Model-based Analysis Research Articles

Modeled estimates of HIV-serodifferent couples in tuberculosis-affected households in four sub-Saharan African countries.

Household-based tuberculosis (TB) contact evaluation may be an efficient strategy to reach people who could benefit from oral pre-exposure prophylaxis (PrEP) because of the epidemiological links between HIV and TB. This study estimated the number of HIV serodifferent couples in TB-affected households and potential HIV acquisitions averted through their PrEP use in 4 TB-HIV high-burden countries. We conducted a model-based analysis set in Ethiopia, Kenya, South Africa, and Uganda using parameters from population-based household surveys, systematic literature review and meta-analyses, and estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. We parameterized the nonlinear relationship between the proportion of serodifferent couples among people living with HIV and population-level HIV prevalence using Markov chain Monte Carlo methods. We integrated all parameters in a mathematical model and propagated uncertainty using a Monte Carlo approach. We estimated the HIV prevalence among adults aged 15-49 living in TB-affected households to be higher than in the general population in all 4 countries. The proportion of serodifferent couples among all couples in TB-affected households was also higher than in the general population (South Africa: 20.7% vs. 15.7%, Kenya: 15.7% vs. 5.7%, Uganda: 14.5% vs. 6.0%, Ethiopia: 4.1% vs. 0.8%). We estimated that up to 1,799 (95% UI: 1,256-2,341) HIV acquisitions in South Africa could be prevented annually by PrEP use in serodifferent couples in TB-affected households, 918 (95% UI: 409-1,450) in Kenya, 686 (95% UI: 505-871) in Uganda, and 408 (95% UI: 298-522) in Ethiopia. As couples in TB-affected households are more likely to be serodifferent than couples in the general population, offering PrEP during household TB contact evaluation may prevent a substantial number of HIV acquisitions.

Mission analysis and design of launch vehicle based on MBSE

A reusable launch vehicle is the basis for carrying out large-scale space activities and reducing the cost of space entry. It will greatly enhance the ability to enter space independently and come up with higher requirements for mission planning, demonstration, overall design, and system development of launch vehicles. Based on the model-based system engineering (MBSE) method, the forward process of model-based launch vehicle mission analysis and design is proposed first. Then, taking a launch vehicle as an example, the mission analysis and design are carried out, the mission is defined, the stakeholder requirements and mission requirements are identified, the mission application scenario is analyzed, the mission architecture is built, and the mission requirements model is established. Finally, by establishing the traceability relationship of mission requirements, the forward process of launch vehicle mission analysis is preliminarily realized, and the feasibility of the process is verified. It is of great engineering significance to improve the efficiency of launch vehicle mission analysis and system design.

6301857 A model-based cost-effectiveness analysis of epidural blood patching versus conservative management in the treatment of post dural puncture headache

6301857 A model-based cost-effectiveness analysis of epidural blood patching versus conservative management in the treatment of post dural puncture headache

A model-based quantitative analysis of efficacy and associated factors of platelet rich plasma treatment for osteoarthritis: Erratum.

A model-based quantitative analysis of efficacy and associated factors of platelet rich plasma treatment for osteoarthritis: Erratum.

Electric power prediction of a two-body hinge-barge wave energy converter using machine learning techniques

Investigating the electric power generated by wave energy converters (WECs) has attracted increasing attention along with the developments of wave energy converting technologies in recent years. Electric power estimation currently depends on wave-to-wire model-based numerical analysis and experimental studies of scale-down prototypes. However, traditional numerical simulations are time-consuming and expensive for power orediction of a full-scale WECs operated in real sea. More importantly, they cannot accurately predict WECs’ power performance in real time, thereby cannot be used for prediction-based real-time control. To address those problems, four machine learning techniques are employed as an alternative to traditional, complex physical modelling processes to evaluate and predict the generated electric power, based on historical experimental data of a two-body hinge-barge WEC. Comparative results of a Backpropagation (BP) Neural Network algorithm, a Long Short-Term Memory (LSTM) algorithm, a Support Vector Machine (SVM) algorithm and a Radial Basis Neural Network (RBFF) algorithm indicate that machine-learning-based approaches can accurately predict the electric performance of this complex multi-degree-of-freedom WEC, where the coefficient of determination (R2) of the BP Neural Network on the test dataset is up to 0.9987.

Transient deoxyhemoglobin formation as a contrast for perfusion MRI studies in patients with brain tumors: a feasibility study.

Background: Transient hypoxia-induced deoxyhemoglobin (dOHb) has recently been shown to represent a comparable contrast to gadolinium-based contrast agents for generating resting perfusion measures in healthy subjects. Here, we investigate the feasibility of translating this non-invasive approach to patients with brain tumors. Methods: A computer-controlled gas blender was used to induce transient precise isocapnic lung hypoxia and thereby transient arterial dOHb during echo-planar-imaging acquisition in a cohort of patients with different types of brain tumors (n = 9). We calculated relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit time (MTT) using a standard model-based analysis. The transient hypoxia induced-dOHb MRI perfusion maps were compared to available clinical DSC-MRI. Results: Transient hypoxia induced-dOHb based maps of resting perfusion displayed perfusion patterns consistent with underlying tumor histology and showed high spatial coherence to gadolinium-based DSC MR perfusion maps. Conclusion: Non-invasive transient hypoxia induced-dOHb was well-tolerated in patients with different types of brain tumors, and the generated rCBV, rCBF and MTT maps appear in good agreement with perfusion maps generated with gadolinium-based DSC MR perfusion.

A study on the improvement of bicycle transportation in Sivas city using hybrid multi-criteria model based network analysis

AbstractThe bicycle stands out as a sustainability-friendly vehicle due to its benefits for health, the environment, and the economy. However, the deficiencies and inadequacies in the cycling infrastructure prevent a safe and comfortable riding environment, in addition to indirectly preventing the widespread use of bicycles. This study aims to integrate a few disconnected bicycle routes in a city and create a bicycle road network that will allow the efficient use of bicycles in the urban area by using a Hybrid Multi-Criteria Model-Based Network Analysis. In the study, considering the physical, social, and visual criteria that should be used when determining bicycle paths, the suitability weight of each road line for bicycle transportation was determined. In this step, the bicycle path network was created using the weighted roads between the important points of the city in terms of tourism, trade, education, health, culture, transportation, and recreation areas. The most suitable bicycle routes were generated by network analysis considering the weighted roads that were determined by a model hybridized with the analytic hierarchy process (AHP) and analytic network process (ANP) methods and minimum distances. When the eligibility checks of the obtained routes were made, it was determined that while 99.7% of the roads passed through moderately suitable, suitable, and very suitable roads, only 0.3% of the new routes passed through unsuitable roads. The findings revealed that both efficient and less costly bicycle lines can be created using the proposed method.

Cost-effectiveness of single-visit cervical cancer screening in KwaZulu-Natal, South Africa: a model-based analysis accounting for the HIV epidemic.

Women living with human immunodeficiency virus (WLHIV) face elevated risks of human papillomavirus (HPV) acquisition and cervical cancer (CC). Coverage of CC screening and treatment remains low in low-and-middle-income settings, reflecting resource challenges and loss to follow-up with current strategies. We estimated the health and economic impact of alternative scalable CC screening strategies in KwaZulu-Natal, South Africa, a region with high burden of CC and HIV. We parameterized a dynamic compartmental model of HPV and HIV transmission and CC natural history to KwaZulu-Natal. Over 100 years, we simulated the status quo of a multi-visit screening and treatment strategy with cytology and colposcopy triage (South African standard of care) and six single-visit comparator scenarios with varying: 1) screening strategy (HPV DNA testing alone, with genotyping, or with automated visual evaluation triage, a new high-performance technology), 2) screening frequency (once-per-lifetime for all women, or repeated every 5 years for WLHIV and twice for women without HIV), and 3) loss to follow-up for treatment. Using the Ministry of Health perspective, we estimated costs associated with HPV vaccination, screening, and pre-cancer, CC, and HIV treatment. We quantified CC cases, deaths, and disability-adjusted life-years (DALYs) averted for each scenario. We discounted costs (2022 US dollars) and outcomes at 3% annually and calculated incremental cost-effectiveness ratios (ICERs). We projected 69,294 new CC cases and 43,950 CC-related deaths in the status quo scenario. HPV DNA testing achieved the greatest improvement in health outcomes, averting 9.4% of cases and 9.0% of deaths with one-time screening and 37.1% and 35.1%, respectively, with repeat screening. Compared to the cost of the status quo ($12.79 billion), repeat screening using HPV DNA genotyping had the greatest increase in costs. Repeat screening with HPV DNA testing was the most effective strategy below the willingness to pay threshold (ICER: $3,194/DALY averted). One-time screening with HPV DNA testing was also an efficient strategy (ICER: $1,398/DALY averted). Repeat single-visit screening with HPV DNA testing was the optimal strategy simulated. Single-visit strategies with increased frequency for WLHIV may be cost-effective in KwaZulu-Natal and similar settings with high HIV and HPV prevalence.

Ensemble model-based analysis of the effects of environmental factors on the distribution and chemical composition of Asparagus cochinchinensis (Lour.) Merr.

Asparagus cochinchinensis (Lour.) Merr., is a traditional Chinese herbal medicine with pharmacological effects such as cough and asthma suppression, anti-inflammation, and immunity enhancement. Currently, as a wild resource it is scarce and its distribution has not been well defined. Especially in the context of climate change, the effect of the environment on its chemical composition is also unknown. In this study, we constructed an ensemble model of species distribution for A. cochinchinensis to predict its current and future potential suitable habitat areas, and explored the relationship between its chemical composition and key environmental factor using statistical analysis. The results showed that the Mean Diurnal Range (Bio2) contributed 67.74% to the model and was the key environmental factor influencing the distribution of A. cochinchinensis. The current habitat of A. cochinchinensis was mainly distributed in south-central and southeastern China, such as Sichuan, Chongqing, Guangxi, Guangdong and Fujian. Under the influence of climate change, its potential habitat would gradually decrease, especially in highly suitable areas. The results of chemical analysis showed that the quality of A. cochinchinensis was higher in Neijiang than other locations, which might be related to the fact that Bio2 in Neijiang was most suitable for growth. In addition, the correlation results showed that 12 out of 36 chemical indicators had significant correlation with Bio2, and all of them except protein and Tyr had significantly negative correlation with Bio2, indicating that the environment affected the biosynthesis of chemical composition of A. cochinchinensis. The results of the study provide theoretical guidance for the cultivation, management, and sustainable use of A. cochinchinensis resources, as well as a theoretical basis for their quality improvement.

Vaccination and screening strategies to accelerate cervical cancer elimination in Norway: a model-based analysis.

Experts have proposed an 'EVEN FASTER' concept targeting age-groups maintaining circulation of human papillomavirus (HPV). We explored effects of the vaccination component of these proposals compared with cervical cancer (CC) screening-based interventions on age-standardized incidence rate (ASR) and CC elimination (<4 cases/100,000) timing in Norway. We used a model-based approach to evaluate HPV vaccination and CC screening scenarios compared with a status-quo scenario reflecting previous vaccination and screening. For cohorts ages 25-30 years, we examined 6 vaccination scenarios that incrementally increased vaccination coverage from current cohort-specific rates. Each vaccination scenario was coupled with three screening strategies that varied screening frequency. Additionally, we included 4 scenarios that alternatively increased screening adherence. Population- and cohort-level outcomes included ASR, lifetime risk of CC, and colposcopy referrals. Several vaccination strategies coupled with de-intensified screening frequencies lowered ASR, but did not accelerate CC elimination. Alternative strategies that increased screening adherence could both accelerate elimination and improve ASR. The vaccination component of an 'EVEN FASTER' campaign is unlikely to accelerate CC elimination in Norway but may reduce population-level ASR. Alternatively, targeting under- and never-screeners may both eliminate CC faster and lead to greater health benefits compared with vaccination-based interventions we considered.

Contralateral Neck-shaft Angle Lower Than 130° Is Associated With Clinical Failure in Nongeriatric Individuals: Analysis of the National Femoral Neck Fracture Database of 1066 Patients.

Treatment of femoral neck fractures in patients who are nongeriatric (≤ 60 years) is challenging because of high failure rates. Anatomic parameters influence the biomechanical environment for fracture healing, but their associations with clinical prognosis remains unclear. (1) Which anatomic parameter that is identifiable on pelvic radiographs shows a statistical correlation with a higher risk of clinical failure defined as nonunion, avascular necrosis (AVN), reoperation, and functional failure (decrease in Harris hip score reaching the minimum clinically important difference) in the screw fixation of femoral neck fractures among nongeriatric patients? (2) How does the influence of anatomic parameters on clinical prognosis manifest: directly or mediated by additional mechanisms? This retrospective, multicenter study used a nationwide database in China. Between January 2014 and December 2020, we evaluated 1066 patients with femoral neck fractures with a median age of 53 years (interquartile range 46 to 56) and median follow-up period of 62 months. Anatomic parameters including femoral neck-shaft angle (NSA), femoral head radius, femoral neck width, femoral offset, acetabular center-edge angle, and acetabular sharp angle were variables of interest. The primary outcome was clinical failure including nonunion, AVN, reoperation, and functional failure (decrease in Harris hip score reaching the minimum clinically important difference). Risk factors for failure were first filtered using the Bayesian information criterion and then assessed with multiple regression adjusting for confounders. The mediation effect was further explored using model-based causal mediation analysis with a quasi-Bayesian Monte Carlo method. Of all anatomic parameters we assessed, the contralateral NSA was associated with clinical failure, after adjusting for all potential covariates and confounding variables (adjusted odds ratio 0.92 [95% confidence interval 0.89 to 0.95]; p < 0.001). The optimal threshold for the NSA was 130°, with the highest Youden index of 0.27. Patients with an NSA < 130° (41% [441 of 1066]) demonstrated an increased occurrence of nonunion (15% [68 of 441] versus 5% [33 of 625]; p < 0.001), AVN (32% [141 of 441] versus 22% [136 of 625]; p < 0.001), functional failure (25% [110 of 441] versus 15% [93 of 625]), and reoperations (28% [122 of 441] versus 13% [79 of 625]). The impact of an NSA less than 130° on clinical failure was direct and substantially mediated by the type of displaced fracture (mediation proportion: 18.7%). In our study of screw fixations for femoral neck fractures among nongeriatric patients, we identified that a contralateral NSA < 130° correlates with an increased risk of clinical failure including nonunion, AVN, functional failure, and reoperation. The effect is either direct or mediated through displaced fracture types. This is important for surgeons in order to recognize the elevated rate of clinical failure and nature of the challenging biomechanical environment, which should guide them in refining surgical details and selecting appropriate fixation and rehabilitation plans. Approaches to managing these fractures require further validation with large-scale clinical trials. Level III, prognostic study.

A millennium of arable land use – the long-term impact of tillage and water erosion on landscape-scale carbon dynamics

Abstract. In the last decades, soils and their agricultural management have received great scientific and political attention due to their potential to act as a sink of atmospheric carbon dioxide (CO2). Agricultural management has strong potential to accelerate soil redistribution, and, therefore, it is questioned if soil redistribution processes affect this potential CO2 sink function. Most studies analysing the effect of soil redistribution upon soil organic carbon (SOC) dynamics focus on water erosion and analyse only relatively small catchments and relatively short time spans of several years to decades. The aim of this study is to widen this perspective by including tillage erosion as another important driver of soil redistribution and by performing a model-based analysis in a 200 km2 sized arable region of northeastern Germany for the period since the conversion from forest to arable land (approx. 1000 years ago). The spatially explicit soil redistribution and carbon (C) turnover model SPEROS-C was applied to simulate lateral soil and SOC redistribution and SOC turnover. The model parameterisation uncertainty was estimated by simulating different realisations of the development of agricultural management over the past millennium. The results indicate that, in young moraine areas, which are relatively dry but have been intensively used for agriculture for centuries, SOC patterns and dynamics are substantially affected by tillage-induced soil redistribution processes. To understand the landscape-scale effect of these redistribution processes on SOC dynamics, it is essential to account for long-term changes following land conversion as typical soil-erosion-induced processes, e.g. dynamic replacement, only take place after former forest soils reach a new equilibrium following conversion. Overall, it was estimated that, after 1000 years of arable land use, SOC redistribution by tillage and water results in a current-day landscape-scale C sink of up to 0.66 ‰ yr−1 of the current SOC stocks.

Climate warming contributes to the record-shattering 2022 Pakistan rainfall

A sequence of torrential rainstorms pounded Pakistan in the summer of 2022, shattering records by massive margins (7 sigma). The severe socioeconomic damages underscore the urgency of identifying its dynamic drivers and relationship with human-induced climate change. Here, we find that the downpours were primarily initiated by the synoptic low-pressure systems, whose intensity and longevity far exceeded their counterparts in history as fueled by a historically-high cross-equatorial moisture transport over the Arabian Sea. The moisture transport has been trending upward since the 1960s and, in 2022, along with the anomalous easterly moisture influx caused by the combination of La Niña and negative Indian Ocean Dipole events, created a corridor of heavy rainfall extending from central India toward southern Pakistan. While it is not yet established whether the observed trend of the cross-equatorial moisture transport has exceeded natural variability, model-based analysis confirms that it is consistent with the fingerprint of anthropogenic climate warming and will raise the likelihood of such rare events substantially in the coming decades.

Interpretable GBDT model-based multi-objective optimization analysis for the lateral inlet/outlet design in pumped-storage power stations

ABSTRACT The uneven velocity distribution formed at the lateral inlet/outlet poses a significant risk of damaging the trash racks. Reasonable design of the inlet/outlet structures requires the consideration of two major aspects: the average velocity (Vm) and the coefficient of unevenness (Uc). This paper developed an optimization framework that combines an interpretable Gradient Boosting Decision Tree (SOBOL-GBDT) with a Non-dominated Sorting Genetic Algorithm (NSGA-II). 125 conditions are simulated by performing CFD simulations to generate the dataset, followed by GBDT implemented to establish a nonlinear mapping between the input parameters including vertical (α) and horizontal (β) diffusion angles, diffusion segment length (LD), channel area (CA), and the objectives Uc and Vm. The SOBOL analysis reveals that in Uc prediction, CA and α play more significant roles in the model development compared to β and LD. Besides, GBDT is observed to better capture interactive effects of the input parameters compared with other machine learning models. Subsequently, a multi-objective optimization framework using GBDT-NSGA-II is developed. The framework calculates the optimal Pareto front and determines the best solution using a pseudo-weight method. The results demonstrate that this framework leads to significant improvements in flow separation reduction in the diffusion segment and the normalized velocity distribution. The SOBOL-GBDT-NSGA-II framework facilitates a rational and effective design of the inlet/outlet.

Phoenix–A model-based human reliability analysis methodology: Data sources and quantitative analysis procedure

Human Reliability Analysis (HRA) has continuously evolved to adopt more advanced human error probability (HEP) quantification approaches. Despite several efforts and enhancements of HRA methods, many still present limitations. Phoenix HRA Methodology aims to overcome known issues by incorporating strong elements of current HRA good practices, leveraging lessons learned from empirical studies and existing and emerging HRA methods' features. Phoenix models performance influencing factors (PIFs) and their impact on failure modes through Bayesian Networks (BBNs). Despite increased use in HRA applications, the large amount of data BBNs may require is still challenging. Thus, to populate Phoenix BBNs, several data sources were combined, including other HRA methods, expert judgment, and operating experience. The paper discusses Phoenix's quantitative analysis process, the data sources used to estimate the BBN parameters, and the applied data aggregation method. It further provides the quantitative analysis procedure guide with the significant steps and sub-steps an HRA analyst requires to implement this Phoenix methodology phase. Furthermore, the method for mapping the data to Phoenix BBN parameters and for aggregating the data considering non-homogeneous data sources can be further used for other HRA methods that apply BBNs or other applications.

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.

Time Transfer Link fusion algorithm based on wavelet multi-resolution analysis

To raise the precision and stability of time transfer links, a wavelet-based multi-resolution data fusion technique is offered. This approach fixes the “Day-Boundary discontinuities” in global positioning system (GPS) precise point positioning (PPP) link and lessens the diurnal in Two-way satellite time and frequency transfer (TWSTFT) link by integrating the model-based dynamic system analysis method with the statistically based multi-scale signal transformation method. It can also handle data from GPS PPP links and TWSTFT links at different sampling rates. More specifically, after applying wavelet transformation to the raw data and Kalman filtering estimation on various scales, this method uses the Mallat fast reconstruction algorithm of wavelet transform to achieve the fused results. The National Time Service Center (NTSC) and the Physikalisch-Technische Bundesanstalt (PTB) handle time transfer results using this method. Experiments show that this strategy greatly improves the link's short-term stability and can manage atypical interruptions while also boosting the link's robustness and reliability, which has a wide range of possible applications.

Fully passive model-based numerical analysis of the trailing-edge flexibility of hydrofoil on energy harvesting performance

The oscillating hydrofoil, a device used for collecting environmentally friendly tidal energy, is the focus of the study. The flexibility of the hydrofoil's trailing edge can impact its surface pressure distribution, lift, and moment characteristics. To improve the energy harvesting performance of oscillating hydrofoils, it is important to conduct thorough research on their energy harvesting mechanism. Therefore, numerical analysis is employed to develop a numerical model of the fully passive oscillating hydrofoil with the flexible trailing edge. The dynamic development behavior of surface vortices on hydrofoils is analyzed, demonstrating that the fluid–structure interaction between the hydrofoil and the surrounding fluid alters the hydrofoil's motion. The vortex patterns and pressure distribution on the hydrofoil surface are also affected, ultimately influencing the energy harvesting performance. By optimizing the flexibility coefficient of the fully passive oscillating hydrofoil with a flexible trailing edge, the energy harvesting performance of the oscillating hydrofoil is improved. When the maximum chord offset δm= 0.1c and the flexibility coefficient n= 2, the energy harvesting efficiency is 31.37%, and the average power coefficient is 1.17. Therefore, increasing the tail flexibility can be considered to enhance energy harvesting performance when designing the fully passive oscillating hydrofoil. The research provides a comprehensive analysis of energy harvesting performance, addressing the dynamic problem of the fully passive oscillating hydrofoils with flexible trailing edges. The findings of this study may provide guidance for the design and optimization of tidal energy harvesting devices with similar structures.

An integrative dynamical perspective for graph theory and the analysis of complex networks.

Built upon the shoulders of graph theory, the field of complex networks has become a central tool for studying real systems across various fields of research. Represented as graphs, different systems can be studied using the same analysis methods, which allows for their comparison. Here, we challenge the widespread idea that graph theory is a universal analysis tool, uniformly applicable to any kind of network data. Instead, we show that many classical graph metrics-including degree, clustering coefficient, and geodesic distance-arise from a common hidden propagation model: the discrete cascade. From this perspective, graph metrics are no longer regarded as combinatorial measures of the graph but as spatiotemporal properties of the network dynamics unfolded at different temporal scales. Once graph theory is seen as a model-based (and not a purely data-driven) analysis tool, we can freely or intentionally replace the discrete cascade by other canonical propagation models and define new network metrics. This opens the opportunity to design-explicitly and transparently-dedicated analyses for different types of real networks by choosing a propagation model that matches their individual constraints. In this way, we take stand that network topology cannot always be abstracted independently from network dynamics but shall be jointly studied, which is key for the interpretability of the analyses. The model-based perspective here proposed serves to integrate into a common context both the classical graph analysis and the more recent network metrics defined in the literature which were, directly or indirectly, inspired by propagation phenomena on networks.

Retraction Note: AISAS model-based statistical analysis for intelligent eldercare products consumption research

Retraction Note: AISAS model-based statistical analysis for intelligent eldercare products consumption research