Automated segmentation of microtomography imaging of Egyptian mummies

Propagation Phase Contrast Synchrotron Microtomography (PPC-SRμCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains. In this analysis, the virtual specimen needs to be segmented to separate different parts or materials, a process that normally requires considerable human effort. In the Automated SEgmentation of Microtomography Imaging (ASEMI) project, we developed a tool to automatically segment these volumetric images, using manually segmented samples to tune and train a machine learning model. For a set of four specimens of ancient Egyptian animal mummies we achieve an overall accuracy of 94–98% when compared with manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97–99%) at much lower complexity. A qualitative analysis of the segmented output shows that our results are close in terms of usability to those from deep learning, justifying the use of these techniques.

An Improved Biogeography-based Optimization with Hybrid Migration and Feedback Differential Evolution and its Performance Analysis

Biogeography-based optimization (BBO) is not suitable for solving high-dimensional or multi-modal problems. To improve the optimization efficiency of BBO, this study proposes a novel BBO variant, which is named ZGBBO. For the selection operator, an example learning method is designed to ensure inferior solution will not destroy the superior solution. For the migration opeartor, a convex migration is proposed to increase the convergence speed, and the probability of finding the optimal solution is increased by using opposition-based learning to generate opposite individuals. The mutation operator of BBO is deleted to eliminate the generation of poor solutions. A differential evolution with feedback mechanism is merged to improve the convergence accuracy of the algorithm for multi-modal and irregular problems. Meanwhile, the greedy selection is used to make the population always moves in the direction of a better area. Then, the global convergence of ZGBBO is proved with Markov model and sequence convergence model. Quantitative evaluations, compared with three self-variants, seven improved BBO variants and six state-of-the-art evolutionary algorithms, experimental results on 24 benchmark functions show that every improved strategy is indispensable, and the overall performance of ZGBBO is better. Besides, the complexity of ZGBBO is analyzed by comparing with BBO, and ZGBBO has less computation and lower complexity.

Design of dual band slotted reconfigurable antenna using electronic switching circuit

This paper proposes a dual band reconfigurable microstrip slotted antenna for supporting the wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications, providing coverage where both directive and omni-directive radiations are needed. The design consists of a feedline, a ground plane with two slots and two gaps between them to provide the switching capability and a 1.6 mm thick flame retardant 4 (FR4) substrate (dielectric constant Ɛ=4.3, loss tangent δ=0.019), modeling an antenna size of 30x35x1.6 mm3. The EM simulation, which was carried out using the connected speech test (CST) studio suite 2017, generated dual wide bands of 40% (2-3 GHz) with -55 dB of S11 and 24% (5.2-6.6 GHz) higher than its predecessors with lower complexity and -60 dB of S11 in addition to the radiation pattern versatility while maintaining lower power consumption. Moreover, the antenna produced omnidirectional radiation patterns with over than 40% bandwith at 2.4 GHz and directional radiation patterns with 24% bandwith at the 5.8 GHz band. Furthermore, a comprehensive review of previously proposed designs has also been made and compared with current work.

Engineering of a Promoter Repressed by a Light-Regulated Transcription Factor in Escherichia coli

Light-regulated gene expression systems allow controlling gene expression in space and time with high accuracy. Contrary to previous synthetic light sensors that incorporate two-component systems which require localization at the plasma membrane, soluble one-component repression systems provide several advantageous characteristics. Firstly, they are soluble and able to diffuse across the cytoplasm. Secondly, they are smaller and of lower complexity, enabling less taxing expression and optimization of fewer parts. Thirdly, repression through steric hindrance is a widespread regulation mechanism that does not require specific interaction with host factors, potentially enabling implementation in different organisms. Herein, we present the design of the synthetic promoter PEL that in combination with the light-regulated dimer EL222 constitutes a one-component repression system. Inspired by previously engineered synthetic promoters and the Escherichia coli lacZYA promoter, we designed PEL with two EL222 operators positioned to hinder RNA polymerase binding when EL222 is bound. PEL is repressed by EL222 under conditions of white light with a light-regulated repression ratio of five. Further, alternating conditions of darkness and light in cycles as short as one hour showed that repression is reversible. The design of the PEL-EL222 system herein presented could aid the design and implementation of analogous one-component optogenetic repression systems. Finally, we compare the PEL-EL222 system with similar systems and suggest general improvements that could optimize and extend the functionality of EL222-based as well as other one-component repression systems.

Optimum Elements Selection in IRS-Assisted UAV Communications with Phase Errors

<div>This letter considers minimizing the bit error rate (BER) of unmanned aerial vehicle (UAV) communications assisted by intelligent reflecting surfaces (IRSs). By noting that increasing the number of IRS elements in the presence of phase errors does not necessarily improve the BER, it is crucial to use only the elements that contribute to reducing the BER. Consequently, we propose an efficient algorithm to activate only the elements that improve the BER. The proposed algorithm has lower complexity and comparable BER to the optimum selection process, which is an NP-hard problem. The accuracy of the estimated phase is evaluated by deriving the probability distribution function (PDF) of the least-square (LS) channel estimator, and showing that the PDF can be closely approximated by the von Mises distribution at high signal-to-noise ratios (SNRs). The obtained analytical and simulation results show that using all the available reflectors can significantly deteriorate the BER, and thus, elements’ selection is necessary. In particular scenarios, using about 26% of the reflectors provides more than 10 fold BER reduction.</div>

Optimum Elements Selection in IRS-Assisted UAV Communications with Phase Errors

<div>This letter considers minimizing the bit error rate (BER) of unmanned aerial vehicle (UAV) communications assisted by intelligent reflecting surfaces (IRSs). By noting that increasing the number of IRS elements in the presence of phase errors does not necessarily improve the BER, it is crucial to use only the elements that contribute to reducing the BER. Consequently, we propose an efficient algorithm to activate only the elements that improve the BER. The proposed algorithm has lower complexity and comparable BER to the optimum selection process, which is an NP-hard problem. The accuracy of the estimated phase is evaluated by deriving the probability distribution function (PDF) of the least-square (LS) channel estimator, and showing that the PDF can be closely approximated by the von Mises distribution at high signal-to-noise ratios (SNRs). The obtained analytical and simulation results show that using all the available reflectors can significantly deteriorate the BER, and thus, elements’ selection is necessary. In particular scenarios, using about 26% of the reflectors provides more than 10 fold BER reduction.</div>

Unfounded Sets for Disjunctive Hybrid MKNF Knowledge Bases

Combining the closed-world reasoning of answer set programming (ASP) with the open-world reasoning of ontologies broadens the space of applications of reasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in some cases without increasing the complexity of reasoning tasks. However, in many cases, solver development is lagging behind. As the result, the only known method of solving disjunctive hybrid MKNF knowledge bases is based on guess-and-verify, as formulated by Motik and Rosati in their original work. A main obstacle is understanding how constraint propagation may be performed by a solver, which, in the context of ASP, centers around the computation of \textit{unfounded atoms}, the atoms that are false given a partial interpretation. In this work, we build towards improving solvers for hybrid MKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded sets for these knowledge bases, identify lower complexity bounds, and demonstrate how we might integrate these developments into a DPLL-based solver. We discuss challenges introduced by ontologies that are not present in the development of solvers for disjunctive logic programs, which warrant some deviations from traditional definitions of unfounded sets. We compare our work with prior definitions of unfounded sets.

Finite-Time Command-Filtered Approximation-Free Attitude Tracking Control of Rigid Spacecraft

This paper presents a finite-time command-filtered approximation-free attitude tracking control for rigid spacecraft. A novel finite-time prescribed performance function (FTPPF) is first constructed to ensure that the attitude tracking errors converge to the predefined region in finite time. Then, a finite-time error compensation mechanism is constructed and incorporated into the backstepping control design, such that the differentiation of virtual control signals in recursive steps can be avoided to overcome the singularity issue. Compared with most of approximation-based attitude control methods, less computational burden and lower complexity are guaranteed by the proposed approximation-free control scheme due to the avoidance of using any function approximations. Simulations are given to illustrate the efficiency of the proposed method.

Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training time, lower complexity, and statistical flexibility. In this work, we investigate the utility of three prominent unsupervised embedding techniques (principal component analysis—PCA, uniform manifold approximation and projection—UMAP, and variational autoencoders—VAEs) for solving classification tasks in the domain of toxicology. To this end, we compare these embedding techniques against a set of molecular fingerprint-based models that do not utilize additional pre-preprocessing of features. Inspired by the success of transfer learning in several fields, we further study the performance of embedders when trained on an external dataset of chemical compounds. To gain a better understanding of their characteristics, we evaluate the embedders with different embedding dimensionalities, and with different sizes of the external dataset. Our findings show that the recently popularized UMAP approach can be utilized alongside known techniques such as PCA and VAE as a pre-compression technique in the toxicology domain. Nevertheless, the generative model of VAE shows an advantage in pre-compressing the data with respect to classification accuracy.

Development of a Nonenzymatic Electrochemical Sensor for Organophosphate Pesticide Detection Using Copper (II) Oxide Nanorod Electrodes

Inorganic electrode materials of low cost, lower complexity, and high stability have become the more preferred choice over enzyme usage in electrochemical sensors. In this work, copper oxide (CuO) nanorods (NRs) were synthesized on copper foil as electrodes through anodization and annealing processes. The synthesized electrodes were used to analyse the organophosphate pesticides (OPPs) and interference molecules by cyclic voltammetry. The CuO NR sensor was able to identify and quantify different kinds of OPPs with an elevated sensitivity of 1.269, 1.425, 1.657, and 2.833 μA/ng mL-1 for chlorpyrifos, parathion, paraoxon, and pirimiphos and explicitly separate them from interference molecules (i.e., carbaryl, paraquat, sodium nitrate, sodium sulphate, and toluene). Moreover, this electrochemical pesticide sensor achieved a very low limit of detection (LOD) in the 10-7 molar level with a high selectivity among all tested analytes. The LOD for each pesticide ranged from 0.29 to 0.61 μM, revealing the ability to define the maximum residue limit in food. In short, our enzyme-free CuO NR sensor is a promising platform to deliver a fast, low-cost, and reliable pesticide detection unit.