Improve Processing Speed Research Articles

An Energy-Efficient Dynamic Feedback Image Signal Processor for Three-Dimensional Time-of-Flight Sensors

With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices.

UAV Geo-Localization Dataset and Method Based on Cross-View Matching

The stable flight of drones relies on Global Navigation Satellite Systems (GNSS). However, in complex environments, GNSS signals are prone to interference, leading to flight instability. Inspired by cross-view machine learning, this paper introduces the VDUAV dataset and designs the VRLM network architecture, opening new avenues for cross-view geolocation. First, to address the limitations of traditional datasets with limited scenarios, we propose the VDUAV dataset. By leveraging the virtual–real mapping of latitude and longitude coordinates, we establish a digital twin platform that incorporates 3D models of real-world environments. This platform facilitates the creation of the VDUAV dataset for cross-view drone localization, significantly reducing the cost of dataset production. Second, we introduce a new baseline model for cross-view matching, the Virtual Reality Localization Method (VRLM). The model uses FocalNet as its backbone and extracts multi-scale features from both drone and satellite images through two separate branches. These features are then fused using a Similarity Computation and Feature Fusion (SCFF) module. By applying a weighted fusion of multi-scale features, the model preserves critical distinguishing features in the images, leading to substantial improvements in both processing speed and localization accuracy. Experimental results demonstrate that the VRLM model outperforms FPI on the VDUAV dataset, achieving an accuracy increase to 83.35% on the MA@20 metric and a precision of 74.13% on the RDS metric.

Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm

Artificial intelligence (AI)-assisted computer vision is an evolving field in medical imaging. However, accuracy and precision suffer when using the existing AI models for small, easy-to-miss objects such as bone fractures, which affects the models’ applicability and effectiveness in a clinical setting. The proposed integration of the Hybrid-Attention (HA) mechanism into the YOLOv8 architecture offers a robust solution to improve accuracy, reliability, and speed in medical imaging applications. Experimental results demonstrate that our HA-modified YOLOv8 models achieve a 20% higher Mean Average Precision (mAP 50) and improved processing speed in arm fracture detection.

Efficacy of transcranial magnetic stimulation treatment in reducing neuropsychiatric symptomatology after traumatic brain injury.

Neuropsychiatric disorders are highly disabling in traumatic brain injury (TBI) patients, and psychopharmacological treatments often fail to adequately mitigate their detrimental effects. Repetitive transcranial magnetic stimulation (rTMS) is an emerging treatment in neurology and psychiatry, showing potential in treating psychiatric disorders. This study investigates the efficacy of a novel, dual-site sequential rTMS protocol designed to treat neuropsychiatric symptoms in a TBI patient who was refractory to conventional treatments. A 34-year-old woman with severe head trauma and complex psychopathology underwent 20 daily sessions of focal-coil rTMS, combining inhibitory stimulation (1 Hz) on the right dorsolateral prefrontal cortex (DLPFC) and excitatory (10 Hz) on the left DLPFC, guided by a neuronavigation system. Psychiatric and neurocognitive assessments were conducted at baseline and at 2, 4, and 8 weeks following the beginning of rTMS treatment. After 2 weeks of treatment, the patient showed decreased impulsivity and obsessive-compulsive symptoms, along with improvements in attention and processing speed. After 4 weeks, impulsivity further declined, though no other significant changes were noted. At 8 weeks, a persistent positive effect was observed, including enhanced positive emotions. These findings suggest that guided, alternating neurostimulation of the DLPFC may modulate activity within cortico-striato-thalamo-cortical circuits, providing a promising alternative for managing neuropsychiatric symptoms in TBI patients who are resistant to traditional treatments.

Effects of aerobic, strength, and combined exercise on cognitive functions in older adults: a systematic review

Introduction: Aging is associated with a decline in cognitive functions, increasing the risk of neurocognitive impairment and limitation of activities of daily living and participation. Previous literature has pointed to physical exercise as an effective intervention; however, its characteristics still need to be specified. Objective: The aim was to synthesize the appropriate intensity, duration, frequency, and type of exercise for different types of cognitive variables. Method: A systematic review was carried out according to PRISMA, using three databases: PubMed, Scopus, and Web of Science. Experimental longitudinal studies were included, whether randomised or not, which performed physical exercise and with a control group in people over 60 years of age. Methodological quality was assessed using the PEDro scale. Results: The results yielded 11 studies finding significant improvements in all of them after performing the intervention (7 of aerobic exercise, 3 of resistance exercise and 2 of combined exercise) finding improvements in attention, memory, processing speed, executive function, inhibition, concentration, perceptual reasoning, orientation, visuospatial perception, and visuomotor organisation. Conclusions: Physical exercise appears to be a beneficial intervention for cognition in older people, with aerobic exercise being the most studied, although further research is needed. The results could benefit decision-making in rehabilitation to work on cognition in cognitively healthy older people. Keywords: prevention; aging; cognition; cognitive impairment; rehabilitation.

Frequency domain attention network for copper chain defect detection in tobacco cutting machine

AbstractThe detection of defects on the copper chain in the production process of tobacco cutters is crucial for ensuring product quality. Traditional defect detection methods often rely on spatial domain image analysis, which not only has a large computational load but also performs poorly in handling high‐frequency noise and complex backgrounds. To address this issue, this paper proposes a novel neural network model based on frequency domain analysis, called frequency domain attention network. This network first utilizes discrete cosine transform to transform the image from the spatial domain to the frequency domain, effectively reducing computational complexity and improving processing speed. Subsequently, through the innovative frequency domain attention module, the network automatically identifies and enhances key discriminative features in the frequency domain, thereby strengthening the model's ability to identify defects. Finally, the frequency domain attention map, after feature extraction and integration, is inputted into the coupling detection head to achieve high‐precision defect detection. The experimental results show that our method outperforms the SOTA method with an increase of 0.03 in AP and 21 in FPS.

Квантовые вычисления в медицинской физике: особенности алгоритмов и их перспективы

Background: Quantum computing represents a paradigm leap in computational capabilities, with potential applications across various scientific disciplines. The complicated computations required for imaging, radiation therapy, and molecular modeling in medical physics give novel prospects for quantum computing. Objective: This article investigates the role of quantum computing in medical physics research, with an emphasis on its potential to improve computational efficiency and accuracy and develop novel treatment approaches. Methods: A thorough literature analysis was undertaken to investigate recent advances in quantum computing and their implications in medical physics. Case studies were reviewed to demonstrate practical applications and possible advantages. Theoretical models were tested to forecast future developments. Statistical data from quantum computing implementations were gathered from many sources, particularly on improving processing speed and accuracy in medical physics applications. Results: The findings show that quantum computing can significantly improve the processing speed of complex simulations and imaging techniques, with some quantum algorithms outperforming classical algorithms by up to 100 times. In radiation therapy planning, quantum computing has exhibited a 25% boost in dose distribution calculation precision. Quantum algorithms have lowered calculation times in molecular modeling by about 40%, potentially boosting drug development and customized therapy by 30%. Conclusion: Quantum computing has the potential to alter medical physics by increasing computational capacity, which could lead to advancements in diagnostics, treatment planning, and medication development. The statistical results corroborate the promise of quantum computing in these domains, emphasizing the importance of ongoing interdisciplinary collaboration and research to fully achieve these benefits and address present hurdles in incorporating quantum computing into clinical practice.

Топологические изоляторы в электронных и спинтронных устройствах

Background: Topological insulators (TIs) are materials with distinctive features, including insulating interiors and conducting surface states protected by time-reversal symmetry. These features make TIs extremely promising for electrical and spintronic device applications, where manipulating electron spin without charge transfer is advantageous. Objective: This study aims to investigate of TIs to improve the performance and efficiency of electrical and spintronic devices. We look at the special qualities of TIs that may be used to improve device functionality, such as reduced power consumption and higher operational speed. Methods: To investigate electron transport behaviour on the surfaces of different TIs, we used a mix of quantum mechanical models and experimental settings. Devices based on bismuth telluride (Bi2Te3) and thallium arsenide (TlAs) were built to test their performance in real-world applications. Results: TI devices showed considerable gains in spin transport efficiency and thermal stability compared to conventional materials. Spintronic devices based on TIs demonstrated a 50% reduction in energy usage and a 30% improvement in data processing speed. Conclusion: Including topological insulators in electrical and spintronic devices offers a promising path to more efficient and speedier technologies. Future research should concentrate on the scalable integration of TIs into commercial devices and the discovery of novel materials with topological insulating characteristic.

Cognitive processing speed improvement after cochlear implantation.

Untreated hearing loss has an effect on cognition. It is hypothesized that the additional processing required to compensate for the sensory loss affects the cognitive resources available for other tasks and that this could be mitigated by a hearing device. The impact on cognition of cochlear implants (CIs) was tested in 100 subjects, ≥60 years old, with bilateral moderately-severe to profound post linguistic deafness using hearing aids. Data was compared pre and 12 and 18 months after cochlear implantation for the speech spatial qualities questionnaire, Mini Mental State Examination (MMSE), Trail making test B (TMTB) and digit symbol coding (DSC) from the Wechsler Adult Intelligence Scale version IV and finally the timed up and go test (TUG). Subjects were divided into young old (60-64), middle old (65-75) and old old (75+) groups. Cognitive test scores and times were standardized according to available normative data. Hearing significantly improved pre- to post-operatively across all age groups. There was no change post-implant in outcomes for TMTB, TUG or MMSE tests. Age-corrected values were within normal expectations for all age groups for the TUG and MMSE. However, DSC scores and TMTB times were worse than normal. There was a significant increase in DSC scores between baseline and 12-months for 60- to 64-year-olds (t[153] = 2.608, p = 0.027), which remained at 18 months (t[153] = 2.663, p = 0.023). The improved attention and processing speed in the youngest age group may be a consequence of reallocation of cognitive resources away from auditory processing due to greatly improved hearing. The oldest age group of participants had cognition scores closest to normal values, suggesting that only the most able older seniors tend to come forward for a CI. Severe to profoundly deaf individuals with hearing aids or cochlear implants were still poorer than age-equivalent normally hearing individuals with respect to cognitive flexibility, attention, working memory, processing speed and visuoperceptual functions. Due to a lack of data for the TUG, TMTB and DSC in the literature for hearing impaired individuals, the results reported here provide an important set of reference data for use in future research.

An improved YOLOv8 model enhanced with detail and global features for underwater object detection

Underwater object detection is significant for the practical research of mastering existing marine biological resources. In response to the challenges posed by complex underwater environments such as water scattering and variations in object scales, researchers have developed YOLOv8 for object detection, driven by the rising popularity and iteration of deep learning. Building upon this model, we propose an enhanced underwater object detection model named YOLOv8-DGF. Firstly, we replace the convolutional layers of Spatial Pyramid Pooling Fusion (SPPF) with Invertible Neural Networks to further augment the fusion capacity of detailed features, facilitating the preservation of pivotal information while mitigating the impact of noise. Additionally, we introduce a global attention mechanism into Convolution to Fully Connected (C2f), which weights the input features, thereby emphasizing or suppressing feature information from different locations. Through our ‘Detail to Global’ strategy, the model achieved mAP@0.5 scores of 87.7% and 84.8% on the RUOD and URPC2020 datasets, respectively, with improved processing speed. Extensive ablation experiments on the Pascal VOC dataset demonstrate that YOLOv8-DGF outperforms other methods, achieving the best overall performance.

Evaluating cognitive assessment tools for patients with major depressive disorder receiving electroconvulsive therapy: A systematic review and meta-analysis

BackgroundMajor Depressive Disorder (MDD) affects 350 million people worldwide. Electroconvulsive therapy (ECT) is effective, yet research on cognitive assessments post-treatment is lacking. This study systematically reviews and meta-analyzes the effectiveness of cognitive assessment tools post-ECT to optimize MDD treatment. MethodsFollowing PRISMA guidelines, this review was pre-registered on PROSPERO (CRD42023470318). Searches were conducted across nine databases up to November 12, 2023. Quality assessment for Randomized Controlled Trials (RCTs) and quasi-experimental studies was performed using the Cochrane risk of bias tool, JBI critical appraisal tools, and the Jadad scale. Meta-analyses for short-term and long-term cognitive function involved 24 and 18 tools, respectively. FindingsThirty studies (20 RCTs and 10 quasi-experimental) involving 2462 MDD patients were evaluated. Results indicated no significant differences in overall short-term and long-term cognitive functions post-ECT. Short-term analysis showed impairments in memory, learning, and verbal abilities but improvements in attention and processing speed. Long-term analysis revealed enhancements in memory, learning, verbal, and visuospatial abilities compared to baseline. Based on GRADE classification, we recommend 11 tools for assessing acute cognitive function and 10 tools for chronic cognitive impairment. These tools demonstrated high reliability and validity, supporting their clinical use. InterpretationThese findings provide critical evidence for future ECT clinical guidelines in managing MDD. The recommended tools can aid clinicians in adjusting ECT regimens, identifying early cognitive changes, and improving therapeutic outcomes in MDD treatment.

An optimized hybrid encryption framework for smart home healthcare: Ensuring data confidentiality and security

This study proposes an optimized hybrid encryption framework combining ECC-256r1 with AES-128 in EAX mode, tailored for smart home healthcare environments, and conducts a comprehensive investigation to validate its performance. Our framework addresses current limitations in securing sensitive health data and demonstrates resilience against emerging quantum computing threats. Through rigorous experimental evaluation, we show that the proposed configuration outperforms existing solutions by delivering unmatched security, processing speed, and energy efficiency. It employs a robust yet streamlined approach, meticulously designed to ensure simplicity and practicality, facilitating seamless integration into existing systems without imposing undue complexity. Our investigation affirms the framework's capability to resist common cybersecurity threats like MITM, replay, and Sybil attacks while proactively considering quantum resilience. The proposed method excels in processing speed (0.006 seconds for client and server) and energy efficiency (3.65W client, 95.4W server), offering a quantum-resistant security level comparable to AES-128. This represents a security-efficiency ratio of 21.33 bits per millisecond, a 25.6% improvement in client-side processing speed, and up to 44% reduction in server-side energy consumption compared to conventional RSA-2048 methods. These improvements enable real-time encryption of continuous health data streams in IoT environments, making it ideal for IoT devices where AES-128′s smaller footprint is advantageous. By prioritizing high-grade encryption alongside ease of use and implementation, the proposed framework presents a future-proof solution that anticipates the trajectory of cryptographic standards amid advancing quantum computing technologies, signifying a pivotal advancement in safeguarding IoT-driven healthcare data.

Immediate post-concussion assessment and cognitive testing Pediatric (ImPACT Pediatric) change scores and factors associated with performance in patients aged 5-9 years following concussion: Preliminary findings

Background Computerized neurocognitive testing is one component of a multidomain assessment of concussion. However, the use of computerized neurocognitive testing has been limited to patients aged 11 years and up, leaving clinicians with few options to evaluate younger children. Purpose To examine the change in Immediate Post-concussion Assessment and Cognitive Testing Pediatric (ImPACT Pediatric) (ImPACT Applications, 2021) scores and factors associated with performance in children aged 5-9 years following a concussion. Methods Participants included 63 children (42% [n = 27] female) aged 5-9 (M = 7.5 ± 1.0) years within 30 (M = 8.5 ± 5.9) days of a concussion. All participants completed the ImPACT Pediatric at their initial visit and at medical clearance for their return to activity (RTA) visit. The ImPACT Pediatric test is a computerized neurocognitive battery that includes 5 tests that assess memory and visual processing speed. Multivariate and univariate analyses of variance and paired t-tests were used to compare ImPACT Pediatric scores from the initial visit to medical clearance. Multivariate and univariate analyses of covariance and multiple linear regression examined factors associated with ImPACT Pediatric performance. Results Participants demonstrated improved overall performance from the initial visit to the medical clearance visit (F(4, 59)=3.08, p = 0.02, Wilks’ Λ = 0.83, ηp 2=0.17), with significant improvement in Rapid Processing Speed (F(1, 62)=7.48, p < 0.01, ηp 2=0.11). When controlling for age, sex, history of ADHD, and days to clinic, the improvement in overall performance remained significant (F(4, 51)=2.99, p = 0.03, Wilks’ Λ = 0.81, ηp 2=0.19). Older age was significantly associated with the Rapid Processing composite score at the initial visit (F(4, 59)=5.9, p < 0.001, Adj. R2=0.25) and medical clearance visit (F(4, 59)=3.8, p = 0.008, Adj. R2=0.16), with older children associated with better performance at both time points (Initial visit: B = 8.17, p < 0.001; Medical Clearance: B = 3.62, p = 0.03). Conclusion Our main findings suggest that children aged 5-9 years improved significantly in Rapid Processing on the ImPACT Pediatric from the initial visit to medical clearance. However, no differences were found for the memory components of the ImPACT Pediatric. Older children also performed better on processing speed than younger children. The findings suggest that the processing speed components of ImPACT Pediatric are useful for monitoring improvements in neurocognitive functioning following concussion in children aged 5-9 years, but that age differences need to be considered when interpreting performance.

Efficient Processing-in-Memory System Based on RISC-V Instruction Set Architecture

A lot of research on deep learning and big data has led to efficient methods for processing large volumes of data and research on conserving computing resources. Particularly in domains like the IoT (Internet of Things), where the computing power is constrained, efficiently processing large volumes of data to conserve resources is crucial. The processing-in-memory (PIM) architecture was introduced as a method for efficient large-scale data processing. However, PIM focuses on changes within the memory itself rather than addressing the needs of low-cost solutions such as the IoT. This paper proposes a new approach using the PIM architecture to overcome memory bottlenecks effectively in domains with computing performance constraints. We adopt the RISC-V instruction set architecture for our proposed PIM system’s design, implementation, and comprehensive performance evaluation. Our proposal expects to efficiently utilize low-spec systems like the IoT by minimizing core modifications and introducing PIM instructions at the ISA level to enable solutions that leverage PIM capabilities. We evaluate the performance of our proposed architecture by comparing it with existing structures using convolution operations, the fundamental unit of deep-learning and big data computations. The experimental results show our proposed structure achieves a 34.4% improvement in processing speed and 18% improvement in power consumption compared to conventional von Neumann-based architectures. This substantiates its effectiveness at the application level, extending to fields such as deep learning and big data.

Optimizing Data Pipeline Efficiency with Machine Learning Techniques

In the era of big data, efficient data processing is crucial for timely insights and decision-making. Traditional data pipelines face challenges such as latency, scalability, and fault tolerance. This paper explores the application of machine learning (ML) techniques to optimize data pipeline efficiency. We propose a framework that integrates ML models for predictive resource allocation, anomaly detection, and dynamic scaling within data pipelines. Our experiments demonstrate significant improvements in processing speed, resource utilization, and reliability. Key Words: Data Engineering, Data Pipelines, Machine Learning, Predictive Resource Allocation, Anomaly Detection, Dynamic Scaling

Design and implementation of deep learning-based object detection and tracking system

Many human tracking methods by deep learning rely on powerful computing resources. For embedded platforms with limited resources, efficient use of resources is a priority. In this paper, we design an object detection and tracking system based on deep learning methods. We propose an efficient system with software and hardware design. We apply the framework of Vitis AI and its Deep Learning Processing Unit using a hardware/software co-design approach. This approach capitalizes on a higher-level acceleration design framework, where the convolutional models can be updated more flexibly and rapidly. This design approach not only provides a fast design flow but also has good performance in terms of throughput. We facilitate the design and accelerate the object detection model YOLO v3 to achieve higher throughput and energy efficiency. Our tracking method achieves a 1.27x improvement in processing speed with the addition of a single-object tracker. Our proposed human tracking methods can achieve better performance than the others in precision with the same test sequences.

Improving processing speed in adolescents at clinical high risk for psychosis with the Specific COgnitive REmediation plus Surround (SCORES) intervention: Study protocol.

Recent preventative approaches with young people at clinical high risk for psychosis (CHR-P) have focused on the remediation of the cognitive deficits that are readily apparent and predictive of future illness. However, the small number of trials using cognitive remediation with CHR-P individuals have reported mixed results. The proposed 2-phased study will test an innovative internet-based and remotely-delivered Specific COgnitive REmediation plus Surround (or SCORES) intervention that targets early processing speed deficits in CHR-P adolescents aged 14-20 years old. In the first R61 phase, a single-arm 2-year proof of concept study, 30 CHR-P individuals will receive SCORES for 10 weeks (4 h per week/40 h total) with a midpoint assessment at 20 h (5 weeks) to demonstrate target engagement and identify the optimal dose needed to engage the target. The Go/No-Go criteria to move to the R33 phase will be processing speed scores improving by a medium effect size (Cohen's d ≥ .6). The proposed package includes a set of complimentary support surround procedures to increase enjoyment and ensure that participants will complete the home-based training. In the second R33 phase, a 3-year pilot study, we will replicate target engagement in a new and larger sample of 54 CHR-P individuals randomized to SCORES (optimized dose) or to a video game playing control condition. In addition, the R33 phase will determine if changes in processing speed are associated with improved social functioning and decreasing attenuated positive symptoms. The support surround components of the intervention will remain constant across phases and conditions in the R33 phase to firmly establish the centrality of processing speed training for successful remediation. The SCORES study is a completely virtual intervention that targets a core cognitive mechanism, processing speed, which is a rate-limiting factor to higher order behaviours and clinical outcomes in CHR-P adolescents. The virtual nature of this study should increase feasibility as well improve the future scalability of the intervention with considerable potential for future dissemination as a complete treatment package.

ANALYSIS OF THE IMPACT OF SHARDING ON THE SCALABILITY AND EFFICIENCY OF BLOCKCHAIN TECHNOLOGIES FOR THE CREATION OF INFORMATION-ANALYTICAL SYSTEMS FOR ENVIRONMENTAL MONITORING OF EMISSIONS INTO THE ENVIRONMENT

This study examines the impact of sharding on the scalability and efficiency of blockchain systems, specifically in the development of a complex of intelligent information and communication systems for environmental monitoring of emissions into the environment for decision-making in the context of carbon neutrality. Utilizing the Ikarus Network infrastructure, sharding was implemented on masternodes as a key technology to optimize transaction processing. Sharding enables the blockchain to be divided into multiple parallel chains, significantly increasing throughput and reducing the load on individual nodes. The results demonstrate a 70% increase in transaction processing speed, allowing the system to handle up to 5000 transactions per second, compared to the previous 3000 transactions per second. Network throughput increased by 50%, ensuring more efficient load distribution and stable operation even with high data volumes. Statistical analysis using ANOVA confirmed significant improvements in transaction processing speed, confirmation time, and resource usage post-sharding implementation. The F-value for transaction processing speed was 4567 with a P-value of 0.0001, indicating substantial improvements. Visual data analysis further confirmed these results, showing noticeable performance enhancements in the blockchain system. Distribution charts and histograms of transaction processing speed and confirmation time revealed an increase in the average number of transactions per second and greater system stability post-sharding. Sharding not only increased throughput but also enhanced system security by decentralizing data among shards, complicating potential cyberattacks. The study aimed to determine how sharding can improve the scalability and efficiency of blockchain systems. These improvements position the Ikarus Network as a promising solution for scalable and secure blockchain-based applications, especially for tasks related to carbon emission monitoring and management. These findings can underpin further study and the development of more efficient blockchain technologies.

GAN-BodyPose: Real-time 3D human body pose data key point detection and quality assessment assisted by generative adversarial network

With the rapid advancement of deep learning and computer vision, these technologies are becoming increasingly vital in areas like virtual reality, medical diagnosis, and sports training. Existing methods for real-time 3D human body pose keypoint detection and quality assessment face significant challenges such as insufficient detection accuracy, low computational efficiency, and high data quality requirements. To address these challenges, we propose an innovative solution, GAN-BodyPose. This approach integrates 3D convolutional neural networks, self-attention mechanisms, and generative adversarial networks to deliver efficient and accurate detection and assessment in real time. The GAN-BodyPose framework combines 3D-CNN and self-attention for effective feature extraction and keypoint detection, enhanced further by generative adversarial networks for superior data quality and accuracy. Our extensive evaluations using a large-scale 3D human body pose dataset demonstrated that GAN-BodyPose outperforms traditional methods, showing improvements in processing speed (15% faster), accuracy in terms of Mean Per Joint Position Error (reduced by approximately 2.2%), and an Area Under the Curve (AUC) score increased by approximately 9.5% compared to HR-Net and other datasets. Additionally, it achieves lower Floating-Point Operations (FLOPs) by about 9.3%, indicating more efficient computational performance. These advancements underline the potential of our approach to significantly enhance user experiences in virtual reality, motion capture, and other real-time applications. The successful application of GAN-BodyPose promises greater efficiency and precision in fields ranging from game development to medical diagnostics, and robust support for human-computer interaction and gesture recognition. This research represents a substantial contribution to deep learning applications in robot control, decision-making, and broadens the research foundation in these domains.

QOL-08. THE LONG-TERM UTILITY OF METHYLPHENIDATE FOR THE PRESERVATION OF INTELLECTUAL DEVELOPMENT IN CHILDHOOD SURVIVORS OF BRAIN TUMOUR

Abstract BACKGROUND Enduring neurocognitive late effects are a common consequence of childhood brain tumour survivorship. Conceptual modelling of intelligence suggests initial deficits to processing speed and attention lead to the subsequent plateauing of intellectual and academic development. The utility of methylphenidate in improving attentional and processing speed deficits within the early phase of recovery has been previously reported. The longer-term contribution of methylphenidate in preserving intellectual ability through ameliorating these early deficits is yet to be evaluated. METHODS Twenty-nine childhood survivors were matched retrospectively with a no-treatment synthetic control. Intellectual assessments were conducted prior to starting methylphenidate and a minimum of 12 months thereafter. For the no-treatment control group, intellectual data were collected retrospectively from archival records, employing a similar assessment period. Linear Mixed Models were used to assess the influence of methylphenidate on intellectual trajectories between groups and fatigue scores over time. RESULTS Methylphenidate was associated with raw score improvements across all intellectual indices compared to no-treatment controls. Linear Mixed Modelling reported a borderline significant improvement in Processing Speed (t = 1.98, p =.05) for the methylphenidate group. In comparison, a borderline significant (p =.05) within-group decrease in Working Memory scores was reported for the no-treatment group. There was a significant within-group decrease in pooled PedsQL MFS scores between assessments for the treatment group (t = -4.01, p &amp;lt;.01), with the most notable decrease observed in Sleep/Rest Fatigue (t = -10.17, p &amp;lt;.01). CONCLUSIONS Methylphenidate may pose a valuable long-term rehabilitative strategy in preserving Processing Speed in childhood survivors of brain tumour. Whilst our sample size is small, there is tentative evidence for the utility of methylphenidate in alleviating fatigue experienced by childhood survivors.