Slower Processing Speed Research Articles

Lightweight video object segmentation: Integrating online knowledge distillation for fast segmentation

The typical shortcoming of STM (Space-Time Memory Network) mode video object segmentation algorithms is their high segmentation performance coupled with slow processing speeds, which poses challenges in meeting real-world application demands. In this work, we propose using an online knowledge distillation method to develop a lightweight video segmentation algorithm based on the STM mode, achieving fast segmentation while maintaining performance. Specifically, we utilize a novel adaptive learning rate to tackle the issue of inverse learning during distillation. Subsequently, we introduce a Smooth Block mechanism to reduce the impact of structural disparities between the teacher and student models on distillation outcomes. Moreover, to reduce the fitting difficulty of the student model on single-frame features, we design the Space-Time Feature Fusion (STFF) module to provide appearance and position priors for the feature fitting process of each frame. Finally, we employ a simple Discriminator module for adversarial training with the student model, to encourage the student model to learn the feature distribution of the teacher model. Extensive experiments show that our algorithm attains performance comparable to the current state-of-the-art on both DAVIS and YouTube datasets, despite running up to ×4 faster, with ×20 fewer parameters and ×30 fewer GFLOPS.

New obstacle detection method for a mobile robot based on improved disparity map processing

Stereovision is a crucial technique for vision-based obstacle detection as it provides accurate 3D information for analyzing scenes and detecting obstacles. Despite its effectiveness, existing methods often struggle with real-time performance and computational efficiency. This paper introduces a new stereovision-based obstacle detection system that integrates two novel approaches: Optimized Disparity Map Computation (ODMC), which enhances Hirschmüller’s method for faster disparity map generation, and Disparity Seeded Region Growing (D-SRG), an improved version of the SRG algorithm optimized for identifying obstacles in real-time. Our system is designed to address key challenges such as high memory consumption and slow processing speed, enabling efficient real-time processing from image acquisition to obstacle detection using a 3D camera. Experimental results show that:

Improvement of fatigue, depression, and processing speed two weeks post Natalizumab infusion in Multiple Sclerosis: No difference between standard and extended interval dosing schedules

BackgroundFatigue, depression and slow processing speed are debilitating symptoms in people with Relapsing-Remitting Multiple Sclerosis (RRMS) that significantly impacts on the quality of life. Natalizumab, a disease-modifying treatment, improves clinical symptoms but questions remain about the comparative efficacy between its standard interval dosing (SID) and extended interval dosing (EID) schedules. ObjectiveTo examine the impact of short term natalizumab dosing schedules—SID versus EID—on the so called “invisible symptoms”, specifically focusing on symptom exacerbation during the 'wearing-off' phase before infusion and the subsequent relief post-infusion. MethodsForty-two RRMS patients were assessed one week before (T0) and two weeks after pre-and post-natalizumab infusion (T1) for fatigue symptoms using the Fatigue Scale for Motor and Cognitive Functions (FSMC), the Modified Fatigue Impact Scale (MFIS), and the Fatigue Symptoms and Impacts Questionnaire–Relapsing Multiple Sclerosis (FSIQ-RMS). Processing speed and depression were measured by the symbol digit modality test (SDMT), and the Beck Depression Inventory-II (BDI-II). Participants were categorized into either the SID or EID dosing schedules of natalizumab, and their outcomes were compared. ResultsForty-two patients (21 SID; 21 EID) completed the study. Fatigue severity scales, SDMT, and BDI-II scores improved from T0 to T1. No significant differences in fatigue symptoms were found between the SID and EID groups, whether during the "wearing-off" period (T0) or post-infusion (T1). ConclusionsBoth SID and EID dosing regimens of natalizumab are similarly effective in reducing fatigue symptoms, depression and improving processing speed in individuals with RRMS, with no observed differences during the "wearing-off" periods or after re-infusion.

GMDIC: a digital image correlation measurement method based on global matching for large deformation displacement fields

The digital image correlation method is a non-contact optical measurement method, which has the advantages of full-field measurement, simple operation, and high measurement accuracy. The traditional DIC method can accurately measure displacement and strain fields, but there are still many limitations. (i) In the measurement of large displacement deformations, the calculation accuracy of the displacement field and strain field needs to be improved due to the unreasonable setting of parameters such as subset size and step size. (ii) It is difficult to avoid under-matching or over-matching when reconstructing smooth displacement or strain fields. (iii) When processing large-scale image data, the computational complexity will be very high, resulting in slow processing speeds. In recent years, deep-learning-based DIC has shown promising capabilities in addressing the aforementioned issues. We propose a new, to the best of our knowledge, DIC method based on deep learning, which is designed for measuring displacement fields of speckle images in complex large deformations. The network combines the multi-head attention Swin-Transformer and the high-efficient channel attention module ECA and adds positional information to the features to enhance feature representation capabilities. To train the model, we constructed a displacement field dataset that conformed to the real situation and contained various types of speckle images and complex deformations. The measurement results indicate that our model achieves consistent displacement prediction accuracy with traditional DIC methods in practical experiments. Moreover, our model outperforms traditional DIC methods in cases of large displacement scenarios.

Executive functions and processing speed in covert cerebral small vessel disease.

Executive dysfunction and slowed processing speed are central cognitive impairments in cerebral small vessel disease (cSVD). It is unclear whether the subcomponents of executive functions become equally affected and whether computerized tests are more sensitive in detecting early cognitive changes over traditional tests. The associations of specific executive abilities (cognitive flexibility, inhibitory control, working memory) and processing speed with white matter hyperintensities (WMHs) and Instrumental Activities of Daily Living (IADL) were examined. In the Helsinki Small Vessel Disease Study, 152 older individuals without stroke or dementia were assessed with brain magnetic resonance imaging and comprehensive neuropsychological evaluation. WMH volumes were obtained with automated segmentation. Executive functions and processing speed measures included established paper-and-pencil tests and the computer-based Flexible Attention Test (FAT), Simon task and Sustained Attention to Response Task. White matter hyperintensity volume and IADL were associated with multiple cognitive measures across subdomains independently of demographic factors. The highest effect sizes were observed for FAT numbers and number-letter tasks (tablet modifications from the Trail Making Test), FAT visuospatial span, Simon task and semantic verbal fluency. Some of the widely used tests such as Stroop inhibition, phonemic fluency and digit span were not significantly associated with either WMHs or IADL. Processing speed and executive function subcomponents are broadly related to functional abilities and WMH severity in covert cSVD, but the strength of associations within subdomains is heavily dependent on the assessment method. Digital tests providing precise measures of reaction times and response accuracy seem to outperform many of the conventional paper-and-pencil tests.

Executive Function Associations With Audibility-Adjusted Speech Perception in Noise.

Speech recognition in noise is challenging for listeners and appears to require support from executive functions to focus attention on rapidly unfolding target speech, track misunderstanding, and sustain attention. The current study was designed to test the hypothesis that lower executive function abilities explain poorer speech recognition in noise, including among older participants with hearing loss who often exhibit diminished speech recognition in noise and cognitive abilities. A cross-sectional sample of 400 younger-to-older adult participants (19 to < 90 years of age) from the community-based Medical University of South CarolinaLongitudinal Cohort Study of Age-related Hearing Loss were administered tasks with executive control demands to assess individual variability in a card-sorting measure of set-shifting/performance monitoring, a dichotic listening measure of selective attention/working memory, sustained attention, and processing speed. Key word recognition in the high- and low-context speech perception-in-noise (SPIN) tests provided measures of speech recognition in noise. The SPIN scores were adjusted for audibility using the Articulation Index to characterize the impact of varied hearing sensitivity unrelated to reduced audibility on cognitive and speech recognition associations. Set-shifting, dichotic listening, and processing speed each explained unique and significant variance in audibility-adjusted, low-context SPIN scores (ps < .001), including after controlling for age, pure-tone threshold average (PTA), sex, and education level. The dichotic listening and processing speed effect sizes were significantly diminished when controlling for PTA, indicating that participants with poorer hearing sensitivity were also likely to have lower executive function and lower audibility-adjusted speech recognition. Poor set-shifting/performance monitoring, slow processing speed, and poor selective attention/working memory appeared to partially explain difficulties with speech recognition in noise after accounting for audibility. These results are consistent with the premise that distinct executive functions support speech recognition in noise.

Cognition in (pre)symptomatic Dutch-type hereditary and sporadic cerebral amyloid angiopathy.

Cerebral amyloid angiopathy (CAA) is a main cause of cognitive dysfunction in the elderly. We investigated specific cognitive profiles, cognitive function in the stage before intracerebral hemorrhage (ICH), and the association between magnetic resonance imaging (MRI) based cerebral small vessel disease (cSVD) burden in CAA because data on these topics are limited. We included Dutch-type hereditary CAA (D-CAA) mutation carriers with and without ICH, patients with sporadic CAA (sCAA), and age-matched controls. Cognition was measured with a standardized test battery. Linear regression was performed to assess the association between MRI-cSVD burden and cognition. D-CAA ICH- mutation carriers exhibited poorer global cognition and executive function compared to age-matched controls. Patients with sCAA performed worse across all cognitive domains compared to D-CAA ICH+ mutation carriers and age-matched controls. MRI-cSVD burden is associated with decreased processing speed. CAA is associated with dysfunction in multiple cognitive domains, even before ICH, with increased MRI-cSVD burden being associated with slower processing speed. Cognitive dysfunction is present in early disease stages of cerebral amyloid angiopathy (CAA) before the occurrence of symptomatic intracerebral hemorrhage (sICH). Presymptomatic Dutch-type CAA (D-CAA) mutation carriers show worse cognition than age-matched controls. More early awareness of cognitive dysfunction in CAA before first sICH is needed. Increased cerebral small vessel disease CAA-burden on magnetic resonance imaging is linked to a decrease in processing speed.

Perinatal risk factors and neurocognitive outcomes in children and adolescents with sickle cell disease.

The literature on cognitive and academic outcomes for children with sickle cell disease (SCD) who experience perinatal risk factors is limited. We aimed to evaluate if low birthweight (LBW), gestational age, and history of neonatal intensive care unit (NICU) admission were associated with neurocognitive functioning, grade retention, or receipt of early intervention or formal educational support in children with SCD. This prospective birth cohort study included 336 participants, ages 8-18, with SCD, who received cognitive testing as part of standard of care and whose caregivers completed behavioral rating scales. Multivariable generalized linear regression models were used to examine associations between perinatal risks and outcome variables, after adjusting for demographic and medical covariates. The prevalence of NICU admission and LBW were 12.03% and 13.50%, respectively. Lower birthweight, earlier gestational age, and NICU admission were associated with worse working memory performance and receipt of early intervention services. Lower birthweight and NICU admission were also associated with slower processing speed. History of NICU admission was associated with caregiver ratings of hyperactivity and emotional dysregulation. The effects of perinatal risk factors on neurocognitive, academic, or educational outcomes were not dependent on SCD genotype. History of LBW or NICU admission was associated with worse cognitive outcomes and increased use of early intervention services among children with SCD. Early identification of perinatal risk factors will help identify children who will benefit from formal developmental or neuropsychological evaluations to manage the comorbidity of SCD and perinatal risks and facilitate increased intervention.

Analysis of methods and algorithms for image filtering and quality enhancement

This paper addresses the prevalent issue of image noise and presents methods for its mitigation. The paper describes, analyses and tests a variety of image filtering techniques, with specific reference to their use in different contexts. The filtering methods can be classified into two principal categories: linear filters, which include the Gaussian and mean filters, and non-linear filters, which comprise the median filter, the Fast Fourier Transform (FFT), the Non-Local Means (NLM) filter, and the anisotropic diffusion filter. The efficacy of each filter is mathematically described and evaluated on RGB images using the Python programming language. The study delineates the evaluation metrics and their respective advantages and disadvantages. The Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) are employed as criteria for the analysis of algorithm efficiency. Furthermore, the mean execution time for each algorithm is also monitored. The experimental data suggests that linear filters are relatively fast but produce inferior results and are best employed as preparatory measures. Non-linear filters have been demonstrated to be more robust and applicable to a variety of noise types, although it has been established that they require parameter fine-tuning. The study demonstrates that anisotropic diffusion is suitable for both manual image processing and real-time applications, offering an optimal balance between processing speed and denoised image quality. NLM is optimal for high-quality single image processing due to its superior results, despite a slower processing speed. FFT is noted for its efficiency in eliminating periodic noise. Further research will be conducted on advancing filtering techniques for different real-world scenarios and autonomous systems.

Adynamic spoken language in corpus callosum dysgenesis

Corpus callosum dysgenesis (CCD) is a congenital brain malformation that occurs when the development of the corpus callosum is disrupted, either partially or completely. The cognitive outcomes in individuals with CCD vary greatly, but generally the neuropsychological profile is characterised by slow processing speed, poor transfer of interhemispheric sensory-motor information, and impaired complex problem solving. Core language skills are often preserved in CCD, but there is some evidence that complex language may be impaired. Thus, the current study sought to examine whether spontaneous speech output was reduced in a cohort of individuals with CCD compared to age-matched controls. We further explored a series of factors that may be contributing to poor spontaneous speech in CCD, such as difficulties generating, selecting, and sequencing ideas for expression, as well as apathy and slowed processing speed. A cohort of 25 individuals with CCD and 39 neurotypical controls were enrolled in this study. Participants completed a picture description task to measure spontaneous speech output, alongside a series of cognitive and language baseline tests. Verbal and nonverbal fluency tasks gauged idea generation and sequencing, and sentence-level selection tasks measured idea selection. We found that, despite having largely intact core language skills, individuals with CCD produced significantly less spontaneous speech on the picture description task than controls. This language profile may be described as “adynamic”. Further, we found that poor spontaneous speech output in CCD was related to problems generating ideas for expression, as individuals with CCD performed below controls on the verbal and nonverbal fluency tasks. Exploratory analyses revealed that apathy and slowed processing speed may be contributing factors. Adynamia in CCD is a novel finding that may be an intervention target for improving communication skills in this population.

Research on Lightweight Method of Insulator Target Detection Based on Improved SSD.

Aiming at the problems of a large volume, slow processing speed, and difficult deployment in the edge terminal, this paper proposes a lightweight insulator detection algorithm based on an improved SSD. Firstly, the original feature extraction network VGG-16 is replaced by a lightweight Ghost Module network to initially achieve the lightweight model. A Feature Pyramid structure and Feature Pyramid Network (FPN+PAN) are integrated into the Neck part and a Simplified Spatial Pyramid Pooling Fast (SimSPPF) module is introduced to realize the integration of local features and global features. Secondly, multiple Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanisms are introduced in the Neck part to make the model pay more attention to the channels containing important feature information. The original six detection heads are reduced to four to improve the inference speed of the network. In order to improve the recognition performance of occluded and overlapping targets, DIoU-NMS was used to replace the original non-maximum suppression (NMS). Furthermore, the channel pruning strategy is used to reduce the unimportant weight matrix of the model, and the knowledge distillation strategy is used to fine-adjust the network model after pruning, so as to ensure the detection accuracy. The experimental results show that the parameter number of the proposed model is reduced from 26.15 M to 0.61 M, the computational load is reduced from 118.95 G to 1.49 G, and the mAP is increased from 96.8% to 98%. Compared with other models, the proposed model not only guarantees the detection accuracy of the algorithm, but also greatly reduces the model volume, which provides support for the realization of visible light insulator target detection based on edge intelligence.

B - 8 Preemie-Pediatric Left Middle Cerebral Arterial Cerebrovascular Accident and Comorbid Focal Left Central Epilepsy: a Case Study

Abstract Objective Literature regarding neurocognitive functioning in pediatric middle cerebral arterial (MCA) is limited. The aim of this study was to enhance the field’s knowledge and understanding of the presentation and impact of preemie-pediatric left MCA in utero with comorbid left-brain epilepsy in overall neurocognitive and behavioral functioning. Method The subject is a 12-year-old left-handed male with history of previously described events. The purpose of this evaluation was to determine current levels of neurocognitive and behavioral functioning as it relates to his medical history, treatment planning, and prognosis. Data collection was part of a pre-surgical consult to reduce epileptic activity. Results Results suggest overall cognitive functioning fell within the average range. However, relative difficulties in areas of attention and executive functioning were indicated. While all other domains were grossly intact, his profile indicates strengths in visual compared to verbal memory. Interestingly, the patient performed better on verbal tasks after a delay both with and without cuing. This suggests the presentation of a unique trait of pediatric epilepsy in that due to slowed processing speed as seen on the WISC, this patient is likely to remember more information over time. His academic achievements also fell within age-expectations. Conclusions This study impresses the importance of prompt neuropsychological evaluation in providing insight to the patient’s impairments and contribute to adaptive neurocognitive functioning moving forward. While idiopathic, this case demonstrates the need for continued research in pediatric-infantile infarct and provides the opportunity to improve the field’s understanding of the interpretation of similar cases.

Combining Transfer Learning and Ensemble Algorithms for Improved Citrus Leaf Disease Classification

Accurate categorization and timely control of leaf diseases are crucial for citrus growth. We proposed the Multi-Models Fusion Network (MMFN) for citrus leaf diseases detection based on model fusion and transfer learning. Compared to traditional methods, the algorithm (integrating transfer learning Alexnet, VGG, and Resnet) we proposed can address the issues of limited categories, slow processing speed, and low recognition accuracy. By constructing efficient deep learning models and training and optimizing them with a large dataset of citrus leaf images, we ensured the broad applicability and accuracy of citrus leaf disease detection, achieving high-precision classification. Herein, various deep learning algorithms, including original Alexnet, VGG, Resnet, and transfer learning versions Resnet34 (Pre_Resnet34) and Resnet50 (Pre_Resnet50) were also discussed and compared. The results demonstrated that the MMFN model achieved an average accuracy of 99.72% in distinguishing between diseased and healthy leaves. Additionally, the model attained an average accuracy of 98.68% in the classification of multiple diseases (citrus huanglongbing (HLB), greasy spot disease and citrus canker), insect pests (citrus leaf miner), and deficiency disease (zinc deficiency). These findings conclusively illustrate that deep learning model fusion networks combining transfer learning and integration algorithms can automatically extract image features, enhance the automation and accuracy of disease recognition, demonstrate the significant potential and application value in citrus leaf disease classification, and potentially drive the development of smart agriculture.

Impaired Emotional Processing in Major Depressive Disorder. Accuracy versus Processing Speed

Depression is one of the most frequent conditions within the general population, ranking among the most burdensome in terms of public health expenses, productivity loss and social costs. The condition has been linked with cognitive deficits that may even continue in remission phases, social cognition being one potentially affected cognitive function. In our study, the Penn’s Emotional Acuity Test included in the CogtestTM battery was used to assess emotion recognition accuracy and processing speed. The Structured Clinical Interview for the Montgomery-Asberg Depression Rating Scale was used to assess the intensity of the symptomatology. A total of 48 depressed individual individuals (65% females), with a mean age of 49.8 ± 10.4 years, and 40 healthy controls (75% females), with a mean age of 35.2 ± 6.9 years were included. As compared to controls, depressive participants recorded significantly less correct answers (9.3 ± 3.8 vs. 11.2 ± 3.6, p = 0.019 – Student’s t test) and slower processing speeds (6795 ± 3366 vs. 4042 ± 1623, p 0.001 – Mann-Whitney U test) in emotion processing. Furthermore, symptom severity significantly influences only processing speed and not accuracy in emotion recognition tasks. Thus, we conclude that depressive individuals have a significant deficit in processing speed and accuracy when identifying the intensity of other peoples’ emotions.

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Deep learning (DL) has brought new perspectives and methods to automatic modulation recognition (AMR), enabling AMR systems to operate more efficiently and reliably in modern wireless communication environments through its powerful feature learning and complex pattern recognition capabilities. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are used for sequence recognition tasks, face two main challenges, respectively: the ineffective utilization of global information and slow processing speeds due to sequential operations. To address these issues, this paper introduces CTRNet, a novel automatic modulation recognition network that combines a CNN with Transformer. This combination leverages Transformer’s ability to adequately capture the long-distance dependencies between global sequences and its advantages in sequence modeling, along with the CNN’s capability to extract features from local feature regions of signals. During the data preprocessing stage, the original IQ-modulated signals undergo sliding-window processing. By selecting the appropriate window sizes and strides, multiple subsequences are formed, enabling the network to effectively handle complex modulation patterns. In the embedding module, token vectors are designed to integrate information from multiple samples within each window, enhancing the model’s understanding and modeling ability of global information. In the feedforward neural network, a more effective Bilinear layer is employed for processing to capture the higher-order relationship between input features, thereby enhancing the ability of the model to capture complex patterns. Experiments conducted on the RML2016.10A public dataset demonstrate that compared with the existing algorithms, the proposed algorithm not only exhibits significant advantages in terms of parameter efficiency but also achieves higher recognition accuracy under various signal-to-noise ratio (SNR) conditions. In particular, it performs relatively well in terms of accuracy, precision, recall, and F1-score, with clearer classification of higher-order modulations and notable overall accuracy improvement.

Research on tumors segmentation based on image enhancement method

One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital image segmentation of the liver and its tumor. However, traditional liver parenchymal segmentation techniques often face several challenges in performing liver segmentation: lack of precision, slow processing speed, and computational burden. These shortcomings limit the efficiency of surgical planning and execution. In this work, the model initially describes in detail a new image enhancement algorithm that enhances the key features of an image by adaptively adjusting the contrast and brightness of the image. Then, a deep learning-based segmentation network was introduced, which was specially trained on the enhanced images to optimize the detection accuracy of tumor regions. In addition, multi-scale analysis techniques have been incorporated into the study, allowing the model to analyze images at different resolutions to capture more nuanced tumor features. In the presentation of the experimental results, the study used the 3Dircadb dataset to test the effectiveness of the proposed method. The experimental results show that compared with the traditional image segmentation method, the new method using image enhancement technology has significantly improved the accuracy and recall rate of tumor identification.

Reduced P300 amplitude in children and adolescents with autism is associated with slowed processing speed, executive difficulties, and social-communication problems.

Selective attention to auditory input is reflected in the brain by an electric amplitude called the P3b amplitude, which is measured using electroencephalography. Previous research has shown that children and adolescents with autism have an attenuated P3b amplitude when they have to attend specific sounds while ignoring other sounds. However, it is unknown whether a reduced P3b amplitude in autistic children and adolescents is associated with their autism features, daily functioning and/or cognitive functions. This study aimed to examine these questions. Therefore, we assessed selective attention to auditory input in 57 children with autism aged 7-14 years and 57 neurotypically developing controls while measuring their brain activity with electroencephalography. Participants further underwent cognitive assessment, and parents reported on autistic traits and daily functioning. As expected, children with autism had lower P3b amplitude compared to their neurotypical peers. Importantly, an attenuated P3b amplitude was associated with more parent-reported social-communication problems and difficulties with daily functioning. Children with autism further had reduced processing speed of visual input, which also was coupled to a lower P3b amplitude. In conclusion, we found attenuated P3b amplitude in children with autism performing an auditory selective attention task, which was related to difficulties with processing visual input and allocating attentional resources critical for social and daily functioning. The results suggest that autistic children are more vulnerable to being disturbed when the environment is filled with conflicting sensory input.

YOLO-TSL: A lightweight target detection algorithm for UAV infrared images based on Triplet attention and Slim-neck

When performed from an Unmanned Aerial Vehicle (UAV) perspective, infrared target detection often suffers from low accuracy, excessive model parameters, and slow processing speed. To address these challenges, this paper proposes a lightweight infrared target detection algorithm, named YOLO-TSL, which is based on an improved version of YOLOv8n. Building on an in-depth analysis of UAV infrared image characteristics, this paper introduces Triplet Attention to the model backbone to enhance target detection accuracy and effectively suppress background interference. Moreover, YOLO-TSL incorporates the Slim-Neck architecture, which features GSConv and GSbottleneck in the neck structure and employs a one-time aggregation method to design the cross-level partial network known as the VoV-GSCSP module. This architecture significantly reduces the model’s computational complexity while maintaining high detection accuracy. Furthermore, this paper introduces an innovative inner-MPDIoU loss function that optimizes IoU loss computation by enhancing bounding box similarity and adaptive auxiliary bounding box scale adjustments, based on both inner-IoU and MDPIoU. Following experimental validation, YOLO-TSL demonstrate significant improvements over YOLOV8n, including a 3.9% increase in mAP50 and a 3% increase in recall. Additionally, YOLO-TSL has 15.7% fewer parameters, requires 11% less computation, and offers 26% faster inference. Comparative experiments on the FLIR dataset reveal that the algorithm not only outperforms YOLOv8n by 1.4% in mAP50, but also offers significant advantages over other algorithms in terms of parameter reduction and computational efficiency, thus demonstrating YOLO-TSL’s superiority in accuracy, efficiency, and speed.

Study on the Application Status of 3D Point Cloud

This paper aims to discuss the application and development trend of 3D point cloud technology in different fields. As an effective means of digitizing the real world, 3D point cloud has been widely used in many fields, including computer-aided design, building information modeling, geographic information systems, robot navigation, autonomous vehicles, and the digitalization of cultural relics. In this paper, the techniques of acquiring 3D point cloud data are summarized, including laser scanning, structured light scanning and time flight measurement. Then, the latest developments in point cloud data preprocessing were analyzed. Further, the challenges of 3D point cloud technology are discussed, such as large data volume, slow processing speed and high precision requirements. Finally, this paper looks forward to the future development direction of 3D point cloud technology, especially in the integration of artificial intelligence and machine learning technology application potential. Through this study, we expect to provide researchers and practitioners in related fields with a comprehensive understanding of 3D point cloud technology and promote its application in a wider range of fields.

Cortical lobar volume reductions associated with homocysteine-related subcortical brain atrophy and poorer cognition in healthy aging.

Homocysteine (Hcy) is a cardiovascular risk factor implicated in cognitive impairment and cerebrovascular disease but has also been associated with Alzheimer's disease. In 160 healthy older adults (mean age = 69.66 ± 9.95 years), we sought to investigate the association of cortical brain volume with white matter hyperintensity (WMH) burden and a previously identified Hcy-related multivariate network pattern showing reductions in subcortical gray matter (SGM) volumes of hippocampus and nucleus accumbens with relative preservation of basal ganglia. We additionally evaluated the potential role of these brain imaging markers as a series of mediators in a vascular brain pathway leading to age-related cognitive dysfunction in healthy aging. We found reductions in parietal lobar gray matter associated with the Hcy-SGM pattern, which was further associated with WMH burden. Mediation analyses revealed that slowed processing speed related to aging, but not executive functioning or memory, was mediated sequentially through increased WMH lesion volume, greater Hcy-SGM pattern expression, and then smaller parietal lobe volume. Together, these findings suggest that volume reductions in parietal gray matter associated with a pattern of Hcy-related SGM volume differences may be indicative of slowed processing speed in cognitive aging, potentially linking cardiovascular risk to an important aspect of cognitive dysfunction in healthy aging.