Alignment Requirements Research Articles

Concept of a Double Tilted Rowland Spectrograph for X-Rays

High-resolution spectroscopy in soft X-rays (<2 keV) requires diffractive elements to resolve any astrophysically relevant diagnostics, such as closely spaced lines, weak absorption lines, or line profiles. The Rowland torus geometry describes how gratings and detectors need to be positioned to optimize the spectral resolving power. We describe how an on-axis Rowland geometry can be tilted to accommodate blazed gratings. In this geometry, two channels with separate optical axes can share the same detectors (double tilted Rowland spectrograph). Small offsets between the channels can mitigate the effect of chip gaps and reduce the alignment requirements during the construction of the instrument. The double tilted Rowland spectrograph concept is especially useful for subapertured mirrors, because it allows an effective use of space in the entrance aperture of a spacecraft. One mission that applies this concept is the Arcus Probe.

Initial results from neoclassical tearing mode stabilization experiment in KSTAR high normalized beta plasmas

Abstract Active stabilization of neoclassical tearing modes (NTMs) is critical for high beta plasma operation in the KSTAR tokamak. In recent device operation, an experiment was conducted to develop a m/n = 2/1 NTM stabilization in high normalized beta (βN) plasmas having βN &gt; 3 by using electron cyclotron current drive (ECCD). The experiment is designed to first demonstrate the direct mode stabilization effect of the device EC system by varying the ECCD deposition location around the mode rational surface to prepare for future NTM stabilization in KSTAR using feedback schemes. In the experiment, the toroidal magnetic field strength, BT, is reduced to 1.5–1.6 T to create a highly localized ECCD profile on a large island width expected to produce a high stabilization effect. To align the ECCD with the mode, BT is varied between discharges by keeping the EC wave injection angles fixed to move the current deposition location across the q = 2 mode rational surface in ~1 cm steps along the plasma midplane. The result shows a prompt reduction of the mode amplitude by ~60% with a partial recovery of the loss of stored energy when the ECCD with 0.7 MW power is applied promptly after the mode onset at BT = 1.54 T. This abrupt reduction of the mode amplitude is significantly weaker or disappears in other discharges having slightly different BT in which the ECCD is deposited a few centimeters away. This result indicates a direct interaction between the driven EC current and the radially localized island structure first observed in the device. The EC ray-tracing analysis using the TORAY code shows that the applied EC-driven current aligns with the mode rational surface when the mode amplitude is observed to decrease. The stability of the observed 2/1 NTM is examined by constructing the modified Rutherford equation (MRE). The calculated EC power requirement for complete mode stabilization by assuming perfect alignment of ECCD on the mode is 0.8-1.7 MW by considering the uncertainties in the equation. This MRE-computed mode stability agrees with the experiment. The result projects that the present KSTAR EC system can stabilize the 2/1 NTM disrupting high βN plasmas, and provides the requirement of the mode-ECCD alignment for complete mode stabilization in future experiments in which an accurate alignment is planned to be made by feedback schemes being developed.&amp;#xD;

Arcus X-ray telescope performance predictions and alignment requirements

Arcus X-ray telescope performance predictions and alignment requirements

Place-based and sectoral patterns in urban experimentation: Implications for deep transitions research

Transforming critical infrastructure systems, such as water and energy, is crucial to achieving global sustainability and climate change targets in many cities. Whilst experimentation has been studied extensively in urban sustainability scholarships, there have been no large-N cross-sector comparative studies. Existing research is potentially blind to different patterns of urban experiments across multiple sectors. This is particularly relevant to advancing deep transitions thinking, which has increasingly foregrounded the notion of multi-system alignment across socio-technical domains. Our research aims to fill this knowledge gap using a database to characterise urban experiments across water and energy domains while integrating sectoral and place-based perspectives. We analysed 40 experiments across Melbourne and Adelaide, Australia. Our results show that on a collective level, these experiments skew towards technological interventions, while their transfer and impact trajectories are underpinned by distinct territorial and sectoral logics. We show that cross-sectoral analysis can reveal plurality in urban experiments across multiple systems and places while offering a more refined understanding of multi-system alignment requirements for deep transitions.

Self-supervised Entity Alignment Model Based on Joint Embedding

Abstract Entity alignment is an important problem for constructing Web-scale KGs. Self-supervised entity alignment models represented by SelfKG, proposed in 2022, have eliminated the essential requirement of accurate alignment: human labeling, which significantly saves resources. However, when SelfKG scales up to improve entity alignment accuracy, it introduces neighbor noise, leading to a decrease in alignment performance. This indicates that the ability of self-supervised models to aggregate and utilize knowledge graph information needs further exploration. To address this issue, we proposed the following approach: using a Graph Convolutional Network(GCN) to collect global entity and structural information, and a Graph Attention Network(GAT) to collect local neighborhood information from subgraphs. Subsequently, we introduced a novel fusion method to merge the two parts through weighted sum and residual connection, and completed self-supervised entity alignment using the Relative Similarity Metric(RSM) mechanism proposed by SelfKG. This model has both the ability to scale up and the flexibility to avoid neighbor noise, resulting in a significant improvement in performance.

An Air-Stable Carbon-Centered Triradical with a Well-Addressable Quartet Ground State.

Organic polyradicals with a high-spin ground state and quantum magnetic properties suitable for spin manipulation are valuable materials for diverse innovative technologies, including quantum devices. However, the typically high reactivity and low stability of conventional polyradicals present a major obstacle to such applications. In this study, a highly stable carbon-centered triradical TR with a quartet ground state and excellent stability (τ1/2 of ∼90 days in air-saturated toluene at room temperature) is achieved, which shows apposite magnetic anisotropy and Zeeman splitting partition with favorable addressability. By virtue of the optimal stability, thorough structural and magnetic characterizations are realized. With X-ray crystallography unambiguously proving the molecular structure, the quartet ground state (ΔED-Q = 0.78 kcal/mol) is confirmed by the SQUID measurements, while the cw- and pulsed EPR techniques offer additional supportive evidence for the high-spin nature. Remarkably, owing to the easily attained magnetic anisotropy, selective excitations between different Zeeman splitting levels are successfully demonstrated with TR in its frozen toluene solution without the requirement for special alignment, which is unprecedented for organic polyradicals. Along with the millisecond spin-lattice relaxation and microsecond coherence time manifested by TR, this triradical is promising for potential coherent spin manipulation applications as a multienergy-level quantum information carrier.

Pseudo-Multispectral Pedestrian Detection with Deep Thermal Feature Guidance

With complementary multi-modal information (i.e. visible and thermal), multispectral pedestrian detection is essential for around-the-clock applications, such as autonomous driving, video surveillance, and vicinagearth security. Despite its broad applications, the requirements for expensive thermal device and multi-sensor alignment limit the utilization in real-world applications. In this paper, we propose a pseudo-multispectral pedestrian detection (called PseudoMPD) method, which employs the gray image converted from the RGB image to replace the real thermal image, and learns the pseudo-thermal feature through deep thermal feature guidance (TFG). To achieve this goal, we first introduce an image base-detail decomposition (IBD) module to decompose image information into base and detail parts. Afterwards, we design a base-detail hierarchical feature fusion (BHFF) module to deeply exploit the information between these two parts, and employ a TFG module to guide pseudo-thermal base and detail feature learning. As a result, our proposed method does not require the real thermal image during inference. The comprehensive experiments are performed on two public multispectral pedestrian datasets. The experimental results demonstrate the effectiveness of our proposed method.

Comparative study of the early functional outcome of anatomic reconstruction of chronic lateral ankle instability with polyester tape reinforcement versus anchor repair

Purpose This study aims to compare the early functional outcome of anatomic reconstruction of chronic lateral ankle instability with polyester tape reinforcement versus suture anchor repair. Patients and Methods Sixty patients suffering from chronic lateral ankle instability were included. Thirty patients were treated by anatomic reconstruction of the injured ligaments and reinforcement with polyester tape and the other half was treated by suture anchors in our institution with a minimum period of 6 months follow-up. The postoperative results were assessed according to the American Orthopedic Foot and Ankle Surgery (AOFAS) scoring system for hind-foot. Results AOFAS score is improved postoperatively in both techniques without statistically significant difference (P=0.084). The results of the two techniques were comparable without statistically significant difference regarding pain, walking distance, hindfoot motion, foot alignment, activity limitation and support requirement (P=0.602, 0.067, 0.582, 0.448, 0.606, respectively). All the studied cases returned to their original activity level.

Machine-learning-assisted orbital angular momentum recognition using nanostructures

The recognition of orbital angular momentum (OAM) in light beams holds significant importance in optical communication. The majority of current OAM recognition techniques are highly sensitive to stringent alignment issues. The speckle-based OAM recognition method reported in J. Opt. Soc. Am. A 39, 759 (2022)JOAOD61084-752910.1364/JOSAA.446352 is alignment-free in the transverse direction of light propagation and has been shown to operate successfully in the far-field region using macrostructures. This study introduces a proof-of-concept for speckle-learned OAM recognition with nanostructures, relaxing the strict alignment requirements in both the transverse and along the direction of light propagation. When the OAM beam interacts with random inhomogeneities at micron and/or nanoscale, it generates an OAM speckle field. Initially, a comprehensive examination of the dynamic evolution of OAM speckle fields, ranging from near field to far field, has been conducted using a ground glass diffuser, featuring random phase inhomogeneities at the micron scale. Subsequently, the investigation proceeds to randomly grown ZnO nanosheets on an aluminum substrate. To achieve rapid and precise OAM recognition, a tailored three-layer CNN is trained and tested on OAM speckle fields ranging from near field to far field to attain an accuracy surpassing 92%. This research expands the technique’s applicability, enabling recognition of OAM across near-field to far-field regimes, while leveraging micro- to nanostructures.

Tri-Flow-YOLO: Counter helps to improve cross-domain object detection

The excellence of intelligent detection models has been widely recognized, but in terms of cross-domain scenes, they still face performance degradation and low accuracy. A multi-supervised Tri-Flow-YOLO model is proposed to improve the accuracy of objects with various scales under cross-domain conditions. Based on the full-supervised traditional detection branch of YOLOv5, another two mutually supporting task branches are designed intently. In brief, we add unsupervised adversarial classification training flow to the backend, to realize the feature alignment requirements and improve the cross-domain performance stability of the model. Meanwhile, a weakly-supervised object counting flow is proposed to improve the model's attention to all the objects and the detection ability is efficiently enforced. In addition, I-Mosaic and iCIOU are designed especially for small hard objects, enriching the positive samples during the training process. With the auxiliary of both improved strategies, the imbalance of positive and negative samples in the anchor-based model is relieved accordingly. The experimental results show that the improved Tri-Flow-YOLO model achieves 56.0 mAP in the Cityscapes→Foggy-Cityscapes task, and 49.8 mAP in the VOC→Clipart task.

Ultraviolet collaborative networking method based on a novel neighbor discovery algorithm

Ultraviolet (UV) optical communication presents several advantages, such as non-line-of-sight propagation, high confidentiality, reliability, and the absence of precise alignment requirements. UV optical communication networks find application in various scenarios in complex electromagnetic environments. In this study, we propose a UV collaborative networking method based on a neighbor discovery algorithm (UVCN–NDA), which establishes neighbor tables within nodes and selects the shortest transmission path between the nodes for frame transmission. During data transmission, nodes dynamically adjust the transmission time of each bit to enhance throughput and use the distance information in the neighbor table for path selection. This strategy prevents ineffective data forwarding and subsequently reduces network power consumption. Simulation results demonstrate that the neighbor discovery algorithm implemented in this protocol exhibits higher efficiency in neighbor discovery and lower message load than existing algorithms. Furthermore, when compared to UV networks based on bit–simultaneous–transmission, the UVCN–NDA–based UV networks achieve an approximate 86.2% reduction in average power consumption and approximately 52% increase in throughput, thereby providing a considerable performance advantage. Finally, the conducted network experiments prove the shortest path selection capability of UVCN–NDA.

Direct measurement on stretched wire to constrain the longitudinal network in CSNS

The elongated and non-closed structure of the longitudinal control network can lead to the accumulation of measurement errors from station to station, causing distortion and preventing the attainment of high-precision longitudinal control network coordinates. This fails to meet the high-precision alignment requirements of the accelerator components. In this study, the absolute scanner AS1 and Leica AT960 laser tracker were used to directly measure the 180-m long stretched wire in the CSNS LINAC tunnel and obtain its three-dimensional coordinates. The obtained data was used to constrain the transverse of the longitudinal control network, effectively improving its accuracy. The Helmert variance component estimation method was incorporated into the data adjustment algorithm, which reasonably allocated weights between horizontal distance, azimuth angle and offset. As a result, the transverse accuracy of the control network improved from 0.151 mm without wire constraint to 0.086 mm with wire constraint. In the future, we plan to apply this method to connect the control network on both sides of the lengthy sight-hole in CSNS-II.

Experimental Research on Shipborne SINS Rapid Mooring Alignment with Variance-Constraint Kalman Filter and GNSS Position Updates.

Analytical coarse alignment and Kalman filter fine alignment based on zero-velocity are typically used to obtain initial attitude for inertial navigation systems (SINS) on a static base. However, in the shipboard mooring state, the static observation condition is corrupted. This paper presents a rapid alignment method for SINS on swaying bases. The proposed method begins with a coarse alignment technique in the inertial frame to obtain an initial rough attitude. Subsequently, a Kalman filter with position updates is employed to estimate the remaining misalignment error. To enhance the filter estimation performance, an appropriate lower boundary is set to the target states' variances according to a carefully designed relative convergence index. The variance-constraint Kalman filter (VCKF) approach is proposed in this paper, and the shipborne experiments validate its effectiveness. The results demonstrate that the VCKF approach significantly reduces the time requirement for fine alignment to achieve the same accuracy on a swaying base, from 90 min in the classic Kalman filter to 30 min. Additionally, the parameter estimation performance in the Kalman filter is also improved, particularly in situations where unpredicted external interference is involved during fine alignment.

Gait-based identification using wearable multimodal sensing and attention neural networks

Advances in sensor technology have sparked a wave of innovation and progress in the domain of wearable devices. However, ensuring the security of these devices remains a critical concern for their users. This study proposes a novel identification framework that employs custom multimodal sensing insoles and deep learning techniques to contribute to the data security of wearable devices in a user-transparent manner. The insole incorporates an inertial sensing unit and ten force sensors to capture kinematic and kinetic information. For this implementation, we propose a lightweight multiclass classification deep model based on depthwise separable convolution and an enhanced attention mechanism, which operates over multiple signal channels. In designing the recognition model, we focused on two pivotal factors: (1) achieving an optimal balance between computational complexity and recognition accuracy, which is essential for models that operate on devices with limited computational power, and (2) introducing an enhanced attention mechanism that ensures high recognition accuracy without the stringent requirement for precise temporal alignment between sensor channels, which can be technically challenging to attain. We conducted extensive experiments to validate the proposed approach, utilizing a walking dataset collected from 82 young subjects. The results indicate that when using data from the multimodal sensing insole, a high recognition accuracy of 97.96 % for person identification is achieved, with a notably low model parameter count of 22,462. The results support the feasibility and effectiveness of sensing footwear-based gait identification. The proposed approach holds significant potential for enhancing user authentication methods across a broad range of scenarios while ensuring transparency and user-friendliness.

Speech Recognition for Air Traffic Control Utilizing a Multi-Head State-Space Model and Transfer Learning

In the present study, a novel end-to-end automatic speech recognition (ASR) framework, namely, ResNeXt-Mssm-CTC, has been developed for air traffic control (ATC) systems. This framework is built upon the Multi-Head State-Space Model (Mssm) and incorporates transfer learning techniques. Residual Networks with Cardinality (ResNeXt) employ multi-layered convolutions with residual connections to augment the extraction of intricate feature representations from speech signals. The Mssm is endowed with specialized gating mechanisms, which incorporate parallel heads that acquire knowledge of both local and global temporal dynamics in sequence data. Connectionist temporal classification (CTC) is utilized in the context of sequence labeling, eliminating the requirement for forced alignment and accommodating labels of varying lengths. Moreover, the utilization of transfer learning has been shown to improve performance on the target task by leveraging knowledge acquired from a source task. The experimental results indicate that the model proposed in this study exhibits superior performance compared to other baseline models. Specifically, when pretrained on the Aishell corpus, the model achieves a minimum character error rate (CER) of 7.2% and 8.3%. Furthermore, when applied to the ATC corpus, the CER is reduced to 5.5% and 6.7%.

Alignment measurement of immersed tunnels based on laser differential imaging

The precision of underwater alignment for tunnel elements directly impacts the closure state of immersed tunnels. The current global navigation satellite system measurement tower method provides centimeter-level precision; however, this is insufficient due to the tower deformation at great water depths, which fails to meet alignment requirements. In this study, a novel immersed tunnel alignment measurement method using laser differential imaging is developed. A unique sensor layout is designed to measure the axial angle and distance between tunnel elements. The system design is implemented, and its laser data processing scheme is optimized to improve performance base on this sensor layout. The experimental results demonstrate that the accuracy of underwater laser spot extraction is better than 1.16 pixels, and the precision of axial angle and axial distance alignment measurements is better than 25″ and 1.6 mm, with repeatability precision exceeding 10.55″ and 0.53 mm. The laser-based alignment measurement method is not affected by the depth of water, satisfying the required precision of immersed tunnel alignment. These results can be applied to future immersed tunnel installations without measuring towers, providing a novel method for achieving automated and unmanned intelligent construction of immersed tunnels.

Spatial wavefront shaping with a multipolar-resonant metasurface for structured illumination microscopy [Invited

Structured illumination microscopy (SIM) achieves superresolution in fluorescence imaging through patterned illumination and computational image reconstruction, yet current methods require bulky, costly modulation optics and high-precision optical alignment, thus hindering the widespread implementation of SIM. To address this challenge, this work demonstrates how nano-optical metasurfaces, rationally designed to tailor the far-field optical wavefront at sub-wavelength dimensions, hold great potential as ultrathin, single-surface, all-optical wavefront modulators for SIM. We computationally demonstrate this principle with a multipolar-resonant metasurface composed of silicon nanostructures that generate versatile optical wavefronts in the far field upon variation of the polarization or angle of incident light. Algorithmic optimization is performed to identify the seven most suitable illumination patterns for SIM generated by the metasurface based on three key criteria. We quantitatively demonstrate that multipolar-resonant metasurface SIM (mrm-SIM) achieves resolution gain that is comparable to conventional methods by applying the seven optimal metasurface-generated wavefronts to simulated fluorescent objects and reconstructing the objects using proximal gradient descent. Notably, we show that mrm-SIM achieves these resolution gains with a far-field illumination pattern that circumvents complex equipment and alignment requirements of comparable methodologies. The work presented here paves the way for a metasurface-enabled experimental simplification of structured illumination microscopy.

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

The cross-domain diagnosis of tie rod bolt loosening is essential for guaranteeing the healthy operation of rod-fastening rotor (RFR) systems. The unsupervised domain adaptation (UDA) method effectively alleviates the impact of domain discrepancy and has been applied for cross-domain diagnosis. Traditional UDA methods mainly focus on the marginal and conditional distributions with fixed weights to adapt the domain distribution discrepancy. However, the fixed distribution combination cannot satisfy the requirement of feature domain alignment under different working conditions, and the relative importance of the two distributions cannot be evaluated quantitatively. This paper proposes an improved dynamic distribution adaptive graph convolutional network (DDAGCN) for the cross-domain diagnosis of tie rod bolt loosening under different working conditions. This method can quantitatively evaluate the relative significance of each distribution in representing the distribution discrepancy. First, it combines the convolutional neural network and the graph convolutional network to extract the features in the graph structure by using the connection relationship between nodes, and realizes the full extraction of neighbourhood information of nodes. Then, the dynamic distribution adaptive alignment strategy is introduced to construct the dynamic linear combination of marginal and conditional distributions, so as to measure the distribution discrepancy between domains. Meanwhile, the domain adversarial module is combined to further reduce the domain gap and finally realize feature alignment. The extracted domain invariant features can effectively enhance the generalization ability and fault identification ability of the model. The case of the public bearing dataset verifies that the effectiveness and generalization ability of the proposed method for cross-domain fault diagnosis under different working conditions is superior to other compared methods. In addition, the identification ability of the proposed method for the degree of tie rod bolt loosening is verified by the self-made bolt loosening dataset of the RFR system.

BDM: An Assessment Metric for Protein Complex Structure Models Based on Distance Difference Matrix.

Protein complex structure prediction is an important problem in computational biology. While significant progress has been made for protein monomers, accurate evaluation of protein complexes remains challenging. Existing assessment methods in CASP, lack dedicated metrics for evaluating complexes. DockQ, a widely used metric, has some limitations. In this study, we propose a novel metric called BDM (Based on Distance difference Matrix) for assessing protein complex prediction structures. Our approach utilizes a distance difference matrix derived from comparing real and predicted protein structures, establishing a linear correlation with Root Mean Square Deviation (RMSD). BDM overcomes limitations associated with receptor-ligand differentiation and eliminates the requirement for structure alignment, making it a more effective and efficient metric. Evaluation of BDM using CASP14 and CASP15 test sets demonstrates superior performance compared to the official CASP scoring. BDM provides accurate and reasonable assessments of predicted protein complexes, wide adoption of BDM has the potential to advance protein complex structure prediction and facilitate related researches across scientific domains. Code is available at http://mialab.ruc.edu.cn/BDMServer/ .

Self‐Aligned Photonic Defect Microcavity Lasers with Site‐Controlled Quantum Dots

AbstractSelf‐assembled semiconductor quantum dots face challenges in terms of scalable device integration because of their random growth positions, originating from the Stranski–Krastanov growth mode. Even with existing site‐controlled growth techniques, for example, nanohole or buried stressor concepts, a further lithography and etching step with high spatial alignment requirements is necessary to accurately integrate quantum dots into the nanophotonic devices. Here, the fabrication and characterization of strain‐induced site‐controlled microcavities are reported, where site‐controlled quantum dots are positioned at the antinode of the optical mode field in a self‐aligned manner without the need of any further nano‐processing. It is shown that the cavity properties such as Q‐factor, mode volume, and mode splitting can be tailored by the geometry of the integrated buried stressor, with an opening &lt;4 µm. The experimental results are complemented with theory calculations based on continuum elasticity. Lasing signatures, including super‐linear input‐output response and linewidth narrowing, are observed for a 3.6‐µm self‐aligned cavity with a Q‐factor of 18 000. Furthermore, the quasi‐planar site‐controlled cavities exhibit no detrimental thermal effects. This approach integrates seamlessly with the industrial‐matured manufacturing process and the buried‐stressor technique, paving the way for exceptional scalability and straightforward manufacturing of high‐β microlasers and bright quantum light sources.