Enzyme activities and glyphosate biodegradation in a riparian soil affected by simulated saltwater incursion

Soil salinization due to saltwater incursion, is a major threat to biochemical activities and thus strongly alters biogeochemical processes in a freshwater riparian of coastal estuary region. A pot incubation experiment was conducted to investigate the effects of simulated saltwater incursion on some key enzymatic activities and biodegradation dynamics of herbicide glyphosate in a riparian soil in Chongming Island located in the Yangtze River estuary, China. The results showed that saltwater addition with 10% artificial seawater significantly increased the biodegradation efficiency of glyphosate with the lowest residual concentration among all the treatments. However, glyphosate degradation was markedly decreased in the riparian soil with high levels of saltwater treatment. As compared with no saltwater treatment, the half-lives for 20% and 50% seawater treatments were prolonged by 4.9% and 21.1%, respectively. Throughout the incubation period, saltwater addition with 10% seawater stimulated the enzymatic activities in the glyphosate-spiked riparian soil, as compared to the treatment with 0% seawater. Flourescein diacetate (FDA) hydrolysis rate, dehydrogenase activity (DHA), catalase activity, and alkaline phosphatase activity in the glyphosate-spiked riparian soil treated with 10% seawater were 68.5%, 49.2%, 38.7%, and 28.6% higher than those for no saltwater treatment, respectively. The effect of 20% seawater treatment on the glyphosate-spiked riparian soil enzymatic activities fluctuated between promotion and inhibition depending on the type of enzymes. Soil enzymatic activities were severely depressed by increasing salinity level with 50% seawater treatment significantly inhibited, relative to no saltwater treatment. Especially, FDA hydrolysis rate and DHA were decreased by 73.8% and 64.8%, respectively, as compared to no saltwater treatment. Glyphosate degradation percentages were strongly positively correlated to the FDA hydrolysis rate and DHA, indicating that as compared to the other enzymes, the two enzymes contributed more to the herbicide biodegradation in the salt-affected riparian soil

Shortage Analysis and Strategies for the Water Resource in Saudi Arabia under the Rapid Development of the Tourism Industry

This study evaluates water sustainability in Saudi Arabia amid expanding tourism using a mixed-methods approach. Primary data were collected through surveys targeting 150 stakeholders in water management and tourism, while secondary data were sourced from official reports. Quantitative analysis revealed significant challenges, with an average daily per capita water consumption of 299 liters and severe stress in regions like Riyadh and Jeddah. Groundwater quantity for 2022 was recorded at 1.48 km³, and desalinated water production totaled 1.95 km³, revealing a supply-demand gap of 1.82 km³. The correlation analysis indicated a significant positive relationship (r=0.440) between tourism growth and water demand. Survey results showed 70% of respondents reported no difficulties in accessing clean water during Hajj, while 30% indicated occasional issues. Stakeholders emphasized the need for integrating renewable energy with desalination to reduce operational costs and carbon emissions. The study recommends enhancing Reverse Osmosis (RO) technology with solar energy to improve sustainability and efficiency, aligning with Saudi Arabia's Vision 2030. These findings highlight the necessity for sustainable water management strategies to balance economic growth with resource sustainability, guiding policymakers in developing effective practices for future water security.&nbsp

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme (TDACP). The scheme divides the resource allocation and management work into three stages in this paper: In the first stage, it designs reasonably strategy to allocate resources to different task slices according to demand. In the second stage, it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement (SLA) of the virtual machine in different slices. In the third stage, it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers. Thus, it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost. The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy. It adjusts the number of equivalent virtual machines based on the SLA range of system parameter, and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear. The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices

Application of Value Assessment Weights in Conservation of Modern Architectural Heritage

This study presents the weights of various indicators in the integrated conservation of our modern architectural heritage. In the AHP (Analytic Hierarchy Process), the Delphi method and Entropy method are integrally adopted to set up the evaluation indicator system of the conservation efforts, and the weight coefficient of evaluation indicators. Through the analysis, we can find that modern architectural heritages not only have the three basic values historical, artistic and scientific values, but also have significant environmental，cultural emotional and real estate values. In the assessment system, artistic and historical values are the priorities among those first-level indicators, and the real estate value is the last one. Among the second-level indicators, representative architectural art is the most important factor. Consequently, the emphases should be placed on the artistic and historical values of modern architectural heritages

From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models

Diffusion models have revolted the field of text-to-image generation recently. The unique way of fusing text and image information contributes to their remarkable capability of generating highly text-related images. From another perspective, these generative models imply clues about the precise correlation between words and pixels. In this work, a simple but effective method is proposed to utilize the attention mechanism in the denoising network of text-to-image diffusion models. Without re-training nor inference-time optimization, the semantic grounding of phrases can be attained directly. We evaluate our method on Pascal VOC 2012 and Microsoft COCO 2014 under weakly-supervised semantic segmentation setting and our method achieves superior performance to prior methods. In addition, the acquired word-pixel correlation is found to be generalizable for the learned text embedding of customized generation methods, requiring only a few modifications. To validate our discovery, we introduce a new practical task called "personalized referring image segmentation" with a new dataset. Experiments in various situations demonstrate the advantages of our method compared to strong baselines on this task. In summary, our work reveals a novel way to extract the rich multi-modal knowledge hidden in diffusion models for segmentation

Optimal filter design for power converters regulated by FCS-MPC in the MEA

For the DC electrical power distribution system onboard more electric aircraft, the voltage quality of DC bus is of a great concern since there could be significant harmonics distortions when feeding different power electronics loads. This problem can be potentially addressed by introducing a dc filter to the point-of-load converters regulated by the finite control set model predictive control (FCS-MPC). To optimize this filter, Genetic Algorithm (GA) is utilized for searching the optimal design which guarantees a low mass and low power losses. Different from the conventional filter design methods, the proposed method treats LC as design variables which need to be optimised while ensuring the output power quality. First, relations among variables, operation conditions and constraints are built based on commercial data and circuit simulations. Then, the design and optimization are developed with these relations and a Pareto-front is finally given by GA. After that, the best design is obtained by an index integrating two objectives. Lastly, the design approach is verified by experiment where an FCS-MPC regulated converter was used as a particular example fed by three different LC filters