Agriculture

This study used bibliometric methods to systematically analyze the development trend, knowledge structure and evolution path of the field of “quantitative research on agricultural soil respiration based on machine learning” from 2021 to 2025, and further explored its implications for agricultural soil carbon sinks. Based on 966 articles included in the core collection of Web of Science, this paper comprehensively uses tools such as Biblioshiny, CiteSpace and VOSviewer to carry out multi-dimensional analysis from the aspects of annual publication trends, international and institutional cooperation networks, keyword clustering and emergent evolution. It is found that this field has shown phased evolution characteristics of “technology-driven mechanism deepening–application expansion” in the past five years. At the beginning of the 5-year period of research, the introduction of machine learning methods and model verification were the core, then gradually expanding to multi-algorithm comparison, environmental factor coupling mechanisms and multi-source data fusion. Recently, the field has focused on regional-scale simulation, uncertainty quantification and model interpretability research. Keyword clustering identifies three thematic clusters—machine learning algorithm and model optimization, environmental driving factors and process mechanism, and remote sensing fusion and regional application—which form a knowledge system of “method–mechanism–application” collaborative evolution. The national cooperation network presents a pattern of “Asia-led, China–US dual-core, and European connectivity”. China dominates in scientific research output, and the United States plays a key role in international cooperation. This study further points out that the development of this field provides important methodological support and a scientific basis for accurate assessment, intelligent management and carbon neutralization decision-making for agricultural soil carbon sinks. Based on the above findings, future research should focus on the development of intelligent models of mechanisms and data fusion, the construction of multi-source data assimilation and uncertainty assessment frameworks, the expansion of global diversified agricultural system cases, and the promotion of an open and shared international scientific research cooperation ecology. This study provides empirical evidence and a direction reference for academic development, scientific research layout, carbon sink management and international collaboration in this field.

Aerobic composting shows state-dependent dynamics in key parameters such as temperature, moisture content, oxygen concentration, and pH, and these variables are strongly coupled over time. This coupling makes accurate state identification and process regulation challenging when relying on single-parameter thresholds or experience-based control. To enable robust recognition of composting states throughout the process, we propose an IRRTO-optimized CNN–LSTM–attention model (IRRTO–CNN–LSTM–attention). The model uses a convolutional neural network (CNN) to extract discriminative multivariate features, a long short-term memory (LSTM) network to model temporal dependencies, and an attention module to adaptively emphasize informative features. To address the hyperparameter selection challenge, the Rapidly-exploring Random Tree Optimizer (RRTO) was introduced and further enhanced via four strategies (fluctuating attenuation adaptive regulation, dual-mode guided update, dynamic dimension adaptive perturbation, and dual-mechanism adaptive perturbation regulation), forming the improved IRRTO. The proposed approach was validated using sensor data from windrow composting of pig manure and corn straw. The IRRTO–CNN–LSTM–attention model achieved an overall accuracy of 98.31% in classifying the four states (mesophilic/heating, thermophilic, cooling, and abnormal) on the independent test set, which was 3.39 percentage points higher than the RRTO-based model. These results suggest that the proposed method can accurately identify composting states and support early warning and state-specific regulation in practical aerobic composting systems.

Cuticular proteins (CPs)—key components of the insect exoskeleton—not only regulate development but also serve as structural barriers that enhance resistance against environmental stressors. This study identified CP gene families in Apis mellifera and analyzed their expression patterns during the worker capped brood development stages from mature larva to pre-eclosion. Using a comprehensive genome-wide bioinformatic approach, we identified 85 CP genes in A. mellifera which comprise six families: CPR (n = 43), CPAPs (n = 27), CPF (n = 2), Tweedle (n = 2), CPLCP (n = 8) and Apidermin (n = 3). Analysis of CP gene evolutionary relationship revealed that each CP family forms a distinct, relatively independent clade. Domain and motif analyses confirmed that all CPR members harbor a conserved Chitin_Bind_4 domain, consistent with CPR family structures in other taxa. Additionally, CPAP members possess one or three Chitin-binding Peritrophin-A domain (CBM_14), CPF members possess a conserved Pupal cuticle protein C1 domain (Cuticle_3), and Tweedle members contain a conserved domain of unknown function (DUF243). In addition, the analysis found no conserved domain within the CPLCP and Apidermin families. RNA-seq data revealed dynamic expression patterns of AmCPs during pupal development, with each gene family displaying a relatively characteristic temporal profile. Quantitative PCR validation of eight highly expressed CPR genes at 9 days post-capping confirmed the RNA-seq results. This work provides a comprehensive bioinformatic characterization and transcriptional analysis of CP genes in A. mellifera, offering a foundation for future functional studies on cuticle formation and identifying candidate genes potentially involved in cuticle development in honeybees. This work relies on transcriptomic data and in silico analyses. All proposed biological roles are hypothetical and require experimental validation.