Background： Basal ileal endogenous amino acid（AA） losses（IAAend） and standardized ileal digestibility（SID） values of cereal grains, such as barley, are apparently underestimated when determined according to the...Background： Basal ileal endogenous amino acid（AA） losses（IAAend） and standardized ileal digestibility（SID） values of cereal grains, such as barley, are apparently underestimated when determined according to the nitrogen（N）-free method. Regression analysis between the dietary apparent ileal digestible content（c AID） and total crude protein（CP） and AA can be considered as alternative approach to obtain more accurate values for IAAendand SID of AA in cereal grains.Methods： Eight hulled barley genotypes were used, with barley being the only source of CP and AA in the assay diets. The diets contained 95 % as-fed of these eight barley genotypes each, ranging in CP content between 109.1 and 123.8 g/kg dry matter（DM）. Nine ileally T-cannulated barrows, average body weight（BW） 30 ± 2 kg, were allotted to a row-column design comprising eight periods with 6 d each and nine pigs. On d 5 and the night of d 6 of every period, ileal digesta were collected for a total of 12 h. The IAAend and the SID were determined by linear regression analysis between c AID and total dietary CP and AA.Results： There exist linear relationships between cA ID and total CP and AA（P 〈 0.001）. The IAAend of CP, Lys, Met, Thr and Trp amounted to 35.34, 1.08, 0.25, 1.02 and 0.38 g/kg DM intake（DMI）, respectively, which are greater compared to average IAAend determined previously under N-free feeding conditions. The SID of CP, Lys, Met, Thr and Trp was 90,79, 85, 79 and 86 %, respectively, and was greater when compared to tabulated values. Moreover, these SID values were greater than those reported in literature, based on correction of apparent ileal digestibility（AID） of CP and AA for their IAAendvalues. Summarized, the results of the present regression analysis indicate greater IAAendin barley-based diets compared to those obtained by N-free feeding.Conclusions： For low-protein feed ingredients like barley the regression method may be preferred over correction of AID values for their IAAenddetermined under N-free feeding conditions, as intercepts and slopes of the linear regression equations between cA ID and total dietary CP and AA provide direct estimates of IAAendand SID of CP and AA in the presence of the assay feed ingredient.展开更多
Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order t...Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.展开更多
Autonomous driving technology has entered a period of rapid development,and traffic sign detection is one of the important tasks.Existing target detection networks are difficult to adapt to scenarios where target size...Autonomous driving technology has entered a period of rapid development,and traffic sign detection is one of the important tasks.Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced,and traffic sign targets are small and have unclear features,which makes detection more difficult.Therefore,we propose aHybrid Feature Fusion Traffic Sign detection algorithmbased onYOLOv7(HFFTYOLO).First,a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales;Secondly,the cross-scale fusion part of the neck introduces a bottom-up multi-path fusion method.Design reuse paths at the end of the neck,paying particular attention to cross-scale fusion of highlevel features.In addition,we found the appropriate channel width through a lot of experiments and reduced the superfluous parameters.In terms of training,a newregression lossCMPDIoUis proposed,which not only considers the problem of loss degradation when the aspect ratio is the same but the width and height are different,but also enables the penalty term to dynamically change at different scales.Finally,our proposed improved method shows excellent results on the TT100K dataset.Compared with the baseline model,without increasing the number of parameters and computational complexity,AP0.5 and AP increased by 2.2%and 2.7%,respectively,reaching 92.9%and 58.1%.展开更多
For the general fixed effects linear model: Y = X_T+ε, ε～N(0, V), V≥0, weobtain the necessary and sufficient conditions for LY +a to be admissible for a linear estimablefunction S_r in the class of all estimators ...For the general fixed effects linear model: Y = X_T+ε, ε～N(0, V), V≥0, weobtain the necessary and sufficient conditions for LY +a to be admissible for a linear estimablefunction S_r in the class of all estimators under the loss function (d -- Sr)'D(d --Sr), whereD≥0 is known. For the general random effects linear model: Y = Xβ+ε,(βε)～N((Aα 0), (V_(11)V_(12)V_(21)V_(22))), ∧= XV_(11)X'+XV_(12)+ V_(21)X+V_(22)≥0, we also get the necessaryand sufficient conditions for LY+a to be admissible for a linear estimable function Sα+Qβin the class of all estimators under the loss function (d-Sα-Qβ)'D(d-Sα-Qβ).whereD≥0 is known.展开更多
基金supported in the framework of Grain Up by funds of the Federal Ministry of Food,AgricultureConsumer Protection(BMELV)based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture Food and(BLE)under the innovation support program
文摘Background： Basal ileal endogenous amino acid（AA） losses（IAAend） and standardized ileal digestibility（SID） values of cereal grains, such as barley, are apparently underestimated when determined according to the nitrogen（N）-free method. Regression analysis between the dietary apparent ileal digestible content（c AID） and total crude protein（CP） and AA can be considered as alternative approach to obtain more accurate values for IAAendand SID of AA in cereal grains.Methods： Eight hulled barley genotypes were used, with barley being the only source of CP and AA in the assay diets. The diets contained 95 % as-fed of these eight barley genotypes each, ranging in CP content between 109.1 and 123.8 g/kg dry matter（DM）. Nine ileally T-cannulated barrows, average body weight（BW） 30 ± 2 kg, were allotted to a row-column design comprising eight periods with 6 d each and nine pigs. On d 5 and the night of d 6 of every period, ileal digesta were collected for a total of 12 h. The IAAend and the SID were determined by linear regression analysis between c AID and total dietary CP and AA.Results： There exist linear relationships between cA ID and total CP and AA（P 〈 0.001）. The IAAend of CP, Lys, Met, Thr and Trp amounted to 35.34, 1.08, 0.25, 1.02 and 0.38 g/kg DM intake（DMI）, respectively, which are greater compared to average IAAend determined previously under N-free feeding conditions. The SID of CP, Lys, Met, Thr and Trp was 90,79, 85, 79 and 86 %, respectively, and was greater when compared to tabulated values. Moreover, these SID values were greater than those reported in literature, based on correction of apparent ileal digestibility（AID） of CP and AA for their IAAendvalues. Summarized, the results of the present regression analysis indicate greater IAAendin barley-based diets compared to those obtained by N-free feeding.Conclusions： For low-protein feed ingredients like barley the regression method may be preferred over correction of AID values for their IAAenddetermined under N-free feeding conditions, as intercepts and slopes of the linear regression equations between cA ID and total dietary CP and AA provide direct estimates of IAAendand SID of CP and AA in the presence of the assay feed ingredient.
基金supported by the National Natural Science Foundation of China(Nos.62373215,62373219 and 62073193)the Natural Science Foundation of Shandong Province(No.ZR2023MF100)+1 种基金the Key Projects of the Ministry of Industry and Information Technology(No.TC220H057-2022)the Independently Developed Instrument Funds of Shandong University(No.zy20240201)。
文摘Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.
基金funded by National Natural Science Foundation of China(Grant No.U2004163).
文摘Autonomous driving technology has entered a period of rapid development,and traffic sign detection is one of the important tasks.Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced,and traffic sign targets are small and have unclear features,which makes detection more difficult.Therefore,we propose aHybrid Feature Fusion Traffic Sign detection algorithmbased onYOLOv7(HFFTYOLO).First,a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales;Secondly,the cross-scale fusion part of the neck introduces a bottom-up multi-path fusion method.Design reuse paths at the end of the neck,paying particular attention to cross-scale fusion of highlevel features.In addition,we found the appropriate channel width through a lot of experiments and reduced the superfluous parameters.In terms of training,a newregression lossCMPDIoUis proposed,which not only considers the problem of loss degradation when the aspect ratio is the same but the width and height are different,but also enables the penalty term to dynamically change at different scales.Finally,our proposed improved method shows excellent results on the TT100K dataset.Compared with the baseline model,without increasing the number of parameters and computational complexity,AP0.5 and AP increased by 2.2%and 2.7%,respectively,reaching 92.9%and 58.1%.
文摘For the general fixed effects linear model: Y = X_T+ε, ε～N(0, V), V≥0, weobtain the necessary and sufficient conditions for LY +a to be admissible for a linear estimablefunction S_r in the class of all estimators under the loss function (d -- Sr)'D(d --Sr), whereD≥0 is known. For the general random effects linear model: Y = Xβ+ε,(βε)～N((Aα 0), (V_(11)V_(12)V_(21)V_(22))), ∧= XV_(11)X'+XV_(12)+ V_(21)X+V_(22)≥0, we also get the necessaryand sufficient conditions for LY+a to be admissible for a linear estimable function Sα+Qβin the class of all estimators under the loss function (d-Sα-Qβ)'D(d-Sα-Qβ).whereD≥0 is known.
