Time Variability Research Articles

Impact of Annually Variable Dust Activity on the Recession of the Martian South Polar Seasonal Cap

Abstract Suspended dust is a vital component of Martian climatic system and its temporal-spatial variation can influence the recession of the South Polar Seasonal Cap (SPSC). However, dust activity varies across different stages and years, affecting the SPSC recession process differently. Continuous observations of SPSC/South Polar Residual Cap (SPRC) across several Martian years, alongside segmented analyses of annual dust activity, will provide new insights into the climate conditions of Martian south polar region. Here, we performed a combined analysis on the extents of SPSC from MY28-MY31 and MY36, together with the concurrent Martian atmospheric optical thickness, to assess the influence of dust activity at various recession time steps. Results show that dust activity peaks in both spring and summer can impact the SPSC/SPRC recession. The recession process is accelerated if the dust activity peak occurs earlier. If the timing of the peak dust activity is identical, the intensity of the dust activity influences the recession. Specific regions, such as the “Cryptic Region” and the Mountains of Mitchel, respond uniquely to dust activity. Higher dust intensity slows the recession of the Cryptic Region, while the SPSC in the Mountains of Mitchel area shows variability in recession timing based on local dust activity intensity. This research provides observational constraints that enhance our understanding of Martian atmospheric circulation and can potentially aid the future development of polar dust activity models.

Differential gait features across Parkinson's disease clinical subtypes.

Clinical subtypes in Parkinson's disease including non-motor manifestations may be more beneficial than subtypes based upon motor manifestations alone. Inclusion of gait metrics may help identity targets for rehabilitation and potentially predict development of non-motor symptoms for individuals with Parkinson's disease. This study aims to characterize gait differences across established multi-domain subtypes. "Motor Only", "Psychiatric & Motor" and "Cognitive & Motor" clinical subtypes were established through motor, cognitive, and psychiatric assessment. Walking was assessed in the "OFF" medication state. Multivariate analysis of variance identified differences in gait domains across clinical subtypes. The "Motor Only" subtype exhibited the fastest velocity, longest step length, and least timing variability (swing, step, stance), compared to "Psychiatric & Motor" and "Cognitive & Motor" subtypes. Stance time differed across subtypes; "Psychiatric & Motor" subtype had the longest stance time, followed by "Cognitive & Motor", then "Motor only". The "Psychiatric & Motor" group had different asymmetry from the "Cognitive & Motor" subtype, as "Psychiatric & Motor" walked with longer steps on their less-affected side while the "Cognitive & Motor" subtype displayed the opposite pattern. No differences were observed for swing time, step velocity variability, step length variability, width measures, or other asymmetry measures. Cognitive and Psychiatric subtypes displayed worse gait performance than the "Motor only" group. Stance time and step length asymmetry were different between Psychiatric and Cognitive subtypes, indicating gait deficits may be related to distinct aspects of non-motor manifestations. Gait signatures may help clinicians distinguish between non-motor subtypes, guiding personalized treatment.

Paced Breathing Associated With Pupil Diameter Oscillations at the Same Rate and Reduced Lapses in Attention.

A dynamical systems model proposes that respiratory, locus coeruleus-noradrenaline (LC-NA), and cortical attentional systems interact, producing emergent states of attention. We tested a prediction that fixing respiratory pace (versus spontaneous respiration) stabilizes oscillations in pupil diameter (LC-NA proxy) and attentional state. Primary comparisons were between 'Instructed Breath' (IB) and 'No Instructed Breath' (NIB) groups. Secondarily, we investigated the effects of shifting respiratory frequency in the IB group from 0.15 to 0.1-0.15 Hz in Experiment 1 (n = 55) and 0.15-0.1 Hz only in Experiment 2 (n = 48) (replication). In the Paced Auditory Cue Entrainment (PACE) task, participants heard two auditory tones, alternating higher and lower pitches, cycling continuously. Tones acted as a breath guide for IB and an attention monitor for both groups. Participants gave rhythmic mouse responses to the transition points between tones (left for high-to-low, right for low-to-high). We derived accuracy of mouse click timing (RTm), variability in click timing (RTVL), and counts of erroneously inverting the left/right rhythm (IRs and Switches). Despite no differences between groups in RTm or RTVL, IB committed significantly fewer IRs and switches, indicating less lapses in attention during paced breathing. Differences in behavioral metrics were present across tone cycle frequencies but not exclusive to IB, so breath frequency did not appear to have a specific effect. Pupil diameter oscillations in IB closely tracked the frequency of the instructed breathing, implicating LC-NA activity as being entrained by the breath intervention. We conclude that pacing respiratory frequency did stabilize attention, possibly through stabilizing fluctuations in LC-NA.

Wavelet-based time-frequency intermuscular beta-band coherence decreases with age but increases after mental fatigue in ankle muscles during gait independent of age

Mental fatigue can affect cognitive function and interfere with motor performance. We examined if mental fatigue affected gait through age-specific modulation of wavelet-based time–frequency intermuscular beta-band coherence in muscles while walking on a treadmill at 1.2 m·s-1. The Psychomotor Vigilance Task, and the AX-Continuous Performance and the Stroop tests were used to induce mental fatigue in groups of healthy young and older participants. Mental fatigue reduced stance time, stride length, and marginally step width and increased cadence, stride length and stance time variability. In older compared with young participants before the induction of mental fatigue, wavelet-based time–frequency intermuscular beta-band coherence measured during walking was lower in the tibialis-peroneus and tibialis-gastrocnemius muscle pairs in specific phases of the gait cycle. In both age groups, after induction of mental fatigue, selected clusters of wavelet-based time–frequency intermuscular beta-band coherence measured during walking increased in the biceps-semitendinosus, rectus-vastus, tibialis-peroneus, gastrocnemius-soleus, and tibialis-gastrocnemius muscle pairs. In conclusion, age and mental fatigue seem to induce different effects on wavelet-based time–frequency intermuscular beta-band coherence measured during gait in healthy young and older adults’ ankle muscles.

Application of the age-period-cohort model in tuberculosis

Up to now, tuberculosis (TB) remains a global public health problem, posing a serious threat to human health. Traditional methods for analyzing time-varying trends, such as age and period, tend to ignore the poor impact of birth cohorts, which is an important factor in the development of TB. The age-period-cohort (APC) model, a statistical method widely used in recent decades in economics, sociology, and epidemiology, can quantitatively estimate the efficacy of different age, period, and birth cohort groups for TB by separating the effects of these three dimensions and controlling for confounding factors among the time variables. The purpose of this paper is to briefly review the model, focus on the application of the existing APC model in the field of TB, and explain its advantages and disadvantages. This study will help to provides a theoretical basis and reference for using the APC model in TB analysis and prediction.

Optimization and Implementation Framework for Connected Demand Responsive Transit (DRT) Considering Punctuality

Demand Responsive Transit (DRT) is gaining attention as a flexible and efficient solution for connecting urban transit hubs, but challenges such as travel time variability and punctuality remain significant barriers. This study develops a robust optimization framework with variable travel speed to address these issues, minimizing user and operator costs while reducing transfer waiting times. The framework incorporates variable travel speeds and employs a genetic algorithm to optimize routes and operations compared to many studies using constant commercial speed. Experiments conducted in Hwaseong, South Korea, analyzed scenarios with varying service rates, vehicle capacities, and detour ratios. Results show that implementing punctuality-constrained DRT reduces total travel times by 14% compared to subways and 36% compared to buses, highlighting its potential to significantly improve user convenience and operational efficiency. The findings suggest that carefully designed DRT systems with highly reliable punctuality can enhance urban mobility by integrating seamlessly with existing transit networks, providing a cost-effective and reliable alternative to traditional public transport.

Dwell Time Variability at Bus Bays at Six Lane Road of Araniko Highway: Impact of Passenger Attributes and Bus Variation

Dwell Time Variability at Bus Bays at Six Lane Road of Araniko Highway: Impact of Passenger Attributes and Bus Variation

Identifying gait differences between Alzheimer's disease and dementia with Lewy bodies and their associations with regional amyloid deposition.

We aimed to compare gait between individuals with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and cognitively unimpaired (CU) individuals and to evaluate the association between gait and regional amyloid beta (Aβ) burden in AD and DLB. We included 420 participants (70 AD, 70 DLB, 280 CU) in the Mayo Clinic Study of Aging (MCSA). Gait was assessed using a pressure-sensor walkway. Aβ deposition was analyzed with Pittsburgh compound B (PiB) positron emission topography (PET). The DLB group had reduced stride velocity, step length, and stride width variability, as well as increased double support percentage (%DS) and variability in step length, swing time, and step time compared to the AD and CU groups. Aβ burden was not associated with any gait outcomes. This study provides additional evidence that gait differs between AD and DLB. Larger studies are needed to investigate associations between Aβ burden and gait outcomes in dementia. Gait was more impaired in dementia than in cognitively unimpaired (CU) controls. Compared with Alzheimer's disease (AD), Dementia with Lewy bodies (DLB) had more impaired pace, variability, and postural control. Step length and double support (%) distinguished DLB and AD with moderate accuracy.

Rapid focusing of light through turbid water based on a digital micromirror device

The strong scattering and time variability of dynamic scattering media pose a major challenge to optical focusing and imaging applications. In this paper, a feedback wavefront shaping technology based on digital micromirror devices is proposed, which combines the hardware design and algorithm optimization. Through a new hardware-triggered image acquisition system and dynamic subgroup genetic algorithm, the image acquisition rate and focusing efficiency are significantly improved, and the light is quickly focused in dynamic turbid water. The imaging speed is about 34.89 times that of the traditional method based on a software-triggered image acquisition system and genetic algorithm. The experimental results of focusing through a milk solution show that this technique has a remarkable effect in improving the efficiency of dynamic focusing and the light intensity of focusing region, which provides an effective optimization strategy for optical imaging technology through a dynamic scattering medium in the future.

Performance comparison in workflow efficiency between a remotely installed 3D workstation and an on-premises image processing workstation for dental cone-beam CT image reconstruction.

This study aims to compare the image processing times of dental cone beam CT (CBCT) images using a remote medical image processing workstation (RW) versus on-premises image processing (OP) and assess its impact on workflow efficiency. Data from 100 CBCT cases were randomly selected and processed using the OP3D VISION 17-19DX (EH Japan Co., Ltd.). In the OP environment, OnDemand 3D Dental (Cybermed Inc.) was used on a local terminal, while the RW setup involved a remote workstation-ZIO STATION (Ziosoft Inc.) connected via a 2 Gbps network. Seven experienced dentists processed the same data in both environments, and various processing times, including data transfer, re-slicing, 3D reconstruction, and PACS transfer, were compared. The RW environment showed significantly shorter data transfer and re-slicing times than the OP environment. However, 3D image reconstruction times were similar between the two setups. Overall, processing time was significantly reduced in the RW environment. Variability in processing times among operators was observed, with most achieving reductions in the RW environment. Remote processing of dental CBCT images using a dedicated image processing device offers equivalent or improved performance compared to on-premises processing. This approach can enhance workflow efficiency by reducing processing times and freeing up local resources, although further research is needed to optimize remote display protocols and multi-client access.

Optimization of Ultrasound Assisted Extraction on Total Phenolic Content of Saussurea lappa Root Using Response Surface Methodology and Evaluation of their Antioxidant Properties

Response Surface Methodology (RSM) is a scientific method are useful in optimization of experimental set ups. It requires observing and gathering information about how process and system works. In an experiment, some input x’s transform into an output that has one or more observable response variables y. Therefore, useful results and conclusions can be drawn by experiment. In the present paper, Box-Behnken Design (BBD) was applied to the ultrasound assisted reaction using three factors and three variables to optimize the extraction process. The three independent variables during the extraction of phytochemicals from the Saussurea lapparoot were temperature, concentration of solvent a time of sonication and dependent variable is yield, total phenolic content and antioxidant activity. The total phenol contents of the optimized extraction process were studied. The optimized extraction was evaluated for antioxidant studies using DPPH (diphenyl-1-picrylhydrazyl) method. The optimized conditions for the maximum extraction 50% were when temperature was maintained at 45 °C with 60% methanol concentration and sonication time around 50 mins and the 80% of inhibition of DPPH free radical was achieved.

Child Maltreatment Characteristics and Adult Physical Multimorbidity in Germany

Associations between child maltreatment (CM) and health have been studied broadly, but most studies focus on multiplicity (number of experienced subtypes of CM). Studies assessing multiple CM characteristics are scarce, partly due to methodological challenges, and were mostly conducted in patient samples. To determine the importance of CM characteristics in association with physical multimorbidity in adulthood for women and men in a German representative sample. This survey study used data from a representative sample of the German population between July and October 2021. Households were randomly selected within regional areas by a German demographic consultation company, and the Kish-Grid method was applied to ensure random participation. Reasons for nonparticipation were refusal of the selected household or target person to participate and failure to contact a household after 4 visits. Analyses took place between June 2023 and July 2024. Characteristics of CM, including subtypes of CM, multiplicity, age at time of maltreatment (timing), number of years of experienced maltreatment (duration), frequency, and subjective severity (measured with the ISPCAN Child Abuse Screening Tools Retrospective version questionnaire). The main outcome was physical multimorbidity, defined as the sum score of lifetime leading morbidity and mortality causes in Western countries (obesity, diabetes, cancer, hypertension, myocardial infarction, chronic obstructive pulmonary disease, and incident stroke). Conditioned random forest regression analyses (a machine learning regression technique) were conducted to examine what characteristics of CM were most importantly associated with physical multimorbidity in adulthood while considering all other variables in the model. Of 5908 individuals invited, the study sample included 2514 participants (response rate, 42.6%), with 1297 (51.6%) women (mean [SD] age, 50.6 [17.9] years) and 1217 (48.4%) men (mean [SD] age, 49.5 [18.2] years). Duration of CM was the most important factor for physical multimorbidity in adulthood for both women (importance = 0.595; 95% CI, 0.599-0.601) and men (importance = 1.389; 95% CI, 1.386-1.394). Duration and timing variables were more importantly associated with outcomes than multiplicity in women and men. For women, severity and experiencing CM at age 4 years was significantly associated with physical multimorbidity in adulthood. For men, experiencing CM at age 11 years was most importantly associated with physical multimorbidity in adulthood. In this survey study, conditioned random forest regression analyses were applied to provide insights in the importance of duration and timing of CM for physical health in addition to the frequently studied multiplicity. These findings suggest that CM assessments should be considered in diagnostics of individuals with physical health conditions and may also inform strategies to mitigate the risk.

Compound Poisson particle approximation for McKean–Vlasov SDEs

Abstract We present a comprehensive discretization scheme for linear and nonlinear stochastic differential equations (SDEs) driven by either Brownian motions or $\alpha $-stable processes. Our approach utilizes compound Poisson particle approximations, allowing for simultaneous discretization of both the time and space variables in McKean–Vlasov SDEs. Notably, the approximation processes can be represented as a Markov chain with values on a lattice. Importantly, we demonstrate the propagation of chaos under relatively mild assumptions on the coefficients, including those with polynomial growth. This result establishes the convergence of the particle approximations towards the true solutions of the McKean–Vlasov SDEs. By only imposing moment conditions on the intensity measure of compound Poisson processes our approximation exhibits universality. In the case of ordinary differential equations (ODEs) we investigate scenarios where the drift term satisfies the one-sided Lipschitz assumption. We prove the optimal convergence rate for Filippov solutions in this setting. Additionally, we establish a functional central limit theorem for the approximation of ODEs and show the convergence of invariant measures for linear SDEs. As a practical application we construct a compound Poisson approximation for two-dimensional Navier–Stokes equations on the torus and demonstrate the optimal convergence rate.

Evaluation and Adjustment of Historical Hydroclimate Data: Improving the Representation of Current Hydroclimatic Conditions in Key California Watersheds

The assumption of stationarity in historical hydroclimatic data, fundamental to traditional water resource planning models, is increasingly challenged by the impacts of climate change. This discrepancy can lead to inaccurate model outputs and misinformed management decisions. This study addresses this challenge by developing a novel monthly data adjustment approach, the Runoff Curve Year–Type–Monthly (RC-YTM) method. The application of this method is exemplified at five key California watersheds. The RC-YTM method accounts for the increasing variability and shifts in seasonal runoff timing observed in the historical data (1922–2021), aligning it with the contemporary climate conditions represented by the period from 1992 to 2021 at the study watersheds. This method adjusts both annual and monthly streamflow values using a combination of precipitation–runoff relationships, quantile mapping, and water year classification. The adjusted data, reflecting current climatic conditions more accurately than the raw historical data, serve as valuable inputs for operational water resource planning models like CalSim3, commonly used in California for water management. This approach, demonstrably effective in capturing the observed climate change impacts on streamflow at monthly timesteps, enhances the reliability of model simulations representing contemporary conditions, which can lead to better-informed decision-making in water management, infrastructure investment, drought and flood risk assessment, and adaptation strategies. While focused on specific California watersheds, this study’s findings and the adaptable RC-YTM method hold significant implications for water resource management in other regions facing similar hydroclimatic challenges in a changing climate.

An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network

As renewable energy continues to penetrate modern power systems, accurate short-term load forecasting is crucial for optimizing power generation resource allocation and reducing operational costs. Traditional forecasting methods often overlook key factors such as holiday load variations and differences in user electricity consumption behavior, resulting in reduced accuracy. To address this, we propose an optimized short-term load forecasting method based on time and weather-fused features using a ConvLSTM-3D neural network. The Prophet algorithm is first employed to decompose historical electricity load data, extracting feature components related to time variables. Simultaneously, the SHAP algorithm filters weather variables to identify highly correlated weather features. A time attention mechanism is then applied to fuse these features based on their correlation weights, enhancing their impact within the time series. Finally, the ConvLSTM-3D model is trained on the fused features to generate short-term load forecasts. A case study using real-world data validates the proposed method, demonstrating significant improvements in forecasting accuracy.

An update on drug repurposing in Parkinson's disease: Preclinical and clinical considerations.

The strategy of drug repositioning has historically played a significant role in the identification of new treatments for Parkinson's disease. Still today, numerous clinical and preclinical studies are investigating drug classes, already marketed for the treatment of metabolic disorders, for their potential use in Parkinson's disease patients. While drug repurposing offers a promising, fast, and cost-effective path to new treatments, these drugs still require thorough preclinical evaluation to assess their efficacy, addressing the specific neurodegenerative mechanisms of the disease. This review explores the state-of-the-art approaches to drug repurposing for Parkinson's disease, highlighting particularly relevant aspects. Preclinical studies still predominantly rely on traditional neurotoxin-based animal models, which fail to effectively replicate disease progression and are characterized by significant variability in model severity and timing of drug treatment. Importantly, for almost all the drugs analyzed here, there is insufficient data regarding the mechanism of action responsible for the therapeutic effect. Regarding drug efficacy, these factors may obviously render results less reliable or comparable. Accordingly, future preclinical drug repurposing studies in the Parkinson's disease field should be carried out using next-generation animal models like α-synuclein-based models that, unfortunately, have to date been used mostly for studies of disease pathogenesis and only rarely in pharmacological studies.

Influence of the Variability in Motor Unit Discharge Times and Neural Drive on Force Steadiness During Submaximal Contractions with a Hand Muscle.

Our purpose was to compare the influence of the spectral content of motor unit recordings on the calculation of electromechanical delay and on the prediction of force fluctuations from measures of the variability in discharge times and neural drive during steady isometric contractions with the first dorsal interosseus muscle. Participants (n = 42; 60 ± 13 yrs) performed contractions at 5% and 20% MVC. After satisfying inclusion criteria, high-density surface EMG recordings from a subset of 23 participants were decomposed into the discharge times of 530 motor units. The force and Cumulative Spike Train (CST) signals were cross-correlated with a novel filtering approach to determine the electromechanical delay. Force and CST signals were bandpass filtered with three bandwidths (0.75 - 5 Hz, 0.75 - 2 Hz, 2 - 5 Hz) to determine the influence of spectral content on the precision of the electromechanical-delay measurement. Subsequently, the variability in the discharge times of motor units was quantified as the coefficient of variation for interspike interval (CVISI) and the variability in neural drive was represented as the standard deviation of the cumulative spike train (SDCST). The main findings were that all frequencies (0.75 - 5 Hz) were needed to determine the electromechanical delay and that the force fluctuations were best explained by measures of variability in both discharge times and neural drive (CVISI and SDCST) at 5% MVC force but only the variability in neural drive (SDCST) at 20% MVC force. These findings indicate that the source of the force fluctuations during the steady submaximal contractions with the hand muscle differed for the two target forces.

How do red foxes (Vulpes vulpes) explore their environment? Characteristics of movement patterns in time and space

BackgroundMany animals must adapt their movements to different conditions encountered during different life phases, such as when exploring extraterritorial areas for dispersal, foraging or breeding. To better understand how animals move in different movement phases, we asked whether movement patterns differ between one way directed movements, such as during the transient phase of dispersal or two way exploratory-like movements such as during extraterritorial excursions or stationary movements.MethodsWe GPS collared red foxes in a rural area in southern Germany between 2020 and 2023. Using a random forest model, we analyzed different movement parameters, habitat features—for example landclasses and distances to linear structures—and time variables (season and time of day) within red fox exploratory, transient and stationary movement phases to characterize phase specific movement patterns and to investigate the influence of different variables on classifying the movement phases.ResultsAccording to the classification model, the movement patterns in the different phases were characterized most strongly by the variables persistence velocity, season, step length and distance to linear structures. In extraterritorial areas, red foxes either moved straight with high persistence velocity, close to anthropogenic linear structures during transient movements, or more tortuously containing a higher variance in turning angles and a decrease in persistence velocity during exploratory-like movements. Transient movements mainly took place during autumn, whereas exploratory-like movements were mainly conducted during winter and spring.ConclusionMovement patterns of red foxes differ between transient, exploratory and stationary phases, reflecting displacement, searching and resident movement strategies. Our results signify the importance of the combined effect of using movement, habitat and time variables together in analyzing movement phases. High movement variability may allow red foxes to navigate in extraterritorial areas efficiently and to adapt to different environmental and behavioral conditions.

Alcohol withdrawal syndrome presentations to emergency departments in the United States from 2015 to 2023.

Alcohol withdrawal syndrome (AWS) is a common condition prompting emergency department (ED) presentation. However, there are limited recent, large-scale, robust data available on the incidence, admission, and medical treatment of AWS in the ED. This was a retrospective cohort study of ED presentations for AWS from January 1, 2016, to December 31, 2023, using Epic Cosmos. All ED visits with ICD-10 codes corresponding to AWS were included. Outcomes included percentage of total ED visits, percentage admitted, length of stay (LOS), and medications administered. Binary logistic regression models were used to measure the relationship between time and dependent variables and reported as odds ratios (ORs) with 95% confidence intervals (CIs). Out of 242,804,798 ED encounters, 670,430 (0.28%) visits were due to AWS with a rise over time (OR 1.074, 95% CI 1.072-1.075). Of these, 386,618 (57.7%) were admitted (46.2% inpatient floor, 11.5% ICU). Median (IQR) hospital LOS was 3 (2-5) days and median (IQR) ICU LOS was 2 (1-4) days. Among all ED patients, benzodiazepine use declined over time (84.9% to 77.1%; OR 0.917, 95% CI 0.914-0.920), while phenobarbital (4.0% to 21.2%; OR 1.255, 95% CI 1.250-1.259) and gabapentin (11.0% to 16.3%; OR 1.054, 95% CI 1.050-1.057) use increased. Oral and intravenous (IV) benzodiazepines were common (63.1% and 66.6%, respectively). Among IV benzodiazepines, lorazepam was most common (59.9%). Among those discharged from the ED, 29.0% were prescribed benzodiazepines (chlordiazepoxide 21.1%, lorazepam 5.5%, diazepam 1.9%). Anticraving medications, such as gabapentin (1.5%), naltrexone (0.4%), and acamprosate (<0.1%) were uncommon, but rising over time. AWS represents a common reason for ED presentation, with most patients being admitted. We identified a rising incidence with a shift in management to include agents such as phenobarbital and gabapentin. These findings provide important evidence on current trends in AWS to inform health policy and knowledge translation efforts as well as emphasizing the need for ongoing research and evaluation of clinical practices to optimize outcomes for patients with AWS.

Dynamic Supervised Principal Component Analysis for Classification

This paper introduces a novel framework for dynamic classification in high dimensional spaces, addressing the evolving nature of class distributions over time or other index variables. Traditional discriminant analysis techniques are adapted to learn dynamic decision rules with respect to the index variable. In particular, we propose and study a new supervised dimension reduction method employing kernel smoothing to identify the optimal subspace, and provide a comprehensive examination of this approach for both linear discriminant analysis and quadratic discriminant analysis. We illustrate the effectiveness of the proposed methods through numerical simulations and real data examples. The results show considerable improvements in classification accuracy and computational efficiency. This work contributes to the field by offering a robust and adaptive solution to the challenges of scalability and non-staticity in high-dimensional data classification.