A nanogel-in-microfiber device, whose release can be switched on and off in response to a temperature change, is successfully developed. The release behaviors are realized through the deswelling and swelling of the nanogels in shell layer of fiber by alternatively elevating and lowering the environmental temperature.
Large language models (LLMs) are vulnerable to universal jailbreaks-prompting strategies that systematically bypass model safeguards and enable users to carry out harmful processes that require many model interactions, like manufacturing illegal substances at scale. To defend against these attacks, we introduce Constitutional Classifiers: safeguards trained on synthetic data, generated by prompting LLMs with natural language rules (i.e., a constitution) specifying permitted and restricted content. In over 3,000 estimated hours of red teaming, no red teamer found a universal jailbreak that could extract information from an early classifier-guarded LLM at a similar level of detail to an unguarded model across most target queries. On automated evaluations, enhanced classifiers demonstrated robust defense against held-out domain-specific jailbreaks. These classifiers also maintain deployment viability, with an absolute 0.38% increase in production-traffic refusals and a 23.7% inference overhead. Our work demonstrates that defending against universal jailbreaks while maintaining practical deployment viability is tractable.
This study reports a pH/magnetic dual-responsive hemicellulose-based nanocomposite hydrogel with nearly 100 % carbohydrate polymer-based and biodegradable polymer compositions for drug delivery. We synthesized pure Fe
The 2060 carbon neutral target reflects the long-term equilibrium and stability of production activities and the natural environment. As an important part of Chinese energy structure, the operation and transformation of power enterprises will face higher requirements. Although the rapid development of smart grids provides necessary technical support for power enterprises to build a modern energy system with green power as the core, whether power enterprises can use smart grids to improve their operating performance and environmental performance has yet to be discussed. The differences caused by the heterogeneity of property rights will also have an impact on the green transformation and development of enterprises. This paper selects 25 Chinese power enterprises as the research objects and uses the 2011–2019 enterprise panel data and the data envelopment analysis model to evaluate the operating performance and environmental performance of power enterprises. The results show that the overall fluctuation trend of the total factor productivity index and green total factor productivity index of power enterprises are W-shaped, and technological progress is the main driving force for the improvement of power operating performance and environmental performance; Compared with enterprises with a single power generation method, enterprises with diversified power generation methods performed better in their overall total factor productivity index. After that, text mining and machine learning methods are used to classify the text of the enterprise’s annual report to determine whether the enterprise applies smart grid technology for production and operation activities. Finally, using feasible generalized least squares method (FLGS) and dynamic panel system generalized moment estimation (SYS-GMM) to analyze the impact of smart grid on the operating performance and environmental performance of power enterprises, and the nature of corporate property rights in this process. It is found that smart grids can improve the operating performance and environmental performance of power enterprises; compared with state-owned enterprises, non-state-owned enterprises can achieve better performance in the application of smart grids to improve operating performance and environmental performance. Finally, this study provides corresponding policy recommendations for power enterprises to achieve performance improvement and green transformation development.
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