作为一家提供管理服务的国际机构，通过为客户提供认证、验证、评估和培训服务，确保客户在组织、产品、人员、设施以及供应链管理方面取得卓越的业绩。综合运用技术、数字化及行业专业知识，助力客户的决策及行动。始终与客户通力合作，帮助客户构建一个业务可持续发展的平台，并赢得利益相关方的信任。 成立于1864年，DNV GL如今在100多个国家运营，专家致力于服务广大客户，共同打造一个更安全、更智能和更环保的未来。
Forests interact with the local climate through a variety of biophysical mechanisms. Observational and modelling studies have investigated the effects of forested vs. non-forested areas, but the influence of forest management on surface temperature has received far less attention owing to the inherent challenges to adapt climate models to cope with forest dynamics. Further, climate models are complex and highly parameterized, and the time and resource intensity of their use limit applications. The availability of simple yet reliable statistical models based on high resolution maps of forest attributes representative of different development stages can link individual forest management practices to local temperature changes, and ultimately support the design of improved strategies. In this study, we investigate how forest management influences local surface temperature (LSTs) in Fennoscandia through a set of machine learning algorithms. We find that more developed forests are typically associated with higher LST than young or undeveloped forests. The mean multi-model estimates from our statistical system can accurately reproduce the observed LST. Relative to the present state of Fennoscandian forests, fully develop forests are found to induce an annual mean warming of 0.26 degrees C (0.03/0.69 degrees C as 5th/95th percentile), and an average cooling effect in the summer daytime from-0.85 to-0.23 degrees C (depending on the model). On the contrary, a scenario with undeveloped forests induces an annual average cooling of-0.29 degrees C (-0.61/-0.01 degrees C), but daytime warming in the summer that can be higher than 1 degrees C. A weak annual mean cooling of-0.01 degrees C is attributed to forest harvest from 2015 to 2018, with an increased daytime temperature in summer of about 0.04 degrees C. Overall, this approach is a flexible option to study effects of forest management on LST that can be applied at various scales and for alternative management scenarios, thereby helping to improve local management strategies with consideration of effects on local climate.
Demand for the construction of new structures is increasing all over the world. Since the construction sector dominates the global carbon footprint, new construction methods are needed with reduced embodied carbon and high resource efficiency to realize a sustainable future. In this direction, Metal Additive Manufacturing, also known as metal 3D printing, can be an opportunity. Many studies are underway to answer open questions about the metal 3D printing processes and products for high-tech industries. The construction sector must join the metal 3D printing research more actively to enrich the knowledge and experience on this technology, and correctly adapt the process parameters suitable to the construction sector requirements. This paper states the opinion of a research group composed of academics and practitioners from Europe, the US, Japan, and South Africa on how metal 3D printing can be a complementary tool/technology to conventional manufacturing to increase productivity rates, and reduce the costs and CO2 emissions in the construction industry.
In order to help technicians master the external noise of the ship, the class notations of external noise issued by three major classification societies are introduced, the processes of obtaining external noise notations by different classification societies are summarized and compared, and the special requirements of each external noise notation are emphasized. The research results can provide some references for relevant practitioners to control the external noise of ships.
