esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Based on our insight, there is only one scientific publication that deals with precisely genotyped _A. salmonicida_ subsp. _salmonicida_ biofilms [25]. _Aeromonas salmonicida_ has been proven to have a \"social life\" and participates in the construction of multispecies biofilms with other aquatic bacteria (although it is unclear which subspecies of _A. salmonicida_), and this is mostly found in biofilms in natural river and sea water environments [26]. It has also been proven that in such multispecies-tight environments, resistance genes from other bacteria are much easier to transfer to _A. salmonicida_[26]. It is still not well understood which genes are responsible for the synthesis of biofilm components in _Aeromonas salmonicida_ subsp. _salmonicida_. Genes that are not strictly typical for _A. salmonicida_ are mostly listed, and most often, these are the genes that encode theproduction of components/processes that are important not only for biofilm but also for other bacterial functions. Thus, the genes for the Type 1, Type 2, and Type 3 autoinducer (AI-1, AI-2, AI-3) system that encode _Quorum sensing_ and swimming motility [26; 27; 28]; genes for the production of lateral flagella [29]; polar flagella (which are even considered essential for biofilm production in the genus _Aeromonas_); type IV pilli [14]; and transcriptional regulator [30] are most often cited in research. The _vapA_ gene encodes an A-layer protein, which is of great importance for _A. salmonicida_ to attach to a surface and which is the basic prerequisite for biofilm formation [25]. In this study, the experiment was designed to fit the hypothesis that _Aeromonas salmonicida_ subsp. _salmonicida_ produces stronger biofilms in nutritionally poor environments and at suboptimal temperatures [31]. Another goal of this study was to establish the optimal temperature of _Aeromonas salmonicida_ subsp. _salmonicida_ for the _in vitro_ production of biofilms, whether it correlates with its natural environment (8 \({}^{\circ}\)C or 16 \({}^{\circ}\)C) or the one that is mostly used for its isolation and identification (25 \({}^{\circ}\)C). To the best of our knowledge, this is the first study to examine precisely typed _Aeromonas salmonicida_ subsp. _salmonicida_ clinical isolates of fish origin at lower temperatures and in scarce nutritional media with three methods, in comparison with rich media and elevated temperatures, during a prolonged incubation that lasted 7 days to determine its ability to produce biofilm. With the partial genome sequencing analysis, we believe this will contribute to a better understanding of the physiology and virulence of this still insufficiently investigated pathogenic agent which is exceptionally important for the aquaculture.
## 5 Summary and Conclusions  
This study investigated the agricultural drought impacts on water use efficiency (WUE) and its components; gross primary productivity (GPP) and evapotranspiration (ET), as well as the recovery duration that terrestrial ecosystems require to revert to pre-drought normal conditions. WUE was analyzed for different land cover types and arid and coastal ecosystems indicated the highest WUE and Midwest US was associated with the lowest WUE. Drought recovery was analyzed according to the GPP rate and it revealed that the required time for each region to revert to its pre-drought condition is positively correlated with drought severity. Therefore, a more severe drought will most likely result in a longer drought recovery time. Additionally, a prolonged drought episode increases the likelihood of protracted drought recovery. During drought, WUE showed a tendency to increase in response to extreme drought condition. Decomposing WUE anomalies to its components during drought illustrated that if the relative anomaly of ET is larger than that of GPP, WUE anomaly will be positive and if the relative anomaly of ET is smaller than that of GPP, WUE anomaly will be negative. Moreover, the spatial distribution of ET anomaly showed that ET has a tendency to be below average in the regions affected by severe (or more intense) and prolonged drought in both dry and humid climates, corroborating the dominance of water availability on evapotranspiration.  
B.A., A.A., G.T. and H.M. conceived the research idea; B.A. processed the data; B.A. and A.A. analyzed the results. All the authors contributed to the manuscript.  
This research was partially funded by NOAA grant NA18 OAR4310319.  
The MODIS evapotranspiration and gross primary productivity data were obtained from the Numerical Terradynamic Simulation Group at University of Montana ([[http://www.ntsg.umt.edu](http://www.ntsg.umt.edu)]([http://www.ntsg.umt.edu](http://www.ntsg.umt.edu))). Additionally, the MODIS biome land cover types were acquired from the global land cover facility of the University of Maryland ([[http://glcf.umd.edu/data/lc/](http://glcf.umd.edu/data/lc/)]([http://glcf.umd.edu/data/lc/](http://glcf.umd.edu/data/lc/))).  
The authors declare no conflict of interest.
## 1 Introduction
### Related Work  
In the manufacturing domain, including assembly, encompassing processes like assembly, machining, and additive manufacturing, the existing KG inference methods are predominantly oriented towards applications such as knowledge recommendation [21], process decision [22], fault diagnosis [23], quality prediction [24], and others [25]. However, there is a scarcity of studies regarding tasks such as knowledge completion and knowledge judgment oriented toward KG construction. For example, [PERSON] [26] proposed an attention-based graph embedded model (ABGE) for discovering and supplementing tacit missing relations in KGs to obtain a complete industrial KG. For additive manufacturing process, [PERSON] et al. [27] adopted bidirectional entity/relation encoder based on BERT model to convert extracted relations and entities into vectors to support additive process knowledge inference. [PERSON] et al. [28] proposed a manufacturing knowledge-oriented relation extraction model (MKREM), which uses the Bi-LSTM layer for word embedded to improve robustness, and uses the simplified Graph Convolutional network (SGC) layer to quickly mine entity information and complete relation information. [PERSON] et al. [29] proposed a product innovation design method based on the tacit knowledge relation completion in the patent KG, which generates new relations and outputs novel knowledge by constructing an implicit relation completion model based on entity similarity. [PERSON] et al. propose [30] propose a time-varying KG (TVGN) based on knowledge embedment, and a time-varying KG (time-varying KG), which enables the context vector and global spatial semantic information to perceive and understand the environment in real time and update the environment.  
From the analysis of KG inference studies regarding KG construction in the manufacturing domain, although researchers have incorporated diverse information to accurately express manufacturing knowledge, this added information remains unaccounted in KG inference for KG construction. In view of this, this paper proposes a KG inference method integrating logical rules and embedded learning for KG construction tasks such as knowledge judgment, entity and relation completion, which is used to solve the problem that domain KG lacks effective inference methods, and this method can be extended to KG inference tasks in other domains. The theoretical framework of the proposed approach is based on quintuple form to express assembly process knowledge and consider all information in quintuple to realize complete construction of assembly process KG.  
The organization of this paper is as follows. Section 2 introduces the KG inference method framework integrating logical rules and embedded learning. The proposed methodology is explained in Section 3. The experiment to verify proposed method is carried out in Section 4. At the end, conclusions and future work are highlighted.
## 4 Challenges, Sustainability, and Potential in Application of Biochar in Sludge Management  
The most common challenge within communities using pit latrines is the ethical norm on the acceptance to repurpose biochar-treated faecal sludge for crop production [17]. Even though the biochar-treated sludge by-product can have acceptable threshold levels for most heavy metals and inorganic and organic contaminants, societies and communities in most developing countries treat human sludge as undesirable waste. In addition, the lack of knowledge in the biochar production process remains a challenge. The International Biochar Initiative [73] set guidelines on standards for production of biochar for use as soil amendments. However, information on biochar production for use in the treatment of contaminates and faecal sludge is limited. Moreover, most communities using latrines have livestock, which relies on the biomass material also needed to produce biochar. Nonetheless, the use of biochar in pit latrine sludge management can also be made sustainable, as the production process of biochar is regarded as an efficient management method to dispose of many organic wastes. However, advantages and disadvantages between the economic cost (production) and benefit value (application) of biochar need to be carefully measured. In addition, to enhance economic availability, easier production processes and cheaper sources of raw biomass materials need to be discovered to enhance economic availability [116]. Heavy metals can contaminate faecal sludge if toilets are also used to dispose of materials other than faecal sludge [52]. Studies have reported that for any new technology to be successfully integrated in a society, community awareness and engagement is important [13; 17; 24].  
Education on the appropriate use of toilets is important [52], and application of biochar in latrines can be viable since a typical standard pit latrine only measures an approximate pit area of 2 m \(\times\) 2 m [31; 42; 61]. In comparison to uses for amendment purposes in soil fertility and agriculture, sludge treatment can be more cost-effective, as the required biochar quantities are less bulky. The use of biochar has also been proven to increase faecal sludge decomposition, which can reduce the pit filling rates and increase the lifespan of a latrine. Biochar is also now commercially produced, which can also increase accessibility for sludge treatment and management uses. The high adsorption properties of biochar for water pollutants can assist with in situ sorbent and faecal sludge treatments. Such low-cost adsorbents can improve water quality through contaminant management.
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
## 2 Bioapplications of AA ILs: An Overview
### Stability and Activity of Enzymes and Proteins
#### 2.5.2 Effect of AA-ILs on the Stability and Structure of Proteins and Enzymes
In addition, MD simulations revealed strong interactions between the IL and the protein, as evidenced by the significant Stokes shifts observed in the spectra of IL-protein complexes. The strong hydrophobic interactions are responsible for the [TMG][Ala]-induced unfolding of red fluorescent mCherry, even at room temperature [95]. The effect of IL anions on the structure and stability of bromelin has been clearly demonstrated in several publications [83; 84]. For example, when [Bmim][Gly] is added to an aqueous solution of bromelain, its thermal unfolding temperature decreases in a concentration-dependent manner. In addition, conformational changes are observed with increasing IL content, as evidenced by a marked decrease in fluorescence intensity and a significant red shift of the maximum at 345 nm in the fluorescence spectra. Notably, [Bmim][Br], when tested in the same concentration range, has a lesser effect on the thermal stability of bromelain and almost no effect on its secondary structure [84]. Molecular docking experiments showed that the Gly anion can dissociate existing hydrogen bonds in the protein and form up to 9-10 new strong hydrogen bonds with several regions of the polypeptide backbone of the enzyme, thus destabilizing the protein. In contrast, for [Bmim][Br], the Br anion is expelled from the protein surface and interacts with water molecules, preventing protein-water hydrogen bonding and stabilizing the protein. It is noteworthy to mention that choline-based ILs containing Gly, Ala, Leu, and Pro anions, when added to a solution at concentrations up to 100 mM, increased the thermal stability of calf thymus DNA by up to 3-4 \({}^{\circ}\)C [96]. Additionally, it was shown that BmimILs with the same anions enter the solvation shell of the DNA and displace water molecules from the hydration shell [97]. These ILs stabilize the double helix structure through strong electrostatic and van der Waals interactions and help to maintain the DNA conformation over the long term.
# Sensor Micro and Nanoparticles for Microfluidic Application
## 5 Microfluidic Systems for Biomedical Applications
### Colorimetric Particle Sensor for Microbiology and Biomedical Measurements
Despite the fact that the flow of the solution was extremely slow and detection time was longer than 10 min, the limit of detection (LOD) was 4 \(\upmu\)g for each drug. An Ag nanoparticle-based sensor was presented by [PERSON] et al. [70]. Easy and straightforward protein analysis was performed in a microfluidic chip via a colorimetric assay, which was based on the interaction of analytes with an aptamer-AgNPs complex. The aptamer Apt 29, which affected the silver SPR, was successfully immobilized onto the surface of AgNPs with morphology close to sphere and 35 nm in diameter on average. The produced complex of Apt 29-AgNPs was used for the subsequent reaction with thrombin. The surfaces of microfluidic channels were functionalized with streptavidin and later biotinylated Apt 15 was immobilized in order to form a capture probe for the targeted protein--thrombin. After the injection of thrombin and the aptamer-AgNPs mixture into the microfluidic device, thrombin was captured on the channel surface specifically. Due to thrombin and aptamer-AgNPs bonding, the color change in the channel appeared and could be analyzed by the naked eye or a flatbed scanner. By increasing the concentration of thrombin, the amount of bounded aptamer-AgNPs complex increased proportionally to the thrombin concentrations. It was demonstrated that this method had a good linear relationship with the detection limit of 20 pM thrombin. Additionally, the protein detection was operated simply, highly specific, showed a low detection limit and required a low sample volume.
## 4 IDPSO Algorithm Analysis and Design
### Encoding and Decoding  
As encoding and decoding involve two aspects of process arrangement and machine selection, the two-layer coding method of process and machine is adopted here, and the length of the coding in the process layer and the machine layer is equal, while the positions are one-to-one. Table 3 shows an example of the FJSP. The first number, 111, in the operation column represents the first operation of the first job in the first type, namely \(O_{111}\), the second number, 112, represents the second operation of the first job in the first type, namely \(O_{112}\), the third number, 211, represents the first operation of the first job in the second type, namely \(O_{211}\), and so on. Therefore, the operation code uses the type of job number in the operation arrangement, and the number of occurrences indicates the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Special Parameters** & \begin{tabular}{c} **General** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Parameter** \\ **Number** \\ \end{tabular} \\ \hline MOABC [17] & \begin{tabular}{c} Maximum evaluation times, \\ weighted value, maximum \\ searches limit, search threshold. \\ Pheromone factor, heuristic \\ information factor, evaporation \\ coefficient, pheromone increment \\ constant, initial \\ pheromone concentration. \\ Inertia factor, learning factors. \\ Number of objective divisions, \\ mutation rate, crossover rate, \\ selection rate. \\ Mutation rate, crossover rate, \\ selection rate. \\ DQNSGA [24] & \
\begin{tabular}{c} Number of objective divisions, \\ mutation rate, crossover rate. \\ \end{tabular} &
\begin{tabular}{c} 6 \\ 6 \\ 5 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: A statistical table of six algorithms’ parameters.  
processing sequence of the job operation. As shown in Figure 2, if an operation is arranged as \(O_{111}-O_{211}-O_{221}-O_{112}-O_{311}-O_{212}-O_{222}-O_{312}-O_{213}-O_{223}\), its operation code can be expressed as \([11\ 21\ 22\ 11\ 31\ 21\ 22\ 31\ 21\ 22\ ]\).  
Before coding the machine, the machines \(WC_{11}\), \(WC_{21}\), \(WC_{22}\), \(WC_{31}\) and \(WC_{32}\) are numbered 1, 2, 3, 4 and 5 in sequence; then, the machine number can be selected according to the operation in the operation sequence (OS). As shown in Figure 3, according to the OS, a machine is successively found from the optional machining machine set in each process, and then one of the machine sequences (MS) is \([1\ 2\ 3\ 4\ 2\ 5\ 1\ 3\ 2\ 1\ ]\). The first element, 1, in the coding sequence means that \(O_{111}\) chooses to process on the first equipment on the first work center with the number of 1, and the second element, 2, means that \(O_{211}\) chooses to process on the first equipment on the second work center with the number 2, and so on.  
In this paper, the multi-factor flexible job shop scheduling problem under parallel multi-equipment is studied, including machine setup time, job handling time, and machine processing time, for multi-equipment production systems with multi-variety and small-batch job processing task orders.
## 3 Results
### Numerical Validations
#### 3.1.2 D High-Velocity Impact  
Figure 2 presents the 2D computational model of an aluminum circular projectile impacting an aluminum plate. The diameter of the circular projectile is 9.53 mm. The length and width of the plate are 30.0 mm and 0.47 mm, respectively. The projectile has an initial horizontal velocity \(v_{0}\) of 4.71 km/s. Both projectile and plate are used with the Mie-Gruneisen equation of state and the Steinberg-Guinan constitutive model. The material parameters of aluminum are provided in Table 1. The initial particle distance is 0.01 mm, and the total number of particles is 995,297.  
Figure 3 shows the SPH simulations and experimental results of the debris cloud at 6 \(\upmu\)s and 19 \(\upmu\)s. The overall profile shows that the simulation results agree with the experimental results [58], and the SPH simulations accurately capture the characteristic structure of the debris cloud, with a spallation region appearing inside the spherical projectile, forming a shell that was loosely attached to the rear of the spherical projectile. At 6 \(\upmu\)s, the ratio of debris cloud (length of debris cloud/width of debris cloud) is 1.08, which is in close agreement with the experimental result of 1.11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\rho_{0}\left(\mathrm{g/cm^{3}}\right)\) & \(c_{0}\left(\mathrm{km/s}\right)\) & \(s\) & \(\tau_{P}\) & \(\mathcal{Q}\left(\mathrm{GPa}\right)\) & \(q\) \\ \hline
2.71 & 5.35 & 1.35 & 2.0 & 37.89 & 8.419 \\ \hline \(\rho_{\mathrm{Rx}}\left(\mathrm{g/cm^{3}}\right)\) & \(T_{\mathrm{s00}}\left(\mathrm{K}\right)\) & \(V_{\mathrm{s00}}\left(\mathrm{cm^{3}/g}\right)\) & \(\tau_{T}\) & \(c_{e}\left(J/\left(\mathrm{g\cdot K}\right)\right)\) & \(G_{0}\left(\mathrm{GPa}\right)\) \\ \hline
2.76 & 933.0 & 0.3986 & 2.35 & 0.850 & 27.6 \\ \hline \(Y_{0}\left(\mathrm{GPa}\right)\) & \(Y_{\mathrm{max}}\left(\mathrm{GPa}\right)\) & \(G_{p}^{\prime}/G_{0}\left(\mathrm{GPa^{-1}}\right)\) & \(G_{T}^{\prime}/G_{0}\left(\mathrm{T^{-1}}\right)\) & \(Y_{p}^{\prime}/Y_{0}\left(\mathrm{GPa^{-1}}\right)\) & \(Y_{T}^{\prime}/Y_{0}\left(\mathrm{T^{-1}}\right)\) \\ \hline
0.29 & 0.68 & 0.065 & 0.00062 & 0.065 & 0.00062 \\ \hline \(\beta\) & \(n\) & & & & \\ \hline
125.0 & 0.10 & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Material parameters of aluminum [51; 52; 53].  
Figure 2: Schematic diagram of the 2D computational model of a circular projectile impacting a target plate.
## 2 Theoretical Analysis of Horizontal Bearing Capacity of Squeezed Branch Pile
### Calculation Method of Horizontal Bearing Capacity of Squeezed Branch Pile  
_The \(m\)_-method based on the Winkler foundation beam model is a typical method to calculate the horizontal bearing capacity of straight piles. It is considered that the horizontal resistance of foundation soil to the pile is proportional to the horizontal displacement at this point of the pile body. The interaction between the pile and soil in the straight section of the squeezed branch pile body is exactly the same as that in the straight pile section of the pile body, so the \(m\)-method can be used to analyze the force in the straight section. The pile-soil interaction at the bearing plate can be described by the calculation equation for the bearing plate resisting moment considering the nonlinear pile-soil interaction obtained in Section 2.2.  
Based on the m-method of the Winkler foundation beam model, [PERSON] [38] established the deflection curve equation of the straight pile:  
\[E_{i}I_{i}\frac{d^{4}x}{d^{4}y}=-b_{i}p=-m_{i}(y_{0i}+y)b_{i}x \tag{8}\]  
where \(E_{i}\) is the elastic modulus of the pile body material in section \(i\), \(I_{i}\) is the moment of inertia of the pile section, \(b_{i}\) is the calculated width of the pile body of this section, \(p\) is the foundation soil reaction force, \(m_{i}\) is the proportional coefficient of the horizontal foundation coefficient changing with depth, and \(y_{0i}\) is the soil resistance parameter at the top of the pile in section \(i\).  
The calculation formula for the horizontal displacement \(x_{i-1}\), rotational displacement \(\varphi_{i-1}\), bending moment \(M_{i-  
For the squeezed single-branched pile, the pile body can be divided into the upper part of the plate numbered 1 and the lower part of the plate numbered 2 along the length direction. And the influence of the resisting moment of the bearing plate existing at the joint of the pile segment is introduced into Equation (8). Thus, the calculation formula for the displacement and internal force of the pile segment applicable to the squeezed branch pile is as follows:  
\[\left\{\begin{array}{c}x_{i}\\ \varphi_{i}\\ M_{i}\\ V_{i}\end{array}\right\}=K_{i}\left\{\begin{array}{c}x_{i-1}\\ \varphi_{i-1}\\ M_{i-1}-M(\varphi_{i-1})\\ V_{i-1}\end{array}\right\} \tag{12}\]  
Since there is no bearing plate and bearing plate resisting moment at the top of pile segment 1 above the squeezed branch plate, letting \(D=d\) can make \(M(\varphi_{0})\) calculated by Equation (7) = 0, so that Equation (10) is also applicable to pile segment 1.  
Obviously, for a squeezed branch single pile with two pile segments, eight equations can be determined by using Equation (10), but there are six unknown displacements and six unknown internal forces. As long as four unknowns are determined by boundary conditions, the equation can be solved.