استكشف المجموعة الواسعة من العناوين المتاحة.

Mastering a handicraft skill requires not only being proficient in the use of tools and body coordination, but also the visual observation skills including planning, inspection and evaluation. In recent years, many studies of heritage education have focused on the development and promotion of traditional handicraft teaching. However, these studies rarely pay attention to effectively conveying the visual observation experience in procedural knowledge from experts to novices to help them learn handicraft skills. By analysing the eye-tracking metrics and visualizing the visual pattern of the expert, this study constructs an Eye-Movement Modelling Example (EMME) to demonstrate the expert model to novices to improve their ability to recognize and make appropriate judgements based on visual observations. A multi-dimensional evaluation was carried out to test the effectiveness of this novel instructional method in helping novices perceive visual knowledge from experts and learn handicrafts. Finally, this study successfully used eye-tracking technology to reveal the potential visual patterns of the expert in traditional handicraft making and confirm the role of EMME in promoting traditional handicraft teaching and inheritance. We provided a new way for the inheritance of traditional handicraft skills.

This article is formed from the experiences of the authors when observations occurred at Pasar Baru in Bandung, Indonesia. The experience is the essential part of the Lived Space perspective by Henri Lefebvre, and that is the right approach to studying the sense of place of Pasar Baru Bandung. This strategy intends to provide a different view of the distinct values and spirit of Pasar Baru Bandung through the numerous everyday activities in and around it. Answering the research objectives was optimized with the interview, visual data, and exploratory literature. The concept of lived space also can lead to a sense of attachment in those who visit Pasar Baru Bandung. Recognizing a place contributes to its sustainability, and the sense of Pasar Baru is an identity that is no longer seen by its appearance but includes experiences. This article provides another understanding of lived space through the experience in Pasar Baru Bandung. This article also provides knowledge about Indonesian local values to a wider audience.

This study addresses the pose estimation problem of an aircraft runway using visual observations in a landing approach scenario. The authors utilised the fact that the geodetic coordinates of most runways are known precisely with highly visible markers. Thus, the runway observations can increase the level of situational awareness during the landing approach, providing additional redundancy of navigation and less reliance on global positioning system. A novel pose optimisation algorithm is proposed utilising unit dual quaternion for the runway corner observations obtained from a monocular camera. The estimated runway pose is further fused with an inertial navigation system in an extended Kalman filter. An open‐source flight simulator is used to collect and process the visual and flight dataset during the landing approach, demonstrating reliable runway pose estimates and the improved inertial navigation solution.

Pilot visual behavior safety assessment is a cross-disciplinary technology that analyzes pilots’ gaze behavior and neurocognitive responses. This paper proposes a multimodal analysis method for pilot visual behavior safety, specifically for cognitive state decoding. This method aims to achieve a quantitative and efficient assessment of pilots’ observational behavior. Addressing the subjective limitations of traditional methods, this paper proposes an observational behavior detection model that integrates facial images to achieve dynamic and quantitative analysis of observational behavior. It addresses the “Midas contact” problem of observational behavior by constructing a cognitive analysis method using multimodal signals. We propose a bidirectional long short-term memory (LSTM) network that matches physiological signal rhythmic features to address the problem of isolated features in multidimensional signals. This method captures the dynamic correlations between multiple physiological behaviors, such as prefrontal theta and chest-abdominal coordination, to decode the cognitive state of pilots’ observational behavior. Finally, the paper uses a decision-level fusion method based on an improved Dempster–Shafer (DS) evidence theory to provide a quantifiable detection strategy for aviation safety standards. This dual-dimensional quantitative assessment system of “visual behavior–neurophysiological cognition” reveals the dynamic correlations between visual behavior and cognitive state among pilots of varying experience. This method can provide a new paradigm for pilot neuroergonomics training and early warning of vestibular-visual integration disorders.

The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm 2 (VASIA2) algorithms were used as well as the database of visual ice observations accumulated in the course of 15 Arctic expeditions. The comparison was performed in line with the SIC gradation (in tenths) into very open (1–3), open (4–6), close (7–8), very close and compact (9–10,10) ice, separately for summer and winter seasons. On average, in summer NT underestimates SIC by 0.4 tenth as compared to ship observations, while ASI and VASIA2 by 0.3 tenth. All three algorithms overestimate total SIC in regions of very open ice and underestimate it in regions of close, very close, and compact ice. The maximum average errors are typical of open ice regions that are most common in marginal ice zones. In winter, NT and ASI also underestimate SIC on average by 0.4 and 0.8 tenths, respectively, while VASIA2, on the contrary, overestimates by 0.2 tenth against the ship data, however, for open and close ice the average errors are significantly higher than in summer. In the paper, we also estimate the impact of ice melt stage and presence of new ice and nilas on SIC derived from NT, ASI, and VASIA2.

How is it that groups of neurons dispersed through the brain interact to generate complex behaviors? Three papers in this issue present brain-scale studies of neuronal activity and dynamics (see the Perspective by Huk and Hart). Allen et al. found that in thirsty mice, there is widespread neural activity related to stimuli that elicit licking and drinking. Individual neurons encoded task-specific responses, but every brain area contained neurons with different types of response. Optogenetic stimulation of thirst-sensing neurons in one area of the brain reinstated drinking and neuronal activity across the brain that previously signaled thirst. Gründemann et al. investigated the activity of mouse basal amygdala neurons in relation to behavior during different tasks. Two ensembles of neurons showed orthogonal activity during exploratory and nonexploratory behaviors, possibly reflecting different levels of anxiety experienced in these areas. Stringer et al. analyzed spontaneous neuronal firing, finding that neurons in the primary visual cortex encoded both visual information and motor activity related to facial movements. The variability of neuronal responses to visual stimuli in the primary visual area is mainly related to arousal and reflects the encoding of latent behavioral states. Science , this issue p. eaav3932 , p. eaav8736 , p. eaav7893 ; see also p. 236 Neurons in the primary visual cortex encode both visual information and motor activity. Neuronal populations in sensory cortex produce variable responses to sensory stimuli and exhibit intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Recording more than 10,000 neurons in mouse visual cortex, we observed that spontaneous activity reliably encoded a high-dimensional latent state, which was partially related to the mouse’s ongoing behavior and was represented not just in visual cortex but also across the forebrain. Sensory inputs did not interrupt this ongoing signal but added onto it a representation of external stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

How are behaviorally relevant representations of the outside world initiated and manifested in the mammalian brain? Marshel et al. combined a channelrhodopsin with an improved holographic stimulation technique to examine activity in the mouse visual cortex, including its deep layers. Optogenetic stimulation of neurons previously activated by natural visual stimuli recreated the original activity and behavior. Neuronal population activity typically propagated from cortical layer 2/3 to layer 5 rather than in the reverse direction. Stimulation of a larger number of cells was required to initiate activity in layer 2/3 than in layer 5. This indicates differences in ensemble coding between the two layers. Science , this issue p. eaaw5202 An optical neural interface reveals the dynamics of cortical network activity underlying mammalian behavior. Perceptual experiences may arise from neuronal activity patterns in mammalian neocortex. We probed mouse neocortex during visual discrimination using a red-shifted channelrhodopsin (ChRmine, discovered through structure-guided genome mining) alongside multiplexed multiphoton-holography (MultiSLM), achieving control of individually specified neurons spanning large cortical volumes with millisecond precision. Stimulating a critical number of stimulus-orientation-selective neurons drove widespread recruitment of functionally related neurons, a process enhanced by (but not requiring) orientation-discrimination task learning. Optogenetic targeting of orientation-selective ensembles elicited correct behavioral discrimination. Cortical layer–specific dynamics were apparent, as emergent neuronal activity asymmetrically propagated from layer 2/3 to layer 5, and smaller layer 5 ensembles were as effective as larger layer 2/3 ensembles in eliciting orientation discrimination behavior. Population dynamics emerging after optogenetic stimulation both correctly predicted behavior and resembled natural internal representations of visual stimuli at cellular resolution over volumes of cortex.

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes the cortical activity of nearly 60,000 neurons from six visual areas, four layers, and 12 transgenic mouse lines in a total of 243 adult mice, in response to a systematic set of visual stimuli. We classify neurons on the basis of joint reliabilities to multiple stimuli and validate this functional classification with models of visual responses. While most classes are characterized by responses to specific subsets of the stimuli, the largest class is not reliably responsive to any of the stimuli and becomes progressively larger in higher visual areas. These classes reveal a functional organization wherein putative dorsal areas show specialization for visual motion signals.

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from −3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.

Four methods have been used for the estimation of the total cloud amount and cloud amount for low clouds: visual observations, the Long method applied on pyranometer measurements, the Automatic Partial Cloud Amount Detection Algorithm (APCADA) method applied on pyrgeometers measurements, and ceilometer measurements of the cloud base height. Records from meteorological observers indicate that clear days (0–1 octa) represent the most frequent cloud amount for low clouds. In contrast, the total cloud amount is more aleatory. Results obtained from the Long method show maximum frequency in the extreme cloud amount values. The APCADA method also indicates the predominance of cloudless skies. The ceilometer method shows a predominance of completely clear skies, but the completely cloudy (8 octas) is the second most frequent case. Automatic methods report more cloudless and overcast skies than the observer. Automatic methods agree with the visual method or differ in ±1 octa for 60–76% cases for low cloud amount and for 56–63% cases for total cloud amount. In general, low cloud amount agrees more with observer measurements than total cloud amount and the automatic methods underestimated total cloud amount observer values possibly due to the difficulty in monitoring high clouds.