The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ...The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.展开更多
This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are d...This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.展开更多
This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a co...This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a comprehensive literature review with case studies of five multinational corporations, allows us to investigate the applications, challenges, and ethical implications of leveraging predictive models in communication strategies. While our findings reveal significant potential for enhancing personalized content delivery, real-time sentiment analysis, and proactive crisis management, we stress the need for careful consideration of challenges such as data privacy concerns and algorithmic bias. This emphasis on ethical implementation is crucial in navigating the complex landscape of predictive analytics in corporate communications. To address these issues, we propose a framework that prioritizes ethical considerations. Furthermore, we identify key areas for future research, thereby contributing to the evolving field of data-driven communication management.展开更多
Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on ...Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.展开更多
The paper reviews some of the major issues that occur in the application of big data analytics and predictive modeling in health, as obtained from the original study. It highlights challenges related to data integrati...The paper reviews some of the major issues that occur in the application of big data analytics and predictive modeling in health, as obtained from the original study. It highlights challenges related to data integration, quality, model interpretability, and clinical relevance. It suggests improvements in terms of hybrid machine learning models, enhanced methods for data preprocessing, and considerations on ethics. In such a way, it is trying to provide a roadmap for future research and practical implementation of predictive analytics in healthcare.展开更多
Modern aquaponic systems can be highly successful,but they require intensive monitoring,control and management.Consequently,the Automation Pyramid(AP)with its layers of Supervisory Control and Data Acquisition(SCADA),...Modern aquaponic systems can be highly successful,but they require intensive monitoring,control and management.Consequently,the Automation Pyramid(AP)with its layers of Supervisory Control and Data Acquisition(SCADA),Enterprise Resource Planning(ERP)and Manufacturing Execution System(MES)is applied for process control.With cloudbased IoT-based Predictive Analytics at the fore marsh,it is worth finding out if IoTwill make these technologies obsolete,or they can work together to gain more beneficial results.In this paper,we will discuss the enhancement of SCADA,ERP and MES with IoT in aquaponics and likewise how IoT-based Predictive Analytics can help to get more out of it.An example use case of an aquaponics project with five demonstration sites in different geographical locations will be presented to show the benefits of IoT on example Predictive Analytics services.Innovative is the collection of data from the five demonstration sites over IoT to make the models of fish,tomatoes,technical components such as filters used for remote monitoring,predictive remote maintenance and economical optimization of the individual plants robust.Robustness of the various models,fish and crop growth models,models for econometric optimization were evaluated using Monte Carlo Simulations revealing as expected the superiority of the IoT-based models.Our analysis suggest that the models are generally tolerant to the temperature coefficient variations of up to 15%and the econometric models tolerated a variation of for example feed ration size for fish of up to 4%and by the energy optimization models a tolerance of up to 14%by variations of solar radiation could be noticed.Furthermore,from the analysis made,it can be concluded that MES has several capabilities which cannot be replaced by IoT such as responsiveness to trigger changes on anomalies.It act as proxy when there is no case for sensors and reliably ensure correct execution in the aquaponics plants.IoT systems can produce unprecedented improvements in many areas but need MES to leverage their true potential and benefits.展开更多
This study investigates the transformative potential of big data analytics in healthcare, focusing on its application for forecasting patient outcomes and enhancing clinical decision-making. The primary challenges add...This study investigates the transformative potential of big data analytics in healthcare, focusing on its application for forecasting patient outcomes and enhancing clinical decision-making. The primary challenges addressed include data integration, quality, privacy issues, and the interpretability of complex machine-learning models. An extensive literature review evaluates the current state of big data analytics in healthcare, particularly predictive analytics. The research employs machine learning algorithms to develop predictive models aimed at specific patient outcomes, such as disease progression and treatment responses. The models are assessed based on three key metrics: accuracy, interpretability, and clinical relevance. The findings demonstrate that big data analytics can significantly revolutionize healthcare by providing data-driven insights that inform treatment decisions, anticipate complications, and identify high-risk patients. The predictive models developed show promise for enhancing clinical judgment and facilitating personalized treatment approaches. Moreover, the study underscores the importance of addressing data quality, integration, and privacy to ensure the ethical application of predictive analytics in clinical settings. The results contribute to the growing body of research on practical big data applications in healthcare, offering valuable recommendations for balancing patient privacy with the benefits of data-driven insights. Ultimately, this research has implications for policy-making, guiding the implementation of predictive models and fostering innovation aimed at improving healthcare outcomes.展开更多
Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transforma...Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.展开更多
AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource managem...AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource management.As the global energy demand is predicted to rise,traditional energy management methods are proved to be inefficient,calling for new,innovative AI-driven solutions.This research unfolds the revolutionary impact of AI in energy optimization,focusing on its modern approaches,most significantly,predictive maintenance and analytics.A notable achievement is reflected by Stem Inc.,whose AI-powered energy storage system reduced its electricity costs by 60%,through predictive analytics of demand-based battery charging and discharging.Additionally,the study also investigates the logic behind AI’s energy optimization methods and AI’s role in crucial sectors like oil extraction,solar energy maintenance,and smart buildings,showcasing its flexibility across various fields.Finally,the study also uncovers a groundbreaking solution to improve AI’s role in energy optimization.Ultimately,this paper highlights the significance of AI in energy optimization and efficiency in the 21st century,the current methods used,and its projected growth and potential in the future.展开更多
Prophylactic fixation(ProFix)of the proximal femur in elderly patients with osteoporosis presents a forward-thinking approach to preventing debilitating fractures and their associated complications.By addressing fract...Prophylactic fixation(ProFix)of the proximal femur in elderly patients with osteoporosis presents a forward-thinking approach to preventing debilitating fractures and their associated complications.By addressing fracture risk before an injury occurs,ProFix has the potential to enhance patient outcomes,promote long-term mobility,and reduce healthcare costs.Early intervention in individuals at high risk can significantly lower hospital admissions,shorten recovery periods,and preserve independence,mitigating challenges such as chronic pain and reduced life expectancy.Given the high prevalence of undiagnosed osteoporosis,prioritising early risk assessment and targeted prevention is essential.Advancements in minimally invasive surgical techniques and safer anaesthesia methods further support ProFix as a feasible and effective strategy to decrease fracturerelated morbidity,improve overall patient well-being,and optimise the use of healthcare resources.This opinion review details the evidence supporting this concept,its efficacy,the challenges in its implementation,and a strategic plan for future implementation.展开更多
Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements to track key performance parameters such as vehicle speed,state of charge(SoC),battery temperature,power consumption,motor RPM,and...Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements to track key performance parameters such as vehicle speed,state of charge(SoC),battery temperature,power consumption,motor RPM,and regenerative braking.While these systems enable real-time data acquisition,they are often hindered by sensor noise,communication delays,andmeasurement uncertainties,which compromise their reliability for critical decision-making.To overcome these limitations,this study introduces a comparative framework that integrates reference signals,a digital twin model emulating ideal system behavior,and real-time IoT measurements.The digital twin provides a predictive and noise-resilient representation of EV dynamics,enabling enhanced monitoring accuracy.Six critical parameters are evaluated using root mean square error(RMSE),mean absolute error(MAE),maximum deviation,and correlation coefficient(R^(2)).Results show that the digital twin significantly improves estimation fidelity,with RMSE for speed reduced from 2.5 km/h(IoT)to 1.2 km/h and R^(2) values generally exceeding 0.99,except for regenerative braking which achieved 0.982.These findings demonstrate the framework’s effectiveness in improving operational safety,energy management,and system reliability,offering a robust foundation for future advancements in adaptive calibration,predictive analytics,and fault detection in EV systems.展开更多
High dropout rates in short-term job skills training programs hinder workforce development.This study applies machine learning to predict program completion while addressing class imbalance challenges.A dataset of6548...High dropout rates in short-term job skills training programs hinder workforce development.This study applies machine learning to predict program completion while addressing class imbalance challenges.A dataset of6548 records with 24 demographic,educational,program-specific,and employment-related features was analyzed.Data preprocessing involved cleaning,encoding categorical variables,and balancing the dataset using the Synthetic Minority Oversampling Technique(SMOTE),as only 15.9% of participants were dropouts.six machine learning models-Logistic Regression,Random Forest,SupportVector Machine,K-Nearest Neighbors,Naive Bayes,and XGBoost-were evaluated on both balanced and unbalanced datasets using an 80-20 train-test split.Performance was assessed using Accuracy,Precision,Recall,F1-score,and ROC-AUC.XGBoost achieved the highest performance on the balanced dataset,with an F1-score of 0.9200 and aROC-AUC of0.9684,followed by Random Forest.These findings highlight the potential of machine learning for early identification of dropout trainees,aiding in retention strategies for workforce training.The results support the integration of predictive analytics to optimize intervention efforts in short-term training programs.展开更多
In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes....In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes.Current techniques for personalized medicine,disease diagnosis,treatment recommendations,and resource optimization in the Internet of Medical Things(IoMT)vary widely,including methods such as rule-based systems,machine learning algorithms,and data-driven approaches.However,many of these techniques face limitations in accuracy,scalability,and adaptability to complex clinical scenarios.This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT.Through the integration of advanced data analytics methodologies with NLP capabilities,we propose a comprehensive framework designed to enhance personalized medicine,streamline disease diagnosis,provide treatment recommendations,and optimize resource allocation.Using a systematic methodology data was collected from open data repositories,then preprocessed using data cleaning,missing value imputation,feature engineering,and data normalization and scaling.Optimization algorithms,such as Gradient Descent,Adam Optimization,and Stochastic Gradient Descent,were employed in the framework to enhance model performance.These were integrated with NLP processes,including Text Preprocessing,Tokenization,and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices.Lastly,through a synthesis of existing research and real-world case studies,we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency.The simulation produced compelling results,achieving an average diagnostic accuracy of 93.5%for the given scenarios,and excelled even further in instances involving rare diseases,achieving an accuracy rate of 98%.With regard to patient-specific treatment plans it generated them with an average precision of 96.7%.Improvements in early risk stratification and enhanced documentation were also noted.Furthermore,the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes,offering insights into risk-mitigating strategies.This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics,with implications for healthcare practitioners,researchers,and policymakers.By leveraging AI and NLP technologies in IoMT environments,this study paves the way for innovative strategies to enhance patient care and operational effectiveness.Ultimately,this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare.展开更多
This comparative review explores the dynamic and evolving landscape of artificial intelligence(AI)-powered innovations within high-tech research and development(R&D).It delves into both theoreticalmodels and pract...This comparative review explores the dynamic and evolving landscape of artificial intelligence(AI)-powered innovations within high-tech research and development(R&D).It delves into both theoreticalmodels and practical applications across a broad range of industries,including biotechnology,automotive,aerospace,and telecom-munications.By examining critical advancements in AI algorithms,machine learning,deep learning models,simulations,and predictive analytics,the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies.The review integrates both qualitative and quantitative data derived from academic studies,industry reports,and real-world case studies to showcase the tangible impacts of AI on product innovation,process optimization,and strategic decision-making.Notably,it discusses the challenges of integrating AI within complex industrial systems,such as ethical concerns,technical limitations,and the need for regulatory oversight.The findings reveal a mixed landscape where AI has significantly accelerated R&D processes,reduced costs,and enabled more precise simulations and predictions,but also highlighted gaps in knowledge transfer,skills adaptation,and cross-industry standardization.By bridging the gap between AI theory and practice,the review offers insights into the effectiveness,successes,and obstacles faced by organizations as they implement AI-driven solutions.Concluding with a forward-looking perspective,the review identifies emerging trends,future challenges,and promising opportunities inAI-poweredR&D,such as the rise of autonomous systems,AI-driven drug discovery,and sustainable energy solutions.It offers a holistic understanding of how AI is shaping the future of technological innovation and provides actionable insights for researchers,engineers,and policymakers involved in high-tech Research and Development(R&D).展开更多
BACKGROUND Gastric cancer is a leading cause of cancer-related deaths worldwide.Prognostic assessments are typically based on the tumor-node-metastasis(TNM)staging system,which does not account for the molecular heter...BACKGROUND Gastric cancer is a leading cause of cancer-related deaths worldwide.Prognostic assessments are typically based on the tumor-node-metastasis(TNM)staging system,which does not account for the molecular heterogeneity of this disease.LATS2,a tumor suppressor gene involved in the Hippo signaling pathway,has been identified as a potential prognostic biomarker in gastric cancer.AIM To construct and validate a nomogram model that includes LATS2 expression to predict the survival prognosis of advanced gastric cancer patients following ra-dical surgery,and compare its predictive performance with traditional TNM staging.METHODS A retrospective analysis of 245 advanced gastric cancer patients from the Fourth Hospital of Hebei Medical University was conducted.The patients were divided into a training group(171 patients)and a validation group(74 patients)to deve-lop and test our prognostic model.The performance of the model was determined using C-indices,receiver operating characteristic curves,calibration plots,and decision curves.RESULTS The model demonstrated a high predictive accuracy with C-indices of 0.829 in the training set and 0.862 in the validation set.Area under the curve values for three-year and five-year survival prediction were significantly robust,suggesting an excellent discrimination ability.Calibration plots confirmed the high concordance between the predictions and actual survival outcomes.CONCLUSION We developed a nomogram model incorporating LATS2 expression,which significantly outperformed conven-tional TNM staging in predicting the prognosis of advanced gastric cancer patients postsurgery.This model may serve as a valuable tool for individualized patient management,allowing for more accurate stratification and im-proved clinical outcomes.Further validation in larger patient cohorts will be necessary to establish its generaliza-bility and clinical utility.展开更多
Objective:Non-small cell lung cancer(NSCLC)patients often experience significant fear of recurrence.To facilitate precise identification and appropriate management of this fear,this study aimed to compare the efficacy...Objective:Non-small cell lung cancer(NSCLC)patients often experience significant fear of recurrence.To facilitate precise identification and appropriate management of this fear,this study aimed to compare the efficacy and accuracy of a Backpropagation Neural Network(BPNN)against logistic regression in modeling fear of cancer recurrence prediction.Methods:Data from 596 NSCLC patients,collected between September 2023 and December 2023 at the Cancer Hospital of the Chinese Academy of Medical Sciences,were analyzed.Nine clinically and statistically significant variables,identified via univariate logistic regression,were inputted into both BPNN and logistic regression models developed on a training set(N=427)and validated on an independent set(N=169).Model performances were assessed using Area Under the Receiver Operating Characteristic(ROC)Curve and Decision Curve Analysis(DCA)in both sets.Results:The BPNN model,incorporating nine selected variables,demonstrated superior performance over logistic regression in the training set(AUC=0.842 vs.0.711,p<0.001)and validation set(0.7 vs.0.675,p<0.001).Conclusion:The BPNN model outperforms logistic regression in accurately predicting fear of cancer recurrence in NSCLC patients,offering an advanced approach for fear assessment.展开更多
This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous m...This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.展开更多
With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry ...With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership （AMP） in 2011 and the National Network for Manufacturing Innovation （NNMI） in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further ＂leverage existing resources... to nurture manufacturing innovation and accelerate commercialization＂ by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10- year plan and roadmap toward manufacturing： Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features： integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing （i2M） systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Intemet of Things and Services （IoTS）, cyber-physical systems （CPSs）, and cloud computing are discussed. Challenges are addressed with applica- tions that are based on commercially available platforms such as General Electric （GE）＇s Predix and PTC＇s ThingWorx.展开更多
The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in o...The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness.We present a first step towards building an artificial intelligence(AI)framework,with predictive analytics(PA)capabilities applied to real patient data,to provide rapid clinical decision-making support.COVID-19 has presented a pressing need as a)clinicians are still developing clinical acumen given the disease’s novelty,and b)resource limitations in a rapidly expanding pandemic require difficult decisions relating to resource allocation.The objectives of this research are:(1)to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes,and(2)to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation.The predictive models learn from historical data to help predict specifically who will develop acute respiratory distress syndrome(ARDS),a severe outcome in COVID-19.Our experimental results based on two hospitals in Wenzhou,Zhejang,China identify features most predictive of ARDS in COVID-19 initial presentation which would not have stood out to clinicians.A mild increase in elevated alanine aminotransferase(ALT)(a liver enzyme)),a presence of myalgias(body aches),and an increase in hemoglobin,in this order,are the clinical features,on presentation,that are the most predictive.Those two centers’COVID-19 case series symptoms on initial presentation can help predict severe outcomes.Predictive models that learned from historical data of patients from two Chinese hospitals achieved 70%to 80%accuracy in predicting severe cases.展开更多
This paper discusses smart body sensor objects （BSOs）, including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive comput...This paper discusses smart body sensor objects （BSOs）, including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive computing and communications capabilities. A few use cases including the relevant privacy and protocol requirements are also presented. General usage and deployment eti-quette along with the relevant regulatory implications are then discussed.展开更多
基金The research project,“Research on Power Safety Assisted Decision System Based on Large Language Models”(Project Number:JSDL24051414020001)acknowledges with gratitude the financial and logistical support it has received.
文摘The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.
文摘This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.
文摘This study explores the integration of predictive analytics in strategic corporate communications, with a specific focus on stakeholder engagement and crisis management. Our mixed-methods approach, which combines a comprehensive literature review with case studies of five multinational corporations, allows us to investigate the applications, challenges, and ethical implications of leveraging predictive models in communication strategies. While our findings reveal significant potential for enhancing personalized content delivery, real-time sentiment analysis, and proactive crisis management, we stress the need for careful consideration of challenges such as data privacy concerns and algorithmic bias. This emphasis on ethical implementation is crucial in navigating the complex landscape of predictive analytics in corporate communications. To address these issues, we propose a framework that prioritizes ethical considerations. Furthermore, we identify key areas for future research, thereby contributing to the evolving field of data-driven communication management.
文摘Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.
文摘The paper reviews some of the major issues that occur in the application of big data analytics and predictive modeling in health, as obtained from the original study. It highlights challenges related to data integration, quality, model interpretability, and clinical relevance. It suggests improvements in terms of hybrid machine learning models, enhanced methods for data preprocessing, and considerations on ethics. In such a way, it is trying to provide a roadmap for future research and practical implementation of predictive analytics in healthcare.
基金The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7-ENV-2013-WATER-INNO-DEMO under Grant agreement N°619137.
文摘Modern aquaponic systems can be highly successful,but they require intensive monitoring,control and management.Consequently,the Automation Pyramid(AP)with its layers of Supervisory Control and Data Acquisition(SCADA),Enterprise Resource Planning(ERP)and Manufacturing Execution System(MES)is applied for process control.With cloudbased IoT-based Predictive Analytics at the fore marsh,it is worth finding out if IoTwill make these technologies obsolete,or they can work together to gain more beneficial results.In this paper,we will discuss the enhancement of SCADA,ERP and MES with IoT in aquaponics and likewise how IoT-based Predictive Analytics can help to get more out of it.An example use case of an aquaponics project with five demonstration sites in different geographical locations will be presented to show the benefits of IoT on example Predictive Analytics services.Innovative is the collection of data from the five demonstration sites over IoT to make the models of fish,tomatoes,technical components such as filters used for remote monitoring,predictive remote maintenance and economical optimization of the individual plants robust.Robustness of the various models,fish and crop growth models,models for econometric optimization were evaluated using Monte Carlo Simulations revealing as expected the superiority of the IoT-based models.Our analysis suggest that the models are generally tolerant to the temperature coefficient variations of up to 15%and the econometric models tolerated a variation of for example feed ration size for fish of up to 4%and by the energy optimization models a tolerance of up to 14%by variations of solar radiation could be noticed.Furthermore,from the analysis made,it can be concluded that MES has several capabilities which cannot be replaced by IoT such as responsiveness to trigger changes on anomalies.It act as proxy when there is no case for sensors and reliably ensure correct execution in the aquaponics plants.IoT systems can produce unprecedented improvements in many areas but need MES to leverage their true potential and benefits.
文摘This study investigates the transformative potential of big data analytics in healthcare, focusing on its application for forecasting patient outcomes and enhancing clinical decision-making. The primary challenges addressed include data integration, quality, privacy issues, and the interpretability of complex machine-learning models. An extensive literature review evaluates the current state of big data analytics in healthcare, particularly predictive analytics. The research employs machine learning algorithms to develop predictive models aimed at specific patient outcomes, such as disease progression and treatment responses. The models are assessed based on three key metrics: accuracy, interpretability, and clinical relevance. The findings demonstrate that big data analytics can significantly revolutionize healthcare by providing data-driven insights that inform treatment decisions, anticipate complications, and identify high-risk patients. The predictive models developed show promise for enhancing clinical judgment and facilitating personalized treatment approaches. Moreover, the study underscores the importance of addressing data quality, integration, and privacy to ensure the ethical application of predictive analytics in clinical settings. The results contribute to the growing body of research on practical big data applications in healthcare, offering valuable recommendations for balancing patient privacy with the benefits of data-driven insights. Ultimately, this research has implications for policy-making, guiding the implementation of predictive models and fostering innovation aimed at improving healthcare outcomes.
文摘Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.
文摘AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource management.As the global energy demand is predicted to rise,traditional energy management methods are proved to be inefficient,calling for new,innovative AI-driven solutions.This research unfolds the revolutionary impact of AI in energy optimization,focusing on its modern approaches,most significantly,predictive maintenance and analytics.A notable achievement is reflected by Stem Inc.,whose AI-powered energy storage system reduced its electricity costs by 60%,through predictive analytics of demand-based battery charging and discharging.Additionally,the study also investigates the logic behind AI’s energy optimization methods and AI’s role in crucial sectors like oil extraction,solar energy maintenance,and smart buildings,showcasing its flexibility across various fields.Finally,the study also uncovers a groundbreaking solution to improve AI’s role in energy optimization.Ultimately,this paper highlights the significance of AI in energy optimization and efficiency in the 21st century,the current methods used,and its projected growth and potential in the future.
文摘Prophylactic fixation(ProFix)of the proximal femur in elderly patients with osteoporosis presents a forward-thinking approach to preventing debilitating fractures and their associated complications.By addressing fracture risk before an injury occurs,ProFix has the potential to enhance patient outcomes,promote long-term mobility,and reduce healthcare costs.Early intervention in individuals at high risk can significantly lower hospital admissions,shorten recovery periods,and preserve independence,mitigating challenges such as chronic pain and reduced life expectancy.Given the high prevalence of undiagnosed osteoporosis,prioritising early risk assessment and targeted prevention is essential.Advancements in minimally invasive surgical techniques and safer anaesthesia methods further support ProFix as a feasible and effective strategy to decrease fracturerelated morbidity,improve overall patient well-being,and optimise the use of healthcare resources.This opinion review details the evidence supporting this concept,its efficacy,the challenges in its implementation,and a strategic plan for future implementation.
文摘Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements to track key performance parameters such as vehicle speed,state of charge(SoC),battery temperature,power consumption,motor RPM,and regenerative braking.While these systems enable real-time data acquisition,they are often hindered by sensor noise,communication delays,andmeasurement uncertainties,which compromise their reliability for critical decision-making.To overcome these limitations,this study introduces a comparative framework that integrates reference signals,a digital twin model emulating ideal system behavior,and real-time IoT measurements.The digital twin provides a predictive and noise-resilient representation of EV dynamics,enabling enhanced monitoring accuracy.Six critical parameters are evaluated using root mean square error(RMSE),mean absolute error(MAE),maximum deviation,and correlation coefficient(R^(2)).Results show that the digital twin significantly improves estimation fidelity,with RMSE for speed reduced from 2.5 km/h(IoT)to 1.2 km/h and R^(2) values generally exceeding 0.99,except for regenerative braking which achieved 0.982.These findings demonstrate the framework’s effectiveness in improving operational safety,energy management,and system reliability,offering a robust foundation for future advancements in adaptive calibration,predictive analytics,and fault detection in EV systems.
文摘High dropout rates in short-term job skills training programs hinder workforce development.This study applies machine learning to predict program completion while addressing class imbalance challenges.A dataset of6548 records with 24 demographic,educational,program-specific,and employment-related features was analyzed.Data preprocessing involved cleaning,encoding categorical variables,and balancing the dataset using the Synthetic Minority Oversampling Technique(SMOTE),as only 15.9% of participants were dropouts.six machine learning models-Logistic Regression,Random Forest,SupportVector Machine,K-Nearest Neighbors,Naive Bayes,and XGBoost-were evaluated on both balanced and unbalanced datasets using an 80-20 train-test split.Performance was assessed using Accuracy,Precision,Recall,F1-score,and ROC-AUC.XGBoost achieved the highest performance on the balanced dataset,with an F1-score of 0.9200 and aROC-AUC of0.9684,followed by Random Forest.These findings highlight the potential of machine learning for early identification of dropout trainees,aiding in retention strategies for workforce training.The results support the integration of predictive analytics to optimize intervention efforts in short-term training programs.
基金the Researchers Supporting Project number(RSP2024R281),King Saud University,Riyadh,Saudi Arabia.
文摘In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes.Current techniques for personalized medicine,disease diagnosis,treatment recommendations,and resource optimization in the Internet of Medical Things(IoMT)vary widely,including methods such as rule-based systems,machine learning algorithms,and data-driven approaches.However,many of these techniques face limitations in accuracy,scalability,and adaptability to complex clinical scenarios.This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT.Through the integration of advanced data analytics methodologies with NLP capabilities,we propose a comprehensive framework designed to enhance personalized medicine,streamline disease diagnosis,provide treatment recommendations,and optimize resource allocation.Using a systematic methodology data was collected from open data repositories,then preprocessed using data cleaning,missing value imputation,feature engineering,and data normalization and scaling.Optimization algorithms,such as Gradient Descent,Adam Optimization,and Stochastic Gradient Descent,were employed in the framework to enhance model performance.These were integrated with NLP processes,including Text Preprocessing,Tokenization,and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices.Lastly,through a synthesis of existing research and real-world case studies,we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency.The simulation produced compelling results,achieving an average diagnostic accuracy of 93.5%for the given scenarios,and excelled even further in instances involving rare diseases,achieving an accuracy rate of 98%.With regard to patient-specific treatment plans it generated them with an average precision of 96.7%.Improvements in early risk stratification and enhanced documentation were also noted.Furthermore,the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes,offering insights into risk-mitigating strategies.This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics,with implications for healthcare practitioners,researchers,and policymakers.By leveraging AI and NLP technologies in IoMT environments,this study paves the way for innovative strategies to enhance patient care and operational effectiveness.Ultimately,this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare.
文摘This comparative review explores the dynamic and evolving landscape of artificial intelligence(AI)-powered innovations within high-tech research and development(R&D).It delves into both theoreticalmodels and practical applications across a broad range of industries,including biotechnology,automotive,aerospace,and telecom-munications.By examining critical advancements in AI algorithms,machine learning,deep learning models,simulations,and predictive analytics,the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies.The review integrates both qualitative and quantitative data derived from academic studies,industry reports,and real-world case studies to showcase the tangible impacts of AI on product innovation,process optimization,and strategic decision-making.Notably,it discusses the challenges of integrating AI within complex industrial systems,such as ethical concerns,technical limitations,and the need for regulatory oversight.The findings reveal a mixed landscape where AI has significantly accelerated R&D processes,reduced costs,and enabled more precise simulations and predictions,but also highlighted gaps in knowledge transfer,skills adaptation,and cross-industry standardization.By bridging the gap between AI theory and practice,the review offers insights into the effectiveness,successes,and obstacles faced by organizations as they implement AI-driven solutions.Concluding with a forward-looking perspective,the review identifies emerging trends,future challenges,and promising opportunities inAI-poweredR&D,such as the rise of autonomous systems,AI-driven drug discovery,and sustainable energy solutions.It offers a holistic understanding of how AI is shaping the future of technological innovation and provides actionable insights for researchers,engineers,and policymakers involved in high-tech Research and Development(R&D).
文摘BACKGROUND Gastric cancer is a leading cause of cancer-related deaths worldwide.Prognostic assessments are typically based on the tumor-node-metastasis(TNM)staging system,which does not account for the molecular heterogeneity of this disease.LATS2,a tumor suppressor gene involved in the Hippo signaling pathway,has been identified as a potential prognostic biomarker in gastric cancer.AIM To construct and validate a nomogram model that includes LATS2 expression to predict the survival prognosis of advanced gastric cancer patients following ra-dical surgery,and compare its predictive performance with traditional TNM staging.METHODS A retrospective analysis of 245 advanced gastric cancer patients from the Fourth Hospital of Hebei Medical University was conducted.The patients were divided into a training group(171 patients)and a validation group(74 patients)to deve-lop and test our prognostic model.The performance of the model was determined using C-indices,receiver operating characteristic curves,calibration plots,and decision curves.RESULTS The model demonstrated a high predictive accuracy with C-indices of 0.829 in the training set and 0.862 in the validation set.Area under the curve values for three-year and five-year survival prediction were significantly robust,suggesting an excellent discrimination ability.Calibration plots confirmed the high concordance between the predictions and actual survival outcomes.CONCLUSION We developed a nomogram model incorporating LATS2 expression,which significantly outperformed conven-tional TNM staging in predicting the prognosis of advanced gastric cancer patients postsurgery.This model may serve as a valuable tool for individualized patient management,allowing for more accurate stratification and im-proved clinical outcomes.Further validation in larger patient cohorts will be necessary to establish its generaliza-bility and clinical utility.
基金Supported by Beijing Hope Run Special Fund of Cancer Foundation of China(LC2022C05).
文摘Objective:Non-small cell lung cancer(NSCLC)patients often experience significant fear of recurrence.To facilitate precise identification and appropriate management of this fear,this study aimed to compare the efficacy and accuracy of a Backpropagation Neural Network(BPNN)against logistic regression in modeling fear of cancer recurrence prediction.Methods:Data from 596 NSCLC patients,collected between September 2023 and December 2023 at the Cancer Hospital of the Chinese Academy of Medical Sciences,were analyzed.Nine clinically and statistically significant variables,identified via univariate logistic regression,were inputted into both BPNN and logistic regression models developed on a training set(N=427)and validated on an independent set(N=169).Model performances were assessed using Area Under the Receiver Operating Characteristic(ROC)Curve and Decision Curve Analysis(DCA)in both sets.Results:The BPNN model,incorporating nine selected variables,demonstrated superior performance over logistic regression in the training set(AUC=0.842 vs.0.711,p<0.001)and validation set(0.7 vs.0.675,p<0.001).Conclusion:The BPNN model outperforms logistic regression in accurately predicting fear of cancer recurrence in NSCLC patients,offering an advanced approach for fear assessment.
文摘This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.
文摘With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership （AMP） in 2011 and the National Network for Manufacturing Innovation （NNMI） in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further ＂leverage existing resources... to nurture manufacturing innovation and accelerate commercialization＂ by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10- year plan and roadmap toward manufacturing： Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features： integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing （i2M） systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Intemet of Things and Services （IoTS）, cyber-physical systems （CPSs）, and cloud computing are discussed. Challenges are addressed with applica- tions that are based on commercially available platforms such as General Electric （GE）＇s Predix and PTC＇s ThingWorx.
文摘The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness.We present a first step towards building an artificial intelligence(AI)framework,with predictive analytics(PA)capabilities applied to real patient data,to provide rapid clinical decision-making support.COVID-19 has presented a pressing need as a)clinicians are still developing clinical acumen given the disease’s novelty,and b)resource limitations in a rapidly expanding pandemic require difficult decisions relating to resource allocation.The objectives of this research are:(1)to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes,and(2)to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation.The predictive models learn from historical data to help predict specifically who will develop acute respiratory distress syndrome(ARDS),a severe outcome in COVID-19.Our experimental results based on two hospitals in Wenzhou,Zhejang,China identify features most predictive of ARDS in COVID-19 initial presentation which would not have stood out to clinicians.A mild increase in elevated alanine aminotransferase(ALT)(a liver enzyme)),a presence of myalgias(body aches),and an increase in hemoglobin,in this order,are the clinical features,on presentation,that are the most predictive.Those two centers’COVID-19 case series symptoms on initial presentation can help predict severe outcomes.Predictive models that learned from historical data of patients from two Chinese hospitals achieved 70%to 80%accuracy in predicting severe cases.
文摘This paper discusses smart body sensor objects （BSOs）, including their networking and internetworking. Smartness can be incorpo-rated into BSOs by embedding virtualization, predictive analytics, and proactive computing and communications capabilities. A few use cases including the relevant privacy and protocol requirements are also presented. General usage and deployment eti-quette along with the relevant regulatory implications are then discussed.
