This study aimed to investigate the effects of fermented puffed feather meal(FPFM)on growth performance,serum biochemical indices,meat quality,and intestinal microbiota in Arbor Acres(AA)broilers.A single-factor desig...This study aimed to investigate the effects of fermented puffed feather meal(FPFM)on growth performance,serum biochemical indices,meat quality,and intestinal microbiota in Arbor Acres(AA)broilers.A single-factor design was adopted,and four treatments were administered with five replicates to 240 one-day-old AA broilers.The control group(group A)received a basal diet,while the experimental groups received a basal diet plus 33%(group B),67%(group C)and 100%(group D)FPFM,respectively.Compared with group A,(1)the average daily gain(ADG)in group C decreased(P<0.05),and the feed conversion ratio(FCR)in group D increased(P<0.05);(2)the level of serum urea nitrogen in treatment groups decreased(P<0.05),and the levels of triglyceride,high density lipoprotein,low density lipoprotein,cholesterol,and glucose contents in group D increased(P<0.05)at day 21;(3)the serum immunoglobulin M and immunoglobulin G in group B and the immunoglobulin A in group C increased(P<0.05)at day 21,and the serum immunoglobulin M and immunoglobulin G in group D decreased(P<0.05)at day 42;(4)the share force of breast muscle and thigh muscle in group D increased(P<0.05);(5)the villus height to crypt depth ratio in the jejunum of group B increased(P<0.05)at day 21,and the villus height in group C and D increased(P<0.05)at day 42;(6)the proteobacteria counts in the cecum digesta in treatment groups decreased(P<0.05)at day 21.The basal diet supplemented with 33%FPFM promoted protein metabolism,enhanced immunity and improved meat quality,promoted the digestion and absorption of nutrients,increased intestinal microbial diversity,and improved the content of beneficial bacteria without affecting the growth performance,it was possible to be used as a good substitute for fish meal.展开更多
As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools...As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools for enhancing IoT security.PUFs leverage the inherent randomness found in the embedded hardware of IoT devices.However,it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches.In this paper,a new deep learning(DL)-based modeling attack is introduced to break the resistance of complex XAPUFs.Because training DL models is a problem that falls under the category of NP-hard problems,there has been a significant increase in the use of meta-heuristics(MH)to optimize DL parameters.Nevertheless,it is widely recognized that finding the right balance between exploration and exploitation when dealing with complex problems can pose a significant challenge.To address these chal-lenges,a novel migration-based multi-parent genetic algorithm(MBMPGA)is developed to train the deep convolutional neural network(DCNN)in order to achieve a higher rate of accuracy and convergence speed while decreas-ing the run-time of the attack.In the proposed MBMPGA,a non-linear migration model of the biogeography-based optimization(BBO)is utilized to enhance the exploitation ability of GA.A new multi-parent crossover is then introduced to enhance the exploration ability of GA.The behavior of the proposed MBMPGA is examined on two real-world optimization problems.In benchmark problems,MBMPGA outperforms other MH algorithms in convergence rate.The proposed model are also compared with previous attacking models on several simulated challenge-response pairs(CRPs).The simulation results on the XAPUF datasets show that the introduced attack in this paper obtains more than 99%modeling accuracy even on 8-XAPUF.In addition,the proposed MBMPGA-DCNN outperforms the state-of-the-art modeling attacks in a reduced timeframe and with a smaller number of required sets of CRPs.The area under the curve(AUC)of MBMPGA-DCNN outperforms other architectures.MBMPGA-DCNN achieved sensitivities,specificities,and accuracies of 99.12%,95.14%,and 98.21%,respectively,in the test datasets,establishing it as the most successful method.展开更多
基金Supported by Harbin Applied Technology Research and Development Project(2016RAXXJ015)。
文摘This study aimed to investigate the effects of fermented puffed feather meal(FPFM)on growth performance,serum biochemical indices,meat quality,and intestinal microbiota in Arbor Acres(AA)broilers.A single-factor design was adopted,and four treatments were administered with five replicates to 240 one-day-old AA broilers.The control group(group A)received a basal diet,while the experimental groups received a basal diet plus 33%(group B),67%(group C)and 100%(group D)FPFM,respectively.Compared with group A,(1)the average daily gain(ADG)in group C decreased(P<0.05),and the feed conversion ratio(FCR)in group D increased(P<0.05);(2)the level of serum urea nitrogen in treatment groups decreased(P<0.05),and the levels of triglyceride,high density lipoprotein,low density lipoprotein,cholesterol,and glucose contents in group D increased(P<0.05)at day 21;(3)the serum immunoglobulin M and immunoglobulin G in group B and the immunoglobulin A in group C increased(P<0.05)at day 21,and the serum immunoglobulin M and immunoglobulin G in group D decreased(P<0.05)at day 42;(4)the share force of breast muscle and thigh muscle in group D increased(P<0.05);(5)the villus height to crypt depth ratio in the jejunum of group B increased(P<0.05)at day 21,and the villus height in group C and D increased(P<0.05)at day 42;(6)the proteobacteria counts in the cecum digesta in treatment groups decreased(P<0.05)at day 21.The basal diet supplemented with 33%FPFM promoted protein metabolism,enhanced immunity and improved meat quality,promoted the digestion and absorption of nutrients,increased intestinal microbial diversity,and improved the content of beneficial bacteria without affecting the growth performance,it was possible to be used as a good substitute for fish meal.
文摘As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools for enhancing IoT security.PUFs leverage the inherent randomness found in the embedded hardware of IoT devices.However,it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches.In this paper,a new deep learning(DL)-based modeling attack is introduced to break the resistance of complex XAPUFs.Because training DL models is a problem that falls under the category of NP-hard problems,there has been a significant increase in the use of meta-heuristics(MH)to optimize DL parameters.Nevertheless,it is widely recognized that finding the right balance between exploration and exploitation when dealing with complex problems can pose a significant challenge.To address these chal-lenges,a novel migration-based multi-parent genetic algorithm(MBMPGA)is developed to train the deep convolutional neural network(DCNN)in order to achieve a higher rate of accuracy and convergence speed while decreas-ing the run-time of the attack.In the proposed MBMPGA,a non-linear migration model of the biogeography-based optimization(BBO)is utilized to enhance the exploitation ability of GA.A new multi-parent crossover is then introduced to enhance the exploration ability of GA.The behavior of the proposed MBMPGA is examined on two real-world optimization problems.In benchmark problems,MBMPGA outperforms other MH algorithms in convergence rate.The proposed model are also compared with previous attacking models on several simulated challenge-response pairs(CRPs).The simulation results on the XAPUF datasets show that the introduced attack in this paper obtains more than 99%modeling accuracy even on 8-XAPUF.In addition,the proposed MBMPGA-DCNN outperforms the state-of-the-art modeling attacks in a reduced timeframe and with a smaller number of required sets of CRPs.The area under the curve(AUC)of MBMPGA-DCNN outperforms other architectures.MBMPGA-DCNN achieved sensitivities,specificities,and accuracies of 99.12%,95.14%,and 98.21%,respectively,in the test datasets,establishing it as the most successful method.
