Quantum computing formulation of some classical Hadamard matrix searching methods and its implementation on a quantum computer

Finding a Hadamard matrix (H-matrix) among all possible binary matrices of corresponding order is a hard problem that can be solved by a quantum computer. Due to the limitation on the number of qubits and connections in current quantum processors, only low order H-matrix search of orders 2 and 4 were implementable by previous method. In this paper, we show that by adopting classical searching techniques of the H-matrices, we can formulate new quantum computing methods for finding higher order ones. We present some results of finding H-matrices of order up to more than one hundred and a prototypical experiment of the classical-quantum resource balancing method that yields a 92-order H-matrix previously found by Jet Propulsion Laboratory researchers in 1961 using a mainframe computer. Since the exactness of the solutions can be verified by an orthogonality test performed in polynomial time; which is untypical for optimization of hard problems, the proposed method can potentially be used for demonstrating practical quantum supremacy in the near future.

A posteriori verification of the positivity of solutions to elliptic boundary value problems

The purpose of this paper is to develop a unified a posteriori method for verifying the positivity of solutions of elliptic boundary value problems by assuming neither $$H^2$$ H 2 -regularity nor $$ L^{\infty } $$ L ∞ -error estimation, but only $$ H^1_0 $$ H 0 1 -error estimation. In (J Comput Appl Math 370:112647, 2020), we proposed two approaches to verify the positivity of solutions of several semilinear elliptic boundary value problems. However, some cases require $$ L^{\infty } $$ L ∞ -error estimation and, therefore, narrow applicability. In this paper, we extend one of the approaches and combine it with a priori error bounds for Laplacian eigenvalues to obtain a unified method that has wide application. We describe how to evaluate some constants required to verify the positivity of desired solutions. We apply our method to several problems, including those to which the previous method is not applicable.

New method for route efficient energy calculations with mobile-sink for wireless sensor networks

<p>Despite proposing a number of algorithms and protocols, especially those related to routing, for the purpose of reducing energy consumption in wireless sensor networks, which is one of the most important issues facing this type of network. In this research paper, energy consumption and cost are calculated taking into account energy consumption and the amount of data transferred to a thousand nodes through specific paths towards the mobile sink. The proposed model simulated by sending various amounts of data with specific path to know the energy consumption of each track and the network life time with 250, 500, and 1000 bits. Cost calculated using various weight for each track of these paths and the coefficient of movement time and path loss factor and others related to the transmission and receiving circuits. And finally, the results compared with a previous method it showed the efficiency of our method used and calculating 1000 nodes with various amount of bits to show the experimental results. Deep learning used to remember each and every path of each position or nearby to avoid calculation cost later.</p>

Hybrid Scheduling Strategy in Cloud Computing based on Optimization Algorithms

As a platform for offering on-demand services, cloud computing has increased in relevance and appeal. It has a pay-per-use model for its services. A cloud service provider's primary goal is to efficiently use resources by reducing execution time, cost, and other factors while increasing profit. As a result, effective scheduling algorithms remain a key issue in cloud computing, and this topic is categorized as an NP-complete problem. Researchers previously proposed several optimization techniques to address the NP-complete problem, but more work is needed in this area. This paper provides an overview of strategy for successful task scheduling based on a hybrid heuristic approach for both regular and larger workloads. The previous method handles the jobs adequately, but its performance degrades as the task size becomes larger. The proposed optimum scheduling method employs two distinct techniques to select the suitable VM for the specified job. To begin, it enhances the LJFP method by employing OSIG, an upgraded version of the Genetic Algorithm, to choose solutions with improved fitness factors, crossover, and mutation operators. This selection returns the best machines, and PSO then chooses one for a specific job. The appropriate machine is chosen depending on several factors, including the expected execution time, current load, and energy usage. The proposed algorithm's performance is assessed using two distinct cloud scenarios with various VMs and tasks, and overall execution time and energy usage are calculated. The proposed algorithm outperforms existing techniques in terms of energy and average execution time usage in both scenarios.

Fractionation of Soil Organic Matter into Labile and Stable Fractions

The present study aims to test and evaluate the efficiency of a new modified method of organic matter evaluation. It allows the assessment of the quality and quantity of the primary soil organic matter and the stable organic fractions separately. The new method was tested in six soil samples of different localities in the Czech Republic. This method is based on observing reaction kinetics during the oxidation of soil organic matter and measuring the cation-exchange capacity of stable organic fractions. The results were compared with classical methods, which rely on the isolation of humic substances, determination of the content of humic acids and fulvic acids and their ratio CHA:CFA, quotient E4/6, and fractionation of soil organic matter according to resistance to oxidation. It turned out that the results of the new modified method are more sensitive in comparison with the results obtained by classical procedures. The linear regression demonstrated the dependence between the amounts of soil organic matter determined by the classical method compared with the modified method. Moreover, the new modified method was found to be faster and not demanding on laboratory equipment. The new method has been improved to be easily repeatable, and some shortcomings of the previous method were eliminated. Based on our results and other recent studies, the modified method may be recommended for the practical evaluation of soil organic matter conditions.

A compliance modeling method of flexible rotary joint for collaborative robot using passive network synthesis theory

Collaborative robots have become a research focus because of their wide applications. However, the previous compliance design method of the flexible rotary joint for collaborative robot mainly relied on experience of designers, and “trial and error” method is usually adopted, no feasible and systematic theory for the designer to select numerical value and series-parallel connection mode of the springs and dampers for the flexible rotary joint. Thus, developing a feasible compliance modeling theory to guide the design of the flexible rotary joint is a particularly challenging task. The main contribution of this paper is to present a novel and effective compliance modeling theory of the flexible rotary joint for collaborative robot based on electrical and mechanical passive network synthesis, to provide theoretical and systematic guidances for compliance design of the flexible rotary joint. First, inerter element is introduced into the mechanical system, and the compliance of the flexible rotary joint is expressed as an angular velocity admittance function using electrical and mechanical network analogy. Then, by passive network synthesis theory, the three kinds of compliance realization forms of rational function and four-element compliance realization conditions of biquadratic function for the flexible rotary joint are given using inerters, springs, and dampers. Moreover, numerical examples and simulations are conducted to illustrate effectiveness of the proposed compliance realization method. Finally, discussions are given to illustrate advantages of the proposed compliance modeling and design methods compared with the previous method.

Optimasi Analisis Melting Curve untuk Skrining Cepat dan Sensitif Mutasi V1016G pada Aedes aegypti Resisten Sintetik Piretroid dengan Reaksi Rantai Polimerase Spesifik Alel

Detection of V1016G mutation is important for identifying the mechanism of synthetic pyrethroid resistance in Aedes aegypti population. The previous method has described an allele specific polymerase chain reaction (AS-PCR) using conventional PCR to detect the mutation. Although the method has great differentiating power and reproducibility, faster and more sensitive genotyping method is essential to accurately detect the mutation. This study evaluate the used of SYBR® Green real-time PCR and melting curve analysis (MCA) to identify the V1016G mutation. The collection of homozygous 1016G, heterozygous, and wild type (1016 V) mosquitoes DNA genome was extracted using genomic DNA mini kit. The SsoAdvanced™ Universal SYBR® Green Supermix was used to identify alleles by real-time PCR followed melting curve analysis of the amplicons. Melting curve analysis produced reproducible results for the loci tested. The melting temperature was reached at 78.5 oC for homozygous 1016G mosquito and at 86 oC for wild type mosquito. Meanwhile, the heterozigous mosquito revealed two peaks of melting temperature at both 78.5 oC and 86 oC. These easily interpretable and distinguishable melting curve results were consistent with AS-PCR results obtained for the same alleles. The described MCA application for screening V1016G mutation is fast and widely accessible also could be implemented under field conditions

An Improved Method for the Extraction of Nucleic Acids from Plant Tissue without Grinding to Detect Plant Viruses and Viroids

Gene amplification techniques such as polymerase chain reaction (PCR) are widely used for the diagnosis of plant diseases caused by viruses and viroids. It is preferable that sample preparation methods for PCR or reverse transcription (RT) PCR are rapid, straightforward, and inexpensive. We previously reported a method for the extraction of nucleic acids without mechanical tissue grinding using a buffer containing potassium ethyl xanthogenate (PEX) to detect viroid RNAs. In the present report, the previous PEX method was improved and simplified. In the simplified PEX (SPEX) method, the process of PEX buffer treatment for plant cell wall disruption is improved to one step of incubation at 80 °C for 10 min, instead of three steps that took more than 26 min at 65 °C in the previous method. Total nucleic acids could be extracted from fresh, frozen, or dried leaves of a cultivar or wild species of tobacco, tomato, citron, hop plants, and pericarps of persimmon fruits by the SPEX method. Several RNA viruses and viroids were successfully detected from the extracted nucleic acids together with an internal mRNA by RT-PCR. The SPEX method may be useful for detecting not only viruses and viroids, but also other plant pathogens.

Measurements and Modeling of Optical-Equivalent Snow Grain Sizes under Arctic Low-Sun Conditions

The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (ropt) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Nonhydrostatic—Aerosols and Reactive Trace gases) simulations. The retrieval methods are based on optical measurements and rely on the ropt-dependent absorption of solar radiation in snow. The measurement data were taken during a three-week campaign that was conducted in the North of Greenland in March/April 2018, such that the retrieval methods and radiation measurements are affected by enhanced uncertainties under these low-Sun conditions. An adjusted airborne retrieval method is applied which uses the albedo at 1700 nm wavelength and combines an atmospheric and snow radiative transfer model to account for the direct-to-global fraction of the solar radiation incident on the snow. From this approach, we achieved a significantly improved uncertainty (<25%) and a reduced effect of atmospheric masking compared to the previous method. Ground-based in situ measurements indicated an increase of ropt of 15 µm within a five-day period after a snowfall event which is small compared to previous observations under similar temperature regimes. ICON-ART captured the observed change of ropt during snowfall events, but systematically overestimated the subsequent snow grain growth by about 100%. Adjusting the growth rate factor to 0.012 µm2 s−1 minimized the difference between model and observations. Satellite-based and airborne retrieval methods showed higher ropt over sea ice (<300 µm) than over land surfaces (<100 µm) which was reduced by data filtering of surface roughness features. Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals revealed a large spread within a series of subsequent individual overpasses, indicating their limitations in observing the snow grain size evolution in early spring conditions with low Sun.

Water vapor estimation based on one-year data of E-band millimeter-wave link in the northeast of China

The amount of water vapor in the atmosphere is very small, but its content varies greatly in different humidity areas. The change of water vapor will affect the transmission of microwave link signals, and most of the water vapor is concentrated in the lower layer, so the water vapor density can be measured by the change of the near-ground microwave link transmission signal. This study collected one-year data of the E-band millimeter-wave link in Hebei, China, and used a model based on the ITU-R to estimate the water vapor density. An improved method of extracting the water vapor induced attenuation value is also introduced. It has a higher time resolution and the estimation error is lower than the previous method. In addition, this paper conducts the seasonal analysis of water vapor inversion for the first time. The monthly and seasonal evaluation index results show a high correlation between the retrieved water vapor density the actual water vapor density value measured by the local weather station. The correlation value for the whole year is up to 0.95, the root mean square error is as low as 0.35, and the average relative error is as low as 0.05. This research shows that millimeter-wave backhaul link provides high-precision data for the measurement of water vapor density and has a positive effect on future weather forecast research.