esa-sceva/satcom-chunk-collection huggingface dataset:
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 8 Lipase-Catalyzed Synthesis of Sustainable Polyesters and Polyamides from Biobased Monomers
### Biobased Saturated Aliphatic Polyesters  
#### 8.1.1 Poly(lactic acid)  
PLA is commonly produced by ring-opening polymerization of lactides or by direct polycondensation of lactic acid, using chemical catalysts. It is also possible to synthesize PLA via lipase-catalyzed ring-opening polymerization (Scheme 21).  
Figure 19: N435-catalyzed ring-opening addition-condensation of ethylene tridecanedioate with various diamines.  
However, the direct enzymatic ring-opening polymerization of lactides generally resulted in PLA with low molecular weights or low reaction yields, indicating that the enzymatic polymerization efficiency was quite low. It was also found that the enzymatic polymerization of d,l-lactide resulted in higher molecular weight products compared to d,d- and l,l-lactide [239; 240; 241]. Nevertheless, after careful adjusting the reaction conditions, high molecular weight poly(d,d-lactide) (PDLA) can be synthesized from the N435-catalyzed ring-opening polymerization, with a \(\overline{M_{\mathrm{n}}}\), dispersity and conversion of 12,000 g/mol, 1.1 and 60%, respectively [242]. In addition, poly(l,l-lactide) (PLLA) can be produced from the N435-catalyzed ring-opening polymerization in supercritical CO\({}_{2}\)[243]. Although the resulting PLLA possessed a high \(\overline{M_{\mathrm{w}}}\) (12,900 g/mol) and a good dispersity (around 1.2), the reaction yield was quite low, less than 12%.  
A new biocatalytic approach was developed for the efficient synthesis of high molecular weight PLLA and PDLA, starting from an \(O\)-carboxylic anhydride derived from lactic acid (l- or d-lacOCA) (Scheme 22) [244]. The \(\overline{M_{\mathrm{n}}}\), dispersity and reaction yield of the resulting PLLA and PDLA were up to 38,400 g/mol, \(\leqslant\)1.4, and around 90%, respectively. In addition, the tested lipases showed slight preference to l-lacOCA over d-lacOCA. Moreover, the molecular weights of the obtained PLLA can be controllable by altering the concentration of N435 in the reaction media.  
By employing alcohol initiators containing different hydroxyl numbers (2, 4, 6, 8, and 22), various linear and branched PLAs were synthesized via the _Pseudomonas fluoresaeus_ lipase-catalyzed ring-opening polymerization of l,l-, d,d-, and d,l-lactide in bulk [245]. The enzymatic polymerization yielded PLAs with \(\overline{M_{\mathrm{n}}}\)'s and dispersities of around 1500-36,700 g/mol, and 1.0-1.5, respectively.  
Moreover, many PLA \(co\)-polyesters were successfully synthesized via lipase-catalyzed \(co\)-polymerization, including poly(lactide-\(co\)-trimethylene carbonate) [246], poly(lactide-\(co\)-glycolide) [247], and poly(lactide-\(co\)-alkylene dicarboxylate) [248] (Scheme 23). The corresponding \(\overline{M_{\mathrm{w}}}\)'s were around 12,000-21,000, 2200-20,600, and 10,000-38,000 g/mol, respectively. Besides, the obtained \(co\)-polyesters can be optically active, due to the retention of the chiral configuration of lactate units after the enzymatic polymerization [248].
## 1 Introduction
### Motivation and Contributions  
First of all, let us clearly emphasize that this manuscript is a _tutorial_ (invited and submitted as the tutorial article type to the special issue \"Advanced Digital Signal Processing and Its Applications\" of the _Applied Sciences_). It is _not_ a regular research paper. Therefore, the primary goal of this tutorial is educational, and its value should be assessed in terms of educational contributions to build a better understanding of the flows of signals and gradients in deep neural networks.  
Let us refer here to two remarkable examples of tutorials well known in the machine learning community. The first one is the tutorial on hidden Markov models by [PERSON] [1] from 1989. The second is the tutorial on boosting (the ensemble learning technique) by [PERSON], [PERSON], and [PERSON] [2] from the year 2000. Both of these works have been, and continue to be up to this date, great sources of knowledge and tutoring material for researchers in those fields. Our motivation and ambition is that this tutorial on deep neural networks can also become well recognized in the machine learning community.  
We regard the following elements as the key **contributions** of this tutorial:  
* The proposition of R and S operators for tensors--_rashape_ and _stack_, respectively--that facilitate algebraic notation for computations (forward and backward) involved in convolutional, pooling, and flattening layers;
* A Python project named _hmdl_ (\"home-made deep learning\")--a practical implementation of the ideas presented in this tutorial;
* A careful analysis of a _small example_--tracking flows of signals and gradients through a four-layer convolutional network (Section 3);
* A concise explanation of _DL computations_ (the aforementioned topics)--automatic differentiation, initialization of weights, learning algorithms, and regularization;
* Clear and consistent _notation_ across all mathematical contexts involved.  
Let us briefly elaborate here on the second element--the _hmdl_ project. It constitutes the postulated \"from scratch\" implementation that can be treated as an example and a reference. It provides the common layer types and functionalities, and it does that in approximately 2 k lines of code. Therefore, the task of developing a \"home-made\" DL framework is not as difficult as it might seem. In sum, we think the hmdl project helps the reader understand how DL's mathematics and algorithms become translated into the realm of programming and computations.  
Apart from tutoring contributions, this manuscript is additionally intended to constitute a short survey of crucial scientific contributions in the development of DL. We focus especially on the ones that took place during the last thirteen years (since the \"DL big bang\") and have paved the way for DL's popularity and frameworks. We present those contributions on the canvas of the five aforementioned topics and with the main goal in mind--guiding the reader towards implementation.
## 1 Introduction
In the literature, malnutrition has been emphasized as a modifiable risk factor [7]. PNI plays a critical role in assessing the prognosis of patients with many diseases, such as cancer, lymphoma, and postoperative changes, by reflecting the reaction and presentation of inflammatory abnormalities [11]. PNI is considered an early sign of malnutrition, with low albumin levels and low lymphocyte counts, which makes its effects evident in gastrointestinal testinal emergencies. These findings suggest that the PNI may enhance the sensitivity of risk assessment in patients with gastrointestinal emergencies. The prognostic value of PNI can also be explained by underlying pathophysiological factors. This index is calculated using a simple formula based on serum albumin level and lymphocyte count, and reflects the immunological and nutritional status of the individual. Albumin is a negative acute phase reactant and can fall rapidly during inflammation, while lymphocytopenia is associated with stress and impaired immune response [12]. Therefore, a low PNI may indicate a severe inflammatory response and/or chronic nutritional deficiency. Furthermore, hypoalbuminemia is associated with endothelial dysfunction, impaired host defense, and increased inflammatory response in sepsis. Several recent studies have reported associations between a low PNI and elevated cytokine levels, such as IL-6 and TNF-\(\alpha\), supporting its role as a surrogate marker of systemic inflammation [6]. The question of whether a low PNI is a factor leading to poor outcomes alone or a reflection of an already serious illness remains debatable. In the literature, PNI has been reported to be a risk factor independent of other scores, even in acute critical illnesses, such as COVID-19 [6]. This suggests that the PNI is not only an indicator of disease severity but also a comprehensive risk index that reflects an individual's capacity to cope with the disease [13]. In this study, we aimed to evaluate the efficacy of PNI in predicting the clinical course and patient outcomes in patients with GI emergencies in the emergency department. We investigated whether PNI is a useful prognostic tool in addition to existing scoring systems and whether it can contribute to the early recognition and management of high-risk patients.
## 2 Materials and Methods
### Organic Speciation  
For the determination of organic molecular markers, the quartz filter sample was extracted by sonication using dichloromethane for non-polar organic compounds (i.e., Polyaromatic hydrocarbons (PAHs), n-alkanes, cycloalkanes and steranes and hopanes) analyzed using gas chromatography-mass spectrometry (GC-MS) and methanol or purified water for polar organic compounds (i.e., levoglucosan, amino acids, resin acids, alkanoic acids, aromatic diacids and alkane dioic acids) quantified using liquid chromatography -tandem mass spectrometry (LC-MSMS). All data were blank corrected using field blank data. For each nonpolar organic sample, the final volume was adjusted to 500 \(\mu\)L to match the volume of the internal standard (samples and blanks were spiked with internal standards). Underivatized polar organic compounds were analyzed using LC-MSMS with internal standards (e.g., phthal acid (D4)), the milli-Q water of 5.0 mL (or methanol some polar organic compounds (e.g., phthalates and cholesterol, etc.) was sonicated into the sample tube for the final extract volume. Hydrophilic interaction LC used an Eclipse XDB-C18 4.6-mm ID \(\times\) 150 mm (5 mm) column as stationary phase with 10-mM ammonium acetate and acetonitrile in milli-Q water. Polar organic compounds was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Forest tree Types**} & \multicolumn{3}{c}{**Agricultural Crop Residues**} \\ \cline{3-4}  & & \multicolumn{3}{c}{**(Herbaceous Plants)**} \\ \hline
**Item** & **Academic Name** & **Item** & **Academic Name** \\ \hline Pine needles & _Pinus densiflora_ & Rice straw & _Oryza sativa_ \\ Pine stem & _Ginkgo biloba L._ & Red pepper stem & _Capsicum annuum_ \\ Maple leaves & _Acer palmatum_ & & _glycine max_ \\ Cherry leaves & _Prunus serrulata var._ & Soybean stem & _glycine max_ \\ Cherry stem & _spontanea_ & Green perilla stem & _Perilla frutescens var._ \\  & & & _japonica Hara_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Research materials used in biomass burning.  
Figure 1: Schematic diagram of biomass burning chamber.  
analyzed in multiple reaction monitoring application for the separation and detection of underivatized compounds. Regression coefficients of determination for seven point calibrations were from higher than 0.998. Absolute method of detection limits were in the range of 1.0-4.6 pg/m\({}^{3}\). For all polar organic compounds, the final mass fragment transitions of quantification application such as fragmentor voltage, collision energy, quantifier and qualifier ions, were determined. The detailed analytical condition can be found at the previous study [27; 28; 29]
## 2 Materials and Methods
### Corrosion Rate Calculation by Volume Loss and Mass Loss  
The experiments to analyze corrosion rates were conducted based on the ASTM in vitro testing standard (ASTM-G31-72 [32]). Pure Zn, Mg and Mg alloys were scanned by micro-computed tomography (micro-CT, GE Phoenix Nanotom-Mtm, GE Sensing & Inspection Technologies GmbH, Boston, MA, USA). The materials were scanned at 6.7 \(\upmu\)m voxel resolution with X-ray emission parameters 110 kV, 90 \(\upmu\)A. Each rotational position 2D X-ray was collected from averaging 3 images. The volumes were reconstructed using Phoenix datos | x software. The volume of residual samples was calculated using VG Studio Max software (v 2.1). The micro-CT images before and after removing corrosion product (Figure 3) were used in the software to calculate the volume loss.  
\begin{table}
\begin{tabular}{c c} \hline
**Alloys** & **Composition** \\ \hline
47–7–2 & Zn 1\% wt, Ca \textless{} 0.5\% wt, Mg remainder \\
60–13 & Zn 4\% wt, REE 1\% wt, Zr 1\% wt, Mg 94\% wt. \\
60–14 & Zn 3\% wt, REE \textless{} 0.5\% wt, Zr 1\% wt, Mg remainder \\
59B & Zn 1\% wt, Ca \textless{} 0.5\% wt, REE 1\% wt, Mg remainder \\ Mg & \(\geq\)99.99\% Mg \\ Zn & \(\geq\)99.99\% Zn \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical composition of tested alloys.  
Figure 1: Grain micro-structures of tested biodegradable alloys. Grain sizes in diameter are: D (47–7–2) = 17 \(\pm\) 16 \(\upmu\)m, D (59B) = 11 \(\pm\) 5 \(\upmu\)m, D (Mg) = 60 \(\pm\) 19 \(\upmu\)m, D (Zn) = 78 \(\pm\) 43 \(\upmu\)m, D (60-13) and D (60-14) <1 \(\upmu\)m.  
Calculated change in volume used numerically to compute the volume loss. The empirical equation for corrosion rates determination from volume change of individual samples [25] is given as:  
\[\text{CR}=\frac{\Delta V}{A\cdot t} \tag{1}\]  
where:  
CR: Corrosion rate (mm/year);  
\(\Delta V\): reduction in volume that is equal to the remaining implant volume subtracted from the initial implant volume (mm\({}^{3}\));  
\(A\): the initial surface area exposed to corrosion (mm\({}^{2}\));  
\(t\): immersion time (year).  
The mass loss obtained from by subtracting the corroded mass (mass without corrosion products) from the original mass of degradable samples. The corrosion rate unit used here chosen to be consistent with the literature in this dissertation. The empirical equation used to compute corrosion rate of magnesium by mass loss as the following formula [33]:  
\[\text{CR}=\text{K}\cdot\frac{\Delta W}{D\cdot A\cdot t} \tag{2}\]  
where:  
CR: Corrosion rate (mm/year);  
K: the constant, 8.76 \(\times\) 104;  
\(\Delta W\): weight loss, which is calculated by reduction of corrosion products-removed remaining mass from initial mass (mg);  
\(D\): density of alloy (g/cm\({}^{3}\));  
\(A\): the initial surface area exposed to corrosion (cm\({}^{2}\));  
\(t\): immersion time (hour).
# Numerical Investigations of Micro-Scale Diffusion Combustion: A Brief Review  
[PERSON]  
1 INEGI--Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal 1  
[PERSON]  
2 Division of Engineering Technology, College of Engineering, Wayne State University, Detroit, MI 48202, USA  
process and (b) the higher heat losses from the combustion chamber walls due to the larger surface to volume ratios [1; 2].  
Designing and conducting numerous micro-scale experiments are both time consuming and expensive. Therefore, it is more reasonable to direct most of the efforts towards numerical simulations and to limit experimental investigations to when it is necessary especially for validation purposes. Hence, the literature in combustion fields is very rich with numerical methodologies and simulations that have been conducted to investigate combustion physics at micro scales and seek ways to (a) develop more proficient and accurate numerical models, (b) capture different phenomena in micro-scale combustion and (c) optimize micro-scale power generation devices.  
In the last two decades, significant efforts have been invested in the modeling of the micro-scale combustion devices, through which numerous methodologies have emerged and prominent conclusions have been drawn for different operating conditions such as different fuel types and phases, different geometries, different fuel/oxidizer compositions, and so forth. Currently, such numerical methodologies and simulation tools are widely adopted to conduct research in micro combustion. With that, most micro-combustion studies consist of numerical simulations to complement the experimental research, allowing a better understanding of the dynamics of the flames and the optimization of the corresponding devices.  
Numerical simulations for both premixed and non-premixed (diffusion) combustion at micro scales have been evolving extensively as the topic is becoming more and more interesting to researchers. In premixed combustion, researchers have concluded that combustion characteristics and flame behavior are very sensitive to the dimensional and thermo-physical parameters [3; 4; 5; 6]. Other than premixed combustion at micro scales, non-premixed or diffusion combustion at such scales has also been under great attention due to the fact that diffusion combustion is a more typical combustion regime to use in practical systems [7]. By comprehending the effects of the complex transport phenomena associated with the chemical kinetics of such flames, researchers can further investigate complex phenomena such as flame ignition and extinction and pollution formations and eventually improve the efficiency of combustion-oriented devices such as combustion engines [8].  
In this review paper, the authors will present a succinct review of the progress that has been made in numerical investigations and methodologies on the topic of diffusion combustion at micro scales, where a summary of the state of the art will be provided to further direct researchers on adding to the body of knowledge in this field.
# Study on Lubrication Performance of Journal Bearing with Multiple Texture Distributions
## 5 Concave-Convex Composite Spherical Texture
### Effect of Texture Depth  
With a fixed area density \(\lambda\) of 0.5, simulations with different texture depths \(h_{p}\) from \(5\times 10^{-7}\) to \(3\times 10^{-6}\) m are performed. The load capacity \(W\) and the friction coefficient \(\mu\) of the bearing with concave-convex composite spherical texture, convex spherical texture and no texture are plotted in Figure 7. It is found that the load capacities of the convex and the composite textured bearings both increase with increasing texture depth. The increment of the composite texture is slightly higher than  
Figure 6: Distribution of concave-convex composite spherical texture: (**a**) circumferential film pressure distribution, (**b**) circumferential film thickness distribution diagram.  
that of the convex texture. The load capacities of both sorts of textured bearing reach the maximum value at \(h_{p}=3\times 10^{-6}\) m together. The load capacity of the composite textured bearing is greatly improved to 1173 N by 8.11% relative to that of the plain bearing (1085 N), and the improvement of the convex textured bearing is quite similar (7.9%).  
In the texture depth range considered, the initial friction coefficient (at \(h_{p}=5\times 10^{-7}\)m) of the composite textured bearing is lower than those of plain bearing and the convex textured bearing. The friction coefficients of the bearings with the composite texture and the convex texture both decrease with increasing texture depth. However, Figure 7b clearly shows an obviously greater reduction in friction coefficient for the composite textured bearing than the convex one. Since both the friction coefficient trends are monotonous, the lowest values can be obtained at the greatest texture depth of \(3\times 10^{-6}\) m. A significant reduction in the friction coefficient is achieved by the composite textured bearing from 0.072 (plain bearing) to 0.0684 by 5.91%, which can be regarded as a considerable beneficial effect in lubrication performance of the journal bearing.
## 8 Conclusions  
We have developed a simplified analytical model for swabs and surges in this study. This model considers eccentricity, diameter ratio, fluid rheological properties, and tripping speed. We conducted experiments using two power-law and two yield-power-law fluids formulated with PAC- and xanthan-gum-based polymers to validate the model. Our findings can be summarized as follows:  
* The model has shown good prediction of the experimental data when utilizing specific \(\beta^{\star}\) values and R-factors for power-law and yield-power-law fluids.
* The rheological properties of fluids, diameter ratio, and tripping speed influence pressure variations during swabs and surges.
* The modified R-factor presented in this paper suggests that it could be further adapted for different fluid properties.
* The \(\beta^{\star}\) value for yield-power-law fluids is lower compared to power-law fluids, resulting in lower pressure loss in yield-power-law fluids.
* Increasing the \"\(n\)\" value increases pressure losses in both power-law and yield-power-law fluids.
* Similarly, an increase in the \"\(k\)\" value results in higher pressure losses in both power-law and yield-power-law fluids.
* The overall model comparison results showed average percentile error deviations of 9.9% and 6.2% for the power-law and yield-power-law fluids, respectively.  
Finally, this paper demonstrates that physics-based models are adequate for the given type of drilling fluid, highlighting the suitability of a rheology model to describe the drilling fluid. It does mean that the hydraulics model predicts the pressure loss for that particular fluid type. It is, therefore, always important to have measurements and do model calibration. In the calibration factors, hidden physical parameters exist that the model did not consider. In this regard, applying a machine learning model that utilizes a measured dataset includes all the factors affecting the measurement. Hence, machine learning models have shown promising results in many fields.  
A.M.: conceptualization, methodology, testing, sensitivity analysis, model performance analysis, interpretation, and writing. M.B.: methodology, draft manuscript preparation, supervision, review, and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.