Biopharmaceutical processes typically generate abundant high-frequency online sensor data but suffer from sparse and delayed offline quality measurements, creating a “data-rich but label-poor” dilemma that hinders effective process monitoring. Furthermore, these processes are subject to significant distribution shifts due to batch-to-batch variability and time-varying metabolic states. To address these challenges, this article proposes a novel online time-series contrastive learning framework for soft sensing, validated on an industrial erythromycin fermentation process. We introduce a mechanism-informed soft contrastive learning strategy that utilizes information derived from domain knowledge to construct instance-level and temporal similarity matrices, guiding the model to learn physically meaningful representations from unlabeled data. In addition, we develop a dual-timescale online learning architecture comprising a slow branch for robust representation learning and a fast branch for real-time adaptation. Experimental results on a large-scale industrial dataset demonstrate that the proposed framework significantly outperforms state-of-the-art time-series contrastive learning baselines, particularly in predicting complex rheological indicators like broth viscosity under temporal distribution shifts.

Traditional agriculture often relies heavily on pesticides for distributed pest management, which increases production costs and can lead to environmental pollution. The inefficiency and environmental impact of these methods highlight the need for more sustainable solutions. Artificial Intelligence Internet of Things (AIoT) enables precise monitoring and prediction of pest infestations in agriculture, facilitating targeted interventions to effectively reduce crop loss rates. This paper proposes a unified pest prevention and control system based on Agricultural Social Internet of Things (Agri-Social IoT) and artificial intelligence algorithm. By social relationships among devices, the system aims to enhance agricultural production efficiency through unified pest prediction and prevention. To achieve rapid and accurate detection of plant pests, this paper introduces an innovative detection method named Ghost-YOLO-ShuffleAttention (GhostYOLOSA). This artificial intelligence method improves the capture of spatial relationships and contextual information, enhancing the detection accuracy for small targets. It significantly reduces the computational load and model parameters while ensuring high detection accuracy, making it suitable for resource-limited devices. Experimental results from datasets and real-world applications demonstrate that the unified pest control system performs well in pest detection and prevention tasks, promoting sustainable agricultural development.

Diffusion models have demonstrated competitive performance in missing data imputation (MDI) task. However, directly applying diffusion models to MDI produces suboptimal performance due to two primary defects. First, the sample diversity promoted by diffusion models hinders the accurate inference of missing values. Second, data masking reduces observable indices for model training, obstructing imputation performance. To address these challenges, we introduce $\underline{\text{N}}$egative $\underline{\text{E}}$ntropy-regularized $\underline{\text{W}}$asserstein gradient flow for $\underline{\text{Imp}}$utation (NewImp), enhancing diffusion models for MDI from a gradient flow perspective. To handle the first defect, we incorporate a negative entropy regularization term into the cost functional to suppress diversity and improve accuracy. To handle the second defect, we demonstrate that the imputation procedure of NewImp, induced by the conditional distribution-related cost functional, can equivalently be replaced by that induced by the joint distribution, thereby naturally eliminating the need for data masking. Extensive experiments validate the effectiveness of our method. Code is available at [https://github.com/JustusvLiebig/NewImp](https://github.com/JustusvLiebig/NewImp).

The increased frequency of forest fires in recent years has raised concerns about the high cost associated with traditional forest fire prevention methods. To address this issue, this paper presents a novel forest fire detection and segmentation application for UAV-assisted mobile edge computing system. Traditional target detection and segmentation systems for forest fire detection are often large and unsuitable for deployment on edge equipment such as UAVs. Deploying such models on edge gateways can also lead to high costs and delays. To overcome these challenges, this paper proposes a lightweight fire target detection and precision segmentation model that can be used on UAVs and other edge equipment. The proposed algorithm achieves more accurate image segmentation, thereby improving the efficiency of fire location. Additionally, an edge computing system is built to link the feedback of the edge model with the edge gateway, administrators, and other intelligent devices promptly. Extensive experiments with large datasets and in real environments demonstrate the efficacy of the proposed algorithm, effectively enhancing the efficiency of forest inspection and forest fire warning capabilities.

Forecasting governing key factors in industrial bioprocesses is crucial for ensuring stability and efficiency in production. However, the accurate prediction is challenged by the strong coupling and uncertainty characteristic of industrial bioprocess data. To capture the common dynamics and comprehensively model the interrelationships among multivariate time series in bioprocesses, this study introduces a predictive model called the causality enhanced global-local graph neural network. A global-local decomposition module is first constructed utilizing time regularization, thereby, explicitly obtaining global and local bioprocess series while preserving temporal structure. Subsequently, we construct node embedding for both the global and local series. Finally, we presents an innovative graph generation module that creates an explicit causality graph based on transfer entropy and an implicit static-dynamic graph for the downstream graph neural network, considering causal information, static and dynamic dependencies among variables. Application results based on real industrial bioprocess data demonstrate that this method has high predictive accuracy.

Missing data imputation is a critical aspect of the Industrial Internet-of-Things (IIoT), which is uniquely challenged by local relationships within data due to different operational contexts and phases. Current imputation methods struggle to accommodate local relationships due to their black-box nature or limited capacity. To bridge this gap, we approach data imputation as a distribution alignment problem and leverage optimal transport to instantiate it for enhanced capacity. Specifically, we first introduce the similarity preserved optimal transport (SPOT) problem, with a conditional gradient solution to compute the transport cost. Subsequently, we propose the SPOT for imputation (SPOT-I) framework. It minimizes the transport cost of SPOT for distribution alignment and uses the gradient to update imputations, which maintains local similarity and refines imputation due to the characteristics of SPOT. Experiments on IIoT datasets showcase the superiority of SPOT-I over state-of-the-art imputation methods.

Monitoring of energy conversion process confronts great difficulties due to extreme value jumps or data packet loss under extreme operating conditions, consequently resulting in data missing. To tackle these issues, researchers propose diffusion-based time series imputation approaches. However, there are critical limitations of methods adopted by these models: first, dense reverse inference (DRI), where conventional diffusion methods suffer from time-consuming imputation procedures and thus the loss of effective information over a long transmission process; second, indirect prediction strategy (IPS), which causes inaccurate temporal data imputation results. To address these limitations, we propose denoising diffusion straightforward models (DDSMs) for missing data imputation in energy conversion process monitoring. Specifically, based on the conditional mechanism, we innovatively use the accelerated sampling strategy in temporal models to reduce the lengthy inference time caused by DRI and propose a well-designed straightforward training algorithm for a straightforward estimation to improve the imprecise inference results acquired by IPS. Extensive experiments on real-world dataset demonstrate the effectiveness and superiority of DDSM and our approach consistently outperforms previous temporal generative methods significantly.