Robust Admissibility and Stabilization of Uncertain Singular Fractional-Order Linear Time-Invariant Systems

This paper addresses the robust admissibility problem in singular fractional-order continuous time systems. It is based on new admissibility conditions of singular fractional-order systems expressed in a set of strict linear matrix inequalities(LMIs). Then, a static output feedback controller is designed for the uncertain closed-loop system to be admissible. Numerical examples are given to illustrate the proposed methods.

Admissibility analysis and control synthesis for switched linear singular systems

The problem of admissibility analysis and control synthesis of discrete-time switched linear singular （SLS） systems for arbitrary switching laws is solved. By using the switched Lyapunov function approach, some new sufficient conditions under which the SLS system is admissible for arbitrary switching laws are derived in terms of linear matrix inequalities （LMIs）. Based on the admissibility results, control synthesis is then to design switched state feedback and static output feedback controllers, guaranteeing that the resulting closed-loop system is admissible. The presented results can be viewed as the extensions of previous works on switched Lyapunov function approach from the regular switched systems to singular switched cases. Examples are provided to demonstrate the reduced conservatism and effectiveness of the proposed conditions.

Admissibility of Bayes estimate with inaccurate prior in surveying adjustment

Based on the concept of admissibility in statistics, a definition of generalized admissibility of Bayes estimates has been given at first, which was with inaccurate prior for the application in surveying adjustment. Then according to the definition, the generalized admissibility of the normal linear Bayes estimate with the inaccurate prior information that contains deviations or model errors, as well as how to eliminate the effect of the model error on the Bayes estimate in surveying adjustment were studied. The results show that if the prior information is not accurate, that is, it contains model error, the generalized admissibility can explain whether the Bayes estimate can be accepted or not. For the case of linear normal Bayes estimate, the Bayes estimate can be made generally admissible by giving a less prior weight if the prior information is inaccurate. Finally an example was given.

ADMISSIBILITY OF LINEAR ESTIMATORS IN A GROWTH CURVE MODEL SUBJECT TO AN INCOMPLETE ELLIPSOIDAL RESTRICTION

This article considers the admissibility of the linear estimators for the regression coefficients in the growth curve model subject to an incomplete ellipsoidal restriction. The necessary and sufficient conditions for linear estimators to be admissible in classes of the homogeneous and non-homogeneous linear estimators, respectively, are obtained under the quadratic loss function. They are generalizations of some existing results in literature.

ADMISSIBILITY IN THE GENERAL GROWTH CURVE MODEL WITH RESPECT TO RESTRICTED PARAMETER SETS UNDER MATRIX LOSS FUNCTION

In this paper, we study the issue of admissibility in the growth curve model with respect to restricted parameter sets under matrix loss function. We obtain some neces- sary and sufficient conditions that the linear estimators of KBL are admissible in the class of homogeneous linear estimators and in the class of non-homogeneous linear estimators under the growth curve model with respect to restricted parameter sets, respectively.

Sufficient and Necessary Condition of Admissibility for Fractional-order Singular System

这份报纸与顺序为部分顺序的单个系统集中于考虑条件吗？(0， 1 ) 。整齐的定义，没有推动并且考虑被给第一，然后，为部分顺序的单个系统的考虑的一个足够、必要的条件被建立。一个数字例子被包括说明建议条件。

Admissibility Conditions for Symbolic Sequences in Dynamics of Digital Filter with Two's Complement Arithmetic

In this paper, we discuss a class of piecewise linear hyperbolic maps on the 2-torus. These maps arise in the second-order digital fdter with two＇ s complement arithmetic. By introducing codings underlying the map operations, we give some admissibility conditions for symbolic sequences and find some periodic properties of these symbolic sequences. Then we use these conditions to check the admissibility of periodic symbol sequences.

A necessary and sufficient condition for generalized admissibility of Bayes estimate

A definition of generalized admissibility of Bayes estimates has been given. This generalized admissibility is a rule to identify whether Bayes estimates is acceptable or not under the condition of incorrect prior information. In this paper, a sufficient and necessary condition for the generalized admissibility is derived under quadratic loss. From this we can conclude that, when deviation of prior mean and deviation of prior variance do not go beyond the bound, the Bayes estimation is acceptable and it is discussed that how the deviation of the prior information influences on generalized admissibility. Because the precise distribution of prior information is unknown, the example gives a way to select the prior distribution. The example shows that this method is efficient and feasible.

Meteorological factors, ambient air pollution, and daily hospital admissions for depressive disorder in Harbin: A time-series study

BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.

Admissibility for Nonhomogeneous Linear Estimates on Multivariate Random Regression Coefficients and Parameters

In this paper,we consider the admissibility for nonhomogeneous linear estimates on regression coefficients and parameters in multivariate random effect linear model and give eight definitions of different forms for admissibility. We not only prove that they can be divided into three identical subclasses,but also gain three kinds of necessary and sufficient conditions.

A 365-Day Record of Maternal Admissions in Intensive Care Unit at the University of Port Harcourt Teaching Hospital in Nigeria

BACKGROUND. The intensive care unit provides critically ill patients with the necessary monitoring, care and supports to optimize their organ/system functions. Parturients are often at risk of sudden deteriorations or exacerbation of chronic illnesses from direct or indirect causes and would often require admissions into the intensive care or high dependency unit. This study is aimed at looking at the trends of maternal admissions in 365 days at the intensive care unit of a tertiary teaching hospital in Southern part of Nigeria, a country that contributes significantly to the global proportion of maternal morbidity and mortality. METHODS. The study adopted a retrospective approach. All critically ill parturients admitted and requiring organ support or close monitoring in the ICU had their files and ICU documents reviewed. The review was held from January-December 2018. RESULTS. Thirty-nine (39) parturients with a mean age (years) of 33 ± 1.3 were admitted, representing 2.9% of annual deliveries. All admissions were postnatal and came predominantly from the unbooked labour ward (51%) and the time lag from maternal deteriorations to presentation to the ICU was 72 ± 10 hours. The main indications for admissions were due to postpartum haemorrhage (33.3%), complications of hypertensive disorders of pregnancy (30.9%) and sepsis (25.6%). Nineteen (48.7%) patients died from obstetric haemorrhage, complications of hypertensive disorders of pregnancy, sepsis and pulmonary embolism. CONCLUSION. The trend of maternal admissions at this specific time frame reflects the burden of maternal critical care in our environment. It highlights the need to holistically tackle the known scourge with improved care.

Admission Cardiotocography: Its Role in Predicting Perinatal Outcome in Term, Uncomplicated (Low Risk) Pregnant Women in Spontaneous Labour

Introduction: Labour admission cardiotocography (CTG) is commonly used non-invasive method of fetal monitoring in Sri Lanka. It may have a potentialto predict perinatal outcome in low-risk term pregnancies. Objectives: Objectives of the study were to determine the perinatal outcomes of normal, suspicious and pathological admission CTGs and role of labour admission cardiotocography as a predictive test for perinatal outcome in low-risk term pregnancies in spontaneous labour. Methods: This study was a prospective observational study done involving 445 low risk, term pregnancies in spontaneous labour. Labour admission CTG was performed in each pregnancy and categorized into normal, suspicious and pathological CTG according to criteria depicted by National Institute of Clinical Excellence (NICE) guideline 2007. Apgar score less than 7 at five minutes, resuscitation at birth, admission to neonatal intensive care unit (NICU), seizure within first 24 hours of birth and meconium-stained amniotic fluid were the primary outcome measures to assess fetal asphyxia. Mode of delivery in each category, nuchal cord at birth were also assessed. Results: Majority of participants were in 25-to-29-year age group and were nulliparous. Frequencies of normal, suspicious and pathological CTG were 74.8%, 18% and 7.2% respectively. Pathological CTG was significantly associated with low Apgar score compared to non-pathological CTG group (p 0.005) while other outcome measures were not significant. Rate of operative delivery was 68% in pathological group and 20.8% in non-pathological CTG group. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of labour admission CTG to detect fetal asphyxia were 51.85%, 95.69%, 43.75% and 96.85% respectively. Conclusions: Incidence of pathological labour admission CTG was 7.2%. Apgar score less than 7 at five minutes of birth was significantly associated with pathological CTG group compared to non-pathological CTG (p 0.05). Worsening of CTG from normal to pathological showed increasing rate of operative delivery. Even though sensitivity and positive predictive values of labour admission CTG were low, specificity and negative predictive values were high for detecting low Apgar score. Therefore, labour admission CTG has a value in excluding adverse perinatal outcomes in low-risk term pregnancies in spontaneous labour.

Opportunistic admission and resource allocation for slicing enhanced IoT networks

Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configuration.In the context of slicing enhanced IoT networks,both the Service Provider(SP)and Infrastructure Provider(InP)face challenges of ensuring efficient slice construction and high profit in dynamic environments.These challenges arise from randomly generated and departed slice requests from end-users,uncertain resource availability,and multidimensional resource allocation.Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments.To this end,we propose an Opportunistic Admission and Resource allocation(OAR)policy to deal with the issues of random slicing requests,uncertain resource availability,and heterogeneous multi-resources.The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources.To cope with the random slice requests and uncertain resource availability,we formulated this issue as a Markov Decision Process(MDP)to obtain the optimal admission policy,with the aim of maximizing the system reward.Furthermore,the buyer-seller game theory approach was adopted to realize the optimal resource allocation,while motivating each SP and InP to maximize their rewards.Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily,and outperforms the baseline schemes in terms of the system reward.

The Reasons for Admission of Elderly Subjects in Intensive Care at the CHU Ignace Deen

Objective: To determine the reasons for admission of elderly subjects and the prognosis in general intensive care. Patients and Methods: Observational descriptive and analytical study with prospective collection of data over a period of one year from January 1 to December 31, 2021. Patients aged 65 or over were included. Abstract: During the study period, 223 cases were collected out of 587 patients admitted, giving a prevalence of 37.9%. The average age was 74.127.39 ± years with extremes of 65 and 96 years and a male predominance (58.7%). The comorbidities were dominated by arterial hypertension (71.3%). The patients were: transferred from medical and surgical emergencies (75.8%). The average admission time was 48.8 ± 29.8 hours. One hundred and eight patients had a Glasgow score between 3 and 7. The reasons for admission were dominated by vascular causes (51.6%). Strokes of any type accounted for 43.9% of these reasons for admission. The average time for carrying out the biological assessments and imaging was 41.8 ± 27.3 hours with the extremes of 3 and 89 hours, 37.2% had a complete assessment within 24 hours. The average duration of hospitalization was 7.10 ± 8.87 days with extremes of 1 and 72 days. The mortality rate was 71.7%. Conclusion: This study has made it possible to take stock of the reasons for the admission of elderly subjects to intensive care. It appears that vascular causes are the main reasons for admission with heavy comorbidities which results in high mortality.

An Ontology-Based Question Answering System for University Admissions Advising

Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in manyfields.However,these systems depend on learning methods and training data.Therefore,it is necessary to prepare such a good dataset,but it is not an easy job.An ontol-ogy-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions.This study proposes a novel chatbot model involving ontology to generate efficient responses automatically.A case study of admissions advising at the International University–VNU HCMC is taken into account in the proposed chatbot.A domain ontology is designed and built based on the domain knowledge of university admissions using Protégé.The Web user interface of the proposed chatbot system is developed as a prototype using NetBeans.It includes a search engine reasoning the ontology and generat-ing answers to users’questions.Two experiments are carried out to test how the system reacts to different questions.Thefirst experiment examines questions made from some templates,and the second one examines normal questions taken from frequent questions.Experimental results have shown that the ontology-based chatbot can release meaningful and long answers.The results are analysed to prove the proposed chatbot is usable and promising.

Whole-brain CT Perfusion at Admission and During Delayed Time-window Detects the Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage

Objective To evaluate the utility of computed tomography perfusion(CTP)both at admission and during delayed cerebral ischemia time-window(DCITW)in the detection of delayed cerebral ischemia(DCI)and the change in CTP parameters from admission to DCITW following aneurysmal subarachnoid hemorrhage.Methods Eighty patients underwent CTP at admission and during DCITW.The mean and extreme values of all CTP parameters at admission and during DCITW were compared between the DCI group and non-DCI group,and comparisons were also made between admission and DCITW within each group.The qualitative color-coded perfusion maps were recorded.Finally,the relationship between CTP parameters and DCI was assessed by receiver operating characteristic(ROC)analyses.Results With the exception of cerebral blood volume(P=0.295,admission;P=0.682,DCITW),there were significant differences in the mean quantitative CTP parameters between DCI and non-DCI patients both at admission and during DCITW.In the DCI group,the extreme parameters were significantly different between admission and DCITW.The DCI group also showed a deteriorative trend in the qualitative color-coded perfusion maps.For the detection of DCI,mean transit time to the center of the impulse response function(Tmax)at admission and mean time to start(TTS)during DCITW had the largest area under curve(AUC),0.698 and 0.789,respectively.Conclusion Whole-brain CTP can predict the occurrence of DCI at admission and diagnose DCI during DCITW.The extreme quantitative parameters and qualitative color-coded perfusion maps can better reflect the perfusion changes of patients with DCI from admission to DCITW.

Learning-Based Admission Control for Low-Earth-Orbit Satellite Communication Networks

Satellite communications has been regarded as an indispensable technology for future mobile networks to provide extremely high data rates,ultra-reliability,and ubiquitous coverage.However,the high dynamics caused by the fast movement of low-earth-orbit(LEO)satellites bring huge challenges in designing and optimizing satellite communication systems.Especially,admission control,deciding which users with diversified service requirements are allowed to access the network with limited resources,is of paramount importance to improve network resource utilization and meet the service quality requirements of users.In this paper,we propose a dynamic channel reservation strategy based on the Actor-Critic algorithm(AC-DCRS)to perform intelligent admission control in satellite networks.By carefully designing the longterm reward function and dynamically adjusting the reserved channel threshold,AC-DCRS reaches a long-run optimal access policy for both new calls and handover calls with different service priorities.Numerical results show that our proposed AC-DCRS outperforms traditional channel reservation strategies in terms of overall access failure probability,the average call success rate,and channel utilization under various dynamic traffic conditions.

The effects of cold region meteorology and specific environment on the number of hospital admissions for chronic kidney disease:An investigate with a distributed lag nonlinear model

Objective:To explore the effects of daily mean temperature(°C),average daily air pressure(hPa),humidity(%),wind speed(m/s),particulate matter(PM)2.5(μg/m3)and PM10(μg/m3)on the admission rate of chronic kidney disease(CKD)patients admitted to the Second Affiliated Hospital of Harbin Medical University in Harbin and to identify the indexes and lag days that impose the most critical influence.Methods:The R language Distributed Lag Nonlinear Model(DLNM),Excel,and SPSS were used to analyze the disease and meteorological data of Harbin from 01 January 2010 to 31 December 2019 according to the inclusion and exclusion criteria.Results:Meteorological factors and air pollution influence the number of hospitalizations of CKD to vary degrees in cold regions,and differ in persistence or delay.Non-optimal temperature increases the risk of admission of CKD,high temperature increases the risk of obstructive kidney disease,and low temperature increases the risk of other major types of chronic kidney disease.The greater the temperature difference is,the higher its contribution is to the risk.The non-optimal wind speed and non-optimal atmospheric pressure are associated with increased hospital admissions.PM2.5 concentrations above 40μg/m3 have a negative impact on the results.Conclusion:Cold region meteorology and specific environment do have an impact on the number of hospital admissions for chronic kidney disease,and we can apply DLMN to describe the analysis.

NOTE OF ADMISSIBILITY IN LINEAR ESTIMATION

１ＩＮＴＲＯＤＵＣＴＩＯＮＡｍｏｄｅｌｉｓｇｉｖｅｎａｓｆｏｌｏｗｓＹｎｘｍ＝ＸＢｐｘｍ＋ε；Ｂε～ΝＡΘ０，Σ△（１）ｗｈｅｒｅＹｉｓａｍａｔｒｉｘｏｆｏｂｓｅｒｖａｔｉｏｎｓ．Ｂεｈａｓａｍａｔｒｉｘｍｏｒｍａｌｄｉｓｔｒｉｂｕｔｉｏｎｗｉｔｈ...

Admissibility of Linear Systems in Banach Spaces

In this paper, infinite-time p-admissibility of unbounded operators is introduced and the C0-semigroup characterization of the infinite-time p-admissibility of unbounded observation operators is given. Moreover, the analogous result for the infinite-time p-admissibility of unbounded control operators is presented.