Operator Research Articles

A Novel Step-T-Junction Microchannel for the Cell Encapsulation in Monodisperse Alginate-Gelatin Microspheres of Varying Mechanical Properties at High Throughput.

Cell encapsulation has been widely employed in cell therapy, characterization, and analysis, as well as many other biomedical applications. While droplet-based microfluidic technology is advantageous in cell microencapsulation because of its modularity, controllability, mild conditions, and easy operation when compared to other state-of-art methods, it faces the dilemma between high throughput and monodispersity of generated cell-laden microdroplets. In addition, the lack of a biocompatible method of de-emulsification transferring cell-laden hydrogel from cytotoxic oil phase into cell culture medium also hurtles the practical application of microfluidic technology. Here, a novel step-T-junction microchannel was employed to encapsulate cells into monodisperse microspheres at the high-throughput jetting regime. An alginate–gelatin co-polymer system was employed to enable the microfluidic-based fabrication of cell-laden microgels with mild cross-linking conditions and great biocompatibility, notably for the process of de-emulsification. The mechanical properties of alginate-gelatin hydrogel, e.g., stiffness, stress–relaxation, and viscoelasticity, are fully adjustable in offering a 3D biomechanical microenvironment that is optimal for the specific encapsulated cell type. Finally, the encapsulation of HepG2 cells into monodisperse alginate–gelatin microgels with the novel microfluidic system and the subsequent cultivation proved the maintenance of the long-term viability, proliferation, and functionalities of encapsulated cells, indicating the promising potential of the as-designed system in tissue engineering and regenerative medicine.

Image Encryption Algorithm Based on Plane-Level Image Filtering and Discrete Logarithmic Transform

Image encryption is an effective way to protect image data. However, existing image encryption algorithms are still unable to strike a good balance between security and efficiency. To overcome the shortcomings of these algorithms, an image encryption algorithm based on plane-level image filtering and discrete logarithmic transformation (IEA-IF-DLT) is proposed. By utilizing the hash value more rationally, our proposed IEA-IF-DLT avoids the overhead caused by repeated generations of chaotic sequences and further improves the encryption efficiency through plane-level and three-dimensional (3D) encryption operations. Aiming at the problem that common modular addition and XOR operations are subject to differential attacks, IEA-IF-DLT additionally includes discrete logarithmic transformation to boost security. In IEA-IF-DLT, the plain image is first transformed into a 3D image, and then three rounds of plane-level permutation, plane-level pixel filtering, and 3D chaotic image superposition are performed. Next, after a discrete logarithmic transformation, a random pixel swapping is conducted to obtain the cipher image. To demonstrate the superiority of IEA-IF-DLT, we compared it with some state-of-the-art algorithms. The test and analysis results show that IEA-IF-DLT not only has better security performance, but also exhibits significant efficiency advantages.

Dynamic Learning From Adaptive Neural Control for Discrete-Time Strict-Feedback Systems.

This article first investigates the issue on dynamic learning from adaptive neural network (NN) control of discrete-time strict-feedback nonlinear systems. To verify the exponential convergence of estimated NN weights, an extended stability result is presented for a class of discrete-time linear time-varying systems with time delays. Subsequently, by combining the n -step-ahead predictor technology and backstepping, an adaptive NN controller is constructed, which integrates the novel weight updating laws with time delays and without the σ modification. After ensuring the convergence of system output to a recurrent reference signal, the radial basis function (RBF) NN is verified to satisfy the partial persistent excitation condition. By the combination of the extended stability result, the estimated NN weights can be verified to exponentially converge to their ideal values. The convergent weight sequences are comprehensively represented and stored by constructing some elegant learning rules with some novel sequences and the mod function. The stored knowledge is used again to develop a neural learning control scheme. Compared with the traditional adaptive NN control, the proposed scheme can not only accomplish the same or similar tracking tasks but also greatly improve the transient control performance and alleviate the online computation. Finally, the validity of the presented scheme is illustrated by numerical and practical examples.

Study on Sensitivity of Observation Error Statistics of Doppler Radars to the Radar forward Operator in Convective-Scale Data Assimilation

In the present work, we investigate the impacts on the observation error (OE) statistics due to different types of errors in the forward operator (FE) for both radar reflectivity and radial wind data, in the context of convective-scale data assimilation in the summertime. A series of sensitivity experiments were conducted with the Efficient Modular VOlume RADar Operator (EMVORADO), using the operational data assimilation system of the Deutscher Wetterdienst (DWD, German Weather Service). The investigated FEs are versatile, including errors caused by neglecting the terminal fall speed of hydrometeor, the reflectivity weighting, and the beam broadening and attenuation effects, as well as errors caused by different scattering schemes and formulations for melting particles. For reflectivity, it is found that accounting for the beam broadening effect evidently reduces the standard deviations, especially at higher altitudes. However, it does not shorten the horizontal or along-beam correlation length scales. In comparison between the Rayleigh and the Mie schemes (with specific configurations), the former one results in much smaller standard deviations for heights up to 4 km, and aloft, slightly larger standard deviations. Imposing the attenuation to the Mie scheme slightly reduces the standard deviations at lower altitudes; however, it largely increases the standard deviations at higher altitudes and it also leads to longer correlation length scales. For radial wind, positive impacts of considering the beam broadening effect on standard deviations and neutral impacts on correlations are observed. For both reflectivity and radial wind, taking the terminal fall speed of hydrometeor and the reflectivity weighting into account does not make remarkable differences in the estimated OE statistics.

Evaluation of the practical clinical use of the Horse Grimace Scale translated into French

ObjectiveTo assess the reliability of a French version of the Horse Grimace Scale (HGSfv). Study designProspective, randomized, clinical study. AnimalsThe operated (OP) group included 13 horses undergoing elective surgery. The positive (PC) and negative control (NC) groups included seven colicking horses and eight exercising sport horses, respectively. MethodsPhotographs were extracted from videos of the horses’ heads. Videos were taken before and immediately after surgery in OP, on arrival of the horse in PC, and at rest in their stalls in NC. Pictures were evaluated by three anaesthetists [Diplomates (DIPs)] and four riders (RIDs) using Horse Grimace Scale translated into French (HGSfv) at two points, 2 weeks apart (E1 and E2). Each evaluator gave each image a score (1–3) for six identified facial action units. The scores given by DIPs and RIDs were compared using a Wilcoxon test. Intra- and inter-evaluator reliability were assessed using Spearman correlation tests (rs) and intra-class coefficients (ICCs), respectively. ResultsRIDs and DIPs gave significantly higher scores in the PC group than in the NC group [RIDsE1PC 5.0 (4.2–9.8) versus RIDsE1NC 2.2 (0.0–6.5), p = 0.02; RIDsE2PC 5.2 (3.2–9.5) versus RIDsE2NC 2.0 (0.2–5.8), p < 0.01; DIPsE1PC 4.0 (1.3–6.3) versus DIPsE1NC 2.2 (1.0–4.7), p = 0.04; DIPsE2PC 2.7 (1.0–6.0) versus DIPsE2NC 1.0 (0.0–2.3), p = 0.03]. Scores given by RID or DIPs 2 weeks apart were highly correlated [rs (RIDsE1, RIDsE2) r = 0.86, p < 0.0001] and [rs (DIPsE1, DIPsE2) r = 0.81 p < 0.0001]. The ICC between RIDs and DIPs in E1 and E2 was 0.94 (0.92–0.95) and 0.91 (0.89–0.93), respectively. The specificity and sensitivity of the HGSfv was 94% and 43%, respectively. Conclusions and clinical relevanceUsing the HGSfv, knowledge of horses rather than specialization in veterinary anaesthesia and analgesia appears to differentiate horses with visceral pain from those assumed to be pain free.

Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map (3D-ILM), an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman (RSA) is presented in this paper. In the first stage, a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence, Lyapunov exponent, and Shannon entropy. In the second stage, combined with the public key RSA algorithm, a new key acquisition mathematical model (MKA) is constructed to obtain the initial keys for the 3D-ILM. Consequently, the key stream can be produced depending on the plain image for a higher security. Moreover, a novel process model (NPM) for the input of the 3D-ILM is built, which is built to improve the distribution uniformity of the chaotic sequence. In the third stage, to encrypt the plain image, a pre-process by exclusive OR (XOR) operation with a random matrix is applied. Then, the pre-processed image is performed by a permutation for rows, a downward modulo function for adjacent pixels, a permutation for columns, a forward direction XOR addition-modulo diffusion, and a backward direction XOR addition-modulo diffusion to achieve the final cipher image. Moreover, experiments show that the the proposed algorithm has a better performance. Especially, the number of pixels change rate (NPCR) is close to ideal case 99.6094%, with the unified average changing intensity (UACI) close to 33.4634%, and the information entropy (IE) close to 8.

Investigation on the Spatial-Temporal Distribution of Electromagnetic Gun Rail Temperature in Single and Continuous Launch Modes

In order to study the spatial-temporal distribution characteristics of electromagnetic rail gun temperature, based on moving grid processing method and mod function method, a 3-D geometric model of electromagnetic rail gun under single launch mode, a 2-D geometric model of electromagnetic rail gun under continuous-fire mode, and the corresponding transient electromagnetic field-temperature field coupling model are established by using the finite element analysis software COMSOL Multiphysics, and the temperature spatial-temporal distribution characteristics in the rail-armature contact area, cross section of rail, and along the rail axis is simulated under single launch and continuous launch. The results show that, under the conditions of single launch and natural cooling, the high temperature area in the rail-armature contact surface is located at the rear of the armature due to the skin effect. The temperature distribution along the axial inner side of rail tends to be higher near the tail and lower near the muzzle, it increases with time, but the peak temperature does not exceed the melting point of copper. Under the continuous launch and natural cooling, after five launches, the peak temperature of the rail increases to 709 °C, which is close to the melting point of copper. Continued launching will cause rapid accumulation of heat in the rail and a rapid increase of peak temperature, resulting in shortened life of the launcher and even launch failure. Therefore, without active cooling measures, the electromagnetic rail gun cannot be used in actual combat. The results of the study are an important guide for the design and thermal management of the cooling system of electromagnetic rail gun.

Hyperchaotic Modulo Operator Encryption Technique for Massive Multiple Input Multiple Output Generalized Frequency Division Multiplexing system

Hyperchaotic Modulo Operator Encryption Technique for Massive Multiple Input Multiple Output Generalized Frequency Division Multiplexing system

Space Modular Operations: A Powerful Method to Guarantee Flight Rules’ Compliance

Reducing operational risk and complexity is a key feature when designing mission operations. One of the metrics that influences complexity is the command and control concept for the flight segment. Flight rules’ and constraints’ compliance has always been a difficult endeavor: rules for every single command have to be considered, depending on its interaction with the spacecraft state and ongoing activities: particularly during critical operations when it is frequently necessary to handle real-time decision-making during ground contacts. We present here a space modular operations framework to be used by both mission development and operations teams from the very beginning of mission design until the mission end of life. This framework is based on a few types of reusable modular elements, which are used in specific roles to implement the mission testing and operations effort, as well as to ensure compliance with flight rules and constraints. We show how this approach reduces operational complexity by an order of magnitude, proportionally increasing the confidence in complying with all the mission flight rules and constraints, improving mission safety.

Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids.

With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input’s privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side.

Möbius orthogonality of sequences with maximal entropy

We prove that strongly b-multiplicative functions of modulus 1 along squares are asymptotically orthogonal to the Möbius function. This provides examples of sequences having maximal entropy and satisfying this property.

Model-Predictive Control for Modular Multilevel Converters Operating at Wide Frequency Range With a Novel Cost Function

Modular multilevel converters (MMCs) have been a promising topology in medium-voltage motor drive applications over the past few decades. The main challenge that hinders the widespread use of the MMC in medium-voltage motor drives is the large voltage fluctuation under low-speed conditions. In this article, we propose a model-predictive control (MPC) for the MMC operating in a wide frequency range. Unlike conventional MPC methods, a novel cost function has been designed to realize the output current control and the voltage balancing control for both low- and high-frequency operations. Compared with the high-frequency circulating current injection method, the proposed method can easily achieve a tradeoff between the modulation range and the amplitude of injected common-mode voltage under different operating conditions. In addition, due to the exclusion of the switching process between low- and high-frequency operations, the control structure of this method is more straightforward. Finally, the experimental results verify the effectiveness and superiority of the proposed method.

Science and Business Developments

Science and Business Developments

Numerical investigation of non-Gaussianities in the phase and modulus of density Fourier modes

ABSTRACT We numerically investigate the non-Gaussianities in the late-time cosmological density field in Fourier space. We explore various statistics, including the two- and three-point probability distribution function (PDF) of phase and modulus, and their two- and three-point correlation function. Significant non-Gaussianity is observed for certain configurations. Comparing the measurement from simulation with the theoretical expansion prediction, we find that for (600 Mpc h-1)3 volume, the $\mathcal {O}(V^{-1/2})$ order term alone is sufficiently accurate to describe all the measured non-Gaussianities in not only the PDFs, but also the correlations. We also numerically find that the phase-modulus cross-correlation contributes $\sim 50{{\ \rm per\ cent}}$ to the bispectrum, further verifying the accuracy of the $\mathcal {O}(V^{-1/2})$ order prediction. This work demonstrates that the non-Gaussianity of cosmic density field is simpler in Fourier space, and may facilitate the data analysis in the era of precision cosmology.

Charged moments in W3 higher spin holography

We consider the charged moments in SL(3, ℝ) higher spin holography, as well as in the dual two-dimensional conformal field theory with W3 symmetry. For the vacuum state and a single entangling interval, we show that the W3 algebra of the conformal field theory induces an entanglement W3 algebra acting on the quantum state in the entangling interval. The algebra contains a spin 3 modular charge which commutes with the modular Hamiltonian. The reduced density matrix is characterized by the modular energy and modular charge, hence our definition of the charged moments is also with respect to these conserved quantities. We evaluate the logarithm of the charged moments perturbatively in the spin 3 modular chemical potential, by computing the corresponding connected correlation functions of the modular charge operator up to quartic order in the chemical potential. This method provides access to the charged moments without using charged twist fields. Our result matches known results for the charged moment obtained from the charged topological black hole picture in SL(3, ℝ) higher spin gravity. Since our charged moments are not Gaussian in the chemical potential any longer, we conclude that the dual W3 conformal field theories must feature breakdown of equipartition of entanglement to leading order in the large c expansion.

MeNTT: A Compact and Efficient Processing-in-Memory Number Theoretic Transform (NTT) Accelerator

Lattice-based cryptography (LBC), exploiting learning with error (LWE) problems, is a promising candidate for postquantum cryptography. The number theoretic transform (NTT) is the latency- and energy-dominant process in the computation of LWE problems. This article presents a compact and efficient in-MEmory NTT accelerator, named MeNTT, which explores an optimized computation in and near a 6T SRAM array. Specifically designed peripherals enable fast and efficient modular operations. Moreover, a novel mapping strategy reduces the data flow between the NTT stages into a unique pattern, which greatly simplifies the routing among processing units (i.e., the SRAM column in this work), reducing the energy and area overheads. The accelerator achieves significant latency and energy reductions over prior arts.

Unified modeling behavior of rockfill materials along different loading stress paths

Rockfill materials used for dam construction show obvious stress path dependency during construction process. The constitutive model for rockfill materials are mainly established based on conventional triaxial test. This brings some deviation to the numerical calculation for earth rockfill dam engineering, and it is necessary to develop a constitutive model considering stress paths effect. In the framework of generalized plasticity, plastic modulus can be expressed as the function of dilatancy equation and tangent modulus, hence the influence of stress paths on model can be transfer to the dilatancy equation and tangent modulus. This leads to a new method to describe stress-strain response of rockfill under various loading stress paths. Based on the analysis of large-scale triaxial experiments of rockfill materials with various stress paths, we proposed new dilatancy equation and tangent modulus to better describe stress paths effect and corresponding modified generalized plasticity model is established. Through the prediction and comparison of three groups of rockfill materials triaxial tests, the model established in this paper can simulate behaviors of rockfill materials under various stress paths, using only a set of parameters.

Canceling microwave crosstalk with fixed-frequency qubits

Scalable quantum information processing requires that modular gate operations can be executed in parallel. The presence of crosstalk decreases the individual addressability, causing erroneous results during simultaneous operations. For superconducting qubits which operate in the microwave regime, electromagnetic isolation is often limited due to design constraints, leading to signal crosstalk that can deteriorate the quality of simultaneous gate operations. Here, we propose and demonstrate a method based on the alternative-current Stark effect for calibrating the microwave signal crosstalk. The method is suitable for processors based on fixed-frequency qubits, which are known for high coherence and simple control. The optimal compensation parameters can be reliably identified from a well-defined interference pattern. We implement the method on an array of seven superconducting qubits and show its effectiveness in removing the majority of crosstalk errors.

Image Encryption and Decryption Using Chua’s Circuits

In recent years, chaos studies have been developing tremendously. The importance of using chaos is its deterministic nature. Though the chaos is interpreted as random in nature but it is deterministic. This is a very useful aspect that can be used in encryption of signals. In this paper, the very well-known Chua’s circuit is used to generate random sequences based on initial conditions. These sequences are going to be used for encrypting and decrypting an image. The key is going to be encrypted using a function with a modulus operation then used as an initiation for Chua circuit. The output of Chua circuit is three arrays X, Y, and Z. The process can be divided into two steps. First, one of these sequences is going to be used to form 2D array and the other two sequences also form another 2D array where both have the same size. Then, it is going to be XORed with the image and then the rows and the columns are going to be swapped horizontally and vertically. Second, the other matrix coming from X-Y arrays after mod operation is going to be used as an index for the first processed image. Simulation results show that the scheme sensitivity and complexity to such factors. The importance of this method is that it has plenty of factors that can be unique during generating of these chaotic sequences which makes it very difficult to decrypt the message.

Neural-Network-Based Nonlinear Tomlinson-Harashima Precoding for Bandwidth-Limited Underwater Visible Light Communication

Underwater visible light communication (UVLC) based on light-emitting diodes (LEDs) is considered a potential candidate for underwater wireless data transmission. However, the implementation of high-speed UVLC remains a challenge due to bandwidth limitation and nonlinear effects. In this paper, we investigate the performance of Tomlinson-Harashima precoding (THP) in the bandwidth-limited UVLC system for the first time. Apart from research on linear and Volterra series-based nonlinear THP, neural network (NN)-based THP is first proposed and conducted. At the transmitter, the intersymbol interference (ISI) and nonlinearity can be partially mitigated through a feedback neural network (FBN) without error propagation. A modulo operator is employed to eliminate instability. In addition, for the precoded signals, the power spectral density (PSD) is near-white. Therefore, it is possible to avoid the spectrum-shaping-induced signal-to-noise ratio (SNR) loss problem that is common in pre-emphasis methods. At the receiver, another modulo operator is used for decoding. An adaptive feedforward neural network (FFN) is applied to compensate for the channel state information (CSI) mismatch of the FBN and mitigate the remaining ISI and nonlinear impairments. In the demonstrated UVLC system, the experimental results prove the feasibility of the proposed method. The Q factor is increased by 3.39 dB at a data rate of 2.2 Gbps. 630 MBd CAP-16 transmission under a 7&#x0025; HD-FEC threshold is realized, which is 90 MBd higher than that when employing linear postequalization only.