In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternati...In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives （FPISA） and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment.展开更多
Quantitative real-time PCR(qPCR)is widely used for gene expression analysis,but its accuracy critically depends on stable internal reference genes for normalization.In marine invertebrates,especially non-model taxa su...Quantitative real-time PCR(qPCR)is widely used for gene expression analysis,but its accuracy critically depends on stable internal reference genes for normalization.In marine invertebrates,especially non-model taxa such as cephalopods,systematic evaluation of reference genes is limited,leading to potential bias.The cuttlefish Sepiella japonica is ecologically and economically important in China,yet previous molecular studies have often relied on single unvalidated reference genes,which may compromise data reliability.This study aimed to systematically evaluate the stability of five commonly used reference genes(18S,ef-1α,ef-1γ,gapdh,andβ-actin)across multiple tissues and sexes of S.japonica,and to identify the most suitable reference genes and optimal number for qPCR normalization.Fifteen to sixteen tissue types were collected from ten healthy adults(five males and five females).Total RNA was extracted,reverse-transcribed,and analyzed by qPCR.Gene stability was assessed using four algorithms(geNorm,NormFinder,BestKeeper,andΔCt)integrated with RefFinder,and the optimal gene number was determined using geNorm pairwise variation(V_(n/n+1)<0.15).Four transcriptome-derived genes(creld2,cd109,acy1,and miox)were used for validation.The C_(t)values of the five genes ranged from 15.47 to 20.83.β-actin and gapdh showed pronounced variability in expression stability among tissues and sexes,indicating their limited suitability for normalization.18S exhibited the highest expression(mean C_(t):15.47-16.29)and lowest variability but displayed sex-biased expression,whereas ef-1αand ef-1γremained consistently stable across most tissues in both sexes,with ef-1αbeing the most robust and showing no sex-related bias.Although specific rankings varied among tissues and sexes,the comprehensive results indicated that ef-1αand ef-1γpossessed the highest overall stability,followed by 18S,whileβ-actin and gapdh were the least stable.The final comprehensive rankings were ef-1γ>ef-1α>18S>gapdh>β-actin(male)and ef-1α>ef-1γ>18S>gapdh>β-actin(female).geNorm analysis(V2/3<0.15)indicated that two genes,mainly ef-1αand ef-1γ,were generally sufficient for reliable normalization in most tissues.Validation confirmed that normalization using the stable ef-1αand ef-1γaccurately reflected the expression differences among tissues,whereasβ-actin and gapdh can bias or confound statistical analyses.ef-1αand ef-1γare identified as the most reliable reference gene combination for qPCR analysis in S.japonica,while 18S can serve as an auxiliary gene for within-sex comparisons.The use ofβ-actin or gapdh alone is not recommended.This study establishes a systematic framework for selecting reliable reference genes in S.japonica,thereby facilitating robust qPCR normalization and providing a foundation for future gene expression research in S.japonica and other cephalopods.展开更多
WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstr...WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstrate a method to schedule the magnitude of the reference input to achieve a faster response.展开更多
Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a fr...Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration.展开更多
Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effect...Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.展开更多
With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,exist...With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration.展开更多
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the ...A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making （MCGDM） and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.展开更多
Based on the analyses of existing preference group decision-making(PGDM)methods with intuitionistic fuzzy preference relations(IFPRs),we present a new PGDM framework with incomplete IFPRs.A generalized multiplicative ...Based on the analyses of existing preference group decision-making(PGDM)methods with intuitionistic fuzzy preference relations(IFPRs),we present a new PGDM framework with incomplete IFPRs.A generalized multiplicative consistent for IFPRs is defined,and a mathematical programming model is constructed to supplement the missing values in incomplete IFPRs.Moreover,in this study,another mathematical programming model is constructed to improve the consistency level of unacceptably multiplicative consistent IFPRs.For group decisionmaking(GDM)with incomplete IFPRs,three reliable sources influencing the weights of experts are identified.Subsequently,a method for determining the weights of experts is developed by simultaneously considering three reliable sources.Furthermore,a targeted consensus process(CPR)is developed in this study with reference to the actual situation of the consensus level of each IFPR.Meanwhile,in response to the proposed multiplicative consistency definition,a novel method for determining the optimal priority weights of alternatives is redefined.Lastly,based on the above theory,a novel GDM method with incomplete IFPRs is developed,and the comparative and sensitivity analysis results demonstrate the utility and superiority of this work.展开更多
To better reflect the psychological behavior characteristics of loss aversion,this paper builds a double reference point decision making method for dynamic multi-attribute decision-making(DMADM)problem,taking bottom-l...To better reflect the psychological behavior characteristics of loss aversion,this paper builds a double reference point decision making method for dynamic multi-attribute decision-making(DMADM)problem,taking bottom-line and target as reference pints.First,the gain/loss function is given,and the state is divided according to the relationship between the gain/loss value and the reference point.Second,the attitude function is constructed based on the results of state division to establish the utility function.Third,the comprehensive utility value is calculated as the basis for alternatives classification and ranking.Finally,the new method is used to evaluate the development level of smart cities.The results show that the new method can judge the degree to which the alternatives meet the requirements of the decision-maker.While the new method can effectively screen out the unsatisfactory alternatives,the ranking results of other alternatives are consistent with those of traditional methods.展开更多
The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a pati...The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of life.By integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments accordingly.The deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment plans.Moreover,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health conditions.However,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and providers.Overcoming these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of PROMs.The paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.展开更多
Bamboo was one of the first plants to be cultivated in China and is widely used in industry and daily life.The study of gene function has become an important part of bamboo breeding,whereas quantitative real-time PCR(...Bamboo was one of the first plants to be cultivated in China and is widely used in industry and daily life.The study of gene function has become an important part of bamboo breeding,whereas quantitative real-time PCR(qRT-PCR)is a powerful tool for gene expression analysis.The accuracy of qRT-PCR results largely depends on suitable reference genes.In this study,a transcriptome-wide identification of reference genes was conducted based on 447 transcriptome datasets,comprising 200 tissue samples,107 treated samples,and 140 samples from various moso bamboo(Phyllostachys edulis)forms.A total of 3444,1013,and 3962 stably expressed genes were identified from these three groups,respectively.Functional enrichment analysis revealed significant enrichment of these genes in pathways,including the spliceosome,proteasome,and oxidative phosphorylation.Eight candidate genes(ADPRE,GAPDH,TRX,TUBA,NRP,MBF,UNK,and CAM1),were selected for qRT-PCR validation using 112 samples.To assess their stability,five statistical methods(geNorm,NormFinder,BestKeeper,Delta-Ct,and RefFinder)were employed.The most suitable reference genes were ADPRE and GAPDH for different tissues,GAPDH and CAM1 for different treatments,and GAPDH and TRX for various moso bamboo forms.Overall,ADPRE and GAPDH were the most stable reference genes across all conditions,while TUBA and TRX were the least stable reference genes.In addition,a significant negative correlation was found between the Ct values of RT-qPCR and the log2TPM values from the transcriptome data(Ct=-1.534x+37.221),providing a potential method for estimating gene expression levels.The identified reference genes,particularly ADPRE and GAPDH,provide a robust set of references for gene expression studies in moso bamboo.展开更多
To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforce...To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.展开更多
This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode sche...This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode scheme with chopped op-amps and internal clock generators to eliminate op-amp offset.A low dropout regulator(LDO)and a pre-regula-tor enhance output driving and LS,respectively.Curvature compensation enhances the TC by addressing higher-order nonlinear-ity.These approaches,effective near room temperature,employs trimming at both 20 and 60°C.When combined with fixed cur-vature correction currents,it achieves an ultra-low TC for each chip.Implemented in a CMOS 180 nm process,the BGR occu-pies 0.548 mm²and operates at 2.5 V with 84μA current draw from a 5 V supply.An average TC of 2.69 ppm/℃ with two-point trimming and 0.81 ppm/℃ with multi-point trimming are achieved over the temperature range of-40 to 125℃.It accommo-dates a load current of 1 mA and an LS of 42 ppm/V,making it suitable for precise BMS applications.展开更多
Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pP...Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
Background:Multiparametric magnetic resonance imaging(mpMRI)has significantly advanced prostate cancer(PCa)detection,yet decisions on invasive biopsy with moderate prostate imaging reporting and data system(PI-RADS)sc...Background:Multiparametric magnetic resonance imaging(mpMRI)has significantly advanced prostate cancer(PCa)detection,yet decisions on invasive biopsy with moderate prostate imaging reporting and data system(PI-RADS)scores remain ambiguous.Methods:To explore the decision-making capacity of Generative Pretrained Transformer-4(GPT-4)for automated prostate biopsy recommendations,we included 2299 individuals who underwent prostate biopsy from 2018 to 2023 in 3 large medical centers,with available mpMRI before biopsy and documented clinical-histopathological records.GPT-4 generated structured reports with given prompts.The performance of GPT-4 was quantified using confusion matrices,and sensitivity,specificity,as well as area under the curve were calculated.Multiple artificial evaluation procedures were conducted.Wilcoxon’s rank sum test,Fisher’s exact test,and Kruskal-Wallis tests were used for comparisons.Results:Utilizing the largest sample size in the Chinese population,patients with moderate PI-RADS scores(scores 3 and 4)accounted for 39.7%(912/2299),defined as the subset-of-interest(SOI).The detection rates of clinically significant PCa corresponding to PI-RADS scores 2-5 were 9.4%,27.3%,49.2%,and 80.1%,respectively.Nearly 47.5%(433/912)of SOI patients were histopathologically proven to have undergone unnecessary prostate biopsies.With the assistance of GPT-4,20.8%(190/912)of the SOI population could avoid unnecessary biopsies,and it performed even better[28.8%(118/410)]in the most heterogeneous subgroup of PI-RADS score 3.More than 90.0%of GPT-4-generated reports were comprehensive and easy to understand,but less satisfied with the accuracy(82.8%).GPT-4 also demonstrated cognitive potential for handling complex problems.Additionally,the Chain of Thought method enabled us to better understand the decision-making logic behind GPT-4.Eventually,we developed a ProstAIGuide platform to facilitate accessibility for both doctors and patients.Conclusions:This multi-center study highlights the clinical utility of GPT-4 for prostate biopsy decision-making and advances our understanding of the latest artificial intelligence implementation in various medical scenarios.展开更多
BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpfu...BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpful in uncertain situations is clinical judgment.Clinicians must deal with contradictory information,lack of time to make decisions,and long-term factors when emergencies occur.AIM To examine the ethical issues healthcare professionals faced during the coronavirus disease 2019(COVID-19)pandemic and the factors affecting clinical decision-making.METHODS This pilot study,which means it was a preliminary investigation to gather information and test the feasibility of a larger investigation was conducted over 6 months and we invited responses from clinicians worldwide who managed patients with COVID-19.The survey focused on topics related to their professional roles and personal relationships.We examined five core areas influencing critical care decision-making:Patients'personal factors,family-related factors,informed consent,communication and media,and hospital administrative policies on clinical decision-making.The collected data were analyzed using the χ^(2) test for categorical variables.RESULTS A total of 102 clinicians from 23 specialties and 17 countries responded to the survey.Age was a significant factor in treatment planning(n=88)and ventilator access(n=78).Sex had no bearing on how decisions were made.Most doctors reported maintaining patient confidentiality regarding privacy and informed consent.Approximately 50%of clinicians reported a moderate influence of clinical work,with many citing it as one of the most important factors affecting their health and relationships.Clinicians from developing countries had a significantly higher score for considering a patient's financial status when creating a treatment plan than their counterparts from developed countries.Regarding personal experiences,some respondents noted that treatment plans and preferences changed from wave to wave,and that there was a rapid turnover of studies and evidence.Hospital and government policies also played a role in critical decision-making.Rather than assessing the appropriateness of treatment,some doctors observed that hospital policies regarding medications were driven by patient demand.CONCLUSION Factors other than medical considerations frequently affect management choices.The disparity in treatment choices,became more apparent during the pandemic.We highlight the difficulties and contradictions between moral standards and the realities physicians encountered during this medical emergency.False information,large patient populations,and limited resources caused problems for clinicians.These factors impacted decision-making,which,in turn,affected patient care and healthcare staff well-being.展开更多
Introduction: Childbirth on a scarred uterus is a major issue for health centers, especially peripheral, due to the major obstetric risks it presents. The objectives were to evaluate the frequency, route of delivery a...Introduction: Childbirth on a scarred uterus is a major issue for health centers, especially peripheral, due to the major obstetric risks it presents. The objectives were to evaluate the frequency, route of delivery and maternal-fetal prognosis of this type of delivery at csref of Kolondiéba. Materials and Methods: This was a retrospective cross-sectional study for one year (1 January 2023-31 December 2023). All patients admitted to the maternity ward of the center with at least one uterine scar and treated in the center were included. We extracted data from partograms, OR records, birth records and obstetric records. Input was done on Excel 2010 and analysis on SPSS.23. Results: The frequency of scarring uterus was 16.8% (217/1285 births). The average age was 27. Pauciparous were most represented (59%). Patients were received from community health centers (44.7%). Prenatal consultation sessions (1 - 3 sessions) were performed at (64.9%). Uterine scars were obstetric in (99%). The cesarean section was performed immediately in (59.4%), it was prophylactic in 17%. The uterine test was attempted in (25.34%) with (69%) success. We recorded 3.6% uterine ruptures, 8.7% postoperative complications, 5.5% stillbirths and one maternal death (0.46%). Conclusion: Births on a scarred uterus are frequent and associated with a high rate of complications.展开更多
Computed tomography is an indispensable X-ray imaging modality used to diagnose numerous pathologies, but it can also involve the delivery of high ionizing radiation doses harmful to the health of patients. This study...Computed tomography is an indispensable X-ray imaging modality used to diagnose numerous pathologies, but it can also involve the delivery of high ionizing radiation doses harmful to the health of patients. This study aims to survey the level of radiation doses delivered to child patients during head exams in CT imaging to set up the Dosimetric Reference Levels (DRLs), a routine dose optimization tool, based on data acquired at the University Hospital of Angré (UHA), the University Hospital of Treichville (UHT) and the Polyclinic Hospital Farah (Farah) for optimizing procedures in Ivorian hospitals. Prospectively performed on 334 CT images of 186 child patients, this study was carried out on CT systems such as Hitachi Scenaria, Sinovision Insitum, and Philips Incisive used respectively at UHA, UHT and Farah. Children’s scan data were classified into four age bands: vol or dose-length product as DLP) value, whatever the hospital, increases with respect to the age of child patients. Based on the 75th percentile of the whole dose distributions, the DRLs of the CTDIvol is 54.37 mGy whatever the age groups and those of the DLP with respect to age bands are 1224.55 mGy∙cm, 1414.06 mGy∙cm, 1632.24 mGy.cm and 1544.57 mGy∙cm, respectively. The averaged values of CTDIvol and DLP smaller than the corresponding DRLs values suggest that practices in our three facilities are optimized. However, comparing our results with those from different international studies, we see that the CTDIvol and DLP values obtained in the present work are higher. These results suggest additional surveys to ensure our DRLs values and efforts from radiologists, imaging technicians and medical physicists to strengthen clinical procedures for the radiation protection of children undergoing CT scans in Côte d’Ivoire.展开更多
文摘In presented fuzzy multi-attribute decision-making （FMADM） problems, the information about attribute weights is interval numbers and the decision maker （DM） has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives （FPISA） and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment.
文摘Quantitative real-time PCR(qPCR)is widely used for gene expression analysis,but its accuracy critically depends on stable internal reference genes for normalization.In marine invertebrates,especially non-model taxa such as cephalopods,systematic evaluation of reference genes is limited,leading to potential bias.The cuttlefish Sepiella japonica is ecologically and economically important in China,yet previous molecular studies have often relied on single unvalidated reference genes,which may compromise data reliability.This study aimed to systematically evaluate the stability of five commonly used reference genes(18S,ef-1α,ef-1γ,gapdh,andβ-actin)across multiple tissues and sexes of S.japonica,and to identify the most suitable reference genes and optimal number for qPCR normalization.Fifteen to sixteen tissue types were collected from ten healthy adults(five males and five females).Total RNA was extracted,reverse-transcribed,and analyzed by qPCR.Gene stability was assessed using four algorithms(geNorm,NormFinder,BestKeeper,andΔCt)integrated with RefFinder,and the optimal gene number was determined using geNorm pairwise variation(V_(n/n+1)<0.15).Four transcriptome-derived genes(creld2,cd109,acy1,and miox)were used for validation.The C_(t)values of the five genes ranged from 15.47 to 20.83.β-actin and gapdh showed pronounced variability in expression stability among tissues and sexes,indicating their limited suitability for normalization.18S exhibited the highest expression(mean C_(t):15.47-16.29)and lowest variability but displayed sex-biased expression,whereas ef-1αand ef-1γremained consistently stable across most tissues in both sexes,with ef-1αbeing the most robust and showing no sex-related bias.Although specific rankings varied among tissues and sexes,the comprehensive results indicated that ef-1αand ef-1γpossessed the highest overall stability,followed by 18S,whileβ-actin and gapdh were the least stable.The final comprehensive rankings were ef-1γ>ef-1α>18S>gapdh>β-actin(male)and ef-1α>ef-1γ>18S>gapdh>β-actin(female).geNorm analysis(V2/3<0.15)indicated that two genes,mainly ef-1αand ef-1γ,were generally sufficient for reliable normalization in most tissues.Validation confirmed that normalization using the stable ef-1αand ef-1γaccurately reflected the expression differences among tissues,whereasβ-actin and gapdh can bias or confound statistical analyses.ef-1αand ef-1γare identified as the most reliable reference gene combination for qPCR analysis in S.japonica,while 18S can serve as an auxiliary gene for within-sex comparisons.The use ofβ-actin or gapdh alone is not recommended.This study establishes a systematic framework for selecting reliable reference genes in S.japonica,thereby facilitating robust qPCR normalization and providing a foundation for future gene expression research in S.japonica and other cephalopods.
文摘WE observe that the response speed of a linear timeinvariant system to a step reference input depends not only on the system parameters but also on the magnitude of the step input.Based on this observation,we demonstrate a method to schedule the magnitude of the reference input to achieve a faster response.
文摘Background:Despite the promise shown by large language models(LLMs)for standardized tasks,their multidimensional performance in real-world oncology decision-making remains unevaluated.This study aims to introduce a framework for evaluating LLMs and physician decisions in challenging lung cancer cases.Methods:We curated 50 challenging lung cancer cases(25 local and 25 published)classified as complex,rare,or refractory.Blinded three-dimensional,five-point Likert evaluations(1–5 for comprehensiveness,specificity,and readability)compared standalone LLMs(DeepSeek R1,Claude 3.5,Gemini 1.5,and GPT-4o),physicians by experience level(junior,intermediate,and senior),and AI-assisted juniors;intergroup differences and augmentation effects were analyzed statistically.Results:Of 50 challenging cases(18 complex,17 rare,and 15 refractory)rated by three experts,DeepSeek R1 achieved scores of 3.95±0.33,3.71±0.53,and 4.26±0.18 for comprehensiveness,specificity,and readability,respectively,positioning it between intermediate(3.68,3.68,3.75)and senior(4.50,4.64,4.53)physicians.GPT-4o and Claude 3.5 reached intermediate physician–level comprehensiveness(3.76±0.39,3.60±0.39)but junior-to-intermediate physician–level specificity(3.39±0.39,3.39±0.49).All LLMs scored higher on rare cases than intermediate physicians but fell below junior physicians in refractory-case specificity.AIassisted junior physicians showed marked gains in rare cases,with comprehensiveness rising from 2.32 to 4.29(84.8%),specificity from 2.24 to 4.26(90.8%),and readability from 2.76 to 4.59(66.0%),while specificity declined by 3.2%(3.17 to 3.07)in refractory cases.Error analysis showed complementary strengths,with physicians demonstrating reasoning stability and LLMs excelling in knowledge updating and risk management.Conclusions:LLMs performed variably in clinical decision-making tasks depending on case type,performing better in rare cases and worse in refractory cases requiring longitudinal reasoning.Complementary strengths between LLMs and physicians support case-and task-tailored human–AI collaboration.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R259)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.Ashit Kumar Dutta would like to thank AlMaarefa University for supporting this research under project number MHIRSP2025017.
文摘Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.
基金support of the National Key Research and Development Plan(No.2021YFB3302501)the financial support of the National Science Foundation of China(No.12161076)the financial support of the Fundamental Research Funds for the Central Universities(No.DUT25GF207).
文摘With the rapid development of artificial intelligence,intelligent air combat maneuver decision-making(ACMD)has garnered global attention.Although deep reinforcement learning provides a promising approach to ACMD,existing methods often suffer from rigid reward functions and limited adaptability to evolving adversarial strategies.Moreover,most research assumes open airspace,overlooking the influence of potential obstacles.In this paper,we address one-on-one within-visual-range ACMD in obstructed environments,and propose an improved Soft Actor-Critic(SAC)algorithm trained under a curriculum self-play framework.A maneuver strategy mirroring inference module is integrated to estimate each other's likely positions when visual obstruction occurs.By leveraging curriculum learning to guide progressive experience accumulation and self-play for adversarial evolution,our method enhances both training efficiency and tactical diversity.We further integrate an attention mechanism that dynamically adjusts the weights of sub-rewards,enabling the learned policy to adapt to rapidly changing air combat situations.Numerical simulations demonstrate that our enhanced SAC converges more quickly and achieves higher win rates than other baseline methods.An animation is available at bilibili.com/video/BV1BHVszHE98 for better illustration.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
基金supported by the National Natural Science Foundation of China (60904059 60975049)+1 种基金the Philosophy and Social Science Foundation of Hunan Province (2010YBA104)the National High Technology Research and Development Program of China (863 Program)(2009AA04Z107)
文摘A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making （MCGDM） and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.
基金supported by the National Natural Science Foundation of China(Nos.71740021,11861034,and 61966030)the Humanities Social Science Programming Project of Ministry of Education of China(No.20YJA630059)+1 种基金the Natural Science Foundation of Jiangxi Province of China(No.20192BAB207012)the Natural Science Foundation of Qinghai Province of China(No.2019-ZJ-7086).
文摘Based on the analyses of existing preference group decision-making(PGDM)methods with intuitionistic fuzzy preference relations(IFPRs),we present a new PGDM framework with incomplete IFPRs.A generalized multiplicative consistent for IFPRs is defined,and a mathematical programming model is constructed to supplement the missing values in incomplete IFPRs.Moreover,in this study,another mathematical programming model is constructed to improve the consistency level of unacceptably multiplicative consistent IFPRs.For group decisionmaking(GDM)with incomplete IFPRs,three reliable sources influencing the weights of experts are identified.Subsequently,a method for determining the weights of experts is developed by simultaneously considering three reliable sources.Furthermore,a targeted consensus process(CPR)is developed in this study with reference to the actual situation of the consensus level of each IFPR.Meanwhile,in response to the proposed multiplicative consistency definition,a novel method for determining the optimal priority weights of alternatives is redefined.Lastly,based on the above theory,a novel GDM method with incomplete IFPRs is developed,and the comparative and sensitivity analysis results demonstrate the utility and superiority of this work.
基金supported in part by the National Natural Science Foundation of China under Grant 62003379Natural Science Foundation of Guangdong Province under Grant 2018A030313317+3 种基金Special Research Project on the Prevention and Control of COVID-19 Epidemic in Colleges and Universities of Guangdong under Grant 2020KZDZX1118Guangzhou Science and Technology Program under Grant 202002030246Research Project and Development Plan for Key Areas of Guangdong Province under Grant 2020B0202080002Guangzhou Key Research Base of Humanities and Social Sciences(Research Center of Agricultural Products Circulation in Guangdong-Hong Kong-Macao Greater Bay Area).
文摘To better reflect the psychological behavior characteristics of loss aversion,this paper builds a double reference point decision making method for dynamic multi-attribute decision-making(DMADM)problem,taking bottom-line and target as reference pints.First,the gain/loss function is given,and the state is divided according to the relationship between the gain/loss value and the reference point.Second,the attitude function is constructed based on the results of state division to establish the utility function.Third,the comprehensive utility value is calculated as the basis for alternatives classification and ranking.Finally,the new method is used to evaluate the development level of smart cities.The results show that the new method can judge the degree to which the alternatives meet the requirements of the decision-maker.While the new method can effectively screen out the unsatisfactory alternatives,the ranking results of other alternatives are consistent with those of traditional methods.
文摘The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of life.By integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments accordingly.The deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment plans.Moreover,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health conditions.However,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and providers.Overcoming these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of PROMs.The paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFD2200502)the National Natural Science Foundation of China(Grant No.31971736).
文摘Bamboo was one of the first plants to be cultivated in China and is widely used in industry and daily life.The study of gene function has become an important part of bamboo breeding,whereas quantitative real-time PCR(qRT-PCR)is a powerful tool for gene expression analysis.The accuracy of qRT-PCR results largely depends on suitable reference genes.In this study,a transcriptome-wide identification of reference genes was conducted based on 447 transcriptome datasets,comprising 200 tissue samples,107 treated samples,and 140 samples from various moso bamboo(Phyllostachys edulis)forms.A total of 3444,1013,and 3962 stably expressed genes were identified from these three groups,respectively.Functional enrichment analysis revealed significant enrichment of these genes in pathways,including the spliceosome,proteasome,and oxidative phosphorylation.Eight candidate genes(ADPRE,GAPDH,TRX,TUBA,NRP,MBF,UNK,and CAM1),were selected for qRT-PCR validation using 112 samples.To assess their stability,five statistical methods(geNorm,NormFinder,BestKeeper,Delta-Ct,and RefFinder)were employed.The most suitable reference genes were ADPRE and GAPDH for different tissues,GAPDH and CAM1 for different treatments,and GAPDH and TRX for various moso bamboo forms.Overall,ADPRE and GAPDH were the most stable reference genes across all conditions,while TUBA and TRX were the least stable reference genes.In addition,a significant negative correlation was found between the Ct values of RT-qPCR and the log2TPM values from the transcriptome data(Ct=-1.534x+37.221),providing a potential method for estimating gene expression levels.The identified reference genes,particularly ADPRE and GAPDH,provide a robust set of references for gene expression studies in moso bamboo.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFE0117100)National Science Foundation of China(Grant No.52102468,52325212)Fundamental Research Funds for the Central Universities。
文摘To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62204235。
文摘This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode scheme with chopped op-amps and internal clock generators to eliminate op-amp offset.A low dropout regulator(LDO)and a pre-regula-tor enhance output driving and LS,respectively.Curvature compensation enhances the TC by addressing higher-order nonlinear-ity.These approaches,effective near room temperature,employs trimming at both 20 and 60°C.When combined with fixed cur-vature correction currents,it achieves an ultra-low TC for each chip.Implemented in a CMOS 180 nm process,the BGR occu-pies 0.548 mm²and operates at 2.5 V with 84μA current draw from a 5 V supply.An average TC of 2.69 ppm/℃ with two-point trimming and 0.81 ppm/℃ with multi-point trimming are achieved over the temperature range of-40 to 125℃.It accommo-dates a load current of 1 mA and an LS of 42 ppm/V,making it suitable for precise BMS applications.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University(QU-APC-2024-9/1).
文摘Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
基金supported by the Beijing Key Clinical Specialty Project(20240930)the National Natural Science Foundation of China(NSFC 82373436)+7 种基金the Beijing Hospitals Authority’Youth Program(BHAYP,QML20230114)the Beijing Natural Science Foundation(BNSF Z200027)the Beijing Chaoyang Hospital Multi-disciplinary Team Program(CYDXK202204),the NSFC(62331001)the BNSF(Z200027)the NSFC(82202097)the BHAYP(QML20230113)the Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University(CCMU2022ZKYXY010)the Beijing Scholars Program(No.[2015]160).
文摘Background:Multiparametric magnetic resonance imaging(mpMRI)has significantly advanced prostate cancer(PCa)detection,yet decisions on invasive biopsy with moderate prostate imaging reporting and data system(PI-RADS)scores remain ambiguous.Methods:To explore the decision-making capacity of Generative Pretrained Transformer-4(GPT-4)for automated prostate biopsy recommendations,we included 2299 individuals who underwent prostate biopsy from 2018 to 2023 in 3 large medical centers,with available mpMRI before biopsy and documented clinical-histopathological records.GPT-4 generated structured reports with given prompts.The performance of GPT-4 was quantified using confusion matrices,and sensitivity,specificity,as well as area under the curve were calculated.Multiple artificial evaluation procedures were conducted.Wilcoxon’s rank sum test,Fisher’s exact test,and Kruskal-Wallis tests were used for comparisons.Results:Utilizing the largest sample size in the Chinese population,patients with moderate PI-RADS scores(scores 3 and 4)accounted for 39.7%(912/2299),defined as the subset-of-interest(SOI).The detection rates of clinically significant PCa corresponding to PI-RADS scores 2-5 were 9.4%,27.3%,49.2%,and 80.1%,respectively.Nearly 47.5%(433/912)of SOI patients were histopathologically proven to have undergone unnecessary prostate biopsies.With the assistance of GPT-4,20.8%(190/912)of the SOI population could avoid unnecessary biopsies,and it performed even better[28.8%(118/410)]in the most heterogeneous subgroup of PI-RADS score 3.More than 90.0%of GPT-4-generated reports were comprehensive and easy to understand,but less satisfied with the accuracy(82.8%).GPT-4 also demonstrated cognitive potential for handling complex problems.Additionally,the Chain of Thought method enabled us to better understand the decision-making logic behind GPT-4.Eventually,we developed a ProstAIGuide platform to facilitate accessibility for both doctors and patients.Conclusions:This multi-center study highlights the clinical utility of GPT-4 for prostate biopsy decision-making and advances our understanding of the latest artificial intelligence implementation in various medical scenarios.
文摘BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpful in uncertain situations is clinical judgment.Clinicians must deal with contradictory information,lack of time to make decisions,and long-term factors when emergencies occur.AIM To examine the ethical issues healthcare professionals faced during the coronavirus disease 2019(COVID-19)pandemic and the factors affecting clinical decision-making.METHODS This pilot study,which means it was a preliminary investigation to gather information and test the feasibility of a larger investigation was conducted over 6 months and we invited responses from clinicians worldwide who managed patients with COVID-19.The survey focused on topics related to their professional roles and personal relationships.We examined five core areas influencing critical care decision-making:Patients'personal factors,family-related factors,informed consent,communication and media,and hospital administrative policies on clinical decision-making.The collected data were analyzed using the χ^(2) test for categorical variables.RESULTS A total of 102 clinicians from 23 specialties and 17 countries responded to the survey.Age was a significant factor in treatment planning(n=88)and ventilator access(n=78).Sex had no bearing on how decisions were made.Most doctors reported maintaining patient confidentiality regarding privacy and informed consent.Approximately 50%of clinicians reported a moderate influence of clinical work,with many citing it as one of the most important factors affecting their health and relationships.Clinicians from developing countries had a significantly higher score for considering a patient's financial status when creating a treatment plan than their counterparts from developed countries.Regarding personal experiences,some respondents noted that treatment plans and preferences changed from wave to wave,and that there was a rapid turnover of studies and evidence.Hospital and government policies also played a role in critical decision-making.Rather than assessing the appropriateness of treatment,some doctors observed that hospital policies regarding medications were driven by patient demand.CONCLUSION Factors other than medical considerations frequently affect management choices.The disparity in treatment choices,became more apparent during the pandemic.We highlight the difficulties and contradictions between moral standards and the realities physicians encountered during this medical emergency.False information,large patient populations,and limited resources caused problems for clinicians.These factors impacted decision-making,which,in turn,affected patient care and healthcare staff well-being.
文摘Introduction: Childbirth on a scarred uterus is a major issue for health centers, especially peripheral, due to the major obstetric risks it presents. The objectives were to evaluate the frequency, route of delivery and maternal-fetal prognosis of this type of delivery at csref of Kolondiéba. Materials and Methods: This was a retrospective cross-sectional study for one year (1 January 2023-31 December 2023). All patients admitted to the maternity ward of the center with at least one uterine scar and treated in the center were included. We extracted data from partograms, OR records, birth records and obstetric records. Input was done on Excel 2010 and analysis on SPSS.23. Results: The frequency of scarring uterus was 16.8% (217/1285 births). The average age was 27. Pauciparous were most represented (59%). Patients were received from community health centers (44.7%). Prenatal consultation sessions (1 - 3 sessions) were performed at (64.9%). Uterine scars were obstetric in (99%). The cesarean section was performed immediately in (59.4%), it was prophylactic in 17%. The uterine test was attempted in (25.34%) with (69%) success. We recorded 3.6% uterine ruptures, 8.7% postoperative complications, 5.5% stillbirths and one maternal death (0.46%). Conclusion: Births on a scarred uterus are frequent and associated with a high rate of complications.
文摘Computed tomography is an indispensable X-ray imaging modality used to diagnose numerous pathologies, but it can also involve the delivery of high ionizing radiation doses harmful to the health of patients. This study aims to survey the level of radiation doses delivered to child patients during head exams in CT imaging to set up the Dosimetric Reference Levels (DRLs), a routine dose optimization tool, based on data acquired at the University Hospital of Angré (UHA), the University Hospital of Treichville (UHT) and the Polyclinic Hospital Farah (Farah) for optimizing procedures in Ivorian hospitals. Prospectively performed on 334 CT images of 186 child patients, this study was carried out on CT systems such as Hitachi Scenaria, Sinovision Insitum, and Philips Incisive used respectively at UHA, UHT and Farah. Children’s scan data were classified into four age bands: vol or dose-length product as DLP) value, whatever the hospital, increases with respect to the age of child patients. Based on the 75th percentile of the whole dose distributions, the DRLs of the CTDIvol is 54.37 mGy whatever the age groups and those of the DLP with respect to age bands are 1224.55 mGy∙cm, 1414.06 mGy∙cm, 1632.24 mGy.cm and 1544.57 mGy∙cm, respectively. The averaged values of CTDIvol and DLP smaller than the corresponding DRLs values suggest that practices in our three facilities are optimized. However, comparing our results with those from different international studies, we see that the CTDIvol and DLP values obtained in the present work are higher. These results suggest additional surveys to ensure our DRLs values and efforts from radiologists, imaging technicians and medical physicists to strengthen clinical procedures for the radiation protection of children undergoing CT scans in Côte d’Ivoire.
