Optimal Response Time Research Articles

Comparative exploration on EEG signal filtering using window control methods

Electroencephalography (EEG) is used to monitor brain activity. The brain signals consist of different frequency band signals delta, theta, alpha, beta, and gamma waves. The signals are affected by external noise which reduces the quality of the EEG signal due to which it becomes difficult to do further processing of EEG signals like feature extraction or extraction of meaningful features from EEG signal. Therefore, it becomes important to filter the noise from the EEG signal before feature extraction or classification of the EEG signal. The research article presents an overview of different types of windowing filter techniques like Rectangular, Bartlett, Hamming, Hanning, and Kaiser windows applied for finite impulse response (FIR) behavior which is used for EEG signal processing for different brain waves processed in different frequency bands. The comparative analysis is carried out in terms of the response time of brain frequency bands for different windowing filter techniques using the MATLAB 2023 signal processing simulation tool. The novelty of the work lies in estimating minimum latency and appropriate filter selection for various typical EEG waves, since the EEG signals are pre-supposed in the hardware chip design, noise elimination is the first step in high-performance computing applications. The Bartlett window band stop has an optimal response time of 12.666 s for delta waves, a highpass filter with a response time of 16.187 s for theta waves, a bandpass with a response time of 13.122 s for alpha waves, a highpass filter with a response time of 17.866 s for beta waves, and a highpass filter with a response time of 13.797 s for gamma waves. The Barlett window FIR filter is well-suited for EEG applications.

The comparison study of PI and Sliding Mode control techniques for Buck-Boost converters

This paper compares the performance of a Sliding Mode Control (SMC) and Proportional-Integral (PI) controller under different voltage and load conditions. The study's findings underscore the need to account for load variations in system designs and to continuously seek system optimization irrespective of the controller type chosen. PI controller demonstrates effectiveness under certain circumstances. However, significant drops in voltage and current during abrupt load changes are obtained. On the other hand, SMC enables superior adaptability, efficiently managing voltage transients and load variations without any electrical disturbances, thereby maintaining system stability. A comparative analysis further emphasizes the SMC's superior time response and robustness against reference voltage changes. Consequently, SMC is proven to be a preferable choice over the PI controller in systems experiencing frequent voltage and load variations. However, both controllers achieve the potential for further response time optimization and stability.

Empowering Microservices: A Deep Dive into Intelligent Application Component Placement for Optimal Response Time

Empowering Microservices: A Deep Dive into Intelligent Application Component Placement for Optimal Response Time

Intelligent prediction of incipient fault in vinyl chloride production process based on deep learning

With the development of industrial information technology, deep learning (DL) has been successfully applied in chemical process fault detection. However, the features of incipient faults could not be more evident at the initial stage, which makes it difficult for deep learning to fully mine the feature probability information, resulting in poor performance of incipient fault monitoring. This paper proposes an intelligent predictive model based on mechanistic modeling to predict incipient faults and determine optimal response times for vinyl chloride production (VCP) processes. Firstly, a dynamic simulation of the VCP production process is performed to obtain datasets of early faults. Secondly, data dimensionality reduction is performed by cleverly combining spearman ranking correlation coefficient (SRCC) and slow feature analysis (SFA). Then, a long short-term memory network (LSTM) with attention mechanism (AM) is built to predict the future trends of key variables. Finally, the optimal response time for different types of incipient faults is determined by comparing the effectiveness of various control schemes. Compared with traditional methods, the R2 of the proposed prediction model corresponding to different step sizes can reach 99.7%, 99.5%, 99.2%, and 98.7%. In addition, the response times for single and parallel faults are 2 and 2.5 h, which helps to control and eliminate potential incidents in advance.

The Influence of Response Time and Feedback Type on the User Experience of Voice Interaction among Older Adults

With the maturity and development of digital technology, voice interaction has been widely used in different devices or scenarios. However, older adults have low satisfaction and acceptance levels when using voice interaction. To improve older adults’ user experience of voice interaction, the current study explored the effect of response time and feedback type on the user experience of voice interaction among older adults. A Xiaodu smart speaker was used as the experimental device, and the response time was recorded using artificial breakpoints. Our study consisted of two experiments that explored the voice wake-up and voice dialogue stages under different combinations of response time and feedback type and older adults rated their satisfaction, comfort, anthropomorphism, and perceived speed level. The results showed that: (1) the optimal wake-up response time for older adults is 250–1000 ms, and the optimal dialogue response time is 600–1500 ms. (2) As response time increases, older adults’ satisfaction, comfort, and anthropomorphism toward smart speakers decrease. In the wake-up stage, the scores in the time period >2000 ms is significantly lower than those shorter response times. In the dialogue stage, the scores in the time periods >3000 ms are significantly lower than those shorter response times. (3) In the wake-up stage, the voice-based prompt “Hello, I’m Xiaodu” scores highest among all kinds of feedback types. In the dialogue stage, the social-oriented feedback scores higher in satisfaction and comfort than task-oriented feedback. The results of the current study could provide implications for age-friendly voice interaction design.

AdaBoost‐powered cloud of things framework for low‐latency, energy‐efficient chronic kidney disease prediction

AbstractThe United Nations' sustainable development agenda has set an ambitious goal of reducing premature mortality from non‐communicable diseases by 33% by 2030. Among these diseases, chronic kidney disease (CKD) is a significant contributor to both morbidity and mortality. Integrating the Internet of Things (IoT) and cloud computing in healthcare has gained momentum, particularly in remote patient monitoring. However, it is essential to acknowledge that cloud computing has limitations, particularly in handling vast volumes of Big Data, mainly due to scalability and latency concerns. This article proposes a novel framework, AdaBoostCoTCKD, to mitigate latency issues, minimize response times, reduce power consumption, and optimize network resources for predicting CKD. The framework leverages the synergy between the AdaBoost machine learning technique and fog computing paradigms to enhance the precision and efficiency of CKD prediction methods. In addition, it introduces an auxiliary cloud‐based database, enriching the pool of future insights and facilitating prospective database infrastructure expansions. This augmentation is expected to impact predictive accuracy positively. We conducted comprehensive experiments to demonstrate the effectiveness of our approach. Our model achieved an impressive training accuracy of 99.928% and testing accuracy of 99.975%, while the fog environment reduced latency by 31% and energy consumption by 75% compared to traditional cloud‐based solutions. Our proposed system enables early CKD detection and offers advantages over cloud‐only solutions, providing a robust and efficient platform for healthcare IoT applications with significant clinical value. These promising results underscore the potential of combining fog computing and the AdaBoost machine learning technique to advance healthcare by addressing latency, response time, power consumption, and network resource optimization challenges in CKD prediction.

Cost and response time optimization of edge architectures

The cloud service provider market has recently expanded its offerings by providing edge as a service. This involves offering resources equivalent to those already available in the cloud, but through data centers located closer to the end user, with the goal of improving service latencies. Application providers face the challenge of selecting appropriate resources, both from the edge and cloud, to deploy their applications in a way that minimizes deployment costs while satisfying latency requirements. This paper presents Edarop (EDge ARchitecture OPtimization), an innovative orchestration mechanism for the optimal allocation of virtual machines in geographically distributed edge and cloud infrastructures. Edarop is capable of handling different edge and cloud vendors, each offering various types of VMs in different regions, with different prices, and network latencies. It also supports multiple simultaneous applications with different latency requirements and load profiles. Edarop employs Integer Linear Programming (ILP) to ensure the globally optimal solution within a reasonable time frame for the considered use cases. Several variants of the mechanism are provided, depending on whether the objective is to minimize cost, response times, or both. These variants are compared to each other and to alternative approaches, with the results showing that, unlike other methods, Edarop consistently respects latency constraints while minimizing the proposed objectives.

Wearable cassette for paper-based aptasensor designed to detection of illicit drug; ketamine- a wearable cassette prototype comparison study

The advent of 3D printing technology has spurred innovation, particularly in healthcare and biosensing. One notable application is the creation of wearable biosensors for detecting substances like ketamine, a potent anesthetic and pain reliever with medical and recreational uses. Monitoring ketamine levels is crucial due to potential misuse and health risks. Utilizing 3D printing, manufacturers can produce intricate and customizable wearable biosensors designed for ketamine detection. This flexibility permits the incorporation of various sensor types, enhancing accuracy. Traditional detection methods are often cumbersome, making 3D printing a transformative tool for real-time monitoring. The application of 3D printing in wearable biosensors has the potential to revolutionize personalized healthcare, ensuring the safe and effective usage of ketamine. In this paper 3D printed paper-based wearable aptamer cassette (3DP-PWC) has been developed by immobilizing Ketamine Aptamer on ZnO-NPs electrodes. Electrochemical techniques such as cyclic voltammetry (CV), linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS) were employed for validating results. The sensor’s versatility was demonstrated across beverages encompassing both alcoholic and non-alcoholic options. Two prototypes—a bracelet and a pendant—were developed and compared, showing promising results. Here, we reported a 3D-printing paper based wearable aptasensor for the ketamine detection. This pioneering developed sensor showed a low limit detection (LOD) of 0.01 μg/mL (lower than the physiological detection threshold 0.084 μg/mL) with linear-range was between 0.01 and 5 μmL and an optimal response time of 25 s.

Modeling the Temporal Response of Gated ZnO Nanowire Field Emitter by Considering the Charging and Self-Heating Effect for Improving the Response Speed

ZnO nanowire is a promising candidate for large-area gated field emitter arrays. How to improve its temporal response is one of the key problems to be solved for applications. In this work, a device model for a gated ZnO nanowire field emitter with consideration of charging and self-heating effect has been established to investigate its temporal response. It is found that while the charging effect is responsible for the delay at the beginning of the pulse, the self-heating effect which induces delay due to the thermal conduction process can shorten the charging time because of its lowering of nanowire resistance. The response time can be minimized when these two effects are balanced at an optimal field which is below the critical field for thermal runaway. We further investigate the optimal response time of a nanowire with the same resistance but a different length, radius, and electrical properties. The results imply that a lower heat capacity and higher critical temperature for thermal runaway are in favor of a shorter response time, which must be taken into account in the reduction in nanowire resistance for improving response speed. All the above should be useful for the device design of a fast-response gated ZnO nanowire field emitter array.

When Does the Gittins Policy Have Asymptotically Optimal Response Time Tail in the M/G/1?

Looking Beyond Mean Delay in Queue Scheduling A central aim of queueing theory is to design scheduling policies that reduce delays. Much of literature focuses on minimizing mean delay, but, in practice, it can be more important to reduce the delay tail—that is, the probability of especially large delays. There is a subtle interplay between these objectives. For instance, the SRPT policy (Shortest Remaining Processing Time) prioritizes short jobs over long jobs, which optimizes mean delay. Sometimes, SRPT also has optimal delay tail, but other times, its delay tail is pessimal. Thus, good mean delay need not imply good delay tail. The article When Does the Gittins Policy Have Asymptotically Optimal Response Time Tail in the M/G/1? explores the interplay between mean delay and delay tail for systems with unknown job durations. The article studies the Gittins policy, which optimizes mean delay in this setting. Curiously, its delay tail can be optimal, pessimal, or in between, depending on the job duration distribution. Fortunately, the worst case can be avoided: the article shows how to modify Gittins to avoid pessimal delay tail while maintaining near-optimal mean delay.

Influence of samarium on the structural, magnetic, and gas sensing performance of cadmium zinc ferrites

Cadmium zinc ferrites Cd0.5Zn0.5SmxFe2−xO4 nanoparticles were synthesized with different concentrations x = 0.00, 0.01, 0.02, 0.04, 0.06, and 0.08, via the wet chemical co-precipitation method. The effects of the Sm3+ doping on the structural, morphological, compositional, and magnetic properties have been investigated. The structural analysis is performed using x-ray diffraction (XRD) with Rietveld refinement. The results indicate great crystallinity in the FCC Fd3m spinel structure of Cd0.5Zn0.5SmxFe2−xO4 nanoparticles. The crystallite size was estimated using Debye–Scherrer, Williamson–Hall, Size-strain plot (SSP), and Halder-Wagner (H-W) methods. It revealed a decreasing trend with the increase of Sm-doping concentrations until the solubility limit at around x = 0.04. The spherical morphology of the samples was investigated using transmission electron microscopy (TEM) with minor agglomeration as a benefit of using the capping agent polyvinylpyrrolidone (PVP). Raman spectroscopy validates the incorporation of trivalent Sm3+ in the octahedral sites. X-ray photoelectron spectroscopy (XPS) verified the elemental compositions as well as the purity of the samples and the incorporation of the dopants. A vibrating sample magnetometer (VSM) was used to study the magnetic properties, and which indicates the superparamagnetic behavior of the prepared samples. The prepared samples were tested as liquefied petroleum gas (LPG) sensors by studying their sensitivity, optimum working temperature, response time, and recovery time. The doping of samarium ions reveals a great increase in LPG sensing sensitivity and optimum temperature with decreasing response and recovery times.

Evaluation of Car-Following Model for Mixed Autonomous and Human Driven Vehicles on Road Facilities.

Driverless cars are emerging slowly but bear the opportunity to improve the traffic system efficiency and user comfort. For the near future, a mix of human-driven and driver-less vehicle co- existence is inevitable. The quest to integrate autonomous and human-driven vehicles has created numerous critical questions in the road traffic system: How can autonomous and human- driven vehicles co-exist efficiently? What are the prospects of breaching the existing car-following model widely adopted to control the longitudinal movement of vehicles and control the driver’s maintenance of a safe distance? There is no doubt, that the emergence of autonomous vehicles and their integration is altering the existing traffic theories and the idea behind the car-following model. Currently, researchers are working hard to efficiently describe the physics behind the traffic flow theory of car-following models in mixed traffic. This work investigated a homogeneous car-following model sensitivity with varying human drivers’ reaction time in mixed traffic. The ability to predict the optimal vehicle response time in a platoon to the behaviour of its predecessor is vital in predicting the effect a change in the driving environment of mixed traffic has on traffic flow. The major complexities surrounding the car- following model in a mixed traffic flow environment are how the traffic participants share the road intersection space and the car-following behavior of human-driven vehicles. Investigation in microscopic traffic flow describes the way a single car be- haves and then introduces the impact another car has on a car’s behavior to address the mixed traffic flow. The traffic speed performance under the different traffic conditions suggests that the proposed mixed traffic car-following model outperforms the existing car-following model for human-driven vehicles with an average of 22.59mph. There is a significant throughput improvement in a mixed traffic environment when the distance headway of autonomous vehicles is adjusted to support the research hypothesis.

Core-shell molecularly imprinted polymer sensor for enrofloxacin determination in various matrices: a novel, sustainable One Health analytical strategy.

Antibiotics are essential in treating infectious diseases in both humans and animals, and they are also utilized to enhance animal growth. However, their widespread use has led to significant environmental concerns. After administration of antibiotics, a substantial portion of them is excreted by animals, contaminating various environmental compartments. This problem is examined from the One Health perspective which seeks to balance human, animal, and environmental health for the benefit of global well-being. Enrofloxacin (ENR) is a commonly used antibiotic in veterinary medicine. Despite its efficacy in animal health, ENR is not approved for human use due to its associated toxicities. To address ENR detection, a sensor built upon a core-shell molecularly imprinted polymer (MIP) was created for the determination and testing of ENR in different matrices. Offering a miniaturized and reproducible tool for determining antibiotic residues in biological and environmental samples helps in revolutionizing the way we monitor and control antibiotic usage and contamination in various settings. The fabricated sensor demonstrated an optimum response time and functioned effectively across the pH range of 2.0 to 5.0. The potential profile displayed a linear correlation within a varying concentration spectrum of 1.0 × 10-5 M to 1.0 × 10-2 M characterized by a slope of 57.21 mV per decade. Furthermore, a comprehensive assessment of the environmental sustainability of the developed method was carried out using the Analytical Greenness calculator, AGREE algorithm. Lastly, an examination of the method's level of environmental friendliness was pursued using the newly developed RGB12 model to evaluate its "whiteness" level.

System-Level Energy-Aware Design Methodology Towards End-To-End Response Time Optimization

Energy provision is known and limited for battery-constrained/powered systems (e.g., mobile phones, electric cars, robots, and wireless sensor networks). System-level energy-aware design methodologies have been proposed to control energy usage for energy provision guarantee, and to optimize the end-to-end response time for high-performance in heterogeneous energy-aware systems, where energy pre-assignment strategies are considered as their main contributions. However, when pre-assigning energy to tasks, these strategies either are overly pessimistic or adopt an average manner without a customizable approach to specific tasks. In this paper, we propose an effective algorithm called ratio-based energy pre-assignment (REP) to be customizable to split the available energy according to the energy ratios of individual tasks. Experiments confirm that REP outperforms the state-of-the-art three algorithms of the same type. After REP is used, we further propose a new innovation algorithm called energy surplus reduction (ESR) to reduce the execution time of small partial tasks, thereby optimizing the end-to-end response time within its energy provision. Experiments confirm ESR can reduce the energy usages of small partial tasks to achieve sufficient response time optimization.

Multi-Network Latency Prediction for IoT and WSNs

The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks.

(Invited) Electrochemically Driven Ion-Exchange Remediation of Water Containing High Fluoride Content

Remediation of water contaminated by anthropogenic activities is required to provide clean water for human consumption and prevent damage to the environment. The scale of the challenge is signaled by its inclusion in the UN 17 Sustainable Development Goals [1]. Major contributors to the problem are industrial activity and agriculture. The former commonly results in the release of metals; analysis and separation of such cationic species have received considerable attention [2]. In the latter instance, run-off of fertilizers used to enhance crop yields can result in unacceptable levels of nitrate or phosphate in natural waters. While monitoring of anionic species is widely undertaken using ion selective electrodes [3], the promise of electrochemical separation technologies [4] in this area is less well developed.For anthropogenic causes, remediation is the last resort following failure to prevent release of toxic materials into the environment. In contrast, there are instances where the “contaminant” is natural: fluoride is a prime example. The beneficial dental health effects of fluoride are widely appreciated, but excessive amounts are harmful to teeth, bones and various organs [5]. The World Health Organization (WHO) identifies the optimum fluoride level in drinking water to be 0.5-1.0 mg/L. In many regions, water is fluoridated to realize the health benefits. However, local geological conditions many countries can result in natural water fluoride levels significantly above the recommended upper limit of 1.5 mg/L.This presentation addresses the challenges associated with remediation of anions, with particular focus on fluoride. At the outset, it is recognized that for potable water the requirement is not full de-ionization but selective removal of contaminants: this is a separation challenge. The attributes, performance and potential for scale-up of a range of (electro)chemical remediation strategies will be discussed in general. We then focus on electrochemically switched ion-exchange (ESIX) [6], for which application to water softening [7] and perchlorate removal [8] has been demonstrated.We explore the facility of aniline-based (co-)polymer films to reversibly extract and eject fluoride ions as the charge (oxidation state) of the films are subject to electrochemical control. Homopolymer and copolymer films based on aniline, o-aminophenol and o-toluidine were deposited potentiodynamically. Film thickness was controlled via the number of potential cycles: the resultant electroactive site population was assayed coulometrically in monomer-free background electrolyte. Film mass was assayed gravimetrically using the EQCM. For relatively thin films, correlation of film mass and charge capacity revealed the solvent content. During deposition of thicker films, acoustic admittance data revealed the progressive evolution of viscoelastic effects. Co-monomer feedstock has a significant influence on film deposition rate, absolute solvent population and redox-driven solvent population change [9].The ion-exchange capacity of the films for redox-driven fluoride uptake was determined upon exposure to aqueous solutions of varying fluoride concentration, both in the absence and presence of chloride as a competing anion. EQCM mass and charge responses, respectively, provide overall “ion+solvent” and “solvent” population changes: correlation permits separation of fluoride and water uptake/release. Copolymer composition significantly influences the ease of fluoride uptake and release and reveals ion trapping. Complementary data on the dynamics of ion and solvent transfers are provided by shorter timescale experiments, involving voltammetry at faster scan rates and chronoamperometry. Correlation of gravimetric and coulometric data permits temporal separation of fluoride and solvent entry and exit.Practical exploitation of these promising fundamental observations will require scale-up and optimization of cycle life, film viscoelasticity and response time as functions of film thickness. We speculate that spatially variant film solvation will be a critical parameter. To this end, we consider the viability of neutron reflectivity measurements with isotopic substitution to determine the spatial distributions of fluoride and solvent in films of graded composition.

Improving Accuracy of Sensor Data by Frequent Pattern Mining Algorithm Using Edge Computing

Sensor data plays a crucial role in various applications, including industrial automation, environmental monitoring, and healthcare. However, the accuracy of sensor data can be adversely affected by factors such as noise, latency, and data transmission issues in existing systems. This study focuses on identifying the disadvantages associated with current sensor data collection and analysis methods and explores the use of frequent pattern mining to enhance data accuracy. The research presents a comprehensive overview of Edge computing in conjunction with sensor systems and the Internet of Things, highlighting the complexities in processing sensor data using conventional methods and the advantages of employing frequent pattern mining. The study concludes that the utilization of frequent pattern mining in edge sensor data processing offers optimized response time, resource utilization, and better scalability. It is also capable of handling the massive amount of data generated from sensors and mobile devices in the Internet of Things.

A task allocation schema based on response time optimization in cloud computing

A task allocation schema based on response time optimization in cloud computing

Deep Reinforcement Learning Framework with Q Learning For Optimal Scheduling in Cloud Computing

Cloud computing is an emerging technology that is increasingly being appreciated for its diverse uses, encompassing data processing, The Internet of Things (IoT) and the storing of data. The continuous growth in the number of cloud users and the widespread use of IoT devices have resulted in a significant increase in the volume of data being generated by these users and the integration of IoT devices with cloud platforms. The process of managing data stored in the cloud has become more challenging to complete. There are numerous significant challenges that must be overcome in the process of migrating all data to cloud-hosted data centers. High bandwidth consumption, longer wait times, greater costs, and greater energy consumption are only some of the difficulties that must be overcome. Cloud computing, as a result, is able to allot resources in line with the specific actions made by users, which is a result of the conclusion that was mentioned earlier. This phenomenon can be attributed to the provision of a superior Quality of Service (QoS) to clients or users, with an optimal response time. Additionally, adherence to the established Service Level Agreement further contributes to this outcome. Due to this circumstance, it is of utmost need to effectively use the computational resources at hand, hence requiring the formulation of an optimal approach for task scheduling. The goal of this proposed study is to find ways to allocate and schedule cloud-based virtual machines (VMs) and tasks in such a way as to reduce completion times and associated costs. This study presents a new method of scheduling that makes use of Q-Learning to optimize the utilization of resources.The algorithm's primary goals include optimizing its objective function, building the ideal network, and utilizing experience replay techniques.

Detection of the nonlinear response of vegetation to terrestrial water storage changes in central Asian endorheic basins

The terrestrial water storage anomaly (TWSA) is an important parameter for assessing the land water budget, and it interacts well with terrestrial ecosystems via complex hydrological processes. Recently, the decline in central Asian terrestrial water storage (TWS) has threatened the health of local ecosystems. Therefore, it is of great significance to adopt an efficient approach to explore and identify the nonlinear relationship between two important indicators, i.e., the TWSA and normalized difference vegetation index (NDVI) in the arid central Asian endorheic basins. In this study, we analysed the long-term trends of the TWSA and NDVI, and identified the lag month (1 month) as the optimal moving window of the nonlinear Granger causality test embedded in random forest to detect the nonlinear NDVI change response of NDVI changes in vegetation to the TWSA from 2003 to 2015. There are decreasing trends in TWSAs over approximately 81.7% of the study area and the NDVI generally decreased resulting in approximately 36% vegetation browning in the study area. The nonlinear Granger unidirectional causes of the TWSA were responsible for 97.9% of the NDVI variation in the study area considering the optimal response time for moving windows. The causes of vegetation browning in the central Asian Aral Sea basin and vegetation greening in the basins of Northwest China could be mostly explained by the changes in TWS. Our findings contribute to understanding the nonlinear causal linkages between vegetation and the TWSA in endorheic basins, and these findings provide insights for obtaining terrestrial water consumption patterns and water resource management under the joint influence of climate change and human activities.