Time Data Research Articles

A Practical Approach of Data Visualization from Geographic Information Systems by Using Mobile Technologies

The purpose of this paper is to demonstrate a practical approach to data visualization by using mobile technologies and desktop geographic information systems. To do that, we analyze public data for Black Sea water pollution on Varna’s beaches by using real data in real time. Geographic information systems (GISs) are used for visualization, and statistical software is used for correlation analysis. In our approach, the use of open-source software products is essential, namely QGIS and PSPP software. The methodology includes using one dataset, and after extract-transform-load procedures, additional analysis is conducted. Mobile technologies are used to demonstrate the results of the correlation analysis to a wide audience, and data visualization helps for better understanding. A similar visualization approach may be conducted for other subjects and fields of interest.

Recognition memory decisions made with short- and long-term retrieval.

In the present research, we produce a coherent account of the storage and retrieval processes in short- and long-term event memory, and long-term knowledge, that produce response accuracy and response time in a wide variety of conditions in our studies of recognition memory. Two to nine pictures are studied sequentially followed by a target or foil test picture in four conditions used in Nosofsky et al. Journal of Experimental Psychology: Learning, Memory, and Cognition, 47, 316-342, (2021) and in our new paradigm: VM: target and foil responses to a given stimulus change from trial to trial; CM: the responses do not change from trial to trial; AN: every trial uses new stimuli; MIXED: combinations of VM, CN, and AN occur on each trial. In the new paradigm a given picture is equally often tested as old or new, but only in CM is the response key the same and learnable. Our model has components that have appeared in a variety of prior accounts, including learning and familiarity, but are given support by our demonstration that accuracy and response time data from a large variety of conditions can be predicted by these processes acting together, with parameter values that largely are unchanged.A longerversion of this article, containing information not found here due to space, is available online https://doi.org/10.31234/osf.io/h8msp .The avalibility of the data (supplement materials), info and link is attached at the end section ( https://psyarxiv.com/h8msp .).

Instructed speed and accuracy affect binding

In the past few decades, binding and retrieval mechanisms have gained increased interest in research on human action control. Recent studies show that these mechanisms also play a role in the control of multiple independent actions. Here, two or more successively executed responses seem to be bound to each other so that repeating one of them can retrieve the other, affecting performance in this second response and resulting in so-called response–response binding effects. Binding effects are typically found in the response time data and, somewhat less reliably, also in the error rates. Whether binding effects show in the response times, the error rates, or both, is likely influenced by the current speed–accuracy settings of the participants, with binding effects more likely showing in error rates under a speed setting, while more likely showing in RTs under an accuracy setting. Alternatively, different speed–accuracy settings might also entail changes in executive control, affecting the size of observed binding effects. In this study, we tested these assumptions by comparing binding effects under different speed–accuracy settings that were induced via instructions focusing on speed, accuracy, or both (ambivalent). Binding effects were observed in response times independent of instructions, while in error rates, they only showed under speed or ambivalent instructions. These findings indicate that binding effects can be affected by instructions regarding speed and accuracy.

Appointment Scheduling with Delay Tolerance Heterogeneity

In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service time uncertainty. We aim to capture the delay tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance sensitivity, monotonicity, and convexity and positive homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the convexity of TAD index ensures a tractable modeling of the collective delay dissatisfaction in the appointment scheduling problem. Using the TAD index, we then develop an appointment model with known empirical service time distribution that minimizes the overall tolerance-aware delays of all users. We analyze the impact of delay tolerance on the sequence and schedule decisions and show that the resultant TAD appointment model can be reformulated as a mixed-integer linear program (MILP). Furthermore, we extend the TAD appointment model by considering service time ambiguity. In particular, we encode into the TAD index a moment ambiguity set and a Wasserstein ambiguity set, respectively. The former captures effectively the correlation among service times across positions and user types, whereas the latter captures directly the service time data information. We show that both of the resultant TAD models under ambiguity can be reformulated as polynomial-sized, mixed-integer conic programs (MICPs). Finally, we compare our TAD models with some existing counterpart approaches and the current practice using synthetic data and a case of real hospital data, respectively. Our results demonstrate the effectiveness of the TAD appointment models in capturing the user delay tolerance with heterogeneity and mitigating the worst-case delays. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled “Econometric Modeling and Economic Policy Studies”], the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2], and the Major Program of National Natural Science Foundation of China [Grant 72192843]. S. Wang was also supported by a grant from MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0025 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0025 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Feasibility of Leveraging Consumer Wearable Devices with Data Platform Integration for Patient Vital Monitoring in Low-Resource Settings.

Manual monitoring of vital signs, which often fails to capture the onset of deterioration, is the main monitoring modality in most Ghanaian hospitals due to the high cost and inadequate supply of patient bedside monitors. Consumer wearable devices (CWDs) are emerging, relatively low-cost technologies for continuous monitoring of physiological status; however, their validity has not been established in low-resource clinical settings. We aimed to (1) investigate the validity of the heart rate (HR) and oxygen saturation (SpO2) data from two widely used CWDs, the Fitbit Versa 2 and Xiaomi Mi Smart Band 6, against gold standard bedside monitors in one Ghanaian hospital and (2) develop a web application to capture and display CWD data in a clinician-friendly way. A healthy volunteer simultaneously wore both CWDs and blood pressure cuffs to measure HR and SpO2. To test for concordance, we conducted the Bland-Altman and mean absolute percentage error analyses. We also developed a web application that retrieves and displays CWD data in near real time as text and graphical trends. Compared to gold standards (patient monitor and manual), the Fitbit Versa 2 had 96.87% and 96.67% measurement accuracies for HR, and the Xiaomi Mi Smart Band 6 had 94.24% and 93.21% measurement accuracies for HR. The Xiaomi Mi Smart Band 6 had 98.79% measurement accuracy for SpO2. The strong concordance between CWD and gold standards supports the potential implementation of these devices as a novel method of vital sign monitoring to replace manual monitoring, thus saving costs and improving patient outcomes. Further studies are needed for confirmation.

Digital twins a digital transformation for management of the construction industry

In recent years technologies have been transforming working relationships in the construction industry. To dynamize the sector, new ways are sought to obtain better control over its lifecycle. The goals are to achieve cost savings, improvement in the quality of services, greater security and less environmental impact. Important information, to reach such goals, is generated during the project, the construction, the use/upkeep of assets. Nevertheless, this information is not used properly, because it requires participative management for the data to be kept updated and available in real time. A process that allows for this management right from the project-planning phase is Building Information Modeling (BIM). Its association with cloud-based communication platforms allows users to access all the data in real time. Allied to the resources of the Internet of Things (IoT), BIM makes it possible to transfer data to specific software that assist this information to remain alive during the whole lifecycle of the construction. The cloud-based platforms integrated to the BIM and the IoT, make it possible to digitalise and transfer the information related to the construction in real time, to create the Digital Twin (DT). With this DT all this information can be managed on one only digital platform which permits intelligent constructions and operational excellence in the sector. In this article, concepts relating to the creation of a DT are analysed by means of the Life Cycle Assessment (LCA) for buildings. A review of the literature was also carried out on the recent applications of the DT and, especially, in the construction industry. It became clear in the study that in recent years the DT is gradually being implemented in different sectors. Most of the research presents a review of the literature followed by small-scale experiments or case studies. It was found that the concept of DT has attracted the attention of the construction industry, and debate on the theme is developing rapidly in the academic sector, so as to establish the ways of effectively developing it in the construction industry.

The Spatially Adaptable Filter for Error Reduction (SAFER) Process: Remote Sensing-Based LANDFIRE Disturbance Mapping Updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Quantitate evaluation of photogrammetry with CT scanning for orbital defect

Facial deformities can be caused by cancer, tumours, trauma, infections, congenital or acquired defects and may lead to alteration in basic functions such as communication, breathing, and mastication and aesthetic thereby affecting quality of life. Traditional processes for manufacturing maxillofacial prostheses involve complicated, time-consuming and tedious processes for the patient and the operator. Impression of the defect area, which is the one of the crucial step in fabrication of prosthesis, is the longest and most difficult process as it requires a long contact with the patient. The digital revolution is now changing the landscape of prosthetic production and making the impression making procedure simpler. Digital technology reduces patient chair side time by providing more accurate display data in less time (3–5 min) than traditional methods. Digital impressions eliminate the need for bulky impression materials and provide a more comfortable patient experience.

METHODS FOR STUDYING THE CHEMICAL COMPOSITION OF PRODUCTS FROM NON-FERROUS METALS OF THE X–XVIII CENTURIES FROM THE TERRITORY OF NORTH AND CENTRAL BELARUS

The article provides an overview of modern methods for studying the chemical composition of products made of non-ferrous metals of the X–XVIII centuries. It has been established that the main methods for studying the elemental composition of archaeological non-ferrous metals are laser emission spectral analysis, optical emission spectral analysis and X-ray fluorescence spectral analysis. It is noted that the most suitable for studying products made of non-ferrous metals are the X–XVIII centuries should be considered standard-free X-ray fluorescence spectral analysis, which allows obtaining quantitative and qualitative data in real time, does not require preliminary sample preparation and does not leave traces on the surface of artifacts. As a result of the author’s analysis of the statistical error in determining the main components of copper-based alloys using the Student’s t-test, it was established that the data obtained during research in different laboratories using different methods are comparable and can be used to study the characteristics of non-ferrous metalworking in the Northern and Central Belarus in the X–XVIII centuries.

Enhancing RNA Payload and Temperature Stability and Activity with Cationic Peptide-Coated Zinc Oxide Nanoparticles.

The lipid nanoparticle (LNP) mRNA vaccine was first tested through clinic but suffered from relatively low RNA payloads and poor temperature stability. Our lab patented a protamine-coated particle approach for temperature-stabilizing DNA vaccines, translating this successfully to the clinic. In subsequent work, we have characterized RNA interaction and delivery by zinc oxide nanoparticles, filing a patent most recently entitled RNA-stabilizing nanoparticles, similarly utilizing protamine-coated zinc oxide nanoparticles for RNA. Here, we present this data for the first time. Briefly, ZnO, ZnO-protamine, and ZnO-protamine-RNA were characterized by size and zeta potential analyses and the RNA-loaded nanoparticles were visualized by transmission electron microscopy. UV spectroscopic analysis demonstrated up to 95-98% loading efficiency with protamine and approximately 75% loading efficiency with LL37, another cationic antiviral peptide. Elution of the RNA isolated from the particles afforded a calculation in three independent trials where RNA payloads ranged from 18 to 45 μg of RNA per 0.5 mg of coated particles. Circular dichroism (CD) analysis indicated that binding of RNA to ZnO NPs stabilized, enhancing the pattern with a clear dependence on the RNA:ZnO stoichiometry. Enhanced temperature stability was shown by differential scanning calorimetry (DSC), gel electrophoresis, and in vitro mRNA expression analysis. Using poly I:C RNA with a well-defined melting point (64.3 ± 0.32 °C), formation of the ZnO:RNA complex increased the RNA melting point (70.9 ± 0.62 °C). After refrigerated or room-temperature storage or incubation at 30, 40, or 50 °C, RNA comigration with the control RNA was recovered from all samples, exposed to either 14 or 100 nm ZnO, and coated with protamine. Furthermore, the ZnO-protamine-mRNA samples retained significantly higher expression activity when incubated at these elevated temperatures. Finally, the ZnO-protamine-mRNA was functionally active for in vitro translation, in cell extracts, and in cells for expression of GFP, luciferase, and COVID spike protein. These data support further preclinical development of ZnO-protamine-mRNA.

Air pollution seasons in urban moderate climate areas through big data analytics

High particulate matter (PM) concentrations have a negative impact on the overall quality of life and health. The annual trends of PM can vary greatly depending on factors such as a country’s energy mix, development level, and climatic zone. In this study, we aimed to understand the annual cycle of PM concentrations in a moderate climate zone using a dense grid of low-cost sensors located in central Europe (Krakow). Over one million unique records of PM, temperature, humidity, pressure and wind speed observations were analyzed to gain a detailed, high-resolution understanding of yearly fluctuations. The comprehensive big-data workflow was presented with the statistical analysis of the meteorological factors. A big data-driven approach revealed the existence of two main PM seasons (warm and cold) in Europe’s moderate climate zone, which do not correspond directly with the traditional four main seasons (Autumn, Winter, Spring, and Summer) with two side periods (early spring and early winter). Our findings also highlighted the importance of high-resolution time and space data for sustainable spatial planning. The observations allowed for distinguishing whether the source of air pollution is related to coal burning for heating in cold period or to agricultural lands burning during the warm period.

Prediction of SOC for lead-acid battery based on LSTM-Attention and LightGBM

Abstract Accurately estimating the SOC of batteries is crucial for the objective of extending battery life and enhancing power supply reliability. Currently, machine learning methods are commonly used to predict the SOC of batteries, however, their accuracy in capturing the sequential nature of battery charging and discharging is insufficient. To address the problem of SOC prediction, a deep learning model that employs Long Short-Term Memory (LSTM) with Attention mechanism is proposed. The LSTM model is designed to connect the current SOC with historical time data and to extract multi-dimensional features from groups of batteries. Additionally, introducing the Attention mechanism allows for the model to prioritize key information while disregarding insignificant data. This work utilizes two different approaches to the multi-cell case and the single-cell case for several reasons. Considering that the failure of a single cell can affect the entire group of batteries, SOC prediction models for individual batteries need not take a long training time. Thus, the LightGBM model is developed to predict the SOC of a single battery whose training speed surpasses that of the deep learning model and has superior prediction accuracy and greater speed when employed with small-scale data, error within 3%. Conversely, the LSTM-Attention model yields higher prediction accuracy when processing large-scale datasets, error within 5%.Two models are proposed: one for predicting the SOC of groups of batteries and another for a single battery.

Research on Improvement of Data Synchronization Method for Distribution Network Differential Protection Based on 5G

To enhance the reliability of traditional current protection systems in the evolving landscape of future power grids, this paper proposes a differential protection data synchronization approach that leverages satellite and 5G network timing. This method addresses the risk of inadvertent operation due to 5G communication jitter. It employs the BeiDou satellite system for synchronizing the timing of 5G base stations and terminals. This synchronization extends to the 5G terminal and the protection device, thereby mitigating time deviations caused by air interface delays and reducing the 5G network’s end-to-end delay by approximately 4 ms on average. A simulation was conducted to assess the reliability and effectiveness of the proposed method under normal satellite signal conditions. The results indicated a significant improvement in protection accuracy, with the method reducing the protection activation time by approximately 5–7 ms compared to the 5G timing data synchronization method. This advancement offers technical support for the safe and stable operation of new power systems.

Survival Analysis of Battalion-Level Commanders(leaders) Using Big Data as Results of Brigade-Level KCTC Training - Focused on Infantry Battalion Defensive Operations -

In this study, we conducted a survival analysis on battalion-level commanders(leaders), focusing on infantry battalion defensive operations using the big data of brigade-level KCTC(Korea Combat Training Center) training results.Unlike previous studies, we utilized the brigade-level KCTC training results data for the first time to conduct a survival analysis, and the research subjects were battalion-level commanders(leaders), which can affect the battle. At this time, the battle results were defined, and through cluster analysis, infantry battalions were divided into excellent, average, and insufficient units, and the difference in the survival rate of the commanders was analyzed through the Kaplan-Meier survival analysis. This provided an opportunity to objectively compare the differences between excellent and insufficient units. Subsequently, factors affecting the survival of commanders were derived using the Cox proportional hazard model, and it was possible to confirm the influencing factors from various angles by also using the survival tree model. Significance and limitations confirmed in the research process were presented as policy suggestions and future research directions.

Survival tree averaging by functional martingale-based residuals

A large literature has been established for random survival forest (RSF), a popular tool developed to analyze right-censored failure time data, under various situations. However, its prediction performance may not be optimal sometimes. To address this issue, we propose two optimal model averaging methods based on martingale residual processes. In particular, an in-of-bag and out-of-bag (IBOB) data process is defined, and two new IBOB functionals criteria are derived for the selection of weights. Furthermore, for their implementation, a greedy algorithm is presented, and the asymptotic optimality of the proposed model averaging approaches is established along with the convergence of the greedy averaging algorithms. Finally, an extensive simulation study is conducted, which indicates that the proposed methods work well, and an illustration is provided.

Prevalence and Psychosocial Risk Factors Associated with Depression in Older People in A Rural Community

Depression is a multifactorial disease that globally impacts all ages and sectors of the population. In this sense, it has become a public health problem, since it affects 280 million people in the world, according to information from the World Health Organization. Older adults represent 5.7% of the population that experiences this disease, with the moderate to severe stage being the one that most impacts their health, and, therefore, quality of life. For this reason, the objective of the research is to know the prevalence and psychosocial risk factors associated with depression in older people in the community of Santa Cruz, Hecelchakán, Campeche. The methodology used is with a quantitative approach because information was collected, based on the application of the Yesavage scale of geriatric depression, and the sociodemographic registry of 60 older adults of both sexes, in order to measure the percentage of prevalence through its respective epidemiological formula. Likewise, it is cross-sectional because it collected data in a single time: November 2022 – October 2023. Results: 46.7% experienced mild depression, 41.7% moderate depression and 11.6% severe depression. 55% of those interviewed were male and 45% female. The actual prevalence of depression is 100%. Likewise, the psychosocial risk factors identified were: the absence of recreational activities and social support networks, the economy, health services and family disintegration. Finally, it is necessary to design and implement socio-health intervention projects in the locality and with a socio-cultural perspective that impacts this age group, the family and from an intergenerational perspective, with the promotion of mental health being a priority goal in their execution.

Situational diagnosis of the practice of professionals in the work process of the school health program: a convergent care research

Objective: To carry out a situational diagnosis on the practice of professionals in the work process and collection of data on actions in the context of the School Health Program (PSE), in the municipality of Sobral, Ceará, Brazil. Method: This is an exploratory, descriptive research, with a qualitative approach, in which the stages of Convergent Care Research (PCA) were used. Results and discussion: Difficulties were found on the part of professionals in recording the data collected from students, in the printed form of collective activity, due to the high quantity of student data to be entered and, in addition, difficulties in monitoring the data of the PSE. Final considerations: The proposal to carry out a critical-reflective process on the work process in the PSE, with the detection of the situational diagnosis, resulting in recognition by management and professionals, the creation of proposals for improvements in the PSE, identifying and minimizing difficulties in data collection, monitoring data in real time, in a shared manner, improving the organization and monitoring of data collected by professionals.

Prediction of Water Content in Subgrade Soil in Road Construction Using Hyperspectral Information Obtained through UAV

In road construction, the compaction of the subgrade layer, which is one of the earthwork fields, is an essential procedure to support the pavement layer and traffic load. For the quality control of subgrades, water content must be measured. Currently, the measurement of water content is performed at specific locations in a large area of subgrades and has the disadvantage of taking a long time to derive. Because this is difficult to immediately confirm, inefficiencies arise in terms of construction schedule and quality control. Therefore, in this study, a CCM (Color-Coded Map) was proposed through hyperspectral remote sensing using drones. This method is a range-type water-content measurement method that can acquire data in a short time (about 20 min) and can be easily confirmed visually. For this, a predicted equation that can convert hyperspectral information into water content information is developed. Multivariate linear regression, a machine learning technique, was applied to the database (of actual measured water content and hyperspectral information). The predicted and measured water contents showed a coefficient of determination of 0.888, and it was confirmed that CCMs can also be presented in various ways depending on user settings.

Flow-Time Minimization for Timely Data Stream Processing in UAV-Aided Mobile Edge Computing

Unmanned Aerial Vehicle (UAV) has gained increasing attentions by both academic and industrial communities, due to its flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this paper, we provide timely services on the data streams of mobile users in a UAV-aided Mobile Edge Computing (MEC) network, in which each UAV is equipped with a 5G small-cell base station for communication and data processing. Specifically, we first formulate a flow-time minimization problem by jointly caching services and offloading tasks of mobile users to the UAV-aided MEC with the aim to minimize the flow-time, where the flow-time of a user request is referred to the time duration from the request issuing time point to its completion point, subject to resource and energy capacity on each UAV. We then propose a spatial-temporal learning optimization framework. We also devise an online algorithm with a competitive ratio for the problem based upon the framework, by leveraging the round-robin scheduling and dual fitting techniques. Finally, we evaluate the performance of the proposed algorithms through experimental simulation. The simulation results demonstrated that the proposed algorithms outperform their comparison counterparts, by reducing the flow-time no less than 19% on average.

Inter-hospital moderate and advanced Alzheimer's disease detection through convolutional neural networks

Electroencephalography (EEG) has been a fundamental technique in the identification of health conditions since its discovery. This analysis specifically centers on machine learning (ML) and deep learning (DL) methodologies designed for the analysis of electroencephalogram (EEG) data to categorize individuals with Alzheimer's Disease (AD) into two groups: Moderate or Advanced Alzheimer's dementia.Our study is based on a comprehensive database comprising 668 volunteers from 5 different hospitals, collected over a decade. This diverse dataset enables better training and validation of our results. Among the methods evaluated, the CNN (deep learning) approach outperformed others, achieving a remarkable classification accuracy of 97.45% for patients with Moderate Alzheimer's Dementia (ADM) and 97.03% for patients with Advanced Alzheimer's Dementia (ADA). Importantly, all the compared methods were rigorously assessed under identical conditions. The proposed DL model, specifically CNN, effectively extracts time domain features from EEG data in time, resulting in a significant reduction in learnable parameters and data redundancy.