esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
The evolution of resistance to Bt maize by western corn rootworm illustrates the potential for insect pests to develop resistance to Bt crops when a high dose is not present. Key factors that facilitated field-evolved resistance by the western corn rootworm in cluded non-recessive inheritance of resistance and minimal fitness costs of resistance (Tables 1 and 2). Furthermore, the use of Bt events singly before pyramiding and cross-resistance among Bt traits, likely hastened resistance development [31; 32]. The use of novel, pyramided transgenic traits, for which resistance allele frequency is low, should provide a more durable approach for managing this pest [25; 30; 32].  
Another important factor facilitating resistance evolution is the limited dispersal displayed by western corn rootworm adults [52; 53]. This factor is intrinsic to the biology of the pest and cannot be manipulated. However, resistance management approaches can be refined to take into account this important aspect of pest biology. Specifically, integrated refuges should be used to increase mating between Bt-selected individuals and those emerging from refuge plants [91]. Additionally, continuous cultivation of Bt maize, coupled with limited adult dispersal, appears to be an important driver of resistance development within the agricultural landscape [3; 5; 76]. Consequently, more diversified management, including crop rotation and use of non-Bt maize with soil-applied insecticide, should help to delay the development of resistance to current and future transgenic traits for management of western corn rootworm [82; 90].  
This work was supported by the National Institute of Food and Agricultural, Hatch Project IOW05617.  
I thank [PERSON], [PERSON], [PERSON] and [PERSON] for helpful comments on this manuscript.  
A.J.G. has received research funding, not related to this work, from AMVAC, Bayer, Dow AgroSciences, DuPont, FMC, Monsanto, Syngenta, and Valent.
## 3 Results and Discussion
### HMs in the Sediments

Table 1: The concentrations of HMs in the dumping area (unit: mg/kg). station in the samples. If the variation is greater than 0.5, the spatial distribution of HM concentrations in the sediments is uneven, and local point source pollution may exist [32]. The CV% of Hg in the sediments of the Huangmao Island dumping area was greater than 0.5, indicating that Hg entering the dumping area was mainly sourced from external input, that is, dumping. The coefficients of variation of Pb, Cu, As, and Zn were all smaller than 0.5, indicating that these HMs were relatively stable. There is a dynamic process between the release and absorption of HMs in marine sediments. In this process, different physicochemical characteristics (e.g., pH, redox potential, temperature, particle size, and salinity), ocean current, and dumping intensity will cause changes in the CV% values of HMs in sediments [33]. Compared with other dumping areas, HM concentrations in the Huangmao Island dumping area were generally at an intermediate level (Table 2). For individual HMs, the Hg concentration was close to the levels recorded in the Jinzhou Port Dumping Area [34] and Marine Dumping Area outside Jiaozhou Bay [35]. The As concentration was close to the levels recorded in the Hulu Island Port Dumping Area [34] and Tianjin Dumping Area [34]. The Zn concentration was close to the levels recorded in the Hulu Island Port Dumping Area [34], Fangchenggang Dumping Area [36], and Dumping Site at the Taiwan Shelf [37]. The Cd concentration was close to the levels recorded in the Jinzhou Port Dumping Area [34], Huangye Port Dumping [34], Lanshan Port Temporary Marine Dumping Area [38], and Marine Dumping Area outside Jiaozhou Bay [35]. Last, the Pb concentration was similar to those recorded in the Huangye Port Dumping Area [34], Laizhou Port Dumping Area [34], and Fangchenggang Dumping Area [36].
## 4 HSI in Fresh-Cut Product Quality Assessment
### Fresh-Cut Fruits  
#### 4.3.1 Tomato  
Tomatoes are one of the major fresh-cut vegetables consumed, and the detection of cracking defects is very important to avoid any development of pathogenic microbes that may have harmful consequences on consumer health. Thus, [PERSON] et al. (2011) applied HSI (1000-1700 nm) to detect damaged tomatoes. The authors applied PCA on the full spectrum and four selected wavelengths (1078 nm, 1194 nm, 1425 nm, and 1642 nm) to extract and compare PC images for crack detection. Finally, LDA was applied for improving the discriminant ability between sound and cracked tomatoes, showing a classification accuracy of 91.7% (using full NIR spectrum) and 80.6% (using only four wavelengths) [35].
# Study on Mechanical Properties and Application of a New Flexible Bolt
## 1 Introduction
### Evolution Process of Bolts  
Bolt support has a long history of development. Due to different geological conditions and technological levels, many types of bolts have been developed [7, 8, 9, 10]. Bolt support is initially applied to the roadway whose surrounding rock is relatively stable and mechanicalbolts like the inverted wedge bolt and expansion shell bolt are the mainstream [11]. Generally speaking, mechanical bolts improve the anchorage force mainly through pressing the drill hole wall, which in turn raises a much higher demand on the borehole diameter. If the surrounding rock is unstable and fractures are common, mechanical bolts will not function as designed. In this case, friction bolts are developed [12; 13]. Split-set bolts and expanded-tube bolts are typical friction bolts. This type of bolt boasts merits of full-length anchorage, but its loading capacity is relatively low, thus failing to satisfy the demand of long-term support, especially in watery or broken surrounding rocks.  
To solve this problem, bonded bolts are employed gradually to bolt support in complex geological conditions for the purpose of permanent support [14; 15]. The bonding material can tightly bond the bolt body and the borehole wall to achieve permanent anchorage. Cement bonding materials that harden quickly in contact with water were first used as anchoring agents in bolts, and this series of bolts are called \"cement bolts\" [16]. However, given that it is difficult to control precisely the setting time, the installation quality often fails to reach the design requirements. This can cause low anchorage force and an unreliable supporting effect. Therefore, cement is gradually replaced by epoxy resin as the binding material. Now, resin bolts have become the most widely used bolts [17]. The setting rate of epoxy resin is much higher, enabling the bolts to provide bearing capacity in a short period and thus guaranteeing the installation quality. Resin bolts can be divided into two types: resin anchorage bolts and resin anchorage cable [18; 19; 20]. Resin bolts are generally high-strength finish-rolled threaded steel bars, which are rigid materials and cannot be bent. Restricted by the height of the roadway, the length of the bolt is generally between 1.8 and 2.5 m. The bolts adopt a nut-rotating installation method, which can only provide a pretightening force of about 30 kN to the roadway surface, and the initial anchoring force is small. However, the resin cables are made of steel-stranded wire, which is a flexible material, and the length of the cables is not restricted by the roadway section. The cables are installed with anchor tensioning, which can provide higher pretightening force. The initial anchor force can reach 100-200 kN, which can better control the roadway deformation.
## 5 In Vitro Antimicrobial Susceptibility Pattern of the _B. burgdorferii_ Complex
### Tetracyclines and Glyglycyclines  
Tigecycline is a primarily bacteriostatic agent belonging to the glycylcycline class of antimicrobial agents, which are modified substances of the tetracycline family. They bind to the 30S subunit of the bacterial ribosome and are known to be very efficacious in vitro against a variety of Gram-negative and Gram-positive bacteria, including multi-drug resistant microorganisms [90; 91]. Studies investigating, under standardized conditions, the activity of tigecycline against all genospecies of borreliae isolates in parallel with traditional tetracyclines, such as tetracycline, doxycycline, minocycline and ceftaxone, as well as cefotaxime [57], revealed the following MIC\({}_{90}\) rank order: tigecycline > ceftaxone > cefotaxime > doxycycline > tetracycline. The MBC\({}_{90}\) rank order was: tigecycline > ceftaxone > tetracycline > doxycycline > cefotaxime [17; 52; 55; 57; 65]. The high in vitro activity of the glycylcycline against borreliae was further substantiated by time-kill experiments performed on _B. afzeli_ isolate EB1. Parallel testing of tigecycline and ceftaxone demonstrated a bacteriostatic effect for 0.016 \(\upmu\)g/mL of tigecycline and for 0.03 \(\upmu\)g/mL of ceftaxone after 72 h of incubation. Moreover, tigecycline was bactericidal at a concentration of 0.25 \(\upmu\)g/mL, showing a >3 log reduction in the initial inoculum. A concentration of 2 \(\upmu\)g/mL was needed for ceftaxone [17; 52; 55; 57; 65]. Taking into consideration the results of the study, the susceptibility testing of mutants with altered tetracycline resistance to tigecyline would be a very interesting future topic for antibiotic research in borreliae [57].
# Time-Dependent Probabilistic Approach of Failure Mode and Effect Analysis
## 3 Application  
### Parameters Setting of RPM and RPN Values  
FMEA of product design, performed by [PERSON] and [PERSON] [4] for an organization that manufactures automotive leaf springs, was adopted in this research as a case study to demonstrate the utility of the proposed TD-FMEA model. Table 1 describes the FMEA chart for design documented as part of this case study. The modified chart describes analysis of various failure causes--from FC\({}_{11}\) to FC\({}_{23}\)--listed under column 1 with \(O\), \(S\), and \(D\). Numerical values were assigned to parameters \(\varepsilon\), \(\lambda\), \(\alpha\), \(\beta\), and \(\theta\), or simulations of the TD-FMEA model for design FMEA. A few of these parameter values were slightly modified as necessary to suit the proposed model. Values concerning the detection and occurrence rates--\(\mu\) and \(\varepsilon\)--were obtained by modifying the corresponding ratings of factors \(O\) and \(D\), respectively, in accordance with Table 2, which in turn, was modified with reference to the FMEA handbook [30].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Potential Failure-Causes (O, S, D)**}} & \multicolumn{3}{c}{**Modified**} & \multicolumn{3}{c}{**Fixed**} & \multicolumn{3}{c}{**Variable**} \\ \cline{2-13} \cline{5-  
In Table 2, occurrence rate of failure-cause and failure mode--\(\varepsilon\) and \(\lambda\)--for _RPM_ are set to satisfy the given guideline such that cumulative probability of failure occurrence during unit time for the exponential distribution can be determined. Moreover, detection rate--\(\mu\) for _RPM_ is 1 over square-root of \(\lambda\). This is because the effect of \(\mu\) is opposite to that of \(\lambda\), and \(\mu\) is related to inspection policy compared to \(\lambda\) as an inherent property of the system. So, it assumes that the \(\mu\) does not have dramatic range compared to \(\lambda\). Parameters concerning the coefficient loss function--\(\alpha\), \(\beta\) and \(\theta\)--were randomly assigned with reference to the \(S\) (severity) value of first column in Table 1. For parameters \(\alpha\), \(\beta\), and \(\theta\), fixed and variable parameters were allocated to derive various simulation results.
## 5 Numerical Results  
In this section, we demonstrate how bias factors and network parameters associated with MEC servers affect network performance via numerical results. We consider a two-layer MEC network, where the computing capabilities of MEC servers in the first layer are higher than those in the second layer. The network parameter values used for numerical results are presented in Table 3, except where otherwise stated. The service load data are generated by simulating the network parameters shown in Table 3 (such as MEC server computing power, node distribution density, signal power, etc.) to verify the impact of bias factors and network parameters on system performance.
# Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model
## 5 Experiments and Results
### Experimental Results
Result for Answering RQ3: To demonstrate whether our model can detect multiple kinds of smart contract vulnerabilities, the proposed HAM in this paper was trained and tested for five common vulnerabilities, including reentrancy attack, timestamp dependency, arithmetic overflow, tx.origin, and unchecked return value. Arithmetic overflow refers to Integer overflow and Integer Overflow. Table 5 shows the quantitative results, in which we observe that all deep learning models can detect the target vulnerabilities to some extent. Apart from this, except for HAM, the performances of the other three approaches are relatively low, whose F1-score and recall are about 60% and 50%, respectively. Apart from the Arithmetic overflow having an F1-score of 75.57% with the HAM model, the reason is that the features of this vulnerability are not obvious in the Solidity code. As for Reentrancy and Timestamp, they have rich semantic features that are easier to identify. This results in slight accuracy and recall. In summary, the proposed approach is able to automatically learn the vulnerability feature from the vector representation and has good generalization ability. Figure 12: Visualization of the quantitative results: (**a**–**e**) present comparison results of reentrancy vulnerability, arithmetic vulnerability, unchecked return value, timestamp dependence and tx.origin vulnerability detection, respectively. (**f**) shows comparison results of reentrancy vulnerability detection on Smartcheck, Oyente, and Securify.
## 3 Results and Discussion
### Model Validation
#### 3.1.2 Power Consumption  
The power consumption measured is obtained by integrating the Lissajous curve measured in a typical cycle from the capacitor as shown in Figure 5. It is noted that only a few data points are plotted for presentation purposes and the solid line is regressed from measured data to calculate the power consumption. The average power consumption measured from measurements is around 2.1 W.  
Figure 6 shows the comparison between the measured and simulated current densities of a typical cycle. It is noted that the experimental current densities in the positive half period (HP) are similar to those in the negative HP, which shows that the discharge is symmetrical under current operating conditions. The simulated current density reaches around 29 A m\({}^{-2}\) which agrees with that measured on average as 35 A m\({}^{-2}\). The simulated cyclic average power consumption can be calculated from the integration of the externally applied voltage and simulated current as 2.0 W which agrees with the measured average power consumption as 2.1 W.  
Figure 4: The intensified CCD (ICCD) photograph is taken with the 1 \(\upmu\)s exposure time during the breakdown to confirm the discharge uniformity.
###### Abstract  
Xanthine oxidase (XO) plays an important role in purine degradation in humans. The study aimed to determine the XO inhibitory potential of _Chrysanthemum morifolium_ dried flower ethyl acetate sub-fractions and its anti-hyperuricemic effect in rat models. Bioassay-guided fractionation based on XO inhibitory assay was employed to obtain bioactive fractions and sub-fractions. In vitro cytotoxicity and cellular antioxidant capacity of the sub-fraction and its mode of XO inhibition were also investigated. The anti-hyperuricemic effect of the bioactive sub-fraction was investigated using rat models via oral consumption, and followed by an XO mRNA gene expression study. The compounds in the bioactive sub-fractions were identified putatively using HPLC-Q-TOF-MS/MS. Ethyl acetate (EOAc) fraction exhibited the highest XO inhibition among the fractions. It was further fractionated into 15 sub-fractions. F10 exhibited high XO inhibitory activity, cellular pro-proliferative effect, and intracellular antioxidant activity among the sub-fractions tested. This sub-fraction was non-cytotoxic at 0.1-10 \(\upmu\)g/mL, and very effective in lowering serum and urine uric acid level in rat models upon oral consumption. A total of 26 known compounds were identified and seven unknown compounds were detected via HPLC-Q-TOF-MS/MS analysis. The possible mechanisms contributing to the anti-hyperuricemic effect were suggested to be the non-competitive inhibition of XO enzyme, XO gene expression down-regulation, and the enhancement of uric acid excretion.  
Chrysanthemum morifolium; anti-hyperuricemic; hyperuricemic rat models; xanthine oxidase inhibitor; uric acid reduction; XO gene expression +
Footnote †: journal: Applied Sciences
## Results and Discussions
### Effect of Catechin on Cyclin E1 and P-AKT Expressions in A549 Cancer Cells  
Cyclin E1 is a positive regulator in the cell cycle and the overexpression of cyclin E1 in tumors would contribute to tumorigenesis [27]. Cyclin E1 is important in cancer tumor growth and proliferation, particularly in breast cancer [28]. Phosphorylated RAC-\(\alpha\) serine/threonine-protein kinase (P-AKT) is the activated form of AKT. It plays a critical role in the development, progression and metastatic spread of breast cancer [29]. Thus, cyclin E1 and P-AKT were used as the primary indicators to evaluate the efficiency of catechin in inhibiting A549 cell proliferation. As shown in Figure 3, the levels of P-AKT  
Figure 2: Relative expression levels of cyclin kinase inhibitor p21 and p27 in control, 200 and 600 \(\upmu\)mol\(\cdot\)L\({}^{-1}\) treatment groups. * Significant difference between each treatment and control in each cyclin kinase inhibitor at \(p<\) 0.05.  
significantly decreased in the 200 \(\upmu\)mol\(\cdot\)L\({}^{-1}\) treatment group. Catechin inhibited cyclin E1 expression in a dose-dependent manner. When the catechin concentration was 200 \(\upmu\)mol\(\cdot\)L\({}^{-1}\), the E1 expression was approximately 50% lower than the control group, and it was 70% lower than control group when the caetchin concentration was the 600 \(\upmu\)mol\(\cdot\)L\({}^{-1}\) treatment, Figure 3. This was in accordance with the trend of the catechin-treated A549 cell, where the A549 proliferation was significantly inhibited in a dose-dependent manner, Table 2. Thus, the inhibition of P-AKT expression led to the downregulation of cyclin E1, and subsequently inhibited cancer cell growth. This was in agreement with the theory that the tea polyphenols, EGCG, reduced the viability of cancer cell by inhibiting signaling proteins like AKT directly [15]. Red wine was reported to cause a remarkable inhibition of basal AKT phosphorylation in A549 cells [18]. Catechin is one of the major phenolics in red wine, thus it would partially contribute to the inhibition of efficiency for inhibiting AKT expression and phosphorylation in red wine [18]. Therefore, catechin was involved in the regulation of A549 cancer cell cycle arrest through directly inhibiting cyclin E1 expression, or indirectly via p21 signaling pathway, in this study.  
Figure 3: Images of Western blot analysis (top) and relative expression levels (bottom) of cyclin E1/\(\beta\)-actin and P–AKT/AKT in control, 200 and 600 \(\upmu\)mol\(\cdot\)L\({}^{-1}\) treatment groups.
## 4 Experiment and Results  
Schematic diagram of the measurement setup as shown in Figure 5. The key components of the experiment are a tunable laser source (TLS photonetics Wavelength Tunable Laser) and two symmetrical AWGRs (Gemfire 80 ch AWG DMX). TLS controls the true-time delay value via adjusting the wavelength. Tunable optical carriers generated by the TSL are coupled and fed into a Mach-Zehnder modulator(MZM) via a polarization controller. Since the FSR of the two symmetrical AWGRs is 40 GHz, the maximum modulable RF signal for this structure is 40 GHz. The vector network analyzer (VNA, Keysight FieldFox N9952A) generates millimeter wave RF signals in the frequency range from 25 GHz to 36 GHz. The optical carrier is modulated by the RF signal generated by the VNA. Erbium-doped fiber amplifier (EDFA) is used to compensate for the loss of symmetrical AWGR to ensure that the optical power at the input PD(PD THORLABS RXM42 AF) is 5 dBm, which is in the linear area of the PD. The variable gain amplifier (VGA) on the RF board is used to balance the amplitude of the two sub-arrays. The \(S_{21}\) phase response of each link is obtained experimentally by detecting the signals fed back from the different output points (e.g., Figure 6, green monitoring point).  
Wavelength switching-over will switch the optical carrier into a different channel of AWGR to obtain an adjustable true time delay. The phase response obtained at the measuring point PD allows for analyzing the delay generated by the different wavelengths of optical carriers passing through the AWGR. As illustrated in Figure 7 by fitting the data, different delays of AWGR can be obtained by phase vs. frequency slope: 0.058434 \(\upmu\)s, 0.71755 \(\upmu\)s, 0.91651 \(\upmu\)s, 2.736 \(\upmu\)s, 1.5178 \(\upmu\)s, 3.3476 \(\upmu\)s. Due to the instability of the PD, there are 5 \(\upmu\)s errors in the delay time.
# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
## 3 Experiments and Analysis
### Comparison of the Classification Framework Based on Cobb Angle Measurement and Shape Description  
As shown in Figure 2, we present two alternative schemes for the Lenke classification of scoliosis. The first was to use Cobb angle measurement and classifying criteria (denoted as Cobb angle + criteria for short) based on the segmentation results, and the second was to use the shape description and KNN classifier (denoted as Shape + KNN for short) for the segmentation results. In this section, we conducted an experiment to compare the two schemes; the results are listed in Table 7.  
From Table 7, we note that the scheme using the shape description and KNN classifier achieved better experimental results than that using the Cobb angle and classifying criteria, where most of the indicators are significant. This is because the Lenke classification criteria built on the Cobb angle is very strict and is extremely dependent on the accuracy of the segmentation. However, the proposed automatic Lenke classification method based on the shape descriptor and KNN classifier achieved a considerable improvement and overcame the influence of the segmentation error caused by deep learning to a great extent.