Search

This paper presents a new design for a low-profile, miniaturized microstrip diplexer, specifically engineered for qubits mobile wireless networks, emphasizing the crucial roles of computational modeling and fabrication in its development. Employing AWR simulator for the modeling of microstrip device, this approach integrates Step Impedance Resonators as well as T-shaped resonators, resulting in a diplexer that meets the stringent demands of modern communication systems while exhibiting remarkable performance characteristics, including the ability for demonstrating negative group delay (NGD). The importance for microstrip diplexers lies in their ability to efficiently separate and route signals at different frequencies, thereby enhancing overall system performance. Operating at resonant frequencies of 3.19 GHz and 4.42 GHz, this diplexer achieves highly narrowband performance, effectively minimizing interference and enhancing signal integrity within resonant frequency bands that are vital requirements for contemporary mobile applications. The diplexer achieves insertion losses of merely 0.4 dB and 0.5 dB at the respective channels, alongside return losses of 16 dB and 33 dB. Furthermore, it boasts an isolation level exceeding 30 dB between the two channels, ensuring reliable communication with minimal cross-talk, which is vital for maintaining high-quality signal transmission in crowded spectral environments. An integration for NGD not only allows for advanced signal processing capabilities but as well facilitates faster data transmission and improved system responsiveness, attributes increasingly critical in today's fast-paced mobile landscape. Constructed on an FR4 substrate, the low-profile topology for this diplexer makes it particularly suitable for integration into space-constrained mobile devices and wireless communication systems, where considerations of size and weight are paramount. The design's effectiveness has been thoroughly validated through comprehensive simulations and experimental measurements, demonstrating a strong correlation between predicted and observed consequences. [ABSTRACT FROM AUTHOR]

The cross-regional spread of epidemics, such as COVID-19, poses significant challenges due to the spillover of false-negative individuals resulting from incubation periods, detection errors, and individual irrationality. This study develops a stylized model to address the trade-offs faced by the planner in designing optimal lockdown policies: curbing the cross-regional spread of epidemics while balancing economic costs and ensuring long-term sustainability. The model integrates a queuing network to calculate the influx of false-negative cases, which more accurately reflects real-world scenarios and captures the complexity of regional interactions during an outbreak. Subsequently, a SIR network is used to estimate the spread of infections. Unlike similar studies, our approach focuses specifically on the cross-regional dynamics of epidemic spread and the formulation of optimal lockdown policies that consider both public health and economic impacts. By optimizing the lockdown threshold, the model aims to minimize the total costs associated with lockdown implementation and infection spread. Our theoretical and numerical results underscore the crucial role of timely nucleic acid testing in reducing infection rates and highlight the delicate balance between public health benefits and economic sustainability. These findings provide valuable insights for developing sustainable epidemic management strategies. [ABSTRACT FROM AUTHOR]

Abstract Tuberculosis (TB) is a highly contagious disease that remains a global concern affecting numerous countries. Kazakhstan has been facing considerable challenges in TB prevention and treatment for decades. This study aims to understand TB transmission dynamics by developing and comparing statistical and deterministic models: Seasonal Autoregressive Integrated Moving Average (SARIMA) and the basic Susceptible-Infected-Recovered (SIR). TB data from 2014 to 2019 were collected from the Unified National Electronic Health System (UNEHS) using retrospective quantitative analysis. SARIMA models were able to capture seasonal variations, with Model 2 exhibiting superior predictive accuracy. Both models showed declining TB incidence and revealed a notable predictive performance evaluated by statistical metrics. In addition, the SIR model calculated the basic reproduction number ( $${R_0}$$ ) below 1, indicating a receding epidemic. Models proved the capability of each to accurately capture trends (SARIMA) and provide mathematical insights (SIR) into TB transmission dynamics. This study contributes to the general knowledge of TB transmission dynamics in Kazakhstan thus laying the foundation for more comprehensive studies on TB and control strategies.

Abstract For simplicity of mathematical modeling of epidemic spreading, the assumption is that hosts have identical rates of disease-causing contacts. However, in the real world, the scenario is different. The network-based framework allows us to capture the complex interdependencies and structural heterogeneity present in real-world systems. We examine two distinct scenarios involving the dynamics of susceptible-infected-recovered (SIR) in interconnected networks. In the first part, we show how the epidemic threshold of a contact network changes as a result of being coupled with another network for a fixed infection strength. The model employed in this work considers both the contact networks and interconnections as generic. We have depicted the epidemic threshold curve for interconnected networks, considering the assumption that the infection could be initially present in either one or both of the networks. If the normalized infection strengths are above the threshold curve, the infection spreads, whereas if the normalized infection strengths are below the threshold curve, the disease does not spread. This is true for any level of interconnection. In the second part, we investigate the spillover phenomenon, where the disease in a novel host population network comes from a reservoir network. We have observed a clear phase transition when the number of links or the inter-network infection rate exceeds a certain threshold, keeping all other parameters constant. We observe two regimes for spillover: a major spillover region and a minor spillover region based on interpopulation links (fraction of links between two networks) and inter-network infection strength (infection rate between reservoir and host network). If the interpopulation links and inter-network infection strength are in the major spillover region, the spillover probability is high, while if the former parameters are in the minor spillover region, the spillover probability is low. When the number of infected individuals within a reservoir network is nearly equal, and the inter-network infection strength remains constant, the threshold number of links required to achieve the spillover threshold condition varies based on the network topology. Overall, this work contributes to the understanding of SIR dynamics in interconnected networks and sheds light on the behavior of epidemics in complex systems.

The reproduction number, R0, is an important parameter in epidemic models. It is interpreted as the average number of new cases resulted from each infected individual during the course of infection. In this paper, the R0 estimates since the outbreak of COVID-19 till 10 August 2020 for eight countries were computed using the package R{eSIR}. The computed values were examined and compared with the daily R0 estimates obtained by a static SIR model by aligning the days of infection, assuming a fixed number of days for the infected person to become confirmed/recover/die. The results showed that running R{eSIR} to obtain R0 estimates provided an easy mean of exploring epidemic data. Care must be taken in the interpretation of R0 as a measure of severity of the spread of an epidemic. Other factors, such as imported cases, need to be considered.

Abstract Avoiding physical contact is regarded as one of the safest and most advisable strategies to follow to reduce pathogen spread. The flip side of this approach is that a lack of social interactions may negatively affect other dimensions of health, like induction of immunosuppressive anxiety and depression or preventing interactions of importance with a diversity of microbes, which may be necessary to train our immune system or to maintain its normal levels of activity. These may in turn negatively affect a population’s susceptibility to infection and the incidence of severe disease. We suggest that future pandemic modelling may benefit from relying on ‘SIR+ models’: epidemiological models extended to account for the benefits of social interactions that affect immune resilience. We develop an SIR+ model and discuss which specific interventions may be more effective in balancing the trade-off between minimizing pathogen spread and maximizing other interaction-dependent health benefits. Our SIR+ model reflects the idea that health is not just the mere absence of disease, but rather a state of physical, mental and social well-being that can also be dependent on the same social connections that allow pathogen spread, and the modelling of public health interventions for future pandemics should account for this multidimensionality.

Estimating the COVID‐19 infection fatality rate, inferring the latent incidence and predicting the future epidemic evolution are critical to public health surveillance, but often challenging due to limited data availability or quality. Recently, a Bayesian framework combining time series deconvolution of deaths with a parametric Susceptible–Infectious–Recovered (SIR) model was proposed by Irons and Raftery, 2021. We assess the parameter identifiability of the model using the profile likelihood approach and simulations, when only the time series of deaths and seroprevalence survey data are available. The robustness of the model to the more complex but also more realistic Susceptible–Exposed–Infectious–Recovered (SEIR)‐based epidemics is evaluated through simulations; the influence of potential biases in the serosurveys on the inference is also investigated. We use a stationary first‐order autoregressive prior to account for the variability of transmission rate over time. The results suggest that the model is relatively robust to SEIR‐based epidemics, especially when the reproductive number is low, given sufficient information from serosurveys or priors. However, the lack of parameter identifiability under limited data availability cannot be neglected. We apply the model to infer the COVID‐19 infections in Ontario and Quebec, Canada during the Omicron era. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, several forecasting models were released to predict the spread of the virus along variables vital for public health policymaking. Of these, the susceptible--infected--recovered (SIR) compartmental model was the most common. In this paper, we investigated the forecasting performance of The University of Texas COVID-19 Modeling Consortium SIR model. We considered the following daily outcomes: hospitalizations, ICU patients, and deaths. We evaluated the overall forecasting performance, highlighted some stark forecast biases, and considered forecast errors conditional on different pandemic regimes. We found that this model tends to overforecast over the longer horizons and when there is a surge in viral spread. We bolstered these findings by linking them to faults with the SIR framework itself. [ABSTRACT FROM AUTHOR]

The reproduction number, R 0 , is an important parameter in epidemic models. It is interpreted as the average number of new cases resulted from each infected individual during the course of infection. In this paper, the R 0 estimates since the outbreak of COVID-19 till 10 August 2020 for eight countries were computed using the package R{eSIR}. The computed values were examined and compared with the daily R 0 estimates obtained by a static SIR model by aligning the days of infection, assuming a fixed number of days for the infected person to become confirmed/recover/die. The results showed that running R{eSIR} to obtain R 0 estimates provided an easy mean of exploring epidemic data. Care must be taken in the interpretation of R 0 as a measure of severity of the spread of an epidemic. Other factors, such as imported cases, need to be considered. [ABSTRACT FROM AUTHOR]

We focus on an epidemiological model (the archetypical SIR system) defined on graphs and study the asymptotic behavior of the solutions as the number of vertices in the graph diverges. By relying on the theory of graphons we provide a characterization of the limit and establish convergence results. We also provide approximation results for both deterministic and random discretizations. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel, compact, and high-performance dual-band filtering power divider is presented. The proposed circuit is constructed from a dual-band power divider and open-circuited stepped-impedance resonators. The dual-band power divider is realized by using two-section impedance transformers. To achieve the filtering function, three open-circuited stepped-impedance resonators are attached to three ports of the dual-band power divider to create a transmission zero between two operating bands. The proposed dual-band filtering power divider has a compact size, ease of fabrication, and high performance in terms of low insertion loss, high isolation, and good return losses. In order to validate the design concept, a dual-band filtering power divider operating at 1 GHz and 2.3 GHz with the size of 0.26 λg×0.23λg is designed, fabricated, and measured. The experiment results show that the dual-band filtering power divider has better than 37.1 dB isolation, 23.3 dB return loss, and 3.49 dB insertion loss at two central frequencies. The measured fractional bandwidths (|S11| < -14 dB) are 30.7% for the first passband and 13.8% for the second.

This study addresses a research gap by introducing fractional order derivatives into the SIR model for tracking COVID-19 in Malaysia. The Caputo sense fractional derivative and the Adams Bashforth Moulton method are employed to analyse the COVID-19 behavior and stability. By manipulating fractional order derivative values, this study investigates their impact on key SIR parameters, observing that lower values accelerate the attainment of asymptotic behavior in populations. The stability analysis reveals two equilibrium points: an unstable disease-free equilibrium and a stable endemic equilibrium within the system. This pioneering exploration of fractional order derivatives in the context of Malaysia's COVID-19 modeling contributes valuable insights, enhancing our understanding the behavior of the disease.

With the continuous evolution of the coronavirus, the Omicron variant has gradually replaced the Delta variant as the prevalent strain. Their inducing epidemics last longer, have a higher number of asymptomatic cases, and are more serious. In this article, we proposed a nonautonomous time-delayed susceptible-infected-removed (NATD-SIR) model to predict them in different regions of China. We obtained the maximum and its time of current infected persons, the final size, and the end time of COVID-19 epidemics from January 2022 in China. The method of the fifth-order moving average was used to preprocess the time series of the numbers of current infected and removed cases to obtain more accurate parameter estimations. We found that usually the transmission rate $ \beta(t) $ was a piecewise exponential decay function, but due to multiple bounces in Shanghai City, $ \beta(t) $ was approximately a piecewise quadratic function. In most regions, the removed rate $ \gamma(t) $ was approximately equal to a piecewise linear increasing function of (a*t+b)*H(t-k), but in a few areas, $ \gamma(t) $ displayed an exponential increasing trend. For cases where the removed rate cannot be obtained, we proposed a method for setting the removed rate, which has a good approximation. Using the numerical solution, we obtained the prediction results of the epidemics. By analyzing those important indicators of COVID-19, we provided valuable suggestions for epidemic prevention and control and the resumption of work and production.

There has been a lot of interest in microstrip diplexers lately due to their potential use in numerous wireless and computer communication technologies, including radio broadcasts, mobile phones, broadband wireless, and satellite-based communication systems. It can do this because it has a communication channel that can combine two distinct filters into one. This article presents a narrow-band microstrip diplexer that uses a stepped impedance resonator, a uniform impedance resonator, tiny square patches, and a meander line resonator. The projected diplexer might be made smaller than its initial dimensions by utilizing the winding construction. To model the microstrip diplexer topology for WiMAX and WIFI/WLAN at 1.66 GHz and 2.52 GHz, the Advanced Wave Research (AWR) solver was employed. It exhibited an insertion loss of 3.2 dB and a return loss of 16 dB for the first channel, while the insertion loss and return loss were 2.88 dB and 21 dB, respectively, for the second channel. When both filters were simulated, the band isolation was 31 dB. The projected microstrip diplexer has been fabricated using an FR4 epoxy laminate with dimensions of 32 × 26 mm2. The simulated S-parameters phase and group delay closely matched the measurements. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANET) has attracted many researchers in recent days because of its inevitable applications in smart transportation structure, autonomous vehicles and Internet of Vehicles (IoV). Establishing and maintaining secured end to end connections in a VANET is challenging due to the dynamic changing environment and due to malicious vehicles. To overcome this problem, this paper proposes novel secured protocol TT-SHO which integrates the Tent Tuned Spotted Hyena Optimization (TT-SHO) for routing algorithm and Hybrid Chaotic Encryption for secured data transmission. The proposed routing protocol adopts the TT-SHO for shortest path optimization and transmits the chaotic encrypted data in the shortest path by maintaining the Quality of Services (QoS). The experimentation is carried out in terms of latency (5 ms), Packet Delivery Ratio (PDR) (90), throughput (1 bps) and also compared with existing frameworks. Final results proved that the proposed TT-SHO with Hybrid Chaotic Encryption is suitable for VANET environment by providing safe, reliable and robust data transmission. And the model proves that it provides more reliable secured data transmission by maintaining QoS. [ABSTRACT FROM AUTHOR]

Traditional online communities suffer from false, repetitive or low-level content, with blockchain technology able to solve these problems. Specifically, the incentive mechanism is the blockchain's core value, including positive and negative incentive mechanisms. The former strengthens people's behaviour positively, while the latter, on the contrary, adopts mandatory methods such as punishment to eliminate the occurrence of certain types of behaviour. The negative incentive mechanism is the key factor to solve the problems presented above that traditional online communities face. Specifically, this article develops a solution that utilises the negative incentive mechanism, based on the classic infectious disease model (SIR model), introduces smart nodes, puts forward the SSIR model of information dissemination in the blockchain network community, and establishes a set of differential equations reflecting the information dissemination rules. Based on the parameter assumption and solving the equations with MATLAB, this article compares and reveals the changes of different user types on the SIR and SSIR models. Furthermore, we utilise the data collected from the Steemit blockchain community and Sina Weibo platform and apply the Social Network Analysis method to compare and analyse the information dissemination between the blockchain and the traditional network community. The research results highlight that the negative incentive mechanism in the blockchain network community affords a more rational behaviour of user information dissemination, a simpler interaction between users, and reducing to a certain extent the dissemination of 'distorted' or 'uncertain' information. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to reach the meaning of a word which the Holy Qur'an does not explicitly declare its essence through the narrations of the occasions of revelation (asbāb al-nuzūl, .(أسباب النزول In line with this purpose, the reasons of the commentators for making different preferences between the narrations of the occasions of revelation are examined. What is meant by a phrase that the Qur'an does not explicitly declare is the secret mentioned in the first verses of Surat al-Tahrīm as something that the Prophet wanted to be kept secret. It is seen that there are different narrations about the nature of this secret in the books of tafsīr, hadīth, and the book of al-asbāb al-nuzūl, and the preferences of the commentators between the related narrations differ. In the sources, two narrations, which are famous with the names "Honey" and "Māriya", are commonly mentioned about the reason for the revelation of the group of verses in which this secret is mentioned. The commentators who preferred the "Honey" narration mostly relied on the grounds that the narration was reported in the Sahihayn and that it was not morally right to attribute the "Māriya" narration to the Prophet. Those who prefer the narration of "Māriyah", on the other hand, are of the opinion that the narration is both mentioned in authentic sources and is more appropriate to the context of the verse and the sīrah-nuzūl relationship. Since the owner of the secret in question is expressed as the Prophet in the verse, the sīrah-nuzūl relationship was taken into consideration in order to clarify the issue and to create the possibility of preference among the occasions of revelation. In terms of both the context of the verses and the sīrah-nuzūl relationship, it is concluded that it is more appropriate to accept the narration of "Māriya" as the occasion of revelation of the relevant verses, and therefore, the secret in question is the information that the Prophet shared with Hafsa that he had forbidden Māriya to her. [ABSTRACT FROM AUTHOR]

Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages meta- paths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network. [ABSTRACT FROM AUTHOR]

With the continuous evolution of the coronavirus, the Omicron variant has gradually replaced the Delta variant as the prevalent strain. Their inducing epidemics last longer, have a higher number of asymptomatic cases, and are more serious. In this article, we proposed a nonautonomous time-delayed susceptible-infected-removed (NATD-SIR) model to predict them in different regions of China. We obtained the maximum and its time of current infected persons, the final size, and the end time of COVID-19 epidemics from January 2022 in China. The method of the fifth-order moving average was used to preprocess the time series of the numbers of current infected and removed cases to obtain more accurate parameter estimations. We found that usually the transmission rate β (t) was a piecewise exponential decay function, but due to multiple bounces in Shanghai City, β (t) was approximately a piecewise quadratic function. In most regions, the removed rate γ (t) was approximately equal to a piecewise linear increasing function of (a*t+b)*H(t-k), but in a few areas, γ (t) displayed an exponential increasing trend. For cases where the removed rate cannot be obtained, we proposed a method for setting the removed rate, which has a good approximation. Using the numerical solution, we obtained the prediction results of the epidemics. By analyzing those important indicators of COVID-19, we provided valuable suggestions for epidemic prevention and control and the resumption of work and production. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, several forecasting models were released to predict the spread of the virus along variables vital for public health policymaking. Of these, the susceptible–infected–recovered (SIR) compartmental model was the most common. In this paper, we investigated the forecasting performance of The University of Texas COVID-19 Modeling Consortium SIR model. We considered the following daily outcomes: hospitalizations, ICU patients, and deaths. We evaluated the overall forecasting performance, highlighted some stark forecast biases, and considered forecast errors conditional on different pandemic regimes. We found that this model tends to overforecast over the longer horizons and when there is a surge in viral spread. We bolstered these findings by linking them to faults with the SIR framework itself.

The relay function of unmanned aerial vehicles (UAVs) is playing an increasingly important role in wireless communication systems, as UAVs often perform tasks at high altitudes and can avoid the impact of ground obstructions on the communication links between ground communication devices, thereby enhancing the reliability and communication capacity of the links. Meanwhile, UAVs have more degrees of freedom and can achieve optimal position deployment and robust anti-jamming performance through flexible adjustment of spatial position. However, the premise for UAVs to achieve the above advantages is accurate modeling and analysis of the wireless communication channel, as well as the deployment of reasonable joint geographic-electromagnetic domain optimization (JGEDO) algorithms to fully utilize their own degrees of freedom. Due to the increasing threat posed by the current electronic warfare environment to the normal communication of UAVs, this article focuses on environments with malicious jamming and geographic obstacles, and constructs a communication system composed of a base station (BS), a relay UAV, and several remote ground robots (RGRs). On the basis of considering multipath fading and estimation error of jammer position, the expected value of instantaneous average signal-to-interference-ratio (SIR) for the entire system is derived. Taking this indicator as the optimization objective, this article develops a joint optimization algorithm for the position of the UAV and the mainlobe width of UAV antenna beams. Numerical simulation verifies the effectiveness of this algorithm compared to traditional algorithms.

Although Friedrich Hirth’s idea that Xueyantuo of Chinese sources was Sir Tardush was generally accepted for a short time, subsequent research has revealed different results. Hirth, while confident in asserting that Xue corresponds to Sir based on the Tonyukuk Inscription, faced challenges in providing conclusive evidence for the proposition that Yantuo is synonymous with Tardush. Tongdian’s identify Yantuo with Helan gave shape to new studies. Researches focused on to find out Turkish original name for Yantuo and it is understood that Yantuo has not been illuminated throughout the course of the ongoing process, leaving significant gaps. Additionally, there has been no thorough examination of the meaning of Xue and the history of the Xues. In this study, the identity equivalence established by Tongdian between Hela-Helan and Yantuo has been critically examined. Efforts have been undertaken in this study to fill the gaps in the history of Yantuo by researching records on the state of Helans until the emergence of the name Yantuo in the sources. Records on the origins of Xue and their history before Xueyantuo name appeared in sources are evaluated. It is tried to find out when Xue seized Yantuo. The fate of the Xueyantuo tribe has been questioned following the downfall of the Xueyantuo Khaganate, and an examination has been conducted to determine whether the Sir mentioned in the Tonyukuk Inscription, as a tribal name, corresponds to the Xue Tribe recorded in Chinese sources.

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of “alarm” in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.

Introduction The global impact of the coronavirus disease 2019 (COVID-19) pandemic has continually jeopardized vulnerable populations encompassing children, youth, elders, and individuals with immunodeficiency and comorbidities. Methods In recognizing the crucial role of predictive analytics in shaping public health decisions, this study utilizes a predictive design, drawing on official data from the Department of Health (DOH) in the Davao Region, Philippines, spanning 57 days from March 15 to May 10, 2020. By comparing the Susceptible, Infected, Recovered (SIR) model and the Autoregressive Integrated Moving Average (ARIMA) model, the research aims to provide a scientific foundation for informed decision-making by public health authorities. Results Analysis revealed that the SIR model emerged as the most effective in identifying trends and forecasting future cases. Despite both models indicating a substantial reduction in infection rates, caution is advised against discontinuing control and preventive measures due to the latent potential for another surge. The findings underscore the necessity for scientifically forecasted data to guide decision-makers in enhancing the responsiveness of public health services during similar and potentially worsening conditions. Conclusions Hence, this study contributes to the ongoing pandemic preparedness and responsiveness discourse. Its emphasis on predictive analytics, particularly the SIR model, offers valuable insights for authorities tasked with safeguarding public health. The significance lies in addressing the current situation in the Davao region and providing a template for future scenarios. As the world grapples with the unpredictable nature of infectious diseases, informed decision-making based on scientific forecasts becomes imperative for effective public health management. [ABSTRACT FROM AUTHOR]

Aim: A raised systemic inflammatory response correlates with poorer colorectal cancer (CRC) outcomes. Faecal immunochemical test bowel screening aims to detect early‐stage disease. We assessed the relationship between systemic inflammatory response, screen detection and CRC survival. Method: A retrospective, observational cohort study compared screen‐detected and non‐screen‐detected CRC patients undergoing resection. Systemic inflammatory response was measured using lymphocyte/monocyte, neutrophil/lymphocyte and platelet/lymphocyte ratios (LMR, NLR, PLR). Covariables were compared using χ2 testing and survival with Cox regression. Results: A total of 761 patients were included (326 screen‐detected, 435 non‐screen‐detected). Screen‐detected patients had lower systemic inflammatory response: low (<2.4) LMR (28.8% vs. 44.6%; P < 0.001), moderate (3–5) or high (>5) NLR (26.1% vs. 30.6%, P < 0.001; and 7.7% vs. 19.5%, P < 0.001) and high (>150) PLR (47.2% vs. 64.6%; P < 0.001). Median follow‐up was 63 months. On univariate analysis, non‐screen detection (hazard ratio [HR] 2.346, 95% CI 1.687–3.261; P < 0.001), advanced TNM (P < 0.001), low LMR (HR 2.038, 95% CI 1.514–2.742; P < 0.001), moderate NLR (HR 1.588, 95% CI 1.128–2.235; P = 0.008), high NLR (HR 2.382, 95% CI 1.626–3.491; P < 0.001) and high PLR (HR 1.827, 95% CI 1.326–2.519; P < 0.001) predicted poorer overall survival (OS). Non‐screen detection (HR 2.713, 95% CI 1.742–4.226; P < 0.001), TNM (P < 0.001), low LMR (HR 1.969, 95% CI 1.340–2.893; P < 0.001), high NLR (HR 2.368, 95% CI 1.448–3.875; P < 0.001) and high PLR (HR 2.110, 95% CI 1.374–3.240; P < 0.001) predicted poorer cancer‐specific survival (CSS). On multivariate analysis, non‐screen detection (HR 1.698, 95% CI 1.152–2.503; P = 0.008) and low LMR (HR 1.610, 95% CI 1.158–2.238; P = 0.005) independently predicted poorer OS. Non‐screen detection (HR 1.847, 95% CI 1.144–2.983; P = 0.012) and high PLR (HR 1.578, 95% CI 1.018–2.444; P = 0.041) predicted poorer CSS. Conclusion: Screen‐detected CRC patients have a lower systemic inflammatory response. Non‐screen detection and systemic inflammatory response (measured by LMR and PLR respectively) were independent predictors of poorer OS and CSS. [ABSTRACT FROM AUTHOR]

Ultrafiltered (UF) white cheese is one of the most consumed types of cheese and characterized by mild flavour and semi-hard texture. The aim of this research was to investigate the effect of salt concentration on the texture profile and mineral contents of this type of cheese. The cheese samples were produced salt free and using 1 %, 2.5 % and 4 % (by mass) of salt (NaCl) and ripened at 9±1 °C for 90 days. The chemical properties and texture profiles of the cheeses were analysed during 90 days of ripening. The mineral content of the cheeses was also determined at the first day of ripening. The results showed that the chemical properties, texture profile and mineral contents of the cheeses were significantly (p<0.05) influenced by changes in salt concentration. Increasing salt concentration caused an increase in the pH, protein and salt to moisture ratio, while there was a decrease in the hardness, cohesiveness, gumminess and chewiness of the cheeses. Differences in salt concentration caused changes in the contents of all minerals analysed. In conclusion, increasing concentration of salt provided more soft structure and less calcium and phosphorus contents in UF white cheese. [ABSTRACT FROM AUTHOR]

In this article, we demonstrated the minimum significant dielectric thickness of silicon dioxide (SiO2) below the diffraction limit for the confinement in the metal–insulator–metal (MIM) waveguide and utilizing this idea, we further exhibited the detail design analysis of dual‐band plasmonic meandering step impedance resonator (MSIR), which can achieve a high quality‐factor compared to a traditional step impedance resonator (SIR). MSIRs have sharper wavelength selectivity compared to SIRs, making them more suitable for applications that require precise wavelength tuning. The tunability of reflection and transmission characteristics with respect to design parameters has been reported. The designed MSIR has demonstrated a good passband response at wavelengths λ = 1414‐nm (E‐band) and 1577‐nm (L‐band) as well as turnability with design parameters. This filter achieved a large frequency spectral range of 139 nm and a high Q factor of ~3 × 104. This work enhances the capability of on‐chip filtering in the plasmonic platform. [ABSTRACT FROM AUTHOR]

This research article proposes a novel MCDM (multi-criteria decision making) technique with interval type-2 hesitant fuzzy set (IT-2 HFS). We amalgamate an algorithm of the Superiority and inferiority ranking method (SIR) method with this IT-2 HFS. The interval type-2 hesitant fuzzy sets demonstrate the uncertainty involved in the context. The SIR method analyzed the navigation of the alternatives in the directions of superiority and inferiority. The proposed interval type-2 hesitant fuzzy superiority and inferiority ranking (IT-2 HFSIR) method was used to analyze and rank Korea's industry–university collaboration performance level. In the industry–university collaboration association, research universities and industries interchange innovations. This research investigated industry–university collaboration through five dimensions: human resources, technical resources, various environments, institutions, and systems. Under these five dimensions, we consider eleven alternatives; we evaluate these alternatives under eight different criteria. By extending the SIR method with the interval type-2 hesitant fuzzy set, we can easily integrate human opinion in the computing method. We can therefore effectively handle the uncertainty and ambiguity from the available information by the interval type-2 fuzzy set. As a result, we can utilize both benefits by using them together in a combined form as the interval type-2 hesitant fuzzy set. The proposed method considers ambient intelligence from almost all aspects for real-life decision making, and it integrates humans' opinions in computing. [ABSTRACT FROM AUTHOR]

Copyright of Dokuz Eylül University Law Review is the property of Dokuz Eyluel University, Faculty of Law Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate. [ABSTRACT FROM AUTHOR]

This paper reviews changes in behavior due to the COVID-19 pandemic in various regions of Russia. Firstly, derivation of the behavioral SIR model shows that the activity level turns out to be an important factor affecting the pandemic. Secondly, I have collected data for the spread of the disease, vaccination, and public behavior (a link to this dataset is provided in Section 3). Behavior is represented by the data from Google Mobility Trends. Thirdly, correlation analysis has been applied to select the best proxies for the activity level. It is described by the intensity of visiting parks and transit stations, as well as that of retail and recreation. In addition, this research highlights special circumstances due to which responses in some regions deviate from the general trend. Both the behavioral proxies and peculiarities of the regions are the main results of my paper. [ABSTRACT FROM AUTHOR]

So far, about 770.1 million confirmed cases of COVID-19 have been counted by August 2023, and around 7 million deaths have been reported from these cases to the World Health Organization. In Ecuador, the first confirmed COVID-19 case was registered on 19 February 2020, and the country's mortality rate reached 0.43% with 12986 deaths, suggesting the need to establish a mechanism to show the virus spread in advance. This study aims to build a dynamic model adapted to health and socio-environmental variables as a multivariate model to understand the virus expansion among the population. The model is based on Susceptible-Infected-Recovered (SIR), which is a standard model in which the population is divided into six groups with parameters such as susceptible S(t), transit stage E(t), infected I(t), recovered R(t), deceased Me(t), infected asymptomatic Ia(t), infected symptomatic Is(t) and deceased by other causes M(t) to be considered and adapted. The model was validated by using consistent data from Chile and run by inconsistent data from Ecuador. The forecast error was analyzed based on the mean absolute error between real data and model forecast, showing errors within a range from 6.33% to 8.41% for Chile, with confidence a interval of 6.17%, then 3.87% to 4.70% range for Ecuador with a confidence interval of 2.59% until 23rd December 2020 of the database. The model forecasts exponential variations in biosecurity measures, exposed population, and vaccination. [ABSTRACT FROM AUTHOR]

Objective: To determine the prognostic factors in the pediatric cochlear implant (CI) outcome. Materials and methods: This prospective cohort study was conducted on 289 pediatric cases with prelingual hearing loss who received cochlear implantation. Several possible salient factors have been recorded. Auditory and speech evaluations were performed before CI, as well as 6 and 12 months after surgery, using Categories of Auditory Performance (CAP) and Speech Intelligibility Rating (SIR) tests. Results: According to univariate analysis, age at the time of surgery was a statistically significant factor. Neurological problems in the child, history of newborn infectious diseases, history of hearing aid use, proper parental cooperation, and round window approach were all significantly related to better auditory or speech outcomes. On the other hand, good parental cooperation and age (for CAP) and good parental cooperation, age, history of infectious disease, and hearing aids use (for SIR) are the significant factors in the multivariate setting. Conclusion: As evidenced by the obtained results, age, background diseases, history of rehabilitation with hearing aids, and surgical details are essential factors to be taken into account in the case-selection process. [ABSTRACT FROM AUTHOR]

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in realtime. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of datadriven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics. [ABSTRACT FROM AUTHOR]

Almost all models used in analysis of infectious disease outbreaks contain some notion of population size, usually taken as the census population size of the community in question. In many settings, however, the census population is not equivalent to the population likely to be exposed, for example if there are population structures, outbreak controls or other heterogeneities. Although these factors may be taken into account in the model: adding compartments to a compartmental model, variable mixing rates and so on, this makes fitting more challenging, especially if the population complexities are not fully known. In this work we consider the concept of effective population size in outbreak modelling, which we define as the size of the population involved in an outbreak, as an alternative to use of more complex models. Effective population size is an important quantity in genetics for estimation of genetic diversity loss in populations, but it has not been widely applied in epidemiology. Through simulation studies and application to data from outbreaks of COVID-19 in China, we find that simple SIR models with effective population size can provide a good fit to data which are not themselves simple or SIR. [ABSTRACT FROM AUTHOR]

Microwave filters with compact size are highly demandable in today's 5G communication systems. Such Filters are designed using different techniques and steps. This review article presents a survey of techniques used in reconfigurable microwave filter design. It also gives a brief overview of various applications of such filters in the communication area. [ABSTRACT FROM AUTHOR]

Replication studies help solidify and extend knowledge by evaluating previous studies' findings. Software engineering literature showed that too few replications are conducted focusing on software artifacts without the involvement of humans. This study aims to replicate an artifact-based study on software testing to address the gap related to replications. In this investigation, we focus on (i) providing a step-by-step guide of the replication, reflecting on challenges when replicating artifact-based testing research and (ii) evaluating the replicated study concerning the validity and robustness of the findings. We replicate a test case prioritization technique proposed by Kwon et al. We replicated the original study using six software programs, four from the original study and two additional software programs. We automated the steps of the original study using a Jupyter notebook to support future replications. Various general factors facilitating replications are identified, such as (1) the importance of documentation; (2) the need for assistance from the original authors; (3) issues in the maintenance of open-source repositories (e.g., concerning needed software dependencies, versioning); and (4) availability of scripts. We also noted observations specific to the study and its context, such as insights from using different mutation tools and strategies for mutant generation. We conclude that the study by Kwon et al. is partially replicable for small software programs and could be automated to facilitate software practitioners, given the availability of required information. However, it is hard to implement the technique for large software programs with the current guidelines. Based on lessons learned, we suggest that the authors of original studies need to publish their data and experimental setup to support the external replications. [ABSTRACT FROM AUTHOR]

Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages meta-paths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network.

Abstract This study discusses the behavioral analysis model of the Susceptible-Infected-Recovered (SIR) epidemic of the spread of measles based on age structure. The total population of measles is grouped into four age groups, namely the first age group (0-4 years), the second age group (5-9 years), the third age group (10-14 years) and the fourth age group (> 15 years). The steps in modeling behavior can be done by determining the equilibrium point, and the basic reproduction number and performing a global stability analysis by building the Lyapunov function. This research contributes to providing information both to the government and the community. Keywords: Epidemic Model; SIR; Lyapunov function; Measles. Abstrak Penelitian ini membahas model analisis perilaku epidemi Susceptible-Infected-Recovered (SIR) penyebaran campak berdasarkan struktur umur. Jumlah penduduk yang terkena campak dikelompokkan menjadi empat kelompok umur, yaitu kelompok umur pertama (0-4 tahun), kelompok umur kedua (5-9 tahun), kelompok umur ketiga (10-14 tahun) dan kelompok umur keempat. (> 15 tahun). Langkah-langkah dalam pemodelan perilaku dapat dilakukan dengan menentukan titik ekuilibrium, bilangan reproduksi dasar dan melakukan analisis stabilitas global dengan membangun fungsi Lyapunov. Penelitian ini memberikan kontribusi untuk memberikan informasi baik kepada pemerintah maupun masyarakat. Kata Kunci: Model Epidemi; PAK; fungsi Lyapunov; Campak. 2020MSC: 00A71.

Abstract Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.

The outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.

Abstract In this study, we model the transmission of COVID-19 by considering vaccination and quarantine interventions. The focus of our study is to measure the effect of these two interventions on controlling the spread of COVID-19. We demonstrate the use of the Kermack-McKendrik model as an SIR model for the number of people infected with COVID-19 applied in Jember, Indonesia. The model parameters are estimated using the Levenberg-Marquardt approach and the model equations are solved using the Runge-Kutta 4th-order method. Through the simulation study, we can determine the peak of the spread of COVID-19 cases and obtain several parameters related to vaccination and quarantine interventions that significantly affected the transmission rate of COVID-19. It is found that a faster rate of vaccinations will reduce the rate of transmission of COVID-19. Moreover, COVID-19 can be fully controlled if the infected patients carry out proper quarantine procedures. Keywords: COVID-19; Kermack-McKendrik; Levenberg-Marquardt; quarantine; SIR; vaccination. Abstrak Dalam penelitian ini, kami memodelkan penularan COVID-19 dengan mempertimbangkan intervensi vaksinasi dan karantina. Fokus dari penelitian kami adalah untuk mengukur pengaruh dari kedua intervensi tersebut dalam mengontrol penyebaran COVID-19. Kami mendemonstrasikan penggunaan model Kermack-McKendrik sebagai model SIR untuk kasus pasien yang terinfeksi COVID-19 di Jember, Indonesia. Parameter model diestimasi menggunakan pendekatan Levenberg-Marquardt dan menyelesaikan model menggunakan metode orde-4 Runge-Kutta. Melalui studi simulasi, kami dapat menentukan waktu puncak penyebaran kasus COVID-19 dan mendapatkan beberapa parameter terkait intervensi vaksinasi dan karantina yang berpengaruh signifikan terhadap laju penularan COVID-19. Hasil simulasi menunjukan bahwa laju vaksinansi yang cepat akan mengurangi laju penyebaran COVID-19. Selain itu, COVID-19 dapat dikontrol dengan penuh jika pasien melakukan prosedur karantina yang tepat. Kata Kunci: COVID-19; Kermack-McKendrik; Levenberg-Marquardt; karantina; vaksinasi. 2020MSC: 00A71, 92B05