Spatial Positions Research Articles

Study on the effect of ocean rolling motion on the thermal–hydraulic performance of supercritical carbon dioxide in horizontal tube

The effect of ocean rolling motion on the flow and heat transfer characteristics of the S-CO2 has been numerically investigated in this article. The results showed that increasing the rolling period makes the influence of rolling motion tend to be gentle. Increasing the maximum rolling angle and the rolling radius will increase the centrifugal and tangential forces on the fluid inside the tube, further making the swing motion of the flow field become more intense. Thus, this directly results in the tube wall temperature oscillation of 10 K to 50 K as well as the total pressure drop fluctuation of about 6%. In addition, the effect of three spatial positions relative to the rolling axis is also investigated. This research work can provide a theoretical reference for the development of marine equipment.

Adaptive Adjacent Layer Feature Fusion for Object Detection in Remote Sensing Images

Object detection in remote sensing images faces the challenges of a complex background, large object size variations, and high inter-class similarity. To address these problems, we propose an adaptive adjacent layer feature fusion (AALFF) method, which is developed on the basis of RTMDet. Specifically, the AALFF method incorporates an adjacent layer feature fusion enhancement (ALFFE) module, designed to capture high-level semantic information and accurately locate object spatial positions. ALFFE also effectively preserves small objects by fusing adjacent layer features and employs involution to aggregate contextual information in a wide spatial range for object essential features extraction in complex backgrounds. Additionally, the adaptive spatial feature fusion (ASFF) module is introduced to guide the network to select and fuse the crucial features to improve the adaptability to objects with different sizes. The proposed method achieves mean average precision (mAP) values of 77.1%, 88.9%, and 95.7% on the DIOR, HRRSD, and NWPU VHR-10 datasets, respectively. Notably, our approach achieves mAP75 values of 60.8% and 79.0% on the DIOR and HRRSD datasets, respectively, surpassing the state-of-the-art performance on the DIOR dataset.

Engineering Super‐Poissonian Photon Statistics of Spatial Light Modes

AbstractThe nature of light sources is defined by the statistical fluctuations of the electromagnetic field. As such, the photon statistics of light sources are typically associated with distinct emitters. Here, the possibility of producing light beams with various photon statistics through the spatial modulation of coherent light is demonstrated. This is achieved by the sequential encoding of controllable Kolmogorov phase screens in a digital micromirror device. Interestingly, the flexibility of this scheme allows for the shaping of spatial light modes with engineered photon statistics at different spatial positions. The performance of this scheme is assessed through the photon‐number‐resolving characterization of different families of spatial light modes with engineered photon statistics. It is believed that the possibility of controlling the photon fluctuations of the light field at arbitrary spatial locations has important implications for quantum spectroscopy, sensing, and imaging.

DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters

Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts of data to train, given, that the number of their trainable parameters has a polynomial relationship to their neuron counts. This property renders deep NNs challenging to apply in fields operating with small, albeit representative datasets such as healthcare. In this paper, we propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma distances of connected neurons. We refer to this method as distance-encoding biomorphic-informational (DEBI) neural network. This concept significantly minimizes the number of trainable parameters compared to conventional neural networks. We demonstrate that DEBI models can yield comparable predictive performance in tabular and imaging datasets, where they require a fraction of trainable parameters compared to conventional NNs, resulting in a highly scalable solution.

Control of a rectangular impinging jet: Experimental investigation of the flow dynamics and the acoustic field

Passive control techniques of impinging jets are of high interest for many industrial applications and particularly for noise generation issues encountered in such configurations. Thus, an experimental study was carried out to simultaneously show the effect of a mechanism of control on the acoustic and the dynamic fields involved in a rectangular jet of air impinging on a slotted plate. A Reynolds number of Re = 5900 presenting an intense acoustic level was considered. The mechanism of control consists on a thin rod which was introduced in different positions of the flow. A total number of 1085 spatial positions of the rod were tested in order to identify the optimal position for noise reduction. Combined Stereoscopic Particle Image Velocimetry measurements were performed to obtain the kinematic field in the whole area of interest from the both sides of the introduced rod. A new representation of the acoustic levels (cartography of acoustic level as function of the location of the rod) is provided to identify the optimal positions of control. It was found that when the self-sustaining tone loop disappears, the sound pressure levels can drop by almost 23% depending on the location of the rod. A Dynamic Mode Decomposition (DMD) was established and cross-correlations were calculated between temporal modes and acoustic signals for both controlled and not controlled cases. The cross-correlations between the acoustic signal and the temporal modes were found to be insignificant in case of controlled flow. Moreover, in case of controlled flow, spatial modes were found to be significant far from the slot which plays a principal role in the self-sustaining tones by interacting with the passage of vortices through it. These results are of interest since the visualization of the flow dynamics and the corresponding vortex activity explains the disappearance of the self-sustaining loop and the sound pressure level changes. Such results are of high interest for developing new strategies of noise control.

Impact of solar radiation on indoor thermal comfort near highly glazed façades in a hot-humid subtropical climate: An experimental evaluation

Solar radiation has a profound impact on indoor thermal conditions, particularly in highly glazed spaces. However, there have been limited field measurements on this subject. Focusing on subtropical climate, this study investigated the effects of irradiation on thermal comfort from two aspects, disparities in glass properties and spatial positions, through a field measurement. Six test cells, each equipped with a representative type of glass, were established; two measurement points were set at inner and near-window positions to analyze the spatial disparities and impacts of solar radiation asymmetry. PMV-PPD-based evaluation criteria were employed to assess the long-term comfort levels and classified by environmental quality index (EQI); additionally, we assessed the risk of overheating by considering two indices: overheating hours and overheating severity, which represent the duration and extent of thermal discomfort. The results indicated that radiation asymmetry varied depending on glass properties and spatial/temporal difference, leading to partial thermal discomfort. Specifically, the MRT at the near-window point increased significantly, reaching 50.8 °C for single-pane glass, 40.1 °C for single-pane tinted glass, and a minimum of 32.8 °C for double-pane reflective glass after 14:30 due to direct sunlight. In contrast, the MRT at the indoor positions remained relatively stable at around 25 °C–27 °C, irrespective of the glazing type. SHGC of glass is critical for thermal comfort, as higher SHGC values intensify the sensation of extreme warmth. Through experiments, this study quantified the relationship between thermal discomfort and glazing properties, providing valuable insights for glazing facades design that can optimize indoor thermal environments.

Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping

Soil fertility plays a crucial role in crop growth, so it is important to study the spatial distribution and variation of soil fertility for agricultural management and decision-making. However, traditional methods for assessing soil fertility are time-consuming and economically burdensome. Moreover, it is hard to capture the spatial variation of soil properties across continuous geographic space using the conventional methods. As key techniques of digital soil mapping (DSM), spatial interpolation techniques have been widely applied in soil surveys and analysis in recent years, since they can predict soil properties at unknown points in continuous space based on limited sample points. However, further research is needed on spatial interpolation models for DSM in regions with variable climates and complex terrains, which are characterized by strong spatial variation in both environmental variables and soil fertility. In this study, taking a typical hilly area in a subtropical monsoon climate, i.e., Gaozhou, Guangdong Province, China, as an example, the performances of four popular spatial interpolation models (Random Forest (RF), Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function) for digital soil mapping on available phosphorus (AP) are compared. Based on RF, the spatial variation and its driving factors of the AP of Gaozhou are then analyzed. Furthermore, by selecting three typical truncation lines from different directions, the correlations between environmental variables and AP in different spatial positions are demonstrated. The root mean square error (RMSE) results of the above four models are 32.01, 32.08, 32.74, and 33.08, respectively, which indicate that the RF has a higher interpolation accuracy. Based on the mapping results of RF, the minimum, maximum, and mean values of AP in the study area are 38.90, 95.24, and 64.96 mg/kg, respectively. The high-value areas of AP are mainly distributed in forested and orchard areas, while the low-value areas are primarily found in urban and cultivated areas in the eastern and western regions. Vegetation and topography are identified as the key factors shaping the spatial variations of AP in the study area. Furthermore, the spatial heterogeneity of the influence strength of altitude and EVI is revealed, providing a new direction for further research on DSM in the future, i.e., spatial interpolation models considering the spatial heterogeneity of the influence of environmental variables.

Multi-dimensional spatial–temporal graph convolution for urban sensors imputation and enhancement

Spatio-temporal data are critical for intelligent systems, such as smart transportation and smart cities. However, due to sensor failure or power failure, the spatiotemporal data missing tends to have a big impact on downstream tasks. Meanwhile, if sensors are scarce, some spatial positions without sensors need data enhancement through intelligent methods. Existing workarounds focus on modeling temporal information (such as time series), often ignoring spatial dependency, or modeling the spatial and temporal domain separately for imputation. In this paper, we propose a Long-term Multi-dimensional Spatial–Temporal Graph Convolution Network (LMSTGCN), which not only inductively estimates some missing data, but also achieves data augmentation of target locations. It contains a periodic temporal encoding mechanism, a gated temporal capture module, and a multi-dimensional spatial–temporal GCN module. The long-term temporal dependencies are captured by the periodic temporal encoding mechanism. The spatial and extra-short-term temporal dependencies are simultaneously modeled by the multi-dimensional GCN module, which can achieve exponential growth in the range of receptive fields. Corresponding to this module, we designed a spatiotemporal adjacency matrix construction method. It generates spatiotemporal adjacency matrices of corresponding time length as needed. The short-term dependencies in sequences are captured by the gated temporal capture module. In experimental analysis, results demonstrate that the proposed model outperforms the state-of-the-art baselines on real-world data sets.

Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting

Recent works demonstrate that capturing correlations between road network nodes is crucial to improving traffic flow forecasting accuracy. In general, there are spatial, temporal, and joint spatial-temporal correlations between two nodes, whose strength is related to spatial and temporal position factors. For example, traffic congestion that occurs at a traffic hub has a wider and stronger impact than that at a branch road. Moreover, the above impacts can vary with temporal position. Although spatial-temporal graph convolution networks have become a popular paradigm for modeling those correlations, there are still three problems with existing models: (i) failing to effectively model joint spatial-temporal correlations; (ii) ignoring spatial and temporal position factors when modeling the aforementioned correlations; and (iii) failing to capture distinct spatial-temporal patterns of each node. To cope with the above issues, this paper proposes a novel Spatial-Temporal Position-aware Graph Convolution Network (STPGCN) for traffic flow forecasting. Specifically, a trainable embedding module is constructed to represent the spatial and temporal positions of the nodes. Subsequently, a spatial-temporal position-aware relation inference module is proposed to adaptively infer the correlation weights of the three important spatial-temporal relations. Based on this, the generated spatial-temporal relations are integrated into a graph convolution layer for aggregating and updating node features. Finally, we design a spatial-temporal position-aware gated activation unit in the graph convolution, to capture the node-specific pattern features under the guidance of position embedding. Extensive experiments on six real-world datasets demonstrate the superiority of our model in terms of prediction performance and computational efficiency.

A deep learning-based simulator for comprehensive two-dimensional GC applications.

Among the main approaches for predicting the spatial positions of eluates in comprehensive two-dimensional gas chromatography, the still under-explored computational models based on deep learning algorithms emerge as robust and reliable options due to their high adaptability to the structure and complexity of the data. In this work, an open-source program based on deep neural networks was developed to optimize chromatographic methods and simulate operating conditions outside the laboratory. The deep neural networks models were fit to convenient experimental predictors, resulting in scaled losses (mean squared error) equivalent to 0.006 (relative average deviation=8.56%, R2 =0.9202) and 0.014 (relative average deviation=1.67%, R2 =0.8009) in the prediction of the first- and second-dimension retention times, respectively. Good compliance was observed for the main chemical classes, such as environmental contaminants: volatile, semivolatile organic compounds, and pesticides; biochemistry molecules: amino acids and lipids; pharmaceutical industry and personal care products and residues: drugs and metabolites; among others. On the other hand, there is a need for continuous database updates to predict retention times of less common compounds accurately. Thus, forming a collaborative database is proposed, gathering voluntary findings from other users.

Effect of visual lateralization on the spatial position of individuals within a school of oval squid (Sepioteuthis lessoniana).

The spatial position of individuals within a social group, which provides the group members with benefits and costs, is determined by several physical and physiological factors. Lateralization (left and right asymmetry of morphology and behavior) could also be factors determining the individual's positions within a group. However, this possibility has been documented in some fish species, but never in an invertebrate species. This study investigates the association between spatial positions and lateralization in oval squid, Sepioteuthis lessoniana, which displays social behavior, such as schooling and lateralization for eye use (visual lateralization). The direction and strength of visual lateralization were determined for single squid by observing which eye was used to detect the prey, predators, and conspecifics. The spatial positions of individuals were determined by identifying whether the squids were in the left or right side from the center of the school. When the prey was presented to schooling squids, strongly lateralized squids against prey positioned themselves on the right side, whereas weakly lateralized squids positioned themselves on the left side. When the predator was presented to squids, the strongly lateralized squids against the conspecifics positioned themselves on the right side, and the weakly lateralized squids positioned themselves on the left side. When no targets were presented, the strongly lateralized squids against the predator positioned themselves on the right side, whereas the weakly lateralized squids positioned themselves on the left side. The strength of visual lateralization of oval squid could offer the defensive and offensive functions of schools with specific individual positions.

Rodent ultrasonic vocal interaction resolved with millimeter precision using hybrid beamforming.

Ultrasonic vocalizations (USVs) fulfill an important role in communication and navigation in many species. Because of their social and affective significance, rodent USVs are increasingly used as a behavioral measure in neurodevelopmental and neurolinguistic research. Reliably attributing USVs to their emitter during close interactions has emerged as a difficult, key challenge. If addressed, all subsequent analyses gain substantial confidence. We present a hybrid ultrasonic tracking system, Hybrid Vocalization Localizer (HyVL), that synergistically integrates a high-resolution acoustic camera with high-quality ultrasonic microphones. HyVL is the first to achieve millimeter precision (~3.4-4.8 mm, 91% assigned) in localizing USVs, ~3× better than other systems, approaching the physical limits (mouse snout ~10 mm). We analyze mouse courtship interactions and demonstrate that males and females vocalize in starkly different relative spatial positions, and that the fraction of female vocalizations has likely been overestimated previously due to imprecise localization. Further, we find that when two male mice interact with one female, one of the males takes a dominant role in the interaction both in terms of the vocalization rate and the location relative to the female. HyVL substantially improves the precision with which social communication between rodents can be studied. It is also affordable, open-source, easy to set up, can be integrated with existing setups, and reduces the required number of experiments and animals.

Rainbow trapping for sound waves in one-dimensional topological insulator

Over the recent decade, topological insulators, originating from the condensed matter physics, have resided at the frontier in the field of acoustics owing to their novel topological properties for manipulating robust wave propagation, which have also opened an intriguing landscape for potential applications. At the meantime, gradually slowing down acoustic waves with metamaterials allows temporary storage of sound, leading to the exploration of so-called trapped rainbow. However, most of the current studies are reported in a topological trivial context with complex structures, and it is hitherto still a challenge to obtain the high-efficient acoustic rainbow trapping effect in a straightforward setup. Here, we propose an acoustic gradient topological insulator in the one-dimensional system to realize a highly efficient rainbow trapping device. Based on the acoustic analogous Su–Schrieffer–Heeger model, we tune the eigenfrequencies of the topological interface states through modulating the neck widths of Helmholtz resonators. The experimentally measured pressure spectra clearly show that the proposed structure could tightly trap the broad-band sound waves at the target spatial positions. Our proposal may provide versatile possibilities for the design of topological acoustic devices.

Optimization design generation of the rural residential building towards energy saving by using “Digital intelligence” technology

Based on the “Digital Intelligence” technology, the scheme of building design can be automatically generated and optimized simultaneously. However, this method has not been reported, in terms of the optimization design generation by improving the building energy saving potential for the rural residential building. Here, according to the data of floor plans of the rural residential buildings collected from the hot summer and cold winter climate region (China), five types of floor plans were deduced. Firstly, we proposed the method of comprehensive score and weight assignment to achieve the optimum functional layout, according to evaluation indicators of the sum of the helpful and harmful radiations, the energy consumptions corresponding to the individual rooms in various spatial positions and the functional layout characters in the study region. Secondly, the plan configuration was also taken into account to improve the energy saving potential of design building, by using the evaluation indicator of plan-shaped coefficient. Based on the criteria of functional layout and plan configuration, the optimum modes for the five types of floor plans were derived. Thirdly, the component parameters of individual rooms in the design building were optimized by genetic algorithm. This study reveals the principle of the automatic generation of building design in synchronicity with improvement of its energy saving potential, and provides an achievement approach to the strategy of “rural revitalization” and the goal of “carbon peaking and carbon neutrality” in China.

Augmented Reality-Supported Rod Bending in Multilevel Spinal Fusion Using the ADVISE Software

One of the most common reasons for poor patient outcomes and revision surgery in spinal fusion is hardware failure. Screw loosening or pullout occurs in up to one-quarter of all cases. It is known that even small screw-rod misalignments can cause significant mechanical overloads during rod fixation, which can result in hardware failure. To address this crucial surgical step, a novel augmented reality-assisted software was developed to generate custom rod templates that are precisely adapted to the individual patient. The novel software, which runs on a tablet, is used in spinal fusion surgery and is based on the use of a specific pedicle screw system, in which the polyaxial screw heads are connected to detachable guides. These guides can be recognized by the tablet camera and a light detection and ranging scanner. This image information is processed to determine the spatial positions of the screw heads and to calculate an ideally fitting rod template. The calculated rod template is displayed in a 1-to-1 scale on the tablet screen. This template is used to cut and bend the rods of the pedicle screw system. Finally, the custom bent rod can be inserted into the screw heads without tension. The augmented reality-assisted software is intended to give surgeons access to patient-specific intraoperative real-time data, helping them in bending rods that are more precisely adapted to the individual patient compared with the freehand technique.

Hydrodynamics of Swirling Steam–Water Two-Phase Flows under External Shear

The hydrodynamics of steam–water two-phase flows under the effects of shearing, swirling, and large-scale discrete boundary conditions were investigated on an experimental basis. The steam was injected in a swirling configuration into concurrently flowing water. Both the steam and water were injected at gauge pressures of 1.0 and 2.0 bars, whereas the swirling was caused by a propeller moving at rotational speeds of 60 and 300 rpm. The ensembled normalized amplitudes of the velocity fluctuations across a layer defined by spatial positions along the radial and axial directions inhibited the swirling steam–water two-phase flows. This ensembled nature and the normalized amplitudes of the velocity fluctuations were investigated under the action of the above-stated operating conditions, and it was found that the swirling steam–water two-phase body of the flow showed proportionally varying percentages of ensembled-normalized amplitudes of the velocity fluctuations.

ISMOD: an integrative browser for image-based single-cell multi-omics data.

Genomic and transcriptomic image data, represented by DNA and RNA fluorescence in situ hybridization (FISH), respectively, together with proteomic data, particularly that related to nuclear proteins, can help elucidate gene regulation in relation to the spatial positions of chromatins, messenger RNAs, and key proteins. However, methods for image-based multi-omics data collection and analysis are lacking. To this end, we aimed to develop the first integrative browser called iSMOD (image-based Single-cell Multi-omics Database) to collect and browse comprehensive FISH and nucleus proteomics data based on the title, abstract, and related experimental figures, which integrates multi-omics studies focusing on the key players in the cell nucleus from 20 000+ (still growing) published papers. We have also provided several exemplar demonstrations to show iSMOD's wide applications-profiling multi-omics research to reveal the molecular target for diseases; exploring the working mechanism behind biological phenomena using multi-omics interactions, and integrating the 3D multi-omics data in a virtual cell nucleus. iSMOD is a cornerstone for delineating a global view of relevant research to enable the integration of scattered data and thus provides new insights regarding the missing components of molecular pathway mechanisms and facilitates improved and efficient scientific research.

Linear epitope identification of monoclonal antibodies against the duck Tembusu virus NS1

Since 2010, the duck Tembusu virus (DTMUV) has caused a severe outbreak of egg drop syndrome in laying ducks in China, which has resulted in substantial financial losses in the poultry industry. DTMUV nonstructural protein 1 (NS1), as the only secreted protein, could aid in the development of therapeutic antibodies and diagnostic techniques; however, there are few studies on the preparation and epitope identification of monoclonal antibodies (mAbs) against DTMUV NS1. In this study, by indirect enzyme-linked immunosorbent assay (ELISA), Western blotting, and indirect immunofluorescence assay, we screened 6 mAbs (8A4, 8E6, 10F12, 1H11, 3D5, 5C11) that could specifically recognize DTMUV NS1. For epitope mapping of mAbs, a series of GST-tagged truncated fusion proteins of DTMUV NS1 were constructed by prokaryotic expression. Finally, the 4 shortest linear epitopes were identified by indirect ELISA and Western blotting. The epitope 133FVIDGPK139 was recognized by 8A4, the epitope 243IPKTLGGP250 was recognized by 8E6, the epitope 267PWDEK271 was recognized by 10F12, and 156EDFGFGVL163 was recognized by 1H11, 3D5, and 5C11. By sequence alignment and cross-reaction tests, we found that 8A4 and 8E6 had high specificity for DTMUV NS1 compared with that of other mAbs, but 10F12, 1H11, 3D5, and 5C11 exhibited a clear degree of cross-reaction with dengue virus (DENV), Japanese encephalitis virus (JEV), West Nile virus (WNV), and Zika virus (ZIKV) NS1. Finally, the predicted crystal structure analysis showed the approximate spatial positions of the 4 epitopes on the NS1 dimer. In summary, our study revealed 2 specific mAbs for DTMUV NS1 recognition and 4 multiflavivirus mAbs for DENV, JEV, WNV, and ZIKV NS1 recognition.

How to Optimize High-Value GEP Areas to Identify Key Areas for Protection and Restoration: The Integration of Ecology and Complex Networks

Identifying and protecting key sites of ecological assets and improving spatial connectivity and accessibility are important measures taken to protect ecological diversity. This study takes Guangxi as the research area. Based on the gross ecosystem product (GEP), the ecological source is identified, and the initial ecological network (EN) is constructed by identifying the ecological corridor with the minimum cumulative resistance model. The internal defects of the initial ecological network are extracted using the circuit theory, the priority areas for restoration and protection with clear spatial positions are determined according to the complex network analysis, and the network’s performance before and after optimization is comprehensively evaluated. The results show that 456 initial ecological sources and 1219 ecological corridors have been identified, forming the initial ecological network of Guangxi. Based on the circuit theory, 168 ecological barriers, 83 ecological pinch points, and 71 ecological stepping stones were extracted for network optimization. After optimizing the ecological network, there are 778 ecological sources with a total area of 73,950.56 km2 and 2078 ecological corridors with a total length of 23,922.07 km. The GEP of the optimized structure is 13.33% higher than that of the non-optimized structure. The priority areas for protection are distributed in a large area, and the attached GEP reaches USD 118 billion, accounting for 72% of the total GEP attached to the optimized ecological source area. The priority areas for restoration are scattered in small patches, with a GEP of USD 19.27 billion. The robustness and connectivity of the optimized ecological network have been improved obviously. This study attempts to identify key sites of ecological assets and the priority regions for restoration and conservation using genuine geographical location and reference materials for regional ecological network optimization and implementation.

Influence of Heterogeneous Foundation on the Safety of Inverted Cone Bottom Oil Storage Tanks under Earthquakes

In order to study the mechanical properties of inverted cone bottom oil storage tanks under earthquakes when the foundation is uneven, finite element modeling calculation for a 20,000 m3 storage tank is carried out based on ANSYS Workbench. Wind load, hydraulic pressure load, and seismic load are all equalized as distributed loads with varying spatial positions. Considering various combinations of different heterogeneous foundations and seismic loads, and by adjusting the preset foundation bed coefficient, the final foundation bed coefficient and the maximum foundation settlement value when the equivalent stress of the tank floor reaches yield strength under different conditions are calculated. The results show that under the condition of heterogeneous foundation stiffness considering seismic action, when the coefficient of local foundation bed is higher than that of natural silty clay, the requirement for safe use of the inverted cone bottom storage tank can be met. Among the seven simulated heterogeneous foundation forms, the form with high foundation stiffness on the windward side has a great influence on the safety of storage tanks.