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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.

Maritime accidents, such as ship collisions and oil spills, directly affect maritime transportation, pollute the water environment, and indirectly threaten life and property safety. Predicting the maritime accident susceptibility and taking measures in advance can effectively avoid the accident probability and reduce the risk. Therefore, this study established dynamic multi-period (monthly, yearly, and five-yearly) maritime accident prediction models based on the random forest (RF) algorithm and Automatic Identification System (AIS) data for susceptibility assessment. First, based on historical maritime accidents and influencing factor data, we generated the feature matrixes and selected the conditioning factors using the Pearson correlation coefficient. Then, we constructed the accident susceptibility models using the RF method and evaluated the model performances based on the accuracy, recall, precision, F1-measure, ROC, and AUC values. Finally, we developed accident susceptibility maps for different period scales. The results show that the monthly, yearly, and five-yearly models performed well according to the validation values. And the three-period susceptibility maps show similar patterns. The high-susceptibility areas are close to the shore, especially from the Shanghai shore to the Guangxi shore. In addition, the ship density and bathymetry are the most critical factors among the ten influencing factors in the three models, contributing around 25% and 20% of the total information. These models and maps can provide technological support for maritime accident susceptibility assessment on a multi-period scale, which can be helpful for route planning and resource allocation in marine management.

Micro-image strain sensing (MISS) is an innovative technology to measure strain within a measurement range of ±8300 microstrains. It has been proved to be effective and satisfy all requirements in the field of structural health monitoring. However, there is still room for improvement and extend the range of measurement. In this paper, an improved method is proposed to increase the measurement range of strain and displacement. Moreover, various tests were conducted to verify the efficiency of the improved method. The results showed that the modified method is efficient and accurate and can be readily used to extend the measurement range of both strain and displacement. This research will likely help stimulate the development of multifunctional sensors to obtain abundant useful information about structures in the field of structural health monitoring. It will allow measuring strain and displacement, which require different levels of accuracy, using one unified sensor.

Strain is one of the key concerns of structural health monitoring (SHM), and effective measurement of structural strain is very important for structural health condition assessment. The purpose of this paper is to investigate the application of the micro image strain sensing (MISS) sensor based on smartphone and microscope for measuring strain parameters of structural members. Firstly, metal tensile tests were performed to verify the effectiveness of the MISS sensor, and the strain results measured by the fiber Bragg grating (FBG) sensor and the MISS sensor were compared. The results showed that the mean error between the MISS and FBG sensor measured results is 7.1 με in the metal tensile test. Then, a concrete beam bending test was carried out; the mean relative error of strain values between the FBG sensor and the MISS sensor is 2.5%. Overall, the strain measurements of the MISS sensor matched well with those from the FBG sensor in each experiment. This study validates the reliability and accuracy of the MISS sensor for strain detection and the MISS sensors can play a more powerful role in SHM due to their robustness, simplicity, and low cost.

Dehydrins, also known as Group II late embryogenesis abundant (LEA) proteins, are classic intrinsically disordered proteins, which have high hydrophilicity. A wide range of hostile environmental conditions including low temperature, drought, and high salinity stimulate dehydrin expression. Numerous studies have furnished evidence for the protective role played by dehydrins in plants exposed to abiotic stress. Furthermore, dehydrins play important roles in seed maturation and plant stress tolerance. Hence, dehydrins might also protect plasma membranes and proteins and stabilize DNA conformations. In the present review, we discuss the regulatory networks of dehydrin gene expression including the abscisic acid (ABA), mitogen-activated protein (MAP) kinase cascade, and Ca2+ signaling pathways. Crosstalk among these molecules and pathways may form a complex, diverse regulatory network, which may be implicated in regulating the same dehydrin.

The Chinese government has taken great efforts to popularize rural household scale biogas digesters, since they are regarded as an effective approach to address energy shortage issues in rural areas and as a potential way of reducing greenhouse gas (GHG) emissions. Focusing on a typical rural household biogas system, the aim of this study is to systematically quantify its total direct and indirect energy, concentrating on non-renewable energy and the associated GHG emission cost over the entire life cycle to understand its net dynamic benefits. The results show that the total energetic cost for biogas output is 2.19 J/J, of which 0.56 J is from non-renewable energy sources and the GHG emission cost is 4.54 × 10−5 g CO2-equivalent (CO2-eq), with respect to its design life cycle of 20 years. Correspondingly, a net non-renewable energy saving of 9.89 × 1010 J and GHG emission reduction of 50.45 t CO2-eq can be obtained considering the coal substitution and manure disposal. However, it must be run for at least 10 and 3 years, to obtain positive net non-renewable energy savings and GHG emission reduction benefits, respectively. These results have policy implications for development orientation, follow-up services, program management and even national financial subsidy methods.

Waste management is a distinct practice aimed at reducing its effects on health and the environment and increasing energy and material recovery. The urban waste management industry has been slow to adopt new technologies, such as sanitary landfills and incineration, which enable better treatment results. The aim of a thorough ecological-economic evaluation of different treatment technologies is to extract the maximum practical benefits from investments and to ensure the minimum environmental impacts of wastes. This paper compares four garbage treatment systems, including sanitary landfills systems, fluidized bed incineration system, grate type incineration system and the current landfills system in Liaoning Province, China. By considering the economic and environmental impacts of waste treatment and disposal, impact of emissions, and contribution of wastes input, this paper constructed an emergy-based urban solid waste model for evaluating the sustainability of the holistic systems. The results in Liaoning indicate that the human health losses caused by the harmful air emissions are ranked in this order: fluidized bed incineration > grate type incineration > current landfills > sanitary landfills, while the ecosystem losses are ranked: grate type incineration > fluidized bed incineration > sanitary landfills > current landfills. The electricity yield ratios are ranked: grate type incineration > fluidized bed incineration > sanitary landfills > current landfills. Taken together this suggests that in considering the incineration option, decision makers must weigh the benefits of incineration against the significant operating costs, potential environmental impacts, and technical difficulties of operating. Emergy analysis of the urban solid treatment systems can provide a set of useful tools which can be used to compare the comprehensive performances of different waste treatment processes for decision-making and optimizing the whole process.

Urban ecosystem health assessments can be applied extensively in urban management to evaluate the status quo of the urban ecosystem, identify the limiting factors, identify key problems, optimize the scheme and guide ecological regulation. Regarding the multi-layer roles of urban ecosystems, urban ecosystem health should be assessed at different scales with each assessment providing a specific reference to urban management from its own viewpoint. Therefore, a novel framework of multi-scale urban ecosystem health assessment is established on global, national, regional and local scales. A demonstration of the framework is shown by using a case study in Guangzhou City, China, where urban ecosystem health assessment is conducted in the order of global, national, regional, and local scales, from macro to micro, and rough to detailed analysis. The new multi-scale framework can be utilized to generate a more comprehensive understanding of urban ecosystem health, more accurate orientation of urban development, and more feasible regulation and management programs when compared with the traditional urban ecosystem health assessment focusing at the local scale.

Small-scale bio-energy projects have been launched in rural areas of China and are considered as alternatives to fossil-fuel energy. However, energetic and environmental evaluation of these projects has rarely been carried out, though it is necessary for their long-term development. A village-level biomass gasification project provides an example. A hybrid life-cycle assessment (LCA) of its total nonrenewable energy (NE) cost and associated greenhouse gas (GHG) emissions is presented in this paper. The results show that the total energy cost for one joule of biomass gas output from the project is 2.93 J, of which 0.89 J is from nonrenewable energy, and the related GHG emission cost is 1.17 × 10−4 g CO2-eq over its designed life cycle of 20 years. To provide equivalent effective calorific value for cooking work, the utilization of one joule of biomass gas will lead to more life cycle NE cost by 0.07 J and more GHG emissions by 8.92 × 10−5 g CO2-eq compared to natural gas taking into consideration of the difference in combustion efficiency and calorific value. The small-scale bio-energy project has fallen into dilemma, i.e., struggling for survival, and for a more successful future development of village-level gasification projects, much effort is needed to tide over the plight of its development, such as high cost and low efficiency caused by decentralized construction, technical shortcomings and low utilization rate of by-products.

The establishment of low-carbon cities has been suggested all over the World, since cities are key drivers of energy usage and the associated carbon emissions. This paper presents a scenario analysis of future energy consumption and carbon emissions for the city of Beijing. The Long-range Energy Alternatives Planning (LEAP) model is used to simulate a range of pathways and to analyze how these would change energy consumption and carbon emissions from 2007 to 2030. Three scenarios have been designed to describe future energy strategies in relation to the development of Beijing city, namely a reference scenario (RS), control scenario (CS), and integrated scenario (IS). The results show that under the IS the total energy demand in Beijing is expected to reach 88.61 million tonnes coal equivalent (Mtce) by 2030 (59.32 Mtce in 2007), 55.82% and 32.72% lower than the values under the RS and the CS, respectively. The total carbon emissions in 2030 under the IS, although higher than the 2007 level, will be 62.22% and 40.27% lower than under the RS and the CS, respectively, with emissions peaking in 2026 and declining afterwards. In terms of the potential for reduction of energy consumption and carbon emissions, the industrial sector will continue to act as the largest contributor under the IS and CS compared with the RS, while the building and transport sectors are identified as promising fields for achieving effective control of energy consumption and carbon emissions over the next two decades. The calculation results show that an integrated package of measures is the most effective in terms of energy savings and carbon emissions mitigation, although it also faces the largest challenge to achieve the related targets.

The development of the complex and multi-dimensional urban socio-economic system creates impacts on natural capital and human capital, which range from a local to a global scale. An emergy-based multiple spatial scale analysis framework and a rigorous accounting method that can quantify the values of human-made and natural capital losses were proposed in this study. With the intent of comparing the trajectory of Beijing over time, the characteristics of the interface between different scales are considered to explain the resource trade and the impacts of emissions. In addition, our improved determination of emergy analysis and acceptable management options that are in agreement with Beijing’s overall sustainability strategy were examined. The results showed that Beijing’s economy was closely correlated with the consumption of nonrenewable resources and exerted rising pressure on the environment. Of the total emergy use by the economic system, the imported nonrenewable resources from other provinces contribute the most, and the multi‑scale environmental impacts of waterborne and airborne pollution continued to increase from 1999 to 2006. Given the inputs structure, Beijing was chiefly making greater profits by shifting resources from other provinces in China and transferring the emissions outside. The results of our study should enable urban policy planners to better understand the multi-scale policy planning and development design of an urban ecological economic system.

With support of GIS tools and Theil index, the spatial variance of urban energy consumption in China was discussed in this paper through the parallel comparison and quantitative analysis of the 30 provincial capital cities of mainland China in 2005, in terms of scale, efficiency and structure. The indicators associated with urban energy consumption show large spatial variance across regions, possibly due to diversities of geographic features, economic development levels and local energy source availability in China. In absolute terms, cities with the highest total energy consumption are mostly distributed in economic-developed regions as Beijing-Tianjin-Tangshan Area, Yangtze River Delta and Pearl River Delta of China, however, the per capita urban energy use is significantly higher in the Mid-and-Western regions. With regard to the energy mix, coal still plays the dominant role and cities in Mid-and-Western regions rely more on coal. In contrast, high quality energy carrier as electricity and oils are more used in southeast coastal zone and northern developed areas. The energy intensive cities are mainly located in the northwest, while the cities with higher efficiency are in southeast areas. The large spatial variance of urban energy consumption was also verified by the Theil indices. Considering the Chinese economy-zones of East, Middle and West, the within-group inequalities are the main factor contributing to overall difference, e.g., the Theil index for per capita energy consumption of within-group is 0.18, much higher than that of between group (0.07), and the same applies to other indicators. In light of the spatial variance of urban energy consumptions in China, therefore, regionalized and type-based management of urban energy systems is badly needed to effectively address the ongoing energy strategies and targets.

Water, energy, and food are lifelines for modern societies. The continuously rising world population, growing desires for higher living standards, and inextricable links among the three sectors make the water-energy-food (WEF) nexus a vibrant research pursuit. For the integrated delivery of WEF systems, quantifying WEF connections helps understand synergies and trade-offs across the water, energy, and food sectors, and thus is a critical initial step toward integrated WEF nexus modeling and management. However, current WEF interconnection quantifications encounter methodological hurdles. Also, existing calculation results are scattered across a wide collection of studies in multiple disciplines, which increases data collection and interpretation difficulties. To advance robust WEF nexus quantifications and further contribute to integrated WEF systems modeling and management, this study: (i) summarizes the estimate results to date on WEF interconnections; (ii) analyzes methodological and practical challenges associated with WEF interconnection calculations; and (iii) points out opportunities for enabling robust WEF nexus quantifications in the future.