Variable Rate Transmission Research Articles

Understanding Climate Variability and Malaria Transmission in West Africa: Case Studies, Regional Insights, and Future Directions

Climate variability plays a crucial role in malaria transmission in West Africa. Variable temperature, rainfall, and humidity patterns in diverse climatic zones, such as tropical rainforests and semi-arid regions, significantly influence mosquito breeding sites and transmission rates. Tropical rainforests experience high transmission rates due to consistent rainfall and humidity, while savanna regions experience seasonal peaks. Semi-arid regions with limited rainfall and extreme temperatures show lower but variable transmission rates. Temperature fluctuations affect the development and survival of Anopheles mosquitoes and malaria parasites. Rising temperatures can extend transmission seasons and spread the disease to unaffected areas. Rainfall patterns affect breeding site availability, with heavy rains increasing breeding sites and droughts reducing mosquito populations. Humidity influences mosquito survival and parasite development. Extreme weather events, such as cyclones and floods, exacerbate malaria transmission by increasing breeding sites and damaging infrastructure. Socioeconomic factors, including urbanization, land use changes, and economic instability, also interact with climate variability to impact malaria dynamics. The review highlights the need for integrated malaria control strategies that incorporate climate considerations, including enhanced surveillance, climate-resilient infrastructure, and community engagement. Future research should address knowledge gaps regarding the interaction between climate variables and malaria transmission, and the socioeconomic impacts of climate change on malaria dynamics. By tailoring control strategies to specific climatic and socio-economic contexts, stakeholders can better manage malaria risks and improve health outcomes across West Africa. Keywords: Climate, Variability, Malaria, Transmission, West Africa, Case Studies.

Insight into the optimal control strategies on corruption dynamics using fractional order derivatives

The fractional-order design for conceptualizing corruption offers several advantages on the corruption spreading over the integer-order corruption models such as acknowledgement of the intricate dynamics of corruption, including corruption super-spreading aspects, variable corruption transmission rates, it capture the persistence of memory effects, accounting for the past trajectory of corruption epidemic, and also providing a more accurate explanation of corruption dynamics. The author of the study has formulated the fractional order model on corruption dynamics by dividing the total population into five sub-groups namely the susceptible group, the exposed group, the moderately corrupted group, the highly corrupted group, and the corruption repented group. In the qualitative analyses section the author of this study has: shown the model solutions existence and uniqueness by applying the well-known Picard–Lindelöf criteria, computed the model basic reproduction number using the next generation approach, computed the model equilibrium points and investigated their stabilities, re-formulated the corresponding fractional order optimal control problem using the proposed three time-dependent controlling variables (prevention measure, moderate corruption treatment measure and high corruption treatment measure). The necessary optimality conditions for the fractional order optimal control problem and the existence of optimal control strategies are derived and presented by applying Pontryagin's Maximum Principle. Eventually, the study demonstrated the effectiveness of the combinations of the three controlling strategies through numerical methods (simulations) and the analysis results shows that the implementation of the three time-dependent controlling strategies significantly minimizes the number of corrupted individuals in the community.

More than a decade of research on Schmallenberg virus-Knowns and unknowns.

Schmallenberg virus, an arbovirus of the Orthobunyavirus genus that primarily infects ruminants, emerged in 2011 near the Dutch-German border region and subsequently caused a large number of abortions and the births of severely malformed newborns in the European livestock population. Immediate intensive research led to the development of reliable diagnostic tests, the identification of competent Culicoides vector species, and the elucidation of the pathogenesis in infected vertebrate hosts. In addition, the structure of the major antigenic domain has been elucidated in great detail, leading to the development of effective marker vaccine candidates. The knowledge gained over the last decade on the biology and pathogenesis of SBV and the experience acquired in its control will be of great value in the future for the control of any similar emerging pathogen of veterinary or public health importance such as Shuni or Oropouche virus. However, some important knowledge gaps remain, for example, the factors contributing to the highly variable transmission rate from dam to fetus or the viral factors responsible for the vector competence of Culicoides midges are largely unknown. Thus, questions still remain for the next decade of research on SBV and related viruses.

Real-time transponder architecture for hitless transmission baud rate switching in association with an auto-negotiation protocol

We propose a novel digital signal processing architecture for a coherent receiver that allows instantaneous and hitless variable baud rate transmission for integer sub-division values. This solution allows the receiver to self-adapt a new baud rate without processing modification, re-initialization, or any knowledge of the transmission speed modification. As for implantation concern, this architecture requires negligible extra logic compared to the standard one. Its development and operation developed on a simulation basis are presented; then its dynamic behavior and performance are demonstrated in a real-time experiment attesting a zero-bit loss. We also show how an in-line auto-negotiation protocol between a transmitter and a receiver minimizing the mediation through the control plane can leverage this instantaneous baud rate variation.

Modeling the variable transmission rate and various discharges on the spread of Malaria

<abstract><p>Natural and household discharges are the natural breeding grounds of various mosquito species, including female <italic>Anopheles</italic> mosquitoes, which transmit the <italic>Plasmodium</italic> parasite, causing the spread of the life-threatening disease malaria. Apart from that, population migrations also have a substantial impact on malaria transmission, claiming about half a million lives every year around the world. To assess the effects of the cumulative density of households and other natural discharges, and emigration-dependent interaction rates on the dissemination of the vector-borne infectious disease malaria, we propose and analyze a non-linear mathematical model. The model comprises five dependent variables, namely, the density of the susceptible human population, the density of the infective human population, the density of the susceptible female <italic>Anopheles</italic> mosquito population, the density of the infective mosquito population and cumulative density of household and other natural discharges. In the model, the density of the mosquito population is supposed to follow logistic growth, whose intrinsic growth rate is a linear function of the cumulative density of household and other natural discharges. The nonlinear model is analyzed by using the stability theory of differential equations, numerical simulations and sensitivity analysis. The analysis shows that an increase in non-emigrating population causes increased incidence of malaria. It is also found that an increase in household and other natural discharges accelerates the occurrence of malaria. A basic differential sensitivity analysis is carried out to assess the sensitivity of model solutions with respect to key parameters. The model's numerical simulations demonstrate the analytical findings.</p></abstract>

An Immunological Review of SARS-CoV-2 Infection and Vaccine Serology: Innate and Adaptive Responses to mRNA, Adenovirus, Inactivated and Protein Subunit Vaccines.

The coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, which is defined by its positive-sense single-stranded RNA (ssRNA) structure. It is in the order Nidovirales, suborder Coronaviridae, genus Betacoronavirus, and sub-genus Sarbecovirus (lineage B), together with two bat-derived strains with a 96% genomic homology with other bat coronaviruses (BatCoVand RaTG13). Thus far, two Alphacoronavirus strains, HCoV-229E and HCoV-NL63, along with five Betacoronaviruses, HCoV-HKU1, HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2, have been recognized as human coronaviruses (HCoVs). SARS-CoV-2 has resulted in more than six million deaths worldwide since late 2019. The appearance of this novel virus is defined by its high and variable transmission rate (RT) and coexisting asymptomatic and symptomatic propagation within and across animal populations, which has a longer-lasting impact. Most current therapeutic methods aim to reduce the severity of COVID-19 hospitalization and virus symptoms, preventing the infection from progressing from acute to chronic in vulnerable populations. Now, pharmacological interventions including vaccines and others exist, with research ongoing. The only ethical approach to developing herd immunity is to develop and provide vaccines and therapeutics that can potentially improve on the innate and adaptive system responses at the same time. Therefore, several vaccines have been developed to provide acquired immunity to SARS-CoV-2 induced COVID-19-disease. The initial evaluations of the COVID-19 vaccines began in around 2020, followed by clinical trials carried out during the pandemic with ongoing population adverse effect monitoring by respective regulatory agencies. Therefore, durability and immunity provided by current vaccines requires further characterization with more extensive available data, as is presented in this paper. When utilized globally, these vaccines may create an unidentified pattern of antibody responses or memory B and T cell responses that need to be further researched, some of which can now be compared within laboratory and population studies here. Several COVID-19 vaccine immunogens have been presented in clinical trials to assess their safety and efficacy, inducing cellular antibody production through cellular B and T cell interactions that protect against infection. This response is defined by virus-specific antibodies (anti-N or anti-S antibodies), with B and T cell characterization undergoing extensive research. In this article, we review four types of contemporary COVID-19 vaccines, comparing their antibody profiles and cellular aspects involved in coronavirus immunology across several population studies.

Modeling pandemic to endemic patterns of SARS-CoV-2 transmission using parameters estimated from animal model data.

The contours of endemic coronaviral disease in humans and other animals are shaped by the tendency of coronaviruses to generate new variants superimposed upon nonsterilizing immunity. Consequently, patterns of coronaviral reinfection in animals can inform the emerging endemic state of the SARS-CoV-2 pandemic. We generated controlled reinfection data after high and low risk natural exposure or heterologous vaccination to sialodacryoadenitis virus (SDAV) in rats. Using deterministic compartmental models, we utilized in vivo estimates from these experiments to model the combined effects of variable transmission rates, variable duration of immunity, successive waves of variants, and vaccination on patterns of viral transmission. Using rat experiment-derived estimates, an endemic state achieved by natural infection alone occurred after a median of 724 days with approximately 41.3% of the population susceptible to reinfection. After accounting for translationally altered parameters between rat-derived data and human SARS-CoV-2 transmission, and after introducing vaccination, we arrived at a median time to endemic stability of 1437 (IQR = 749.25) days with a median 15.4% of the population remaining susceptible. We extended the models to introduce successive variants with increasing transmissibility and included the effect of varying duration of immunity. As seen with endemic coronaviral infections in other animals, transmission states are altered by introduction of new variants, even with vaccination. However, vaccination combined with natural immunity maintains a lower prevalence of infection than natural infection alone and provides greater resilience against the effects of transmissible variants.

Exploring Radial Kernel on the Novel Forced SEYNHRV-S Model to Capture the Second Wave of COVID-19 Spread and the Variable Transmission Rate

The transmission rate of COVID-19 varies over time. There are many reasons underlying this mechanism, such as seasonal changes, lockdowns, social distancing, and wearing face masks. Hence, it is very difficult to directly measure the transmission rate. The main task of the present paper was to identify the variable transmission rate (β1) for a SIR-like model. For this, we first propose a new compartmental forced SEYNHRV-S differential model. We then drive the nonlinear differential equation and present the finite difference technique to obtain the time-dependent transmission rate directly from COVID-19 data. Following this, we show that the transmission rate can be represented as a linear combination of radial kernels, where several forms of radial kernels are explored. The proposed model is flexible and general, so it can be adapted to monitor various epidemic scenarios in various countries. Hence, the model may be of interest for policymakers as a tool to evaluate different possible future scenarios. Numerical simulations are presented to validate the prediction of our SEYNHRV and forced SEYNHRV-S models, where the data from confirmed COVID-19 cases reported by the Ministry of Health in Saudi Arabia were used. These confirmed cases show the second wave of the infected population in Saudi Arabia. By using the COVID-19 data, we show that our model (forced SEYNHRV-S) is able to predict the second wave of infection in the population in Saudi Arabia. It is well known that COVID-19 epidemic data cannot be accurately represented by any compartmental approach with constant parameters, and this is also true for our SEYNHRV model.

Modeling the impact of public response on the COVID-19 pandemic in Ontario.

The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses. However, the implementation of these policies can be difficult and costly, making it imperative that both policy makers and the citizenry understand their potential benefits and the risks of non-compliance. In this work, a mathematical model is developed to study the impact of social behaviour on the course of the pandemic in the province of Ontario. The approach is based upon a standard SEIRD model with a variable transmission rate that depends on the behaviour of the population. The model parameters, which characterize the disease dynamics, are estimated from Ontario COVID-19 epidemiological data using machine learning techniques. A key result of the model, following from the variable transmission rate, is the prediction of the occurrence of a second wave using the most current infection data and disease-specific traits. The qualitative behaviour of different future transmission-reduction strategies is examined, and the time-varying reproduction number is analyzed using existing epidemiological data and future projections. Importantly, the effective reproduction number, and thus the course of the pandemic, is found to be sensitive to the adherence to public health policies, illustrating the need for vigilance as the economy continues to reopen.

Dynamically tuneable pre‐modulation filter for an airborne PCM/FM telemetry system

In the conventional airborne telemetry system, the pre-modulation filter with a multi-pole active Bessel filter is preferred to use in Pulse Code Modulator (PCM)/FM transmission. In the existing system, it is not possible to change the cut-off frequency for different data rates dynamically as required in the launch scenario of a long-range aerospace vehicle. In general, aerospace vehicle has multiple propellant stages to travel a desired trajectory path. Each stage gets separated from the vehicle at different instances. Each stage measurement plan is defined and correspondingly the PCM format is generated with an optimum data rate. Hence, the telemetry system is required to transmit variable data rates at various instances of a long-range aerospace vehicle from launch point to end point of a vehicle. This can be addressed by designing a dynamically tuneable pre-modulation (DTPM) filter. Here, a suitable DTPM filter scheme is proposed to mitigate variable data rate transmission in the telemetry system. The scheme is analysed as per IRIG-106 standard and simulated using MATLAB. The same has been modelled using VHDL and implemented targeting 28 nm technology Xilinx Zynq FPGA device.

Change in global transmission rates of COVID-19 through May 6 2020.

We analyzed COVID-19 data through May 6th, 2020 using a partially observed Markov process. Our method uses a hybrid deterministic and stochastic formalism that allows for time variable transmission rates and detection probabilities. The model was fit using iterated particle filtering to case count and death count time series from 55 countries. We found evidence for a shrinking epidemic in 30 of the 55 examined countries. Of those 30 countries, 27 have significant evidence for subcritical transmission rates, although the decline in new cases is relatively slow compared to the initial growth rates. Generally, the transmission rates in Europe were lower than in the Americas and Asia. This suggests that global scale social distancing efforts to slow the spread of COVID-19 are effective although they need to be strengthened in many regions and maintained in others to avoid further resurgence of COVID-19. The slow decline also suggests alternative strategies to control the virus are needed before social distancing efforts are partially relaxed.

A lightweight load balancing and route minimizing solution for routing protocol for low-power and lossy networks

Routing Protocol for Low-Power and Lossy Networks (RPL) is considered as one of the essential information forwarding options for the Internet of Thing appliances, which possesses a flexible design. However, RPL suffers from the lack of an applicable load balancing mechanism in massive traffic scenarios. Uneven traffic distribution leads to congestion and deteriorates packet losses, delay periods, power consumption handling, and finally, reduces network lifetime. The present study proposed a scheme named “Lightweight Load Balancing and Route Minimizing solution for RPL” (L2RMR), compromising a novel Objective Function (OF) and a new routing metric based on the minimization of path routes. Also, a Probability Function was introduced, which prevents from creating the general Herd Decampment Phenomenon (HDP) problem. The proposed solution enables delayed parent joining for the nodes in order to achieve a lower rank instead of a greedy thundering, leading to multiple instabilities in the network topology. The present study evaluated L2RMR performance using the Contiki-Cooja simulator. To this end, L2RMR was examined under scenarios, including variable network sizes, transmission rates, and density. The simulation results revealed that this mechanism could enhance the average Packet Loss Ratio, End-to-End Delay, and energy consumption criteria in an environment incorporated with RPL and other comparative approaches.

Probabilistic Constrained Secure Transmissions: Variable-Rate Design and Performance Analysis

In a wiretap channel, due to the passive nature of eavesdropper and the inevitable errors during channel estimation or feedback, the channel state information is usually imperfectly known at the transmitter. While probabilistic constrained secure transmission provides an elegant formulation to tackle these uncertainties, current works mostly focus on the fixed-rate secure transmission design. To exploit the dynamic channel state information for performance enhancement, this paper investigates a variable-rate transmission scheme with adjustable rate and power, under the outage probabilistic constraints and upper bounding rate constraint. By leveraging the first-order and log-concavity properties of the Marcum Q-function, closed-form optimal secure transmission design is obtained. Furthermore, the optimality of the proposed method empowers us to concisely quantify the performance gain brought by rate variation. Numerical results show that the proposed scheme achieves significantly lower average outage probability and higher throughput than the fixed-rate scheme no matter with or without upper bound rate limitation.

Energy Efficiency Versus Reliability Tradeoff Improvement in Low-Power Wireless Communications

Energy efficiency (EE) and reliability are two important but conflicting objectives in low-power, short-range wireless communications, usually treated as the single objective problems under constraints. Unlike others, we treat the objectives simultaneously as a multiobjective problem without preferences and analyze the opportunity of feasible tradeoff improvement in an objective space for communication over block-fading Rayleigh channel. We consider the improvement relative to the referent case that includes the current practice, where decision space consists of packet length, the maximal number of allowed transmission attempts (MNATA), and transmission power. Our hypothesis is that we can improve the tradeoff by modifying decision space and mapping functions from decision to objective space. We consider three modification techniques applied singly and jointly: Introduction of the expected MNATA as a real variable; Pareto optimal control of transmission power and transmitter power consumption; and introduction of the new decision variable—transmission rate. We show by mathematical modeling and numerical evaluation based on SI4455 transceiver that Pareto fronts (PF) attained by the application of techniques at least weakly dominate the referent PF; PF set cardinality can even be doubled; the joint application of techniques improves EE, reliability objectives, averaged over the entire PF, by approximately 49% and 74%, respectively.

Natural selection favoring more transmissible HIV detected in United States molecular transmission network

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters.

Performance Analysis and FPGA Prototype of Variable Rate GO-OFDMA Baseband Transmission Scheme

To fulfill the increasing demand for high speed Variable Bit Rate (VBR) broadcast services with reliable Quality of Service, Group Orthogonal-Orthogonal Frequency Division Multiple Access (GO-OFDMA) and Variable Spreading Length Multicarrier Code Division Multiple Access transmission schemes are examined over Land Mobile Satellite (LMS) channel. This paper presents the performance analysis of aforementioned multiple access transmission schemes in terms of computation complexity, Peak-to-Average-Power-Ratio (PAPR) and the average Bit Error Rate (BER) performance over L-band LMS channel. Minimum Mean Square Equalizer (MMSE) equalizer is employed at the receiver. Based on the simulation results, GO-OFDMA stems out to be a better variable rate transmission scheme in respect of both PAPR and BER performance for higher data rate users. Further, GO-OFDMA based VBR transmitter and MMSE equalizer are prototyped on commercially available virtex5 xc5vlx110t–1ff1136 Field Programmable Gate Array (FPGA) device. A block-by-block analysis of hardware resource utilization and power dissipation for the VBR GO-OFDMA transmitter is provided. The achieved baud rate for VBR GO-OFDMA transmission scheme on FPGA is 103.874 Msps. Thus, GO-OFDMA transmission scheme could be appropriate for VBR communication over LMS channel.

Outage of Periodic Downlink Wireless Networks With Hard Deadlines

We consider a downlink periodic wireless communications system, where multiple access points cooperatively transmit packets to a number of devices, e.g., actuators in an industrial control system. Each period consists of two phases: an uplink training phase and a downlink data transmission phase. Each actuator must successfully receive its unique packet within a single transmission phase; else, an outage is declared. Such an outage can be caused by two events: a transmission error due to transmission at a rate that the channel cannot actually support or time overflow, where the downlink data phase is too short, given the channel conditions to successfully communicate all the packets. We determine the closed-form expressions for the time overflow probability when there are just two field devices, as well as the transmission error probability for an arbitrary number of devices. In addition, we provide upper and lower bounds on the time overflow probability for an arbitrary number of devices. We propose a novel variable-rate transmission method that eliminates time overflow. Detailed system-level simulations are used to identify system design guidelines, such as the optimal amount of training time, as well as for benchmarking the proposed system design versus non-cooperative cellular, cooperative fixed-rate, and cooperative relaying.

Performance improvement of vehicular ad-hoc network with nature inspired biological computing algorithm

VANET is a hybrid ad-hoc network between vehicles and road side units. Due to the high mobility of nodes, to find a stable data communication route in VANET is an open challenge. This paper introduces a new hybrid routing algorithm to find a stable route by using zone-based routing (ZBR), fuzzy logic, and NIBC algorithm. In proposed algorithm ZBR is used to divide the network into small and stable zones, fuzzy logic is used to find the quality of links between nodes, and NIBC to find the stable route in short time. Six techniques for the VANET has been implemented and compared in this paper. NS2.34 network simulator is used for simulations. Simulations were carried out for variable transmission rate and variable speed. Five performance parameters have been taken to analyse the results. Simulation results have shown that NIBC algorithms improve the performance of VANET.

Nanostructured electrospun nonwovens of poly(ε-caprolactone)/quaternized chitosan for potential biomedical applications

Blend solutions of poly(ε-caprolactone) (PCL) and N-(2-hydroxy)-propyl-3-trimethylammonium chitosan chloride (QCh) were successfully electrospun. The weight ratio PCL/QCh ranged in the interval 95/5–70/30 while two QCh samples were used, namely QCh1 (DQ¯ = 47.3%; DPv¯ = 2218) and QCh2 (DQ¯ = 71.1%; DPv¯ = 1427). According to the characteristics of QCh derivative and to the QCh content on the resulting PCL/QCh nonwoven, the nanofibers displayed different average diameter (175 nm–415 nm), and the nonwovens exhibited variable porosity (57.0%–81.6%), swelling capacity (175%–425%) and water vapor transmission rate (1600 g m−2 24 h–2500 g m−2 24 h). The surface hydrophilicity of nonwovens increases with increasing QCh content, favoring fibroblast (HDFn) adhesion and spreading. Tensile tests revealed that the nonwovens present a good balance between elasticity and strength under both dry and hydrated state. Results indicate that the PCL/QCh electrospun nonwovens are new nanofibers-based biomaterials potentially useful as wound dressings.

Global stability and persistence of HIV models with switching parameters and pulse control

This paper studies some HIV (Human Immunodeficiency Virus) models with switching parameters and pulse control. By taking into account of the effects of reverse transcriptase inhibitor (RTI) drugs, protease inhibitor (PI) drugs and the variable transmission rate, we propose a new HIV model with switching parameters and derive a general threshold value that measures the persistence of the disease. Furthermore, pulse vaccination is applied to the HIV model and some novel threshold conditions are established to ensure the existence and stability of the periodic infection-free solution. In contrast to the standard HIV models, it is shown that our proposed models are more practical and useful. Moreover, pulse vaccination strategies are proven to be more effective in determining whether or not the disease is eradicated. Numerical simulations are carried out to illustrate the effectiveness of the obtained results, and future research directions are suggested.