Can Large Language Models Understand Real-World Complex Instructions?

Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with complex instructions, which can be either complex task descriptions that require multiple tasks and constraints, or complex input that contains long context, noise, heterogeneous information and multi-turn format. Due to these features, LLMs often ignore semantic constraints from task descriptions, generate incorrect formats, violate length or sample count constraints, and be unfaithful to the input text. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions, as they are close-ended and simple. To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically. We design eight features for complex instructions and construct a comprehensive evaluation dataset from real-world scenarios. We also establish four criteria and develop corresponding metrics, as current ones are inadequate, biased or too strict and coarse-grained. We compare the performance of representative Chinese-oriented and English-oriented models in following complex instructions through extensive experiments. Resources of CELLO are publicly available at https://github.com/Abbey4799/CELLO

Appropriate Management Scale of Farmland and Regional Differences under Different Objectives in Shaanxi Province, China

Agricultural development is facing two problems: insufficient grain production and low profit of farmers. There is a contradiction between the government’s goal of increasing production and the farmer’s goal of increasing profit. Exploring the appropriate management scale of farmland under different objectives is of great significance to alleviate the conflict of interests between the government and farmers. In this study the Cobb-Douglas production function model was used to measure the appropriate management scale of farmland under different objectives in Shaanxi Province and analyze the regional differences. Under the two objectives, the appropriate management scale of the Loess Plateau was the largest in the three regions, followed by Qinba Mountains and Guanzhong Plain. Farmland area and quality were the main influencing factors for the appropriate management scale of farmland under the goal of maximizing the farmland yield, while the nonagricultural employment rate and farmland transfer rate were the main influencing factors under the goal of maximizing farmers’ profits. It is easy for Shaanxi Province to increase farmers’ profits, but more land needed to be transferred to increase farmland yield. These results suggest that in order to balance the goal of increasing yield and profit, the transfer of rural surplus labor should be promoted, and the nonagricultural employment rate should be improved. In Loess Plateau, restoring the ecological environment and enhancing the farmland quality. In Guanzhong Plain, avoiding urban land encroachment on farmland. In Qinba Mountains, developing farming techniques and moderately increasing the intensity of farmland exploit

Ultrasonic Welding of Aluminum to Steel: A Review

As a solid-state bonding technology, ultrasonic welding (USW) has the characteristics of green energy saving and environmental friendliness. It is more suitable for joining dissimilar metals than other welding technologies. The aluminum-to-steel USWed joint has been widely used in the automotive and aviation industries. Currently, there is no review literature report on aluminum-to-steel USW. The main physical phenomena of the USW process include interface temperature increase, ultrasonic softening, plastic deformation, formation and growth of the IMCs, and dynamic recrystallization. Hence, the microstructures and mechanical properties of aluminum-alloy-to-low-carbon-steel, aluminum-alloy-to-stainless steel, and aluminum-alloy-to-galvanized-steel-joints by USW are reviewed. Moreover, the effect of interface temperature, interface plastic deformation, and interface macrostructure and microstructure is explored. Lastly, tensile-shear and fatigue strength of joints and numerical simulation of the USW process are also discussed. In addition, some new application types of aluminum-to-steel USW are introduced. Finally, the future trends of aluminum-to-steel USW with guidance are provided

Stable isotopic composition of submerged plants living in karst water and its eco-environmental importance.

The stable carbon isotopic composition of submerged plants (δ13CP) can be controlled by physiological and environmental factors. Herein, we took advantage of a short, natural karst river with an annual mean bicarbonate (HCO3−) value of 3.8 mmol L−1 to study the stable carbon isotopic composition of submerged plants along the river and the influence of environmental conditions on the δ13CP values. The δ13CP values of Ottelia acuminata, Potamogeton wrightii, Vallisneria natans, and Hydrilla verticillata from upstream to downstream show a gradient and ranged from −34.8‰ to −27.8‰, −36.6‰ to −23.7‰, −35.1‰ to −25.3‰, and −38.6‰ to −26.3‰, respectively and even more depleted values for the first two species at the uppermost site. Diurnal variation of water chemistry and concentration of the dissolved inorganic carbon (DIC) and the stable carbon isotopic composition of DIC (δ13CD) indicate that the macrophytes and other primary producers in the river have a very high net photosynthetic rate. The gradient of δ13CP values was consistent with CO2 being a declining source of inorganic carbon for photosynthesis in the downstream transect. The results demonstrate that the high DIC concentration with lower negative δ13C value, particularly in karst water environment has a significant role in controlling the stable carbon isotopic composition of submerged plants living in it

Water Use Effectiveness Is Enhanced Using Film Mulch Through Increasing Transpiration and Decreasing Evapotranspiration

Water shortage is a main limitation of crop growth and yield in drought northwest China, which is an important area of seed maize growth. Plastic film mulch is widely adopted to reduce soil evaporation (E) and conserve water resources, which changes evapotranspiration (ET) and its components, E and transpiration (Tr) and crop growth. The AquaCrop model, one of widely used crop models powered by water, can well simulate crop ET components and growth. However, there are few studies that examine ET partitioning and growth with and without plastic film mulch. The calibrated AquaCrop model was used to partition ET and simulate growth of seed maize with and without plastic film mulch in a drought region of northwest China in 2014 and 2015. The AquaCrop model can well simulate canopy cover curve (CC), and the dynamic and accumulated courses of ET and ET components. Plastic film mulch could advance the growth stage of seed maize and reduce seasoned ET. The initial stage with plastic film mulch was 37–42 days, while it was 46–48 days for no-mulch. Plastic film mulch increased Tr by 14.16% and 14.48% and significantly decreased E by 57.25% and 34.28% in 2014 and 2015, respectively, resulting in the reduction of seasonal total ET. Plastic film mulch increased averaged mid-season crop coefficient for transpiration (Kc Tr) by 0.88% and decreased soil evaporation coefficient (Ke) by 62.50%. Collectively, the results suggest that, in comparison with no-mulch, plastic film mulch advanced crop growth, and decreased total ET and increased Tr related with crop production, i.e., improve water use effectiveness

Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes

Possible improvements of global optimization methods inspired by nature

This study focuses on the global optimization of functions of real variables using methods inspired by nature. It contains a description of selected global optimization techniques (Differential Evolution, Self-Organizing Migrating Algorithm, Steady-State Evolutionary Algorithm, Particle Swarm Optimization, Gregarious Particle Swarm Optimizer a Hybrid Particle Swarm with Differential Evolution Operator). I have found four improvements of these techniques, discovered their suitable parameter configurations and compared them on chosen trial functions. Experimental results proved that described improvements can increase performance of the optimization techniques inspired by nature

Lignin characteristics in soil profiles in different plant communities in a subtropical mixed forest

Aims Lignin is generally considered as an important indicator of soil organic carbon (SOC) storage and dynamics. To evaluate the effects of plant communities and soil depth on soil lignin is critical to better understand forest carbon cycling. Methods We compared lignin content and chemical signature in three soil depths of four major plant communities in a subtropical forest, which located in the north part of Wuling Mountains, China. Lignin was measured using CuO oxidation method. Important Findings Both lignin content and its biochemical signature in plant litter varied among communities. However, these differences were mostly no longer exist in the upper soil layers. Lignin chemistry in soils inherited some of the biochemical signature of lignin in litter, but in a diminished magnitude. These results suggest that different plant communities had similar decomposition process with varying rates, caused diminished differences in lignin content and its biochemical signature. Lignin content decreased with soil depth, but the biochemical signature of lignin was not significantly different among soil layers for all communities, which suggests that vertical movement of lignin within the soil profile is very likely a key process causing this similar biochemical signature. These results emphasized the important roles of lignin inputs and soil eluviation in shaping lignin characteristics and distribution in forest soils, which pinpoint the urgent need to consider hydrological processes in studying forest soil carbon cycling