Providing Global Awareness in Distributed Dynamic Systems

The paper investigates applicability of the developed high-level model and technology for solution of diverse problems in large distributed dynamic systems which can provide sufficient awareness of their structures,organization,and functionalities.After the review of meanings of awareness and existing approaches for its expression and support,the paper shows application of the Spatial Grasp Model and Technology(SGT)and its basic Spatial Grasp Language(SGL)for very practical awareness solutions in large distributed dynamic systems,with obtaining any knowledge from any point inside or outside the system.The self-evolving,self-replicating,and self-recovering scenario code in SGL can effectively supervise distributed systems under any circumstances including rapidly changing number of their elements.Examples are provided in SGL for distributed networked systems showing how in any node any information about other nodes and links,including the whole system,can be obtained by using network requesting patterns based on recursive scenarios combining forward and backward network matching and coverage.The returned results may be automatically organized in networked patterns too.The presented exemplary solutions are parallel and fully distributed,without the need of using vulnerable centralized resources,also very compact.This can be explained by fundamentally different philosophy and ideology of SGT which is not based on traditional partitioned systems representation and multiple agent communications.On the contrary,SGT and its basic language supervise and control distributed systems by holistic self-spreading recursive code in wavelike,virus-like,and even“soul-like”mode.

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.

Promoting Sickle Cell Trait Awareness and Education: A Typology of Interventions in the United States to Inform Ongoing Efforts to Patients and Providers

Research Background: Sickle cell trait has no treatment or cure and predominantly affects people who are Black, but can affect anyone of any race or ethnicity. While commonly incorrectly considered benign by providers and the public, people with a sickle cell trait experience life-threatening outcomes that are exacerbated by extreme conditions. There is a severe lack of awareness and understanding of sickle cell trait and the associated health complications among sickle cell trait carriers and healthcare providers. Purpose/Aim: Interventions that aim to improve awareness of sickle cell trait differ in approaches and are not well documented in the literature. This typology aims to highlight current efforts to inform targeted interventions that raise awareness through consistent messaging, educate people and providers on sickle cell trait and the related health complications, and support the design and implementation of comprehensive sickle cell trait awareness initiatives. Methods: We conducted a scoping review of United States-based sickle cell trait interventions and performed a content analysis to identify the categories and characteristics of these efforts. We then organized the results into a typology according to established protocols. Results: Among 164 interventions, twenty-five (15%) met the typology inclusion criteria described above and were grouped into categories: Seven of twenty-five interventions were Educational Interventions (28%), three of twenty-five interventions (12%) were Combined Screening and Educational-Based Interventions, eight of twenty-five interventions (32%) were Policy and Guideline-Based Intervention, and six of twenty-five interventions (24%) were Sickle Cell Trait Organization-Led Interventions. Conclusions: There is a lack of consistency in messaging across interventions whether delivered by credible healthcare institutions or national organizations, which can result in lack of education and awareness and confusion around sickle cell trait. Categorizing interventions through a typology allows clarity and informs consistency in messaging, which should be at the forefront of future sickle cell trait efforts.

Prevalence, Awareness and Risk Factors for Hypertension in Adults Attending a Tertiary Hospital in South-East Nigeria

Hypertension, a non-communicable disease, is considered a major public health challenge because of its widespread prevalence globally coupled with its huge morbidity and mortality burden, which is largely preventable if early detection and prompt initiation of management are done. Hypertension prevalence is increasing especially in the developing world, despite this, its awareness among the general population is low. This study aimed at determining the prevalence of hypertension among adult attendees of the General Outpatient Clinic of the Federal University Teaching Hospital (FUTH), Owerri, with an assessment of the proportion of hypertensives who were aware of their hypertensive status, and identifying risk factors of hypertension in the study participants. A cross-sectional analytical study was conducted between October and November 2022 at the General Outpatient Clinic of the FUTH, Owerri. A total of 257 consenting and eligible adult patients made up of 135 males and 122 females, aged 18 years and above, were selected by systematic random sampling method. The overall prevalence of hypertension was 34.6%. The prevalence was higher in females than in males (37.7% vs 31.9%, P = 0.325). Among the hypertensive subjects 56.2% had awareness of their hypertensive status. Following a multiple regression analysis, hypertension was independently associated with age, family history of hypertension, occupation (retirees, traders, farmers and the unemployed), and marital status (being widowed). Hypertension is prevalent in our environment;the prevalence rate from this study is higher than in most studies in our environment, suggesting possibly, a rising burden. The results from the study underscore the need for increased and sustained advocacy for implementation of policies and programs directed at increased detection and management of hypertension in the different population groups such as annual wellness check for employees in the formal sector, largescale dietary and lifestyle adjustments, and know your numbers (an approach to population driven blood pressure check for all adults). Also, health workers should use any opportunity of contact with a patient to screen for hypertension.

Household Solid Waste Awareness and Practices among Residents of Windhoek, Namibia

This study explores household solid waste management (HSWM) practices and awareness among residents of Windhoek West, a rapidly urbanizing constituency in the Khomas Region of Namibia. Employing a descriptive methodology, the research investigates the interplay between public awareness, regulatory frameworks, and the availability of waste management facilities to assess their impact on waste management behaviors. Our findings indicate significant gaps in both knowledge and infrastructure that hinder effective waste management. The study reveals that while there is a high willingness among residents to engage in recycling and waste reduction, actual practices are limited due to inadequate facilities and lack of stringent enforcement of waste policies. This research identifies key factors that influence waste management practices, including demographic characteristics and access to waste management facilities. It also proposes actionable strategies such as expanding recycling and sorting facilities, enhancing educational campaigns tailored to local needs, and implementing regular enforcement mechanisms. These strategies are aimed at improving compliance with waste management protocols and fostering a culture of environmental responsibility. The results of this investigation show the critical role of ongoing education and infrastructural improvement in bridging existing knowledge gaps and facilitating effective waste management practices. This research lays a foundational step toward enhancing sustainable urban development and effective waste management in Windhoek, providing valuable insights for policymakers, community leaders, and stakeholders engaged in urban environmental management.

Breast cancer awareness in reproductive women in the low-and middle-income countries:a scoping review

Objective:This study aimed to assess breast cancer(BC)awareness among reproductive women in low-and middle-income countries(LMICs),identify influencing factors,and propose feasible interventions or programs.Methods:We followed a 5-step process using a modified version of Arksey and O’Malley framework methodology.A comprehensive search was conducted on the Embase,PubMed,and CINAHL electronic databases for literature published within 10 years(from 2012 to 2022).Results:Thirty-three papers published between 2012 and 2020,spanning 18 countries,were included.Of these,45.6%described a good level of knowledge,while 24.2%reported that women at reproductive ages had good awareness.Twelve influencing factors were identified in 3 categories:socio-demographic(family history,personal history,marital status,age,religion,income status,living place,and occupation),personal(self-efficacy,education,and perceived level),and external(advertisements promoting awareness).Educational programs were recommended in most(>72%)of the included studies.Conclusions:While most studies reported high levels of knowledge and awareness,some found low prevalence among certain groups.Factors affecting knowledge and awareness were classified into socio-demographic,personal,and external categories,with socio-demographic factors such as age,education,income,and marital status being the most frequently cited.The studies recommended implementing educational programs,health prevention strategies,and social interventions to increase BC knowledge and awareness.

Research on Enhancing Students’Intercultural Awareness and Competence in English Teaching

With the deepening of globalization,intercultural communication has become an important part of people’s daily lives and work.English,as an international language,plays a vital role in intercultural communication.However,simple language knowledge can no longer meet the needs of modern society.Students need to have intercultural awareness and competence to better adapt to the development of globalization.Therefore,it is particularly important to improve students’intercultural awareness and competence in English teaching.This paper will discuss how to improve students’intercultural awareness and competence in English teaching,which includes three parts.The first part introduces the definition of intercultural awareness and competence.The second part explores the importance of intercultural awareness and competence to students,which can help students better understand and respect different cultures,stimulate students’innovative thinking and creativity,and cultivate critical thinking skills,and is also of great significance to students’professional development,personal growth,and social responsibility.The third part discusses the effective methods and strategies to improve students’intercultural awareness and competence in teaching,including carrying out cultural discussion and debate,introducing diverse materials,creating intercultural experience teaching activities,and using virtual learning tools.To a certain extent,this study can enrich the theoretical research in the field of intercultural awareness and competence,help to improve students’intercultural awareness and competence,and provide certain practical guidance for educators to teach intercultural courses.

Effect of Health Management in Raising Awareness Rate of Disease Prevention and Treatment in Patients with Prehypertension

Objective: To analyze the effect of health management on improving the awareness rate of disease prevention and treatment in patients with prehypertension, so as to provide guidance for clinical management of patients with prehypertension. Methods: 108 patients diagnosed with prehypertension in our hospital were divided into a control group and an experimental group. The control group was not given management measures, while the experimental group was given health management. The incidence of hypertension and cognition level of hypertension knowledge were compared between the two groups after management. Results: The incidence of hypertension in the experimental group was 7.41% lower than that in the control group 29.63%. The cognitive level of hypertension in the patients (66.54 ± 1.25) was significantly higher than that in the patients without health management (41.45 ± 2.45), and P < 0.05;Conclusion: For patients with prehypertension, the implementation of health management is helpful to improve their cognition of hypertension, master related prevention knowledge, and reduce the incidence of hypertension.

采用世界卫生组织AWaRe工具促进我国抗菌药物的合理使用

本文介绍世界卫生组织推荐的AWaRe工具,包括AWaRe抗菌药物分级目录和AWaRe抗菌药物使用手册,以促进我国抗菌药物的合理使用。

Knowledge and awareness of human mpox infection among healthcare workers:A cross-sectional study in southwestern Nigeria

Objective:To identify the knowledge and awareness level of human mpox viral infection among healthcare workers in southwestern Nigeria.Methods:A cross-sectional study was conducted in Ekiti State,southwest Nigeria among 316 healthcare workers that were selected through a systematic random sampling.Data were collected with the aid of a semi-structured,self-administered questionnaire.The Chi-square test and binary logistic regression were used to find the association between the independent and dependent variables.The significance level was set at P-value<0.05.Results:Two hundred and twenty-two(70.3%)of the respondents were aged≤40 years,mean age(36±9)years,189(59.8%)were female,306(96.8%)were Christians,and 203(64.2%)were married.Three hundred and fourteen(99.4%)of the respondents were aware of mpox infection.Main sources of information about mpox were medical education(44.0%),radio/television(32.0%)and newspaper(21.0%).However,among those aware of the disease,209(67.0%)demonstrated poor knowledge levels.Longer than 5 years’experience of medical practice was the only significant predictor of higher knowledge level of the disease(OR 1.76,95%CI 1.01-3.06;P=0.046).Conclusions:Despite the high awareness level of mpox infection among healthcare workers,there still exists a huge knowledge gap.It is recommended that targeted intervention could be directed towards continuous medical education and simulation exercises on re-emerging infectious diseases like mpox to improve the knowledge of the healthcare workers.

Improving myopia awareness via school-based myopia prevention health education among Chinese students

AIM:To investigate the myopia awareness level,knowledge,attitude,and skills at baseline and to implement and evaluate the efficacy of myopia prevention health education among Chinese students.METHODS:A total of 1000 middle school students from 2 middle schools were invited to participate in the study,and myopia prevention health education was conducted.The students were assessed at baseline,followed by a survey.The efficacy of health education was evaluated using the self-comparison method pre-and post-health education.RESULTS:The study included 957 and 850 pre-and post-health education par ticipants,respectively.The baseline knowledge of all respondents on myopic symptoms(87.5%),myopia is a risk of eyes(72.9%),myopia prevention(91.3%),myopia increases with age(86.7%),performing periodic eye examinations(92.8%),and one first,one foot,and one inch(84.8%)significantly increased after health education(P<0.001 for all).However,the percentage of students who still did not think it necessary to take breaks after 30-40min of continuous near work was 27.0%.The opinion that“myopia can be cured”was still present in 38.3%.CONCLUSION:Implementing school-based myopia prevention health education improves knowledge,attitudes,and skills regarding myopia among Chinese middle school students.

Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

Awareness and perspectives of audiologists on assessment and management of misophonia in India

Background:The assessment and management of misophonia need a team approach,and audiologists are essential team members.However,the role of an audiologist in this condition is not well understood,and there is a lack of awareness even among professionals about their role in the assessment and management of misophonia.Purpose:The main aim of our study is to document the present level of awareness and knowledge about misophonia assessment and management among audiologists in India.Methods:A descriptive cross-sectional study was carried out among audiologists from all over India.Descriptive statistical procedures were measured based on the type of questions being addressed,and a non-parametric chi-square test was done to see the association among variables.Results:The results show a lack of knowledge about misophonia even among audiologists,as only 15.3%of the audiologist reported being confident in handling cases with misophonia.Conclusion:Although the exact assessment and management of misophonia is still the topic of debate,it is clear that audiologists are the team's key members.However,the results clearly show a lack of confidence in handling cases of misophonia among audiologists in India.This result shows the future need for Research in misophonia from an audiological perspective.

Reduced differential transform and Sumudu transform methods for solving fractional financial models of awareness

In that paper,we new study has been carried out on previous studies of one of the most important mathematical models that describe the global economic movement,and that is described as a non-linear fractional financial model of awareness,where the studies are represented at the steps following:One:The schematic of the model is suggested.Two:The disease-free equilibrium point(DFE)and the stability of the equilibrium point are discussed.Three:The stability of the model is fulfilled by drawing the Lyapunov exponents and Poincare map.Fourth:The existence of uniformly stable solutions have discussed.Five:The Caputo is described as the fractional derivative.Six:Fractional optimal control for NFFMA is discussed by clarifying the fractional optimal control through drawing before and after control.Seven:Reduced differential transform method(RDTM)and Sumudu Decomposition Method(SDM)are used to take the resolution of an NFFMA.Finally,we display that SDM and RDTM are highly identical.

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Live Virtual Machine(VM)migration is one of the foremost techniques for progressing Cloud Data Centers’(CDC)proficiency as it leads to better resource usage.The workload of CDC is often dynamic in nature,it is better to envisage the upcoming workload for early detection of overload status,underload status and to trigger the migration at an appropriate point wherein enough number of resources are available.Though various statistical and machine learning approaches are widely applied for resource usage prediction,they often failed to handle the increase of non-linear CDC data.To overcome this issue,a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory(CDB-LSTM)model is proposed.The CDB-LSTM adopts Helly property of Hypergraph and Savitzky–Golay(SG)filter to select informative samples and exclude noisy inference&outliers.The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computa-tional complexity during live VM migration.Further,the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration.A Hypergraph based CDB-LSTM was vali-dated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.

Context Awareness by Noise-Pattern Analysis of a Smart Factory

Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.

Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.

Spatial Multi-Presence System to Increase Security Awareness for Remote Collaboration in an Extended Reality Environment

Enhancing the sense of presence of participants is an important issue in terms of security awareness for remote collaboration in extended reality.However,conventional methods are insufficient to be aware of remote situations and to search for and control remote workspaces.This study pro-poses a spatial multi-presence system that simultaneously provides multiple spaces while rapidly exploring these spaces as users perform collaborative work in an extended reality environment.The proposed system provides methods for arranging and manipulating remote and personal spaces by creating an annular screen that is invisible to the user.The user can freely arrange remote participants and their workspaces on the annular screen.Because users can simultaneously view various spaces arranged on the annular screen,they can perform collaborative work while simultaneously feeling the presence of multiple spaces and can be fully immersed in a specific space.In addition,the personal spaces where users work can also be arranged through the annular screen.According to the results of the performance evaluations,users participating in remote collaborative works can visualize the spaces of multiple users simultaneously and feel their presence,thereby increasing their understanding of the spaces.Moreover,the proposed system reduces the time to perform tasks and gain awareness of the emergency in remote workspaces.

PCATNet: Position-Class Awareness Transformer for Image Captioning

Existing image captioning models usually build the relation between visual information and words to generate captions,which lack spatial infor-mation and object classes.To address the issue,we propose a novel Position-Class Awareness Transformer(PCAT)network which can serve as a bridge between the visual features and captions by embedding spatial information and awareness of object classes.In our proposal,we construct our PCAT network by proposing a novel Grid Mapping Position Encoding(GMPE)method and refining the encoder-decoder framework.First,GMPE includes mapping the regions of objects to grids,calculating the relative distance among objects and quantization.Meanwhile,we also improve the Self-attention to adapt the GMPE.Then,we propose a Classes Semantic Quantization strategy to extract semantic information from the object classes,which is employed to facilitate embedding features and refining the encoder-decoder framework.To capture the interaction between multi-modal features,we propose Object Classes Awareness(OCA)to refine the encoder and decoder,namely OCAE and OCAD,respectively.Finally,we apply GMPE,OCAE and OCAD to form various combinations and to complete the entire PCAT.We utilize the MSCOCO dataset to evaluate the performance of our method.The results demonstrate that PCAT outperforms the other competitive methods.