Processing Speed Research Articles

Fast‐MedNeXt: Accelerating the MedNeXt Architecture to Improve Brain Tumour Segmentation Efficiency

ABSTRACTWith the rapid development of medical imaging technology, 3D image segmentation technology has gradually become a mainstream method, especially in brain tumour detection and diagnosis showing its unique advantages. The technique makes full use of 3D spatial information to locate and analyze tumours more accurately, thus playing an important role in improving diagnostic accuracy, optimising treatment planning and promoting research. However, it also suffers from significant computational expenditure and delayed processing pace. In this paper, we propose an innovative optimisation scheme to address this problem. We thoroughly investigate the MedNeXt network and propose Fast‐MedNeXt, which aims to increase the processing speed while maintaining accuracy. First, we introduce the partial convolution (PConv) technique, which replaces the deep convolutional layers in the network. This improvement effectively reduces computation and memory requirements while maintaining efficient feature extraction. Second, based on PConv, we propose PConv‐Down and PConv‐Up modules, which are applied to the up‐sampling and down‐sampling modules to further optimise the network structure and improve efficiency. To confirm the efficacy of the approach, we carried out a sequence of tests in the multimodal brain tumour segmentation challenge 2021 (BraTS2021). By comparing with the MedNeXt series network, the Fast‐MedNeXt reduced the latency by 22.1%, 20.5%, 15.8%, and 11.4% respectively, while the average accuracy also increased by 0.475% and 0.2% respectively. These significant performance improvements demonstrate the effectiveness of Fast‐MedNeXt in 3D medical image segmentation tasks and provide a new and more efficient solution for the field.

Growing Semiconductor Industry in India

This paper delves into the growing semiconductor industry in India, and its importance in every aspect of present and future high data processing speed. Semiconductors are crucial electronic components for quantum computing, artificial intelligence (AI), medical devices, aerospace and defence systems, etc. Thus, the use of semiconductors for these most important and future ease concepts, this paper is an understanding approach to the growing importance of semiconductors in India

Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction

To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents a comprehensive review of the current state of the art in tunnel blasting evaluation, organized into five key areas: Blasting Techniques and Optimization, 3D Reconstruction and Visualization, Monitoring and Assessment Technologies, Automation and Advanced Techniques, and Half Porosity in Tunnel Blasting. Each section provides an indepth analysis of the latest research and developments, offering insights into enhancing blasting efficiency, improving safety, and optimizing tunnel design. Building on this foundation, we introduce a digital identification method for assessing half porosity through 3D image reconstruction. Utilizing the Structure from Motion (SFM) technique, we re-construct the 3D contours of tunnel surfaces and bench faces after blasting. Curvature values are employed as key indicators for extracting 3D point cloud data from boreholes. The acquired postblasting point cloud data is processed using advanced software that incorporates the RANSAC algorithm to accurately project and fit the borehole data, leading to the determination of the target circle and borehole axis. The characteristics of the boreholes are analyzed based on the fitting results, culminating in the calculation of half porosity. Field experiments conducted on the Huangtai Tunnel (AK20 + 970.5 to AK25 + 434), part of the new National Highway 109 project, provided data from shell holes generated during blasting. These data were analyzed and compared with traditional onsite measurements to validate the proposed method’s effectiveness. The computed half porosity value using this technique was 58.7%, showing minimal deviation from the traditional measurement of 60%. This methodology offers significant advantages over conventional measurement techniques, including easier equipment acquisition, non-interference with construction activities, a comprehensive detection range, rapid processing speed, reduced costs, and improved accuracy. The findings demonstrate the method’s potential for broader application in tunnel blasting assessments.

Keypoint-Based Bee Orientation Estimation and Ramp Detection at the Hive Entrance for Bee Behavior Identification System

This paper addresses the challenge of accurately estimating bee orientations on beehive landing boards, which is crucial for optimizing beekeeping practices and enhancing agricultural productivity. The research utilizes YOLOv8 pose models, trained on a dataset created using an open-source computer vision annotation tool. The annotation process involves associating bounding boxes with keypoints to represent bee orientations, with each bee annotated using two keypoints: one for the head and one for the stinger. The YOLOv8-pose models demonstrate high precision, achieving 98% accuracy for both bounding box and keypoint detection in 1024×576 px images. However, trade-offs between model size and processing speed are addressed, with the smaller nano model reaching 67 frames per second on 640×384 px images. The entrance ramp detection model achieves 91.7% intersection over union across four keypoints, making it effective for detecting the hive’s landing board. The paper concludes with plans for future research, including the behavioral analysis of bee colonies and model optimization for real-time applications.

All‐Optical Imaging Using Perovskite Nanocrystals Based on Spectro‐Spatial Correlation

AbstractThe exponential growth of data from sensors and internet‐connected devices has challenged traditional electronic computing, particularly in processing speed and power efficiency. Optical information processing, with its inherent high speed and parallelism, offers a promising solution. Moreover, optical processing is vital for developing all‐optical networks, where all‐optical imaging is essential. In this paper, a novel all‐optical imaging technique that leverages in situ anion exchange reactions between perovskite nanocrystals to create an array of pixels with tunable emission wavelengths is proposed. This method enables the conversion of UV light, whether transmitted or reflected by an object, into the visible spectrum, thereby establishing a direct correlation between spectral and spatial information. By employing wavelength division multiplexing (WDM), the approach reduces the dimensionality of information acquisition from two dimensions to one, enhancing imaging speed. This innovation paves the way for significant advancements in all‐optical imaging and image processing, offering new possibilities for faster and more efficient imaging technologies.

Subjective memory complaints and the effect of a multidomain lifestyle intervention on cognition - the FINGER trial.

Older people reporting subjective memory complaints (SMCs) may have greater risk of cognitive decline. Multidomain lifestyle interventions are a promising strategy for the prevention of cognitive decline. The aim of this study was to investigate whether the presence of SMCs affects the efficacy of a 2-year multidomain lifestyle intervention on cognition. This study is part of the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) project. Participants (a sub-sample of 568 individuals, baseline age 60 to 77 years) were randomized (1:1) to receive a 2-year multidomain lifestyle intervention group including dietary advice, exercise, cognitive training, and vascular risk management, or regular health advice control group. Cognitive performance was assessed at baseline and at 1- and 2-year visits, using a neuropsychological test battery, including tests assessing memory, executive functions, and processing speed. Participants rated the frequency of SMCs using the Prospective and Retrospective Memory Questionnaire. Having more retrospective SMCs was linked to a less favorable cognitive trajectory over 2 years. The difference between the intervention and control groups in annual change in tested memory performance was 0.077 (95 % CI 0.008-0.146) among those reporting more retrospective SMCs and -0.011 (-0.074-0.053) among those with less SMCs; interaction effect p=0.019. No other interactions between SMCs and intervention allocation were observed. A lifestyle intervention may be beneficial for older adults with and without SMCs. Persons having more retrospective SMCs may benefit more from the intervention regarding memory functioning.

Road Pothole Detection Model Based on Local Attention Resnet18-CNN-LSTM

Abstract. In response to the low detection accuracy and slow speed of existing road pothole detection methods, a road pothole classification detection model based on local attention Resnet18-CNN-LSTM (Long Short-Term Memory network) is proposed. On the basis of Resnet18, a local attention mechanism and a CNN-LSTM combined model are added to propose a road pothole detection model based on local attention Resnet18-CNN-LSTM. The local attention mechanism is used to accurately extract specific target feature values, CNN is used to extract the spatial features of the input data, and LSTM enhances the detection model's extraction of sequential features and performs classification, thereby improving the accuracy of the road and pothole model. Experimental results show that on the training set, the accuracy of the local attention mechanism-based ResNet18-CNN-LSTM model reached 99.2188%, which is an increase of 0.7813% and 2.3438% compared to the ResNet34-CNN-LSTM and ResNet50-CNN-LSTM models under the same conditions, respectively. On the test set, the model's accuracy was 93.4437%, an increase of 0.5437% and 1.9867% compared to the ResNet34-CNN-LSTM and ResNet50-CNN-LSTM models, respectively. After dealing with overfitting issues through early stopping, the detection accuracy of this model has significantly improved compared to the detection models based on ResNet34 and ResNet50, with an increase of 1.2% and 1.49% respectively. The model shows faster processing speed in identification time, effectively retains the correlation and sequence features of the data, overcomes the problem of gradient disappearance in deep networks, and thereby enhances the extraction capability of local target features of road pothole images. The above results indicate that the local attention mechanism-based ResNet18-CNN-LSTM model shows superior performance in road pothole detection.

Multifunctional GAN-based optimization for X-ray tomography under different conditions

Based on the generative adversarial network (GAN), we present a multifunctional X-ray tomographic protocol for artifact correction, noise suppression, and super-resolution of reconstruction. The protocol mainly consists of a data preprocessing module and multifunctional GAN-based loss function simultaneously dealing with ring artifacts and super-resolution. The experimental protocol removes ring artifacts and improves the contrast-to-noise ratio (CNR) and spatial resolution (SR) of reconstructed images successfully, which shows the capability to adaptively rectify ring artifacts with varying intensities and types while achieving super-resolution. Compared with the main existing deep learning models or conventional tomographic correction methods, it also enables higher processing speed and minimal information loss, especially for images of smaller dimensions. This study provides a robust optimization tool for the equivalent realization of large fields of view and high-resolution X-ray tomography. The experimental datasets were collected from a series of X-ray cone-beam computed tomography scans of biological samples.

The Internal Structure of the WISC-V in Chile: Exploratory and Confirmatory Factor Analyses of the 15 Subtests

The WISC-V is a widely used scale in clinical and educational settings in Chile. Given that its use guides critical decision-making for children and adolescents, it is essential to have evidence of its psychometric properties, including validity based on internal structure. This study analyzed the factor structure of the WISC-V through an exploratory (EFA) and confirmatory (CFA) approach considering the age range of 853 children and adolescents between 6 and 16 years. We obtained evidence favoring the four-factor structure in the EFA, which is a clearer organization in the 15–16 age group. In the confirmatory stage, the best four- and five-factor models showed factor loadings greater than 0.4, except for one subtest in the processing speed domain in the 6–8 age group. The internal consistency ranged from acceptable to good estimates for the best two models. The results support the use of hierarchical factor structures of four and five factors, which offer specific advantages and disadvantages discussed in the article. The implications of these findings in both the professional area of psychology and future research are discussed.

Cognitive Impairments Related to COMT and Neuregulin 1 Phenotypes as Transdiagnostic Markers in Schizophrenia Spectrum Patients

Background: Research on the interaction between antipsychotic treatment and cognitive dysfunction in schizophrenia spectrum disorders (SSDs) is extensive, yet the role of genetic polymorphisms in catechol-O-methyltransferase (COMT) and neuregulin 1 (NRG1) remains underexplored. Methods: This study evaluates the impact of COMT (rs4680) and NRG1 (rs3924999 and rs35753505) polymorphisms on cognitive functions in SSD patients. A cross-sectional study was conducted with fifty-four patients, assessed using the Positive and Negative Syndrome Scale (PANSS) and the CNS Vital Signs battery. Results: Significant cognitive function differences were observed across SSD diagnostic categories (p < 0.001). The NRG1 rs35753505 TT genotype was significantly associated with better verbal memory performance compared to the CC genotype (p = 0.03), while no significant differences were observed for other genotypes. The NRG1 rs3924999 AA genotype showed superior reasoning performance compared to AG and GG genotypes (p = 0.01), with AG and GG associated with lower scores (p = 0.01 and p = 0.02, respectively). Additionally, the COMT Val158Met genotype significantly influenced processing speed, with patients at the first episode of psychosis showing higher scores than chronic patients (p = 0.01). Conclusions: These findings suggest that NRG1 and COMT polymorphisms may influence cognitive domains in schizophrenia spectrum disorders, potentially informing personalized treatment and cognitive rehabilitation strategies.

Electric Trucks Pantograph-Catenary Interaction Condition Monitoring Method Based on Semantic Segmentation Network and Linear Fitting

Abstract The pantograph–catenary system (PCS) of dual source electric trucks is an important way for the vehicle to obtain electricity from the grid. Power quality depends on the reliability of the PCS contact. It is therefore necessary to monitor the PCS by detecting the contact point (CPT) between the pantograph and the catenary. However, due to structural variations between train PCS and electric truck PCS, existing CPT detection methods designed for trains are not suitable for electric trucks. To solve this problem, this paper proposes a CPT detection method that combines semantic segmentation and linear fitting. First, a lightweight pantograph and contact line segmentation network (PCSN) is proposed to extract the pantograph and contact line regions. The PCSN comprise a lightweight encoder, Cross-Attention Fusion decoder and Hybrid Atrous Spatial Pyramid Pooling (HASPP). The lightweight encoder comprise mainly of focus and lightweight feature extraction unit. The cross-attention fusion decoder is used to fuse low-level and high-level features, and the HASPP is used to obtain multi-scale context
information. Second, we use position correction and least squares linear fitting algorithm to detect the CPT of electric trucks. Experimental results show that the proposed method's detection error is concentrated around ±5 pixels. In terms of processing speed, the proposed method can reach a high speed of 76.9 FPS (Frames Per Second) on RTX 3080 desktop computer, 47.13 FPS on CPU I9-12900, and 11.63 FPS on embedded device Jeston TX2.

Improved lane detection for autonomous vehicles using deep learning, semantic segmentation, edge detection and multi-sensor data fusion

Automated driving has gained significant attention because it can eliminate severe driving risks in real time. While autonomous vehicles rely heavily on sensors for lane detection, obstacle identification, and environmental awareness, accurate lane recognition remains a persistent challenge due to factors such as noise from shadows, poor lane markings, and obstructed views. Despite advances in computer vision, this problem is yet to be fully resolved, presenting a gap in the current literature. The primary objective of this research is to address these challenges by developing an enhanced lane-detection system. To achieve this, the study integrates advanced techniques, including semantic segmentation, edge detection, and deep learning, coupled with multi-sensor data fusion from cameras, LIDAR, and radar. By employing this methodology, the research examines various lane-detection methods and benchmarks the proposed model against existing systems in terms of accuracy, specificity and processing speed. Initial findings demonstrate that the combination of semantic segmentation and multi-sensor fusion improves lane detection in real-time scenarios. The proposed model achieved a lane detection accuracy of 97.8%, a specificity of 99.28%, and an average processing time of 0.0047 seconds per epoch. This study not only addresses the limitations of existing lane detection systems but also offers insights into improving road safety for autonomous vehicles.

Development of laser felling modes of gas-thermal coating

The use of copper and its alloys to create parts for metallurgical equipment is associated with an increase in abrasive wear and high-temperature corrosion. In this regard, there is a need to apply a protective coating. In particular, to prevent wear and premature chipping of the metal of copper tuyeres, the surface is hardened with a coating of zirconium dioxide stabilized with yttria oxide by thermal spraying in an air atmosphere. Due to the difference in the coefficient of thermal expansion of copper (at T = 300 K: 16.7 µm/m оС and at T = 750 K: 19.7 µm/m оС) and its low resistance to gas corrosion, the application of zirconium oxide (produced by a preapplied intermediate layer that plays a role in matching the coefficient of thermal expansion (CTE) between the copper base and the ceramic coating. In addition, the intermediate layer protects copper from gas corrosion. In this case, The use of copper and its alloys to create parts for metallurgical equipment is associated with an increase in abrasive wear and high-temperature corrosion. In this regard, there is a need to apply a protective coating. In particular, to prevent wear and premature chipping of the metal of copper tuyeres, the surface is hardened with a coating of zirconium dioxide stabilized with yttria oxide by thermal spraying in an air atmosphere. Due to the difference in the coefficient of thermal expansion of copper (at T = 300 K: 16.7 pm/m °G and at T = 750 K: 19.7 pm/m оG) and its low resistance to gas corrosion, the application of zirconium oxide (produced by a pre-applied intermediate layer that plays a role in matching the coefficient of thermal expansion (CTE) between the copper base and the ceramic coating. In addition, the intermediate layer protects copper from gas corrosion. In this case, nickel-based alloys were used as intermediate layers. The use of nickel as the basis of intermediate layers is due to the fact that copper and nickel form a continuous series of solid solutions, such as cupronickel or monel metal-like structures. This, in turn, assumes a smooth transition of thermophysical properties from copper to nickel alloy. To ensure increased adhesion of the transition layer to copper by increasing the area of mutual contact between copper and the sublayer (dagger penetration) and significantly increasing the homogeneity of the material of the intermediate layer made of a nickel alloy, laser melting of the intermediate sublayer (Ni–B–Si system) was used on a laser complex based on laser LS-5 with a power of 5 kW with a KUKA KR-60HA robot in an argon atmosphere. To test the modes, experiments were carried out on copper samples of a flat shape and a body of rotation. The optimal parameters for the process of melting flat samples were: processing speed 33 mm/s, power from 400 to 3900 W, focal length from 200 to 230 mm, pitch between tracks: 0.25, 0.5 and 1 mm. The optimal parameters for the process of melting rotating samples were: laser radiation power 400–450 W, processing step 0.125; 0.5, focal length from 200 to 210 mm.

Sex differences in the association of overweight with cognitive performance in individuals with first-episode psychosis

Cognitive deficits and overweight are prominent challenges in the treatment of psychosis, which have a direct impact on patients’ quality of life. We aim to determine whether there is an association of overweight with cognitive performance and whether there are sex differences in this association. We included 170 individuals with first-episode psychosis (FEP) (mean age 23.08 years, 32.9% females) attending an early intervention service who underwent clinical, biometric, and cognitive assessments by the MATRICS Consensus Cognitive Battery. A set of two-way analyses of covariance (ANCOVAs) were conducted for each cognitive test. Sex, overweight, and their interaction were included as factors. Nearly 34% of the participants were overweight without differences between males and females. The excess of weight did not exert any main effect on cognition; however, overweight females performed significantly worse than non-overweight females in processing speed, verbal learning and memory, reasoning and problem-solving, and global cognitive function, whereas in males, there were no differences. Our findings highlight that sex matters in the study of metabolic and cognitive factors in FEP to develop targeted interventions based on sex perspectives.

Cognitive recovery of post critical care patients with and without COVID-19: differences and similarities, an observational study

BackgroundCoronavirus disease (COVID-19) patients treated in an intensive care unit (ICU) are at high risk of developing cognitive impairments of a “post-intensive care syndrome” (PICS). We explored whether critically ill COVID-19 and non-COVID-19 survivors differ in their post-ICU recovery course in terms of severity and affected cognitive domains.MethodsAn observational prospective study was conducted in a German post-acute neurological early rehabilitation clinic. Critically ill patients with or without SARS-CoV-2 infection (at least mechanically ventilated for one week) underwent repeated standardized assessments during their subsequent inpatient rehabilitation stay. Cognitive functions (information processing speed, learning, recognition, short-term and working-memory, word fluency, flexibility) assigned to different domains (attention, memory, executive functions) were assessed as primary outcome. Secondary outcomes included mental (depression, anxiety) and physical (Barthel index, modified ranking scale) state.ResultsOut of 92 eligible patients (screened between June 2021 and August 2023), 34 were examined, and 30 were available for analysis (15 per group). Both groups were ventilated for a similar period (COVID-19 vs. Non-COVID-19: median: 48 vs. 53 days). Patients of COVID-19 group spend on average 10 days longer at ICU and developed slightly more complications, but subsequent inpatient rehabilitation was of comparable duration (median: 36.5 vs. 37 days). On the group-level both groups showed similar cognitive dysfunctions with striking impairments (normative T-scores < 41) in information processing speed, word fluency, flexibility, and recognition memory on admission. Significant gains until discharge were only revealed for information processing speed in both groups (main effect visit, mean difference [95%CI] − 7.5 [− 13.1, − 2.0]). Physical and mental state were also similarly affected in both groups on admission, but improved over time, indicating that overall recovery for higher-order cognitive functions is slowest. Interestingly, majority of patients stated correctly being still physically disabled, while a discrepancy was found between subjective and objective evaluation of cognitive health.ConclusionsResults suggest a substantial overlap of cognitive, mental and physical dysfunction in post-acute recovery of ICU survivors independent of SARS-CoV-2 infection which warrants further monitoring to reduce the risk of long-term burden and enable a return to previous functionality.Trial registrationRetrospectively registered at https://drks.de/search/de/trial/DRKS00025523, 21.06.2021.

Increased 1H-NMR metabolomics-based health score associates with declined cognitive performance and functional independence in older adults at risk of cardiovascular disease.

The 1-HMR metabolomics-based MetaboHealth score, comprised of 14 serum metabolic markers, associates with disease-specific mortality, but it is unclear whether the score also reflects cognitive changes and functional impairment. We aimed to assess the associations between the MetaboHealth score with cognitive function and functional decline in older adults at increased cardiovascular risk. A total of 5292 older adults free of dementia at baseline with mean age 75.3years (SD = 3.4) from the Prospective Study of Pravastatin in the Elderly (PROSPER). MetaboHealth score were measured at baseline, and cognitive function and functional independence were measured at baseline and every 3months during up to 2.5years follow-up. Cognitive function was assessed using the Stroop test (selective attention), the Letter Digit Coding test (LDCT) (processing speed), and the two versions of the Picture Learning test (delayed and immediate; memory). Two tests of functional independence were used: Barthel Index (BI) and instrumental activities at daily living (IADL). A higher MetaboHealth score was associated with worse cognitive function (in all domains) and with worse functional independence. For example, after full adjustments, a 1-SD higher MetaboHealth score was associated with 9.02s (95%CI 7.29, 10.75) slower performance on the Stroop test and 2.79 (2.21, 3.26) less digits coded on the LDCT. During follow-up, 1-SD higher MetaboHealth score was associated with an additional decline of 0.53s (0.23, 0.83) on the Stroop test and - 0.08 (- 0.11, - 0.06) points on the IADL. Metabolic disturbance, as reflected by an increased metabolomics-based health score, may mark future cognitive and functional decline.

High-speed formation of pure copper layers via multibeam laser metal deposition with blue lasers

To realize a carbon-neutral society, it is necessary to shift from gasoline-powered vehicles to electric vehicles. Electric vehicles use copper components such as coil motors and batteries for which copper joining technology is important. Additive manufacturing is one of the most essential technologies for the next generation of manufacturing. Therefore, the technology for forming pure copper layer on a pure copper substrate is important. Laser metal deposition (LMD) is one of the additive manufacturing technologies. A blue diode laser is expected to be effective in shaping pure copper parts because light absorptance of pure copper at blue light is higher than that of near-infrared light. Thus, a multibeam LMD system with the blue diode laser in which metal powder was supplied perpendicular to the processing point and multiple lasers were irradiated from the surroundings was developed for additively manufactured pure copper. In our previous study, we succeeded in forming a pure copper layer on a pure copper substrate at a speed of 1.5 mm/s with blue diode lasers. However, an increase in processing speed was necessary for industrial application. It is considered that the improvement of energy density at the processing point and the control of heat input to the powder and the substrate by lasers are essential to improve the processing speed. Therefore, in this study, we designed an optical system with ten times higher energy density and calculated the heat input to the powder to form a pure copper layer at a speed of 10 mm/s or faster.

Biopsychosocial Impact of Multiple Sclerosis in Omani Patients: A Multicenter Comparative Study

Background/Objectives: Multiple sclerosis (MS) is a chronic neurological disorder characterized by various clinical presentations and manifestations that include biopsychosocial impediments. This study has three interrelated goals relevant to biopsychosocial functioning: (i) compare reasoning ability, neuropsychological functioning, affective range, and quality of life (QoL) between people with multiple sclerosis (PwMS) and healthy controls; (ii) explore gender differences in reasoning ability and neuropsychological functioning, affective symptoms, and QoL among PwMS; and (iii) examine the relationship between QoL and cognitive performance in PwMS, focusing on those with inadequate vs. adequate QoL. Methods: This multicenter study was carried out among clinically stable PwMS (no relapse in the last two months) at follow-up in two tertiary care units in urban Oman. Healthy controls, matched for age and sex, were also recruited as a comparison group. Data were collected using cognitive batteries sensitive to current reasoning ability and conventional neuropsychological batteries designed to measure verbal learning, visual-spatial ability, and processing speed. The affective range (anxiety and depressive symptoms) and quality of life (QoL) were also evaluated. Results: The PwMS group scored lower on current reasoning ability, verbal learning, visual-spatial ability, and processing speed compared to the control group. The incidence of anxiety was higher in the PwMS group, but there were no statistically significant differences in depressive symptoms. No significant differences were found in cognitive variables between the two sexes, except in visual-spatial ability, where women outperformed men. PwMS with low QoL scored lower on attention and concentration indices than those with adequate QoL. According to QoL, no significant differences were observed in reasoning, verbal learning, or visual-spatial ability. Conclusions: The present sentinel study suggests that the Omani cohort with MS tends to have lower indices of current reasoning ability, visual and spatial memory, and cognitive speed compared to control subjects. Gender differences are minimal, except for visual-spatial abilities, where women outperform men. Quality of life significantly affects cognitive functioning. In general, the biopsychosocial impediment appears to be significant, indicating the need for comprehensive evaluation and care in the management of MS.

Accelerating and Compressing Transformer-Based PLMs for Enhanced Comprehension of Computer Terminology

Pretrained language models (PLMs) have significantly advanced natural language processing (NLP), establishing the "pretraining + fine-tuning" paradigm as a cornerstone approach in the field. However, the vast size and computational demands of transformer-based PLMs present challenges, particularly regarding storage efficiency and processing speed. This paper addresses these limitations by proposing a novel lightweight PLM optimized for accurately understanding domain-specific computer terminology. Our method involves a pipeline parallelism algorithm designed to accelerate training. It is paired with an innovative mixed compression strategy that combines pruning and knowledge distillation to effectively reduce the model size while preserving its performance. The model is further fine-tuned using a dataset that mixes source and target languages to enhance its versatility. Comprehensive experimental evaluations demonstrate that the proposed approach successfully achieves a balance between model efficiency and performance, offering a scalable solution for NLP tasks involving specialized terminology.

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.