Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single...Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.展开更多
Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo developm...Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo development is of paramount importance.In this study,we established a stable porcine trophectoderm cell line expressing dual fluorescent reporter genes driven by the CDX2 and TEAD4 gene promoter segments using lentiviral transfection.Results Three amino acid metabolites—kynurenic acid,taurine,and tryptamine—met the minimum z-score criteria of 2.0 for both luciferase and Renilla luciferase activities and were initially identified as potential metabolites for embryo development,with their beneficial effects validated by qPCR.Given that the identified metabolites are closely related to methionine,arginine,and tryptophan,we selected these three amino acids,using lysine as a standard,and employed response surface methodology combined with our high-throughput screening cell model to efficiently screen and optimize amino acid combination conducive to early embryo development.The optimized candidate amino acid system included lysine(1.87 mmol/L),methionine(0.82 mmol/L),tryptophan(0.23 mmol/L),and arginine(3 mmol/L),with the ratio of 1:0.43:0.12:1.60.In vitro experiments confirmed that this amino acid system enhances the expression of key genes involved in early embryonic development and improves in vitro embryo adhesion.Transcriptomic analysis of blastocysts suggested that candidate amino acid system enhances early embryo development by regulating early embryonic cell cycle and differentiation,as well as improving nutrient absorption.Furthermore,based on response surface methodology,400 sows were used to verify this amino acid system,substituting arginine with the more cost-effective N-carbamoyl glutamate(NCG),a precursor of arginine.The optimal dietary amino acid requirement was predicted to be 0.71%lysine,0.32%methionine,0.22%tryptophan,and 0.10%NCG for sows during early gestation.The optimized amino acid system ratio of the feed,derived from the peripheral release of essential amino acids,was found to be 1:0.45:0.13,which is largely consistent with the results obtained from the cell model optimization.Subsequently,we furtherly verified that this optimal dietary amino acid system significantly increased total litter size,live litter size and litter weight in sows.Conclusions In summary,we successfully established a dual-fluorescent high-throughput screening cell model for the efficient identification of potential nutrients that would promote embryo development and implantation.This innovative approach overcomes the limitations of traditional amino acid nutrition studies in sows,providing a more effective model for enhancing reproductive outcomes.展开更多
To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized pur...To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized purge-and-trap(μP&T) device integrating semiconductor refrigeration storage was developed in this work. Water samples were poured into the purge vessels and purged with purified air generated by an air pump. The VOCs in water samples were then separated and preconcentrated with sorbent tubes. After their complete separation and preconcentration, the tubes were subsequently preserved in the semiconductor refrigeration unit of the μP&T device. Notably, the high integration, small size, light weight, and low power consumption of the device makes it easy to be hand-carried to the field and transport by drone from remote locations, significantly enhancing the flexibility of field sampling. The performances of the device were evaluated by comparing analytical figures of merit for the detection of four cyclic volatile methylsiloxanes(cVMSs) in water. Compared to conventional collection and preservation methods, our proposed device preserved the VOCs more consistently in the sorbent tubes, with less than 5% loss of all analytes, and maintained stability for at least 20 days at 4℃. As a proof-of-concept,10 municipal wastewater samples were pretreated using this device with recoveries ranging from 82.5% to 99.9% for the target VOCs.展开更多
Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Me...Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.展开更多
Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous ...Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.展开更多
The rapid growth of marine applications leads to a significant increase in Maritime Devices(MDs).Traditional shore-based maritime communication networks face limitations,such as overloaded and transmission distance to...The rapid growth of marine applications leads to a significant increase in Maritime Devices(MDs).Traditional shore-based maritime communication networks face limitations,such as overloaded and transmission distance to provide network services for MDs.Unmanned Aerial Vehicles(UAVs)act as relays that can expand coverage and enhance the quality of service for offshore communication networks.We consider a multi-UAV-assisted Offshore Internet of Things(mUAV-OloT),and formulate a throughput maximization problem by jointly optimizing channel allocation,Leader MD(LMD)selection,UAV-LMD association,and LMD-MD association.Firstly,we propose the Hypergraph-based Two-Stage Matching(HTSM)algorithm where a Hypergraph-based LMD Selection(HLMDS)strategy is employed to identify the set of LMDs.Secondly,the Kuhn-Munkres algorithm is used to optimize the UAV-LMD association and a Weighted Threedimensional Hypergraph Matching(WTHM)algorithm is designed to solve the LMD-MD association and channel allocation.Numerical results show that the HTSM algorithm outperforms benchmark algorithms regarding throughput.展开更多
Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst cas...Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples;the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully;the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.展开更多
To overcome disadvantages of traditional worst-case execution time （WCET） analysis approaches, we propose a new WCET analysis approach based on independent paths for ARM programs. Based on the results of program flo...To overcome disadvantages of traditional worst-case execution time （WCET） analysis approaches, we propose a new WCET analysis approach based on independent paths for ARM programs. Based on the results of program flow analysis, it reduces and partitions the control flow graph of the program and obtains a directed graph. Using linear combinations of independent paths of the directed graph, a set of feasible paths can be generated that gives complete coverage in terms of the program paths considered. Their timing measurements and execution counts of program segments are derived from a limited number of measurements of an instrumented version of the program. After the timing measurement of the feasible paths are linearly expressed by the execution times of program seg-ments, a system of equations is derived as a constraint problem, from which we can obtain the execution times of program segments. By assigning the execution times of program segments to weights of edges in the directed graph, the WCET estimate can be calculated on the basis of graph-theoretical techniques. Comparing our WCET estimate with the WCET measurement obtained by the exhaustive measurement, the maximum error ratio is only 8.259 3 %. It is shown that the proposed approach is an effective way to obtain the safe and tight WCET estimate for ARM programs.展开更多
In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver u...In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.展开更多
This paper studies a dual-hop Simultaneous Wireless Information and Power Transfer(SWIPT)-based multi-relay network with a direct link.To achieve high throughput in the network,a novel protocol is first developed,in w...This paper studies a dual-hop Simultaneous Wireless Information and Power Transfer(SWIPT)-based multi-relay network with a direct link.To achieve high throughput in the network,a novel protocol is first developed,in which the network can switch between a direct transmission mode and a Single-Relay-Selection-based Cooperative Transmission(SRS-CT)mode that employs dynamic decode-and-forward relaying accomplished with Rateless Codes(RCs).Then,under this protocol,an optimization problem is formulated to jointly optimize the network operation mode and the resource allocation in the SRS-CT mode.The formulated problem is difficult to solve because not only does the noncausal Channel State Information(CSI)cause the problem to be stochastic,but also the energy state evolution at each relay is complicated by network operation mode decision and resource allocation.Assuming that noncausal CSI is available,the stochastic optimization issue is first to be addressed by solving an involved deterministic optimization problem via dynamic programming,where the complicated energy state evolution issue is addressed by a layered optimization method.Then,based on a finite-state Markov channel model and assuming that CSI statistical properties are known,the stochastic optimization problem is solved by extending the result derived for the noncausal CSI case to the causal CSI case.Finally,a myopic strategy is proposed to achieve a tradeoff between complexity and performance without the knowledge of CSI statistical properties.The simulation results verify that our proposed SRS-and-RC-based design can achieve a maximum of approximately 40%throughput gain over a simple SRS-and-RC-based baseline scheme in SWIPT-based multi-relay networks.展开更多
The demersal fish populations constitute a valuable commercial resource in the Beibu Gulf,northern South China.However,our understanding of the ontogenetic dietary shifts of fish,particularly Lagocephalus spadiceus,wh...The demersal fish populations constitute a valuable commercial resource in the Beibu Gulf,northern South China.However,our understanding of the ontogenetic dietary shifts of fish,particularly Lagocephalus spadiceus,which is the most widely distributed fish in the northern South China Sea,is lacking.Therefore,we conducted a study using high-throughput sequencing and stable isotope analyses to examine the diet of L.spadiceus.The individuals were collected during 2 bottom trawl surveys in April and August 2022.The length distribution showed that small L.spadiceus was more abundant in shallow water,whereas large fish were generally found in offshore water.High-throughput sequencing indicated that L.spadiceus mainly fed on crustaceans and fish.Ontogenetic changes in diet composition and stable isotope values were also observed.As L.spadiceus grew,they started preying on larger individuals,specifically fish.The^(15)N enrichment in large fish supported to small fish supports these findings.The trophic niche indicated a segregation between individuals in shallow waters(mostly small fish)and offshore waters(mostly large fish),which helps in reducing the competition within the species.The L.spadiceus occupied the role of mid-predator(trophic level~3.66)and increased with body length and water depth.Consequently,both high-throughput sequencing and stable isotope analyses provided evidence of intraspecific dietary variation and trophic niche partitioning of L.spadiceus in the Beibu Gulf,South China Sea.展开更多
为研究消毒剂对猪粪厌氧发酵系统的影响,选取不同浓度(质量分数0.02%、0.1%、0.5%)卫可(Virkon^(TM))消毒剂进行试验。结果表明:在高浓度Virkon^(TM)胁迫下,厌氧发酵系统出水中总氮(total nitrogen,TN)、氨氮(ammonia nitrogen,NH_(4)^(...为研究消毒剂对猪粪厌氧发酵系统的影响,选取不同浓度(质量分数0.02%、0.1%、0.5%)卫可(Virkon^(TM))消毒剂进行试验。结果表明:在高浓度Virkon^(TM)胁迫下,厌氧发酵系统出水中总氮(total nitrogen,TN)、氨氮(ammonia nitrogen,NH_(4)^(+)-N)、总磷(total phosphorus,TP)和化学需氧量(chemical oxygen demand,COD)等含量异常剧增,伴随TS和VS的降解率下降。对照组(CK组)和0.02%、0.1%和0.5%Virkon^(TM)试验组(分别记为L组、M组和H组)的最大产CH_(4)速率分别为63.20、71.63、73.10和38.17mL/g且CH_4总产量分别降低4.48%、16.58%(P<0.001)和86.33%(P<0.001)。随着暴露时间的延长,试验组的关键酶活呈先升后降趋势,其中,H组的S-α-GC、S-β-GC、S-ACP、S-NP及S-CAT等土壤酶活被显著性抑制(P<0.05)。进一步结合高通量测序发现,在整个厌氧发酵阶段,H组Ace指数、Chao指数和Shannon指数均显著性低于CK组(P<0.01)。在门水平上,第一优势菌群为厚壁菌门,其次为变形菌门,其中厚壁菌门相对丰度随Virkon^(TM)浓度的上升而降低,而变形菌门则相反;在属水平上,随着厌氧发酵时间的延长,束毛球菌属(Trichococcus)相对丰度均出现不同程度上升,由0.19%~0.39%提升至2.80%~4.20%,而H组中史密斯氏菌属(Smithella)的相对丰度受到极显著性(P<0.001)的抑制,较CK组下降91%。同时通过PICRUSt2功能预测分析发现,各试验组微生物群落COG(clusters of orthologous groups of proteins)功能组成结构差异并不明显,未知功能及氨基酸运输和代谢为主要优势功能;结合KO(KEGG orthology)结果与KEGG(kyoto encyclopedia of genes and genomes)数据库相关基因分析发现,高浓度Virkon^(TM)显著抑制微生物群落的生长代谢活性并削弱厌氧发酵系统的甲烷合成效能,另一方面,却诱导关键功能基因K00531(anfG)的表达活性发生超量级响应,较对照组(CK)激增约272倍。其表达水平的显著上调可能通过强化关键酶活性,使系统对Virkon^(TM)消毒副产物中典型氯代污染物(氯代烷烃及氯代烯烃类化合物)的降解效率获得提升,结果可为猪粪厌氧消化处理提供参考。展开更多
基金the financial support of the National Natural Science Foundation of China(No.22168009)。
文摘Highly toxic phosgene,diethyl chlorophosphate(DCP)and volatile acyl chlorides endanger our life and public security.To achieve facile sensing and discrimination of multiple target analytes,herein,we presented a single fluorescent probe(BDP-CHD)for high-throughput screening of phosgene,DCP and volatile acyl chlorides.The probe underwent a covalent cascade reaction with phosgene to form boron dipyrromethene(BODIPY)with bright green fluorescence.By contrast,DCP,diphosgene and acyl chlorides can covalently assembled with the probe,giving rise to strong blue fluorescence.The probe has demonstrated high-throughput detection capability,high sensitivity,fast response(within 3 s)and parts per trillion(ppt)level detection limit.Furthermore,a portable platform based on BDP-CHD was constructed,which has achieved high-throughput discrimination of 16 analytes through linear discriminant analysis(LDA).Moreover,a smartphone adaptable RGB recognition pattern was established for the quantitative detection of multi-analytes.Therefore,this portable fluorescence sensing platform can serve as a versatile tool for rapid and high-throughput detection of toxic phosgene,DCP and volatile acyl chlorides.The proposed“one for more”strategy simplifies multi-target discrimination procedures and holds great promise for various sensing applications.
基金supported by National Natural Science Foundation of China (32172747 and 32425052)
文摘Background Early embryo development plays a pivotal role in determining pregnancy outcomes,postnatal development,and lifelong health.Therefore,the strategic selection of functional nutrients to enhance embryo development is of paramount importance.In this study,we established a stable porcine trophectoderm cell line expressing dual fluorescent reporter genes driven by the CDX2 and TEAD4 gene promoter segments using lentiviral transfection.Results Three amino acid metabolites—kynurenic acid,taurine,and tryptamine—met the minimum z-score criteria of 2.0 for both luciferase and Renilla luciferase activities and were initially identified as potential metabolites for embryo development,with their beneficial effects validated by qPCR.Given that the identified metabolites are closely related to methionine,arginine,and tryptophan,we selected these three amino acids,using lysine as a standard,and employed response surface methodology combined with our high-throughput screening cell model to efficiently screen and optimize amino acid combination conducive to early embryo development.The optimized candidate amino acid system included lysine(1.87 mmol/L),methionine(0.82 mmol/L),tryptophan(0.23 mmol/L),and arginine(3 mmol/L),with the ratio of 1:0.43:0.12:1.60.In vitro experiments confirmed that this amino acid system enhances the expression of key genes involved in early embryonic development and improves in vitro embryo adhesion.Transcriptomic analysis of blastocysts suggested that candidate amino acid system enhances early embryo development by regulating early embryonic cell cycle and differentiation,as well as improving nutrient absorption.Furthermore,based on response surface methodology,400 sows were used to verify this amino acid system,substituting arginine with the more cost-effective N-carbamoyl glutamate(NCG),a precursor of arginine.The optimal dietary amino acid requirement was predicted to be 0.71%lysine,0.32%methionine,0.22%tryptophan,and 0.10%NCG for sows during early gestation.The optimized amino acid system ratio of the feed,derived from the peripheral release of essential amino acids,was found to be 1:0.45:0.13,which is largely consistent with the results obtained from the cell model optimization.Subsequently,we furtherly verified that this optimal dietary amino acid system significantly increased total litter size,live litter size and litter weight in sows.Conclusions In summary,we successfully established a dual-fluorescent high-throughput screening cell model for the efficient identification of potential nutrients that would promote embryo development and implantation.This innovative approach overcomes the limitations of traditional amino acid nutrition studies in sows,providing a more effective model for enhancing reproductive outcomes.
基金the National Natural Science Foundation of China (No. 22306146)the PhD Scientific Research Startup Foundation of Xihua University (No. RX2200002003) for their financial support。
文摘To accomplish on-site separation, preconcentration and cold storage of highly volatile organic compounds(VOCs) from water samples as well as their rapid transportation to laboratory, a high-throughput miniaturized purge-and-trap(μP&T) device integrating semiconductor refrigeration storage was developed in this work. Water samples were poured into the purge vessels and purged with purified air generated by an air pump. The VOCs in water samples were then separated and preconcentrated with sorbent tubes. After their complete separation and preconcentration, the tubes were subsequently preserved in the semiconductor refrigeration unit of the μP&T device. Notably, the high integration, small size, light weight, and low power consumption of the device makes it easy to be hand-carried to the field and transport by drone from remote locations, significantly enhancing the flexibility of field sampling. The performances of the device were evaluated by comparing analytical figures of merit for the detection of four cyclic volatile methylsiloxanes(cVMSs) in water. Compared to conventional collection and preservation methods, our proposed device preserved the VOCs more consistently in the sorbent tubes, with less than 5% loss of all analytes, and maintained stability for at least 20 days at 4℃. As a proof-of-concept,10 municipal wastewater samples were pretreated using this device with recoveries ranging from 82.5% to 99.9% for the target VOCs.
基金sponsored by the National Key Research and Development Program of China(No.2023YFB4604800,2021YFA1202300)the Natural and Science Foundation of China(Grant Nos.52201041,52275331,52205358)+1 种基金the Key Research and Development Program of Hubei Province(Nos.2024BCB091,2022CFA031)the Hong Kong Scholars Program(No.XJ2022014)。
文摘Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.
基金supported by Shanghai Municipal Commission of Science and Technology,China(Grant No.:19XD1400300)the National Natural Science Foundation of China(Grant Nos.:821040821,82273867,and 82030107).
文摘Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.
基金supported by the National Natural Science Foundation of China(Nos.51939001,62371085,62122069,and 62071431)the Fundamental Research Funds for the Central Universities(No.3132023514)the FDCT-MOST Joint Project(No.0066/2019/AMJ).
文摘The rapid growth of marine applications leads to a significant increase in Maritime Devices(MDs).Traditional shore-based maritime communication networks face limitations,such as overloaded and transmission distance to provide network services for MDs.Unmanned Aerial Vehicles(UAVs)act as relays that can expand coverage and enhance the quality of service for offshore communication networks.We consider a multi-UAV-assisted Offshore Internet of Things(mUAV-OloT),and formulate a throughput maximization problem by jointly optimizing channel allocation,Leader MD(LMD)selection,UAV-LMD association,and LMD-MD association.Firstly,we propose the Hypergraph-based Two-Stage Matching(HTSM)algorithm where a Hypergraph-based LMD Selection(HLMDS)strategy is employed to identify the set of LMDs.Secondly,the Kuhn-Munkres algorithm is used to optimize the UAV-LMD association and a Weighted Threedimensional Hypergraph Matching(WTHM)algorithm is designed to solve the LMD-MD association and channel allocation.Numerical results show that the HTSM algorithm outperforms benchmark algorithms regarding throughput.
文摘Search-based software engineering has mainly dealt with automated test data generation by metaheuristic search techniques. Similarly, we try to generate the test data (i.e., problem instances) which show the worst case of algorithms by such a technique. In this paper, in terms of non-functional testing, we re-define the worst case of some algorithms, respectively. By using genetic algorithms (GAs), we illustrate the strategies corresponding to each type of instances. We here adopt three problems for examples;the sorting problem, the 0/1 knapsack problem (0/1KP), and the travelling salesperson problem (TSP). In some algorithms solving these problems, we could find the worst-case instances successfully;the successfulness of the result is based on a statistical approach and comparison to the results by using the random testing. Our tried examples introduce informative guidelines to the use of genetic algorithms in generating the worst-case instance, which is defined in the aspect of algorithm performance.
基金Supported by the National High Technology Research and Development Program of China(863 Program,2009AA011705)the National Natural Science Foundation of China(60903033)
文摘To overcome disadvantages of traditional worst-case execution time （WCET） analysis approaches, we propose a new WCET analysis approach based on independent paths for ARM programs. Based on the results of program flow analysis, it reduces and partitions the control flow graph of the program and obtains a directed graph. Using linear combinations of independent paths of the directed graph, a set of feasible paths can be generated that gives complete coverage in terms of the program paths considered. Their timing measurements and execution counts of program segments are derived from a limited number of measurements of an instrumented version of the program. After the timing measurement of the feasible paths are linearly expressed by the execution times of program seg-ments, a system of equations is derived as a constraint problem, from which we can obtain the execution times of program segments. By assigning the execution times of program segments to weights of edges in the directed graph, the WCET estimate can be calculated on the basis of graph-theoretical techniques. Comparing our WCET estimate with the WCET measurement obtained by the exhaustive measurement, the maximum error ratio is only 8.259 3 %. It is shown that the proposed approach is an effective way to obtain the safe and tight WCET estimate for ARM programs.
基金supported by the Key Research and Development Program of China(No.2022YFC3005401)Key Research and Development Program of China,Yunnan Province(No.202203AA080009,202202AF080003)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0482).
文摘In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.
基金supported in part by the National Natural Science Foundation of China under Grant 61872098 and Grant 61902084the Natural Science Foundation of Guangdong Province under Grant 2017A030313363.
文摘This paper studies a dual-hop Simultaneous Wireless Information and Power Transfer(SWIPT)-based multi-relay network with a direct link.To achieve high throughput in the network,a novel protocol is first developed,in which the network can switch between a direct transmission mode and a Single-Relay-Selection-based Cooperative Transmission(SRS-CT)mode that employs dynamic decode-and-forward relaying accomplished with Rateless Codes(RCs).Then,under this protocol,an optimization problem is formulated to jointly optimize the network operation mode and the resource allocation in the SRS-CT mode.The formulated problem is difficult to solve because not only does the noncausal Channel State Information(CSI)cause the problem to be stochastic,but also the energy state evolution at each relay is complicated by network operation mode decision and resource allocation.Assuming that noncausal CSI is available,the stochastic optimization issue is first to be addressed by solving an involved deterministic optimization problem via dynamic programming,where the complicated energy state evolution issue is addressed by a layered optimization method.Then,based on a finite-state Markov channel model and assuming that CSI statistical properties are known,the stochastic optimization problem is solved by extending the result derived for the noncausal CSI case to the causal CSI case.Finally,a myopic strategy is proposed to achieve a tradeoff between complexity and performance without the knowledge of CSI statistical properties.The simulation results verify that our proposed SRS-and-RC-based design can achieve a maximum of approximately 40%throughput gain over a simple SRS-and-RC-based baseline scheme in SWIPT-based multi-relay networks.
基金supported by the Key Research and Development Program of Guangxi(Guike AB22035050)the National Natural Science Foundation of China(U20A2087)the Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation,Beibu Gulf University(2022KA01)
文摘The demersal fish populations constitute a valuable commercial resource in the Beibu Gulf,northern South China.However,our understanding of the ontogenetic dietary shifts of fish,particularly Lagocephalus spadiceus,which is the most widely distributed fish in the northern South China Sea,is lacking.Therefore,we conducted a study using high-throughput sequencing and stable isotope analyses to examine the diet of L.spadiceus.The individuals were collected during 2 bottom trawl surveys in April and August 2022.The length distribution showed that small L.spadiceus was more abundant in shallow water,whereas large fish were generally found in offshore water.High-throughput sequencing indicated that L.spadiceus mainly fed on crustaceans and fish.Ontogenetic changes in diet composition and stable isotope values were also observed.As L.spadiceus grew,they started preying on larger individuals,specifically fish.The^(15)N enrichment in large fish supported to small fish supports these findings.The trophic niche indicated a segregation between individuals in shallow waters(mostly small fish)and offshore waters(mostly large fish),which helps in reducing the competition within the species.The L.spadiceus occupied the role of mid-predator(trophic level~3.66)and increased with body length and water depth.Consequently,both high-throughput sequencing and stable isotope analyses provided evidence of intraspecific dietary variation and trophic niche partitioning of L.spadiceus in the Beibu Gulf,South China Sea.
文摘为研究消毒剂对猪粪厌氧发酵系统的影响,选取不同浓度(质量分数0.02%、0.1%、0.5%)卫可(Virkon^(TM))消毒剂进行试验。结果表明:在高浓度Virkon^(TM)胁迫下,厌氧发酵系统出水中总氮(total nitrogen,TN)、氨氮(ammonia nitrogen,NH_(4)^(+)-N)、总磷(total phosphorus,TP)和化学需氧量(chemical oxygen demand,COD)等含量异常剧增,伴随TS和VS的降解率下降。对照组(CK组)和0.02%、0.1%和0.5%Virkon^(TM)试验组(分别记为L组、M组和H组)的最大产CH_(4)速率分别为63.20、71.63、73.10和38.17mL/g且CH_4总产量分别降低4.48%、16.58%(P<0.001)和86.33%(P<0.001)。随着暴露时间的延长,试验组的关键酶活呈先升后降趋势,其中,H组的S-α-GC、S-β-GC、S-ACP、S-NP及S-CAT等土壤酶活被显著性抑制(P<0.05)。进一步结合高通量测序发现,在整个厌氧发酵阶段,H组Ace指数、Chao指数和Shannon指数均显著性低于CK组(P<0.01)。在门水平上,第一优势菌群为厚壁菌门,其次为变形菌门,其中厚壁菌门相对丰度随Virkon^(TM)浓度的上升而降低,而变形菌门则相反;在属水平上,随着厌氧发酵时间的延长,束毛球菌属(Trichococcus)相对丰度均出现不同程度上升,由0.19%~0.39%提升至2.80%~4.20%,而H组中史密斯氏菌属(Smithella)的相对丰度受到极显著性(P<0.001)的抑制,较CK组下降91%。同时通过PICRUSt2功能预测分析发现,各试验组微生物群落COG(clusters of orthologous groups of proteins)功能组成结构差异并不明显,未知功能及氨基酸运输和代谢为主要优势功能;结合KO(KEGG orthology)结果与KEGG(kyoto encyclopedia of genes and genomes)数据库相关基因分析发现,高浓度Virkon^(TM)显著抑制微生物群落的生长代谢活性并削弱厌氧发酵系统的甲烷合成效能,另一方面,却诱导关键功能基因K00531(anfG)的表达活性发生超量级响应,较对照组(CK)激增约272倍。其表达水平的显著上调可能通过强化关键酶活性,使系统对Virkon^(TM)消毒副产物中典型氯代污染物(氯代烷烃及氯代烯烃类化合物)的降解效率获得提升,结果可为猪粪厌氧消化处理提供参考。
