Motion Categories Research Articles

Influence of ground motion parameters on seismic response of a large-longitudinal-slope and small-radius curved girder bridge.

The mechanical performance of curved bridges under the action of an earthquake is complex. To obtain the influence of seismic parameters on the seismic response of curved girder bridges, this paper relies on a large slope small-radius curved steel box girder bridge (LSCGB) and selects seismic wave incidence angle, vertical component of ground motion, and site category as seismic parameters to carry out nonlinear time history analysis. Based on the analysis results of the case bridge, it is shown that the torsional vibration of the first 10 modes of LSCGB is significant, the modes are dispersed, and the contribution of high-order modes of vibration cannot be ignored. The most unfavorable seismic wave incidence angle is in the direction of 45°∼60° counterclockwise Angle from the central connection line of Pier No. 1 and Pier No. 4 of the bridge. The seismic response of the curved bridge components increases with the vertical seismic intensity, and the influence on displacement responses is more significant. The basic vibration period of curved girder bridges built on soft soil sites is extended by approximately 18.23%, and the seismic response of key components increases with the softening of the site soil. Therefore, when analyzing the seismic response of LSCGBs, the influence of vertical component of ground motion and site category should not be ignored.

A Systematic Review of the Most Recent Concepts in Kinetic Shading Systems with a Focus on Biomimetics: A Motion/Deformation Analysis

In the context of sustainability and ambitious goals for reducing CO2 emissions, modifying transparency in architecture becomes a crucial tool for managing energy flow into buildings. Kinetic shading systems (KSSs) regulate light and heat entry into a room, thereby reducing energy consumption and CO2 emissions and improving daylight comfort. Recent advancements in KSSs have led to a significant increase in published papers since early 2022. This paper systematically reviews recent technological innovations in KSSs and presents the mechanical principles utilized in these systems. Given the kinetic/mechanical nature of all case studies examined, a categorization based on ‘type of motion and deformation’ was used, ranging from the simplest to the most complex solutions. In the context of kinetic systems, the motion category addresses the displacement (translation, rotation, or both) of rigid façade elements, while deformation describes the transformation that changes the shape of these elements. The data are presented in tabular form, including details about building type, climate zone, research type, evaluation, and before and after values. Additionally, some reviewed systems’ authors drew inspiration from nature, employing biomimetic methods to design KSSs. Despite considerable growth, these solutions still represent only 21% of all analyzed shading system cases. This topic is extensively discussed, considering tropical and nastic plant movements towards this paper’s conclusion. The PRISMA protocol was used to review, screen, select, and retrieve all cited papers. This review covers the most recent publications from 2022 to April 2024, recorded in the WoS and Scopus databases, and includes 66 papers.

Fictive motion categories in modern Persian: A cognitive semantic approach

Motion is the cornerstone of cognitive structures which can be signified factively and fictively. Fictive motion events in English are classified into six categories (Talmy 2000a). While Iranian linguists have worked on verbs and typology of fictive motion, they almost failed to discover these categories. This study made an attempt to examine these categories by addressing the questions: What are fictive motion categories in Persian? To what extent is the speakers’ cognition influenced by the structure of the language they speak? What are Persian speakers’ preferable tenses? With this aim, using convenience sampling and qualitative research design, sixteen sentences extracted from Talmy’s model plus nine fillers were rendered into Persian. To avoid ordering effect, sixty participants were organized in a counterbalanced technique and accomplished completion and picture description tasks so that one half of the participants filled the completion task first and dealt with the picture description task later, while others acted in the reverse order. In completion task the participants performed accurately except the pattern paths category. However, they did not produce fictive motion sentences for the prospect, pattern, and advent paths, frame relative with factively stationary observer and site manifestation in the picture description task. They tended to adopt present tense in producing fictive motion sentences for the depicted categories. More importantly, the paper analyses the participants’ performance in completing the tasks related to the sixteen fictive motion categories and shows that cognition is influenced by the linguistic structure of the speakers. It is hoped that this study can be a contribution to the field of cognitive and semantic linguistics and also advance fictive motion studies in different languages.

Skeleton-aware Graph-based Adversarial Networks for Human Pose Estimation from Sparse IMUs

Recently, sparse-inertial human pose estimation (SI-HPE) with only a few IMUs has shown great potential in various fields. The most advanced work in this area achieved fairish results using only six IMUs. However, there are still two major issues that remain to be addressed. First, existing methods typically treat SI-HPE as a temporal sequential learning problem and often ignore the important spatial prior of skeletal topology. Second, there are far more synthetic data in their training data than real data, and the data distribution of synthetic data and real data is quite different, which makes it difficult for the model to be applied to more diverse real data. To address these issues, we propose “Graph-based Adversarial Inertial Poser (GAIP)”, which tracks body movements using sparse data from six IMUs. To make full use of the spatial prior, we design a multi-stage pose regressor with graph convolution to explicitly learn the skeletal topology. A joint position loss is also introduced to implicitly mine spatial information. To enhance the generalization ability, we propose supervising the pose regression with an adversarial loss from a discriminator, bringing the ability of adversarial networks to learn implicit constraints into full play. Additionally, we construct a real dataset that includes hip support movements and a synthetic dataset containing various motion categories to enrich the diversity of inertial data for SI-HPE. Extensive experiments demonstrate that GAIP produces results with more precise limb movement amplitudes and relative joint positions, accompanied by smaller joint angle and position errors compared to state-of-the-art counterparts. The datasets and codes are publicly available at https://cslinzhang.github.io/GAIP/ .

Cognitive style of field dependence-independence modulates the working memory storage of biological motion.

The biological motion refers to the continuous configuration movement of live agents in space. The perceptual processing of biological motion has the specificity of the dissociation between body form and body motion. However, there is limited evidence for whether such specificity continues when holding biological motion in working memory. We explored this question from the perspective of field dependence (FD) and field independence (FI) cognitive styles in the current study. Three categories of biological motion have been developed: intact movement, motion feature, and form feature. We examined the working memory capacity of motion features, form features, intact movements (Experiments 1-3), and the recognition of three categories of biological motion when remembering intact movements (Experiment 4). The results showed that for the motion features, FI individuals had better memory performance when remembering five items and showed greater working memory capacity and recognition compared with FD individuals, whereas the opposite pattern was observed between FI and FD individuals for the form features. The cognitive style could modulate the working memory storage of biological motion when the task becomes demanding, suggesting that body form and body motion are dissociable in working memory. Our study provided additional evidence for the specificity of biological motion processing in working memory, extending the hierarchical neural model. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Virtual range of motion is a safe and acceptable alternative to intra-operative manual range of motion testing in direct anterior robotic total hip arthroplasty

IntroductionIn robotic total hip arthroplasty (THA), virtual range of motion (VROM) modeling allows the surgeon to account for spinopelvic motion and create an impingement-free range of motion that is patient-specific. The primary purpose of this study was to evaluate the risk of dislocation in patients undergoing direct anterior THA using VROM rather than manual ROM trialing. MethodsProspectively collected data was reviewed retrospectively of all consecutive anterior THAs performed by a single fellowship-trained surgeon. No patients were excluded from the study. VROM identified bone and implant impingement, which was recorded in degrees of hip external rotation(ER) at 0° of hip extension (standing), and in degrees of hip internal rotation(IR) at 90° of hip flexion (sitting). No patients had manual ROM trialing performed. Dislocation events were recorded during the first 3 months of follow-up. 362 patients, with a mean age of 67 and mean BMI of 28.8, were included. ResultsThis cohort, including 154 patients (42.5%) with abnormal spinopelvic motion, demonstrated zero dislocations using VROM. The average ER impingement occurred at 60.9° ER (range 20-90°), and 50.8° IR (range 25-90°). Patients with ER impingement <55° had significantly less acetabular anteversion (16.4° ±3.3°, p < 0.001). Compared to the entire cohort, anteversion of the acetabulum was also decreased in the stuck-sitting subgroup (17.4° ±3.3°, p < 0.001) and increased in the stuck standing subgroup (20.5° ±3.6°, p < 0.001). ER impingement was a stronger predictor of acetabular anteversion than spinopelvic motion category (r = 0.458). Patients with ER impingement <45° (6.4%) or IR impingement <35° (6.6%) were “early impingers”. ConclusionIn this cohort of anterior THA patients with a high proportion of abnormal spinopelvic motion, a technique utilizing only VROM produced no dislocations. An impingement-free zone of 45° ER standing and 35° IR sitting is recommended.

Self-Supervised Pretraining Based on Noise-Free Motion Reconstruction and Semantic-Aware Contrastive Learning for Human Motion Prediction

Human motion prediction is to forecast future human motions based on the observed ones. Currently, a prediction model always includes an encoder to extract the features of the observed motions and a decoder for motion prediction with the extracted features. However, existing encoders have limited abilities of anti-interference and semantic attribute learning; thus, the prediction results are not fully satisfactory. To solve this problem, we propose two novel pretext tasks, i.e., noise-free motion reconstruction and semantic-aware contrastive learning, to implement self-supervised pretraining on the encoder. The former extracts the features of a noise-added motion to reconstruct a noise-free motion. This pretext task can improve the ability of anti-interference of the encoder. The latter shortens the distance between two motions with the same motion category while widening the distance between two motions from different motion categories. This pretext task enables the encoder to capture the semantic commonality of the motions with the same motion category, thereby enhancing the encoder's ability of semantic attribute learning. After implementing the self-supervised pretraining, the pretrained encoder is connected with a decoder to build a prediction model called ReLe-GCN, which is fine-tuned for human motion prediction. The results on public datasets show the high accuracies of ReLe-GCN in both short-term and long-term motion predictions.

A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar.

Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range-time-Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m.

Posterior Parietal Cortex Plays a Causal Role in Abstract Memory-Based Visual Categorical Decisions.

Neural activity in the lateral intraparietal cortex (LIP) correlates with both sensory evaluation and motor planning underlying visuomotor decisions. We previously showed that LIP plays a causal role in visually-based perceptual and categorical decisions, and preferentially contributes to evaluating sensory stimuli over motor planning. In that study, however, monkeys reported their decisions with a saccade to a colored target associated with the correct motion category or direction. Since LIP is known to play a role in saccade planning, it remains unclear whether LIP's causal role in such decisions extend to decision-making tasks which do not involve saccades. Here, we employed reversible pharmacological inactivation of LIP neural activity while two male monkeys performed delayed match to category (DMC) and delayed match to sample (DMS) tasks. In both tasks, monkeys needed to maintain gaze fixation throughout the trial and report whether a test stimulus was a categorical match or nonmatch to the previous sample stimulus by releasing a touch bar. LIP inactivation impaired monkeys' behavioral performance in both tasks, with deficits in both accuracy and reaction time (RT). Furthermore, we recorded LIP neural activity in the DMC task targeting the same cortical locations as in the inactivation experiments. We found significant neural encoding of the sample category, which was correlated with monkeys' categorical decisions in the DMC task. Taken together, our results demonstrate that LIP plays a generalized role in visual categorical decisions independent of the task-structure and motor response modality.SIGNIFICANCE STATEMENT Neural activity in the lateral intraparietal cortex (LIP) correlates with perceptual and categorical decisions, in addition to its role in mediating saccadic eye movements. Past work found that LIP is causally involved in visual decisions that are rapidly reported by saccades in a reaction time based decision making task. Here we use reversible inactivation of LIP to test whether LIP is also causally involved in visual decisions when reported by hand movements during delayed matching tasks. Here we show that LIP inactivation impaired monkeys' task performance during both memory-based discrimination and categorization tasks. These results demonstrate that LIP plays a generalized role in visual categorical decisions independent of the task-structure and motor response modality.

Development and validation of a four-dimensional registration technique for DCE breast MRI

BackgroundPatient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.ResultsAfter applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman’s rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient Ktrans as surrogate parameter for motion artifacts. Mean Ktrans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001).ConclusionsFour-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.

Design spectra to be used in Base Isolation Design in light of recent strong motion records

Strong motion records have shown an increase of the maximum values of PGA and PGV, also associated to the increase of the archived strong motion records. Nowadays, the worldwide largest PGA values are: 3,94g (vertical) and 2,75g (horizontal), from the 2008 Iwate-Miyagi and 2011 Tohoku earthquakes, while the current PGV maxima are larger than 2.0 m/s (horizontal and vertical). These unforeseen values have encouraged a more conscious approach in BIS design, being necessary to move to larger design periods and to face wider design displacements. Many changes have occurred in the history of BIS, as indicated in Design Recommendations for Seismically Isolated Buildings by AIJ (2016), tracing the improvements of BIS in Japan, and in De Luca and Guidi (2019). In this excursion, a significant contribution has been given by Miyazaki (2008) that defined a “Next Generation of seismic isolation” to counteract the unprecedented spectral values of displacement and acceleration. In this perspective, the paper analyzes the acceleration, velocity and displacement spectra of different sets of strong motion records to provide suggestions for seismic design of base-isolated structures in the light of higher spectral values. In particular, we will consider: the set defined in Trevor Kelly (2001), that divided motions into pre-1971 and post-1971; the set of FEMA307 (1999), in which three categories of ground motion are considered (short duration, long duration and forward directivity); the set used by Miyazaki (2008) for the Next Generation of Seismic Isolation; the set defined by Suzuki and Iervolino (2017) to trace the history of largest PGA and PGV; the set used by De Luca and Guidi (2019) to trace the improvements in BIS design and propose a classification of BIS in three successive generations. A comparison between results, for different periods, will lead to possible design indications for BIS.

Abstract Encoding of Categorical Decisions in Medial Superior Temporal and Lateral Intraparietal Cortices.

Categorization is an essential cognitive and perceptual process for decision-making and recognition. The posterior parietal cortex, particularly the lateral intraparietal (LIP) area has been suggested to transform visual feature encoding into abstract categorical representations. By contrast, areas closer to sensory input, such as the middle temporal (MT) area, encode stimulus features but not more abstract categorical information during categorization tasks. Here, we compare the contributions of the medial superior temporal (MST) and LIP areas in category computation by recording neuronal activity in both areas from two male rhesus macaques trained to perform a visual motion categorization task. MST is a core motion-processing region interconnected with MT and is often considered an intermediate processing stage between MT and LIP. We show that MST exhibits robust decision-correlated motion category encoding and working memory encoding similar to LIP, suggesting that MST plays a substantial role in cognitive computation, extending beyond its widely recognized role in visual motion processing.SIGNIFICANCE STATEMENT Categorization requires assigning incoming sensory stimuli into behaviorally relevant groups. Previous work found that parietal area LIP shows a strong encoding of the learned category membership of visual motion stimuli, while visual area MT shows strong direction tuning but not category tuning during a motion direction categorization task. Here we show that the medial superior temporal (MST) area, a visual motion-processing region interconnected with both LIP and MT, shows strong visual category encoding similar to that observed in LIP. This suggests that MST plays a greater role in abstract cognitive functions, extending beyond its well known role in visual motion processing.

Development of an Early Fire Detection Technique Using a Passive Infrared Sensor and Deep Neural Networks

Early detection of fire is key to mitigate fire related damages. This paper presents a differential pyro-electric infrared (PIR) sensor and deep neural networks (DNNs) based method to detect fire in real-time. Since the PIR sensor is sensitive to sudden body motions and emits a continuous time-varying signal, experiments are carried out to collect human and fire motions using a PIR sensor. These signals are processed using one-dimensional continuous wavelet transform to perform feature extraction. The corresponding wavelet coefficients are converted into RGB spectrum images that are then used as inputs for a deep convolutional neural network. Various pre-trained DNN architectures are adopted to train and identify the collected data for background (no motion), human motion, and fire categories: small quasi-static and spreading fires. Experimental results show that the ShuffleNet architecture yields the highest prediction accuracy of 87.8%. Experimental results for the real-time strategy which works at a speed of 12 frames-per-second show 95.34% and 92.39% fire and human motion detection accuracy levels respectively.

The impact of atmospheric motions on source-specific black carbon and the induced direct radiative effects over a river-valley region

Abstract. Black carbon (BC) is one of the most important short-lived climate forcers, and atmospheric motions play an important role in determining its mass concentrations of pollutants. Here an intensive observation was launched in a typical river-valley city to investigate relationships between atmospheric motions and BC aerosols. Equivalent BC (eBC) source apportionment was based on an aethalometer model with the site-dependent absorption Ångström exponents (AAEs) and the mass absorption cross sections (MACs) retrieved using a positive matrix factorization (PMF) model based on observed chemical components (i.e., EC, POC, K+, Mg, Al, Si, S, Cl, Ca, V, Mn, Fe, Ni, Cu, As, Se, Br, Sr, Pb, Ga, and Zn) and primary absorption coefficients at selected wavelengths from λ=370 to 880 nm. The derived AAEs from 370 to 880 nm were 1.07 for diesel vehicular emissions, 2.13 for biomass burning, 1.74 for coal combustion, and 1.78 for mineral dust. The mean values for eBCfossil and eBCbiomass were 2.46 and 1.17 µg m−3, respectively. Wind run distances and the vector displacements of the wind in 24 h were used to construct a self-organizing map, from which four atmospheric motion categories were identified (local-scale dominant, local-scale strong and regional-scale weak, local-scale weak and regional-scale strong, and regional-scale dominant). BC pollution was found to be more likely when the influence of local-scale motions outweighed that of regional-scale motions. Cluster analysis for the back-trajectories of air mass calculated by the Hybrid Single-Particle Lagrangian Integrated Trajectory model at the study site indicated that the directions of air flow can have different impacts for different scales of motion. The direct radiative effects (DREs) of source-specific eBC were lower when the influence of regional-scale motions outweighed that of the local ones. However, due to chemical aging of the particles during transport – the DRE efficiencies under regional-scale motions were ∼ 1.5 times higher than those under more local influences. The finding that the DRE efficiency of BC increased during the regional transport suggested significant consequences in regions downwind of pollution sources and emphasizes the importance of regionally transported BC for potential climatic effects.

Fluid physics of telescoping cardboard boxes

Telescoping boxes are widely used to store and transport, e.g., board games, yet knowledge of the physical processes relevant to the end-user experience is currently unavailable. We combine observations on real product packaging with low-Reynolds-number theory and controlled experiments. Three distinct categories of lid motion are identified, controlled by flow in a thin film of air in the gap separating the lid and the base of the box. Finally, the optimal box design that combines the antagonistic criteria of safety and speed is identified.

Enhanced Intra String Copy for Screen Content Coding in AVS3

Driven by growing applications that use computer screens as interfaces for daily remote interactions, almost all current video coding standards have included screen content coding (SCC) tools. Recently, an efficient SCC tool called intra string copy (ISC) has been adopted in the third-generation of audio video coding standard in China (AVS3). ISC has two coding unit (CU) level sub-modes: fully-matching-string and partially-matching-string based string prediction (FPSP) sub-mode and equal-value-string, unit-basis-vector-string, and unmatched-pixel-string based string prediction (EUSP) sub-mode. To further improve the coding efficiency of SCC, this paper proposes four enhancement techniques of ISC (EISC), including CU partition improvements, point vector (PV) relocation and reactivation, line-based overlapping string prediction, and an optimized coding method for string length in the EUSP sub-mode. Compared with the latest AVS3 reference software HPM with EISC disabled, using AVS3 SCC common test condition and YUV test sequences in text and graphics with motion and mixed content categories, the proposed technique achieves an average Y BD-rate reduction of 2.39% and 1.49% for all intra (AI) and low-delay B (LDB) configurations, respectively, with low additional encoding complexity and almost no additional decoding complexity. All proposed ISC enhancement techniques have been adopted in AVS3.

Head movement during cerebral CT perfusion imaging of acute ischaemic stroke: Characterisation and correlation with patient baseline features

PurposeTo quantitatively characterise head motion prevalence and severity and to identify patient-based risk factors for motion during cerebral CT perfusion (CTP) imaging of acute ischaemic stroke. MethodsThe head motion of 80 stroke patients undergoing CTP imaging was classified retrospectively into four categories of severity. Each motion category was then characterised quantitatively based on the average head movement with respect to the first frame for all studies. Statistical testing and principal component analysis (PCA) were then used to identify and analyse the relationship between motion severity and patient baseline features. Results46/80 (58%) of patients showed negligible motion, 19/80 (24%) mild-to-moderate motion, and 15/80 (19%) considerable-to-extreme motion sufficient to affect diagnostic/therapeutic accuracy even with correction. The most prevalent movement was “nodding” with maximal translation/rotation in the sagittal/axial planes. There was a tendency for motion to worsen as scan proceeded and for faster motion to occur in the first 15 s. Statistical analyses showed that greater stroke severity (National Institutes of Health Stroke Scale (NIHSS)), older patient age and shorter time from stroke onset were predictive of increased head movement (p < 0.05 Kruskal-Wallis). Using PCA, the combination of NIHSS and patient age was found to be highly predictive of head movement (p < 0.001). ConclusionsQuantitative methods were developed to characterise CTP studies impacted by motion and to anticipate patients at-risk of motion. NIHSS, age, and time from stroke onset function as good predictors of motion likelihood and could potentially be used pre-emptively in CTP scanning of acute stroke.

Development of a laparoscopic surgical skills simulation curriculum: Enhancing resident training through directed coaching and closed-loop feedback

BackgroundPerformance feedback through peer coaching and rigorous self-assessment is a critical part of technical skills improvement. However, formal collaborative programs using operative video-based skills assessments to generate peer coaching feedback have only been validated among attending surgeons. In this study, we developed a unique longitudinal, simulation video-based laparoscopic skills resident curriculum using video-based peer coaching and evaluated its association with skills acquisition among surgical trainees. MethodsThe laparoscopic simulation curriculum consists of a pre-practice laparoscopic skill video recording, followed by receipt of directed coaching and feedback on performance from a faculty coach, a peer coach, and self-coaching. Residents then completed 6 weeks of feedback-directed practice and submitted a second post-practice laparoscopic skill video recording of the same skill, which was evaluated by a minimally invasive surgery expert grader. All general surgery residents in a single institution were enrolled, with 107 residents completing the curriculum in its initial 2 years. ResultsOverall, more than two-thirds of residents achieved skills proficiency on their expert assessments, with similar rates of residents achieving skills proficiency at all postgraduate year levels. Significant improvements between the pre-practice assessments and post-practice assessments were most frequently seen in the instrument handling, precision, and motion & flow categories (P < .05 each). Faculty provided the highest number and proportion of closed-loop comments; residents’ self-coaching feedback had the lowest number of closed-loop comments, with 83% of self-assessments containing none. ConclusionIn this study, we describe the successful implementation of a longitudinal laparoscopic skills video-based coaching curriculum designed to improve residents’ laparoscopic technical abilities through iterative directed practice supplemented by formative closed-loop feedback. This feasible, reproducible, and low-cost simulation curriculum can be adapted to other training programs and skills acquisition endeavors. This program also prepares trainees for ongoing performance feedback after completion of residency through rigorous self-assessment and peer-to-peer coaching.

Pupillary response to real, illusory, and implied motion.

The perception of moving objects (real motion) is a critical function for interacting with a dynamic environment. Motion perception can be also induced by particular structural features of static images (illusory motion) or by photographic images of subjects in motion (implied motion, IM). Many cortical areas are involved in motion processing, particularly the medial temporal cortical area (MT), dedicated to the processing of real, illusory, and implied motion. Recently, there has been a growing interest in the influence of high-level visual processes on pupillary responses. However, just a few studies have measured the effect of motion processing on the pupil, and not always with consistent results. Here we systematically investigate the effects of real, illusory, and implied motion on the pupil diameter for the first time, by showing different types of stimuli (movies, illusions, and photos) with the same average luminance to the same observers. We find different pupillary responses depending on the nature of motion. Real motion elicits a larger pupillary dilation than IM, which in turn induces more dilation than control photos representing static subjects (No-IM). The pupil response is sensitive even to the strength of IM, as photos with enhanced IM (blur, motion streaks, speed lines) induce larger dilation than simple freezed IM (subjects captured in the instant they are moving). Also, the subject represented in the stimulus matters: human figures are interpreted as more dynamic and induce larger dilation than objects/animals. Interestingly, illusory motion induces much less dilation than all the other motion categories, despite being seen as moving. Overall, pupil responses depend on the individual perception of dynamicity, confirming that the pupil is modulated by the subjective interpretation of complex stimuli. We argue that the different pupillary responses to real, illusory, and implied motion reflect the top-down modulations of different cortical areas involved in their processing.

Prototype Development of Small Mobile Robots for Mallard Navigation in Paddy Fields: Toward Realizing Remote Farming

This study was conducted to develop robot prototypes of three models that navigate mallards to achieve high-efficiency rice-duck farming. We examined two robotics navigation approaches based on imprinting and feeding. As the first approach, we used imprinting applied to baby mallards. They exhibited follow behavior to our first prototype after imprinting. Experimentally obtained observation results revealed the importance of providing imprinting immediately up to one week after hatching. As another approach, we used feed placed on the top of our second prototype. Experimentally obtained results showed that adult mallards exhibited wariness not only against the robot, but also against the feeder. After relieving wariness with provision of more than one week time to become accustomed, adult mallards ate feed in the box on the robot. However, they ran away immediately at a slight movement. Based on this confirmation, we developed the third prototype as an autonomous mobile robot aimed for mallard navigation in a paddy field. The body width is less than the length between rice stalks. After checking the waterproof capability of a body waterproof box, we conducted an indoor driving test for manual operation. Moreover, we conducted outdoor evaluation tests to assess running on an actual paddy field. We developed indoor and outdoor image datasets using an onboard monocular camera. For the outdoor image datasets, our segmentation method based on SegNet achieved semantic segmentation for three semantic categories. For the indoor image datasets, our prediction method based on CNN and LSTM achieved visual prediction for three motion categories.