Nutrient Uptake and Utilization and Antioxidants of Fruits in Red Raspberry (Rubus idaeus L.) Cultivar ‘Autumn Bliss’ in response to Fertilization under Extended Photoperiod

Annual-fruiting cultivars of red raspberry (Rubus idaeus L.) complete its entire cycle of vegetative growth, flowering, and fruiting in one growing season, which has not been well studied in response to treatments of photoperiod and fertilization. In this study, micro-propagated plantlets of ‘Autumn Bliss’ were planted in a greenhouse at Dalian, Northeast China. Some were treated with fertilizers at the rate of 12 g per plant (N-P2O5-K2O, 14-14-14) under extended photoperiod of 17 h with PPFD of 240 μmol m-2 s-1 at dark-time (Pho.+Fert.), while others were treated with longer photoperiod (Pho.) or with the control. Compared to the control, both growth and biomass in shoot part of ‘Autumn Bliss’ were promoted by the Pho.+Fert. treatment, but foliar nutrients declined as the symptom of nutrient dilution. The Pho. treatment had no effect on either growth or nutrient uptake in leaves, resulting in the symptom of nutrient depletion compared to the Control. Soil P availability positively correlated with foliar P concentration but negatively correlated with root length. Only the Pho.+Fert. treatment resulted in the advance of fruiting by two months at July when fruits were measured to have acceptable fruit weight of about 3 g and contents of anthocyanin at ~26.4 mg cyaniding-3-glucoside equiv. 100 g-1 Fw and total phenolic content at ~17.5 mg GAE 100 g-1 Fw

Management and planning of a collaborative construction planning process

Construction planning is performed in a multi-disciplinary environment in which it is crucial to explore interdependencies, manage the uncertainty of the information exchange and the understanding of the context. Current construction planning often works on a “throw over the wall” basis - plans are developed only or mainly for control purpose, and ignore the “how” aspect. Construction method planning is treated as a linear process and isolated from information and logistics management. Planners are often puzzled by information; they usually receive a large amounts of formal and informal communications with different formats, some of which are not relevant to their role. The quality of the information received is also often poor (i.e. incomplete design information). In order to deal with the uncertainty caused by insufficient information, guesses are frequently made in the planning process, which neither the initial planner, nor the downstream planner will later check. They are usually ignored and left until execution of the plan, when the problems reveal themselves. This paper argues the importance of effective management of information flow in a planning process and the need to improve the management and planning of construction planning. A collaborative planning process model using a dependency structure matrix tool to manage and optimize the construction planning process is presented

Two-Person Interaction Augmentation with Skeleton Priors

Close and continuous interaction with rich contacts is a crucial aspect of human activities (e.g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc. However, acquiring such skeletal motion is challenging. While direct motion capture is expensive and slow, motion editing/generation is also non-trivial, as complex contact patterns with topological and geometric constraints have to be retained. To this end, we propose a new deep learning method for two-body skeletal interaction motion augmentation, which can generate variations of contact-rich interactions with varying body sizes and proportions while retaining the key geometric/topological relations between two bodies. Our system can learn effectively from a relatively small amount of data and generalize to drastically different skeleton sizes. Through exhaustive evaluation and comparison, we show it can generate high-quality motions, has strong generalizability and outperforms traditional optimization-based methods and alternative deep learning solutions

Human Motion Prediction under Unexpected Perturbation

We investigate a new task in human motion prediction, which is predicting motions under unexpected physical perturbation potentially involving multiple people. Compared with existing research, this task involves predicting less controlled, unpremeditated and pure reactive motions in response to external impact and how such motions can propagate through people. It brings new challenges such as data scarcity and predicting complex interactions. To this end, we propose a new method capitalizing differentiable physics and deep neural networks, leading to an explicit Latent Differentiable Physics (LDP) model. Through experiments, we demonstrate that LDP has high data efficiency, outstanding prediction accuracy, strong generalizability and good explainability. Since there is no similar research, a comprehensive comparison with 11 adapted baselines from several relevant domains is conducted, showing LDP outperforming existing research both quantitatively and qualitatively, improving prediction accuracy by as much as 70%, and demonstrating significantly stronger generalization

Two-Person Interaction Augmentation with Skeleton Priors

Close and continuous interaction with rich contacts is a crucial aspect of human activities (e.g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc. However, acquiring such skeletal motion is challenging. While direct motion capture is expensive and slow, motion editing/generation is also non-trivial, as complex contact patterns with topological and geometric constraints have to be retained. To this end, we propose a new deep learning method for two-body skeletal interaction motion augmentation, which can generate variations of contact-rich interactions with varying body sizes and proportions while retaining the key geometric/topological relations between two bodies. Our system can learn effectively from a relatively small amount of data and generalize to drastically different skeleton sizes. Through exhaustive evaluation and comparison, we show it can generate high-quality motions, has strong generalizability and outperforms traditional optimization-based methods and alternative deep learning solutions

A Nonlinear African Vulture Optimization Algorithm Combining Henon Chaotic Mapping Theory and Reverse Learning Competition Strategy

In order to alleviate the main shortcomings of the AVOA, a nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy (HWEAVOA) is proposed. Firstly, the Henon chaotic mapping theory and elite population strategy are proposed to improve the randomness and diversity of the vulture's initial population; Furthermore, the nonlinear adaptive incremental inertial weight factor is introduced in the location update phase to rationally balance the exploration and exploitation abilities, and avoid individual falling into a local optimum; The reverse learning competition strategy is designed to expand the discovery fields for the optimal solution and strengthen the ability to jump out of the local optimal solution. HWEAVOA and other advanced comparison algorithms are used to solve classical and CEC2022 test functions. Compared with other algorithms, the convergence curves of the HWEAVOA drop faster and the line bodies are smoother. These experimental results show the proposed HWEAVOA is ranked first in all test functions, which is superior to the comparison algorithms in convergence speed, optimization ability, and solution stability. Meanwhile, HWEAVOA has reached the general level in the algorithm complexity, and its overall performance is competitive in the swarm intelligence algorithms

Theory and technical conception of carbon-negative and high-efficient backfill mining in coal mines

Safe, high-efficient, green and low-carbon mining is an eternal theme of coal mines. Near zero rock burst, near zero ecological damage and low-carbon, zero-carbon and carbon-negative green mining will become new requirements to ensure China's energy security supply and green low-carbon development. Backfill mining is the inevitable way to achieve these requirements. However, the existing theories, technologies, and methods of backfill mining are difficult to overcome the technical bottlenecks of high yield, high efficiency, and low-carbon mining, and it is imperative to reform the filling materials and filling modes. In view of the strategic goal of low-carbon coal mining of “kilometer deep mine resource development and ten-million-ton productivity mine filling (two thousands) ” and “near zero ecological damage and near zero rock burst (two near zeros)”. The definition and concept of carbon-negative & high-efficient backfill mining in coal mines has been systematically expounded, and the theoretical development for carbon-negative & high-efficient backfill mining in coal mines has been proposed, including the topological configuration and strength theory of CGIF (CO2 Gangue Innovative Framework) for high porosity filling materials structure, the carbon sequestration theory of CGIF mixture filling body, the reaction kinetics theory of fast adhesive gel bonding material, and the prevention and control of rock burst by filling mining in mining area. The key technical systems have been proposed, such as the preparation technology of gangue fast and efficient cementation high porosity filling material, the green and efficient preparation technology of fast and efficient cementation gel binding material, the negative carbon efficient filling mining technology of CGIF backfill, the negative carbon efficient filling mining technology, the technology of multi-face mining, and the full cycle three-dimensional efficient filling mining and rock burst prevention technology. On this basis, the “three stage” development plan of “basic research, technical research, and engineering demonstration” for carbon-negative & high-efficient backfill mining in coal mines has been clarified, and a theoretical and technical system for carbon-negative & high-efficient backfill mining in coal mines has been constructed. The CO2 storage capacity with carbon-negative & high-efficient backfill mining in coal mines has been evaluated. It is expected to achieve a new pattern of carbon neutrality in the entire process of coal development and utilization through carbon-negative mining and low-carbon utilization

Microecology in vitro model replicates the human skin microbiome interactions

Skin microecology involves a dynamic equilibrium among the host, microbiome, and internal/external environments. This equilibrium, shaped by multifactorial interactions, reflects individual specificity and diversity. Creating a replicable in vitro skin microecological model is highly challenging. Here, we introduce a mimicked stratum corneum microecology model (SCmic). It uses light cured crosslinked hydrogels as a scaffold and moisture source, and nonviable epidermal cells as the main nutrient. This setup establishes a suitable, stable, and reproducible microecology for microbiome colonization. Notably, it replicates the normal/oily skin microbiota with no significant differences from the original native microbiota at the genus level. Simultaneously, we have developed a standardized human skin microbiota model (Hcm), featuring seven dominant strains that form a representative microbial community. The models provide highly convenient approaches for exploring the intricate mutual interactions among skin microecology, influence of microbiota on skin health, and metabolism of chemical substances by microbiota