The performance of the presurgical test (preSurg) for temporal lobe epilepsy (TLE) was evaluated via video-electroencephalography (VEEG), electroencephalography (EEG), 99mTc-HmPAO single-photon emission tomography (SPECT) and 1.5 T magnetic resonance imaging (MRI) in a group of 112 men (37.0 ± 1.1 years) and 106 women (39.7 ± 1.1 years) operated on for TLE. The epileptic zone (EZ) was adequately identified to determine whether the patient reached an Engel I grade (EI) at least one year postop. Accuracy was evaluated by the coefficient α, ranging from 3 (when result = EZ) to 2 (result in the same hemisphere as the EZ), 1 (noninformative result) or 0 (EZ in the contralateral hemisphere). The simplicity of diagnosis was defined as the number of preSurg surgeries needed to identify the EZ. EI was obtained in 85.8% of patients even though 42.2% of patients had noninformative MRI results. For preSurg α was (mean ± SEM) = VEEG (2.83 ± 0.04) > MRI (2.14 ± 0.08) > EEG (1.87 ± 0.09) > SPECT (1.71 ± 0.09). The accuracy (combination of sensitivity and specificity) was calculated as follows: VEEG (0.797) > MRI (0.518) > EEG (0.446) > SPECT (0.360). The likelihood positive ratio was greater, and the likelihood negative ratio was lower for VEEG. The most relevant factor for the regression model was VEEG (3.106) > MRI (2.558) > EEG (1.905), and the factors were not significantly related to SPECT. EZs in EI patients with low simplicity were identified mainly by VEEG. A very good postoperative outcome can be obtained even in TLE patients with no lesions on MRI. The VEEG is the most reliable preSurg test and may be the only reliable test for patients with very low simplicity (i.e., difficult).single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

The use of video in education has become ubiquitous as technological developments have markedly improved the ability and facility to create, deliver and view videos. The concomitant pedagogical transformation has created a sense of urgency regarding how video may be used to advance learning. Initial reviews have suggested only limited potential for the use of video in higher education. More recently, a systematic review of studies on the effect of video use on learning in higher education, published in the prestigious Review of Educational Research, found, overall, effects to be positive. In the present paper we critique this study and provide some needed clarity to the state of research on learning via video in higher education. We reveal significant gaps in the study methodology and write-up and use a cognitive processing lens to critically assess and reanalyse study data. We found results of this study to be only applicable to learning requiring lower-level cognitive processing and conclude, consistent with prior research, learning benefits remain limited.

Keywords: Wall-like RC columns; Strengthening; FRP; NSM rebars; Steel plates; Testing; FE modeling; Preloading.

The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Pre-operative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared localization accuracies between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. Patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

Thirteen benzylethylenearyl ureas and one carbamate have been designed, synthesized and biologically evaluated as regards their antiproliferative activity on tumor cell lines HEK-293 and A-549 and on the immune and endothelial Jurkat T cells and HMEC-1, respectively. A relationship between the structure of the synthetic compounds and their antiproliferative activity has been established. Compounds C.1, C.3, C.12 and C.14 were chosen for further biological studies in order to determine their potential as oncoimmunomodulating agents. Some compounds exhibited significant inhibitory effects on both total PD-L1 and VEGFR-2 in HT-29 cell line, showing urea C.12 active against both targets. Some compounds could reduce more than 50% of cancer living cells, compared to non-treated ones, when assessed in co-cultures using HT-29 and THP-1 cells. In addition, these compounds showed no effect on the immune cell viability and significantly reduce CD11b expression, which is a promising target for immune modulation in anti-cancer therapies.

This paper presents an approach to teacher identity heritage as result of the implementation of a research device created through Arts-based Research (ABR) methods, specifically with video elicitation and video essay used as research tools. Two main objectives were raised. The first one, to establish the real relevance of focusing performance on teacher identity. The second one, testing a new methodological proposal specifically designed for this purpose, but still useful in other contexts where heritage identity is as present as in teaching. The device, Body-camera, involves a process that allows new ways to understand the creation of identities using video to encourage the production of new meanings by visual and oral data. The participants were teachers in training during their internship period. Some notions about teachers' identity heritage were revealed, and also preserved, firstly, through personal perspectives by video elicitations and secondly, through collective perspectives by video essays. Both are video structures used in ABR which mix creative experience, memories, life experiences, relationships and links that shape the teachers’ professional identity.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

Naturally fermented dairy products are an important component of the human diet. They are a valuable source of nutrients as well as vitamins and minerals. Their importance as a source of probiotic bacterial strains should not be overlooked. A number of studies highlights the positive effects of species of the Lactobacillacea on the intestinal microbiota, the overall homeostasis of the body, as well as a complementary treatment for some diseases. However, data on the effects on intestinal epithelial cells of postmetabolites released by bacteria are incomplete. This is likely due to the fact that these effects are species and strain specific. In the present study, we investigated the effects of postmetabolites produced by a pre-selected candidate probiotic strain Limosilactobacillus fermentum on HT-29 intestinal epithelial cells. Our data showed a pronounced proliferative effect, evaluated by flow cytometry, quantification of cell population, and determination of mitotic index. This was accompanied by stabilization of the cell monolayer, measured by increase of TEER (trans epithelial electric resistance) and reorganization of actin filaments. The data obtained are a clear indication of the positive effects, that the products secreted by L. fermentum strain 53 have on the intestinal epithelial cells.

While current video quality assessment research predominantly revolves around resolutions of 4K and beyond, targeted at Ultra High-Definition (UHD) displays, effective video quality for mobile video streaming remains primarily within the range of 480p to 1080p. In this study, we conducted a comparative analysis of the Quality of Experience (QoE) for widely implemented video codecs on mobile devices, specifically Advanced Video Coding (AVC), its successor High-Efficiency Video Coding (HEVC), and Google's VP9. Our choice of 720p video sequences from a newly developed database, all with identical bitrates, aimed to maintain a manageable subjective assessment duration, capped at 35-40 minutes. To mimic real-time network conditions, we generated stimuli by streaming original video clips over a controlled emulated setup, subjecting them to eight different packet loss scenarios. We evaluated the quality and structural similarity of the distorted video clips using objective metrics, including Video Quality Metric (VQM), Peak Signal-to-Noise Ratio (PSNR), Video Multi-Method Assessment Fusion (VMAF), and Multi-Scale Structural Similarity Index (MS-SSIM). Subsequently, we collected subjective ratings through a custom mobile application developed for Android devices. Our findings revealed that VMAF accurately represents the degradation in video quality compared to other metrics. Moreover, in most cases, HEVC exhibits an advantage over both AVC and VP9 under low packet loss scenarios. However, it is noteworthy that in our test cases, AVC outperformed HEVC and VP9 in scenarios with high packet loss, based on both subjective and objective assessments. Our observations further indicate that user preferences for the presented content contribute to video quality ratings, emphasizing the importance of additional factors that influence the perceived video quality of end-users.

Well-being is one of the pillars of positive psychology, which is known to have positive effects not only on the personal and professional lives of individuals, but also on teams and organizations. Understanding and promoting individual well-being is essential for staff health and long-term success, but current tools for assessing subjective well-being rely on time-consuming surveys and questionnaires, which limit the possibility of providing the real-time feedback needed to raise awareness and change individual behavior. This paper proposes a framework for understanding the process of non-verbal communication in teamwork, using video data to identify significant predictors of individual well-being in teamwork. It relies on video acquisition technologies and state-of-the-art artificial intelligence tools to extract individual, relative, and environmental characteristics from panoramic video. Statistical analysis is applied to each time series, leading to the generation of a dataset of 125 features, which are then linked to PERMA (Positive Emotion, Engagement, Relationships, Meaning, and Accomplishments) surveys developed in the context of positive psychology. Each pillar of the PERMA model is evaluated as a regression or classification problem using machine learning algorithms. Our approach was applied to a case study, where 80 students collaborated in 20 teams for a week on a team task in a face-to-face setting. This enabled us to formulate several hypotheses identifying factors influencing individual well-being in teamwork. These promising results point to interesting avenues for research, for instance fusing different media for the analysis of individual well-being in teamwork.

Delay in data transmission is one of key performance indicators (KPIs) of a network. The planning and project value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in an Long Term Evolution (LTE) network using three ML techniques - Neural Networks (NN), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that, in all three optimization approaches, the k-NN model is selected as the best solution. For the RReliefF optimization algorithm, the best model has 6 inputs and minimum relative error (RE), RE=0.109; for the Backward selection via the recursive feature elimination algorithm, the best model has 4 inputs and RE=0.041; and for the Pareto 80/20 rule, the best model has 11 inputs and RE= 0.049. The comparative analysis of the results concludes that according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

With the proliferating of video surveillance system deployment and related applications, real-time video analysis is very critical to achieve intelligent monitoring, autonomous driving, etc. It is non-trivial to achieve high accuracy and low latency video stream analysis through the traditional cloud computing. In this paper, we propose a non-orthogonal multiple access (NOMA) based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the QoE of UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimension, which respectively select the offloading and resource allocation action, the resolution scaling action. Experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of transmission and computation, and have better performance in convergence compared to other baseline.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

In recent years, the scientific and technological developments led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, key frame selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines.

Short video addiction refers to a psychological dependency on short video applications, characterized by an intense attachment and an inability to control usage, leading to typical addiction symptoms. While its definition bears similarities to substance addiction, significant differences also exist. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), published in 2013, short video addiction has not yet been officially classified as an addiction disorder. To facilitate more in-depth and standardized research on short video addiction, it is essential to synthesize and organize existing studies on its theoretical foundations and mechanisms.

To assess the usefulness of an attachable video laryngoscope (AVL) by attaching a camera and monitor to a conventional Macintosh laryngoscope (CML). Normal and tongue edema airway scenarios were simulated using a manikin. Twenty physicians performed tracheal intubations using CML, AVL, Pentax AWS® (AWS), and McGrath MAC® (MAC) in each scenario. Ten physicians with more than 10 cases of clinical experience with tracheal intubation using CML were included in the skilled group. Ten physicians with less than 10 cases of clinical experience with tracheal intubation using CML were included in the unskilled groups. The time required to intubate and the success rate were recorded. The degree of difficulty of use and glottic view assessment scored by participants. All 20 participants successfully completed the study. There was no difference in the success rate and time required for tracheal intubation in the normal airway scenarios. For the skilled group, in the tongue edema airway scenario, the AWS had the highest success rate (100%) followed by AVL (60%), MAC (60%), and CML (20%) (p = 0.001). The time required to intubate with the AWS was significantly shorter than with AVL (10.2 vs 22.3 s; p=0.002) or MAC (10.2 vs 23.7 s; p=0.001). There was no significant (p > 0.05) difference between AVL and MAC. The degree of Difficulty of Use for AVL was significantly lower than that for CML (7.8 vs 2.8; p < 0.001). There was no significant (p > 0.05) difference between AVL and other video laryngoscopes. AVL had a higher rate of successful tracheal intubation than CML in the tongue edema scenario. AVL showed a similar success rate and time of tracheal intubation to the MAC. Therefore, AVL may be a possible alternative video laryngoscope device.

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. By leveraging computer vision technologies to extract key traffic parameters from video streams, the approach demonstrated a notable improvement in aligning the generated data from the calibrated simulation model with car sensor data, achieving an average improvement of over 50% compared to the uncalibrated macroscopic model. Moreover, there was a substantial reduction in data drift for the machine learning model integrated into the virtual transport space using vehicle-to-everything technology, resulting in a more than fourfold decrease in the average absolute error of the model.