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Agroforestry systems are characterized by a high complexity between vegetation components and niche partitioning. In a crop-grass-tree agroforestry system, rape, willow, and grasses were in situ pulse labeled separately with 13CO2 for 6 h, and 13C was traced in shoots, roots, topsoil (0–15 cm) and subsoil (15–30 cm), microbial biomass carbon (C), and dissolved organic C, as well as respiration losses (CO2) up to 28 days after labeling to investigate the effects of vegetation components on C allocation belowground. 13C recovery in roots after 28 days was 7.0% of total assimilated C for grassland, which was 3.5- and 5.2-fold higher than that for rape and willow, respectively. The larger C allocation belowground in grassland was ascribed to its higher root/shoot ratio compared to willow and rape. Grassland facilitated higher accumulation of root-derived C in soil compared to rape (9.2% of recovered 13C) and compared to willow (1.6% of 13C). Willow retained more photosynthetic C aboveground and less was allocated to roots compared to rape. Although the C allocated to the top 15-cm soil was similar between willow and rape, willow facilitated C allocation in deeper soil compared to rape (0.6% vs. 0.2%). This could be explained by the lower microbial activity and subsequent weaker decomposition of rhizodeposits in 15–30-cm depth under willow. The net belowground C inputs in grassland, willow, and rape were 0.53, 0.06, and 0.10 g C m−2 month−1 of vegetation period, including rhizodeposition of 0.24, 0.05, and 0.04 g C m−2 month−1, respectively. Overall, integrating trees and grassland within cropland facilitates higher root-derived C input into soil, thus contributing to the soil C sequestration in agroforestry systems.

This paper presents a systematic methodology to enable environmental sustainability and productivity performance assessment for integrated process and operation plans at the machine cell level of a manufacturing system. This approach determines optimal process and operation plans from a range of possible alternatives that satisfy the objectives and constraints. The methodology provides a systematic procedure to highlight parameters that have significant impact on both sustainability and productivity performance metrics. We developed models and applied them to analyze manufacturing life cycle scenarios for collecting and categorizing key concepts towards building a material information model for sustainability. Integration of process and operation plans allows globalized assessment of sustainability and productivity, while development of a multi-criteria decision-making method leads to optimization of process planning activities based on the impact of conflicting sustainability and productivity metrics. A case study is detailed to demonstrate the sustainability-focused methodology, wherein integrated simulation and optimization techniques are used to support analysis of candidate scenarios and selection of preferred alternatives from a finite set of alternate process and operation plans. A discrete event simulation tool is used to model evolution of sustainability metrics (e.g., energy consumption) and productivity metrics (e.g., production time, cost) of a shop floor. The outcomes of this work include determination of optimized feature sequence plans which optimize various key performance indicators depending on stakeholder interest based on time, sustainability and production cost.

We report on the experimental observation of dual-wavelength dissipative soliton operation of a fiber laser with net anomalous cavity dispersion. Different from the dual- or multi-wavelength soliton operation of fiber lasers where mode locking is used to initiate soliton formation, no mode locking occurs in our fiber laser. Instead, soliton formation is through the dissipative mechanism caused by the effective gain bandwidth limitation. Either dual-wavelength scalar, or vector, or induced dissipative solitons are experimentally obtained. Their robustness is experimentally confirmed.

Nitrogen fertilizers are the major source of nitrous oxide (N2O) emissions from arable land. The addition of nitrification inhibitors to fertilizers may improve the nitrogen (N) use efficiency and reduce N2O emissions. However, it is still unclear how crop rotations affect nitrification and urease inhibitors to reduce N2O emissions. We conducted a field experiment with two-year winter wheat and one-year oilseed rape cultivation in Germany from 2016 to 2017. We applied five different fertilizer treatments: (1) a control treatment without fertilization (N0); (2) calcium ammonium nitrate (CAN); (3) ammonium sulfate nitrate with the nitrification inhibitor 3,4-dimethylepyrazole phosphate (ENTEC); (4) urea; and (5) urea with the urease inhibitor N-(n-butyl) thiophosphoric triamide (UTEC). Crop yield, grain and straw N content, and N2O fluxes were measured to assess yield-scaled N2O emissions under different treatments. We found that in all fertilized treatments, the aboveground N uptake of wheat after wheat was 199–203 kg N ha−1, which was much lower than that of wheat after oilseed rape (252–271 kg N ha−1). The apparent N recovery of oilseed rape (13–23%) was much lower than in wheat after wheat (63–66%). The enhanced-efficiency fertilizers increased aboveground N uptake by 0–5% compared to fertilizers without inhibitors. The oilseed rape field had the highest yield-scaled N2O emissions (18.0, 15.1, 16.7 and 15.6 g N2O-N kg−1 aboveground N uptake in CAN, ENTEC, urea and UTEC, respectively). These results indicate that urease and nitrification inhibitors hold the potential to increase crop yield and reduce N2O emissions. Oilseed rape straw should be carefully managed to avoid high N2O emissions. Future research should be focused on different fertilizer level and optimized N application strategy to increase the efficiency of urease and nitrification inhibitors, for instance, by increasing the N application rate in wheat after wheat, but reducing it in oilseed rape.

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

With the continuous increase in plantation areas, the reduction of natural forest areas, and the unreasonable management of plantations by human beings, the service function of forest ecosystems has gradually reduced. Therefore, close-to-natural forestry has become important for the sustainable development of modern forestry. However, the differences in soil properties, enzyme activities, microbial diversities, and undergrowth vegetation species diversities have not been systematically explained for Chinese fir (Cunninghamia lanceolata). How do these indicators interact? The purpose of this paper was to study the difference in soil properties and biodiversity in different aged Chinese fir plantations and close-to-natural forests to explore their interactions and to provide direction for close-to-nature management. The results showed that the above indicators were significantly different in different aged Chinese fir plantations, soil pH, organic matter, total nitrogen, total phosphorus, total potassium, alkali-hydrolyzed nitrogen, available phosphorus concentrations, and urease activities in close-to-natural forests were significantly higher than plantations. It is worth mentioning that the richness of undergrowth vegetation species diversity (especially shrubs) and soil microbial diversities (especially fungi) in the close-to-natural forests were obviously higher than those in plantations. The correlation analysis results showed that the diversity of shrub species with respect to soil properties presented a higher correlation than herb species, the diversity of fungi with respect to soil properties presented a higher correlation than that observed for bacteria, and the diversity of fungi were significantly correlated with the diversity of undergrowth vegetation species diversities, but the correlation between bacteria and undergrowth vegetation species diversities was not significant. Our results suggest that the above factors are fundamental factors for the transformation of Chinese fir plantations to close-to-natural forests. To realize close-to-natural forestry, we must change the tree structure, combine the difference of those factors in different aged plantations, restore undergrowth vegetation species diversity, and thus improve microbial diversity and increase decomposition, transformation, and improvement of soil properties.

Sustainability has become a very significant research topic as it impacts many different manufacturing industries. Therefore, the technologies for monitoring, analysing, evaluating and optimising the sustainability performance of manufacturing processes and systems are very critical for decision makers on the shop floor. This paper introduces a decision guidance management system that provides actionable recommendations through quantitative analysis of the sustainability measures of manufacturing processes and systems based on life cycle assessment. The system determines decision preferences through dynamically collected data and decision makers' responses, taking into account the prevailing constraints. Optimal decisions can be derived using mathematical and constraint programming. By using decision guidance query language, this methodology allows users to make optimal decisions without an extensive mathematical or operations research background. Knowledge of relational databases is sufficient for a user...

We show both theoretically and experimentally the temporal cavity soliton formation in a fiber ring laser. The cavity-induced modulation instability can automatically destabilize the CW operation of a fiber ring laser, transferring the CW laser beam into a periodic pulse train. Under strong laser emission, and particularly under the action of the effective gain bandwidth limitation, each pulse of the periodic pulse train could be further shaped into a dissipative soliton. The features of the temporal cavity solitons thus formed are experimentally studied and their similarity and difference to those of the mode-locked fiber lasers are discussed.