Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neur...Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neural networks(PINN)provide a new way to solve the nonlinear Schrodinger equation describing the soliton evolution by fusing data-driven and physical constraints.However,the grid point sampling strategy of traditional PINN suffers from high computational complexity and unstable gradient flow,which makes it difficult to capture the physical details efficiently.In this paper,we propose a residual-based adaptive multi-distribution(RAMD)sampling method to optimize the PINN training process by dynamically constructing a multi-modal loss distribution.With a 50%reduction in the number of grid points,RAMD significantly reduces the relative error of PINN and,in particular,optimizes the solution error of the(2+1)Ginzburg–Landau equation from 4.55%to 1.98%.RAMD breaks through the lack of physical constraints in the purely data-driven model by the innovative combination of multi-modal distribution modeling and autonomous sampling control for the design of all-optical communication devices.RAMD provides a high-precision numerical simulation tool for the design of all-optical communication devices,optimization of nonlinear laser devices,and other studies.展开更多
The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health ...The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health settings across customs,cultures,and languages in two provinces,on the basis of qualitative interviews with healthcare professionals including doctors,nurses,other healthcare workers,patients,and traditional healers.We emphasize the views of consent with participants of customs,cultural,and languages regarding informed consent.There are factors between peoples of differing circumstances which can greatly alter how they view consent.Some groups would involve people in the decision-making process that may not traditionally be involved in the decision making of a medical decision.Other groups may dislike certain medical procedures as in Papua New Guinea(PNG).And certain people have different views on what should be disclosed of the patient’s condition.Customs,cultures,and languages are common phenomena which continue to affect the daily lives of many thousands of people.It is unclear in PNG about the characteristics of customs,culture,and language on health care because there is no published information on informed consent and issues that affect the making of informed consent.展开更多
Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process...Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.展开更多
BACKGROUND Brain-computer interface(BCI)technology is rapidly advancing in psychiatry.Informed consent competency(ICC)assessment among psychiatric patients is a pivotal concern in clinical research.AIM To analyze the ...BACKGROUND Brain-computer interface(BCI)technology is rapidly advancing in psychiatry.Informed consent competency(ICC)assessment among psychiatric patients is a pivotal concern in clinical research.AIM To analyze the assessment of ICC and form a framework with multi-dimensional elements involved in ICC of BCI clinical research among psychiatric disorders.METHODS A systematic review of studies regarding ICC assessments of BCI clinical research in patients with six kinds of psychiatric disorders was conducted.A systematic literature search was performed using PubMed,ScienceDirect,and Web of Science.Peer-reviewed articles and full-text studies were included in the analysis.There were no date restrictions,and all studies published up to February 27,2025,were included.RESULTS A total of 103 studies were selected for this review.Fifty-eight studies included ICC factors,and forty-five were classified in ICC related ethical issues of BCI research in six kinds of psychiatric disorders.Executive function impairment is widely recognized as the most significant factor impacting ICC,and processing speed deficits are observed in schizophrenia,mood disorders,and Alzheimer’s disease.Memory dysfunction,particularly episodic and working memory,contributes to compromised ICC.Five core ethical issues in BCI research should be addressed:BCI specificity,vulnerability,autonomy,dynamic ICC,comprehensiveness,and uncertainty.CONCLUSION A Five-Dimensional evaluative framework,including clinical,ethical,sociocultural,legal,and procedural dimensions,is constructed and proposed for future ICC research in BCI clinical research involving psychiatric disorders.展开更多
With the civilization and modernization of human society,occupational health has emerged as a fundamental goal of social justice,as highlighted in the United Nations'Sustainable Development Goals(SDGs)since 2016.S...With the civilization and modernization of human society,occupational health has emerged as a fundamental goal of social justice,as highlighted in the United Nations'Sustainable Development Goals(SDGs)since 2016.Specifically,"SDG Goal 1:No Poverty","SDG 3:Good Health and Well-being",and"SDG 8:Decent Work and Economic Growth",are interconnected with other SDGs to support the pursuit of occupational health.展开更多
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev...Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.展开更多
Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-eng...Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.展开更多
With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent years.In this paper,physics informed memory networks(PIM...With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent years.In this paper,physics informed memory networks(PIMNs)are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of PDEs.Unlike the fully connected structure of the PINNs,the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory network.Meanwhile,the PDEs residuals are approximated using difference schemes in the form of convolution filter,which avoids information loss at the neighborhood of the sampling points.Finally,the performance of the PIMNs is assessed by solving the Kd V equation and the nonlinear Schr?dinger equation,and the effects of difference schemes,boundary conditions,network structure and mesh size on the solutions are discussed.Experiments show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.展开更多
Heat transport has been significantly enhanced by the widespread usage of extended surfaces in various engi-neering domains.Gas turbine blade cooling,refrigeration,and electronic equipment cooling are a few prevalent ...Heat transport has been significantly enhanced by the widespread usage of extended surfaces in various engi-neering domains.Gas turbine blade cooling,refrigeration,and electronic equipment cooling are a few prevalent applications.Thus,the thermal analysis of extended surfaces has been the subject of a significant assessment by researchers.Motivated by this,the present study describes the unsteady thermal dispersal phenomena in a wavy fin with the presence of convection heat transmission.This analysis also emphasizes a novel mathematical model in accordance with transient thermal change in a wavy profiled fin resulting from convection using the finite difference method(FDM)and physics informed neural network(PINN).The time and space-dependent governing partial differential equation(PDE)for the suggested heat problem has been translated into a dimensionless form using the relevant dimensionless terms.The graph depicts the effect of thermal parameters on the fin’s thermal profile.The temperature dispersion in the fin decreases as the dimensionless convection-conduction variable rises.The heat dispersion in the fin is decreased by increasing the aspect ratio,whereas the reverse behavior is seen with the time change.Furthermore,FDM-PINN results are validated against the outcomes of the FDM.展开更多
The publisher would like to draw the reader's attention to the following errors.Informed consents were not included in the published version of the following articles that appeared in previous issues of Grain&...The publisher would like to draw the reader's attention to the following errors.Informed consents were not included in the published version of the following articles that appeared in previous issues of Grain&Oil Science and Technology.The authors were contacted after publication to request informed consents for the following articles.The appropriate informed consents,provided by the authors,are included below.展开更多
Recent advancements in next generation sequencing have allowed for genetic information become more readily available in the clinical setting for those affected by cancer and by treating clinicians.Given the lack of ac...Recent advancements in next generation sequencing have allowed for genetic information become more readily available in the clinical setting for those affected by cancer and by treating clinicians.Given the lack of access to geneticists,medical oncologists and other treating physicians have begun ordering and interpreting genetic tests for individuals with cancer through the process of"mainstreaming".While this process has allowed for quicker access to genetic tests,the process of"mainstreaming"has also brought several challenges including the dissemination of variants of unknown significance results,ordering of appropriate tests,and accurate interpretation of genetic results with appropriate followup testing and interventions.In this editorial,we seek to explore the process of informed consent of individuals before obtaining genetic testing and offer potential solutions to optimize the informed consent process including categorization of results as well as a layered consent model.展开更多
Informed consent is necessary in good clinical practice.It is based on the patient’s ability to understand the information about the proposed procedure,the potential consequences and complications,and alternative opt...Informed consent is necessary in good clinical practice.It is based on the patient’s ability to understand the information about the proposed procedure,the potential consequences and complications,and alternative options.The information is written in understandable language and is fortified by verbal discussion between physician and patient.The aim is to explain the problem,answer all questions and to ensure that the patient understands the problems and is able to make a decision.The theory is clear but what happens in daily practice?展开更多
Since the first publication describing the identification of prostate-specific antigen （PSA） in the 1960s, much progress has been made. The PSA test changed from being initially a monitoring tool to being also used ...Since the first publication describing the identification of prostate-specific antigen （PSA） in the 1960s, much progress has been made. The PSA test changed from being initially a monitoring tool to being also used as a diagnostic tool. Over time, the test has been heavily debated due to its lack of sensitivity and specificity. However, up to now the PSA test is still the only biomarker for the detection and monitoring of prostate cancer. PSA-based screening for prostate cancer is associated with a high proportion of unnecessary testing and overdiagnosis with subsequent overtreatment. In the early years of screening for prostate cancer, high rates of uptake were very important. However, over time the opinion on PSA-based screening has shifted towards the notion of informed choice. Nowadays, it is thought to be unethical to screen men without them being aware of the pros and cons of PSA testing, as well as the fact that an informed choice is related to better patient outcomes. Now, as the results of three major screening studies have been presented and the downsides of screening are becoming better understood, informed choice is becoming more relevant.展开更多
Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black ...Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.展开更多
This study explores how doctors and patients in Papua New Guinea(PNG)perceive informed consent in medical settings.Doctors and patients from National Capital District and Central Province who responded to the survey w...This study explores how doctors and patients in Papua New Guinea(PNG)perceive informed consent in medical settings.Doctors and patients from National Capital District and Central Province who responded to the survey were the participants of the study.Researchers asked the participants to fill out questionnaires regarding their knowledge about informed consent for each group,namely,the doctors and participants.From those who responded,six randomly selected participants were chosen to join the focus group discussion which aimed to get experiences from the doctors and patients regarding medical procedures.Results show that both doctors and patients lack knowledge of the legalities of informed consent.Based on the experiences of doctors,they do not use consent forms when seeking permission from patients.Patients,on the other hand,do not see consent forms as important and only served as a formality.Customs,culture surrounding PNG were found to have an impact on how patients perceived informed consent.展开更多
基金supported by the National Key R&D Program of China(Grant No.2022YFA1604200)National Natural Science Foundation of China(Grant No.12261131495)+1 种基金Beijing Municipal Science and Technology Commission,Adminitrative Commission of Zhongguancun Science Park(Grant No.Z231100006623006)Institute of Systems Science,Beijing Wuzi University(Grant No.BWUISS21)。
文摘Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neural networks(PINN)provide a new way to solve the nonlinear Schrodinger equation describing the soliton evolution by fusing data-driven and physical constraints.However,the grid point sampling strategy of traditional PINN suffers from high computational complexity and unstable gradient flow,which makes it difficult to capture the physical details efficiently.In this paper,we propose a residual-based adaptive multi-distribution(RAMD)sampling method to optimize the PINN training process by dynamically constructing a multi-modal loss distribution.With a 50%reduction in the number of grid points,RAMD significantly reduces the relative error of PINN and,in particular,optimizes the solution error of the(2+1)Ginzburg–Landau equation from 4.55%to 1.98%.RAMD breaks through the lack of physical constraints in the purely data-driven model by the innovative combination of multi-modal distribution modeling and autonomous sampling control for the design of all-optical communication devices.RAMD provides a high-precision numerical simulation tool for the design of all-optical communication devices,optimization of nonlinear laser devices,and other studies.
文摘The work in this paper is based on primary research on how to obtain informed consent to medical treatment and or procedure among patients;this study was carried out in Papua New Guinea in both urban and rural health settings across customs,cultures,and languages in two provinces,on the basis of qualitative interviews with healthcare professionals including doctors,nurses,other healthcare workers,patients,and traditional healers.We emphasize the views of consent with participants of customs,cultural,and languages regarding informed consent.There are factors between peoples of differing circumstances which can greatly alter how they view consent.Some groups would involve people in the decision-making process that may not traditionally be involved in the decision making of a medical decision.Other groups may dislike certain medical procedures as in Papua New Guinea(PNG).And certain people have different views on what should be disclosed of the patient’s condition.Customs,cultures,and languages are common phenomena which continue to affect the daily lives of many thousands of people.It is unclear in PNG about the characteristics of customs,culture,and language on health care because there is no published information on informed consent and issues that affect the making of informed consent.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12122403 and 12327807).
文摘Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.
基金Supported by the Ministry of Science and Technology of the People's Republic of China(2021ZD0201900)Project 5,No.2021ZD0201905Capital’s Funds for Health Improvement and Research,No.CFH 2022-2-4115.
文摘BACKGROUND Brain-computer interface(BCI)technology is rapidly advancing in psychiatry.Informed consent competency(ICC)assessment among psychiatric patients is a pivotal concern in clinical research.AIM To analyze the assessment of ICC and form a framework with multi-dimensional elements involved in ICC of BCI clinical research among psychiatric disorders.METHODS A systematic review of studies regarding ICC assessments of BCI clinical research in patients with six kinds of psychiatric disorders was conducted.A systematic literature search was performed using PubMed,ScienceDirect,and Web of Science.Peer-reviewed articles and full-text studies were included in the analysis.There were no date restrictions,and all studies published up to February 27,2025,were included.RESULTS A total of 103 studies were selected for this review.Fifty-eight studies included ICC factors,and forty-five were classified in ICC related ethical issues of BCI research in six kinds of psychiatric disorders.Executive function impairment is widely recognized as the most significant factor impacting ICC,and processing speed deficits are observed in schizophrenia,mood disorders,and Alzheimer’s disease.Memory dysfunction,particularly episodic and working memory,contributes to compromised ICC.Five core ethical issues in BCI research should be addressed:BCI specificity,vulnerability,autonomy,dynamic ICC,comprehensiveness,and uncertainty.CONCLUSION A Five-Dimensional evaluative framework,including clinical,ethical,sociocultural,legal,and procedural dimensions,is constructed and proposed for future ICC research in BCI clinical research involving psychiatric disorders.
文摘With the civilization and modernization of human society,occupational health has emerged as a fundamental goal of social justice,as highlighted in the United Nations'Sustainable Development Goals(SDGs)since 2016.Specifically,"SDG Goal 1:No Poverty","SDG 3:Good Health and Well-being",and"SDG 8:Decent Work and Economic Growth",are interconnected with other SDGs to support the pursuit of occupational health.
基金supported by the National Natural Science Foundation of China(Grant Nos.1217211 and 12372244).
文摘Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.
基金supported in part by the National Science and Technology Major Project of China(No.2019-I-0019-0018)the National Natural Science Foundation of China(Nos.61890920,61890921,12302065 and 12172073).
文摘Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.
文摘With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent years.In this paper,physics informed memory networks(PIMNs)are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of PDEs.Unlike the fully connected structure of the PINNs,the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory network.Meanwhile,the PDEs residuals are approximated using difference schemes in the form of convolution filter,which avoids information loss at the neighborhood of the sampling points.Finally,the performance of the PIMNs is assessed by solving the Kd V equation and the nonlinear Schr?dinger equation,and the effects of difference schemes,boundary conditions,network structure and mesh size on the solutions are discussed.Experiments show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.
基金supported by the Researchers Supporting Project number (RSPD2024R526),King Saud University,Riyadh,Saudi Arabi.
文摘Heat transport has been significantly enhanced by the widespread usage of extended surfaces in various engi-neering domains.Gas turbine blade cooling,refrigeration,and electronic equipment cooling are a few prevalent applications.Thus,the thermal analysis of extended surfaces has been the subject of a significant assessment by researchers.Motivated by this,the present study describes the unsteady thermal dispersal phenomena in a wavy fin with the presence of convection heat transmission.This analysis also emphasizes a novel mathematical model in accordance with transient thermal change in a wavy profiled fin resulting from convection using the finite difference method(FDM)and physics informed neural network(PINN).The time and space-dependent governing partial differential equation(PDE)for the suggested heat problem has been translated into a dimensionless form using the relevant dimensionless terms.The graph depicts the effect of thermal parameters on the fin’s thermal profile.The temperature dispersion in the fin decreases as the dimensionless convection-conduction variable rises.The heat dispersion in the fin is decreased by increasing the aspect ratio,whereas the reverse behavior is seen with the time change.Furthermore,FDM-PINN results are validated against the outcomes of the FDM.
文摘The publisher would like to draw the reader's attention to the following errors.Informed consents were not included in the published version of the following articles that appeared in previous issues of Grain&Oil Science and Technology.The authors were contacted after publication to request informed consents for the following articles.The appropriate informed consents,provided by the authors,are included below.
文摘Recent advancements in next generation sequencing have allowed for genetic information become more readily available in the clinical setting for those affected by cancer and by treating clinicians.Given the lack of access to geneticists,medical oncologists and other treating physicians have begun ordering and interpreting genetic tests for individuals with cancer through the process of"mainstreaming".While this process has allowed for quicker access to genetic tests,the process of"mainstreaming"has also brought several challenges including the dissemination of variants of unknown significance results,ordering of appropriate tests,and accurate interpretation of genetic results with appropriate followup testing and interventions.In this editorial,we seek to explore the process of informed consent of individuals before obtaining genetic testing and offer potential solutions to optimize the informed consent process including categorization of results as well as a layered consent model.
基金Supported by The research project,No.MZO 00179906 from the Ministry of Health,Czech Republic
文摘Informed consent is necessary in good clinical practice.It is based on the patient’s ability to understand the information about the proposed procedure,the potential consequences and complications,and alternative options.The information is written in understandable language and is fortified by verbal discussion between physician and patient.The aim is to explain the problem,answer all questions and to ensure that the patient understands the problems and is able to make a decision.The theory is clear but what happens in daily practice?
文摘Since the first publication describing the identification of prostate-specific antigen （PSA） in the 1960s, much progress has been made. The PSA test changed from being initially a monitoring tool to being also used as a diagnostic tool. Over time, the test has been heavily debated due to its lack of sensitivity and specificity. However, up to now the PSA test is still the only biomarker for the detection and monitoring of prostate cancer. PSA-based screening for prostate cancer is associated with a high proportion of unnecessary testing and overdiagnosis with subsequent overtreatment. In the early years of screening for prostate cancer, high rates of uptake were very important. However, over time the opinion on PSA-based screening has shifted towards the notion of informed choice. Nowadays, it is thought to be unethical to screen men without them being aware of the pros and cons of PSA testing, as well as the fact that an informed choice is related to better patient outcomes. Now, as the results of three major screening studies have been presented and the downsides of screening are becoming better understood, informed choice is becoming more relevant.
文摘Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.
文摘This study explores how doctors and patients in Papua New Guinea(PNG)perceive informed consent in medical settings.Doctors and patients from National Capital District and Central Province who responded to the survey were the participants of the study.Researchers asked the participants to fill out questionnaires regarding their knowledge about informed consent for each group,namely,the doctors and participants.From those who responded,six randomly selected participants were chosen to join the focus group discussion which aimed to get experiences from the doctors and patients regarding medical procedures.Results show that both doctors and patients lack knowledge of the legalities of informed consent.Based on the experiences of doctors,they do not use consent forms when seeking permission from patients.Patients,on the other hand,do not see consent forms as important and only served as a formality.Customs,culture surrounding PNG were found to have an impact on how patients perceived informed consent.
