Chemically Mediated Interactions between Grapevine, Aphid, Ladybird, and Ant in the Context of Insect Chemical Ecology

This study simplifies the complex relationship among grapevine plants,aphids,ladybirds,and ants,which is essential for effective pest management and ecological balance.This study investigated the impact of aphid attacks and the presence of ants and ladybirds on the volatile compounds profile released into the chemosphere of the community consisting of the common vine Vitis vinifera,the aphid Aphis illinoisensis,the ladybird Cocci-nella undecimpunctata-and the ant Tapinoma magnum.This study aims to analyze the volatile compounds emitted by the grapevine and surrounding insects in response to these intricate interactions.The extraction of volatile organic compounds(VOCs)was carried out using closed-loop stripping(CLS)and then analyzed via gas chromatography-mass spectrometry(GC-MS)and principles coordinated analysis(PCA)was performed.The grapevine was exposed to different types and order of treatments,including non-infested,aphid-infested,aphid-infested with ant,aphid-infested with ladybird,and various combinations of ant and ladybird.After the aphid attack,the outcomes uncovered massive alterations in the volatile compound profiles.Infested grapevine displayed distinct emissions of germacrene D,an alcohol,and an alkene compared to non-infested plants.The characteristic VOC profile was the share of infested grapes in the presence of ants,with benzene derivatives and sesquiterpenes dominating the components.The coexistence of ladybirds with ants and aphids resulted in a dif-ferent volatile profile characterized by elevated levels of aldehydes,ketones,α-farnesene,and its hydroxy deriva-tive.It was concluded that the emission of VOCs into the chemosphere of the grapevine communities varied qualitatively and quantitatively depending on the level of the relationship complexity within each community in response to the infestation of grapevines by aphids,the presence of ladybirds as natural predators,and the presence of ant as protector.The grapevine’s status-dependent compounds can serve as indicators of infestation status and contribute to non-destructive early-stage diagnosis of the aphid.

Psoriasis treatment: Unconventional and non-standard modalities in the era of biologics

Psoriasis is a potentially debilitating inflammatory dermatosis affecting 0.2%-4.8% of the population worldwide causing a significant occupational, personal or psychosocial morbidity to these patients for life. The basic aim of psoriasis therapy is to control the disease to maximum possible extent and improve the patient's quality of life. Management of triggers for flareups, lifestyle modifications, and dietary supplements are often recommended. Intermittent or rotational therapy with frequent alterations in treatment options is usually needed to reduce toxicity of anti-psoriatic drugs in the absence of safer alternatives. Currently, several biological agents categorized as either T-cell targeted(e.g., Alefacept, Efalizumab) or cytokine modulating(e.g., Adalimumab, Infliximab, Etanercept) are available for treating severe psoriasis. However, their high cost is often precluding for most patients. The usefulness of systemic(methotrexate, cyclosporine, acitretin or several other therapeutic agents) or topical(tar, anthralin, corticosteroids or calcipotriol ointments, phototherapy with or without psoralens) therapies has been well established for the management of psoriasis. The literature is also replete with benefits of less used non-standard and unconventional treatment modalities(hydroxycarbamide, azathioprine, leflunomide, mycophenolate mofetil, isotretinoin, fumarates, topical calcineurin inhibitors, peroxisome proliferator-activated receptors agonists, statins, sulfasalazine, pentoxifylline, colchicine, grenz ray therapy, excimer laser, climatotherapy and balneophototherapy, peritoneal dialysis, tonsillectomy, ichthyotherapy, etc.). These can be used alternatively to treat psoriasis patients who have mild/minimal lesions, are intolerant to conventional drugs, have developed side effects or achieved recommended cumulative dose, where comorbidities pose unusual therapeutic challenges, or may be as intermittent, rotational or combination treatment alternatives.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry.There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch.In this study,a data-driven model of the uncertainty in the annual carbon price was created.Subsequently,a collaborative,robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation.The model is solved using the column-and-constraint generation algorithm.An operation and cost model of a carbon-capture power plant(CCPP)that couples the carbon market and the economic operation of the power system is also established.The critical,profitable conditions for the economic operation of the CCPP were derived.Case studies demonstrated that the proposed low-carbon,robust dispatch model reduced carbon emissions by 2.67%compared with the traditional,economic,dispatch method.The total fuel cost of generation decreases with decreasing,conservative,carbon-price-uncertainty levels,while total carbon emissions continue to increase.When the carbon-quota coefficient decreases,the system dispatch tends to increase low-carbon unit output.This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

Polar codes based OFDM-PLC systems in the presence of impulsive noise

Power line communication(PLC)has the potential to become the preferred technique for providing broadband communication to homes and offices with advantage of eliminating the need for new wiring infrastructure and reducing the cost.But it suffers from the impulsive noise because it introduces significant time variance into the power line channel.In this paper,a polar codes based orthogonal frequency division multiplexing(OFDM)PLC system is proposed to deal with the impulsive noise and thereby improve the transmission performance.Firstly,the impulsive noise is modelled with a multi-damped sine function by analyzing the time behavior of impulse events.Then the polar codes are used to combat the impulsive noise of PLC channel,and a low complexity bit-flipping decoding method based on CRC-aided successive cancellation list(CA-SCL)decoding algorithm is proposed.Simulations evaluate the proposed decoding algorithm and the results validate the suggested polar codes based OFDM-PLC scheme which can improve the BER performance of PLC with impulsive interference.

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.

High-Secured Image LSB Steganography Using AVL-Tree with Random RGB Channel Substitution

Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data.This property makes it difficult for steganalysts’powerful data extraction tools to detect the hidden data and ensures high-quality stego image generation.However,using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical equations.In addition,these numbers may cluster in certain ranges.The hidden data in these clustered pixels will reduce the image quality,which steganalysis tools can detect.Therefore,this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego image.This paper employs the AdelsonVelsky and Landis(AVL)tree data structure algorithm to implement the randomization pixel selection technique for data concealment.The AVL tree algorithm provides several advantages for image steganography.Firstly,it ensures balanced tree structures,which leads to efficient data retrieval and insertion operations.Secondly,the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels,thereby preserving the stego image quality.The data structure employs the pixel indicator technique for Red,Green,and Blue(RGB)channel extraction.The green channel serves as the foundation for building a balanced binary tree.First,the sender identifies the colored cover image and secret data.The sender will use the two least significant bits(2-LSB)of RGB channels to conceal the data’s size and associated information.The next step is to create a balanced binary tree based on the green channel.Utilizing the channel pixel indicator on the LSB of the green channel,we can conceal bits in the 2-LSB of the red or blue channel.The first four levels of the data structure tree will mask the data size,while subsequent levels will conceal the remaining digits of secret data.After embedding the bits in the binary tree level by level,the model restores the AVL tree to create the stego image.Ultimately,the receiver receives this stego image through the public channel,enabling secret data recovery without stego or crypto keys.This method ensures that the stego image appears unsuspicious to potential attackers.Without an extraction algorithm,a third party cannot extract the original secret information from an intercepted stego image.Experimental results showed high levels of imperceptibility and security.

Mechanisms underlying the role of endoplasmic reticulum stress in the placental injury and fetal growth restriction in an ovine gestation model

Background Exposure to bisphenol A(BPA),an environmental pollutant known for its endocrine-disrupting properties,during gestation has been reported to increase the risk of fetal growth restriction(FGR)in an ovine model of pregnancy.We hypothesized that the FGR results from the BPA-induced insufficiency and barrier dysfunction of the placenta,oxidative stress,inflammatory responses,autophagy and endoplasmic reticulum stress(ERS).However,precise mechanisms underlying the BPA-induced placental dysfunction,and subsequently,FGR,as well as the potential involvement of placental ERS in these complications,remain to be investigated.Methods In vivo experiment,16 twin-pregnant(from d 40 to 130 of gestation)Hu ewes were randomly distributed into two groups(8 ewes each).One group served as a control and received corn oil once a day,whereas the other group received BPA(5 mg/kg/d as a subcutaneous injection).In vitro study,ovine trophoblast cells(OTCs)were exposed to 4 treatments,6 replicates each.The OTCs were treated with 400μmol/L BPA,400μmol/L BPA+0.5μg/m L tunicamycin(Tm;ERS activator),400μmol/L BPA+1μmol/L 4-phenyl butyric acid(4-PBA;ERS antagonist)and DMEM/F12 complete medium(control),for 24 h.Results In vivo experiments,pregnant Hu ewes receiving the BPA from 40 to 130 days of pregnancy experienced a decrease in placental efficiency,progesterone(P4)level and fetal weight,and an increase in placental estrogen(E2)level,together with barrier dysfunctions,OS,inflammatory responses,autophagy and ERS in type A cotyledons.In vitro experiment,the OTCs exposed to BPA for 24 h showed an increase in the E2 level and related protein and gene expressions of autophagy,ERS,pro-apoptosis and inflammatory response,and a decrease in the P4 level and the related protein and gene expressions of antioxidant,anti-apoptosis and barrier function.Moreover,treating the OTCs with Tm aggravated BPA-induced dysfunction of barrier and endocrine(the increased E2 level and decreased P4 level),OS,inflammatory responses,autophagy,and ERS.However,treating the OTCs with 4-PBA reversed the counteracted effects of Tm mentioned above.Conclusions In general,the results reveal that BPA exposure can cause ERS in the ovine placenta and OTCs,and ERS induction might aggravate BPA-induced dysfunction of the placental barrier and endocrine,OS,inflammatory responses,and autophagy.These data offer novel mechanistic insights into whether ERS is involved in BPA-mediated placental dysfunction and fetal development.

Computational Investigation of Brownian Motion and Thermophoresis Effect on Blood-Based Casson Nanofluid on a Non-linearly Stretching Sheet with Ohmic and Viscous Dissipation Effects

Motivated by the widespread applications of nanofluids,a nanofluid model is proposed which focuses on uniform magnetohydrodynamic(MHD)boundary layer flow over a non-linear stretching sheet,incorporating the Casson model for blood-based nanofluid while accounting for viscous and Ohmic dissipation effects under the cases of Constant Surface Temperature(CST)and Prescribed Surface Temperature(PST).The study employs a twophase model for the nanofluid,coupled with thermophoresis and Brownian motion,to analyze the effects of key fluid parameters such as thermophoresis,Brownian motion,slip velocity,Schmidt number,Eckert number,magnetic parameter,and non-linear stretching parameter on the velocity,concentration,and temperature profiles of the nanofluid.The proposed model is novel as it simultaneously considers the impact of thermophoresis and Brownian motion,along with Ohmic and viscous dissipation effects,in both CST and PST scenarios for blood-based Casson nanofluid.The numerical technique built into MATLAB’s bvp4c module is utilized to solve the governing system of coupled differential equations,revealing that the concentration of nanoparticles decreases with increasing thermophoresis and Brownian motion parameters while the temperature of the nanofluid increases.Additionally,a higher Eckert number is found to reduce the nanofluid temperature.A comparative analysis between CST and PST scenarios is also undertaken,which highlights the significant influence of these factors on the fluid’s characteristics.The findings have potential applications in biomedical processes to enhance fluid velocity and heat transfer rates,ultimately improving patient outcomes.

In silico prospective analysis of the medicinal plants activity on the CagA oncoprotein from Helicobacter pylori

BACKGROUND Colonization with Helicobacter pylori(H.pylori)has a strong correlation with gastric cancer,and the virulence factor CagA is implicated in carcinogenesis.Studies have been conducted using medicinal plants with the aim of eliminating the pathogen;however,the possibility of blocking H.pylori-induced cell differentiation to prevent the onset and/or progression of tumors has not been addressed.This type of study is expensive and time-consuming,requiring in vitro and/or in vivo tests,which can be solved using bioinformatics.Therefore,prospective computational analyses were conducted to assess the feasibility of interaction between phenolic compounds from medicinal plants and the CagA oncoprotein.AIM To perform a computational prospecting of the interactions between phenolic compounds from medicinal plants and the CagA oncoprotein of H.pylori.METHODS In this in silico study,the structures of the phenolic compounds(ligands)kaempferol,myricetin,quercetin,ponciretin(flavonoids),and chlorogenic acid(phenolic acid)were selected from the PubChem database.These phenolic compounds were chosen based on previous studies that suggested medicinal plants as non-drug treatments to eliminate H.pylori infection.The three-dimensional structure model of the CagA oncoprotein of H.pylori(receptor)was obtained through molecular modeling using computational tools from the I-Tasser platform,employing the threading methodology.The primary sequence of CagA was sourced from GenBank(BAK52797.1).A screening was conducted to identify binding sites in the structure of the CagA oncoprotein that could potentially interact with the ligands,utilizing the GRaSP online platform.Both the ligands and receptor were prepared for molecular docking using AutoDock Tools 4(ADT)software,and the simulations were carried out using a combination of ADT and AutoDock Vina v.1.2.0 software.Two sets of simulations were performed:One involving the central region of CagA with phenolic compounds,and another involving the carboxy-terminus region of CagA with phenolic compounds.The receptor-ligand complexes were then analyzed using PyMol and BIOVIA Discovery Studio software.RESULTS The structure model obtained for the CagA oncoprotein exhibited high quality(C-score=0.09)and was validated using parameters from the MolProbity platform.The GRaSP online platform identified 24 residues(phenylalanine and leucine)as potential binding sites on the CagA oncoprotein.Molecular docking simulations were conducted with the three-dimensional model of the CagA oncoprotein.No complexes were observed in the simulations between the carboxy-terminus region of CagA and the phenolic compounds;however,all phenolic compounds interacted with the central region of the oncoprotein.Phenolic compounds and CagA exhibited significant affinity energy(-7.9 to-9.1 kcal/mol):CagA/kaempferol formed 28 chemical bonds,CagA/myricetin formed 18 chemical bonds,CagA/quercetin formed 16 chemical bonds,CagA/ponciretin formed 13 chemical bonds,and CagA/chlorogenic acid formed 17 chemical bonds.Although none of the phenolic compounds directly bound to the amino acid residues of the K-Xn-R-X-R membrane binding motif,all of them bound to residues,mostly positively or negatively charged,located near this region.CONCLUSION In silico,the tested phenolic compounds formed stable complexes with CagA.Therefore,they could be tested in vitro and/or in vivo to validate the findings,and to assess interference in CagA/cellular target interactions and in the oncogenic differentiation of gastric cells.

Performance Comparison of Hyper-V and KVM for Cryptographic Tasks in Cloud Computing

As the extensive use of cloud computing raises questions about the security of any personal data stored there,cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment.A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware.The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment.An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance;Each hypervisor should be examined to meet specific needs.The main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based Virtual Machine(KVM)while implementing different cryptographic algorithms to guide cloud service providers and end users in choosing the most suitable hypervisor for their cryptographic needs.This study evaluated the efficiency of two hypervisors,Hyper-V and KVM,in implementing six cryptographic algorithms:Rivest,Shamir,Adleman(RSA),Advanced Encryption Standard(AES),Triple Data Encryption Standard(TripleDES),Carlisle Adams and Stafford Tavares(CAST-128),BLOWFISH,and TwoFish.The study’s findings show that KVM outperforms Hyper-V,with 12.2%less Central Processing Unit(CPU)use and 12.95%less time overall for encryption and decryption operations with various file sizes.The study’s findings emphasize how crucial it is to pick a hypervisor that is appropriate for cryptographic needs in a cloud environment,which could assist both cloud service providers and end users.Future research may focus more on how various hypervisors perform while handling cryptographic workloads.

Targeted anti-tumor synergistic effects of Myc decoy oligodeoxynucleotides-loaded selenium nanostructure combined with chemoradiotherapy on LNCaP prostate cancer cells

In the present study,we investigated the synergistic effects of targeted methotrexate-selenium nanostructure containing Myc decoy oligodeoxynucleotides along with X-irradiation exposure as a combination therapy on LNCaP prostate cancer cells.Myc decoy ODNs were designed based on the promoter of Bcl-2 gene and analyzed by molecular docking and molecular dynamics assays.ODNs were loaded on the synthesized Se@BSA@Chi-MTX nanostructure.The physicochemical characteristics of nanostructures were determined by FTIR,DLS,UV-vis,TEM,EDX,in vitro release,and hemolysis tests.Subsequently,the cytotoxicity properties of them with and without X-irradiation were investigated by uptake,MTT,cell cycle,apoptosis,and scratch assays on the LNCaP cell line.The results of DLS and TEM showed negative charge(−9 mV)and nanometer size(40 nm)for Se@BSA@Chi-DEC-MTX NPs,respectively.The results of FTIR,UV-vis,and EDX showed the proper interaction of different parts and the correct synthesis of nanoparticles.The results of hemolysis showed the hemocompatibility of this nanoparticle in concentrations less than 6 mg/mL.The ODNs release from the nanostructures showed a pH-dependent manner,and the release rate was 15%higher in acidic pH.The targeted Se@BSA@Chi-labeled ODN-MTX NPs were efficiently taken up by LNCaP cells by targeting the prostate-specific membrane antigen(PSMA).The significant synergistic effects of nanostructure(containing MTX drug)treatment along with X-irradiation showed cell growth inhibition,apoptosis induction(~57%),cell cycle arrest(G2/M phase),and migration inhibition(up to 90%)compared to the control.The results suggested that the Se@BSA@Chi-DEC-MTX NPs can potentially suppress the cell growth of LNCaP cells.This nanostructure system can be a promising approach for targeted drug delivery and chemoradiotherapy in prostate cancer treatment.

Stepped-up development of accelerator mass spectrometry method for the detection of ^(60)Fe with the HI-13 tandem accelerator

The Moon provides a unique environment for investigating nearby astrophysical events such as supernovae.Lunar samples retain valuable information from these events,via detectable long-lived“fingerprint”radionuclides such as^(60)Fe.In this work,we stepped up the development of an accelerator mass spectrometry(AMS)method for detecting^(60)Fe using the HI-13tandem accelerator at the China Institute of Atomic Energy(CIAE).Since interferences could not be sufficiently removed solely with the existing magnetic systems of the tandem accelerator and the following Q3D magnetic spectrograph,a Wien filter with a maximum voltage of±60 kV and a maximum magnetic field of 0.3 T was installed after the accelerator magnetic systems to lower the detection background for the low abundance nuclide^(60)Fe.A 1μm thick Si_(3)N_(4) foil was installed in front of the Q3D as an energy degrader.For particle detection,a multi-anode gas ionization chamber was mounted at the center of the focal plane of the spectrograph.Finally,an^(60)Fe sample with an abundance of 1.125×10^(-10)was used to test the new AMS system.These results indicate that^(60)Fe can be clearly distinguished from the isobar^(60)Ni.The sensitivity was assessed to be better than 4.3×10^(-14)based on blank sample measurements lasting 5.8 h,and the sensitivity could,in principle,be expected to be approximately 2.5×10^(-15)when the data were accumulated for 100 h,which is feasible for future lunar sample measurements because the main contaminants were sufficiently separated.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Prevalence of visual impairment and estimation of refractive errors among school children in Kakamega,Kenya

AIM:To investigate the prevalence of visual impairment(VI)and provide an estimation of uncorrected refractive errors in school-aged children,conducted by optometry students as a community service.METHODS:The study was cross-sectional.Totally 3343 participants were included in the study.The initial examination involved assessing the uncorrected distance visual acuity(UDVA)and visual acuity(VA)while using a+2.00 D lens.The inclusion criteria for a subsequent comprehensive cycloplegic eye examination,performed by an optometrist,were as follows:a UDVA<0.6 decimal(0.20 logMAR)and/or a VA with+2.00 D≥0.8 decimal(0.96 logMAR).RESULTS:The sample had a mean age of 10.92±2.13y(range 4 to 17y),and 51.3%of the children were female(n=1715).The majority of the children(89.7%)fell within the age range of 8 to 14y.Among the ethnic groups,the highest representation was from the Luhya group(60.6%)followed by Luo(20.4%).Mean logMAR UDVA choosing the best eye for each student was 0.29±0.17(range 1.70 to 0.22).Out of the total,246 participants(7.4%)had a full eye examination.The estimated prevalence of myopia(defined as spherical equivalent≤-0.5 D)was found to be 1.45%of the total sample.While around 0.18%of the total sample had hyperopia value exceeding+1.75 D.Refractive astigmatism(cil<-0.75 D)was found in 0.21%(7/3343)of the children.The VI prevalence was 1.26%of the total sample.Among our cases of VI,76.2%could be attributed to uncorrected refractive error.Amblyopia was detected in 0.66%(22/3343)of the screened children.There was no statistically significant correlation observed between age or gender and refractive values.CONCLUSION:The primary cause of VI is determined to be uncorrected refractive errors,with myopia being the most prevalent refractive error observed.These findings underscore the significance of early identification and correction of refractive errors in school-aged children as a means to alleviate the impact of VI.

Robust optimal dispatch strategy of integrated energy system considering CHP-P2G-CCS

Integrated energy systems(IESs)can improve energy efficiency and reduce carbon emissions,essential for achieving peak carbon emissions and carbon neutrality.This study investigated the characteristics of the CHP model considering P2G and carbon capture systems,and a two-stage robust optimization model of the electricity-heat-gascold integrated energy system was developed.First,a CHP model considering the P2G and carbon capture system was established,and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived,which proved that the model could weaken the electric-thermal coupling characteristics,increase the electric power regulation range,and reduce carbon emissions.Subsequently,a two-stage robust optimal scheduling model of an IES was constructed,in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs.The objective function in the real-time scheduling stage was to minimize the equipment operating costs,carbon emission costs,wind curtailment,and solar curtailment costs,considering multiple uncertainties.Finally,after the objective function is linearized with a ψ-piecewise method,the model is solved based on the C&CG algorithm.Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.

Optimal operation of Internet Data Center with PV and energy storage type of UPS clusters

With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

Primary frequency control considering communication delay for grid-connected offshore wind power systems

Offshore wind farms are becoming increasingly distant from onshore centralized control centers,and the communication delays between them inevitably introduce time delays in the measurement signal of the primary frequency control.This causes a deterioration in the performance of the primary frequency control and,in some cases,may even result in frequency instability within the power system.Therefore,a frequency response model that incorporates communication delays was established for power systems that integrate offshore wind power.The Padéapproximation was used to model the time delays,and a linearized frequency response model of the power system was derived to investigate the frequency stability under different time delays.The influences of the wind power proportion and frequency control parameters on the system frequency stability were explored.In addition,a Smith delay compensation control strategy was devised to mitigate the effects of communication delays on the system frequency dynamics.Finally,a power system incorporating offshore wind power was constructed using the MATLAB/Simulink platform.The simulation results demonstrate the effectiveness and robustness of the proposed delay compensation control strategy.

Nanocomposite superstructure of zinc oxide mesocrystal/reduced graphene oxide with effective photoconductivity

Metal oxide mesocrystals are the alignment of metal oxide nanoparticles building blocks into the ordered superstructure,which have potentially tunable optical,electronic,and electrical properties suitable for practical applications.Herein,we report an effective method for synthesizing mesocrystal zinc oxide nanorods(ZnONRs).The crystal,surface,and internal structures of the zinc oxide mesocrystals were fully characterized.Mesocrystal zinc oxide nanorods/reduced graphene oxide(ZnONRs/rGO)nanocomposite superstructure were synthesized also using the hydrothermal method.The crystal,surface,chemical,and internal structures of the ZnONRs/rGO nanocomposite superstructure were also fully characterized.The optical absorption coefficient,bandgap energy,band structure,and electrical conductivity of the ZnONRs/rGO nanocomposite superstructure were investigated to understand its optoelectronic and electrical properties.Finally,the photoconductivity of the ZnONRs/rGO nanocomposite superstructure was explored to find the possibilities of using this nanocomposite superstructure for ultraviolet(UV)photodetection applications.Finally,we concluded that the ZnONRs/rGO nanocomposite superstructure has high UV sensitivity and is suitable for UV detector applications.