One-Pot Hydrothermal-Derived rGO/MXene/Sulfur Composite Aerogels as Free-Standing Cathodes in Lithium-Sulfur Batteries.

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.

Exploring the mechanisms of organic fertilizers on Cd bioavailability in rice fields: Environmental behavior and effect factors.

The problem of paddy Cadmium (Cd) contamination is currently the focus of global research. Earlier researches have confirmed that utilization of organic fertilizers regulates Cd chemical fraction distribution by increases organic bound Cd. However, environmental behaviours of organic fertilizers in paddy are still lack exploration. Here, we critical reviewed previous publications and proposed a novel research concept to help us better understand it. Three potential impact pathways of utilization of organic fertilizers on the bioavailability of Cd are presented: (i) use of organic fertilizers changes soil physicochemical properties, which directly affects Cd bioavailability by changing chemical form of Cd(II); (ii) use of organic fertilizers increases soil nutrient content, which indirectly regulates Cd supply and bioaccumulation through ion adsorption and competition for ion-transport channels between nutrients and Cd; and (iii) use of organic fertilizers increases activity of microorganisms and efflux of rice root exudates, which indirectly affects Cd bioavailability of through complexation and sequestration of these organic materials with Cd. Meanwhile, dissolved organic matter (DOM) in the rhizosphere of rice is believed to be the key to revealing the effects of organic fertilizers on Cd. DOM is capable of adsorption and complexation-chelation reactions with Cd and the fractionation of Cd(II) is regulated by DOM. Molecular mass, chemical composition, major functional groups and reaction sequence of DOM determine the formation and solubilization of DOM-Cd complexes.

Effects of Habitual Dietary Change on the Gut Microbiota and Health of Silkworms.

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.

Simple and efficient registration of 3D point cloud and image data for an indoor mobile mapping system.

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.

A Robust Indoor Positioning Method based on Bluetooth Low Energy with Separate Channel Information.

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.

Effective Cycle Slip Detection and Repair for PPP/INS Integrated Systems.

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.

High CXC Chemokine Ligand 16 (CXCL16) Expression Promotes Proliferation and Metastasis of Lung Cancer via Regulating the NF-κB Pathway.

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.

Wide Swath Stereo Mapping from Gaofen-1 Wide-Field-View (WFV) Images Using Calibration.

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.

Accurate Smartphone Indoor Visual Positioning Based on a High-Precision 3D Photorealistic Map.

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.

Extrinsic Calibration of 2D Laser Rangefinders Using an Existing Cuboid-Shaped Corridor as the Reference.

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.

A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability.

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.

Inferring Human Activity in Mobile Devices by Computing Multiple Contexts.

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.

LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments.

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.

Fast fingerprint database maintenance for indoor positioning based on UGV SLAM.

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.

Influence of Zr Microalloying on the Microstructure and Room-/High-Temperature Mechanical Properties of an Al-Cu-Mn-Fe Alloy.

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.

Human behavior cognition using smartphone sensors.

This research focuses on sensing context, modeling human behavior and developing a new architecture for a cognitive phone platform. We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior. Contexts in this research are abstracted as a Context Pyramid which includes six levels: Raw Sensor Data, Physical Parameter, Features/Patterns, Simple Contextual Descriptors, Activity-Level Descriptors, and Rich Context. To achieve implementation of the Context Pyramid on a cognitive phone, three key technologies are utilized: ubiquitous positioning, motion recognition, and human behavior modeling. Preliminary tests indicate that we have successfully achieved the Activity-Level Descriptors level with our LoMoCo (Location-Motion-Context) model. Location accuracy of the proposed solution is up to 1.9 meters in corridor environments and 3.5 meters in open spaces. Test results also indicate that the motion states are recognized with an accuracy rate up to 92.9% using a Least Square-Support Vector Machine (LS-SVM) classifier.

Arsenic and Heavy Metals in Sediments Affected by Typical Gold Mining Areas in Southwest China: Accumulation, Sources and Ecological Risks.

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.

A hybrid smartphone indoor positioning solution for mobile LBS.

Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user's motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.

iParking: an intelligent indoor location-based smartphone parking service.

Indoor positioning technologies have been widely studied with a number of solutions being proposed, yet substantial applications and services are still fairly primitive. Taking advantage of the emerging concept of the connected car, the popularity of smartphones and mobile Internet, and precise indoor locations, this study presents the development of a novel intelligent parking service called iParking. With the iParking service, multiple parties such as users, parking facilities and service providers are connected through Internet in a distributed architecture. The client software is a light-weight application running on a smartphone, and it works essentially based on a precise indoor positioning solution, which fuses Wireless Local Area Network (WLAN) signals and the measurements of the built-in sensors of the smartphones. The positioning accuracy, availability and reliability of the proposed positioning solution are adequate for facilitating the novel parking service. An iParking prototype has been developed and demonstrated in a real parking environment at a shopping mall. The demonstration showed how the iParking service could improve the parking experience and increase the efficiency of parking facilities. The iParking is a novel service in terms of cost- and energy-efficient solution.

Using LS-SVM based motion recognition for smartphone indoor wireless positioning.

The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in "Static Tests" and a 3.53 m in "Stop-Go Tests".