Polarization-insensitive vortex beam generator by the holographic grating on an integrated multi-layer waveguide.

An integrated polarization-insensitive vortex beam generator is proposed in this study. It is composed of a holographic grating on a multi-layer waveguide, which enables conversion of Transverse Electric (TE) and Transverse Magnetic (TM) waveguide modes to y-polarized and x-polarized optical vortex beams, respectively. The conversion efficiency and the phase fidelity are numerically analyzed, and the working bandwidth is about 100 nm from 1500 nm to 1600 nm with a phase fidelity above 0.7. Moreover, the vortex beam with the superposition of the y-polarization and x-polarization states can be obtained with the incident of the superposition of TE and TM waveguide modes.

Association between Periodontitis and Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

INTRODUCTION: The connection between periodontitis and mild cognitive impairment (MCI) continues to receive attention. However, whether periodontitis is a risk factor for MCI remains still uncertain. This study aims to systematically analyze the available literature regarding the relationship between periodontitis and the risk of developing MCI and whether the periodontal health of MCI patients is poorer. METHODS: A literature search of PubMed, Scopus, Embase, and Web of Science databases was conducted to include all studies on the relationship between periodontitis and MCI from inception to April 2023. The studies were independently screened by 2 researchers, and those meeting the inclusion criteria were extracted and cross-checked. Pooled odds ratio (OR) or mean difference (MD) with 95% confidence intervals (CI) was calculated using either a fixed-effects or random-effects model. RESULTS: Seven studies with a total of 3,973 participants were included. Meta-analysis results showed a statistically significant higher incidence of MCI in patients with periodontitis (OR, 1.70 (95% CI: 1.24-2.32, p < 0.001) compared to healthy participants. A subgroup meta-analysis showed that the pooled OR for the risk of MCI in patients with severe periodontitis was 2.09 (95% CI: 1.49-2.92, p < 0.001). In addition, attachment loss (MD = 0.44, 95% CI: 0.12-0.75, p < 0.001) and plaque index (MD = 0.72, 95% CI: 0.50-0.93, p < 0.001) were higher in MCI patients compared with the control group, but the pocket probing depth (MD = 0.21, 95% CI: -0.08 to 0.49, p = 0.15) was not significantly different between the two groups. CONCLUSIONS: Patients with periodontitis are at a higher risk of developing MCI, and the periodontal health of MCI patients is generally compromised. However, further well-designed studies should be conducted to confirm this relationship between MCI and periodontitis.

Learning-Based Hierarchical Decision-Making Framework for Automatic Driving in Incompletely Connected Traffic Scenarios.

The decision-making algorithm serves as a fundamental component for advancing the level of autonomous driving. The end-to-end decision-making algorithm has a strong ability to process the original data, but it has grave uncertainty. However, other learning-based decision-making algorithms rely heavily on ideal state information and are entirely unsuitable for autonomous driving tasks in real-world scenarios with incomplete global information. Addressing this research gap, this paper proposes a stable hierarchical decision-making framework with images as the input. The first step of the framework is a model-based data encoder that converts the input image data into a fixed universal data format. Next is a state machine based on a time series Graph Convolutional Network (GCN), which is used to classify the current driving state. Finally, according to the state's classification, the corresponding rule-based algorithm is selected for action generation. Through verification, the algorithm demonstrates the ability to perform autonomous driving tasks in different traffic scenarios without relying on global network information. Comparative experiments further confirm the effectiveness of the hierarchical framework, model-based image data encoder, and time series GCN.

Detection of Sulfoquinovosidase Activity in Cell Lysates Using Activity-Based Probes.

The sulfolipid sulfoquinovosyl diacylglycerol (SQDG), produced by plants, algae, and cyanobacteria, constitutes a major sulfur reserve in the biosphere. Microbial breakdown of SQDG is critical for the biological utilization of its sulfur. This commences through release of the parent sugar, sulfoquinovose (SQ), catalyzed by sulfoquinovosidases (SQases). These vanguard enzymes are encoded in gene clusters that code for diverse SQ catabolic pathways. To identify, visualize and isolate glycoside hydrolase CAZY-family 31 (GH31) SQases in complex biological environments, we introduce SQ cyclophellitol-aziridine activity-based probes (ABPs). These ABPs label the active site nucleophile of this enzyme family, consistent with specific recognition of the SQ cyclophellitol-aziridine in the active site, as evidenced in the 3D structure of Bacillus megaterium SQase. A fluorescent Cy5-probe enables visualization of SQases in crude cell lysates from bacteria harbouring different SQ breakdown pathways, whilst a biotin-probe enables SQase capture and identification by proteomics. The Cy5-probe facilitates monitoring of active SQase levels during different stages of bacterial growth which show great contrast to more traditional mRNA analysis obtained by RT-qPCR. Given the importance of SQases in global sulfur cycling and in human microbiota, these SQase ABPs provide a new tool with which to study SQase occurrence, activity and stability.

Nanoscale Investigation of Bubble Nucleation and Boiling on Random Rough Surfaces.

Surface roughness is one of the significant factors affecting liquid-vapor phase change heat transfer. This paper explores the effect of surface roughness on bubble nucleation and boiling heat transfer, as well as the microscopic mechanism, by constructing random rough surfaces using molecular dynamics (MD) simulation. Bubbles randomly nucleate on a flat surface and tend to nucleate in pits on rough surfaces. The pits on the random rough surface gather more argon atoms than the protrusions, forming low potential energy regions on the surface, thus providing stable nucleation sites for bubbles. As the surface roughness increases, bubble generation, merging, and growth are advanced. In addition, rough surfaces offer a larger effective heat transfer area for the heat transfer process, increase the strength of solid-liquid coupling, and obtain smaller solid-liquid interaction energy. The critical heat flux (CHF) value positively correlates with surface roughness. As the roughness increases, the surface superheat at the onset of CHF decreases accordingly. This paper provides new insights into the mechanism of heat transfer enhancement on rough surfaces and surface design in thermal management.

The prediction of contact force networks in granular materials based on graph neural networks.

The contact force network, usually organized inhomogeneously by the inter-particle forces on the bases of the contact network topologies, is essential to the rigidity and stability in amorphous solids. How to capture such a "backbone" is crucial to the understanding of various anomalous properties or behaviors in those materials, which remains a central challenge presently in physics, engineering, or material science. Here, we use a novel graph neural network to predict the contact force network in two-dimensional granular materials under uniaxial compression. With the edge classification model in the framework of the deep graph library, we show that the inter-particle contact forces can be accurately estimated purely from the knowledge of the static microstructures, which can be acquired from a discrete element method or directly visualized from experimental methods. By testing the granular packings with different structural disorders and pressure, we further demonstrate the robustness of the optimized graph neural network to changes in various model parameters. Our research tries to provide a new way of extracting the information about the inter-particle forces, which substantially improves the efficiency and reduces the costs compared to the traditional experiments.

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends.

The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaborative decision-making technology for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) methods have become an efficient way in solving decision-making problems. However, with the development of computing technology, graph reinforcement learning (GRL) methods have gradually demonstrated the large potential to further improve the decision-making performance of CAVs, especially in the area of accurately representing the mutual effects of vehicles and modeling dynamic traffic environments. To facilitate the development of GRL-based methods for autonomous driving, this paper proposes a review of GRL-based methods for the decision-making technologies of CAVs. Firstly, a generic GRL framework is proposed in the beginning to gain an overall understanding of the decision-making technology. Then, the GRL-based decision-making technologies are reviewed from the perspective of the construction methods of mixed autonomy traffic, methods for graph representation of the driving environment, and related works about graph neural networks (GNN) and DRL in the field of decision-making for autonomous driving. Moreover, validation methods are summarized to provide an efficient way to verify the performance of decision-making methods. Finally, challenges and future research directions of GRL-based decision-making methods are summarized.

Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning.

As one of the main elements of reinforcement learning, the design of the reward function is often not given enough attention when reinforcement learning is used in concrete applications, which leads to unsatisfactory performances. In this study, a reward function matrix is proposed for training various decision-making modes with emphasis on decision-making styles and further emphasis on incentives and punishments. Additionally, we model a traffic scene via graph model to better represent the interaction between vehicles, and adopt the graph convolutional network (GCN) to extract the features of the graph structure to help the connected autonomous vehicles perform decision-making directly. Furthermore, we combine GCN with deep Q-learning and multi-step double deep Q-learning to train four decision-making modes, which are named the graph convolutional deep Q-network (GQN) and the multi-step double graph convolutional deep Q-network (MDGQN). In the simulation, the superiority of the reward function matrix is proved by comparing it with the baseline, and evaluation metrics are proposed to verify the performance differences among decision-making modes. Results show that the trained decision-making modes can satisfy various driving requirements, including task completion rate, safety requirements, comfort level, and completion efficiency, by adjusting the weight values in the reward function matrix. Finally, the decision-making modes trained by MDGQN had better performance in an uncertain highway exit scene than those trained by GQN.

Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving.

In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.

Catalytic Synthesis of 1H-2-Benzoxocins: Cobalt(III)-Carbene Radical Approach to 8-Membered Heterocyclic Enol Ethers.

The metallo-radical activation of ortho-allylcarbonyl-aryl N-arylsulfonylhydrazones with the paramagnetic cobalt(II) porphyrin catalyst [CoII(TPP)] (TPP = tetraphenylporphyrin) provides an efficient and powerful method for the synthesis of novel 8-membered heterocyclic enol ethers. The synthetic protocol is versatile and practical and enables the synthesis of a wide range of unique 1H-2-benzoxocins in high yields. The catalytic cyclization reactions proceed with excellent chemoselectivities, have a high functional group tolerance, and provide several opportunities for the synthesis of new bioactive compounds. The reactions are shown to proceed via cobalt(III)-carbene radical intermediates, which are involved in intramolecular hydrogen transfer (HAT) from the allylic position to the carbene radical, followed by a near-barrierless radical rebound step in the coordination sphere of cobalt. The proposed mechanism is supported by experimental observations, density functional theory (DFT) calculations, and spin trapping experiments.

Palladium-catalyzed cascade 5-exo-trig radical cyclization/aromatic C-H alkylation with unactivated alkyl iodides.

A novel and convenient palladium-catalyzed cascade 5-exo-trig radical cyclization/aromatic C-H alkylation with unactivated alkyl iodides has been described. This strategy provides an efficient access to a variety of 3a-methyl-1,2,3,3a,4,8b-hexahydroindeno[1,2-b]pyrrole derivatives, which facilitate access to a series of medically important heterocyclic bioactive molecules. This protocol involves mild catalytic reaction conditions and shows high functional group tolerance with high stereoselectivity. Mechanistic investigations reveal that an alkyl radical pathway is involved in this reaction.

Review on Vehicle Detection Technology for Unmanned Ground Vehicles.

Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology-vehicle detection-are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

Dairy Product Consumption and Bladder Cancer Risk: A Meta-Analysis.

To explore the potential relationship between dairy product consumption and bladder cancer risk, we retrieved eligible studies published up to March 15, 2018, via online database search and manual review of the selected articles. Summary relative risk (RR) estimates were calculated using random-effects models based on high to low intake values. Inter-study heterogeneity was explored using stratified analyses of study design, geographic region, or whether studies adjusted for the confounders age, sex, body mass index, smoking, and education level. We extracted data from 16 studies on milk (5,193 subjects) and 10 studies on dairy products (20,434 subjects). The total study population included 220,952 individuals. Dairy product intake and bladder cancer risk were significantly associated, and milk intake and bladder cancer risk more so. Stratified analysis revealed that the trend was more pronounced in case-control studies, and in studies with impact factor <3 and in Asia. The relationship was confirmed after adjusting for sex and Newcastle-Ottawa Scale score of 7 and 8. Our study shows an inverse association between milk consumption and bladder cancer risk.

Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array.

The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.

Pressure-Modulated Selective Electrokinetic Trapping for Direct Enrichment, Purification, and Detection of Nucleic Acids in Human Serum.

Micro total-analysis systems (µTAS) have been extensively developed for the detection of nucleic acids (NAs) in resource-limited settings in recent years, yet the sample-preparation steps that interface real-world samples with on-chip analytics remain as the technical bottleneck. We report pressure-modulated selective electrokinetic trapping (PM-SET) for the direct enrichment, purification, and detection of NAs in human serum in one step without involving tedious solid-phase extraction, chemical amplification, and surface-hybridization-based assays. Under appropriately modulated hydrostatic pressures, NAs in human serum were selectively enriched in an electrokinetic concentrator with the majority of background proteins removed, achieving an enrichment factor of >4800 in 15 min. A sequence-specific NA was detected simultaneously during the enrichment process using a complementary morpholino (MO) probe, realizing a limit of detection of 3 pM in 15 min. PM-SET greatly reduces the cost, time, and complexity of sample preparation for NA detection and could be easily interfaced with existing NA-detection devices to achieve true sample-to-answer biomolecular analytics.

Direct numerical simulation of continuous lithium extraction from high Mg2+/Li+ ratio brines using microfluidic channels with ion concentration polarization.

A novel ion concentration polarization-based microfluidic device is proposed for continuous extraction of Li+ from high Mg2+/Li+ ratio brines. With simultaneous application of the cross-channel voltage that drives electroosmotic flow and the cross-membrane voltage that induces ion depletion, Li+ is concentrated much more than other cations in front of the membrane in the microchannel. The application of external pressure produces a fluid flow that drags a portion of Li+ (and Na+) to flow through the microchannel, while keeping most of Mg2+ (and K+) blocked, thus implementing continuous Li+ extraction. Two-dimensional numerical simulation using a microchannel of 120 µm length and 4 µm height and a model, highly concentrated brine, shows that the system may produce a continuous flow rate of 1.72 mm/s, extracting 25.6% of Li+, with a Li+/Mg2+ flux ratio of 2.81×103, at a pressure of 100 Pa and cross-membrane voltage of 100 times of thermal voltages (25.8 mV). Fundamental mechanisms of the system are elaborated and effects of the cross-membrane voltage and the external pressure are analyzed. These results and findings provide clear guidance for the understanding and designing of microfluidic devices not only for Li+ extraction, but also for other ionic or molecular separations.

[3 + 2] Cycloaddition of Azaoxyallyl Cations with Cyclic Ketones: Access to Spiro-4-oxazolidinones.

The first formal [3 + 2] cycloaddition reaction of in situ generated azaoxyallyl cation with cyclic ketones has been developed using mild reaction conditions. A variety of spiro-4-oxazolidinones was obtained in excellent yields (up to 99%). The high efficiency of this process, coupled with the operational simplicity, makes it an attractive method for the synthesis of spiro-4-oxazolidinones.

Enantioselective Synthesis of Alkylthioetherpyrrolidine Derivatives via [3+2] Cycloaddition of α-Thioacrylates with Isocyanoacetates.

An unprecedented catalytic asymmetric method for the [3+2] cycloaddition of isocyanoacetates with α-thioacrylates/α-phthalimidoacrylates has been developed with excellent enantioselectivities. The generated pyrrolines could be readily further reduced to an array of structurally various and biologically important pyrrolidine derivatives. α-Tosyloxyacrylate with isocyanoacetates as well as tosylmethylisocyanide could be used to produce 2,4-disubstituted pyrroles.

Association between monocyte to lymphocyte ratio and diabetic foot ulcer in the population of the US with diabetes based on the 1999-2004 National Health and Nutrition Examination Survey data: a retrospective cross-sectional study.

Background: Diabetic foot ulcer (DFU) is a severe complication that occurs in patients with diabetes and is a primary factor that necessitates amputation. Therefore, the occurrence and progression of DFU must be predicted at an early stage to improve patient prognosis and outcomes. In this regard, emerging evidence suggests that inflammation-related markers play a significant role in DFU. One such potential marker, the monocyte-lymphocyte ratio (MLR), has not been extensively studied in relation to DFU. This study aimed to define a connection between MLR and DFU. Methods: A cross-sectional study was conducted using National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2004. DFU was defined based on survey questionnaires assessing the presence of nonhealing ulcers in the lower extremities for more than 4 weeks in diabetes patients. The MLR was calculated as the ratio of the monocyte count to the lymphocyte count, which was directly obtained from laboratory data files. Logistic regression analysis was performed to assess the relationship between the MLR and DFU. Stratified analysis according to age, sex, body mass index, blood glucose, hemoglobin, and glycated hemoglobin categories was conducted, and multiple imputations were applied to missing data. Results: In total, 1246 participants were included; the prevalence of DFU was 9.4% (117/1246). A multivariable regression model revealed a significant association between DFU and a 0.1 unit increase in MLR after adjusting for all covariates (adjusted odds ratio=1.16, 95% confidence interval: 1.02-1.33). Subgroup analyses revealed consistent findings regarding the impact of MLR on the presence of DFU (p > 0.05). Conclusion: MLR is significantly associated with DFU in diabetes patients, and can be used as one of the indicators for predicting the occurrence of DFU. MLR assessment may be a valuable component in the follow-up of patients with diabetes.

Microplastic separation and enrichment in microchannels under derivative electric field gradient by bipolar electrode reactions.

The decomposed plastic products in the natural environment evolve into tiny plastic particles with characteristics such as small size, lightweight, and difficulty in removal, resulting in a significant pollution issue in aquatic environments. Significant progress has been made in microplastic separation technology benefiting from microfluidic chips in recent years. Based on the mechanisms of microfluidic control technology, this study investigates the enrichment and separation mechanisms of polystyrene particles in an unbuffered solution. The Faraday reaction caused by the bipolar electrodes changes the electric field gradient and improves the separation efficiency. We also propose  an evaluation scheme to measure the separation efficiency. Finite element simulations are conducted to parametrically analyze the influence of applied voltages, channel geometry, and size of electrodes on plastic particle separation. The numerical cases indicate that the electrode-installed microfluidic channels separate microplastic particles effectively and precisely. The electrodes play an important role in local electric field distribution and trigger violent chemical reactions. By optimizing the microchannel structure, applied voltages, and separation channel angle, an optimal solution for separating microplastic particles can be found. This study could supply some references to control microplastic pollution in the future.