Suchergebnisse

This study aims to assess the perception of villagers in two villages for forest resources management and analyzes the forest resources uses patterns of the people residing surrounding the Khammouane provincial protected area (PPA), Khaymaeb and Houayhaed villages, Boualapha district, Khammouane province. The research findings revealed that: the involvement of the villagers in forestry resources management was somewhat limited, just only 1 or 2 times per year. Forestry resources management prior the year 2000 was at the medium level of 59.91%. Forestry resources management after the year 2000 is at the good level of 52.72%. Compliance level with laws and regulations of the villagers is about 69.09. Established the management regulations by the government is 69.09%, the concept of establishing the provincial protected area is 61.82%. Forestry resources management is very important for livelihood and 49.09% said that it has improved their livelihoods. The forestry resources management regulations set by the government is said to be 74.44% appropriate. Construction activities that need less than 5 cubic meters of wood, 94.55% of them have asked permission from village authority. Construction activities that need above 5 cubic meters of wood, 64.45% of them have asked permission from village authority and 34.55% requested from district authority. The sources of wood for construction about 61.82% taken from the village collective forest. 50.91% of the fire wood also collected from the village collective forest and 10.91% from village protected area. For the prohibition on utilizing the non timber forest products (NTFPs) is that it is prohibited to collect NTFPs unlimitedly covered 87.27%. the sectors hat monitored and inspected the forest resources in the village areas, 60% will be done by the security and defense units based in such 2 villages, 43.73% of the interviewees did not receive the data/information on forestry resources management, the benefits of involvement in forestry management is 43.34% which is highly ...

"This book covers the implementation of green processes into software systems, contributing novel principles, methodologies, and tools to improve software development by featuring comprehensive and timely coverage on various areas in service strategy and modeling, engineering, and sustainability"--Provided by publisher

The usage of pesticides is deemed essential to ensure crop production for global food security. Conventional chemical pesticides have significant effects on ecosystems. Nanopesticides are increasingly considered an emerging alternative due to their higher efficiency and lower environmental impacts. However, large knowledge gaps exist in the public perceptions and willingness-to-pay (WTP) for nanopesticides. Thus, we conducted a regional survey of pesticide users and food consumers on perceptions and WTP for nanopesticides across China. We found that 97.4% pesticide users were willing to pay for nanopesticides, with a main price from 25% to 40% higher than for conventional pesticides. Experience with applying pesticides, income, familiarity with and attitude toward nanopesticides, and trust in industries were significant determinants of WTP. Although the public were not familiar with nanopesticides, they had positive attitudes toward their future development and supported labeling nanoscale ingredients on products. Pesticide users presented high trust levels in governments and industries, while 34% of food consumers neutrally or distrusted industries in selling and production. This study highlights the socioeconomic and technological aspects of nanopesticides, which could provide guidance for industries to develop market strategies and for governments to design relevant regulation policies effectively, contributing to crop yield improvement and sustainable agriculture.

Underwater Acoustic Sensor Networks (UANs) have emerged as a promising technology recently which can be applied in many areas such as military and civil, where the communication between devices is crucial and challenging due to the unique characteristics of underwater acoustic-based environment, such as high latency and low bandwidth. In this paper, context awareness is applied to the design of an underwater communication approach, called Context-Aware Communication Approach for a UAN (CACA-UAN), which aims to improve the overall performance of the underwater communication. According to the results, the proposed CACA-UAN can increase the efficiency and reliability of the underwater communication system

The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called "filter-bubbles" leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolves some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents.

Long-term exposure to air environments full of suspended particles, especially PM2.5, would seriously damage people's health and life (i.e., respiratory diseases and lung cancers). Therefore, accurate PM2.5 prediction is important for the government authorities to take preventive measures. In this paper, the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) models are combined. Then a hybrid CNN-LSTM model is proposed to predict the daily PM2.5 concentration in Beijing based on spatiotemporal correlation. Specifically, a Pearson's correlation coefficient is adopted to measure the relationship between PM2.5 in Beijing and air pollutants in its surrounding cities. In the hybrid CNN-LSTM model, the CNN model is used to learn spatial features, while the LSTM model is used to extract the temporal information. In order to evaluate the proposed model, three evaluation indexes are introduced, including root mean square error, mean absolute percent error, and R-squared. As a result, the hybrid CNN-LSTM model achieves the best performance compared with the Multilayer perceptron model (MLP) and LSTM. Moreover, the prediction accuracy of the proposed model considering spatiotemporal correlation outperforms the same model without spatiotemporal correlation. Therefore, the hybrid CNN-LSTM model can be adopted for PM2.5 concentration prediction.