TPDC: Point Cloud Completion by Triangular Pyramid Features and Divide-and-Conquer in Complex Environments.

Point cloud completion recovers the complete point clouds from partial ones, providing numerous point cloud information for downstream tasks such as 3-D reconstruction and target detection. However, previous methods usually suffer from unstructured prediction of points in local regions and the discrete nature of the point cloud. To resolve these problems, we propose a point cloud completion network called TPDC. Representing the point cloud as a set of unordered features of points with local geometric information, we devise a Triangular Pyramid Extractor (TPE), using the simplest 3-D structure-a triangular pyramid-to convert the point cloud to a sequence of local geometric information. Our insight of revealing local geometric information in a complex environment is to design a Divide-and-Conquer Splitting Module in a Divide-and-Conquer Splitting Decoder (DCSD) to learn point-splitting patterns that can fit local regions the best. This module employs the Divide-and-Conquer approach to parallelly handle tasks related to fitting ground-truth values to base points and predicting the displacement of split points. This approach aims to make the base points align more closely with the ground-truth values while also forecasting the displacement of split points relative to the base points. Furthermore, we propose a more realistic and challenging benchmark, ShapeNetMask, with more random point cloud input, more complex random item occlusion, and more realistic random environmental perturbations. The results show that our method outperforms both widely used benchmarks as well as the new benchmark.

Efficacy of Random Forest Models in Predicting Multidrug-Resistant Gram-Negative Bacterial Nosocomial Infections Compared to Traditional Logistic Regression Models.

This study evaluates whether random forest (RF) models are as effective as traditional Logistic Regression (LR) models in predicting multidrug-resistant Gram-negative bacterial nosocomial infections. Data were collected from 541 patients with hospital-acquired Gram-negative bacterial infections at two tertiary-level hospitals in Urumqi, Xinjiang, China, from August 2022 to November 2023. Relevant literature informed the selection of significant predictors based on patients' pre-infection clinical information and medication history. The data were split into a training set of 379 cases and a validation set of 162 cases, adhering to a 7:3 ratio. Both RF and LR models were developed using the training set and subsequently evaluated on the validation set. The LR model achieved an accuracy of 84.57%, sensitivity of 82.89%, specificity of 80.10%, positive predictive value of 84%, negative predictive value of 85.06%, and a Yoden index of 0.69. In contrast, the RF model demonstrated superior performance with an accuracy of 89.51%, sensitivity of 90.79%, specificity of 88.37%, positive predictive value of 87.34%, negative predictive value of 91.57%, and a Yoden index of 0.79. Receiver operating characteristic curve analysis revealed an area under the curve of 0.91 for the LR model and 0.94 for the RF model. These findings indicate that the RF model surpasses the LR model in specificity, sensitivity, and accuracy in predicting hospital-acquired multidrug-resistant Gram-negative infections, showcasing its greater potential for clinical application.

Enhancing Stability and Performance in Mobile Robot Path Planning with PMR-Dueling DQN Algorithm.

Path planning for mobile robots in complex circumstances is still a challenging issue. This work introduces an improved deep reinforcement learning strategy for robot navigation that combines dueling architecture, Prioritized Experience Replay, and shaped Rewards. In a grid world and two Gazebo simulation environments with static and dynamic obstacles, the Dueling Deep Q-Network with Modified Rewards and Prioritized Experience Replay (PMR-Dueling DQN) algorithm is compared against Q-learning, DQN, and DDQN in terms of path optimality, collision avoidance, and learning speed. To encourage the best routes, the shaped Reward function takes into account target direction, obstacle avoidance, and distance. Prioritized replay concentrates training on important events while a dueling architecture separates value and advantage learning. The results show that the PMR-Dueling DQN has greatly increased convergence speed, stability, and overall performance across conditions. In both grid world and Gazebo environments the PMR-Dueling DQN achieved higher cumulative rewards. The combination of deep reinforcement learning with reward design, network architecture, and experience replay enables the PMR-Dueling DQN to surpass traditional approaches for robot path planning in complex environments.

Cyclodextrin Polymer-Loaded Micro-Ceramic Balls for Solid-Phase Extraction of Triazole Pesticides from Water.

A citric acid cross-linked ß-cyclodextrin (ß-CD) polymer was synthesized and loaded on micro-ceramic balls to fabricate the solid-phase adsorbents (P-MCB) for adsorption and extraction of triazole pesticides from water. The stability of ß-CD polymer and P-MCB was investigated in solutions with different pH values at different temperatures. The adsorption properties and the influence of kinetics, sorbent amount, pesticide concentration, and temperature on the adsorption capacity were evaluated. The results showed P-MCB had favorable adsorption of 15.98 mg/g flutriafol in 3.5 h. The equilibrium data followed the Freundlich equation, and the adsorption of flutriafol and diniconazole followed the second-order kinetics. The recovery rate of P-MCB for triazole pesticides in water was satisfactory, and the recovery rate was still 80.1%, even at the 10th cycle. The P-MCB had good stability, with a degradation rate of 0.2% ± 0.08 within 10 days, which could ensure extraction and recycling.

CircPSMC3 Inhibits Colorectal Cancer Cell Growth and Migration by Controlling miR-31-5p/YAP/ß-Catenin.

INTRODUCTION: Colorectal cancer (CRC) is hackneyed cancer and a major lethiferous cancer. Circular RNAs (CircRNAs) have been discovered to own important roles in controlling CRC progression. CircPSMC3 is known to exhibit lower expression in diversified cancers. However, the regulatory function of CircPSMC3 in CRC keeps unclear. METHODS: The expression of CircPSMC3 and miR-31-5p was confirmed through RT-qPCR. The cell proliferation was measured through CCK-8 and EdU assays. The protein expression of genes was examined through a western blot. The cell invasion and migration were tested through Transwell and wound healing assays. The binding ability between CircPSMC3 and miR-31-5p was confirmed through the luciferase reporter assay. RESULTS: CircPSMC3 exhibited lower expression in CRC tissues and cell lines. Additionally, CircPSMC3 was revealed to suppress cell proliferation in CRC. Moreover, through Transwell and wound healing assays, CircPSMC3 was discovered to repress CRC cell invasion and migration. In CRC tissues, miR-31-5p expression was up-regulated and negatively correlated with CircPSMC3 expression. Further mechanism exploration experiments disclosed that CircPSMC3 is bound with miR-31-5p to modulate the YAP/ß-catenin axis in CRC. At last, through rescue assays, CircPSMC3 inhibited cell proliferation, invasion and migration through sponging miR-31-5p in CRC. CONCLUSION: Our work was the first time to probe the potential regulatory effects of CircPSMC3 in CRC, and these above results uncovered that CircPSMC3 inhibited CRC cell growth and migration through regulating miR-31-5p/YAP/ß-catenin. This discovery hinted that CircPSMC3 may serve as a useful therapeutic candidate for CRC.

BASeg: Boundary aware semantic segmentation for autonomous driving.

Semantic segmentation is a critical component for street understanding task in autonomous driving field. Existing various methods either focus on constructing the object's inner consistency by aggregating global or multi-scale context information, or simply combine semantic features with boundary features to refine object details. Despite impressive, most of them neglect the long-range dependences between the inner objects and boundaries. To this end, we present a Boundary Aware Network (BASeg) for semantic segmentation by exploiting boundary information as a significant cue to guide context aggregation. Specifically, a Boundary Refined Module (BRM) is proposed in the BASeg to refine coarse low-level boundary features from a Canny detector by high-level multi-scale semantic features from the backbone, and based on which, the Context Aggregation Module (CAM) is further proposed to capture long-range dependences between the boundary regions and the object inner pixels, achieving mutual gains and enhancing the intra-class consistency. Moreover, our method can be plugged into other CNN backbones for higher performance with a minor computation budget, and obtains 45.72%, 81.2%, and 77.3% of mIoU on the datasets ADE20K, Cityscapes, and CamVid, respectively. Compared with some state-of-the-art ResNet101-based segmentation methods, extensive experiments demonstrate the effectiveness of our method. Our code is available at https://github.com/Lature-Yang/BASeg.

An Optimization-Based Motion Planner for Car-like Logistics Robots on Narrow Roads.

Thanks to their strong maneuverability and high load capacity, car-like robots with non-holonomic constraints are often used in logistics to improve efficiency. However, it is difficult to plan a safe and smooth optimal path in real time on the restricted narrow roads of the logistics park. To solve this problem, an optimization-based motion planning method inspired by the Timed-Elastic-Band algorithm is proposed, called Narrow-Roads-Timed-Elastic-Band (NRTEB). Three optimization modules are added to the inner and outer workflow of the Timed-Elastic-Band framework. The simulation results show that the proposed method achieves safe reversing planning on narrow roads while the jerk of the trajectory is reduced by 72.11% compared to the original method. Real-world experiments reveal that the proposed method safely and smoothly avoids dynamic obstacles in real time when navigating forward and backward. The motion planner provides a safer and smoother trajectory for car-like robots on narrow roads in real time, which greatly enhances the safety, robustness and reliability of the Timed-Elastic-Band planner in logistics parks.

Polyphenylene Sulfide Ultrafine Viscous Fibrous Membrane Modified by ZIF-8 for Highly Effective Oil/Water Separation under High Salt or Alkaline Conditions.

The oil/water separation in harsh environments has always been a challenging topic all over the world. In this study, the ZIF-8/PPS fiber membranes were fabricated via the combination of hot pressing and in situ growth. The distribution of ZIF-8 in the membranes was adjusted by changing the ZIF-8 in situ growth time, which could control the oil/water separation effect. Due to the hydrophilic nature of the ZIF-8/PPS fiber membranes, the water molecules in the oil-in-water emulsion could quickly penetrate into the fiber membrane under the drive of pressure, gravity, and capillary force, forming a water layer on the surface of the fiber membranes. The coupling of the water layer and the fiber structure prevented direct contact between the oil molecules and the fiber membrane, thereby realizing the separation of the emulsion. The results show that when the ZIF-8 in situ growth time was 10 h, the contact angle, the porosity, and the pure water flux of the ZIF-8/PPS fiber membranes were 72.5°, 52.3%, and 12,351 L/h·m2, respectively. More importantly, the separation efficiency of M10 was 97%, and the oil/water separation efficiency reached 95% after 14 cycles. This study provides a novel strategy for preparing MOFs/fiber materials for oil/water separation in harsh environments.

How hindrance stress, proactive personality, and the employment relationship atmosphere affect employees' innovative behavior.

Hindrance stress is a stimulus factor in the workplace that has a certain impact on the innovative behavior of employees. Most existing studies focus on the analysis of individual-level factors, ignoring the important role of organizational-level factors. This study uses multiple linear models to empirically analyze the interaction mechanisms among hindrance stress, proactive personality, employment relationship atmosphere, and employee innovative behavior factors in the workplace. This study found the following: (1) Hindrance stress negatively affects employees' innovative behavior. (2) A proactive personality positively affects employees' innovative behavior. (3) A proactive personality plays a moderating role in the relationship between hindrance stress and employees' innovative behavior. (4) The employment relationship atmosphere has a positive impact on employees' innovative behavior. (5) The employment relationship atmosphere plays a moderating role in the relationship between hindrance stress and employees' innovative behavior. This study enriches theoretical knowledge in the field of human resources and provides guidance for business managers on the effective encouragement of employees' innovative behavior.

Surgical outcomes associated with sleep apnea syndrome in Stanford A aortic dissection patients.

BACKGROUND: Patients suffering from aortic dissection (AD) often experience sleep apnea syndrome (SAS), which aggravates their respiratory function and aortic false lumen expansion. METHODS: We analyzed the peri-operative data of Stanford A AD patients, with or without SAS, between January 2017 and June 2019. Subjects were separated into SAS positive (SAS+) and SAS negative (SAS-) cohorts, based on the Apnea-Hypopnea Index (AHI) and the Oxygen Desaturation Index (ODI). We next analyzed variables between the SAS+ and SAS- groups. RESULTS: 155, out of 198 AAD patients, were enlisted for this study. SAS+ patients exhibited higher rates of pneumonia (p < 0.001), heart failure (HF, p = 0.038), acute kidney injury (AKI, p = 0.001), ventilation time (p = 0.009), and hospitalization duration (p < 0.001). According to subsequent follow-ups, the unstented aorta false lumen dilatation (FLD) rate increased markedly, with increasing degree of SAS (p < 0.001, according to AHI and ODI). The SAS+ patients exhibited worse cumulative survival rate (p = 0.025). The significant risk factors (RF) for poor survival were: severe (p = 0.002) or moderate SAS (p = 0.008), prolonged ventilation time (p = 0.018), AKI (p = 0.015), HF New York Heart Association (NYHA) IV (p = 0.005) or III (p = 0.015), pneumonia (p = 0.005), Marfan syndrome (p = 0.010), systolic blood pressure (BP) upon arrival (p = 0.009), and BMI ≥ 30 (p = 0.004). CONCLUSIONS: SAS+ Stanford A AD patients primarily exhibited higher rates of complications and low survival rates in the mid-time follow-up. Hence, the RFs associated with poor survival must be monitored carefully in SAS patients. Moreover, the FLD rate is related to the degree of SAS, thus treating SAS may mitigate FLD.

Myocardial injury and inflammatory response in percutaneous device closures of pediatric patent ductus arteriosus.

BACKGROUND: The percutaneous device closure of patent ductus arteriosus (PDA) is widely used in clinical practice, however full data on the changes in myocardial injury and systemic inflammatory markers' levels after PDA in children are not fully reported. METHODS: We have conducted a retrospective analysis of the medical records of 385 pediatric patients in our hospital from January 2017 to December 2019. The patients were distributed into five groups. The first four (A, B, C and D) included patients divided by the type of the surgical closure methods, namely ligation, clamping, ligation-combined suturing and ligation-combined clamping, respectively. The fifth group E comprised of percutaneous device PDA patients. All recorded medical and trial data from the five groups were statistically studied. RESULTS: No serious complications in the patients regardless of the classification group were reported. Our results suggested that there were no considerable differences between the groups at the baseline (with all P > 0.05). Group E demonstrated a significantly smaller operative time (42.39 ± 3.88, min) and length of hospital stay (LOS) (4.49 ± 0.50, day), less intraoperative blood loss (7.12 ± 2.09, ml) while on the other hand, a higher total hospital cost (24,001.35 ± 1152.80, RMB) than the other four groups (with all P < 0.001). Interestingly, the comparison of the inflammatory factors such as white blood cells (WBC) count, C-reactive protein (CRP), procalcitonin (PCT) and interleukin-6 (IL-6), as well as the myocardial injury markers (CKMB and troponin I) did not show a significant increase (P > 0.05) among the four groups. On the contrary, when the aforementioned factors and markers of all the surgical groups were compared to those in group E, we observed significantly higher speed and magnitude of changes in group E than those in groups A, B, C, and D (with all P < 0.001). CONCLUSION: Although the percutaneous device closure of PDA is more comforting and drives fast recuperation in comparison to conventional surgery, it provokes myocardial injury and overall inflammation. Timely substantial and aggressive intervention measures such as the use of antibiotics before operation and active glucocorticoids to suppress inflammation and nourish the myocardium need be applied if the myocardial and inflammatory markers are eminent.

Intelligent Bus Platoon Lateral and Longitudinal Control Method Based on Finite-Time Sliding Mode.

Considering the rapid convergence of the longitudinal and lateral tracking errors of the platoon, a finite-time tracking control method for the longitudinal and lateral directions of the intelligent bus platoon is proposed. Based on the bus platoon model and desired motion trajectory, a distributed longitudinal and lateral finite-time sliding mode tracking control framework of the platoon is designed. Considering the finite-time convergence of the sliding mode of the system, a nonsingular integral terminal sliding mode (NITSM) is designed. An adaptive power integral reaching law (APIRL) is proposed for the finite-time accessibility of the system approaching mode. Based on NITSM-APIRL, a distributed longitudinal and lateral finite-time sliding mode tracking controller for the bus platoon is designed, and a Lyapunov function is created to analyze the finite-time stability and string stability of the system. Based on the Trucksim/Simulink joint simulation experiment platform, the control performance of the method is contrasted with the existing methods, and the actual vehicle test verification is completed by relying on the National Intelligent Connected Vehicle testing zone, which proves the practicability of the method.

A Collision Relationship-Based Driving Behavior Decision-Making Method for an Intelligent Land Vehicle at a Disorderly Intersection via DRQN.

An intelligent land vehicle utilizes onboard sensors to acquire observed states at a disorderly intersection. However, partial observation of the environment occurs due to sensor noise. This causes decision failure easily. A collision relationship-based driving behavior decision-making method via deep recurrent Q network (CR-DRQN) is proposed for intelligent land vehicles. First, the collision relationship between the intelligent land vehicle and surrounding vehicles is designed as the input. The collision relationship is extracted from the observed states with the sensor noise. This avoids a CR-DRQN dimension explosion and speeds up the network training. Then, DRQN is utilized to attenuate the impact of the input noise and achieve driving behavior decision-making. Finally, some comparative experiments are conducted to verify the effectiveness of the proposed method. CR-DRQN maintains a high decision success rate at a disorderly intersection with partially observable states. In addition, the proposed method is outstanding in the aspects of safety, the ability of collision risk prediction, and comfort.

An Abdominal Registration Technology for Integration of Nanomaterial Imaging-Aided Diagnosis and Treatment.

Image registration technology is a key technology used in the process of nanomaterial imaging-aided diagnosis and targeted therapy effect monitoring for abdominal diseases. Recently, the deep-learning based methods have been increasingly used for large-scale medical image registration, because their iteration is much less than those of traditional ones. In this paper, a coarse-to-fine unsupervised learning-based three-dimensional (3D) abdominal CT image registration method is presented. Firstly, an affine transformation was used as an initial step to deal with large deformation between two images. Secondly, an unsupervised total loss function containing similarity, smoothness, and topology preservation measures was proposed to achieve better registration performances during convolutional neural network (CNN) training and testing. The experimental results demonstrated that the proposed method severally obtains the average MSE, PSNR, and SSIM values of 0.0055, 22.7950, and 0.8241, which outperformed some existing traditional and unsupervised learning-based methods. Moreover, our method can register 3D abdominal CT images with shortest time and is expected to become a real-time method for clinical application.

3D Fast Object Detection Based on Discriminant Images and Dynamic Distance Threshold Clustering.

The object detection algorithm based on vehicle-mounted lidar is a key component of the perception system on autonomous vehicles. It can provide high-precision and highly robust obstacle information for the safe driving of autonomous vehicles. However, most algorithms are often based on a large amount of point cloud data, which makes real-time detection difficult. To solve this problem, this paper proposes a 3D fast object detection method based on three main steps: First, the ground segmentation by discriminant image (GSDI) method is used to convert point cloud data into discriminant images for ground points segmentation, which avoids the direct computing of the point cloud data and improves the efficiency of ground points segmentation. Second, the image detector is used to generate the region of interest of the three-dimensional object, which effectively narrows the search range. Finally, the dynamic distance threshold clustering (DDTC) method is designed for different density of the point cloud data, which improves the detection effect of long-distance objects and avoids the over-segmentation phenomenon generated by the traditional algorithm. Experiments have showed that this algorithm can meet the real-time requirements of autonomous driving while maintaining high accuracy.

Impact of meteorological factors on the occurrence of acute aortic dissection in Fujian Province, China: a single-center seven-year retrospective study.

BACKGROUND: The aim of this study was to investigate the correlation between meteorological factors and the occurrence of acute aortic dissection (AAD) in Fujian Province, China. METHODS: The clinical data of 2004 patients diagnosed with AAD in our hospital and the relevant local meteorological data from January 2013 to November 2019 were retrospectively analyzed. RESULTS: The incidence of AAD had a clear tendency toward concentration, and the corresponding peak in terms of the occurrence date was from January 13 to 14. The average minimum temperature, the average maximum temperature, and the average daily temperature differences on the "day with AAD" were significantly lower than those on the "day without AAD". From 5 days to 3 days before AAD onset, the average daily temperature difference showed a downward trend, but statistical analysis showed that the average minimum, average maximum and average daily temperature differences were not significantly different from the values 5 days to 0 days before AAD onset. CONCLUSIONS: The incidence of AAD is related to the season and month. The lowest average temperature may increase the incidence of AAD in patients with complicated cardiovascular diseases.

Comparative Study between Surgical Repair of Atrial Septal Defect via Median Sternotomy, Right Submammary Thoracotomy, and Right Vertical Infra-Axillary Thoracotomy.

OBJECTIVE: To compare the results of surgical repair via median sternotomy, right submammary thoracotomy, and right vertical infra-axillary thoracotomy for atrial septal defect (ASD). METHODS: This is a retrospective analysis of the relative perioperative and postoperative data of 136 patients who underwent surgical repair for ASD with the abovementioned three different treatments in our hospital from June 2014 to December 2017. RESULTS: The results of the surgeries were all satisfactory in the three groups. No statistically significant difference was found in operative time, duration of cardiopulmonary bypass, blood transfusion amount, postoperative mechanical ventilation time, duration of intensive care unit, length of hospital stay, and hospital costs. However, the median sternotomy group had the longest incision. Meanwhile, there was no significant difference in postoperative complications. CONCLUSION: All three types of surgical incisions can be safely and effectively used to repair ASD. The treatments via right submammary thoracotomy and right vertical infra-axillary thoracotomy have advantages over the treatment via median sternotomy in cosmetic results and should be the recommended options.

Liver segmentation from abdominal CT volumes based on level set and sparse shape composition.

BACKGROUND AND OBJECTIVE: Liver segmentation from abdominal CT volumes is a primary step for computer-aided surgery and liver disease diagnosis. However, accurate liver segmentation remains a challenging task for intensity inhomogeneity and serious pathologies occurring in liver CT volume. This paper presents a novel framework for accurate liver segmentation from CT images. METHODS: Firstly, a novel level set integrated with intensity bias and position constraint is applied, and for normal liver, the generated liver regions are regarded as the final results. Then, for pathological liver, a sparse shape composition (SSC)-based method is presented to refine liver shapes, followed by an improved graph cut to further optimize segmentation results. The level set-based method is capable of overcoming intensity inhomogeneity in object regions, and the SSC- and graph cut-based strategy has outstanding power to address under-segmentation appearing in pathological livers. RESULTS: The experiments conducted on public databases SLIVER07 and 3Dircadb show that the proposed method can segment both healthy and pathological liver effectively. The segmentation performance in terms of mean ASD, RMSD, MSD, VOE and RVD on SLIVER07 are 0.9mm, 1.8mm, 19.4mm, 5.1% and 0.1%, respectively, and on 3Dircadb are 1.6mm, 3.1mm, 27.2mm, 9.2% and 0.5%, respectively, which outperforms many existing methods. CONCLUSIONS: The proposed method does not require complex training procedure on numerous liver samples, and has satisfying and robust segmentation performance on both normal and pathological liver in various shapes.

Comparative study between surgical repair of atrial septal defect via median sternotomy, right submammary thoracotomy, and right vertical infra-axillary thoracotomy

Abstract Objective: To compare the results of surgical repair via median sternotomy, right submammary thoracotomy, and right vertical infra-axillary thoracotomy for atrial septal defect (ASD). Methods: This is a retrospective analysis of the relative perioperative and postoperative data of 136 patients who underwent surgical repair for ASD with the abovementioned three different treatments in our hospital from June 2014 to December 2017. Results: The results of the surgeries were all satisfactory in the three groups. No statistically significant difference was found in operative time, duration of cardiopulmonary bypass, blood transfusion amount, postoperative mechanical ventilation time, duration of intensive care unit, length of hospital stay, and hospital costs. However, the median sternotomy group had the longest incision. Meanwhile, there was no significant difference in postoperative complications. Conclusion: All three types of surgical incisions can be safely and effectively used to repair ASD. The treatments via right submammary thoracotomy and right vertical infra-axillary thoracotomy have advantages over the treatment via median sternotomy in cosmetic results and should be the recommended options.