A Survey on Handover and Mobility Management in 5G HetNets: Current State, Challenges, and Future Directions.

Fifth-generation (5G) networks offer high-speed data transmission with low latency, increased base station volume, improved quality of service (QoS), and massive multiple-input-multiple-output (M-MIMO) channels compared to 4G long-term evolution (LTE) networks. However, the COVID-19 pandemic has disrupted the achievement of mobility and handover (HO) in 5G networks due to significant changes in intelligent devices and high-definition (HD) multimedia applications. Consequently, the current cellular network faces challenges in propagating high-capacity data with improved speed, QoS, latency, and efficient HO and mobility management. This comprehensive survey paper specifically focuses on HO and mobility management issues within 5G heterogeneous networks (HetNets). The paper thoroughly examines the existing literature and investigates key performance indicators (KPIs) and solutions for HO and mobility-related challenges while considering applied standards. Additionally, it evaluates the performance of current models in addressing HO and mobility management issues, taking into account factors such as energy efficiency, reliability, latency, and scalability. Finally, this paper identifies significant challenges associated with HO and mobility management in existing research models and provides detailed evaluations of their solutions along with recommendations for future research.

First-principles calculations to investigate structural, electronic, elastic and optical properties of radium based cubic fluoro-perovskite materials.

Perovskite materials play a vital role in the field of material science via experimental as well as theoretical calculations. Radium semiconductor materials are considered the backbone of medical fields. These materials are considered in high technological fields to be used as controlling the decay ability. In this study, radium-based cubic fluoro-perovskite XRaF3 (where X = Rb and Na) are calculated using a DFT (density functional theory). These compounds are cubic nature with 221 space groups that construct on CASTEP (Cambridge-serial-total-energy-package) software with ultra-soft PPPW (pseudo-potential plane-wave) and GGA (Generalized-Gradient-approximation)-PBE (Perdew-Burke-Ernzerhof) exchange-correlation functional. The structural, optical, electronic, and mechanical properties of the compounds are calculated. According to the structural properties, NaRaF3 and RbRaF3 have a direct bandgap with 3.10eV and 4.187eV of NaRaF3 and RbRaF3, respectively. Total density of states (DOS) and partial density of states (PDOS) provide confirmation to the degree of electrons localized in distinct bands. NaRaF3 material is semiconductors and RbRaF3 is insulator, according to electronic results. The imaginary element dispersion of the dielectric function reveals its wide variety of energy transparency. In both compounds, the optical transitions are examined by fitting the damping ratio for the notional dielectric function scaling to the appropriate peaks. The absorption and the conductivity of NaRaF3 compound is better than the RbRaF3 compound which make it suitable for the solar cell applications increasing the efficiency and work function. We observed that both compounds are mechanically stable with cubic structure. The criteria for the mechanical stability of compounds are also met by the estimated elastic results. These compounds have potential application in field of solar cell and medical. Objectives: The band gap, absorption and the conductivity are necessary conditions for potential applications. Here, literature was reviewed to check computational translational insight into the relationships between absorption and conductivity for solar cell and medical applications of novel RbRaF3 and NaRaF3 compounds.

Lithium-based perovskites materials for photovoltaic solar cell and protective rays window applications: a first-principle calculations.

Perovskites are the key enabler materials for the solar cell applications in the achievement of high performance and low production costs. In this article, the structural, mechanical, electronic, and optical properties of rubidium-based cubic nature perovskite LiHfO3 and LiZnO3 are investigated. These properties are investigated using density-functional theory with the aid of CASTEP software by introducing ultrasoft pseudo-potential plane-wave (USPPPW) and GG-approximation-PB-Ernzerhof exchange-correlation functionals. It is investigated that the proposed compounds exhibit stable cubic phase and meet the criteria of mechanical stability by the estimated elastic properties. Also, according to Pugh's criterion, it is noted that LiHfO3 is ductile and LiZnO3 is brittle. Furthermore, the electronic band structure investigation of LiHfO3 and LiZnO3 shows that they have indirect bandgap (BG). Moreover, the BG analysis of the proposed materials shows that these are easily accessible. Also, the results for partial density of states (DOS) and total DOS confirm the degree of a localized electron in the distinct band. In addition, the optical transitions in the compounds are examined by fitting the damping ratio for the notional dielectric functions scaling to the appropriate peaks. At absolute zero temperature, the materials are observed as semiconductors. Therefore, it is evident from the analysis that the proposed compounds are excellent candidates for solar cells and protective rays applications.

Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN.

Vehicular Named Data Networking (VNDN) is one of the potential and future networking architectures that allow Connected and Autonomous Vehicles (CAV) to exchange data by simply disseminating the content over the network. VNDN only supports a pull-based data forwarding model, where the content information is forwarded upon request. However, in critical situations, it is essential to design a push-based data forwarding model in order to broadcast the critical data packets without any requests. One of the challenges of push-based data forwarding in VNDN is the broadcasting effect, which occurs when every vehicle broadcasts critical information over the network. For instance, in emergency situations such as accidents, road hazards, and bad weather conditions, the producer generates a critical data packet and broadcasts it to all the nearby vehicles. Subsequently, all vehicles broadcast the same critical data packet to each other, which leads to a broadcast storm on the network. Therefore, this paper proposes a Fuzzy Logic-based Push Data Forwarding (FLPDF) scheme to mitigate the broadcast storm effect. The novelty of this paper is the suggestion and application of a fuzzy logic approach to mitigate the critical data broadcast storm effect in VNDN. In the proposed scheme, vehicles are grouped into clusters using the K-means clustering algorithm, and then Cluster Heads (CHs) are selected using a fuzzy logic approach. A CH is uniquely responsible for broadcasting the critical data packets to all other vehicles in a cluster. A Gateway (GW) has the role of forwarding the critical data packets to the nearest clusters via their GWs. The simulation results show that the proposed scheme outperforms the naive method in terms of transmitted data packets and efficiency. The proposed scheme generates five times fewer data packets and achieves six times higher efficiency than the naive scheme.

Regression analysis of hydro-meteorological variables for climate change prediction: A case study of Chitral Basin, Hindukush region.

In the present study, hydro-meteorological variables of Chitral Basin in Hindukush region of Pakistan were studied to predict the changes in climatic components such as temperature, precipitation, humidity and river flow based on observed data from 1990 to 2019. Uncertainties in climate change projection were studied using various statistical methods, such as trend variability analysis via stationarity test and validation of regression assumptions prior to fitting of regression estimates. Also, multiple regression models were estimated for each hydro-meteorological variables for the given 30 years of observed data. Results demonstrated that temperature and, precipitation were inversely related with one another. It was observed from the regression model that temperature is decreases by 0.309 °C on the average increases in precipitation by one unit. Temperature also decreases for the increase in humidity by average 0.086 °C. Since, precipitation is negatively related with temperature, thus for increases in temperature the annual precipitation decreases by 0.278 mm annually. Humidity on the other hand, increases by 0.207% by increasing in precipitation and the temperature that causes humidity to decrease by 0.99%. Thus, it demonstrated that the flow in Chitral river increases due to precipitation by 0.306 m3/s for the change in precipitation by one unit. Findings from the present study negated the general perceptions that flow in the Chitral river has increased due to recession of glaciers with increase in the intensity of temperature.

Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak.

BACKGROUND: Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. METHODS: In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. RESULTS: Statistical measures-Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)-are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.

A Gull Alpha Power Weibull distribution with applications to real and simulated data.

In this paper, we produced a new family of distribution called Gull Alpha Power Family of distributions (GAPF). A Special case of GAPF is derived by considering the Weibull distribution as a baseline distribution called Gull Alpha Power Weibull distribution (GAPW). The suitability of the proposed distribution derives from its ability to model both the monotonic and non-monotonic hazard rate functions which are a common practice in survival analysis and reliability engineering. Various statistical properties were derived in addition to their special cases. The unknown parameters of the model are estimated using the maximum likelihood method. Moreover, the usefulness of the proposed distribution is supported by using two real lifetime data sets as well as simulated data.

Risk factors associated with childhood asthma in District Mardan, Pakistan.

OBJECTIVE: To estimate the prevalence of asthma in children aged <10 years, and to identify important risk factors for asthma.. METHODS: The case-control study was conducted at Mardan Medical Complex and District Head Quarters Hospital, Mardan, Pakistan, from June to September 2017. Data was collected from paediatric patients of asthma as well as healthy controls through a self-designed questionnaire. SPSS 19 was used for data analysis. RESULTS: Of the 647 subjects, 349(54%) were asthmatic cases and 298(46%) were controls. Among the cases, 201(57.6%) were females, while 148(42.4%) were males. There were 332(51%) subjects whose fathers were smokers, and of them 224(67%) had asthma and 125(37%) were non-asthmatic. Overall, 323(50%) subjects had carpet in their rooms, and of them 221(68%) had asthma. Among other risk factors, subjects aged <5 years had 1.49 time more likely to have asthma with (odds ratio: 1.49, 95% confidence interval: 0.963-1.988). CONCLUSIONS: Female gender, fathers' smoking, having carpet in the room and age <5 year were found to be the main risk factors associated with asthma.

Sample size issues in multilevel logistic regression models.

Educational researchers, psychologists, social, epidemiological and medical scientists are often dealing with multilevel data. Sometimes, the response variable in multilevel data is categorical in nature and needs to be analyzed through Multilevel Logistic Regression Models. The main theme of this paper is to provide guidelines for the analysts to select an appropriate sample size while fitting multilevel logistic regression models for different threshold parameters and different estimation methods. Simulation studies have been performed to obtain optimum sample size for Penalized Quasi-likelihood (PQL) and Maximum Likelihood (ML) Methods of estimation. Our results suggest that Maximum Likelihood Method performs better than Penalized Quasi-likelihood Method and requires relatively small sample under chosen conditions. To achieve sufficient accuracy of fixed and random effects under ML method, we established ''50/50" and ''120/50" rule respectively. On the basis our findings, a ''50/60" and ''120/70" rules under PQL method of estimation have also been recommended.

Alpha-Power Pareto distribution: Its properties and applications.

In Statistical theory, inclusion of an additional parameter to standard distributions is a usual practice. In this study, a new distribution referred to as Alpha-Power Pareto distribution is introduced by including an extra parameter. Several properties of the proposed distribution, including moment generating function, mode, quantiles, entropies, mean residual life function, stochastic orders and order statistics are obtained. Parameters of the proposed distribution have been estimated using maximum likelihood estimation technique. Two real datasets have been considered to examine the usefulness of the proposed distribution. It has been observed that the proposed distribution outperforms different variants of Pareto distribution on the basis of model selection criteria.

Bayesian Analysis of Three-Parameter Frechet Distribution with Medical Applications.

The medical data are often filed for each patient in clinical studies in order to inform decision-making. Usually, medical data are generally skewed to the right, and skewed distributions can be the appropriate candidates in making inferences using Bayesian framework. Furthermore, the Bayesian estimators of skewed distribution can be used to tackle the problem of decision-making in medicine and health management under uncertainty. For medical diagnosis, physician can use the Bayesian estimators to quantify the effects of the evidence in increasing the probability that the patient has the particular disease considering the prior information. The present study focuses the development of Bayesian estimators for three-parameter Frechet distribution using noninformative prior and gamma prior under LINEX (linear exponential) and general entropy (GE) loss functions. Since the Bayesian estimators cannot be expressed in closed forms, approximate Bayesian estimates are discussed via Lindley's approximation. These results are compared with their maximum likelihood counterpart using Monte Carlo simulations. Our results indicate that Bayesian estimators under general entropy loss function with noninformative prior (BGENP) provide the smallest mean square error for all sample sizes and different values of parameters. Furthermore, a data set about the survival times of a group of patients suffering from head and neck cancer is analyzed for illustration purposes.

Rules for resolving Mendelian inconsistencies in nuclear pedigrees typed for two-allele markers.

Gene-mapping studies, regularly, rely on examination for Mendelian transmission of marker alleles in a pedigree as a way of screening for genotyping errors and mutations. For analysis of family data sets, it is, usually, necessary to resolve or remove the genotyping errors prior to consideration. At the Center of Inherited Disease Research (CIDR), to deal with their large-scale data flow, they formalized their data cleaning approach in a set of rules based on PedCheck output. We scrutinize via carefully designed simulations that how well CIDR's data cleaning rules work in practice. We found that genotype errors in siblings are detected more often than in parents for less polymorphic SNPs and vice versa for more polymorphic SNPs. Through computer simulations, we conclude that some of the CIDR's rules work poorly in some circumstances, and we suggest a set of modified data cleaning rules that may work better than CIDR's rules.

Unit Root Testing and Estimation in Nonlinear ESTAR Models with Normal and Non-Normal Errors.

Exponential Smooth Transition Autoregressive (ESTAR) models can capture non-linear adjustment of the deviations from equilibrium conditions which may explain the economic behavior of many variables that appear non stationary from a linear viewpoint. Many researchers employ the Kapetanios test which has a unit root as the null and a stationary nonlinear model as the alternative. However this test statistics is based on the assumption of normally distributed errors in the DGP. Cook has analyzed the size of the nonlinear unit root of this test in the presence of heavy-tailed innovation process and obtained the critical values for both finite variance and infinite variance cases. However the test statistics of Cook are oversized. It has been found by researchers that using conventional tests is dangerous though the best performance among these is a HCCME. The over sizing for LM tests can be reduced by employing fixed design wild bootstrap remedies which provide a valuable alternative to the conventional tests. In this paper the size of the Kapetanios test statistic employing hetroscedastic consistent covariance matrices has been derived and the results are reported for various sample sizes in which size distortion is reduced. The properties for estimates of ESTAR models have been investigated when errors are assumed non-normal. We compare the results obtained through the fitting of nonlinear least square with that of the quantile regression fitting in the presence of outliers and the error distribution was considered to be from t-distribution for various sample sizes.

Sufficient Sample Size and Power in Multilevel Ordinal Logistic Regression Models.

For most of the time, biomedical researchers have been dealing with ordinal outcome variable in multilevel models where patients are nested in doctors. We can justifiably apply multilevel cumulative logit model, where the outcome variable represents the mild, severe, and extremely severe intensity of diseases like malaria and typhoid in the form of ordered categories. Based on our simulation conditions, Maximum Likelihood (ML) method is better than Penalized Quasilikelihood (PQL) method in three-category ordinal outcome variable. PQL method, however, performs equally well as ML method where five-category ordinal outcome variable is used. Further, to achieve power more than 0.80, at least 50 groups are required for both ML and PQL methods of estimation. It may be pointed out that, for five-category ordinal response variable model, the power of PQL method is slightly higher than the power of ML method.