Assessing biogeochemical effects and best management practice for a wheat–maize cropping system using the DNDC model

Contemporary agriculture is shifting from a single-goal to a multi-goal strategy, which in turn requires choosing best management practice (BMP) based on an assessment of the biogeochemical effects of management alternatives. The bottleneck is the capacity of predicting the simultaneous effects of different management practice scenarios on multiple goals and choosing BMP among scenarios. The denitrification–decomposition (DNDC) model may provide an opportunity to solve this problem. We validated the DNDC model (version 95) using the observations of soil moisture and temperature, crop yields, aboveground biomass and fluxes of net ecosystem exchange of carbon dioxide, methane, nitrous oxide (N2O), nitric oxide (NO) and ammonia (NH3) from a wheat–maize cropping site in northern China. The model performed well for these variables. Then we used this model to simulate the effects of management practices on the goal variables of crop yields, NO emission, nitrate leaching, NH3 volatilization and net emission of greenhouse gases in the ecosystem (NEGE). Results showed that no-till and straw-incorporated practices had beneficial effects on crop yields and NEGE. Use of nitrification inhibitors decreased nitrate leaching and N2O and NO emissions, but they significantly increased NH3 volatilization. Irrigation based on crop demand significantly increased crop yield and decreased nitrate leaching and NH3 volatilization. Crop yields were hardly decreased if nitrogen dose was reduced by 15% or irrigation water amount was reduced by 25%. Two methods were used to identify BMP and resulted in the same BMP, which adopted the current crop cultivar, field operation schedules and full straw incorporation and applied nitrogen and irrigation water at 15 and 25% lower rates, respectively, than the current use. Our study indicates that the DNDC model can be used as a tool to assess biogeochemical effects of management alternatives and identify BMP

Towards Accelerated Model Training via Bayesian Data Selection

Mislabeled, duplicated, or biased data in real-world scenarios can lead to prolonged training and even hinder model convergence. Traditional solutions prioritizing easy or hard samples lack the flexibility to handle such a variety simultaneously. Recent work has proposed a more reasonable data selection principle by examining the data's impact on the model's generalization loss. However, its practical adoption relies on less principled approximations and additional clean holdout data. This work solves these problems by leveraging a lightweight Bayesian treatment and incorporating off-the-shelf zero-shot predictors built on large-scale pre-trained models. The resulting algorithm is efficient and easy-to-implement. We perform extensive empirical studies on challenging benchmarks with considerable data noise and imbalance in the online batch selection scenario, and observe superior training efficiency over competitive baselines. Notably, on the challenging WebVision benchmark, our method can achieve similar predictive performance with significantly fewer training iterations than leading data selection methods

How Does Mobile Computing Develop Transactive Memory in Virtual Team? A Social Identification View

The advancement in mobile computing technologies has shown great potential to drive efficiency and effectiveness of knowledge work in virtual teams. Despite their ubiquity, theoretical and empirical research investigating the impact of mobile computing artifacts on development of transactive memory in virtual teams is in its infancy. Drawing on the social psychology literature, we propose a social identity based view to understand how the use of mobile computing artifacts is associated with the development of transactive memory system (TMS) in virtual teams. Specially, the use of four categories of mobile computing artifacts (i.e., ubiquitous co-presence, status disclosure, context search, and customized notification) is proposed to enhance social identification, which thereafter promotes TMS development in terms of specialization, credibility, and coordination. This study offers a new perspective on the mechanisms through which mobile computing artifacts facilitate TMS development, and it yields important implications for the design of mobile strategy in organizations

An attribute-based framework for secure communications in vehicular ad hoc networks

In this paper, we introduce an attribute-based framework to achieve secure communications in vehicular ad hoc networks (VANETs), which enjoys several advantageous features. The proposed framework employs attribute-based signature (ABS) to achieve message authentication and integrity and protect vehicle privacy, which greatly mitigates the overhead caused by pseudonym/private key change or update in the existing solutions for VANETs based on symmetric key, asymmetric key, and identity-based cryptography and group signature. In addition, we extend a standard ABS scheme with traceability and revocation mechanisms and seamlessly integrate them into the proposed framework to support vehicle traceability and revocation by a trusted authority, and thus, the resulting scheme for vehicular communications does not suffer from the anonymity misuse issue, which has been a challenge for anonymous credential-based vehicular protocols. Finally, we implement the proposed ABS scheme using a rapid prototyping tool called Charm to evaluate its performance