Search Results (7,886)

The large-scale generation of waste graphite not only poses environmental challenges but also provides an opportunity for resource recovery. This study proposes a sustainable strategy that utilizes the graphite cutting waste produced during the production of large graphite electrodes through chemical intercalation, microwave-assisted expansion, and in situ urea nitrogen doping techniques to prepare nitrogen-containing micro-expanded graphite (NMG) composite materials. Structural analysis reveals that the nitrogen-doped amorphous carbon layer formed on the expanded graphite (EG) matrix effectively suppresses excessive expansion while preserving its typical worm-like interlayer morphology and porous structure. XPS confirms successful nitrogen doping with predominant pyridinic-N configuration, introducing abundant defect sites and enhancing lithiophilicity. As an anode for LIBs, NMG delivers an exceptional initial discharge capacity of 1907.5 mAh g⁻¹ at 20 mA g⁻¹ and maintains 798.2 mAh g⁻¹ after 50 cycles, nearly twice that of purified waste graphite (G). Remarkably, after 1000 cycles at 1 A g⁻¹, it retains 650.4 mAh g⁻¹ with 89.9% capacity retention, indicating an electrochemical activation process. Kinetic analysis reveals that the superior performance originates from synergistic diffusion-controlled intercalation and surface-dominated pseudocapacitance, with nitrogen-doped defect sites and hierarchical pore architecture promoting rapid ion/electron transport and surface faradaic reactions. This work demonstrates a viable pathway for value-added upcycling of waste graphite while providing insights into designing high-performance anodes through integrated defect engineering and heteroatom doping. Full article

Objective: Influenza and pertussis vaccines are recommended during pregnancy; however, uptake remains insufficient in many European countries, increasing the risk of preventable infections. Recent recommendations for maternal respiratory syncytial virus vaccination have been endorsed by scientific societies. This study evaluated maternal vaccination coverage, knowledge, attitudes, and factors influencing vaccine uptake among Lithuanian women. Methods: A retrospective cross-sectional online survey was conducted between 4 and 14 November 2025 in Lithuania among women aged 18–55 years with at least one previous pregnancy. The questionnaire contained 29 questions on sociodemographic characteristics, obstetric history, vaccination history, attitudes, and informational sources influencing decisions. Internal reliability was confirmed (Cronbach’s α = 0.83). Descriptive statistics were used to summarize the data. Associations between categorical variables were assessed using the Chi-square test or exact tests (Fisher’s exact or Fisher–Freeman–Halton). Binary and multivariable logistic regression analyses were performed to evaluate factors associated with self-reported vaccination uptake and the relationship between influenza and pertussis vaccination. Odds ratios with 95% confidence intervals were calculated. Statistical significance was set at p < 0.05. Results: A total of 241 women participated. Self-reported vaccination coverage during pregnancy was 28.7% for influenza, 43.8% for tetanus–diphtheria–pertussis, and 4.2% for respiratory syncytial virus. Physician’s recommendation was the strongest predictor: women advised to vaccinate were 17.0 times more likely to receive influenza, 16.5 times more likely to receive pertussis, while RSV vaccination occurred almost exclusively among women who reported receiving a physician’s recommendation. Higher uptake was associated with younger maternal age and university education. Reasons for declining vaccination were avoidance of medical interventions and concerns about safety or side effects. Conclusions: Maternal vaccination coverage in Lithuania remains low despite public funding and national recommendations. Strengthening provider communication, improving information strategies, and integrating vaccination counseling into routine antenatal care may increase uptake and enhance maternal and neonatal protection. Full article

Radon is a significant indoor air pollutant and a leading cause of lung cancer in non-smokers. While geogenic radon potential is well-documented, the specific contribution of building materials—particularly historic stones and those containing industrial by-products—requires precise in situ characterization to ensure public safety. This study investigates radon activity concentrations and surface exhalation rates across three distinct case studies in Slovakia: a mid-20th-century structure with cinder blocks, a UNESCO-protected Gothic building featuring volcanic andesite, and a historic stone plinth. Continuous radon monitoring and accumulation chamber measurements were employed, integrated with the tracking of meteorological parameters. The results revealed the highest surface exhalation rate in cinder block masonry (8.98 Bq m⁻² h⁻¹), followed by andesite ashlars (7.9 Bq m⁻² h⁻¹) and stone (1.87 Bq m⁻² h⁻¹). A clear correlation was observed between indoor radon levels and barometric pressure, whereas the influence of outdoor temperature appeared negligible. An estimated Activity Concentration Index of 0.30 suggests that the volcanic rock is likely radiologically safe for use as a bulk building material. The study concludes that while specific materials contribute to exhalation, indoor radon stability is primarily governed by barometric variations and the effectiveness of floor barriers against geogenic ingress rather than the masonry itself. Full article

Multipath propagation presents a major challenge to acoustic communication, causing signal distortion, delay spread, and inter-symbol interference, which degrade data integrity. This study investigates the use of Orthogonal Frequency Division Multiplexing (OFDM) as a robust modulation strategy for communication in complex acoustic environments where radio frequency (RF) propagation is severely attenuated. Using a software-defined radio (SDR) platform implemented in GNU Radio, OFDM performance was experimentally evaluated against Binary Frequency Shift Keying (BFSK) and Binary Phase Shift Keying (BPSK) under simulated and real multipath conditions in materials including air, water, and steel. The results show that OFDM achieves consistently lower bit error rates (BERs) and greater resilience to multipath interference due to its sub-carrier orthogonality and cyclic-prefix structure. The research also highlights how the frequency selectivity and coherence bandwidth of acoustic channels influence modulation performance across different media. By implementing custom transducers and real-time baseband processing, the study demonstrates how software-defined acoustics can be adapted for highly reflective and frequency-dependent environments. The observed improvements in BER and signal stability validate OFDM’s effectiveness in maintaining data integrity despite time and frequency dispersion effects. These findings demonstrate that OFDM enables reliable acoustic data transmission across heterogeneous media and is well suited to sensor-network applications in RF-hostile environments such as railway infrastructure, sealed containers, and submerged systems. Future work will include quantitative channel characterisation—specifically measuring delay spread, coherence bandwidth, and impulse response profiles—to further optimise OFDM parameters and provide a generalisable framework for adaptive modulation in dynamic acoustic channels. Full article

Wound healing involves the coordinated regulation of inflammation, angiogenesis, and extracellular matrix remodeling, processes modulated by natural bioactives. In this context, Chelidonium majus L. (C. majus), a plant rich in alkaloids and flavonoids, remains mechanistically underexplored. This study, therefore, investigates its metabolites using an integrated computational–experimental approach and evaluates their applicability in sericin-based wound-healing systems. A curated database of 83 C. majus bioactive compounds was analyzed using cheminformatics and molecular docking against key wound-healing targets (iNOS, VEGF, MMP-3, and tyrosinase), followed by ADMET and toxicity prediction (StopTox). Selected plant–sericin formulations were subsequently evaluated for wound-healing activity using an in vitro fibroblast scratch assay. Docking revealed strong binding affinities for several metabolites, particularly protopine, kaempferol-3-rutinoside, cynaroside, hesperidin, quercetin-3-rhamnosylrutinoside, and vitexin, indicating multi-target modulation across inflammatory, proliferative, and remodeling phases of tissue repair. ADMET and toxicity analyses predicted favorable dermal safety and pharmacokinetic profiles for most compounds. Consistently, in vitro assays demonstrated that C. majus–sericin systems had fibroblast migration and wound closure in a concentration- and ratio-dependent manner, with improved healing kinetics observed at 150 µg/mL and for formulations containing higher relative proportions of both components. The experimental outcomes supported the pro-angiogenic and matrix-stabilizing mechanisms predicted in silico. Overall, C. majus metabolites exhibit polypharmacological wound-healing activity, supporting their integration into sericin-based systems as a promising strategy for topical therapies. Full article

The red cusk-eel (Genypterus chilensis) is an endemic Chilean teleost fish of significant importance to fisheries and aquaculture; however, no reference genome is available for this species. In this study, we present the first hybrid genome assembly of G. chilensis using Nanopore long-reads and Illumina short-reads, integrated with structural and functional annotations from RNA-seq data of the intestine and head kidney. The resulting genome assembly was 439.89 Mb in size, with an N50 of 7.96 Mb, containing 35,029 coding genes. Comparative genomics with G. blacodes revealed high similarity in genome size and completeness. Additionally, 14,681 lncRNAs were annotated, with 641 lncRNAs and 7323 coding genes differentially expressed in a tissue-specific expression pattern. These findings provide a high-quality genomic resource that enhances the understanding of lncRNA regulation and genome structure in the Genypterus genus. This study establishes a foundation for future research on commercial traits, conservation, and the evolution of the Ophidiiformes order. Full article

Soy protein concentrate (SPC) undergoes continuous thermal and structural changes during passage through a cooling die, yet these changes are often interpreted using viscosity-based descriptions that do not explicitly account for structural development rate (SDR). This study developed a rheology-guided framework to analyze SPC behavior in a three-stage cooling die by integrating isothermal and non-isothermal rheological characterization with computational fluid dynamics (CFD). SPC samples containing 76, 78, and 80% moisture were evaluated using strain sweep, frequency sweep, viscosity, time sweep, and temperature sweep tests. Lower moisture promoted stronger structure development, higher viscosity, and faster gelation. For the 76% moisture sample, peak SDR increased from 6.66 Pa/s at 50 °C to 22.46 Pa/s at 100 °C, while the time to peak decreased from 937 to 360 s. During non-isothermal cooling, the major structure development occurred in the 80–50 °C interval, where ΔG′ reached 4902.54 Pa at 76% moisture. CFD analysis showed that the gelation-kinetics-based model predicted both pressure and extrudate temperature more accurately than the viscosity-based model. Pressure RMSE ranged from 8.57 to 14.43 kPa for the kinetic model, compared with 11.31 to 22.39 kPa for the viscosity model. These results demonstrate that the three-stage cooling die should be interpreted as a coupled thermal, flow, and structure-development domain. Full article

Lithium is a rare metal widely used in the renewable energy industry. The Altyn region in Xinjiang, China, contains abundant granitic pegmatite-type lithium resources; however, the deeply incised and complex terrain limits the accuracy of conventional two-dimensional remote sensing approaches for dike identification and segmentation. To address this limitation, a remote sensing segmentation method incorporating terrain information was proposed. A digital elevation model (DEM) derived from LiDAR data, together with its associated topographic factors, was integrated into the Spatial–Spectral Mamba framework to enable the joint utilization of spectral and terrain features. Rather than performing explicit three-dimensional geometric modeling, the proposed approach enhances a two-dimensional segmentation framework by introducing elevation-derived information, allowing the model to capture terrain-related spatial variations of pegmatite dikes. This design enables improved representation of both the planar distribution and terrain-influenced morphological characteristics of dikes under deeply incised conditions. The Xichanggou lithium deposit in the Altyn region is a large-scale, economically valuable pegmatite-type lithium deposit, and was therefore selected as the study area for pegmatite dike segmentation. The results demonstrated that, compared with conventional two-dimensional approaches and representative machine learning methods, the proposed method achieved higher segmentation accuracy in complex terrain. Improvements were also observed in the continuity and spatial consistency of the extracted dike patterns. Field verification indicated that the major pegmatite dikes delineated by the model were highly consistent with their actual surface exposures. Sampling analyses further confirmed the validity and reliability of the identification results. Overall, the terrain-integrated remote sensing segmentation approach exhibited good applicability and robustness under deeply incised and complex geomorphological conditions. Full article

This paper evaluates the feasibility of real-time traffic data analysis on resource-constrained edge devices using a hybrid processing approach. The proposed architecture integrates an LF Edge eKuiper complex event processing engine, deployed within Docker containers, with a native YOLO deep learning model for pedestrian detection. The model processes video frames at 480 × 240 resolution on CPU-only Raspberry Pi devices, achieving up to 30 FPS. The research specifically investigates the performance limits of Raspberry Pi 3 and Raspberry Pi 4 platforms when simultaneously processing high-throughput simulated traffic data from the SUMO simulator (Belgrade scenario, with vehicle distributions and densities adjusted for small, medium, and large traffic volumes) and live video streams, respectively. Experimental results indicate that while both platforms can process up to 2600 messages per second in the settings without image processing, the introduction of a camera sensor reveals a significant hardware bottleneck. The Raspberry Pi 4 maintains robust real-time performance with an average complex event detection latency of less than 500 ms. In contrast, the Raspberry Pi 3 exhibits severe performance degradation, with image processing delays exceeding 8 s, rendering it unsuitable for real-time safety alerts. The findings demonstrate that with appropriate hardware selection, edge-based complex event processing can successfully detect critical safety events, such as sudden vehicle acceleration near pedestrians, without relying on cloud infrastructure. Full article

An automotive welding unit is a modular production cell within a welding workshop that integrates industrial manipulators, welding equipment, fixtures, and control systems to perform specific welding and assembly tasks. A large number of industrial manipulators are utilized in the automotive welding unit. The capability to quickly plan a short and collision-free path in the workspace of the manipulator is of great importance for improving the manipulator’s intelligence level and production efficiency. The RRT* algorithm, based on random sampling, has been widely applied in path planning for high-dimensional manipulators due to its probabilistic completeness and powerful exploration capabilities. However, the RRT* algorithm performs poorly in spaces containing narrow passages. Research on the practical application of path planning for 6-DOF manipulators is still insufficient, particularly in planning posture. To solve these two problems, an improved RRT* algorithm is proposed in this paper. New sampling and node connection strategies are designed to improve the expansion and convergence speed of the random tree in spaces containing narrow passages. A distance-constrained posture quaternion interpolation method is presented to generate smooth and continuous paths for manipulators of the automotive welding unit. Simulations and experiments are carried out to validate the proposed method, which confirms that the method can plan collision-free paths for manipulators more quickly compared to other methods. Full article

Port cities represent an interdependent system in which port and urban activities overlap and develop. While ports serve as the gateway for the city, expanding market reach and attracting investments, cities provide the necessary labor and services required for the operation of the ports. However, the mutual relationship between ports and cities is threatened by conflicts such as urban sprawl, which leads to friction by taking the space needed for storing containers at ports. Similarly, ports generate high noise and air pollution, threatening the quality of life in urban centers. Therefore, implementing best practices to manage the port–city dichotomy is essential to ensure the coexistence of the port and city. This study re-examined the port–city relationship in the framework of urban planning to guide redevelopment decisions within the Piraeus city port in Greece. Data were collected through a mixed-methods approach involving secondary research and roundtable discussions. The findings showed that a key design parameter of the Piraeus city port is the development and exploitation of the city’s relationship with water, from a functional, spatial, and aesthetic point of view. Furthermore, a guide was developed to facilitate the redevelopment of the city port and improve decision-making. The recommendations also emphasize the integration of the port city into a global economic forum and highlight its dynamism, ensuring mutual benefits for the city and port. Full article

Leakage identification in district heating networks is challenging because leakage-induced transient pressure waves often overlap with pressure disturbances triggered by routine operations such as valve regulation, pump speed variation, and emergency shut-off. In addition, the scarcity of high-quality labeled leakage samples limits the robustness of data-driven models under small-sample conditions. To address these issues, this study proposes a compact and moderately interpretable framework for multi-condition identification from transient pressure-wave signals, integrating signal preprocessing, handcrafted statistical feature extraction, multiclass ReliefF-based feature selection, and class-wise generative adversarial network augmentation in the selected feature space. A dataset containing four representative conditions, namely leakage, valve regulation, pump speed regulation, and emergency valve shut-off, was constructed using an integrated indoor district heating network testbed. After Hampel-based spike suppression and zero-phase Butterworth band-pass filtering within 0.5 to 300 Hz, time- and frequency-domain statistical features were extracted, and a compact subset was selected by multiclass ReliefF. A class-wise generative adversarial network was then used to augment the training set in feature space, while all evaluations were performed strictly on real samples. The results show that feature-space augmentation improves robustness and generalization under operational disturbances and noise. Using random forest as the representative classifier, Accuracy and Macro-F1 increased from 0.960 to 0.985, while leakage recall improved from 0.920 to 0.980. Further comparisons confirmed that the ReliefF-selected subset outperformed representative alternatives such as LASSO and mRMR. Overall, the proposed framework provides an effective solution for distinguishing leakage events from operational disturbances and offers practical support for online monitoring and intelligent operation of district heating networks. Full article

Background: The aim of this in vitro study is to comparatively evaluate the effects of toothpaste formulations containing and not containing vitamin C on the surface roughness and microhardness of different composite resin materials. Methods: Four different toothpastes (Sensodyne, Colgate, Klorhex, Dentiste) and three composite resin materials (Arabesk—microhybrid, Charisma Smart—nanohybrid, Estelite Sigma Quick—supra-nano filled) were used in the study. Composite discs measuring 10 mm in diameter and 2 mm in thickness were prepared and subjected to brushing simulations equivalent to 1 month (150 s) and 3 months (450 s). Surface roughness was measured using a mechanical profilometer, and microhardness was evaluated with a Vickers hardness tester. Surface morphology was further examined in detail using scanning electron microscopy (SEM) and atomic force microscopy (AFM). For statistical analyses, one-way ANOVA, repeated measures ANOVA, Kruskal–Wallis test, and Friedman test were employed, with the significance level set at p < 0.05. Results: Brushing procedures resulted in statistically significant changes in the surface roughness (ΔRa) and microhardness of the composites across all toothpaste groups (p < 0.05). The increase in surface roughness varied depending on the composite type, with the highest increase observed in the ESQ composite. In the ESQ composite, higher ΔRa values were obtained, particularly in the Dentiste (≈1.70 µm) and Colgate (≈1.52 µm) groups. Microhardness results, however, differed depending on the composite and toothpaste type. While a general trend toward increased microhardness was observed, a significant decrease in microhardness was detected in the Colgate and Dentiste groups of the ESQ composite (p < 0.05). Conclusions: This study demonstrates that the addition of vitamin C to toothpaste formulations increases the surface roughness of restorative materials and results in significant changes in their microhardness properties. These findings highlight the importance of considering the type of toothpaste used by patients in clinical practice, particularly in terms of restorative material selection and the long-term preservation of surface integrity. Full article

Background: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to late diagnosis. Although machine learning (ML) techniques have been widely applied for lung cancer classification, many studies lack a fully optimized end-to-end pipeline using routine clinical data. This study proposes an optimized ML framework that integrates demographic, lifestyle, and clinical features with systematic hyperparameter tuning to improve classification performance. Methods: A dataset of 309 patient records containing demographic, lifestyle, and clinical attributes was used. The data were preprocessed and split into training and testing sets in an 80:20 ratio. Feature selection was performed using metaheuristic algorithms, including Red Deer Optimization, Binary Grasshopper Optimization, Gray Wolf Optimization, and Bee Colony Optimization. Six ML classifiers—Logistic Regression, Support Vector Classifier, Gradient Boosting, Random Forest, K-Nearest Neighbors, and Gaussian Naive Bayes—were trained with optimized hyperparameters. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC–AUC. Results: The optimized pipeline significantly improved classification performance. Logistic Regression achieved the highest accuracy of 91.07% with an AUC of 0.91, outperforming more complex ensemble models. Gradient Boosting and Random Forest both achieved an accuracy of 87.5%, while other classifiers demonstrated moderate performance. Conclusions: The proposed optimized ML pipeline enhances lung cancer classification accuracy using routine clinical data. The results highlight that simpler, well-optimized models can outperform complex approaches on structured datasets. This framework shows strong potential for early lung cancer risk screening and clinical decision support, although further validation on larger datasets is recommended. Full article

The Temporal Analysis of Products (TAP) pulse response technique provides valuable insights into catalytic function and reaction kinetics. However, complex fragmentation patterns in the TAP mass spectrometry signals can complicate precise quantification, particularly when analyzing transient gas flux data typical of TAP experiments. This work demonstrates a standard defragmentation method that deconvolves transient TAP signals while maintaining the temporal resolution of the experiment. First, the integrals of calibration gas fluxes are used to determine the fingerprint fragmentation pattern and construct a fragmentation matrix. This matrix is then used to defragment experimental flux data at each recorded time point via a non-negative least squares regression. The effectiveness of this method is demonstrated using virtual data and control experiments with a TAP reactor system. The defragmentation is then applied to the more complex propane dehydrogenation reaction on a chromia/alumina catalyst, which can contain up to ten significant gas species in the reactor outlet. Initial propane pulsing reveals an induction period during which propane is fully oxidized to CO_2, followed by partial reduction to CO. Afterwards, there is a transition in chemistries towards coking and propylene production. Our example illustrates a practical method for the accurate determination of the time-dependent reactant/product concentrations and rates for a thorough analysis of the propane dehydrogenation kinetics. This approach can be broadly applied to any transient mass spectrometry experiment for a better understanding of catalyst-reaction dynamics. Full article