Force and impulse multi-sensor based on flexible gate dielectric field effect transistor

Nowadays,force sensors play an important role in industrial production,electronic information,medical health,and many other fields.Two-dimensional material-based filed effect transistor(2D-FET)sensors are competitive with nano-level size,lower power consumption,and accurate response.However,few of them has the capability of impulse detection which is a path function,expressing the cumulative effect of the force on the particle over a period of time.Herein we fabricated the flexible polymethyl methacrylate(PMMA)gate dielectric MoS_(2)-FET for force and impulse sensor application.We systematically investigated the responses of the sensor to constant force and varying forces,and achieved the conversion factors of the drain current signals(I_(ds))to the detected impulse(I).The applied force was detected and recorded by I_(ds)with a low power consumption of~30 nW.The sensitivity of the device can reach~8000%and the 4×1 sensor array is able to detect and locate the normal force applied on it.Moreover,there was almost no performance loss for the device as left in the air for two months.

Compositional Grounded Language for Agent Communication in Reinforcement Learning Environment

In a context of constant evolution of technologies for scientific,economic and social purposes,Artificial Intelligence(AI)and Internet of Things(IoT)have seen significant progress over the past few years.As much as Human-Machine interactions are needed and tasks automation is undeniable,it is important that electronic devices(computers,cars,sensors…)could also communicate with humans just as well as they communicate together.The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines,illustrated with chat-bots.Nonetheless,using this technology is not sufficient,as they often give inappropriate or unrelated answers,usually when the subject changes.To improve this technology,the problem of defining a communication language constructed from scratch is addressed,in the intention to give machines the possibility to create a new and adapted exchange channel between them.Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment,the convergence toward a common“language”is analyzed,exactly as it is supposed to have happened for humans in the past.By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality,rapidly converging evolution of syntactic communication is obtained,opening the way of a meaningful language between machines.

ForceCT双能量与低kV扫描技术在头颈CTA中的对比分析

探索并比较Force CT双能量与低kV扫描技术在头颈CTA中的作用。方法 以系统随机化法,将2022.01-2023.01在我院接受头颈CTA检查的40例患者均分为两组各20例。其中观察组患者采用低kV扫描技术,对照组则进行Force CT双能量扫描。比较两组的辐射剂量、图像质量。结果 观察组的辐射剂量指数低于对照组,P<0.05。观察组的大脑中动脉与颈内动脉图像质量均高于对照组,P<0.05。结论 运用低kV扫描技术,能较好地降低头颈CTA检查时的辐射剂量,与此同时还能得到更为高质量的图像,帮助病情的更快诊断,患者能尽快得到个性化的治疗,早日恢复健康。

Force CT高级建模迭代重建算法对门静脉图像质量的影响

目的:探讨Force CT高级建模迭代重建算法(ADMIRE)对门静脉图像质量影响。方法:回顾性分析2022年1—4月于中山大学附属第八医院行上腹部CT平扫及增强的40例患者影像资料,在syngo.via后处理工作站重建,采用门脉期原始数据重建出滤波反投影算法(FBP)、ADMIRE1、ADMIRE3、ADMIRE5 4组不同重建算法的图像,在4组不同算法图像上测量门静脉干中心位置、肝实质及同层竖脊肌的CT值和噪声值,计算门静脉和肝实质的信噪比(SNR)和对比噪声比(CNR)。此外,由两位具有5年以上诊断经验的医生对图像进行主观评分。结果:随着迭代等级的增高,图像的CNR、SNR呈现上升的趋势,图像的噪声值呈现下降的趋势,其中ADMIRE5组图像中门静脉和肝脏具有最高的CNR和SNR且具有最低的噪声值,以及最高的主观评分,采用ADMIRE5重建算法的门静脉与周围组织对比非常好,门静脉5级分支显示良好。结论:高级建模迭代重建ADMIRE算法相比于FBP重建算法,降噪效果更好,门静脉的显示更加锐利,能提升门静脉的图像质量。

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still lacking.Unlike other SLs,the visuals of the Urdu Language are different.This study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this purpose.Unlike existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited resources.We conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and prediction.To enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise filtering.Comparative analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of 0.95.Additionally,our model exhibited superior performance in Precision,Recall,and F1-score evaluations.This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

Comparisons of Wave Force Model Effects on the Structural Responses and Fatigue Loads of a Semi-Submersible Floating Wind Turbine

The selection of wave force models will significantly impact the structural responses of floating wind turbines.In this study,comparisons of wave force model effects on the structural responses and fatigue loads of a semi-submersible floating wind turbine(SFWT)were conducted.Simulations were performed by employing the Morison equation(ME)with linear or second-order wave kinematics and potential flow theory(PFT)with first-or second-order wave forces.A comparison of regular waves,irregular waves,and coupled wind/waves analyses with the experimental data showed that many of the simulation results and experimental data are relatively consistent.However,notable discrepancies are found in the response amplitude operators for platform heave,tower base bending moment,and tension in mooring lines.PFT models give more satisfactory results of heave but more significant discrepan-cies in tower base bending moment than the ME models.In irregular wave analyses,low-frequency resonances were captured by PFT models with second-order difference-frequency terms,and high-frequency resonances were captured by the ME models or PFT models with second-order sum-frequency terms.These force models capture the response frequencies but do not reasonably predict the response amplitudes.The coupled wind/waves analyses showed more satisfactory results than the wave-only analyses.However,an important detail to note is that this satisfactory result is based on the overprediction of wind-induced responses.

Study of the Lift Force Induced by An Interceptor on A High-Speed Mono-Hull:The Affecting Factors and Estimation Formula

To find a better way to estimate the lift force induced by an interceptor on a high-speed mono-hull ship,a series of high-speed mono-hull ship models are designed and investigated under different conditions.Different lift forces are obtained by numerical calculations and validated by a model test in a towing tank.The factors that influence the force are the interceptor height,velocity,draft,and deadrise angle.The relationship between each factor and the induced lift force is investigated and obtained.We found that the induced lift mainly depends on the interceptor height and advancing velocity,and is proportional to the square of the interceptor height and velocity.The results also showed that the effects of the draft and deadrise angle are relatively less important,and the relationship between the induced lift and these two factors is generally linear.Based on the results,a formula including the combined effect of all factors used to estimate the lift force induced by the interceptor is developed based on systematic analysis.The proposed formula could be used to estimate the lift force induced by interceptors,especially under high-speed condition.

Culturally competent care across borders: Implementing culturally responsive teaching for nurses in diverse workforces

The migration of healthcare professionals,including nurses,is a global phenomenon.It is driven by various factors,including the pursuit of better opportunities,living conditions,and professional development,as well as political instability or conflict in their home countries.The World Health Organization(WHO)has noted that high-income countries often rely on foreign-trained nurses to fill gaps in their healthcare systems[1].For instance,as of 2021,over 40%(52 million)of all nurses in the United States(US)were expatriates[2].In the United Kingdom(UK),the percentage of expatriate nurses was even higher,reaching approximately 18%in 2021[3].Owing to globalization and migration,healthcare providers must possess cultural competence to deliver improved care[4,5].Culturally responsive teaching(CRT)is rooted in the idea that culture plays a vital role in shaping people’s behaviors,beliefs,values,and communication styles[6].Furthermore,these cultural factors influence patients’perspectives on health,illness,healing,and their preferences for care and communication[7].By recognizing and embracing these cultural differences,nurses can provide more effective and compassionate care to their diverse patient population[8].

Systematizing Teacher Development:A Review of Foreign Language Teacher Learning

Foreign language teaching practice is developing rapidly,but research on foreign language teacher learning is currently relatively fragmented and unstructured.The book Foreign Language Teacher Learning,written by Professor Kang Yan from Capital Normal University,published in September 2022,makes a systematic introduction to foreign language teacher learning,which to some extent makes up for this shortcoming.Her book presents the lineage of foreign language teacher learning research at home and abroad,analyzes both theoretical and practical aspects,reviews the cuttingedge research results,and foresees the future development trend,painting a complete research picture for researchers in the field of foreign language teaching and teacher education as well as front-line teachers interested in foreign language teacher learning.This is an important inspiration for conducting foreign language teacher learning research in the future.And this paper makes a review of the book from aspects such as its content,major characteristics,contributions and limitations.

Imaging a force field via an optically levitated nanoparticle array

Levitated optomechanical systems represent an excellent candidate platform for force and acceleration sensing.We propose a force-sensing protocol utilizing an optically levitated nanoparticle array.In our scheme,N nanoparticles are trapped in an optical cavity using holographic optical tweezers.An external laser drives the cavity,exciting N cavity modes interacting simultaneously with the N nanoparticles.The optomechanical interaction encodes the information of the force acting on each nanoparticle onto the intracavity photons,which can be detected directly at the output ports of the cavity.Consequently,our protocol enables real-time imaging of a force field.

Self-assembly of perovskite nanocrystals:From driving forces to applications

Self-assembly of metal halide perovskite nanocrystals(NCs)into superlattices can exhibit unique collective properties,which have significant application values in the display,detector,and solar cell field.This review discusses the driving forces behind the self-assembly process of perovskite NCs,and the commonly used self-assembly methods and different self-assembly structures are detailed.Subsequently,we summarize the collective optoelectronic properties and application areas of perovskite superlattice structures.Finally,we conclude with an outlook on the potential issues and future challenges in developing perovskite NCs.

Experimental and numerical simulation study on forced ventilation and dust removal of coal mine heading surface

In order to study the problems of unreasonable airflow distribution and serious dust pollution in a heading surface,an experimental platform for forced ventilation and dust removal was built based on the similar principles.Through the similar experiment and numerical simulation,the distribution of airflow field in the roadway and the spatial and temporal evolution of dust pollution under the conditions of forced ventilation were determined.The airflow field in the roadway can be divided into three zones:jet zone,vortex zone and reflux zone.The dust concentration gradually decreases from the head to the rear of the roadway.Under the forced ventilation conditions,there is a unilateral accumulation of dust,with higher dust concentrations away from the ducts.The position of the equipment has an interception effect on the dust.The maximum error between the test value and the simulation result is 12.9%,which verifies the accuracy of the experimental results.The research results can provide theoretical guidance for the application of dust removal technology in coal mine.

Electric field and force characteristic of dust aerosol particles on the surface of high-voltage transmission line

High-voltage transmission lines play a crucial role in facilitating the utilization of renewable energy in regions prone to desertification. The accumulation of atmospheric particles on the surface of these lines can significantly impact corona discharge and wind-induced conductor displacement. Accurately quantifying the force exerted by particles adhering to conductor surfaces is essential for evaluating fouling conditions and making informed decisions. Therefore, this study investigates the changes in electric field intensity along branched conductors caused by various fouling layers and their resulting influence on the adhesion of dust particles. The findings indicate that as individual particle size increases, the field strength at the top of the particle gradually decreases and eventually stabilizes at approximately 49.22 k V/cm, which corresponds to a field strength approximately 1.96 times higher than that of an unpolluted transmission line. Furthermore,when particle spacing exceeds 15 times the particle size, the field strength around the transmission line gradually decreases and approaches the level observed on non-adhering surface. The electric field remains relatively stable. In a triangular arrangement of three particles, the maximum field strength at the tip of the fouling layer is approximately 1.44 times higher than that of double particles and 1.5 times higher compared to single particles. These results suggest that particles adhering to the transmission line have a greater affinity for adsorbing charged particles. Additionally, relevant numerical calculations demonstrate that in dry environments, the primary adhesion forces between particles and transmission lines follow an order of electrostatic force and van der Waals force. Specifically, at the minimum field strength, these forces are approximately74.73 times and 19.43 times stronger than the gravitational force acting on the particles.

Distributed Dynamic Load in Structural Dynamics by the Impulse-Based Force Estimation Algorithm

This paper proposes a novel approach for identifying distributed dynamic loads in the time domain.Using polynomial andmodal analysis,the load is transformed intomodal space for coefficient identification.This allows the distributed dynamic load with a two-dimensional form in terms of time and space to be simultaneously identified in the form of modal force,thereby achieving dimensionality reduction.The Impulse-based Force Estimation Algorithm is proposed to identify dynamic loads in the time domain.Firstly,the algorithm establishes a recursion scheme based on convolution integral,enabling it to identify loads with a long history and rapidly changing forms over time.Secondly,the algorithm introduces moving mean and polynomial fitting to detrend,enhancing its applicability in load estimation.The aforementioned methodology successfully accomplishes the reconstruction of distributed,instead of centralized,dynamic loads on the continuum in the time domain by utilizing acceleration response.To validate the effectiveness of the method,computational and experimental verification were conducted.

Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

Post-Traumatic Stress Disorder among Defence and Security Forces in Northern Benin (2023)

Introduction: Post-traumatic stress disorder (PTSD) is defined as “actual exposure to death or the threat of death, serious injury or sexual violence”, either directly or indirectly, resulting in a symptomatic procession of repetition, avoidance, neurovegetative hyperactivity and individualized symptoms, with or without negative cognitive and mood changes. It therefore goes without saying that the defence and security forces constitute a high-risk population in need of attention. Objective: To study post-traumatic stress disorder in defence and security forces in the city of Parakou in 2023. Methods: Descriptive cross-sectional study conducted from December 2022 to July 2023. The study population consisted of active military, republican police and firefighters in the city of Parakou in 2023. Non-proportional stratified sampling was used, given the inaccessibility of the source population size for national security reasons. Post-traumatic stress disorder was assessed using the “post-traumatic stress disorder checklist-5 (PCLS-5) scale. Results: A total of 305 subjects participated in the survey. Males dominated 90.2%. The most represented corps was the Republican Police (41.6%), most of whom were non-commissioned officers (46.6%). The majority count between 11 and 20 years of service (48.9%), with 2 to 5 missions completed (67.5%). The calculated prevalence of post-traumatic stress disorder was 11.8%, based on the post-traumatic stress disorder checklist-5 (PCL-5). Of the 36 respondents with post-traumatic stress disorder, 20 (55.6%) had experienced an armed attack, 25 (69.4%) had witnessed a violent death, 18 (50.0%) had witnessed the agony of a colleague, 15 (41.7%) had been exposed to a fire or explosion, while 26 (72.2%) had been traumatized by physical and/or verbal aggression. 5 (13.9%) had consulted a specialist psychiatrist, while 6 (16.7%) were on medication and 26 (72.2%) used sport as a means of maintaining physical and mental health. Respectively 22 (61.1%) and 21 (58.3%) had definite symptoms of anxiety and depression. Multivariate analysis revealed a significant association between post-traumatic stress disorder and the following variables: total number of children ≤ 2 (p = 0.015), comorbidities such as arterial hypertension (p = 0.007), history of hepatitis (p = 0.017), work accidents (p = 0.016), alcohol dependence (p = 0.004), domestic violence (p = 0.004), psychological violence (p = 0.017) and anxiety disorders (p Conclusion: Defence and security personnel can also be prey to post-traumatic stress disorder (PTSD), which needs to be systematically taken into account when they are subjected to trauma in the course of their duties. Mental health should be an integral part of the periodic medical check-up objectives for defence and security forces throughout the country.

Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.

Visualizing Language and Aging From 2013-2022

Declining cognitive abilities can be a concomitant of advanced age.As language is closely associated with cognitive abilities,changes in language abilities can be an important marker of changes in cognitive abilities.The current study is to review cognitive studies of language and aging by first identifying and exploring the major clusters and pivotal articles and then detecting emerging trends.Data of 3,266 articles on language and aging from 2013 to 2022 were collected from the Web of Science Core Collection database.Adopting Document Co-citation Analysis,Freeman’s betweenness centrality metric(Freeman,2002)and Kleinberg’s burst detection algorithm(Kleinberg,2002),we explored major clusters,pivotal articles and emerging trends in this field.Cognition appears to be the most remarkable cluster.Bilingualism,speech production,listening effort,and reading comprehension are other major active clusters in a certain period.The most recent active cluster concerns the studies of Alzheimer’s disease.Articles serving as pivotal points concentrate on cognitive studies of the Framework for Understanding Effortful Listening(FUEL),the new Ease of Language Understanding model(EUL)and a hierarchical multi-representational generative framework of language comprehension.The progress in statistical methods,the relationship between language and cognitive impairment and the relationship between language abilities and cognition are the emerging trends.These emerging trends will provide some insights into how cognitive abilities influence language abilities in aging.