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The protein kinase adenosine monophosphate-activated γ3-subunit (PRKAG3) gene encodes the muscle specific isoform of the regulatory γ3 subunit of adenosine monophosphate activated protein kinase (AMPK), which participates in regulation of energy metabolism and food intake in animals. In the present study, we investigated the genetic polymorphisms of the PRKAG3 gene and the association with carcass performance in 470 Daheng meat-type chickens. One nucleotide substitution (c. 3207 A>G) was detected in the eleventh exon, which caused an amino acid change (p. 1069 T>A). The frequencies of the three genotypes were 44.22% (AA), 36.98% (AB), and 18.80% (BB), respectively. The association of genotypes with chicken carcass traits showed that the genotypes were significantly associated with live weight (LW), carcass weight (CW), breast muscle weight (BMW), leg muscle weight (LMW), abdominal fat weight (AW) and subcutaneous fat thickness (SFT) traits. The BB genotype has higher performance than which of genotypes AB and AA in traits LW, CW, BMW, LMW and AW (P≤0.01 or P≤0.05), which would be genetically advantageous to improve carcass traits in Daheng meat-type chickens. To our knowledge, this is the first report to study the genetic polymorphism of the PRKAG3 gene and its association with carcass traits in chicken.

LST (Land Surface Temperature) is a significant parameter that represents the ground energy balance and plays a crucial role in understanding climate change. The LST of the Tibetan Plateau (TP) has a direct influence on the climate and environmental changes of the TP, and it also has a significant impact on global climate and atmospheric circulation. Although there are various factors that drive the spatial and temporal distribution of LST on the TP, the primary driving forces and its seasonal variations of LST are not yet well understood. The research focuses specifically on the TP region, selecting three types of LST data, using geodetector model, to analyze the driving factors affecting the spatial pattern of LST in different seasons. The results indicate that the three factors, Air Temperature (AT), Elevation (Ele), and Permafrost Thermal Stability (PTS), have a significant influence on LST throughout all seasons, whereas other variables demonstrate varying contributions to LST depending on the season. This study contributes to the understanding of the spatial variability of surface thermal conditions and the intricate relationships between their driving factors. It also emphasizes the potential changes in these relationships throughout the year.

IntroductionRecently, Google introduced Pathways as its next-generation AI architecture. Pathways must address three critical challenges: learning one general model for several continuous tasks, ensuring tasks can leverage each other without forgetting old tasks, and learning from multi-modal data such as images and audio. Additionally, Pathways must maintain sparsity in both learning and deployment. Current lifelong multi-task learning approaches are inadequate in addressing these challenges.MethodsTo address these challenges, we propose SEN, a Sparse and Expandable Network. SEN is designed to handle multiple tasks concurrently by maintaining sparsity and enabling expansion when new tasks are introduced. The network leverages multi-modal data, integrating information from different sources while preventing interference between tasks.ResultsThe proposed SEN model demonstrates significant improvements in multi-task learning, successfully managing task interference and forgetting. It effectively integrates data from various modalities and maintains efficiency through sparsity during both the learning and deployment phases.DiscussionSEN offers a straightforward yet effective solution to the limitations of current lifelong multi-task learning methods. By addressing the challenges identified in the Pathways architecture, SEN provides a promising approach for developing AI systems capable of learning and adapting over time without sacrificing performance or efficiency.

Basic oxygen furnace slag (BOF slag), also known as Linz-Donauwitz slag, is an industrially by-product of the steel industry and produced during the converter process. In this research we have investigated the effect of grinding aids and particle fineness on the hydration of potassium citrate activated BOF slag. It was investigated the effects of five different grinding aids on the grindability and hydration of activated BOF slag in laboratory batch milling experiments. The grinding aids generally improve the grindability of BOF slag and therefore increase the particle fineness of BOF slag. This had the consequence that the hydration of activated BOF slag was improved. Only one grinding aid, triethanolamine (TEA) also improved the hydration of BOF slag besides providing higher particle fineness. The combination of the higher particle fineness and additional effect of TEA on the hydration improved the compressive strength to 60 and 78 MPa of activated BOF slag. An additional important of this research is that the hydration of BOF slag is significantly more sensitive on particle fineness compared to ordinary Portland cement.

Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system; consequently, a long-term SM product with high quality is urgently needed. In this study, five SM products, including one microwave remote sensing product – the European Space Agency's Climate Change Initiative (ESA CCI) – and four reanalysis data sets – European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis – Interim (ERA-Interim), National Centers for Environmental Prediction (NCEP), the 20th Century Reanalysis Project from National Oceanic and Atmospheric Administration (NOAA), and the ECMWF Reanalysis 5 (ERA5) – are systematically evaluated using in situ measurements during 1981–2013 in four climate regions at different timescales over the Chinese mainland. The results show that ESA CCI is closest to the observations in terms of both the spatial distributions and magnitude of the monthly SM. All reanalysis products tend to overestimate soil moisture in all regions but have higher correlations than the remote sensing product except in Northwest China. The largest inconsistency is found in southern Northeast China region, with an unbiased root mean square error (ubRMSE) value larger than 0.04. However, all products exhibit certain weaknesses in representing the interannual variation in SM. The largest relative bias of 144.4 % is found for the ERA-Interim SM product under extreme and severe wet conditions in northeastern China, and the lowest relative bias is found for the ESA CCI SM product, with the minimum of 0.48 % under extreme and severe wet conditions in northwestern China. Decomposing mean square errors suggests that the bias terms are the dominant contribution for all products, and the correlation term is large for ESA CCI. As a result, the ESA CCI SM product is a good option for long-term hydrometeorological applications on the Chinese mainland. ERA5 is also a promising product, especially in northern and northwestern China in terms of low bias and high correlation coefficient. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.

In textile printing and dyeing industry, a novel type of separator called high gravity rotary gas-liquid separator (HGRGS) is designed, which includes a rotary drum with multi-layer fins and an impeller. First, the structure and separation principle of HGRGS are introduced in this paper. Then, the flow field and separation efficiency are studied by CFD techniques. To ensure the accuracy of the numerical simulation, the results are verified by the available experimental data. Compared with the typical cyclone, the maximum pressure drop reduction rate in HGRGS is 64.7% when the gas enters at 10 m/s. Besides, for droplets less than 5 μm, the separation performance in HGRGS is more efficient and it will be greatly improved by 30% for 1 μm droplets. The numerical results also show that the tangential velocity inside the rotary drum is linear with the radius and the higher the rotating speed, the greater the tangential velocity. Moreover, the maximum tangential velocity between the forced and quasi-free vortex has moved to the vicinity of the outer wall, which is beneficial for droplets to move outward. Additionally, the droplets in HGRGS can be captured with enough residence time owing to the lower axial velocity than that in a typical cyclone.

This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise continuous. Firstly, simple linear iterative cluster method (SLIC) is used to segment the base image into a series of patches. Then, a global energy function, which consists of a data term and a smoothness term, is built on the patches. The data term is the second-order Taylor expansion of correlation coefficients, and the smoothness term is built by combing connectivity constraints and the coplanarity constraints are combined to construct the smoothness term. Finally, the global energy function can be built by combining the data term and the smoothness term. We rewrite the global energy function in a quadratic matrix function, and use least square methods to obtain the optimal solution. Experiments on Adirondack stereo and Motorcycle stereo of Middlebury benchmark show that the proposed method can remove fronto-parallel bias effectively, and produce continuous smooth surfaces.