Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Decarbonising energy: The developing international activity in hydrogen technologies and fuel cells

Hydrogen technologies and fuel cells offer an alternative and improved solution for a decarbonised energy future.Fuel cells are electrochemical converters;transforming hydrogen (or energy sources containing hydrogen) and oxygen directly into electricity.The hydrogen fuel cell,invented in 1839,permits the generation of electrical energy with high efficiency through a non-combustion,electrochemical process and,importantly,without the emission ofits point of use.Hitherto,despite numerous efforts to exploit the obvious attractions of hydrogen technologies and hydrogen fuel cells,various challenges have been encountered,some of which are reviewed here.Now,however,given the exigent need to urgently seek low-carbon paths for humankind’s energy future,numerous countries are advancing the deployment of hydrogen technologies and hydrogen fuel cells not only for transport,but also as a means of the storage of excess renewable energy from,for example,wind and solar farms.Furthermore,hydrogen is also being blended into the natural gas supplies used in domestic heating and targeted in the decarbonisation of critical,large-scale industrial processes such as steel making.We briefly review specific examples in countries such as Japan,South Korea and the People’s Republic of China,as well as selected examples from Europe and North America in the utilization of hydrogen technologies and hydrogen fuel cells.

Application of 2-D geoelectrical resistivity tomography for subsurface cavity detection in the eastern part of Saudi Arabia

The presence of natural voids and cavities in subsurface karstic limestones causes severe problem for civil engineering and environmental management. The presence of such features hinders the extension of urbanizations particularly in the new metropolitan. The eastern part of Saudi Arabia contains various types of karstic limestone, sinkholes, solution cavities and voids. In this context, geophysical methods particularly electrical resistivity technique is used as a cost-effective solution for investigating subsurface caves, voids, and shallow weathered zones. 2-D electrical resistivity data sets have been acquired along seven profiles in the new urbanization at AI Hassa area. Data processing has been carried out taking into consideration the response of synthetic models, which simulates physical models of the most common karstic features in the area. The results are very useful to determine the extension of shallow weathered zones and to locate different cavities underneath them. The hard limestone bedrock can also be detected and traced along the surveyed profiles.

Determination of gamma-ray parameters for polyethylene glycol of different molecular weights

Mass attenuation coefficient(μ_m) for polyethylene glycol(PEG) of different molecular weights was determined by using NaI(Tl) scintillator and Win Xcom mixture rule at gamma energies of 59.5, 302.9, 356.0, 661.7, 1173.2 and 1332.5 keV. The total atomic, molecular and electronic cross sections, half-value layer, effective atomic and electron numbers, mass energy-absorption coefficients and kerma relative to air are calculated. The energy and compositional dependence of μ_m values, and the related radiation absorption parameters, are evaluated and discussed. The experimental results agree well with the theoretical ones, within an uncertainty of 1% in the effective atomic number for all PEG samples at the designated energies.

Allocation and Migration of Virtual Machines Using Machine Learning

Cloud computing promises the advent of a new era of service boosted by means of virtualization technology.The process of virtualization means creation of virtual infrastructure,devices,servers and computing resources needed to deploy an application smoothly.This extensively practiced technology involves selecting an efficient Virtual Machine(VM)to complete the task by transferring applications from Physical Machines(PM)to VM or from VM to VM.The whole process is very challenging not only in terms of computation but also in terms of energy and memory.This research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data centres.Machine Learning(ML)based Artificial Bee Colony(ABC)is used to rank the VM with respect to the load while considering the energy efficiency as a crucial parameter.The most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy,applications are migrated fromoneVMto another.The simulation analysis is performed inMatlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.

Feasible Multiple Satellite Mission Scenarios Flying in a Constellation for Refinement of the Gravity Field Recovery

Improving the gravity field recovery in terms of error levels and more isotropic noise distribution by adding cross-track and radial information to the satellite observables has been investigated through a number of studies by a variety of satellite constellations, i.e. satellite pairs that orbit the Earth in alternative configurations than the current GRACE (Gravity Recovery And Climate Experiment) gravity mission. This contribution gives for the first time a comparative study considering the recovery of the global gravity field from three constellations flying in satellite pairs in different directions (i.e. along-track, cross-track and radial). The three constellations include: 1) Foursatellite Bender configuration (flying in two pairs) of type along-track observations, 2) Three-satellite GRAPEN (combined GRACE with Pendulum formations) configuration of type cross-alongtrack observations, 3) Four-satellite Cartwheel configuration (flying in two pairs) of type radialalong-track observations. Additionally, a GRACE mission scenario is added as a reference “comparative” mission. The orbits of all satellites are considered to fly with drag-free system, however, realistic white noise has been added to the simulated observations to mimic the error associated with the drag-free measurement. The results are analyzed in the spectral wavelength spectrum of the gravity field up to a spherical harmonics degree of n = 100 and are plotted spatially on earth maps. The results show that the Three-satellite GRAPEN constellation provides, besides its low economically launches, an improved gravity field solution with respect to the Four-satellite Bender and the Four-satellite Cartwheel constellations.

Determining the Basaltic Sequence Using Seismic Reflection and Resistivity Methods

This study was carried out in Harat Rahat (south of Almadinah Almonwarah) using seismic reflection and resistivity methods. The main objectives of this study are to determine the extent of the basaltic layer and to define the subsurface faults and fractures that could affect and control the groundwater movement in the study area. A 2D seismic profile was acquired and the result shows that the subsurface in the study area has a major fault. We obtained a well match when the seismic result was compared with drilled wells. As a complementary tool, the resistivity method was applied in order to detect the groundwater level. The results of the resistivity method showed that six distinct layers have been identified. The interpretation of these six layers show that the first three layers, the fourth layer, the fifth layer and the bottom of the section indicated various subsurface structures and lithologies;various basaltic layers, fractured basalt, weathered basement and fresh basaltic layers, respectively. It is obvious that the eventual success of geophysical surveys depend on the combination with other subsurface data sources in order to produce accurate maps.

Performing High Resolution Seismic Reflection for Mapping Bauxite Layers

The seismic method is able to produce highly accurate images of the Earth's subsurface. Having such detail is not only an important factor in mining, but also in civil engineering. Bauxite exploration attracts both government and industrialists to invest in it because of the high percentage of aluminum present. The economic importance of extracting aluminum from bauxite encouraged us to take this challenge;to image bauxite layers by using a high-resolution seismic reflection method at Al Qassim, Saudi Arabia. Since the subsurface structure of the area is complex, this high-resolution reflection method was carried out along a 2D line with geophone and source interval, with settings at 5 m. The result for the seismic section shows that the depth and thickness of the bauxite layer varied from 20 to 34 m, and 3 to 7 m respectively. In addition, the bauxite layer was sandwiched between clay layers. In order to achieve an even more precise depth than presented by seismic section alone, we tied the drilled wells to the seismic data and we accomplished a well match with an approximation error of 1 - 2 m, which may have been caused by the upper clay layer or by very shallow loose subsurface material. The seismic method thus applied shows the ability to detect significant details within the near surface of the earth, and considers more cost-effective than only drilled wells.

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

In the domain of artificial neural networks,the learning process represents one of the most challenging tasks.Since the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at hand.However,to a very large search space,it is very difficult to find the proper values of connection weights and biases.Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima.Most commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient issue.As an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network training.In thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural networks.In the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm.The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local minima.The proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on average.When mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

结合催化气化过程的高效直接碳固体氧化物燃料电池(英文)

This study explores strategies to develop highly efficient direct carbon fuel cells(DCFCs) by com-bining a solid-oxide fuel cell(SOFC) with a catalyst-aided carbon-gasification process. This system employs Cu/CeO 2 composites as both anodic electrodes and carbon additives in a cell of the type: carbon|Cu-CeO 2/YSZ/Ag|air. The study investigates the impact on in situ carbon-gasification and DCFC performance characteristics of catalyst addition and variation in the carrier gas used(inert He versus reactive CO2). The results indicate that cell performance is significantly improved by infusing the catalyst into the carbon feedstock and by employing CO2 as the carrier gas. At 800 ℃, the maxi-mum power output is enhanced by approximately 40% and 230% for carbon/CO2 and car-bon/catalyst/CO2 systems, respectively, compared with that of the carbon/He configuration. The increase observed when employing the catalyst and CO2 as the carrier gas can be primarily at-tributed to the pronounced effect of the catalyst on carbon-gasification through the re-verse-Boudouard reaction, and the subsequent in situ electro-oxidation of CO at the anode three-phase boundary.

A Vicenary Analysis of SARS-CoV-2 Genomes

Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory syndrome.However,a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health crisis.Therefore,there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic targets.The nucleotide sequences of SARS-CoV-2 are consist of several bases.These bases can be classified into purines and pyrimidines according to their chemical composition.Purines include adenine(A)and guanine(G),while pyrimidines include cytosine(C)and tyrosine(T).There is a need to understand the spatial distribution of these bases on the nucleotide sequence to facilitate the development of antivirals(including neutralizing antibodies)and epitomes necessary for vaccine development.This study aimed to evaluate all the purine and pyrimidine associations within the SARS-CoV-2 genome sequence by measuring mathematical parameters including;Shannon entropy,Hurst exponent,and the nucleotide guanine-cytosine content.The Shannon entropy is used to identify closely associated sequences.Whereas Hurst exponent is used to identifying the auto-correlation of purine-pyrimidine bases even if their organization differs.Different frequency patterns can be used to determine the distribution of all four proteins and the density of each base.The GC-content is used to understand the stability of the DNA.The relevant genome sequences were extracted from the National Center for Biotechnology Information(NCBI)virus database.Furthermore,the phylogenetic properties of the COVID-19 virus were characterized to compare the closeness of the COVID-19 virus with other coronaviruses by evaluating the purine and pyrimidine distribution.

NOx Emission Reduction Using Hydrous Ethanol-Gasoline Blend with Syngas in SI Engine

This paper presents a comparatively experimental study of nitrogen oxide (NOx) emissions from an internal combustion engine fed by gasoline available in the Saudi Arabian market rating octane number (RON 91 and RON 95) with admixtures of syngas with 0% E0, 5% E5 and 10% E10—by volume of pure ethanol—and HE5 and HE10 with water concentrations of 5%, 10%, 30% and 40%—by volume of hydrous ethanol—at stoichio-metric mixtures. An on-board plasma system used to produce syngas through the partial oxidation of gasoline with air in a plasma-assisted fuel reformer. The syngas injected in a gasoline engine with a fuel injection system modified for operation with addition of some amount of syngas. The experimental results demonstrated a significant total reduction in NOx emissions and slightly increased in fuel consumption when mixing gasoline (RON 91 and RON 95), ethanol (E5 and E10) and hydrous ethanol (HE5 and HE10) with syngas. For the use of hydrous ethanol (HE5 and HE10) along with the addition of syngas, for both RON 91 and RON 95, the lowest NOx emissions are found 72% with a water concentration of 40%.

Fabrication-tolerant Fourier transform spectrometer on silicon with broad bandwidth and high resolution

We report an advanced Fourier transform spectrometer(FTS)on silicon with significant improvement compared with our previous demonstration in[Nat.Commun.9,665(2018)].We retrieve a broadband spectrum(7 THz around 193 THz)with 0.11 THz or sub nm resolution,more than 3 times higher than previously demonstrated[Nat.Commun.9,665(2018)].Moreover,it effectively solves the issue of fabrication variation in waveguide width,which is a common issue in silicon photonics.The structure is a balanced Mach–Zehnder interferometer with 10 cm long serpentine waveguides.Quasi-continuous optical path difference between the two arms is induced by changing the effective index of one arm using an integrated heater.The serpentine arms utilize wide multi-mode waveguides at the straight sections to reduce propagation loss and narrow single-mode waveguides at the bending sections to keep the footprint compact and avoid modal crosstalk.The reduction of propagation loss leads to higher spectral efficiency,larger dynamic range,and better signal-to-noise ratio.Also,for the first time to our knowledge,we perform a thorough systematic analysis on how the fabrication variation on the waveguide widths can affect its performance.Additionally,we demonstrate that using wide waveguides efficiently leads to a fabrication-tolerant device.This work could further pave the way towards a mature silicon-based FTS operating with both broad bandwidth(over 60 nm)and high resolution suitable for integration with various mobile platforms.

MXene-GaN van der Waals metal-semiconductor junctions for high performance multiple quantum well photodetectors

A MXene-GaN-MXene based multiple quantum well photodetector was prepared on patterned sapphire substrate by facile drop casting.The use of MXene electrodes improves the responsivity and reduces dark current,compared with traditional Metal-Semiconductor-Metal(MSM)photodetectors using Cr/Au electrodes.Dark current of the device using MXene-GaN van der Waals junctions is reduced by three orders of magnitude and its noise spectral intensity shows distinct improvement compared with the traditional Cr/Au–GaN–Cr/Au MSM photodetector.The improved device performance is attributed to low-defect MXene-GaN van der Waals interfaces.Thanks to the high quality MXene-GaN interfaces,it is possible to verify that the patterned substrate can locally improve both light extraction and photocurrent collection.The measured responsivity and specific detectivity reach as high as 64.6 A/W and 1.93×1012 Jones,respectively,making it a potential candidate for underwater optical detection and communication.The simple fabrication of MXene-GaN-MXene photodetectors spearheaded the way to high performance photodetection by combining the advantages of emerging 2D MXene materials with the conventional III-V materials.

A large-scale gene discovery for the red palm weevil Rhynchophorus ferrugineus （Coleoptera： Curculionidae）

The red palm weevil （RPW; Rhynchophorusferrugineus） is a devastating pest of palms, prevalent in the Middle East as well as many other regions of the world. Here, we report a large-scale de novo complementary DNA （cDNA） sequencing effort that acquired ~5 million reads and assembled them into 26 765 contigs from 12 libraries made from samples of different RPW developmental stages based on the Roche/454 GS FLX platform. We annotated these contigs based on the publically available known insect genes and the Tribolium castaneum genome assembly. We find that over 80% of coding sequences （CDS） from the RPW contigs have high-identity homologs to known proteins with complete CDS. Gene expression analysis shows that the pupa and larval stages have the highest and lowest expression levels, respectively. In addition, we also identified more than 60 000 single nucleotide polymorphisms and 1 200 simple sequence repeat markers. This study provides the first large-scale eDNA dataset for RPW, a much-needed resource for future molecular studies.

Novel spectral-shaping building block:a narrowband Mach-Zehnder interferometer

We present an experimental demonstration of notch filters with arbitrary center wavelengths capable of tunable analog output power values varying between full extinction of 15 and 0 dB.Fach filter is composed of highly modular apodized four-port Bragg add/drop filters to reduce the crosstalk between concatenated devices.The constructed photonic integrated circuit experimentally demonstrates spectra shaping using four independent notch filters.Fach notch filter supports a bandwidth of^2 nm and is shown to be suitable for realization of programmable photonic integrated circuits.

Perovskite Solar Cells Yielding Reproducible Photovoltage of 1.20 V

High photovoltages and power conversion efciencies of perovskite solar cells(PSCs)can be realized by controlling the undesired nonradiative charge carrier recombination.Here,we introduce a judicious amount of guanidinium iodide into mixed-cation and mixed-halide perovskite flms to suppress the parasitic charge carrier recombination,which enabled the fabrication of>20%efcient and operationally stable PSCs yielding reproducible photovoltageas high as 1.20 V.By introducing guanidinium iodide into the perovskite precursor solution,the bandgap of the resulting absorber material changed minimally;however,the nonradiative recombination diminished considerably as revealed by time-resolved photoluminescence and electroluminescence studies.Furthermore,using capacitance-frequency measurements,we were able to correlate the hysteresis features exhibited by the PSCs with interfacial charge accumulation.Tis study opens up a path to realize new record efciencies for PSCs based on guanidinium iodide doped perovskite flms.

Transcriptomic study of the red palm weevil Rhynchophorus ferrugineus embryogenesis

The red palm weevil （RPW）, Rhynchophorus ferrugineus （Coleoptera： Cur- culionidae）, is an invasive, concealed and destructive tissue borer, and it becomes a lethal pest of the palm family of plants and has been reported to attack 20 palm species around the globe. Here we report a systematic transcriptomic study on embryogenesis of RPW, where we analyze the transcriptomes across five developmental stages of RPW embryogenesis, involving four embryonic stages （El, E2, E3 and E4） and one larval stage （L1）. Using the RNA-seq and next-generation platforms, we generated 80 to 91 million reads for each library and assemble 22 532 genes that are expressed at different embryonic stages. Among the total transcripts from the five embryonic development stages, we found that 30.45 % are differentially expressed, 10.10 % show stage-specificity and even a larger fraction, 62.88 %, exhibit constitutive expression in all the stages. We also analyzes the expression dynamics of several conserved signaling pathways （such as Hedgehog, JAK-STAT, Notch, TGF-β, Ras/MAPK and Writ）, as well as key developmental genes, including those related to apoptosis, axis formation, Hox complex, neurogenesis and segmentation. The datasets provide an essential resource for gene annotation and RPW functional genomics, including studies by using tools and concepts from multiple disciplines, such as development, physiology, biochemistry, molecular biology and genetics.