Enhancing milk quality and modulating rectal microbiota of dairy goats in starch‑rich diet:the role of bile acid supplementation

Background Diets rich in starch have been shown to increase a risk of reducing milk fat content in dairy goats.While bile acids(BAs)have been used as a lipid emulsifier in monogastric and aquatic animals,their effect on ruminants is not well understood.This study aimed to investigate the impact of BAs supplementation on various aspects of dairy goat physiology,including milk composition,rumen fermentation,gut microbiota,and BA metabolism.Results We randomly divided eighteen healthy primiparity lactating dairy goats(days in milk=100±6 d)into two groups and supplemented them with 0 or 4 g/d of BAs undergoing 5 weeks of feeding on a starch-rich diet.The results showed that BAs supplementation positively influenced milk yield and improved the quality of fatty acids in goat milk.BAs supplementation led to a reduction in saturated fatty acids(C16:0)and an increase in monounsaturated fatty acids(cis-9 C18:1),resulting in a healthier milk fatty acid profile.We observed a significant increase in plasma total bile acid concentration while the proportion of rumen short-chain fatty acids was not affected.Furthermore,BAs supplementation induced significant changes in the composition of the gut microbiota,favoring the enrichment of specific bacterial groups and altering the balance of microbial populations.Correlation analysis revealed associations between specific bacterial groups(Bacillus and Christensenellaceae R-7 group)and BA types,suggesting a role for the gut microbiota in BA metabolism.Functional prediction analysis revealed notable changes in pathways associated with lipid metabolism,suggesting that BAs supplementation has the potential to modulate lipid-related processes.Conclusion These findings highlight the potential benefits of BAs supplementation in enhancing milk production,improving milk quality,and influencing metabolic pathways in dairy goats.Further research is warranted to elucidate the underlying mechanisms and explore the broader implications of these findings.

Novel Hybrid Physics‑Informed Deep Neural Network for Dynamic Load Prediction of Electric Cable Shovel

Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly influences the planning of digging trajectories and energy consumption.Load prediction of ECS mainly consists of two types of methods:physics-based modeling and data-driven methods.The former approach is based on known physical laws,usually,it is necessarily approximations of reality due to incomplete knowledge of certain processes,which introduces bias.The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization,which introduces variance.In addition,some parts of load are non-observable and latent,which cannot be measured from actual system sensing,so they can’t be predicted by data-driven methods.Herein,an innovative hybrid physics-informed deep neural network(HPINN)architecture,which combines physics-based models and data-driven methods to predict dynamic load of ECS,is presented.In the proposed framework,some parts of the theoretical model are incorporated,while capturing the difficult-to-model part by training a highly expressive approximator with data.Prior physics knowledge,such as Lagrangian mechanics and the conservation of energy,is considered extra constraints,and embedded in the overall loss function to enforce model training in a feasible solution space.The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset.

Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Toward autonomous mining:design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning

With the proposal of intelligent mines,unmanned mining has become a research hotspot in recent years.In the field of autonomous excavation,environmental perception and excavation trajectory planning are two key issues because they have considerable influences on operation performance.In this study,an unmanned electric shovel(UES)is developed,and key robotization processes consisting of environment modeling and optimal excavation trajectory planning are presented.Initially,the point cloud of the material surface is collected and reconstructed by polynomial response surface(PRS)method.Then,by establishing the dynamical model of the UES,a point to point(PTP)excavation trajectory planning method is developed to improve both the mining efficiency and fill factor and to reduce the energy consumption.Based on optimal trajectory command,the UES performs autonomous excavation.The experimental results show that the proposed surface reconstruction method can accurately represent the material surface.On the basis of reconstructed surface,the PTP trajectory planning method rapidly obtains a reasonable mining trajectory with high fill factor and mining efficiency.Compared with the common excavation trajectory planning approaches,the proposed method tends to be more capable in terms of mining time and energy consumption,ensuring high-performance excavation of the UES in practical mining environment.