Search

The treatment of locally advanced rectal cancer (LARC) is extremely challenging, and it is difficult to achieve satisfactory results with surgical resection alone. In recent years, the diagnosis and treatment of LARC tends to be multi-disciplinary (MDT) mode. The emerging neoadjuvant treatment strategy is a milestone. At present, the preferred treatment for LARC is neoadjuvant chemoradiotherapy combined with total mesorectal excision. This article summarizes the main treatments of LARC neoadjuvant therapy, hoping to provide reference for clinical diagnosis and treatment.

Improving energy efficiency by adjusting the structure of energy consumption types is of great significance for reducing carbon emissions in the short term. The present paper constructs new data envelopment analysis models for evaluating energy utilization under different structural conditions and calculating potential emissions reductions. We conducted empirical research on 30 provinces in China from 2003 to 2019—a time frame that coincides with the instituting of China's “coal‐to‐gas” program. Our results show that technological progress is the main way for China to reduce carbon emissions and that it is possible to reduce the total amount of carbon emissions by 35%. Additionally, optimizing the energy consumption structure following the coal‐to‐gas program guidelines could reduce the country's carbon emissions by a further 25%. Finally, this paper provides specific policy recommendations based on the efficiency analysis results to guide each province in reducing carbon emissions under the conditions of energy demand growth. [ABSTRACT FROM AUTHOR]

Improving green productivity (GP) is critical for achieving global sustainable green development. Yet, past efficiency studies on the African continent have ignored Africa's GP, estimation, and driving factors. Specifically, the role of sovereign debt vulnerability, environmental entrepreneurship, and institutional quality in ensuring Africa's green productivity development is lacking. Therefore, this study built a meta‐frontier biennial weighted modified data envelopment analysis (DEA) model to evaluate green productivity across resource‐intensive and non‐resource‐intensive African countries from 2010 to 2019. Further, the study adopted the global Malmquist‐Luenberger productivity index (GML) to investigate the dynamic changes in African green productivity growth. The results suggest that only a few African countries are efficient, implying more room for improvement for the other countries. The heterogeneity of green productivity across the resource's classification exhibits significant disparity. The technology gap also prevails among the two resource classifications of African countries. The GML index indicates that Africa's green productivity is mainly explained by technical change instead of efficiency change. The bootstrap truncation regression suggests an inverted U‐shaped nexus between growth and green productivity in Africa and resource‐intensive countries (RIC). Green environmental entrepreneurship positively affects green productivity in Africa. On the components of institutional quality, we observed that the rule of law positively influences green productivity in non‐resource‐intensive countries (NRIC) and the whole of Africa. In contrast, political stability, government effectiveness, voice and corruption negatively affected green productivity in NRIC. Also, foreign direct investment (FDI) and sovereign debt vulnerability negatively correlate to green productivity in Africa as a whole and NRIC, respectively. The findings of this study aid in raising awareness of green productivity in developing countries, which is crucial in boosting global green development. [ABSTRACT FROM AUTHOR]

When developing strategies aimed at mitigating air pollution in densely populated urban areas, it is vital to accurately investigate the vertical distribution of airborne particulate matter (PM) and its primary influencing factors. For this study, field experiments were conducted to quantify the vertical distribution and dispersion processes of PM at five vertical heights related to trees—including at street level near vehicular emission sources (0.3 m), pedestrian breathing height (1.5 m), beneath the canopy (6 m), mid-canopy (9 m), and the top of the canopy (12 m)—within a street-facing building in Wuhan, China. Comparing the vertical dispersion patterns of PM with six particle sizes (PM1, PM2.5, PM4, PM7, PM10, and total suspended particulates—TSPs), larger particles exhibited more pronounced variations with height, notably TSPs (correlation coefficient of −0.95) and PM10 (−0.84). The findings consistently revealed a downward trend in PM concentrations across various particle sizes with increasing height, indicating a negative linear correlation between particle concentrations and altitude within the street canyon. For every 1% increase in vertical height, the PM2.5 concentration decreased by approximately 5.44%, the PM10 concentration decreased by 132.1%, and the TSP concentration decreased by 180.6%. These findings show potential for guiding building designers in developing effective strategies, such as optimal vent placement, in order to mitigate the intrusion of outdoor air pollution—particularly PM2.5—into indoor environments. Furthermore, this research provides novel insights for residents living in street-facing buildings and individuals with respiratory diseases, aiding them in the selection of residential floors to minimize health risks associated with exposure to respirable PM. [ABSTRACT FROM AUTHOR]

This study examines the cyclic shear-slip performance of a novel prefabricated composite shear stud (PCSS) connector designed for accelerated bridge construction (ABC) of composite structures. An in-depth experimental procedure is employed involving the design and fabrication of four push-out specimens with two different stud configurations. The specimens are then tested under both monotonic and cyclic-to-monotonic loading protocols. Upon completion of the testing phase, a meticulous inspection of the fractography is conducted to delineate and qualify the failure mode of the PCSS connectors. Simultaneously, a comprehensive shear-slip curve is derived from the measured data, enabling a detailed analysis over the mechanical performance. Furthermore, the study calculates a series of deformation-associated indicators from the shear-slip curve, effectively quantifying the ductility, recoverability, and capacity of the PCSS. The test results accentuate a well-deformed and ductile failure mode of the tested PCSS specimens, marked by the stud fracture and crushing of surrounding concrete. This could be attributed to the constraint of vertical plates of the PCSS on the concrete, which improves the capacity and recoverability of the PCSS. Whereas, the performance of the PCSS is also notably influenced by the group nail effect, for which the ductility and per-stud capacity degrade with the increase in the number of studs. Especially, the PCSS specimen exhibits full elastic-to-plastic hysteresis loops under cyclic loads, together with the satisfied ductility, implying an excellent potential of the PCSS to dissipate energy under impact loads. In addition, the PCSS displays a robust stiffness across different cyclic loading blocks. Hence, satisfactory post-damage ductility has still been observed in the PCSS under the monotonic loading after the cyclic loading. In conclusion, this work elucidates the superiority of the PCSS in terms of capacity, ductility, and recoverability, providing a promising basis for their application in the accelerated construction of composite bridges. [ABSTRACT FROM AUTHOR]

Due to the progress in light detection and ranging (LiDAR) technology, the collection of road point cloud data containing depth information and spatial coordinates has become more accessible. Consequently, utilizing point cloud data for pavement distress detection and quantification emerges as a crucial approach to improving the precision and reliability of road maintenance procedures. This paper aims to automatically detect and visualize pavement distress using LiDAR, deep learning-based 3D object detection method, and building information modeling (BIM). A pavement distress data set is first established using the point cloud data obtained from LiDAR. Then, the 3D object detection network, namely PointPillar, is employed for pavement distress detection, and the detection results will be quantified at a region-level. Finally, pavement BIM model integrating parametrically modeled distress families is built to visually manage the detected distress. After training and validating the model with the pavement distress data set, a detection performance index of recall is 78.5%, mean average precision (mAP) is 62.7%, which is better than other compared point cloud-based methods though the detection performance can be further improved. In addition, a newly untrained section of road is applied for the experiment. The detected distress is integrated in BIM environment for a visual management, providing a better maintenance guidance. [ABSTRACT FROM AUTHOR]