Large language models (LLMs) excel at complex reasoning and achieve human-like performance in many natural language processing tasks. However, they still struggle to reason effectively when faced with inconsistent or contradictory information. This capability gap raises significant concerns for real-world applications where reliable decision-making depends on reconciling conflicting evidence, such as legal analysis, medical diagnosis, and commonsense reasoning. In this paper, we focus on defeasible reasoning in natural language, a task that challenges LLMs to handle and resolve contradictory information. To improve the defeasible reasoning capability of LLMs, we propose LLM-ASPIC+, a framework combining neural language understanding with formal argumentation. Our framework harnesses LLMs’ capacity for grounding and contextual reasoning while integrating formal argumentation frameworks to establish systematic conflict resolution mechanisms lacking in LLMs. We also create MineQA, a newly synthesized dataset designed to evaluate multi-step defeasible reasoning under both strict and defeasible rules. LLM-ASPIC+ achieves state-of-the-art results on multi-step defeasible reasoning, with 87.1% accuracy on BoardGameQA-2 and 82.6% on BoardGameQA-3. These results show that integrating neural language models with formal argumentation effectively supports defeasible reasoning in natural language.

3-D NAND flash memory is the ubiquitous nonvolatile memory (NVM) on the market because of its large storage capacities, high reliability, and low bit cost. The reliability characteristics of 3-D NAND flash memory, however, are considerably different from those of 2-D NAND flash memory due to the peculiar architectures. In this article, read disturb (RD) at various program/erase (P/E) stages is thoroughly explored. To adjust the low-density parity check (LDPC) codes dynamically and extend the lifetime of 3-D NAND flash memory, short-term lifetime prediction models of RD and endurance are proposed based on in-depth studies on the correlations of fail bit count (FBC) at various lifetime stages, and their accuracy is tested experimentally. A new short-term warning system (STWS) is proposed to extend the lifetime of 3-D NAND-based storages. It consists of the error-bits’ prediction module (EBPM) and the self-adjustable LDPC codes module (SLDPC), where EBPM predicts FBC periodically and SLDPC preallocates LDPC codes for future use based on the result of EBPM. The experimental result shows that our prediction models have high reliability, and STWS can effectively prolong the lifetime of NAND flash. The findings of this study provide fundamental insights into FBC degradation in 3-D NAND flash, as well as a simple and practical method for building 3-D NAND-based storage with high reliability.

In this paper, read disturb (RD) at various program/erase (P/E) stages has been thoroughly explored, and short-term lifetime prediction models of RD and endurance are proposed. Based on these, a new short-term warning system (STWS) is proposed, which can extend the lifetime of 3D NAND-based storage by adjusting LDPC codes dynamically. The experimental result shows that the proposed prediction models have high reliabilities, and STWS can effectively prolong the lifetime of NAND flash.

This study proposes three‐node cooperative computing schemes for wireless sensor networks (WSNs), including parallel offload cooperative computing (POCC) and serial offload cooperative computing (SOCC). The cooperation system includes a task node with execution tasks and two peer help nodes. Specifically, the authors fully mobilise the computing and communication resources in the network by means of cooperative computing to minimise the energy consumption of processing tasks in the WSN while satisfying the delay constraints. The authors characterise these problems as optimal problems and find the optimal solutions using corresponding iterative algorithms, respectively. The simulation results show the superior performance of the proposed schemes in terms of energy consumption and node fairness. For different cases the superiority of POCC and SOCC is verified separately, and the SOCC is more suitable for the case where task node is located at the edge of WSN. Furthermore, the authors introduce a trade‐off scheme between POCC and SOCC, which can always achieve the optimal scheme in different cases.

Sudden Oak Death (SOD), a kind of plant disease, is caused by Phytophthora ramorum. It was discovered in 1993 for the first time, has a wide range of host plants, rapid spreading, serious harmful consequences. The outbreak of SOD is influenced by various factors. Thus there are obvious spatial variations in its distribution due to the influence of different environmental factors. In this study, an integrated datasets of the outbreak points and the associated environmental variables at global and regional scales were collected and processed. The Support Vector Machine (SVM) model was adopted to predict the potential occurrence risk of SOD under future climate scenarios. In addition to the traditional bioclimatic variables, Leaf Area Index (LAI) was introduced into the potential risk prediction models to achieve the forecasting of SOD in China. Four future climate scenarios of RCP2.6, RCP4.5, RCP6.0 and RCP8.5 were considered and compared. The optimal thresholds were determined for different climate scenarios and different years. The predictive accuracies were assessed using the indices of OPS, Sensitivity, Specificity, Kappa coefficient and AUC. Areas of the potential invasion risk of SOD under different climate scenarios in China were analyzed. Results showed that, under the future climate scenarios in 2050 and 2070, Yunnan, Sichuan, Guizhou, Tibet and Chongqing all have high risks. This study could provide the long-term early warning about the outbreak and invasion risk of SOD, serving for the prevention and treatment of forest diseases as well as ensuring the forest ecological security globally and nationally.