Suchergebnisse

The development of artificial intelligence and related technologies has a potential serious impact on the industrial development and the military operations. The Chinese Communist Party has also classified this area as a key direction of the future development, hoping that in this new wave of military affairs innovation, under the absolute superiority of economic and military affairs, China could surpass the United States in one fell swoop, changing the world situation in which the United States is the only military superpower since the end of the Cold War. In the report of the 19th National Congress of the Communist Party of China, President Xi Jinping clearly stated that it should accelerate the development of military intelligence and strengthen the joint combat capability of the network information system, including the R&D of the innovative weapons and equipment. As Xi Jinping took over the presidency of China in 2013, he initiated the "dream of strong army". In the future, artificial intelligence is bound to be an important part of the CCP's military modernization, and it is also a foresighted preparation for winning the next war. At present, most of the CCP's research on military development in terms of artificial intelligence tends to be on hardware devices, such as automated combat vehicles, autonomous drones and remote-controlled submarines. These related technologies rely heavily on the mechanical engineering and traditional R&D. The CCP intends to combine the development of military science and technology with the advanced weapons as a means of "killer" conception for future regional wars against the United States and other major powers. In this concept, the Communist Army will carry out paralytic asymmetrical attacks to its potential enemies. In the past, the "killer" weapons may be the attack missiles that attack large ships, but now they may include a new generation of artificial intelligence weapons that use the big data, the Internet of Things, or the cloud computing. In the face of the ...

With limited indigenous conventional energy resources, Taiwan imports over 97% of its energy supply from foreign countries, mostly from the Middle East. Developing independent renewables is thus of priority concern for the Taiwanese government. A medium subtropical island surrounded by the Pacific Ocean, Taiwan has enormous potential to develop various renewables, such as solar energy, biomass energy, wind power, geothermal energy, hydropower, etc. According to the estimation, the total renewable energy reserve is about 194 GW, which equals to 4 times of national installed power capacity in 2015, e.g., 48.7 GW, so Taiwan has abundant renewable energy resources indeed. However, owing to the importance of conventional fossil energy in generating exceptionally cheap electricity, renewable energy has not yet fully developed in Taiwan, resulting from a lack of market's competition. In 2016, after the new government came to power, the Bureau of Energy (BOE) Ministry of Economic Affairs (MOEA) set up an active promotion goal for the renewable energies (RE) in Taiwan: the total RE power capacity will be 20% share of the national power installation capacity by 2025. In the meantime, the four inherent shortcomings—low energy density, high cost of power generation, instability of power supply, and current cost of renewable energy being still higher than that of fossil energy—have to be overcome first, before renewable energy is actually formed as a main component in national energy mix. The development of renewable energy not only contributes to the independence of energy supply, but also achieves the benefits of economic development and environmental protection. This study reviews the current status, achievements, polices and future plans in these areas for Taiwan.

This study aims to measure the ability of 29 countries in producing competitive products and services that fulfill individual needs and improve the level of welfare with less utilization of natural resources. We build a two-stage network production process model to investigate the ecology efficiency and social welfare efficiency of the countries and then further discriminate the efficient countries in post-analysis. The two-stage network directional distance function is applied to assess the efficiencies of countries, and the network-based ranking approach is used to further discriminate the efficient countries following the panel data between the years 2013 and 2016. Results show that Poland and Spain are strongly referenced by other countries in the ecology stage, whereas Bulgaria, the United States, and Sweden are leaders in the social welfare stage. A remarkable observation is an absence of countries' efficiency in both ecology and social welfare efficiencies. Most of the 29 countries have lower efficiency in the social welfare stage than in the ecology stage. This study suggests the strengths and highlights the weaknesses of the countries to help the governments efficiently improve and operate their countries.

The emergence of coronavirus disease 2019 (COVID-19) in the United States has forced federal and local governments to implement containment measures. Moreover, the severity of the situation has sparked engagement by both the research and clinical community with the goal of developing effective treatments for the disease. This article proposes a time dynamic prediction model with competing risks for the infected individual and develops a simple tool for policy makers to compare different strategies in terms of when to implement the strictest containment measures and how different treatments can increase or suppress infected cases. Two types of containment strategies are compared: (1) a constant containment strategy that could satisfy the needs of citizens for a long period; and (2) an adaptive containment strategy whose strict level changes across time. We consider how an effective treatment of the disease can affect the dynamics in a pandemic scenario. For illustration we consider a region with population 2.8 million and 200 initial infectious cases assuming a 4% mortality rate compared with a 2% mortality rate if a new drug is available. Our results show compared with a constant containment strategy, adaptive containment strategies shorten the outbreak length and reduce maximum daily number of cases. This, along with an effective treatment plan for the disease can minimize death rate.

PurposeThe purpose of this study is twofold. First, this research estimates banks' efficiencies from the perspectives of resource utilization and investment after incorporating risk measures as an exogenous input in the investment-efficiency stage. Second, the current study examines the relationship between intellectual capital (IC) and banks' efficiencies.Design/methodology/approachFirst, this study uses a dynamic network data envelopment analysis approach in investigating the efficiencies of 24 Taiwanese banks in 2007–2018 from two perspectives. Second, this research utilizes various regression techniques, namely, ordinary least squares (OLS), robust least squares and truncated regression, to gauge the impact of IC on banks' efficiencies. Typically, IC is determined based on a monetary value-based measure and value-added intellectual coefficient (VAICTM).FindingsResource-utilization (investment) efficiencies were observed as 0.941 (0.964), thereby contributing to the mean overall efficiency of the sample banks at 0.952. However, the related efficiency changes decline over the sample period, thereby suggesting that the average banks' efficiencies hardly increase. Regression analyses show a significantly positive relationship between IC and banks' overall resource-utilization and investment efficiencies.Research limitations/implicationsOverall, this study suggests that researchers should consider risks when estimating banks' efficiencies owing to their connection to banks' investment performance. From banks' dynamic two-stage efficiencies, this study demonstrated that investments in IC will bring improved future economic benefits.Originality/valueDifferent from prior studies, this study improves banks' efficiency evaluation models by incorporating risk measures and assuming weighted periods for the 2007–2008 global financial crisis. Moreover, the use of monetary value-based measure of IC provides consistent results as the commonly-used VAICTM does.