Visual Evaluation Research Articles

The Effect of Lumbopelvic Manipulation for Pain Reduction in Patellofemoral Pain Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Patellofemoral pain syndrome (PFPS) is one of the most common etiologies of knee pain and might be relieved with lumbopelvic manipulation (LPM). This meta-analysis aimed to investigate the effects of LPM on pain reduction in patients with PFPS. Electronic databases were searched from inception to December 2023 for randomized controlled trials (RCTs) investigating the effects of LPM on PFPS. The primary outcome was the change in visual analog or numeric rating scale scores assessing pain. Ten studies comprising 346 participants were included. Significant pain reduction was noted in the LPM group (Hedges' g = -0.706, 95% confidence interval [CI] = -1.197 to -0.214, p = 0.005, I2 = 79.624%) compared with the control group. Moreover, pain relief was more pronounced when LPM was combined with other physical therapies (Hedges' g = -0.701, 95% CI = -1.386 to -0.017, p = 0.045, I2 = 73.537%). No adverse events were reported during the LPM. The LPM appears to be a safe and effective adjuvant therapy for pain reduction in patients with PFPS. Clinicians should consider adding LPM to other physical therapies (e.g., quadriceps muscle strengthening) during the management of these patients.

MOX-NET: Multi-stage deep hybrid feature fusion and selection framework for monkeypox classification

Background:Monkeypox virus has quickly expanded throughout several nations, raising serious public health concerns. Lack of precautionary measures raises concerns about the possibility of global pandemic. As a result, early detection of monkeypox is essential for efficient diagnosis and treatment before broad community transmission. Artificial intelligence (AI) and machine learning (ML) have recently shown tremendous promise in early monkeypox diagnosis using image-based diagnoses methods. But, challenges such as low contrast lesions, overloaded datasets, high memory complexity, and redundant feature extraction still need to be addressed. Methods:This work proposes a coalesced CAD system for monkeypox classification using a deep learning framework. The proposed approach includes multiple stages: First, the given dataset images are pre-processed by a proposed fusion-based contrast enhancement method. Second, six pre-trained deep learning models i.e., Vision Transformers (ViT), Shifted Windows (Swin) Transformers, ResNet-50, ResNet-101, EfficientNetV2, and ConvNeXt-V2 are modified and transfer learning is used for training. Third, from all the deep learning networks, the deep feature vectors are acquired and integrated through the convolutional sparse image decomposition fusion strategy. Subsequently, an entropy-controlled firefly feature selection approach is applied to identify the most suitable features for classification. Lastly, the multi-class support vector machine is utilized to classify the input data based on the selected features. Results:The proposed algorithm utilized the MSLID dataset and obtained an accuracy of 98.64% which is better than other state-of-the-art works. Conclusions:Experimental outcomes reveals that the proposed monkeypox classification algorithm outperforms well-established existing algorithms in both visual and quantitative evaluations, resulting in improved accuracy.

REMA: A Rich Elastic Mixed Attention Module for Single Image Super-Resolution.

Detail preservation is a major challenge for single image super-resolution (SISR). Many deep learning-based SISR methods focus on lightweight network design, but these may fall short in real-world scenarios where performance is prioritized over network size. To address these problems, we propose a novel plug-and-play attention module, rich elastic mixed attention (REMA), for SISR. REMA comprises the rich spatial attention module (RSAM) and the rich channel attention module (RCAM), both built on Rich Structure. Based on the results of our research on the module's structure, size, performance, and compatibility, Rich Structure is proposed to enhance REMA's adaptability to varying input complexities and task requirements. RSAM learns the mutual dependencies of multiple LR-HR pairs and multi-scale features, while RCAM accentuates key features through interactive learning, effectively addressing detail loss. Extensive experiments demonstrate that REMA significantly improves performance and compatibility in SR networks compared to other attention modules. The REMA-based SR network (REMA-SRNet) outperforms comparative algorithms in both visual effects and objective evaluation quality. Additionally, we find that module compatibility correlates with cardinality and in-branch feature bandwidth, and that networks with high effective parameter counts exhibit enhanced robustness across various datasets and scale factors in SISR.

Auto-evaluation of skull radiograph accuracy using unsupervised anomaly detection.

Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic value are rejected, and retakes are required. However, determining the suitability of retaking radiographs is a qualitative evaluation. To evaluate skull radiograph accuracy automatically using an unsupervised learning-based autoencoder (AE) and a variational autoencoder (VAE). In this study, we eliminated visual qualitative evaluation and used unsupervised learning to identify skull radiography retakes from the quantitative evaluation. Five skull phantoms were imaged on radiographs, and 1,680 images were acquired. These images correspond to two categories: normal images captured at appropriate positions and images captured at inappropriate positions. This study verified the discriminatory ability of skull radiographs using anomaly detection methods. The areas under the curves for AE and VAE were 0.7060 and 0.6707, respectively, in receiver operating characteristic analysis. Our proposed method showed a higher discrimination ability than those of previous studies which had an accuracy of 52%. Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations.

The performance of grooved turning tools under distinct cooling environments

This work investigates the performance of modified tungsten carbide inserts when turning supermartensitic stainless steel with cutting fluid and/or air jet applied in two directions: either cutting fluid under high pressure or compressed air under low pressure supplied on the back of the chip (through a hole produced near the principal cutting edge), together with either cutting fluid or compressed air, both under low pressure, directed at the clearance face. Tool performance is evaluated by means of the principal cutting force, temperature distribution (using FEM), visual evaluation of the rake face, morphology of the chips and the quality of the machined surface. The findings indicate that the groove reduces the cutting force when dry cutting. Cutting fluid applied under low pressure at the clearance face is more efficient in decreasing both the cutting force and chip temperature than cutting fluid at high pressure supplied at the back of the chip. A chrome oxide layer is formed on the chip when air jet is applied, thus leading to an unfavorable cutting condition. The simultaneous application of cutting fluid under high pressure to reach the back of the chip and compressed air supplied through the clearance face provides the best surface quality.

Assessing CT-based Volumetric Analysis via Transfer Learning with MRI and Manual Labels for Idiopathic Normal Pressure Hydrocephalus.

Brain computed tomography (CT) is an accessible and commonly utilized technique for assessing brain structure. In cases of idiopathic normal pressure hydrocephalus (iNPH), the presence of ventriculomegaly is often neuroradiologically evaluated by visual rating and manually measuring each image. Previously, we have developed and tested a deep-learning-model that utilizes transfer learning from magnetic resonance imaging (MRI) for CT-based intracranial tissue segmentation. Accordingly, herein we aimed to enhance the segmentation of ventricular cerebrospinal fluid (VCSF) in brain CT scans and assess the performance of automated brain CT volumetrics in iNPH patient diagnostics. The development of the model used a two-stage approach. Initially, a 2D U-Net model was trained to predict VCSF segmentations from CT scans, using paired MR-VCSF labels from healthy controls. This model was subsequently refined by incorporating manually segmented lateral CT-VCSF labels from iNPH patients, building on the features learned from the initial U-Net model. The training dataset included 734 CT datasets from healthy controls paired with T1-weighted MRI scans from the Gothenburg H70 Birth Cohort Studies and 62 CT scans from iNPH patients at Uppsala University Hospital. To validate the model's performance across diverse patient populations, external clinical images including scans of 11 iNPH patients from the Universitatsmedizin Rostock, Germany, and 30 iNPH patients from the University of Alabama at Birmingham, United States were used. Further, we obtained three CT-based volumetric measures (CTVMs) related to iNPH. Our analyses demonstrated strong volumetric correlations (ϱ=0.91, p<0.001) between automatically and manually derived CT-VCSF measurements in iNPH patients. The CTVMs exhibited high accuracy in differentiating iNPH patients from controls in external clinical datasets with an AUC of 0.97 and in the Uppsala University Hospital datasets with an AUC of 0.99. CTVMs derived through deep learning, show potential for assessing and quantifying morphological features in hydrocephalus. Critically, these measures performed comparably to gold-standard neuroradiology assessments in distinguishing iNPH from healthy controls, even in the presence of intraventricular shunt catheters. Accordingly, such an approach may serve to improve the radiological evaluation of iNPH diagnosis/monitoring (i.e., treatment responses). Since CT is much more widely available than MRI, our results have considerable clinical impact.

Interventional Prospective Studies on Xerostomia in Patients Undergoing Palliative and End-of-Life Care: A Scoping Review.

Patients undergoing palliative care often develop debilitating oral conditions, including xerostomia. These conditions may significantly impact patients' quality of life. Despite the high prevalence and adverse impact of xerostomia, effective management strategies remain unclear. This scoping review was performed to elucidate effective interventions for xerostomia in patients undergoing palliative and end-of-life care. A comprehensive search strategy was employed to identify relevant studies up to August 2023. Full-text primary articles focusing on xerostomia in patients receiving palliative care were included in the review. Eleven articles were selected for analysis, and data were extracted by six reviewers. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among the 11 studies, interventions ranged from oral care to saliva substitutes and methods to stimulate saliva secretion. The primary method of assessing xerostomia was the performance of subjective evaluations using visual analog scale scores or numerical rating scale scores. Various interventions including oral care regimens, topical treatments, and mixed efficacy outcomes were reported. Notably, only one study directly measured the saliva volume, highlighting a reliance on subjective endpoints in most studies. Although no definitive conclusions can be drawn regarding the most effective intervention, oral care was a preferred option for managing xerostomia in patients undergoing palliative care. Additionally, adjunctive treatments such as ice cubes, saline, and moisturizers showed promise but require further investigation. Objective measures should be incorporated into future intervention trials to complement subjective assessments and provide a comprehensive evaluation of xerostomia management strategies in this patient population.

New objective simple evaluation methods of amyloid PET/CT using whole-brain histogram and Top20%-Map

ObjectiveThis study aims to assess the utility of newly developed objective methods for the evaluation of intracranial abnormal amyloid deposition using PET/CT histogram without use of cortical ROI analyses.MethodsTwenty-five healthy volunteers (HV) and 38 patients with diagnosed or suspected dementia who had undergone 18F-FPYBF-2 PET/CT were retrospectively included in this study. Out of them, 11C-PiB PET/CT had been also performed in 13 subjects. In addition to the conventional methods, namely visual judgment and quantitative analyses using composed standardized uptake value ratio (comSUVR), the PET images were also evaluated by the following new parameters: the skewness and the mode-to-mean ratio (MMR) obtained from the histogram of the brain parenchyma; Top20%-map highlights the areas with high tracer accumulation occupying 20% volume of the total brain parenchymal on the individual’s CT images. We evaluated the utility of the new methods using histogram compared with the visual assessment and comSUVR. The results of these new methods between 18F-FPYBF-2 and 11C-PiB were also compared in 13 subjects.ResultsIn visual analysis, 32, 9, and 22 subjects showed negative, border, and positive results, and composed SUVR in each group were 1.11 ± 0.06, 1.20 ± 0.13, and 1.48 ± 0.18 (p < 0.0001), respectively. Visually positive subjects showed significantly low skewness and high MMR (p < 0.0001), and the Top20%-Map showed the presence or absence of abnormal deposits clearly. In comparison between the two tracers, visual evaluation was all consistent, and the ComSUVR, the skewness, the MMR showed significant good correlation. The Top20%-Maps showed similar pattern.ConclusionsOur new methods using the histogram of the brain parenchymal accumulation are simple and suitable for clinical practice of amyloid PET, and Top20%-Map on the individual’s brain CT can be of great help for the visual assessment.

Pediatric diffuse intrinsic pontine glioma radiotherapy response prediction: MRI morphology and T2 intensity-based quantitative analyses.

An easy-to-implement MRI model for predicting partial response (PR) postradiotherapy for diffuse intrinsic pontine glioma (DIPG) is lacking. Utilizing quantitative T2 signal intensity and introducing a visual evaluation method based on T2 signal intensity heterogeneity, and compared MRI radiomic models for predicting radiotherapy response in pediatric patients with DIPG. We retrospectively included patients with brainstem gliomas aged ≤ 18 years admitted between July 2011 and March 2023. Applying Response Assessment in Pediatric Neuro-Oncology criteria, we categorized patients into PR and non-PR groups. For qualitative analysis, tumor heterogeneity vision was classified into four grades based on T2-weighted images. Quantitative analysis included the relative T2 signal intensity ratio (rT2SR), extra pons volume ratio, and tumor ring-enhancement volume. Radiomic features were extracted from T2-weighted and T1-enhanced images of volumes of interest. Univariate analysis was used to identify independent variables related to PR. Multivariate logistic regression was performed using significant variables (p < 0.05) from univariate analysis. Of 140 patients (training n = 109, and test n = 31), 64 (45.7%) achieved PR. The AUC of the predictive model with extrapontine volume ratio, rT2SRmax-min (rT2SRdif), and grade was 0.89. The AUCs of the T2-weighted and T1WI-enhanced models with radiomic signatures were 0.84 and 0.81, respectively. For the 31 DIPG test sets, the AUCs were 0.91, 0.83, and 0.81, for the models incorporating the quantitative features, radiomic model (T2-weighted images, and T1W1-enhanced images), respectively. Combining T2-weighted quantification with qualitative and extrapontine volume ratios reliably predicted pediatric DIPG radiotherapy response. Combining T2-weighted quantification with qualitative and extrapontine volume ratios can accurately predict diffuse intrinsic pontine glioma (DIPG) radiotherapy response, which may facilitate personalized treatment and prognostic assessment for patients with DIPG. Early identification is crucial for radiotherapy response and risk stratification in diffuse intrinsic pontine glioma. The model using tumor heterogeneity and quantitative T2 signal metrics achieved an AUC of 0.91. Using a combination of parameters can effectively predict radiotherapy response in this population.

A physical-based semi-distributed continuous hydrological modelling for Brahmani River Basin, Eastern India

ABSTRACT This paper describes a case study of continuous hydrologic modeling in the Brahmani River basin in India using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC–HMS). The soil moisture algorithm (SMA) was employed as loss method for continuous simulations in the thirteen sub-basins. The sensitivity analysis showed that soil storage, tension storage and soil percolation are most sensitive parameters. The model performance was assessed for calibration period of 15 years (1990–2004) and validation period of 12 years (2005–2016), using statistical and visual evaluation techniques. During the calibration period, the observed and simulated values showed a good fit, with R2 ranging from 0.67 to 0.74, NSE from 0.66 to 0.72, and PBIAS from −8.5 to −9.5%. Similar performances are obtained during the validation period, with R2 ranging from 0.62 to 0.70, NSE from 0.60 to 0.68, and PBIAS from −2.5 to 21.4%. The results of the SMA procedure employed in the HEC-HMS model give valuable information for flood control and water supply management in the Brahmani Basin and similar other regions in Eastern India.

Olfactory Dysfunction as a Marker for Cognitive Impairment in General Paresis of the Insane: A Clinical Study.

BACKGROUND General paresis of the insane (GPI) is characterized by cognitive impairment, neuropsychiatric symptoms, and brain structural abnormalities, mimicking many neuropsychiatric diseases. Olfactory dysfunction has been linked to cognitive decline and neuropsychiatric symptoms in numerous neuropsychiatric diseases. Nevertheless, it remains unclear whether patients with GPI experience olfactory dysfunction and whether olfactory dysfunction is associated with their clinical manifestations. MATERIAL AND METHODS Forty patients with GPI and 37 healthy controls (HCs) underwent the "Sniffin Sticks" test battery, Mini-Mental State Examination, and Neuropsychiatric Inventory to measure olfactory function, cognitive function, and neuropsychiatric symptoms, respectively. Brain structural abnormalities were evaluated using visual assessment scales including the medial temporal lobe atrophy (MTA) visual rating scale and Fazekas scale. RESULTS Compared with HCs, patients with GPI exhibited significant olfactory dysfunction, as indicated by deficits in the odor threshold (OT) (P=0.001), odor discrimination (OD) (P<0.001), and odor identification (OI) (P<0.001). In patients with GPI, the OI was positively correlated with cognitive function (r=0.57, P<0.001), but no significant correlation was found between olfactory function and neuropsychiatric symptoms, blood, or cerebrospinal fluid biomarkers (rapid plasma reagin circle card test and Treponema pallidum particle agglutination test), or brain structural abnormalities (MTA and Fazekas scale scores). Mediation analysis indicated that the impaired OI in patients with GPI was mediated by cognitive impairment and impaired OT respectively. CONCLUSIONS Patients with GPI exhibited overall olfactory dysfunction. OI is correlated with cognitive function and the impaired OI is mediated by cognitive impairment in patients with GPI. Thus, OI may serve as a marker for reflecting cognitive function in patients with GPI.

The time-course augmentation of perivascular space enlargement in the basal ganglia among a community-dwelling elder population

PurposeWe examined whether time-course augmentation of perivascular space enlargement in the basal ganglia (BG-PVS) reflected cerebral small vessel disease (CSVD) severity by considering white matter hyperintensity lesion (WMHL) as an indicator for CSVD.Materials and methodsThis study population included 416 older participants from a community-based cohort. They participated in magnetic resonance imaging (MRI) studies more than once during the study period. The grades for BG-PVS and WMHL were evaluated by visual rating scales; BG-PVS time-course augmentation in 4–9 years was also evaluated. At baseline, the participants were asked about their smoking and drinking history, and medical history. They also underwent a blood examination and their office blood pressure (BP) examination. In addition, 24-h ambulatory BP monitoring was also performed within the study period.ResultsOf the 416 participants, 48 participants (11.5%) had BG-PVS time-course augmentation. The participants with BG-PVS augmentation had significantly lower LDL levels, hyper-nighttime BP, and lower nighttime BP fall in univariate analysis (p = 0.03, p = 0.03, p = 0.003, respectively). In multivariate analysis, lower nighttime BP fall and male sex showed significance (p = 0.02, 0.03, respectively). Additionally, BG-PVS time-course augmentation was significantly associated with subsequent WMHL severity in univariate analysis (p < 0.001), which remained significant in multivariate analysis adjusted by imaging and demographic factors (p = 0.03). In multivariate analysis, additionally adjusted by the clinical factors, the significance disappeared (p = 0.07).ConclusionThis study revealed that the lower nighttime BP fall in ambulatory blood pressure monitoring was a factor significantly associated with BG-PVS augmentation. Moreover, the BG-PVS time-course augmentation would be a notable finding that was associated with the subsequent WMHL.

(114) HPV-AUTOMATED VISUAL EVALUATION (PAVE) STUDY: VALIDATING A NOVEL CERVICAL SCREENING STRATEGY. PRELIMINARY RESULTS OF PHASE 1 FROM CAMBODIA

Abstract Introduction The HPV-Automated Visual Evaluation (PAVE) Study is an international, multi-centric study designed to evaluate a novel cervical screen-triage-treat strategy for resource-limited settings as part of a global strategy to reduce cervical cancer burden as part of the National Cancer Institute’s Cancer Cures Moonshot Initiative. The PAVE strategy involves: 1) screening with self-sampled HPV testing; 2) triage of HPV-positive participants with a combination of extended genotyping and visual evaluation of the cervix assisted by deep-learning-based automated visual evaluation (AVE); and 3) treatment with thermal ablation or excision (LLETZ). The PAVE study has two phases: efficacy (2023-2024) and effectiveness (planned to begin in 2024-2025). The efficacy phase aims to refine and validate the screen-triage portion of the protocol. The effectiveness phase will examine implementation of the PAVE strategy into clinical practice, cost-effectiveness, and health communication. The study will enroll a total 100,000 women in 9 different countries. Objective The Cambodia section of the study will enroll a total of 10,000 women 25 to 49 years of age. Two different cohorts are being studied: lower-risk women who are employed (and screened) at garment factories, and higher-risk women living with HIV (WLWH) who are enrolled in the Cambodian National Centre for HIV/AIDS Dermatology and STDS (NCHADS) clinics. Very few women in either cohort have ever been screened for cervical cancer. Methods After an initial educational campaign including videos and written literature, eligible women were consented and performed HPV self-sampling. HPV testing was performed with the modified AmpFire HPV isothermal testing system (Atila Biosystems, California) which provides genotyping information in following groups: 16, 18/45, 31/33/35/52/58, 39/59/68/51/56/59. All women were informed of the HPV status via SMS. All HPV positive women had digital colposcopy with 5% acetic acid using an IRIS digital colposcope (Liger, Utah) and digital images were obtained. All women hrHPV+ had ectocervical and/or endocervical biopsies with SoftBiopsy or SoftECC brush (Histologics, California). Women with CIN2+ lesions then had LEEP. Results As of October 31st, 2023, 2097 Garment workers and 530 WLWH have been enrolled. From 259 of 2097 garment workers were hrHPV+ (12.4%). 10 of the 259 (3.9%) had biopsy confirmed CIN2+ lesions. Only 9 (3.4%) of the garment workers were lost to follow up after testing positive for hrHPV. 128 of 530 WLWH were hrHPV+ (24.15%), 101 of these 128 women presented for visual evaluation of their Cervices and 21 had a CIN2+ lesions and 11 had invasive cancer. Currently, 27 of the hrHPV+ WLWH have been lost-to-follow-up, though efforts continue to reach these women. Conclusions Women in low resource settings often have great socioeconomic barriers to cervical cancer screening. Screening and treating in garment factories appears to be a very successful strategy as high numbers of women can be screened in a short period of time. Additionally, there has been very minimal loss-to-follow-up even though HPV testing was performed up to one month prior to the visual inspection of hrHPV+ women. Screening WLWH, however, is a greater challenge as they often have to travel greater distances to get screened and frequently face greater economic burdens. Strategies such as arranging and paying for all transportation and compensation for lost wages will be used in the future to limit loss-to-follow-up. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Dare Bioscience.

Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles

To achieve the transition of rural areas from traditional to modern, the visualization of rural landscape data and feature evaluations are essential. Landscape character assessment (LCA) is a well-established tool that was developed to assess and understand rural landscape features. In recent years, drones have become increasingly attractive for various applications and services due to their low costs and relative ease of operation. Unlike most previous studies that relied solely on drone-based remote sensing or visual esthetic evaluations, this study proposes an innovative assessment method based on landscape characteristic assessment (LCA) and oblique drone photography technology, supported by specific data and survey results. These include various landscape metrics, such as the Shannon diversity index (SHDI), Shannon evenness index (SHEI), vegetation coverage, landscape character zoning, and delineations of various ecologically sensitive areas. This method was applied to study Zhanqi Village in Chengdu, Sichuan Province, China and revealed some unique characteristics of this village. By categorizing and describing the landscape features, the study makes judgments and decisions about them. This is a beneficial attempt to apply the scientific methods of landscape assessments to the production management of aerial drone surveys. This method provides a comprehensive framework for evaluating rural landscape features and demonstrates that the combination of LCA and oblique drone photography technology is feasible for rural landscape research. Additionally, this study emphasizes the need for further research to explore the potential application of this method in continuously evolving urban and rural environments in the future.

MCRNet: Underwater image enhancement using multi-color space residual network

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images, which can impede the ever-growing demand for underwater robot operations. To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. Our method takes advantage of the unique features of color representation in the RGB, HSV, and Lab color spaces. By utilizing the distinct feature representations of images in different color spaces, we can highlight and fuse the most informative features of the three color spaces. Our approach employs a self-attention mechanism in the multi-color space feature fusion module. Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images, particularly in severely degraded scenes. Consequently, our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Efficacy and safety of venlafaxine versus nortriptyline for the preventive treatment of migraine: A double-blind randomized clinical trial

ObjectiveMigraine, as a primary headache disorder, stands as one of the primary causes of disability worldwide. Consequently, prophylactic treatments are highly recommended for individuals experiencing recurrent migraine episodes. Our study aimed to compare the efficacy and safety profiles of venlafaxine and nortriptyline in the prophylactic management of migraine. MethodsIn this single-center, randomized, double-blind clinical trial, 210 migraine patients were allocated into two groups in a 1:1 ratio. One group received venlafaxine (37.5 mg, orally twice daily), while the other group administered nortriptyline (25 mg, orally once daily). A neurologist documented (1) headache intensity using the Visual Analog Scale (VAS) and 6-point Behavioral Rating Scale (BRS-6), (2) headache frequency (per month), and (3) headache duration (in hours) of participants on days 0, 45, and 90 of the intervention. ResultsFollowing the 90-day intervention, a significant decrease was observed in VAS, BRS-6, frequency, and duration of headaches within both groups (all with p-values <0.001). No difference in VAS, BRS-6, or headache durations was observed between the two groups after 45 and 90 days of treatment (all p-values > 0.05). Although the headache frequency exhibited no difference between the groups after 45 days (p-value = 0.097), a significantly lower frequency in the venlafaxine group was observed at day 90 of the intervention (p-value = 0.011). The reductions in attack parameters in the 0–45- and 0–90-day intervals did not meet statistical significance between the two groups (p-values > 0.05). 77.0 % of the participants in the venlafaxine group and 79.2 % in the nortriptyline group experienced a minimum of 50 % improvement in all attack parameters. Venlafaxine demonstrated a statistically significant lower incidence of adverse reactions in comparison to nortriptyline (p-value = 0.005). A total of 33 adverse drug reactions were documented in the venlafaxine group and 53 in the nortriptyline group, with insomnia observed in the former and xerostomia in the latter as the most prevalent side effects. ConclusionsVenlafaxine and nortriptyline demonstrate clinically significant and comparable therapeutic efficacy for migraine patients in reducing the intensity, frequency, and duration of headache attacks. Venlafaxine may be preferred to nortriptyline in the context of migraine preventive treatment under comparable conditions due to its lower incidence of adverse effects.

Remote expert viewing, laboratory tests or objective metrics: which one(s) to trust?

We present a study on the validity of quality assessment in the context of the development of visual media coding schemes. The work is motivated by the need for reliable means for decision-taking in standardization efforts of MPEG and JVET, i.e., the adoption or rejection of coding tools during the development process of the coding standard. The study includes results considering three means: objective quality metrics, remote expert viewing, which is a method designed in the context of MPEG standardization, and formal laboratory visual evaluation. The focus of this work is on the comparison of pairs of coded video sequences, e.g., a proposed change and an anchor scheme at a given rate point. An aggregation of performance measurements across multiple rate points, such as the Bjøntegaard Delta rate, is out of the scope of this paper. The paper details the test setup for the subjective assessment methods and the objective quality metrics under consideration. The results of the three approaches are reviewed, analyzed, and compared with respect to their suitability for the decision-taking task. The study indicates that, subject to the chosen test content and test protocols, the results of remote expert viewing using a forced-choice scale can be considered more discriminatory than the results of naïve viewers in the laboratory tests. The results further that, in general, the well-established quality metrics, such as PSNR, SSIM, or MS-SSIM, exhibit a high rate of correct decision-making when their results are compared with both types of viewing tests. Among the learning-based metrics, VMAF and AVQT appear to be most robust. For the development process of a coding standard, the selection of the most suitable means must be guided by the context, where a small number of carefully selected objective metrics, in combination with viewing tests for unclear cases, appears recommendable.

ReLU-oscillator: Chaotic VGG10 model for real-time neural style transfer on painting authentication

Neural Style Transfer (NST) has exerted algorithms to generate animation images in computer vision for decades. The Convolution Neural Network (CNN) applied to image content and styles in the NST has improved the extraction of functionalities and the calculation of the convergence speed to recognize and generate high quality structure images, but unpredictable loss elements are inadequate to iterate human learning ability of unique artists’ paintings or styles. This paper offers a chaotic VGG10 NST model based on CNN, ReLU and Lee-Oscillator. The proposed ReLU-Oscillator dynamically relies on activation functions in a chaotic state that dynamically improves high-frequency iterative training of high-quality image and high-speed time optimization. In addition, the best parameters recognizing and determining a personalized painting style from the search for ReLU-Oscillator by corresponding to a set of parameters from proposed Optimal Oscillator Parameter Search Algorithm. Experimental results showed that the stylized image generated by the Chaotic VGG 10 model with high-frequency oscillation succeeded in reducing the training time in magnitude models with the smallest Params and FLOPs in model performance and image quality with the lowest content loss for preserving semantic information and moderate style loss for style similarity balanced with comparison to 8 state-of-the-art models in visual perception evaluation. Chaotic NST has a unique identification for each artist supplemented with a set of oscillator parameters to evaluate the loss performance based on the relative error between the famous painting and its imitation, indicating that the authentication of paintings can be detected through specific ReLU-Oscillator parameters for each stylization, estimated from the loss value performance.

Modernization of the stamping process using eddy current and load sensors in the manufacturing of automotive parts

In this research, an experimental study is presented, which extends the usage of eddy current and load sensors in progressive stamping tools to optimize and continuously monitor the stamping process. The purpose of the research was to automatically detect material scrap before it leaves imprints on the part and based on the sensor’s readings, determine the optimal tool bottom position. The scrap thickness that needs to be detected was established in an experiment by visual evaluation of the result. To determine the optimal bottom position, a linear regression method was used, and the results were evaluated by part quality parameter. The research results consist of separate detection steps and the conclusion was made only after the serial type production. Overall results of scrap detection were influenced by the design of the existing tool. The bottom position detection consists of various readings interpretations and multi-step method descriptions. Based on the acquired results of both methods, implementing the in–die sensors was considered successful and applicable to new tools.

Adaptive block imaging based on compressive sensing in AFM.

Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. HIGHLIGHTS: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm.