esa-sceva/satcom-chunk-collection huggingface dataset:
Bioengineering_**2015**, \(2\), 184-203; doi:10.3390/bioengineering2040184  
_bioengineering_  
**ISSN 2306-5354**  
www.mdpi.com/journal/bioengineering  
_Review_  
Converting Sugars to Biofuels: Ethanol and Beyond  
**[PERSON] 1,2 and [PERSON] 1,2,*  
* 1 Joint BioEnergy Institute, Emeryville, CA 94608, USA
* 2 Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; E-Mail: [EMAIL_ADDRESS]
* * Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-510-495-2469; Fax: +1-510-495-2437.  
Academic Editor: [PERSON]  
_Received: 17 September 2015 / Accepted: 20 October 2015 / Published: 27 October 2015_  
**Abstract:** To date, the most significant sources of biofuels are starch- or sugarcane-based ethanol, which have been industrially produced in large quantities in the USA and Brazil, respectively. However, the ultimate goal of biofuel production is to produce fuels from lignocellulosic biomass-derived sugars with optimal fuel properties and compatibility with the existing fuel distribution infrastructure. To achieve this goal, metabolic pathways have been constructed to produce various fuel molecules that are categorized into fermentative alcohols (butanol and isobutanol), non-fermentative alcohols from 2-keto acid pathways, fatty acids-derived fuels and isoprenoid-derived fuels. This review will focus on current metabolic engineering efforts to improve the productivity and the yield of several key biofuel molecules. Strategies used in these metabolic engineering efforts can be summarized as follows: (1) identification of better enzymes; (2) flux control of intermediates and precursors; (3) elimination of competing pathways; (4) redox balance and cofactor regeneration; and (5) bypassing regulatory mechanisms. In addition to metabolic engineering approaches, host strains are optimized by improving sugar uptake and utilization, and increasing tolerance to toxic hydrolysates, metabolic intermediates and/or biofuel products.  
**Keywords:** biofuels; ethanol; advanced biofuels; lignocellulosic biomass; metabolic engineering
# Arabidopsis Plastid-RNA Polymerase RPOTp Is Involved in Abiotic Stress Tolerance
## 1 Introduction
Along these lines, tobacco PEP-defective mutants are impaired in photosynthesis, and display an albino phenotype [18; 20; 21]. Knocking out Arabidopsis genes _RPOTmp_[22], and principally _RPOTp_, the latter in _scabra3_ (_sca3_) mutants [23], has resulted in abnormal chloroplast development, stunted growth, reduced pigmentation and altered leaf morphogenesis. Interestingly, the _rpoTmp sca3-2_ double mutant individuals exhibit a more severe phenotype, and even arrest growth early in development [23]. Chloroplasts may play a fundamental role in plant stress responses by sensing abiotic stress and transmitting this information to the nucleus, in order to coordinate the expression of nuclear and plastome genes. In this way, organelle activity adjusts to the new environmental conditions,which allows plants to cope with stress [24]. Accordingly, PGE has to adequately respond to changes in development, and also to environmental stimuli. Accordingly, it has recently been reported that a disruption of the function of genes involved in PGE alters a plant's response to abiotic stress [24; 25]. In the last few years, several nuclear genes sensitive to salinity that encode plastid proteins have been described, and some are also involved in PGE regulation [26; 27]. Besides, Arabidopsis mutants affected in nuclear genes required for PGE show altered responses to different abiotic stresses [24; 25]. Notwithstanding, currently available information on the effects of altered PGE on plant stress tolerance is still limited. To gain insight into the role of PGE in plant responses to abiotic stress, we decided to investigate whether NEP RPOTp could be involved in plant salt tolerance. Two strong loss-of-function mutant alleles of the _RPOTp_ gene (_sca3-2_ and _sca3-3_) were morphologically and molecularly characterized in a previous work [23]. However, whether the _SCA3_ (_RPOTp_) gene plays a role in tolerance to abiotic stress remains to be assessed. We report herein a study of the sensitivity of _sca3_ mutants to not only ionic and osmotic stresses, but also to the abscisic acid (ABA) hormone involved in plant responses to stress. We performed an analysis of gene ontologies (GO) for the genes differentially expressed in the _sca3-2_ mutant, and investigated the effect of salt stress and ABA on the expression of NEP and plastome genes. We then examined _RPOTp_ expression in response to carotenoid inhibitor norflurazon (NF). Our results reveal that RPOTp function, which is required for abiotic stress tolerance, is controlled by plastid-to-nucleus retrograde signaling, which is triggered when chloroplast functionality is perturbed by environmental stress. As far as we know, this is the first study to suggest a role for NEP in salt stress tolerance in plants.
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings
### SOC Automation Application Areas
#### 3.2.3 Automated Alert Context  
Once threats and vulnerabilities have been detected, the next step is to supplement these alerts with additional context. Truly aiding security analysts extends beyond simply flagging alerts as malicious. It involves providing further background information about these alerts in the form of alert characteristics and properties, where they have originated from, attacker behaviors and techniques, and what the alert impact could be if not resolved [45]. Ref. [58] implemented Context2 Vector to reduce security analysts' burden during the triage process by enhancing alerts' informative information beyond what traditional intrusion detection systems offer. In the cybersecurity domain, ref. [45] (p. 02) define alert context as \"a comprehensive concept, cover[ing] different kinds of data sources, such as the provenance..., the logs collected within the same period, [and] the alert sequences\".  
**Findings:** Within this theme, our analysis led us to interpret that alert context is first extracted and then generated:  
1. Context extraction refers to collating organizational information affected by security alerts (i.e., pulling context from additional sources beyond the SOC). The more information that is collected from around the organization, such as business assets affected and processes disrupted, the better decision-making will be [58,59]
2. Context generation refers to utilizing and generating the auxiliary extracted information and generating from it, such as the threat's potential impact and risk level. As a result, prioritization techniques are ameliorated [44,45,50].  
The multi-phase approach of context extraction and generation illustrates that the automation of alert context can be implemented to varying degrees, with the recommendation that both phases be adhered to.
## 4 Simulations of Tightening, Vibration-Induced Loosening, and Fatigue  
### Tightening  
There are many methods to generate preloads of bolted joints in FEA. In this study, the preload of a bolted joint was generated by applying torque to the nut, which conformed to engineering practice. In this way, the torsion-tension relationship of bolted joints could be obtained, which helped us to evaluate and compare the tightening characteristics of different types of anti-loosening bolted connections. For a bolted joint with a single nut, the target tightening torque was generated by applying clockwise shear stress distributed evenly on each node around the outer cylindrical surface of the nut [41], as shown in Figure 9. The letter \(r\) denotes the equivalent radius of the outer cylindrical nut surface. In terms of a bolted joint with double nuts, the target tightening torque was applied to the outer cylindrical surface of the lower and upper nuts successively using the same method. The rotation angle of the nut during the tightening process was controlled by changing the value of shear stress. The FEA was performed using ANSYS 19.2 software [19; 42]. In this study, the generated preload of different threaded fasteners was maintained at 30 \(\pm\) 1 kN by controlling the rotation angle of the nut.  
Some scholars [43] have accurately established a model of the mathematical relationship between tightening torque (\(T\)) and preload (\(F\)) through theoretical analysis, as shown in Equation (1).  
\[T=F\Big{(}\mu_{b}r_{b}+\frac{p}{2\pi}+\frac{\mu_{t}r_{t}}{\cos\alpha}\Big{)} \tag{1}\]  
where \(u_{b}\) is the friction coefficient between the turning nut and its surface, \(u_{t}\) is the friction coefficient between threads, \(r_{b}\) is the effective radius of the bearing surface, \(r_{t}\) is the effective thread contact radius, \(p\) is the pitch of the thread, and \(\alpha\) is the helix angle. The accuracy of FEA results was validated by comparing the torque-tension relationship obtained by FEA and that obtained using Equation (1). The results are presented in Figure 10. It is obvious that the discrete points of torque and preload obtained by FEA almost lay in a straight line based on Equation (1), meaning that the simulation of nut tightening using the torque method is reliable.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Item** & **Value** \\ \hline Element type & Solid 185 \\ Young’s modulus & 210 GPa \\ Poisson’s ratio & 0.3 \\ Friction coefficient & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of FEMs.  
Figure 9: Tightening torque generated by shear stress.
## 4 Quality Prediction Stage  
In the final assessment stage, the selected characteristics are used to construct a regression model that is capable of predicting the score for the quality of the distorted images. Due to its capacity to handle feature vectors with high dimensionality and nonlinear correlations between features and quality ratings, Support Vector Machine (SVM) regression is employed as the prediction model in this research work. We extract numerous image characteristics from the proposed system, such as wavelet coefficients, edge detection findings, and filter responses. These characteristics contribute to a multidimensional feature space. The capacity of SVM to handle such complicated and high-dimensional data makes it ideal for our needs. The strength of SVM is its capacity to deal with non-linear connections between characteristics and target variables. The connection between image attributes and quality scores in image quality evaluation can be non-linear and complicated. The kernel-based trick of SVM allows it to implicitly translate characteristics to a higher-dimensionalspace, allowing for the successful modeling of non-linear connections. Moreover, SVM offers high generalization characteristics and is less susceptible to overfitting, both of which are important when constructing a robust and trustworthy BIQA model.  
The SVM regression model establishes the relationship between the set of features \(F\) and the quality ratings \(Q\). A set of training data \(D\) of \(N\) labeled image data is used to train the function of regression \(f(p)\) which is defined as,  
\[f(p)=\sum_{l=1}^{M}\alpha_{l}y_{l}k(p,p_{l})+w \tag{6}\]  
In this formula, \(p\) is an image sample expressed as a feature vector, and \(\alpha_{l}\) and \(y_{l}\) are the initial samples' Lagrange multipliers and labels for classes, respectively. The kernel function \(k(p,p_{l})\) shifts the feature vector \(p\) to a higher-dimensional feature space, allowing for nonlinear decision boundaries. The radial basis function (RBF) kernel function is utilized in this research paper,  
\[k(p,p_{l})=\exp(-\gamma||p-p_{l}||^{2}) \tag{7}\]  
where \(\gamma\) denotes the kernel parameter.  
The below-mentioned optimization equation is used to learn the regression coefficients \(\alpha_{l}\) and bias term \(w\):  
\[\begin{split}\min_{\alpha,w}&\frac{1}{2}\sum_{l=1}^ {M}\sum_{j=1}^{M}\alpha_{l}\alpha_{j}y_{l}y_{j}k(p_{l},p_{j})-\sum_{l=1}^{M} \alpha_{i}\\ \text{s.t.}& 0\leq\alpha_{l}\leq C\\ &\sum_{l=1}^{M}\alpha_{l}y_{l}=0\end{split} \tag{8}\]  
\(C\) is a hyperparameter in this optimization problem that determines the tradeoff between maximizing the margin and reducing the training error. The optimization issue is tackled iteratively using Sequential Minimal Optimization (SMO). Once trained on the training set, the SVM regression model can be used to predict the quality scores of fresh, unseen image samples using their associated feature vectors and the learnt regression function \(f(p)\).
## 1 Introduction  
### Background  
Taiwan is an island country surrounded by sea and relies highly on air and sea transportation for foreign traffic and trade. Considering 2019 as an example, the loading and unloading freight through domestic, international, cross-strat route, and re-export flights across all airports in Taiwan reached 2.315 million metric tons [1]. As for shipping, the loading and unloading freight through domestic ports and international commercial ports amounted to 734.64 million metric tons. This accounted for 99.69% of all cargo transportation in 2019. Obviously, sea transportation plays an important role in economic and trade development in Taiwan.  
Located at the pivot of Asia, the surrounding waters of Taiwan will inevitably see an increase in the number of incidences of collisions and other maritime accidents due to the complexity of maritime traffic flows and traffic density under the premise of constant and limited sea routes [2]. For example, ship collisions between merchant ships and inshorse fishing beats sometimes occur. According to \"Casualty Return\" from Lloyd's Registry of Shipping, that assessed the risk of marine environments on the basis of the rate of incidents, traffic density, complexity of traffic flow, visibility, variation of tides, currents, etc., the surrounding waters in Taiwan are evaluated as a moderate risk environment. The count of maritime incidents of various types in Taiwan was 2105 from 2011 to 2020, of which 1643 incidents, or 78.05% of all incidents, occurred in the surrounding waters of Taiwan. They resulted in 177 people injured, 133 missing, 181 dead, 962 ships damaged, and 41 ships sunk [3].  
Most maritime accidents not only influence social and economic aspects but also involve personnel safety and even cause irreversible damage to marine environments [4; 5]. For example, a ship collision would result in damage to the ship and sunken ships in conjunction with personal injury, missing persons, and even death [6; 7; 8]. It is, therefore, imperative to mitigate the risk of maritime accidents by exploring the risk factors and then providing preventive measures.  
Maritime accidents are categorized into eight categories by the IMO, including foundering, missing, fire/explosion, collision, contact, grounding, heavy weather/ice damage, and hull/machinery. On the basis of the definition of the IMO and consideration of Taiwanese national affairs, maritime accidents were defined by the Central Disaster Prevention and Response Council in 2019 in Taiwan as malfunction, foundering, grounding, collision, fire, explosion, and other accidents related to the ship, cargo, crew, or passengers. Among all, ship collisions are the main cause of maritime accidents, which encompasses the incidents between two ships and between ships and other objects [9]. In addition, the ships include not only merchant ships but also fishing boats, traffic boats, and recreational fishing boats. The total number of ship collisions was 559 from 2011 to 2020, indicating a high frequency of incident occurring rate, with an average of one ship collision every 6.5 days.
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 2 Methods of Synthesizing Coordinated Polymer-Inorganic Hybrids  
The complexation routes for polymer-inorganic hybrids can pose significant challenges due to their low mixing entropy compared to small-molecule hybrids [38]. Furthermore, the poorly ordered macromolecular structure typically found in polymers could disrupt and alter the coordination geometry [38]. Therefore, the synthetic routes to polymer-inorganic hybrids need to be carefully curated to obtain specific products, such as when selecting precursors [39], reaction conditions [38, 40], and necessary pre- [41, 42] and post-processing techniques [43]. A comprehensive review of such routes is essential to understand the complexities of polymer-inorganic hybrids. In the synthesis of polymer-inorganic hybrid materials, various methods are employed, which can be broadly categorized into two main routes: homogeneous and heterogeneous phases. The homogeneous phase generally involves combining polymers and inorganic components directly in a solution. However, this method is exclusive to water-soluble complexes. On the other hand, the heterogeneous phase includes infiltration techniques such as vapor phase infiltration (VPI) and liquid phase infiltration (LPI) as post-process methods, providing more selective methods that extend beyond the limitations of hydrophilic polymers.
## 3 Results and Discussion
### Effect of Nitrogen Fertilizer Reduction on Volatile Metabolomics of Suijing 18  
#### 3.2.1 Qualitative and Quantitative Volatile Metabolomics Analysis  
Within GC-MS analysis, the matrix effect should be considered. Generally, in this study, matrix effects (MEs) were calculated by slope differences using the following equation: ME (%) = 100% \(\times\) (slope of calibration curve in matrix/slope of calibration curve in solvent \(-1\)). MEs were divided into \"no MEs\" (\(-20<\) no MEs \(<20\%\)), \"medium MEs\" (20-50%), and \"strong MEs\" (MEs \(>\pm 50\%\)) [27; 28]. The internal standard used in this study was 3-hexanone-2,2,4,4-d4, which has a slope of 1085.7 in the solution and 1096.7 in the matrix. The ME calculated according to the above equation was 0.6, which reveals that the matrix effect can be neglected in the present method.  
We then compared the volatile compound contents of Suijing 18 after different treatments with reduced nitrogen fertilization, as measured by GC-MS. Figure S1 shows the total ion chromatogram of the mixed samples. Table S1 lists all the VOCs detected in Suijing 18. After the removal of compounds that contributed less to the aroma and the common column loss impurities, 15 classes of aromatic compounds were identified, totaling 358 aromatic components, among which esters, terpenoids, heterocyclic compounds, and hydrocarbons accounted for higher percentages (16.25%, 15.41%, 15.13%, and 13.45%,  
Figure 1: Effect of reduced nitrogen fertilizer on the eating quality of Suijing 18. (\(\alpha=0.05\); different letters indicate significant differences between groups).  
respectively) (Figure 2). We hypothesized that some of the volatile compounds may be closely related to the aromatic components of _japonica_ rice in different treatments.
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 3 Shortcomings of a Centralized PKI System  
1. **Single Point of Failure:** The centralized nature of CAs presents a significant vulnerability, leading to widespread distrust, regardless of their theoretical trustworthiness. Multiple operational errors and breaches in well-known CAs have raised significant concerns [8, 34], such as the 2017 Symantec incident, where over a hundred certificates were improperly issued, resulting in distrust from major platforms [5]. The large number of CAs and their dependent hierarchy also pose a significant challenge, as a single compromised or rogue CA among the hundreds or thousands could lead to widespread failure, often exploited through MitM attacks [20, 34]. In addition, compromised CAs can be exploited for malicious purposes, potentially undermining the entire infrastructure and compromising sensitive data; they are also able to issue a legitimate certificate for any domain [6, 21, 34]. 2. **Challenges in Certificate Revocation Process:** Revoking specific certificates issued by a CA is essential for various reasons, such as compromised private keys, compromised CAs, change of affiliation, or certificate supersession [7, 35]. The MITRE Corporation highlights the expense and time-consuming nature of a centralized PKI's revocation information distribution, particularly in revoking certificates before expiration [1].
## 2 Experiment One: Localisation Thresholds for Natural Sound Sources
### Results
#### 2.2.3 The Effect of Sound Source  
Figure 6 shows the median localisation thresholds for each stimulus at each ICTD. The medians have been plotted with notch edges. The notch edges alone suggest that the effect of sound source on the localisation threshold was not significant. However, it should be noted that there are a number of notch edges that have minimal overlap (e.g., the guitar and oboe at 0 ms). A Friedman test conducted on the data indicated that the effect of sound source was significant for the 0 (\(p\) = 0.001) and 10 ms (\(p\) = 0.039) ICTDs. A Wilcoxon test was subsequently conducted to identify which pairs of stimuli were significantly different from one another, again with the Bonferroni correction being applied. The results of this analysis showed no significantly different pairs for the 10 ms ICTD. This suggests that sound source had no significant effect on the localisation threshold for this ICTD, which agrees with the overlap of notch edges in Figure 6. It should also be noted that although the overlap between conga and speech is notably minimal, the effect size indicated a small effect (\(r\) = 0.28). For the 0 ms ICTD, significant differences were identified between the oboe and both the guitar (\(p\) = 0.01) and speech (\(p\) = 0.05). However, the effect size was not large in either case (\(r\) = 0.49 between the oboe and guitar and \(r\) = 0.42 between the oboe and speech). In addition, there is overlap between all notch edges. Further, the effect size (Kendall's W), which was calculated during the Friedman test, was low (0.262). Based on this analysis, it can therefore be concluded that the effect of sound source on localisation threshold was not significant. This would suggest that the same localisation thresholds could be applied to all sources tested in the present study.  
Figure 5: Medians and associated notch edges with the results for both presentation methods combined.  
Figure 6: Medians and associated notch edges with the results for both presentation methods combined, arranged to compare the localisation thresholds for each sound source at each ICTD.