Simple Computer Research Articles

A Simplified Analytical Solution for the Computation of Machine Path in Filament Winding of Cylindrical Angle-Ply and Double-Double Structures

This paper presents a simplified computation approach for the machine path (or winding trajectory) of grid structures and tubes with a circular cross-section and angle-ply or double-double layup. The solution for the machine path is given through controllable degrees of freedom of a low-cost two-axis filament winding machine (FWM): mandrel rotation and translation of the delivery eye along the axis of the mandrel. The efficiency of the analytical solution for the machine path of the FWM was ascertained by automated laying the cotton thread over the geodesic and non-geodesic groove imprinted on the surface of a cylindrical polylactide mandrel. These results validated the possibility of manufacturing cylindrical composite structures with an angle-ply layup or double-double stacking sequence, without the need for expensive software, making the winding technology accessible to society and promoting university extension.

Entropy‐based complex proportional assessment for efficient task scheduling in fog computing

AbstractThe tasks are offloaded to fog computing instead of cloud computing to enhance the quality of service (QoS) of internet of things (IoT) applications. Though it provides improved QoS the accessibility of unremitting computation properties of fog servers is one among the limitations of IoT applications as transmitting a huge quantity of data may induce more energy consumption, cost, and increased makespan. Much research has been made to build up an efficient technique to address the multi–objective task scheduling in fog computing. One such method used to solve multiple objective problems in different domains is multi–criteria decision–making methods (MCDM). The MCDM Method named complex proportional assessment (COPRAS) has risen in popularity due to simple and fast computation. In this paper, a task scheduling algorithm (ECOPRAS) based on the integration of the entropy weight method and COPRAS method is proposed. The proposed technique consists of two phases that is, Task prioritization, and Task assignment. In the first phase, the task is ordered based on the heuristic method, B‐level and in the second phase, the task will be assigned based on the ranking provided by the ECOPRAS method. The proposed method aims at minimizing energy consumption, price, and makespan and maximizing the reliability and utilization rate. The simulation results are compared with similar algorithms through various workflows and the proposed method outperforms well.

Rotations of F-ATPase and V-ATPase analyzed by a torque approach

F-type ATP synthase (F-ATPase) and vacuolar ATP hydrolase (V-ATPase) are well-known biomolecular motors, which play significant catalytic roles in ATP synthesis and ATP hydrolysis reactions. Their rotational torques are important factors involved in their rotational behavior that can be measured experimentally but with considerable difficulty. To overcome this difficulty and thereby provide an in-depth understanding of their operation mechanism, we herein carry out simple and fast computer modelling to study the two proteins, using our torque approach that relies on interatomic forces and coordinates of unequilibrated configurations taken from brief molecular dynamics (MD) simulations. As predicted by the torque approach, F-ATPase is demonstrated to be a random rotor, but it prefers to rotate in clockwise direction (as seen from the membrane toward the protein) for ATP synthesis, owing to the predominantly negative angle-averaged torques. By contrast, V-ATPase tends to rotate only in counterclockwise direction for ATP hydrolysis, due to the almost uniform averaged positive torques generated by the unidirectional rotation near the three catalytic sites. The rotational behaviors of both proteins are also affected by the surrounding solvent which can promote or hinder the internal rotation. By combining the torque approach with classic force-field MD simulations, the torques of two biomolecular motors can be calculated economically, and are found to agree with previous experiments and theoretical calculations. This work demonstrates that our torque approach can be extended to the field of biology and can help gain a deeper insight into the mechanistic rotation of biomolecular motors with modest computation time. Communicated by Ramaswamy H. Sarma

A Novel Control-Theory-Based Approach to Scheduling of High-Throughput Screening System for Enzymatic Assay

Nowadays, high-throughput screening (HTS) systems are widely used in pharmaceutical industries and laboratories for the discovery of new drugs and biomedical substances. It is important to efficiently schedule them so as to reduce the cost. In the operation of an HTS system, a microplate may visit some resources more than once and complex time window constraints are imposed on some activities and activity sequences. Moreover, with the consistency requirement, a one-microplate cyclic schedule is necessary. Thus, its scheduling problem is very challenging. This article studies the scheduling problem of an HTS system for an enzymatic assay, a typical application of HTSs, from the perspective of control theory. The system is modeled by resource-oriented Petri nets (ROPNs). With the model, necessary and sufficient conditions under which a feasible cyclic schedule exists are established. Then, we determine how many microplates should be in the system for concurrent processing and the transition firing sequence to obtain the activity sequence for an optimal and feasible schedule. In this way, a feasible and optimal cyclic schedule can be found by very simple computations, which shows that polynomial algorithms for an optimal schedule exist, while the existing studies apply mathematical programming with exponential computation complexity. Also, its efficient implementation is given.

Coordinated Control of High-Order Fully Actuated Multiagent Systems and Its Application: A Predictive Control Strategy

This research is devoted to the coordinated control of high-order fully actuated multiagent systems (HOFA-MASs). A predictive control strategy is developed to realize the coordination of HOFA-MASs and simultaneous stability and output consensus. In this strategy, a Diophantine equation is introduced to construct an incremental HOFA prediction model rather than a reduced-order one, then a cost function focused on coordinated relationship is minimized by the design of predictive control such that the optimal coordination controllers can be achieved with simple and easy computation. Further discussion derives a simple criterion to guarantee the simultaneous stability and output consensus of the closed-loop HOFA-MASs. Two experiments of formation control of three air-bearing simulators are given to demonstrate the availability of the predictive control strategy.

Theoretical Study of Fair-Trade Sustainable Hatcheries - Sizing for Cameroon and Indonesia

Fair-trade sustainable hatcheries are designed for developing and emerging countries, such as Cameroon and Indonesia respectively so that they can use their often-generous sunshine at the service of harmonious development. These hatcheries are sustainable by their functioning exclusively from solar irradiation and nocturnal radiative emission. They are fair-trade because of their simplicity which allows them to be manufactured by many local professionals. They are developed so that the compensation in thermal losses is ensured by thermosiphon heat transfer loops thermo-regulated by bimetallic strip. These hatcheries are also designed for tropical or equatorial climatic hazards due to fine modeling of physical phenomena which can also be implemented by using a simple computer. An excellent economic return is expected.

Adaptive spectral trend based optimized EWT for monitoring the parameters of multiple power quality disturbances

The growing penetration of renewable energy sources leads to power quality disturbances (PQDs) being multiple. The real-time monitoring of the parameters of multiple power quality disturbances (MPQDs) can help electricity distributors better control power quality problems. In this paper, an adaptive spectral trend-based optimized empirical wavelet transform (EWT) is proposed to analyze MPQDs. First, the upper and lower envelopes of the signal frequency spectrum are fitted by piecewise cubic Hermite interpolation. Then the sum of the upper and lower envelopes is halved as a spectral trend. These steps are repeated until all peak-to-peak distances are greater than the key threshold, indicating that the spectral trend has already been optimized. Third, main frequency components are calculated based on peaks of the optimal spectral trend, which can be used as a basis for spectrum segmentation. Eventually, the components decomposed by EWT represent different disturbance elements, whose parameters can be further calculated by Hilbert transform (HT). To verify the effectiveness of the proposed algorithm, two databases are applied for analysis: synthetic signals, and recording signals by the experimental platform. At the same time, four advanced algorithms are used for comparison, which are scale-space optimized EWT (SOEWT), improved EWT (IEWT), variational mode decomposition (VMD) and fast Stockwell transform (FST). The results showed that the proposed method can accurately segment the spectrum and prevent the inappropriate segmentation of the original EWT so that it can monitor the disturbance parameters with high precision. In addition, the proposed algorithm has a simple computation.

Toy models for hierarchy studies

We provide a simple computation in order to estimate the probability of a given hierarchy between two scales. In particular, we work in a model provided with a gauge symmetry, with two scalar doublets. We start from a scale-invariant classical Lagrangian, but by taking into account the Coleman–Weinberg mechanism, we obtain masses for the gauge bosons and the scalars. This approach typically provides a light (L) and a heavy (H) sector related to the two different vacuum expectation values of the two scalars. We compute the size of the hypervolume of the parameter space of the model associated with an interval of mass ratios between these two sectors. We define the probability as proportional to this size and conclude that probabilities of very large hierarchies are not negligible in the type of models studied in this work.

A Noise-robust Pulse Wave Estimation from NIR Video using Wiener Estimation Method

In this paper, we propose a noise-robust pulse wave estimation method from NIR video. Pulse wave estimation in the near-infrared region is expected to be applied to non-contact monitoring in dark areas. Conventional method cannot consider noise when performing estimation, so the accuracy of pulse wave estimation in noisy environment is not very high. This may adversely affect the accuracy of heart rate and other data obtained from pulse waves. Therefore, the objective of this study is to perform pulse wave estimation robust to noise. The Wiener estimation method was used in this study. The Wiener estimation method is a simple linear computation that can consider noise. The proposed method is expected to enable non-contact and accurate estimation of pulse wave from near-infrared video images. Experimental results show that the proposed method estimates the pulse wave more robustly to noise than the conventional method. Furthermore, the heart rate was estimated from the estimated pulse wave and the proposed method was able to obtain a value closer to the ground truth.

A low computational cost optimization procedure for axial load carrying capacity of the multi-beam structure

The paper presents an improved algorithm to compute an axial load carrying capacity of the specific multi-beam structure in order to failure avoidance. The former approach based on the finite element method is reduced to determine a relation between the axial load and axial displacement for a single beam, while the time-consuming FE procedure applied on several beams is replaced with a numerical optimization process. The developed approach provides faster and simpler computation of the minimum axial load carrying capacity of the structure considering the beam length tolerances, where the results are in a good agreement with the complex FE analysis. Supporting theoretical and experimental analyses were performed to confirm the correctness of the mentioned method where the load carrying capacity of the multi-beam structure made up of six steel circular beams was analyzed.

Validation of Cloud-Gap-Filled Snow Cover of MODIS Daily Cloud-Free Snow Cover Products on the Qinghai–Tibetan Plateau

Accurate daily snow cover extent is a significant input for hydrological applications in the Qinghai–Tibetan Plateau (QTP). Although several Moderate Resolution Imaging Spectroradiometer (MODIS) daily cloud-free snow cover products over the QTP are openly accessible, the cloud-gap-filled snow cover from these products has not yet been validated. This study assessed the accuracy of cloud-gap-filled snow cover from three open accessible MODIS daily products based on snow maps retrieved from Landsat TM images. The F1-score (FS) from daily cloud-free MODIS snow cover for the combined MOD10A1F and MYD10A1F (SC1) was 64.4%, which was 7.4% points and 5.3% points higher than the other two commonly used products (SC2 and SC3), respectively. The superior accuracies from SC1 were more evident in regions with altitudes lower than 5000 m, with a weighted average FS by the area percentage of the altitude regions of 58.3%, which was 6.9% points and 9.1% points higher than SC2 and SC3. The improved SC1 accuracies also indicated regional clustering characteristics with higher FS values compared to SC2 and SC3. The lower accuracies of cloud-gap-filled snow cover from SC2 and SC3 were mainly due to the limitation in determining snow cover based on the method of the inferred snow line and the overestimation of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) snow water equivalent (SWE). These results indicate that the temporal filter approach used in SC1 is a good solution to produce daily cloud-gap-filled snow cover data for the QTP because of its higher accuracy and simple computation. The findings can be helpful for the selection of cloud-removal algorithms for determining snow cover dynamics and phenological parameters on the QTP.

Robust Output Tracking of Boolean Control Networks over Finite Time

With an increase in tracking time, the operating cost of the controller will increase accordingly. Considering the biological applications of Boolean control networks (BCNs), it is necessary to study the control problem of BCNs over finite time. In this paper, we study the output tracking problem of a BCN with disturbance inputs in a given finite time. First, the logical form of BCNs is transformed into an algebraic form using the semi-tensor product (STP) method. Then, the robust output tracking problems of a reference output trajectory and the outputs of a reference system over finite time are transformed into the robust reachability problem of the BCNs. Based on the truth matrix technique, two necessary and sufficient conditions are provided for the trackability of the reference outputs over finite time. Moreover, two algorithms are proposed to design the controllers in the case of the traceable outputs. It should be pointed out that the truth matrix method we used here has some unique features, including its simple computation and concise expression. Finally, two illustrative examples are presented to demonstrate the results in this paper.

Driving quality improvement with nudges: True interventions in cardiology.

Key Points Quality improvement research brings evidence‐based interventions to clinical practice. This single‐center study demonstrated that simple clinical guidelines, computer prompts, and education were able to reduce the duration of triple therapy after percutaneous coronary intervention. Behavioral interventions are an important tool to drive clinical improvement but must be tailored to respect physician autonomy and professionalism.

Optimizing a superconducting radio-frequency gun using deep reinforcement learning

Superconducting photoelectron injectors are a promising technique for generating high brilliant pulsed electron beams with high repetition rates and low emittances. Experiments such as ultra-fast electron diffraction, experiments at the Terahertz scale, and energy recovery linac applications require such properties. However, optimization of the beam properties is challenging due to the high amount of possible machine parameter combinations. In this article, we show the successful automated optimization of beam properties utilizing an already existing simulation model. To reduce the amount of required computation time, we replace the costly simulation by a faster approximation with a neural network. For optimization, we propose a reinforcement learning approach leveraging the simple computation of the derivative of the approximation. We prove that our approach outperforms common optimization methods for the required function evaluations given a defined minimum accuracy.

Observer-based motion axis control for hydraulic actuation systems

Unknown dynamics including mismatched mechanical dynamics (i.e., parametric uncertainties, unmodeled friction and external disturbances) and matched actuator dynamics (i.e., pressure and flow characteristic uncertainties) broadly exist in hydraulic actuation systems (HASs), which can hinder the achievement of high-precision motion axis control. To surmount the practical issue, an observer-based control framework with a simple structure and low computation is developed for HASs. First, a simple observer is utilized to estimate mismatched and matched unknown dynamics for feedforward compensation. Then combining the backstepping design and adaptive control, an appropriate observer-based composite controller is provided, in which nonlinear feedback terms with updated gains are adopted to further improve the tracking accuracy. Moreover, a smooth nonlinear filter is introduced to shun the “explosion of complexity” and attenuate the impact of sensor noise on control performance. As a result, this synthesized controller is more suitable for practical use. Stability analysis uncovers that the developed controller assures the asymptotic convergence of the tracking error. The merits of the proposed approach are validated via comparative experiment results applied in an HAS with an inertial load as well.

New Insights into the Lamb Shift: The Spectral Density of the Shift

In an atom, the interaction of a bound electron with the vacuum fluctuations of the electromagnetic field leads to complex shifts in the energy levels of the electron, with the real part of the shift corresponding to a shift in the energy level and the imaginary part to the width of the energy level. The most celebrated radiative shift is the Lamb shift between the 2s1/2 and the 2p1/2 levels of the hydrogen atom. The measurement of this shift in 1947 by Willis Lamb Jr. proved that the prediction by Dirac theory that the energy levels were degenerate was incorrect. Hans Bethe’s non-relativistic calculation of the shift using second-order perturbation theory demonstrated the renormalization process required to deal with the divergences plaguing the existing theories and led to the understanding that it was essential for theory to include interactions with the zero-point quantum vacuum field. This was the birth of modern quantum electrodynamics (QED). Numerous calculations of the Lamb shift followed including relativistic and covariant calculations, all of which contain a nonrelativistic contribution equal to that computed by Bethe. The semi-quantitative models for the radiative shift of Welton and Power, which were developed in an effort to demonstrate physical mechanisms by which vacuum fluctuations lead to the shift, are also considered here. This paper describes a calculation of the shift using a group theoretical approach which gives the shift as an integral over frequency of a function, which is called the “spectral density of the shift.“ The energy shift computed by group theory is equivalent to that derived by Bethe yet, unlike in other calculations of the non-relativistic radiative shift, no sum over a complete set of states is required. The spectral density, which is obtained by a relatively simple computation, reveals how different frequencies of vacuum fluctuations contribute to the total energy shift. The analysis shows, for example, that half the radiative shift for the ground state 1S level in H comes from virtual photon energies below 9700 eV, and that the expressions of Power and Welton have the correct high-frequency behavior, but not the correct low-frequency behavior, although they do give approximately the correct value for the total shift.

Laplace's demon in biology: Models of evolutionary prediction.

Our ability to predict natural phenomena can be limited by incomplete information. This issue is exemplified by "Laplace's demon," an imaginary creature proposed in the 18th century, who knew everything about everything, and thus could predict the full nature of the universe forward or backward in time. Quantum mechanics, among other things, has cast doubt on the possibility of Laplace's demon in the full sense, but the idea still serves as a useful metaphor for thinking about the extent to which prediction is limited by incomplete information on deterministic processes versus random factors. Here, we use simple analytical models and computer simulations to illustrate how data limits can be captured in a Bayesian framework, and how they influence our ability to predict evolution. We show how uncertainty in measurements of natural selection, or low predictability of external environmental factors affecting selection, can greatly reduce predictive power, often swamping the influence of intrinsic randomness caused by genetic drift. Thus, more accurate knowledge concerning the causes and action of natural selection is key to improving prediction. Fortunately, our analyses and simulations show quantitatively that reasonable improvements in data quantity and quality can meaningfully increasepredictability.

Computer model of sensory-motor dilemma in stuttering of spoken sounds

The reaction time of any typical person is usually noticeable by others but not perceived by the same person. Whenever such an individual performs any activity even if very fast such as eye blinking, the individual apparently perceives simultaneity between the instant of activating the muscles and the instant of receiving a sensory, visual, tactile, or auditory confirmation of accomplishing the task. Obviously, any motor response involves some unavoidable neural processing delay. Any sensory perception involves some unavoidable neural processing delay also. Therefore, the real instant of the sensory perception cannot be perceived to coincide with the perception of the instant of motor action without a very precise neural adjustment of perception in temporal position. This situation reminds of the merging of the two visual perspectives from two eyes merging together to provide a single visual perspective. It appears that there are two clocks maintained in the central nervous system: one clock for recording the time when a motor command was sent, and another clock for recording the time when a sensory confirmation was received. These two clocks must differ by a small delay to give the illusion of zero reaction time to the individual's own activities and sensory feedback. We will discuss implications of disruptions of that neural adjustment of timing perception with a simple computer model.

Online Radiation Experiment on Gamma-Ray Absorption from an Early Quack Device

Using a Geiger–Müller tube, sound-capturing software, and a simple computer program, students measured to reasonable precision the half-value layer of steel in absorbing high-energy gamma rays from a Radium Ore Revigator (pronounced re-vig-a-tor with the accent on “vig”), an early quack medical device. This article describes the process used for conducting online radiation exposure experiments as part of an in-class Course-Based Undergraduate Research Experience (CURE) project, with their instructor acting in a more subservient role carrying out actions requested by the students. The manner in which this student-centered approach can drive similar online radiation experiments without compromising student safety is discussed.

Measurement and modeling of an electromechanical spring reverb device

A spring reverb is an electromechanical device used to artificially reverberate an audio signal, i.e. to impart a sense of spaciousness to the audio signal, as if the sound were being emitted into a reverberant acoustic space. The device consists of a number of helical springs set into motion by electrically driven magnetized beads, with the elastic wave motion along the springs being analogous to acoustic waves propagating along the various dimensions of a room. While originally invented by the Hammond organ company to add a sense of spaciousness to the sound of their electric organs, spring reverbs have been used in a number of electronic instruments and audio devices over the years, with probably the most iconic and enduring use being in classic electric guitar amplifiers. In this talk, a general overview of the history and technical aspects of spring reverb devices will be given, with particular emphasis placed on the highly dispersive nature of elastic wave propagation along helical springs, which it will be argued is primarily responsible for the distinctive sound of the device. Finally, a simplified computation model will be presented that captures the salient features of a spring reverb device.