Camouflage evaluation typically involves human visual search and detection experiments that are time-consuming and expensive. Hence, there is a need for models that compute camouflage effectiveness from digital imagery. Convolutional neural networks are a powerful tool for automatic object detection and recognition. We investigated whether such a network (YOLO) can also provide a measure of camouflage effectiveness that is related to human perception. To this end, human performance measures of camouflage effectiveness such as detection time and target conspicuity were obtained in observer experiments and compared with the performance of the (standard, i.e. pre-trained) YOLO-V4-tiny algorithm. YOLO provides the probability that a detected object is correctly classified, and this is adopted as our measure of camouflage effectiveness. We compared the YOLO-predicted classification probability for a person in camouflage clothing moving through rural and urban scenes to human detection performance. Overall, camouflage effectiveness predicted by YOLO correlates with human observer performance. At close distances, YOLO’s classification performance appears sensitive to high-contrast shape-breaking elements of a camouflage pattern. This suggests that YOLO may be adapted to assess camouflage effectiveness for a wide range of applications, such as evaluating or optimizing one’s signature and predicting optimal hiding locations in a given environment. Further research is required to fully establish YOLO’s limitations and applicability for this purpose.

: In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV's environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the independent monitoring and touchdown of UAV. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAV was established. Tracking performance was verified by tracking three trajectories of unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

Nowadays, semantic segmentation is increasingly used in exploration by underwater robots, for example in autonomous navigation, so that the robot can recognise the nature and elements of its environment during the mission and act according to this classification to avoid collisions. Other applications can be found in the search for archaeological artefacts, in the inspection of underwater structures or in species monitoring. Therefore, it is necessary to try to improve the performance in these tasks as much as possible. To this end, we compare some methods for improving image quality and for data augmentation and test whether higher performance metrics can be achieved with both strategies. The experiments are performed with the SegNet implementations and the SUIM dataset with 8 common underwater classes to compare the obtained results with the already known ones. The results obtained with both strategies show that they are beneficial and lead to better performance results by achieving a mean IoU of 56% and an increased overall accuracy of 81.8%. The single result shows that there are 5 classes with an IoU value above 60% and only one class with an IoU value below 30%, which is a more reliable result and easier to use in real contexts.

This paper explores the application of wavelet analysis, a multiscale approach for enhancing visual clarity, in the context of image denoising, which reduces the impact of noisy pixels, caused by various factors such as electronic sensor limitations, low-light conditions, or transmission errors in digital imaging systems. This paper introduces some common noise types like Gaussian noise and processes of image denoising, eliciting the strength of wavelet analysis. As a powerful image denoising technology, wavelet analysis needs five steps to process the image, and the key step is thresholding. There are many kinds of wavelet, and each wavelet has different advantages and functions, which makes it suitable for different applications. Hence, wavelet analysis also makes a contribution in various fields beyond signal processing and data analysis, such as Biomedical Imaging and Geophysics and Seismology, where preserving image quality is essential for accurate analysis and interpretation. In short, this research highlights the promise of wavelet analysis, emphasizing the use of high- quality image data.

Background and Objectives: Uveitis, a prevalent eye disorder characterized by inflammatory processes, often leads to cataract formation and significant visual impairment. This study aimed to evaluate preoperative conditions and postoperative outcomes following cataract surgery in uveitis patients. Materials and Methods: A retrospective study was conducted at the University Hospital Center Rebro Zagreb, Croatia, involving uveitis patients who underwent cataract surgery between 2013 and 2022. Eligible patients had uveitic cataracts affecting visual acuity or posterior segment visualization in a "quiet eye" and were disease-inactive for at least three months. Patients with certain preexisting ocular conditions were excluded. Data collected included patient demographics, uveitis type, preoperative therapy, preexisting lesions, and postoperative outcomes such as visual acuity, intraocular pressure, central macular thickness, and complications. Statistical analysis was performed to identify risk factors associated with complications. Results: The study included 105 patients. After cataract surgery, there was a significant improvement in visual acuity at various time points, with 90% of eyes showing improvement. Intraocular pressure decreased over time. Central macular thickness increased at three months post-surgery but remained stable thereafter. Early and late complications were observed in 52.4% and 63.8% of eyes, respectively. The most common complications were posterior capsular opacification, macular edema, and epiretinal membrane. Factors associated with complications varied between early and late stages but included age, age at onset of uveitis, and uveitis type. Conclusion: Cataract surgery in uveitis patients presents challenges but can lead to significant visual improvement. This study highlights the importance of careful patient selection, preoperative and postoperative inflammation management, and precise surgical techniques. Although complications were common, they were manageable, and most patients experienced improved vision, emphasizing the benefits of cataract surgery in this population. However, future investigations should address the study's limitations and further refine perioperative strategies.

Pain is a complex multidimensional feeling combined with sensorial and emotional features. The majority of patients undergoing orthodontic treatment reported various degrees of pain, which is perceived as widely variable between individuals, even when the stimulus is the same. Orthodontic pain is considered the main cause of poor-quality outcomes, patients' dissatisfaction, and lack of collaboration up to the interruption of the therapy. A deep understanding of pain and how it influences the patient’s daily life is fundamental to establishing proper therapeutic procedures and obtaining the correct collaboration. Because of its multifaced and subjective nature, pain is a difficult dimension to measure. The use of questionnaires and their relative rating scales is actually considered the gold standard for pain assessment. Depending on the patient’s age and cognitive abilities is possible to choose the most appropriate instrument for self-reported pain records. Although several scales have been proposed and a lot of them are applied, it remains uncertain which of these tools represents the standard and performs the most precise, universal, and predictable task. This review aims to give an overview of the aspects which describe pain, specifically the pain experienced during orthodontic treatment, the main tool to assess self-perceived pain in a better and more efficient way, the different indications for each of them, and their correlated advantages or disadvantages.

Pattern recognition has long been regarded as key role for crime prevention and reduction. Crime analysts and policy makers can formulate effective strategies and allocate resources with reference to spatial and temporal pattern of crime. In order the combat and prevent severe crime in New York City (NYC), this study analyzed Felony Crime data of NYC in previous 5 years (2015 2020) and discovered criminal hotspots pattern and temporal patterns with open criminal complaint data provided by New York Police Department (NYPD). This study adapt a human computer interactive appraoch to draw patterns from crime data, whereas computations and visualization are performed by Python libraries, and human to inform the decision of visualization methods, computational parameters and direction of this exploratary analysis. Density based clustering algorithms, Grid Thematic Mapping and Density Heatmap are displayed to identify hotspots and demonstrates their associations with spatial features. Timeline analysis on moments of crime occurance demonstrates seasonality where crimes are mostly commited, while aoristic analysis showed hours of day when crime is mostly committed considering their timespan. Lastly, 3D visualization improved recognition of the displacement of hotspot over time, and suggested long term hotspots in NYC in 3 D visualization. This inform strategic plans for police deployment.

Foveal vision loss has been shown to reduce efficient visual search guidance due to contextual cueing by incidentally learned contexts. However, previous studies used artificial (T among L-shape) search paradigms that prevent the memorization of a target in a semantically meaningful scene. Here, we investigated contextual cueing in real-life scenes that allow explicit memory of target locations in semantically rich scenes. In contrast to the contextual cueing deficits in artificial scenes, contextual cueing in patients with age-related macular degeneration (AMD) did not differ from age-matched normal-sighted controls. We discuss this in the context of visuospatial working memory demands for which both eye-movement control in the presence of central vision loss and for memory-guided search may compete. Memory-guided search in semantically rich scenes may depend less on visuospatial working memory than search in abstract displays, potentially explaining intact contextual cueing in the former but not the latter. In a practical sense, our findings may indicate that Patients with AMD are less deficient than expected after previous lab experiments. This shows the usefulness of realistic stimuli in experimental clinical research.

Severe postoperative pain affects most patients after thoracotomy and is a risk factor for post-thoracotomy pain syndrome (PTPS). This randomized controlled trial compared preemptively administered ketamine versus paravertebral block (PVB) versus control in patients undergoing posterolateral thoracotomy. The primary outcome was acute pain intensity on the visual analog scale (VAS) on the first postoperative day. Secondary outcomes included morphine consumption, patient satisfaction, and PTPS assessment with Neuropathic Pain Syndrome Inventory (NPSI). Acute pain intensity was significantly lower with PVB compared to other groups at four out of six time points. Patients in the PVB group used significantly less morphine via a patient-controlled analgesia pump than participants in other groups. Moreover, patients were more satisfied with postoperative pain management after PVB. PVB, but not ketamine, decreased PTPS intensity at 1, 3, and 6 months after posterolateral thoracotomy. Acute pain intensity at hour 8 and PTPS intensity at month 3 correlated positively with PTPS at month 6. Bodyweight was negatively associated with chronic pain at month 6. Thus, PVB but not preemptively administered ketamine decreases both acute and chronic pain intensity following posterolateral thoracotomies. The trial was prospectively registered at the Australian New Zealand Clinical Trial Registry (https://www.anzctr.org.au/; ACTRN12616000900415; 07 July 2016).

Proprioceptive deficits accompanied with motor paresis after stroke, namely sensory ataxic motor paresis has a poorer prognosis of recovery than pure motor deficit. We aimed to investigate the effects of repetitive peripheral magnetic stimulation (rPMS) on sensory ataxic upper extremity (UE) paresis at acute stroke. Eighteen stroke participants with sensory ataxic UE paresis were randomized. rPMS group received five sessions of intervention per week, where 20 minutes of rPMS prior to 20 minutes of physical therapy (PT) every other day, alternating with 40 minutes of PT. The control group received five sessions of 40 minutes of PT/week. Outcome measures were Wolf motor function test (WMFT), UE motor section of Fugl-Meyer Motor Assessment Scale (FMA-UE), and Thumb localizing test (TLT). All assessments were compared before and after intervention. rPMS group received mean 6.5 sessions and the control group received 12.8 sessions, showing significantly imbalanced intervention due to different timing of hospital discharge. The imbalance was corrected by calculating gains of motor scores divided by sessions as Progress Rate (PR). Corrected gains (PR) of rPMS group was more significantly enhanced in WMFT and FMA-UE than control groups. rPMS group tended to improve TLT more than control one. rPMS might improve sensory ataxic UE paresis after stroke and proprioceptive deficit.

Previous studies on the mechanisms underlying willed actions reported that the premotor cortex may be involved in the construction of motor awareness. However, its exact role is still under investigation. Here we investigated the role of the dorsal premotor cortex (PMd) in motor awareness by modulating its activity applying inhibitory rTMS to PMd, before a specific motor awareness task (under three conditions: without stimulation, after rTMS and after sham stimulation). During the task, subjects had to trace straight lines to a given target, receiving a visual feedback of the line trajectories on a computer screen. Crucially, in most trials the trajectories on the screen were deviated and to produce straight lines, subjects had to correct their movements towards the opposite direction. After each trial, participants were asked to judge whether the line seen on the computer screen corresponded to the line actually drawn. Results show that participants in the no stimulation condition did not recognize the perturbation until 14 degrees of deviation. Importantly, active, but not sham, rTMS significantly modulated motor awareness, decreasing the amplitude of the angle at which participants became aware of the trajectory correction. These results suggest that PMd plays a crucial role in action self- monitoring.

Previous studies have found that individuals with limited motor capabilities due to acquired neurological injury (e.g., spinal cord injury and stroke) can make accurate action possibility judgements for neurologically healthy individuals. Previous studies have shown that people with limited motor capabilities may rely on previous motor experience (i.e., pre-injury) when making action possibility judgments for others. In the present study, we examined whether having severely limited previous motor experience from birth, as a consequence of spinal muscle atrophy (SMA), alters the action possibility judgments made for neurologically healthy individuals. Participants with SMA and Neurologically Healthy (NH) sex- and age-matched controls performed a perceptual-motor judgment task using the Fitts’s law paradigm (see Fitts, 1954). Participants observed apparent motion videos of reciprocal aiming movements with varying indices of difficulty (ID, see: Manson et al., 2014). For each movement, participants predicted the shortest movement time (MT) at which a neurologically healthy young adult could perform the task while maintaining accuracy. Between-group comparisons revealed that participants with SMA predicted significantly longer MTs compared to controls. Regression analyses revealed that predicted MTs of both NH and SMA participants exhibited a Fitts’s law relationship (i.e., the predicted MTs significantly increased as movement difficulty increased). A supplementary analysis on the SMA group revealed no differences in predicted MTs between the participants with some and no motor function as assessed by the SMA health index. Overall, these results provide evidence that participants with SMA who have limited or no motor experience may make more conservative action possibility judgments for others. Critically, our finding that the pattern of action possibility judgements (e.g., the slopes of the regression lines) were not different between SMA and NH groups provides evidence that limited previous motor experience may not completely impair action possibility judgements.

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

The transportation sector is seeing an increased adoption rate of electric vehicles (EV), which comprise battery electric vehicles (BEV), hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV). This mode of transportation is likely to displace automobiles powered by internal combustion engines (ICE) in the nearest future, as indicated by the current trend. Each of the key EC components is home to a variety of technologies that are either currently in use or will be in the near future. Electric vehicles have the potential to have a significant impact on a variety of fields, including the economy, the energy infrastructure, and the natural environment. As a result of the production of carbon dioxide gas from the combustion of fossil fuels, transportation is the sector that is responsible for the second-highest amount of greenhouse gas emissions. Electric vehicles, often known as EVs, are getting a lot of attention as a potentially game-changing solution to this issue. It is possible for electric vehicles to produce fewer emissions of carbon dioxide (CO2) than conventional automobiles due to the fact that an electric motor serves as the vehicle's propeller rather than an internal combustion engine. EVs have the potential to become zero-emission vehicles if they are paired with renewable energy sources. This document presents an overview of the numerous types of EV drive circuits, covering their design as well as the benefits and drawbacks associated with each type. The current state of battery technology, particularly that pertaining to the batteries utilized in electronic vehicles, is discussed in this paper. This article also discusses electric motor efficiency, power density, fault tolerance, dependability, cost, and the best electric motor for EVs. Then, a thorough study of future EV implementation's obstacles and prospects is done. Charging times and battery performance are examples of technological challenges, but government regulation of EVs continues to be a substantial non-technical hurdle.

The stator winding insulation system is the most critical and weak part of the EMU's (electric multiple unit) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent the fault from expanding into phase-to-phase or ground short-circuits. The TCU(traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is stationary. Three ITSC fault diagnostic conditions are based on different IGBTs control logic. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on random forest. The proposed method was validated using a simulation experimental platform for ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with the increase of ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location both in train and test samples are 100%.

: Background: Parkinson’s disease (PD) is a chronic, progressive illness with a profound impact on health-related quality of life, and it is crucial to know what factors influence quality of life throughout the course of the disease. This study aimed to evaluate PD patients’ motor and non-motor symptoms to compare symptom severity between PD clinical phenotypes and to assess the impact of disease symptoms on quality of life in a cohort of Latvian patients. Methods: We evaluated 43 patients with Parkinson’s disease. Fourteen patients had tremor-dominant (TD) PD, twenty-five patients had postural instability/gait difficulty (PIGD), and four patients had a mixed phenotype. Results: The patients’ mean age was 65.21 years, and the disease’s mean duration was 7 years. The most common non-motor symptoms were fatigue (95.3%), sleep disturbance (83.7%), daytime sleepiness (83.7%), and pain and other sensations (81.4%). PIGD patients had a higher prevalence of depressed mood, daytime sleepiness, constipation, light headedness on standing, cognitive impairment, and severe gastrointestinal and urinary disturbances (as assessed using the SCOPA-AUT domains) compared with TD patients. A high prevalence of fatigue was assessed in both disease subtypes. Health-related quality of life significantly statistically correlated with MDS-UPDRS parts III and IV (r = 0.704), the Hoehn and Yahr scale (r = 0.723), as well as the SCOPA-AUT scale’s gastrointestinal (r = 0.639), cardiovascular (r = 0.586), thermoregulatory (r = 0.566) and pupillomotor domains (r = 0.597). Conclusion: The severity of motor symptoms, as well as non-motor symptoms, such as fatigue, apathy, sleep problems and daytime sleepiness, pain, and disturbances in gastrointestinal and cardiovascular function, negatively affect PD patients’ health-related quality of life. Thermoregulatory and pupillomotor symptoms also significantly affect PD patients’ well-being.

Increased definite power, reduced cost, and dynamic construction are the important features required by electric motors for various applications. One such motor accommodating all the requisites mentioned above is Switched reluctance. Applications pertaining to small, commercial, and EV find Switched reluctance machine as a developing competitor to the conventional induction and permanent magnet motors. Though SRMS contribute a considerable number of advantages over induction motors and permanent magnet motors, the primary disadvantages pertaining to SRMs are the torque ripple, especially at high-speed operation, and the resulting acoustic noise. These disadvantages may not always be detrimental to the systems in entirely all cases, but depending on the application to be employed, torque ripple and acoustic noise form are detrimental to the system. This paper reviews the technology status and recent trends in the minimization of torque ripples switched reluctance machines.

Learning dental procedures is a complex task involving the development of fine motor skills. The reported use of theories and/ or evidence for designing learning activities to develop the fine motor skills needed for dental practice is limited. The aim of this review is to explore the available body of knowledge related to learning fine motor skills in dentistry. Evidence from studies investigating motor skill learning highlights the negative impact of self-focus and self-regulation on learning outcomes, particularly during the early stages of learning. The development of activities and schedules that enable novices to demonstrate characteristics similar to experts, without the reported long period of ‘deliberate practice’, is clearly of value. Outcomes of learning implicitly are important in dentistry because working under stressful conditions is common, either during undergraduate study or in practice. It is suggested that learning implicitly in the simulation stage can reduce disrupted performance when transitioning to clinical settings. Therefore, further investigation of effective methods for learning dental fine motor skills is indicated, using approaches that result in robust performance, even under stressful conditions.

The efficiency of performance in various sports has the development of certain specific skills at its core. In ice hockey, both the technical aspects (techniques, stance) and the cognitive ones (keeping attention on the puck, game strategy, etc.) are highly important. This study is aimed at the identification of specific features that determine the performance efficiency of professional hockey players. We used virtual reality (VR) to study the differences between professional ice hockey players and novices in terms of motor responses to the puck’s presentation on different levels of difficulty. The study involved 22 participants, 13 of them being professional ice hockey players (M_age=20±2.9; mean age of training experience М=14.18±3.8) and 9 being not experienced participants (M_age=20±1.4). The study showed that the stick response time of professional hockey players is significantly higher (0.98 ms vs 1.5 ms, p≤0.05) in more difficult situations close to a real game. Moreover, professionals proved to have more stable movement patterns of the knee and hip joints. They also make fewer head movements as a response to stimuli during all runs (0.66 vs 1.25, p≤0.05). Therefore, the results indicate specific spatial-temporal, technical and tactical, and energetic determinants, that ensure higher performance efficiency in hockey players

Cortical actomyosin flows, among other mechanisms, scale up spontaneous symmetry breaking and thus play pivotal roles in cell differentiation, division, and motility. According to many model systems, myosin motor-induced local contractions of initially isotropic actomyosin cortices are nucleation points for generating cortical flows. However, the positive feedback mechanisms by which spontaneous contractions can be amplified towards large-scale directed flows remain mostly speculative. To investigate such a process on spherical surfaces, we reconstituted and confined initially isotropic minimal actomyosin cortices to the interfaces of emulsion droplets. The presence of ATP leads to myosin-induced local contractions that self-organize and amplify into directed, large-scale actomyosin flows. By combining our experiments with theory, we found that the feedback mechanism leading to a coordinated, directional motion of actomyosin clusters can be described as asymmetric cluster vibrations, caused by intrinsic non-isotropic ATP consumption, in conjunction with spatial confinement. By tracking individual actomyosin clusters, we identified fingerprints of vibrational states as the basis of directed motions. These vibrations may represent a generic key driver of directed actomyosin flows under spatial confinement in vitro and in living systems.

In this paper, a voice coil motor (VCM) actuated fast tool servo (FTS) system is developed for diamond turning. To guide motions of the VCM actuator, a crossed double parallelogram flexure mechanism is selected featuring totally symmetric structure with high lateral stiffness. To facilitate the determination of the multi-physical parameters, analytical models of both electromagnetic and mechanical systems are developed. The designed FTS with balanced stroke and natural frequency is then verified through the finite element analysis. Finally, the prototype of the VCM actuated FTS is fabricated and experimentally demonstrated to have a stroke of ±59.02 μm and a first natural frequency of 253 Hz. By constructing a closed-loop control using PID controller with the internal-model based resonant controller, the error for tracking a harmonic trajectory with ±10 μm amplitude and 120 Hz frequency is obtained to be ±0.2 μm, demonstrating the capability of the FTS for high accuracy trajectory tracking.

In healthy ageing, capacity declines in the neural, muscular, and skeletal systems, and each system decline has its effect on the execution of complex motor tasks. This decline in capacity can result in the inability to stand up (sit-to-stand, sit-to-walk), which is a key movement for independence. The mechanisms leading to mobility limitations or inabilities are complex, overlapping, and interdependent and the complementary fields of biomechanics, motor control, and physiology need to be combined to understand these mechanisms. The aim of this review is to provide an overview of the current knowledge of age-related compensation in standing up and to consider the limitations of these results when analysing standing up in daily life using the Capacity, Reserve, Movement Objectives, and Compensation (CaReMoOC) framework that combines biomechanics, motor control, and physiology. A literature search was performed in the search engine Scopus, using the keywords and their synonyms: strateg*(approach, technique, way) AND, sit-to-walk OR sit-to-stand OR rise (raise, arise, stand, stand-up) AND chair (seat). Inclusion criteria were: biomechanics or motor control on sit-to-stand or sit-to-walk in healthy and/or frail adults (<60y) and elderly (>60y), and/or osteoarthritis patients as a specific case of ageing related decline. The review shows that movement compensations in standing up manifest as changes in planned trajectory (Compensation by Selection) and in muscle recruitment (Compensation by Reorganisation). However, as most studies in the literature typically use standardized experimental protocols where movement compensation is restricted, these studies cannot be directly translated to functional tasks, such as the mobility of the elderly in their homes, communities, and clinic. Compensation must be included in future studies in order to facilitate clinical translation. Specifically, future studies in the standing up task should 1) determine the effect of varying arm use strategies (e.g., armrests, knees, chair, cane) on trunk and both lower limb and upper limb joint loading, 2) analyse control strategies in elderly people, 3) determine the biomechanical implications of asymmetry, and 4) incorporate assessments of age-related physical and neural decline as well as changes in psychological priorities.

Previous studies have reported certain sex differences in motor performance precision. The aim of the present study was to analyse sex differences in fine motor precision performance for both hands in different tests conditions. 220 Spanish participants (ages: 12-95) performed fine motor tasks - tracing over the provided models – lines of 40 mm for both hands, two sensory conditions (PV – proprioceptive-visual; P – proprioceptive only) and three movement types (F – frontal, T – transversal and S - Sagittal). Differences in line length (the task focused on precision) were observed through MANOVA analysis for all test conditions, both sexes and different age groups. Sex differences in precision were observed in F and T movement types (statistically significance level and higher Cohens’ d was observed in condition with vision). No any statistically significant differences were observed in both hands and sensory conditions in sagittal type. Sex differences in fine motor precision were more frequently observed in the PV sensory condition in the frontal movement type and less in the sagittal one.

The link between language processing and motor systems has been the focus of increasing interest to Cognitive Neuroscience. Some classical papers studying Event Related Potentials (ERPs) induced by linguistic stimuli have found differences in electrophysiological activity when comparing action and non-action words; more specifically, a bigger p200 for action words. On the other hand, a series of studies have validated the use of a grip force sensor (GFS) to measure language-induced motor activity during both isolated words and sentence listening, finding that action words induce an augmentation in the grip force around 250-300 ms after the onset of the stimulus. The purpose of the present study is to combine both techniques to assess if the p200 is related to the augmentation of the grip force measured by a GFS. We measured ERP and GFS changes elicited by listening to action and non-action words while maintaining an active grasping task in 10 healthy subjects. Our results show that the amplitude of the p200 in central electrodes is correlated to the augmentation in the GFS around 300 ms induced by linguistic stimuli. To our knowledge, this is the first study where the electrophysiological activity and the changes in the grip force induced by auditory language processing are put together, opening new venues of interpretation for the sensorimotor interaction in language processing.

Objective: In this pilot study, we described the gross motor development of infants aged 4 to 24 months with congenital heart disease (CHD) and assessed through a systematic develop­mental screening programme, with individualised motor interventions. Methods: Thirty infants who had cardiac repair underwent gross motor evaluations using the AIMS at 4 months, and the Bayley-III at 12 and 24 months. Results: Based on AIMS, 80% of 4-month-old infants had a delay in gross motor development and required physical therapy. Gross motor abilities significantly improved by 24 months. Infants who benefited from regular physiotherapy tended to show better improvement in motor scores. Conclusion: Our study highlights the importance of early motor screening in infants with CHD and suggests a potential benefit of early physical therapy in those at-risk. Further research is needed to assess the effectiveness of systematic developmental screening and individualized inter­vention programmes at identifying at risk patients, and their impact on developmental outcomes.

In this paper, we present a method for estimating GPS signals from visual information captured by a monocular camera mounted on a fixed-wing Unmanned Aerial Vehicle. The main challenge in visual odometry from aerial images is the computation of scale due to irregularities in the elevation of the terrain. That is, it is not possible to accurately convert from pixels in the image to meters in space, and the error accumulates. The contribution of this work is to reduce the accumulated error by comparing the images from the camera with satellite images. The algorithm has been tested in real-time experiments, and the results show that it is possible to eliminate the accumulated error using satellite images.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

Visual Sensor Networks (VSNs) have been the focus of numerous studies, emphasizing the quality of the information they generate. The primary goal of these networks is to capture data from an area, extract the pertinent information from the visual scene, and facilitate communication. However, the presence of objects in the study area can impacted the network's efficiency, linking the quality of acquired data directly to the positioning of the cameras. Strategically positioning multiple cameras in a potentially complex area, while ensuring effective communication between nodes, presents a challenging issue in VSNs applications. This paper introduces a specific method to enhance the deployment of visual sensors in indoor environments. The research addresses the theme of multi-objective optimization of VSN deployment, integrating various criteria: maximizing overall visual coverage, maximizing the observation of zones of interest, maximizing observation redundancy of zones of interest and ensuring communication connectivity for data transfers. To tackle this problem, we simulate the observed environment incorporating cameras, obstacles, and considering zones of interest. Camera positioning is performed while considering visual constraints and the scene's characteristics. The quality of positioning is evaluated based on the defined objective functions. Initially, a comparative study is conducted with a state-of-the-art approach, integrating the first three objective functions. Subsequently, we incorporate the fourth objective function to ensure data transfers. The proposed method demonstrates efficient camera network deployments in indoor areas, considering global coverage and observation redundancy. In contrast to the state-of-the-art, these results are achieved in areas with visual obstacles while ensuring communication connectivity between the different cameras.

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can integrate seamlessly into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms, using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual-semantic gap, steering the model towards enhanced performance in zero-shot instance retrieval. This work not only benchmarks the cognition alignment of AI, but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.}

Past research suggests a continuity between perception and memory, as reflected in influences of orienting of spatial attention by cues presented after a visual target offset (post-target cues) on target perception. Conducting two experiments, we tested and confirmed this claim. Our study revealed an elevated reliance on post-target cues for target detection with diminishing target visibility, leading to better performance in validly versus invalidly cued trials, indicative for contrast gain. We demonstrated this post-target cueing impact on target perception without a postcue response prompt, meaning that our results truly reflected a continuity between perception and memory rather than a task-specific impact of having to memorize the target due to a response prompt. We further showed an influence of attention by the post-target cues with cues presented away from a clearly visible target, meaning that visual interactions at the target location provided no better explanation of our results. Our results generalize prior research with liminal targets and confirm the view of a perception-memory continuum, so that visual target processing is not shielded against visuospatial orienting of attention elicited by events following offset of the visual target.

The study of paraneoplastic syndromes in humans has a long history, whereas for domestic animals – only since the beginning of the 21^th century. There are various forms of paraneoplastic syndromes; many classifications based on pathogenetic, organ-system principles are described. In accordance with the modern classification, paraneoplastic syndromes are divided into endocrinopathies, hematological paraneoplastic syndromes, gastrointestinal, renal, dermatological, neurological, ophthalmological. The purpose of this study was to develop a scientifically based algorithm for diagnosing ophthalmopathies against the background of paraneoplastic syndrome in dogs, including a multifactorial system of “hormonal-drug treatment” of paraneoplastic ophthalmopathies. The work used a comprehensive methodological approach, including the collection of anamnestic data, clinical examination of the animal, examination of the area of the pathological process, hematological studies, cytomorphological studies, visual diagnostic methods with complete oncological and ophthalmological examinations using the necessary equipment. Treatment for cancer included surgical removal of the tumor, chemotherapy and radiotherapy. The choice of treatment protocol depended on the location of the disease, tumor phenotype (if present) and stage of the malignancy. The only chemotherapy was applied using complex protocols in the treatment of dog lymphomas. Treatment of melanoma in dogs was carried out using an integrated approach which combined methods of surgery, chemotherapy, and radiotherapy. The obtained results can be recommended for use in the diagnosis and treatment of paraneoplastic ophthalmopathies in dogs.

Dry eye syndrome (DES) has become increasingly prevalent due to the prolonged use of digital devices in modern society. Individual supplementation with lutein or docosahexaenoic acid (DHA) demonstrated potential benefits for eye health. This study aimed to investigate the effects of lutein plus DHA complex supplementation on DES, visual function, and memory in healthy individuals. Thirty-four participants, aged 40~60 years, received lutein/DHA complex supplements (containing 10 mg free-form lutein, 200 mg DHA, 30 mg red algae extract (astaxanthin), phosphatidylserine, and vitamin A) for 12 weeks. Ophthalmological examinations and memory tests were carried out, and a questionnaire was administered at weeks 0, 6, and 12 of the study. After 12 weeks of the intervention, the tear break-up time had significantly increased, and intraocular pressure (IOP) had significantly decreased. This suggests that lutein/DHA complex supplementation may reduce the IOP and offer potential benefits for DES, as indicated by improved tear stability.

In the manufacturing of optical lenses, the lack of digital monitoring and inspection for centring processing not only results in product consistency not being guaranteed but also leads to inefficiency in processing. A centring processing digital twin system is proposed to address the problems of low virtual visualization and insufficient monitoring capability of centring processing of high-precision optical components. The layered architecture of the system is determined based on the five-dimensional model of the digital twin, coupled with a dimension-driven geometric model and a physical model that integrates data and turning mechanisms to improve the fidelity and interactivity of the virtual model of the centring lathe. The fusion of heterogeneous data from multiple sources in the centring machining process at the semantic and physical levels is realized by using an information model and an OPC UA-based data interaction method. The VMD-GRU method based on feature fusion is used to monitor the key components of the centring lathe. Finally, the feasibility and effectiveness of the proposed method are verified by a case study of the development of a digital twin system for centring lathe, which provides a reference for the digitization and intelligence of the machining process of high-precision optical components.

People with Down syndrome have more visual problems than general population. They experience a premature aging, and it would be expected they will also have an acceleration in worsening visual function. A prospective observational study which includes visual acuity, refractive error, accommodation, binocular and colour vision was performed to young adults with (n=69) and without (n=65) Down syndrome and to a senior group (n=55) without Down syndrome. Results showed significant differences in visual acuity between groups (p&lt;0.001) and it can be improved with a new prescription in 40% of the participants with Down syndrome. Regarding to accommodative state, no significant differences were found between groups of young people. Concerning binocular vision, 64.7% of strabismus was observed in the group with Down syndrome (p&lt;0.001). Visual abnormalities are significant in young adults with Down syndrome and are different from those of older people without Down syndrome, some of which can be improved by providing the optimal prescription as well as regular eye examinations.

The recent public release of the generative AI language model ChatGPT has captured the public imagination and has resulted a rapid uptake and widespread experimentation by the general public and academia alike. The number of academic publications focusing on the capabilities as well as practical and ethical implications of generative AI has been growing exponentially. One of the concerns with this unprecedented growth in scholarship related to generative AI, in particular ChatGPT, is that in most cases the raw data, that is the text of the original ‘conversations,’ have not been made available to the audience of the papers and thus cannot be drawn on to assess the veracity of the arguments made and the conclusions drawn therefrom. This paper provides a protocol for the documentation and archiving of these raw data.

This study, based on the AISAS model, explores the impact of the interaction effect between visual and auditory signals on consumer purchase behavior. Using experimental methods, 120 participants were randomly assigned to four different visual and auditory signal combinations, and their purchase intentions and actual purchase behavior were measured. The results show that the interaction effect between visual and auditory signals has a significant impact on both purchase intentions and actual purchase behavior, and there is a significant positive relationship. Specifically, when visual and auditory signals are mutually consistent, consumers have the highest purchase intentions and actual purchase behavior; when both visual and auditory signals are absent, consumers have the lowest purchase intentions and actual purchase behavior; when either the visual or auditory signal is missing, consumers' purchase intentions and actual purchase behavior are in between the two extremes. This study provides a new perspective for understanding consumers' decision-making processes in multi-sensory environments and offers valuable insights for the development of marketing strategies.

The promised dataset was obtained from the daily Turkish words and phrases pronounced by various people in the videos posted on YouTube. The purpose of collecting the dataset is to provide detection of the spoken word by recognizing patterns or classifying lip movements with supervised, unsupervised, semi-supervised learning and machine learning algorithms. Most of the datasets related with lip reading consist of people recorded on camera with fixed backgrounds and the same conditions, but the dataset presented here consists of images compatible with machine learning models developed for real-life challenges. It contains a total of 2335 instances taken from TV series, movies, vlogs, and song clips on YouTube. The images in the dataset vary due to factors such as the way people say words, accent, speaking rate, gender and age. Furthermore, the instances in the dataset consist of videos with different angles, shadows, resolution, and brightness that are not created manually. The most important feature of our lip reading dataset is that we contribute to the non-synthetic Turkish dataset pool, which does not have wide dataset varieties. Machine learning studies can be carried out in many areas, such as the defense industry and social life, with this dataset.

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and L1 loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.

While shoulder injuries represent the musculoskeletal disorders (MSDs) most encountered in physical therapy, there is no consensus on their management. As attempts to provide standardized and personalized treatment, a robot-ic-assisted device combined with EMG biofeedback specifically dedicated to shoulder MSDs has been developed. The aim of this study was to determine the efficacy of an 8-week rehabilitation program (≈3 sessions a week) using a ro-botic-assisted device combined with EMG biofeedback (RA-EMG group) in comparison with a conventional program (CONV group) in patients presenting with shoulder MSDs. This study is a retrospective cohort study including data from 2010 to 2013 on patients initially involved in a physical rehabilitation program in a private clinic of Chicoutimi (Canada) for shoulder MSDs. Shoul-der flexion strength and range of motion were collected before and after the rehabilitation program. Forty-four patients participated in a conventional pro-gram using dumbbell (CONV group) while 72 of them completed a program on robot-assisted device with EMG and visual biofeedback (RA-EMG group), whereby both programs consisted in 2 sets of 20 repetitions at 60% of maximal capacity. Results showed that the RA-EMG had significantly greater benefits than the Conv group for shoulder flexion strength (+103.1% vs 67%, p = 0.016) and range of motion (+14.4% vs 6.1%, p = 0.046). The current retrospective co-hort study showed that a specific and tailored rehabilitation program with constant effort by automatic adjustment of the level of resistance was able to potentiate strength and range of motion shoulder flexion after an 8-week reha-bilitation period in comparison with a conventional approach in patients with shoulder MSDs. This study provides new insight on shoulder MSD rehabilita-tion and future research should be pursued to determine the added potential of this approach for abduction and external rotation with a randomized controlled design.

We explore gaps in Wikipedia's coverage of the visual arts by comparing the representation of 100 artists and 100 artworks from the Western canon against corresponding sets of notable artists and artworks from non-Western cultures. We measure the coverage of these two sets of topics across Wikipedia as a whole and for its individual language versions. We also compare the coverage for Wikimedia Commons and Wikidata, sister-projects of Wikipedia that host digital media and structured data. We show that all these platforms strongly favour the Western canon, giving many times more coverage to Western art. We highlight specific examples of differing coverage of visual art inside and outside the Western canon. We find that European language versions of Wikipedia are generally more "Western" in their coverage and Asian languages more "global", with interesting exceptions. We suggest how both Wikipedia and the wider cultural sector can address this gap in content and thus give Wikipedia a truly global perspective on the visual arts.

Purpose of the study was to explore the association of cognition with hazardous drinking, binge drinking and alcohol use disorder in schizophrenia and schizoaffective disorder. Cognitive deficits are common in schizophrenia. Alcohol might be associated with additional cognitive impairment in schizophrenia patients. The study population included 3362 schizophrenia and schizoaffective disorder patients in Finland. Hazardous drinking was screened with the AUDIT-C (Alcohol Use Disorders Identification Test for Consumption) screening tool. Binge drinking was obtained from the AUDIT-C. Alcohol use disorder (AUD) diagnoses were obtained from the national registrar data. Participants performed two computerized tasks from the Cambridge automated neuropsychological test battery (CANTAB) on tablet computer: the 5-choice serial reaction time task (5-CSRTT), or, reaction time (RT) test and the Paired Associative Learning (PAL) test. Association of alcohol use with RT test and PAL test was analyzed with log-linear regression and logistic regression, respectively. After adjustment for age, education and age at first psychotic episode, hazardous drinking in females was associated with lower median RT. Compared to never binge drinkers, male and female participants drinking 6 or more doses of alcohol monthly or less had lower median RT. In the PAL test both first trial memory score (FTMS) and total errors adjusted score (TEAS) were associated with better performance in males drinking 6 or more doses of alcohol weekly or more and in females drinking 6 or more doses monthly or less. Higher PAL TEAS was associated with AUD in females Some positive associations between alcohol and cognition were found in male and female schizophrenia and schizoaffective disorder patients with hazardous drinking and binge drinking.

To characterize the mechanisms by which the highly-conserved exocyst trafficking complex regulates eye physiology in zebrafish and mice, we focused on exoc5 (aka sec10), a central exocyst component. We analyzed both exoc5 zebrafish mutants and retinal pigmented epithelium (RPE)-specific Exoc5 knockout mice. Exoc5 is present in both the non-pigmented epithelium of the ciliary body and in the RPE. In this study we set out to establish an animal model to study the mechanisms underlying the ocular phenotype and to establish if loss of visual function is induced by postnatal RPE Exoc5-deficiency. Exoc5-/- zebrafish showed smaller eyes, with decreased number of melanocytes in the RPE and shorter photoreceptor outer segments. At 3.5 days post fertilization, loss of rod and cone opsins were observed in zebrafish Tg:exoc5 mutants. Mice with postnatal RPE-specific loss of Exoc5 showed retinal thinning associated with compromised visual function, and loss of visual photoreceptor pigments. This retinal phenotype in Exoc5-/- mice was present at 20-weeks, and the phenotype was more severe at 27-weeks, indicating progressive disease phenotype. We previously showed that the exocyst is necessary for photoreceptor ciliogenesis and retinal development. Here, we report that exoc5 mutant zebrafish and mice with RPE-specific genetic ablation of Exoc5 develop abnormal RPE pigmentation, resulting in retinal cell dystrophy and loss of visual pigments associated with compromised vision. As RPE cells are “downstream” of photoreceptor cells in the visual process, these data suggest exocyst-mediated retrograde communication and dependence between the RPE and photoreceptors.

Aim: Reporting 3 year outcomes of a community shared care scheme run by specialised independent prescribing (IP) optometrists for stable glaucoma and ocular hypertension (OHT) patients in West Kent, England. Purpose: Shared Care Schemes for glaucoma exist to alleviate the burden on Hospital Eye Services (HES) glaucoma clinics. We studied the effectiveness of community care by highly trained and qualified IP optometrists in terms of disease stability and referral rate into HES.Methods: Retrospective longitudinal review of 200 eyes with stable early to moderate stage glaucoma and OHT followed-up in two specialist optometry practices. Outcome measures included visual field mean deviation (VFMD), intraocular pressure (IOP), changes to treatment and referral rate into HES. Inclusion criteria included all patients with OHT and glaucoma (open angle and primary angle closure) referred for community follow-up. Incomplete data sets were excluded.Results: Mean age 71yrs (range 28 - 93yrs) and equal male: female ratio. n= 159 at year 3. The results for both outcomes showed no significant change from baseline at 12 or 24-month time points. However, a significant change from baseline at 36 months was observed for both outcomes: mean reduction of 0.7 mmHg in IOP, and a mean reduction of 0.3 dB in VFMD. There was a statistically significant change in the number of drops used at 36 months (p=0.001). 11 patients had a change in medication within 3 years. One patient was referred back to HES for uncontrolled IOP and consideration of trabeculectomy.Conclusion: Community follow-up of stable cases of glaucoma and OHT by highly qualified IP optometrists was safe, with stability of disease maintained and few referrals back to HES.

The electrical stimulation of the visual cortices has the potential to restore vision to blind individuals. Until now, the results of visual cortical prosthetics has been limited as no prosthesis has restored a full working vision but the field has shown a renewed interest these last years thanks to wireless and technological advances. However, several scientific and technical challenges are still open in order to achieve the therapeutic benefit expected by these new devices. One of the main challenges is the electrical stimulation of the brain itself. In this review, we analyze the results in electrode-based visual cortical prosthetics from the electrical point of view. We first briefly describe what is known about the electrode-tissue interface and safety of electrical stimulation. Then we focus on the psychophysics of prosthetic vision and the state-of-the-art on the interplay between the electrical stimulation of the visual cortex and phosphene perception. Lastly, we discuss the challenges and perspectives of visual cortex electrical stimulation and electrode array design to develop the new generation implantable cortical visual prostheses.

A number of studies have been conducted to improve the accessibility of images using touchscreen devices for screen reader users. In this study, we conducted a systematic review of 33 papers to get a holistic understanding of existing approaches and to suggest a research road map given identified gaps. As a result, we identified types of images, visual information, input device and feedback modalities that were studied for improving image accessibility using touchscreen devices. Findings also revealed that little has studied how to automate the generation of image-related information, and that screen reader users play important roles during the evaluation but the design process. Then we introduce two of our recent studies on the accessibility of artwork and comics, AccessArt and AccessComics respectively. Based on the identified key challenges, we suggest a research agenda for improving image accessibility for screen reader users.

Citrus Greening, which is mainly caused by bacteria, is one of the severe citrus diseases affecting all citrus cultivars and causing the deliberate abolition of trees worldwide. This infectious disease cannot be spread by wind, rain, or contact by contaminated personnel. The primary vector that spreads this disease through feeding citrus leaves is the Asian citrus psyllid (ACP), a minuscule insect. The management of citrus greening is also very costly as there is no fruitful technique is developed to cure this disease except removing all infected plants from good ones to eliminate the dissemination of the pathogen. Citrus greening identification is also the most difficult job, as the symptoms are similar to other citrus diseases and nutrient deficiency. Asymmetrical blotchy mottling patterns on leaves are the main symptoms to detect this disease. Here we have discussed some visual signs of citrus greening, which will ultimately help root level farmers to identify and prevent this disease before it drastically impacts citrus plants. Whether it is affected by citrus greening or lack of nutrients, we have also discussed the pen test method of determining the symptoms as symmetrical or asymmetrical across the mid-vein.

Recent studies suggest that the developmental curves in adolescence, related to the development of executive functions, could be fitted to a non-linear trajectory of development with progressions and retrogressions. Therefore, the present study proposes to analyze the pattern of development in Perceptual Inhibition (PI), considering all stages of adolescence (early, middle, and late) in intervals of one year. To this aim, we worked with a sample of 275 participants between 10 and 25 years, who performed a joint visual and search task (to measure PI). We have fitted exGaussian functions to the probability distributions of the mean response time across the sample and performed a covariance analysis (ANCOVA). The results showed that the 10- to 13-year-old groups performed similarly in the task and differ from the 14- to 19-year-old participants. We found significant differences between the older group and all the rest of the groups. We discuss the important changes that can be observed in relation to the nonlinear trajectory of development that would show the PI during adolescence.

The aim of this study was to examine brightness effect, which is the perceptual property of visual stimuli, on brain responses obtained during visual processing of these stimuli. For this purpose, brain responses of the brain to changes in brightness were explored comparatively using different emotional images (pleasant, unpleasant and neutral) with different luminance levels. Moreover, electroencephalography recordings from 12 different electrode sites of 31 healthy participants were used. The power spectra obtained from the analysis of the recordings using short time Fourier transform were analyzed, and a statistical analysis was performed on features extracted from these power spectra. Statistical findings obtained from electrophysiological data were compared with those obtained from behavioral data. The results showed that the brightness of visual stimuli affected the power of brain responses depending on frequency, time and location. According to the statistically verified findings, the distinctive effect of brightness occurred in the parietal and occipital regions for all the three types of stimuli. Accordingly, the increase in the brightness of pleasant and neutral images increased the average power of responses in the parietal and occipital regions whereas the increase in the brightness of unpleasant images decreased the average power of responses in these regions. However, the increase in brightness for all the three types of stimuli reduced the average power of frontal and central region responses (except for 100-300 ms time window for unpleasant stimuli). The statistical results obtained for unpleasant images were found to be in accordance with the behavioral data. The results also revealed that the brightness of visual stimuli could be represented by changing the activity power of the brain cortex. The main contribution of this research was to comprehensively examine brightness effect on brain activity for images with different emotional content and different frequency bands at different time windows of visual processing for different brain regions. The findings emphasized that the brightness of visual stimuli should be viewed as an important parameter in studies using emotional image techniques such as image classification, emotion evaluation and neuro-marketing.

Visual question answering (VQA) is receiving increasing attention from researchers in both the computer vision and natural language processing fields. There are two key components in the VQA task: feature extraction and multi-modal fusion. For feature extraction, we introduce a novel co-attention scheme by combining Sentence-guide Word Attention (SWA) and Question-guide Image Attention (QIA) in a unified framework. To be specific, the textual attention SWA relies on the semantics of the whole question sentence to calculate contributions of different question words for text representation. For the multi-modal fusion, we propose a “Cross-modal Multistep Fusion (CMF)” network to generate multistep features and achieve multiple interactions for two modalities, rather than focusing on modeling complex interactions between two modals like most current feature fusion methods. To avoid the linear increase of the computational cost, we share the parameters for each step in the CMF. Extensive experiments demonstrate that the proposed method can achieve competitive or better performance than the state-of-the-art.

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

The multiphase motors due to their reliability find new areas of application in the industry and transportation. Developing of any system with multiphase motors at first requires elaborate model of the system, define control mode and controllers. The model of control system for the six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. Simulink model of six-phase drive with indirect field-oriented control and simulated with two types PID and PID fuzzy controllers. The simulation results, reflecting step response and provided speed reference law as well as load application at constant speed, are presented and analyzed. Fuzzy controller with 49 rules is considered and applied. Six-phase electric drive with field-oriented control at well-tuned PID controller has good step response specifications: settling time 0.13 s at starting without load, step response has no overshoot, small about 1 % short time decrease of speed at load of 5 Nm application. The system employing PID fuzzy controller shows slightly better results in response to torque application: the decrease in speed 1 % is eliminated in shorter time. Simulation results indicate the robustness of both systems with six-phase drive to disturbance and provide high quality transient specifications at determined reference speed.

The clinical neurophysiological tests allow determine the type, extent, and nature of brachial plexus damage. They are crucial in decision-making regarding surgical procedures or conservative treatment. This report aimed to present an algorithm for rehabilitation procedures in patients with brachial plexus injury of various origins based on the results of neurophysiology findings for the selection of procedures supporting the process of nerve and muscle regeneration. The research group consisted of patients whose medical documentation was analyzed concerning the reason, level, and localization of damage to the brachial plexus structures, surgical and or rehabilitative treatment as well as the MRI results. Among the group of ten patients, the clinical studies showed the greatest incidence of brachial plexus injuries of the mixed (both pre- and postganglionic), all trunks, and cervical root injuries, respectively. Results of the motor evoked potentials (MEP) and electroneurography (ENG) recordings induced at levels of spinal roots and Erb's point showed a decrease of more than 40% in amplitudes on the symptomatic side in comparison to the asymptomatic side. This difference was recorded for the axillary and radial innervation and the C5, C6, and C7 root domains, ranging from 56% to 71%; the lowest decrease was recorded following electrical stimulation at Erb's point for the ulnar nerve (41.5%). The latency prolongation on the symptomatic side in CMAP and MEP tests ranged from 0.2 to 1.9 ms, the most following magnetic stimulation of the C5 cervical root for the axillary innervation. All results indicated the axonotmesis and neuropraxia type of injury in motor fibers (40%) confirmed by EMG results. The sensory conduction studies (SNCS) in distal nerve branches did not confirm the severe advancement of the brachial plexus injury (22%). The proposed algorithm of the physiotherapeutic procedures should be mainly targeted for recovery of motor dysfunction as the consequence of brachial plexus injury. Rehabilitation should incorporate the treatment supporting nerve regeneration, muscle strengthening, and maintaining functional ranges of motion of the injured extremities. The rehabilitation treatment for patients with brachial plexus injuries is an individualized process, and the selection of procedures and the effectiveness of the treatment undertaken should be confronted with results of neurophysiological tests verifying the motor neural transmission from the level of the cervical motor center to the effector, peripheral nerve function, and muscle’s motor unit activity.

Studies in the field of experimental hypnosis highlighted the role of hypnotizability in the physiological variability of the general population. It is associated, in fact, with a few differences which are observable in the ordinary state of consciousness and in the absence of suggestions. The aim of the present scoping review is summarizing them, and indicate their relevance to the neural mechanisms of hypnosis and to the prognosis and treatment of a few medical conditions. Individuals with high, medium and low hypnotizability scores display different cerebral functional differences – i.e., functional equivalence between imagery and perception/action, excitability of the motor cortex, interoceptive accuracy - possibly related to brain structural and functional characteristics -, and different control of blood supply at peripheral and cerebral level, likely due to different availability of endothelial nitric oxide. These differences are reviewed to support the idea of their participation in hypnotic behaviour and to indicate their prognostic and therapeutic usefulness in a few medical conditions.

Due to its high power, high efficiency, low pollution, and compact size, permanent magnet synchronous motors (PMSMs) have been widely used in a variety of fields, including electric vehicles, aerospace, wind turbines, and marine devices which are used in renewable, sustainable, and environmentally friendly energy resources. However, in these practical scenarios, the motor operating conditions are complex and variable. Under high temperatures and high current conditions, PMSMs may experience demagnetization failures, not only leading to performance degradation but also inducing unexpected failures of the motors. To reduce the risk of unexpected losses caused by demagnetization faults and improve the safety and reliability of motor systems, it is necessary to apply automated monitoring of the magnet flux of the motor's permanent magnets and achieve real-time diagnosis of early demagnetization faults, ensuring the safe operation of the motor. This review article tries to summarize the current detecting methods of the automated monitoring of demagnetization faults in PMSMs. The main online monitoring technologies from both practical and academic perspectives were summarized and their benefits and challenges have been reviewed. Finally, the research trends and suggestions for future improvements are provided. This review article will not only shed light on the origins of the automated monitoring of demagnetization faults but also help to design highly effective and sustainable permanent magnet synchronous motors.

Physical activity (PA) is a modifiable factor that may have an influence on the course of Parkinson’s disease (PD). This study aimed to apply a simple behavioral intervention to encourage PD sufferers to increase their everyday physical activity and assess which parameters of motor functions will be improved. The research covered 50 PD patients (28 men and 22 women) aged 40-81 years (65.38 ± 9.23), with a duration of the disease 2-4 years, in stages 1-3 on the Hoehn and Yahr scale. The patients were randomly divided into two groups: the experimental group with behavioral therapy, and the control group without intervention. During 12 weeks the patients from the experimental group had five phone conversations. Each conversation lasted 15 minutes and was an interview about the subjects’ physical activity in the last month. The results were assessed by the Timed Up and Go test (TUG), Unified Parkinson's Disease Rating Scale (UPDRS) – part III, FIR (Functional Index “Repty” – own modification of Functional Independence Measure (FIM)), Functional Ambulation Category (FAC) and International Physical Activity Questionnaire (IPAQ). The results showed that spontaneous physical activity increased in the experimental group and motor functions improved. Physical activity improvement depended on age, body mass index, and gender.

Surgical neurolysis is a procedure designed to liberate an injured nerve from scar tissue or adjacent structure, thereby facilitating nerve regeneration in cases of brachial plexus neuropathy (BPN). This study presents a case series of patients diagnosed with BPN who underwent surgical neurolysis. The primary focus was on the clinical assessment of recovery using the British Medical Research Council motor grading scale (BMRC). Additionally, a comprehensive literature review was conducted to analyze motor recovery outcomes related to surgical neurolysis for BPN. 18 patients with BPN who underwent surgical neurolysis where included. The results experienced a notable increase of 58% in muscle strength as assessed by the BMRC. The average preoperative state of 2.17±1.15 improved significantly to a postoperative condition of 3.44±1.34 (p = 0.003, d= 0.913) The systematic review identified 2298 articles which were selected for qualitative analysis, demonstrated that surgical neurolysis was associated with favorable motor recovery outcomes in 75.82% of the patients. Both the case series and the literature review reveal significant motor recovery following surgical neurolysis. It is crucial to conduct well-designed, adequately powered, randomized, and blinded clinical trials. Such studies will provide robust evidence to support or refute the utility of this approach in motor recovery.

Objective: Design and validate a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90\% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An accelerometer provides monitoring of head movements. The EEG amplifier operates with 24 bits resolution up to 500 Hz sampling frequency, and can communicate with other devices using 802.11 {b/g/n/WiFi}. It has high SNR and CMRR (121 dB and 110 dB, respectively), and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as backend coding language and JS, CSS, HTML as front-end coding languages, and includes training and optimization of Support Vector Machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation provides proof-of-concept validation for device use at both the clinic and at home. Significance: The low-cost, accessibility, usability, interoperability, and programmability of the proposed closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

The present study illustrates a brain-computer interface designed and developed to be wearable, portable, and user-friendly. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: the "neurofeedback" group, which performed motor imagery while receiving feedback, and the "control" group, which performed motor imagery with no feedback. Several questionnaires were administrated to participants aiming to investigate the usability of the proposed system and individual’s ability to imagine movements. The highest mean classification accuracy across subjects of control group was about 62 % with 3 % associated type A uncertainty, and 69 % with 3 % uncertainty for neurofeedback group. Moreover, in some cases the results were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study highlights the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, the results and the perceived usability pave the way for the employment of the proposed system in tele-rehabilitation.

In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

In the speed control system of Interior Permanent Magnet Synchronous Motor (IPMSM) without a speed sensor, the speed under the traditional PI control suffers from poor tracking performance and step response overshoot. This paper proposes a Compound Variable Structure PI (CVSPI) controller to improve the system control performance. It can choose whether to include an integral term according to the size of the system deviation to speed up the response. It also introduces a Model Reference Adaptive System (MRAS) speed observer in the controller to estimate the speed and adaptively adjust the size of the anti-integration saturation gain to improve the dynamic response following performance and immunity of the system. A feed-forward link is added for a given input differential to achieve an accurate answer to time-varying inputs. As the linear compensation matrix of the conventional MRAS is a unit matrix, the speed can only be accurately observed in a specific speed range. In this paper, a new linear compensation matrix is designed, and a new speed adaptive law is derived, allowing the improved MRAS to measure speed over a wide range accurately. Simulation results validate the excellent control performance of the CVSPI and the accuracy of the enhanced MRAS over a wide speed range.

In the speed control system of Interior Permanent Magnet Synchronous Motor (IPMSM) without a speed sensor, the speed under the traditional PI control suffers from poor tracking performance and step response overshoot. This paper proposes a Compound Variable Structure PI (CVSPI) controller to improve the system control performance. It can choose whether to include an integral term according to the size of the system deviation to speed up the response. It also introduces a Model Reference Adaptive System (MRAS) speed observer in the controller to estimate the speed and adaptively adjust the size of the anti-integration saturation gain to improve the dynamic response following performance and immunity of the system. A feed-forward link is added for a given input differential to achieve an accurate answer to time-varying inputs. As the linear compensation matrix of the conventional MRAS is a unit matrix, the speed can only be accurately observed in a specific speed range. In this paper, a new linear compensation matrix is designed, and a new speed adaptive law is derived, allowing the improved MRAS to measure speed over a wide range accurately. Simulation results validate the excellent control performance of the CVSPI and the accuracy of the enhanced MRAS over a wide speed range.

The synuclein family consists of α-, β-, and γ-Synuclein (α-Syn, β-Syn, and γ-Syn), expressed in the neurons and concentrated in synaptic terminals. While α-Syn is at the center of interest due to its implication in the pathogenesis of Parkinson’s disease (PD) and other synucleinopathies, limited information exists on the other members. The current study aimed at investigating the biological role of γ-Syn controlling the midbrain dopamine (DA) function. We generated two different mouse models with i) γ-Syn overexpression induced by an adeno-associated viral vector and ii) γ-Syn knockdown induced by a ligand-conjugated antisense oligonucleotide, to modify the endogenous γ-Syn transcription levels in midbrain DA neurons. The progressive overexpression of γ-Syn decreased DA neurotransmission in the nigrostriatal and mesocortical pathways. In parallel, mice evoked motor deficits in the rotarod and impaired cognitive performance as assessed by novel object recognition, passive avoidance, and Morris water maze tests. Conversely, acute γ-Syn knockdown selectively in DA neurons facilitated forebrain DA neurotransmission. Importantly, modifications in γ-Syn expression did not induce the loss of DA neurons or changes in α-Syn expression. Collectively, our data strongly suggest that DA re-lease/re-uptake processes in the nigrostriatal and mesocortical pathways are partially dependent on SNc/VTA γ-Syn transcription levels, and are linked to modulation of DA transporter function, similar to α-Syn.

Traumatic brain injury represents one of the main health problems in developed countries. Growth Hormone (GH) and rehabilitation have been claimed to significantly contribute to the recovery of lost motor function after acquired brain injury, but the mechanisms by which this occurs are not well understood. In this work, we have investigated cell proliferation in the piriform cortex (PC) of adult rats with ablation of the frontal motor cortex treated with GH and rehabilitation, in order to evaluate if this region of the brain, related to the sense of smell, could be involved in benefits of GH treatment. Male rats were either ablated the frontal motor cortex in the dominant hemisphere or sham-operated and treated with GH or vehicle at 35 days post-injury (dpi) for five days. At 36 dpi, all rats received daily injections of bromodeoxyuridine (BrdU) for 4 days. We assessed motor function through the paw-reaching-for-food task. GH treatment and rehabilitation at 35 dpi significantly improved the motor deficit caused by the injury and promoted an increase of cell proliferation in the PC ipsilateral to the injury, which could be involved in the improvement observed. Cortical ablation promoted greater number of BrdU+ cells in the piriform cortex that was maintained long-term, which could be involved in the compensatory mechanisms of the brain after injury.

Aiming for the problems emerging in the PWM drive system with long cables, accurate modeling of power cables is the premise to predict and analyze the relevant phenomenon, and a proper filter design is the key solution to these problems. This paper proposes high-frequency cable models to represent the frequency-dependent characteristics, especially for the high-frequency resistance of the cable that is an easily overlooked factor but determines the damping of overvoltage. The proposed models can be used for accurately representing the cable parameters in a wide frequency range, and correctly simulating the differential mode (DM) overvoltage and common mode (CM) current, including the peak value, oscillation frequency and damping of the transient waveform. In addition, improved filter networks are proposed to suppress the DM voltage and CM current, with the merit of low losses, small volume and an excellent ability of suppressing overvoltage. The proposed cable models and the filter design have been validated in a 750W PWM drive system with 200m power cables.

Permanent magnet synchronous motor (PMSM) AC servo system shows the characteristics of uncertainty, time-varying and non-linearity, which makes it difficult for traditional PID control to achieve ideal control effect. Fuzzy control has strong adaptability to the problems of parameter variation, non-linearity and model inaccuracy of the controlled object. Vector control strategy is used to study its control principle and the realization of SVPWM. Because the motor object has certain fractional order characteristics, the fuzzy parameter self-tuning PIλ control is chosen as the position regulator of servo motor. It combines the accuracy of fractional order PI control and the adaptability of fuzzy control. A simulation model of PMSM three-closed-loop system is built in MATLAB/Simulink environment. The results show that the control method is effective and can satisfy the trajectory tracking of servo control.

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by a long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. A short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.

Visual exploration of complex data helps interpretation, especially in the case of omics data analysis in life sciences and clinical research. Generating and analysing omics data requires bioinformatic skills, while they are interpreted by domain experts, for whom parsing large and complex data structures may be challenging. However, outcomes of visual analytics are often difficult to quantify, which emphasises precision and reproducibility, especially for research on human diseases. Here, we propose a workflow combining a reproducible computational environment with a dedicated visualisation functionality for systems biology diagrams. By linking the Galaxy with the MINERVA Platform, we visualise and explore COVID-19 transcriptomic data to demonstrate the utility of our workflow. Visualised data recapitulate findings of the original publication and offer new insights about the COVID-19 pathology. Our results offer a blueprint for quick prototyping of computational workflows that facilitate communication and exploration of complex data in biomedical research.

In this review, we outline our current understanding of the mechanisms involved in the absorption, storage, and transport of dietary vitamin A to the eye, the trafficking of rod opsin protein to the photoreceptor outer segments, which encompasses the logistical backbone required for visual function. Two key mechanisms of this process are emphasized in this manuscript: Ocular and systemic vitamin A membrane transporters, and rhodopsin transporters. Understanding the complementary mechanisms responsible for the generation and proper transport of the retinylidene opsin to the photoreceptor outer segment will eventually shed light on the importance of genes encoded by these proteins, and their relationship on normal visual function and in the pathophysiology of retinal degenerative diseases.

The lack of tactile experience is a significant flaw in online product evaluation and purchase, but visual information can be utilized to compensate for tactile deficits. In this study, we constructed a mechanistic model of the role of tactile perceived diagnosticity on consumer purchase intention based on visual compensation based on mental imagery theory, innovativeness theory, and personal goals framework. We collected data from consumers (n=406) in the Chinese market and tested the research hypotheses using structural equation modeling. The findings suggest that visually compensated tactile perceptual diagnosticity promotes mental imagery and sensory similarity, which in turn affects purchase intention. Theoretically, this study enriches current explanations of online haptics by explaining the mechanisms by which haptic demonstration videos influence consumers' haptic evaluations and behavioral responses, as well as the moderating role of personal goals therein; practically offers advice for retailers seeking to build or expand their tactile marketing strategies.

This study investigated clinical nurses’ knowledge and visual differentiation ability of the pressure injury classification system (PICS) and incontinence-associated dermatitis (IAD), additionally analyzing possible influencing factors. A convenience sample of 248 nurses took the PICS and IAD knowledge test (KT) and completed the visual differentiation ability test (VDAT), consisting of 21 photographs with clinical information. The overall mean score for correct answers was 12.65±2.90 points in PICS & IAD KT and 11.43±4.57 points in VDAT. Incorrect responses were most common for statements related to stage Ⅱ, Ⅲ, IAD for PICS & IAD KT, and deep tissue pressure injury (DTPI), unstageable, and stage Ⅲ for VDAT. Significant correlations were found between PICS & IAD KT and VDAT (r=.252, p<.001). Factors affecting scores for VDAT were the scores of PICS & IAD KT, debridement experience in nursing patients with PI, and the management frequency of PI and IAD. Results indicate that nurses have an overall understanding of PICS and IAD, but low visual differentiation ability regarding stage Ⅲ, DTPI, and unstageable PI. Continuing education is needed to further improve knowledge and visual differentiation ability for PICS and IAD.

Background: Machine learning can analyze vast amounts of data and make predictions for events in the future. Our group created machine learning models for vital sign predictions. To transport the information of these predictions without numbers and numerical values and make them easily usable for human caregivers, we aimed to integrate them into the Philips Visual-Patient-avatar, an avatar-based visualization of patient monitoring. Methods: We conducted a computer-based simulation study with 70 participants in three European university hospitals. We validated the vital sign prediction visualizations by testing their identification by anesthesiologists and intensivists. Each prediction visualization consisted of a condition (e.g., blood pressure low) and an urgency (a visual indication of the timespan in which the condition is expected to occur). To obtain qualitative user feedback, we also conducted standardised interviews and derived statements that participants later rated in an online survey. Results: The mixed logistic regression model showed 77.9% (95%CI 73.2-82.0%) correct identification of prediction visualizations (i.e. condition and urgency both correctly identified) and 93.8% (95%CI 93.7-93.8%) for conditions only (i.e. without considering urgencies). Forty-nine of 70 participants completed the online survey. The online survey participants agreed that the prediction visualizations were fun to use (32/49, 65.3%), and that they could imagine working with them in the future (30/49, 61.2%). They also agreed that identifying the urgencies was difficult (32/49, 65.3%). Conclusions: This study found that care providers correctly identified >90% of the conditions (i.e. without considering urgencies). The accuracy of identification decreased when considering urgencies in addition to conditions. Therefore, in future development of the technology, we will focus on either only displaying conditions (without urgencies) or improve the visualizations of urgency to enhance usability for human users.

To enhance system reliability and mitigate vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute position and reducing data gaps. To address shortcomings of traditional Kalman Filter (KF) such as sensor error, imperfect nonlinear system model, and KF estimation error, a GRU-aided ESKF architecture is proposed to enhance positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to priories and identify the potential faults in the urban environment facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association error and navigation environment error during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting higher efficiency in complex environments.

This study aimed to compare the efficacies and safety of enhanced and standard monofocal intraocular lenses (IOLs) in eyes with early glaucoma. Patients with concurrent cataracts and open-angle glaucoma (OAG) were enrolled. They underwent cataract surgery with IOL implantation. Manifest refraction; monocular uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), uncorrected intermediate visual acuity (UIVA), and uncorrected near visual acuity (UNVA); visual field (VF); contrast sensitivity (CS); defocus curves and questionnaires were assessed three months postoperatively. Thirty-four and 38 patients had enhanced and standard monofocal IOLs, respectively. The enhanced monofocal IOL provided better UIVA than the standard monofocal IOL (p = 0.003) but similar UDVA, CDVA and UNVA. The enhanced monofocal IOL had more consistent defocus curves than the standard monofocal IOL, especially at -1 (p = 0.042) and -1.5 (p = 0.026) diopters. The enhanced monofocal IOL provided better satisfaction (p = 0.019) and lower spectacle dependence (p = 0.004) than the standard monofocal IOL for intermediate vision, with similar VF and CS. In conclusion, the enhanced monofocal IOL is recommended for patients with OAG because they provide better intermediate vision, higher satisfaction, and lower dependence on spectacles than standard monofocal IOL, without worsening other visual outcomes.

To investigate the intricate dynamics of brain activity when interacting with a dynamic environment, it is imperative to continuously generate and update expectations regarding forthcoming events and their corresponding sensory and motor responses. This study aims to explore the interconnectivity in time perception across predictable and unpredictable conditions. The necessary data for this study were acquired from EEG signals, sourced from an existing database that involved an experiment conducted on a group of healthy participants. The individuals were subjected to two distinct conditions, predictable and unpredictable, encompassing various time delays. The functional connectivity between brain regions was estimated using a method known as the phase lag index. This method was employed to discern disparities in time perception between the two conditions. A comprehensive comparison of the two conditions across different delays demonstrated noteworthy variations, particularly in the gamma, beta, and theta frequency bands. The differences between delays were more pronounced in the predictable condition. Subsequently, an in-depth analysis was conducted to scrutinize the dissimilarities between the conditions within each delay. Notably, significant differences were observed across all delays. In the unpredictable condition, an increase in connectivity was detected within the alpha band during the 400-ms delay, specifically between occipital and temporal regions. Moreover, the mean connectivity in the unpredictable condition surpassed that of the predictable condition. In the delta band, distinct connectivity patterns were observed across different delays, involving connections between central and frontal regions. Specifically, a heightened connectivity between central and prefrontal regions was noted during the 83-ms delay. Notably, the right hemisphere of the prefrontal cortex played a vital role in time perception. Furthermore, a decline in connectivity across the delta, theta, and beta bands was observed in both conditions during the longest delay (800 ms) when compared to other delays

The rural road landscape is crucial in forming rural areas' landscape character (LC). As a platform for portraying the rural landscape, the rural roads demonstrate the area's unique natural and cultural characteristics to the visitors. However, with the continuous development of rural areas, the rural LC has been severely impacted, thus impacting visitors' visual experience. In order to preserve and protect the rural landscape, this study aims to assess the visual quality of rural road landscapes based on public preference and heatmap analysis. The results indicated that most of the participants had a higher level of preference for rural landscapes with open horizontal views represented by agricultural areas such as paddy fields. It was also found that different paddy field characters based on their planting stages can also positively affect the visual quality of rural road landscapes. The study also revealed that rural LCs with roadside settlements, commercial structures, mixed agricultural crops, and vegetation received low preference ratings. These characters negatively impact the visual quality of the rural road landscape. These findings provide significant insight for planners and decision-makers regarding protecting and preserving the essential rural road landscapes for the rural tourism experience.

In recent years, with the rapid development of computer vision technology and the popularization of intelligent hardware, as well as people’s increasing demand for intelligent products for human-computer interaction, visual grounding technology can help machines and humans identify and locate objects, thereby promoting human-computer interaction and intelligent manufacturing. At the same time, human-computer interaction is constantly evolving and improving, becoming increasingly intelligent, humane, and efficient. This paper proposes a new VG model and designs a language verification module that uses language information as the main information to increase the model’s interactivity. Additionally, we propose the combination of visual grounding and human-computer interaction, aiming to explore the research status and development trends of visual grounding and human-computer interaction technology, as well as their application in practical scenarios and optimization directions, to provide references and guidance for relevant researchers and promote the development and application of visual grounding and human-computer interaction technology.

The Covid-19 pandemic caused a shift in teaching practice towards blended learning for many Higher Education institutions. This led to the rapid adoption of certain digital technologies within existing teaching structures as a means to meet student access needs and facilitate learning. Integration of these technologies caused numerous challenges for practitioners and often provided mixed results. This paper is an attempt to summarise and extend pre-Covid pedagogical research to leverage digital immersive technologies for blended teaching in the post-pandemic era. Focus is given towards the evolution of Virtual Learning Environments through elements of immersive audio-visual technologies, which are shown to be effective when coupled in a blended approach. It is both a review of these methodologies and a case study of the I-Ulysses: Virtual Learning Environment as a point of comparison for evaluating the review.

Over the years, there has been a significant rise in the world's scientific knowledge. However, most of it lacks structure and is often termed as Dark Data. Both humans and expert systems have continually faced difficulty in analyzing and comprehending such overwhelming amounts of information which is crucial in solving several real-world problems. Information and data visualization techniques proffer a promising solution to explore such data by allowing quick comprehension of information, the discovery of emerging trends, identification of relationships and patterns, etc. In this tutorial, we utilize the rich corpus of PubMed comprising of more than 30 million citations from biomedical literature to visually explore and understand the underlying key-insights using various information visualization techniques. With this study, we aim to diminish the limitation of human cognition and perception in handling and examining such large volumes of data by speeding up the process of decision making and pattern recognition and enabling decision-makers to fully understand data insights and make informed decisions.

The growing awareness of the biological effects of artificial light on humans has stimulated ample research. It is now widely accepted that asynchrony between artificialand natural light-dark cycles can elicit severe detrimental health effects. New research has been devoted to lighting solutions that dynamically change their color to mimic spectral changes of daylight and to account for human needs. However, in some situations, the visual properties of light must be preserved: For example professional TV video editors and shift workers who must work under standardized lighting conditions to do color correction in post-production. We have investigated the possibility to tune circadian effects using white lights that are spectrally different but nonetheless have similar color coordinates thus appear as a similar white tone. Our simulation results indicate that it is possible to modulate circadian light effects by combining LEDs for neutral white (4000 K), a widely used white tone for indoor lighting in europe. The results also show that the solutions combining single-color LEDs do however not meet the quality criteria from the visual point of view because their color rendering ability decays to unacceptable low levels. Combining narrowband LEDs with a broadband white LED improves the color rendering quality and we show how far circadian light effects can be tuned according to common theoretical models. The aim is to reflect daylight situations with artificial lighting, thus having a high melatonin suppression in the morning and low melatonin suppression in the evening. As a consequence our aim is to show what maximum and minimum circadian effect is possible with the same set of LEDs.

To efficiently use its finite resources, the visual system selects for further processing only a subset of the rich sensory information. Visual masking and spatial attention control the information transfer from visual sensory-memory to visual short-term memory. There is still a debate whether these two processes operate independently or interact, with empirical evidence supporting both arguments. However, recent studies pointed out that earlier studies showing significant interactions between common-onset masking and attention suffered from ceiling and/or floor effects. Our review of previous studies reporting metacontrast-attention interactions revealed similar artifacts. Therefore, we investigated metacontrast-attention interactions by using an experimental paradigm in which ceiling/floor effects were avoided. We also examined whether metacontrast masking is differently influenced by endogenous and exogenous attention. We analyzed mean absolute-magnitude of response-errors and their statistical distribution. Our results support the hypothesis that metacontrast and endogenous/exogenous attention are largely independent with negligible likelihood for interactions. Moreover, statistical modeling of the distribution of response-errors suggests weak interactions modulating the probability of “guessing” behavior for some observers in both types of attention. Nevertheless, our data suggest that any joint effect of attention and metacontrast can be adequately explained by their independent and additive contributions.

Human visual system (HVM) can quickly localize the most salient object in scenes, which has been widely applied on natural image segmentation [15]-[19]. In ultrasound (US) breast images, compared with background areas, tumor is more salient because of its higher contrast. In this paper, we develop a novel automatic localization method based on HVM for automatic segmentation of ultrasound (US) breast tumors. First, the input image is smoothed by convolution with a linearly separable Gaussian filter and then subsampled into a 9-layer Gaussian pyramid. Then intensity, blackness ratio, and superpixel contrast features are combined to compute saliency map, in which Winner Take All algorithm is used to localize the most salient region, presenting with a circle on the localized target. Finally the circle is taken as the initial contour of CV level set to finish the extraction of breast tumor. The localization method has been tested on 400 US beast images, among which 378 images have higher saliency than background areas and succeed in localization, with high accuracy 92.00%. The HVM localization method can be used to localize the tumors, combined with this method, CV level set can achieve the fully automatic segmentation of US breast tumors. By combing intensity, blackness ratio and superpixel contrast features, the proposed localization method can successfully avoid the interference caused by background areas with low echo and high intensity. Moreover, multi-object localization of US breast images can be considered in future employment.

Background: Cell proliferation and the cell cycle are fundamental to tumor development, with mitosis playing a crucial role in cell division. Understanding the complexities of the cell cycle and mitosis is critical for cancer research, especially in the context of developing therapeutic strategies. Objective: This review highlights mitotic poisons as potential anti-cancer pharmacotherapeutics, particularly their structural and functional group diversity presenting opportunity for semi-synthesis of new derivatives with multi-target mechanisms relevant for cancer pharmacotherapeutics. Also, the review explores naturally occurring mitotic poisons and inhibitors of microtubule-dependent molecular motors as promising cancer pharmacotherapeutics. Methods: A comprehensive review of literature from 2004 to 2023 was conducted, covering studies on cell proliferation, mechanism of cell cycle regulation, mitosis, cytokinesis, and anti-cancer drug development. The published literature was collected primarily from PubMed, Scopus, Medline, Web of Science Core Collections, Google Scholar and other relevant scientific databases using keywords including but not limited to mitotic poisons and cancer, sources of mitotic poisons, mechanism of mitotic poisons, targets of mitotic poisons and structural diversity of mitotic poisons. The efficacy and limitations of various mitotic poisons and their impact on different cancer types were examined. Recent advancements in targeted therapeutic interventions for various cancers were also explored. Results: The effectiveness and limitations of mitotic poisons, including Taxanes, Epothilones, Colchicine, Vinca alkaloids, Berberine, Laulimalide, and Pseudolaric acid B, were highlighted. Drugs targeting mitotic kinases such as CDKs, Aurora kinases, PLK1, and WEE1 inhibitors were reported. Inhibiting kinesin motor proteins such as Eg5 and KIF15 to interfere with spindle formation and cell division were discussed, along with specific genomic alterations in KIF genes and the discovery of KIFC1 as potential for future research and drug development. Conclusion: Targeting microtubules and mitotic kinases, along with inhibiting kinesin motor proteins, show promise for cancer pharmacotherapeutics. The combination of targeted therapies, patient stratification based on biomarkers (such as low alpha-1 acid glycoprotein and high p63 expression), and exploration of alternative natural sources of mitotic poisons and analogues can significantly advance cancer pharmacotherapy to improve patient outcomes.

This paper presents an innovative multi-agent, data-driven reinforcement learning (RL) approach to develop and utilize the thermal equivalent network model that represents the motor's thermal dynamics. A multi-agent reinforcement learning is designed and trained to adjust the model parameters using data from several motor driving cycles. To ensure the incoming driving cycle matches the historical data before employing the pre-trained RL agents, offline statistical analysis and clustering techniques are developed and used. Numerical simulations highlighted the RL agent's ability to develop strategies that effectively address the variability of driving cycles. The proposed RL framework showed its capability to accurately reflect the motor's thermal behavior under various driving conditions.

Background: Previous investigations have found a correlation between abnormal curvatures and a variety of patient complaints such as cervical pain and disability. However, no study has shown that loss of the cervical curve is a direct result of exposure to a motor vehicle collision (MVC). This investigation presents a retrospective consecutive case series of patients with both a pre-injury cervical lateral radiograph (CLR) and a post-injury CLR after exposure to a MVC. Computer analysis of digitized vertebral body corners on CLRs was performed to investigate the possible alterations in the geometric alignment of the sagittal cervical curve. Methods: Three spine clinic records were reviewed, over a 2-year period, looking for patients where both an initial lateral cervical x-ray and an examination were performed prior to the patient being exposed to a MVC; afterwards an additional exam and radiographic analysis were required. A total of 41 patients met the inclusion criteria. Examination records of pain intensity on numerical pain rating scores (NPRS) and neck disability index (NDI), if available, were analyzed. The CLRs were digitized and modeled in the sagittal plane using curve fitting and the least squares error approach. Radiographic variables included total cervical curve (ARA C2-C7), Chamberlain’s line to horizontal (skull flexion), horizontal translation of C2 relative to C7, segmental translations (retrolisthesis and anterolisthesis), and circular modelling radii. Results: There were 15 males and 26 females with an age range of 8-65 years. Most participants were drivers (28) involved in rear end impacts (30). The pre-injury NPRS was 2.7 while the post injury was 5.0; p &lt; 0.001. The NDI was available on 24 /41 (58.5%) patients and was found to increase following exposure to the MVC: 15.7% to 32.8%, p &lt; 0.001. An altered cervical curvature was identified following exposure to MVC, characterized by an increase in radius of curvature and an approximate 8° reduction of lordosis from C2-C7; p &lt; 0.001. The mid cervical spine (C3-C5) showed the greatest curve reduction with localized mild kyphosis at these levels on average. Several participants developed segmental translations that approached the instability value of translation &gt; 3.5mm. Conclusions: Abnormalities of cervical lordosis occurred following exposure to a MVC. The average loss of cervical curvature was 8° from C2-C7, with mild flexion of the mid cervical segments. Snap through type or dynamic buckling during the MVC may explain our findings. The primary contribution of this study to the motor vehicle collision literature is that altered cervical lordosis may result from MVC exposures and the consequences of an altered cervical alignment, in turn, may lead to future pain, disability and decreased health-related quality of life.

This paper aims at achieving optimal rotor speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets, with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the 5th order nonlinear model of the induction motor, rotor speed is estimated with the optimized EKF algorithm and the used algorithm is tested experimentally.

The characteristics of response to stressful situations depend on a complex of factors such as personality traits, emotional regulation, health state, features of the nervous system, motor functions and proprioception. In cases of disruption of the brain's dopamine system due to disease or chemotherapy, hormone discharge due to stress, changes in emotions and proprioception may occur. The aim of our study was to find the relationship of stress resilence in verbal test and individual differences performed in proprioceptive test in onco-patients, sportsmen and group of control; as well as to observe the differences between these groups in the verbal stress resistance and individual differences proprioceptive tests. The results showed that onco-patients were less resistant as per verbal stress resistance outcomes for a major part of variables compared to other groups. The sportsmen were superior in verbal Risk and Control dimensions compared to other groups; but less in Positive Affectiveness. The proprioceptive test perfomance revealed the only statistically significant difference in performance in the dimension related to Mood, showing more Pessimism in the sportsmen group, but more Optimism in the oncopatients at temperamental (or intrisinc) level. The obtained results can be used by psychologists, therapists, coach and healthcare when needed to attend the specific needs of public, being pateints or sportsmen, etc.

Neuromonitoring of the efferent nerve impulses transmission in the spinal cord tracts during surgical scoliosis treatment makes it possible to assess whether the functional status is deteriorating, affecting the procedure's safety. Is there any relationship between pre- and intraoperative motor evoked potentials recordings and does idiopathic scoliosis (IS) surgical correction improve directly the spinal efferent transmission? This study aimed to compare the results of surface recorded electromyography (EMG), electroneurography (ENG, M and F-waves), and especially motor evoked potentials (MEP) from tibialis anterior (TA) muscle bilaterally in 353 girls with the right idiopathic scoliosis (types 1A-4C according to Lenke classification). It has not yet been documented whether the results of MEPs recordings induced by transcranial single magnetic stimulus (TMS, pre – and postoperatively) and trains of electrical stimuli (TES; intraoperatively in T0-before surgery, T1 – after pedicle screws implantation, T2 – after scoliosis curvature distraction and derotation following two rods implantation) can be compared for diagnostic verification of improvement of the spinal neural transmission. The study also attempted to determine whether the constant level of optimal anesthesia during the certain surgical steps of scoliosis treatment affects the parameters of MEPs recorded during neuromonitoring procedures. No neurological deficits have been observed postoperatively. Values of amplitudes but not latencies in MEPs recordings evoked with TMS in IS patients compared before and after surgery indicated a slight improvement in the efferent transmission of neural impulses within the fibers of the spinal tracts postoperatively. Results of all neurophysiological studies in IS patients were significantly asymmetrical and recorded worse on the concave side, suggesting greater neurological motor deficits at p=0.04. This asymmetry had been significantly reduced following IS surgery. The surgeries in IS patients brought significant improvement (p=0.04) in parameters of amplitudes of sEMG recordings, however reflecting still the consequences of the neurogenic injury of TA muscle motor units. ENG studies results indicated the symptoms of the axonal type injury in peroneal motor fibers improved only on the concave side at p=0.04 in parallel with the significant improvement of F-waves parameters, which suggests that surgeries might result in the lumbar ventral roots decompression. There were not detected significant differences in amplitudes or latencies of MEPs induced with TMS or TES comparing the parameters recorded preoperatively (one day before surgery) and intraoperatively in T0. The amplitudes of TES evoked MEPs increased gradually at p=0.04 in the subsequent periods (T1 and T2) of observation. The significant reduction of MEPs latency at p=0.05 was observed only at the end of the IS surgery. Studies on the possible connections between the level of anesthesia fluctuations and the required TMS stimulus strength, as well as the MEPs amplitude changes measured in T0-T2 revealed lack of relationships. It is not likely that they could be the factors influencing the efferent transmission in spinal pathways beside the surgical procedures. Considering that MEPs amplitude parameter reflects the number of axons excited from the motor cortex and transmitting the efferent impulses via spinal descending tracts in the white matter, pre- (TMS evoked) and intraoperative (TES evoked) recordings are reliable for evaluating the patient’s neurological status before and during surgical scoliosis correction procedures. The results of this study indicate an agreement between preoperative and early-intraoperative evaluations with these both diagnostic methods. An increase of MEPs amplitude parameters recorded on both sides after scoliosis surgery proves immediate improvement of the total efferent spinal cord transmission. Considering comparative pre- and postoperative sEMG and ENG recordings it can be concluded that surgeries might directly result in the additional lumbar ventral roots decompression. Our results of the tests on the possible variability of the anesthesia level on the parameters of intraoperative recorded MEPs show no clear relationships. We can conclude that MEPs parameters changes are determined by the surgery procedures during neuromonitoring, not the anesthesia conditions if they are kept stable, which influences a decrease in the number of false-positive neuromonitoring warnings. Further studies on a larger population of patients with long-lasting observation postoperatively are required to confirm the presented conclusions on the direct influences of scoliosis surgery on improvement of the motor function in patients with IS.

In response to the structural optimization problem of PCB stator coreless disc motors, the orthogonal response surface method was used to optimize the motor structure, determine the basic parameters of the motor preliminarily, and conduct orthogonal experiments on the motor parameters based on the optimization design objectives, The optimization factors were the quantity of magnetic pole of the motor rotor, the ratio of main/auxiliary pole size, the thicknessof the permanent magnet, and the air gap length.The motor torque，the amplitude of magnetic density of the air gap and waveform distortion rateare used as optimization objectives. The motor parameters that make the motor torque reach the maximum, the air gap magnetic density maximum, and the waveform distortion rate minimum. Due to the use of PCB plates instead of motor stator cores in PCB coreless disc motors,The service life of the PCB board during motor operation will be reduced with the temperature rise generated by the stator winding To solve this problem, the response surface analysis of the motor is carried out to reduce the temperature rise of the stator windings during the operation of the motor. PCB board and stator winding are the main factors affecting the temperature rise of the motor.Taking the thickness of the PCB board, the hole diameter on the board, and the uneven width of the stator winding as optimization factors, the motor parameters with the lowest temperature rise of the motor winding are obtained. Simulation analysis is conducted using Ansys/Maxwell software, and the results prove the feasibility of optimization.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Treatment of neurological disease is hampered by the lack of validated specific and sensitive biomarkers, resulting in delayed diagnosis and nonspecific disease modifying therapies. For example, in ALS the lack of a sensitive and specific biomarker impedes the ability to administer a treatment prior to or at the onset of motor neuron dysfunction. Although viral or other infectious etiologies have been proposed as a contributing factor to neurodegeneration, it can be argued that these are casual and not causal associations. In the case of ALS, evidence for direct causality would require in vivo validation of specific nucleotide sequences of microbial origin which are known to be neuroinvasive with tropism for motor neurons, such as polio and non-polio. Several viral enteropathogens recapitulate the pathological events in sporadic ALS, including the ability to cleave TDP-43 resulting in accumulation of misfolded proteins in the cytoplasm. This review provides supporting evidence that motor neuron disease is causally related to specific enteroviruses and argues that prospective validation is imperative for effective prevention and treatment.

Motor imagery(MI)-based Brain-Computer Interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their low accuracy performance. To date, although a lot of research has been made in benchmarking and highlighting the most valuable classification algorithms in BCI configurations, most of them are using offline data and not from real BCI performance during the closed-loop (or online) sessions. Since rehabilitation training relies on the availability of an accurate feedback system, we surveyed articles of current and past EEG-based BCI frameworks who report the online classification of the movement of two upper limbs in both healthy volunteers and stroke patients. We found that the recently developed Deep Learning methods do not outperform the traditional machine learning algorithms. In addition, patients and healthy subjects exhibit similar classification accuracy in current BCI configurations. Lastly, in terms of neurofeedback modality, Functional Electrical Stimulation (FES) yielded the best performance compared to non-FES systems.

Physical activity (PA) is a factor that may have an influence on the symptoms of Parkinson’s disease (PD). The aim of this study was to identify the potential determinants of spontaneous PA in the PD patient group. 134 PD patients aged 65.2±9.2 years, Hoehn-Yahr scale ≤ 4, Mini Mental State Examinaton (MMSE) ≥ 24 were examined. For the study purposes, the authors analyzed: age, sex, education, history of PD, dopaminergic treatment, the severity of PD symptoms using Unified Parkinson’s Disease Rating Scale (UPDRS) and Hoehn-Yahr scale. Additionally all participants were evaluated through a set of scales for specific neuropsychiatric symptoms including: depression, anxiety, apathy, fatigue and sleep disorders. An analysis of linear regression was used with backward elimination. In the total explanatory model, 12% of the variability in activity (R2=0.125; F(16.133)=2.185; p<0.01), the significant predictor was starting therapy with the dopamine agonist (DA) (β= 0.420; t= 4.068; p=0.000), which was associated with a longer duration of moderate PA. In the total explanatory model, for more than 13% of the variance in time spent sitting (R2=0.135; F(16.130)=2.267; p<0.01), the significant predictors were secondary education and the results of the UPDRS. The patients with secondary and vocational education, those starting treatment with DA and those with a less severe degree of Parkinson’s symptoms (UPDRS) spent less time sitting in a day. It is possible to identify determinants of spontaneous PA. It may elucidate consequences in terms of influence on modifiable conditions of PA and the proper approach to patients with unmodifiable PA factors.

How does the human neurophysiological system self-organize to achieve optimal phase relationships among joints and limbs, such as in the composite rhythms of butterfly and front crawl swimming, drumming, or dancing? We conducted a systematic review of literature relating to CNS control of phase among joint/limbs in continuous rhythmic activities. SCOPUS and Web of Science were searched using keywords ‘Phase AND Rhythm AND Coordination’. This yielded 998 matches from which 23 papers were extracted for inclusion based on screening criteria. The empirical evidence arising from in-vivo, fictive, in-vitro, and modelling of neural control in humans, other species, and robots indicates that the control of movement is facilitated and simplified by innervating muscle synergies by way of spinal central pattern generators (CPGs). These typically behave like oscillators enabling stable repetition across cycles of movements. This approach provides a foundation to guide the design of empirical research in human swimming and other limb independent activities. For example, future research could be conducted to explore whether the two-layer CPG model proposed by Saltiel et al [1] to explain locomotion in cats might also explain the complex relationships among the cyclical motions in human swimming.

This study applies a cluster analysis to identify typical physical activity (PA) and sedentary behaviour (SB) patterns in people with chronic obstructive pulmonary disease (COPD) before starting pulmonary rehabilitation (PR). We implemented an observational design which assessed baseline data on objectively measured PA and SB from the STAR (Stay Active after Rehabilitation) study. 355 persons wore a accelerometer (Actigraph wGT3X) for seven days before the start of their PR. Sociodemographic and disease-related parameters were assessed at the start of PR. We applied cluster analysis and compared groups applying univariate variance analyses. Data was available for 326 persons (31.6% women; age ø = 58 yr.). Cluster analysis revealed four movement clusters with distinct PA and SB patterns: sedentary non-movers (28.5%), sedentary occasional movers (41.7%), sedentary movers (19.6%), and sedentary exercisers (10.1%). The four clusters showed very different levels of moderate PA before rehabilitation (Ø daily minutes: 9; 28; 38; 70). Notably, all four clusters displayed considerably long average sitting time per day (Ø daily minutes: 644; 561; 490; 446). The groups differed significantly in disease-related parameters of GOLD severity, FEV1, CAT, and 6-Min-Walk-Test. In addition to PA promotion, PR programs should consider the reduction of sitting time as a valuable goal.

Lead (Pb) is a toxic environmental heavy metal that induces serious clinical defect on all organs with the nervous system being the primary target. Curcumin is the main active constituent of turmeric rhizome (Curcuma longa) with strong antioxidant and anti-inflammatory properties. This study is aimed at evaluating the therapeutic potentials of curcumin on Pb-induced neurotoxicity. Thirty six male Sprague Dawley rats were randomly assigned into five (5) groups with 12 rats in the control (normal saline) and 6 rats for the lead treated group (LTG) (50 mg/kg lead acetate for 4 weeks), recovery group (RC) (50 mg/kg lead acetate for 4 weeks), treatment group 1 (Cur100) (50 mg/kg lead acetate for 4 weeks, followed by 100 mg/kg curcumin for 4 weeks) and treatment group 2 (Cur200) (50 mg/kg lead acetate for 4 weeks, followed by 200 mg/kg curcumin for 4 weeks) groups each. All experimental groups received the oral treatments through orogastric-tube on alternate days. Motor function was assessed using horizontal bar method while Pb concentration in the cerebellum of the rats were evaluated using ICP-MS techniques. Pb-administered rats showed significant decrease in motor scores, SOD activity with increase MDA levels and Pb concentration in their cerebellum with marked alterations in the histological architecture of the cerebellar cortex layers. However, treatment with curcumin improved the motor score, reduced Pb concentration in the cerebellum and ameliorates the markers of oxidative stress as well as restored the histological architecture of the cerebellum. The results this in study suggested that curcumin attenuates Pb-induced neurotoxicity via inhibition of oxidative stress and chelating activity.

Lead (Pb) is a toxic environmental heavy metal that induces serious clinical defect on all organs with the nervous system being the primary target. Curcumin is the main active constituent of turmeric rhizome (Curcuma longa) with strong antioxidant and anti-inflammatory properties. This study is aimed at evaluating the therapeutic potentials of curcumin on Pb-induced neurotoxicity. Thirty six male Sprague Dawley rats were randomly assigned into five (5) groups with 12 rats in the control (normal saline) and 6 rats for the lead treated group (LTG) (50 mg/kg lead acetate for 4 weeks), recovery group (RC) (50 mg/kg lead acetate for 4 weeks), treatment group 1 (Cur100) (50 mg/kg lead acetate for 4 weeks, followed by 100 mg/kg curcumin for 4 weeks) and treatment group 2 (Cur200) (50 mg/kg lead acetate for 4 weeks, followed by 200 mg/kg curcumin for 4 weeks) groups each. All experimental groups received the oral treatments through orogastric-tube on alternate days. Motor function was assessed using horizontal bar method while Pb concentration in the cerebellum of the rats were evaluated using ICP-MS techniques. Pb-administered rats showed significant decrease in motor scores, SOD activity with increase MDA levels and Pb concentration in their cerebellum with marked alterations in the histological architecture of the cerebellar cortex layers. However, treatment with curcumin improved the motor score, reduced Pb concentration in the cerebellum and ameliorates the markers of oxidative stress as well as restored the histological architecture of the cerebellum. The results this in study suggested that curcumin attenuates Pb-induced neurotoxicity via inhibition of oxidative stress and chelating activity.

Objective: To evaluate the effects of left intraparietal sulcus (IPS) inhibition by repetitive transcranial magnetic stimulation (rTMS) on grip force modulation (GFM) for both hands during a unimanual task. Methods: GFM induced by manual action-verb listening was evaluated for each hand in a unimanual task, and the motor-evoked potentials (MEP) were recorded for both left and right hemispheres prior to and following the left IPS inhibition. Left IPS inhibition was obtained by rTMS (5 min of 1.0 Hz, 60% of maximal stimulator output) of the international 10–20 system P3 point. Seven healthy right-handed subjects were evaluated. Results: One-way repeated measures ANOVA found that MEP amplitude and duration increased following IPS inhibition in the left hemisphere and did not change in the right hemisphere. Language-induced modulation did not change in the left hemisphere, while it was significantly attenuated in the right hemisphere. Since IPS inhibition increased the left primary motor cortex (M1) excitability, the maintenance of language-induced modulation intensity suggests it was also attenuated. Conclusion: Left IPS inhibition increased left M1 excitability without changing right M1 excitability, while attenuating the language-induced GFM for both the left and right hands.

Children’s motor competence (MC) has declined in the past decades, while sedentary behavior (SB) has increased. This study examined the association between MC and physical activity (PA) levels among primary schoolchildren. Demographics, body height and weight, MC (Athletic Skills Track) and PA levels (ActiGraph, GT3X+) were assessed among 595 children (291 boys, mean age = 9.2 years, SD = 1.1). MC was standardized into five categories: from very low to very high. PA levels were classified into sedentary behavior (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Mixed model analyses were conducted with PA levels as dependent variables and MC as the independent variable, while adjusting for age, gender, and BMI z-score on the individual level. Additional moderation analyses between covariates and MC and PA levels were also conducted. A negative association between MC and SB (β = -3.17) and a positive association between MC and MVPA (β =1.41) were found. The strength of both associations increased as children expressed lower or higher levels of MC. Increased age and being a girl were significant predictors of higher levels of SB and lower levels of MVPA. Non-overweight children spent significantly more minutes in MVPA compared with overweight children. No moderating effects of BMI, gender, and age were found on the association between MC and PA levels. MC is an important correlate of both SB and MVPA, particularly for children with very high or low MC. Developing and improving children’s MC may contribute to spending less time in SB and more time in MVPA, particularly for high-risk groups, i.e. children with low MC. Moreover, addressing MC development and PA promotion simultaneously might create positive feedback loops for both children’s MC and PA levels.

Background/Objectives: We defined the value of a machine learning algorithm to distinguish between the EEG response to no light or any light stimulation, and between red or green light stimulation in awake volunteers with closed eyelids. This new method of EEG analysis is visionary in the understanding of visual signal processing and will facilitate anaesthetic depth research. Methods: X-gradient boosting models were used to classify the cortical response to VEP stimulation (no light vs. any light stimulation; red vs. green light stimulation). For each of the two classifications, three scenarios were tested: training and prediction in all participants (all), training and prediction in one participant (individual), training across all but one participant with prediction in the participant left out (one out). Results: Ninety-four Caucasian adults were included. The machine learning algorithm had a very high predictive value and accuracy in differentiating between no light and any light stimulation (AUCROCall: 0.96; accuracyall: 0.94; AUCROCindividual: 0.96±0.05, accuracyindividual: 0.94±0.05; AUCROConeout: 0.98±0.04; accuracyo-neout:0.96±0.04). The machine learning algorithm was highly predictive and accurate in distinguishing between green and red colour stimulation (AUCROCall: 0.97; accuracyall: 0.91; AUCROCin-dividual: 0.98±0.04, accuracyindividual: 0.96±0.04; AUCROConeout: 0.96±0.05; accuracyoneout: 0.93±0.06). The predictive value and accuracy of both classification tasks was comparable between males and females. Conclusions: Machine learning algorithms could almost continuously and reliably differentiate between the cortical EEG responses to no light or any light stimulation and between green or red colour stimulation using VEPs in awake female and male volunteers with eyes closed. Our findings may open new possibilities for using VEPs in the intraoperative setting.

Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual-semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual-semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual-semantic features using self-distillation and disentanglement network (SDDN). The aim is to utilize self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual-semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual-semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual-semantic features. Finally, the consistent visual-semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN).