Search Results (4,217)

Cross-Laminated Timber (CLT) is ideal for tall timber structures but relies on environmentally concerning chemical adhesives. Nailed Cross-Laminated Timber (NCLT) offers a sustainable alternative by using densified wooden nails that form eco-friendly, adhesive-free bonds through lignin’s thermoplastic properties. However, significant uncertainties remain regarding the synergistic effects of multiple wooden nails. To address this, this study systematically analyzed the impact of the group effect on the mechanical performance of wooden nail joints. The results show that within the elastic range, the number of wooden nails has no significant effect on the elastic behavior of a structure. However, it is significantly positively correlated with both the joint yield load and yield displacement, enabling the accurate prediction of the structural yield point based on the number of wooden nails. With consistent nail arrangements, the group effect coefficient for the load-bearing capacity remains highly stable and shows no significant correlation with the number of nails. Additionally, an increase in the number of wooden nails significantly enhances the deformation resistance and structural stiffness, while having a minimal impact on ductility. This study reveals the linear additive nature of the group effect in wooden nails, providing important theoretical support for the design of NCLT. Full article

This study investigates the age, growth, and mortality of the common pandora (Pagellus erythrinus) in the Central Aegean Sea, providing critical insights into its population dynamics and sustainability. A total of 589 specimens were analyzed, identifying nine age cohorts with mean total lengths ranging from 13.18 cm to 32.94 cm. Growth parameters, estimated using the von Bertalanffy growth model, yielded an asymptotic length (L∞) of 39.53 cm and a growth coefficient (k) of 0.16 year⁻¹, indicating moderate growth rates. The population exhibited non-isomorphic growth (b = 2.49, R² = 98.4), suggesting slower weight gain relative to length. Mortality estimates indicated natural mortality (M) at 0.321 year⁻¹, total mortality (Z) at 0.52 year⁻¹, and fishing mortality (F) at 0.2 year⁻¹, resulting in an exploitation rate (E) of 0.38. The fishing mortality at maximum sustainable yield (FMSY) was estimated at 0.33, with an exploitation rate at MSY (EMSY) of 0.51, suggesting that the population is currently harvested sustainably but close to the threshold of overexploitation. These findings provide essential reference points for fisheries management and highlight the need for continuous monitoring to ensure the long-term sustainability of P. erythrinus in Greek waters. Full article

Marine researchers rely heavily on ocean sound velocity, a crucial hydroacoustic environmental metric that exhibits large geographical and temporal changes. Nowadays, spatio-temporal series prediction algorithms are emerging, but their prediction accuracy requires improvement. Moreover, in terms of ocean sound speed, most of these models predict an ocean sound speed profile (SSP) at a single coordinate position, and only a few predict multi-spatial-scale SSPs. Hence, this paper proposes a new data-driven method called STA-Conv-LSTM that combines convolutional long short-term memory (Conv-LSTM) and spatio-temporal attention (STA) to predict SSPs. We used a 234-month dataset of monthly mean sound speeds in the eastern Pacific Ocean from January 2004 to June 2023 to train the prediction model. We found that using 24 months of SSPs as the inputs to predict the SSPs of the following month yielded the highest accuracy. The results demonstrate that STA-Conv-LSTM can achieve predictions with an accuracy of more than 95% for both single-point and three-dimensional scenarios. We compared it against recurrent neural network, LSTM, and Conv-LSTM models with optimal parameter settings to demonstrate the model’s superiority. With a fitting accuracy of 95.12% and the lowest root-mean-squared error of 0.8978, STA-Conv-LSTM clearly outperformed the competition with respect to prediction accuracy and stability. This model not only predicts SSPs well but also will improve the spatial and temporal forecasts of other marine environmental factors. Full article

This study explores the potential role of precision agricultural technologies (PATs) in enhancing the physical, natural, human, financial, and social capitals of farming communities in the southern Punjab region of Pakistan, specifically focusing on the districts of Bahawalpur, Rahim Yar Khan, Dera Ghazi Khan, and Multan. A stratified random sampling method with proportional allocation was employed to gather insights from four heterogeneous key stakeholder groups, including progressive farmers, researchers, extension agents, and academicians, yielding a total sample of 287 respondents. A structured questionnaire utilizing a five-point Likert scale was administered, allowing the respondents to assess the perceived potential impacts of the PATs on various livelihood assets. The findings reveal that while stakeholders recognized some potential for PATs to improve physical assets, natural resources, and human capital, the overall perceived impact remained limited across all dimensions. The highest-rated potential impact was noted in crop diversity, with an average score of 2.26 in the physical capital category. In the category of natural capital, precise plant protection practices were rated the highest, with an average score of 2.31 that showed little potential change. A reduction in labor displacement issues and generating skilful employment resources, with average scores of 2.12, were rated the highest in the human capital category. A slight increase in family income, with an average score of 2.28, was observed in the financial capital category, highlighting cautious optimism among respondents. Additionally, reducing family problems and social issues, with an average score of 2.20, was rated the highest, leading to a minimal perceived change in social capital, indicating a need for integrated approaches to foster stronger community ties. The results underscore the necessity for targeted interventions that combine technological adoption with community engagement to enhance the overall resilience of farming systems. This research contributes valuable insights into adopting PATs and their implications for sustainable livelihoods, emphasizing the importance of aligning technological advancements with the unique needs of farming communities in the face of a changing climate. Full article

Biogas (primarily biomethane), as a carbon-neutral renewable energy source, holds great potential to replace fossil fuels for sustainable hydrogen production. Conventional biogas reforming systems adopt strategies similar to industrial natural gas reforming, posing challenges such as high temperatures, high energy consumption, and high system complexity. In this study, we propose a novel multi-product sequential separation-enhanced reforming method for biogas-derived hydrogen production, which achieves high H₂ yield and CO₂ capture under mid-temperature conditions. The effects of reaction temperature, steam-to-methane ratio, and CO₂/CH₄ molar ratio on key performance metrics including biomethane conversion and hydrogen production are investigated. At a moderate reforming temperature of 425 °C and pressure of 0.1 MPa, the conversion rate of CH₄ in biogas reaches 97.1%, the high-purity hydrogen production attains 2.15 mol-H₂/mol-feed, and the hydrogen yield is 90.1%. Additionally, the first-law energy conversion efficiency from biogas to hydrogen reaches 65.6%, which is 11 percentage points higher than that of conventional biogas reforming methods. The yield of captured CO₂ reaches 1.88 kg-CO₂/m³-feed, effectively achieving near-complete recovery of green CO₂ from biogas. The mild reaction conditions allow for a flexible integration with industrial waste heat or a wide selection of other renewable energy sources (e.g., solar heat), facilitating distributed and carbon-negative hydrogen production. Full article

This study aims to develop a method for predicting walnut (Juglans regia L.) yield based on the walnut orchard point cloud model, addressing issues such as low efficiency, insufficient accuracy, and high costs in traditional methods. The walnut orchard point cloud is reconstructed using unmanned aerial vehicle (UAV) images, and the semantic segmentation technique is applied to extract the individual walnut tree point cloud model. Furthermore, the tree height, canopy projection area, and volume of each walnut tree are calculated. By combining these morphological features with statistical models and machine learning methods, a prediction model between tree morphology and yield is established, achieving prediction accuracy with a mean absolute error (MAE) of 2.04 kg, a mean absolute percentage error (MAPE) of 17.24%, a root mean square error (RMSE) of 2.81 kg, and a coefficient of determination (R²) of 0.83. This method provides an efficient, accurate, and economically feasible solution for walnut yield prediction, overcoming the limitations of existing technologies. Full article

Pest infestations remain a critical threat to global agriculture, significantly compromising crop yield and quality. While accurate pest detection forms the foundation of precision pest management, current approaches face two primary challenges: (1) the scarcity of comprehensive multi-scale, multi-category pest datasets and (2) performance limitations in detection models caused by substantial target scale variations and high inter-class morphological similarity. To address these issues, we present three key contributions: First, we introduce Insect25—a novel agricultural pest detection dataset containing 25 distinct pest categories, comprising 18,349 high-resolution images. This dataset specifically addresses scale diversity through multi-resolution acquisition protocols, significantly enriching feature distribution for robust model training. Second, we propose GC-Faster RCNN, an enhanced detection framework integrating a hybrid attention mechanism that synergistically combines channel-wise correlations and spatial dependencies. This dual attention design enables more discriminative feature extraction, which is particularly effective for distinguishing morphologically similar pest species. Third, we implement an optimized training strategy featuring a cosine annealing scheduler with linear warm-up, accelerating model convergence while maintaining training stability. Experiments have shown that compared with the original Faster RCNN model, GC-Faster RCNN has improved the average accuracy mAP0.5 on the Insect25 dataset by 4.5 percentage points, and mAP0.75 by 20.4 percentage points, mAP0.5:0.95 increased by 20.8 percentage points, and the recall rate increased by 16.6 percentage points. In addition, experiments have also shown that the GC-Faster RCNN detection method can reduce interference from multiple scales and high similarity between categories, improving detection performance. Full article

Elaeis oleifera and Elaeis guineensis, two oil palm species capable of intercrossing to produce interspecific Elaeis oleifera × Elaeis guineensis (O × G) hybrids, exhibit genetic variability in key agronomic traits such as fruit development, oil accumulation, and bunch composition. This variability influences the productivity and oil quality of the resulting hybrids. Harvesting, a critical practice in oil palm production, significantly impacts oil yield and quality. Therefore, this study aimed to ascertain the optimum harvest point (OHP) in widely cultivated O × G hybrids and its correlation with genetic backgrounds. The O × G cultivars, “Coari × La Mé” (C × LM), “Manaos × Compacta” (M × C), and “Brazil × Djongo” (B × DJ), were examined to identify notable changes during various phenological stages of bunch ripening using the O × G BBCH scale, a standardized system for describing plant growth stages based on phenological development. The research was conducted in the Southwest Colombian oil palm zone during dry and rainy seasons. Observations revealed distinctive fruit coloration patterns and increased bunch weights throughout the maturation process. However, final fruit coloration did not consistently align with maximum oil rates, indicating it as an unsuitable descriptor for OHP. The C × LM cultivar exhibited the shortest ripening period (173 days after anthesis, DAA), while M × C showed the longest at 207 DAA, followed by B × DJ at 187 DAA. Pollination efficiency varied among cultivars, with C × LM and M × C displaying higher proportions of parthenocarpic fruits. Findings suggest harvesting can occur for all cultivars between phenological stages 807 and 809—corresponding to late maturity stages in fruit development—regardless of the time of year, when maximum oil per bunch is attained. Fruit opacity, fruit cracking, and fruit detachment at stages 807 and 809 were identified as pivotal descriptors for determining the right OHP, albeit unique to each cultivar. Implementing two of these three descriptors by field workers will likely result in the highest oil yields for O × G cultivars. In conclusion, this research provides valuable insights into optimizing oil palm harvest practices, emphasizing the importance of considering genetic variability and phenological indicators for determining the optimum harvest point in interspecific O × G hybrids. Full article

Following various natural and man-made disasters, a critical challenge in emergency response is establishing an emergency living supplies distribution center that minimizes service costs while ensuring rapid and efficient delivery of essential goods to affected populations, thereby safeguarding their lives and material well-being. The study addresses this challenge by developing an optimization function to minimize the total service cost for locating such distribution centers, using connection points as a foundation. Utilizing a robust optimization approach that incorporates constraint conditions and bounded intervals as the value set for uncertain demand, the optimization function is transformed into a robust equivalent model through the dual principle. The tabu search method, integrated with MATLAB R2015b software, is employed to perform statistical analysis on the data, yielding the optimal solution. Case study analysis demonstrates that the minimum total service cost escalates with increases in robustness level and disturbance parameters. Furthermore, the model incorporating connection points consistently yields better results than the model without connection points, highlighting the efficacy of the proposed approach. Full article

In semiconductor manufacturing, the final testing phase is critical for ensuring chip quality and operational efficiency. Accurate yield prediction at this stage optimizes testing workflows, boosts production efficiency, and enhances quality control. However, existing research primarily focuses on wafer-level yield prediction, leaving the unique challenges of final testing—such as test condition variability and complex failure patterns—insufficiently addressed. This is especially critical for Radio Frequency Front-End (RFFE) chips, where high precision is essential, highlighting the need for a specialized prediction approach. In our study, a rigorous RF correlation parameter selection process was applied, leveraging metrics such as Spearman’s correlation coefficient and variance inflation factors to identify key RF-related features, such as multiple frequency-point PAE measurements and other critical electrical parameters, that directly influence final test yield. To overcome the limitations of traditional methods, this study proposes a multistrategy dynamic weighted fusion model for yield prediction. The proposed approach combines convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with sliding window averaging to capture both local features and long-term dependencies in RFFE test data, while employing a learnable weighting mechanism to dynamically fuse outputs from multiple submodels for enhanced prediction accuracy. It further incorporates incremental training to adapt to shifting production conditions and utilizes principal component analysis (PCA) in data preprocessing to reduce dimensionality and address multicollinearity. Evaluated on a dataset of over 24 million RFFE chips, the proposed model achieved a Mean Absolute Error (MAE) below 0.84% and a Root Mean Square Error (RMSE) of 1.24%, outperforming single models by reducing MAE and RMSE by 7.69% and 13.29%, respectively. These results demonstrate the high accuracy and adaptability of the fusion model in predicting semiconductor final test yield. Full article

Certain fixed-income securities, such as callable bonds and mortgage-backed securities subject to prepayment, typically exhibit negative convexity at low yields and cannot be adequately immunized through duration and convexity-matching alone. To address this residual risk, we examine the concepts of bond tilt and bond agility. We provide explicit calculations and derive several approximation formulas that incorporate higher-order terms. With the help of these methods, we are able to track the price-yield dynamics of callable bonds remarkably well, achieving mean absolute errors below 2.5% across a wide variety of callable bonds for parallel yield shifts of up to ±200 basis points. Full article

In Mexico, around 2.4 million people (1.9% of the national population) are deaf, and Mexican Sign Language (MSL) support is essential for people with communication disabilities. Research and technological prototypes of sign language recognition have been developed to support public communication systems without human interpreters. However, most of these systems and research are closely related to American Sign Language (ASL) or other sign languages of other languages whose scope has had the highest level of accuracy and recognition of letters and words. The objective of the current study is to develop and evaluate a sign language recognition system tailored to MSL. The research aims to achieve accurate recognition of dactylology and the first ten numerical digits (1–10) in MSL. A database of sign language and numeration of MSL was created with the 29 different characters of MSL’s dactylology and the first ten digits with a camera. Then, MediaPipe was first applied for feature extraction for both hands (21 points per hand). Once the features were extracted, Machine Learning and Deep Learning Techniques were applied to recognize MSL signs. The recognition of MSL patterns in the context of static (29 classes) and continuous signs (10 classes) yielded an accuracy of 92% with Support Vector Machine (SVM) and 86% with Gated Recurrent Unit (GRU) accordingly. The trained algorithms are based on full scenarios with both hands; therefore, it will sign under these conditions. To improve the accuracy, it is suggested to amplify the number of samples. Full article

The mushroom farming industry struggles to automate harvesting due to limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, and pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes of white Agaricus bisporus and brown baby bella mushrooms, capturing realistic variations in quantity, position, orientation, and growth stages. Our two-stage pose estimation pipeline combines 2D object detection and instance segmentation with a 3D point cloud-based pose estimation network using a Point Transformer. By employing a continuous 6D rotation representation and a geodesic loss, our method ensures precise rotation predictions. Experiments show that processing point clouds with 1024 points and the 6D Gram–Schmidt rotation representation yields optimal results, achieving an average rotational error of $1.67°$ on synthetic data, surpassing current state-of-the-art methods in mushroom pose estimation. The model, further, generalizes well to real-world data, attaining a mean angle difference of $3.68°$ on a subset of the M18K dataset with ground-truth annotations. This approach aims to drive automation in harvesting, growth monitoring, and quality assessment in the mushroom industry. Full article

This study investigates the impact of different noise levels on thermal comfort in outdoor environments. The research was conducted in two university squares in Xi’an, China, exhibiting distinct noise exposures, with twenty volunteers participating in the study. These individuals provided subjective evaluations of thermal comfort through questionnaires while situated in environments with disparate acoustic conditions in conjunction with the documentation of prevailing meteorological circumstances. The analysis yielded three salient findings. Initially, a marked elevation in perceived warmth was noted in environments experiencing higher noise levels, with 35.29% of subjects in the high-noise plaza (HP) reporting feeling warm (TSV = 2), which was 11.76 percentage points higher than in the low-noise plaza (LP). This included a 5.88 percentage point uptick in the frequency of “hot” (TSV = 3) thermal sensations reported in the HP. Furthermore, an intensification of thermal discomfort was observed in noisier settings, with the thermal comfort vote (TCV) in HP encompassing a spectrum from very uncomfortable to neutral and a predominant 90% of TCVs indicating discomfort, 35.29% of which were deemed very uncomfortable. Lastly, the findings suggest that high-decibel noise exposure notably amplifies the perception of heat within a specific high-temperature bandwidth. Beyond this delineated thermal threshold, the influence of noise on thermal sensation substantially diminishes. Full article

The Wenchuan earthquake, an unexpected magnitude 8.0 mega-earthquake that struck on 12 May 2008, significantly changed land cover (LC), particularly affecting vegetation and rock cover. However, the long-term effects of LC changes on ecosystem services (ESs) remain unclear in earthquake-affected regions, especially across different spatial scales. This study, focusing on Wenchuan County, employs a multi-model framework that integrates fractional vegetation coverage (FVC), rock exposure rate (FR), and ecosystem services (ESs), combining correlation analysis, geographically weighted regression (GWR), Self-organizing map (SOM) clustering, and XGBoost-SHAP model, to analyze the spatiotemporal dynamics, interrelationships, and driving mechanisms of land cover (LC) and ESs before and after the earthquake. Results show that: (1) From 2000 to 2020, FVC and FR fluctuated markedly under earthquake influence, with slight declines in habitat quality (HQ) and carbon storage (CS) and notable improvements in soil conservation (SC) and water yield (WY). (2) With increasing elevation, the FVC–CS–SC group exhibited a downward trend and synergy, while the FR–HQ–WY group increased and also showed synergy; trade-offs and synergies became more pronounced at larger scales, displaying strong spatiotemporal heterogeneity. (3) Elevation (explaining 10–60% of variance) was the main driver for LC and ESs, with land use, slope, human activities, climate, and geological conditions significantly impacting individual indicators. At the same time, the existing geological hazard points are mainly concentrated along both sides of the river valleys, which may be associated with intensified human–land conflicts. These findings offer valuable insights into ecological restoration and sustainable development in earthquake-affected regions. Full article