Execution Speed Research Articles

A Potential Measure of Premorbid Functioning: Evaluating the Construct Validity of the Author and Magazine Recognition Tests.

To establish convergent and discriminant validity for a combined measure of print exposure (i.e., Author Recognition Test and Magazine Recognition Test [ART/MRT]) and assess its potential utility for estimating premorbid cognitive functioning. Community-dwelling older adults (N = 84; 95% non-Hispanic White) completed the ART/MRT, Test of Premorbid Functioning (ToPF), Dementia Rating Scale - 2nd Edition (DRS-2), Hopkins Verbal Learning Test - Revised (HVLT-R-DR), and select subtests from the Delis-Kaplan Executive Functioning System (D-KEFS) as measures of executive functioning (i.e., D-KEFS-EF) and processing speed (i.e., D-KEFS-PS). Pearson correlations and linear regressions were used to examine the relationships between the ART/MRT, cognition, and demographics. Cognitive scores, with the exception of HVLT-R-DR, were positively correlated with ART/MRT score such that better cognitive performance was associated with greater print exposure (range r = 0.39-0.49). ART/MRT score was positively correlated with years of education and negatively correlated with age. ToPF and DRS-2 differentially and uniquely predicted ART/MRT score beyond the other cognitive and demographic variables and beyond each other. Findings indicate that measures of print exposure reflect crystallized knowledge but may also capture fluid abilities that may be more vulnerable to age-related decline or neurodegeneration. Assessment of print exposure may offer an alternative to word reading measures that may be inappropriate for translation into other languages and for use with individuals with certain language difficulties.

Use of transcranial magnetic stimulation (TMS) for studying cognitive control in depressed patients: A systematic review.

Major depressive disorder (MDD) is a debilitating mental disorder and the leading cause of disease burden. Major depressive disorder is associated with emotional impairment and cognitive deficit. Cognitive control, which is the ability to use perceptions, knowledge, and information about goals and motivations to shape the selection of goal-directed actions or thoughts, is a primary function of the prefrontal cortex (PFC). Psychotropic medications are one of the main treatments for MDD, but they are not effective for all patients. An alternative treatment is transcranial magnetic stimulation (TMS). Previous studies have provided mixed results on the cognitive-enhancing effects of TMS treatment in patients with MDD. Some studies have found significant improvement, while others have not. There is a lack of understanding of the specific effects of different TMS protocols and stimulation parameters on cognitive control in MDD. Thus, this review aims to synthesize the effectiveness of the TMS methods and a qualitative assessment of their potential benefits in improving cognitive functioning in patients with MDD. We reviewed 21 studies in which participants underwent a treatment of any transcranial magnetic stimulation protocol, such as repetitive TMS or theta-burst stimulation. One of the primary outcome measures was any change in the cognitive control process. Overall, the findings indicate that transcranial magnetic stimulation (TMS) may enhance cognitive function in patients with MDD. Most of the reviewed studies supported the notion of cognitive improvement following TMS treatment. Notably, improvements were predominantly observed in inhibition, attention, set shifting/flexibility, and memory domains. However, fewer significant improvements were detected in evaluations of visuospatial function and recognition, executive function, phonemic fluency, and speed of information processing. This review found evidence supporting the use of TMS as a treatment for cognitive deficits in patients with MDD. The results are promising, but further research is needed to clarify the specific TMS protocol and stimulation locations that are most effective.

Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization

This study aims to generate visually useful imagery by preventing cropping while maintaining resolution and minimizing the degradation of stability and distortion to enhance the stability of a video for Augmented Reality applications. The focus is placed on conducting research that balances maintaining execution speed with performance improvements. By processing Inertial Measurement Unit (IMU) sensor data using the Versatile Quaternion-based Filter algorithm and optical flow, our research first applies motion compensation to frames of input video. To address cropping, PCA-flow-based video stabilization is then performed. Furthermore, to mitigate distortion occurring during the full-frame video creation process, neural rendering is applied, resulting in the output of stabilized frames. The anticipated effect of using an IMU sensor is the production of full-frame videos that maintain visual quality while increasing the stability of a video. Our technique contributes to correcting video shakes and has the advantage of generating visually useful imagery at low cost. Thus, we propose a novel hybrid full-frame video stabilization algorithm that produces full-frame videos after motion compensation with an IMU sensor. Evaluating our method against three metrics, the Stability score, Distortion value, and Cropping ratio, results indicated that stabilization was more effectively achieved with robustness to flow inaccuracy when effectively using an IMU sensor. In particular, among the evaluation outcomes, within the “Turn” category, our method exhibited an 18% enhancement in the Stability score and a 3% improvement in the Distortion value compared to the average results of previously proposed full-frame video stabilization-based methods, including PCA flow, neural rendering, and DIFRINT.

LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)

Abstract. The single-column model, with its advantages of low computational cost and fast execution speed, can assist users in gaining a more intuitive understanding of the impact of parameters on the simulated results of climate models. It plays a crucial role in the study of parameterization schemes, allowing for a more direct exploration of the influence of parameters on climate model simulations. In this paper, we employed various methods to conduct sensitivity analyses on the 11 parameters of the Single Column Atmospheric Model (SCAM). We explored their impact on output variables such as precipitation, temperature, humidity, and cloud cover, among others, across five test cases. To further expedite experimentation, we utilized machine learning methods to train surrogate models for the aforementioned cases. Additionally, three-parameter joint perturbation experiments were conducted based on these surrogate models to validate the combined parameter effects on the results. Subsequently, targeting the sensitive parameter combinations identified from the aforementioned experiments, we further conducted parameter tuning for the corresponding test cases to minimize the discrepancy between the results of SCAM and observational data. Our proposed method not only enhances model performance but also expedites parameter tuning speed, demonstrating good generality at the same time.

Self-reported cognitive function mediates the relationship between employment status and cognitive functioning in persons with multiple sclerosis

BackgroundCognitive impairment (CI) is common in people with MS (PwMS). Evidence is lacking for the self-reported CI's mediation effect on employment status and objective cognitive performance. Self-reported CI was found to be unreliable and seemed to be more associated with depression rather than formal cognitive performance. We hypothesized that the link between subjective and objective assessments of cognitive functions, mood, and employment status may be more complex in PwMS than previously reported. ObjectiveThe aims of this study are the following: (Romero-Pinel et al., 2022) to determine whether employment status could affect performance in cognitive function testing and (Rao et al., 1991) whether their relationship may be mediated by self-reported CI; and (Deluca et al., 2013) to determine whether self-reported depression interacts with self-reported CI in influencing performance in various cognitive domains in PwMS. MethodologyA retrospective study was performed involving PwMS who completed the self-report Multiple Sclerosis Neuropsychological Questionnaire (MSNQ), Hospital Anxiety and Depression Scale-depression scale (HADS-D), Minimal Assessment of Cognitive Function in MS (MACFIMS) and had data regarding employment status. Included PwMS were classified as employed or unemployed. A structural equation modeling (SEM) approach was taken due to the advantage of examining multiple cognitive outcomes simultaneously while accounting for shared associations. First, a latent factor of memory and executive functioning modeled the error-free associations between both factors and a processing speed task (SDMT). Next, the model tested for the indirect effect of self-reported cognition (MSNQ) on employment status differences in each outcome (memory, speed, and executive functioning). Finally, we tested interactions between MSNQ and HADS-D on each of the outcomes. ResultsWe included 590 PwMS: 72.5% female, mean age 44.2 years (SD = 10.5), mean disease duration 8.6 years (SD 9.0). The majority (n = 455, 77.1%) had relapsing MS; 357 (60.5%) were employed. About half (n = 301, 51%) did not report CI on the MSNQ; of those, 213 (70.8%) were employed. The mean MSNQ for employed PwMS was 24.5 (SD = 10.7) and 29.8 (SD = 11.2) for unemployed PwMS. Employed PwMS had significantly better memory (β = .16, p < .05), executive functioning (β = .25, p < .05), and processing speed (β = .22, p < .05). MSNQ partially indirectly mediated the effect of employment status on memory (Δβ = .03, p < .05) and executive functioning (Δβ = .03, p < .05) and processing speed (Δβ = .04, p < .05), indicating that self-report CI partially explains the influence of employment status on these cognitive domains. The association between MSNQ with both memory and executive functioning was moderated by depression, meaning that in PwMS with high HADS-D scores, MSNQ was more strongly related to worse memory and executive functioning. The final model was an acceptable fit to the data (χ2(87) = 465.07, p < .05; CFI = .90, RMSEA = .08, 90% CI [.06, .09], SRMR = .05) explaining 41.20%, 38.50% and 33.40% of the variability in memory, executive functioning, and processing speed, respectively. ConclusionSelf-reported CI partially explains the associations between employment status and objective cognitive assessment in PwMS. Depression may moderate the relationship between self-reported cognitive assessment and objective cognitive performance. Thus, employment status and mood may guide the interpretation of self-reported CI.

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.

Optimizing strength training protocols in young females: A comparative study of velocity-based and percentage-based training programs

The purpose of this study was to compare the effects of velocity-based strength training (VBT) and percentage-based strength training (PBT) on absolute strength, explosive strength, speed, and agility, as well as markers of muscle damage after 6 weeks of exercise programs. The study included 30 young female individuals, divided into three groups of 10 participants: VBT, PBT, and control group. The main findings indicated that the VBT group and PBT group showed significant improvement in 1RM squat exercise (Δ% 27.87 and Δ% 8.98, respectively) and 1RM bench press (Δ% 14.47 and Δ% 8.65, respectively), but a greater enhancement was observed in the VBT group. In addition, VBT induced substantial changes in SJ (Δ% 14.32) and CMJ height (Δ% 7.69), while PBT had an improvement only in the SJ test (Δ% 6.72). The improvement noted in the VBT group could be attributed to its ability to tailor training intensity according to the speed of movement execution. This approach allows athletes to perform each repetition as fast as possible, thus maintaining an optimal intensity for explosive strength development. The capacity of VBT to adapt training intensity based on the speed of movement execution may be the key factor contributing to these results. Therefore, coaches and athletes should consider implementing VBT as a valuable tool to optimize strength and power development. In conclusion, VBT induced greater improvement in the 1RM squat, 1RM bench press, SJ, and CMJ compared to the group that performed the traditional strength training modality. Therefore, VBT is considered a more effective training tool regarding the development of absolute and explosive strength in young women.

Chemical radioanalysis of production of positron-emitting radioisotope Gallium-68 via (p,n) and (p,2n) reactions using compact cyclotron for tomography applications

In this study, the proton-induced reactions of 68Zn and 69Ga aimed at generating 68Ga were simulated and modeled using Talys code and neural network software. In the first step, both targets were simulated under different proton energies and at different bombardments times to generate a total of six thousand data. Then, the obtained data from the Talys, including the various cross-sections, contaminations, the main product i.e. 68Ga, and other options were completely saved in the output file. Afterwards, the inputs of the neural network were selected from the output of the Talys by analyzing and considering most of the key features. A total of four inputs, two of which are different energies related to the reaction, the other is the process sequence and the fourth input is the bombardment time, were recognized as suitable inputs and the model was trained differently depending on the type of target. The selected model was a feed-forward neural network with 5 nodes in a middle layer, which was able to estimate the output of Talys code by changing the input parameters with extremely high accuracy. Two different models including the main model for estimating the output of the main sample (product) and the sub-model for estimating process pollution or impurity were trained, and then the trained model was tested on the deduced process data. The implementation results fully demonstrated the high accuracy of the method. The neural network model is much easier to implement than the Talys code, and its execution speed is very high. In addition, it can be used appropriately as a system alternative for optimization and different structures in medical and biological engineering.

Support vector regression-based state of charge estimation for batteries: cloud vs non-cloud

Embracing the potential of cloud technology in the field of electric vehicle advancements, this paper explores the application of support vector regression (SVR) for accurate state of charge (SOC) estimation of lithium-ion batteries in various computational landscapes. This study aims to scrutinize and compare the performance of SOC estimation, with a specific focus on precision, computational efficiency, and execution speed. The investigation is conducted across diverse environments, including a traditional non-cloud setup and two cloud-based platforms-a standard cloud environment employing Amazon web services (AWS) EC2 servers and an enhanced configuration utilizing the MATLAB production server. The investigation not only emphasizes the effectiveness of cloud integration but also provides valuable insights into the strengths and weaknesses of the proposed methodology. The experimental results contribute to a nuanced understanding of the methodology’s performance, shedding light on its potential implications for advancing electric vehicle technologies. This study thus extends its significance beyond technical considerations, providing a broader perspective on its relevance to global electrification initiatives.

Optimization of the technology of hoisting operations when drilling oil and gas wells

In the process of drilling deep wells, hoisting operations are, although unproductive, a necessary expenditure of working time and occupy 25-60% of the total time of well construction. In the case of major repair of wells, this share can be even higher. It is clear that the technical and economic indicators of drilling and capital repair of wells largely depend on the speed of execution of hoisting operations. Existing technologies and time standards for their implementation are outdated and do not take into account the current state of technology development. The article deals with the issue of optimization of the rationing of time for the execution of hoisting operations during drilling and capital repair of oil and gas wells. This problem is solved using the example of the TW-125 CA A6 well overhaul installation, which are used during overhaul of wells by branches of JSC “Ukrgazvydobuvannya”. With the help of the kinematic scheme of the installation, the rotation speeds of the winch shaft at each gear of the gearbox were determined, which made it possible to determine the optimal time for performing hoisting operations, and, as a result, to reduce the time spent on drilling, depending on the type of rock, in general.

The phenomenon of anticipation in fencing. An applicability approach.

The aim of the study was to determine the structure of muscular activity and ground reaction forces during the preparatory period and the execution of a fencing lunge at the opponent's torso. The analysis focused on the correlations between three phases of a fencing technical action in the context of factors of temporal anticipation. Six female épée fencers from the Polish National Fencing Team participated in the study. The research tools included electromyography (EMG), ground reaction force (GRF) platforms, and the OptiTrack motion capture system. The fencers performed the lunge three times in response to visual cues from the coach. By integrating the testing system, the EMG signal indices of the fencers' upper and lower limbs and the vertical force values of the fencers' front and rear leg muscles were obtained simultaneously. The results of the study demonstrated the key role of five muscles: BICEPS BRACHII, LAT TRICEPS, EXTCARP RAD, BICEPS FEMORIS and MED GAS in influencing the speed of lunge execution. In addition, a significant correlation was found between the EMG signal of the gastrocnemius muscle of the rear leg and the movement time (MT) phase of the lunge execution. The anticipatory activation of the EMG signal in relation to the vertical force waveforms generated by the ground forces response platform in the 15-30 ms interval was demonstrated. Finally, the importance of the preparatory period for the effectiveness of the fencing lunge was highlighted based on the phenomenon of anticipation.

ASV station keeping under wind disturbances using neural network simulation error minimization model predictive control

AbstractStation keeping is an essential maneuver for autonomous surface vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a model predictive controller using neural network simulation error minimization (NNSEM–MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the robotics operating system and the multipurpose simulation environment Gazebo. A set of six tests was conducted by combining two varying wind speeds that are modeled as the Harris spectrum and three wind directions (, , and ). The simulation results clearly show the advantage of the NNSEM–MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD‐MPC), neural ordinary differential equation MPC (NODE‐MPC), and knowledge‐based NODE MPC. The proposed NNSEM–MPC approach performs better than the rest in five out of the six test conditions, and it is the second best in the remaining test case, reducing the mean position and heading error by at least % ( m) and % (), respectively, across all the test cases. In terms of execution speed, the proposed NNSEM–MPC is at least 36% faster than the rest of the MPC controllers. The field experiments on two different ASV platforms showed that ASVs can effectively keep the station utilizing the proposed method, with a position error as low as m and a heading error as low as within time windows of at least s. This would increase the potential applications of ASVs for launch, recovery, and replenishment in long‐term surveys in collaboration with other autonomous systems.

P058 A service evaluation of rheumatologist-led ultrasound clinics at a tertiary care centre

Abstract Background/Aims Rheumatologist-led ultrasound (USG) clinics were established at UCLH over 10 years ago with an intention to improve speed of diagnosis and/or execution of treatment plan with the benefit of clinical assessment and diagnostic imaging in one appointment. The benefits for patients may include reduced time taken from work and reduced anxiety. This project aimed to evaluate reasons for referral to this clinic, the outcome of a patient review and the impact of this service. Methods Retrospective Health-record data were collected from EPIC using Google forms for patients who had USG of their hands in the specialist clinics for 3 months (Jan - March 2022). Data included patient demographics, clinical data, USG findings, intended change or initiation of treatment, and outcome of the review. Results 54 patients had been reviewed in the three months. More than half (61%) were referred to help answer a treatment-related question and the remaining were to confirm diagnosis (39%). Most patients referred for diagnosis had their diagnosis confirmed (86%). Majority (62%) also had appropriate follow up and two were discharged; a further six had potential to be discharged at this time. Out of the 33 referred for treatment questions, there were 23(70%) patients who needed no change to their treatment; 15(65%) of these would have potentially been initiated on biologics otherwise. This resulted in potential savings of between £76,497-£107,601 per 6-month (trial of DMARD) period due to drug cost. Table 1 lists potential indirect savings. Conclusion This service evaluation found considerable direct and indirect savings that emphasises the need for such specialist service in the cost-strapped NHS. The direct cost savings relate to the drugs that were not required to be initiated, with the majority attributed to the avoidance of biologics. In addition to the indirect cost savings to the NHS , the reduction of risk to the patient of unnecessary immunosuppression remains undefined. As a result of this service evaluation, to enhance potential discharges from this clinic, referring clinicians manage patient expectations at referral and communicatepotential for discharge at referral to the scanning clinician. Disclosure A. Zmuda: None. J. Manson: None. M. Castelino: None.

Cognitive impairment in children and adolescents living with perinatal HIV disease in the ART era: a meta-analysis

Despite improved survival and overall health outcomes from modern antiretroviral therapy (ART), children and adolescents living with HIV are facing pervasive impairments in neurodevelopment including cognitive impairment, but there remains a lack of consensus on the cognitive domains that are affected in those children and adolescents. The objective of this meta-analysis was to evaluate the impact of perinatal HIV-infection on executive function, working memory, and speed of information processing in the ART era. The PubMed database was searched for studies published between 1997 and 2024, plus additional search with the ScienceDirect, bioRxiv, and medRxiv databases. A meta-analysis was conducted on thirty-five studies published between 2012 and 2023 that encompassed a total of 4066 perinatally-infected HIV patients, 2349 HIV-exposed uninfected (HEU) controls, and 2466 HIV-unexposed, uninfected (HUU) controls. Performance scores on executive function, working memory, and processing speed were pooled using random-effects meta-analysis. Compared to HEU and HUU controls, perinatally HIV-infected children and adolescents presented with significant impairments in processing speed (Hedges g=-0.64, p<0.00001), working memory (Hedges g=-0.69, p<0.00001), and to a lesser degree, executive function (Hedges g=-0.35, p=0.02). Meta-regression analysis suggested that the effect estimate of processing speed impairment negatively correlated with Gross National Income (GNI) per capita of the study countries (CALHIV vs HUU, p=0.0016; CALHIV vs HEU, p=0.0019), even though HIV-infected cases were compared to sociodemographically matched HUU controls from the same countries. Sub-group meta-analyses with participants from high-income or low-/middle-income countries provided further evidence suggesting that the performance gap between HIV-infected cases and HUU/HEU controls may be larger in low-/middle-income countries than high-income countries. In the ART era, cognitive impairment (especially reduced processing speed and working memory) persists in children and adolescents living with HIV. These impairments may be more pronounced among those children and adolescents living with HIV in low-income countries, suggesting that there may be global health inequities in treatment outcomes with perinatal HIV-infection. However, meta-analysis and meta-regression analysis have their limitations, which calls for future collaborative multi-country international studies to directly investigate this important topic. Nevertheless, there is an unmet need to assure equity in timely assessments and interventions to optimize neurocognitive development and outcomes among children and adolescents with perinatal HIV globally. This research was supported in part by NIH R01MH108466, NIH R56NS124422, and NIH R01NS124422.

Investigation of approaches to designing complex database structures in systems of integrated monitoring of environmental, economic, energy and social parameters of the territory

The object of research is the traditional and universal approach of designing the database structure in systems of integrated monitoring of ecological, economic, energy and social parameters of the territory, which include diverse data from various subject areas. In the course of the study, an analysis was performed based on a set of criteria such as scalability, ease of updating data, absence of empty fields, volume of the database, number of tables and fields, ease and speed of execution of requests for a sample set of indicators of the research object. The comparison of these approaches took place on the example of water resources monitoring, since it has several subsystems and a large number of indicators that are used for assessment. It is established that the proposed universal approach to designing complex database structures made it possible to reduce the volume of the database by 2.25 times due to the absence of empty fields. In particular, in the considered example, the filling factor of the database with the traditional approach is 1.75 times less than with the proposed universal approach. It should be noted that the rate of table filling for the traditional design approach can vary depending on the number of indicator values, while the table filling rate for the universal approach is always close to 100 %. Also, the proposed database design approach makes it possible to speed up data loading and processing. For example, with the same volume of significant information, the minimum speed of sampling the characteristics of one research object is 3.87 times greater in a database developed according to the principles of the universal approach than according to the rules of the traditional approach. The proposed structure of the database is successfully used in the system of complex eco-energy-economic monitoring. The developed structure of the database can serve as an effective basis for the formation of an electronic data bank at the level of the enterprise, region and country.

Experimental Identification of a Coupled-Circuit Model for the Digital Twin of a Wound-Rotor Induction Machine

The development of monitoring and diagnostic methods for electrical machines requires the use of transient models capable of operating in real time and producing signal signatures with high precision. In this context, coupled-circuit models offer numerous advantages due to their speed of execution and accuracy. They have been successfully employed to create real-time digital twins of electrical machines. The main challenge of this modeling method lies in the preparation of the model, which involves numerous preliminary calculations and takes time to identify all its parameters. This is particularly due to the variation in inductances based on the rotor position. To determine these inductance values with great precision, the classical approach involves using finite-element field calculation software. However, the computation time quickly becomes an issue due to the large number of values to calculate and simulations to perform. This article introduces an innovative experimental approach to identify a coupled-circuit model and develop a digital twin of a wound-rotor induction machine. This method relies solely on simple electrical measurements and tests conducted at extremely low rotation speeds (1 rpm) to obtain inductance variations as a function of the rotor position. By employing this technique, the need for analytical models or finite-element field calculation simulations, which typically require precise knowledge of the machine’s geometry and materials, is circumvented. The measurement processing employs optimization methods to extract the inductances as a function of the rotor position, which are then used as input data for the coupled-circuit model. The final parameters are specific to each machine and replicate all its manufacturing imperfections such as eccentricity and geometric or winding defects. This experimental identification method significantly enhances the model’s accuracy and reduces the usually required preliminary calculation time in a finite-element-based identification process.

Experimental Evaluation of Noise Exposure Effects on Subjective Perceptions and Cognitive Performance

Individuals exposed to elevated noise levels experience heightened emotional intensity, leading to increased cognitive disruption and a higher likelihood of accidents. This study seeks to investigate the impact of noise exposure on human cognitive performance, and the moderating role of emotion. Twelve healthy male college-age students underwent exposure to three noise conditions, each characterized by different sound pressure levels and sharpness. Each condition included an initial acoustic/thermal adaption period lasting approximately 40 min, followed by intermittent questionnaire tests and a battery of computerized cognitive tests. Statistical analysis revealed that reducing noise levels proved advantageous, enhancing perceived sound quality, positive emotions, and auditory perception abilities, while concurrently reducing false alerts and accelerating execution speed. Many of these effects were found to be counteracted by elevated sharpness. Correlation analyses and partial least squares structural equation modeling (PLS-SEM) results suggested that human emotions mediate the relationship between noise exposure and cognitive performance. The potential underlying mechanism suggests that negative feelings towards noise contribute to poor emotional states, subsequently influencing cognitive processes and impairing executive function. The outcomes of this study provide valuable insights into the mechanism of noise exposure and its effects on human cognition and subjective perceptions.

INFERIOR VENA CAVA COLLAPSIBILITY INDEX AS A NON-INVASIVE METHOD OF ASSESSING THE VOLEMIC STATUS OF PATIENTS DURING SPINE INTERVENTIONS

Objective. To prove the possibility of using non-invasive diagnostics of the volemic state of postoperative patients using ultrasound assessment of inferior vena cava collapsibility index. Methods. The study included 67 patients who underwent transpedicular fixation of the spine with laminectomy. Volemiс status was determined for all participants in two ways: by catheterization of the central vein and determination of central venous pressure, as well as by ultrasound examination of the inferior vena cava and calculation of inferior vena cava collapsibility index. Results. According to the results of the invasive assessment of central venous pressure, patients were divided into 3 groups: group I (patients in hypovolemic state, n = 31), group II (patients in euvolemic state, n = 25) and group III (patients in hypervolemic state, n = 11 ). The average values of central venous pressure indicators in patients of these groups were statistically significantly different from each other (p &lt; 0.01). The difference between the mean values of the inferior vena cava collapse index in the respective groups was statistically significant (p &lt; 0.01). A reliable inverse correlation of very high strength was found between the indicators of inferior vena cava collapsibility index and central venous pressure (p &lt; 0.05). Conclusions. Determination of the patient's volemic state is an extremely important tool for the correct selection of the fluid volume management. Ultrasound assessment of volemic status has a number of advantages, such as the non-invasiveness of the method, wide availability, low price and speed of execution. According to the results of our study, the possibility of using inferior vena cava collapsibility index to assess the volemic status of patients has been demonstrated.

Performance Comparison of Apache Spark and Hadoop for Machine Learning based iterative GBTR on HIGGS and Covid-19 Datasets

In the realm of distributed computing frameworks, such as Apache Spark and MapReduce Hadoop, the efficacy of these frameworks varies across diverse applications and algorithms contingent upon distinctive evaluation metrics and critical parameters. This research paper diligently scrutinizes the extant body of research that compares these two frameworks concerning said evaluation metrics and parameters. Subsequently, it conducts empirical investigations to authenticate the performance of these frameworks in the context of an iterative Gradient Boosting Tree Regression (GBTR) algorithm. Remarkably, the comparative analyses in previous studies encompass a spectrum of iterative machine learning regression and classification techniques, batch processing, SQL, and Graph processing algorithms. Furthermore, numerous investigations have explored the application of machine learning algorithms encompassing logistic regression, Page Rank, K-Means, KNN, and the HiBench suite. This paper presents the comparison between the two distributed computing platforms on iterative GBTR for classification task on the HIGGS dataset from the physics domain and for the regression task on the Covid-19 dataset from the healthcare domain. The empirical findings corroborate that Apache Spark exhibits superior execution speed in iterative tasks when the available physical memory significantly exceeds the dataset size. Conversely, Hadoop outperforms Spark when dealing with substantial datasets or constrained physical memory resources.

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Handling unreliable detections and avoiding identity switches are crucial for the success of multiple object tracking (MOT). Ideally, MOT algorithm should use true positive detections only, work in real-time and produce no identity switches. To approach the described ideal solution, we present the BoostTrack, a simple yet effective tracing-by-detection MOT method that utilizes several lightweight plug and play additions to improve MOT performance. We design a detection-tracklet confidence score and use it to scale the similarity measure and implicitly favour high detection confidence and high tracklet confidence pairs in one-stage association. To reduce the ambiguity arising from using intersection over union (IoU), we propose a novel Mahalanobis distance and shape similarity additions to boost the overall similarity measure. To utilize low-detection score bounding boxes in one-stage association, we propose to boost the confidence scores of two groups of detections: the detections we assume to correspond to the existing tracked object, and the detections we assume to correspond to a previously undetected object. The proposed additions are orthogonal to the existing approaches, and we combine them with interpolation and camera motion compensation to achieve results comparable to the standard benchmark solutions while retaining real-time execution speed. When combined with appearance similarity, our method outperforms all standard benchmark solutions on MOT17 and MOT20 datasets. It ranks first among online methods in HOTA metric in the MOT Challenge on MOT17 and MOT20 test sets. We make our code available at https://github.com/vukasin-stanojevic/BoostTrack.