Arbitrary Period Research Articles

How quickly do patients with cancer view the results of their imaging studies?

419 Background: The 21 st Century Cures Act requires electronic health data to be available to patients. However, such data are useful to patients only if they access it. For example, imaging data can indicate the effectiveness of treatment and progression of disease and engagement with this information empowers patients to become more active in their care. While prior studies have explored the prevalence and timing of patient access to clinical notes, the timing of patient access to other data types remains unknown. We analyzed how rapidly oncology patients access the results of their imaging studies. Methods: Memorial Sloan Kettering Cancer Center (MSK) is a cancer center in New York that provides 700,000 imaging studies annually. At the time of this analysis, MSK had an internally developed patient portal, MyMSK, which notified patients via email about the presence of new data in the portal. The default timing for notifications was daily at 8 a.m. (patients could change notifications to be real-time, but very few did). We selected an arbitrary 7-day period (4/1/2023-4/7/2023) for analysis. For that week, for all imaging studies performed at MSK, we measured the time from when the report was available in the database (and thus available to the patient in MyMSK) to the time the report was read by either a patient or a proxy. The day the imaging study was performed was defined as “Day 0”. We determined (i) the proportion of imaging study reports that were read by 8 a.m. on Day 1, i.e., before any notification was sent to the patient, (ii) the proportion of reports that were read on Day 1 between 8 a.m. and 5 p.m., (iii) the proportion of reports that were read after 5 p.m. on Day 1, and (iv) the proportion not yet read by 8/23/2023 (when the data were collected). Results: In this one-week period, a total of 7,110 patients had 10,298 imaging studies. The average age of the patients was 61.4 years, 61.3% were female, 85.5% were non-Hispanic, 75.8% were White, and 95.1% reported English as their preferred language. Of the 10,298 imaging reports, 3203 (31.1%) were read before 8 a.m. on Day 1, 2090 (20.3%) were read between 8 a.m. and 5 p.m. on Day 1, 2389 (23.2%) were read after 5 p.m. on Day 1, and 2616 (25.4%) were not read as of 8/23/2023. Conclusions: This analysis indicates that a substantial proportion of cancer patients seek the results of imaging studies very soon after the tests are performed. Almost a third of the time, the patient read the report before any notification arrived. In oncology, it is easy to imagine patients frequently refreshing their portal to see if results have been posted. It is vital for provider organizations to understand the eagerness with which patients await results. Organizations may also want to develop methods to answer questions patients may have about their results prior to speaking with providers; this may be a key opportunity to leverage emerging artificial intelligence techniques.

On modeling seismicity in seismic hazard assessment problems

Seismicity modeling is an important part of creating General Seismic Zoning maps within the framework of a probabilistic approach. We consider the main disadvantages of individual elements of the recent seismicity models. A variant of the methodology is proposed, which, due to the improvements of those elements, should provide more accurate estimates of the future seismicity. For the first time, a stochastic seismicity model has been proposed in the form of a synthetic earthquake catalog, generated for an arbitrary conditional period and reproducing the properties of the catalog of actual earthquakes, including spatiotemporal grouping. A methodology for verifying seismicity models is proposed to check the compliance of the models with the initial data, to assess the predictive efficiency of the models, and to compare efficiency of different models.

455 Evaluating the effect of a phenology-based timeline on the interpretation of enteric methane emission results

Abstract The methodology of analyzing methane emissions in cattle grazing native rangeland is imperative for producing reliable and repeatable estimations of total enteric emissions on an individual and herd basis. However, due to seasonal differences in climate from year to year, there are distinct differences in forage biomass availability and nutrient composition on extensive rangelands. Thus, our objective was to evaluate the differences in methane emissions using phenological forage phases compared with specific time periods. Yearling Angus steers (n = 127) were allocated to one of six pastures grazed from June to September (2023) at the South Dakota State University Cottonwood Field Station (Cottonwood, SD). Each pasture was equipped with a GreenFeed (C-Lock Inc., Rapid City, SD) pasture system to measure individual animal CH4 emissions. A subset of CH4 data from two cool-season-dominated pastures were aggregated from the overall study. We used two methods to segment CH4 by either 1) month (June, July, August) or 2) phenology (vegetative, reproductive, and senescence) to derive average CH4 (gּ animalּ -1ּ d-1) for each period. Western wheatgrass (WWG), a cool-season native, was used as a proxy for cool season grass development. On May 28th, 2023, the growing degree days (GDD) reached 632, representing the 3.5 leaf stage. WWG has been documented to reach maturity at approximately 1,100 GDD, which occurred on June 20th, 2023. Nutrient content of the forage (i.e., lignin) was used to approximate the start of senescence on August 11th. These developmental markers created estimates of the vegetative, reproductive, and senescence periods within our emissions data. Differences in enteric emissions for each approach were analyzed using a linear mixed effects model in R, and post-hoc contrast between methods was assessed using the lsmeans package. There were no statistical differences in CH4 by month (P > 0.05), and the means for June, July, and August were 217, 214, and 211 ± 4, respectively. By phenological phases, there was a difference (P < 0.05) between maturity and senescence, with means for vegetative, reproductive, and senescence as 214, 219, and 206 ± 4, respectively. Methane intensity (gּ animal-1ּ d-1) per period was then multiplied by the number of days in each month or phase to estimate total emissions for the entire grazing season (86 d). The monthly approach totaled 18,383 g CH4 per animal, while phenology totaled 18,504 g for the grazing season. This is a difference of 0.654% between the same data segmented by month or by phenology. Therefore, because of the dynamic nature of forage and cattle production, it is likely that accounting for phenology will provide greater clarity for GHG on rangeland systems rather than arbitrary periods of time and will require the use of dynamic models to handle the complexity of enteric emissions of grazing animals.

Zero-Order-Hold Triggered Control of a Chain of Integrators with an Arbitrary Sampling Period

Zero-Order-Hold Triggered Control of a Chain of Integrators with an Arbitrary Sampling Period

Central venous pressure waveform analysis during sleep/rest: a novel approach to enhance intensive care unit post-extubation monitoring of extubation failure.

This pilot study aimed to investigate the relation between cardio-respiratory parameters derived from Central Venous Pressure (CVP) waveform and Extubation Failure (EF) in mechanically ventilated ICU patients during post-extubation period. This study also proposes a new methodology for analysing these parameters during rest/sleep periods to try to improve the identification of EF. We conducted a prospective observational study, computing CVP-derived parameters including breathing effort, spectral analyses, and entropy in twenty critically ill patients post-extubation. The Dynamic Warping Index (DWi) was calculated from the respiratory component extracted from the CVP signal to identify rest/sleep states. The obtained parameters from EF patients and patients without EF were compared both during arbitrary periods and during reduced DWi (rest/sleep). We have analysed data from twenty patients of which nine experienced EF. Our findings may suggest significantly increased respiratory effort in EF patients compared to those successfully extubated. Our study also suggests the occurrence of significant change in the frequency dispersion of the cardiac signal component. We also identified a possible improvement in the differentiation between the two groups of patients when assessed during rest/sleep states. Although with caveats regarding the sample size, the results of this pilot study may suggest that CVP-derived cardio-respiratory parameters are valuable for monitoring respiratory failure during post-extubation, which could aid in managing non-invasive interventions and possibly reduce the incidence of EF. Our findings also indicate the possible importance of considering sleep/rest state when assessing cardio-respiratory parameters, which could enhance respiratory failure detection/monitoring.

Quantitative assessment of the extent of damage to timber structures during the full course of a real fire using the energy equivalence method

In this study, an innovative method, the Energy Equivalence Method (EEM), is proposed in the context of the Performance-Based Fire Engineering (PBFE) framework for quantitatively assessing the degree of damage of timber structures during the full process of a real fire. To verify the validity of the method, glued laminated timber (GLT) specimens and GLT columns were selected as the subjects of fire action, and a series of equivalent fire experiments were conducted by using a parametric fire model (referred to as the DNI fire model), which is currently used in Germany, to simulate real fire scenarios. By comparing the equivalence of charring depth (CD), pyrolysis zone temperature, and residual load-carrying capacity of the test specimens after the action of the DNI fire and its equivalent standard fire, the validity of the EEM in quantifying the degree of damage to timber members after the DNI fire was fully verified. Subsequently, based on the fire experimental data, accurate thermal and structural models were developed, and these models were utilized to further validate the effectiveness of the EEM in quantifying the extent of damage to timber members under the action of a DNI fire over an arbitrary time period. The results of the study show that the proposed EEM not only can be used to assess the extent of structural damage during fire action and the residual load carrying capacity of the structure after fire action, but also promotes the transition from standard fire safety design to the PBFE design.

A novel indicator in epidemic monitoring through a case study of Ebola in West Africa (2014-2016).

The E/S (exposed/susceptible) ratio is analyzed in the SEIR model. The ratio plays a key role in understanding epidemic dynamics during the 2014-2016 Ebola outbreak in Sierra Leone and Guinea. The maximum value of the ratio occurs immediately before or after the time-dependent reproduction number (Rt) equals 1, depending on the initial susceptible population (S(0)). It is demonstrated that transmission rate curves corresponding to various incubation periods intersect at a single point referred to as the Cross Point (CP). At this point, the E/S ratio reaches an extremum, signifying a critical shift in transmission dynamics and aligning with the time when Rt approaches 1. By plotting transmission rate curves, β(t), for any two arbitrary incubation periods and tracking their intersections, we can trace CP over time. CP serves as an indicator of epidemic status, especially when Rt is close to 1. It provides a practical means of monitoring epidemics without prior knowledge of the incubation period. Through a case study, we estimate the transmission rate and reproduction number, identifying CP and Rt = 1 while examining the E/S ratio across various values of S(0).

A Review Study of Prime Period Perfect Gaussian Integer Sequences

Prime period sequences can serve as the fundamental tool to construct arbitrary composite period sequences. This is a review study of the prime period perfect Gaussian integer sequence (PGIS). When cyclic group {1,2,…,N−1} can be partitioned into k cosets, where N=kf+1 is an odd prime number, the construction of a degree-(k + 1) PGIS can be derived from either matching the flat magnitude spectrum criterion or making the sequence with ideal periodic autocorrelation function (PACF). This is a systematic approach of prime period N=kf+1 PGIS construction, and is applied to construct PGISs with degrees 1, 2, 3 and 5. However, for degrees larger than 3, matching either the flat magnitude spectrum or achieving the ideal PACF encounters a great challenge of solving a system of nonlinear constraint equations. To deal with this problem, the correlation and convolution operations can be applied upon PGISs of lower degrees to generate new PGISs with a degree of 4 and other higher degrees, e.g., 6, 7, 10, 11, 12, 14, 20 and 21 in this paper. In this convolution-based scheme, both degree and pattern of a PGIS vary and can be indeterminate, which is rather nonsystematic compared with the systematic approach. The combination of systematic and nonsystematic schemes contributes great efficiency for constructing abundant PGISs with various degrees and patterns for the associated applications.

Generalized sequential state equation method for moving subsystem-induced structural parametric resonance

Parametric excitation is a unique type of excitation that arises from the time-varying parameters of a dynamical system, typically induced by the periodic movements of the system components. However, the structure-moving subsystems problem creates a new type of parametric excitation where the subsystems do not have exact periodic movements. Under the circumstances, dimension of the mathematical model of such a system exhibits time dependency, which prohibits the employment of conventional analytical methods to predict its stability. In this paper, we propose a novel Generalized Sequential State Equation Method that provides a distinct solution framework for analysis of the subsystem-induced parametric resonance. With the time-domain system partition and state mapping, the proposed method reconfigures the coupled system with a sequence of state space equations and solves them chronologically. The proposed method relaxes the single-subsystem-assumption in its original form and applies to a general scenario where the subsystems travel with arbitrary characteristic period. With the proposed method, occurrences of the subsystem-induced parametric resonance are precisely predicted via a set of analytical stability criteria and the steady state response is determined in an exact analytical form.

Accumulation of Large Ion Populations with High Ion Densities and Effects Due to Space Charge in Traveling Wave-Based Structures for Lossless Ion Manipulations (SLIM) IMS-MS.

The accumulation of very large ion populations in traveling wave (TW)-based Structures for Lossless ion Manipulations (SLIM) has been studied to better understand aspects of "in-SLIM" ion accumulation, and particularly its use in conjunction with ion mobility spectrometry (IMS). A linear SLIM ion path was implemented that had a "gate" for blocking and accumulating ions for arbitrary time periods. Removing the gate potential caused ions to exit, and the spatial distributions of accumulated ions examined. The ion populations for a set of peptides increased approximately linearly with increased accumulation times until space change effects became significant, after which the peptide precursor ion populations decreased due to growing space charge-related ion activation, reactions, and losses. Ion activation increased with added storage times and the TW amplitude. Lower amplitude TWs in the accumulation/storage region prevented or minimized ion losses or ion heating effects that can also lead to fragmentation. Our results supported the use of an accumulation region close to the SLIM entrance for speeding accumulation, minimizing ion heating, and avoiding ion population profiles that result in IMS peak tailing. Importantly, space charge-driven separations were observed for large populations of accumulated species and attributed to the opposing effects of space charge and the TW. In these separations, ion species form distributions or peaks, sometimes moving against the TW, and are ordered in the SLIM based on their mobilities. Only the highest mobility ions located closest to the gate in the trapped ion population (and where the highest ion densities were achieved) were significantly activated. The observed separations may offer utility for ion prefractionation of ions and increasing the dynamic range measurements, increasing the resolving power of IMS separations by decreasing peak widths for accumulated ion populations, and other purposes benefiting from separations of extremely large ion populations.

Extending the Multi-Tone Sinusoidal Frequency Modulation Signal Model by Fourier Expansions With Arbitrary Periods

Extending the Multi-Tone Sinusoidal Frequency Modulation Signal Model by Fourier Expansions With Arbitrary Periods

OPTIMIZATION OF THE USE OF DISTRIBUTED ENERGY RESOURCES IN LOCAL ELECTRICAL POWER SYSTEMS ACCORDING TO THE CRITERION OF MINIMUM LOSS OF ELECTRICITY

The issue of optimizing the use of distributed energy resources in local electric power systems (LES) based on the criterion of minimum power loss is considered. It was determined that an important step in optimizing the use of distributed energy resources in the LES is the development of an energy router that allows devices that generate, store, and consume electricity to be combined into a single system at low voltage (without integration into medium voltage electrical networks). The energy router itself can be positioned as a basic device that ensures the operation of LES, the interaction of neighboring LES at the level of energy and information exchange, and the integration of specific LES to medium voltage distribution networks.
 It is shown that the basic component of the evaluation of the effective functioning of energy routers and the platform approach in the LES, in accordance with the requirements of the Energy Internet, is the analysis of the components of electricity losses, the influence on their level of various factors, as well as the formation of appropriate energy efficiency criteria and the assessment of the partial impact of LES elements. It is proposed to carry out a description of energy processes during the decomposition of Frize reactive power with the allocation of reactive and active current in the controlled intersections of the system. The decomposition of the QF power into components under the action of various causes (factors) of additional electricity losses in the LES, in particular, due to different modes of operation of generators and LES loads (voltage and current spectra), both during the period of system operation and for an arbitrary period of time, was considered. which is determined by technological factors of system operation.
 The use of Frize power made it possible to take into account the energy supply processes of LES loads on alternating and direct current from a single point of view, in particular, to assess the impact of a combination of various factors of the appearance of additional electricity losses; comparison of electricity losses for different time intervals through a selected controlled intersection, when the direction of the flow of electricity is constant or the direction of the flow of electricity changes during separate time intervals; analysis of electricity losses in direct and alternating current systems of LES or hybrid systems, where modified capacities are introduced, as integral estimates of the discrepancy, which determines the influence of distorting factors; assessment of the impact of changes in voltage and current in the intersection of LES of limited power on the components of additional losses; analysis of changes in load consumption of active power according to the first harmonic and higher harmonics as integral indicators.

Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study.

Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices. Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed. The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance. The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications. This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods

Predicting user engagement -- whether a user will engage in a given information cascade -- is an important problem in the context of social media, as it is useful to online marketing and misinformation mitigation just to name a couple major applications. Based on split population multi-variate survival processes, we develop a discriminative approach that, unlike prior works, leads to a single model for predicting whether individual users of an information network will engage a given cascade for arbitrary forecast horizons and observation periods. Being probabilistic in nature, this model retains the interpretability of its generative counterpart and renders count prediction intervals in a disciplined manner. Our results indicate that our model is highly competitive, if not superior, to current approaches, when compared over varying observed cascade histories and forecast horizons.

A stochastic description of pH within nanoscopic water pools

The nature of pH in a pure water pool that is too small to have a continuous population of ions formed by water dissociation is not settled. Here, we use stochastic kinetics simulations of the water dissociation reaction in pools ranging from 103 to 1016 waters to characterize water ion lifetimes and populations. An availability coefficient is defined to quantify the intermittent presence of ion pairs during an arbitrary observation period and used in a proposed definition for the effective pH of nanoscopic pools. The effective pH converges to the bulk value of 7 for water pools in the range of 1010 waters. The lifetimes of water ion pairs are found to increase with increasing water pool size due to the balance between their formation and recombination kinetics, with a maximum near the size where bulk-like characteristics begin to dominate and ion pairs are continuously present.

Chaotic renormalization group flow and entropy gradients over Haros graphs.

Haros graphs have been recently introduced as a set of graphs bijectively related to real numbers in the unit interval. Here we consider the iterated dynamics of a graph operator R over the set of Haros graphs. This operator was previously defined in the realm of graph-theoretical characterization of low-dimensional nonlinear dynamics and has a renormalization group (RG) structure. We find that the dynamics of R over Haros graphs is complex and includes unstable periodic orbits of arbitrary period and nonmixing aperiodic orbits, overall portraiting a chaotic RG flow. We identify a single RG stable fixed point whose basin of attraction is associated with the set of rational numbers, and find periodic RG orbits that relate to (pure) quadratic irrationals and aperiodic RG orbits, related with (nonmixing) families of nonquadratic algebraic irrationals and transcendental numbers. Finally, we show that the graph entropy of Haros graphs is globally decreasing as the RG flows towards its stable fixed point, albeit in a strictly nonmonotonic way, and that such graph entropy remains constant inside the periodic RG orbit associated to a subset of irrationals, the so-called metallic ratios. We discuss the possible physical interpretation of such chaotic RG flow and put results regarding entropy gradients along RG flow in the context of c-theorems.

Online coal consumption characteristics fitting for daily economic dispatch using a data-driven hybrid sequential model

To motivate the transition to the future smart and low-carbon energy systems, it is essential to make full use of the online coal consumption data flow to reduce the dispatch cost of thermal power. This paper proposed a hybrid sequential model for coal consumption characteristics (CCC) modeling in daily economic dispatch (ED) based on the online coal consumption data flow. The hybrid sequential model, combined convolution neural network (CNN) and long short term memory (LSTM), is developed to model the temporal and spatial dynamics of actual CCC in arbitrary period and fluctuation. Furthermore, ED based on the proposed sequential CCC model is constructed, and the simplicial homology global optimization (SHGO) method is used to solve the ED optimization, for cost evaluation. Simulation studies are conducted to validate the accuracy and economy of our model and other reference models in terms of CCC regression and daily economic dispatch in plant level respectively. Results indicate that the proposed sequential CCC model shows competitive accuracy and significant energy saving.

Nothing Is a Memory: Remembering Bernadette Mayer

Nothing Is a MemoryRemembering Bernadette Mayer Daniel Poppick (bio) For her 1971 project Memory, Bernadette Mayer took a roll of film every day for the month of July and transcribed what it depicted. With Memory's monumental torrent of images and words from what appears to be a scrappily halcyon summer—Manhattan streets, Hudson Valley diners and forests, stray shots of Mayer herself and intimate, dimly lit gatherings of friends and lovers—the poet attempts to put everything down for a limited and arbitrary period of time. And yet the painstaking effort only underscores how much cannot be included. "It's astonishing to me that there is so much in Memory, yet so much is left out: emotions, thoughts, [End Page 152] sex, the relationship between poetry and light, storytelling, walking, and voyaging to name a few," she wrote in 2019. (The photographs were exhibited in 1972 by the gallerist Holly Solomon alongside a six-hour looped recording of Mayer reading the accompanying poem, but the text and images were not available together in a book until 2020.) Mayer knew as well as any poet of her generation that there isn't enough time within a single life to give a full account of one's memory, much less to record it. You can try in every medium imaginable—language, images, sound, whatever. It can't be done. Memory is circumscribed, but language cannot keep up with it. Mayer tried anyway, and in her thrilling snapshots of the present in the instant it slipped into the past, she helped push poetry forward into places it had never been before. This paradox is one of several that defined Mayer, who passed away late last year. Her work is possessed of a spirit of ceaseless creative reinvention, anarchic sexuality, and anticapitalist fervor, but it is also preoccupied with the classics and traditional form. She was a rigorous lyric thinker and autodidact who committed her life to the idea that poetry was an egalitarian art, that it could and should be taken up by anyone. As a committed avant-gardist, she was always ahead of her time, but she never stopped experimenting with new ways of capturing memory. Halfway through Milkweed Smithereens, the last book that she saw to publication, Mayer offers a window into her writing processes, something she artfully recapitulated again and again throughout her life: "the idea that writing is easy comes from the frank o'hara method. but it is in fact easy, especially if you don't try to say more than you are thinking, to say other than what you're thinking, for instance you might be trying to say what somebody else is thinking." The Frank O'Hara method, as she calls it—the flaneurish, chatty style that O'Hara made his own in the 1950s, in which poetry flows as naturally as a babbling stream through city strolls, encounters with art, and flirty cocktail conversation—looks simple enough. But Mayer's poems, like O'Hara's, are something else: as gorgeous, sad, [End Page 153] elated, horny, distracted, coy, angry, kinetic, and complex as people. How did something as difficult as life emerge from something as easy as living? Writing might in fact be easy. But it's also impossible to capture the entirety of what you are thinking, feeling, and doing in words. Mayer's magic trick was to make it look like she could, even as she dramatized this impossibility. Like O'Hara, Mayer was a threshold poet, writing between aesthetic realms. In the late 1960s and '70s, when she first emerged onto New York's creatively molten downtown scene, she was an interlocutor with both the rowdy, anti-hierarchical energy of the New York School's second generation and the intellectual rigor of a nascent conceptual art and poetics emerging in real time. She co-founded and edited the mimeographed magazine 0 to 9 with her brother-in-law, the artist Vito Acconci, and her galvanizing workshops at the St. Mark's Poetry Project were attended by Charles Bernstein, Hannah Weiner, Bruce Andrews, and others who in short time would come to form Language poetry. In her books, Mayer swings...

Symmetrizable Boolean networks

In this work, we provide a procedure that allows us to transform certain kinds of deterministic Boolean networks on minterm or maxterm functions into symmetric ones, so inferring that such symmetrizable networks can present only periodic points of periods 1 or 2. In particular, we deal with generalized parallel (or synchronous) dynamical systems (GPDS) over undirected graphs, i.e., discrete parallel dynamical systems over undirected graphs where some of the self-loops may not appear. We also study the class of anti-symmetric GPDS (which are non-symmetrizable), proving that their periodic orbits have period 4. In addition, we introduce a class of non-symmetrizable systems which admit periodic orbits with arbitrary large periods.

Deep learning based highway vehicles detection and counting system using computer vision

In the field of highway management, intelligent vehicle detection and counting are becoming increasingly significant. City planners have been fighting the issue of traffic analysis for years. To analyze traffic and streamline the process, intelligent methods are being created. The type of vehicles and the number of vehicles in an area over some arbitrary time period may be taken into account. Over the years, humans have created a variety of mechanisms, because the vehicles are come in different sizes, it is still difficult to identify them, which can directly impact the accuracy of vehicles count. In the field of vision research, computer vision is one of the most often used applications for traffic scenarios. A variety of computer vision applications, including object detection and categorization, have proven that deep learning techniques like Convolutional Neural Networks (CNNs) greatly outperform conventional approaches. Hence in this work, deep learning-based highway vehicles detection and count system suing computer vision is presented. Vehicles on the highway can be efficiently detected by this technique. YOLOv3 (You Look Only Once Version 3) is a real-time object detection technique which is used to detect vehicles in real-time. Precision, recall and real-time rate are measured to evaluate the performance of this system.