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The confinement and high utilization of sulfur in the cathodes is critical for improved cycling performance of lithium-sulfur batteries. In this case one-pot hydrothermal strategy is developed to produce rGO/MXene/sulfur composite aerogels where sulfur is in situ trapped in the 3D rGO/MXene conductive skeleton. The optimized composite aerogels as free-standing cathodes delivery a specific capacity of 951 mAhg-1 after 100 cycles at 0.2 C with a low fading rate of 0.062 % per cycle. The excellent cycling performance is correlated with highly oxidized MXene and in situ formed sulfate/thiosulfate complex layer in the long-term cycles.

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Here, 0.3 wt.%Zr was introduced in an Al-4 wt.%Cu-0.5 wt.%Mn-0.1 wt.%Fe alloy to investigate its influence on the microstructure and mechanical properties of the alloy. The microstructures of both as-cast and T6-treated Al-Cu-Mn-Fe (ACMF) and Al-Cu-Mn-Fe-Zr (ACMFZ) alloys were analyzed. The intermetallic compounds formed through the casting procedure include Al2Cu and Al7Cu2Fe, and the Al2Cu phase dissolves into the matrix and re-precipitates as θ' phase during the T6 process. The introduction of Zr results in the precipitation of L12-Al3Zr nanometric precipitates after T6, while the θ' precipitates in ACMFZ alloy are much finer than those in ACMF alloy. The L12-Al3Zr precipitates were found coherently located with θ', which was assumed beneficial for stabilizing the θ' precipitates during the high-temperature tensile process. The tensile properties of ACMF and ACMFZ alloys at room temperature and elevated temperatures (200, 300, and 400 °C) were tested. Especially, the yield strength of ACMFZ alloys can reach 128 MPa and 65 MPa at 300 °C and 400 °C, respectively, which are 31% and 33% higher than those of ACMF alloys. The strengthening mechanisms of grain size, L12-Al3Zr, and θ' precipitates on the tensile properties were discussed. This work may be referred to for designing Al-Cu alloys for application in high-temperature fields.

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Diet plays a crucial role in shaping the gut microbiota and overall health of animals. Traditionally, silkworms are fed fresh mulberry leaves, and artificial diets do not support good health. The aim of this study was to explore the relationship between the dietary transition from artificial diets to mulberry leaves and the effects on the gut microbiota and physiological changes in silkworms as a model organism. With the transition from artificial diets to mulberry leaves, the diversity of the silkworm gut microbiota increased, and the proportion of Enterococcus and Weissella, the dominant gut bacterial species in silkworms reared on artificial diets, decreased, whereas the abundance of Achromobacter and Rhodococcus increased. Dietary transition at different times, including the third or fifth instar larval stages, resulted in significant differences in the growth and development, immune resistance, and silk production capacity of silkworms. These changes might have been associated with the rapid adaptation of the intestinal microbiota of silkworms to dietary transition. This study preliminarily established a dietary transition-gut microbial model in silkworms based on the conversion from artificial diets to mulberry leaves, thus providing an important reference for future studies on the mechanisms through which habitual dietary changes affect host physiology through the gut microbiome.

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Gold mining is associated with serious heavy metal pollution problems. However, the studies on such pollution caused by gold mining in specific geological environments and extraction processes remain insufficient. This study investigated the accumulation, fractions, sources and influencing factors of arsenic and heavy metals in the sediments from a gold mine area in Southwest China and also assessed their pollution and ecological risks. During gold mining, As, Sb, Zn, and Cd in the sediments were affected, and their accumulation and chemical activity were relatively high. Gold mining is the main source of As, Sb, Zn and Cd accumulation in sediments (over 40.6%). Some influential factors cannot be ignored, i.e., water transport, local lithology, proportion of mild acido-soluble fraction (F1) and pH value. In addition, arsenic and most tested heavy metals have different pollution and ecological risks, especially As and Sb. Compared with the other gold mining areas, the arsenic and the heavy metal sediments in the area of this study have higher pollution and ecological risks. The results of this study show that the local government must monitor potential environmental hazards from As and Sb pollution to prevent their adverse effects on human beings. This study also provides suggestions on water protection in the same type of gold-mining areas.

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Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation, and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Both traditional machine learning algorithms and deep learning algorithms were investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at 3-m height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOGs) were selected as texture features and combined with the support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9, 85.9, 84.9, and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficientnet family and other state-of-art CNN models (VGG16, Resnet50, and Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47, 99.53, 99.39, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tool to detect rice seedling growth stages.

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The piezoelectric injection-system provides a reliable approach for precise small-quantity fuel injection due to its fast, dynamic response. Considering the nonlinearity of a piezoelectric actuator, the complete electro-mechanical-hydraulic model of the piezoelectric injector was established and verified experimentally, which showed that it could accurately predict the fuel injection quantity. The small-quantity fuel injection with different driving voltages, pulse widths, and rail pressures was analyzed. The effects of key structural parameters of the injector on the delivery, control-chamber pressure fluctuation, and small-quantity injection characteristics were studied. The results show that the linearity of the curve of the injection volume with the pulse width was relatively poor, and there was a significant inflection point when the piezoelectric injector worked in the small pulse width region (PW < 0.6 ms). The bypass valve significantly accelerated the establishment of the control-chamber pressure, reduced the pressure fluctuation in the chamber, shortened the closing delay and duration of the needle valve, and reduced the rate of the fuel-quantity change so that it provided a greater control margin for the pulse width over the same fuel volume change interval. Under the condition of a small-quantity fuel injection of 20 mm3, decreasing the inlet orifice diameter and increasing the outlet orifice diameter shortened the minimum control pulse width and fuel injection duration required for the injector injection, which is beneficial for multiple and small-quantity fuel injection. However, these behaviors reduce the control margin for the pulse width, especially in small pulse width regions.

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Registration of 3D lidar point clouds with optical images is critical in the combination of multisource data. Geometric misalignment originally exists in the pose data between lidar point clouds and optical images. To improve the accuracy of the initial pose and the applicability of the integration of 3D points and image data, we develop a simple but efficient registration method. We first extract point features from lidar point clouds and images: point features are extracted from single-frame lidar and point features are extracted from images using a classical Canny operator. The cost map is subsequently built based on Canny image edge detection. The optimization direction is guided by the cost map, where low cost represents the desired direction, and loss function is also considered to improve the robustness of the proposed method. Experiments show positive results.

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BACKGROUND: Long non-coding RNAs (lncRNAs) have been demonstrated to play critical roles in various diseases. Our bioinformatics analysis showed that lncRNA TNFα and heterogenous nuclear ribonucleoprotein L (hnRNPL) related immunoregulatory LincRNA (THRIL) may interact with miR-19a, which targets TNF-α. This study aimed to explore the role of THRIL, an enhancer of LPS-induced inflammatory, in sepsis. METHODS: Research subjects of the present study included 66 sepsis patients and 66 healthy volunteers. The expression levels of THRIL, miR-19a and TNF-α in plasma samples from these participants were determined by RT-qPCR. The interaction between THRIL and miR-19a was explored by performing overexpression experiments in human bronchial epithelial cells (HBEpCs). The roles of THRIL, miR-19a and TNF-α in regulating the apoptosis of HBEpCs were analyzed by cell apoptosis assay. RESULTS: We found that THRIL was upregulated in sepsis patients. THRIL is predicted to interact with miR-19a, and the interaction was confirmed by dual-luciferase activity assay. However, THRIL and miR-19a did not affect the expression of each other. Instead, overexpression of THRIL resulted in the increased expression levels of TNF-α, a downstream target of miR-19a in HBEpCs. In HBEpCs, LPS treatment induced the overexpression of THRIL. Cell apoptosis analysis showed that overexpression of THRIL and TNF-α promoted the apoptosis of HBEpCs induced by LPS, while overexpression of miR-19a played an opposite role. Overexpression of THRIL attenuated the effects of overexpression of miR-19a. CONCLUSION: Therefore, THRIL is upregulated in sepsis and may sponge miR-19a to upregulate TNF-α, thereby promoting lung cell apoptosis.

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Among the current indoor positioning technologies, Bluetooth low energy (BLE) has gained increasing attention. In particular, the traditional distance estimation derived from aggregate RSS and signal-attenuation models is generally unstable because of the complicated interference in indoor environments. To improve the adaptability and robustness of the BLE positioning system, we propose making full use of the three separate channels of BLE instead of their combination, which has generally been used before. In the first step, three signal-attenuation models are separately established for each BLE advertising channel in the offline phase, and a more stable distance in the online phase can be acquired by assembling measurements from all three channels with the distance decision strategy. Subsequently, a weighted trilateration method with uncertainties related to the distances derived in the first step is proposed to determine the user's optimal position. The test results demonstrate that our proposed algorithm for determining the distance error achieves a value of less than 2.2 m at 90%, while for the positioning error, it achieves a value of less than 2.4 m at 90%. Compared with the traditional methods, the positioning error of our method is reduced by 33% to 38% for different smartphones and scenarios.

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Cycle slip (CS) is a primary error source in Precise Point Positioning/Inertial Navigation System (PPP/INS) integrated systems. In this study, an INS-aided CS detection and repair method is presented. It utilizes high-precision INS information instead of a pseudorange to remove the satellite⁻receiver geometric range in the wide-lane (WL) and ionospheric-free (IF) phase combinations and creates an INS-aided WL (WL-INS) model and an INS-aided IF (IF-INS) model. Since INS information is superior to pseudorange, the INS-aided models have high detection accuracy. However, the effectiveness of INS-aided models cannot persist for a long time because of INS accumulation error. To overcome the disturbance of INS error, improved INS-aided models are proposed. This idea takes advantage of the long wavelength of WL combination and tries to fix WL CS. Once it succeeds, the INS error can be evaluated and removed. The proposed method was tested using land vehicle data, in which simulated cycle slips and signal interruption were introduced. The results show that this method can accurately detect and repair different cycle slips between the continuous Global Positioning System (GPS) epoch. When it comes to the cycle slip after a GPS interruption, the method can also accelerate PPP re-convergence, as it is not affected by the inertial accumulation error.

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Laser rangefinders (LRFs) are widely used in autonomous systems for indoor positioning and mobile mapping through the simultaneous localization and mapping (SLAM) approach. The extrinsic parameters of multiple LRFs need to be determined, and they are one of the key factors impacting system performance. This study presents an extrinsic calibration method of multiple LRFs that requires neither extra calibration sensors nor special artificial reference landmarks. Instead, it uses a naturally existing cuboid-shaped corridor as the calibration reference, and it hence needs no additional cost. The present method takes advantage of two types of geometric constraints for the calibration, which can be found in a common cuboid-shaped corridor. First, the corresponding point cloud is scanned by the set of LRFs. Second, the lines that are scanned on the corridor surfaces are extracted from the point cloud. Then, the lines within the same surface and the lines within two adjacent surfaces satisfy the coplanarity constraint and the orthogonality constraint, respectively. As such, the calibration problem is converted into a nonlinear optimization problem with the constraints. Simulation experiments and experiments based on real data verified the feasibility and stability of the proposed method.

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Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, a classical Wi-Fi-based indoor positioning method, consists of two phases: radio map learning and position inference. Thus far, the application of Bayesian fingerprinting positioning is limited due to its poor usability; radio map learning requires an adequate number of received signal strength indication (RSSI) observables at each reference point, long-term fieldwork, and high development and maintenance costs. In this paper, based on a statistical analysis of actual RSSI observables, a Weibull⁻Bayesian density model is proposed to represent the probability density of Wi-Fi RSSI observables. The Weibull model, which is parameterized with three parameters that can be calculated with fewer samples, can calculate the probability density with a higher accuracy than the traditional histogram method. Furthermore, the parameterized Weibull model can simplify the radio map by storing only three parameters that can restore the whole probability density, i.e., it is not necessary to store the probability distribution based on traditionally separated RSSI bins. Bayesian positioning inference is performed in the positioning phase using probability density rather than the traditional probability distribution of predefined RSSI bins. The proposed method was implemented on an Android smartphone, and the performance was evaluated in different indoor environments. Results revealed that the proposed method enhanced the usability of Wi-Fi Bayesian fingerprinting positioning by requiring fewer RSSI observables and improved the positioning accuracy by 19⁻32% in different building environments compared with the classic histogram-based method, even when more samples were used.

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Indoor positioning is in high demand in a variety of applications, and indoor environment is a challenging scene for visual positioning. This paper proposes an accurate visual positioning method for smartphones. The proposed method includes three procedures. First, an indoor high-precision 3D photorealistic map is produced using a mobile mapping system, and the intrinsic and extrinsic parameters of the images are obtained from the mapping result. A point cloud is calculated using feature matching and multi-view forward intersection. Second, top-K similar images are queried using hamming embedding with SIFT feature description. Feature matching and pose voting are used to select correctly matched image, and the relationship between image points and 3D points is obtained. Finally, outlier points are removed using P3P with the coarse focal length. Perspective-four-point with unknown focal length and random sample consensus are used to calculate the intrinsic and extrinsic parameters of the query image and then to obtain the positioning of the smartphone. Compared with established baseline methods, the proposed method is more accurate and reliable. The experiment results show that 70 percent of the images achieve location error smaller than 0.9 m in a 10 m × 15.8 m room, and the prospect of improvement is discussed.

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The development of Earth observation systems has changed the nature of survey and mapping products, as well as the methods for updating maps. Among optical satellite mapping methods, the multiline array stereo and agile stereo modes are the most common methods for acquiring stereo images. However, differences in temporal resolution and spatial coverage limit their application. In terms of this issue, our study takes advantage of the wide spatial coverage and high revisit frequencies of wide swath images and aims at verifying the feasibility of stereo mapping with the wide swath stereo mode and reaching a reliable stereo accuracy level using calibration. In contrast with classic stereo modes, the wide swath stereo mode is characterized by both a wide spatial coverage and high-temporal resolution and is capable of obtaining a wide range of stereo images over a short period. In this study, Gaofen-1 (GF-1) wide-field-view (WFV) images, with total imaging widths of 800 km, multispectral resolutions of 16 m and revisit periods of four days, are used for wide swath stereo mapping. To acquire a high-accuracy digital surface model (DSM), the nonlinear system distortion in the GF-1 WFV images is detected and compensated for in advance. The elevation accuracy of the wide swath stereo mode of the GF-1 WFV images can be improved from 103 m to 30 m for a DSM with proper calibration, meeting the demands for 1:250,000 scale mapping and rapid topographic map updates and showing improved efficacy for satellite imaging.

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BACKGROUND CXC chemokine ligand 16 (CXCL16) is a soluble chemokine with a transmembrane domain, playing an important role in inflammatory regulation. NF-κB has a critical role in tumor progression. Recent studies focused on the effect of CXCL16 on tumor progression. However, few reports showed the influence of CXCL16 on lung cancer, especially in regulating NF-κB activity. Here we investigated CXCL16 expression and its clinical significance in lung cancer, as well as the effect on lung cancer cell biological characteristics by regulating NF-κB. MATERIAL AND METHODS CXCL16 expression in lung cancer was detected and its associations with clinical characteristics were analyzed. Proliferation and invasion of A549 and PC-9 cells was measured before and after silencing CXCL16 or inhibiting the NF-κB pathway, separately. RESULTS The positive rate of CXCL16 in lung cancer tissue was significantly higher than that in adjacent tissue, and that in patients with lymphatic metastasis was significantly higher than that in patients without (all, P<0.05). The positive rate of CXCL16 was significantly (P<0.05) positively corrected with poor prognosis of lung cancer. Silencing CXCL16 not only suppressed proliferation and invasion of A549 and PC-9 cells, but also significantly (P<0.05) inhibited c-Rel, p105, and Rel-B in the NF-κB pathway. Inhibiting NF-κB also suppressed proliferation and invasion of A549 and PC-9 cells, which was similar to the results after silencing CXCL16. CONCLUSIONS Enhanced CXCL16 expression in lung cancer tissue promoted the proliferation and invasion of lung cancer cells. CXCL16 might promote proliferation and invasion of lung cancer by regulating the NF-κB pathway.

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A plasma-induced p-type MoS2 flake and n-type ZnO film diode, which exhibits an excellent rectification ratio, is demonstrated. Under 365 nm optical irradiation, this p-n diode shows a strong photoresponse with an external quantum efficiency of 52.7% and a response time of 66 ms. By increasing the pressure on the junction to 23 MPa, the photocurrent can be enhanced by a factor of four through the piezophototronic effect.

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In this work, by employing halogen elements (fluorine, chlorine, bromine, and iodine) as dopant we demonstrate a unique strategy to enhance the output performance of ZnO-based flexible piezoelectric nanogenerators. For a halogen-doped ZnO nanowire film, dopants and doping concentration dependent lattice strain along the ZnO c-axis are established and confirmed by the EDS, XRD, and HRTEM analysis. Although lattice strain induced charge separation was theoretically proposed, it has not been experimentally investigated for wurtzite structured ZnO nanomaterials. Tuning the lattice strain from compressive to tensile state along the ZnO c-axis can be achieved by a substitution of halogen dopant from fluorine to other halogen elements due to the ionic size difference between dopants and oxygen. With its focus on a group of nonmetal element induced lattice strain in ZnO-based nanomaterials, this work paves the way for enhancing the performance of wurtzite-type piezoelectric semiconductor nanomaterials via lattice strain strategy which can be employed to construct piezoelectric nanodevices with higher efficiency in a cost-effective manner.

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This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user's mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution.

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A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies-INS and LiDAR SLAM-into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform-NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment.

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Indoor positioning technology has become more and more important in the last two decades. Utilizing Received Signal Strength Indicator (RSSI) fingerprints of Signals of OPportunity (SOP) is a promising alternative navigation solution. However, as the RSSIs vary during operation due to their physical nature and are easily affected by the environmental change, one challenge of the indoor fingerprinting method is maintaining the RSSI fingerprint database in a timely and effective manner. In this paper, a solution for rapidly updating the fingerprint database is presented, based on a self-developed Unmanned Ground Vehicles (UGV) platform NAVIS. Several SOP sensors were installed on NAVIS for collecting indoor fingerprint information, including a digital compass collecting magnetic field intensity, a light sensor collecting light intensity, and a smartphone which collects the access point number and RSSIs of the pre-installed WiFi network. The NAVIS platform generates a map of the indoor environment and collects the SOPs during processing of the mapping, and then the SOP fingerprint database is interpolated and updated in real time. Field tests were carried out to evaluate the effectiveness and efficiency of the proposed method. The results showed that the fingerprint databases can be quickly created and updated with a higher sampling frequency (5Hz) and denser reference points compared with traditional methods, and the indoor map can be generated without prior information. Moreover, environmental changes could also be detected quickly for fingerprint indoor positioning.