High-resolution Video Research Articles

A Comparative Analysis of Drone and Boat Monitoring for the Endangered Bolivian River Dolphin

Accurate population monitoring is essential for effective wildlife conservation. This study compares the effectiveness of drone-based vs traditional boat-based methods for assessing an endangered Bolivian river dolphin (Inia geoffrensis boliviensis; BRD) population in Bolivia. Data were collected using high-resolution video recorded with a DJI Mavic 2 zoom drone and standardized methodologies for boat surveys. Two mixed-effects linear models incorporating Poisson and negative binomial error structures were used to compare counts obtained from drone- and boat-based surveys. Results show that drone-based surveys detected 1.15% more individuals on average than boat-based surveys. Drone counts were higher at sites with larger group sizes, leading to congruent estimates. The aerial perspective that drones offer lets researchers overcome potential challenges from boat-based surveys—for example, difficulties related to confirming individual IDs because of limited visibility due to sun glare. Transitioning from boat- to drone-based surveys offers advantages such as reduced disturbance from placement closer to the animals and improved detection rates. Ethical considerations and responsible flight practices are crucial. Standardizing methodologies and prioritizing ethical research factors are key for successful implementation. Drone-based surveys offer a promising approach to enhance wildlife monitoring and conservation practices. This study is the first of this kind for Bolivia, a national natural heritage site, and contributes to the conservation and knowledge of the BRD.

Development of Conflict-Based Safety Performance Function for Safety Evaluation of Urban Roundabouts in India

This study evaluates the safety performance of urban roundabouts in India using conflict-based Safety Performance Functions (SPFs). High-resolution aerial videos were utilized to extract vehicle trajectories, and the Surrogate Safety Assessment Model (SSAM) was employed for conflict identification. Data were collected from 27 roundabouts across various Indian cities, focusing on non-lane-based heterogeneous traffic conditions. Four SPFs were developed utilizing a Generalized Linear Model with Negative Binomial (NB) distribution and log link function for different proximity levels based on Time-to-Collision (TTC) and Post-Encroachment Time (PET) values. Key factors influencing conflict frequency included conflicting traffic volume, occupancy time, ratio of approaching and conflicting traffic volume, percentage of cars and motorcycles, inscribed circle diameter, roundabout symmetry, and number of legs. The developed conflict frequency prediction models fitted well and demonstrated robust predictive performance. This proactive framework for roundabout safety assessment can be followed to enhance road safety in developing nations.

Lightweight Reference-Based Video Super-Resolution Using Deformable Convolution

Super-resolution is a technique for generating a high-resolution image or video from a low-resolution counterpart by predicting natural and realistic texture information. It has various applications such as medical image analysis, surveillance, remote sensing, etc. However, traditional single-image super-resolution methods can lead to a blurry visual effect. Reference-based super-resolution methods have been proposed to recover detailed information accurately. In reference-based methods, a high-resolution image is also used as a reference in addition to the low-resolution input image. Reference-based methods aim at transferring high-resolution textures from the reference image to produce visually pleasing results. However, it requires texture alignment between low-resolution and reference images, which generally requires a lot of time and memory. This paper proposes a lightweight reference-based video super-resolution method using deformable convolution. The proposed method makes the reference-based super-resolution a technology that can be easily used even in environments with limited computational resources. To verify the effectiveness of the proposed method, we conducted experiments to compare the proposed method with baseline methods in two aspects: runtime and memory usage, in addition to accuracy. The experimental results showed that the proposed method restored a high-quality super-resolved image from a very low-resolution level in 0.0138 s using two NVIDIA RTX 2080 GPUs, much faster than the representative method.

Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation.

Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of hidden objects. Despite recent advances, real-time video of complex and dynamic scenes remains a major challenge owing to the weak signal of multiply scattered light. Here we propose and demonstrate a framework of spectrum filtering and motion compensation to realize high-quality NLOS video for room-sized scenes. Spectrum filtering leverages a wave-based model for denoising and deblurring in the frequency domain, enabling computational image reconstruction with a small number of sampling points. Motion compensation tailored with an interleaved scanning scheme can compute high-resolution live video during the acquisition of low-quality image sequences. Together, we demonstrate live NLOS videos at 4 fps for a variety of dynamic real-life scenes. The results mark a substantial stride toward real-time, large-scale and low-power NLOS imaging and sensing applications.

High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields.

In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation. This module can capture natural eye blinks and high-frequency details, and maintain a similar rendering time as previous fast methods. Secondly, we present an efficient conditional super-resolution module on the dynamic scene to directly generate the high-resolution portrait with our low-resolution head. Incorporated with the prior information, such as depth map and audio features, our new proposed efficient conditional super resolution module can adopt a lightweight network to efficiently generate realistic and distinct high-resolution videos. Extensive experiments demonstrate that our method can generate more distinct and fidelity talking portraits on high resolution (900 × 900) videos compared to state-of-the-art methods. Our code is available at https://github.com/muyuWang/HHNeRF.

ND15, subunit of the electron transport chain protein-complex I, age-dependently worsens cardiac performance in Drosophila melanogaster

Abstract Introduction In the natural aging process of the heart, aside others, mitochondrial dysfunction is an essential driver for the development of reduced cardiac performance. Previous analysis of protein expression levels in cardiomyocytes of aged mice and have found aberrancies in subunits of the mitochondrial Complexes I – IV of the electron transport chain. Especially, the complex I subunit ND15 was highly downregulated. To elucidate the functional effects of this gene on the cardiac performance, we generated cardiomyocyte specific ND15 knock-downs in Drosophila melanogaster. Methods We generated Drosophila with heart-specific (+/+; Hand4.2-GAL4/UAS-ND15; tdtK/+) (ND15-KD)knockdown of ND15 using the UAS/GAL4 system. The flies' cardiomyocytes endogenously express tdTomato, facilitating fluorescence-based live imaging of the heart by high-resolution video fluorescence microscopy. The recorded data was processed using a R-Script, resulting in Results We compared wild type (WT) flies to ND15-KD flies at age 3 weeks(young) and 7 weeks(old). Knockdown of ND15 resulted in impaired cardiac function. Most parameters deteriorated in aged flies in comparison to young flies. The heart frequency was significantly reduced (WT:6 beats/s – KD:4 beats/s – p<0.0001) in the 7 weeks old group, end-diastolic diameter of the heart tube increased significantly in 3 weeks old ND15KD flies compared to controls(WT:65µm – KD:74µm p<0,0001), resulting in a dilated heart tube Interestingly enough, this dilation in ND15-KD disappeared in 7 weeks old flies. The fractional shortening was significantly decreased in young(WT:33,5% - KD:30% p<0,01) and deteriorated in old flies (WT:35% - KD:22% p<0,0001), indicating a reduced contraction capability. For both 3 weeks and 7 weeks old groups, the heart-rate adjusted interbeat interval variance (WT:22% -KD: 40% p<0,0001) as well as the time of no cardiac action (WT: 22% - KD: 35% p<0,0001) increased and showed significant irregularities. The duration of systole and diastole combined also exhibited arrhythmic patterns (WT:15% - KD:20.5% p<0,0001). A general arrhythmia index (AI) increased significantly (WT: 0.25 – KD:0.5 p<0,0001) These arrhythmia parameters emphasize the arrhythmogenic potential of the ND15-knockdown. The relative relaxation capabilities drastically worsened with age, displaying a diastolic dysfunction in the 7 weeks old group. At half-time relaxation, the WT was at 60% relaxation, ND15KD at 46%). Conclusion This study demonstrates that heart-specific deletion of ND15 results in a marked impairment of systolic and diastolic cardiac function in Drosophila accompanied by pro-arrhythmogenic actions. We could identify ND15 as a new mitochondrial molecular target for the maintenance of regular cardiac function and rhythmicity, especially with increasing age. Further mechanistic studies and translation to mammalian organisms are ongoing.

Adipose tissue/ heart lipid transport determines cardiac diastolic function

Abstract Introduction Heart failure with preserved ejection fraction (HFpEF) is characterized by diastolic dysfunction and often occurring in obese individuals. Previous research has demonstrated that the modification of lipid metabolism can prevent the onset of diastolic dysfunction. In our project we have investigated how lipid transport influences the onset of diastolic dysfunction utilizing heart- and adipose tissue-specific knockdowns of the fatty acid (FA) transport protein 1 (FatP1) in Drosophila melanogaster. Methods We generated Drosophila with heart-specific (+/+; Hand4.2-GAL4/UAS-FatP1; tdtK/+) (cFatP1-KD) and adipose tissue-specific (+/+; ppl-GAL4/UAS-FatP1; tdtK/+) (atFat-P1-KD) FatP1 knockdowns using the UAS/GAL4 system. The flies' cardiomyocytes endogenously express tdTomato, facilitating fluorescence-based live imaging of the heart by high-resolution video fluorescence microscopy, as previously described. Cardiac morphology and cardiac lipid accumulation was monitored by confocal microscopy. Additionally, mitochondrial function was evaluated using the glycolytic Seahorse assay of whole hearts. Results Blocking FA-uptake into adipose tissue in atFatP1-KD flies resulted in a highly significant reduction in relaxation velocity (p=0.0009, WT vs. atFatP1-KD), indicative of diastolic dysfunction in flies, while contraction velocity and fractional shortening (systolic function) remained unchanged. Diastolic dysfunction was associated with a significant increase of cardiac lipid droplet (LD) quantity and size in flies lacking FatP1 in adipose tissue (p=0.0275 and p<0.0001, respectively, WT vs. atFatP1-KD). Cardiac mitochondrial function was significantly impaired in atFatP1-KD flies compared to WT flies. Reducing cardiac FA uptake in heart-specific FatP1 KD flies led to a decrease in end-diastolic diameter and a significant reduction in relaxation and contraction velocity (p<0.0001 and p=0.0001, respectively, WT vs. cFatP1-KD), suggesting impairment in both diastolic and systolic function. Interestingly, the number of cardiac LDs increased in cFATP1-KD, however their size decreased significantly resulting in an overall unchanged lipid content compared to WT flies (p=0.0475 and p=0.0367, respectively, WT vs. atFatP1-KD). Mitochondrial function showed no significant alterations. Conclusion This study demonstrates that reducing FA-uptake in adipose or cardiac tissue results in cardiac dysfunction in Drosophila melanogaster. Reduction of adipose tissue FA-uptake results exclusively in diastolic dysfunction likely mediated by cardiac lipid overload and subsequent mitochondrial dysfunction. In contrast, blockade of Fatp1-mediated cardiac FA-uptake induces diastolic/ systolic dysfunction which was unrelated to alterations in cardiac lipid content and mitochondrial dysfunction suggesting alternative mechanisms. These study identifies FatP1-mediated FA-uptake in adipose tissue as a potential therapeutic target for the treatment of diastolic dysfunction.

Hydromechanical properties of metachronal swimming in polychaetes

Free-swimming polychaetes are common in marine habitats and exhibit a unique form of swimming whereby a metachronal wave occurs simultaneously with a bending body wave. This body wave is unusual among swimming animals because it travels in the same direction as the animal’s swimming direction. However, we currently lack a mechanistic understanding of this unusual form of locomotion. In this study we use a combination of high-speed, high-resolution video and particle image velocimetry (PIV) to quantify kinematics and fluid dynamics for three species of swimming polychaetes, spanning two orders of magnitude in size. We find that in all species, flows generated by metachronal waves of parapodia dominate while typical flows associated with body bending is absent. However, the parapodia are less flexible than propulsive structures in other metachronal swimmers. This creates a localized, but substantial upstream flow during the recovery stroke. Using body bending, the recovery stroke can occur mostly beneath the bulk flow from the power strokes, resulting in minimal inference while the subsequent power stroke can benefit from the pressure field generated during recovery. These results may have implications for future vehicle designs that incorporate metachronal locomotion.

Visual Communication Method of Multi frame Film and Television Special Effects Images Based on Deep Learning

For the dynamic film and television special effects industry, creating visually stunning and valuable graphics requires the integration of excellent deep-learning algorithms. This paper presents an enhanced version of the 3-D Convolutional Neural Network (3-D CNN), specifically tailored to meet the demanding needs of multi-frame television and film special effects. The version’s key feature is its efficient data handling through coupled precision training, significantly increasing computational efficiency while reducing memory needs. This method, which combines floating-issue operations of 16 and 32 bits, is ideal for efficiently processing large amounts of high-resolution video data. The proposed 3-D CNN structure excels in extracting and analysing complex spatiotemporal capabilities from video sequences, capturing the spatial and temporal nuances crucial in film imagery. This capability is important for accomplishing excessive fidelity in visual results, ensuring seamless integration with live motion pics. Incorporating a cutting-edge attention mechanism inspired by the aid of transformer-based architectures, the model specialises inside the maximum pertinent components of video frames to enhance the quality and realism of outcomes. Furthermore, the model boasts an excessive-resolution processing characteristic, permitting simultaneous capture of first-rate records and broader scene context. This guarantees consistency and realism in outcomes, from complicated textures to the overarching visual narrative. Advanced regularisation strategies are hired to prevent overfitting, permitting the model to generalise efficiently through numerous film manufacturing conditions. The state-of-the-art 3-D information augmentation techniques make the model robust and prepare it to deal with an intensive variety of challenging situations involving computer special effects. Real-time processing competencies make the model a game-changer for on-set visible outcomes and adjustments. Designed for seamless integration with the famous CGI software program software utility, it allows a harmonious aggregate of AI and ingenious creativity. Acknowledging the environmental impact of high-powered computing, the model consists of power-efficient computational techniques that align with sustainable computing practices, lower operational costs, and are related to processing complex video statistics. model boasts an excessive-resolution processing characteristic, permitting simultaneous capture of first-rate records and broader scene context. This guarantees consistency and realism in outcomes, from complicated textures to the overarching visual narrative. Advanced regularisation strategies are hired to prevent overfitting, permitting the model to generalize efficiently through numerous film manufacturing conditions. The state-of-the-art 3-D information augmentation techniques, in addition, make the model robust, and prepare it to deal with an intensive variety of computer special effects challenging situations.

Neuroethology: A Review of Studies on Combat Behavior in Mice

Optogenetics has revolutionized neuroscience, enabling precise control and monitoring of neural circuits. This study focuses on the neural mechanisms underlying combat behaviors in mice, particularly the roles of the ventromedial hypothalamus (VMH) in freezing, jumping escape, and defensive attack behaviors. The research employed a combination of optogenetic tools, behavioral assays, and high-resolution imaging techniques. Genetically modified mice expressing channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR) were used. Viral vectors were injected into specific brain regions, and optogenetic stimulation was performed to activate or inhibit neuronal populations. Behavioral responses were documented and analyzed using specialized software, and brain tissues were prepared for histological examination. Activation of VMH neurons increased jumping escape behavior, while inhibition reduced defensive attack behaviors, highlighting the VMH's critical role in these responses. High-resolution video analysis provided detailed metrics on behavior, and histological examination confirmed precise targeting and expression of optogenetic proteins in the brain. The results demonstrated the direct influence of specific neural circuits on combat behaviors. The study elucidated the roles of VMH and associated neural circuits in mediating combat behaviors in mice. Optogenetic techniques provided precise control over neuronal activity, revealing critical insights into the neural mechanisms underlying these behaviors. The findings support the VMH's central role in regulating instinctual defensive actions and offer potential applications for developing therapeutic strategies for anxiety and pain disorders.

Scalable video transformer for full-frame video prediction

Vision Transformers (ViTs) have shown success in many low-level computer vision tasks. However, existing ViT models are limited by their high computation and memory cost when generating high-resolution videos for tasks like video prediction. This paper presents a scalable video transformer for full-frame video predication. Specifically, we design a backbone transformer block for our video transformer. This transformer block decouples the temporal and channel features to reduce the computation cost when processing large-scale spatial–temporal video features. We use transposed attention to focus on the channel dimension instead of the spatial window to further reduce the computation cost. We also design a Global Shifted Multi-Dconv Head Transposed Attention module (GSMDTA) for our transformer block. This module is built upon two key ideas. First, we design a depth shift module to better incorporate the cross-channel or temporal information from video features. Second, we introduce a global query mechanism to capture global information to handle large motion for video prediction. This new transformer block enables our video transformer to predict a full frame from multiple past frames at the resolution of 1024 × 512 with 12 GB VRAM. Experiments on various video prediction benchmarks demonstrate that our method with only RGB input outperforms state-of-the-art methods that require additional data, like segmentation maps and optical flows. Our method exceeds the state-of-the-art RGB-only methods by a large margin (1.2 dB) in PSNR. Our method is also faster than state-of-the-art video prediction transformers.

Abnormal hyperactivity of specific striatal ensembles encodes distinct dyskinetic behaviors revealed by high-resolution clustering.

L-DOPA-induced dyskinesia (LID) is a debilitating complication of dopamine replacement therapy in Parkinsońs disease and the most common hyperkinetic disorder of basal ganglia origin. Abnormal activity of striatal D1 and D2 spiny projection neurons (SPNs) is critical for LID, yet the link between SPN activity patterns and specific dyskinetic movements remains unknown. To explore this, we developed a novel method for clustering movements based on high-resolution motion sensors and video recordings. In a mouse model of LID, this method identified two main dyskinesia types and pathological rotations, all absent during normal behavior. Using single-cell resolution imaging, we found that specific sets of both D1 and D2-SPNs were abnormally active during these pathological movements. Under baseline conditions, the same SPN sets were active during behaviors sharing physical features with LID movements. These findings indicate that ensembles of behavior-encoding D1- and D2-SPNs form new combinations of hyperactive neurons mediating specific dyskinetic movements.

Real-Time CNN Training and Compression for Neural-Enhanced Adaptive Live Streaming.

We propose a real-time convolutional neural network (CNN) training and compression method for delivering high-quality live video even in a poor network environment. The server delivers a low-resolution video segment along with the corresponding CNN for super resolution (SR), after which the client applies the CNN to the segment in order to recover high-resolution video frames. To generate a trained CNN corresponding to a video segment in real-time, our method rapidly increases the training accuracy by promoting the overfitting property of the CNN while also using curriculum-based training. In addition, assuming that the pretrained CNN is already downloaded on the client side, we transfer only residual values between the updated and pretrained CNN parameters. These values can be quantized with low bits in real time while minimizing the amount of loss, as the distribution range is significantly narrower than that of the updated CNN. Quantitatively, our neural-enhanced adaptive live streaming pipeline (NEALS) achieves higher SR accuracy and a lower CNN compression loss rate within a constrained training time compared to the state-of-the-art CNN training and compression method. NEALS achieves 15 to 48% higher quality of the user experience compared to state-of-the-art neural-enhanced live streaming systems.

Design and Implementation of an Interactive Embedded System as a Low-Cost Remotely Operated Vehicle for Underwater Applications

The underwater environment is harsh and challenging for human life, prompting companies and researchers to develop advanced technologies for exploration. Building on previous work that applied a CNN-based method for underwater object classification, this paper focuses on the design and implementation of an interactive embedded system for a compact Remotely Operated Vehicle (ROV) with specific dimensions and weight. The primary goal is to capture real-time underwater video using remote control communication via Ethernet. The ROV is powered by five brushless motors controlled by a smart PID controller. Precise pulse-width modulation signals enhance stability during movements along three axes, enabling high-resolution video capture. The system utilizes the Raspberry Pi 3's computing power for motion control, positioning, temperature monitoring, and video acquisition. Experimental results demonstrate the system's capability to process 42 frames per second. A user-friendly graphical interface allows for remote ROV control across various operating systems. With a depth rating of 100 meters and speed of 0.148 m/s. This ROV surpasses human divers' limitations and holds significant potential for applications in surveillance, operations, maintenance, and measurement tasks underwater. The proposed ROV contributes to the advancement of underwater exploration technology by combining high performance with cost-effectiveness.

Observational study of close contact behaviors in a multinational graduate student office

Understanding human contact behavior plays a crucial role in improving the design of indoor environments, for example, by enhancing thermal comfort, workplace performance and productivity, and controlling infection transmission. Although baseline information for indoor environmental design is available, data on person-to-person contact behavior are limited, especially in populations of diverse nationalities, genders, and age ranges. In this study, we used high-resolution video cameras to collect close contact data for five consecutive school days in a multinational graduate school student office in Japan with a reserved cultural background. We characterized human close-contact behavior using stochastic interactions in terms of interpersonal distance, assortativity, posture, duration, and frequency of contact in a multinational population. Our results showed a remarkable difference compared to the study results from person-to-person contact behavior in an ethnically homogenous population. The interpersonal distance averaged 0.75 m, and the close contact frequency was notably lower, but the close contact duration was significantly longer. Nationality showed stronger homophily. We considered the degree centrality measures of the nodes in the observed networks. The observed contact network links were dense with high reciprocity. High contact heterogeneity was observed in twenty-five percent of the nodes. These data can be used to improve targeted epidemiological analyses for indoor occupant health decision-making, and infection spread prevention strategies.

Hardware-accelerated integrated optoelectronic platform towards real-time high-resolution hyperspectral video understanding

Recent advancements in artificial intelligence have significantly expanded capabilities in processing language and images. However, the challenge of comprehensively understanding video content still needs to be solved. The main problem is the requirement to process real-time multidimensional video information at data rates exceeding 1 Tb/s, a demand that current hardware technologies cannot meet. This work introduces a hardware-accelerated integrated optoelectronic platform specifically designed for the real-time analysis of multidimensional video. By leveraging optical information processing within artificial intelligence hardware and combining it with advanced machine vision networks, the platform achieves data processing speeds of 1.2 Tb/s. This capability supports the analysis of hundreds of frequency bands with megapixel spatial resolution at video frame rates, significantly outperforming existing technologies in speed by three to four orders of magnitude. The platform demonstrates effectiveness for AI-driven tasks, such as video semantic segmentation and object understanding, across indoor and aerial scenarios. By overcoming the current data processing speed limitations, the platform shows promise in real-time AI video understanding, with potential implications for enhancing human-machine interactions and advancing cognitive processing technologies.

Data safety prediction using YOLOv7+G3HN for traffic roads

Pulau Pinang has introduced several measures to enhance traffic safety and promote sustainability, including the installation of CCTV systems and the implementation of smart solutions and green technology as part of the Penang 2030 vision, aligning with the Sustainable Development Goals (SDGs). However, despite these efforts, road accidents persist due to non-optimised detection models, incomplete data from manual reporting, and technological constraints in real-time video analysis and predictive modelling. This study evaluates the effectiveness of the YOLOv7+G3HN framework for vehicle detection and near-miss analysis, with a focus on the influence of video quality on detection performance. The research aims to understand how high- and low-quality video inputs affect the accuracy and computational efficiency of detection algorithms. High[1]quality videos resulted in significantly faster computation times for vehicle detection than low-quality videos, highlighting the importance of video resolution in optimising detection processes. Despite the robustness of the algorithm, with no errors detected in both video qualities, higher miss detection rates in low-quality videos suggest that lower resolution may compromise detection accuracy and the effectiveness of monitoring systems. Near-miss analysis revealed that high-quality videos had a lower probability of near-miss occurrences than low-quality videos, highlighting the importance of video resolution for detection efficacy. These findings emphasise the critical role of high-resolution video inputs in enhancing detection accuracy and reliability, advocating for their implementation to optimise vehicle detection and improve road safety. Additionally, YOLOv7+G3HN outperforms YOLOv7 in both accuracy and speed. The study concludes that the YOLOv7+G3HN framework is effective for vehicle detection and near-miss analysis, provided that video quality is considered in system design and implementation.

Supporting Differentiated Streaming Services in Heterogeneous Vehicle-to-Everything Networks.

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm's effectiveness in meeting EV users' bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved.

Unicortical Button Fixation Provide Higher Strength Compared with Transosseous Repair for Subscapularis Tendon in Total Shoulder Arthroplasty

Subscapularis tendon (SSc) dysfunction following total shoulder arthroplasty (TSA) results in poor functional outcomes. There have been numerous SSc repair constructs tested biomechanically and clinically, however, none has been demonstrated as superior. Newer techniques and implants have emerged, but have not been fully tested. We hypothesized that the unicortical button (UB) fixation will provide significantly improved restoration of the anatomic footprint and biomechanical properties when compared to transosseous (TO) repair of the SSc. A digital footprint of SSc humeral insertion was obtained in 6 pairs of fresh-frozen cadaveric shoulders using a three-dimensional (3-D) digitizer. A complete SSc tear was created, and each pair of shoulders was randomized to either SSc repair with UB or TO repair. Each specimen underwent a cyclic loading protocol followed by pull-to-failure. The failure load, elongation at failure, gapping failure, number of cycles until failure, the load at key gapping points (1 mm, 3 mm, 5 mm, and 10 mm) and the failure mode were recorded using high-resolution video recording. 3-D surfaces of the insertion footprint and repair site were obtained, and surface areas were calculated using a custom MATLAB script and laser scanner. Paired t-tests were conducted to compare differences between two repair groups. Failure load was significantly higher in the UB group (382.4 N ± 56.5 N) than in the TO group (253.6 N ± 103.4 N, p=0.005). TO repair provided higher gapping at failure (28.8 mm ± 8.2 mm) than UB repair (10.4 mm ± 6.8 mm, p=0.0017). UB repair had significantly higher load at the 1-mm, 5-mm, and 10-mm gapping compared with TO repair with p=0.042, p=0.033, and p=0.0076, respectively. There were no significant differences between elongation failure, the difference in footprint area from native to repair states, or the percentage of restored footprint area between groups. (p=0.26, p=0.18 and p=0.21 respectively) CONCLUSION: The UB fixation showed a significantly lower gap at failure, higher failure load and number of cycles until failure, and higher gap loads compared with the traditional TO repair for SSc. Although more clinical research is necessary, the UB fixation that utilizes cortical bone presents promising results.

A Markovian dynamics for Caenorhabditis elegans behavior across scales

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.