The NHS is right now confronting huge pressures relating to demand and capacity in radiology. The purpose of this research has been to provide information about MRI usage, details of operational aspects of MRI service...The NHS is right now confronting huge pressures relating to demand and capacity in radiology. The purpose of this research has been to provide information about MRI usage, details of operational aspects of MRI services, and to ascertain the planning intentions of NHS radiology services to keep up and create MRI capacity. The report expands on using Discrete Event Simulation (DES) to inspect and plan the utilisation of NHS hospital resources for the radiology department to help a 24 hr service that is available to outpatients which will help with diminishing patient waiting time, better resource usage, understanding the capacity and demand. Consequently, this research examines to adjust staff and resources with the demand of the MRI. The research was investigated using DES in various scenarios to find which resources are inactive;patients are treated slowly. DES helped in discovering resource utilisation and outpatient throughout the system. It additionally helped in distinguishing the bottlenecks in patient flow. The DES simulation results demonstrated that time for the outpatient in the system is less and more outpatients have been treated too. There is a higher level of outpatient patients leaving the system under 120 minutes. The report uncovered an MRI report interpretation time. Reception room time and MRI waiting room time are decreased significantly. It additionally exhibited with an expanded outflow of outpatients, resources, for example, MRI capacity and radiographer utilisation expanded.展开更多
Cloud computing(CC)is developing as a powerful and flexible computational structure for providing ubiquitous service to users.It receives interrelated software and hardware resources in an integrated manner distinct f...Cloud computing(CC)is developing as a powerful and flexible computational structure for providing ubiquitous service to users.It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment.The variation of software and hardware resources were combined and composed as a resource pool.The software no more resided in the single hardware environment,it can be executed on the schedule of resource pools to optimize resource consumption.Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation.This study introduces a Battle Royale Optimization-based Resource Scheduling Scheme for Cloud Computing Environment(BRORSS-CCE)technique.The presented BRORSS-CCE technique majorly schedules the available resources for maximum utilization and effectual makespan.In the BRORSS-CCE technique,the BRO is a population-based algorithm where all the individuals are denoted by a soldier/player who likes to go towards the optimal place and ultimate survival.The BRORSS-CCE technique can be employed to balance the load,distribute resources based on demand and assure services to all requests.The experimental validation of the BRORSS-CCE technique is tested under distinct aspects.The experimental outcomes indicated the enhancements of the BRORSS-CCE technique over other models.展开更多
As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.Exploring drug‐target interaction strength in terms of drug‐...As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.Exploring drug‐target interaction strength in terms of drug‐target affinity(DTA)is an emerging and effective research approach for drug development.However,it is challenging to model drug‐target interactions in a deep learning manner,and few studies provide interpretable analysis of models.This paper proposes a DTA prediction method(mutual transformer‐drug target affinity[MT‐DTA])with interactive learning and an autoencoder mechanism.The proposed MT‐DTA builds a variational autoencoders system with a cascade structure of the attention model and convolutional neural networks.It not only enhances the ability to capture the characteristic information of a single molecular sequence but also establishes the characteristic expression relationship for each substructure in a single molecular sequence.On this basis,a molecular information interaction module is constructed,which adds information interaction paths between molecular sequence pairs and complements the expression of correlations between molecular substructures.The performance of the proposed model was verified on two public benchmark datasets,KIBA and Davis,and the results confirm that the proposed model structure is effective in predicting DTA.Additionally,attention transformer models with different configurations can improve the feature expression of drug/protein molecules.The model performs better in correctly predicting interaction strengths compared with state‐of‐the‐art baselines.In addition,the diversity of drug/protein molecules can be better expressed than existing methods such as SeqGAN and Co‐VAE to generate more effective new drugs.The DTA value prediction module fuses the drug‐target pair interaction information to output the predicted value of DTA.Additionally,this paper theoretically proves that the proposed method maximises evidence lower bound for the joint distribution of the DTA prediction model,which enhances the consistency of the probability distribution between actual and predicted values.The source code of proposed method is available at https://github.com/Lamouryz/Code/tree/main/MT‐DTA.展开更多
Climate change has become a global phenomenon and is adversely affecting agricultural development across the globe.Developing countries like Pakistan where 18.9%of the GDP(gross domestic product)came from the agricult...Climate change has become a global phenomenon and is adversely affecting agricultural development across the globe.Developing countries like Pakistan where 18.9%of the GDP(gross domestic product)came from the agriculture sector and also 42%of the labor force involved in agriculture.They are directly and indirectly affected by climate change due to an increase in the frequency and intensity of climatic extreme events such as floods,droughts and extreme weather events.In this paper,we have focused on the impact of climate change on farm households and their adaptation strategies to cope up the climatic extremes.For this purpose,we have selected farm households by using multistage stratified random sampling from four districts of the Potohar region i.e.Attock,Rawalpindi,Jhelum and Chakwal.These districts were selected by dividing the Potohar region into rain-fed areas.We have employed logistic regression to assess the determinants of adaptation to climate change and its impact.We have also calculated the marginal effect of each independent variable of the logistic regression to measure the immediate rate of change in the model.In order to check the significance of our suggested model,we have used hypothesis testing.展开更多
Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propos...Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System （IDS） to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service （DOS） and black hole attacks on vehicular ad hoe networks （VANETs）. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis （LDA） and Quadratic Diseriminant Analysis （QDA） which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.展开更多
A thermoelectric generation Stirling engine (TEG-Stirling engine) is discussed by employing a low temperature Stirling engine and the dissipative equation of motion derived from the method of thermomechanical dynamics...A thermoelectric generation Stirling engine (TEG-Stirling engine) is discussed by employing a low temperature Stirling engine and the dissipative equation of motion derived from the method of thermomechanical dynamics (TMD). The results and mechanism of axial flux electromagnetic induction (AF-EMI) are applied to a low temperature Stirling engine, resulting in a TEG-Stirling engine. The method of TMD produced thermodynamically consistent and time-dependent physical quantities for the first time, such as internal energy ℰ(t), thermodynamic work Wth(t), the total entropy (heat dissipation) Qd(t)and measure or temperature of a nonequilibrium state T˜(t). The TMD analysis produced a lightweight mechanical system of TEG-Stirling engine which derives electric power from waste heat of temperature (40˚CT100˚C) by a thermoelectric conversion method. An optimal low rotational speed about 30θ′(t)/(2π)60(rpm) is found, applicable to devices for sustainable, clean energy technologies. The stability of a thermal state and angular rotations of TEG-Stirling engine are specifically shown by employing properties of nonequilibrium temperature T˜(t), which is also applied to study optimal fuel-injection and combustion timings of heat engines.展开更多
The traditional thermoelectric energy conversion techniques are explained in detail in terms of the axial flux electromagnetic (AFE) and the radial flux electromagnetic (RFE) inductions, and applications to heat engin...The traditional thermoelectric energy conversion techniques are explained in detail in terms of the axial flux electromagnetic (AFE) and the radial flux electromagnetic (RFE) inductions, and applications to heat engines for the energy-harvesting technologies are discussed. The idea is induced by the analysis of thermomechanical dynamics (TMD) for a nonequilibrium irreversible thermodynamic system of heat engines (a drinking bird, a low temperature Stirling engine), resulting in thermoelectric energy generation different from conventional heat engines. The mechanism of thermoelectric energy conversion can be categorized as the axial flux generator (AFG) and the radial flux generator (RFG). The axial flux generator is helpful for low mechanoelectric energy conversion and activations of waste heat from macroscopic energy generators, such as wind, geothermal, thermal, nuclear power plants and heat-dissipation lines, and the device contributes to solving environmental problems to maintain clean and sustainable energy as one of the energy harvesting technologies.展开更多
Background:Cardiovascular disease remains the leading cause of mortality across the European region.Despite marked regional variations,cross-national differences in underlying risk factors have received comparatively ...Background:Cardiovascular disease remains the leading cause of mortality across the European region.Despite marked regional variations,cross-national differences in underlying risk factors have received comparatively little attention.Objective:To use European Social Survey,a unique cross-European dataset,to examine regional patterns in prevalence and lifestyle risks.Methods:This study employs clustering analysis and nested logistic modelling.Counterfactual analysis was conducted to illustrate how lifestyle modifications could reduce risk.Results:The prevalence of heart problems was highest in Latvia(25.6%,95%CI:23.0 to 28.2),Lithuania(17.6%,95%CI:15.5 to 19.7),and Bulgaria(14.9%,95%CI:13.4 to 19.4).Regionally,heart problems were higher in Northern and Eastern Europe(15%and 11.9%)than Western and Southern Europe(10.8%and 9.5%).Among the risk factors,modelling emphasised the importance of modifiable factors including education,body mass index and physical activity.Conclusion:The results underline that cardiovascular disease is influenced by interrelated socioeconomic,environmental and lifestyle determinants.Public policy interventions could be targeted at those countries where greatest reductions are obtainable and concentrate on interventions on those lifestyle traits identified.The study utilised a social science dataset,thereby illustrating how multidisciplinary resources can benefit epidemiological research.展开更多
文摘The NHS is right now confronting huge pressures relating to demand and capacity in radiology. The purpose of this research has been to provide information about MRI usage, details of operational aspects of MRI services, and to ascertain the planning intentions of NHS radiology services to keep up and create MRI capacity. The report expands on using Discrete Event Simulation (DES) to inspect and plan the utilisation of NHS hospital resources for the radiology department to help a 24 hr service that is available to outpatients which will help with diminishing patient waiting time, better resource usage, understanding the capacity and demand. Consequently, this research examines to adjust staff and resources with the demand of the MRI. The research was investigated using DES in various scenarios to find which resources are inactive;patients are treated slowly. DES helped in discovering resource utilisation and outpatient throughout the system. It additionally helped in distinguishing the bottlenecks in patient flow. The DES simulation results demonstrated that time for the outpatient in the system is less and more outpatients have been treated too. There is a higher level of outpatient patients leaving the system under 120 minutes. The report uncovered an MRI report interpretation time. Reception room time and MRI waiting room time are decreased significantly. It additionally exhibited with an expanded outflow of outpatients, resources, for example, MRI capacity and radiographer utilisation expanded.
文摘Cloud computing(CC)is developing as a powerful and flexible computational structure for providing ubiquitous service to users.It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment.The variation of software and hardware resources were combined and composed as a resource pool.The software no more resided in the single hardware environment,it can be executed on the schedule of resource pools to optimize resource consumption.Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation.This study introduces a Battle Royale Optimization-based Resource Scheduling Scheme for Cloud Computing Environment(BRORSS-CCE)technique.The presented BRORSS-CCE technique majorly schedules the available resources for maximum utilization and effectual makespan.In the BRORSS-CCE technique,the BRO is a population-based algorithm where all the individuals are denoted by a soldier/player who likes to go towards the optimal place and ultimate survival.The BRORSS-CCE technique can be employed to balance the load,distribute resources based on demand and assure services to all requests.The experimental validation of the BRORSS-CCE technique is tested under distinct aspects.The experimental outcomes indicated the enhancements of the BRORSS-CCE technique over other models.
基金supported by Cooperation Project Between Undergraduate Universities in Chongqing and Institutions Affiliated to the Chinese Academy of Sciences(No.HZ2021018)the National Natural Science Foundation of China(Grant 62276037)Special key project of Chongqing technology innovation and application development:CSTB2022TIAD‐KPX0039.
文摘As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.Exploring drug‐target interaction strength in terms of drug‐target affinity(DTA)is an emerging and effective research approach for drug development.However,it is challenging to model drug‐target interactions in a deep learning manner,and few studies provide interpretable analysis of models.This paper proposes a DTA prediction method(mutual transformer‐drug target affinity[MT‐DTA])with interactive learning and an autoencoder mechanism.The proposed MT‐DTA builds a variational autoencoders system with a cascade structure of the attention model and convolutional neural networks.It not only enhances the ability to capture the characteristic information of a single molecular sequence but also establishes the characteristic expression relationship for each substructure in a single molecular sequence.On this basis,a molecular information interaction module is constructed,which adds information interaction paths between molecular sequence pairs and complements the expression of correlations between molecular substructures.The performance of the proposed model was verified on two public benchmark datasets,KIBA and Davis,and the results confirm that the proposed model structure is effective in predicting DTA.Additionally,attention transformer models with different configurations can improve the feature expression of drug/protein molecules.The model performs better in correctly predicting interaction strengths compared with state‐of‐the‐art baselines.In addition,the diversity of drug/protein molecules can be better expressed than existing methods such as SeqGAN and Co‐VAE to generate more effective new drugs.The DTA value prediction module fuses the drug‐target pair interaction information to output the predicted value of DTA.Additionally,this paper theoretically proves that the proposed method maximises evidence lower bound for the joint distribution of the DTA prediction model,which enhances the consistency of the probability distribution between actual and predicted values.The source code of proposed method is available at https://github.com/Lamouryz/Code/tree/main/MT‐DTA.
文摘Climate change has become a global phenomenon and is adversely affecting agricultural development across the globe.Developing countries like Pakistan where 18.9%of the GDP(gross domestic product)came from the agriculture sector and also 42%of the labor force involved in agriculture.They are directly and indirectly affected by climate change due to an increase in the frequency and intensity of climatic extreme events such as floods,droughts and extreme weather events.In this paper,we have focused on the impact of climate change on farm households and their adaptation strategies to cope up the climatic extremes.For this purpose,we have selected farm households by using multistage stratified random sampling from four districts of the Potohar region i.e.Attock,Rawalpindi,Jhelum and Chakwal.These districts were selected by dividing the Potohar region into rain-fed areas.We have employed logistic regression to assess the determinants of adaptation to climate change and its impact.We have also calculated the marginal effect of each independent variable of the logistic regression to measure the immediate rate of change in the model.In order to check the significance of our suggested model,we have used hypothesis testing.
文摘Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoe networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System （IDS） to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service （DOS） and black hole attacks on vehicular ad hoe networks （VANETs）. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis （LDA） and Quadratic Diseriminant Analysis （QDA） which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.
文摘A thermoelectric generation Stirling engine (TEG-Stirling engine) is discussed by employing a low temperature Stirling engine and the dissipative equation of motion derived from the method of thermomechanical dynamics (TMD). The results and mechanism of axial flux electromagnetic induction (AF-EMI) are applied to a low temperature Stirling engine, resulting in a TEG-Stirling engine. The method of TMD produced thermodynamically consistent and time-dependent physical quantities for the first time, such as internal energy ℰ(t), thermodynamic work Wth(t), the total entropy (heat dissipation) Qd(t)and measure or temperature of a nonequilibrium state T˜(t). The TMD analysis produced a lightweight mechanical system of TEG-Stirling engine which derives electric power from waste heat of temperature (40˚CT100˚C) by a thermoelectric conversion method. An optimal low rotational speed about 30θ′(t)/(2π)60(rpm) is found, applicable to devices for sustainable, clean energy technologies. The stability of a thermal state and angular rotations of TEG-Stirling engine are specifically shown by employing properties of nonequilibrium temperature T˜(t), which is also applied to study optimal fuel-injection and combustion timings of heat engines.
文摘The traditional thermoelectric energy conversion techniques are explained in detail in terms of the axial flux electromagnetic (AFE) and the radial flux electromagnetic (RFE) inductions, and applications to heat engines for the energy-harvesting technologies are discussed. The idea is induced by the analysis of thermomechanical dynamics (TMD) for a nonequilibrium irreversible thermodynamic system of heat engines (a drinking bird, a low temperature Stirling engine), resulting in thermoelectric energy generation different from conventional heat engines. The mechanism of thermoelectric energy conversion can be categorized as the axial flux generator (AFG) and the radial flux generator (RFG). The axial flux generator is helpful for low mechanoelectric energy conversion and activations of waste heat from macroscopic energy generators, such as wind, geothermal, thermal, nuclear power plants and heat-dissipation lines, and the device contributes to solving environmental problems to maintain clean and sustainable energy as one of the energy harvesting technologies.
文摘Background:Cardiovascular disease remains the leading cause of mortality across the European region.Despite marked regional variations,cross-national differences in underlying risk factors have received comparatively little attention.Objective:To use European Social Survey,a unique cross-European dataset,to examine regional patterns in prevalence and lifestyle risks.Methods:This study employs clustering analysis and nested logistic modelling.Counterfactual analysis was conducted to illustrate how lifestyle modifications could reduce risk.Results:The prevalence of heart problems was highest in Latvia(25.6%,95%CI:23.0 to 28.2),Lithuania(17.6%,95%CI:15.5 to 19.7),and Bulgaria(14.9%,95%CI:13.4 to 19.4).Regionally,heart problems were higher in Northern and Eastern Europe(15%and 11.9%)than Western and Southern Europe(10.8%and 9.5%).Among the risk factors,modelling emphasised the importance of modifiable factors including education,body mass index and physical activity.Conclusion:The results underline that cardiovascular disease is influenced by interrelated socioeconomic,environmental and lifestyle determinants.Public policy interventions could be targeted at those countries where greatest reductions are obtainable and concentrate on interventions on those lifestyle traits identified.The study utilised a social science dataset,thereby illustrating how multidisciplinary resources can benefit epidemiological research.
