The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem domains.Selection hyper-heuristics are a class of algorithms that dynamic...The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem domains.Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process.Numerous selection hyper-heuristics have different imple-mentation strategies.However,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different components.The question is how to effectively employ them to produce an efficient search heuristic.Furthermore,the algorithms that competed in the inaugural CHeSC have not been collectively reviewed.This work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is presented.The analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter control.This review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these components.The effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic parameters.Findings are also compared with recent trends in hyper-heuristics.This work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.展开更多
In this study, we focus into the non-relativistic wave equation described by the Schrodinger equation, specifically considering angular-dependent potentials within the context of a topological defect background genera...In this study, we focus into the non-relativistic wave equation described by the Schrodinger equation, specifically considering angular-dependent potentials within the context of a topological defect background generated by a cosmic string. Our primary goal is to explore quasi-exactly solvable problems by introducing an extended ring-shaped potential. We utilize the Bethe ansatz method to determine the angular solutions, while the radial solutions are obtained using special functions. Our findings demonstrate that the eigenvalue solutions of quantum particles are intricately influenced by the presence of the topological defect of the cosmic string,resulting in significant modifications compared to those in a flat space background. The existence of the topological defect induces alterations in the energy spectra, disrupting degeneracy.Afterwards, we extend our analysis to study the same problem in the presence of a ring-shaped potential against the background of another topological defect geometry known as a point-like global monopole. Following a similar procedure, we obtain the eigenvalue solutions and analyze the results. Remarkably, we observe that the presence of a global monopole leads to a decrease in the energy levels compared to the flat space results. In both cases, we conduct a thorough numerical analysis to validate our findings.展开更多
This study investigates the hydrochemical formation mechanism of shallow groundwater in the Upper Kebir upstream sub-basin(Northeastern Algeria).The objective is to evaluate water quality suitability for domestic purp...This study investigates the hydrochemical formation mechanism of shallow groundwater in the Upper Kebir upstream sub-basin(Northeastern Algeria).The objective is to evaluate water quality suitability for domestic purposes through the application of water quality index(WQI).A total of 24 water points(wells and borewells)evenly distributed in the basin were collected and analyzed in the laboratory for determining the major ions and other geochemical parameters in the groundwater.The groundwater hydrochemical types were identified as Cl–Na and Cl–HCO_(3)^(–)Na,with the dominant major ions were found in the order of Na^(+)>Ca^(2+)>Mg^(2+)for cations,and Cl^(−)>SO_(4)^(2−)>HCO_(3)^(–)>NO_(3)^(−)for anions.Results suggest that weathering,dissolution of carbonate,sulfate,salt rocks,and anthropogenic activities were the major contributors to ion content in the groundwater.The Water Quality Index(WQI)was calculated to assess the water quality of potable water.Approximately 50%of the sampled sites exhibited good water quality.However,the study highlights significant NO_(3)contamination in the study area,with 50%of samples exceeding permissible limits.Therefore,effective treatment measures are crucial for the safe consumption of groundwater.展开更多
In order to identify the variation and estimate the genetic diversity among the fig (Ficus carica L.) genotypes collected from Algeria and Turkey, the genetic relationships between 86 genotypes were investigated using...In order to identify the variation and estimate the genetic diversity among the fig (Ficus carica L.) genotypes collected from Algeria and Turkey, the genetic relationships between 86 genotypes were investigated using 23 inter primer binding sites (iPBS)-retrotransposon and 16 simple sequence repeat (SSR) primers. A total of 63 polymorphic bands for the iPBS-retrotransposon markers and 25 alleles for the SSR markers were identified with an average of 2.7 and 1.6 per primer, respectively. The average value of polymorphism information content (PIC) for the iPBS markers (0.73) was higher than that for the SSR markers (0.69). Applying the neighbor-joining method to the combined iPBS-retrotransposon and SSR data, the fig genotypes were clustered into two groups. The STRUCTURE software was used to determine the population structure. Among the genotypes studied, two populations (K = 2) were identified indicating a low diversity between the Algerian and Turkish varieties. Both types of markers were able to differentiate all the fig genotypes and were efficient in discriminating the closely related genotypes. Our data also showed that as a universal marker, iPBS-retrotransposon is a useful tool for the molecular characterization of fig genotypes.展开更多
While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness,they suffer from limitations in capturing long-range dependencies.Transformer-based app...While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness,they suffer from limitations in capturing long-range dependencies.Transformer-based approaches are currently prevailing since they enlarge the receptive field to model global contextual correlations.To further extract rich representations,some extensions of U-Net employ multi-scale feature extraction and fusion modules to obtain improved performance.Inspired by this idea,we propose TransCeption for medical image segmentation,a pure transformer-based U-shaped network incorporating an inception-like module in the encoder and adopting a contextual bridge for better feature fusion.The design proposed in this work is based on three core principles.(i)The patch merging module in the encoder is redesigned to use ResInception Patch Merging(RIPM).The Multi-Branch(MB)transformer has the same number of branches as the outputs of RIPM.Combining the two modules enables the model to capture a multi-scale representation within a single stage.(ii)We apply an Intra-stage Feature Fusion(IFF)module following the MB transformer to enhance the aggregation of feature maps from all branches and particularly focus on the interaction between the different channels at all scales.(iii)In contrast to a bridge that only contains tokenwise self-attention,we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective.Extensive experiments on multi-organ and skin lesion segmentation tasks show the superiority of TransCeption to previous work.The code is publicly available on GitHub.展开更多
Magnetic resonance imaging(MRI)is one of the most prevalent imaging modalities used for diagnosis,treatment planning,and outcome control in various medical conditions.MRI sequences provide physicians with the ability ...Magnetic resonance imaging(MRI)is one of the most prevalent imaging modalities used for diagnosis,treatment planning,and outcome control in various medical conditions.MRI sequences provide physicians with the ability to view and monitor tissues at multiple contrasts within a single scan and serve as input for automated systems to perform downstream tasks.However,in clinical practice,there is usually no concise set of identically acquired sequences for a whole group of patients.As a consequence,medical professionals and automated systems both face difficulties due to the lack of complementary information from such missing sequences.This problem is well known in computer vision,particularly in medical image processing tasks such as tumor segmentation,tissue classification,and image generation.With the aim of helping researchers,this literature review examines a significant number of recent approaches that attempt to mitigate these problems.Basic techniques such as early synthesis methods,as well as later approaches that deploy deep learning,such as common latent space models,knowledge distillation networks,mutual information maximization,and generative adversarial networks(GANs)are examined in detail.We investigate the novelty,strengths,and weaknesses of the aforementioned strategies.Moreover,using a case study on the segmentation task,our survey offers quantitative benchmarks to further analyze the effectiveness of these methods for addressing the missing modalities challenge.Furthermore,a discussion offers possible future research directions.展开更多
Purpose–The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem(DCNDP)and dynamic critical links detection problem(DCLDP)in stochastic networks...Purpose–The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem(DCNDP)and dynamic critical links detection problem(DCLDP)in stochastic networks.DCNDP and DCLDP consist of identifying the subset of nodes and links,respectively,whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs,in the graph modeling the transport network.The identification of such nodes(or links)helps to better control the road traffic and predict the necessary measures to avoid congestion.Design/methodology/approach–A Markovian decision process is used to model the shortest path problem underdynamic trafficconditions.Effectivealgorithmstodeterminethe criticalnodes(links)whileconsideringthe dynamicity of the traffic network are provided.Also,sensitivity analysis toward capacity reduction for critical links is studied.Moreover,the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.Findings–The numerical results demonstrate that the use of dynamic shortest path(time dependency)as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.Research limitations/implications–The research in this paper also revealed several challenges,which call for future investigations.First,the authors have restricted our experimentation to a small network where the only focus is on the model behavior,in the absence of historical data.The authors intend to extend this study to very large network using real data.Second,the authors have considered only congestion to assess network’s criticality;future research on this topic may include other factors,mainly vulnerability.Practical implications–Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks.This leads to design optimized smart transport plans particularly in disaster management,to improve the emergency evacuation effeciency.Originality/value–The paper provides a novel approach to solve critical nodes/links detection problems.In contrast to the majority of research works in the literature,the proposed model considers dynamicity and betweennesswhiletakingintoaccount the stochasticaspectof transportnetworks.Thisenables theapproach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.展开更多
Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete succe...Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete successfully in a dynamic market.To identify the technologies and methods that can facilitate the development ofbiologically inspired creative designs,this research briefly reviews the existing biological-knowledge-based theories and methods and examines the application of biological-knowledge-inspired designs in various fields.Afterward,this research thoroughly examines the four dimensions of key technologies that underlie the biologically inspired design(BID)process.This research then discusses the future development trends of the BID process before presenting the conclusions.展开更多
Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine.Here,we developed a deep learning framework called...Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine.Here,we developed a deep learning framework called TINDL,completely trained on preclinical cancer cell lines(CCLs),to predict the response of cancer patients to different treatments.TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors.Moreover,by making the deep learning black box interpretable,this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model,enabling identification of biomarkers of drug response.Using data from two large databases of CCLs and cancer tumors,we showed that this model can distinguish between sensitive and resistant tumors for 10(out of 14)drugs,outperforming various other machine learning models.In addition,our small interfering RNA(siRNA)knockdown experiments on 10 genes identified by this model for one of the drugs(tamoxifen)confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells,and seven of these genes in T47D cells.Furthermore,genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways.In summary,this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer.The code can be accessed at https://github.com/ddhostallero/tindl.展开更多
In this work we present experimental results on the behavior of diamond at megabar pressure. The experiment was performed using the PHELIX facility at GSI in Germany to launch a planar shock into solid multi-layered d...In this work we present experimental results on the behavior of diamond at megabar pressure. The experiment was performed using the PHELIX facility at GSI in Germany to launch a planar shock into solid multi-layered diamond samples. The target design allows shock velocity in diamond and in two metal layers to be measured as well as the free surface velocity after shock breakout. As diagnostics, we used two velocity interferometry systems for any reflector(VISARs). Our measurements show that for the pressures obtained in diamond(between 3 and 9 Mbar),the propagation of the shock induces a reflecting state of the material. Finally, the experimental results are compared with hydrodynamical simulations in which we used different equations of state, showing compatibility with dedicated SESAME tables for diamond.展开更多
Introduction The application of large language models such as generative pre-trained transformers(GPTs)has been promising in medical education,and its performance has been tested for different medical exams.This study...Introduction The application of large language models such as generative pre-trained transformers(GPTs)has been promising in medical education,and its performance has been tested for different medical exams.This study aims to assess the performance of GPTs in responding to a set of sample questions of short-answer management problems(SAMPs)from the certification exam of the College of Family Physicians of Canada(CFPC).Method Between August 8th and 25th,2023,we used GPT-3.5 and GPT-4 in five rounds to answer a sample of 77 SAMPs questions from the CFPC website.Two independent certified family physician reviewers scored AI-generated responses twice:first,according to the CFPC answer key(ie,CFPC score),and second,based on their knowledge and other references(ie,Reviews’score).An ordinal logistic generalised estimating equations(GEE)model was applied to analyse repeated measures across the five rounds.Result According to the CFPC answer key,607(73.6%)lines of answers by GPT-3.5 and 691(81%)by GPT-4 were deemed accurate.Reviewer’s scoring suggested that about 84%of the lines of answers provided by GPT-3.5 and 93%of GPT-4 were correct.The GEE analysis confirmed that over five rounds,the likelihood of achieving a higher CFPC Score Percentage for GPT-4 was 2.31 times more than GPT-3.5(OR:2.31;95%CI:1.53 to 3.47;p<0.001).Similarly,the Reviewers’Score percentage for responses provided by GPT-4 over 5 rounds were 2.23 times more likely to exceed those of GPT-3.5(OR:2.23;95%CI:1.22 to 4.06;p=0.009).Running the GPTs after a one week interval,regeneration of the prompt or using or not using the prompt did not significantly change the CFPC score percentage.Conclusion In our study,we used GPT-3.5 and GPT-4 to answer complex,open-ended sample questions of the CFPC exam and showed that more than 70%of the answers were accurate,and GPT-4 outperformed GPT-3.5 in responding to the questions.Large language models such as GPTs seem promising for assisting candidates of the CFPC exam by providing potential answers.However,their use for family medicine education and exam preparation needs further studies.展开更多
Background:With the rising global prevalence of fatty liver disease related to metabolic dysfunction,the association of this common liver condition with chronic kidney disease(CKD)has become increasingly evident.In 20...Background:With the rising global prevalence of fatty liver disease related to metabolic dysfunction,the association of this common liver condition with chronic kidney disease(CKD)has become increasingly evident.In 2020,the more inclusive term metabolic dysfunction-associated fatty liver disease(MAFLD)was proposed to replace the term non-alcoholic fatty liver disease(NAFLD).The observed association between MAFLD and CKD and our understanding that CKD can be a consequence of underlying metabolic dysfunction support the notion that individuals with MAFLD are at higher risk of having and developing CKD compared with those without MAFLD.However,to date,there is no appropriate guidance on CKD in individuals with MAFLD.Furthermore,there has been little attention paid to the link between MAFLD and CKD in the Nephrology community.Methods and Results:Using a Delphi-based approach,a multidisciplinary panel of 50 international experts from 26 countries reached a consensus on some of the open research questions regarding the link between MAFLD and CKD.Conclusions:This Delphi-based consensus statement provided guidance on the epidemiology,mechanisms,management and treatment of MAFLD and CKD,as well as the relationship between the severity of MAFLD and risk of CKD,which establish a framework for the early prevention and management of these two common and interconnected diseases.展开更多
基金funded by Ministry of Higher Education(MoHE)Malaysia,under Transdisciplinary Research Grant Scheme(TRGS/1/2019/UKM/01/4/2).
文摘The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem domains.Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process.Numerous selection hyper-heuristics have different imple-mentation strategies.However,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different components.The question is how to effectively employ them to produce an efficient search heuristic.Furthermore,the algorithms that competed in the inaugural CHeSC have not been collectively reviewed.This work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is presented.The analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter control.This review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these components.The effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic parameters.Findings are also compared with recent trends in hyper-heuristics.This work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.
文摘In this study, we focus into the non-relativistic wave equation described by the Schrodinger equation, specifically considering angular-dependent potentials within the context of a topological defect background generated by a cosmic string. Our primary goal is to explore quasi-exactly solvable problems by introducing an extended ring-shaped potential. We utilize the Bethe ansatz method to determine the angular solutions, while the radial solutions are obtained using special functions. Our findings demonstrate that the eigenvalue solutions of quantum particles are intricately influenced by the presence of the topological defect of the cosmic string,resulting in significant modifications compared to those in a flat space background. The existence of the topological defect induces alterations in the energy spectra, disrupting degeneracy.Afterwards, we extend our analysis to study the same problem in the presence of a ring-shaped potential against the background of another topological defect geometry known as a point-like global monopole. Following a similar procedure, we obtain the eigenvalue solutions and analyze the results. Remarkably, we observe that the presence of a global monopole leads to a decrease in the energy levels compared to the flat space results. In both cases, we conduct a thorough numerical analysis to validate our findings.
文摘This study investigates the hydrochemical formation mechanism of shallow groundwater in the Upper Kebir upstream sub-basin(Northeastern Algeria).The objective is to evaluate water quality suitability for domestic purposes through the application of water quality index(WQI).A total of 24 water points(wells and borewells)evenly distributed in the basin were collected and analyzed in the laboratory for determining the major ions and other geochemical parameters in the groundwater.The groundwater hydrochemical types were identified as Cl–Na and Cl–HCO_(3)^(–)Na,with the dominant major ions were found in the order of Na^(+)>Ca^(2+)>Mg^(2+)for cations,and Cl^(−)>SO_(4)^(2−)>HCO_(3)^(–)>NO_(3)^(−)for anions.Results suggest that weathering,dissolution of carbonate,sulfate,salt rocks,and anthropogenic activities were the major contributors to ion content in the groundwater.The Water Quality Index(WQI)was calculated to assess the water quality of potable water.Approximately 50%of the sampled sites exhibited good water quality.However,the study highlights significant NO_(3)contamination in the study area,with 50%of samples exceeding permissible limits.Therefore,effective treatment measures are crucial for the safe consumption of groundwater.
文摘In order to identify the variation and estimate the genetic diversity among the fig (Ficus carica L.) genotypes collected from Algeria and Turkey, the genetic relationships between 86 genotypes were investigated using 23 inter primer binding sites (iPBS)-retrotransposon and 16 simple sequence repeat (SSR) primers. A total of 63 polymorphic bands for the iPBS-retrotransposon markers and 25 alleles for the SSR markers were identified with an average of 2.7 and 1.6 per primer, respectively. The average value of polymorphism information content (PIC) for the iPBS markers (0.73) was higher than that for the SSR markers (0.69). Applying the neighbor-joining method to the combined iPBS-retrotransposon and SSR data, the fig genotypes were clustered into two groups. The STRUCTURE software was used to determine the population structure. Among the genotypes studied, two populations (K = 2) were identified indicating a low diversity between the Algerian and Turkish varieties. Both types of markers were able to differentiate all the fig genotypes and were efficient in discriminating the closely related genotypes. Our data also showed that as a universal marker, iPBS-retrotransposon is a useful tool for the molecular characterization of fig genotypes.
基金funded by the German Research Foundation(Deutsche Forschungsgemeinschaft,DFG)project number 455548460.
文摘While CNN-based methods have been the cornerstone of medical image segmentation due to their promising performance and robustness,they suffer from limitations in capturing long-range dependencies.Transformer-based approaches are currently prevailing since they enlarge the receptive field to model global contextual correlations.To further extract rich representations,some extensions of U-Net employ multi-scale feature extraction and fusion modules to obtain improved performance.Inspired by this idea,we propose TransCeption for medical image segmentation,a pure transformer-based U-shaped network incorporating an inception-like module in the encoder and adopting a contextual bridge for better feature fusion.The design proposed in this work is based on three core principles.(i)The patch merging module in the encoder is redesigned to use ResInception Patch Merging(RIPM).The Multi-Branch(MB)transformer has the same number of branches as the outputs of RIPM.Combining the two modules enables the model to capture a multi-scale representation within a single stage.(ii)We apply an Intra-stage Feature Fusion(IFF)module following the MB transformer to enhance the aggregation of feature maps from all branches and particularly focus on the interaction between the different channels at all scales.(iii)In contrast to a bridge that only contains tokenwise self-attention,we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective.Extensive experiments on multi-organ and skin lesion segmentation tasks show the superiority of TransCeption to previous work.The code is publicly available on GitHub.
基金funded by the German Research Foundation(Deutsche Forschungsgemeinschaft,DFG)under project numbers 191948804 and 455548460.
文摘Magnetic resonance imaging(MRI)is one of the most prevalent imaging modalities used for diagnosis,treatment planning,and outcome control in various medical conditions.MRI sequences provide physicians with the ability to view and monitor tissues at multiple contrasts within a single scan and serve as input for automated systems to perform downstream tasks.However,in clinical practice,there is usually no concise set of identically acquired sequences for a whole group of patients.As a consequence,medical professionals and automated systems both face difficulties due to the lack of complementary information from such missing sequences.This problem is well known in computer vision,particularly in medical image processing tasks such as tumor segmentation,tissue classification,and image generation.With the aim of helping researchers,this literature review examines a significant number of recent approaches that attempt to mitigate these problems.Basic techniques such as early synthesis methods,as well as later approaches that deploy deep learning,such as common latent space models,knowledge distillation networks,mutual information maximization,and generative adversarial networks(GANs)are examined in detail.We investigate the novelty,strengths,and weaknesses of the aforementioned strategies.Moreover,using a case study on the segmentation task,our survey offers quantitative benchmarks to further analyze the effectiveness of these methods for addressing the missing modalities challenge.Furthermore,a discussion offers possible future research directions.
基金acknowledgment to Dr Ali Benssam for his invaluable support during all the steps of the project and in the writing of the paper.
文摘Purpose–The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem(DCNDP)and dynamic critical links detection problem(DCLDP)in stochastic networks.DCNDP and DCLDP consist of identifying the subset of nodes and links,respectively,whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs,in the graph modeling the transport network.The identification of such nodes(or links)helps to better control the road traffic and predict the necessary measures to avoid congestion.Design/methodology/approach–A Markovian decision process is used to model the shortest path problem underdynamic trafficconditions.Effectivealgorithmstodeterminethe criticalnodes(links)whileconsideringthe dynamicity of the traffic network are provided.Also,sensitivity analysis toward capacity reduction for critical links is studied.Moreover,the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.Findings–The numerical results demonstrate that the use of dynamic shortest path(time dependency)as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.Research limitations/implications–The research in this paper also revealed several challenges,which call for future investigations.First,the authors have restricted our experimentation to a small network where the only focus is on the model behavior,in the absence of historical data.The authors intend to extend this study to very large network using real data.Second,the authors have considered only congestion to assess network’s criticality;future research on this topic may include other factors,mainly vulnerability.Practical implications–Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks.This leads to design optimized smart transport plans particularly in disaster management,to improve the emergency evacuation effeciency.Originality/value–The paper provides a novel approach to solve critical nodes/links detection problems.In contrast to the majority of research works in the literature,the proposed model considers dynamicity and betweennesswhiletakingintoaccount the stochasticaspectof transportnetworks.Thisenables theapproach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.
基金the National Natural Science Foundation of China(Grant Nos.51675159 and 51475137)the Natural Science Foundation of Hebei Province of China(Grant No.E2015202029)China Scholarship Council and Hebei in the Graduate Student Innovation Ability Training Project.
文摘Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete successfully in a dynamic market.To identify the technologies and methods that can facilitate the development ofbiologically inspired creative designs,this research briefly reviews the existing biological-knowledge-based theories and methods and examines the application of biological-knowledge-inspired designs in various fields.Afterward,this research thoroughly examines the four dimensions of key technologies that underlie the biologically inspired design(BID)process.This research then discusses the future development trends of the BID process before presenting the conclusions.
基金supported by the New Frontiers in Research Fund(NFRF)of Government of Canada(Grant No.NFRFE-2019-01290 to Amin Emad and Junmei Cairns)the Natural Sciences and Engineering Research Council of Canada(NSERC)(Grant No.RGPIN-2019-04460 to Amin Emad)the McGill Initiative in Computational Medicine(MiCM)to Amin Emad.
文摘Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine.Here,we developed a deep learning framework called TINDL,completely trained on preclinical cancer cell lines(CCLs),to predict the response of cancer patients to different treatments.TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors.Moreover,by making the deep learning black box interpretable,this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model,enabling identification of biomarkers of drug response.Using data from two large databases of CCLs and cancer tumors,we showed that this model can distinguish between sensitive and resistant tumors for 10(out of 14)drugs,outperforming various other machine learning models.In addition,our small interfering RNA(siRNA)knockdown experiments on 10 genes identified by this model for one of the drugs(tamoxifen)confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells,and seven of these genes in T47D cells.Furthermore,genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways.In summary,this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer.The code can be accessed at https://github.com/ddhostallero/tindl.
基金the support of the laser technical team at GSI PHELIXhas been carried out within the framework of the EUROfusion Enabling Research Project:ENR-IFE19.CEA-01‘Study of Direct Drive and Shock Ignition for IFE:Theory,Simulations,Experiments,Diagnostics Development’and has received funding from Euratom 2019–2020。
文摘In this work we present experimental results on the behavior of diamond at megabar pressure. The experiment was performed using the PHELIX facility at GSI in Germany to launch a planar shock into solid multi-layered diamond samples. The target design allows shock velocity in diamond and in two metal layers to be measured as well as the free surface velocity after shock breakout. As diagnostics, we used two velocity interferometry systems for any reflector(VISARs). Our measurements show that for the pressures obtained in diamond(between 3 and 9 Mbar),the propagation of the shock induces a reflecting state of the material. Finally, the experimental results are compared with hydrodynamical simulations in which we used different equations of state, showing compatibility with dedicated SESAME tables for diamond.
基金SAR is Canada Research Chair(Tier Ⅱ)in Advanced Digital Primary Health Care,received salary support from a Research Scholar Junior 1 Career Development Award from the Fonds de Recherche du Québec-Santé(FRQS)during a portion of this study,and her research program is supported by the Natural Sciences Research Council(NSERC)Discovery(grant 2020-05246).
文摘Introduction The application of large language models such as generative pre-trained transformers(GPTs)has been promising in medical education,and its performance has been tested for different medical exams.This study aims to assess the performance of GPTs in responding to a set of sample questions of short-answer management problems(SAMPs)from the certification exam of the College of Family Physicians of Canada(CFPC).Method Between August 8th and 25th,2023,we used GPT-3.5 and GPT-4 in five rounds to answer a sample of 77 SAMPs questions from the CFPC website.Two independent certified family physician reviewers scored AI-generated responses twice:first,according to the CFPC answer key(ie,CFPC score),and second,based on their knowledge and other references(ie,Reviews’score).An ordinal logistic generalised estimating equations(GEE)model was applied to analyse repeated measures across the five rounds.Result According to the CFPC answer key,607(73.6%)lines of answers by GPT-3.5 and 691(81%)by GPT-4 were deemed accurate.Reviewer’s scoring suggested that about 84%of the lines of answers provided by GPT-3.5 and 93%of GPT-4 were correct.The GEE analysis confirmed that over five rounds,the likelihood of achieving a higher CFPC Score Percentage for GPT-4 was 2.31 times more than GPT-3.5(OR:2.31;95%CI:1.53 to 3.47;p<0.001).Similarly,the Reviewers’Score percentage for responses provided by GPT-4 over 5 rounds were 2.23 times more likely to exceed those of GPT-3.5(OR:2.23;95%CI:1.22 to 4.06;p=0.009).Running the GPTs after a one week interval,regeneration of the prompt or using or not using the prompt did not significantly change the CFPC score percentage.Conclusion In our study,we used GPT-3.5 and GPT-4 to answer complex,open-ended sample questions of the CFPC exam and showed that more than 70%of the answers were accurate,and GPT-4 outperformed GPT-3.5 in responding to the questions.Large language models such as GPTs seem promising for assisting candidates of the CFPC exam by providing potential answers.However,their use for family medicine education and exam preparation needs further studies.
文摘Background:With the rising global prevalence of fatty liver disease related to metabolic dysfunction,the association of this common liver condition with chronic kidney disease(CKD)has become increasingly evident.In 2020,the more inclusive term metabolic dysfunction-associated fatty liver disease(MAFLD)was proposed to replace the term non-alcoholic fatty liver disease(NAFLD).The observed association between MAFLD and CKD and our understanding that CKD can be a consequence of underlying metabolic dysfunction support the notion that individuals with MAFLD are at higher risk of having and developing CKD compared with those without MAFLD.However,to date,there is no appropriate guidance on CKD in individuals with MAFLD.Furthermore,there has been little attention paid to the link between MAFLD and CKD in the Nephrology community.Methods and Results:Using a Delphi-based approach,a multidisciplinary panel of 50 international experts from 26 countries reached a consensus on some of the open research questions regarding the link between MAFLD and CKD.Conclusions:This Delphi-based consensus statement provided guidance on the epidemiology,mechanisms,management and treatment of MAFLD and CKD,as well as the relationship between the severity of MAFLD and risk of CKD,which establish a framework for the early prevention and management of these two common and interconnected diseases.
