Unknown Time Research Articles

VM queuing optimal scheduling in cloud using heuristic ant colony optimal based multi-objective genetic approach

The usages of cloud based applications are increasing tremendously. The cloud computing task distribution is an unknown polynomial time issue that is challengeable to find the optimal solution. In solve above mentioned issue with large amount user’s job requests, heuristic ant colony optimal based multi-objective genetic (HACOMOG) approach based job allocation and resource optimization is proposed. Utilization basis scheduler recognizes the task order and optimal resources to be scheduled. The primary contribution of the proposed technique is to develop several online techniques to find solution for the virtual machines (VM) Packing problem sharing-aware and for performing a comprehensive number of studies in order to assess their efficiency with online sharing algorithms. The proposed algorithm considers the utilization basis scheduler output and identified the optimzed task allocation technique based on job execution time, MakeSpan and throughput. The experimental outcomes show that the proposed HACOMOG Algorithm reduces 0.70 seconds job execution time (JET), 0.13 MakeSpan and improve 1.98 throughput on given parameters for 100, 200, and 500 tasks with conventional methodologies.

Abstract Number ‐ 149: Mechanical stent retriever thrombectomy of distal medium vessel occlusiono in acute ischemic stroke.

Introduction Randomized clinical trial (RTC) have demonstrated benefit of stent retriever mechanical thrombectomy (SRMT) of large vessel occlusion (LVO) in acute ischemic stroke (AIS) including with those present in 24 hours. However, trials have included predominately proximal middle cerebral artery (MCA, M1) and current recommendation is to perform SRMT in M1/M2. Therefore, AIS patients with distal medium vessel occlusion (DMVO)) with high NIHSS has no recommendation or options leading to disabilities. Objective of our study is to present the safety and feasibility of SRMT of DMVO who underwent SRMT. Additionally, we will describe technical aspect including their clinical and radiographic outcomes. Methods From a stroke‐endovascular databased, consecutive patient with SVO underwent SRMT from 2015–2022 were selected. Patient demographics including recanalization data with clinical and radiographic outcomes were collected. Results 7 patients (4 female) with median age of 65.66 (range 41–86), median NIHSS of 13 (range 7–19) and median ASPECT 8 (7‐10) presented with SVO; right M3 in 2 and left M3/M4 in 5 patients. Of 7 DMVO patients, one presented in 4 hours and receives intravenous rTPA, 2 presented in 12 hours and 4 presented in unknown time of onset but last known normal was 24 hours. One of the left M3/4 patients was a singer who (41 years old) absolutely do not wanted to be alive if she didn’t regain her speech. Similarly, another 42‐year‐old woman with left M3/4 clot was teacher wanted her speech restored for teaching. Endovascular strategies: all but one received rectal 300 mg aspirin and 500 ml normal saline bolus. Under conscious sedation, 8 French balloon catheters were used in all cases and the guide was flushed with heparin and nitroglycerine mixed saline. Solitaire SRMT in 5 cases (4×20 in 1 and 4×40 in 6 cases) and trevo in one (3×20 mm). Patient with solitaire SRMT required single pass in which trap technique (suction through CAT6 and retriever of SRD as a unit without shaving of clots) was utilized using CAT6 132 mm catheter 6 cases and Trevo SRMT required 3 passes. All achieved TICI 3 recanalization without complications. Outcomes: immediate post STMT, median NIHSS 2 (0‐6), 24 hours median NIHSS 1 (1‐6) and 90 days NIHSS 0 for all. 90 days mRS0 in 4, mRS1 in 1 and mRS2 in 1. 24 hours CT scan demonstrated small stroke in 2 patients. One patient lost follow up. 41‐year‐old singer immediately regained her speech after SRMT discharged home in 36 hours. 42‐year‐old teacher improved her speech and continue to teach. Conclusions Our case series demonstrated that SRMT in DMVO with disabling NIHSS is feasible and safe if performed carefully. All patient achieved TICI 3 perfusion and first pass effect was better in trap technique using long solitaire STD. Good function outcomes were achieved in all cases irrespective of the types of SRD used and passes required to achieve TICI 3 perfusion. Further studies are required.

Moral Distress in the Time of COVID-19: Occupational Therapists’ Perspectives

The conditions of COVID-19 have caused moral distress in healthcare workers. Occupational therapists have had to adapt to these unknown times to best treat their clients. The purpose of this study was to explore the experience of moral distress in occupational therapists during the time of COVID-19. Eighteen occupational therapists were included who worked in a variety of settings. Investigators conducted semi-structured interviews to explore experience with moral distress (distress felt when confronting an ethical problem) during the time of COVID-19. The data were analyzed using a hermeneutical phenomenological approach to generate themes regarding the experience of moral distress. Investigators identified themes of occupational therapists’ experiences during the COVID-19 pandemic. These themes included: Experiences of moral distress, capturing participants’ encounters with morally distressing issues; effects of moral distress, exploring the impact of COVID-19 experiences on participants’ well-being and quality of life; and managing moral distress, addressing ways in which occupational therapists tried to mitigate moral distress throughout the pandemic. This study brings awareness to the experience of occupational therapists during the pandemic and explores implications for preparing occupational therapists for future occurrences of moral distress.

Time Evolution of the Modulus of Elasticity of Metakaolin-Based Geopolymer

The objective of the research is to develop a new family of geopolymeric materials and to use an experimental methodology to characterize the mechanical behavior of the materials obtained by alkaline activation of metakaolin using a compound activator. The researchers also intend to study the unknown time evolution of the modulus of elasticity and the influence of the composition of the aggregates on the strength of the material. Like the material’s strength, the results have a direct influence on structural safety evaluations. For the analysis of the mechanical properties of the mixtures, different types of tests were carried out: Flexural and compression tests on parallelepipeds and compression tests on cylinders were performed to assess the main strength characteristics of metakaolin-based geopolymers. Regarding the aggregate composition, the results show that the correction of the aggregate particle size line did not improve the mechanical properties. From about 400 h of curing, at ambient temperatures, the mechanical properties of the geopolymeric material are almost invariable. The highest value of the elastic modulus of elasticity occurs around 420 h, at about 18 GPa. The modulus of elasticity is independent of test load rate as per standards, and 1.7‰ strain was observed during maximum compressive stresses in the rupture tests. Also, the secant modulus values at 60% and 80% of maximum stress are within 12% of the value at 40% of maximum stress.

Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset

Abstract. Data assimilation (DA), the statistical combination of computer models with measurements, is applied in a variety of scientific fields involving forecasting of dynamical systems, most prominently in atmospheric and ocean sciences. The existence of misreported or unknown observation times (time error) poses a unique and interesting problem for DA. Mapping observations to incorrect times causes bias in the prior state and affects assimilation. Algorithms that can improve the performance of ensemble Kalman filter DA in the presence of observing time error are described. Algorithms that can estimate the distribution of time error are also developed. These algorithms are then combined to produce extensions to ensemble Kalman filters that can both estimate and correct for observation time errors. A low-order dynamical system is used to evaluate the performance of these methods for a range of magnitudes of observation time error. The most successful algorithms must explicitly account for the nonlinearity in the evolution of the prediction model.

INFLUENCE OF UNOBSERVED DATA IN THE TIME SERIES OF THE DEPENDENT VARIABLE AND THEIR POSITION IN ANALYSIS OF MULTIPLE LINEAR REGRESSION ON PREDICTION - CASE STUDY ON: FACTORS AFFECTING CO_2 EMISSIONS

Using a variety of statistical techniques, time series forecasting is crucial for preparing for and predicting the future. It is contingent on making an accurate forecast as to the value of a variable at some unknown time in the future. This research analyzed the missing data from the time series (a model with no missing observations and three models were considered to be missing data for the dependent variable at various positions). By a standard multiple linear regression of the four models of the study, it is clear that the series is consistent, transparent, within the bounds of statistical acceptability, the analysis used the Ordinary least square and the weighted least square to find the best prediction model with missed observation.

The influence of elastic ankle exoskeletons on lower limb mechanical energetics during unexpected perturbations.

Passive elastic ankle exoskeletons have been used to augment locomotor performance during walking, running and hopping. In this study, we aimed to determine how these passive devices influence lower limb joint and whole-body mechanical energetics to maintain stable upright hopping during rapid, unexpected perturbations. We recorded lower limb kinematics and kinetics while participants hopped with exoskeleton assistance (0, 76 and 91 Nm rad-1) on elevated platforms (15 and 20 cm) which were rapidly removed at an unknown time. Given that springs cannot generate nor dissipate energy, we hypothesized that passive ankle exoskeletons would reduce stability during an unexpected perturbation. Our results demonstrate that passive exoskeletons lead to a brief period of instability during unexpected perturbations - characterized by increased hop height. However, users rapidly stabilize via a distal-to-proximal redistribution of joint work such that the knee performs an increased energy dissipation role and stability is regained within one hop cycle. Together, these results demonstrate that despite the inability of elastic exoskeletons to directly dissipate mechanical energy, humans can still effectively dissipate the additional energy of a perturbation, regain stability and recover from a rapid unexpected vertical perturbation to maintain upright hopping.

Abstract WP115: DWI/FLAIR Mismatch Is Common In Late Window Patients With Target Perfusion Mismatch

Introduction: The presence of a DWI/FLAIR mismatch has been suggested as a method to identify patients within 4.5 hours from stroke symptom onset. We studied patients from DEFUSE 2 and 3 with the target perfusion mismatch profile to evaluate the rates of DWI/FLAIR mismatch in patients with who have favorable collaterals in early and late time windows. We hypothesize that the persistence of DWI/FLAIR mismatch in late window patients may reflect good collateral circulation. Methods: We identified 58 eligible patients in D2 and 30 patients from D3. Imaging was reviewed by two neuroradiologists and one neurologist who rated the presence of DWI-FLAIR mismatch. Results: The inter-rater agreement for the presence of DWI/FLAIR mismatch was 88.3%. There were 32 patients who were < 6 hours from last known well (LKW) or symptom onset, 17 patients who were ≥ 6 hours from symptom onset, and 26 patients who were ≥ 6 hours from LKW with an unknown time of symptom onset. These are categorized as early, late, and suspected late window, respectively. The rate of DWI/FLAIR mismatch was 56% in the early window group, 41% in the late window group, and 35% in the suspected late window group. The overall rate of DWI/FLAIR mismatch did not differ significantly between these three groups (56% vs. 41% vs. 35%, p=0.239). Of the early window patients, the rate of DWI-FLAIR mismatch was higher in those with the target perfusion mismatch profile (67%) compared to those without (36%). All but one the known onset late window patients had the target perfusion mismatch profile, and the rate of DWI-FLAIR mismatch in this group was 35%. Forthcoming analyses will include predictors of DWI-FLAIR mismatch such as hypoperfusion intensity ratio, time from symptom onset, and NIHSS score. Conclusions: More than one-third of patients presenting >6 hours from symptom onset with a target perfusion mismatch profile have DWI/FLAIR mismatch. This could reflect the presence of good collateral circulation that may delay the appearance of a FLAIR lesion. The rate of DWI/FLAIR mismatch did not differ among early, late, and expected late window patients. This suggests that, among patients with favorable collaterals, DWI/FLAIR mismatch does not reliably identify early window patients.

A distributionally robust optimization model for batch nonlinear switched time-delay system considering uncertain output measurements

In this paper, we consider a nonlinear switched time-delay (NSTD) system with unknown switching times and unknown system parameters, where the output measurement is uncertain. This system is the underling dynamical system for the batch process of glycerol bioconversion to 1,3-propanediol induced by Klebsiella pneumoniae. The uncertain output measurement is regarded as a stochastic vector (whose components are stochastic variables) and the only information about its distribution is the first-order moment. The objective of this paper is to identify the unknown quantities of the NSTD system. For this, a distributionally robust optimization problem (a bi-level optimization problem) governed by the NSTD system is proposed, where the relative error under the environment of uncertain output measurements is involved in the cost functional. The bi-level optimization problem is transformed into a single-level optimization problem with non-smooth term through the application of duality theory in probability space. By applying the smoothing technique, the non-smooth term is approximated by a smooth term and the convergence of the approximation is established. Then, the gradients of the cost functional with respect to switching times and system parameters are derived. A hybrid optimization algorithm is developed to solve the transformed problem. Finally, we verify the obtained switching times and system parameters, as well as the effectiveness of the proposed algorithm, by solving this distributionally robust optimization problem.

Self-synchronized temporal-spectral characterization system for revealing ultrafast fiber laser dynamics

Due to the electronic bottleneck limited real-time measurement speed of common temporal-spectral detection and the particle-like nature of optical soliton enabled nonrepeatable transient behaviors, capturing the ultrafast laser pulses with unknown times of arrival and synchronously characterizing their temporal-spectral dynamic evolution is still a challenge. Here, using the Raman soliton frequency shift based temporal magnifier and dispersive Fourier transform based spectral analyzer, we demonstrate a self-synchronized, ultrafast temporal-spectral characterization system with a resolution of 160 fs and 0.05 nm, and a recording length above milliseconds. The synchronized nonlinear process makes it possible to image full-filled temporal sub-picosecond pulse trains regardless of their arrival times and without extra pump lasers and photoelectric conversion devices. To demonstrate the significance of this improvement, a buildup dynamic process of a soliton laser with a complex breakup and collisions of multisolitons is visually displayed in the spectral and temporal domains. The soliton dynamic evolution processes observed by our characterization system are in one-to-one correspondence with the numerical simulation results. We believe this work provides a new multidimensional technique to break the electronic bottleneck to gain additional insight into the dynamics of ultrafast lasers and nonlinear science.

Estimation of the point source parameters by the adjoint equation in the time-varying atmospheric environment with unknown turn-on time

Bayesian inference coupled with computational fluid dynamics (CFD) has been widely used in the source term estimation (STE). At present, most scholars have studied the pollution source released in the time-invariant flow field and the turn-on time of the source is often regarded as a known parameter which requires no estimation. Nevertheless, it is of greater practical significance to estimate the source parameters by considering the time-varying characteristics of flow field. Besides, estimating the turn-on time is very important as it provides the critical information for post-disaster rescue and accident cause analysis. In this paper, the predicted concentrations are calculated by the adjoint equation in the time-varying flow field, and the turn-on time, taken as an unknown parameter, is estimated together with the source location and the release rate. Accurate estimated results are thereby obtained. Then, how the sensors’ sampling time intervals and the measured data in different dispersion stages influence the estimated results is investigated. It is found that reducing the sampling time interval can decrease the uncertainties of the estimations. In addition, in order to estimate the turn-on time accurately, the measured information in the developing stage of dispersion is indispensable. However, only using the measured information in the stable stage of dispersion cannot predict the turn-on time. Finally, the earliest time when the source parameters can be estimated with accuracy is also explored. The results show that the method of the STE proposed can obtain accurate source information at the early stage of dispersion.

Scientists warning on the ecological effects of radioactive leaks on ecosystems

A nuclear leakage or tactical nuclear weapon use in a limited war could cause immense and long-lasting ecological consequences beyond the direct site of exposure. We call upon all scientists to communicate the importance of the environmental impacts of such an event to all life forms on Earth, including humankind. Changes to ecosystem structure and functioning and species extinctions would alter the biosphere for an unknown time frame. Radiation could trigger cascade effects in marine, atmospheric and terrestrial ecosystems of a magnitude far beyond human capabilities for mitigation or adaptation. Even a “tactical nuclear war” could alter planet Earth’s living boundaries, ending the current Anthropocene era.

Outcomes following thrombectomy for acute large vessel occlusion beyond 24 hours or with unknown time of onset

BackgroundEndovascular thrombectomy (EVT) is recommended in medically eligible patients with large vessel occlusions (LVO) within 24 hours of symptom onset. While there is evidence that EVT ≥24h after last known well (LKW) is associated with favorable outcomes in patients who meet DAWN/DEFUSE-3 criteria, it is unknown if more liberal criteria can be applied. MethodsA single center, prospective observational cohort of consecutive adult stroke patients was queried for symptomatic occlusions of the internal carotid (ICA) or proximal middle cerebral (M1) arteries (October 2019-January 2022), with a National Institutes of Health Stroke Scale (NIHSS) ≥6, pre-stroke modified Rankin Scale (mRS) 0-2, and Alberta Stroke Program Early Computed Tomography Scale score 3-10. These inclusion criteria were extrapolated from recently published data indicating a benefit with EVT with more liberal patient selection. Patients who underwent EVT ≥24h after LKW were compared against those treated medically. The primary outcome was a good functional outcome (90-day mRS 0-2), which was evaluated using multivariable logistic regression. ResultsOf the 27 included patients, the median age was 65y (IQR 49-76) with a median NIHSS of 15 (IQR 8-26), and 17 (63.0%) underwent EVT (median LKW-to-puncture 35.5h (IQR 26.9-65.8h). The primary outcome was no different with EVT in unadjusted regression (OR 1.17, 95%CI 0.17-8.09), and there remained no association across all multivariable models tested. Age, pre-stroke disability, and M1 occlusions were non-significantly associated with the primary outcome (p>0.05). There was a non-significant trend indicating a favorable shift in 90-day mRS with EVT (proportional OR 2.04, 95%CI 0.44-9.48). ConclusionsUsing more liberal inclusion criteria for EVT in the ultra-extended window, there was no statistically significant difference in the rate of good functional outcome with EVT. Larger studies are called upon to evaluate outcomes when more liberal criteria are used to assess thrombectomy eligibility.

Minimizing online retailers’ revenue loss under a time-varying willingness-to-pay distribution

We study revenue management for an online retailer that offers a single product to customers with a time-varying willingness-to-pay (WTP) distribution. When the WTP distribution changes at an unknown time point (i.e., the change point), the original price quoted to customers may not be optimal. The retailer’s objective is to minimize his revenue loss by determining when to quote a new price to incoming customers and what the new price is. We formulate the problem as an optimization model, which is challenging to solve due to the presence of the unknown change point. To overcome this challenge, we first characterize the probability distribution of the estimated change point using maximum likelihood estimation. We then develop two pricing algorithms to solve the model by leveraging the characterized distribution. Lastly, through numerical studies, we illustrate the effectiveness of the proposed algorithms in identifying change points and recommending optimal post-change selling prices. The numerical results suggest that the proposed pricing algorithms significantly reduce the frequency of false change detection in the early stages of a selling season. The proposed pricing algorithms also outperform the benchmark pricing algorithm in reducing the retailer’s revenue loss.

Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery

<abstract> <p>The rotor of the magnetic suspension turbomachinery is supported by the magnetic suspension bearing without contact and mechanical friction, which directly drives the high-efficiency fluid impeller. It has the advantages of high efficiency, low noise, less fault and no lubrication. However, the system often has some unknown mutation, time variation, load perturbation and other un-certainties when working, and the traditional Proportion Integration Differentiation (PID) control strategy has great limitations to overcome the above disturbances. Therefore, this paper firstly establishes a mathematical model of the rotor of magnetic levitation turbomachinery. Then, a linear active disturbance rejection controller (LADRC) is presented, which can not only improve the above problems of PID control, but also avoid the complex parameter tuning process of traditional nonlinear active disturbance rejection control (ADRC). However, LADRC is easy to induce the overshoot of the system and cannot filter the given signal. On this basis, an improved LADRC with a fast-tracking differentiator (FTD) is proposed to arrange the transition process of input signals. The simulation results show that compared with the traditional PID controller and single LADRC, the improved linear active disturbance rejection control method with fast tracking differentiator (FTD-LADRC) can better suppress some unknown abrupt changes, time variation and other uncertainties of the electromagnetic bearing-rotor system. At the same time, the overshoot of the system is smaller, and the parameters are easy to be set, which is convenient for engineering application.</p> </abstract>

K počátkům životní dráhy významného šlechtice husitské doby v českých zemích Haška z Valdštejna

The study provides the first detailed account of the early life of an important nobleman in the Czech lands, Hašek of Valdštejn. While his political career in the period after 1419 is relatively well known, the period prior to this date has not received due attention from historians. Hašek of Valdštejn was the most important member of the Valdštejn family until the time of the great Albrecht Václav Eusebius of Valdštejn in the 17th century. Like his famous relative nearly two hundred years later, Hašek had a major career in the military and diplomatic sphere in the 15th century. The author describes Hašek’s early life in the text. He came from the impoverished Dětenice branch of the family, which held domains in eastern Bohemia, as was first mentioned in written sources in 1404. It can be assumed that he was born sometime between 1385 and 1390. An important strategic decision was his departure from Bohemia to Moravia in 1410, where he managed to establish a prominent position for himself in the local aristocratic society. In Moravia, Hašek acquired the domain of Uherský Ostroh as a pledge by 1411 at the latest. Hašek also acquired the Víckov domain in Moravia at an unknown time. And while it is said that Hašek was the leader of the Budějovice-Hrotovice group of highwaymen in Moravia, the author emphasises that the existence of such a group of outlaws is not sufficiently documented in the sources. He concludes that this is an artificial construct of historians and that no such group existed.

Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples

The problem of quickest anomaly detection in networks with unlabeled samples is studied. At some unknown time, an anomaly emerges in the network and changes the data-generating distribution of some unknown sensor. The data vector received by the fusion center at each time step undergoes some unknown and arbitrary permutation of its entries (unlabeled samples). The goal of the fusion center is to detect the anomaly with minimal detection delay subject to false alarm constraints. With unlabeled samples, existing approaches that combines local cumulative sum (CuSum) statistics cannot be used anymore. Several major questions include whether detection is still possible without the label information, if so, what is the fundamental limit and how to achieve that. Two cases with static and dynamic anomaly are investigated, where the sensor affected by the anomaly may or may not change with time. For the two cases, practical algorithms based on the ideas of mixture likelihood ratio and/or maximum likelihood estimate are constructed. Their average detection delays and false alarm rates are theoretically characterized. Universal lower bounds on the average detection delay for a given false alarm rate are also derived, which further demonstrate the asymptotic optimality of the two algorithms.

Deep neural network based adaptive learning for switched systems

In this paper, we present a deep neural network based adaptive learning (DNN-AL) approach for switched systems. Currently, deep neural network based methods are actively developed for learning governing equations in unknown dynamic systems. However, their efficiency can degenerate for switching systems, where structural changes exist at discrete time instants. In this new DNN-AL strategy, observed datasets are adaptively decomposed into subsets, such that no structural change within each subset. During the adaptive procedures, DNNs are hierarchically constructed, and unknown switching time instants are gradually identified. Especially, network parameters at previous iteration steps are reused to initialize networks for the later iteration steps, which provides efficient training procedures for the DNNs. For the DNNs obtained through our DNN-AL, the bounds of the prediction error are established. Numerical studies are conducted to demonstrate the efficiency of DNN-AL.

Uncertainty in estimated intake for routine lung monitoring of Pu/Am

In routine monitoring of radiation workers to detect internal contamination due to Pu/Am, it is assumed that intake has occurred at the midpoint of the monitoring interval. Radiation worker is normally monitored at the end of the annual monitoring interval, within a span of ±1 month under a routine in vivo monitoring program. An uncertainty is introduced in intake estimation as the time of intake can vary over the entire monitoring interval. In this work, uncertainties introduced in the intake estimation of Type M and S compounds of Pu/Am are evaluated due to unknown time of intake for annual and biennial monitoring frequency.

6D Phase space diagnostics based on adaptively tuned physics-informed generative convolutional neural networks

A physics-informed generative convolutional neural network (CNN)-based 6D phase space diagnostic is presented which generates all 15 unique 2D projections (x, y), (x, y′),...,(z, E) of a charged particle beam’s 6D phase space (x, y, z, x′, y′, E). The CNN is trained by supervised learning over a wide range of input beam distributions, accelerator parameters, and the associated 6D beam phase spaces at multiple accelerator locations. The CNN is applied in an un-supervised adaptive manner without knowledge of the input beam distribution or accelerator parameters and is robust to their unknown time variation. Adaptive feedback automatically tunes the low-dimensional latent space of the encoder-decoder CNN to predict the 6D phase space based only on 2D (z, E) longitudinal phase space measurements from a device such as a transverse deflecting RF cavity (TCAV). This method has the potential to provide diagnostics beyond the existing state of the art at many accelerator facilities. Studies are presented for two very different accelerators: the 5-meter-long ultra-fast electron diffraction (UED) HiRES compact accelerator at LBNL and the kilometer long plasma wakefield accelerator FACET-II at SLAC.