Editing Conceptual Knowledge for Large Language Models

Recently, there has been a growing interest in knowledge editing for Large Language Models (LLMs). Current approaches and evaluations merely explore the instance-level editing, while whether LLMs possess the capability to modify concepts remains unclear. This paper pioneers the investigation of editing conceptual knowledge for LLMs, by constructing a novel benchmark dataset ConceptEdit and establishing a suite of new metrics for evaluation. The experimental results reveal that, although existing editing methods can efficiently modify concept-level definition to some extent, they also have the potential to distort the related instantial knowledge in LLMs, leading to poor performance. We anticipate this can inspire further progress in better understanding LLMs. Our project homepage is available at https://zjunlp.github.io/project/ConceptEdit.Comment: Work in progress. Code: https://github.com/zjunlp/EasyEdit Dataset: https://huggingface.co/datasets/zjunlp/ConceptEdi

Research Progress on Food 3D Printing Based on Starch

Three-dimensional (3D) printing, also known as additive manufacturing, is an emerging manufacturing technology that enables personalized product design and precise modeling through digital control. In recent years, 3D printing technology has gained significant attention in the food industry due to its potential advantages, especially in the field of customized food processing. Starch is an important component of human diet, especially in the eastern diet structure primarily based on plant-based foods. Most starches possess excellent rheological, hydration, and gel properties, making them have natural advantages in outflow nozzle and printing molding and thus have great application potential in food 3D printing. In this article, recent progress in starch-based 3D printing is reviewed with respect to printing equipment types commonly used in starch-based 3D printing, printing technology using common starches as raw materials, the correlation between starch physicochemical properties and printing performance, starch modification for quality improvement of 3D printed products, the post-processing of starch-based 3D printed products and the influence of printing on starch structure. Furthermore, future prospects in the 3D printing field are presented

Carbonized Cow Dung as a High Performance and Low Cost Anode Material for Bioelectrochemical Systems

We develop a high-performance anode formed from carbonized cow dung for bioelectrochemical systems. Thermal gravimetric analysis showed that the CD carbonization process started at 300°C and ended at approximately 550°C; the weight was reduced by 51%. After a heat-treatment at 800°C for 2 h, the treated CD featured a good conductivity and a high specific surface area. The maximum current density of 11.74 ± 0.41 A m-2 was achieved by CD anode (heated at 800°C), which remained relatively stable from more than 10 days. This study shows that a valuable anode material can be produced through conversion of CD by high-temperature carbonization. This approach provides a new way to alleviate environmental problems associated with CD

Plasmonic organic solar cell and its absorption enhancement analysis using cylindrical Ag nano-particle model based on finite difference time domain (FDTD)

We report the plasmon-assisted photocurrent enhancement in Ag nanoparticles (NPs)-embedded PEDOT:PSS/P3HT:PCBM organic solar cells, and theoretically investigate the causes of the improved optical absorption based on a cylindrical Ag-NPs model which is simulated with a finite difference time domain (FDTD) method. The proposed cylindrical Ag-NPs model is able to explain the optical absorption enhancement by the localized surface plasmon resonance (LSPR) modes, and to provide a further understanding of Ag-NPs shape parameters which play an important role to determine the broadband absorption phenomena in plasmonic organic solar cells

Efficiency Improved by H

The photovoltaic (PV) effects have been investigated and improved using efficient treatments both on single-crystalline (sc) and on multicrystalline (mc) silicon (Si) solar cells. The major effect of forming gas (FG) treatment on solar cell performance is the fill-factor values, which increase 3.75% and 8.28%, respectively, on sc-Si and mc-Si solar cells. As for the optimal 15%-H2 ratio and 40-minute FG treatment, the conversion efficiency (η) values drastically increase to 14.89% and 14.31%, respectively, for sc- and mc-Si solar cells. Moreover, we can measure the internal quantum efficiency (IQE) values increase with H2-FG treatment under visible wavelength (400~900 nm) radiation. Thus based on the work in this research, we confirm that H2 passivation has become crucial both in PV as well as in microelectronics fields. Moreover, the developed mc-Si solar cell by proper H2 FG treatment is quite suitable for commercial applications

Absorption and transport enhancement by Ag nanoparticle plasmonics for organic optoelectronics

The organic films such as P3HT/PCBM incorporating Ag metal nanoparticles are fabricated and experimentally characterized. Due to the excited surface plasma induced by Ag metal nanoparticles, the absorption of the active organic material layer is increased by around 30%. The broadened absorption spectrum to the 260-650nm wavelength range is also observed from our measurements because of the enhanced scattering cross section by Ag metal nanoparticles. Furthermore, by incorporating Ag nanoparticles into the active layer, the mobility have also been improved. Finite Difference Time Domain (FDTD) simulations confirm the increase in transmission of electromagnetic radiation at visible wavelength. The hopping model is proposed to explain the transport mechanism for the device operations. These observations suggest a variety of approaches for improving the performance of general organic optoelectronic devices

Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: a perspective from long‐term data assimilation

It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model-data fusion framework and systematically analyzed the SSA-induced biases using time-series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83-fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA-induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young-aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long-term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C-climate feedback