Search Results (13,021)

The responses of nitrous oxide (N₂O) emissions to nitrogen (N) application in acidic, perennial agricultural systems, and the factors driving these emissions, remain poorly understood. To address this gap, a 12-year field experiment was conducted to investigate the effects of different N application rates (0, 112.5, 225, and 450 kg N ha⁻¹ yr⁻¹) on N₂O emissions, tea yield, and the associated driving factors in a tea plantation. The study found that soil pH significantly decreased with long-term N application, dropping by 0.32 to 0.85 units. Annual tea yield increased significantly, by 148–243%. N application also elevated N₂O emission fluxes by 33–277%, with notable seasonal fluctuations observed. N₂O flux was positively correlated with N rates, water-filled pore space (WFPS), soil temperature (T_soil), and inorganic N (NH₄⁺-N and NO₃⁻-N), while showing a negative correlation with soil pH. Random forest (RF) modeling identified WFPS, N rates, and Tsoil as the most important variables influencing N₂O flux. The cumulative N₂O emissions for N112.5, N225, and N450 were 1584, 2791, and 45046 g N ha⁻², respectively, representing increases of 1.33, 2.34, and 3.77 times compared to N0. The N₂O-N emission factors (EF) were 0.35%, 0.71%, and 0.74%, respectively, and increased with higher N rates. These findings highlight the importance of selecting appropriate fertilization timing and improving water and fertilizer management as key strategies for mitigating soil acidification, enhancing nitrogen use efficiency (NUE), and reducing N₂O emissions in acidic tea-plantation systems. This study offers a theoretical foundation for developing rational N fertilizer management practices and strategies aimed at reducing N₂O emissions in tea-plantation soils. Full article

Fires play a significant role in shaping the Miombo woodlands. Understanding how fire affects the Miombo region’s resilience is crucial for ensuring its sustainability. This study evaluated plant composition and structure across different fire frequencies in the Miombo woodlands of the LevasFlor Forest Concession (LFC), central Mozambique. Fire frequency clusters-high (HFF), moderate (MFF), and low (LFF)-were identified using a 21-year remote-sensing dataset. In each cluster, 90 random sampling plots were established (30 per cluster). In each plot, the diameter at breast height (DBH) and total height of the saplings and trees were measured. Subplots were used to count and identify seedlings, herbs, climbers, and grasses. Plant species richness, evenness,—diversity, the importance value index (IVI), and similarity were computed to assess plant composition. For the structure, stem density, biomass, basal area, diameter, and height were assessed. A total of 124 plant species-including trees, saplings, seedlings, herbs, climbers, and grasses-were identified across the three clusters. The Bray-Curtis Dissimilarity Index, tested with an ANOSIM similarity test, revealed significant differences in plant species composition among clusters (p < 0.0003), with an overall average dissimilarity of 71.98%. In the HFF cluster, fire-tolerant species were among the five species with the highest IVI, while fire-sensitive species predominated in the LFF. Additionally, the Kruskal-Wallis test indicated significant differences in seedling stem density (p < 0.005) between the LFF and other clusters. However, overall, the composition and structure attributes suggested that current fire regime does not significantly compromise the plant species resilience of the Miombo woodlands in the LFC. Still, it is essential to concentrate management and conservation efforts on seedlings of some key Miombo species, such as Brachystegia spiciformis, whose ecology is particularly affected by fire. Full article

Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features was also found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features, and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations. Full article

The classification methods of machine learning have been widely used in almost every discipline. A new classification method, called Taba regression, was introduced for analyzing binary, multinomial, and ordinal outcomes. To evaluate the performance of Taba regression, liver cirrhosis data obtained from a Mayo Clinic study were analyzed. The results were then compared with an artificial neural network (ANN), random forest (RF), logistic regression (LR), and probit analysis (PA). The results using cirrhosis data revealed that the Taba regression model could be a competitor to other classification models based on the true positive rate, F-score, accuracy, and area under the receiver operating characteristic curve (AUC). Taba regression can be used by researchers and practitioners as an alternative method of classification in machine learning. In conclusion, the Taba regression provided a reliable result with respect to accuracy, recall, F-score, and AUC when applied to the cirrhosis data. Full article

The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law’s texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire–ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm’s effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, p < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity. Full article

Background: Previous epidemiological studies have shown that diabetes is associated with an increased risk of several cancers, including bladder cancer. However, prediction models for bladder cancer among diabetes patients remain scarce. This study aims to develop a scoring system for bladder cancer risk prediction among diabetes patients who receive routine care in general outpatient clinics using a machine learning-guided approach. Methods: A territory-wide retrospective cohort study was conducted using electronic health records of Hong Kong. Patients who received diabetes care in public general outpatient clinics between 2010 and 2019 without a history of malignancy were identified and followed up until December 2019. To develop a scoring system for bladder cancer risk prediction, random survival forest was employed to guide variable selection, and Cox regression was subsequently applied for weight assignment. Results: Of the 382,770 patients identified, 644 patients developed bladder cancer during follow-up (median: 6.2 years). The incidence rate was 0.29 per 1000 person-years. In the final time-to-event scoring system, age, serum creatinine, sex, and smoking were included as predictors. Serum creatinine ≥94 µmol/L appeared to be associated with an increased risk of developing bladder cancer. The 2-year and 5-year AUCs on test set were 0.88 (95%CI: 0.84–0.92) and 0.86 (95%CI: 0.80–0.92) respectively. Conclusions: Renal dysfunction could be a potential predictor of bladder cancer among diabetes patients. The proposed scoring system could be potentially useful for providing individualized risk prediction among diabetes patients. Full article

From a global ecological management perspective, as a core tree species in the mountain ecosystem of Xinjiang, the study of the spatial distribution characteristics of Picea schrenkiana var. tianschanica is crucial for maintaining the ecological balance in the Tianshan region. This study focuses on the western section of the Tianshan mountains in Xinjiang and employs the variogram analysis technique to explore the spatial heterogeneity of Picea schrenkiana var. tianschanica biomass. Successively, the study implements ordinary kriging, multivariate linear regression, the random forest algorithm, and an innovative random forest residual kriging method to conduct a spatial interpolation analysis of Picea schrenkiana var. tianschanica biomass in the target area. The results indicate that the biomass of Picea schrenkiana var. tianschanica exhibits moderate spatial autocorrelation, with its distribution pattern being influenced by a combination of topography, climate, and soil conditions. After comparing multiple spatial interpolation methods, it is found that the hybrid model combining regression analysis and kriging, delivers the best performance (R² = 0.642, RMSE = 40.18, RMSPE = 44.6). This model not only significantly improves the prediction accuracy, but also provides an intuitive and accurate spatial distribution map of Picea schrenkiana var. tianschanica biomass in the western section of the Tianshan mountains which reveals the global ecological importance of Picea schrenkiana var. tianschanica in an intuitive and accurate way, providing valuable scientific evidence and practical guidance for the field of international ecological protection and resource management. Full article

Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data. Full article

In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the extreme learning machine (ELM) model to enhance detection accuracy and real-time monitoring capabilities. Our approach involves collecting high-frequency current signals from 23 types of loads using a self-developed AC series arc fault data acquisition device. We then extract 14 features from both the time and frequency domains as candidates for arc fault diagnosis, employing a random forest to select the most significantly changed features. Finally, we design an ELM classifier for series arc fault diagnosis, achieving an identification accuracy of 99.00% ± 0.26%. Compared to existing series arc fault diagnosis methods, our ELM-based method demonstrates superior recognition performance. This study contributes to the field by providing a more accurate and efficient diagnostic tool for series AC arc faults, with broad implications for electrical safety and fire prevention. Full article

The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predicted by models with varying feature extraction methods and regression algorithms. Back propagation neural network (BPNN), which has been rarely investigated in previous work, is used to construct regression models, the performances of which are compared with models using random forest (RF) and partial least squares regression (PLSR). Independent component analysis (ICA), competitive adaptive reweighing sampling (CARS), and their dual combinations served to extract spectral features. In camellia oil adulteration with rice bran oil, both the ICA-BPNN and ICA-PLSR models are found to achieve satisfactory performances. For camellia oil adulteration with rice bran oil and corn oil, on the other hand, the performances of BPNN-based models are substantially deteriorated, and the best prediction accuracy is achieved by a PLSR model coupled with CARS-ICA. In addition to performance fluctuations with varying regression algorithms, the output for feature extraction method also played a vital role in ultimate prediction performance. Full article

Tree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datasets can be obtained and analyzed to inherently improve the current understanding of broadleaf tree species distribution. The utility of RPA for mapping broadleaf species at broader scales using satellite data needs to be explored. This study investigates the use of RPA RGB imagery captured during peak fall foliage to leverage coloration commonly exhibited by different broadleaf tree species during phenology transition to delineate individual tree crowns and map species distribution. Initially, a two-step hybrid segmentation procedure was designed to delineate tree crowns for two broadleaf forests using RPA imagery collected during the fall season. With the tree crowns, a subsequent Object-based Random Forest (ORF) model was tested for classifying common and economically important broadleaf tree species groups. The classified map was further utilized to improve ground reference data for mapping species distribution at the stand and landscape scales using multispectral satellite imagery (1.4 m to 10 m). The results indicated an improvement in the overall accuracy of 0.13 (from 0.68 to 0.81) and a MICE metric of 0.14 (from 0.61 to 0.75) using reference samples derived from RPA data. The results of this preliminary study are promising in utilizing RPA for multi-scale mapping of broadleaf tree species effectively. Full article

As the Internet of Things (IoT) expands rapidly and 5G networks become more widespread, the need for strong cybersecurity measures in IoT systems has become increasingly critical. Traditional security methods are no longer sufficient due to the shear volume, diversity, and limited processing capabilities of IoT devices. The high data transmission speeds of 5G networks further intensify the potential risks, making it essential to implement effective security measures. One of the major threats to IoT systems is Cross-Site Scripting (XSS) attacks. To address this issue, we introduce a new machine learning (ML) approach designed to detect and predict XSS attacks on IoT systems operating over 5G networks. By using ML classifiers, particularly the Random Forest classifier, our approach achieves a high classification accuracy of 99.89% in identifying XSS attacks. This research enhances IoT security by addressing the emerging challenges posed by 5G networks and XSS attacks, ensuring the safe operation of IoT devices within the 5G ecosystem through early detection and prevention of vulnerabilities. Full article

Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety and well-being of animals. This study aims to develop a rapid and cost-effective method using an electronic nose (E-nose) and machine learning algorithms to predict whether ZEN levels in pet food exceed the regulatory limits (250 µg/kg), as set by Chinese pet food legislation. A total of 142 pet food samples from various brands, collected between 2021 and 2023, were analyzed for ZEN contamination via liquid chromatography–tandem mass spectrometry. Additionally, the “AIR PEN 3” E-nose, equipped with 10 metal oxide sensors, was employed to identify volatile compounds in the pet food samples, categorized into 10 different groups. Machine learning algorithms, including liner regression, k-nearest neighbors, support vector machines, random forests, XGBoost, and multi-layer perceptron (MLP), were used to classify the samples based on their volatile profiles. The MLP algorithm showed the highest discrimination accuracy at 86.6% in differentiating between pet food samples above and below the ZEN threshold. Other algorithms showed moderate accuracy, ranging from 77.1% to 84.8%. The ensemble model, which combined the predictions from all classifiers, further improved the classification performance, achieving the highest accuracy at 90.1%. These results suggest that the combination of E-nose technology and machine learning provides a rapid, cost-effective approach for screening ZEN contamination in pet food at the market entry stage. Full article

This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. These elements improve energy efficiency and promote sustainability. Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. Real data on electricity, gas, and water consumption are used to optimize load management under isolated conditions. Electric Vehicles (EVs) are also considered in the system. They serve as energy storage devices and, through Vehicle-to-Grid (V2G) technology, can supply power when needed. A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. The metrics considered include accuracy, efficiency, emissions, and cost. The performance was compared with several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), and hybrid LSTM–CNN, and the results show that the proposed model provides the best results. For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and $R^2$ is 0.81. These results indicate that the proposed model provides robust load optimization, particularly in island mode, and highlight the potential of CSGBs for sustainable urban living. Full article

Monitoring the dynamic distribution of irrigated cropland and assessing its cooling effects are essential for advancing sustainable agriculture amid climate change. This study presents an integrated framework for irrigated cropland monitoring and cooling effect assessment. Leveraging dense time series vegetation indices with Google Earth Engine (GEE), we evaluated multiple machine learning algorithms within to identify the most robust approach (random forest algorithm) for mapping irrigated cropland in Inner Mongolia from 2010 to 2020. Furthermore, we developed an effective method to quantify the diurnal, seasonal, and interannual cooling effects of irrigation. Our generated irrigated cropland maps demonstrate high accuracy, with overall accuracy ranging from 0.85 to 0.89. This framework effectively captures regional cropland expansion patterns, revealing a substantial increase in irrigated cropland across Inner Mongolia by 27,466.09 km² (about +64%) between 2010 and 2020, with particularly pronounced growth occurring after 2014. Analysis reveals that irrigated cropland lowered average daily land surface temperature (LST) by 0.25 °C compared to rain-fed cropland, with the strongest cooling effect observed between July and August by approximately 0.64 °C, closely associated with increased evapotranspiration. Our work highlights the potential of satellite-based irrigation monitoring and climate impact analysis, offering a valuable tool for supporting climate-resilient agriculture practices. Full article