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Under the global climate change, variations in climatic elements such as temperature, precipitation, and sunshine duration significantly impact the growth, development, and yield formation of winter wheat. A precise understanding of the impact of climate change on winter wheat growth and the scientific use of meteorological resources are crucial for ensuring food security, optimizing agricultural planting structures and agricultural sustainability. This study uses statistical methods and focuses on the Beijing–Tianjin–Hebei region, utilizing data from 25 meteorological stations from 1961 to 2010 and winter wheat yield data from 1978 to 2010. Twelve refined indicators encompassing temperature, precipitation, and sunshine duration were constructed. Path analysis was employed to determine their weights, establishing a comprehensive climate indicator model. Results indicate: Temperature indicators in the region show an upward trend, with accumulated temperature of the whole growth period increasing at a rate of 61.1 °C·d/10a. Precipitation indicators reveal precipitation of the whole growth period rising at 3.9 mm/10a and pre-winter precipitation increasing at 4.2 mm/10a. Sunshine duration exhibits a declining trend, decreasing at 72.7 h/10a during the whole growth period. Comprehensive climate indicators decrease from south to north, with the southwest region exhibiting the highest tendency rate (18.41), while the central and southern regions show greater variability. This study provides scientific basis for optimizing winter wheat planting patterns and rational utilization of climate resources in the Beijing–Tianjin–Hebei region. It recommends prioritizing cultivation in western southern Hebei and improving water conditions in the central and northern areas through irrigation technology to support sustainable crop production.

Climate change alters climatic factors, which in turn affect the suitability of crops to grow. Winter wheat is a major crop in the Beijing-Tianjin-Heibei region of China. To assess the climate factors on winter wheat production, the meteorological data (temperature, precipitation, sunshine, etc.) from 25 stations in the target region the Beijing-Tianjin-Hebei region of China from 1961 to 2010, the winter wheat yield data from 1978 to 2010, and the growth stages were used. A model of the suitability of light, temperature, and water was subsequently developed to quantitatively analyze the spatial and temporal variability of the suitability of the winter wheat to the climate of the region. Temperature suitability was high during the sowing and grouting periods (temperature suitability peaks at 0.941 during grouting) and lowest in the rejuvenation period. In terms of spatial distribution, it is strong in the south and low in the north, and it exhibits a gradual increase in interannual variation. Precipitation suitability fluctuates steadily, with a peak in the tillering stage and a trough in the jointing stage. In terms of spatial distribution, it is highest in the northeast and decreases in the west; in inter-annual changes, it fluctuates strongly with weak overall growth. Sunshine suitability is stable at 0.9 or above. In spatial distribution, it is high in the northwest and low in the southeast, and it decreases slowly in the interannual variations. The trend of climatic suitability is consistent with temperature and precipitation, showing a pattern of falling first and then rising. In terms of spatial distribution, the overall climate suitability is high in the south and low in the north. In inter-annual changes, climate suitability generally increases slowly. Temperature and precipitation are key factors. Moisture stress became the most important factor for winter wheat cultivation in the region. Sunshine conditions are typically sufficient. This study provides a theoretical basis for a rational layout of winter wheat growing areas in the Beijing-Tianjin-Hebei region and the full utilization of climatic resources.

Sentiment classification (SC) often suffers from low-resource challenges such as domain-specific contexts, imbalanced label distributions, and few-shot scenarios. The potential of the diffusion language model (LM) for textual data augmentation (DA) remains unexplored, moreover, textual DA methods struggle to balance the diversity and consistency of new samples. Most DA methods either perform logical modifications or rephrase less important tokens in the original sequence with the language model. In the context of SC, strong emotional tokens could act critically on the sentiment of the whole sequence. Therefore, contrary to rephrasing less important context, we propose DiffusionCLS to leverage a diffusion LM to capture in-domain knowledge and generate pseudo samples by reconstructing strong label-related tokens. This approach ensures a balance between consistency and diversity, avoiding the introduction of noise and augmenting crucial features of datasets. DiffusionCLS also comprises a Noise-Resistant Training objective to help the model generalize. Experiments demonstrate the effectiveness of our method in various low-resource scenarios including domain-specific and domain-general problems. Ablation studies confirm the effectiveness of our framework's modules, and visualization studies highlight optimal deployment conditions, reinforcing our conclusions.

Hate speech on social media is ubiquitous but urgently controlled. Without detecting and mitigating the biases brought by hate speech, different types of ethical problems. While a number of datasets have been proposed to address the problem of hate speech detection, these datasets seldom consider the diversity and variability of bias, making it far from real-world scenarios. To fill this gap, we propose a benchmark, named HateDebias, to analyze the model ability of hate speech detection under continuous, changing environments. Specifically, to meet the diversity of biases, we collect existing hate speech detection datasets with different types of biases. To further meet the variability (i.e., the changing of bias attributes in datasets), we reorganize datasets to follow the continuous learning setting. We evaluate the detection accuracy of models trained on the datasets with a single type of bias with the performance on the HateDebias, where a significant performance drop is observed. To provide a potential direction for debiasing, we further propose a debiasing framework based on continuous learning and bias information regularization, as well as the memory replay strategies to ensure the debiasing ability of the model. Experiment results on the proposed benchmark show that the aforementioned method can improve several baselines with a distinguished margin, highlighting its effectiveness in real-world applications.

As the fourth largest language family in the world, the Dravidian languages have become a research hotspot in natural language processing (NLP). Although the Dravidian languages contain a large number of languages, there are relatively few public available resources. Besides, text classification task, as a basic task of natural language processing, how to combine it to multiple languages in the Dravidian languages, is still a major difficulty in Dravidian Natural Language Processing. Hence, to address these problems, we proposed a multilingual text classification framework for the Dravidian languages. On the one hand, the framework used the LaBSE pre-trained model as the base model. Aiming at the problem of text information bias in multi-task learning, we propose to use the MLM strategy to select language-specific words, and used adversarial training to perturb them. On the other hand, in view of the problem that the model cannot well recognize and utilize the correlation among languages, we further proposed a language-specific representation module to enrich semantic information for the model. The experimental results demonstrated that the framework we proposed has a significant performance in multilingual text classification tasks with each strategy achieving certain improvements.

Compost can realize the recycling of organic waste. However, it also emits NH 3 and greenhouse gases (GHGs) to the environment, which leads to nitrogen loss and global warming. Adding additives to compost can alleviate the emission of NH 3 and GHGs. The mechanism of nitrogen transformation and GHGs emission was studied with deer manure and corn straw as compost substrate, and biochar and zeolite as additives. The results showed that the addition of zeolite in compost is good for prolonging high-temperature composting time. The addition of zeolite reduced the transformation of NH 3 -N and the N 2 O emission. The addition of zeolite is beneficial to reduce nitrogen loss during composting. CH 4 emission is an important factor affecting the global warming potential of composting, and it is necessary to improve ventilation conditions in order to alleviate anaerobic. This study is of great significance to reduce nitrogen loss and improve composting effect.