Observation of strong attenuation within the photonic band gap of multiconnected networks

We theoretically and experimentally study a photonic band gap (PBG) material made of coaxial cables. The coaxial cables are waveguides for the electromagnetic waves and provide paths for direct wave interference within the material. Using multiconnected coaxial cables to form a unit cell, we realize PBGs via (i) direct interference between the waveguides within each cell and (ii) scattering among different cells. We systematically investigate the transmission of EM waves in our PBG materials and discuss the mechanism of band gap formation. We observe experimentally for the first time the wide band gap with strong attenuation caused by direct destructive interference

Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development

Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making. However, their application in 3D lane detection for effective driving environment perception is hindered by the lack of comprehensive LiDAR datasets. The sparse nature of LiDAR point cloud data prevents an efficient manual annotation process. To solve this problem, we present LiSV-3DLane, a large-scale 3D lane dataset that comprises 20k frames of surround-view LiDAR point clouds with enriched semantic annotation. Unlike existing datasets confined to a frontal perspective, LiSV-3DLane provides a full 360-degree spatial panorama around the ego vehicle, capturing complex lane patterns in both urban and highway environments. We leverage the geometric traits of lane lines and the intrinsic spatial attributes of LiDAR data to design a simple yet effective automatic annotation pipeline for generating finer lane labels. To propel future research, we propose a novel LiDAR-based 3D lane detection model, LiLaDet, incorporating the spatial geometry learning of the LiDAR point cloud into Bird's Eye View (BEV) based lane identification. Experimental results indicate that LiLaDet outperforms existing camera- and LiDAR-based approaches in the 3D lane detection task on the K-Lane dataset and our LiSV-3DLane.Comment: 7 pages, 6 figure

On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still unclear to the public. Robustness is of particular concern in responsible AI, especially for safety-critical applications. In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective. To do so, we employ the AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart review and DDXPlus medical diagnosis datasets for OOD evaluation. We select several popular foundation models as baselines. Results show that ChatGPT shows consistent advantages on most adversarial and OOD classification and translation tasks. However, the absolute performance is far from perfection, which suggests that adversarial and OOD robustness remains a significant threat to foundation models. Moreover, ChatGPT shows astounding performance in understanding dialogue-related texts and we find that it tends to provide informal suggestions for medical tasks instead of definitive answers. Finally, we present in-depth discussions of possible research directions.Comment: Technical report; code is at: https://github.com/microsoft/robustlear

Real Time Planning Algorithm of Automatic Train Driving Based on Global Optimization

Objective The existing target speed planning algorithm for the automatic driving of urban rail transit train cannot handle in real time the temporary change of the line speed limit in case of emergency due to the large computational load and long computation time. With regard to the problem, a real-time target speed planning algorithm based on global optimization is proposed to generate the speed planning curve in real time. Method Based on the conditions of train current position, current speed and speed limit of the forward line, the speed planning curve is firstly generated in the shortest time through point by point calculation. Then the traction and braking levels are being adjusted to keep the train running at uniform acceleration or deceleration, optimizing the comfort index of the train running. Next, the cruising speed in the maximum speed limit section is adjusted to reduce unnecessary traction and braking time, optimizing the energy consumption index of the train running. Finally, the train speed planning curve is output. Result & Conclusion The simulation results show that the real-time speed planning curve generated by the proposed algorithm satisfies the basic constraints of safety, punctuality and accurate parking. Compared with the traditional algorithm, it improves the comfort level and reduces the energy consumption during train operation. Meanwhile, the proposed algorithm can effectively handle the temporary change of the line speed limit in emergency and optimize several operation indicators

Pilot Study on the Deep Treatment of Sulfuric-Acid–Titanium-Dioxide Wastewater Using an Ultrafiltration/Reverse Osmosis Process

The production of titanium dioxide via the sulfuric acid process generates large amounts of acidic wastewater. Investigating the possibility of reusing this wastewater after deep treatment can reduce pollutant discharge and conserve water resources. In a pilot study, a dual-membrane method of ultrafiltration (UF) and reverse osmosis (RO) was employed to perform deep treatments of sulfuric-acid–titanium-dioxide wastewater. The findings showed that the multimedia and precision filters reduced the turbidity of water from an external drainage to as low as 0.18 NTU, with a turbidity removal rate of approximately 50%, reaching a maximum of 68%. When the UF effluent had a membrane flux of 70–100 L/m2 h and a water production rate of 85–90%, the SDI15 was 95%, a CODCr removal rate of 85%, and a desalination rate of >98.5%. At a smooth operation system water recovery rate of 50%, the highest system recovery rate obtained was 64%. The water produced via RO adhered to reuse water standards. UF/RO deep treatment of sulfuric-acid–titanium-dioxide production wastewater and its reuse can realize comprehensive wastewater use and conserve water resources

The Fabrication of Calcium Alginate Beads as a Green Sorbent for Selective Recovery of Cu(Ⅱ) from Metal Mixtures

Calcium alginate (CA) beads as a green sorbent were easily fabricated in this study using sodium alginate crosslinking with CaCl2, and the crosslinking pathway was the exchange between the sodium ion of α-L-guluronic acid and Ca(II). The experimental study was conducted on Cu(II), Cd(II), Ni(II) and Zn(II) as the model heavy metals and the concentration was determined by inductively coupled plasma optical emission spectrometry (ICP-OES). The characterization and sorption behavior of the CA beads were analyzed in detail via using scanning electron microscopy (SEM), fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The adsorption experiments demonstrated that the CA beads exhibited a high removal efficiency for the selective adsorption of Cu(II) from the tetra metallic mixture solution and an excellent adsorption capacity of the heavy metals separately. According to the isotherm studies, the maximum uptake of Cu(II) could reach 107.53 mg/g, which was significantly higher than the other three heavy metal ions in the tetra metallic mixture solution. Additionally, after five cycles of adsorption and desorption, the uptake rate of Cu(II) on CA beads was maintained at 92%. According to the properties mentioned above, this material was assumed to be applied to reduce heavy metal pollution or recover valuable metals from waste water

Reliable and fast automatic artifact rejection of Long-Term EEG recordings based on Isolation Forest

Long-term electroencephalogram (Long-Term EEG) has the capacity to monitor over a long period, making it a valuable tool in medical institutions. However, due to the large volume of patient data, selecting clean data segments from raw Long-Term EEG for further analysis is an extremely time-consuming and labor-intensive task. Furthermore, the various actions of patients during recording make it difficult to use algorithms to denoise part of the EEG data, and thus lead to the rejection of these data. Therefore, tools for the quick rejection of heavily corrupted epochs in Long-Term EEG records are highly beneficial. In this paper, a new reliable and fast automatic artifact rejection method for Long-Term EEG based on Isolation Forest (IF) is proposed. Specifically, the IF algorithm is repetitively applied to detect outliers in the EEG data, and the boundary of inliers is promptly adjusted by using a statistical indicator to make the algorithm proceed in an iterative manner. The iteration is terminated when the distance metric between clean epochs and artifact-corrupted epochs remains unchanged. Six statistical indicators (i.e., min, max, median, mean, kurtosis, and skewness) are evaluated by setting them as centroid to adjust the boundary during iteration, and the proposed method is compared with several state-of-the-art methods on a retrospectively collected dataset. The experimental results indicate that utilizing the min value of data as the centroid yields the most optimal performance, and the proposed method is highly efficacious and reliable in the automatic artifact rejection of Long-Term EEG, as it significantly improves the overall data quality. Furthermore, the proposed method surpasses compared methods on most data segments with poor data quality, demonstrating its superior capacity to enhance the data quality of the heavily corrupted data. Besides, owing to the linear time complexity of IF, the proposed method is much faster than other methods, thus providing an advantage when dealing with extensive datasets

Inhibition effect of covalent carbon nanosheets on mechanochemical wear of diamond

Diamond tools experience severe chemical wear when machining ferrous metals, which hinders their practical applications. In order to improve the wear resistance of diamond cutting tools, in the diamond-graphite strong covalent structure prepared by laser induced solid-phase diffusion, carbon nanosheets (CNS) can be obtained by electrochemical stripping of the graphite layer onto the diamond matrix, which provides a new way to improve the limitations of diamond tools in application. After 14,400 cycles of reciprocating sliding against the GCr15 ball at a normal load of 2–8 N, friction was reduced by 45.9 %–65.6 % with high durability. The oxygen content is reduced by an order of magnitude during this process, suggesting that the CNS can prevent oxidation behavior at the sliding interface. The bare diamond had a relative wear rate of 4.1–15.4 times that of the CNS. It showed competitive inhibition of mechanochemical wear. Our work provides a convenient and green method of preparing in-situ CNS covalently bonded on a diamond surface, extending the way for the prospect of carbon materials

Deciphering and advancing CAR T-cell therapy with single-cell sequencing technologies

Abstract Chimeric antigen receptor (CAR) T-cell therapy has made remarkable progress in cancer immunotherapy, but several challenges with unclear mechanisms hinder its wide clinical application. Single-cell sequencing technologies, with the powerful unbiased analysis of cellular heterogeneity and molecular patterns at unprecedented resolution, have greatly advanced our understanding of immunology and oncology. In this review, we summarize the recent applications of single-cell sequencing technologies in CAR T-cell therapy, including the biological characteristics, the latest mechanisms of clinical response and adverse events, promising strategies that contribute to the development of CAR T-cell therapy and CAR target selection. Generally, we propose a multi-omics research mode to guide potential future research on CAR T-cell therapy

Establishment and Analysis of Energy Consumption Model of Heavy-Haul Train on Large Long Slope

AC heavy-haul trains produce a huge amount of regenerative braking energy when they run on long downhill sections. If this energy can be used by uphill trains in the same power supply section, a reduction in coal transportation cost and an improvement in power quality would result. To accurately predict the energy consumption and regenerative braking energy of heavy-haul trains on large long slopes, a single-particle model of train dynamics was used. According to the theory of railway longitudinal section simplification, the energy consumption and the regenerative braking energy model of a single train based on the train attributes, line conditions, and running speed was established. The model was applied and verified on the Shenshuo Railway. The results indicate that the percentage error of the proposed model is generally less than 10%. The model is a convenient and simple research alternative, with strong engineering feasibility. Based on this foundation, a model of train energy consumption was established under different interval lengths by considering the situation of regenerative braking energy in the multi-train operation mode. The model provides a theoretical foundation for future train diagram layout work with the goal of reducing the total train energy consumption