Duration Of State Research Articles

ДОПОЛНЕННАЯ РЕАЛЬНОСТЬ ЭМУЛЯЦИИ РЕЛЬЕФА МЕСТНОСТИ И НЕШТАТНЫХ СИТУАЦИЙ В ТРЕНАЖЕРАХ ТЕХНИКИ ДЛЯ АПК

The practical skills of agroengineers include the skills of operating technical means used in the agricultural sector. The variety of brands and modifications of training simulators and their functionality creates difficulties in obtaining the full range of skills required fror operating transport and technological machines. To expand the practical skills of students, the authors conducted analytical and patent studies of training simulators. As a result, they present a simulator using the technology of augmented reality terrain emulation, which can simulate dynamic force effects on the vehicle cab, when driving at a uniform speed on roads with different surface profiles and rotation angles, displaying the dynamics of translational motion with visual images. The impact of the vertical road terrain on the vehicle and the operator’s cabin is simulated by four vertical linear pneumatic actuators acting on the inclined platform using control devices, by means of short-term lifting and the return movement of the rods. The lifting height is modeled by the pressure in the receiver and the duration of the open state of the valve and is consistent with the road relief-modeling program. With the controls, the trainee simulates the control of the vehicle movement along the road. The central microprocessor device displays an image of the road with a visible profile of rhe vehicle’s body part and the trainee, who feels the dynamic effect on the receptor-muscular system through mechanical movements of the simulator cabin, creates a psychophysical perception of the full range of real control factors over the machine. The proposed prototype of the training simulator for the movement of a technological vehicle will increase the effectiveness of training operating skills.

Experimental study of the factors on solid adsorption vacuum flash evaporation for ice production

The paper based on the solid adsorption vacuum flash ice slurry production device, the process of distilled water flashing was recorded with a high-speed CCD camera, and the vacuum flashing state of distilled water at different pressures and different initial temperatures was analyzed by experimental phenomena. The effect of system pressure, different wall temperatures and solution volumes on the subcooling state of the ice slurry formation process under solid adsorption vacuum flash evaporation was also experimentally investigated. The results show that the lower the pressure the more intense the flashing phenomenon in the vacuum flashing state, and the higher the initial temperature the more likely the solution is to produce bubbles. Within 20 seconds after the start of the experiment, distilled water will appear subcooling phenomenon in solid adsorption vacuum flash evaporation ice making system, due to the vacuum flash action water is constantly disturbed subcooling state duration is very short. The lower the pressure, the lower the subcooling degree, the subcooling degree is about 2 °C - 4 °C. The wall temperature has a greater impact on the ice crystal phase change accumulation stage, the higher the initial wall temperature the greater the fluctuations in the subsequent stages of flashing and the lower the ice content. When the volume of the solution is small, the liquid film is prone to flash formation of solid ice.

The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model.

Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state's mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states' evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand's daily happiness data for May 2019 -November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.

A Novel Health Prognosis Method for a Power System Based on a High-Order Hidden Semi-Markov Model

Power system health prognosis is a key process of condition-based maintenance. For the problem of large error in the residual lifetime prognosis of a power system, a novel residual lifetime prognosis model based on a high-order hidden semi-Markov model (HOHSMM) is proposed. First, HOHSMM is developed based on the hidden semi-Markov model (HSMM). An order reduction method and a composite node mechanism of HOHSMM based on permutation are proposed. The health state transition matrix and observation matrix are improved accordingly. The high-order model is transformed into the corresponding first-order model, and more node dependency information is stored in the parameter group to be estimated. Secondly, in order to estimate the parameters and optimize the structure of the proposed model, an intelligent optimization algorithm group is used instead of the expectation–maximization (EM) algorithm. Thus, the simplification of the topology of the high-order model by the intelligent optimization algorithm can be realized. Then, the state duration variables in the high-order model are defined and deduced. The prognosis method based on polynomial fitting is used to predict the residual lifetime of the power system when the prior distribution is unknown. Finally, the intelligent optimization algorithm is used to solve the proposed model, and experiments are performed based on a set of power system data sets to evaluate the performance of the proposed model. Compared with HSMM, the proposed model has better performance on the power system health prognosis problem and can get a relatively good solution in a short computation time.

Sensitivity analysis of standardized precipitation index to climate state selection in China

In the calculation of the standardized precipitation index (SPI) index, it is necessary to select a certain period of precipitation samples as the reference climate state, and the SPI obtained by different reference climate states have different size. Therefore, the influence of different reference climate states on the accuracy of SPI calculation is worth analyzing. Based on the monthly precipitation data of 1184 stations in China from 1961 to 2010, the influence of the selection of the reference climatic state in the calculation of SPI was analyzed. Using 30 consecutive years as the duration of the reference climatic state, 1961–2010 is divided into three periods 1961–1990, 1971–2000, 1981–2010. Taking the SPI obtained from the entire period as the standard value, the spatial distribution of SPI error and the accuracy of SPI classification based on each reference period were analyzed. Then, the resampling method was used to analyze the influence of time-continuous precipitation samples on the size of SPI. The results show that the SPI error of most sites is less than 0.2, and the accuracy of SPI classification is more than 80%. Although the errors of SPI mostly come from extreme drought and extremely wet, this does not affect the accuracy of the recognition of extreme drought and extremely wet. In most regions, it is reliable to calculate SPI based on the precipitation data of continuous 30 years, but the reliability of SPI is relatively low in areas with frequent drought. The results of the resampling analysis and 30-year sliding analysis show that the distribution parameters have noticeable turning characteristics, and the precipitation distribution parameters of nearly 85% stations had noticeable turning point before 1985, which led to the precipitation data of continuous 30 years easily overestimate the dry/wet.

Fast–slow variable dissection with two slow variables related to calcium concentrations: a case study to bursting in a neural pacemaker model

Neuronal bursting is an electrophysiological behavior participating in physiological or pathological functions and a complex nonlinear behavior alternating between burst and quiescent state modulated by slow variables. Identification of dynamics of bursting modulated by two slow variables is still an open problem. In the present paper, a novel fast–slow variable dissection method with two slow variables is proposed to analyze the complex bursting simulated in a four-dimensional neuronal model to describe firing associated with pathological pain. The lumenal (\(C_\mathrm{lum}\)) and intracellular (\(C_\mathrm{in}\)) calcium concentrations are the slowest variables, respectively, in the quiescent state and burst duration. Questions encountered when the traditional method with one slow variable is used. When \(C_\mathrm{lum}\) is taken as slow variable, the burst is successfully identified to terminate near the saddle-homoclinic bifurcation point of the fast subsystem and begin not from the saddle-node bifurcation. With \(C_\mathrm{in}\) chosen as slow variable, \(C_\mathrm{lum}\) value of the initiation point of burst is far from the saddle-node bifurcation point, due to \(C_\mathrm{lum}\) not contained in the equation of the membrane potential. To overcome this problem, both \(C_\mathrm{in}\) and \(C_\mathrm{lum}\) are regarded as slow variables; the two-dimensional fast subsystem exhibits a saddle-node bifurcation point, which is extended to a saddle-node bifurcation curve by introducing the \(C_\mathrm{lum}\) dimension. Then, the initial point of burst is successfully identified to be near the saddle-node bifurcation curve. The results present a feasible method for fast–slow variable dissection and a deep understanding of the complex bursting behavior with two slow variables, which is helpful for the modulation to pathological pain.

Readout for simple and precise analog acoustic impact initialization

An economic concept of acoustic shock wave sensing readout system for simple computer processing is introduced in this work. Its application can be found in precise initialization of the stopwatch from the starter sound, handclap or gun in competitive sport races but also in many other places. The proposed device consists of several low-cost commercially available components and it is powered by a 9 V battery. The proposed device reliably reacts on incoming acoustic shock wave by generation of explicit impulse having controllable duration. It significantly overcomes basic implementations using only a microphone and amplifier (generating parasitic burst instead of defined and distinct impulse) or systems allowing a limited number of adjustable features (gain and/or threshold of the comparator—our concept offers the adjustment of gain, cut-off frequency, threshold level and time duration of active state). In comparison with standard methods, the proposed approach simplifies and makes sensing device less expensive and universal for any powder-based starting gun (without necessity to adapt starting gun). The proposed device, among others, has the following features: impulse duration can be controlled from hundreds of μs up to 2.3 s, the gain range of linear part of processing from 6 to 40 dB and open-collector output compatible with 5 V TTL or 3.3 V CMOS logic. The initialization has been tested in the range from tens of centimeters up to four meters. In order to highlight the important spectral components, the spectral character of the signal can be optimally reduced by a low-pass filter. The quiescent power consumption of the designed simple analog circuit reaches 90 mW. Several use cases, response of the designed system on gunshot signature, talking, hand-clapping and hit on the sensing microphone, are studied and compared to each other. Simulation and experimental results confirm functionality of the realized system.

Modeling Of Subscriber Station Energy Consumption Network 802.16

Worldwide Interoperability for Microwave Access (WiMAX) is a Broadband Wireless Access (BWA) technology that has high speed and wide access for multimedia communication. The reception of quality video or voice packets at the receiver is related to how efficient the energy consumption is sent by the Subscriber Station (SS). The more video or voice packets that are sent, the more energy consumption is needed. One way to calculate energy consumption at a WiMAX subscriber station is by using mathematical modeling. This final project is modeling energy consumption in WiMAX. From the simulation, it is found that the change in the bit rate affects the energy consumption of the subscriber station. Through simulation, training data is generated to obtain a mathematical model.The mathematical model contains the components of the state duration and the level of energy consumption. Mathematical models are then used to predict energy consumption in WiMAX. The model is tested through the generation of test data. The test results through simulation showthe percentage deviation of the average energy consumption of training data with mathematical modeling on average is 0.180%. Meanwhile, the energy consumption of test data and mathematical models has an average deviation of 0.187% and 0.191%. The least energy consumption is generated when the MAC Initialization state (state i0) is 0.001 Joule, while the highest energy consumption is the Uplink Frame state (state i14) of 101.017226 Joule.

Study on Transient Queue-Size Distribution in the Finite-Buffer Model with Batch Arrivals and Multiple Vacation Policy.

The transient behavior of the finite-buffer queueing model with batch arrivals and generally distributed repeated vacations is analyzed. Such a system has potential applications in modeling the functioning of production systems, computer and telecommunication networks with energy saving mechanism based on cyclic monitoring the queue state (Internet of Things, wireless sensors networks, etc.). Identifying renewal moments in the evolution of the system and applying continuous total probability law, a system of Volterra-type integral equations for the time-dependent queue-size distribution, conditioned by the initial buffer state, is derived. A compact-form solution for the corresponding system written for Laplace transforms is obtained using an algebraic approach based on Korolyuk’s potential method. An illustrative numerical example presenting the impact of the service rate, arrival rate, initial buffer state and single vacation duration on the queue-size distribution is attached as well.

Use of an ear-tag accelerometer and a radio-frequency identification (RFID) system for monitoring the licking behaviour in grazing cattle

Monitoring the licking behaviour in grazing cattle is a potential means for quantifying block supplement intake. The current study aimed to 1) evaluate the capability of an ear-tag accelerometer to identify the licking behaviour at a block supplement in grazing cattle and 2) validate individual licking state (LS) duration predicted by an accelerometer and radio-frequency identification (RFID) system. Four out of 12 Angus steers weighing 384 ± 9.7 kg (mean ± SD) were given free access to a 900 m2 supplement yard with access to two RFID-equipped automatic supplement feeders provided daily from 15:00 to 18:00 h for 10 days. Each steer was fitted with an ear-tag containing a 3-axis accelerometer set at a frequency of 25 Hz. Accelerometer data were segmented into three window sizes (3, 5, and 10 s) and further processed using four machine learning (ML) algorithms: Random Forest (RF), Extreme Gradient Boosting (XGB), Logistic Regression (LR) and Linear Discriminant Analysis (LDA). The best performance in classifying licking behaviour was obtained from the combination of XGB and 10 s window size with an accuracy, Kappa coefficient, and F1 score of 93%, 0.88, and 0.88, respectively. Accelerometers and RFID systems consecutively under-predicted and over-predicted LS duration by 20% and 6%, with a mean absolute error (MAE) proportion of 22% and 10%, a ratio of root mean square prediction error (RSR) of 0.33 and 0.14 and a modelling efficiency (MEF) of 0.89 and 0.98. Overall, both the ear-tag accelerometer and RFID system was capable of monitoring the licking behaviour and LS duration of grazing cattle accessing block supplements.

Reliability assessment for electrical power generation system based on advanced Markov process combined with blocks diagram

This paper presents the power generation system reliability assessment using an advanced Markov process combined with blocks diagram technique. The effectiveness of the suggested methodology is based on HL-I of IEEE_EPS_24_bus. The proposed method achieved the generation reliability and availability of an electrical power system using the Markov chain which based on the operational transition from state to state which represented in matrix. The proposed methodology has been presented for reliability performance evaluation of IEEE_EPS_24_bus. MATLAB code is developed using Markov chain construction. The transition between probability states is represented using changing the failure and repair rates. The reduced number of generation system are used with Markov process to assess the availability, unavailability, and reliability for the generation system. Additionally, the proposed technique calculates the frequency, time duration of states, the probability of generation capacity state which get out of service or remained in service for each state of failure, and reliability indices. A considerable improvement in reliability indices is found with using blocks diagram technique which is used to reduce the infinity number of transition states and assess the system reliability. The proposed technique succeeded at achieving accurate and faster reliability for the power system.

The “Sandwich Generation” Revisited: Global Demographic Drivers of Care Time Demands

AbstractGenerational overlap affects the care time demands on parents and grandparents worldwide. Here, we present the first global estimates of the experience of simultaneously having frail older parents and young children (“sandwichness”) or young grandchildren (“grandsandwichness”) for the 1970–2040 cohorts, using demographic methods and microsimulations. We find that sandwichness is more prevalent in the Global South—for example, almost twice as prevalent in sub‐Saharan Africa as it is in Europe for the 1970 cohort—but is expected to decline globally by one‐third between 1970 and 2040. The Global North might have reached a peak in the simultaneous care time demands from multiple generations but the duration of the grandsandwich state will increase by up to one year in Africa and Asia. This increasing generational overlap implies more care time demands over the entire adult life course, but also opens up an opportunity for the full potential of grandparenthood to materialize.

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

<p class="0abstract">Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.<strong></strong></p>

Subthalamic dynamic neural states correlate with motor symptoms in Parkinson’s Disease

ObjectiveThis study aims to discriminate the dynamic synchronization states from the subthalamic local field potentials and investigate their correlations with the motor symptoms in Parkinson’s Disease (PD). MethodsThe resting-state local field potentials of 10 patients with PD were recorded from the subthalamic nucleus. The dynamic neural states of multiple oscillations were discriminated and analyzed. The Spearman correlation was used to investigate the correlations between occurrence rate or duration of dynamic neural states and the severity of motor symptoms. ResultsThe proportion of long low-beta and theta synchronized state was significantly correlated with the general motor symptom and tremor, respectively. The duration of combined low/high-beta state was significantly correlated with rigidity, and the duration of combined alpha/high-beta state was significantly correlated with bradykinesia. ConclusionsThis study provides evidence that motor symptoms are associated with the neural states coded with multiple oscillations in PD. SignificanceThis study may advance the understanding of the neurophysiological mechanisms of the motor symptoms and provide potential biomarkers for closed-loop deep brain stimulation in PD.

Brain, behavioral, affective, and sex correlates of recovery from alcohol use disorders

Recovery from alcohol use disorders (AUDs) consists of salutary changes in behavior and affect. While evidence suggests that recovery-related behavioral changes, such as abstinence, emerge in tandem with both neural and affective changes, the precise relationships among these changes are unknown. To understand these relationships, we examined associations between the duration of abstinence (DOA), affective states, and neuroimaging-based structural measures of the brain reward system (BRS) in AUD men (AUDM ) and AUD women (AUDW ). Participants were community respondents from the Boston area comprising right-handed abstinent individuals with AUD (n=60; 30men) and controls without AUD (NC; n=60; 29men). Multivariate linear regressions compared short-/mid-term abstainers (≤5years), long-term abstainers (>5years), and the NC group on measures of BRS volume (3T magnetic resonance imaging scans) and measures of affect (Profile of Mood States [POMS]; Multiple Affect Adjective Check List [MAACL]; Hamilton Rating Scale for Depression [HRSD]). Analyses contrasted sex differences and accounted for age, education, drinking severity, and verbal IQ. Compared to the NC group, short-/mid-term abstainers exhibited larger posterior insular volume (total (β=0.019, 95% CI: 0.004, 0.034)), higher negative affect (POMS Mood Disturbance (β=27.8, 95% CI: 11.56, 44.04), and lower positive affect (POMS Vigor (β=-4.89, 95% CI: -9.06, -0.72)). Compared to the NC group, Long-term abstainers exhibited significantly smaller volumes of aggregate anterior cingulate cortex (β=-0.06, 95% CI: -0.113, -0.008) and higher HRSD scores (β=1.56, 95% CI: 0.14, 2.98). Relative to AUDM , AUDW exhibited significantly larger right anterior insular volumes (β=0.03, 95% CI: 0.01, 0.06) and significantly greater MAACL Positive Affect scores (β=7.56, 95% CI: 0.59, 11.55) in association with DOA. We found that differences in abstinence from alcohol were correlated with differences in both neural recovery and affective dimensions of recovery from AUDs. The observed sex differences extend evidence of dimorphic effects of AUDs and recovery on brain structure and function. Future longitudinal research will test inferences concerning the directionality of these relationships.

Criticality in the duration of quasistationary state.

The duration of the quasistationary states (QSSs) emerging in the d-dimensional classical inertial α-XY model, i.e., N planar rotators whose interactions decay with the distance r_{ij} as 1/r_{ij}^{α} (α≥0), is studied through first-principles molecular dynamics. These QSSs appear along the whole long-range interaction regime (0≤α/d≤1), for an average energy per rotator U<U_{c} (U_{c}=3/4), and they do not exist for U>U_{c}. They are characterized by a kinetic temperature T_{QSS}, before a crossover to a second plateau occurring at the Boltzmann-Gibbs temperature T_{BG}>T_{QSS}. We investigate here the behavior of their duration t_{QSS} when U approaches U_{c} from below, for large values of N. Contrary to the usual belief that the QSS merely disappears as U→U_{c}, we show that its duration goes through a critical phenomenon, namely t_{QSS}∝(U_{c}-U)^{-ξ}. Universality is found for the critical exponent ξ≃5/3 throughout the whole long-range interaction regime.

Characteristics of turbulence over the semi‐fixed desert area north of Xinjiang, China

AbstractAs the largest fixed and semi‐fixed desert in China, the Gurbantünggüt Desert undergoes a long period of snow cover in the winter and the rapid growth of ephemeral plants in the spring, presenting obvious seasonal changes in the underlying desert surface type, which can lead to variation in the turbulence of the near‐surface boundary layer turbulence over the desert. In this study, gradient tower data and eddy covariance data from 2017 were analysed to investigate the turbulence characteristics of the different surface boundary layers in the hinterland of the Gurbantünggüt Desert. The results indicate that stable atmospheric conditions in the desert occur exclusively during the early morning and at night in the desert, and the onset and duration of this stable state varies seasonally. Two regimes of intermittent turbulence occur during the night, a weak turbulent regime that occurs when the wind speed is less than the threshold and a strong turbulent regime when the wind speed exceeds the threshold, and different wind speed thresholds were observed at each level. These parameters follow a seasonal pattern of summer (July) &gt; spring (April) &gt; autumn (October) &gt; winter (January) in terms of magnitude. The mean turbulence intensities of the along‐wind, cross‐wind and vertical wind are 0.5, 0.47 and 0.14, respectively, with Iu &gt; Iv &gt; Iw. The normalized standard deviation of the wind velocity components (σu, σv and σw) generally satisfies a 1/3 power‐law relation. Our results show that the night‐time turbulence regime classification for the Gurbantünggüt Desert strongly depends on meteorological and orographic features, and the intermittent turbulent events have the non‐stationarity of the flow in common. The results can contribute to the study of land surface processes, climate change and desertification in inland arid desert areas.

On State Occupancies, First Passage Times and Duration in Non-Homogeneous Semi-Markov Chains

Semi-Markov processes generalize the Markov chains framework by utilizing abstract sojourn time distributions. They are widely known for offering enhanced accuracy in modeling stochastic phenomena. The aim of this paper is to provide closed analytic forms for three types of probabilities which describe attributes of considerable research interest in semi-Markov modeling: (a) the number of transitions to a state through time (Occupancy), (b) the number of transitions or the amount of time required to observe the first passage to a state (First passage time) and (c) the number of transitions or the amount of time required after a state is entered before the first real transition is made to another state (Duration). The non-homogeneous in time recursive relations of the above probabilities are developed and a description of the corresponding geometric transforms is produced. By applying appropriate properties, the closed analytic forms of the above probabilities are provided. Finally, data from human DNA sequences are used to illustrate the theoretical results of the paper.

A State Duration Model for Brand Choice and Inter-Purchase Time

A new approach for analyzing state duration data in brand-choice studies is explored. This approach not only incorporates the correlation among repeated purchases for a subject, it also models the purchase timing and the brand decision jointly. The former is accomplished by applying transition model approaches from longitudinal studies while the latter is done by conditioning on the brand choice variable. Then mixed multinomial logit models and Cox proportional hazards models are employed to model the marginal densities of the brand choice and the conditional densities of the interpurchase time given the brand choice. We illustrate the approach using a Nielsen household scanner panel data set

Balance between competing spectral states in subthalamic nucleus is linked to motor impairment in Parkinson's disease.

Exaggerated local field potential bursts of activity at frequencies in the low beta band are a well-established phenomenon in the subthalamic nucleus of patients with Parkinson’s disease. However, such activity is only moderately correlated with motor impairment. Here we test the hypothesis that beta bursts are just one of several dynamic states in the subthalamic nucleus local field potential in Parkinson’s disease, and that together these different states predict motor impairment with high fidelity.Local field potentials were recorded in 32 patients (64 hemispheres) undergoing deep brain stimulation surgery targeting the subthalamic nucleus. Recordings were performed following overnight withdrawal of anti-parkinsonian medication, and after administration of levodopa. Local field potentials were analysed using hidden Markov modelling to identify transient spectral states with frequencies under 40 Hz.Findings in the low beta frequency band were similar to those previously reported; levodopa reduced occurrence rate and duration of low beta states, and the greater the reductions, the greater the improvement in motor impairment. However, additional local field potential states were distinguished in the theta, alpha and high beta bands, and these behaved in an opposite manner. They were increased in occurrence rate and duration by levodopa, and the greater the increases, the greater the improvement in motor impairment. In addition, levodopa favoured the transition of low beta states to other spectral states. When all local field potential states and corresponding features were considered in a multivariate model it was possible to predict 50% of the variance in patients’ hemibody impairment OFF medication, and in the change in hemibody impairment following levodopa. This only improved slightly if signal amplitude or gamma band features were also included in the multivariate model. In addition, it compares with a prediction of only 16% of the variance when using beta bursts alone.We conclude that multiple spectral states in the subthalamic nucleus local field potential have a bearing on motor impairment, and that levodopa-induced shifts in the balance between these states can predict clinical change with high fidelity. This is important in suggesting that some states might be upregulated to improve parkinsonism and in suggesting how local field potential feedback can be made more informative in closed-loop deep brain stimulation systems.