Search Results (271)

The environmental impact of companion animals has received little scientific attention compared to that of livestock, even though the global dog population is rapidly increasing, particularly in urban areas. This review addresses the overlooked contribution of dogs to environmental emissions, focusing on feces, urine, packaging waste, and other care-related by-products. The current knowledge from livestock research provides useful analogies for understanding nutrient excretion and gaseous emissions from dog feces, and data on nitrogen and phosphorus inputs highlight their potential to pollute soil and water. We also examine the role of plastic waste from food packaging, waste bags, and accessories, which can degrade into microplastics, and discuss recent developments in biodegradable materials. Evidence shows that owner choices—such as diet composition, protein sources, and product selection—directly affect the environmental pawprint of dogs. Mitigation strategies include optimizing diets to reduce nutrient excretion, applying feed additives developed for livestock, and improving waste management through composting or the use of emission-reducing amendments. In conclusion, dogs should no longer be viewed merely as individual household companions but as a population with a measurable environmental pawprint. Including dogs in emission reporting systems would provide a more accurate basis for mitigation policies and sustainable urban planning. Full article

This study introduces a Transformer-based forecasting tool termed EXPERT (EXchange rate Prediction using Encoder Representation from Transformers) and applies it to exchange rate forecasting. We developed and trained a Transformer-based forecasting model, then evaluated its performance on nine currency pairs with various characteristics. Finally, we benchmarked its effectiveness against six established forecasting models: Linear Regression, Random Forest, Stochastic Gradient Descent, XGBoost, Bagging Regression, and Long Short-Term Memory. Our dataset covers the period from 1999 to 2022. The models were evaluated for their ability to predict the next day’s closing price using three performance metrics. In addition, the EXPERT system was evaluated on its ability to extend forecast horizons and as the core of a trading strategy. The model’s robustness was further evaluated using the Multiple Comparisons with the Best (MCB) metric on five dataset samples. Full article

As a fundamental information carrier in Industrial Internet of Things (IIoT), electromagnetic spectrum data presents critical challenges for efficient spectrum sensing and situational awareness in smart industrial cognitive radio systems. Addressing sparse sampling limitations caused by energy-constrained transceiver nodes in Unmanned Aerial Vehicle (UAV) spectrum monitoring, this paper proposes a compressive sensing-based 3D spectrum tensor completion framework for extrapolative reconstruction in obstructed areas (e.g., building occlusions). First, a Sparse Coding Neural Gas (SCNG) algorithm constructs an overcomplete dictionary adaptive to wide-range spectral fluctuations. Subsequently, a Bag of Pursuits-optimized Orthogonal Matching Pursuit (BoP-OOMP) framework enables adaptive key-point sampling through multi-path tree search and temporary orthogonal matrix dimensionality reduction. Finally, a Neural Gas competitive learning strategy leverages intermediate BoP solutions for gradient-weighted dictionary updates, eliminating computational redundancy. Benchmark results demonstrate 43.2% reconstruction error reduction at sampling ratios r ≤ 20% across full-space measurements, while achieving decoupling of highly correlated overlapping subspaces—validating superior estimation accuracy and computational efficiency. Full article

This study addresses the challenge of accurate classification under missing data conditions by integrating multiple imputation strategies with discriminant analysis frameworks. The proposed approach evaluates six imputation methods (Mean, Regression, KNN, Random Forest, Bagged Trees, MissRanger) across several discriminant techniques. Simulation scenarios varied in sample size, predictor dimensionality, and correlation structure, while the real-world application employed the Cirrhosis Prediction Dataset. The results consistently demonstrate that ensemble-based imputations, particularly regression, KNN, and MissRanger, outperform simpler approaches by preserving multivariate structure, especially in high-dimensional and highly correlated settings. MissRanger yielded the highest classification accuracy across most discriminant analysis methods in both simulated and real data, with performance gains most pronounced when combined with flexible or regularized classifiers. Regression imputation showed notable improvements under low correlation, aligning with the theoretical benefits of shrinkage-based covariance estimation. Across all methods, larger sample sizes and high correlation enhanced classification accuracy by improving parameter stability and imputation precision. Full article

Assessing and forecasting hydrocarbon resource potential (HRP) is of great significance. However, due to the complexity and uncertainty of geological conditions during hydrocarbon accumulation, it is challenging to accurately establish HRP models. This study employs machine learning methods to construct a HRP assessment model. First, nine primary controlling factors were selected from the five key conditions for HRP: source rock, reservoir, trap, migration, and accumulation. Subsequently, three prediction models were developed based on the backpropagation (BP) neural network, BP-Bagging algorithm, and BP-AdaBoost algorithm, with hydrocarbon resources abundance as the output metric. These models were applied to the Dongpu Depression in the Bohai Bay Basin for performance evaluation and optimization. Finally, this study examined the importance of various variables in predicting HRP and analyzed model uncertainty. The results indicate that the BP-AdaBoost model outperforms the others. On the test dataset, the BP-AdaBoost model achieved an R² value of 0.77, compared to 0.73 for the BP-Bagging model and only 0.64 for the standard BP model. Variable importance analysis revealed that trap area, sandstone thickness, sedimentary facies type, and distance to faults significantly contribute to HRP. Furthermore, model accuracy is influenced by multiple factors, including the selection and quantification of geological parameters, dataset size and distribution characteristics, and the choice of machine learning algorithm models. In summary, machine learning provides a reliable method for assessing HRP, offering new insights for identifying high-quality exploration blocks and optimizing development strategies. Full article

Thailand generated 27.2 million tons of municipal solid waste in 2024, of which 12% was plastic waste, predominantly single-use plastics. The mismanagement of plastic waste can lead to significant long-term environmental issues, including the release of toxic chemicals through open burning and air pollution, posing risks to human health. Effective and efficient plastic waste collection and recycling are therefore essential to address the reduction and management of plastic waste, as well as to support a low-carbon energy transition. This study assessed three community-driven initiatives by conducting a comparative sustainability assessment of plastic bag recycling under real-life conditions in Thailand using a multi-criteria decision-making framework. The results of the assessment in three municipalities showed that the actual collection rates in all initiatives remained extremely low (0.0014–0.1555%). The highest rankings were observed with recycling initiatives driven by superior collection rates and favorable economic returns. The hindrances to promoting sustainability are found to be due to policy inconsistency, ineffective leadership, and behavioral barriers. The practical collection rates should increase to at least 25% to be more sustainable in terms of economic, social, and environmental aspects compared to those without the recycling initiative. These findings, thus, provide specific targets for improving plastic waste separation and management strategies in all regions facing similar challenges. Full article

Background/Objectives: Mycophenolic acid (MPA) is used as part of first-line combination immunosuppressive therapy for renal transplant recipients. Personalized dosing approaches are needed to balance efficacy and minimize toxicity due to the pharmacokinetic variability of the drug. In this study, population pharmacokinetic (PopPK) modeling and machine learning (ML) techniques are coupled to provide valuable insights into optimizing MPA therapy. Methods: Using data from 76 renal transplant patients, two PopPK models were developed to describe and predict MPA levels for two different formulations (enteric-coated mycophenolate sodium and mycophenolate mofetil). Covariate effects on drug clearance were assessed, and Monte Carlo simulations were used to evaluate exposure under normal and reduced clearance conditions. ML techniques, including principal component analysis (PCA) and ensemble tree models (bagging and boosting), were applied to identify predictive factors and explore associations between MPA plasma/saliva concentrations and the examined covariates. Results: Total daily dose and post-transplant time (PTP) were identified as key covariates affecting clearance. PCA highlighted MPA dose as the primary determinant of plasma levels, with urea and PTP also playing significant roles. Boosted tree analysis confirmed these findings, demonstrating strong predictive accuracy (R² > 0.91). Incorporating saliva MPA levels improved predictive performance, suggesting that saliva may be a complementary monitoring tool, although plasma monitoring remained superior. Simulations allowed exploring potential dosing adjustments for patients with reduced clearance. Conclusions: This study demonstrates the potential of integrating machine learning with population pharmacokinetic modeling to improve the understanding of MPA variability and support individualized dosing strategies in renal transplant recipients. The developed PopPK/ML models provide a methodological foundation for future research toward more personalized immunosuppressive therapy. Full article

Despite the advances on data analysis methodologies in the last decades, most of the traditional regression methods cannot be directly applied to large-scale data. Although aggregation methods are especially designed to deal with large-scale data, their performance may be strongly reduced in ill-conditioned problems (due to collinearity issues). This work compares the performance of a recent approach based on normalized entropy, a concept from information theory and info-metrics, with bagging and magging, two well-established aggregation methods in the literature, providing valuable insights for applications in regression analysis with large-scale data. While the results reveal a similar performance between methods in terms of prediction accuracy, the approach based on normalized entropy largely outperforms the other methods in terms of precision accuracy, even considering a smaller number of groups and observations per group, which represents an important advantage in inference problems with large-scale data. This work also alerts for the risk of using the OLS estimator, particularly under collinearity scenarios, knowing that data scientists frequently use linear models as a simplified view of the reality in big data analysis, and the OLS estimator is routinely used in practice. Beyond the promising findings of the simulation study, our estimation and aggregation strategies show strong potential for real-world applications in fields such as econometrics, genomics, environmental sciences, and machine learning, where data challenges such as noise and ill-conditioning are persistent. Full article

Pine wilt disease, transmitted primarily by Monochamus alternatus (Hope, 1842) adults, causes severe ecological and economic losses globally. Conventional chemical controls face challenges of resistance and non-target toxicity. This study identified Beauveria bassiana (Bals.-Criv.) Vuill. strain B4 as a high-virulence biocontrol agent against adult M. alternatus. Laboratory bioassays compared four strains (B1–B4), with B4 exhibiting rapid lethality (LT₅₀ = 6.61 days at 1 × 10⁸ spores/mL) and low median lethal concentration (LC₅₀ = 9.63 × 10⁵ spores/mL). Critically, B4 infection induced significant behavioral suppression, including reduced appetite and mobility prior to death. In forest trials, pheromone-enhanced nonwoven fabric bags impregnated with B4 spores reduced trap catches by 66.4% within one month, with effects persisting for over a year without reapplication. The slow-release carrier system enabled continuous spore dissemination and sustained population suppression. These results demonstrate that B4’s dual action—rapid lethality and behavioral disruption—provides an effective, eco-friendly strategy for sustainable pine wilt disease management. Full article

This study examines the effects of reduced nitrogen (N) application on rice straw N depolymerization in coastal saline paddy soil to establish a scientific basis for optimizing N application strategies during straw incorporation in coastal paddy systems. A 360-day field straw bag burial experiment was conducted using four N application levels: N0 (control, without N fertilizer), N1 (225 kg N/ha), N2 (300 kg N/ha), and N3 (375 kg N/ha). The results indicated that applying 300 kg N/ha significantly (p < 0.05) increased dissolved organic N (DON) content, apr and chiA gene copies, and the activities of alkaline protease, chitinase, leucine aminopeptidase, and N-acetylglucosaminidase. In addition, the application of 300 kg N/ha enhanced the synergistic effects of alkaline protein- and chitin-degrading microbial communities. Pseudomonas, Brevundimonas, Sorangium, Cohnella, and Thermosporothrix were identified as keystone taxa predominant in straw N depolymerization. Straw N depolymerization occurred by two primary pathways: direct regulation of enzyme activity by straw properties of total carbon and electrical conductivity, and indirect influence on N hydrolase activity and DON production through modified microbial community structures. The findings suggest that an application rate of 300 kg N/ha is optimal for promoting straw N depolymerization in coastal saline paddy fields. Full article

Metal/composite interfacial interactions are critical to the mechanical performance of Fiber Metal Laminates (FMLs). In this study, the feasibility of successively combining Vacuum-Assisted Resin Transfer Molding (VARTM) and Vacuum Bagging (VB) was investigated, a strategy that has not been reported in the literature for the fabrication of FMLs with 2/1 stacking configuration, using low-cost 3003-H14 aluminum alloy. The substrate was surface modified through mechanical abrasion and chemical etching in an ultrasonic bath with a 0.1 M NaOH solution, varying the exposure time (20, 40, and 60 min). These surfaces were characterized by optical microscopy and atomic force microscopy (AFM), conducting both qualitative and quantitative analyses of the two- and three-dimensional surface features associated with pore morphology. Additionally, their effects on interlaminar strength and Mode I failure modes of the adhesive joint at the metal/composite interface were evaluated. Micrographs of the surface variants revealed a systematic evolution of the metallic microstructure. The T-peel tests demonstrated that the microstructural features influenced the interlaminar behavior. The 40 min treatment exhibited the highest initial peak force (26.4 N) and the highest average peel force (12.4 N), with a predominantly cohesive mixed-mode failure, representing the most favorable configuration for maximizing adhesion at the metal/composite interface. Full article

Macroplastic pollution is a growing environmental concern, threatening the marine environment. Despite growing awareness of marine plastic pollution, few studies have assessed the effectiveness of in situ technologies such as safety nets for macroplastic interception. This study aims to evaluate the effectiveness of safety net (SN) systems in intercepting macroplastic debris in the different zones of recreational Yugang Park Beach (YPB), Zhanjiang Bay, China. Safety nets were installed at stations representing different hydrodynamic conditions, and macroplastic debris (2.5–80 cm) was collected and analyzed for size, color, and shape characteristics. Two survey comparisons revealed a higher debris density in the winter survey (1.8 ± 0.3 items m²) than in the summer survey (1.5 ± 0.3 items m²). Most debris fell within the 10–40 cm range, with transparent low-density polyethylene plastic bags being the dominant type, particularly in the winter survey (80.7%). Statistical analysis indicated that plastic size was likely related to net retention characteristics, while tidal influences accounted for a major portion of spatial variability in debris accumulation. These findings suggest that SN systems are effective tools for macroplastic interception and could inform evidence-based coastal management strategies to reduce plastic pollution in similar coastal environments. Full article

Microplastic pollution is an escalating environmental and public health issue. Defined as plastic particles less than 5 mm in size, microplastics have been found in oceans, rivers, food, drinking water, air, and even human tissues. While scientific research on microplastics has expanded significantly, public understanding and behavioral change remain limited. This literature scan synthesizes global findings on public awareness, perceptions, and responses to microplastics, drawing from surveys, focus groups, and online behavioral data collected by existing studies. It explores the following: (1) general knowledge and perceived environmental and health risks; (2) trust in scientific and governmental sources; (3) willingness to adopt behavioral changes; (4) attitudes toward policy and corporate responsibility. Public concern is high, especially regarding marine life and food safety, but varies across populations based on education, socioeconomic status, and media exposure. Despite growing concern, psychological distance and persistent knowledge gaps hinder meaningful action. Communication strategies such as school programs, media campaigns, and eco-labels show mixed success, while regulatory interventions like plastic bags or microbead bans are more effective when supported by clear public messaging. This literature scan highlights the need for interdisciplinary collaboration to close the knowledge–behavior–policy gap and strengthen public engagement, particularly in urban settings where consumption and waste generation are concentrated. Full article

The paper proposes the use of Bag-of-Words classifiers for the reliable detection of tuberculosis infection from cough recordings. The effect of using both independent and combined distinct feature extraction procedures and encoding strategies is evaluated in terms of standard performance metrics such as the Area Under Curve (AUC), accuracy, sensitivity, and F1-score. Experiments were conducted on two distinct large datasets, using both the original recordings and extended versions obtained by augmentation techniques. Performances were assessed by repeated k-fold cross-validation and by employing external datasets. An extensive ablation study revealed that the proposed approach yields up to 0.77 accuracy and 0.84 AUC values, comparing favorably against existing solutions and exhibiting robustness against various combinations of the setup parameters. Full article

Artificial intelligence has become crucial in sports, leveraging its analytical capabilities to enhance the understanding and prediction of complex events. Machine learning algorithms in sports, especially basketball, are transforming performance analysis by identifying patterns and trends invisible to traditional methods. This technology provides in-depth insights into individual and team performance, enabling precise evaluation of strategies and tactics. Consequently, the detailed analysis of every aspect of a team’s routine can significantly elevate the level of competition in the sport. This study investigates a range of machine learning models, including Logistic Regression (LR), Ridge Regression Classifier (RR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Stacking Classifier (STACK), Bagging Classifier (BAG), Multi-Layer Perceptron (MLP), AdaBoost (AB), and XGBoost (XGB), as well as deep learning architectures such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to compare their effectiveness in predicting game outcomes in the NBA and WNBA leagues. The results show highly acceptable prediction accuracies of 65.50% for the NBA and 67.48% for the WNBA. This study allows us to understand the impact that artificial intelligence can have on the world of basketball and its current state in relation to previous studies. It can provide valuable insights for coaches, performance analysts, team managers, and sports strategists by using machine learning and deep learning models to predict NBA and WNBA outcomes, enabling informed decisions and enhancing competitive performance. Full article