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Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker's activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency. [ABSTRACT FROM AUTHOR]

With the implementation of the green development strategy and the "double carbon goal", as an important energy for sustainable development, wind power has been widely researched and vigorously developed across the world. Wind speed prediction has a major impact on the grid dispatching of wind power connection. Most current studies only focus on the deterministic prediction of wind speed. However, the traditional deterministic forecast only provides the single wind speed prediction results and cannot meet the diverse demands of dispatchers. To bridge the gap, a wind speed point-interval forecasting method is proposed that utilizes empirical wavelet transform, an improved wild horse optimization algorithm, a multi-predictor, and improved kernel density estimation. This method decomposes the wind speed sequence into stationary subsequences through empirical wavelet transform, and then optimizes three basic learners with completely different learning mechanisms to form an ensemble model using the modified wild horse optimization algorithm. Finally, the uncertainty is analysed using an improved kernel density estimation. The datasets of three sites from America's national renewable energy laboratory are used for comparison experiments with other models, and the predictions are discussed from different angles. The simulation results demonstrate that the model can produce high-precision deterministic results and high-quality probabilistic results. The reference information the model provides can be extremely valuable for scheduling operators. [ABSTRACT FROM AUTHOR]

Endosomal cargo recycling lies at the heart of subcellular trafficking processes under the management of several Ras‐related GTP‐binding proteins (Rabs) which are coordinated by their upstream regulators and require their downstream effectors to display their functions. In this regard, several Rabs have been well‐reviewed except Rab22a. Rab22a is a crucial regulator of vesicle trafficking, early endosome and recycling endosome formation. Notably, recent studies demonstrated the immunological roles of Rab22a, which are closely associated with cancers, infection and autoimmune disorders. This review provides an overview of the regulators and effectors of Rab22a. Also, we highlight the current knowledge of the role of Rab22a in endosomal cargo recycling, including the biogenesis of recycling tubules with the help of a complex with Rab22a at its core, and how different internalized cargo chooses different recycling routes thanks to the cooperation of Rab22a, its effectors and its regulators. Of note, contradictions and speculation related to endosomal cargo recycling that Rab22a brings impacts on are also discussed. Finally, this review endeavors to briefly introduce the various events impacted by Rab22a, particularly focusing on the commandeered Rab22a‐associated endosomal maturation and endosomal cargo recycling, in addition to the extensively investigated oncogenic role of Rab22a. [ABSTRACT FROM AUTHOR]

(1) Background: Propolis has attracted attention in recent years due to its important pharmacological effects. The present study aimed to investigate the botanical origins of 39 propolis samples and evaluate their antioxidant activities; (2) Methods: A HPLC-PDA system was used to analyze the phenolic compositions of propolis and poplar bud resin samples. The antioxidant activities of propolis samples were evaluated by oxygen radical absorption capacity (ORAC) and superoxide anion free radical scavenging capacity assay; (3) Results: Our study shows that 17 propolis samples were characterized by five predominant flavonoids, including 5-methoxy pinobanksin, pinobanksin, pinocembrin, pinobanksin-3-acetate, and chrysin, while 22 propolis samples were characterized by four flavonoids (pinobanksin, pinocembrin, pinobanksin-3-acetate, and chrysin). The average contents of characteristic flavonoids reached up to over 70% and 65% of total phenolics, respectively. Furthermore, the botanical origins of the two types of propolis samples were identified as Populus × euramericana cv. 'Neva' and Populus Simonii × P. nigra, respectively; (4) Conclusions: Most notably, our results reveal that these propolis samples presented excellent antioxidant activities due to their high contents of flavonoid. These flavonoid-rich propolis samples can thus be used to develop low-allergen and high-antioxidant nutraceuticals. [ABSTRACT FROM AUTHOR]

Currently, coaxial electrohydrodynamic jet (CE-Jet) printing is used as a promising technique for the alternative fabrication of drop-on-demand micro- and nanoscale structures without using a template. Therefore, this paper presents numerical simulation of the DoD CE-Jet process based on a phase field model. Titanium lead zirconate (PZT) and silicone oil were used to verify the numerical simulation and the experiments. The optimized working parameters (i.e., inner liquid flow velocity 150 m/s, pulse voltage 8.0 kV, external fluid velocity 250 m/s, print height 16 cm) were used to control the stability of the CE-Jet, avoiding the bulging effect during experimental study. Consequently, different sized microdroplets with a minimum diameter of ~5.5 µm were directly printed after the removal of the outer solution. The model is considered the easiest to implement and is powerful for the application of flexible printed electronics in advanced manufacturing technology. [ABSTRACT FROM AUTHOR]