Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to...Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity measures.This paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of SPL.In the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and business.Specifically,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient ecosystems.Our experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in SPLs.As digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.展开更多
Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting...Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.展开更多
AIM:To estimate post-war burdens of trachomatous trichiasis(TT)and multi-level risk factors among displaced population in Raya Kobo districts,implication for urgent action.METHODS:A community-based cross-sectional stu...AIM:To estimate post-war burdens of trachomatous trichiasis(TT)and multi-level risk factors among displaced population in Raya Kobo districts,implication for urgent action.METHODS:A community-based cross-sectional study was conducted among 603 participants from randomly selected 14 displaced slums in the Raya Kobo district.The data was collected from February 16th to March 30th,2023.Study participants were selected using the multistage sampling technique.A structured questionnaire and ophthalmic loupe with×2.5 magnificence were used to collect from participants.Multi-level binary logistic regression was used to determine associated factors with TT infection.Adjusted odds ratio(AOR)with 95%confidence interval(CI)were claimed for the strength of association at P<0.05.RESULTS:We recruited 602(99.9%)participants for the final analysis.From the total,126(20.9%)and 98(16.3%,95%CI:13.5%-19.4%)participants were diagnosed with active trachoma&TT infection,respectively.Being age≥45y(AOR=7.9,95%CI:2.4-25.3),having multiple eye infections(AOR=2.73,95%CI:1.47-5.29),poor wealth index(AOR=9.2;95%CI:2.7-23.7)and twice face washing per day(AOR=0.082,95%CI:0.03-0.21)has identified as individual as factors for TT infection.Whereas,distance between clean water source≥10 km(AOR=6.5,95%CI:3.9-31.3),and latrine availability(AOR=0.35,95%CI:0.21-0.58)were the two community-level factors associated with TT infections.CONCLUSION:The high prevalence of TT infection post-war throughout the study districts indicates a need for urgent clinical intervention in addition to rapid scaling up surgery,antibiotics,facial cleanliness,and environmental improvement(SAFE)strategies,strategy for high-risk population.Age≥45y,distance from the clean water source,poor wealth indexes,and eye infection are identified to be risk factors for TT infection.Furthermore,community-level preventative factors for TT infection are found as latrine availability and face washing practice.展开更多
Mycobacterium tuberculosis is the causative agent of tuberculosis(TB), which is still the leading cause of mortality from a single infectious disease worldwide. The development of novel anti-TB drugs and vaccines is s...Mycobacterium tuberculosis is the causative agent of tuberculosis(TB), which is still the leading cause of mortality from a single infectious disease worldwide. The development of novel anti-TB drugs and vaccines is severely hampered by the complicated and time-consuming genetic manipulation techniques for M. tuberculosis. Here, we harnessed an endogenous type Ⅲ-A CRISPR/Cas10 system of M. tuberculosis for efficient gene editing and RNA interference(RNAi).This simple and easy method only needs to transform a single mini-CRISPR array plasmid, thus avoiding the introduction of exogenous protein and minimizing proteotoxicity. We demonstrated that M. tuberculosis genes can be efficiently and specifically knocked in/out by this system as confirmed by DNA high-throughput sequencing. This system was further applied to single-and multiple-gene RNAi. Moreover, we successfully performed genome-wide RNAi screening to identify M. tuberculosis genes regulating in vitro and intracellular growth. This system can be extensively used for exploring the functional genomics of M. tuberculosis and facilitate the development of novel anti-TB drugs and vaccines.展开更多
基金supported via funding from Ministry of Defense,Government of Pakistan under Project Number AHQ/95013/6/4/8/NASTP(ACP).Titled:Development of ICT and Artificial Intelligence Based Precision Agriculture Systems Utilizing Dual-Use Aerospace Technologies-GREENAI.
文摘Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity measures.This paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of SPL.In the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and business.Specifically,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient ecosystems.Our experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in SPLs.As digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.
基金This research was financially supported in part by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program.(Project No.P0016038)in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2016-0-00312)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and complexity.However,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories.Thus,there is a need for a repository mining technique for relevant and bug-free data prediction.This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software.To predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation measures.Next,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values.To evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects.The evaluation showed that the results of our proposal are promising.Practitioners and developers can utilize these rules for defect prediction during early software development.
文摘AIM:To estimate post-war burdens of trachomatous trichiasis(TT)and multi-level risk factors among displaced population in Raya Kobo districts,implication for urgent action.METHODS:A community-based cross-sectional study was conducted among 603 participants from randomly selected 14 displaced slums in the Raya Kobo district.The data was collected from February 16th to March 30th,2023.Study participants were selected using the multistage sampling technique.A structured questionnaire and ophthalmic loupe with×2.5 magnificence were used to collect from participants.Multi-level binary logistic regression was used to determine associated factors with TT infection.Adjusted odds ratio(AOR)with 95%confidence interval(CI)were claimed for the strength of association at P<0.05.RESULTS:We recruited 602(99.9%)participants for the final analysis.From the total,126(20.9%)and 98(16.3%,95%CI:13.5%-19.4%)participants were diagnosed with active trachoma&TT infection,respectively.Being age≥45y(AOR=7.9,95%CI:2.4-25.3),having multiple eye infections(AOR=2.73,95%CI:1.47-5.29),poor wealth index(AOR=9.2;95%CI:2.7-23.7)and twice face washing per day(AOR=0.082,95%CI:0.03-0.21)has identified as individual as factors for TT infection.Whereas,distance between clean water source≥10 km(AOR=6.5,95%CI:3.9-31.3),and latrine availability(AOR=0.35,95%CI:0.21-0.58)were the two community-level factors associated with TT infections.CONCLUSION:The high prevalence of TT infection post-war throughout the study districts indicates a need for urgent clinical intervention in addition to rapid scaling up surgery,antibiotics,facial cleanliness,and environmental improvement(SAFE)strategies,strategy for high-risk population.Age≥45y,distance from the clean water source,poor wealth indexes,and eye infection are identified to be risk factors for TT infection.Furthermore,community-level preventative factors for TT infection are found as latrine availability and face washing practice.
基金supported by the National Key R&D Program of China(Grant No.2017YFD0500303)the National Natural Science Foundation of China(Grant Nos.C180501 and 31602061)+1 种基金the Huazhong Agricultural University Scientific&Technological Self-innovation Foundation,China(Grant Nos.2662017PY105 and 2662017PY105)the Doctoral Fund of Ministry of Education of China(Grant No.131012).
文摘Mycobacterium tuberculosis is the causative agent of tuberculosis(TB), which is still the leading cause of mortality from a single infectious disease worldwide. The development of novel anti-TB drugs and vaccines is severely hampered by the complicated and time-consuming genetic manipulation techniques for M. tuberculosis. Here, we harnessed an endogenous type Ⅲ-A CRISPR/Cas10 system of M. tuberculosis for efficient gene editing and RNA interference(RNAi).This simple and easy method only needs to transform a single mini-CRISPR array plasmid, thus avoiding the introduction of exogenous protein and minimizing proteotoxicity. We demonstrated that M. tuberculosis genes can be efficiently and specifically knocked in/out by this system as confirmed by DNA high-throughput sequencing. This system was further applied to single-and multiple-gene RNAi. Moreover, we successfully performed genome-wide RNAi screening to identify M. tuberculosis genes regulating in vitro and intracellular growth. This system can be extensively used for exploring the functional genomics of M. tuberculosis and facilitate the development of novel anti-TB drugs and vaccines.
