ABSTRACT Technology is an instrument to build BRI relationships, mitigate the environmental and climate impacts of BRI projects, as well as to enhance environmental sustainability in the regions. This study aims to reposition China in global climate technology transfer in BRI era and to obtain initial knowledge on needs, priorities, and barriers from the receivers’ perspective. Focus group method with aid of questionnaire survey and follow-up face-to-face interviews was adopted to capture the major issues directly expressed by receivers from these countries. A total of 63 valid questionnaires were collected, and 13 respondents were face-to-face interviewed. The results confirmed that energy and agriculture were the most prioritized sectors for mitigation and adaptation in the developing countries alongside OBOR. The prioritized technologies for mitigation included cogeneration, solar photovoltaic, and biomass/biogas electricity. Irrigation, conservation agriculture, and soil management were prioritized for adaptation in agricultural sector, and water recycling and reuse, source water protection, and urban drainage management in water resource sector. Technology cost during installation and operation was stressed as the most important factor constraining the application and diffusion of climate technologies. But communication including language, information, and ways of communication, was also identified as an important factor. This implied that the conventional climate technology transfer need adapt to changing contexts of BRI and be complemented with innovative approaches involving multi-actors in different phases of climate technology development. Due to the limited representativeness of the sample, the results can hardly be generalized to all the countries, but raised interesting topics for future researches.
Abstract Achieving satisfactory separation of N, N‐Dimethylformamide (DMF)/water is still challenging in industrial wastewater treatment. To address this issue, a novel method involving pervaporation (PV) separation using polyvinyl alcohol/chitosan (PVA/CS) blend films is proposed. Various characterization methods confirmed the strong compatibility between PVA and CS, and the blending improved their mechanical properties and thermal stability. Heat treatment significantly reduced the swelling degree of the membranes. In a binary aqueous system with low concentrations of DMF, controlled separation can be achieved by adjusting the blending ratio of PVA to CS. The membranes exhibited preferential selectivity for DMF when the CS content is 40–75 wt%, which allowed for the concentration and recovery of DMF. At 20 °C with a feed of 10 wt% DMF solution, 2 h heat‐treated PVA/CS‐50 membrane exhibited the best performance, which could be attributed to its strong hydrogen bonding force, with a total flux of 484.09 g m −2 h −1 and a separation factor of 5.41 for DMF. The PVA/CS blend membranes exhibit excellent acid and alkali resistant and are promising candidates for the separation and recovery of DMF from industrial wastewater by membrane technology.
Abstract The potential of source-diverted graywater reuse mainly relies on the efficiency and cost of graywater treatment technology. Oxygen (O 2 ) supply and utilization rate directly determine the energy consumption and pollutants removal rate in the biological graywater treatment. This study developed a gravity flow self-supplying O 2 and easy-to-maintain bio-enhanced granular-activated carbon dynamic biofilm reactor (BhGAC-DBfR) for on-site graywater treatment. Results showed that increasing of saturated/unsaturated ratio led to the continuous growth of biomass on GAC surface. Division of saturated and unsaturated zones favors the formation of aerobic-anoxic-anaerobic biofilm in the reactor. A saturated/unsaturated ratio of 1:1.1 achieved the maximum removal rate of chemical oxygen demand (COD), linear alkylbenzene sulfonates (LAS), ammonia nitrogen, and total nitrogen at 98.3%, 99.4%, 99.8%, and 83.5%, respectively. Key is that adsorption and biodegradation play important and distinct roles in the quick uptake and continuous removal of both organics and N in the system. The related genus and enzymes functional for LAS mineralization, deamination of organic N, ammonium oxidation, and nitrate respiration enabled the efficient and simultaneous removal of organics and N in the BhGAC-DBfR. This study offers a promising engineering alternative technology with great potential to achieve efficient and low-energy-input graywater treatment.
In order to reduce the influence of redundant features on the performance of the model in the process of accelerometer behavior recognition, and to improve the recognition accuracy of the model, this paper proposes an improved Whale Optimization algorithm with mixed strategy (IWOA) combined with the extreme gradient boosting algorithm (XGBoost) as a preferred method for chicken behavior identification features. A nine-axis inertial sensor was used to obtain the chicken behavior data. After noise reduction, the sliding window was used to extract 44 dimensional features in the time domain and frequency domain. To improve the search ability of the Whale Optimization algorithm for optimal solutions, the introduction of the good point set improves population diversity and expands the search range; the introduction of adaptive weight balances the search ability of the optimal solution in the early and late stages; the introduction of dimension-by-dimension lens imaging learning based on the adaptive weight factor perturbs the optimal solution and enhances the ability to jump out of the local optimal solution. This method's effectiveness was verified by recognizing cage breeders' feeding and drinking behaviors. The results show that the number of feature dimensions is reduced by 72.73%. At the same time, the behavior recognition accuracy is increased by 2.41% compared with the original behavior feature dataset, which is 95.58%. Compared with other dimensionality reduction methods, the IWOA-XGBoost model proposed in this paper has the highest recognition accuracy. The dimension reduction results have a certain degree of universality for different classification algorithms. This provides a method for behavior recognition based on acceleration sensor data.
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