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As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake. [ABSTRACT FROM AUTHOR]

Extrinsic parameter calibration is the foundation and prerequisite for LiDAR and camera data fusion of the autonomous system. This technology is widely used in fields such as autonomous driving, mobile robots, intelligent surveillance, and visual measurement. The learning-based method is one of the targetless calibrating methods in LiDAR and camera calibration. Due to its advantages of fast speed, high accuracy, and robustness under complex conditions, it has gradually been applied in practice from a simple theoretical model in just a few years, becoming an indispensable and important method. This paper systematically summarizes the research and development of this type of method in recent years. According to the principle of calibration parameter estimation, learning-based calibration algorithms are divided into two categories: accurate calibrating estimation and relative calibrating prediction. The evolution routes and algorithm frameworks of these two types of algorithms are elaborated, and the methods used in the algorithms' steps are summarized. The algorithm mechanism, advantages, limitations, and applicable scenarios are discussed. Finally, we make a summary, pointing out existing research issues and trends for future development. [ABSTRACT FROM AUTHOR]

The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R2) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Exploring the deformation mechanism of the 2021 Mw 7.4 Maduo Earthquake is crucial for better understanding the seismic hazard of the faults with low strain rates inside the Bayan Har block. This study leverages deformation information derived from Sentient-1 A/B images and GPS data to investigate in detail the co- and postseismic deformation mechanisms using multiple methods. The main results are as follows. First, the postseismic InSAR time series robustly identified the reactivation of the Changmahe fault, indicating the impact of the Maduo event on surrounding active faults. Second, the joint inversion of Interferometric Synthetic Aperture Radar and GPS revealed that (1) there was a complementary and partially overlapping relationship between the coseismic slip and postseismic afterslip of the main rupture; and (2) the Changmahe fault exhibited thrust compression dislocation in the early stage and experienced a sustained compressive effect from afterslip in the one year after the mainshock. Third, modeling the processes of viscoelastic relaxation and poroelastic rebound revealed that the postseismic deformation was probably caused by a combination of afterslip (near-field) and viscoelastic relaxation (near and far field). Fourth, the stress changes driven by the Maduo event revealed that the seismic gaps inside the Maqin-Maqu segment and the Kunlun Pass-Jiangcuo fault will be potential seismic risks in the future. [ABSTRACT FROM AUTHOR]

Piceatannol (PIC) and epigallocatechin gallate (EGCG) are polyphenolic compounds with applications in the treatment of various diseases such as cancer, but their stability is poor. β-lactoglobulin (β-LG) is a natural carrier that provides a protective effect to small molecule compounds and thus improves their stability. To elucidate the mechanism of action of EGCG, PIC, and palmitate (PLM) in binding to β-LG individually and jointly, this study applied molecular docking and molecular dynamics simulations combined with in-depth analyses including noncovalent interaction (NCI) and binding free energy to investigate the binding characteristics between β-LG and compounds of PIC, EGCG, and PLM. Simulations on the binary complexes of β-LG + PIC, β-LG + EGCG, and β-LG + PLM and ternary complexes of (β-LG + PLM) + PIC, (β-LG + PLM) + EGCG, β-LG + PIC) + EGCG, and (β-LG + EGCG) + PIC were performed for comparison and characterizing the interactions between binding compounds. The results demonstrated that the co-bound PIC and EGCG showed non-beneficial effects on each other. However, the centrally located PLM was revealed to be able to adjust the binding conformation of PIC, which led to the increase in binding affinity with β-LG, thus showing a synergistic effect on the co-bound PIC. The current study of β-LG co-encapsulated PLM and PIC provides a theoretical basis and research suggestions for improving the stability of polyphenols. [ABSTRACT FROM AUTHOR]

The efficient and accurate identification of diaphragm pump faults is crucial for ensuring smooth system operation and reducing energy consumption. The structure of diaphragm pumps is complex and using traditional fault diagnosis strategies to extract typical fault characteristics is difficult, facing the risk of model overfitting and high diagnostic costs. In response to the shortcomings of traditional methods, this study innovatively combines signal demodulation methods with residual networks (ResNet) to propose an efficient fault diagnosis strategy for diaphragm pumps. By using a demodulation method based on principal component analysis (PCA), the vibration signal demodulation spectrum of the fault condition is obtained, the typical fault characteristics of the diaphragm pump are accurately extracted, and the sample features are enhanced, reducing the cost of fault diagnosis. Afterward, the PCA-ResNet model is applied to the fault diagnosis of diaphragm pumps. A reasonable model structure and advanced residual block design can effectively reduce the risk of model overfitting and improve the accuracy of fault diagnosis. Compared with the visual geometry group (VGG) 16, VGG19, ResNet50, and autoencoder models, the proposed model has improved accuracy by 35.89%, 80.27%, 2.72%, and 6.12%. Simultaneously, it has higher operational efficiency and lower loss rate, solving the problem of diagnostic lag in practical engineering. Finally, a model optimization strategy is proposed through model evaluation metrics and testing. The reasonable parameter range of the model is obtained, providing a reference and guarantee for further optimization of the model. [ABSTRACT FROM AUTHOR]

Thrombin is an important ischemia/reperfusion injury (IRI) mediator in patients with ST-elevation myocardial infarction (STEMI). This study examines the use of bivalirudin, a direct thrombin inhibitor, in reducing IRI in STEMI patients. STEMI patients (n = 21) were treated with bivalirudin and compared to 21 patients treated with unfractionated heparin (UFH) from the EARLY Assessment of Myocardial Tissue Characteristics by CMR in STEMI (EARLY-MYO-CMR) registry (NCT03768453). Infarct size (IS) and left ventricular ejection fraction (LVEF) were comparable between the two groups at follow up. During the first cardiac magnetic resonance (CMR) scan within the first week after percutaneous coronary intervention (PCI), all patients in both the bivalirudin and UFH groups exhibited myocardial edema. However, the myocardium edema volume was significantly less in the bivalirudin group (p < 0.05). At the one-month follow-up, a smaller proportion of patients in the bivalirudin group than in the UFH group exhibited myocardial edema (4.7% vs. 33.3%, p < 0.05). At the three-month follow-up, myocardial edema had completely resolved in the bivalirudin group, while it persisted in two patients in the UFH group. The incidence and volume of microvascular obstruction (MVO) were significantly lower in the bivalirudin group during the acute phase. Additionally, the incidence of intramyocardial hemorrhage (IMH) was significantly lower in the bivalirudin group during both the acute and follow up (p < 0.05). These findings were corroborated by T2 and T1 mapping results. The study concluded that the use of bivalirudin for anticoagulation is associated with attenuated IRI in STEMI patients who receive primary PCI. [ABSTRACT FROM AUTHOR]

In brain-computer interface (BCI) systems, challenges are presented by the recognition of motor imagery (MI) brain signals. Established recognition approaches have achieved favorable performance from patterns like SSVEP, AEP, and P300, whereas the classification methods for MI need to be improved. Hence, seeking a classification method that exhibits high accuracy and robustness for application in MI-BCI systems is essential. In this study, the Sparrow search algorithm (SSA)-optimized Deep Belief Network (DBN), called SSA-DBN, is designed to recognize the EEG features extracted by the Empirical Mode Decomposition (EMD). The performance of the DBN is enhanced by the optimized hyper-parameters obtained through the SSA. Our method's efficacy was tested on three datasets: two public and one private. Results indicate a relatively high accuracy rate, outperforming three baseline methods. Specifically, on the private dataset, our approach achieved an accuracy of 87.83%, marking a significant 10.38% improvement over the standard DBN algorithm. For the BCI IV 2a dataset, we recorded an accuracy of 86.14%, surpassing the DBN algorithm by 9.33%. In the SMR-BCI dataset, our method attained a classification accuracy of 87.21%, which is 5.57% higher than that of the conventional DBN algorithm. This study demonstrates enhanced classification capabilities in MI-BCI, potentially contributing to advancements in the field of BCI. [ABSTRACT FROM AUTHOR]

In the existing recycling system for end-of-life vehicles (ELVs), online recycling based on the Internet platform is a useful supplement. In this paper, a Stackelberg game pricing model, which is dominated by ELV part remanufacturers and composed of remanufacturers, recyclers, and consumers, is constructed considering consumer preferences for recycling channels. The influence of different subsidy strategies on the optimal pricing, profit, and recycling volume of the reverse supply chain (RSC) of ELVs is discussed, and the effects of factors such as subsidy amount and consumer preferences on the RSC of ELVs are analyzed using numerical simulation. The results show that the increase in consumers' online recycling preferences has a positive effect on the recycling volume and profit of the RSC of ELVs. Considering the recycling volume of the RSC, when fewer subsidies are given, more recycling volume can be generated by subsidizing remanufacturers, and, on the contrary, recycling volume will be generated by subsidizing consumers. Considering the profit of the RSC, when subsidies are given at the lower-middle level, higher profits can be earned by subsidizing remanufacturers, and, on the contrary, higher profits can be earned by subsidizing consumers. [ABSTRACT FROM AUTHOR]

Previous camera self-calibration methods have exhibited certain notable shortcomings. On the one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Synthetic adhesives play a crucial role in holding together solid materials through interfacial interactions. Thermoplastic and thermosetting adhesives are important types of synthetic adhesives, with thermoplastic adhesives being reassemblable and thermosetting adhesives exhibiting high adhesive strength and creep resistance. However, there is a need to combine the advantages of both types and develop high bonding strength, reassemblable adhesives. Here, epoxidized soybean oil (ESO) was used to prepare adhesive networks and Diels–Alder bonds were incorporated to enhance reassembly ability. The ESO was functionalized with furyl groups and cross-linked via the reaction between furyl and imide groups to involve the Diels–Alder bonds. The resulting adhesive exhibited good solvent resistance and mechanical properties, which could be regulated by adjusting the quantity of cross-linker. The prepared adhesives also demonstrated self-healing capabilities, as the scratch on the surface gradually diminished with heating. Additionally, the adhesives showed the ability to undergo recycling without significant changes in properties. The prepared adhesives exhibited hydrophilicity and the flow characteristics during reassembly were characterized by a decrease in torque. This study provides a promising approach for the development of synthetic adhesives with reassembly ability, which has important implications for the field of bonding. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing technology significantly enhances the productivity of shale oil and gas reservoirs. Nonetheless, the infiltration of fracturing fluid into shale formations can detrimentally affect the microscopic pore structure, thereby impairing the efficacy of hydraulic stimulation. In this study, nuclear magnetic resonance (NMR) technology was utilized to conduct high-pressure soaking tests on shale specimens treated with EM30+ + guar gum mixed water and CNI nano variable-viscosity slickwater, where various concentrations of a drag reducer were utilized. Additionally, the differences in porosity, permeability, mineral composition, and iron ion concentration before and after the measurements were compared, which were used to analyze the influence on the shale's microscopic pore structure. It features a reduction in the total pore volume after the interaction with the fracturing fluid, with the pore-throat damage degree, porosity damage degree, and permeability damage degree ranging from 0.63% to 5.62%, 1.51% to 6.84%, and 4.17% to 19.61%, respectively. Notably, EM30+ + guar gum mixed water exhibits heightened adsorption retention, alkaline dissolution, and precipitation compared to CNI nano variable-viscosity slickwater, rendering it more deleterious to shale. Moreover, higher concentrations of drag reducers, such as EM30+ or CNI-B, predominantly result in damage to the shale's micropores. Shale compositions characterized by lower content of quartz and elevated proportions of clay minerals and iron-bearing minerals showcase augmented mineral dissolution and precipitation, consequently intensifying the shale damage. The hydration expansion of mixed-layer illite/smectite profoundly diminishes the core permeability. Consequently, the mechanisms underpinning the damage inflicted on shale's microscopic pore structure primarily involve fracturing fluid adsorption and retention, mineral dissolution, and precipitation, such as clay minerals and iron-containing minerals. [ABSTRACT FROM AUTHOR]

Linear conveyors, traditional tools for cargo transportation, have faced criticism due to their directional constraints, inability to adjust poses, and single-item conveyance, making them unsuitable for modern flexible logistics demands. This paper introduces a platform designed to convey and adjust cargo boxes according to their spatial positions and orientations. Additionally, a cargo pose recognition algorithm that integrates image and point cloud data are presented. By aligning depth camera data, the axis-aligned bounding box (AABB) point serves as the image's region of interest (ROI). Peaks extracted from the image's Hough transform are refined using RANSAC-based point cloud linear fitting, then integrated with the point cloud's oriented bounding box (OBB). Notably, the algorithm eliminates the need for deep learning and registration, enabling its use in rectangular cargo boxes of various sizes. A comparative experiment using accelerometer sensors for pose acquisition revealed a deviation of <0.7° between the two processes. Throughout the real-time adjustments controlled by the experimental platform, cargo angles consistently remained stable. The proposed two-dimensional conveyance platform, compared to existing methods, exhibits simplicity, accurate recognition, enhanced flexibility, and wide applicability. [ABSTRACT FROM AUTHOR]

The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D–S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module's training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput. [ABSTRACT FROM AUTHOR]

As global warming continues, heat stress events are expected to increase and negatively affect rice production. Spatiotemporal changes in single-season rice exposure to heat stress were explored along the middle and lower reaches of the Yangtze River (MLRYR) in China during 1971–2020 based on created heat thresholds in different phenological stages, derived from comparison of historical heat records for single-season rice and agro-meteorological data. The feature importance (IF) provided by the Random Forest model was used to modulate the relationship between threshold accumulated temperature and yield reduction rate caused by heat stress. In addition, critical temperature thresholds at different phenological stages were determined by combining Overall Accuracy and the Receiver Operating Characteristic (ROC) curve. According to historical disaster records, the heat stress occurred before the reproductive phase (i.e., the tillering–jointing stage) and ended in the filling stage. Critical temperature thresholds of Tmax at tillering–jointing, booting, flowering and filling were quantified as 36, 35, 35 and 38 °C, with higher IF values of 13.14, 10.93, 17.15 and 13.15, respectively. The respective values of Overall Accuracy and the areas under the ROC curve were greater than 0.85 and 0.930, implying that each threshold performed excellently in identifying heat occurrence. Based on the determined critical thresholds, accumulated harmful temperature (Tcum), number of heat days (HD), first heat date (FHD) and last heat date (LHD) were presented to characterize heat exposure. It was clear that Tcum and HD exhibited a north-to-south increasing trend from 1971 to 2020, with the obvious increasing occurrence in most parts of the study region through the period of 2010 to 2020. FHD occurred earlier in most stations except the northeast parts, while LHD ended later in southern MLRYR. Exploring heat critical thresholds at different phenological stages highlighted in this study can help decision-makers monitor and evaluate heat exposure to single-season rice in MLRYR and further develop mitigation strategies to ensure rice production security. [ABSTRACT FROM AUTHOR]

Porous thermal insulation materials (PTIMs) are a class of materials characterized by low thermal conductivity, low bulk density and high porosity. The low thermal conductivity of the gas enclosed in their pores allows them to achieve efficient thermal insulation, and are they among the most widely used and effective materials in thermal insulation material systems. Among the PTIMs, polyurethane foam (PUF) stands out as particularly promising. Its appeal comes from its multiple beneficial features, such as low density, low thermal conductivity and superior mechanical properties. Such attributes have propelled its broad application across domains encompassing construction, heterogeneous chemical equipment, water conservation and hydropower, and the aviation and aerospace fields. First, this article outlines the structure and properties of porous thermal insulation PUF materials. Next, it explores the methods of preparing porous thermal insulation PUF materials, evaluating the associated advantages and disadvantages of each technique. Following this, the mechanical properties, thermal conductivity, thermal stability, and flame-retardant characteristics of porous thermal insulation PUF materials are characterized. Lastly, the article provides insight into the prospective development trends pertaining to porous thermal insulation PUF materials. [ABSTRACT FROM AUTHOR]

Chlorogenic acid (CGA) is a bioactive substance with anti-inflammatory activities. Clusters of CD36 have been suggested to be widely involved in inflammatory damage. However, the mechanism of CGA protecting against LPS-induced inflammation involving the CD36 regulation is unclear. Here, we demonstrated that CGA protected against LPS-induced cell death and decreased the production of ROS. Moreover, the SOD, CAT, and GSH-Px activities were also upregulated in CGA-treated cells during LPS stimulation. CGA reduced COX-2 and iNOS expression and IL-1β, IL-6, and TNF-α secretion in LPS-stimulated RAW264.7 macrophages. In addition, CGA treatment widely involved in immune-related signaling pathways, including NF-κB signaling, NOD-like receptor signaling, and IL-17 signaling using transcriptomic analysis and CD36 also markedly reduced during CGA pretreatment in LPS-induced RAW264.7 cells. Furthermore, the CD36 inhibitor SSO attenuated inflammation and oxidative stress by enabling activation of the AMPK/PGC-1α cascade. These results indicate that CGA might provide benefits for the regulation of inflammatory diseases by modulating CD36/AMPK/PGC-1α to alleviate oxidative stress. [ABSTRACT FROM AUTHOR]

Asthma is a common respiratory disease with symptoms such as repeated wheezing, shortness of breath, and coughing. However, currently, asthma cannot be cured but only controlled or relieved using medication. The metered-dose inhaler (MDI) is known to lead to high deposition fractions of drug particles in the mouth and throat, resulting in inadequate drug efficacy. Therefore, herein, the factors influencing the deposition fraction of asthma drugs in the mouth and throat regions were explored by computational fluid dynamics and a smart MDI for asthma was designed. The smart MDI was designed based on the obtained simulation results, which demonstrated that the deposition fraction gradually increased from 55.78% to 65.75% with an increase in the peak inspiratory flow rate at an angle of incidence of 0°. The deposition fraction first decreased and then increased as the angle of incidence increased at a constant peak inspiratory flow rate. The deposition fraction increased as the inspiration–press interval time increased at a constant angle of incidence and peak inspiratory flow rate. Meanwhile, performance analysis of the designed smart MDI indicated that the inhaler could effectively reduce the deposition fraction of drugs in the mouth and throat regions by 17% on average. [ABSTRACT FROM AUTHOR]

Thermosetting adhesives are commonly utilized in various applications. However, covalent cross-linked networks prevent thermosetting adhesives from being re-assembled, which necessitates higher machining precision. Additionally, the primary raw materials used in adhesive preparation are derived from non-renewable petroleum resources, which further constrain adhesive development. In this study, a recyclable adhesive was developed by incorporating dynamic boronic esters into epoxy resin derived from soybean oil. The successful synthesis of epoxidized soybean oil and boronic esters was confirmed through the analysis of proton nuclear magnetic resonance spectra and differential scanning calorimetry results. Swelling tests and tensile curves demonstrated the presence of covalently cross-linked networks. Self-healing and reprocessing experiments indicated that the cross-linked network topology could be re-assembled under mild conditions. [ABSTRACT FROM AUTHOR]

Background: This study aimed to determine the efficacy and safety of posttraumatic antifibrinolysis with multidose tranexamic acid (TXA) in reducing perioperative hidden blood loss (HBL) in elderly intertrochanteric fracture patients. Method: Ninety-six elderly intertrochanteric fracture patients admitted to our department from June 2021 to September 2022 were randomized into two groups. The control group (Group A) received 100 mL of normal saline, while the experimental group (Group B) received 1.5 g of TXA intravenously q12 h from postadmission Day 1 (PAD1) to the day before surgery, and both groups received 1.5 g of TXA q12 h from postoperative Day 1 (POD1) to POD3. Haemoglobin (Hb), haematocrit (Hct), coagulation parameters, fibrinogen degradation product (FDP), and D-dimer (D-D) were recorded from PAD1 to POD3. HBL was calculated using the gross formula and recorded as the primary outcome. Result: In all-over analyses, the patients in Group B had lower perioperative HBL (on PAD3, POD1, and POD3), preoperative HBL (HBLpre), decline of haemoglobin (ΔHb-on PAD3), allogeneic blood transfusion (ABT) rate, FDP (on PAD3), and D-D (on PAD3) compared with Group A. No significant differences were exhibited in postoperative HBL (HBLpost) between the 2 groups. In subgroup analyses, for patients who received intervention within 24 h, the result is consistent with the whole. For patients who received intervention over 72 h of injury, there were no significant differences in perioperative HBL, ΔHb, ABT rate, FDP, and D-D between the 2 groups. There were no significant differences in APTT, PT, the rate of venous thromboembolism, wound complications, or 90-day mortality between the 2 groups. Conclusion: For elderly intertrochanteric fracture patients, early posttraumatic antifibrinolysis with multidose TXA is effective in reducing perioperative HBL, which mainly manifests as the reduction of preoperative HBL, especially for patients injured within 24 h. Application of TXA beyond 72 h of injury was ineffective. [ABSTRACT FROM AUTHOR]

Cold forging is suitable for manufacturing thin-walled tubes; however, a poorly planned forging process results in serious quality problems. This paper aims to determine an appropriate cold forging process for thin-walled A286 superalloy tube with ideal forming quality. We analyzed the effects of the two forging processes with reverse forging sequences on forming defects and hardness distribution in the thin-walled tubes via finite element simulation. The methods of optical microscope, micro-hardness, scanning electron microscope, and electron-backscattered diffraction were used to validate the tube forming quality. The simulation results revealed that the Type-I process was an appropriate forging process for meeting the quality requirements. For the Type-I process, an underfilling defect was observed at the bottom of the rod section of the tube. The stress concentration in the head section was lower than that in the Type-II process, potentially reducing the probability of crack initiation. Compared to the rod section, the head section may exhibit higher hardness magnitudes due to the greater strain distribution. The experimental results confirmed the feasibility of the Type-I process. The increased hardness in the head section may be primarily attributed to the more intense plastic deformation applied to the material in this section by the Type-I process. [ABSTRACT FROM AUTHOR]

Multi-GNSS PPP partial ambiguity resolution (PAR) can improve the fixing success rate and shorten the time to first fix (TTFF). Ambiguity subset selection based on the bootstrapping success rate sorting criterion (BSSC) is widely used in PPP PAR due to its ease of computation and comprehensive evaluation of the global quality of ambiguity solutions. However, due to the influence of unmodeled errors, such as atmospheric residuals and gross errors, ambiguity parameter estimation will inevitably introduce bias. For ambiguity parameters with bias, their variance will converge incorrectly and will not accurately reflect the estimation accuracy. As a result, the selected ambiguity subset based on the BSSC becomes inaccurate, affecting the fixing success rate and TTFF. Therefore, we proposed an improved multi-GNSS PPP PAR method based on a two-step sorting criterion (TSSC). This method aims to address the influence of inaccurate variance of ambiguity parameters, particularly those with low observation quality, on the ambiguity subset selection based on the BSSC. The ambiguity subset satisfying the preset success rate threshold is selected to reduce the influence of unconverged ambiguity on the TSSC. In the first step of the sorting process, the observations whose elevation angle is below 30° or whose posterior residual falls into the IGG3 model reduction domain are clustered together. The posterior observation weight criterion (POWC) instead of the BSSC is adopted to sort ambiguities to overcome the false convergence of variance of ambiguity parameters. In the second step of the sorting process, the remaining ambiguities with reasonable variances are sorted based on the BSSC. Finally, the bottom ambiguity is removed one by one from the ambiguity subset sorted based on the two-step sorting criterion (TSSC) until the requirements of the ratio test for LAMBDA are met. The static data from 10 MGEX stations over a period of 30 days, along with urban kinematic data, were collected to validate the proposed method. Compared with the PAR based on the BSSC, the static experiments demonstrated a reduction of 8.7% and 16.8% in the TTFF and convergence time, respectively. Additionally, the positioning accuracy in the east, north, and up directions was improved by 20.1%, 17.1%, and 4.67%, respectively. Furthermore, the kinematic experiment revealed that the TTFF and convergence time decreased from 1.65 min and 10.5 min to 1.3 min and 1.8 min, respectively, with higher positioning accuracy. [ABSTRACT FROM AUTHOR]

Real-time and high-precision water level monitoring is crucial for the fields of hydrology, hydraulic engineering, and disaster prevention and control. The most prevalent method for measuring water level is through the use of water level gauges, which can be costly and have limited coverage. In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) technology has emerged as a promising approach for water level monitoring due to its low cost and high coverage. However, a limitation of current GNSS-R technology is the extended time required to record signals, which hinders its potential for real-time application. This paper introduces a novel real-time water level monitoring method based on GNSS dual-antenna attitude measurement and develops a model to invert water level based on baseline vector. This method uses double-difference observations to eliminate errors caused by various factors, such as satellite and receiver clock, and ionospheric and tropospheric delay. To avoid the impact of detecting and correcting cycle slips during real-time operations, a single-epoch calculation method is introduced. In order to verify the stability and reliability of our method, field tests were carried out at Dongshahe Station in Beijing. We obtained water level data with a time resolution of 1 Hz through field experiments. Experimental data collected from 12 May to 8 June 2022 and from 4 July to 8 August 2022 showed good agreement with on-site water gauge measurements, with root mean square errors of 2.77 cm and 2.54 cm, respectively. Experimental results demonstrate that this method can achieve high-precision, high-temporal-resolution water level monitoring. [ABSTRACT FROM AUTHOR]

Most dams in China have been operating for a long time and are products of the economic and technical limitations at the time of construction. Due to decades of aging engineering and ancillary problems, these reservoirs pose great threats to the safety of local people and the development of the surrounding economy. In this study, the surface deformation information for the Banqiao Reservoir is monitored with the small baseline subset–synthetic aperture radar interferometry (SBAS-InSAR) method using 80 Sentinel-1A images acquired from 3 January 2020 to 20 August 2022. Additionally, ground measurements from the BeiDou ground-based deformation monitoring stations were collected to validate the InSAR results. Based on the InSAR results, the spatiotemporal deformation features of the dam were analyzed in detail. The results show that the deformation in most areas, including the dam in the study area, is relatively stable, and the regional deformation velocity of the Banqiao Reservoir dam and other hydraulic engineering facilities varies between −1 mm/y and −4 mm/y. The Ru River area has a relatively obvious subsidence trend, and the maximum subsidence velocity reaches 30 mm/y. The InSAR monitoring results are consistent with the change trend in the BeiDou ground-based deformation measurement results. The monitoring results for the reservoir dam area provide a reference for local sustainable development and geological disaster prevention. [ABSTRACT FROM AUTHOR]

The flow dynamics of wormlike micellar solutions around a sphere is a fundamental problem in particle-laden complex fluids but is still understood insufficiently. In this study, the flows of the wormlike micellar solution past a sphere in the creeping flow regime are investigated numerically with the two species, micelles scission/reforming, Vasquez–Cook–McKinley (VCM) and the single-species Giesekus constitutive equations. The two constitutive models both exhibit the shear thinning and the extension hardening rheological properties. There exists a region with a high velocity that exceeds the main stream velocity in the wake of the sphere, forming a stretched wake with a large velocity gradient, when the fluids flow past a sphere at very low Reynolds numbers. We found a quasi-periodic fluctuation of the velocity with the time in the wake of the sphere using the Giesekus model, which shows a qualitative similarity with the results found in present and previous numerical simulations with the VCM model. The results indicate that it is the elasticity of the fluid that causes the flow instability at low Reynolds numbers, and the increase in the elasticity enhances the chaos of the velocity fluctuation. This elastic-induced instability might be the reason for the oscillating falling behaviors of a sphere in wormlike micellar solutions in prior experiments. [ABSTRACT FROM AUTHOR]

3-Azabicyclo[3.1.0]hexanes are common structural components in natural products and bioactive compounds. Traditionally, the metal-mediated cyclopropanation domino reaction of chain enzymes is the most commonly used strategy for the construction of this type of aza[3.1.0]bicycle derivative. In this study, a base-promoted intramolecular addition of alkenes used to deliver conformationally restricted highly substituted aza[3.1.0]bicycles is reported. This reaction was tailor-made for saturated aza[3.1.0] bicycle-containing fused bicyclic compounds that may be applied in the development of concise and divergent total syntheses of bioactive compounds. [ABSTRACT FROM AUTHOR]

A new kind of surface material is found and defined in the Balmer–Kapteyn (B-K) cryptomare region, Mare-like cryptomare deposits (MCD), representing highland debris mixed by mare deposits with a certain fraction. This postulates the presence of surface materials in the cryptomare regions. In this study, to objectively verify the existence of the MCD in the cryptomare regions, based on the Chang'E-2 microwave radiometer (MRM) data, the support vector machine (SVM) method was adopted, where the K-means algorithm was used to optimize the training samples and the random forest algorithm was used to select the proper band features. Finally, the extracted MCD is identified with the datasets including Lunar Reconnaissance Orbiter Wide Angle Camera, Diviner, and Clementine UV–VIS. The main findings are as follows: (1) Compared to the range outlined via the TB counter, the range of the MCD is objectively extracted using the SVM method in the B-K cryptomare region, which is reasonably indicated by the FeO abundance, TiO2 abundance, and rock abundance distributions. (2) The MCDs were extracted in the Dewar, Lomonosov–Fleming (L-F), and Schiller–Schickard (S-S) regions, indicating that the MCDs are widely distributed in the cryptomaria. (3) The presence of MCDs is concentrated in a limited region, accounting for 64.9%, 52.3%, 76.4%, and 64%, respectively, in the range of Dewar, L-F, S-S, and B-K regions identified using the optical data. The occurrence of the MCD gives a new understanding of the surface evolution in the cryptomare regions. [ABSTRACT FROM AUTHOR]

Video Synthetic Aperture Radar (SAR) has shown great potential in moving target detection and tracking. At present, most of the existing detection methods focus on the intensity information of the moving target shadow. According to the mechanism of shadow formation, some shadows of moving targets present low contrast, and their boundaries are blurred. Additionally, some objects with low reflectivity show similar features with them. These cause the performance of these methods to degrade. To solve this problem, this paper proposes a new moving target shadow detection method, which consists of background modeling and shadow detection based on intensity information and neighborhood similarity (BIIANS). Firstly, in order to improve the efficiency of image sequence generation, a fast method based on the Back-projection imaging algorithm (f-BP) is proposed. Secondly, due to the low-rank characteristics of stationary objects and the sparsity characteristics of moving target shadows presented in the image sequence, this paper introduces the low-rank sparse decomposition (LRSD) method to perform background modeling for obtaining better background (static objects) and foreground (moving targets) images. Because the shadows of moving targets appear in the same position in the original and the corresponding foreground images, the similarity between them is high and independent of their intensity. Therefore, using the BIIANS method can obtain better shadow detection results. Real W-band data are used to verify the proposed method. The experimental results reveal that the proposed method performs better than the classical methods in suppressing false alarms, missing alarms, and improving integrity. [ABSTRACT FROM AUTHOR]

Mitochondria is an important drug target for ailments ranging from neoplastic to neurodegenerative diseases and metabolic diseases. Here, we describe the synthesis of chloroquine analogs and show the results of mitochondrial ATP inhibition testing. The 2,4-dinitrobenzene-based analogs showed concentration-dependent mitochondrial (mito.) ATP inhibition. The most potent mito. ATP inhibitor was found to be N-(4-((2,4-Dinitrophenyl)amino)pentyl)-N-ethylacetamide (17). [ABSTRACT FROM AUTHOR]

Dispersion management plays an important role in improving the output performance of a mode-locked fiber laser. Therefore, dispersion management is carried out by introducing the grating pair in our experiment. Through adjusting the distance between the grating pair, mode-locked pulses corresponding to different dispersion regimes can be realized, which typically range from soliton state in the anomalous dispersion regime to the dissipative soliton format in the normal dispersion regime. Furthermore, tunable spectrum distribution can be achieved by adjusting two intra-cavity polarization controllers. The proposed dispersion management method complements mode-locking techniques based on nonlinear multimode interference (NL-MMI). The laser can operate with self-start mode locking stably and is useful for practice applications. [ABSTRACT FROM AUTHOR]

Since failure of steam turbines occurs frequently and can causes huge losses for thermal plants, it is important to identify a fault in advance. A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient boosting (XGBoost) is proposed in this paper. First, the t-SNE algorithm was used to map the high-dimensional data to the low-dimensional space; and the data clustering method of K-means was performed in the low-dimensional space to distinguish the fault data from the normal data. Then, the imbalance problem in the data was processed by the synthetic minority over-sampling technique (SMOTE) algorithm to obtain the steam turbine characteristic data set with fault labels. Finally, the XGBoost algorithm was used to solve this multi-classification problem. The data set used in this paper was derived from the time series data of a steam turbine of a thermal power plant. In the processing analysis, the method achieved the best performance with an overall accuracy of 97% and an early warning of at least two hours in advance. The experimental results show that this method can effectively evaluate the condition and provide fault warning for power plant equipment. [ABSTRACT FROM AUTHOR]

High-speed detection of abnormal frames in surveillance videos is essential for security. This paper proposes a new video anomaly–detection model, namely, feature trajectory–smoothed long short-term memory (FTS-LSTM). This model trains an LSTM autoencoder network to generate future frames on normal video streams, and uses the FTS detector and generation error (GE) detector to detect anomalies on testing video streams. FTS loss is a new indicator in the anomaly–detection area. In the training stage, the model applies a feature trajectory smoothness (FTS) loss to constrain the LSTM layer. This loss enables the LSTM layer to learn the temporal regularity of video streams more precisely. In the detection stage, the model utilizes the FTS loss and the GE loss as two detectors to detect anomalies. By cascading the FTS detector and the GE detector to detect anomalies, the model achieves a high speed and competitive anomaly-detection performance on multiple datasets. [ABSTRACT FROM AUTHOR]

The effects of Steam Flash-Explosion (SFE) on the physicochemical properties and molecular structure of high-temperature denatured defatted rice bran protein isolate (RBPI) were investigated. The mechanism of SFE treatment on high-temperature denatured defatted RBPI was revealed. The analysis of the physical and chemical properties of RBPI showed that the surface hydrophobicity, characteristic viscosity, and thermal stability of rice bran protein isolate were significantly affected by the pressure of saturated steam and pressure holding time. Under the conditions of 2.1 MPa and 210 s, the surface hydrophobicity index decreased significantly from 137.5 to 17.5, and the characteristic viscosity increased significantly. The peak temperature of denaturation decreases from 114.2 to 106.7 °C, and the enthalpy of denaturation decreases from 356.3 to 231.4 J/g. The higher structure (circular dichroic spectrum and endogenous fluorescence spectrum) of rice bran protein isolate was analyzed by volume rejection chromatography (SEC). The results showed that steam flash treatment could depolymerize and aggregate RBPI, and the relative molecular weight distribution changed greatly. The decrease in small molecules with poor solubility was accompanied by the increase in macromolecules (>550 kDa) soluble aggregates, which were the products of a Maillard reaction. The contents of free sulfhydryl and disulfide bonds in high-temperature rice bran meal protein isolate were significantly increased, which resulted in the increase in soluble aggregates containing disulfide bonds. Circular dichroism (CD) analysis showed that the α-helix content of the isolated protein was significantly decreased, the random curl content was increased, and the secondary structure of the isolated protein changed from order to disorder. The results of endogenous fluorescence spectroscopy showed that the high-temperature rice bran meal protein isolate was more extended, tryptophan was in a more hydrophilic microenvironment, the fluorescence intensity was reduced, and the tertiary structure was changed. In addition, the mean particle size and net surface charge of protein isolate increased in the aqueous solution, which was conducive to the development of the functional properties of the protein. [ABSTRACT FROM AUTHOR]

The clumping index (CI) is a commonly used vegetation dispersion parameter used to characterize the spatial distribution of the clumping or random distribution of leaves in canopy environments, as well as to determine the radiation transfer of the canopy, the photosynthesis of the foliage, and hydrological processes. However, the method of CI estimation using the measurement instrument produces uncertain values in various forest types. Therefore, it is necessary to clarify the differences in CI estimation methods using field measurements with various segment lengths in different forest types. In this study, three 100 m × 100 m plots were set, and the CI and leaf area index (LAI) values were measured. The CI estimation results were compared. The results show that the accuracy of CI estimation was affected by different forest types, different stand densities, and various segment lengths. The segment length had a significant effect on CI estimation with various methods. The CI estimation accuracy of the LX and CLX methods increased alongside a decrease in the segment length. The CI evidently offered spatial heterogeneity among the different plots. Compared with the true CI, there were significant differences in the CI estimation values with the use of various methods. Moreover, the spatial distribution of the CI estimation values using the ΩCMN method could more effectively describe the spatial heterogeneity of the CI. These results can provide a reference for CI estimation in field measurements with various segment lengths in different forest types. [ABSTRACT FROM AUTHOR]

In this research, three fully biobased poly(hexamethylene 2,5-furandicarboxylate-co-sebacate) (PHFSe) copolyesters with low contents of hexamethylene sebacate (HSe) unit (10 mol%, 20 mol%, and 30 mol%) were successfully synthesized through a two-step transesterification/esterification and polycondensation method. The chemical structure and actual composition of PHFSe copolyesters were confirmed by hydrogen nuclear magnetic resonance. The thermal behavior and mechanical property of PHFSe copolyesters were investigated and compared with those of the poly(hexamethylene 2,5-furandicarboxylate) (PHF) homopolymer. Both PHFSe copolyesters and PHF showed the high thermal stability. The basic thermal parameters, including glass transition temperature, melting temperature, and equilibrium melting temperature, gradually decreased with increasing the HSe unit content. PHFSe copolyesters crystallized more slowly than PHF under both the nonisothermal and isothermal melt crystallization conditions; however, they crystallized through the same crystallization mechanism and crystal structure. In addition, the mechanical property, especially the elongation at break of PHFSe copolyesters, was obviously improved when the HSe unit content was greater than 10 mol%. In brief, the thermal and mechanical properties of PHF may be easily tuned by changing the HSe unit content to meet various practical end-use requirements. [ABSTRACT FROM AUTHOR]

Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic Caenorhabditis elegans hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the C. elegans neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree distributions were neither Poisson distributions nor pure power-law distributions. We have proposed a model of network evolution with different initial attractiveness for in-degrees and out-degrees of nodes and preferential attachment, which reproduces the asymmetry between in-degrees and out-degrees and similar degree distributions via the tuning of the initial attractiveness values. In this study, we present the well-preserved structural properties of C. elegans neuronal networks across development, and provide some insight into understanding the evolutionary processes of biological neural networks through a simple network model. [ABSTRACT FROM AUTHOR]

The photo-induced denitrogenative annulations of a variety of 1-alkenylbenzotriazoles were investigated. By judiciously manipulating the structural variations of 1-alkenylbenzotriazoles, two characteristic polycyclic skeletons associated with monoterpene indole alkaloids were constructed through a diverted and controllable manner. The present work not only enriches the photochemistry of 1-alkenylbenzotriazoles, but also offers a unified approach to access skeletally diverse indole alkaloid scaffolds. [ABSTRACT FROM AUTHOR]

In order to reduce the impact of noise on the accuracy of inversion products based on SAR images, many filtering algorithms have been developed for noise reduction of SAR images. This paper proposes a filtering method based on the spatial autocorrelation feature of the block fast Fourier transform (BFFT). The method statistically analyses the autocorrelation length of speckle noise on Sentinel-1B images for different features and then constructs a relationship between autocorrelation length and noise period. After that, the size of the optimal FFT filtering window radius was determined based on the relationship between the noise period and the components in the image frequency domain. Finally, we filtered the SAR image within the parcels. We compared BFFT with six commonly used filtering methods. The results show that: (1) The noise periods of the soybean, corn, paddy, and water objects on the SAR image have little difference, with noise periods of 3.36, 3.17, 3.13, and 3.14 pixels on the VV polarization and 3.49, 3.17, 2.94, and 2.42 pixels on the VH polarization; (2) after the BFFT filtering in the land parcel area, the mean value of the backscattering coefficient (BC) kept constant, whilst at the same time, the standard deviation (STD) was reduced to half of that before the filtering and (3) the BFFT and NLM filtering methods have a better effect on noise reduction inside the block. The BFFT filtering method retains the variation trend between different regions within the block and preserves the block boundary's clarity. This study provides a new idea for refined image processing. [ABSTRACT FROM AUTHOR]

The accurate estimation of the mass and center of gravity (CG) position is key to vehicle dynamics modeling. The perturbation of key parameters in vehicle dynamics models can result in a reduction of accurate vehicle control and may even cause serious traffic accidents. A dual robust embedded cubature Kalman filter (RECKF) algorithm, which takes into account unknown measurement noise, is proposed for the joint estimation of mass and CG position. First, the mass parameters are identified based on directly obtained longitudinal forces in the distributed drive electric vehicle tires using the whole vehicle longitudinal dynamics model and the RECKF. Then, the CG is estimated with the RECKF using the mass estimation results and the vertical vehicle model. Finally, different virtual tests show that, compared with the cubature Kalman algorithm, the RECKF reduces the root mean square error of mass and CG by at least 7.4%, and 2.9%, respectively. [ABSTRACT FROM AUTHOR]

The application of silver nanoparticles (AgNPs) in antibacterial materials, glucose detection, etc., is of broad interest for researchers around the world. Nanocellulose with many excellent properties can be used as a carrier and stabilizer to assist in the synthesis of AgNPs. In this study, cellulose nanofibrils (CNFs) and cellulose nanocrystals (CNCs) were used to assist in the synthesis of AgNPs under the reduction of glucose and detection of glucose concentration under different conditions. Transmission electron microscopy (TEM) analysis showed that the AgNPs in the nanocellulose-AgNPs (NC-AgNPs) system were roughly spherical and randomly distributed on the nanocellulose. In the whole reaction system, when the concentration of nanocellulose is 0.11 mg/mL, the concentration of silver ammonia solution is 0.6 mM, and the mixing time is 2.5 h, according to the UV-Vis analysis, the absorbance of CNF-AgNPs at 425 nm exhibited a good linear relationship (R2 = 0.9945) with the glucose concentration range (5–50 μM), while the absorbance of CNC-AgNPs at 420 nm showed a good linear relationship (R2 = 0.9956) with the glucose concentration range (5–35 μM). The synthesis of NC-AgNPs can be further developed into a sensor with higher sensitivity and higher stability for detecting glucose concentration and a material with antibacterial effects. [ABSTRACT FROM AUTHOR]

The current satellite network traffic forecasting methods cannot fully exploit the long correlation between satellite traffic sequences, which leads to large network traffic forecasting errors and low forecasting accuracy. To solve these problems, we propose a satellite network traffic forecasting method with an improved gate recurrent unit (GRU). This method combines the attention mechanism with GRU neural network, fully mines the characteristics of self-similarity and long correlation among traffic data sequences, pays attention to the importance of traffic data and hidden state, learns the time-dependent characteristics of input sequences, and mines the interdependent characteristics of data sequences to improve the prediction accuracy. Particle Swarm Optimization (PSO) algorithm is used to obtain the best network model Hyperparameter and improve the prediction efficiency. Simulation results show that the proposed method has the best fitting effect with real traffic data, and the errors are reduced by 26.9%, 37.2%, and 57.8% compared with the GRU, Support Vector Machine (SVM), and Fractional Autoregressive Integration Moving Average (FARIMA) models, respectively. [ABSTRACT FROM AUTHOR]

Capacitive deionization is an emerging desalination technology with mild operation conditions and high energy efficiency. However, its application is limited due to the low deionization capacity of traditional capacitive electrodes. Herein, we report a novel dual-ion capacitive deionization system with a lithium-ion battery cathode LiMn2O4/C and a sodium-ion battery anode NaTi2(PO4)3/C. Lithium ions could enhance the charge transfer during CDI desalination, while NaTi2(PO4)3/C provided direct intercalation sites for sodium ions. The electrochemical capacities of the battery electrodes fitted well, which was favorable for the optimization of the desalination capacity. The low potential of the redox couple Ti3+/Ti4+ (−0.8 V versus Ag/AgCl) and intercalation/deintercalation behaviors of sodium ions that suppressed hydrogen evolution could enlarge the voltage window of the CDI process to 1.8 V. The novel CDI cell achieved an ultrahigh desalination capacity of 140.03 mg·g−1 at 1.8 V with an initial salinity of 20 mM, revealing a new direction for the CDI performance enhancement. [ABSTRACT FROM AUTHOR]

However, the honey bees exhibited a reduction in consuming the sucrose containing not less than 1 ug/mL of CdCl SB 2 sb ; although non-significant, there exists the possibility that sucrose containing more than 1 ug/mL of Cd may affect honey bee sucrose responsiveness that is essential for olfactory learning in honey bees [[38]]. We, therefore, investigated whether honey bees exposed to chronic Cd exhibited altered olfactory learning, and we further compared the patterns of brain gene expression between cadmium-exposed bees and control bees using RNA-seq analysis. Our studies showed that the head weight was significantly lower in cadmium-exposed bees when compared to controls, suggesting chronic Cd exposure may have adverse effects on the total protein content of the mandibular and hypopharyngeal glands in the heads of adult honey bees, thus resulting in a decrease in head weight in Cd-exposed bees. [Extracted from the article]

Appearance quality is one of the most important indexes for many injection-molded products, like optical parts, automotive parts etc., especially at the area near the injection gate. Different from the work that focused on designing the dimensions of the runner, this work proposed a method which is based on the insert technology to improve the appearance quality of a standard automotive part. An insert was introduced into the runner system which located before the gate. Three different shapes of this insert (circular, rectangular and diamond) were used to study the effect of geometrical factors on the appearance quality in this paper. All inserts were parameterized to describe their location and dimensions. Based on the geometrical design parameters, expected improvement optimization problem about the appearance quality were solved by using sequential approximate optimization method. The appearance qualities of three cases are improved by 13.77%, 21.56%, 14.37% respectively. Results showed that the best geometrical design scheme of the insert is rectangular with the optimal geometrical location and dimensions. The reasons were discussed by investigating the flow and thermal history in detail. Compared with the design case without any insert, the heat was absorbed and the velocity field was changed by the insert before the polymer melts ran into the cavity. It changed the complicated thermo-mechanical history inside the material during the entire processing history, which improved the final appearance quality of this auto part. [ABSTRACT FROM AUTHOR]

Simple Summary: β-Thalassemia is one of the most prevalent inherited diseases in China. It is important to develop animal models to accurately simulate human β-thalassemia and there are unique advantages to studying β-thalassemia in pigs. However, there are only few reports on the systematic analysis of the β-thalassemia-related genes and their expression pattern in pigs so far. Therefore, in this study, we firstly predicted 11 porcine hemoglobin-encoding genes and found that there was no HBG gene in pigs, indicating that the globin switches might not exist in pigs. A new hemoglobin-encoding gene, 'HBB-like', was found in pigs, which showed high conservation in its amino sequences between pigs and humans. Then, we studied the evolutionary relationship of hemoglobin-encoding genes in human, pig and mouse. The results showed that the β-chain structure of pig and human was highly similar. In addition, we analyzed the hemoglobin-encoding gene expressions by using the iswine database and qPCR. Our results showed significant differences in the spatiotemporal expression patterns among the four genes (HBA, HBB, HBB-like and HBE) in three developmental stages of six different pig breeds. Our study provides an important theoretical basis for further construction of a gene-edited β-thalassemia miniature pig model to assess β--thalassemia treatments. β-Thalassemia induces hemolytic anemia caused by mutations in the β-chain gene locus. As humans progress from embryo to adulthood, hemoglobin recombines twice. To test whether similar hemoglobin reassembly occurs in pigs, bioinformatics tools were used to predict the pig hemoglobin-encoding gene. We then systematically analyzed the expression patterns of the HBB gene family in three developmental stages (weaning, sexual maturity and physical maturity) of six different pig breeds (Landrace, Yorkshire, Wuzhishan, Songliao black, Meishan and Tibetan). The results showed that the new hemoglobin coding gene 'HBB-like' was found in pigs, while the HBG gene did not exist in pigs, indicating that human-like reassembly might not exist in pigs. The HBB and HBB-like genes shared highly similar amino acid sequences and gene sequences. The genes on the β-chain were highly similar between humans and pigs and the amino acid sequences of human and pig HBB genes at position 26 and positions 41–42 were identical. qPCR results showed that there were significant differences in the spatiotemporal expression patterns of the four genes (HBA, HBB, HBB-like and HBE) across breeds. Our results provide a foundation for follow-up studies assessing the relationship between the gene-encoding hemoglobin and β-thalassemia disease, as well as the construction of a gene-edited β-thalassemia miniature pig model to assess β-thalassemia treatments. [ABSTRACT FROM AUTHOR]

Hyperspectral inversion techniques can facilitate soil quality monitoring and evaluation. In this study, the Yellow River Delta Wetland Nature Reserve was used as the study area. By measuring and analyzing soil samples under different vegetation types and collecting soil reflectance spectra, the relationships between vegetation types, soil depth, and the changes in soil total carbon (TC), total nitrogen (TN), and total phosphorus (TP) contents were assessed. The spectral data set was changed by spectral first derivative processing and division of the sample set according to vegetation type. The correlation between soil carbon, nitrogen, and phosphorus contents, and soil spectra was also analyzed, sensitive bands were selected, and the partial least-squares (PLS) method, support vector machine (SVM) method, and random forest (RF) model were used to establish the inversion model based on the characteristic bands. The optimal combination of spectral transformation, sample set partitioning, and inversion model was explored. The results showed significant differences (p < 0.05) in soil TC, TN, and TP contents under reed and saline alkali poncho vegetation, but not between soil element contents under different stratifications of the same plant species. The first derivative reflectance had higher correlation coefficients with soil TC, TN, and TP contents compared with the original reflectance, while the sensitive bands and quantities of the three elements differed. The division of the sample sets according to vegetation type and the first derivative treatment can improve the prediction accuracy of the model. The best combination of sample set plus FD plus RF for TC, TN, and TP in reed soil and sample set plus FD plus SVM for TC, TN, and TP in saline alkali pine soil provides technical support to further improve the prediction accuracy of TC, TN, and TP in wetland soil. [ABSTRACT FROM AUTHOR]

Lunar cryptomare records both early-stage mare volcanisms and large-scale impact cratering, which can provide important information about the thermal evolution of the Moon. We built a mixing dielectric constant model to represent the cryptomare deposits mixed by highland debris and mare deposits, and the proper radiative transfer simulation was constructed to evaluate the thermal emission features of surface deposits in the cryptomare region. The microwave radiometer (MRM) data in the Balmer-Kapteyn region were extracted, and the linear interpolation method was used to generate brightness temperature (TB) maps at noon and at night. To enhance the correlation between cryptomare deposits and TB performances, normalized TB (nTB) and TB difference (dTB) maps were also generated. Combined with the datasets, including Lunar Reconnaissance Orbiter Wide Angle Camera, Lunar Orbiter Laser Altimeter, and Diviner and Clementine UV–VIS, the main findings are as follows: (1) The mare-like cryptomare deposits were discovered and identified according to the nTB and dTB performances. Combined with the surface compositions, at least two kinds of buried mare deposits were identified in the B-K region, which erupted during different episodes. (2) A construct-like volcanic feature was suggested by the nTB and dTB performances. (3) The results of our analysis indicated the presence of materials with low dTB anomalies in the northern and southwestern parts of the cryptomare region and in the mare unit within the Vendelinus crater, which illustrates the heterogeneity of the lunar crust in the vertical direction. [ABSTRACT FROM AUTHOR]

First-principles calculations were performed to investigate the site preference of alloying elements, and the effect of alloying elements on stability, electronic structure and magnetism of Ni3P-type Fe3B. The calculated energies suggested that all studied compounds are thermodynamically stable while it is relatively difficult to synthesize the (Fe2.875,Cu0.125)B, (Fe2.875,W0.125)B and (Fe2.875,Nb0.125)B. The (Fe2.875,W0.125)B is the most stable compound from the view of cohesive energy. Mn element prefers to occupy the Fe2 site, whereas the others are more likely to reside in the Fe1 site. It can be found from the electronic structures that the DOSs of both Fe3B and alloyed Fe3B are dominated by Fe-d states, and all the compounds mainly contain Fe-B covalent bond, Fe-Fe covalent bond and Fe-Fe metallic bond. Based on the magnetic moments (Ms) results, it can be known that the Fe3B, (Fe2.875,Mn0.125)B, (Fe2.875,Co0.125)B, (Fe2.875,Ni0.125)B and (Fe2.875,Cu0.125)B are ferromagnetic compounds, whereas the others are ferrimagnetic compounds. Only Mn and Co are able to enhance the magnetism of Fe3B. Moreover, Mn is the most favorable candidate for improving the magnetic properties of Fe3B among the alloying elements. These results can be used to guide the composition design and performance optimization of magnetic materials containing Fe3B with Ni3P-type structure. [ABSTRACT FROM AUTHOR]

This paper aims at finding a fixed-time solution to the Sylvester equation by using a gradient neural network (GNN). To reach this goal, a modified sign-bi-power (msbp) function is presented and applied on a linear GNN as an activation function. Accordingly, a fixed-time convergent GNN (FTC-GNN) model is developed for solving the Sylvester equation. The upper bound of the convergence time of such an FTC-GNN model can be predetermined if parameters are given regardless of the initial conditions. This point is corroborated by a detailed theoretical analysis. In addition, the convergence time is also estimated utilizing the Lyapunov stability theory. Two examples are then simulated to demonstrate the validation of the theoretical analysis, as well as the superior convergence performance of the presented FTC-GNN model as compared to the existing GNN models. [ABSTRACT FROM AUTHOR]

The clustering of transgenes at a chromosome location minimizes the number of segregating loci that needs to be introgressed to field cultivars. Transgenes could be efficiently stacked through site-specific recombination and a recombinase-mediated in planta gene stacking process was described previously in tobacco based on the Mycobacteriophage Bxb1 site-specific integration system. Since this process requires a recombination site in the genome, this work describes the generation of target sites in the Japonica rice genome. Agrobacterium-mediated gene transfer yielded ~4000 random-insertion lines. Seven lines met the criteria of being single copy, not close to a centromere, not inserted within or close to a known gene or repetitive DNA, having precise recombination site sequences on both ends, and able to express the reporter gene. Each target line tested was able to accept the site-specific integration of a new gfp-containing plasmid and in three of those lines, we regenerated fertile plants. These target lines could be used as foundation lines for stacking new traits into Japonica rice. [ABSTRACT FROM AUTHOR]