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Existing AI-based medicine prediction systems require substantial training time, computing resources, and extensive labeled data, yet they often lack scalability. To bridge these gaps, this study introduces a novel MED-Prompt framework that employs pretrained models such as BERT, BioBERT, and ClinicalBERT. The core of our framework lies in developing specialized prompts, which act as guiding instructions for the models during the prediction process. MED-Prompt develops prompts that help models interpret and extract medical information from clinical corpus. The clinical text was derived from the widely known MIMIC-III 11 https://physionet.org/content/mimiciii/1.4/. dataset. The study performs a comparative analysis and evaluates the performance of Manual-Prompt and GPT-Prompts. Further, a fine-tuned approach is developed within MED-Prompt, leveraging transfer learning to achieve prompt-guided medicine predictions. The proposed method achieved a maximum F1-score of 96.8%, which is more than 40% F1-score higher than the pretrained model. In addition, the fine-tuned also showed an average of 2.38 times better processing performance. These results revealed that MED-Prompt is scalable regarding the number of training records and input prompts. These results not only demonstrate the proficiency and effectiveness of the framework but also significantly reduce computational requirements. This also indicates that the proposed approach has the potential to significantly improve patient care, reduce resource requirements, and increase the overall effectiveness of AI-driven medical prediction systems.

Accurate pre-harvest crop yield estimation is vital for agricultural sustainability and economic stability. The existing yield estimating models exhibit deficiencies in insufficient examination of hyperparameters, lack of robustness, restricted transferability of meta-models, and uncertain generalizability when applied to agricultural data. This study presents a novel meta-knowledge-guided framework that leverages three diverse agricultural datasets and explores meta-knowledge transfer in frequent hyperparameter optimization scenarios. The framework’s approach involves base tasks using LightGBM and Bayesian Optimization, which automates hyperparameter optimization by eliminating the need for manual adjustments. Conducted rigorous experiments to analyze the meta-knowledge transformation of RGPE, SGPR, and TransBO algorithms, achieving impressive R2 values (0.8415, 0.9865, 0.9708) using rgpe_prf meta-knowledge transfer on diverse datasets. Furthermore, the framework yielded excellent results for mean squared error (MSE), mean absolute error (MAE), scaled MSE, and scaled MAE. These results emphasize the method’s significance, offering valuable insights for crop yield estimation, benefiting farmers and the agricultural sector.

Water and nutrient use efficiencies are important adaptive features of plants in arid and semi-arid environments. In this study, water, nitrogen (N), and phosphorus (P) use efficiencies of Stipa purpurea Griesb., an endemic and dominant grass species, were investigated in the alpine steppe along precipitation gradients on the Tibetan Plateau. The leaf N content of S. purpurea increased along the precipitation gradient, but leaf P decreased, whereas carbon (C) remained unchanged. Leaf δ13C (water use efficiency, WUE) and C:N ratio (N use efficiency, NUE) decreased with increasing precipitation. But leaf C:P (P use efficiency, PUE) and N:P ratios increased with increasing precipitation. A trade-off was found between WUE and PUE but not between WUE and NUE. The changes in leaf traits were associated with changes in soil water, organic C, total N and P. These findings offer insights into understanding alpine plant water and nutrient use strategies along a precipitation gradient, as well as facilitate the prediction of alpine ecosystem responses to precipitation changes.

Learned image compression techniques have achieved considerable development in recent years. In this paper, we find that the performance bottleneck lies in the use of a single hyperprior decoder, in which case the ternary Gaussian model collapses to a binary one. To solve this, we propose to use three hyperprior decoders to separate the decoding process of the mixed parameters in discrete Gaussian mixture likelihoods, achieving more accurate parameters estimation. Experimental results demonstrate the proposed method optimized by MS-SSIM achieves on average 3.36% BD-rate reduction compared with state-of-the-art approach. The contribution of the proposed method to the coding time and FLOPs is negligible.

In this work, a memristive spike-based computing in memory (CIM) system with adaptive neuron (MSPAN) is proposed to realize energy-efficient remote arrhythmia detection with high accuracy in edge devices by software and hardware co-design. A multi-layer deep integrative spiking neural network (DiSNN) is first designed with an accuracy of 93.6% in 4-class ECG classification tasks. Then a memristor-based CIM architecture and the corresponding mapping method are proposed to deploy the DiSNN. By evaluation, the overall system achieves an accuracy of over 92.25% on the MIT-BIH dataset while the area is 3.438 mm2 and the power consumption is 0.178 μJ per heartbeat at a clock frequency of 500 MHz. These results reveal that the proposed MSPAN system is promising for arrhythmia detection in edge devices.

In the High Efficiency Video Coding (HEVC), a variety of CU sizes and intra prediction modes significantly improve coding efficiency, but also bring higher computational complexity. This paper proposes a new compact VLSI architecture for HEVC intra prediction, which is geared towards 8K video decoding. It supports all the transform unit (TU) sizes and 35 HEVC intra prediction modes. First, this paper introduces a TU-oriented intra predictor with a throughput of 32 pixels, which can be newly arranged with the TU size. It can be a line of 32 pixels, two lines of 16 pixels, 4 lines of 8 pixels or four lines of four pixels. This TU-oriented architecture allows intra-prediction and inverse discrete cosine transform (IDCT) to be computed in parallel, removing the memory between them. In addition, a horizontal and vertical line buffer for reference sample is proposed, which only cost 0.8K bit and is implemented by register files with SRAM-free. Finally, to further reduce hardware consumption, multipliers can be shared in the prediction. The implementation result shows that the compact architecture supports 8K video application and costs 66.2K logic gates, which is synthesized with the TSMC 65nm process under 400MHz.

Low-rank and sparse decomposition based image alignment has recently become an important research topic in the computer vision community. However, the reconstruction process often suffers from the perturbations caused by variations of the input samples. The reason behind is that the consistency of the learned low-rank and sparse structures for similar input samples is not well addressed in the existing literature. In this paper, a novel framework that embeds the manifold constraint into low-rank and sparse decomposition is proposed. Particularly, the proposed approach attempts to solve the original optimization problem directly and force the optimization process to satisfy the structure preservation requirement. Therefore, this novel manifold constrained low-rank and sparse decomposition (ManiDec) can consistently integrate the manifold constraint during the non-convex optimization process, and it can contribute a better solution which is robust to the variance of the input samples. Numerical comparisons between our proposed ManiDec and some state-of-the-art solvers, on several accessible databases, are presented to demonstrate its efficiency and effectiveness. In fact, to the best of our knowledge, this is the first time to integrate the manifold constraint into a non-convex framework, which has demonstrated the superiority of performance.

This brief presents a tutorial on multifaceted techniques for high efficiency piezoelectric energy harvesting. For the purpose of helping design piezoelectric energy harvesting system according to different application scenarios, we summarize and discuss the recent design trends and challenges. We divide the design focus into the following three categories, namely, (1) AC-DC rectifiers, (2) CP compensation circuits, (3) maximum power point tracking (MPPT) circuits. The features, problems encountered, and suitable systems of various AC-DC rectifier topologies are introduced and compared. The important role of non-linear methods for piezoelectric energy harvesting is illustrated from the perspective of impedance matching. Energy extraction techniques and voltage flipping techniques based on inductors, capacitors, and hybrid structures are analyzed. MPPT techniques with different features and targets are discussed.

Applications have different preferences for caches, sometimes even within the different running phases. Caches with fixed parameters may compromise the performance of a system. To solve this problem, we propose a real-time adaptive reconfigurable cache based on the decision tree algorithm, which can optimize the average memory access time of cache without modifying the cache coherent protocol. By monitoring the application running state, the cache associativity is periodically tuned to the optimal cache associativity, which is determined by the decision tree model. This paper implements the proposed decision tree-based adaptive reconfigurable cache in the GEM5 simulator and designs the key modules using Verilog HDL. The simulation results show that the proposed decision tree-based adaptive reconfigurable cache reduces the average memory access time compared with other adaptive algorithms.

Often referred to as the “Third Pole,” China's Qinghai–Tibetan Plateau developed large amounts of peatland owing to its unique alpine environment. As a renewable resource, peat helps to regulate the climate as well as performing other important functions. However, in recent years, intensifying climate change and anthropogenic disturbances have resulted in peatland degradation and consequently made sustainable development of peatland more difficult. This review summarizes peatland ecological and economic functions, including carbon sequestration, biodiversity conservation, energy supplies, and ecotourism. It identifies climate change and anthropogenic disturbances as the two key factors attributing to peatland degradation and ecosystem carbon loss. Current problems in environmental degradation and future challenges in peatland management under the effects of global warming are also discussed and highlighted.

Blockchain technology is increasingly being used in Internet of things (IoT) devices for information security and data integrity. However, it is challenging to implement complex hash algorithms with limited resources in IoT devices owing to large energy consumption and a long processing time. This paper proposes a RISC-V processor with memristor-based in-memory computing (IMC) for blockchain technology in IoT applications. The IMC-adapted instructions were designed for the Keccak hash algorithm by virtue of the extendibility of the RISC-V instruction set architecture (ISA). Then, a RISC-V processor with area-efficient memristor-based IMC was developed based on an open-source core for IoT applications, Hummingbird E200. The general compiling policy with the data allocation method is also disclosed for the IMC implementation of the Keccak hash algorithm. An evaluation shows that >70% improvements in both performance and energy saving were achieved with limited area overhead after introducing IMC in the RISC-V processor.

This study used mesocosms to examine the effects of alternate drying and rewetting on CO2 and CH4 emissions from high-altitude peatlands on the Tibetan Plateau. The drying and rewetting experiment conducted in this study included three phases: a 10-day predrying phase, a 32-day drying phase, and an 18-day rewetting phase. During the experiment, the water table varied between 0 and 50 cm with respect to the reference peat column where the water table stayed constant at 0 cm. The study found that drying and rewetting had no significant effect on CO2 emissions from the peatland, while CH4 emissions decreased. The cumulative CH4 emissions in the control group was 2.1 times higher than in the drying and rewetting treatment over the study period. Moreover, CO2 and CH4 emissions were positively correlated with soil temperature, and the drying process increased the goodness of fit of the regression models predicting the relationships between CO2 and CH4 emissions and temperature. These results indicate that small-scale water table variation has a limited effect on CO2 emissions, but might reduce CH4 emissions in high-altitude peatlands on the Tibetan Plateau.