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.

Merkle Tree is a fundamental cryptographic primitive in Zero-Knowledge Proof (ZKP) protocols, sharing significant computational workloads with the Number Theoretic Transform (NTT) in zkSTARK schemes. Merkle Tree is a tree structure where nodes are primarily generated through hash computations. Among them, Poseidon Hash, as a ZK-friendly hash function, has emerged as one of the most widely adopted choices. Therefore, hardware acceleration of building Merkle Tree based on Poseidon Hash can significantly enhance the performance of ZKP protocols. We propose AcclMT, a highly resourceefficient and flexible Poseidon Hash-based Merkle Tree architecture. Our design employs hardware-software co-design and optimizes the hashing data flow, resulting in an area-efficient Poseidon Hash engine that improves modular multiplication resource utilization. Furthermore, AcclMT uses these engines alongside hierarchical on-chip cache and optimized task scheduling for building large Merkle Trees. It also supports flexible parameter configurations for various requirements. Experimental results show that our proposed Poseidon Hash engine achieves a $14.3 \times$ speedup compared to the latest FPGA-based work. By improving resource utilization, it also reduces area usage by 14.8% compared to unoptimized design. AcclMT achieves up to $1665 \times$ speedup over software implementations in building Merkle tree, with average utilization of 95.9% and 99.2% for the two hash engines.

Zero-knowledge proof (ZKP) is a widely used privacy-preserving technology, where multiscalar multiplication (MSM) accounts for over 70% of the computational workload. The acceleration of MSM can enhance the overall performance of ZKP, making it a focal point of community attention. However, in practical applications involving the deployment of multiple MSM accelerators, existing designs often overlook strategies for optimizing bandwidth and area efficiency. To address this, we propose Myosotis, an efficiently pipelined and parameterized MSM architecture. By sharing input data and allocating cache effectively, it mitigates average transmission bandwidth in runtime. Myosotis also supports the use of multiple point addition (PADD) units to achieve performance gains, balancing area overhead and latency for improved area efficiency. Different parameter selection enables a tradeoff between the performance, area, and bandwidth of the MSM accelerator. When benchmarking with MSM degrees between$2^{18}$and$2^{26}$, our proposed baseline design achieves up to$3.32\times $and$6.72\times $speedups over state-of-the-art FPGA and ASIC designs. Compared to the baseline, Myosotis with two window MSMs and one PADD unit reduces bandwidth demand by 43% while maintaining similar area and latency. On the other hand, Myosotis with three window MSMs and two PADD units decreases latency by 43% and bandwidth by 17%, with only a 9% area increase.

Edge servers and mobile devices are often assigned a large number of computing tasks. However, the data involved in computing tasks is often sensitive in terms of privacy. Our initial proposal is a federated edge learning strategy based on real-world scenarios, which combines blurred data or pseudo shared data. Federated learning is used to train device models with the aim of protecting privacy while enabling mobile devices to more effectively utilize data for decision-making. In the case of limited energy on mobile devices, we propose a federated edge learning algorithm with blurred data sharing. This algorithm can generate more accurate models by uploading partially blurred data. In order to further improve model accuracy and protect privacy of mobile devices, we propose a federated edge learning algorithm with pseudo data sharing based on dataset distillation and generative adversarial networks (GANs) in scenarios with relatively sufficient energy. The experimental results on several traditional datasets show that our proposed algorithms outperform traditional algorithms in terms of accuracy and energy consumption.

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.

In this paper the quality of Jason-3 data was evaluated based on Jason-2 GDR data in the tandem stages. The percentage of data loss and data edition and the daily mean changes of the main physical parameters are calculated for each cycle. The sea surface height discrepancy at the intersection point and the sea level anomaly along the track are analyzed, and the system deviation between Jason-2 and Jason-3 are calibrated. Jason-3 and Jason-2 data have uniform change trend and spatial distribution in significant wave heights(SWH), mean backscattering coefficient, ionosphere delay correction mean values and mean wet tropospheric delay differences between microwave radiometer observations and ECMWF model, and there are systematic deviations between Jason-3 and Jason-2. The standard deviations of the sea surface height bias at the self-intersection of the Jason-3 and Jason-2 are 4.98cm and 4.94cm, respectively. It can be indicated that the accuracy of Jason-3 is comparable to that of Jason-2 with the systematic deviation of 2.93cm.