esa-sceva/satcom-chunk-collection huggingface dataset:
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 3 Experiments and Analysis
### Experimental Results and Analysis  
#### 3.4.1 Mixed Iris Dataset for Training and Testing  
In this experiment, the training set and validation set iris images of the three datasets JLU-6.0, CASIA-Iris-Lamp-v4, and MICHE-I were selected and mixed to form a mixed iris training set and validation set. Then, training, validation, and testing were performed. Figure 8 shows the loss curves of the proposed PFSegIris on the mixed iris training set and validation set. Figure 9 shows the accuracy curves of PFSegIris on the mixed iris training set and validation set. Figure 10 shows the iris segmentation results of PFSegIris on the JLU-6.0, CASIA-Iris-Lamp-v4, and MICHE-I test sets, respectively. Table 3 gives the evaluation indexes of PFSegIris on the JLU-6.0, CASIA-Iris-Lamp-v4, and MICHE-I test sets, respectively.  
Figure 8: The loss curves of PFSegIris on the mixed iris training set and validation set.  
Conclusions can be obtained as follows after the analysis of Figures 8-10 and Table 3:  
1. The loss and accuracy curves converged fast with increasing epochs in the process of training and validation, and the curves were stable without oscillations, indicating that the model did not overfit, which avoids the emergence of complex models.
2. PFSegIris performed well on all three kinds of heterogeneous iris images and handled the details of small targets well. It performed best on the JLU-6.0 iris dataset. The main reason was that the JLU-6.0 dataset has high-quality near-infrared images with large iris area, while the other two are visible-light iris images with more noise.
3. The average prediction times of the three heterogeneous iris images were all short.  
Figure 10: The segmentation results of PFSegIris on the JLU-6.0, CASIA-Iris-Lamp-v4, and MICHE-I test sets, respectively: (**a**) original images; (**b**) ground truth; (**c**) segmentation results; (**d**) merged images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **mIoU/\%** & **mPA/\%** & **F1-Score/\%** & **Average Time/s** \\ \hline JLU-6.0 & 97.38 & 99.33 & 98.68 & 0.12 \\ CASIA-Lamp & 97.15 & 98.97 & 97.91 & 0.10 \\ MICHE-I & 96.24 & 97.11 & 97.13 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The evaluation indexes of PFSegIris on the JLU-6.0, CASIA-Iris-Lamp-v4, and MICHE-I test sets, respectively.  
Figure 9: The accuracy curves of PFSegIris on the mixed iris training set and validation set.
## 3 Numerical Results
### Macroscale Analysis  
In this section, sandwich cantilever beams are analysed considering for the PUF cores the homogenised mechanical properties presented in Table 5. First, beams with homogeneous PUF cores bounded by aluminium face sheets are studied. Afterwards, gradually modifying the homogenised mechanical properties of the PUF core along the core thickness, approximated functionally graded sandwich cantilever beam models are constructed and numerically analysed. Regarding the mechanical properties of the aluminium face sheets, the following linear elastic isotropic material properties are considered: [PERSON]'s modulus \(E=70\) GPa and Poisson's ratio \(\
u=0.32\). Considering for all models a regular mesh with \(30\times 120\) divisions (corresponding to a uniform regular mesh of 3751 nodes and 3600 quadrilateral elements), all the studied models were analysed with FEM-4n, NNRPIMv1, and NNRPIMv2.  
This study is a pure numerical research work. No experimental or practical applications were produced or tested. Since in this work all static analyses were performed assuming linear elastic material behaviour, with small strains and no contact assumptions, it was not possible to capture or address some practical problems that recurrently appear in sandwich structures [51], such as the detection, propagation, and mitigation of damage to the sandwich core and at the interface; fatigue and creep testing; delamination and debounding between the core and face sheets; nonlinear material and geometric response; and temperature and humidity effects. Additionally, this paper assumes aluminium face sheets and a PUF core, which can lead to catastrophic scenarios, like the ones documented in the work of [PERSON] et al. [52] for bending of honeycomb PUF cores: the core fails and delaminates from the face sheets; the core and traction face sheet fracture; and the core is crushed, losing thickness and bending stiffness.
# Are Serious Games an Effective Teaching Tool in Anatomy Education? A Systematic Review
## 3 Results
### Study Characteristics and Quality Assessment  
The search yielded studies published between 2015 and 2023. The studies originated from diverse regions: five studies (33.3%) were from Europe (one in Ireland [19], three in the United Kingdom [13,16], and one in Germany [22]). Three were from Asia (20.0%) (one in Indonesia [23] and two in Singapore [1,3]), three from North America (20.0%) (two in Canada [15,20] and one in the United States [10]), two from South America (Brazil (13.3%) [11,24]), and two from Australia (13.3%) [12,21]. Table 2 shows the Newcastle-Ottawa Scale (NOS) quality assessment of the included studies: one study was rated as being of fair quality, five were rated as being of good quality, and the rest were of poor quality. Poor quality ratings often stemmed from the lack of a comparison group in the comparability domain.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S.N.** & **Study** & **Study Design** & **Assessment Domain** & **Sample Size** & **Intervention** & **Exposure Time** & **Main Outcome** \\ \hline \hline \multirow{8}{*}{10} & [PERSON] et al., 2022 [22] & Single group pre-test and post-test & Knowledge and perception & \multirow{8}{*}{36} & The Pelvis Augmented Reality Game & \multirow{8}{*}{NA} & Participants showed & \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } \\  & [PERSON] et al., 2022 [22] & Single group pre-test and post-test & Knowledge and perception & & & & & \\ \cline{1-1}  & [PERSON] et al.  
In terms of publication type, eight studies are journal articles [1; 2; 12; 15; 16; 19; 20; 23], three are conference proceedings [10; 22; 24], three are theses [13; 21], and one is a book chapter [11].  
The studies were evenly distributed according to their types; five were qualitative [10; 11; 12; 19; 21], five were quantitative [13; 15; 20; 23; 24], and five were mixed-methods [1; 2; 13; 16; 22]. Regarding study design, five employed a single-group cross-sectional or post-test-only approach [2; 10; 11; 12; 19]. Six studies were randomized control trials (RCTs) [1; 13; 15; 23; 24], two used two non-randomized groups [16; 20], and two used single groups pre-test and post-test design [21; 22]. In seven studies, informed consent was explicitly reported, but this was unclear in the remainder. Among the studies that reported participant age (6/15), the average age was below 22 years. Ethical approval was documented in most studies, indicating adherence to research standards. Recruitment methods included email advertisements [1; 3], break period advertisements [1; 3], snowball sampling [10; 19], convenience sampling [12; 21], and in-class announcements [16; 20]. The sample sizes vary among the studies, from the lowest of 9 students [19] to the largest of 251 students [16].
# Investigation on the Application of Artificial Intelligence in Prostodontics
## 4 Discussion
### Future Scope  
AI is changing everything from space research to dentistry. Biomedical diagnosis, therapy planning, patient recording, and management have several benefits. AI aids physicians and patients in every profession, and in the future, AI will produce a forecast that can be merged with human diagnosis to increase the chances of appropriate diagnostics, leading to a higher rate of correct diagnoses.  
Despite the encouraging outcomes, it is still important to validate the generalizability and reliability of the provided AI models using adequate external data acquired from newly enrolled patients or gathered from other dental facilities. One of the long-term goals of AI research in dentistry is to improve AI models to the point where they can detect early abnormalities that are undetectable to the naked eye. There will be a significant need for AI-enabled algorithms as CAD/CAM technology advances and prosthodontic implant procedures require more accuracy.  
At all stages of AI development, including pre-modelling, model creation, and post-modelling, explainability can be taken into account. The majority of the research on AI explainability describes post-modelling explainability and seeks to explain an existing black-box model version [96], even though explaining decisions made by artificial intelligence systems can help provide transparency on how the model arrives at its decision. However, at the level of the dentist and specific prosthodontist, the applications of AI is concerned with prosthesis fabrication, the success of which will be tested and determined only when it is placed in the oral cavity and so, if the explainability of AI is not known to us as a prosthodontist, the ultimate effect will be negligible. Despite this, we accept that if it is known it will be beneficial to both the prosthodontist and patient, then it will be beyond the scope of the present review. It is recommended to involve this aspect of AI in future studies and reviews.  
AI will improve the clinical and dental patient experience. To improve patient experience, the system will learn preferences. Improved dental patient experiences will increase appropriate oral health care, enhancing systemic health. The software will offer RPD designs for partial edentulism. Research-based, clinically proven technologies and approaches will modernize dental implant therapy. The software will assist with partial denture design in cases of incomplete edentulism. Dental implant therapy can become standardized via the use of tried-and-true technology and procedures backed by years of scientific study.
## 2 Fractal Model  
The fractal relation of the soil particle size distribution is generally defined using the relationship between the cumulative number of the soil particles with the grain size [16]. However, the particle size distribution of soil is usually expressed as the quality of soil particles at different particle size intervalsand within certain particle size range and is obtained using the screening method or equivalent particle size method. Within the particle size range investigated, \(d_{M}\) and \(d_{m}\) are set as the maximum grain size and the minimum grain size respectively, \(d_{i}\) is the diameter of the \(i\)th sieve pore (\(i=1,\,2,\,\ldots,\,n\)). If the difference of relative density and shape of the soil particles in each grain grade is neglected, [PERSON] et al. [20] put forward a fractal model of particle size distribution expressed with the relationship between the cumulative mass and particle size of the soil particles:  
\[M(\delta<d_{i})/M_{T}=(d_{i}/d_{M})^{3-D} \tag{1}\]  
where \(\delta\) describes the particle size, \(M(\delta<d_{i})\) is the cumulative mass of the soil particles whose grain size is smaller than \(d_{i}\), \(M_{T}\) is the total mass of soil particles and \(D\) is the fractal dimension.  
The fractal model represented by Equation (1) has been widely used in the particle size distribution of the soil. The left side of the equation refers to the quality percentage of the soil particle whose diameter is smaller than \(d_{i}\) and it is consistent with the frequently used grain size distribution curve. As shown in Figure 1, \(d_{M}\) and \(d_{m}\) are given. Equation (1) is used to draw the particle size distribution curve of different \(D\) values on the semi-logarithmic and double-logarithmic coordinate axes and strictly self-similar particle size distribution curve can be seen. It is a cluster of concave upward curves on the semi-logarithmic coordinate axis and a cluster of straight lines on the double-logarithmic coordinate axis. In addition, the larger the value of \(D\) is, the higher the content of fine materials in the particle size distribution is. This paper uses Equation (1) to describe the fractal relationship of the particle size distribution during the particle crushing process.
## 4 Conclusions  
Real-time is mainly used for research purposes and is a well-established methodology.  
The workflow of Q-PCR analysis includes several important steps, crucial for successful experimentation, exhibiting high precision, robustness and reliability.  
Q-PCR data analyses differ significantly in their performance and especially in pre-PCR steps and quality assurance procedures between different laboratories. There is a need for greater standardization  
\begin{table}
\begin{tabular}{c|c|c|c} \hline
**Target (RQ)** & **Age** & **Sex** & **Regression grade (after Dworak)\({}^{\text{\@@cite[cite]{[\@@bibref{}{Dworak}{}{}]}}}\)** \\ \hline _ERCC1_ & p = 0.4910 & p = 0.2179 & p = 0.4279 \\ _RRM1_ & p = 0.4765 & p = 0.7017 & p = 0.1731 \\ _TYMS_ & p = 0.3927 & p = 0.1609 & p = 0.0221 \\ \hline \end{tabular}
\end{table}
Table 7: Correlation between the relative quantification (2\({}^{\text{-}\text{AACT}}\)) of each gene target (_ERCC1_, _RRM1_ and _TYMS_), the age, sex and regression grade status of all samples statistically evaluated by the application of the Chi\({}^{2}\)-test. A statistically significant correlation between _TYMS_ and the regression grade was revealed.  
to make the final result of quantitative expression analysis more comparable between laboratories worldwide (beginning with the usage of isolation kits and ending with final data evaluation).  
The quality of tissue samples must therefore also be closely monitored through detailed histopathological tissue characterization and RNA quality tests which should be reported to classify the resultant final data of the respective publication.  
Additionally, several potential endogenous controls should be investigated on a representative number of samples to identify the most stable one required for the reliable normalization of RT-PCR data.  
PCR-amplification efficiency and primer-specificity have a major impact on the fluorescence history and accuracy of the calculated expression pattern and should be monitored.  
Sample characterization, tissue variations and PCR/Q-PCR efficiencies must be carried out prior to any calculations to determine the final experimental design and kind of quantification methods. If needed, efficiency-corrected quantification corrections should be included. Complete and biologically relevant analysis of gene expression requires additional information, especially that derived from immunohistochemistry and biochemical assays.  
In the present study, gene expression of _TYMS_ in tumor tissue was significantly higher in those patients with resistance to 5-FU chemotherapy. This was also observed in other studies.  
_ERCC1_ and _RRM1_ presented a similar expression pattern. One could hypothesize that there might be a correlation between the increased activity of DNA repair (ERCC1) enzyme and the enzyme (RRM1) essential for the production of deoxyribonucleotides. A further detect study with larger CRC series should be analyzed and compared to immunohistochemical examinations in order to definitely establish clinical relevance.
# Modeling Population Dynamics and Assessing Ecological Impacts of Lampreys via Sex Ratio Regulation
## 5 Sex Ratio Impact on Ecosystem Stability Model (SRIES Model)
### A Simple Logistic Model of Population Growth  
In this case, we adopt Assumption 4. Additionally, the following four assumptions are made about the lamprey population in a given region:  
1. Assumption 2: The effect of migration of lamprey individuals in and out of the population on population size is not considered.
2. Assumption 4: The carrying capacity of the environment in a given region is constant and does not change over time.
3. Assumption 5: The population is homogeneous and all individuals within the population are physiologically and behaviorally identical.
4. The population growth rate is constant, without considering the effects of changes in sex ratio and age composition.  
We define the average number of lamprey species as \(N\). According to the Logistic model, this number increases over time without exceeding the environmental carrying capacity \(K\). We have  
\[\frac{dN}{dt}=rN\bigg{(}1-\frac{N}{K}\bigg{)} \tag{29}\]  
where \(r\) is the intrinsic growth rate, \(K\) is the maximum average number of the species within the environmental limit, and \(\frac{dN}{dt}\) is the rate of change in population size over time.  
However, practical application to lampreys as a population requires the consideration of hypothetical limitations, particularly in variable natural and artificial environments. Lampreys have a uniquely distinct three-stage life cycle, and sex ratios are influenced by food availability, so environmental carrying capacity is not constant in growing environments with variable food availability, which in turn leads to non-constant population growth rates by affecting age composition and sex ratios. At the same time, the contribution of lampreys of different sexes to the population is different because males or females show different sex dominance in environments with different food conditions [1,11].
## 1 Introduction
In particular, the proposed SAL approach has three major phases, including:(i) the utilization of ensemble learning classifiers rather than individual classifiers to overcomes the limitations of a single classifier and accurately predict a class label for a new attack and anomalous, (ii) function agreement to improve the prediction's ability of the first phase to only label the observations which meet an adaptive confidence threshold, and (iii) an increment update of labeled set phase to iteratively and continuously train a hybrid voting-based ensemble learning approach. In particular, the hybrid voting-based ensemble learning approach, including the soft voting-based and hard voting-based, combines the original labeled data along with new labeled observations which meet the confidence threshold (in phase #2) to assign labels to new coming unlabeled data which may contain new and anomalous attacks. To evaluate the performance of the proposed the self-augmentation labeling (SAL), two benchmark data sets have been used in the experiment. The experimental results demonstrate the advantages of our proposed approach in terms of accuracy, recall, and F-score measure in comparison to three of the well-known semi-supervised classification algorithms. The structure of the rest of this paper is organised as follows. Section 2 presents the related work to our proposed approach. Section 3 describes the proposed approach in detail. The performance of SAL in comparison to the other baseline algorithms using two data sets is discussed and summarised in Section 4. Finally, we summarise the research in Section 5.
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
## 4 Conclusions  
In this study, we examine whether the anticancer efficacy of SnO\({}_{2}\) NPs can be improved through Zn doping. Pure and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) were prepared using a modified sol-gel process and characterized by modern analytical tools. XRD data indicate that the crystallite size of SnO\({}_{2}\) NPs increased with increasing levels of Zn doping. Structural characterization was further carried out by TEM, SEM, and EDX. The band gap energy of SnO\({}_{2}\) NPs decreased with increasing dopant concentrations (3.5-3.34 eV). The PL study suggested that the recombination rate of electron holes was reduced with increasing levels of Zn doping. The biological results found that Zn doping improves the anticancer  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Synthesized NPs** & **MCF-7 Cancer Cells** \\ \hline SnO\({}_{2}\) NPs & 184.69 \(\pm\) 0.05 \\ Zn (1%)-SnO\({}_{2}\) NPs & 111.68 \(\pm\) 0.12 \\ Zn (5%)-SnO\({}_{2}\) NPs & 90.74 \(\pm\) 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: IC\({}_{50}\)\(\pm\) SD values of prepared samples in MCF-7 cancer cell lines.  
Figure 7: Cell viability based on the MTT assay on MCF-7 cells exposed to NPs at concentrations (0–300) for 24 h (**A**), based on the biocompatibility of prepared NPs in human umbilical vein endothelial cells (HUVECs) (**B**). * indicates significant differences in treated cells with respect to controls.  
potential of SnO\({}_{2}\) NPs against human breast cancer cells (MCF-7). Moreover, Zn-doped SnO\({}_{2}\) NPs did not cause toxicity to human umbilical vein endothelial cells (HUVECs). This study warrants further research on the anticancer activity of Zn-doped SnO\({}_{2}\) NPs in an appropriate in vivo model.  
Conceptualization, M.A.; Methodology, S.A., Z.M.A., R.L., N.M., A.A. and M.A.; Software, S.A., Z.M.A., R.L., N.M. and A.A.; Validation, Z.M.A., N.M., A.A. and M.A.; Formal analysis, S.A., Z.M.A., R.L., N.M., A.A. and M.A.; Investigation, S.A., Z.M.A., R.L., A.A. and M.A.; Resources, N.M. and M.A.; Data curation, M.A.; Writing--original draft, Z.M.A., R.L. and M.A.; Writing--review & editing, M.A. All authors have read and agreed to the published version of the manuscript.  
Deputyship for Research and Innovation at the Ministry of Education in Saudi Arabia. IFP-IMSIU-2023059.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors appreciate the Deputyship for Research and Innovation at the Ministry of Education in Saudi Arabia. This research was carried out through project number IFP-IMSIU-2023059. The authors also appreciate the Deanship of Scientific Research at Imam Mohammed Ibn Saud Islamic University (IMSIU) for their support and supervision in this project.  
The authors declare no conflict of interest.