Disaster management has become a universal challenge and a top priority for practically every country across the world. Humanitarian activity is a critical strategy for disaster management and development that can leverage big data to improve human and system capacity by linking information, disaster response, and help. Researchers, academics, and legislators are debating the utility of big data analytics (BDA) for disaster management. This study intends to explore the potential of BDA for disaster management by broadening the influence of humanitarian aid. To substantiate the contentions, a qualitative technique based on PRISMA guided desk review was used. It argues that by leveraging big data analytics, humanitarian activity can shift in the makeup of the humanitarian workforce, challenging old power relations and assuring better, faster, and stronger services. Big data has the possibility of providing vast amounts of information to aid in the development of humanitarian skills, allowing a social system to better prepare for disasters. It also emphasizes key aspects of BDA for disaster management, such as open-source tools, solid infrastructure, building local skills, authentic data sources, ethical data sharing, awareness about data rights, and learning from experience. BDA can be used by policymakers, administrators, and other stakeholders for taking effective initiatives during and after disasters, such as early warning systems, weather forecasting, urgent evacuation, instant humanitarian responses, relief delivery, training need assessment, and increasing the number of trained individuals.

Environmental cost refers to the cost paid by enterprises to reduce environmental pollution and resource depletion in production and operation. To help enterprises reduce environmental costs, a manufacturing environmental cost control algorithm based on machine learning is proposed. The probabilistic neural network is used to classify the current environmental cost control level of different manufacturing enterprises. Then, the particle swarm optimization (PSO) algorithm is improved to build a multi-objective backbone PSO algorithm for multi-objective decision-making, which is used in the selection of environmental cost control methods. The experimental results show that there is a strong correlation between the original data classification and the proposed probabilistic neural network, and the correlation reaches 96.1%. PSO performance test results show that the algorithm has the best performance, the best stability, and the shortest time needed to find the optimal solution set when the initial particle number is 140 and the number of iterations is 60. Based on the comprehensive experimental results, the following conclusions are drawn. Enterprises should strengthen collaboration and cooperation with customers, suppliers, and waste-profiting enterprises, so as to well control environmental costs. To sum up, the proposed model provides some references for the adoption of machine learning in environmental cost control of manufacturing enterprises.

Cooperation between government and social capital is an important starting point in the supply-side reform of public services. It is also an effective practice in public governance innovation. Based on policy diffusion theory and event history analysis (EHA), this study analyzes panel data from 282 mainland prefecture-level cities in China from 2004–2020 to explore public–private partnerships’ critical diffusion factors. The study reveals that motivation factors, resource/obstacle factors, and external factors affect government and social capital cooperation policies to different extents. The main driving forces for local governments to adopt these policies are population size, level of economic development, government financial resources, the learning mechanism, and the imitation mechanism. This study proposes the following arguments: firstly, that the ultimate goal of policy innovation is to solve social contradictions and meet public demand; secondly, that economic resources can help to adopt policy innovation and proper diffusion; thirdly, that the public–private partnership (PPP) model has been continuously developed by using experience from other projects or cities through a learning mechanism; and finally, that policy publicity and public opinion expressed via the mainstream media are not only an inducement for policy innovation and diffusion, but also a powerful guarantee. The experience of local governments in China can help to verify whether the “positive factors” that are traditionally considered to be conducive to the cooperation between the government and social capital are effective, and to reveal the internal logic of the innovation diffusion of public policies of local governments in China from a more multidimensional perspective.

Near-space remote sensing image registration is an important foundation of near-space image processing. For large image jitter distortion, geometric and atmospheric distortion of its image, we propose a two-step method based on deep neural networks, which includes a coarse-to-fine registration process. We construct a near-space image registration dataset which is captured from Google Maps and hot air balloon platforms, etc. For obtaining candidates, the coarse alignment stage applies classical geometric validation methods to a corresponding set of pre-trained deep features. The fine alignment network is based on pyramidal feature extraction and optical flow estimation to realize local flow field inference from coarse to fine. We construct a regularization layer for each level to ensure smoothness. Applying our method to our synthetic dataset, the experimental result shows that it has a competitive result that is evaluated based on the root mean square error, peak signal to noise ratio and structural similarity.

Household livelihood assessment is an integral approach to determining the status of the development of rural people and society. Therefore, this study aims to explore the status of the livelihood and adaptation strategies of ethnic mountain people. A capital-based livelihood assessment was conducted on households living in Mount Qingcheng in Sichuan, China, under the guidance of the sustainable livelihood framework. Primary data were gathered through a semi-structured household survey of 42 ethnic mountain households complemented by two focus group discussions with community representatives. This study reveals that ethnic mountain households possess low levels of human capital (education, health, and vocational skills), limited natural capital (cultivable land and homestead areas), scarcity of physical capital (poor housing, production tools, vehicles, and durable assets), limited access to financial capital (income, borrowing informal and formal loans, and charity), and poor social capital (loose social networks within relatives, low opportunities for financial help, and job facilities). The analysis revealed a positive relationship between family size and household income, and cultivable land area. Cultivable land and homestead size are significantly positively related to dwelling quality (0.20*), financial security (0.60*), ease of obtaining informal loans (0.20*), and access to formal loans (0.30*). The major adaptation strategies depend on agriculture, off-farm income activities, social networks, traditional ecological knowledge (TEK), and migration for work. This study recommends that a continuous development program be implemented to help protect the culture and enhance the livelihood security of ethnic mountain people.