Search

The Mars 2020 Entry, Descent, and Landing (EDL) system successfully delivered the Perseverance rover to Jezero Crater, the most hazardous landing site ever attempted on Mars. To mitigate the risk of landing hazards, which included cliffs, inescapable dune fields, and rocks, a novel terrain relative navigation system was developed and integrated with the heritage Mars Science Laboratory EDL system. First the hazards were identified using orbital imagery and stored on-board the spacecraft as a hazard map. Then, during parachute descent, the Lander Vision System (LVS) estimated map relative position by fusing landmarks matched between descent imagery and a map of the landing site with inertial measurement unit data. Finally, the position estimate and hazard map were then used by the powered descent system to identify and then fly to the safest and reachable landing target. Postflight analysis indicated that all required systems worked much better than predicted. In particular, the fully autonomous LVS generated a position estimate in 10 s that was in error by only a few meters relative to a 40 m requirement. This paper describes the LVS design, how it was tested before launch, and the LVS performance during EDL.

Motor-based brain-computer interfaces (BCIs) were developed from the brain signals during motor imagery (MI), motor preparation (MP), and motor execution (ME). Motor-based BCIs provide an active rehabilitation scheme for post-stroke patients. However, BCI based solely on MP was rarely investigated. Since MP is the precedence phase before MI or ME, MP-BCI could potentially detect brain commands at an earlier state. This study proposes a bipedal MP-BCI system, which is actuated by the reduction in frontoparietal connectivity strength. Three substudies, including bipedal classification, neurofeedback, and post-stroke analysis, were performed to validate the performance of our proposed model. In bipedal classification, functional connectivity was extracted by Pearson's correlation model from electroencephalogram (EEG) signals recorded while the subjects were performing MP and MI. The binary classification of MP achieved short-lived peak accuracy of 73.73(±7.99)% around 200-400 ms post-cue. The peak accuracy was found synchronized to the MP-related potential and the decrement in frontoparietal connection strength. The connection strengths of the right frontal and left parietal lobes in the alpha range were found negatively correlated to the classification accuracy. In the subjective neurofeedback study, the majority of subjects reported that motor preparation instead of the motor imagery activated the frontoparietal dysconnection. Post-stroke study also showed that patients exhibit lower frontoparietal connections compared to healthy subjects during both MP and ME phases. These findings suggest that MP reduced alpha band functional frontoparietal connectivity and the EEG signatures of left and right foot MP could be discriminated more effectively during this phase. A neurofeedback paradigm based on the frontoparietal network could also be utilized to evaluate post-stroke rehabilitation training.

Near-infrared 850 nm Cr(iii) emissions with handedness in degassed solutions were induced by ligand carbon-centred chirality.

The bifunctional catalyst CuFeO2/hierarchical HZSM-5 suppresses the total selectivity of CO and CH4 to less than 12% with a high aromatics STY of 431.8 mgCH2 gcat−1 h−1 at a CO2 conversion of 52.8%, which outperforms the previously reported catalysts.

The phosphorescence of bpi-Cr(iii) complexes in degassed fluid solutions is tunable in the near-infrared region and the tunability is governed by the energy gap law.

Under the action of extreme wind load, the overhead transmission line will lead to the fracture of the traditional V-type insulator string, which greatly affects the safety of the power system. Compared with the V-type insulator string, the Y-type insulator string has better stability under the wind load. Therefore, the overhead lines in the mountainous areas of Anhui Province are taken as the research object, considering the combined effect of wind load and conductor dead weight, and through theoretical derivation, the calculation formula of insulator string wind deflection angle is obtained. Using numerical simulation software, the nonlinear mechanical analysis of Y-type insulator strings is conducted, and under the action of different wind speeds, the windage yaw angle and unloading angle of the Y-type insulator string are obtained. Compared with the calculation results of the V-type insulator string, the stability of the Y-type insulator string in the structure is better than that of the V-type insulator string, and the Y-type insulator string can make full use of the distance between layers and the gap margin of the tower head, reduce the length of the cross arm, and reduce the weight of the tower, which has obvious advantages. Combined with the results of theoretical analysis and numerical simulation, the optimal design method of the Y-type insulator string is given. Under the condition of ensuring the safety and stability of insulators, the distance of the cross arm is shortened as much as possible and the weight of the transmission line tower is reduced. The research results will provide a theoretical reference for engineering design and improvement.

Cold-mix asphalt has been developed in recent years by combining foamed asphalt and recycled asphalt pavement (RAP). However, manufacturers struggled due to the less standardized specification. Foaming and mixing performance cannot be accurately controlled and are usually treated with a foaming agent (FA). This research focused on the relationship between the chemical composition of bitumen and its foaming properties. Three groups of AC-20 bitumen and organo-modified siloxanes based FA with water content of 1.5%, 2.0%, 2.5%, 3.0%, 3.5%, 4.0% and 4.5%. were analyzed. Chemical components of different bitumen types were evaluated using the Gaestel colloidal index (CI). The half-life (HL), expansion ratio (ER), Foam index (FI), and bubble surface area index (SAI) were used to evaluate the workability of the binders and discuss the effects of the values on the performance of the mixture. The CI observation revealed that FA obviously changed the saturates value and greatly improved the foam properties in terms of stability and expansion. FI change was found to be 71% of the change in the CI. Similarly, the resulting FI and SAI reach up to 88% in terms of correlation.

Ferroptosis is a newly discovered form of cell death accompanied by iron accumulation and lipid peroxidation. Both biotic and abiotic stresses can induce ferroptosis in plant cells. In the case of plant interactions with pathogenic Phytophthora oomycetes, the roles of ferroptosis are still largely unknown. Here, we performed transcriptome analysis on soybean plants treated with the biocontrol agent Pythium oligandrum, a soilborne and non-pathogenic oomycete capable of inducing plant resistance against Phytophthora sojae infection. Expression of homologous soybean genes involved in ferroptosis and resistance was reprogrammed upon P. oligandrum treatment. Typical hallmarks for characterizing ferroptosis were detected in soybean hypocotyl cells, including decreased glutathione (GSH) level, accumulation of ferric ions, and lipid peroxidation by reactive oxygen species (ROS). Meanwhile, ferroptosis-like cell death was triggered by P. oligandrum to suppress P. sojae infection in soybean. Protection provided by P. oligandrum could be attenuated by the ferroptosis inhibitor ferrostatin-1 (Fer-1), suggesting the critical role of ferroptosis in soybean resistance against P. sojae. Taken together, these results demonstrate that ferroptosis is a P. oligandrum-inducible defence mechanism against oomycete infection in soybean.

Dosing powdered activated carbon (PAC) has been proven to be an economical and effective method to mitigate membrane fouling. However, the effects of pretreated PAC with different redox properties on membrane fouling still need to be further investigated. Here, the impact of commercial PAC, oxidized-PAC, and reduced-PAC on membrane fouling was investigated in membrane bioreactors (MBRs). Surprisingly, the filtration cycles were extended from 12-36 h to 132-156 h only by dosing reduced-PAC and commercial PAC with a finial dosage of 3 g/L, which were provided with reductive properties. However, few improvements of filtration cycle (less than 50 h) were achieved by dosing oxidized-PAC in the same dosage, which had the same adsorption performance as reduced-PAC and commercial PAC. The biomass and foulant concentration suggested that the enhanced anti-fouling performances by PAC with reductive properties were mainly attributed to the reduction of extracellular polymer substances (EPS) and soluble microbial products (SMP) content in the bulk solutions after 14 days of continuous operation. The model foulant degradation tests and the confocal laser scanning microscope (CLSM) images of activated sludge further demonstrated that PAC with reductive properties directly affected the microbial activities by controlling the EPS and SMP concentrations in the bulk solution, thereby suppressing membrane fouling. Such a finding provides new insights into anti-fouling mechanisms that the redox properties of PAC played a decisive role in membrane fouling mitigation, and also provides a strategy to prolong the anti-fouling effects by restoring the reductive properties of PAC. KEY POINTS: • The anti-fouling mechanisms of PAC with reductive property were investigated. • Reductive property was the main reason for fouling control instead of adsorption. • PAC with reductive property hindered the sludge activity to produce fewer foulants.

The production of large-area twisted bilayer graphene (TBG) with controllable angles is a prerequisite for proceeding with its massive applications. However, most of the prevailing strategies to fabricate twisted bilayers face great challenges, where the transfer methods are easily stuck by interfacial contamination, and direct growth methods lack the flexibility in twist-angle design. Here we develop an effective strategy to grow centimetre-scale TBG with arbitrary twist angles (accuracy,1.0°). The success in accurate angle control is realized by an angle replication from two prerotated single-crystal Cu(111) foils to form a Cu/TBG/Cu sandwich structure, from which the TBG can be isolated by a custom-developed equipotential surface etching process. The accuracy and consistency of the twist angles are unambiguously illustrated by comprehensive characterization techniques, namely, optical spectroscopy, electron microscopy, photoemission spectroscopy and photocurrent spectroscopy. Our work opens an accessible avenue for the designed growth of large-scale two-dimensional twisted bilayers and thus lays the material foundation for the future applications of twistronics at the integration level.

Insulators are basic parts of high-voltage transmission, and detecting faults of insulators is a critical task. Most state-of-the-art methods contain two or more stages, including insulator detection and defect locating. Some also involve hand-designed components to improve the performance due to the complicated and misleading background of the wild. To automatically detect faults in UAV-captured insulator images, this paper presents a method that introduces DETR into insulator defect detection. With the self-attention mechanism in Transformer, the model can naturally exploit its advantage in focusing on the target area. However, training DETR requires large data sets and long training schedules to establish spatial relations in sparse locations, which makes it generally not feasible to train in small data sets. To explore the possibility of training a well-performing model with a data set that minimizes the cost of collecting insulator images, transfer learning techniques were applied to this process. To compensate for the disadvantage of DETR in detecting small objects at more precise scales, an improved loss was transplanted to this model. The results show that our proposed method can detect defects directly from UAV images without the need to locate the insulator first, while providing competitive performance with a lower cost of collecting training samples.

We propose a concise method to improve the inference accuracy of a convolutional neural network model for image classification. The characteristics of the input images are sharpened by a modified 5 ´ 5 mask before training and testing. The practice data were acquired from liver cancer MRI scanning at a collaborative hospital. We established the datasets using separated scanned images, which were labeled 1 or 0 to represent images with or without a cancer focal area, respectively. Scanned files from 45 patients were adopted for this study with each of them providing hundreds of separated images. We predicted one patient’s longitudinal cancer position in the liver to illustrate the merit of our approach. &nbsp

Software engineering is a domain characterized by intricate decision-making processes, often relying on nuanced intuition and consultation. Recent advancements in deep learning have started to revolutionize software engineering practices through elaborate designs implemented at various stages of software development. In this paper, we present an innovative paradigm that leverages large language models (LLMs) throughout the entire software development process, streamlining and unifying key processes through natural language communication, thereby eliminating the need for specialized models at each phase. At the core of this paradigm lies ChatDev, a virtual chat-powered software development company that mirrors the established waterfall model, meticulously dividing the development process into four distinct chronological stages: designing, coding, testing, and documenting. Each stage engages a team of agents, such as programmers, code reviewers, and test engineers, fostering collaborative dialogue and facilitating a seamless workflow. The chat chain acts as a facilitator, breaking down each stage into atomic subtasks. This enables dual roles, allowing for proposing and validating solutions through context-aware communication, leading to efficient resolution of specific subtasks. The instrumental analysis of ChatDev highlights its remarkable efficacy in software generation, enabling the completion of the entire software development process in under seven minutes at a cost of less than one dollar. It not only identifies and alleviates potential vulnerabilities but also rectifies potential hallucinations while maintaining commendable efficiency and cost-effectiveness. The potential of ChatDev unveils fresh possibilities for integrating LLMs into the realm of software development., 25 pages, 9 figures, 2 tables

Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with significantly better performance in most benchmarks than traditional optimization-based methods. Nonetheless, 3D HPE in the wild is still the biggest challenge of learning-based models, whether with 2D-3D lifting, image-to-3D, or diffusion-based methods, since the trained networks implicitly learn camera intrinsic parameters and domain-based 3D human pose distributions and estimate poses by statistical average. On the other hand, the optimization-based methods estimate results case-by-case, which can predict more diverse and sophisticated human poses in the wild. By combining the advantages of optimization-based and learning-based methods, we propose the Zero-shot Diffusion-based Optimization (ZeDO) pipeline for 3D HPE to solve the problem of cross-domain and in-the-wild 3D HPE. Our multi-hypothesis ZeDO achieves state-of-the-art (SOTA) performance on Human3.6M as minMPJPE $51.4$mm without training with any 2D-3D or image-3D pairs. Moreover, our single-hypothesis ZeDO achieves SOTA performance on 3DPW dataset with PA-MPJPE $42.6$mm on cross-dataset evaluation, which even outperforms learning-based methods trained on 3DPW.

Peace of mind (PoM) is an index of mental health in Asian culture and emphasizes low arousal, happiness, harmony, and an internal state of peacefulness. While previous studies have found that mindful self-awareness can contribute to PoM, regular physical activity (PA) is also an important factor contributing to one’s PoM due to its function in promoting one's resilience. The study aims to investigate a hypothetical model that assumes PA is associated with resilience while controlling for mindful self-awareness, contributing to PoM. The PoM scale, Connor-Davidson Resilience Scale, Chinese translation of Mindful Attention Awareness Scale, and PA self-report questionnaire were used. A path analysis was applied to test the association between these variables and the mediating role of resilience. A total of 436 students from a university in Taiwan were recruited; the mean age was 20.87, with 46.3% female and 73.6% engaging in over 150 min/week of moderate PA. Gender and age negatively correlated with PA. After controlling for age and gender, there was no direct effect of physical activity on PoM; both mindful self-awareness and PA predict resilience, which in turn predicts PoM, suggesting that both cognitive (i.e., mindful self-awareness) and PA are important to cultivate resilience and thus PoM.

Natural language is expected to be a key medium for various human-machine interactions in the era of large language models. When it comes to the biochemistry field, a series of tasks around molecules (e.g., property prediction, molecule mining, etc.) are of great significance while having a high technical threshold. Bridging the molecule expressions in natural language and chemical language can not only hugely improve the interpretability and reduce the operation difficulty of these tasks, but also fuse the chemical knowledge scattered in complementary materials for a deeper comprehension of molecules. Based on these benefits, we propose the conversational molecular design, a novel task adopting natural language for describing and editing target molecules. To better accomplish this task, we design ChatMol, a knowledgeable and versatile generative pre-trained model, enhanced by injecting experimental property information, molecular spatial knowledge, and the associations between natural and chemical languages into it. Several typical solutions including large language models (e.g., ChatGPT) are evaluated, proving the challenge of conversational molecular design and the effectiveness of our knowledge enhancement method. Case observations and analysis are conducted to provide directions for further exploration of natural-language interaction in molecular discovery.