Static Motion Research Articles

Neural Differential Appearance Equations

We propose a method to reproduce dynamic appearance textures with space-stationary but time-varying visual statistics. While most previous work decomposes dynamic textures into static appearance and motion, we focus on dynamic appearance that results not from motion but variations of fundamental properties, such as rusting, decaying, melting, and weathering. To this end, we adopt the neural ordinary differential equation (ODE) to learn the underlying dynamics of appearance from a target exemplar. We simulate the ODE in two phases. At the "warm-up" phase, the ODE diffuses a random noise to an initial state. We then constrain the further evolution of this ODE to replicate the evolution of visual feature statistics in the exemplar during the generation phase. The particular innovation of this work is the neural ODE achieving both denoising and evolution for dynamics synthesis, with a proposed temporal training scheme. We study both relightable (BRDF) and non-relightable (RGB) appearance models. For both we introduce new pilot datasets, allowing, for the first time, to study such phenomena: For RGB we provide 22 dynamic textures acquired from free online sources; For BRDFs, we further acquire a dataset of 21 flash-lit videos of time-varying materials, enabled by a simple-to-construct setup. Our experiments show that our method consistently yields realistic and coherent results, whereas prior works falter under pronounced temporal appearance variations. A user study confirms our approach is preferred to previous work for such exemplars.

A Multi-Scene Roadside Lidar Benchmark towards Digital Twins of Road Intersections

Abstract. In recent years, the evolution of digital twin technology has paved the way for the construction of intelligent holographic intersections. This can be facilitated by utilizing precise point clouds from roadside lidar. With its capability of real-time monitoring, lidar plays a crucial role in enhancing intersection perception, enabling precise detection and tracking of road objects, as well as providing accurate speed estimates. Despite the introduction of few roadside lidar datasets aimed at enhancing supervised learning algorithms, their applicability to intelligent intersection monitoring remains limited. To address this, this paper presents an Intelligent Intersections (Int2sec) dataset, which exhibits several salient features: 1) it encompasses a broad array of urban intersection scenarios accompanied by a substantial quantity of object annotations; 2) the deployment of dual lidar stations facilitates a thorough scanning of scenes, thereby ensuring expansive scene coverage and mitigating the mutual occlusion phenomenon amongst objects; and 3) the dataset not only catalogues the coordinates, dimensions, and orientations of objects but also encompasses additional attributes such as tracking IDs and real-time motion statuses. Furthermore, the paper evaluates the efficacy of various prominent benchmarking networks, providing a critical analysis and prospective for future research.

From Newton's First Law to the Motion of Celestial Bodies

Newton's original work "Principles" was written in Latin. An English version translated two years after his death changed the first law from "in directum" to "in a right line." Later generations think that only static or uniform straight line motion is inertia, while excluding rotational motion. This article proposes that the gravitational potential energy of a planet's revolution is equal to its centrifugal kinetic energy (mυ2). The work done by an object is a process quantity, but kinetic energy is a state variable that can be divided into instantaneous kinetic energy and average kinetic energy. Uniform motion does not do work, and instantaneous kinetic energy in variable speed motion does not also do work, only the average kinetic energy of variable speed motion does work. Only the energy between work and the average kinetic energy of variable speed motion can be converted into each other. We have clarified the relationship between work and kinetic energy, completed the transition of the kinetic energy formula at low and high speeds, achieved the unification of the kinetic energy formula and the mass energy relationship formula, and explained the √2 factor in the escape velocity..

Analysis of plantar pressure of midsole prepared by 3d printed biomimetic structures with different densities

AbstractThis study investigates the impact of 3D printed midsoles with biomimetic structures of varying densities on plantar pressure during static and dynamic motions. The midsoles were designed with three densities of Tyson polygon (TS) structures: 1TS, 2TS, and 3TS. Plantar pressure tests were conducted on midsoles during static and dynamic motions such as walking, running, and jumping. The data were analyzed based on hypotheses related to samples, motions, and 10 plantar pressure zones. As results, for static motion, all midsoles improved pressure distribution and reduced peak pressure compared to barefoot conditions, with 1TS being the most effective. During dynamic motions, 1TS and 2TS effectively distributed plantar pressure in the midfoot and heel areas, while 3TS provided better support and stability during high-intensity activities like jumping. Statistical analysis revealed that 1TS offered comfort and flexibility but lacked support, 2TS balanced support and cushioning, and 3TS provided superior support and stability but reduced elasticity during jumps. In dynamic motions, 1TS excelled in walking, and 2TS performed best in high-intensity activities such as running and jumping. In the meta areas (M2 and M3), 1TS reduced pressure by over 30% during walking and nearly 40% during running, while 3TS showed similar reductions during jumping, with BF showing higher pressures compared to running. Thus, this study highlights the effectiveness of 1TS and 2TS in reducing pressure in the meta and midfoot areas, emphasizing the importance of selecting the right midsole density for optimal comfort and performance across different activities.

Optical fibre based artificial compound eyes for direct static imaging and ultrafast motion detection

Natural selection has driven arthropods to evolve fantastic natural compound eyes (NCEs) with a unique anatomical structure, providing a promising blueprint for artificial compound eyes (ACEs) to achieve static and dynamic perceptions in complex environments. Specifically, each NCE utilises an array of ommatidia, the imaging units, distributed on a curved surface to enable abundant merits. This has inspired the development of many ACEs using various microlens arrays, but the reported ACEs have limited performances in static imaging and motion detection. Particularly, it is challenging to mimic the apposition modality to effectively transmit light rays collected by many microlenses on a curved surface to a flat imaging sensor chip while preserving their spatial relationships without interference. In this study, we integrate 271 lensed polymer optical fibres into a dome-like structure to faithfully mimic the structure of NCE. Our ACE has several parameters comparable to the NCEs: 271 ommatidia versus 272 for bark beetles, and 180o field of view (FOV) versus 150–180o FOV for most arthropods. In addition, our ACE outperforms the typical NCEs by ~100 times in dynamic response: 31.3 kHz versus 205 Hz for Glossina morsitans. Compared with other reported ACEs, our ACE enables real-time, 180o panoramic direct imaging and depth estimation within its nearly infinite depth of field. Moreover, our ACE can respond to an angular motion up to 5.6×106 deg/s with the ability to identify translation and rotation, making it suitable for applications to capture high-speed objects, such as surveillance, unmanned aerial/ground vehicles, and virtual reality.

Status of off-bottom mariculture in wave-exposed environments. Part 2. Comparative loading and motion of longline designs currently used in exposed commercial farms

A global inventory of extractive species mariculture in wave-exposed temperate waters shows that the longline is the technology used in more than 99% of the sites (Part 1 of this review). In this second part, I compare the static (longline at rest), quasi-static (tidal sea surface elevation, steady currents and mainline lifting operation) and dynamic (wind seas and swells) loading and motion of surface, semi-submerged and fully submerged longlines used to grow bivalves and kelp. This review is based on a hundred papers published on the subject mostly after 2010 and on simple analytical models used to illustrate the many compromises that must be made to ensure the survivability of the structure and the survival (retention), growth and quality of the cultured biomass. Surface longlines are unsuitable for fully exposed environments. To mitigate storm energy it is necessary to minimize the volume of surface buoys and submerge the mainline to the maximum depth possible. There is however a limit to minimizing the volume of surface buoys due to the uplifting of the mainline by currents. In the case of kelp, its optimal growing depth is within a few meters from the sea surface. This limitation can be partly circumvented by having the kelp float above the mainline. In the case of bivalves, mainline depth can be tens of meters below the sea surface. This comes with some disadvantages including difficulties in maintaining the delicate buoyancy balance, particularly for fully submerged longlines without legs, and reduced access to the mainline, particularly for fully submerged longlines with legs. Devices that allow autonomous or remote-controlled changes of mainline depth on a daily, occasional (husbandry and harvest operations) or seasonal basis have been tested but are not yet used commercially on longlines.

Ability of a multi-segment foot model to measure kinematic differences in cavus, neutrally aligned, asymptomatic planus, and symptomatic planus foot types

BackgroundMulti-segment foot models (MFMs) provide a better understanding of the intricate biomechanics of the foot, yet it is unclear if they accurately differentiate foot type function during locomotion. Research questionWe employed an MFM to detect subtle kinematic differences between foot types, including: pes cavus, neutrally aligned, and asymptomatic and symptomatic pes planus. The study investigates how variable the results of this MFM are and if it can detect kinematic differences between pathologic and non-pathologic foot types during the stance phase of gait. MethodsIndependently, three raters instrumented three subjects on three days to assess variability. In a separate cohort, each foot type was statically quantified for ten subjects per group. Each subject walked while instrumented with a four-segment foot model to assess static alignment and foot motion during the stance phase of gait. Statistical analysis performed with a linear mixed effects regression. ResultsModel variability was highest for between-day and lowest for between–rater, with all variability measures being within the true sample variance. Almost all static measures (radiographic, digital scan, and kinematic markers) differed significantly by foot type. Sagittal hindfoot to leg and forefoot to leg kinematics differed between foot types during late stance, as well as coronal hallux to forefoot range of motion. The MFM had low between-rater variability and may be suitable for multiple raters to apply to a single study sample without introducing significant error. The model, however, only detected a few dynamic differences, with the most dramatic being the hallux to forefoot coronal plane range of motion.Significance: Results only somewhat aligned with previous work. It remains unclear if the MFM is sensitive enough to accurately detect different motion between foot types (pathologic and non-pathologic). A more accurate method of tracking foot bone motion (e.g., biplane fluoroscopy) may be needed to address this question.

Geometrically exact 3D arbitrarily curved rod theory for dynamic analysis: Application to predicting the motion of hard-magnetic soft robotic arm

Magnetorheological elastomers are active materials which can be actuated by the applied magnetic field. Hard magnetic soft (HMS) materials, a type of magnetorheological elastomers, show great potential in the fields of biomedical engineering and soft robotics, due to their short response time, remote operation, and shape programmability. To exploit its potential, a series of theoretical frameworks of HMS rods have been developed, but they are mainly limited to the static rod models or classical curved rod models that fail to consider the effect of the “initial curvature” on the distribution of the stress. In this work, we develop a curved rod theory to predict the 3D dynamic motion of the rod-like HMS robotics under large deformation. Based on the geometrically exact rod theory, we include the heterogeneous initial length of the longitudinal fiber caused by “initial curvature” into our model and obtain the reduced balance equations of the HMS robotics. As a result, the “tension-bending” and “shear-torsion” coupling effects of curved rods emerge in the present model. A numerical implementation of our model based on the classical Newmark algorithm is presented. To validate our model, three numerical examples, including the dynamic snap-through behavior of a bistable arch, are performed and compared with the simulation or experiment results reported in literatures, which show a good agreement. Finally, we experimentally study the 2D and 3D static and dynamic motion of a quarter arc HMS robotic arm under an applied magnetic field of 10 mT, and our model gives a satisfactory prediction, especially for static deformation.

Efficient EPID-based quality assurance of beam time delay for respiratory-gated radiotherapy with validation on Catalyst™ and AlignRT™ systems.

To propose a straightforward and time-efficient quality assurance (QA) approach of beam time delay for respiratory-gated radiotherapy and validate the proposed method on typical respiratory gating systems, Catalyst™ and AlignRT™. The QA apparatus was composed of a motion platform and a Winston-Lutz cube phantom (WL3) embedded with metal balls. The apparatus was first scanned in CT-Sim and two types of QA plans specific for beam on and beam off time delay, respectively, were designed. Static reference images and motion testing images of the WL3 cube were acquired with EPID. By comparing the position differences of the embedded metal balls in the motion and reference images, beam time delays were determined. The proposed approach was validated on three linacs with either Catalyst™ or AlignRT™ respiratory gating systems. To investigate the impact of energy and dose rate on beam time delay, a range of QA plans with Eclipse (V15.7) were devised with varying energy and dose rates. For all energies, the beam on time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 92.13 5.79ms, 123.11 6.44ms, and 303.44 4.28ms, respectively. The beam off time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 121.87 1.34ms, 119.33 0.75ms, and 97.69 2.02ms, respectively. Furthermore, the beam on delays decreased slightly as dose rates increased for all gating systems, whereas the beam off delays remained unaffected. The validation results demonstrate the proposed QA approach of beam time delay for respiratory-gated radiotherapy was both reproducible and time-efficient to practice for institutions to customize accordingly.

辊式枝条捡拾机构运动学仿真与试验分析

Apple tree branches have the physical characteristics of high cellulose content and high elasticity, and the use of traditional soft straw pickers will miss a large number of branches. It is necessary to design a picking mechanism that adapts to the physical characteristics of fruit tree branches. In the treatment of branches in modern orchards, the picking mechanism is an important part of the picking device. The success rate of picking directly affects the processing performance of the whole machine. To improve the picking rate, the roller branch picking mechanism is studied. The trajectory of the roller picking and the stress state of the branches are analyzed, and five main factors affecting the picking effect are obtained, i.e. the position relationship between the rotation center of the picking roller and the feeding roller, inclination angle of the steel teeth, the effective working length, the speed and the forward speed of the machine. A mathematical model is established to obtain the static and dynamic motion trajectories of the roller picking mechanism are obtained by using Matlab. Taking the analysis results of influencing factors as the optimization goal, the picking trajectory of the mechanism is simulated to obtain a set of non-inferior solutions. A roller branch picker is manufactured according to the parameter values and tested in the field, and the picking rate can reach 91%.

EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer

Video Motion Magnification (VMM) aims to break the resolution limit of human visual perception capability and reveal the imperceptible minor motion that contains valuable information in the macroscopic domain. However, challenges arise in this task due to photon noise inevitably introduced by photographic devices and spatial inconsistency in amplification, leading to flickering artifacts in static fields and motion blur and distortion in dynamic fields in the video. Existing methods focus on explicit motion modeling without emphasizing prioritized denoising during the motion magnification process. This paper proposes a novel dynamic filtering strategy to achieve static-dynamic field adaptive denoising. Specifically, based on Eulerian theory, we separate texture and shape to extract motion representation through inter-frame shape differences, expecting to leverage these subdivided features to solve this task finely. Then, we introduce a novel dynamic filter that eliminates noise cues and preserves critical features in the motion magnification and amplification generation phases. Overall, our unified framework, EulerMormer, is a pioneering effort to first equip with Transformer in learning-based VMM. The core of the dynamic filter lies in a global dynamic sparse cross-covariance attention mechanism that explicitly removes noise while preserving vital information, coupled with a multi-scale dual-path gating mechanism that selectively regulates the dependence on different frequency features to reduce spatial attenuation and complement motion boundaries. We demonstrate extensive experiments that EulerMormer achieves more robust video motion magnification from the Eulerian perspective, significantly outperforming state-of-the-art methods. The source code is available at https://github.com/VUT-HFUT/EulerMormer.

Deep Reinforcement Learning Car-Following Control Based on Multivehicle Motion Prediction

Reinforcement learning (RL)–based car-following (CF) control strategies have attracted significant attention in academia, emerging as a prominent research topic in recent years. Most of these control strategies focus solely on the motion status of the immediately preceding vehicle. However, with the development of vehicle-to-vehicle (V2V) communication technologies, intelligent vehicles such as connected autonomous vehicles (CAVs) can gather information about surrounding vehicles. Therefore, this study proposes an RL-based CF control strategy that takes multivehicle scenarios into account. First, the trajectories of two preceding vehicles and one following vehicle relative to the subject vehicle (SV) are extracted from a highD dataset to construct the environment. Then the twin-delayed deep deterministic policy gradient (TD3) algorithm is implemented as the control strategy for the agent. Furthermore, a sequence-to-sequence (seq2seq) module is developed to predict the uncertain motion statuses of surrounding vehicles. Once integrated into the RL framework, this module enables the agent to account for dynamic changes in the traffic environment, enhancing its robustness. Finally, the performance of the CF control strategy is validated both in the highD dataset and in two traffic perturbation scenarios. In the highD dataset, the TD3-based prediction CF control strategy outperforms standard RL algorithms in terms of convergence speed and rewards. Its performance also surpasses that of human drivers in safety, efficiency, comfort, and fuel consumption. In traffic perturbation scenarios, the performance of the proposed CF control strategy is compared with the model predictive controller (MPC). The results show that the TD3-based prediction CF control strategy effectively mitigates undesired traffic waves caused by the perturbations from the head vehicle. Simultaneously, it maintains the desired traffic state and consistently ensures a stable and efficient traffic flow.

Prediction and simulation of wearable sensor devices for sports injury prevention based on BP neural network

In the research of sports injury prevention, the recognition of sports action plays an important role in the action recognition model and the prediction evaluation model. In view of the above problems, this paper constructs a new mathematical model through the idea of BP neural network. The model combines wearable technology and can improve the recognition accuracy of sports actions. The model uses the BP network classifier, which can be used in data. Processes such as feature extraction improve efficiency and reliability. In this paper, the algorithm simulation is carried out, and the experiments are carried out for three movements: running, running and static. The results show that for the recognition of running and running action, when the hidden layer node is 11, the BP neural network classifier shows the best recognition effect. For static motion, the recognition effect of each classifier is basically the same. This paper analyzes the wearable sports action recognition system, including perception layer, application layer and service layer, to realize the recognition and classification of sports actions, predict actions in advance and prevent sports injuries. Finally, this paper analyzes the causes of sports injury, puts forward specific measures to prevent sports injury, and further reduces sports injury events through wearable devices.

Numerical Evaluation of Aircraft Aerodynamic Static and Dynamic Stability Derivatives by a Mid-Fidelity Approach

The present study aimed to investigate the capability of mid-fidelity aerodynamic solvers in performing a preliminary evaluation of the static and dynamic stability derivatives of aircraft configurations in their design phase. In this work, the mid-fidelity aerodynamic solver DUST, which is based on the novel vortex particle method (VPM), was used to perform simulations of the static and dynamic motion conditions of the Stability And Control CONfiguration (SACCON): an unmanned combat aerial vehicle geometry developed by NATO’s Research and Technology Organisation (RTO), which is used as a benchmark test case in the literature for the evaluation of aircraft stability derivatives. Two different methods were exploited to extract the dynamic stability derivative values from the aerodynamic coefficient time histories that were calculated with DUST. The results for the mid-fidelity approach were in good agreement with the obtained experimental data, as well as with the results obtained using more demanding high-fidelity CFD simulations. This demonstrates its suitability when implemented in DUST for predicting the static and dynamic behavior of airloads in different conditions, as well as in reliably predicting the values of stability derivatives, with the advantage of requiring limited computational effort with respect to classical high-fidelity numerical approaches and the use of wind tunnel tests.

MA-ST3D: Motion Associated Self-Training for Unsupervised Domain Adaptation on 3D Object Detection.

Recently, unsupervised domain adaptation (UDA) for 3D object detectors has increasingly garnered attention as a method to eliminate the prohibitive costs associated with generating extensive 3D annotations, which are crucial for effective model training. Self-training (ST) has emerged as a simple and effective technique for UDA. The major issue involved in ST-UDA for 3D object detection is refining the imprecise predictions caused by domain shift and generating accurate pseudo labels as supervisory signals. This study presents a novel ST-UDA framework to generate high-quality pseudo labels by associating predictions of 3D point cloud sequences during ego-motion according to spatial and temporal consistency, named motion-associated self-training for 3D object detection (MA-ST3D). MA-ST3D maintains a global-local pathway (GLP) architecture to generate high-quality pseudo-labels by leveraging both intra-frame and inter-frame consistencies along the spatial dimension of the LiDAR's ego-motion. It also equips two memory modules for both global and local pathways, called global memory and local memory, to suppress the temporal fluctuation of pseudo-labels during self-training iterations. In addition, a motion-aware loss is introduced to impose discriminated regulations on pseudo labels with different motion statuses, which mitigates the harmful spread of false positive pseudo labels. Finally, our method is evaluated on three representative domain adaptation tasks on authoritative 3D benchmark datasets (i.e. Waymo, Kitti, and nuScenes). MA-ST3D achieved SOTA performance on all evaluated UDA settings and even surpassed the weakly supervised DA methods on the Kitti and NuScenes object detection benchmark.

Commonly Used Tests in the Assessment of Sacro-illiac Joint Dysfunction by Physiotherapists in India: A Cross-sectional Study

Introduction: Sacroiliac Joint Dysfunction (SIJD) refers to a condition that causes pain and discomfort, commonly due to dysfunction of the Sacroiliac Joint (SIJ). It is a plausible source of Low Back Pain (LBP). Static palpation tests, motion palpation, and pain provocation tests are mostly employed to check for SIJD. Aim: To determine the commonly used tests in the assessment of SIJD by physiotherapists. Materials and Methods: A cross-sectional Questionnairebased study was conducted among physiotherapists in India from November 2022 to April 2023 at the Nitte Institute of Physiotherapy, NITTE (deemed to be University), Mangalore, Karnataka, India. The study included physiotherapists specialising in orthopaedics and obstetrics and gynaecology. The practitioners’ years of experience were considered. An online survey was conducted using a questionnaire developed to determine the frequently used bony landmark for checking SIJ asymmetry, commonly affected structures, and commonly used motion palpation and pain provocation tests for examining SIJD. Questionnaires were sent to at least 100 physiotherapists, and 72 responses were included in the study. Data was collected and analysed using descriptive statistics: frequency and percentage. Data analysis was performed using Statistical Package for Social Sciences (SPSS) software version 26.0. Results: Seventy-two practitioners responded to the online survey. The commonly assessed bony landmark was the Posterior Superior Iliac Spines (PSIS) with 52 responses (28%). The most frequently preferred motion palpation examination test was the compression test with 36 responses (17.2%). Tenderness or tissue texture change was mainly checked at the PSIS with 54 responses (37.8%), and the commonly used pain provocation tests were the thigh thrust test and supine SIJ springing test with 46 responses (26.6%). Conclusion: According to this study, many physiotherapists employed diagnostic techniques congruent with Mitchell’s model, but they also frequently added additional motion tests, measured tenderness, and used pain provocation techniques. Most preferred the ASIS compression and thigh thrust tests to assess SIJD.

Implications of wave–current interaction on the dynamic responses of a floating offshore wind turbine

This study investigates the implications of wave–current interaction on the dynamic responses of the W2Power semisubmersible platform for floating offshore wind turbines under operational and extreme conditions. Firstly, two analytical models based on Airy wave theory are developed to analyse the effects of current interaction with regular and irregular waves. Then, these models are integrated with the well-known engineering tool OrcaFlex for the coupled aero-hydro-servo-elastic analysis. The presence of current was found to significantly modify the wave profiles and influence the static equilibrium, mooring system, and motion dynamics of the FOWT.The results reveal that the translational motion responses, such as surge and heave, are affected by wave–current interaction, with mean and maximum values decreasing under a following current and increasing under an opposing current. However, rotational motion responses are minimally affected. Wave–current interaction also notably affects maximum mooring tensions, with variations of up to ±22% depending on the current direction and mooring layout. Furthermore, reductions in maximum longitudinal acceleration are observed due to such interaction. Incorporating wave–current interaction in simulations enhances our understanding of FOWT dynamics and allows for more reliable estimations of system behaviour, emphasising the importance of ensuring safe operating conditions, particularly in sites with opposing currents.

Microsecond Timescale Conformational Dynamics of a Small-Molecule Ligand within the Active Site of a Protein.

The possible internal dynamics of non-isotope-labeled small-molecule ligands inside a target protein is inherently difficult to capture. Whereas high crystallographic temperature factors can denote either static disorder or motion, even moieties with very low B-factors can be subject to vivid motion between symmetry-related sites. Here we report the experimental identification of internal μs timescale dynamics of a high-affinity, natural-abundance ligand tightly bound to the enzyme human carbonic anhydrase II (hCAII) even within a crystalline lattice. The rotamer jumps of the ligand's benzene group manifest themselves both, in solution and fast magic-angle spinning solid-state NMR 1 H R1ρ relaxation dispersion, for which we obtain further mechanistic insights from molecular-dynamics (MD) simulations. The experimental confirmation of rotameric jumps in bound ligands within proteins in solution or the crystalline state may improve understanding of host-guest interactions in biology and supra-molecular chemistry and may facilitate medicinal chemistry for future drug campaigns.

Konformationeller Austausch eines Liganden in der Bindetasche eines Proteins auf der Mikrosekundenzeitskala

AbstractDie experimentelle Erfassung möglicher interner Dynamik eines Liganden ohne Isotopenmarkierung im proteingebundenen Zustand ist grundsätzlich schwierig. Hohe Temperaturfaktoren aus Kristallstrukturen können statische Heterogenität nicht von Bewegung unterscheiden, zudem können für symmetrische Bewegungen selbst bei niedrigsten B‐Faktoren schnelle Bewegungen vorhanden sein. Hier zeigen wir die sowohl in Lösung als auch im Kristall experimentell beobachtete interne μs‐Dynamik eines nicht‐isotopenmarkierten Liganden, während dieser mit hoher Affinität an das Enzym humane Carboanhydrase II (hCAII) gebunden ist. Der sprunghafte Wechsel zwischen den Rotameren der Benzolgruppe des Liganden verursacht sowohl in Lösungs‐ als auch Festkörper NMR mit schneller Rotation um den magischen Winkel 1H R1ρ Relaxationsdispersion, für die mittels Molecular Dynamics (MD)‐Simulationen mechanistische Einblicke erhalten werden. Die experimentelle Erfassung von konformationellem Austausch in gebundenen Liganden in Lösung und im Festkörper könnte einem besseren Verständnis von Wirt‐Gast‐Interaktionen in Biologie und supramolekularer und medizinischer Chemie dienen.

Multimedia intelligent 3D images for automatic detection of sports injuries

Abstract This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D image plane technology. Using a weight-sharing network and convolution operation, sports injury features are extracted and fused, and a fusion detection framework for sports injury image features is created. Data analysis was conducted using an example to verify the detection framework’s effectiveness. The results show that the peak signal-to-noise ratio of acquiring athletes’ sports injury region imaging by the algorithm in this paper is 43 dB, and the average detection time is 5.91 s. The error control for sports injury detection was reduced from 0.102 to 0.011 after 600 iterations of the algorithm in this paper.