esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dysbiosis and Clinical Implications
### Clinical Evidence for Probiotic Mixtures in CIDs  
Several studies have demonstrated the efficacy of multi-strain probiotics in various CIDs. A randomized controlled trial by [PERSON] et al. showed that a mixture of eight probiotic strains (VSL#3) was effective in maintaining remission in ulcerative colitis patients [28]. [PERSON] et al. reported that a four-strain probiotic combination improved glycemic control and reduced systemic inflammation in individuals with type 2 diabetes [29]. A study by [PERSON] et al. found that a probiotic mixture containing seven strains improved disease activity and inflammatory markers in rheumatoid arthritis patients [30]. [PERSON] et al. demonstrated that a three-strain probiotic mixture improved cognitive function and metabolic status in Alzheimer's disease patients [31]. The development of AI-driven algorithms that integrate clinical data with microbiome profiles shows promise in optimizing probiotic interventions (Figure 2).  
ML algorithms can be used to predict the most effective probiotic combinations, based on an individual's microbiome profile and clinical characteristics [32]. Continuous monitoring of microbiome changes in response to probiotic interventions can allow for real-time adjustments to treatment strategies [33]. Combining microbiome data with other omics data (e.g., metabolomics, proteomics) can provide a more comprehensive understanding of the impact of probiotic interventions [34]. Despite the promising advancements, several challenges remain in the application of AI to microbiome research and antibiotic resistance. These include the following:  
1. Data quality and standardization: Ensuring consistent and high-quality data across different studies and platforms is crucial for developing robust AI models [27].
2. Interpretability: Many AI models, particularly deep learning approaches, operate as \"black boxes,\" making it challenging to interpret their decision-making processes [35].  
Figure 2: AI in probiotic mixtures and clinical settings. NOTA. This diagram represents how AI integrates into different probiotic applications, illustrating the flow from data analysis to actionable insights.  
3. Integration of multi-omics data: Developing AI models that can effectively integrate diverse data types (e.g., metagenomics, metabolomics, and host genomics) remains a significant challenge [36].
4. Ethical considerations: The use of AI in healthcare raises important ethical questions regarding data privacy, algorithmic bias, and clinical decision-making [37]. Future research directions should focus on addressing these challenges and exploring new AI paradigms. The development of explainable AI models, federated learning approaches for privacy-preserving analysis, and the integration of AI with other emerging technologies (e.g., single-cell sequencing and microfluidics) hold great promise for advancing our understanding of the microbiome and combating antibiotic resistance.
# Alpha-Mangostin Suppresses LPS-Induced Inflammation in Human Dental Pulp Cells
###### Abstract  
Xanthones are secondary metabolites isolated from the peel of mangosteen showing medicinal potencies. Alpha-mangostin (\(\alpha\)-MG) is the most plentiful xanthone, which has been reported to possess anti-inflammatory, anti-oxidant, and anti-bacterial activities. We aimed to investigate the anti-inflammatory effects of xanthones on LPS-treated hDPCs. Cell viability was determined using the WST-1 assay. The mRNA and protein expression profiles of inflammatory mediators were evaluated using quantitative real-time polymerase chain reaction (qPCR) and Western blot analysis. Anti-inflammatory effects were assessed using the Western blot analysis to examine underlying mechanisms. A one-way analysis of variance followed by [PERSON]'s post hoc test was used to determine statistically significant differences (\(p<0.05\)). The study found no significant differences between the cytotoxic effects in the \(\alpha\)-MG-treated groups and controls. The mRNA and protein expression levels of inflammatory markers in the \(\alpha\)-MG treated groups decreased. \(\alpha\)-MG significantly inhibited LPS-induced phosphorylation of proteins associated with the MAPK and NF-\(\kappa\)B pathways. This study suggests that \(\alpha\)-MG exerts anti-inflammatory effects by suppressing the MAPK and NF-\(\kappa\)B signaling pathways in LPS-treated hDPCs.  
\(\alpha\)-mangostin; dental pulp; inflammation; lipopolysaccharide-sacharide; xanthones +
Footnote †: journal: Applied Sciences  
expression of pro-inflammatory mediators. A previous study demonstrated that \(\alpha\)-MG prevented lipopolysaccharide-anchardide (LPS)-induced inflammation by inhibiting nuclear factor kappa B (NF-kB) signaling in RAW 264.7 macrophages in vitro and inhibited carrageenan-induced peritonitis in vivo [7]. Studies also showed that \(\alpha\)-MG exerted inhibitory effects on tumor cells by downregulating the mitogen-activated protein kinase (MAPK) and NF-kB signaling pathways [8; 9; 10]. However, the effect of \(\alpha\)-MG on LPS-induced human dental pulp cells (hDPCs) has not been studied.  
Several mechanisms have been proposed to explain the anti-inflammatory effects of \(\alpha\)-MG: (1) anti-oxidant and radical scavenging activity; (2) modulation of inflammation-related cellular activities; (3) modulation of the activities of pro-inflammatory enzymes; (4) modulation of the production of other pro-inflammatory molecules; and (5) modulation of pro-inflammatory gene expression [11].  
This study evaluated the potential of \(\alpha\)-MG to control the levels of inflammatory mediators, such as IL-1\(\beta\), IL-6 intercellular adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1) in LPS-treated hDPCs and aimed to investigate the underlying anti-inflammatory mechanisms.
## 3 Results
### Response Curves of ES Trade-Offs/Synergies
An increase in positive or negative changes in CS causes it to maintain a synergy and then a trade-off with the SC. The trade-offs weaken and gradually converge to synergy as the negative change in CS increases above about \(-5000\) TgC/yr\({}^{2}\). Changes in SC basically do not affect their ESTD. In Figure 8i, the increase in the negative change in CS has little effect on ESTD. The increase in positive change causes the ESTD between CS and HQ to fluctuate: a synergy is created first, followed by trade-off increases. After about 5000 TgC/yr\({}^{2}\), the trade-off starts to decrease. The effect of changes in HQ on the trade-off synergy is negligible. In Figure 8j, the trade-off effect diminishes as the negative change in SC increases, after increasing to \(-1.3\times 10^{5}\), t/km\({}^{2}\). At \(-2\times 10^{5}\), t/km\({}^{2}\), the trade-off tends to become a synergy, and the increase in positive change causes it to fluctuate around the trade-off synergy. Changes in HQ had little effect on ESTD. Figure 8e-j show that FP dominates the ESTD between it and SC; CS is the dominant player between CS and SC; HQ occupies a secondary position of influence except in the trade-off synergy with FP. The GAM fitting results for each ES change were evaluated through a smoothness test (see Table 3). When kindex < 0.9 or >1.1, the smoothing function adds degrees of freedom to improve the smoothness of the curve. \"edf\" is the degree of freedom and higher edf values indicate more complex spline curves. A degree of freedom larger than 1 indicates a curvilinear relationship. Theoretically, when the degree of freedom is close to 1, it indicates a linear relationship. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**WY-FP**} & \multicolumn{2}{c}{**WY-CS**} & \multicolumn{2}{c}{**WY-SC**} & \multicolumn{2}{c}{**WY-HQ**} & \multicolumn{2}{c}{**FP-CS**} & \multicolumn{2}{c}{**FP-SC**} & \multicolumn{2}{c}{**FP-HQ**} & \multicolumn{2}{c}{**CS-SC**} & \multicolumn{2}{c}{**CS-HQ**} & \multicolumn{2}{c}{**SC-HQ**} \\ \hline ES- & \multirow{2}{*}{WY} & \multirow{2}{*}{FP} & \multirow{2}{*}{WY} & \multirow{2}{*}{CS} & \multirow{2}{*}{WY} & \multirow{2}{*}{SC} & \multirow{2}{*}{WY} & \multirow{2}{*}{HQ} & \multirow{2}{*}{FP} & \multirow{2}{*}{CS} & \multirow{2}{*}{FP} & \multirow{2}{*}{SC} & \multirow{2}{*}{FP} & \multirow{2}{*}{HQ} & \multirow{2}{*}{CS} & \multirow{2}{*}{SC} & \multirow{2}{*}{CS} & \multirow{2}{*}{HQ} & \multirow{2}{*}{SC} & \multirow{2}{*}{HQ} \\ \cline{2-2} \cline{11-19} change & & & & & & & & & & & & & & & & & & & & & & \\ \cline{2-19} edf & 6.42 & 1.42 & 4.71 & 1.00 & 6.08 & 1.00 & 7.57 & 1.00 & 1.00 & 1.00 & 6.17 & 1.00 & 1.00 & 7.10 & 8.98 & 1.00 & 6.44 & 1.00 & 7.88 & 1.00 \\ Kindex & 0.99 & 1.01 & 0.94 & 0.90 & 1.01 & 1.02 & 1.
## 1 Introduction
Previous research studies [23; 24; 25; 26; 27; 28; 29; 30; 31; 32] investigated the potential use of ANNs to analyze the complex shear behavior of RC members and verified the existing design code formulation. These studies evaluated a variety of design parameters affecting the shear behavior of RC members with and without transverse reinforcement. Because the current design codes perform a similar design process for the members under shear and torsional moment, an analytical approach for the shear can also be extended to analyze the torsional behavior. However, compared to studies of the shear, few research studies have utilized ANN models to analyze the torsional response of RC beams. [PERSON] [33] used radial basis function neural networks (RBFNs) and [PERSON] et al. [34] utilized a genetic programming (GP) model to analyze the torsional strength of 76 RC beams and compared the results with design code equations. The results of these studies demonstrated that predicting the ultimate strength with RBFN and GP was feasible, and the prediction was better than existing code formulations. Similarly, [PERSON] [35] investigated the efficiency of different artificial neural networks in predicting the torsional strength of 76 RC beams. The study showed that each ANN model provides reasonable predictions of the ultimate torsional strength of RC beams. It is evident from the literature that a large number of previous studies have focused on modeling the shear behavior of RC members using ANNs. However, The studies investigating torsional behavior are not only limited in number, but the databases used for training and validation of ANN models appear insufficient. Therefore, the potential use of ANNs in accurately modeling the torsional behavior of RC beams requires more detailed research with a more extensive test database for training and validation of the model. In this study, the torsional strength of RC beams was predicted using an ANN, and a parametric study of significant characteristic variables was carried out. The ANN model was trained and validated against a larger test database of 159 RC beams reported in the previous literature. A total of 10 main input variables were used to train the regression models. A back-propagation neural network (BPNN) was chosen to predict the torsional strength, and principal component analysis, in addition to autoencoder algorithms, were considered to improve the analysis results. The results of the ANN model were compared with the predictions of ACI 318-19, EC2-04, CSA-14, and JSCE-07.
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
###### Abstract  
In recent years, remote sensing imagery has become indispensable for applications such as environmental monitoring, land use classification, and urban planning. However, the physical constraints of satellite imaging systems frequently limit the spatial resolution of these images, impeding the extraction of fine-grained information critical to downstream tasks. Super-resolution (SR) techniques thus emerge as a pivotal solution to enhance the spatial fidelity of remote sensing images via computational approaches. While deep learning-based SR methods have advanced reconstruction accuracy, their high computational complexity and large parameter counts restrict practical deployment in real-world remote sensing scenarios--particularly on edge or low-power devices. To address this gap, we propose LSANet, a lightweight SR network customized for remote sensing imagery. The core of LSANet is the large separable kernel attention mechanism, which efficiently expands the receptive field while retaining low computational overhead. By integrating this mechanism into an enhanced residual feature distillation module, the network captures long-range dependencies more effectively than traditional shallow residual blocks. Additionally, a residual feature enhancement module, leveraging contrast-aware channel attention and hierarchical skip connections, strengthens the extraction and integration of multi-level discriminative features. This design preserves fine textures and ensures smooth information propagation across the network. Extensive experiments on public datasets such as UC Merced Land Use and NWPU-RESISC45 demonstrate LSANet's competitive or superior performance compared to state-of-the-art methods. On the UC Merced Land Use dataset, LSANet achieves a PSNR of 34.33, outperforming the best-baseline HSENet with its PSNR of 34.23 by 0.1. For SSIM, LSANet reaches 0.9328, closely matching HSENet's 0.9332 while demonstrating excellent metric-balancing performance. On the NWPU-RESISC45 dataset, LSANet attains a PSNR of 35.02, marking a significant improvement over prior methods, and an SSIM of 0.9305, maintaining strong competitiveness. These results, combined with the notable reduction in parameters and floating-point operations, highlight the superiority of LSANet in remote sensing image super-resolution tasks.  
supred 2025, 15, 7497  
[PERSON], [PERSON], [PERSON] LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing, _Appl. Sci._**2025**, 15,7497. [[https://doi.org/10.3390/app15137497](https://doi.org/10.3390/app15137497)]([https://doi.org/10.3390/app15137497](https://doi.org/10.3390/app15137497))
## 3 Results  
### Peak Amplitudes  
A strong shock wave was observed at the focal point due to the nonlinear propagation of the wave pulse. Figure 2a-c shows the focal waveforms of the 2 cm, 5 cm, and 8 cm composite transducers, respectively. The positive peak pressures of the shock waveform correspond to 35 MPa, 42 MPa, and 53 MPa, respectively. Their corresponding negative pressures are \(-\)10 MPa, \(-\)22 MPa, and \(-\)25 MPa, respectively. The frequency spectra of these shock wave signals are shown in Figure 2d-f. The center frequencies, peak amplitudes, and \(-\)6 dB bandwidth values of the CNT-PDMS composite transducers are shown in Table 1. The center frequencies are between 4 and 6.5 MHz and the bandwidths between 6 and 7.8 MHz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Diameter** & \begin{tabular}{c} **Center** \\ **Frequency** \\ **(MHz)** \\ \end{tabular} & \begin{tabular}{c} **\(-\)6 dB** \\ **Bandwidth** \\ **(MHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Peak Positive** \\ **Pressure (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Peak Negative** \\ **Pressure (MPa)** \\ \end{tabular} \\ \hline
2 & 4 & 7 & 35 & 10 \\
5 & 6.5 & 7.8 & 42 & 22 \\
8 & 5 & 6 & 53 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Center frequencies, bandwidths, and peak positive and negative pressures of 2 cm, 5 cm, and 8 cm CNT-PDMS composite transducers.  
Figure 2: Graph representing the peak positive and negative pressures of the shock waves generated by CNT-PDMS composite transducers of different diameters. (**a–c**) show the time domain signals, while (**d–f**) show the frequency domain analysis of the 2 cm, 5 cm, and 8 cm CNT-PDMS composite transducers, respectively.
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 4 Proposed Solution
### Path Generator  
If we add to each node \(N(r,c,v)\) in layer \(r=2\ldots n_{r}\) of the solution space graph, the probabilities of reaching \(N\) from each node of the proceeding layer, the generation of the new path can be modelled as the \((n_{r}-2)\)-stage Markov decision process that starts at the final node \(B\) and goes backwards.  
**Remark 4**: _The search graph \(G_{3}\) enriched by the probability distributions, can be seen as the state space of the path-generating Markov chain._  
The key elements of this generation process--which may be seen as the optimal path-search problem specific adaptation of the corresponding mechanisms of the _Estimation of Distribution Algorithm_ (EDA)--are summarised below:  
* At each \(G_{3}\) node, the predecessor selection problem is two-dimensional (\(c\) and \(v\) are to be chosen) and discrete; hence, the corresponding joint probability distributions are represented as 2D-frequency tables \((p_{j}[c,v])\);  
Figure 2: Solution space representation. (**left**) multi-stage graph \(G_{2}\). (**right**) 3D-graph \(G_{3}\) (for the sake of clarity of the drawing, the coordinate axes have been moved).  
* The prior (initial) distributions are not uniform--they form a _\"gable roof shape\"_ (with the marginal distribution of \(v\) being uniform, and the marginal distribution of \(c\) being triangular);
* Drawing is performed from the joint distribution, i.e., with a single draw, we obtain a pair of values \((c,v)\);
* Distribution adaptation is performed in two steps: first, the value corresponding to \((c,v)\) is increased by a predefined constant, i.e., \(p_{j}[c,v]=p_{j}[c,v]+\delta\), then, the distribution is \"smoothed\" locally (e.g., by weighted averaging).
## 4 Final Discussion  
A serendipitous GNSS-R experiment performed with the SMAP radar receiver has provided a rather unique data set. The use of the Polarimetric Ratio (PR) at H-Pol and V-Pol from a GNSS-R spaceborne mission has been evaluated for the first time. This work provides an advance in the understanding of GNSS-R scatterometry over land surfaces and cryosphere enabled by the high gain of the radar antenna (\(\sim\)36 dB) and the use of two orthogonal polarizations. Additionally, the second added value of this work is the analysis of waveform spreading due to the scattering process over the Earth's surface, showing the ability to detect different levels of AGB over Amazonian and Boreal forests, differentiating from deserts and the cryosphere. Values of soil moisture as provided by the SMAP's radiometer, and SWI and NDVI maps have been used here to help in the interpretation of the GNSS-R observables. It is found that despite the good correlation between PR and the soil moisture values, the GNSS-R signatures simultaneously contain information about AGB, soil moisture, and topography, making it difficult to separate the contributions. These issues need to be addressed to potentially provide accurate soil moisture measurements (key-factor for studies of the water cycle that is a continuous sequence involving water in the atmosphere, on the continents, and in the ocean), and biomass measurements (key-factor for evaluation of the carbon cycle).
## 4 Conclusions  
The objective of this study was to evaluate a machine learning application for the complex near-critical phase behavior modelling of CO\({}_{2}\)-hydrocarbon systems. We compared the physical Peng-Robinson equation of state model performance and machine learning algorithms under varying temperatures, pressures, and composition, including challenging near-critical scenarios. We used a direct neural network model and two hybrid model approaches to capture the physical behavior in a comprehensive compositional space for a CO\({}_{2}\), n-butane, and n-decane system. The key conclusions are as follows:  
1. All machine learning algorithms showed great performance during training and validation. However, when the machine learning algorithms were used directly  
Figure 7: Ternary diagrams for far-from-critical conditions of 50 atm and 60 \({}^{\circ}\)C: (**a**) Peng–Robinson EoS; (**b**) Direct Model; (**c**) Hybrid Model 1; and (**d**) Hybrid Model 2.  
for comprehensive testing in compositional space, it is evident that direct model implementation was not producing physical features, such as fluctuating K-values and inaccuracies near critical point compositions.
2. Hybrid models: both hybrid models produced more physically constrained results, with Hybrid Model 2 providing the smoothest and most reliable predictions.
3. Near-critical conditions: all models faced challenges in the near-critical region, but Hybrid Model 2 was closest to the physical model in capturing accurate phase behavior.
4. Far-from-critical conditions: under far-from-critical conditions, the overall behavior of all three models produced reasonable results.  
In summary, while neural network training is highly accurate, this study demonstrates that physical constraints, such as those incorporated into the hybrid models, are crucial for ensuring reliable phase behavior predictions, especially at near-critical points. Future work could explore expanding the hybrid modeling approach to more complex systems such as real hydrocarbon systems with plus fraction pseudocomponents. Moreover, future studies should address the hybrid model applicability for experimental data, accessing uncertainties and error bars.  
Conceptualization, D.M.; Methodology, D.M.; Software, M.M.; Validation, D.M.; Formal analysis, D.M. and M.M.; Investigation, M.M.; Resources, Z.D.; Data curation, M.M.; Writing--original draft, D.M.; Writing--review & editing, M.M.; Visualization, D.M.; Supervision, D.M. and M.S.; Project administration, Z.D. and M.S.; Funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant no. AP13068661).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction
## 5 Experiment Results
### Case 2 Results  
Some tuned AlexNet models were generated after 30 generations. Figure 10 shows some of these architectures, which obtained high fitness value rankings. As Figure 7 shows, these architectures had five convolutional layers, which is also true with AlexNet. However, they show a novel structure with several links between the same layers, which takes on the appearance of a fork. Model 1 shown in Figure 10a has a three-lever fork link before the first flattened layer. Figure 10b has a five-lever fork link before the first flattened layer. Model 3 shown in Figure 10c has two three-lever fork links before the last convolutional layer and flattened layer. The layers of all of these tuned models are perfectly identical to the initial AlexNet model. However, as Figure 8 shows, they have different links between layers. This creates greater accuracy.  
Figure 11 demonstrates the model accuracies of 50 times of independent running of the model. The average accuracy of the original AlexNet model with 50 epochs was 0.74582, and the maximum accuracy was 0.7536. The average accuracies of the tuned models with fork-like links were improved compared to the original model. The maximum accuracies were also improved, except for in model (a). Table 3 presents the values of Figure 9. It shows that the proposed algorithm improved upon the conventional model by almost 0.7% without a human expert. Although it seems to be a small improvement compared to the effort, it can be substantiated that it is a meaningful result since it has been achieved without human experts.  
Figure 10: Evolved architectures of case 2. (**a**) Model (**a**) has a 3-lever fork link before the flattened layer. (**b**) Model (**b**) has a 5-lever fork link before the flattened layer. (**c**) Model (**c**) has two 3-lever fork links before the last convolutional layer and flattened layer.