esa-sceva/satcom-chunk-collection huggingface dataset:
# Hemodynamic Interference of Serial Stenoses and Its Impact on FFR and iFR Measurements
###### Abstract  
The hemodynamic interference of serial stenoses poses challenges for identifying the functional severity using the fractional flow reserve (FFR) method. The instantaneous wave-free ratio (iFR), i.e., the distal-to-proximal pressure ratio at 75% of diastole, was recently proposed to overcome the disadvantages of the FFR. However, the underlying mechanism remained ambiguous due to the lack of quantitative definition of hemodynamic interference. The objective of this study is to quantitatively define the hemodynamic interference and then examine its role on the FFR and iFR measurements. Pressure distributions, velocity fields, and Q-criterion which identifies vortices, were obtained through the computational fluid dynamics (CFD) for five cases with spacing ratios at 1, 3, 5, 7, and 10. The hemodynamic interference was identified using vortex structures which were quantified by Q-criterion. Results have shown that a spacing ratio of 7 or larger was interference-free. Serial stenoses with a smaller spacing ratio led to a larger hemodynamic interference, and thus, larger errors in the FFR measurements compared to the spacing ratio of 7. Moreover, the underestimation of the first stenosis lesion has been observed, even in interference-free cases due to the nature of hyperemia. However, the hemodynamic interference of the serial stenoses has a negligible impact on the iFR measurement, regardless of the spacing ratio. Our results demonstrated that the quantification of the hemodynamic interference in serial stenoses provided a better understanding of its role on the pressure measurements, which could be further exploited for the optimal treatment of serial stenoses.  
Keywords:serial stenoses; hemodynamic interference; fractional flow reserve (FFR); instantaneous wave-free ratio (iFR); local pressure fluctuation; pressure recovery; vortex structures; computational fluid dynamics (CFD) +
Footnote †: journal: Applied Sciences
## Appendix A Structure of the Stabilization Matrices  
Given the stabilization parameters \(\tau_{c}\), \(\tau_{m}\), and \(\tau_{c}\), the incompressible stabilization matrices for 2D can be written as follows [20; 29; 33].  
\[\mathbf{\tau}_{\text{diag}}=\begin{bmatrix}\tau_{c}&0&0&0\\ 0&\tau_{m}&0&0\\ 0&0&\tau_{m}&0\\ 0&0&0&\tau_{c}\end{bmatrix} \tag{1}\]  
\[\mathbf{\tau}_{\text{HH}}=\begin{bmatrix}\tau_{c}&\rho u_{1}\tau_{m}&\rho u_{2} \tau_{m}&0\\ 0&\tau_{m}&0&0\\ 0&0&\tau_{m}&0\\ -\varepsilon_{1}\tau_{c}&-u_{1}\tau_{c}&-u_{2}\tau_{c}&\tau_{c}\end{bmatrix} \tag{2}\]  
\[\mathbf{\tau}_{\text{polar}}=\mathbf{\tau}_{\text{HH}}+\omega\begin{bmatrix}0&\rho u _{1}\tau_{m}&\rho u_{2}\tau_{m}&0\\ u_{1}\tau_{m}&0&0&0\\ u_{2}\tau_{m}&0&0&0\\ 0&0&0&0\end{bmatrix} \tag{3}\]  
\[\mathbf{\tau}_{\text{polar--comp}}=\begin{bmatrix}\tau_{c}&\rho(\omega+1)fu_{1}&( \omega+1)fu_{2}&\rho\alpha_{p}|\mathbf{u}|^{2}\tau_{c}\\ \omega fu_{1}&\tau_{m}&0&\alpha_{p}u_{1}\tau_{c}\\ \omega fu_{2}&0&\tau_{m}&\alpha_{p}u_{2}\tau_{c}\\ -\varepsilon_{1}\tau_{c}&(\alpha_{p}T-1)u_{1}\tau_{c}&(\alpha_{p}T-1)u_{2}\tau _{c}&\tau_{c}\end{bmatrix}, \tag{4}\]where \(f=\tau_{m}+\bar{\epsilon}_{1}\alpha_{p}\tau_{e}\) and \(e_{1}\) can be found in [20]. The constant  
\[\omega\in\big{(}\frac{1}{2}(-1-\sqrt{5}),\frac{1}{2}(-1+\sqrt{5})\big{)} \tag{10}\]  
is given in [29]. Additionally, from [20; 41],  
\[\tau_{m}=\min\biggl{(}\frac{\Delta t}{2\rho},\frac{1}{\rho}(\mathbf{u}\cdot\mathbf{G}\bm {u})^{-1/2},\frac{4}{12\mu\mathbf{G}:\mathbf{G}}\biggr{)} \tag{11}\]  
\[\tau_{e}=\min\biggl{(}\frac{\Delta t}{2\rho c_{p}},\frac{1}{\rho c_{p}}(\mathbf{u} \cdot\mathbf{G}\mathbf{u})^{-1/2},\frac{4}{12\kappa\mathbf{G}:\mathbf{G}}\biggr{)} \tag{12}\]  
\[\tau_{c}=\frac{1}{\rho\tau_{m}\mathbf{a}\cdot\mathbf{a}} \tag{13}\]  
\[G_{ij}=\sum_{k=1}^{2}\frac{\partial\xi_{k}}{\partial x_{i}}\frac{\partial\xi _{k}}{\partial x_{j}} \tag{14}\]  
\[a_{i}=\sum_{j=1}^{2}\frac{\partial\xi_{j}}{\partial x_{i}}. \tag{15}\]
## 3 Characterization
### Other Properties  
As discussed before, besides the enhancement of thermal conductivity and the reduction of CTE, it is essential for the composite to maintain its electrical and mechanical properties to be used as spin-coating dielectrics. The dielectric property, insulation property and mechanical strength of the composite with the optimized component ratio were tested and the results are shown in Table 1. The corresponding properties of the PI matrix are also listed for comparison. The mechanical strength of the PI matrix was enhanced by the reinforcement materials. The tensile strength increased from 81 MPa to 145 MPa and the Young's modulus increased from 2.5 GPa to 8.4 GPa, at the cost of the reduction of elongation at break from 7.2% to 3.3%. Affected by the poor dielectric properties of the reinforcement materials, the relative permittivity of the composite increased to 4.6 compared with that of the PI matrix of 3.5 at 1 MHz. The addition of SiC which has a relatively low resistivity made the volume resistivity decrease from \(4.3\times 10^{7}\)\(\Omega\)-cm to \(2.1\times 10^{7}\)\(\Omega\)-cm. Although the elongation at break, dielectric property and insulation property were degraded to some degree, the degradation is still acceptable.
## 1 Introduction
Given this, this article proposes an LC/S compensated IPT system that can realize stable CCO and CVO characteristics under two fixed ZPA operating frequencies through reasonable parameter design. Notably, the total number of compensation elements of the system is minimized, with only one compensation capacitor on the receiving side, which ensures not only the low cost of the system, but also a compact receiver. The CLC/N compensated IPT system proposed in [25] has no compensation components on the receiving side, which further optimizes the receiving side of the system from the perspective of compactness. However, the research in [25] achieved only CCO characteristics with ZPA operation through reasonable parameter design. Therefore, the research in [25] is only suitable for CC charging applications, such as LED driving. It is worth noting that the LC/S compensated IPT system proposed in this paper is suitable for charging with fixed coupling or low fluctuation, which is the same as the hybrid topologies of [16; 17; 18; 19] and the dual-frequency topologies of [20; 21; 22; 23; 24]. Section 2 of this article describes a basic analysis of the proposed LC/S compensated IPT system. This section provides an overall analysis of the circuit topology and the principles for realizing CCO/CVO characteristics and ZPA operation. In Section 3, the system parameter tuning scheme is given. Firstly, a simulation analysis of the loosely  
Figure 1: Lithium-ion battery charging curve. coupled transformer (LCT) is carried out using the electromagnetic simulation software Maxwell. Then, the design steps of the system parameters are given. Next, an equivalent model is established in MATLAB to conduct preliminary verification of the proposed IPT system. Section 4 describes the design of the system switching strategy. Section 5 describes experiments for verifying the above theoretical analysis, and the advantages of the LC/S compensated IPT system proposed in this paper are summarized. Finally, Section 6 concludes the article.
## 4 Discussion
The occlusive properties of the occlusion SEMSs may be affected by multiple factors, including the degree of mucosal invasion, diameter of the ureter, and shape of the SEMS. For example, stents with flares at the end may improve occlusive properties by reducing migration, but are more likely to increase mechanical injuries to the ureteral wall. In this study, stents were designed to have straight ends to minimize mucosal injuries, but the occlusive properties may have been compromised to some degree. This study has some limitations. First, the number of sample stents was not large enough for a robust analysis. Second, the occlusion SEMSs were not tested in animal models of ureteral leakage. Third, histological evaluation, to assess potential mucosal damage, was not possible in the phantom. Fourth, the bursting pressure causing leakage of the occlusion SEMS was lower in the 3D-printed phantom than in the ex vivo experiments. Although the 3D-printed phantom was made in the same shape as the human ureter, using silicone rubber, their sizes were slightly off; given that the inner diameter of the phantom was 7 mm, and the diameter of the occlusion SEMSs was 8 mm, it seems likely that loosening was not possible. Therefore, even if the phantom is helpful for stent design, it can hardly replace ex vivo or in vivo studies for property assessment. Despite the need for in vivo investigations involving histological analysis to validate the above-mentioned results, this current study supports the premise that the two types of occlusion SEMSs can successfully occlude the urinary tract.
# Corrosion Behavior of Pre-Rusted Rebars in Cement Mortar Exposed to Harsh Environments
## 3 Results
### Scanning Electron Microscopy Analysis and X-ray Diffraction  
The surface morphology and chemical composition of the rebar specimens were analyzed using a HITACHI S-4800 scanning electron microscope (SEM), from the joint equipment center of the Korea University College of Engineering. The SEM images of the group A, B, and C specimens prepared in Section 2.4 are shown in Figure 6. The outer layer of the pre-rusted specimens in groups B and C was separated from the rebar by a narrow crack along the interface, but the separation was not apparent in the group A specimens. The layer thickness of the non-corroded reinforcement group A was measured to be about 7.1 \(\upmu\)m; other researchers reported that the thickness of the non-corroded reinforcement layer was about 10 \(\upmu\)m [27, 28]. The outer layer measured 15.33 \(\upmu\)m for group B and 35.58 \(\upmu\)m for group C, indicating that the amount of pre-corrosion in the group C rebar was approximately twice that in the group B rebar.  
Figure 5: (a) Half-cell potential vs. time; (b) Corrosion current vs. half-cell potential  
To determine the chemical composition of the outer layer, an energy dispersive spectrometer (EDX) analysis was conducted over the boxed area in Figure 6. The results showed the layer of group B and C specimens was mainly composed of O and Fe, as shown in Table 4; based on the atomic weight of the elements, we determined that the outer layer was mainly composed of Fe and FeO and FeOOH in groups B and C, respectively [27,29]. The absence of O in group A indicated no detectable corrosion on the rebar surface, as expected based on the previous study by [PERSON] et al. [30].  
The chemical analysis of the outer layer collected from the pre-rust rebar surface was also analyzed using an X-ray diffraction spectrometer (XRD). Figure 7 shows the XRD analysis results. The results confirmed the presence of FeO and FeOO, in the form of FeOOH, FeOCl, along with lepidocrocite,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & **S** & **P** & **Mn** & **O** & **Fe** & **C** & **Cl** & **Ca** \\ \hline A & - & - & - & - & 97.52 & 2.48 & - & - \\ B & 2.31 & 0.3 & 0.7 & 32.74 & 62.01 & - & 1.55 & 0.4 \\ C & - & 1.14 & 0.74 & 20.49 & 77.45 & - & 0.18 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical composition measured by Energy Dispersive Spectrometer EDX (%).  
Figure 6: SEM image of steel rebar section: (**a1**) Group A (\(\times\)30), (**b1**) Group B (\(\times\)30), and (**c1**) Group C (\(\times\)30), (**a2**) Group A (\(\times\)1000), (**b2**) Group B (\(\times\)1000), and (**c2**) Group C (\(\times\)1000); -2 image shows the boxed area shown on the image -1 at higher magnification; boxed area shown in -2 image is scanned by EDX.  
akaganeite, magnetite, and goethite, which were consistent with the corrosion products formed on steel after one year of atmospheric exposure [31].
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Physical Methods  
Both top-down and bottom-up strategies are utilized in the process of producing magnetic nanoparticles (MNPs) by means of physical methods of synthesis. They represent the two basic physical procedures that are carried out. Top-down techniques involve the process of breaking down bulk materials into nanosized particles. Bottom-up approaches, on the other hand, are used to construct nanoparticles by assembling them from atomic or molecular components. Both methods are essential in the field of nanoparticle synthesis; each has its own set of advantages and disadvantages, and the decision between the two is frequently determined by the particular requirements of the application that the MNPs are meant to be used for.  
The physical synthesis methods of magnetic nanoparticles (MNPs) involve a range of procedures that mostly rely on mechanical or physical processes to generate nanoparticles.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 4 IT Architecture
#### Data Acquisition, Storage, and Processing  
The data transmission from the sensors to the local server is performed using the MQTT communication protocol, a lightweight and data-agnostic protocol, particularly suitable for IoT applications. MQTT relies on a broker for exchanging data between publishers and subscribers, and it supports various levels of Quality of Service (QoS). In this project, all messages are sent with a QoS of 2, i.e., they are sent exactly once, with a unique reception confirmation. To guarantee data security, all traffic towards the cloud is encrypted by means of SSL/TLS protocol: certificates signed by a public Certification Authority guarantee the identification and proper authorization of devices. Before storing the raw sensor data in the database, an anonymization module is applied to protect user privacy. A pseudo-random identification number is assigned to each patient. The correspondence to the patient identity is stored in a separate table, only accessible to the data controller (the local health authority).  
The behavioral analytical algorithms, are accessible as services on the _IBM Cloud_ through _REST API_ calls, enabling extraction of daily activity diary, longitudinal health-status progressive trajectory, as well as conducting user segmentation (precision cohort), activity prediction, anomaly detection, and device battery level monitoring. The data exchange with the host server is performed through a _Virtual Private Network_ (VPN) link.
###### Abstract
They also found that the distance covered at high intensity was also lower under high environmental stress than when such stress was low, the rate of successful passes was higher under high environmental stress, and the number of sprints was lower under high environmental stress than under moderate or low stress conditions. [PERSON] et al. [15] examined the relation between climatic characteristics and the occurrence of injuries. They compared the Australian Football League (AFL) with European soccer, and the main findings were that AFL teams have a higher probability of injuries in northern (warmer) areas. In contrast, European teams suffer injuries more often in northern (cooler) areas. The interesting observation derived from the latter study was related to the type of injury. The authors found that ankle sprains and anterior cruciate ligament injuries are more likely to appear in areas with warmer climates, whereas Achilles tendinopathy was more common in colder regions. Another observation was carried out by [PERSON] et al. [16] with the aim of analyzing the environmental conditions and impact of jet lag on exercise performance during the 2010 FIFA World Cup in South Africa. From their results, they tried to give valuable advice in order to prevent negative consequences of these two factors. [PERSON] et al. [17] analyzed variations in weather conditions after the 2014 World Cup, in Brazil. Also worth mentioning in this part is the study by [PERSON] et al. [18], whose objective was to predict soccer results using the Bayesian Network model. The model was applied on the matches of Barcelona team in the 2008-2009 season. One of the factors that the authors took into account in the analysis was estimation of the weather with two categories: good and bad. As mentioned earlier, research highlights the importance of the prediction of the results of soccer games and the influence of weather conditions on the final results; however, there is no study focusing on predicting soccer outcomes including weather data as one of the primary features. Therefore, taking into account the power of ML technologies and the importance of the weather conditions on soccer matches, the main aim of this study was to predict soccer results, as the main feature, by considering weather conditions with the help of ML technologies. In addition, the following questions were formulated as the main research questions: (a) How do weather conditions affect the results of soccer matches? ; and (b) Which ML methods can predict the results of soccer matches with greater accuracy, taking the weather conditions into account as a principal feature? The rest of the paper has the following structure: Section 2 introduces the datasets that were utilized and describes each step involved in the workflow of the applied methodology. Section 3 presents the results. Section 4 discusses the obtained results and offers the overall conclusion from the work.
## 4 Discussion
### Development of the AMS Checklist  
A modified Delphi process that included participants involved in partnerships of UK institutions with hospitals in Tanzania, Zambia, Uganda, and Ghana was used to ensure that the final AMS checklist was relevant and understandable for local healthcare staff in Sub-Saharan Africa. The final tool was cognizant of the unique settings in which they operate and the differences in practice from high-income settings. Compared to the original global checklist by [PERSON] et al. [11], the AMS checklist developed in this study included a combination of closed-ended and open-ended questions that give room for a more comprehensive exploration of AMS activities.  
The consensus process targeted the lower resource settings of LMICs in Sub-Saharan Africa by considering context-specific information and the involvement of experts from a broad range of specialities. Our work extends ongoing attempts to develop baseline assessment tools in Africa. This includes a Kenyan study in 2020, investigating the AMS policies and structures in 16 Kenyan hospitals while adapting the UK NICE AMS system to the Kenyan healthcare system [17]. Another study developed a survey questionnaire to investigate existing AMS activities for learners of the Massive Online Open Course (MOOC) [18; 19].  
By involving local healthcare staff in the development of our checklist, we also fostered a sense of ownership and commitment, thus serving as an example of successful co-creation. Compared to the LMIC AMS checklist contained within the WHO Practical Toolkit for healthcare facilities [12], which comprises 28 elements across six sections, our newly developed checklist contains 54 checklist items across eight main sections. While both checklists cover the more essential core elements for the National Antimicrobial Stewardship Programmes, including policy, guidelines and governance, awareness, education and training, IPC and surveillance, our newly developed tool was co-created and tested in the specific healthcare setting of Sub-Saharan Africa, thus increasing its acceptability amongst hospital staff and its level of contextualisation. Acceptability and contextualisation were prioritised to ensure the continued relevance of the tool and, consequently, the substance of antimicrobial stewardship activities in the long run. Similar tools are available in high-income countries, including the CDC Core Elements of Hospital Antibiotic Stewardship Programs developed in 2014 and updated in 2019 to reflect the new evidence from the field of antibiotic stewardship and the lessons learned from five years of experience [20]. The core elements in this checklist are similar to the developed checklist, each having sections on leadership, accountability, reporting, expertise, education, monitoring/tracking, and further actions. Additional checklist variations with adaptations to local healthcare settings could be developed following the modified Delphi consensus procedure employed in our study.
## 2 Computation Model and Methodology
### Governing Equations  
The following set of governing equations of the CONVERGE CFD solver [33] were used as the basis for the implementation of the proposed real fluid RFM model. This implementation is based on the continuity equation, the species transport equation, the mixture momentum equation and the mixture-specific internal energy equation.  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\rho u_{i}}{\partial x_{i}}=0 \tag{1}\]  
\[\frac{\partial\rho Y_{m}}{\partial t}+\frac{\partial\rho Y_{m}u_{j}}{\partial x _{j}}=\frac{\partial}{\partial x_{j}}(\rho D\frac{\partial Y_{m}}{\partial x _{j}}),m=\{1\dots N_{s}-1\} \tag{2}\]  
\[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial\rho u_{i}u_{j}}{\partial x _{j}}=\frac{\partial P}{\partial x_{i}}+\frac{\partial\tau_{ij}}{\partial x_{ j}} \tag{3}\]  
\[\frac{\partial\rho e}{\partial t}+\frac{\partial\rho eu_{j}}{\partial x_{j}}=- P\frac{\partial u_{j}}{\partial x_{j}}+\tau_{ij}\frac{\partial u_{i}}{ \partial x_{j}}+\frac{\partial}{\partial x_{j}}(\lambda\frac{\partial T}{ \partial x_{j}})+\frac{\partial}{\partial x_{j}}(\rho D\sum_{m}h_{m}\frac{ \partial Y_{m}}{\partial x_{j}}) \tag{4}\]  
where \(N_{s}\) is the number of species, \(\tau_{ij}\) is the viscous tensor stress and \(\rho,u_{i},P,T,e\) are the mixture, density, velocity, pressure, temperature and specific internal energy, respectively.  
For the flows dealing with discontinuity due to shock waves, as observed in the de Laval nozzle simulated below, Equation (4) is replaced by the following total energy Equation (5):  
\[\frac{\partial\rho E}{\partial t}+\frac{\partial}{\partial x_{j}}\left(\rho u _{j}E+u_{j}P-u_{i}\tau_{ij}-\lambda\frac{\partial T}{\partial x_{j}}-\rho D \sum_{m}h_{m}\frac{\partial Y_{m}}{\partial x_{j}}\right)=0 \tag{5}\]