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Global navigation satellite system (GNSS) positioning accuracy is degraded in urban canyons due to signal blockages and reflections, which is still a major challenge. Recently, using machine learning to improve the accuracy of GNSS positioning in urban areas has become a new trend. This article summarizes the works focused on GNSS multipath/non-light-of-sight (NLOS) mitigation using machine learning. The review of the studies is categorized based on the input features, algorithms, and outputs. The categorization shows that the received signal strength, elevation angle, and receiver correlator outputs from a single channel of satellite signal are the most popular input features. For the algorithm selection, the support vector machine and fully connected neural network are the algorithms most widely used. In terms of the outputs, most of the works made improvements in measurement status prediction, namely, LOS, multipath, and NLOS. Besides, this article also provides an open-source dataset with four scenarios for machine learning algorithms for the GNSS multipath/NLOS mitigation. Finally, the benchmarks are established based on the proposed dataset and the FCNN and least-squares estimation to enable performance evaluation in Kaggle.

National action plans enumerate many interventions as potential strategies to reduce the burden of bacterial antimicrobial resistance (AMR). However, knowledge of the benefits achievable by specific approaches is needed to inform policy making, especially in low-income and middle-income countries (LMICs) with substantial AMR burden and low health-care system capacity. In a modelling analysis, we estimated that improving infection prevention and control programmes in LMIC health-care settings could prevent at least 337 000 (95% CI 250 200–465 200) AMR-associated deaths annually. Ensuring universal access to high-quality water, sanitation, and hygiene services would prevent 247 800 (160 000–337 800) AMR-associated deaths and paediatric vaccines 181 500 (153 400–206 800) AMR-associated deaths, from both direct prevention of resistant infections and reductions in antibiotic consumption. These estimates translate to prevention of 7·8% (5·6–11·0) of all AMR-associated mortality in LMICs by infection prevention and control, 5·7% (3·7–8·0) by water, sanitation, and hygiene, and 4·2% (3·4–5·1) by vaccination interventions. Despite the continuing need for research and innovation to overcome limitations of existing approaches, our findings indicate that reducing global AMR burden by 10% by the year 2030 is achievable with existing interventions. Our results should guide investments in public health interventions with the greatest potential to reduce AMR burden.

This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops (VTLs) is illustrated by theoretical analysis and numerical simulations, and the following findings are obtained. Firstly, the initial user state bias leads to steady navigation solution bias in the relative VTL, while new measurements can eliminate it in the absolute VTL. Secondly, the initial code phase bias is transferred to the following navigation solutions in the relative VTL, while new measurements can eliminate it in the absolute VTL. Thirdly, the user state bias induced by erroneous navigation solution of VTLs can be eliminated by both of the two VTLs. Fourthly, the multipath/NLOS likely affects the two VTLs, and the induced tracking bias in the duration of the multipath/NLOS would decrease the performance of VTLs. Based on the above analysis, a robust VTL structure is proposed, where the absolute VTL is selected for its robustness to the two kinds of initialization biases; meanwhile, the instant bias detection and correction method is used to improve the performance of VTLs in the duration of the multipath/NLOS. Numerical simulations and experimental results verify the effectiveness of the proposed robust VTL structure.

Dynamic object detection from point clouds has been widely studied in recent years to achieve accurate and robust LiDAR odometry for autonomous driving. Satisfactory accuracy can be achieved by dynamic object detection from point clouds has been widely studied in recent years to achieve accurate and robust LiDAR odometry for autonomous driving. Satisfactory accuracy can be achieved by detecting and removing the object points in the urban environment. However, it is still not clear how dynamic objects numerically affect the performance of LiDAR odometry. In addition, the existing solutions tended to directly remove the LiDAR features belonging to the dynamic object, which can lead to the degradation of the geometry constraints of the surrounding features. This paper aims to give answers to these problems by evaluating the effects of dynamic objects as well as reweighting both dynamic objects and static objects. Three factors affecting the performance of LiDAR odometry in highly dynamic scenarios, including the number, geometry distribution, and velocity of the dynamic objects, are first extensively studied using generated scenarios by leveraging real data. Instead of brutely removing the dynamic features, this paper proposes to adaptively assign weightings to the dynamic features. Then both the dynamic and static features are employed to estimate the LiDAR odometry. The effectiveness of the proposed method is verified using UrbanNav and nuScenes datasets that include numerous dynamic and static objects. To benefit the community, the implementation of the dynamic vehicle simulator and the code for the proposed method are both open-sourced.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Navigating to a destination using smartphones has recently become a primary way in people’s daily lives. Positioning is the prerequisite condition before efficient path planning can be achieved. In urban environments, people can self-localize with the aid of the global navigation satellite system (GNSS). However, last-mile navigation is still a challenge for pedestrians as current indoor positioning techniques cannot provide an accurate position. For the past few years, many researchers in academia and industry have thrown themselves into smartphone-based indoor positioning. Various solutions have been proposed to enable accurate and reliable position solutions. Pedestrian dead reckoning (PDR) is a popular positioning method as it is self-contained and outperforms inertial navigation. Also, other information from sensor data features and infrastructures, such as radio frequency (RF), can ensure the positioning service. At present, there are various indoor positioning reviews available to summarize current techniques. However, they focus primarily on separate positioning methods rather than integration. This article aims to comprehensively review PDR-centric indoor positioning using smartphones. It covers the basic concept of PDR and its integration with other information, examining both infrastructure-free and infrastructure-dependent approaches. Furthermore, this article outlines key considerations for conducting a comprehensive comparative analysis of the algorithm. Finally, the challenges and future research trends are given. This article can help readers understand the features of different PDR-centric integrations. It also contributes to the design of more robust solutions to satisfy the ever-increasing demand for indoor positioning.

A comprehensive understanding of welding penetration and the role of process parameters is crucial for ensuring high-quality joints in laser welding. In-situ process monitoring can aid in detection of defects, reducing material usage and time-consuming inspection operations. In this study, we propose a novel approach for online prediction of keyhole depth in laser welding operations. Using in-process images captured with a coaxial camera and active illumination, our software employs pre-trained CNNs from the EfficientNet and DenseNet families to extract features. These features serve as input for data-efficient regression models, trained to predict the keyhole depth. The results have shown that both methods yield percentage errors of approximately 3%. Ultimately, this methodology facilitates real-time analysis of welding operations.

The weld bead is crucial in identifying defects during laser welding, aiding in the evaluation of weld strength and quality. However, accurately monitoring and identifying defects with cameras can be challenging in the preassembly phase. This paper compares surface topography of laser weld beads using in-process Optical Coherence Tomography (OCT) and post-process Focus Variation Microscopy (FVM). Both techniques were employed on welded A36 structural steel plates to detect the surface defects. The weld profile was evaluated according to EN ISO 13919-1, and the quality level B was given to the surface topography measurement from both techniques. The correspondence between these two methodologies is imperative for implementing profile data in advanced monitoring systems. Thus, by reducing the delay between defect formation and inspection, this paper supports a more intelligent welding process, particularly in the shipbuilding industry.

Accurate positioning from the global navigation satellite system (GNSS) is critical for various civil applications, such as location-based services (LBSs) and intelligent transportation systems (ITSs). GNSS Doppler frequency can provide reliable velocity estimation to improve the positioning performance. Unfortunately, the quality of Doppler frequency measurements can be significantly degraded in urban canyons. This is due to the non-line-of-sight (NLOS) receptions altering the incoming signal direction and its dynamic characteristics. Thus, correcting the NLOS error on Doppler frequency is essential for the velocity as well as position estimation in urban canyons. The 3-D mapping-aided (3DMA) GNSS is a promising technique for NLOS error correction. Its effectiveness on pseudorange measurements has been well proven. However, its feasibility on Doppler frequency correction has not been investigated yet. Therefore, this article first verifies the feasibility of ray tracing in modeling Doppler frequency. Then, an urban Doppler frequency assessment is conducted. Finally, the effectiveness of ray tracing in correcting velocity estimation accuracy is evaluated. The assessment and evaluation assessment are conducted via experiments in both open-sky and urban areas. The results demonstrate that ray tracing has an excellent capability in modeling the NLOS Doppler frequency, which reduces the corresponding measurement error by 62.8% in average and the root-mean-square (RMS) of velocity estimation error by 51.92%.

The global navigation satellite system (GNSS) PPP-RTK technique, which integrates the superiorities of precise point positioning (PPP) and real-time kinematic (RTK), is increasingly attracting people's attention in recent years. This article proposes a factor graph optimization (FGO) based GNSS PPP-RTK method to provide an alternative platform to study urban GNSS precise positioning. Instead of a batch FGO estimator, the proposed method uses the sliding-window method to meet the real-time requirement. Pseudorange, carrier-phase measurements, atmospheric corrections at each epoch, and Doppler velocity and atmospheric variations between the adjacent epochs are integrated to estimate receiver positions. We evaluated the performance of the proposed method by carrying out static and kinematic experiments in different scenarios. Two static experiments based on the data from a base station in an open-sky environment and a low-cost GNSS receiver in a typical urban environment can both obtain centimeter-level positioning results. Two kinematic experiments using a low-cost GNSS receiver experiment in two complex urban canyons in Hong Kong achieve the mean horizontal positioning error of the real-time results of 1.606 m and 4.055 m, respectively. Compared with the existing classic PPP-RTK methods based on Kalman filters, the proposed method improves the availability of the positioning result. It mitigates in part large positioning errors, ensuring that the maximum horizontal positioning error is less than 10 m in complex urban environments.

Accurate and reliable positioning is of great importance for the realization of intelligent vehicles (IV). Factor graph optimization (FGO) has been popularized in the field of robotics for state estimation. As a ubiquitous sensor, the IMU is widely used for vehicular positioning based on the preintegration theory using FGO. However, the existing preintegration model fails to consider the earth's rotation, which challenges the attitude and positioning performance of vehicles equipped with high-precision inertial measurement units (IMU). To fill this gap, an accurate IMU factor that accounts for the earth's rotation rate is designed in this article. First, an exact preintegration measurement model is derived based on a high-precision inertial navigation system (INS) mechanization. The proposed factor is more accurate and it enables the application of the FGO more suitable for inertial-based integrated navigation with different precision IMUs. In both simulation and field tests, the INS/ Global Navigation Satellite System (GNSS) integration, as a representative of inertial-based integrated navigation, is used to verify the performance of the proposed preintegration factor. The navigation results using our experimental car reveal that the proposed model leads to a more accurate estimation, outperforming the traditional preintegration models, especially in the aspect of attitude estimation.

GNSS/INS/LiDAR-based Multi-Sensor Fusion (MSF) systems facilitate the efficient integration of multiple navigation sensors to deliver stable and dependable positioning outcomes in Autonomous Vehicles (AVs). Generally, AVs are bound to operate in diverse scenarios. However, state-of-the-art spoofing attack algorithms can identify vulnerable periods of MSF systems and execute aggressive GNSS spoofing in certain specific scenarios with high success rates. The present study proposes a security defense algorithm for MSF systems based on an enhanced Kalman filter, incorporating the Measurement Variance Monitoring (MVM) and Minimum Constraint of Covariance Matrix (MCCM) model. MVM effectively constrains the impact of GNSS signals with large positioning errors, thereby mitigating the adverse effects caused by spoofing attacks. MCCM fully leverages LiDAR information between two consecutive GNSS signals to enhance the credibility of LiDAR measurements, thus improving the positioning correction capability. Furthermore, considering the diverse application environments of AVs, this paper primarily focuses on sensor uncertainty variation and validates different models in varying scenarios. Ultimately, real-world data demonstrate the effectiveness of the proposed anti-spoofing model, which can significantly mitigate positioning errors caused by GNSS spoofing attacks without affecting navigation accuracy.

Accurate and reliable localization is of great importance for autonomous vehicles (AV). Mainstream localization approaches in autonomous vehicles (AV) are limited by the reliability of onboard sensors, which could be vulnerable to sensor failure, such as signal outages of the camera and signal spoofing of the global navigation satellite systems (GNSS). Different from these active or passive sensors, the vehicle dynamic model (VDM), which is the application of physical laws to a vehicle in motion, is environmentally independent and is capable of providing vehicle motion estimation continuously. However, the performance of the VDM-based motion estimation is dominated by the accuracy of the system dynamics model. To tackle this issue, this study proposes a sensor-free localization method VDM-SI by integrating system identification into the design of vehicle dynamic models (VDM). A system identification process based on low-order process models is proposed to identify the system dynamics of the AV, where the identified system responses are taken as the control input of VDM to estimate the vehicular positioning. The localization experiments in two scenarios show that the mean absolute translation error of VDM-SI can be reduced by 70% compared to conventional VDM methods. In addition, VDM-SI is experimentally proven to improve the localization performance of sensor fusion-based localization systems with high noise levels. Furthermore, in the application of re-localization after sensors fail and recover, VDM-SI shows strength in enhancing the security of AVs in extreme conditions.

Leveraging multiple sensors enhances complex environmental perception and increases resilience to varying luminance conditions and high-speed motion patterns, achieving precise localization and mapping. This paper proposes, ECMD, an event-centric multisensory dataset containing 81 sequences and covering over 200 km of various challenging driving scenarios including high-speed motion, repetitive scenarios, dynamic objects, etc. ECMD provides data from two sets of stereo event cameras with different resolutions (640×480, 346×260), stereo industrial cameras, an infrared camera, a top-installed mechanical LiDAR with two slanted LiDARs, two consumer-level GNSS receivers, and an onboard IMU. Meanwhile, the ground-truth of the vehicle was obtained using a centimeter-level high-accuracy GNSS-RTK/INS navigation system. All sensors are well-calibrated and temporally synchronized at the hardware level, with recording data simultaneously. We additionally evaluate several state-of-the-art SLAM algorithms for benchmarking visual and LiDAR SLAM and identifying their limitations.

Intelligent vehicles demand reliable, continuous, and accurate positioning capability. Light Detection and Ranging (LiDAR)-inertial odometry (LIO) provides precise continuous relative pose estimation but suffers from drift over large-scale operations. Global navigation satellite system (GNSS) offers drift-free absolute positioning capability but the accuracy is strongly affected by non-line-of-sight (NLOS) receptions and multipath effects arising from the reflections of the surrounding environments. The tightly-coupled integration of the GNSS and LIO is highly complementary. However, how to fully utilize the complementariness of LiDAR measurements and the GNSS model is still an open question. More importantly, an open-sourced implementation of their integration is highly expected. To fill these gaps, this article proposes the GLIO, a GNSS/LiDAR/IMU integrated estimator that tightly fuses GNSS pseudorange, Doppler, LiDAR, and IMU measurements using factor graph optimization (FGO). In particular, the corrections from the reference station are adopted to remove the systematic errors involved in the GNSS pseudorange measurements. To fully exploit the complementariness of the LiDAR and GNSS measurements, two stages of the optimization scheme are utilized to achieve global consistent and continuous pose estimation. In the first stage of optimization, the sliding-window-based FGO is employed to integrate the GNSS-related factors, IMU pre-integration factor, and scan-to-map-based LiDAR factor for efficient odometry estimation. In the second stage of optimization, the LiDAR factor is employed as a scan-to-multiscan scheme to maintain global consistency and improve the robustness of the system to the GNSS outlier by large-scale batch optimization. We evaluate the proposed method and verify its effectiveness through the challenge dataset UrbanNav involving highly urbanized areas. The proposed system achieves more than 70% improvement in positioning accuracy compared with the traditional GNSS positioning method and LIO standalone system.

The global navigation satellite system (GNSS) is one of the most popular solutions to localize potential road cracks. Unfortunately, the accurate positioning of the GNSS in urban environments presents a significant challenge due to complex signal blockage and reflection phenomena. To tackle this, we propose a method that enhances GNSS positioning accuracy, particularly suited for the intricate layouts of urban canyons. Our approach integrates the prior map with lane line information into the factor graph optimization (FGO) algorithm, effectively mitigating the impacts of multipath effects and non-line-of-sight (NLOS). However, the poor accuracy of the initial guess from the GNSS positioning can easily lead to incorrect lane matching which is one of the main challenges of lane matching in highly urbanized areas. To fill this gap, this article proposes to use the switchable factor to model the potential incorrect lane matching by leveraging the redundancy of lane information across multiepochs. This article verified the effectiveness of this methodology using two datasets from the dense urban environment of Hong Kong, collected using the low-cost automobile level receiver, and compared the results with conventional methods. Our findings affirm that integrating the FGO-based GNSS positioning system with map information significantly boosts positioning accuracy, demonstrating the robustness of our approach.

Recent urbanization has posed challenges for the global navigation satellite system (GNSS) to provide accurate navigation solutions. This is especially true in GNSS-denied environments, where the clear line of sight (LOS) path between the satellites and receiver is lacking. For such environments, fusion-based techniques relying on external sensors and/or other signals are widely used. However, such external sensors and signals may not be feasible and/or cost-effective every time. To overcome these limitations, this work proposes a system that makes explicit use of past available measurements, under certain assumptions, to generate new synthetic measurements. For this purpose, two functions are proposed in this work: a geometrically decaying series and a linear combination of past measurements. To enhance the overall performance of the system, an inertial measurement unit (IMU) is used as an additional measurement source in the extended Kalman filter (EKF). In addition, an approach to adapt the noise co-variances that support the generation of synthetic measurements is proposed. Furthermore, we derive the optimal gain under specific assumptions for a concrete theoretical understanding of the proposed algorithm. The proposed algorithm is tested and validated through two real-world datasets collected in Hong Kong, one corresponding to a moving vehicle inside a significantly long sea tunnel and another set in a harsh urban area, involving complex trajectories. A detailed analysis of the results has been performed with respect to all the aforementioned contributions. Additionally, the proposed algorithm has been compared with other existing algorithms. Experimental results show that a mean error of about 4 m is attained inside the tunnel, while it is around 4.6 m for the second scenario set in a harsh urban environment.

Abstract: α-Fe2O3 nanowires (NWs) were formed by the thermal oxidation of an iron film in air at 350 °C for 10 h. The rhombohedral structure of the α-Fe2O3 NWs was grown vertically on the substrate with diameters of 8–25 nm and lengths of several hundred nm. It was found that the population density of the NWs per unit area (D NWs) can be varied by the film thickness. The thicker the iron film, the more NWs were grown. The growth mechanism of the NWs is suggested to be a combination effect of the thermal oxidation rate, defects on the film, and selective directional growth. The electrical resistivity of a single NW with a length of 800 nm and a diameter of 15 nm was measured to be 4.42 × 103 Ωcm using conductive atomic force microscopy. The field emission characteristics of the NWs were studied using a two-parallel-plate system. A low turn–on field of 3.3 V/μm and a large current density of 10−3 A/cm2 (under an applied field of about 7 V/μm) can be obtained using optimal factors of D NWs in the cathode.

Many existing works use external devices or information, e.g., real-world maps, reference receivers, dead reckoning (DR) sensors, and multiantennas, to compensate for signal dynamics and mitigate multipath interference in global navigation satellite system (GNSS) signal reception. This article proposes a standalone global positioning system L5 receiver using the fractional Fourier transform (FRFT) in an over-one-second superlong coherent integration (S-LCI) correlating process. Then, a code phase DR method is presented in the baseband processing by leveraging the super-resolution average Doppler rate and instantaneous Doppler frequency from the S-LCI-based FRFT to enhance the code discriminating process. A software-defined radio prototype of the unassisted super-resolution receiver (U-SRR) based on the S-LCI, FRFT, and code phase DR is developed where the pilot-channel L5 signal serves for positioning. The transmitting time splicing comes from the respective frame synchronization and code phase tracking in the data and pilot channels. Real-world experiments verify the proposed U-SRR in an extremely dense urban area. Compared to the legacy L1 coarse/acquisition (C/A) signals from the u-blox ZED-F9P receiver, the code-based positioning precision is increased by 86.8% with 100% measurement availability.

In this article, we report a wearable magnetic-reluctance-based wireless pressure sensing module to measure intraoral pressure for clinical orthodontic treatment. The sensing module consists of two parts: first part is a transmitting/receiving electromagnet (EM $_{\mathrm {T}}$ ) which is connected to a reference electromagnet (EM $_{\mathrm {R}}$ ); second part is a high-permeable sensing diaphragm embedded into a bi-layered wearable orthodontic aligner. These electromagnets are placed outside of the mouth, while the aligner (consisting of the sensing diaphragm) is placed inside of the mouth. An ac current is applied to the transmitting electromagnet to produce ac magnetic flux, consequently by the electromagnetic induction, an ac voltage is induced in the receiving electromagnet. When people use cheek muscle (i.e., stress the cheek muscle), pressure in the oral cavity is changed. Consequently, a pressure difference is formed to deflect the sensing diaphragm toward or backward the EMT. Due to this, the magnetic reluctance between the sensing diaphragm and the EMT is changed. Through the electromagnetic induction, voltage output is induced in the receiving electromagnet. By analyzing the voltage output, we can know the change of the pressure. Based on above principle, we fabricated above parts and conducted pressure sensing test. According to the test results, the sensing module successfully senses the pressure ranging from 0 to 30 kPa with a sensitivity of 3.52 to $0.82 \mu \text{V}$ /kPa, when the EMT is placed 7–13 mm away from the sensing module respectively. This achieves the wearable wireless intraoral pressure sensing and consequently provides useful monitoring information for clinical orthodontic treatment.

The inherent vulnerability of the Global Navigation Satellite System (GNSS) leads to the ease of implementation of spoofing attacks. The latest GNSS spoofing attack schemes still suffer from low success rate, long attack time, and poor concealment. To improve the success rate, an efficient GNSS spoofing attack method for a vehicle-mounted Multi Sensors Fusion (MSF) system is proposed based on the scenario classification models with a spatial database. Firstly, the influence of the two typical urban scenarios, which are 1) the road with buildings on both sides and 2) tunnels, on the GNSS spoofing attack is analyzed. Then a dynamic Bayesian network model considering the sky visibility generated with the 3D building models and tunnel models inside the spatial database is established to quantify the difficulty of the attack. Furthermore, the scenarios of the victim can be classified into high-risk and low-risk scenarios. When the vehicle is just out of the tunnel or in open scenarios, attackers can select these high-risk scenarios and implement aggressive spoofing attacks. Then the efficiency of the GNSS spoofing attack can be significantly improved. Finally, the proposed attack scheme is demonstrated by actual world data with simulated spoofing attacks in urban areas.

Purpose: With the high penetration rate of the Internet and the prevalence of social networking sites (SNSs), the negative emotions caused by stressors on SNS have become an important issue worthy of attention. This study explored the relationship between SNS stressors, negative emotions and prosocial behavior, by employing negative emotions as the mediation role and social support as the moderator. Design/methodology/approach: The samples of this study were Facebook users who have used it constantly in the past six months. An online survey was conducted, from which 547 responses were completed, and 525 were valid, for an effective recovery ratio of 95.98%. Findings: Employing the structural equation model (SEM), the findings show that five paths were supported with a good fit. Negative emotions (anxiety and loneliness) have a moderating effect. Originality/value: This study contributes to the literature by factors divided by negative emotions of anxiety and loneliness from SNS stressors’ perspectives. In addition, this study provides additional support the moderating effect of social support of anxiety on prosocial behavior.

Light detection and ranging (LiDAR) can provide continuous and stable pose estimation with the model of normal distribution transform (NDT), which is widely used in autonomous vehicles (AVs), even under adverse weather conditions. However, there are few studies about the influence of inclement weather on LiDAR positioning results. In this article, different weather scenarios (rain, fog, and snow) are composed of synthetic LiDAR datasets based on state-of-the-art weather simulators. Then, the impacts of different adverse weather conditions are quantitatively evaluated in terms of positioning accuracy and uncertainty. Afterward, we perform the first study to qualitatively analyze the relationship between meteorological weather standards and LiDAR positioning performances, which is significant but unexplored. Evaluated results indicate that NDT matching performance will deteriorate in adverse weather conditions, especially when the meteorological level is “Heavy” or “Violent,” threatening the AVs’ positioning security seriously. Therefore, the results of this article provide more basis for the realization of high-precision positioning in adverse weather conditions, to ensure the positioning safety of AVs.

With the global emergence of drug-resistant bacteria causing difficult-to-treat infections, there is an urgent need for a tool to facilitate studies on key virulence and antimicrobial resistant factors. Mass spectrometry (MS) has contributed substantially to the elucidation of the structure-function relationships of lipid A, the endotoxic component of lipopolysaccharide which also serves as an important protective barrier against antimicrobials. Here, we present LipidA-IDER, an automated structure annotation tool for system-level scale identification of lipid A from high-resolution tandem mass spectrometry (MS2) data. LipidA-IDER was validated against previously reported structures of lipid A in the reference bacteria, Escherichia coliand Pseudomonas aeruginosa. Using MS2data of variable quality, we demonstrated LipidA-IDER annotated lipid A with a performance of 71.2% specificity and 70.9% sensitivity, offering greater accuracy than existing lipidomics software. The organism-independent workflow was further applied to a panel of six bacterial species: E. coliand Gram-negative members of ESKAPE pathogens. A comprehensive atlas comprising 188 distinct lipid A species, including remodeling intermediates, was generated and can be integrated with software including MS-DIAL and Metabokit for identification and semiquantitation. Systematic comparison of a pair of polymyxin-sensitive and polymyxin-resistant Acinetobacter baumanniiisolated from a human patient unraveled multiple key lipid A structural features of polymyxin resistance within a single analysis. Probing the lipid A landscape of bacteria using LipidA-IDER thus holds immense potential for advancing our understanding of the vast diversity and structural complexity of a key lipid virulence and antimicrobial-resistant factor. LipidA-IDER is freely available at https://github.com/Systems-Biology-Of-Lipid-Metabolism-Lab/LipidA-IDER.

This article presents a modified carrier-aided dual-frequency vectorized tracking (CA-DFVT) algorithm for Navigation with Indian Constellation (NavIC) receiver. In signal blockage and interference scenarios, even the most advanced receiver architecture may fail to provide accurate positioning solutions. Therefore, this article proposes an improved CA-DFVT architecture for NavIC signals. The architecture is based on the vector delay frequency lock loop (VDFLL), wherein precise carrier phase measurements from NavIC $S$ and unambiguous code phase measurements from the $L5$ signal are integrated to form new measurements. These measurements are improved by the novel vector tracking (VT) approach introduced in this article. The approach adopts the Savitzky–Golay filtering technique to improve the integrated measurements without deforming the actual measurements. Furthermore, the improved CA-DFVT receiver architecture includes a bidirectional algorithm called the Rauch–Tung–Striebel (RTS). This process involves a forward estimation extended Kalman filter (EKF) and a backward recursion smoother. In addition, this article also introduces VT for CA-DFVT based on an unscented Kalman filter (UKF). Simulations and hardware implementation are carried out to evaluate the effectiveness of the proposed algorithm under conditions of signal blockage and interference. Experiments demonstrate that the improved CA-DFVT can achieve better position accuracy than the single-frequency $L5/S$ VT, CA-DFVT-EKF, and CA-DFVT-UKF approaches. The mean position accuracy of improved CA-DFVT improves by at least 2–6 and 1–2 m for signal blockage and interference conditions, respectively.

Determining how the flow structure affects the heat transfer of a staggered cylindrical array was one of the objectives of this numerical study. The major flow patterns and the corresponding characteristics of heat transfer were investigated, including Shear Layer Reattachment (SLR), Induced Separation (IS), Vortex Pairing and Enveloping (VPE), Vortex Impingement (VI), and Synchronized Vortex Shedding (SVS). As the stagger angle is larger than 35°, the mean Nusselt numbers of array systems are larger than that of a single cylinder. The magnitude of Nu/Cd is suggested as a parameter to measure the efficiency of the heat transfer for an array system.

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

The climate characteristics of remote rainfall events in Taiwan from September to February over 41 years (1980–2020) are studied. These events are induced by the interaction between the northeasterly flow and the typhoon's outer circulation. Our findings reveal that rainfall in northeastern Taiwan becomes more prominent when tropical cyclones move to the remote rainfall‐prone area, located in the north Philippine area to the northern South China Sea, and when the background northeasterly wind speed exceeds 7 m s−1. Under these criteria, the confluence of the typhoon's outer circulation and the northeasterly flow creates a convergence area that enhances rainfall in northeastern Taiwan, increasing the occurrence of moderate to extreme rainfall (ER) events. This leads to an average enhancement in rainfall amount of 80–220 mm per day. Additionally, when typhoons are in the remote rainfall‐prone area, there is a greater than 20% chance for events with maximum rainfall over 200 mm day−1to occur, particularly in the region of 20°–22°N, 116°−124°E, and north to Luzon Island. In this area, the occurrence rate can exceed more than a 45% chance. The highest risk of ER events occurs between 20°–22°N and 118°−120°E, with a probability of over 90%. Notably, the convergence area for the Taiwan cases does not necessarily coincide with the baroclinic forcing as that associated with remote rainfall events observed in Japan, Korea, and North America. We studied a particular pattern of rainfall in Taiwan that occurs between September and February over a 41‐year period (1980–2020). Our research found that when tropical cyclones (TCs) are far from Taiwan and there is a strong northeasterly wind, it causes more rainfall in northeastern Taiwan. This can lead to moderate to extreme rainfall, with an increase in rainfall of 80–220 mm per day. When tropical storms are in a specific area north of Luzon Island, there is a higher chance of heavy rain, with some places receiving more than 200 mm per day. The highest probability of extreme rain events occurs when TCs are in a specific area between 20°–22°N and 118°−120°E, where the chance of extreme rain in Taiwan is over 90%. It happens because the TC circulation meets with the northeasterly flow and creates a convergence area. It's important to note that the way remote rainfall events happen in Taiwan may not be the same as in Japan, Korea, and North America. Northeastern Taiwan experiences heavy rainfall as tropical cyclone (TC) move toward North South China Sea while northeasterly flow >7 m/sTyphoons' outer flow and the northeasterly convergence enhanced remote rainfall events to 80–220 mm/dayAbove 45% of events with max daily rainfall over 200 mm occur when TCs are in the 20°–22°N, 116°–124°E area and north to Luzon Island Northeastern Taiwan experiences heavy rainfall as tropical cyclone (TC) move toward North South China Sea while northeasterly flow >7 m/s Typhoons' outer flow and the northeasterly convergence enhanced remote rainfall events to 80–220 mm/day Above 45% of events with max daily rainfall over 200 mm occur when TCs are in the 20°–22°N, 116°–124°E area and north to Luzon Island

AbstractWe estimated the annual bed days lost and economic burden of healthcare-associated infections to Singapore hospitals using Monte Carlo simulation. The mean (standard deviation) cost of a single healthcare-associated infection was S$1,809 (S$440) [or US$1,362 (US$331)]. This translated to annual lost bed days and economic burden of 55,978 (20,506) days and S$152.0 million (S$37.1 million) [or US$114.4 million (US$27.9 million)], respectively.