Cloud Radiative Effects on MJO Development in DYNAMO

Observed Madden–Julian oscillation (MJO) events are examined with the aid of regional model simulations to understand the role of cloud radiative effects in the MJO development. The importance of this role is demonstrated by the absence of the MJO in the model simulations that contain no cloud radiative effects. Comparisons of model simulations with and without the cloud radiative effects and observation help identify the major processes arising from those effects. Those processes develop essentially from heating in the upper troposphere due to shortwave absorption within anvil clouds in the upper troposphere and the convergence of longwave radiation in the middle to upper troposphere, with a peak at 300 hPa, during deep convection. First, that heating adds extra buoyancy and accelerates the rising motion in the upper troposphere in deep convection. The vertical acceleration in the upper troposphere creates a vacuum effect and demands for more deep convection to develop. Second, in response to that demand and required by mass balance arises the large-scale horizontal and vertical mass, moisture, and energy convergence. It strengthens deep convection and, with the feedback from continuing cloud radiative effect, creates conditions that can perpetuate deep convection and MJO development. That perpetuation does not occur however because those processes arising from the cloud radiative heating in the upper troposphere stabilize the troposphere until it supports no further deep convection. Weakening deep convection reduces cloud radiative effects. The subsequent reduction of the vacuum effect in the upper troposphere diminishes deep convection completing an MJO cycle. These results advance our understanding of the development of the MJO in the radiative–convective system over warm waters in the tropics. They show that while the embryo of intraseasonal oscillation may exist in the system its growth/development is largely dependent on cloud radiative effects and feedbacks

Heterogeneous Value Evaluation for Large Language Models

The emergent capabilities of Large Language Models (LLMs) have made it crucial to align their values with those of humans. Current methodologies typically attempt alignment with a homogeneous human value and requires human verification, yet lack consensus on the desired aspect and depth of alignment and resulting human biases. In this paper, we propose A2EHV, an Automated Alignment Evaluation with a Heterogeneous Value system that (1) is automated to minimize individual human biases, and (2) allows assessments against various target values to foster heterogeneous agents. Our approach pivots on the concept of value rationality, which represents the ability for agents to execute behaviors that satisfy a target value the most. The quantification of value rationality is facilitated by the Social Value Orientation framework from social psychology, which partitions the value space into four categories to assess social preferences from agents' behaviors. We evaluate the value rationality of eight mainstream LLMs and observe that large models are more inclined to align neutral values compared to those with strong personal values. By examining the behavior of these LLMs, we contribute to a deeper understanding of value alignment within a heterogeneous value system.Comment: Our full prompts are released in the repo: https://github.com/zowiezhang/A2E

Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner to catch up with the evolving data distribution. However, conversions usually do not happen immediately after a user click. This may result in inaccurate labeling, which is called delayed feedback problem. In previous studies, delayed feedback problem is handled either by waiting positive label for a long period of time, or by consuming the negative sample on its arrival and then insert a positive duplicate when a conversion happens later. Indeed, there is a trade-off between waiting for more accurate labels and utilizing fresh data, which is not considered in existing works. To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. Then we optimize the expectation of true conversion distribution via importance sampling under the elapsed-time sampling distribution. We further estimate the importance weight for each instance, which is used as the weight of loss function in CVR prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive experiments on a public data and a private industrial dataset. Experimental results confirm that our method consistently outperforms the previous state-of-the-art results.Comment: This paper has been accepted by AAAI 202

The impact of government subsidy and weather on environmentally sustainable investment decision for agricultural supply chain.

This paper studies the environmentally sustainable investment of an agricultural supply chain composed of a farmer and a company, under three subsidy policies which are the non-subsidy policy, the fixed subsidy policy, and the Agriculture Risk Coverage (ARC) subsidy policy. Then, we analyse the impact of different subsidy policy and adverse weather on the costs of the government and profits of the farmer and the company. By comparing with the non-subsidy policy, we find that both the fixed subsidy policy and the ARC policy encourage the farmer to improve the environmentally sustainable investment level and increase the profit of the farmer and the company. We also find that both the fixed subsidy policy and the ARC subsidy policy lead to an increase in government spending. Our results show that the ARC subsidy policy has a significate advantage in encouraging the farmer's environmentally sustainable investment if the adverse weather is relatively serious, comparing with the fixed subsidy policy. In turn, our results also show that the ARC subsidy policy is more beneficial for both the farmer and the company than the fixed subsidy policy if the adverse weather is relatively serious, which then leads to a higher expenditure of the government. Therefore, our conclusion serves as a theoretical basis for governments to formulate agricultural subsidy policies and promote sustainable development of the agricultural environment

Crushing Analysis and Optimization of Adjacent Variable Thickness Hexagonal Tubes

In this study, we proposed a new adjacent variable thickness hexagonal tube (AVTHT) and performed crushing analysis and crashworthiness optimization under multiple loadings. First, the finite element models were constructed and validated by experiments with four configurations of AVTHTs. Then, the numerical simulations under axial loading and multiple oblique loadings indicated that AVTHTs under various loading angles (0°, 10°, 20°, and 30°) and three patterns (α, β, and θ) exhibited different deformation modes, force-displacement characteristics, and crashworthiness indices. This suggested that we could change and determine the plate thickness configuration to make the AVTHTs exhibit the expected crushing performance under multiple loadings. Therefore, multi-objective optimization for minimizing maximum crushing force with multiple loadings (Fmaxw) and maximizing specific energy absorption with multiple loadings (SEAw) by changing the thickness configuration under multiple loadings was conducted. The results determined the thickness design domains and indicated that certain thickness ranges should be avoided, such as the ranges of 1.55≤t1≤1.6 and 1.85≤t1≤1.95, which was helpful for getting AVTHTs to achieve excellent crushing performance in railway vehicles. In the pareto results, increasing t1 would not always increase the Fmaxw and SEAw. For example, when 1.75≤t1≤1.8, increasing t1 would lead to decline of Fmaxw and SEAw

Design of an Automatic Ground Cleaning Machine for Dedusting Rooms of Chicken Houses

In this paper, we designed an automatic ground cleaning machine for the dedusting rooms of chicken houses to replace the manual daily cleaning of dust particles and fluff. The machine mainly comprised a power system, control system, frame and walking structure, ground cleaning system, and dedusting system. The automatic movement of the machine body in two vertical directions without turning, lifting, and lowering of the sweeper; the retraction and expansion of the sweeper support arm; the reciprocating movement of the sweeper relative to the machine body; and the timely separation of the dust particles and fluff from gas mixtures were achieved. Parameter optimization experiments on the machine were performed using a quadratic general rotary combination design considering the movement speed, rotation speed of the sweeper, and distance between the suction head nozzle and ground as experimental factors. The regression equations describing the relationship between the three experimental factors and the dust particle removal rate and fluff removal rate were obtained using Design-Expert 12 software, adequately reflecting the impact of the three experimental factors on the two experimental indexes. Further parameter optimization was conducted to obtain the optimized parameter combination at the same weight as the two experimental indexes: movement speed of 0.1 m/s, rotation speed of the sweeper of 198 r/min, and distance between the suction head nozzle and ground of 12 mm. The performance experiment on the machine was conducted using the optimized parameter combination, yielding a dust particle removal rate of 90.7% and fluff removal rate of 91.7%. The experimental results show that the machine exhibits good performance and stable operation, meeting the daily cleaning needs of large-, medium-, and small-scale rectangular dedusting rooms of chicken houses