Random Positions Research Articles

Comparing a Modified Structured Mix with a Modified Random Rotation Procedure to Teach Auditory-Visual Conditional Discriminations to Children with Autism

We evaluated two procedures to teach auditory-visual conditional discriminations (receptive labeling) to children with autism. The procedures evaluated a modified Structured Mix (SM) procedure and a modified Counterbalanced Random Rotation (RR) procedure. The modified SM procedure was based on the logic of simplifying the task by breaking it down into smaller, successive steps and by requiring mastery of each step before introducing the next. Compared to previous studies, the modified SM procedure contained fewer steps, less prompting, and a less stringent mastery criterion. The modified RR procedure targeted all three stimuli simultaneously by presenting them across consecutive trials, both during training and error correction. Sample stimuli were presented in a counterbalanced random order and the comparison stimuli were presented in counterbalanced random positions. Participants were nine children with autism. An adapted alternating treatments design was used. Results showed that the modified SM procedure was more efficient for four of the nine participants, the modified RR procedure was more efficient for one of the nine participants, both procedures were equally efficient for two participants, and neither procedure was effective for two of the nine participants. The modified SM procedure appeared more efficient than the SM procedure employed in previous studies. Despite results, further research is warranted to examine within subject comparisons between original discrimination training procedures and modified procedures.

Effective Feature Selection Strategy for Supervised Classification based on an Improved Binary Aquila Optimization Algorithm

Feature Selection (FS) is considered a crucial step in machine learning and data mining tasks, which facilitates minimizing the direct consequence of redundant and irrelevant attributes on the model’s accuracy. Hence, the researchers developed various algorithms to choose the most appropriate features for improving the accuracy rate of the presented dataset. Nevertheless, these algorithms may fall into local optima problems when applied to substantial feature sizes’ datasets. In this paper, for handling the FS strategy through dimensionality-lessening while improving the classification accuracy, an effective Aquila Optimization (AO) algorithm is introduced. AO has stable exploration and exploitation capabilities. It is enhanced by integrating a random position amendment approach with Local Search (LS) strategy to avoid local optima and then cloned into a binary version called Improved Binary AO (IBAO). Additionally, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) are quality estimators. On 18 multi-scale benchmarks, the IBAO algorithm is compared with the original BAO algorithm and twelve recent algorithms, such as Binary Artificial Bee Colony (BABC), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), Binary Whale Optimization Algorithm (BWOA), Binary Grey Wolf Optimization (BGWO), Binary Sailfish Optimizer (BSFO), Binary Henry Gas Solubility Optimization (BHGSO), Binary Harris Hawks Optimization (BHHO). According to Wilcoxon’s rank-sum test (α=0.05), the dominance and significant influence of IBAO are apparent on both small and large dimensional benchmarks and obtained classification accuracy up to 100% in some of these benchmarks integrated with a feature reduction size down to 92%.

An efficient hybrid optimization method for Fuzzy Flexible Job-Shop Scheduling Problem: Steady-state performance and analysis

Due to the uncertainty pervasive in manufacturing and production systems, a crisp processing time is no longer appropriate. Thus, the flexible job-shop scheduling issue comes with fuzzy processing times. Fuzzy numbers are used to depict the uncertainty associated with processing times. This paper proposes a Hybrid Fuzzy Flexible job-shop Scheduling Approach (HFFSA) for addressing the Fuzzy Flexible Job-Shop Scheduling Problem (FFJSSP). An encoding-and-decoding scheme is employed for initializing the population. HFFSA incorporates four operators to enhance the quality of solutions: the Different Positions Shuffling (DPS) operator, the Randomly Selected Positions Shuffling (RSPS) operator, the Block Shuffling (BS) operator and the Inversion Mutation (IM) operator. The DPS operator shuffles the different positions between a randomly generated solution and the current one. On the other hand, the RSPS shuffles random positions to maintain population diversity. The BS and IM work on a subset of positions from the current solution to obtain near-optimal solutions. Furthermore, we integrate the pair-wise local search strategy with HFFSA to improve the best solution. HFFSA is compared to twenty-five algorithms using Lei and remanufacturing benchmarks. Several statistical measures are employed, like the CPU time, best, average and worst fuzzy makespan values, boxplots and the Wilcoxon signed-rank test. The statistical analyses affirm the superiority of the proposed HFFSA.

An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks

A measurement matrix is essential to compressed sensing frameworks. The measurement matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand, and enhance the stability and performance of the recovery algorithm. Choosing a suitable measurement matrix for Wireless Multimedia Sensor Networks (WMSNs) is demanding because there is a sensitive weighing of energy efficiency against image quality that must be performed. Many measurement matrices have been proposed to deliver low computational complexity or high image quality, but only some have achieved both, and even fewer have been proven beyond doubt. A Deterministic Partial Canonical Identity (DPCI) matrix is proposed that has the lowest sensing complexity of the leading energy-efficient sensing matrices while offering better image quality than the Gaussian measurement matrix. The simplest sensing matrix is the basis of the proposed matrix, where random numbers were replaced with a chaotic sequence, and the random permutation was replaced with random sample positions. The novel construction significantly reduces the computational complexity as well time complexity of the sensing matrix. The DPCI has lower recovery accuracy than other deterministic measurement matrices such as the Binary Permuted Block Diagonal (BPBD) and Deterministic Binary Block Diagonal (DBBD) but offers a lower construction cost than the BPBD and lower sensing cost than the DBBD. This matrix offers the best balance between energy efficiency and image quality for energy-sensitive applications.

Rab8, Rab11, and Rab35 coordinate lumen and cilia formation during zebrafish left-right organizer development.

An essential process during Danio rerio's left-right organizer (Kupffer's Vesicle, KV) formation is the formation of a motile cilium by developing KV cells which extends into the KV lumen. Beating of motile cilia within the KV lumen directs fluid flow to establish the embryo's left-right axis. However, the timepoint at which KV cells start to form cilia and how cilia formation is coordinated with KV lumen formation have not been examined. We identified that nascent KV cells form cilia at their centrosomes at random intracellular positions that then move towards a forming apical membrane containing cystic fibrosis transmembrane conductance regulator (CFTR). Using optogenetic clustering approaches, we found that Rab35 positive membranes recruit Rab11 to modulate CFTR delivery to the apical membrane, which is required for lumen opening, and subsequent cilia extension into the lumen. Once the intracellular cilia reach the CFTR positive apical membrane, Arl13b-positive cilia extend and elongate in a Rab8 dependent manner into the forming lumen once the lumen reaches an area of 300 μm2. These studies demonstrate the need to acutely coordinate Rab8, Rab11, and Rab35-mediated membrane trafficking events to ensure appropriate timing in lumen and cilia formation during KV development.

Measurements on an Anderson chain

We study the dynamics of a monitored single particle in a one-dimensional, Anderson-localized system. The time evolution is governed by Hamiltonian dynamics for fixed time intervals, interrupted by local, projective measurements. The competition between disorder-induced localization and measurement-induced jumps leads to interesting behaviour of readout-averaged quantities. We find that measurements at random positions delocalize the average position, similar to a classical random walk. Along each quantum trajectory, the particle remains localized, however with a modified localization length. In contrast to measurement-induced delocalization, controlled measurements can be used to introduce transport in the system and localize the particle at a chosen site. In this sense, the measurements provide a controlled environment for the particle.

Image Encryption via Complementary Embedding Algorithm and New Spatiotemporal Chaotic System

Although image encryption is developing rapidly, it lacks the pertinence of specific scenarios. This paper proposes a complementary embedding encryption strategy. The strategy firstly identifies the airport area, then replaces the optimal similar area and embeds the airport image into a random position through the complementary embedding algorithm. In the encryption stage, to generate a better key stream, we propose an improved sine cross coupled mapping lattice (ISCCML). Comprehensive performance analysis shows that ISCCML has a larger parameter space and better chaotic cryptographic properties. Furthermore, a fractal disordered matrix (FDM) with iterative and out-of-order properties is presented for the simultaneous scrambling diffusion of images. In particular, the encryption algorithm has generalization and is also suitable for ordinary image encryption. Our results indicate that the proposed scheme can avoid repeated encryption on the premise of ensuring the security of important information; at the same time, the security analysis shows that our algorithm has security, practicality and scalability.

Detection of tandem repeats in the Capsicum annuum genome

In this study, we modified the multiple alignment method based on the generation of random position weight matrices (RPWM) and used it to search for tandem repeats (TRs) in the Capsicum annuum genome. The application of the modified (m)RPWM method, which considers the correlation of adjusting nucleotides, resulted in the identification of 908,072 TR regions with repeat lengths from 2 to 200bp in the C. annuum genome, where they occupied ~29%. The most common TRs were 2 and 3bp long followed by those of 21, 4, and 15bp. We performed clustering analysis of TRs with repeat lengths of 2 and 21bp and created position-weight matrices (PWMs) for each group; these templates could be used to search for TRs of a given length in any nucleotide sequence. All detected TRs can be accessed through publicly available database (http : //victoria.biengi.ac.ru/capsicum_tr/). Comparison of mRPWM with other TR search methods such as Tandem Repeat Finder, T-REKS, and XSTREAM indicated that mRPWM could detect significantly more TRs at similar false discovery rates, indicating its superior performance. The developed mRPWM method can be successfully applied to the identification of highly divergent TRs, which is important for functional analysis of genomes and evolutionary studies.

The influence of stimulus history on directional coding in the monarch butterfly brain.

The central complex is a brain region in the insect brain that houses a neural network specialized to encode directional information. Directional coding has traditionally been investigated with compass cues that revolve in full rotations and at constant angular velocities around the insect's head. However, these stimulus conditions do not fully simulate an insect's sensory perception of compass cues during navigation. In nature, an insect flight is characterized by abrupt changes in moving direction as well as constant changes in velocity. The influence of such varying cue dynamics on compass coding remains unclear. We performed long-term tetrode recordings from the brain of monarch butterflies to study how central complex neurons respond to different stimulus velocities and directions. As these butterflies derive directional information from the sun during migration, we measured the neural response to a virtual sun. The virtual sun was either presented as a spot that appeared at random angular positions or was rotated around the butterfly at different angular velocities and directions. By specifically manipulating the stimulus velocity and trajectory, we dissociated the influence of angular velocity and direction on compass coding. While the angular velocity substantially affected the tuning directedness, the stimulus trajectory influenced the shape of the angular tuning curve. Taken together, our results suggest that the central complex flexibly adjusts its directional coding to the current stimulus dynamics ensuring a precise compass even under highly demanding conditions such as during rapid flight maneuvers.

Progressively Hybrid Transformer for Multi-Modal Vehicle Re-Identification.

Multi-modal (i.e., visible, near-infrared, and thermal-infrared) vehicle re-identification has good potential to search vehicles of interest in low illumination. However, due to the fact that different modalities have varying imaging characteristics, a proper multi-modal complementary information fusion is crucial to multi-modal vehicle re-identification. For that, this paper proposes a progressively hybrid transformer (PHT). The PHT method consists of two aspects: random hybrid augmentation (RHA) and a feature hybrid mechanism (FHM). Regarding RHA, an image random cropper and a local region hybrider are designed. The image random cropper simultaneously crops multi-modal images of random positions, random numbers, random sizes, and random aspect ratios to generate local regions. The local region hybrider fuses the cropped regions to let regions of each modal bring local structural characteristics of all modalities, mitigating modal differences at the beginning of feature learning. Regarding the FHM, a modal-specific controller and a modal information embedding are designed to effectively fuse multi-modal information at the feature level. Experimental results show the proposed method wins the state-of-the-art method by a larger 2.7% mAP on RGBNT100 and a larger 6.6% mAP on RGBN300, demonstrating that the proposed method can learn multi-modal complementary information effectively.

Dynamic compressive behaviour of basic magnesium sulfate cement–coral aggregate concrete (BMSC–CAC) after exposure to elevated temperatures: Experimental and analytical studies

This study investigates the effects of high temperature and strain rate on the dynamic compression behaviour of basic magnesium sulfate cement–coral aggregate concrete (BMSC–CAC). Specimens are exposed to temperatures ranging from 100 °C to 900 °C and then systematically tested using a split Hopkinson pressure bar (SHPB) apparatus. A three-dimensional mesoscale modelling approach is adopted in this study to account for the random shape and position of the aggregate. Results indicate that the dynamic impact damage is due to the coupled effect of high exposure temperature and high strain rate. At lower exposure temperatures, the dynamic compressive strength is similar to that at ambient temperature. However, at exposure temperatures of 500 °C and 700 ℃, the compressive strength is reduces to 50% and 30% of that at ambient temperature, respectively. Additionally, an increase in the exposure temperature increases the strain rate and continuously decreases the elastic modulus of concrete. A dynamic constitutive model is proposed considering the temperature and strain rate as influencing factor. This mesoscale modelling method is highly reliable and provides the best calculation parameters for simulating and predicting the dynamic compressive effect of BMSC–CAC after exposure to high temperatures.

Random motions in [formula omitted] with orthogonal directions

This paper is devoted to the detailed analysis of three-dimensional motions in R3 with orthogonal directions switching at Poisson times and moving with constant speed c>0. The study of the random position at an arbitrary time t>0 on the surface of the support, forming an octahedron Sct, is completely carried out on the edges and faces (Fct). In particular, the motion on the faces Fct is analyzed by means of a transformation which reduces it to a three-directions planar random motion. This permits us to obtain an integral representation on Fct in terms of integral of products of first order Bessel functions. The investigation of the distribution of the position p=p(t,x,y,z) inside Sct implied the derivation of a sixth-order partial differential equation governing p (expressed in terms of the products of three D’Alembert operators). A number of results, also in explicit form, concern the time spent on each direction and the position reached by each coordinate as the motion develops. The analysis is carried out when the incoming direction is orthogonal to the ongoing one and also when all directions can be uniformly chosen at each Poisson event. If the switches are governed by a homogeneous Poisson process many explicit results are obtained.

Quick Macroscopic Diffusion-Based Molecular Communication With Wavelike Propagation by Use of a Suspension of Relay Cells

In order to enable molecular communication on macroscopic distances without a moving medium, the model of diffusion-based information transmission with a large number of relays suspended in the medium, hence at random positions, is proposed. With an increasing number of relays, the stochastic variations approach zero and the system exhibits a quasi-deterministic behavior. Furthermore, the transmission time becomes inversely proportional to the number of relays and the impulse response independent of the channel length. Thus, the model describes a wave-like propagation, which is fundamentally different from the established flow- and diffusion-based approaches in molecular communication.

Structure formation of rod-like fillers in a contraction flow

Rod-like fillers in a flow field of a viscous fluid may form complex structures after passing a sudden contraction. The rods start with a dilute distribution with random positions and orientations. Behind the contraction, a large amount of rods tumble in a spatially correlated way, such that orientations perpendicular to the flow field occur at regular distances along the channel. The correlated tumbling results from an interplay of several effects, the tumbling inferred by the space dependent shear flow, the accumulation of rods at a certain distance from the wall, and the rod alignment at the contraction. The system is studied numerically for rod-like fillers in a shear-thinning viscous fluid.

A Proposed Algorithm for Hiding a Text in an Image Using QR Code

In this paper, an algorithm to hide information in an image using QR code technology is presented. QR Code “QUICK RESPONSE CODE” is a two-dimensional array that can include different types of data and was first developed in 1993 for the Japanese Toyota Corporation for the purpose of tracking products through production and marketing. The researchers paid great attention to QR code technology especially in the field of information security. The proposed algorithm in this paper hides the secret text in the image in a random way by generating random positions and using the LSB “Least Significant Bit” method. After that, the random positions are saved in the QR code file, and this is the hiding stage. In the extraction stage, the QR code file is read first to extract the positions where the secret text has been hidden. After that, the secret text is read according to the LSB method. The algorithm was implemented using the C-Sharp programming language and Microsoft Visual Studio 2019 development environment. After conducting experiments on a number of images and extracting results based on the PSNR “Peak signal-to-noise ratio “test method, the results were good and the algorithm is very strong against a brute force attack. This algorithm can be used in building authentication systems. Index Terms— Information Security, Text Hiding, QR Code, LSB Steganography, Random Position, LSB.

Particle swarm optimisation strategies for IOL formula constant optimisation.

To investigate particle swarm optimisation (PSO) as a modern purely data driven non-linear iterative strategy for lens formula constant optimisation in intraocular lens power calculation. A PSO algorithm was implemented for optimising the root mean squared formula prediction error (rmsPE, defined as achieved refraction minus predicted refraction) for the Castrop formula in a dataset of N= 888 cataractous eyes with implantation of the Hoya Vivinex hydrophobic acrylic aspheric lens. The hyperparameters were set to inertia: 0.8, accelerations c1=c2=0.1. The algorithm was initialised with NP = 100 particles having random positions and velocities within the box constraints of the constant triplet parameter space C=0.25 to 0.45, H=-0.25 to 0.25 and R=-0.25 to 0.25. The performance of the algorithm was compared to classical gradient-based Trust-Region-Reflective and Interior-Point algorithms. The PSO algorithm showed fast and stable convergence after 37 iterations. The rmsPE reduced systematically to 0.3440 diopters (D). With further iterations the scatter of the particle positions in the swarm decreased but without further reduction of rmsPE. The final constant triplet was C/H/R=0.2982/0.2497/0.1435. The Trust-Region-Reflective/Interior-Point algorithms showed convergence after 27/17 iterations, respectively, resulting in formula constant triplets C/H/R=0.2982/0.2496/0.1436 and 0.2982/0.2495/0.1436, both with the same rmsPE as the PSO algorithm (rmsPE=0.3440 D). The PSO appears to be a powerful adaptive nonlinear iteration algorithm for formula constant optimisation even in formulae with more than 1 constant. It acts independently of an analytical or numerical gradient and is in general able to search for the best solution even with multiple local minima of the target function.

A Fast Path Planning Method of Seedling Tray Replanting Based on Improved Particle Swarm Optimization

In the factory nursery, qualified seedlings can be used to replant unqualified seedlings or missing seedlings in the seedling tray through automatic transplanters. Due to the random positions of unqualified and missing seedlings, the end effector of the automatic replanting machine spends substantial time shuttling between the supply tray and the target tray to complete the replanting task. Therefore, we proposed a fast path planning method based on improved particle swarm optimization and compared it with the fixed sequence method and genetic algorithm in experiments with different replanting numbers in different tray types. The experiment shows that the improved particle swarm optimization algorithm and genetic algorithm can shorten the length of the replantation path by about 20% compared with the fixed sequence method, and the running time of the improved particle swarm optimization algorithm is 57.63% less than the genetic algorithm on average. The replanting path optimization method based on improved particle swarm optimization designed in this research can significantly optimize the length and time of the replanting path of the seedling tray, improve the efficiency of the replanting operation, and meet the real-time requirements.

Active Brownian particles in random and porous environments.

The transport of active particles may occur in complex environments, in which it emerges from the interplay between the mobility of the active components and the quenched disorder of the environment. Here, we explore the structural and dynamical properties of active Brownian particles (ABPs) in random environments composed of fixed obstacles in three dimensions. We consider different arrangements of the obstacles. In particular, we consider two particular situations corresponding to experimentally realizable settings. First, we model pinning particles in (non-overlapping) random positions and, second, in a percolating gel structure and provide an extensive characterization of the structure and dynamics of ABPs in these complex environments. We find that the confinement increases the heterogeneity of the dynamics, with new populations of absorbed and localized particles appearing close to the obstacles. This heterogeneity has a profound impact on the motility induced phase separation exhibited by the particles at high activity, ranging from nucleation and growth in random disorder to a complex phase separation in porous environments.

Porous Si loaded with Ag nanoparticles for ultra-broadband infrared absorption and detection

With the rapid development of semiconductor technology, silicon based infrared detector chips have been greatly optimized with the help of MEMS process. The infrared absorber, as the core part of thermal detector, requires infrared absorption characteristics such as wide spectrum and high absorption. In this paper, two kinds of porous silicon materials loaded with Ag nanoparticles (Si@AgNPs), honeycomb-like and forest-like, were constructed by chemical etching method. The experimental results and corresponding analyses illustrated that micro- and nano-porous structures of silicon with arbitrary sizes and random spatial positions can have a great guiding effect on the light waves, providing multiple reflections and scattering, allowing increased light propagation paths and generating light trapping effect, leading to enhanced infrared absorption in a wide spectral range. AgNPs are loaded, and the collective oscillation of free electrons resonates with incident photons, thereby exciting plasmon resonance effect, which can further improve the infrared absorption properties of the material. Benefiting from the synergistic effect of light trapping and plasmon resonance, the porous Si@AgNPs with honeycomb-like or forest-like structure both exhibited high absorption properties at 2.5–20 μm. Moreover, the honeycomb-like Si@AgNPs also possessed optical interference effect, which can further enhance the absorption, compared with the forest-like Si@AgNPs.

Phase Unwrapping Error Correction Based on Multiple Linear Regression Analysis

Fringe projection profilometry (FPP) is prone to phase unwrapping error (PUE) due to phase noise and measurement conditions. Most of the existing PUE-correction methods detect and correct PUE on a pixel-by-pixel or partitioned block basis and do not make full use of the correlation of all information in the unwrapped phase map. In this study, a new method for detecting and correcting PUE is proposed. First, according to the low rank of the unwrapped phase map, multiple linear regression analysis is used to obtain the regression plane of the unwrapped phase, and thick PUE positions are marked on the basis of the tolerance set according to the regression plane. Then, an improved median filter is used to mark random PUE positions and finally correct marked PUE. Experimental results show that the proposed method is effective and robust. In addition, this method is progressive in the treatment of highly abrupt or discontinuous regions.