Augustine’s Self Ascension and Intelligible Vision

In Confessions,Augustine positively recollects three ascensions that he had experienced ten years earlier.Searching himself in memory,he makes self an object of rational study and thus manifests that he is still influenced by neo-Platonism even after he had been in the Catholic church for a decade.The ascension of self is in the high part of the soul,which fittingly returns to the Intellect and in turn to the One for an ultimate reunion,since the soul descends from the Intellect which emanates from the One.Among the faculties of the soul,Augustine focuses on memory,which is an internal seeing and hearing.In Christianity,the salvation of a sinner comes from the indwelling Holy Spirit.In contrast to the Enneads by Plotinus,this paper analyzes unsolved problems of Augustine,such as saving faith and a distinction between the intelligible world and the spiritual world.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Recent Advances in Artificial Sensory Neurons:Biological Fundamentals,Devices,Applications,and Challenges

Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.

Smart Gas Sensors:Recent Developments and Future Prospective

Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,etc.With the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated feature.Various electronicand optoelectronic gas sensors have been developed for high-performancesmart gas analysis.With the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas analysis.This review highlights recent advances of smart gassensors in diverse applications.The structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are highlighted.Moreover,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are introduced.Finally,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction

Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.

Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

机器学习在智能反射面辅助的通信系统中的应用综述

智能反射面(intelligent reflecting surfaces,IRS)可以通过大量低成本的无源反射元件巧妙地调整信号反射,从而动态改变无线信道,提高通信性能,目前已成为无线通信研究的焦点。然而,由于IRS的加入,整个通信系统变得更加复杂,系统的动态性也更高,使通信系统面临着许多新的挑战。机器学习(machine learning,ML)具有很强的数据处理与自适应能力,能够不断适应变化的环境和需求,可以很好地应对通信系统中的许多挑战。因此,使用ML解决IRS辅助的通信系统中的问题,已经成为当前研究的重点。基于此,对ML在IRS系统中的应用进行了系统性的概述,从IRS辅助的通信系统中存在的问题入手,分别从反射因子的设计与优化、信道处理与建模、资源分配和管理、安全性增强4个方面对ML在IRS系统中的应用进行阐述和分析,并讨论了将ML应用在IRS系统中的优势及未来的发展趋势与挑战。

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Harnessing artificial intelligence for identifying conflicts of interest in research

This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.

Multimodal artificial intelligence system for detecting a small esophageal high-grade squamous intraepithelial neoplasia: A case report

BACKGROUND Recent advancements in artificial intelligence(AI)have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases.AI has shown great promise in clinical practice,particularly for diagnostic support,offering real-time insights into complex conditions such as esophageal squamous cell carcinoma.CASE SUMMARY In this study,we introduce a multimodal AI system that successfully identified and delineated a small and flat carcinoma during esophagogastroduodenoscopy,highlighting its potential for early detection of malignancies.The lesion was confirmed as high-grade squamous intraepithelial neoplasia,with pathology results supporting the AI system’s accuracy.The multimodal AI system offers an integrated solution that provides real-time,accurate diagnostic information directly within the endoscopic device interface,allowing for single-monitor use without disrupting endoscopist’s workflow.CONCLUSION This work underscores the transformative potential of AI to enhance endoscopic diagnosis by enabling earlier,more accurate interventions.

Recent progress on artificial intelligence-enhanced multimodal sensors integrated devices and systems

Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.

Artificial intelligence and the impact of multiomics on the reporting of case reports

The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.

Artificial intelligence-driven strategies for managing renal and urinary complications in inflammatory bowel disease

In this editorial,we discuss the article by Singh et al published in World Journal of Nephrology,stating the need for timely adjustments in inflammatory bowel disease(IBD)patients'long-term management plans.IBD is chronic and lifelong,with recurrence and remission cycles,including ulcerative colitis and Crohn's disease.It's exact etiology is unknown but likely multifactorial.Related to gut flora and immune issues.Besides intestinal symptoms,IBD can also affect various extrain-testinal manifestations such as those involving the skin,joints,eyes and urinary system.The anatomical proximity of urinary system waste disposal to that of the alimentary canal makes early detection and the differentiation of such symptoms very difficult.Various studies show that IBD and it's first-line drugs have nephro-toxicity,impacting the patients'life quality.Existing guidelines give very few references for kidney lesion monitoring.Singh et al's plan aims to improve treatment management for IBD patients with glomerular filtration rate decline,specifically those at risk.Most of IBD patients are young and they need lifelong therapy.So early therapy cessation,taking into account drug side effects,can be helpful.Artificial intelligence-driven diagnosis and treatment has a big potential for management improvements in IBD and other chronic diseases.

Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky board images and an improved Cascade R-CNN

Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.

Artificial sensory neurons and their applications

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core components of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.

ET-Net:A Novel Framework for Fine-Grained Traffic Classification in Intelligent Vehicle Applications

Intelligent vehicle applications provide convenience but raise privacy and security concerns.Misuse of sensitive data,including vehicle location,and facial recognition information,poses a threat to user privacy.Hence,traffic classification is vital for promptly overseeing and controlling applications with sensitive information.In this paper,we propose ETNet,a framework that combines multiple features and leverages self-attention mechanisms to learn deep relationships between packets.ET-Net employs a multisimilarity triplet network to extract features from raw bytes,and exploits self-attention to capture long-range dependencies within packets in a session and contextual information features.Additionally,we utilizing the loss function to more effectively integrate information acquired from both byte sequences and their corresponding lengths.Through simulated evaluations on datasets with similar attributes,ET-Net demonstrates the ability to finely distinguish between nine categories of applications,achieving superior results compared to existing methods.

Reflections on the Inflection Point in Network Development

The development of communication networks is currently undergoing a period of transformation.This paper illustrates this transformation from the growth rate of communication users,network bandwidth,and service revenue.We also analyze the shift in the focus of network technology development from aspects such as information sources,mobile terminals,wireless channels,core networks,edge clouds,data perception,and artificial intelligence.Finally,we briefly outline the new paradigm for network research and development(R&D)in the intelligent era.

Trends in dentomaxillofacial radiology

Oral and maxillofacial diagnostic imaging is of paramount importance in dental clinical diagnosis,treatment planning,and follow-up procedures.Periapical ra-diographic examination and numerous panoramic systems are used in routine clinical dental practice.Cone beam CT is widely used and currently the method of choice in oral and maxillofacial implantology,endodontics,maxillofacial surgery,periodontics,degenerative temporomandibular joint disease,orthodontics,airway studies,sleep disorders,and forensic dentistry.Another innovative laboratory re-search tool that offers three-dimensional(3D)detailed high-resolution images of in vitro teeth and neighboring structures with submicrometric accuracy is micro-computed tomography.Ultra-high radiation doses,long scanning times,and high costs preclude its routine clinical use.In response to the high demand for a te-chnique that could provide real-time images using a cost-effective,rapid,user-friendly,and portable technique without ionizing radiation,some authors pro-posed ultrasound imaging methods as an alternative to X-ray imaging techniques.Ultrasonography can be used in the dentomaxillofacial region for various diagno-stic purposes such as salivary gland and superficial tissue examination.Recently,dedicated dental magnetic resonance imaging with appropriate software,hard-ware,sequences,and field of view tailored to fit dentomaxillofacial anatomy was introduced.Lately,3D printing technologies and their application in dentistry has attracted attention.During 3D printing a given material is added in successive layers to create a 3D object.The application of this technology has the potential to decrease operation time and minimize operator bias and the possibility of proce-dural errors.Another hot topic regarding dentomaxillofacial radiology is artificial intelligence,which is a field related to computer science dedicated to developing systems or machines that can perform tasks traditionally associated with human intelligence.It is obvious that further investigation and research in the field of dentomaxillofacial radiology will make great contributions to diagnostic imaging for various dental specialties.