Discrete Moments Research Articles

Novel use of clinical pathways to identify poor prognosis lung cancer patients: Implementation and outcomes.

1589 Background: Cancer patients in the last year of life have different clinical needs and evolving goals of care. Using our oncology decision-support pathways to help clinicians consistently identify such patients in a systematic and prospective fashion, at a discrete moment in the care trajectory, may be an important step towards matching the care of these patients with their stated goals. Methods: Medical oncologists from each disease group at the Dana-Farber Cancer Institute (DFCI) were tasked with identifying clinical settings in each oncology care pathway associated with an expected median survival of < 12 months. This information was embedded into the underlying data model of the pathways platform, allowing us to determine how often clinicians navigated through each poor prognosis node. Results: From 3/1/20 – 6/30/21, there were 264 navigations in 205 unique lung cancer patients receiving standard of care (i.e., not on clinical trial) for a clinical condition associated with poor prognosis. Overall, the median overall survival from the time of a patient’s first navigation through a poor prognosis node during the defined study period was 6.4 months. Table lists outcomes for each specific setting. Patients with squamous or small cell lung cancer being treated in or beyond the third-line setting had notably poor outcomes, with less than a third of these patients surviving 6 months from the time of navigation. Conclusions: A clinical pathways platform can be a key tool in designating clinical scenarios associated with poor prognosis and identifying patients who may be particularly at risk. Pathways analytics provide real-world evidence corroborating the expected poor prognosis based on published studies and can identify specific clinical subsets for whom specific resources are warranted. By embedding this into the pathways data model, we aim to alert physicians to conduct goals of care conversations, offer supportive care resources, and match patients to appropriate treatment options and clinical trials. [Table: see text]

СРАВНИТЕЛЬНЫЙ АНАЛИЗ ЦЕНТРАЛИЗОВАННОГО И ДЕЦЕНТРАЛИЗОВАННОГО АЛГОРИТМОВ ДВИЖЕНИЯ СТРОЕМ БЛА МУЛЬТИКОПТЕРНОГО ТИПА

The development of robotics makes their group application relevant for solving varioustasks. The effectiveness of performing the tasks of detecting and determining the coordinates ofobjects by a group of robots significantly depends on the accuracy of maintaining a given formation.In this regard, the task of determining motion planning algorithms that ensure the greatestaccuracy of maintaining a given formation is of practical interest. This article is devoted to thestudy of the accuracy of maintaining the formation of a multicopter-type UAV group using a centralizedmotion planning algorithm and a decentralized algorithm. The centralized algorithm usesa master UAV, which transmits its coordinates to the slave UAVs. Based on the coordinates obtainedand the given framework of the formation, the guided UAVs plan their movement. In a decentralizedsystem, neighboring UAV groups transmit their coordinates to each other, on the basisof which the movement of a separate UAV is planned. The accuracy of the control system is investigateddepending on the errors of the navigation system and the frequency of updating data on theposition of the leading or neighboring UAVs. It is assumed that the group's UAVs determine their coordinates in discrete moments of time using an external navigation system. Centralized anddecentralized algorithms are worked out by the same motion control system. The algorithms areinvestigated in this article by numerical modeling methods. In the process of simulation, models ofkinematics, dynamics and actuators are taken into account, as well as models for the formation oferrors in the navigation system. It is shown that the de-centralized algorithm of group motionplanning provides higher accuracy compared to the centralized algorithm. However, the technicalimplementation of a decentralized algorithm is more complicated from the point of view of organizinga group communication system. In a centralized system, data transmission from the masterUAV to the slave should be implemented. In a decentralized system, it is required to implementnetwork communication.

A Nonzero Sequence All of Whose Discrete Moments Are Equal to Zero

We describe a general method to construct a sequence whose discrete moments satisfy for all values of the exponent. We focus on a specific example, but a vast set of analogous constructions is possible.

Orthogonal invariant Lagrange-Fourier moments for image recognition

The orthogonal moments have recently achieved outstanding predictive performance, and become an indispensable tool in a wide range of pattern recognition applications, including the image classification and object detection. Using the orthogonal Lagrange polynomials, we present in this paper three new sets of discrete orthogonal moments and their invariants to translation, scaling and rotation (TSR) for image representation and recognition: the orthogonal Lagrange-Fourier moments (LFMs) for the gray-scale images, the multi-channel orthogonal Lagrange-Fourier moments (MLFMs) and the quaternion orthogonal Lagrange-Fourier moments (QLFMs) for the color images. These orthogonal moments are presented in polar coordinates. We present a set of numerical experiments in the field of classification and pattern recognition to evaluate the performance of the proposed invariant moments. From a comparative study with the recent orthogonal invariant moments, we conclude that our approach is very promising in the pattern recognition field and image analysis.

Adaptive efficient estimation for generalized semi-Markov big data models

In this paper we study generalized semi-Markov high dimension regression models in continuous time, observed at fixed discrete time moments. The generalized semi-Markov process has dependent jumps and, therefore, it is an extension of the semi-Markov regression introduced in Barbu et al. (Stat Inference Stoch Process 22:187–231, 2019a). For such models we consider estimation problems in nonparametric setting. To this end, we develop model selection procedures for which sharp non-asymptotic oracle inequalities for the robust risks are obtained. Moreover, we give constructive sufficient conditions which provide through the obtained oracle inequalities the adaptive robust efficiency property in the minimax sense. It should be noted also that, for these results, we do not use neither sparse conditions nor the parameter dimension in the model. As examples, regression models constructed through spherical symmetric noise impulses and truncated fractional Poisson processes are considered. Numerical Monte-Carlo simulations confirming the theoretical results are given in the supplementary materials.

Deleuze's Elaboration of Eternity: Ontogenesis and Multiplicity

I demonstrate that Deleuze's identification of Aion as an empty form (of time) offers a fascinating model of temporality that prioritises variation. First, I suggest that Deleuze's identification of time as an empty form is supported by ancient Greek and Gnostic concepts of the relation of Aion and Chronos. From Plato, through Aristotle, to Plotinus the concept of time undergoes substantive revision, in the sense that temporal measurement becomes removed from the measurement of existent entities (cosmic or sublunar bodies). This gradual untethering of time from movement gives rise to the development of the concept of eternity as an ontologically comprehensive mode of time that is devoid of content (i.e. a movement not indexed to the movement of cosmic or sublunar entities). It is here, with Deleuze's reading of the Platonic cosmology, that we see the first hints of the suggestion that Aion is involved with ontogenesis. Eternity is characterised as: (1) a temporal ‘all’ (i.e. generality) that is non-reducible to the determinacy implied by any particular temporally localised existent or temporal series (i.e. a succession relation of temporally discrete moments); (2) that which tends towards a diversity of possible states of affairs. Perhaps one of the most interesting aspects in the long history of Aion is that – in the ancient world – it was used in magical incantations. For the Gnostics and Oracles, Aion was a deity, and a potent one at that. From the Gnostic papyri, we get a vision of Aion as a force which enjoys eternal realisation. I suggest that the papyri conjure an image of Aion as a deity that is liberated from time, in the sense that it enjoys a neutrality with respect to the movements of any particular entity or group of entities – a form, in the most general sense of the term. Then, to clarify this mercurial aspect of an empty form of time, I elaborate on a complex analogy between Aion and Deleuze's concept of an ‘ideal game’ – an analogy that Deleuze specifies through reference to Fitzgerald and Borges. The claim is that Aion is an analogue of an ideal game in the (limited) sense that both share essential properties. Both Aion and the ideal game involve the multiplication of chance. Finally, I suggest that the differential aspects of Aion imply that it is pure variability; something which can be illustrated through a differential equation. These yield the suggestion that Aion enjoys realisation as an ontogenetic force. I further claim that ontogenetic forces may enjoy expression as the content of literature and (Riemannian-inspired) mathematics. In concrete terms, temporality is involved in the creation of existent entities, which may be illustrated as various types of continuous multiplicity.

Efficient color face recognition based on quaternion discrete orthogonal moments neural networks

Efficient color face recognition based on quaternion discrete orthogonal moments neural networks

Continuous–discrete cell mass distribution observer design using impulsive ISS Lyapunov functions

The problem of estimating the cell–mass distribution in a yeast cell fermentation process based on non–periodic discrete–time cell mass distribution measurements is addressed. Given that the underlying problem can be cast into a nonlinear error propagation subsystem coupled with an impulsive (i.e., at discrete moments in time) stabilization scheme for the observation error, the design problem is addressed within the framework of impulsive ISS Lyapunov theory. Using a finite–dimensional approximation of the cell population balance equations modeling the cell mass distribution evolution over time, the proposed approach yields conditions on the maximum time between measurements and the resulting ISS gain with respect to the measurement noise disturbance. In order to make the resulting global conditions more accessible and easier to verify in a real process application, the obtained conditions are cast into an additional application–oriented approximation that is amenable to tuning for a given process at hand. The approach is illustrated and tested using experimental data from a yeast batch fermentation.

Plain, Edge, and Texture Detection Based on Orthogonal Moment

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOMs). Therefore, finding a fast PET classification method that accurately classify image pattern is crucial. To this end, this paper proposes a new scheme for accurate and fast image pattern classification using an efficient DOM. To reduce the computational complexity of feature extraction, an election mechanism is proposed to reduce the number of processed block patterns. In addition, support vector machine is used to classify the extracted features for different block patterns. The proposed scheme is evaluated by comparing the accuracy of the proposed method with the accuracy achieved by state-of-the-art methods. In addition, we compare the performance of the proposed method based on different DOMs to get the robust one. The results show that the proposed method achieves the highest classification accuracy compared with the existing methods in all the scenarios considered.

An Energy-Efficient Compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

Looking for the neural basis of memory.

Memory neuroscientists often measure neural activity during task trials designed to recruit specific memory processes. Behavior is championed as crucial for deciphering brain-memory linkages but is impoverished in typical experiments that rely on summary judgments. We criticize this approach as being blind to the multiple cognitive, neural, and behavioral processes that occur rapidly within a trial to support memory. Instead, time-resolved behaviors such as eye movements occur at the speed of cognition and neural activity. We highlight successes using eye-movement tracking with in vivo electrophysiology to link rapid hippocampal oscillations to encoding and retrieval processes that interact over hundreds of milliseconds. This approach will improve research on the neural basis of memory because it pinpoints discrete moments of brain-behavior-cognition correspondence.

Реалізація прийняття рішень організаційного управління на основі онтології діяльності

The complexity of the tasks of making decisions in organizational management is stipulated by the extremely large dimension of the management space, the weak structure of the tasks being solved due to their uniqueness, the uncertainty of conditions, and the changeability of the goals (aspects) of management. The ontological approach provides a range of advantages in solving the problem of organizational management automatization. The use of the activity ontology makes it possible to significantly expand the scope of formalized methods application to organ-izational multi-aspect management. Due to the ontology, the detailing of managerial alterna-tives is increased, and the factors of the performers’ activity and the multi-aspect nature of management are taken into account. The article considers the interpretation of the process of forming managerial alternatives as a process of the threefold grouping of elements of the activi-ty ontology and discrete moments of time. The method of distributed formation of an ontologi-cal network with the grouping of its elements using expert methods, implemented in the prac-tice of designing corporate ACS, is described. The topical scientific tasks of the development of the scientific and methodological apparatus of organizational management based on ontology are formulated. These tasks include the formation of decision-making spaces that are invariant to aspects of management, the concretization of characteristics-invariants and reconfiguration of procedural regulations concerning the actual aspect of management, synthesis of alternatives based on the ontology elements. Some approaches to solving the formulated tasks are shown. The formulation of the problem of synthesizing a system of procedures with given input and output parameters is presented as a problem of tensor transformation of an initial multi-coil electrical network (library of procedures) into a network with the given parameters. A tensor equation is proposed. Its solution allows determining the values of voltages on the intermediate coils of the original electrical network which forms the structural connections for the synthe-sized procedural regulations.

Ekstraksi Fitur Citra Radiografi Thorax Menggunakan DWT dan Moment Invariant

COVID-19 is an infectious disease caused by thecoronavirus family, namely severe acute respiratorysyndrome coronavirus 2 (SARS-CoV-2). The fastest methodto identify the presence of this virus is a rapid antibody or
 antigen test, but confirming the positive status of a COVID-19 patient requires further examination. Lung examination
 using chest X-ray images taken through X-rays of COVID-19patients can be one way to confirm the patient's conditionbefore/after the rapid test. This paper proposes a featureextraction model to detect COVID-19 through chestradiography using a combination of Discrete WaveletTransform (DWT) and Moment Invariant features. In thiscase, haar wavelet transform and seven Hu moments wereused to extract image features in order to find unique featuresthat represent chest radiographic images as suspectedCOVID-19, pneumonia, or normal. To find out theuniqueness of the proposed features, it is coupled with thekNN and generic ANN classification techniques. Based on theperformance parameters assessed, it turns out that thewavelet-based and moment invariant thorax radiographicimage feature model can be used as a unique featureassociated with three categories: Normal, Pneumonia, andCovid-19. This is indicated by the accuracy value of 82.7% inthe kNN classification technique and the accuracy, precision,and recall of 86%, 87%, and 86% respectively with the ANNclassification technique.

Reducing cloud infrastructure costs through task management

The transition of more and more companies from their own computing infrastructure to the clouds is due to a decrease in the cost of maintaining it, the broadest scalability, and thepresence of a large number of tools for automating activities. Accordingly, cloud providers provide an increasing number of different computing resourcesand tools for working in the clouds. In turn, this gives rise to the problem of the rational choice of the types of cloud services in accordance with the peculiarities of the tasks to be solved. One of the most popular areas of effort for cloud consumers is to reduce rental costs. The main base of this direction is theuse of spot resources. The article proposes a method for reducing the cost of renting computing resources in the cloud by dynamically managing the placement of computational tasks, which takes into account the possible underutilization of planned resources, the forecast of the appearance of spot resources and their cost. For each task, a state vector is generated that takes into account the duration of the task and the required deadline. Accordingly, for a suitable set of computing resources, an availability forecast vectorsareformed at a given time interval, counting from the current moment in time. The technique proposes to calculate at each discrete moment of time the most rational option for placing the task on one of the resources and the delay in starting the task on it. The placementoption and launch delays are determined by minimizing the rental cost function over the time interval using a genetic algorithm. One of the fea-tures of using spot resources is the auction mechanism for their provision by a cloud provider. This means that if there are more pref-erable rental prices from any consumer, then the provider can warn you about the disconnection of the resource and make this dis-connection after the announced time. To minimize the consequences of such a shutdown, the technique involves preliminary prepara-tion of tasks by dividing them into substages with the ability to quickly save the current results in memory and then restartfrom the point of stop. In addition, to increase the likelihood that the task will not be interrupted, a price forecast for the types of resources used is used and a slightly higher price is offered for the auction of the cloud provider, compared to the forecast. Using the example of using the Elastic Cloud Computing (EC2) environment of the cloud provider AWS, the effectiveness of the proposed method is shown.

Toward Concurrent Video Multicast Orchestration for Caching-Assisted Mobile Networks

Multicasting does not work well for video applications such as video on demand, where individual users request video content at discrete and unpredictable moments. In this paper, we study a concurrent video multicast orchestration solution for the caching-assisted and centrally controlled mobile networks. The proposed solution strives to obtain rapid video prefetch and effective traffic reduction. By rapid video prefetch, user devices can cache required contents in advance, which can effectively improve the streaming fluency in unsteady wireless environments, especially the highly mobile vehicular environment. We introduce a maximum-rate concurrent multicast (MRCM) problem whose solution is intended to maximize the total video delivery rate by a concurrent multicast manner, and prove that the MRCM problem is NP-hard. Then, we propose a dynamic concurrent multicast tree construction algorithm and a global concurrent multicast tree optimization algorithm to approximately solve the MRCM problem in polynomial time. We also introduce a cache competition mechanism that strives to maximize the total cache hit rate when the cache space is insufficient. Performance evaluations show that compared with the existing cache-assisted multicast delivery solutions, the proposed solution significantly reduces the video delivery duration and the traffic stress of backhaul links.

New color image encryption scheme based on multi-parameter fractional discrete Tchebyshev moments and nonlinear fractal permutation method

A novel color image encryption scheme is presented by combining the multi-parameter fractional discrete Tchebyshev moments (MPFrDTMs) with the nonlinear fractal permutation method. To withstand the brute-force attack on the image encryption algorithm based on the fractional discrete Tchebyshev moments, the MPFrDTMs and their inverse process are put forward for larger key space. Furthermore, a new nonlinear fractal permutation method is constructed by combining the fractal Sierpinski triangle model (FSTM) with the rotation operations. The structural complexity of the FSTM directly determined by the plaintext image could resist known-plaintext attack and chosen-plaintext attack. Simulation results and performance analyses illustrate that the proposed color image encryption scheme is feasible and has a high level of security.

A new approach for $$H_{\infty }$$ deconvolution filtering of 2D systems described by the Fornasini–Marchesini and discrete moments

A new approach for $$H_{\infty }$$ deconvolution filtering of 2D systems described by the Fornasini–Marchesini and discrete moments

Using clinical pathways to prospectively identify poor prognosis cancer patients: A preliminary analysis.

329 Background: Cancer patients in the last year of life may have different clinical needs and evolving goals of care. Helping clinicians consistently identify such patients in a systematic and prospective fashion, at a discrete moment in the care trajectory, is an important step towards optimizing the care of these patients. Methods: Physician medical directors for each clinical pathway at our institution were tasked with identifying nodes within their respective treatment pathways associated with poor prognosis, specifically a median overall survival of < 12 months. This information was embedded into the underlying data model of the pathways platform, allowing us to determine how often clinicians navigated through poor prognosis nodes. Results: Pathways medical directors identified 44 nodes associated with poor prognosis. Some of these nodes encompassed a broader set of patients (metastatic pancreatic cancer, any line), whereas others were more specific (metastatic non-small cell lung cancer, squamous cell histology, second line or beyond). For the period 3/11/20 – 5/31/21, clinicians navigated in the platform for 11,057 unique patients. Of these, 1,234 (11.1%) unique patients were associated with poor prognosis nodes. Such navigations were much more common in patients with solid tumors (table). Conclusions: A well-maintained clinical pathways program can be a mechanism for defining clinical settings associated with poor prognosis and for routinely identifying patients in these settings. By embedding this into the pathways data model, we can provide physicians with important reminders and resources related to goals of care conversations, supportive care resources, and appropriate treatment options and clinical trials.[Table: see text]

Within-Session Reliability and Minimum Detectable Differences for Discrete Lower-Extremity Angles and Moments During Walking.

Differences in walking biomechanics between groups or conditions should be greater than the measurement error to be considered meaningful. Reliability and minimum detectable differences (MDDs) have not been determined for lower-extremity angles and moments during walking within a session, as needed for interpreting differences in cross-sectional studies. Thus, the purpose of this study was to determine within-session reliability and MDDs for peak ankle, knee, and hip angles and moments during walking. Three-dimensional gait analysis was used to record walking at 1.25m/s (±5%) in 18 men, 18-50years of age. Peak angles and moments were calculated for 2 sets of 3 trials. Intraclass correlation coefficients (3, 3) were used to determine within-session reliability. In addition, MDDs were calculated. Within-session reliability was good to excellent for all variables. The MDDs ranged from 0.9° to 3.6° for joint angles and 0.06 to 0.15N·m/kg for joint moments. Within-session reliability for peak ankle, knee, and hip angles and moments was better than the between-session reliability reported previously. Overall, our MDDs were similar or smaller than those previously reported for between-session reliability. The authors recommend using these MDDs to aid in the interpretation of cross-sectional comparisons of lower-extremity biomechanics during walking in healthy men.

Efficient watermarking algorithm for digital audio/speech signal

Ensuring conflicting requirements such as imperceptibility, payload capacity, and robustness, becomes a great challenge for a robust audio/speech watermarking algorithm. To achieve this, this paper proposes a blind and robust audio/speech watermarking algorithm that combines the discrete Tchebichef moment transform (DTMT), the chaotic system of the mixed linear–nonlinear coupled map lattices (MLNCML), and discrete wavelet transform (DWT). The watermark is encrypted by the MLNCML system, and then it is embedded in the norm of the Tchebichef moments of the low frequency components. DTMT and MLNCML are used to achieve high robustness, high payload capacity, and high security, while DWT is used to achieve a satisfactory level of imperceptibility. In addition, the adopted strategy has a blind nature, where no original audio/speech is needed in watermark extraction. Compared to other existing audio/speech watermarking algorithms, the proposed algorithm gives better results in terms of robustness and payload capacity while keeping the embedding effect non-perceptible and undetectable.