Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based ...Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based on something other than arrival time. The Active queue management is important subject to manage this queue to increase the effectiveness of Transmission Control Protocol networks. Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, Random Early Detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. One of these enhancements of RED is FRED or Fair Random Early Detection attempts to deal with a fundamental aspect of RED in that it imposes the same loss rate on all flows, regardless of their bandwidths. FRED also uses per-flow active accounting, and tracks the state of active flows. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Unlike FRED, we propose a new scheme that used hazard rate estimated packet dropping function in FRED. We call this new scheme Enhancement Fair Random Early Detection. The key idea is that, with EFRED Scheme change packet dropping function, to get packet dropping less than RED and other AQM algorithms like ARED, REM, RED, etc. Simulations demonstrate that EFRED achieves a more stable throughput and performs better than current active queue management algorithms due to decrease the packets loss percentage and lowest in queuing delay, end to end delay and delay variation (JITTER).展开更多
Erythromycin fermentation residue(EFR)represents a typical hazardous waste produced by the microbial pharmaceutical industry.Although electrolysis is promising for EFR disposal,its microbial threats remain unclear.Her...Erythromycin fermentation residue(EFR)represents a typical hazardous waste produced by the microbial pharmaceutical industry.Although electrolysis is promising for EFR disposal,its microbial threats remain unclear.Herein,metagenomics was coupled with the random forest technique to decipher the antibiotic resistance patterns of electrochemically treated EFR.Results showed that 95.75%of erythromycin could be removed in 2 hr.Electrolysis temporarily influenced EFRmicrobiota,where the relative abundances of Proteobacteria and Actinobacteria increased,while those of Fusobacteria,Firmicutes,and Bacteroidetes decreased.A total of 505 antibiotic resistance gene(ARG)subtypes encoding resistance to 21 antibiotic types and 150 mobile genetic elements(MGEs),mainly including plasmid(72)and transposase(52)were assembled in EFR.Significant linear regression models were identified among microbial richness,ARG subtypes,and MGE numbers(r^(2)=0.50-0.81,p<0.001).Physicochemical factors of EFR(Total nitrogen,total organic carbon,protein,and humus)regulated ARG and MGE assembly(%IncMSE value=5.14-14.85).The core ARG,MGE,and microbe sets(93.08%-99.85%)successfully explained 89.71%-92.92%of total ARG and MGE abundances.Specifically,gene aph(3 )-I,transposase tnpA,and Mycolicibacterium were the primary drivers of the resistance dissemination system.This study also proposes efficient resistance mitigation measures,and provides recommendations for future management of antibiotic fermentation residue.展开更多
文摘Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based on something other than arrival time. The Active queue management is important subject to manage this queue to increase the effectiveness of Transmission Control Protocol networks. Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, Random Early Detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. One of these enhancements of RED is FRED or Fair Random Early Detection attempts to deal with a fundamental aspect of RED in that it imposes the same loss rate on all flows, regardless of their bandwidths. FRED also uses per-flow active accounting, and tracks the state of active flows. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Unlike FRED, we propose a new scheme that used hazard rate estimated packet dropping function in FRED. We call this new scheme Enhancement Fair Random Early Detection. The key idea is that, with EFRED Scheme change packet dropping function, to get packet dropping less than RED and other AQM algorithms like ARED, REM, RED, etc. Simulations demonstrate that EFRED achieves a more stable throughput and performs better than current active queue management algorithms due to decrease the packets loss percentage and lowest in queuing delay, end to end delay and delay variation (JITTER).
基金supported by the Special Project of Basic Scientific Research Business of Central Public Welfare Scientific Research Institutes (No.2019YSKY-027).
文摘Erythromycin fermentation residue(EFR)represents a typical hazardous waste produced by the microbial pharmaceutical industry.Although electrolysis is promising for EFR disposal,its microbial threats remain unclear.Herein,metagenomics was coupled with the random forest technique to decipher the antibiotic resistance patterns of electrochemically treated EFR.Results showed that 95.75%of erythromycin could be removed in 2 hr.Electrolysis temporarily influenced EFRmicrobiota,where the relative abundances of Proteobacteria and Actinobacteria increased,while those of Fusobacteria,Firmicutes,and Bacteroidetes decreased.A total of 505 antibiotic resistance gene(ARG)subtypes encoding resistance to 21 antibiotic types and 150 mobile genetic elements(MGEs),mainly including plasmid(72)and transposase(52)were assembled in EFR.Significant linear regression models were identified among microbial richness,ARG subtypes,and MGE numbers(r^(2)=0.50-0.81,p<0.001).Physicochemical factors of EFR(Total nitrogen,total organic carbon,protein,and humus)regulated ARG and MGE assembly(%IncMSE value=5.14-14.85).The core ARG,MGE,and microbe sets(93.08%-99.85%)successfully explained 89.71%-92.92%of total ARG and MGE abundances.Specifically,gene aph(3 )-I,transposase tnpA,and Mycolicibacterium were the primary drivers of the resistance dissemination system.This study also proposes efficient resistance mitigation measures,and provides recommendations for future management of antibiotic fermentation residue.
