Search Results (830)

Automated quality control plays a critical role in modern industries, particularly in environments that handle large volumes of packaged products requiring fast, accurate, and consistent inspections. This work presents an anomaly detection system for candle jars commonly used in industrial and commercial applications, where obtaining labeled defective samples is challenging. Two anomaly detection strategies are explored: (1) a baseline model using convolutional neural networks (CNNs) as an end-to-end classifier and (2) a hybrid approach where features extracted by CNNs are fed into One-Class classification (OCC) algorithms, including One-Class SVM (OCSVM), One-Class Isolation Forest (OCIF), One-Class Local Outlier Factor (OCLOF), One-Class Elliptic Envelope (OCEE), One-Class Autoencoder (OCAutoencoder), and Support Vector Data Description (SVDD). Both strategies are trained primarily on non-defective samples, with only a limited number of anomalous examples used for evaluation. Experimental results show that both the pure CNN model and the hybrid methods achieve excellent classification performance. The end-to-end CNN reached 100% accuracy, precision, recall, F1-score, and AUC. The best-performing hybrid model CNN-based feature extraction followed by OCIF also achieved 100% across all evaluation metrics, confirming the effectiveness and robustness of the proposed approach. Other OCC algorithms consistently delivered strong results, with all metrics above 95%, indicating solid generalization from predominantly normal data. This approach demonstrates strong potential for quality inspection tasks in scenarios with scarce defective data. Its ability to generalize effectively from mostly normal samples makes it a practical and valuable solution for real-world industrial inspection systems. Future work will focus on optimizing real-time inference and exploring advanced feature extraction techniques to further enhance detection performance. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

Deforestation driven by plantations, such as Pinus patula Schiede ex Schltdl. et Cham., is a major cause of biodiversity and functional loss in tropical ecosystems. We assessed the diversity and composition of lichens and bryophytes in four size categories of pine cones, small (3–5 cm), medium (5.1–8 cm), large (8.1–10 cm), and very large (10.1–13 cm), with a total of 150 pine cones examined, where the occurrence and cover of lichen and bryophyte species were recorded. Identification keys based on morpho-anatomical features were used to identify lichens and bryophytes. In addition, for lichens, secondary metabolites were tested using spot reactions with potassium hydroxide, commercial bleach, and Lugol’s solution, and by examining the specimens under ultraviolet light. To evaluate the effect of pine cone size on species richness, the Kruskal–Wallis test was conducted, and species composition among cones sizes was compared using multivariate analysis. A total of 48 taxa were recorded on cones, including 41 lichens and 7 bryophytes. A total of 39 species were found on very large cones, 37 species on large cones, 35 species on medium cones, and 24 species on small cones. This is comparable to the diversity found in epiphytic communities of pine plantations. Species composition was influenced by pine cone size, differing from small in comparison with very large ones. The PERMANOVA analyses revealed that lichen and bryophyte composition varied significantly among the pine cone categories, explaining 21% of the variance. Very large cones with specific characteristics harbored different communities than those on small pine cones. The presence of lichen and bryophyte species on the pine cones from managed Ecuadorian P. patula plantations may serve as refugia for the conservation of biodiversity. Pine cones and their scales (which range from 102 to 210 per cone) may facilitate colonization of new areas by dispersal agents such as birds and rodents. The scales often harbor lichen and bryophyte propagules as well as intact thalli, which can be effectively dispersed, when the cones are moved. The prolonged presence of pine cones in the environment further enhances their role as possible dispersal substrates over extended periods. To our knowledge, this is the first study worldwide to examine pine cones as substrates for lichens and bryophytes, providing novel insights into their potential role as microhabitats within P. patula plantations and forest landscapes across both temperate and tropical zones. Full article

Several natural products have been shown to display antiviral activity against the hepatitis B virus (HBV), among a number of other viruses. In a previous study, the hydro-alcoholic extracts (n = 66) of 31 species from the Venezuelan Amazonian rain forest were tested on the hepatoma cell line HepG2.2.15, which constitutively produces HBV. One of the species that exerted inhibitory activity on HBV replication was Senna silvestris. The aim of this study was the bioassay-guided purification of the ethanol fraction of leaves of S. silvestris, which displayed the most significant inhibitory activity against HBV. After solvent extraction and two rounds of reverse-phase HPLC purification, NMR analysis identified salsolinol as the compound that may exert the desired antiviral activity. The purified compound exerted inhibition of both HBV DNA and core HBV DNA. Pure salsolinol obtained from a commercial source also displayed anti-HBV DNA inhibition, with an approximate MIC value of 12 µM. Although salsolinol is widely used in Chinese traditional medicine to treat congestive heart failure, it has also been associated with Parkinson’s disease. More studies are warranted to analyze the effect of changes in its chemical conformation, searching for potent antiviral, perhaps dual agents against HBV and HIV, with reduced toxicity. Full article

Aronia melanocarpa (black chokeberry) is a medicinally valuable small fruit species, yet its commercial propagation remains limited by low rooting and genotype-specific responses. This study developed an efficient, callus-free micropropagation and rooting protocol using a Shrub Plant Medium (SPM) supplemented with 5 mg/L BAP in large 660 mL jars, which yielded up to 27 shoots per explant. Optimal rooting (100%) was achieved with 0.5 mg/L NAA + 0.25 mg/L IBA in half-strength SPM. In the second phase, supervised machine learning models, including Random Forest (RF), XGBoost, Gaussian Process (GP), and Multilayer Perceptron (MLP), were employed to predict morphogenic traits based on culture conditions. XGBoost and RF outperformed other models, achieving R² values exceeding 0.95 for key variables such as shoot number and root length. These results demonstrate that data-driven modeling can enhance protocol precision and reduce experimental workload in plant tissue culture. The study also highlights the potential for combining physiological understanding with artificial intelligence to streamline future in vitro applications in woody species. Full article

Machine learning is reshaping gel-based additive manufacturing by enabling accelerated material design and predictive process optimization. This review provides a comprehensive overview of recent progress in applying machine learning across gel formulation development, printability prediction, and real-time process control. The integration of algorithms such as neural networks, random forests, and support vector machines allows accurate modeling of gel properties, including rheology, elasticity, swelling, and viscoelasticity, from compositional and processing data. Advances in data-driven formulation and closed-loop robotics are moving gel printing from trial and error toward autonomous and efficient material discovery. Despite these advances, challenges remain regarding data sparsity, model robustness, and integration with commercial printing systems. The review results highlight the value of open-source datasets, standardized protocols, and robust validation practices to ensure reproducibility and reliability in both research and clinical environments. Looking ahead, combining multimodal sensing, generative design, and automated experimentation will further accelerate discoveries and enable new possibilities in tissue engineering, biomedical devices, soft robotics, and sustainable materials manufacturing. Full article

The remote sensing classification of urban functional zones provides scientific support for urban planning, land resource optimization, and ecological environment protection. However, urban functional zone classification encounters significant challenges in accuracy and efficiency due to complicated image structures, ambiguous critical features, and high computational complexity. To tackle these challenges, this work proposes a novel XT-SECA algorithm employing a strengthened efficient channel attention mechanism (SECA) to integrate the feature-extraction XGBoost branch and the feature-enhancement Transformer feedforward branch. The SECA optimizes the feature-fusion process through dynamic pooling and adaptive convolution kernel strategies, reducing feature confusion between various functional zones. XT-SECA is characterized by sufficient learning of complex image structures, effective representation of significant features, and efficient computational performance. The Futian, Luohu, and Nanshan districts in Shenzhen City are selected to conduct urban functional zone classification by XT-SECA, and they feature administrative management, technological innovation, and commercial finance functions, respectively. XT-SECA can effectively distinguish diverse functional zones such as residential zones and public management and service zones, which are easily confused by current mainstream algorithms. Compared with the commonly adopted algorithms for urban functional zone classification, including Random Forest (RF), Long Short-Term Memory (LSTM) network, and Multi-Layer Perceptron (MLP), XT-SECA demonstrates significant advantages in terms of overall accuracy, precision, recall, F1-score, and Kappa coefficient, with an accuracy enhancement of 3.78%, 42.86%, and 44.17%, respectively. The Kappa coefficient is increased by 4.53%, 51.28%, and 52.73%, respectively. Full article

This study investigates the potential of Eucalyptus benthamii wood from planted forests in southern Brazil for the production of cross-laminated timber (CLT) panels. The performance of E. benthamii CLT panels is compared to that of Pinus spp. panels and European commercial panels (KLH^®), using the finite element method applied to a two-story building model. Class 2 of the Brazilian standard ABNT NBR 7190-2 was adopted as the reference for the physical and mechanical properties of Pinus spp., while the European commercial specifications from KLH^® were used to represent European reference panels. The results indicate that E. benthamii wood exhibits superior mechanical properties, enabling reductions of 12.5% to 27.3% in panel thickness and a 20.7% decrease in wood volume when compared to Pinus spp., without compromising structural safety. Relative to the KLH^® and ETA 06/0138 standards, E. benthamii wood demonstrates higher stiffness (modulus of elasticity of 15,325 MPa vs. 12,000 MPa) and greater flexural strength (109.11 MPa vs. 24 MPa), allowing for the use of thinner panels. Stress and displacement analyses confirm that E. benthamii CLT slabs can withstand critical loads (wind and vertical) within normative limits, with maximum displacements of 18.5 mm. The reduction in material volume (22.8 m³ versus 28.7 m³ for Pinus spp.) suggests potential benefits in terms of environmental impact and logistical efficiency. It can be concluded that E. benthamii represents a sustainable and efficient alternative for CLT panels, combining high structural performance with resource optimization and contributing to the decarbonization of the construction industry. Full article

The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO₂ and PM_2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city’s busiest transport corridors, analyzing how the concentrations of CO₂, PM_2.5, and PM₁₀, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty’s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems. Full article

Uncaria tomentosa (Ut) is a Rubiaceae widely used in Peru’s traditional medicine. It is mainly known by the vernacular name of Cat’s claw due to its morphological aspects and is found in tropical low mountain forests of Central and South America. A decoction of Ut bark, root and leaves is used traditionally for different health problems, including arthritis, weakness, viral infections, skin disorders, abscesses, allergies, asthma, cancer, fevers, gastric ulcers, haemorrhages, inflammations, menstrual irregularity, rheumatism, urinary tract inflammation and wounds, among others, which gave rise to scientific and commercial interest. The present paper reviews research progress relating to the ethnobotany, phytochemistry and pharmacology of Ut, and some promising research routes are also discussed. We highlight the centrality of its different biological activities, such as antioxidant, anti-inflammatory, antiproliferative, antiviral, and antinociceptive, among others. Recently, studies of the health effects of this plant suggest that novel nutraceuticals can be obtained from it and applied as a preventive or prophylaxis strategy before the start of conventional drug therapy, especially for patients who are not prone to conventional pharmacological approaches to diseases. The present work emphasizes the current pharmacological properties of Uncaria tomentosa, evidencing its therapeutic benefits and encouraging further research on this medicinal plant. Full article

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R² of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Full article

Optimization of energy consumption in urban infrastructures is essential to achieve sustainability and reduce environmental impacts. In particular, accurate regression-based energy forecasting of the energy consumption in various sectors plays a key role in informed decision-making, efficiency improvements, and resource allocation. This paper examines the application of artificial intelligence and supervised machine learning techniques to modeling and predicting the energy consumption patterns in the smart grid sector of a commercial building located in Singapore. By evaluating performance of several regression algorithms using various metrics, this study identifies the most effective method for analyzing sectoral energy consumption. The results show that the Regression Tree Ensemble algorithm outperforms other techniques, achieving an accuracy of 97.00%, followed by Random Forest Regression (96.20%) and Gradient Boosted Regression Trees (95.50%). These results underline the potential of machine learning models to foster intelligent energy management and promote sustainable energy practices in smart cities. Full article

Growing environmental concerns have led to the search for alternative biological repellents against the large pine weevil Hylobius abietis L., Europe’s most important coniferous forest regeneration pest. A laboratory study was carried out to assess the effectiveness (damage intensity) of six combinations of a novel biological repellent, consisting of plant-based oils, beeswax, calcium carbonate, vanillin, pine bark extractives, terpentine, abrasive particles, solvent, and a viscosity agent, in comparison with commercially available repellent Norfort LDW 115. The application complexity of the repellents, their persistence on seedlings, and the extent of H. abietis damage were evaluated. The five alternative repellents had higher protection compared to the control repellent, highlighting the potential for new alternative repellents. The base (without additives) repellent provided the highest protection, indicating a redundancy of admixtures. A mixed cumulative link model, employed to estimate differences between the repellents, estimated 85% undamaged and none significantly damaged saplings in the case of the base repellent. However, the consistency and hence persistence of certain repellents on plantlets would benefit from improvements; further field studies are needed to upscale the test of the stability and efficiency of high levels in real environments under different H. abietis population pressures. Full article

The Białowieża Forest is home to both primary forests under strict protection and commercial forests, which provides an opportunity to compare them in terms of the number of individuals, number of species, and composition of different animal assemblages. The main goal of our study was to compare spider assemblages inhabiting herbaceous vegetation in these two types of forest stands. Spiders were sampled using a sweep net in an oak–lime–hornbeam forest, an ash–alder forest, and an alder carr. More spiders were found in unmanaged stands compared to managed stands, but a significant difference was found only in the alder carr. Total species richness did not significantly vary between managed and unmanaged stands in all forest types. In the oak–lime–hornbeam forest, more species per sample were found in commercial stands compared to primeval stands, while the result was the opposite for the alder carr. Our research did not show a clear negative impact of forest management on plant-dwelling spiders. The impact of forest management was evident in the case of the riparian forest, where the managed stand was characterized by low canopy cover as a result of logging carried out years ago, which is likely to have resulted in differences in family composition. Full article

Urban green spaces (UGSs), including parks, forests, and community gardens, play a critical role in enhancing public health and well-being by providing essential ecosystem services such as improving air quality, reducing surface temperatures, and mitigating harmful substances. As urbanization accelerates, especially in rapidly growing cities like Jinan, China, the demand for UGSs is intensifying, necessitating careful urban planning to balance development and environmental protection. While previous studies have often focused on city-level green coverage, this study shifts the analytical focus from UGS as a whole to urban functional units (UFUs), allowing for a more detailed examination of how green space is distributed across different land use types. We investigate UGS changes in Jinan over the past two decades and assess the influence of socio-economic factors—such as housing prices, land use types, and building age—on UGS distribution within UFUs. Remote sensing technology was employed to analyze the spatiotemporal dynamics of UGS and its correlation with these variables. Our findings reveal a significant shift in UGS distribution, with parks and leisure areas becoming primary drivers of UGS expansion. This study also highlights the growing influence of economic factors, particularly housing prices, on UGS distribution in more affluent UFUs. Additionally, while UGS in Jinan has generally expanded, challenges remain in balancing green space with urban expansion, especially in commercial and residential UFUs. This paper contributes to a more nuanced understanding of UGS distribution by integrating the UFU framework and identifying socio-economic drivers—including housing prices, construction age, and land use type—that shape green space patterns in Jinan. Our findings demonstrate that the spatial pattern of UGS in Jinan mirrors socio-economic and land use disparities observed in other global cities, highlighting both the universality of these patterns and the need for targeted planning in rapidly urbanizing contexts. Full article