligning with circular economy principles and offering sustainable solutions for environmental and industrial applications, such as water purification and energy storage. Full article

The large intelligent surface (LIS) concept represents an architectural advance for enhancing the performance of 6G wireless communication systems. In this work, we address the problem of jointly selecting active panels and associating terminals to outputs of such active panels in a panel-based LIS framework to maximise the minimum signal-to-interference-and-noise ratio (SINR) across all terminals. Due to the nature of the mixed-integer linear programming (MILP) formulation, we propose an alternative approach based on a genetic algorithm (GA) that efficiently explores the solution space through tailored crossover via column swapping and adaptive mutation. We compare the GA’s performance against the CPLEX solver under various configurations and time constraints. The performance results show that the GA provides competitive solutions with reduced computational complexity, showcasing its potential for scalable LIS implementations with complex resource allocation. Full article

Material Extrusion (MEX) process is increasingly used to fabricate components for structural applications, driven by the availability of advanced materials and greater industrial adoption. In these contexts, understanding the mechanical performance of printed parts is crucial. However, conventional methods for assessing anisotropic elastic behavior often rely on expensive equipment and time-consuming procedures. The aim of this study is to evaluate the applicability of the impulse excitation of vibration (IEV) in characterizing the dynamic mechanical properties of a 3D-printed composite material. Tensile tests were also performed to compare quasi-static properties with the dynamic ones obtained through IEV. The tested material, Nylon 12CF, contains 35% short carbon fibers by weight and is commercially available from Stratasys. It is used in the fused deposition modeling (FDM) process, a Material Extrusion technology, and exhibits anisotropic mechanical properties. This is further reinforced by the filament deposition process, which affects the mechanical response of printed parts. Young’s modulus obtained in the direction perpendicular to the deposition plane (E₃₃), obtained via IEV, was 14.77% higher than the value in the technical datasheet. Comparing methods, the Young’s modulus obtained in the deposition plane, in an inclined direction of 45 degrees in relation to the deposition direction (E₄₅), showed a 22.95% difference between IEV and tensile tests, while Poisson’s ratio in the deposition plane (v₁₂) differed by 6.78%. This data is critical for designing parts subject to demanding service conditions, and the results obtained (orthotropic elastic properties) can be used in finite element simulation software. Ultimately, this work reinforces the potential of the IEV method as an accessible and consistent alternative for characterizing the anisotropic properties of components produced through additive manufacturing (AM). Full article

Background: Epidermal growth factor receptor (EGFR) mutations are presented in approximately 50% of East Asian populations with advanced non-small cell lung cancer (NSCLC). While EGFR-tyrosine kinase inhibitors (TKIs) are the standard treatment, patient outcomes are also influenced by host-related factors. This study aimed to investigate clinical and radiological factors associated with early mortality and develop a prognostic prediction model in advanced EGFR-mutated NSCLC. Methods: A retrospective cohort was conducted in patients with EGFR-mutated NSCLC treated with first line EGFR-TKIs from January 2012 to October 2022 at Chiang Mai University Hospital. Clinical data and radiologic findings at the initiation of treatment were analyzed. A multivariable flexible parametric survival model was used to determine the predictors of death at 18 months. The predicted survival probabilities at 6, 12, and 18 months were estimated, and the model performance was evaluated. Results: Among 189 patients, 84 (44.4%) died within 18 months. Significant predictors of mortality included body mass index <18.5 or ≥23, bone metastasis, neutrophil-to-lymphocyte ratio ≥ 5, albumin-to-globulin ratio < 1, and mean pulmonary artery diameter ≥ 29 mm. The model demonstrated good performance (Harrell’s C-statistic = 0.72; 95% CI: 0.66–0.78). Based on bootstrap internal validation, the optimism-corrected Harrell’s C-statistic was 0.71 (95% CI: 0.71–0.71), derived from an apparent C-statistic of 0.75 (95% CI: 0.74–0.75) and an estimated optimism of 0.04 (95% CI: 0.03–0.04). Estimated 18-month survival ranged from 87.1% in those without risk factors to 2.1% in those with all predictors. A web-based tool was developed for clinical use. Conclusions: The prognostic model developed from fundamental clinical and radiologic parameters demonstrated promising utility in predicting 18-month mortality in patients with advanced EGFR-mutated NSCLC receiving first-line EGFR-TKI therapy. Full article

Graphene’s exceptional carrier mobility and broadband absorption make it promising for ultrafast photodetection. However, its low optical absorption limits responsivity, while the absence of a bandgap results in high dark current, constraining the signal-to-noise ratio and efficiency. Although silicon (Si) photodetectors normally offer fabrication compatibility, their performance is severely hindered by interface trap states and optical shading. To overcome these limitations, we demonstrate an epitaxial graphene/n-Si heterojunction photodiode. This device utilizes graphene epitaxially grown on germanium integrated with a transferred Si thin film, eliminating polymer residues and interface defects common in transferred graphene. As a result, the fabricated photodetector achieves an ultralow dark current of 1.2 × 10⁻⁹ A, a high responsivity of 1430 A/W, and self-powered operation at room temperature. This work provides a strategy for high-sensitivity and low-power photodetection and demonstrates the practical integration potential of graphene/Si heterostructures for advanced optoelectronics. Full article