Intermittent Times Research Articles

Numerical simulation analysis of turbulent pulsation drag reduction at different intermittent times

The shear stress generated by wall turbulence is the main cause of wall friction resistance in turbulent flow through pipes. This paper investigates the impact of inserting rest periods (regions of constant Reynolds number) within the pulsating operating cycle of velocity on the fully turbulent flow at large time-averaged Reynolds numbers, using the large eddy simulation (LES) method. The study aims to explore the effect of increasing rest periods within pulsations on drag resistance. The dimensionless shear stress and drag reduction rates during different time periods of rest were analyzed. Numerical simulation results indicate that the pulsating velocity operation mode does not necessarily lead to drag reduction; it may even result in increased resistance. Inserting rest periods within the pulsation cycle can achieve drag reduction effects, with the maximum drag reduction rate reaching 21.8%. This paper utilizes large eddy simulation (LES) to compare and validate the feasibility of LES for pipe pulsating operation modes against experimental results and direct numerical simulation (DNS) results from the literature, providing a rationale and proof of concept for further in-depth studies on other pipe pulsating flows.

The impact of post-activation potentiation on explosive vertical jump after intermittent time: a meta-analysis and systematic review

The optimal intermittent time for post-activation potentiation (PAP) training remains uncertain and contentious. This study employed a meta-analysis to systematically evaluate the effect of different intermittent times on PAP in relation to explosive vertical jump height. Relevant literature was sourced from CNKI, Wanfang, VIP, CBM, PubMed, Web of Science, and Google Scholar databases using keywords such as “postactivation potentiation,” “activation enhancement effect,” “PAP,” “explosive vertical jump,” “explosive vertical high jump,” and “intermittent time.” The search covered publications from the inception of each database until June 2024. Studies involving athletes (regardless of sport type) undergoing PAP training were included, with no restrictions on the methods used to induce PAP. Comparative analysis focused on the heights of countermovement jumps (CMJ) and peak ground reaction force (GRF) before and after interventions. The quality of the included studies was assessed using the Cochrane Risk of Bias Tool, and data were analyzed using RevMan5.3. The study included a total of 21 papers with 327 subjects, primarily using the squat as the method of PAP induction. The meta-analysis revealed that intermittent times of 4 min [MD = − 0.03, 95% CI: − 0.04 ~ − 0.01; Z = 2.71, P = 0.007] and 5–8 min [MD = − 0.03, 95% CI: − 0.04 ~ – 0.01; Z = 3.07, P = 0.002] significantly increased the height of explosive vertical CMJs. However, intermittent times of 1–3 min [MD = –0.00, 95% CI: − 0.01 ~ 0.01; Z = 0.38, P = 0.70] and 10–24 min [MD = − 0.01, 95% CI: − 0.02 ~ 0.00; Z = 1.43, P = 0.15] did not show significant effects on CMJ height. These findings indicate that 4-min and 5–8 min intervals significantly enhance CMJ height, while intervals shorter than 4 min or longer than 8 min do not have a significant impact.

Robotic caudo-peripheral approach for liver parenchymal transection in anatomical liver resections for hepatocellular carcinoma

Liver parenchymal transection is a challenging step during hepatic resection, particularly when using robotic platforms that require specific skills to optimize this phase. Pedicle division at the beginning of the liver parenchyma helps to better identify the resection plane and minimizes blood loss. The three-dimensional (3D) high-definition vision and the robotic Maryland allow for clear identification of the hepatic pedicles that could be dissected or divided without the need for a laparoscopic ultrasonic dissector. The caudo-peripheral technique, combined with the Maryland bipolar Kelly clamp crushing technique, is a useful approach to complete parenchymal transection and achieve safe anatomical resections in cases of hepatocellular carcinoma (HCC) with multi-pronged bleeding control. This is essential for expediting the procedure, reducing the number of intermittent clamping times, and minimizing the risk of ischemia-reperfusion injury. In this setting, perfect synchronization between the surgeon operating at the console and the bedside assistant is crucial. Advances in artificial intelligence (AI) systems have shown great potential to redefine clinical care management, preoperative planning, and intraoperative decision making for patients with HCC. This paper describes the most relevant details of our technique, its theoretical background, advantages, and limitations. Moreover, minimally invasive surgery offers the opportunity to share surgical experiences and technical progress through multimedia videos. This represents a modern and effective teaching tool to accelerate the learning process and overcome the challenges of the most complex procedures by offering surgeons various solutions to common technical problems.

Predicting bite rate of grazing cattle from accelerometry data via semi-supervised regression

Accurate estimation of bite rate, i.e., the number of bites during a given grazing period, is important for the characterisation of cattle grazing behaviour and feed intake. In this paper, we estimate the bite rate of grazing cattle from accelerometer data using semi-supervised linear regression. We use a mixture of labelled and unlabelled accelerometer data collected during the grazing trial to train the semi-supervised model. The labelled data has associated ground-truth values for the bite rate and was recorded by an expert observer at intermittent times over the duration of the trial. The trial involved ten cattle, each fitted with a tri-axial accelerometer mounted on a neck collar, over a period of five weeks. We calculate various features from tri-axial accelerometer data and utilise several feature selection approaches and regression methods to fit bite-rate prediction models to our data and compare their performance. Our leave-one-animal-out cross-validated results show that a semi-supervised linear regression model with the specifically chosen feature set delivers good bite-rate prediction performance with a root mean squared error of 5.73 bites per minute and an R2 value of 0.73. Other regression models tested here (including orthogonal distance regression) yielded similar results.

Strain prediction and benchmark with measurement in the Spallation Neutron Source mercury target without gas injection

The 1.4-MW Spallation Neutron Source (SNS) currently operates as the world's highest time-average power spallation source and uses mercury as the spallation target material. The mercury circulates through a stainless-steel target vessel and heat exchanger loop that removes the heat deposited from the incident proton beam in the target. The mercury vessel is part of the fixed but replaceable target module. The target module is not safety credited in the SNS design, but reliable long life is vital for meeting operational goals and minimizing cost and waste. SNS is a short-pulse ($\ensuremath{\sim}0.7\text{ }\text{ }\ensuremath{\mu}\mathrm{s}$) neutron source with a repetition rate of 60 Hz with 1.0 GeV protons onto the target. The short-pulse operation introduces two mechanisms that threaten the longevity of the mercury vessel. The pulsed target heating is essentially isochoric, which results in a distributed pressure field in the mercury with each pulse. After each pulse, the pressure propagates, interacts with, and strains the stainless-steel vessel. For intermittent times, this pressure field leads to mercury cavitation. The first mechanism of concern for the target lifetime is high-cycle fatigue to the stainless-steel vessel and the second is cavitation erosion. At the onset of the SNS project, the R program sought to address the need for an accurate and practical method to estimate the fatigue life of the mercury vessel. Around 2000, the R team conducted a series of in-beam mercury target experiments at the Weapons Neutron Research Facility at Los Alamos Neutron Science Center. These experiments successfully produced a small database of pulse response strain data. The data were used to develop and benchmark an explicit dynamic finite element analysis technique for estimating target fatigue life. SNS is now a mature facility with about 15 years of operation and 29 SNS target modules consumed. Since 2015, strain measurements have been collected from actual targets, and abundant target vessel strain data are now available in greater detail than was possible in 2000. Variances in pulse response data have been characterized using tolerance interval analysis to benchmark the simulation technique more thoroughly. The simulated results were found to match measurements generally well, but the method is always conservative. A potential adjustment to the cavitation threshold parameter value was suggested. Variances in the pulse response data are moderate, which is largely explained by the fact that data were acquired in 60-Hz burst mode and by the lasting vibration of the vessel between pulses.

Effect of Intermittent Ultrasonic Vibration-Assisted Compression on the Mechanical Properties of Zr-Based Amorphous Alloys

Bulk amorphous alloys have some good mechanical properties due to their special atomic arrangement and are now popular in the field of materials. Zr-based amorphous alloys have good mechanical properties, but they are different from lattice slip materials with high ductility. When these materials are compressed and deformed, it generates a concentrated elastic force in the shear zone that causes instantaneous amorphous fracture. The extremely poor plasticity of Zr-based amorphous materials highlight their shortcomings and make them difficult to use in engineering applications. In this paper, it is found that the plasticity of Zr-based amorphous alloys is enhanced to a certain extent by intermittent ultrasonic vibration-assisted compression (IUVC). The ultrasonic vibration stress of IUVC can increase the extra free volume of Zr-based amorphous alloys and increase their degree of “rejuvenation”, which is manifested as an increase in plasticity. To explore how IUVC affects the plasticity of Zr-based amorphous alloys, we design experiments to analyse the effects of different intermittent times, pre-pressures and ultrasonic amplitudes on the plasticity of amorphous alloys.

The effect of intermittent anode potential regimes on the morphology and extracellular matrix composition of electro-active bacteria

Electro-active bacteria (EAB) can form biofilms on an anode (so-called bioanodes), and use the electrode as electron acceptor for oxidation of organics in wastewater. So far, bioanodes have mainly been investigated under a continuous anode potential, but intermittent anode potential has resulted in higher currents and different biofilm morphologies. However, little is known about how intermittent potential influences the electron balance in the anode compartment. In this study, we investigated electron balances of bioanodes at intermittent anode potential regimes. We used a transparent non-capacitive electrode that also allowed for in-situ quantification of the EAB using optical coherence tomography (OCT). We observed comparable current densities between continuous and intermittent bioanodes, and stored charge was similar for all the applied intermittent times (5 mC). Electron balances were further investigated by quantifying Extracellular Polymeric Substances (EPS), by analyzing the elemental composition of biomass, and by quantifying biofilm and planktonic cells. For all tested conditions, a charge balance of the anode compartment showed that more electrons were diverted to planktonic cells than biofilm. Besides, 27–43% of the total charge was detected as soluble EPS in intermittent bioanodes, whereas only 15% was found as soluble EPS in continuous bioanodes. The amount of proteins in the EPS of biofilms was higher for intermittent operated bioanodes (0.21 mg COD proteins mg COD biofilm−1) than for continuous operated bioanodes (0.05 mg COD proteins mg COD biofilm−1). OCT revealed patchy morphologies for biofilms under intermittent anode potential. Overall, this study helped understanding that the use of a non-capacitive electrode and intermittent anode potential deviated electrons to other processes other than electric current at the electrode by identifying electron sinks in the anolyte and quantifying the accumulation of electrons in the form of EPS.

Helium tribology of Inconel 617 subjected to laser peening for high temperature nuclear reactor applications

Inconel 617 is among the best candidates for utilization in high temperature gas cooled reactor tribo-components. However, the combined effects of sliding contact, along with intermittent idle times and very high temperature material degradation, deteriorates the alloy tribological performance, especially under a helium atmosphere. Laser peening is a surface treatment technique which can enhance the properties at the surface and subsurface by generating deep residual stresses and enhanced microstructure. Herein, we report the tribological behavior of regular laser peened as well as thermally-engineered laser peened Inconel 617 under helium and air atmospheres at 800 °C. In addition to friction and wear studies, the specimens are characterized by different analytical techniques to further understand the mechanisms involved in the peening process and sliding contact. Regardless of the peening process and post-process treatment types, it is observed that laser peening improves the tribological characteristics of Inconel 617. Interestingly, laser peening followed by helium thermal aging shows highly enhanced tribological behavior. This is attributed to the strengthening effect of the laser peening on the surface oxides providing an excellent and lasting protective and lubricating film under helium exposure.

Discrete event optimization of a vehicle charging station with multiple sockets

The relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

Accuracy of Intermittent Demand Forecasting Systems in the Enterprise

Purpose: The main purpose of this article is to find the best forecasting method for intermittent demand times series, from the company’s point of view. Design/Methodology/Approach: Intermittent demand forecasting systems were constructed based on the Croston’s, SBA, TSB, SES and MA methods. A real database from the warehouse center, containing over sixteen thousand items, was used. Accuracy measures were also discussed. Forecasting methods were compared for all products and for separate demand categories (intermittent, lumpy, erratic, smooth). Findings: It was determined that the TSB method outperforms other methods for all products. The worst procedures were found to be Croston’s and SBA, which performed even worse than SES or MA. The same conclusions were true for intermittent and lumpy categories. In case of erratic and smooth items different results were obtained. It was determined that the SBA method performed best, while the TSB method yielded the poorest results. Practical Implications: The main conclusion is that to judge accuracy of forecasting systems first the proper forecast error measures should be chosen. Based on obtained results, TSB method seems to be the best for intermittent demand times series and this method is recommended for enterprises dealing with intermittent demand. Originality/value: Since such error measures as MASE or scaled MAE favored an underestimated (or even zero) forecast, in the article a new error metric is proposed, which was named scaled Compound Error (sCE). It is a scaled error, and it considers forecast biasedness.

Biasedness of Forecasts Errors for Intermittent Demand Data

Purpose: Intermittent demand is defined as infrequent or sporadic. Many forecasting errors are inappropriate for intermittent data. In some periods, there could be no demand, so division by zero must be avoided. Usually, forecasts are computed for many products; therefore, errors should be scale-independent (or relative). Many ex-post forecast errors, such as MASE (Mean Absolute Scaled Error) or MAE (Mean Absolute Error), indicate as best very low forecasts, sometimes even zero forecasts. Therefore, many researchers think that measures taking into account stock and consumer service levels should be used instead of conventional forecasts. It might suggest that typical forecast errors are useless for intermittent data. In this article, the contradictory hypothesis is verified. It is stated that only unbiased forecast errors should be used if the conclusions are to be correct. Design/Methodology/Approach: Definition of unbiased forecast error is proposed and verified for popular forecast errors, such as ME (Mean Error), MSE (Mean Square Error), MAE, or MASE. The theoretical properties of these errors are considered concerning their biasedness. Forecasts are made based on Croston’s and TSB methods, but also average and median were used as forecasting methods to emphasize conclusions. Findings: In the empirical example, forecast errors are computed for intermittent demand times series to verify theoretical conclusions. The general conclusion is that only unbiased forecast errors provide proper indications according to forecast accuracy. This finding is true in general, not only for intermittent demand. Practical Implications: Presented considerations might be useful for enterprises dealing with intermittent demand forecasting such as distribution centers, warehouse centers, and so on. Originality/value: To the author’s knowledge, forecast error bias was not analyzed before in the literature. A new forecast error is proposed, which was named RMSSE (Root Mean Square Scaled Error).

A Mixture Model for Bivariate Interval-Censored Failure Times with Dependent Susceptibility

Interval-censored failure times arise when the status with respect to an event of interest is only determined at intermittent examination times. In settings where there exists a sub-population of individuals who are not susceptible to the event of interest, latent variable models accommodating a mixture of susceptible and nonsusceptible individuals are useful. We consider such models for the analysis of bivariate interval-censored failure time data with a model for bivariate binary susceptibility indicators and a copula model for correlated failure times given joint susceptibility. We develop likelihood, composite likelihood, and estimating function methods for model fitting and inference, and assess asymptotic-relative efficiency and finite sample performance. Extensions dealing with higher-dimensional responses and current status data are also described.

First report of pulmonary sclerosing pneomucytoma with malignant transformation in both cuboidal surface cells and stromal round cells: a case report

BackgroundPulmonary sclerosing pneumocytoma (PSP) is a rare benign tumor. Although lymph node metastasis has been reported, it is still considered benign. No malignant transformation has been reported. This is the first case of malignant transformation of both cuboidal surface cells and stromal round cells.Case presentationA 64-year-old male had been complaining of intermittent hemoptysis several times per day for eight months. Chest computed tomography scan showed parenchymal infiltration with cystic lesion in the right lower lobe accompanied by enlarged right hilar lymph nodes. Lobectomy and systemic lymph node dissection was performed.On grossly pathological examination, the lesion was 50 mm from the bronchial stump. It was a mixture of both cystic and solid components and 30 mm * 20 mm in size with unclear border. Microscopically, the cuboidal surface cells transformed to adenocarcinoma. The stromal round cells also had a malignant transformation. The Ki-67 proliferation index in malignant cuboidal surface cells and stromal round cells were 70 and 55%, respectively. Furthermore, E-cadherin was negative in primary tumor but positive in metastatic lymph node, which suggested that the mesenchymal to epithelial transition may play an important role in lymph node metastasis.ConclusionsTo our knowledge, we present the first case of malignant transformation of both cuboidal surface cells and stromal round cells in PSP. The process of mesenchymal to epithelial transition may play an important role in lymph node metastasis.

Copula-based semiparametric regression method for bivariate data under general interval censoring.

This research is motivated by discovering and underpinning genetic causes for the progression of a bilateral eye disease, age-related macular degeneration (AMD), of which the primary outcomes, progression times to late-AMD, are bivariate and interval-censored due to intermittent assessment times. We propose a novel class of copula-based semiparametric transformation models for bivariate data under general interval censoring, which includes the case 1 interval censoring (current status data) and case 2 interval censoring. Specifically, the joint likelihood is modeled through a two-parameter Archimedean copula, which can flexibly characterize the dependence between the two margins in both tails. The marginal distributions are modeled through semiparametric transformation models using sieves, with the proportional hazards or odds model being a special case. We develop a computationally efficient sieve maximum likelihood estimation procedure for the unknown parameters, together with a generalized score test for the regression parameter(s). For the proposed sieve estimators of finite-dimensional parameters, we establish their asymptotic normality and efficiency. Extensive simulations are conducted to evaluate the performance of the proposed method in finite samples. Finally, we apply our method to a genome-wide analysis of AMD progression using the Age-Related Eye Disease Study data, to successfully identify novel risk variants associated with the disease progression. We also produce predicted joint and conditional progression-free probabilities, for patients with different genetic characteristics.

Fuel Cell Application To Mitigate Load Ramping Impacts Of Rooftop PV System Installation.

This paper is based on a hybrid PV and fuel cell system designed to meet energy requirements of 100000 customers of Southern California in a sustainable way. Solar roof-top PV system is turning out to be a boon to the mankind with its positive effects of reducing Green House Gas (GHG) emission, lowering stress on conventional coal-fired or natural gas generators and even reducing costs associated with electricity pricing for consumers. However, with increased penetration from the grid tied PV systems, there is an inevitable rise in stress faced by conventional generators in terms of meeting and balancing the energy requirements during early morning and late afternoon hours which requires precise switching and control mechanisms. This paper addresses the ramping issues and offers an integrated solution in the form of fuel cells that can complement the existing PV system during intermittent times of energy production and even by reducing the ramp up and ramp down slopes of power production by conventional generators, in a promising way.

Microfluidic Paper-based Analytical Devices (μPADs): Miniaturization and Enzyme Storage Studies.

This paper describes the design and development of miniaturized microfluidic paper-based analytical devices (μPADs) for biological assays and enzyme storage instruments. Here, a glucose assay utilizing glucose oxidase (GOx), horseradish peroxidase (HRP), and potassium iodide (KI) is used as the model system. The efficacy of the miniaturized devices is further examined by assessing the activity of acetylcholinesterase (AChE). Two types of μPADs were developed: one, "strip" chips of detection zones of area 0.5, 0.1 cm2 and, two, "grid" chips of detection zone 0.05 cm2. The devices are easily fabricated via a wax printing process whereby lines of wax are deposited onto chromatographic paper and heated to create rows of hydrophobic barriers. The "strip" chips were subjected to three different temperature environments (-20, 0, and 20°C) over 30 days and glucose assays conducted at intermittent times yielding a correlation between corrected average inverse yellow intensity, days, and glucose concentration. Calculated and experimentally derived color intensity values for 1, 4, and 9 mM glucose concentrations after a 7-day storage study showed a good correlation (0.89 - 15.76% error). Both types of μPADs are effective platforms as potential point-of-care (POC) diagnostic devices and display minimal enzyme denaturation. μPADs of this size show promise as alternative devices for resource-limited regions and especially those areas where materials and instrumentation are not always available.

Anomalous Diffusion of Sand Tracer Particles Under Waves

A model is presented for the horizontal and vertical diffusion of sand tracer particles by waves and is verified using a reanalysis of the experiments of Miller and Komar (1979, https://doi.org/10.1086/628455). In the model, sand particles take random jumps at a rate associated with the wave period but also can bury one another resulting in intermittent long rest times between jumps. The particle jump length between burials is modeled by considering a vortex entrainment and advection mechanism, resulting in a probability distribution as a function of wave orbital excursion amplitude and particle settling velocity. The model is formalized using the theory of continuous time random walks to obtain closed‐form expressions for the horizontal particle spreading rate, and plume shape, without tunable parameters. Importantly, the model predicts subdiffusive particle spreading, a previously unconsidered phenomenon that is apparent in the experimental data. The plume horizontal extent versus time is also well predicted compared to experimental data.

Correlation of Intermittency of Quantum Dot Photoluminescence Intensity, Decay Time, and Energy

Intermittency of photoluminescence (PL) intensity is a common feature of semiconductor quantum dots (QDs). Here, we show for ZnS capped CdSe QDs that the intermittency (blinking) is closely correlated to the intermittency of PL decay times. Furthermore, we find that fluctuations of PL emission energies are also correlated with intensity and decay time blinking. The origin of energy fluctuations is a combination of switching either between energies of electronic states intrinsic to differently charged QDs or correspondingly Stark shifted states caused by slow fluctuations of the QD interface. By the method of change point analysis we assign the distribution of PL intensities intermediate between on/off intensities to distinct intermittent PL decay times and PL energies. Intermittency occurs on slow time scales between sub‐ms and minutes and depends strongly on the embedding matrix. Highly specific experiments allow for identification of a distribution of charged and non‐charged states near to the excitonic band edge. Most of the blinking phenomena are observed on the depopulation pathways of the band edge states. However, also population pathways are intermittent, but on a much shorter time scale. We assign this mechanism to creation of hot electrons followed by charge trapping and release at the QD interface.

Pulse Packet Stochastic Model for Gastric Emptying in the Fasted State: A Physiological Approach.

Fasted-state gastrointestinal (GI) fluid transit is typically represented as a first-order, deterministic process (averaged and viewed as a continuous approximation). It is, however, most likely a discrete process involving fluid packets interrupted by variable time periods of little to no fluid emptying. In this report we present a physiologically based pulsed-packet gastric fluid emptying model and evaluate it with respect to recent gastrointestinal fluid volume emptying results, published gastric emptying of various dosage forms, and gastric fluid emptying as a function of GI motility. We develop the mathematical model for gastric emptying of discrete volumes emptied during intermittent pulse times of variable lengths, defined as a function of gastric motility utilizing a Poisson point process with motility-dependent intensity. We compare the simulations with observed gastric emptying results. The discrete pulse packet gastric volumetric emptying model is a more physiologically realistic mathematical model for gastric emptying, and it accounts well for the average observed emptying rates and, importantly, encompasses the variability of of observed volume and dosage form emptying rates.

Temporal trajectories of novel object recognition performance in mice exposed to intermittent hypoxia.

Intermittent hypoxia is one of the major perturbations of sleep-disordered breathing and has been causally implicated in neurocognitive deficits. However, the reversibility of such deficits is unclear.Male C57BL/6J mice were exposed to either intermittent hypoxia or room air for 3-240 days, and then half were randomly selected and allowed to recover in normoxic conditions for the same duration of the previous exposure. A novel object recognition (NOR) test was performed.NOR performance was stable over time in room air. Intermittent hypoxia induced significant reductions in recognition index that progressed over the first 45 days and stabilised thereafter. Normoxic recovery of recognition index was essentially complete and indistinguishable from room air in mice exposed to shorter intermittent hypoxia times (<90 days). However, significant residual deficits emerged after normoxic recovery following prolonged intermittent hypoxia exposures (p<0.01). In addition, gradual attenuation of the magnitude of recovery in recognition index occurred with increasingly longer intermittent hypoxia exposures (MANOVA p<0.0001).Intermittent hypoxia during the resting period reduces NOR performance in a time-dependent fashion. Reversal of NOR performance deficits is unlikely after prolonged intermittent hypoxia duration. These findings suggest that early recognition of sleep apnoea and effective treatment are critical for restoration of the adverse cognitive effects of the disease.