Hybridized S cathode with N719 dye for a photo-assisted charging Li-S battery

Nowadays,huge consumption of fossil fuels brings about serious energy crisis and environmental problems,which urge researchers to explore novel sustainable energy sources and storage systems[1,2].

Prediction of the Best Three-Breed Hybridized Combination of Imported Meat Sheep Using Genetic Polymorphism of Microsatellite DNA

The allelic frequency, the polymorphic information contents （PIC）, the number of effective alleles, the heterozygosity, and the genetic distances were studied in three imported meat sheep （Suffolk, Dorset, Texel） and their F1 crossbred obtained from those crossed with indigenous Small Tail Hun Sheep （Suffolk♂× Small Tail Hun Sheep, SH; Dorset ♂× Small Tail Han Sheep♂, DH; Texel♂× Small Tail Hart Sheep ♀, TH） using six microsatellite DNA loci. The perpormences of three-breed crossbred （Suffolk ♂× DH ♀, Suffolk ♂× TH ♀, Texel ♂× SH ♀, Texel ♂× DH ♀, Dorset ♂× TH ♀, and Dorset ♂× SH ♀ ） were tested. The results indicated that there were genetic polymorphisms at six microsatellite loci in six sheep populations. Six microsatellite loci could be used for genetic diversity evaluation in sheep populations. The order of three-breed heterosis by the analysis of genetic relationship from large to small was Texel ♂× DH ♀, Suffolk ♂× DH ♀, Suffolki ♂× TH ♀, Texel ♂× SH ♀, Dorset ♂×TH ♀, and Dorset ♂× SH ♀, which was in accordance with the testing results on actual heterosis. These results showed that prediction of the best three-breed hybridized combination among sheep breeds by microsatellite DNA polymorphism was feasible, which will have an important value on the reasonable utilization of introduced meat sheep and sheep breeding in our country in the future.

On the Development of a Hybridized Ant Colony Optimization (HACO) Algorithm

This paper proposes a Hybridized Ant Colony Optimization (HACO) algorithm. It integrates the advantages of Ant System (AS) and Ant Colony System (ACS) of solving optimization problems. The main focus and core of the HACO algorithm are based on annexing the strengths of the AS, ACO and the Max-Min Ant System (MMAS) previously proposed by various researchers at one time or the order. In this paper, the HACO algorithm for solving optimization problems employs new Transition Probability relations with a Jump transition probability relation which indicates the point or path at which the desired optimum value has been met. Also, it brings to play a new pheromone updating rule and introduces the pheromone evaporation residue that calculates the amount of pheromone left after updating which serves as a guide to the successive ant traversing the path and diverse local search approaches. Regarding the computational efficiency of the HACO algorithm, we observe that the HACO algorithm can find very good solutions in a short time, as the algorithm has been tested on a number of combinatorial optimization problems and results shown to compare favourably with analytical results. This strength can be combined with other metaheuristic approaches in the future work to solve complex combinatorial optimization problems.

Simulation of Flux Distribution in Central Metabolism of Saccharo-myces cerevisiae by Hybridized Genetic Algorithm

A scheme of investigating the intracellular metabolic fluxes in central metabolism of Saccharomyces cerevisiae based on isotope model and tracer experiment was developed. The metabolic model applied in this study includes the Embden-Meyerhof-Parnas pathway,the pentose phosphate pathway,the tricarboxylic acid cycle,CO2 anaplerotic reactions,ethanol and acetate formation,and pathways involved in amino acid synthesis. The approach of hybridized genetic algorithm combined with the sequential simplex technique was used to optimize a quadratic error function without the requirement of the information on the partial derivatives. The impact of some key pa-rameters on the algorithm was studied. This approach was proved to be rapid and numerically stable in the analysis of the central metabolism of S.cerevisiae.

A Stable Numerical Scheme Based on the Hybridized Discontinuous Galerkin Method for the Ito-Type Coupled KdV System

The purpose of this paper is to develop a hybridized discontinuous Galerkin(HDG)method for solving the Ito-type coupled KdV system.In fact,we use the HDG method for discre-tizing the space variable and the backward Euler explicit method for the time variable.To linearize the system,the time-lagging approach is also applied.The numerical stability of the method in the sense of the L2 norm is proved using the energy method under certain assumptions on the stabilization parameters for periodic or homogeneous Dirichlet bound-ary conditions.Numerical experiments confirm that the HDG method is capable of solving the system efficiently.It is observed that the best possible rate of convergence is achieved by the HDG method.Also,it is being illustrated numerically that the corresponding con-servation laws are satisfied for the approximate solutions of the Ito-type coupled KdV sys-tem.Thanks to the numerical experiments,it is verified that the HDG method could be more efficient than the LDG method for solving some Ito-type coupled KdV systems by comparing the corresponding computational costs and orders of convergence.

Bifunctional polyaniline electrode tailored hybridized solar cells for energy harvesting from sun and rain

Pursuit of energy-harvesting or-storage materials to realize outstanding electricity output from nature has been regarded as a promising strategy to resolve the energy-lack issue in the future. Among them,the solar cell as a solar-to-electrical conversion device has been attracted enormous interest to improve the efficiency. However, the ability to generate electricity is highly dependent on the weather conditions,in other words, there is nearly zero power output in dark-light conditions, such as rainy, cloudy, and night, lowering the monolithic power generation capacity. Here, we present a bifunctional polyaniline film via chemical bath deposition, which can harvest energy from the rain, yielding an induced current of 2.57 μA and voltage of 65.5 μV under the stimulus of real raindrop. When incorporating the functional PANi film into the traditional dye sensitized solar cell as a counter electrode, the hybridized photovoltaic can experimentally realize the enhanced output power via harvesting energy from rainy and sunny days. The current work may show a new path for development of advanced solar cells in the future.

Tensile properties of strain-hardening cementitious composites containing polyvinyl-alcohol fibers hybridized with polypropylene fibers

Partially replacing polyvinyl-alcohol(PVA)fibers with polypropylene(PP)fibers in strain-hardening cementitious composites(fiber hybridization)modify certain mechanical properties of these materials.The hybridization based on the introduction of low-modulus hydrophobic polypropylene fibers improves the ductility and the strain-hardening behavior of the cementitious composites containing polyvinyl-alcohol fibers of different types(PVA-SHCC).Pull-out tests indicate that adding PP fibers increases the energy capacity of the hybrid composite with respect to the material containing only PVA fibers under tensile loading,and PP-fiber geometry(i.e.,section shape and length)is a key factor in enhancing the strain capacity.

Nonlinear constrained optimization using the flexible tolerance method hybridized with different unconstrained methods

This paper proposes the use of the flexible tolerance method(FTM) modified with scaling of variables and hybridized with different unconstrained optimization methods to solve real constrained optimization problems.The benchmark problems used to analyze the performance of the methods were taken from G-Suite functions.The original method(FTM) and other four proposed methods:(i) FTM with scaling of variables(FTMS),(ii) FTMS hybridized with BFGS(FTMS-BFGS),(iii) FTMS hybridized with modified Powell's method(FTMS-Powell)and(iv) FTMS hybridized with PSO(FTMS-PSO), were implemented. The success rates of the methods were 80%,100%, 75%, 95% and 85%, for FTM, FTMS, FTMS-BFGS, FTMS-Powell and FTMS-PSO, respectively. Numerical experiments including real constrained problems indicated that FTMS gave the best performance, followed by FTMSPowell and FTMS-PSO. Despite the inferior performance compared to FTMS and FTMS-Powell, the FTMS-PSO method presented some advantages since good different initial points could be obtained, which allow exploring different routes through the solution space and to escape from local optima. The proposed methods proved to be an effective way of improving the performance of the original FTM.

Self-powered wireless environmental monitoring system for in-service bridges by galloping piezoelectric-triboelectric hybridized energy harvester

The energy harvesting technology for the ubiquitous natural wind enables a desirable solution to the issue of distributed sensors in the bridge environmental sensing Internet of Things(Io T)system being restricted to conventional energy supply.In this work,a self-powered system based on a compact galloping piezoelectric-triboelectric energy harvester(GPTEH)is developed to achieve efficient wind energy harvesting.The GPTEH is constructed on the prototype of a cantilever structure with piezoelectric macro-fiber composite(MFC)sheets and a rectangular bluff body with triboelectric nanogenerators(TENGs).Through a special swing-type structural design with iron blocks inside the bluff body,the GPTEH exhibits preferable aerodynamic behavior and excellent energy conversion efficiency,compared to conventional cantilever kind of piezoelectric wind energy harvester(PWEH).The GPTEH also demonstrates the capability of high output power density(PEH of 23.65 W m^(-2)and TENG of 1.59 W m^(-2)),superior response wind speed(about 0.5 m s^(-1)),and excellent long-term stability(over 14000 cyclic tests).Furthermore,a power management system is developed to efficiently utilize the output energy from GPTEH to power the sensors and wirelessly transmit environmental data to the terminals.The proposed GPTEH-powered system exhibits a great potential for the bridge environmental monitoring and Io T technologies.

Trojan nanobacteria hybridized with prodrug nanocapsules for efficient combined tumor therapy

Live bacteria-based drug delivery systems have been raised as promising tools for enhancing drug delivery into tumors due to their active tumor targeting and easy surface modifiability.In this work,a“Trojan nanobacteria hybrid”,E.coli@highly integrated nanocapsules(HINCs)hybrid(HINE-Hybrid),was successfully constructed with HINCs of prodrug based on covalent selfassembly and the facultative anaerobic bacterium E.coli MG 1655 for combined chemotherapy,photothermal therapy(PTT),and chemodynamic therapy(CDT).HINCs were constructed by covalent cross-linking of pillar[5]arene derivatives and cisplatin prodrug linker,which can be endocytosed and lysed to release therapeutic agents.Under the near-infrared(NIR)light(at 808 nm)irradiation,the system temperature can be significantly increased by HINCs,which further leads to the highly efficient generation of reactive oxygen species(ROS).In addition,HINE-Hybrid shows significant antitumor effects in in vitro and in vivo studies and also promotes immune cell infiltration and antitumor cytokine expression in the tumor microenvironment(TME).HINEHybrid exerts its anticancer properties efficiently due to selective enrichment and multiplication of E.coli at tumor sites,which is important for the construction of bacterial-assisted antitumor platforms.

颈椎前路Hybrid手术和颈椎后路单开门椎管扩大成形术治疗多节段脊髓型颈椎病临床疗效分析

目的:分析颈椎前路Hybrid手术和颈椎后路单开门椎管扩大成形术(EODL)治疗多节段脊髓型颈椎病的疗效,探讨多节段脊髓型颈椎病患者手术方式的选择。方法:对2017年7月—2020年7月在首都医科大学附属北京中医医院手术治疗的70例多节段脊髓型颈椎病患者进行回顾性分析,根据手术方式不同,分为前路组35例和后路组35例,前路组患者行Hybrid手术[颈椎前路椎间盘切除融合术(ACDF)联合人工颈椎间盘置换术(ACDR)],后路组患者行EODL。记录2组患者住院时间、手术时间、术中出血量和术后引流量,通过日本骨科协会(JOA)评分、JOA改善率、颈椎残障功能指数(NDI)、疼痛视觉模拟评分(VAS)和术后满意度评分进行疗效评价,统计2组患者术后并发症发生情况。结果:与后路组比较,前路组患者术中出血量、术后引流量、住院时间和手术时间均明显减少(P<0.01),术前各项评分差异无统计学意义(P>0.05)。末次随访时,与后路组比较,前路组患者JOA评分和JOA改善率明显升高(P<0.01),NDI评分和VAS评分明显降低(P<0.01)。与术前比较,末次随访时2组患者JOA评分明显升高(P<0.01),NDI和VAS评分均明显降低(P<0.01)。按术后满意度评分评价,2组患者术后满意度均较高。2组患者术后并发症发生率比较差异无统计学意义(P>0.05)。结论:颈椎前路Hybrid手术和EODL在治疗多节段脊髓型颈椎病方面均取得了较为满意的疗效。Hybrid手术具有出血量少和手术时间短等优点,临床上应根据患者实际情况选择最适宜的术式。

溅射技术与基体偏压对TiB_(x)涂层结构和力学性能的影响

目的改善TiB_(x)涂层的残余应力,提高其力学性能。方法采用不同的磁控溅射技术(dcMS、HiPMS、Hybrid)和基体偏压制备TiB_(x)涂层。通过电子探针显微分析仪(EPMA)测试TiB_(x)涂层的化学成分,采用扫描电子显微镜(SEM)和X射线衍射仪(XRD)表征涂层的表面形貌、组织结构特征、相组成和择优取向,通过sin^(2)ψ法、纳米压痕仪和洛氏压痕法分别测试TiB_(x)涂层的残余应力、纳米硬度和膜层/基体结合强度。结果溅射技术和基体偏压对TiB_(x)涂层的组织结构和力学性能都有着显著的影响。在基体旋转条件下,不同溅射技术和基体偏压制备的涂层均呈现出TiB_(2)-(001)择优取向。随基体偏压的升高,涂层的残余应力呈现出先增大、后减小的规律。偏压相同时,HiPIMS技术制备的TiB_(x)涂层具有最高的残余应力。随着基体偏压的增加,TiB_(x)涂层的纳米硬度和弹性模量不断增加,在偏压为–130 V时,Hybrid-TiB_(x)涂层具有最高的纳米硬度(40.1GPa)。偏压相同时,dcMS-TiB_(x)和Hybrid-TiB_(x)涂层表现出优异的膜层/基体结合强度(HF1-HF2级),而HiPIMS涂层表现出较差的膜层/基体结合强度(HF1-HF4级)。结论溅射技术和基体偏压对TiB_(x)涂层的微观组织结构和力学性能有着重要的影响,Hybrid模式结合了dcMS模式和HiPMS模式的优点,Hybrid-TiB_(x)涂层呈现出最优的综合力学性能(高的纳米硬度、沉积速率和膜层/基体结合强度)。

Accelerating Oxygen Electrocatalysis Kinetics on Metal-Organic Frameworks via Bond Length Optimization

Metal-organic frameworks(MOFs)have been developed as an ideal platform for exploration of the relationship between intrinsic structure and catalytic activity,but the limited catalytic activity and stability has hampered their practical use in water splitting.Herein,we develop a bond length adjustment strategy for optimizing naphthalene-based MOFs that synthesized by acid etching Co-naphthalenedicarboxylic acid-based MOFs(donated as AE-CoNDA)to serve as efficient catalyst for water splitting.AE-CoNDA exhibits a low overpotential of 260 mV to reach 10 mA cm^(−2)and a small Tafel slope of 62 mV dec^(−1)with excellent stability over 100 h.After integrated AE-CoNDA onto BiVO_(4),photocurrent density of 4.3 mA cm^(−2)is achieved at 1.23 V.Experimental investigations demonstrate that the stretched Co-O bond length was found to optimize the orbitals hybridization of Co 3d and O 2p,which accounts for the fast kinetics and high activity.Theoretical calculations reveal that the stretched Co-O bond length strengthens the adsorption of oxygen-contained intermediates at the Co active sites for highly efficient water splitting.

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

MicroRNA-502-3p regulates GABAergic synapse function in hippocampal neurons

Gamma-aminobutyric acid(GABA)ergic neurons,the most abundant inhibitory neurons in the human brain,have been found to be reduced in many neurological disorders,including Alzheimer's disease and Alzheimer's disease-related dementia.Our previous study identified the upregulation of microRNA-502-3p(miR-502-3p)and downregulation of GABA type A receptor subunitα-1 in Alzheimer's disease synapses.This study investigated a new molecular relationship between miR-502-3p and GABAergic synapse function.In vitro studies were perfo rmed using the mouse hippocampal neuronal cell line HT22 and miR-502-3p agomiRs and antagomiRs.In silico analysis identified multiple binding sites of miR-502-3p at GABA type A receptor subunitα-1 mRNA.Luciferase assay confirmed that miR-502-3p targets the GABA type A receptor subunitα-1 gene and suppresses the luciferase activity.Furthermore,quantitative reve rse transcription-polymerase chain reaction,miRNA in situ hybridization,immunoblotting,and immunostaining analysis confirmed that overexpression of miR-502-3p reduced the GABA type A receptor subunitα-1 level,while suppression of miR-502-3p increased the level of GABA type A receptor subunitα-1 protein.Notably,as a result of the overexpression of miR-502-3p,cell viability was found to be reduced,and the population of necrotic cells was found to be increased.The whole cell patch-clamp analysis of human-GABA receptor A-α1/β3/γ2L human embryonic kidney(HEK)recombinant cell line also showed that overexpression of miR-502-3p reduced the GABA current and overall GABA function,suggesting a negative correlation between miR-502-3p levels and GABAergic synapse function.Additionally,the levels of proteins associated with Alzheimer s disease were high with miR-502-3p overexpression and reduced with miR-502-3p suppression.The present study provides insight into the molecular mechanism of regulation of GABAergic synapses by miR-502-3p.We propose that micro-RNA,in particular miR-502-3p,could be a potential therapeutic to rget to modulate GABAergic synapse function in neurological disorders,including Alzheimer's disease and Alzheimer's diseaserelated dementia.

Hybrid手术对Stanford B型主动脉夹层疗效的研究进展

主动脉夹层主要通过使用假体移植物的开胸手术修复来治疗。在过去的10年里,胸主动脉腔内修复术(TEVAR)已成为一种创伤更小、潜在更安全的治疗方法。然而,单一的微创腔内修复术在缺乏足够的锚定区的Stanford B型主动脉夹层(TBAD)患者上有其局限性。因此,结合了外科手术及胸主动脉腔内修复术的Hy‐brid手术(或称为杂交手术),通过外科手段重建左颈总动脉和左锁骨下动脉的弓上分流术,再结合微创腔内修复术,显著减少患者在外科手术中并发症的发生率,且提高了那些常规胸主动脉腔内修复术缺乏理想锚定区患者的生存率。

Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability

In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.

Dual-ion carrier storage through Mg^(2+) addition for high-energy and long-life zinc-ion hybrid capacitor

Cation additives can efficiently enhance the total electrochemical capabilities of zinc-ion hybrid capacitors (ZHCs).However their energy storage mechanisms in zinc-based systems are still under debate.Herein,we modulate the electrolyte and achieve dual-ion storage by adding magnesium ions.And we assemble several Zn//activated carbon devices with different electrolyte concentrations and investigate their electrochemical reaction dynamic behaviors.The zinc-ion capacitor with Mg^(2+)mixed solution delivers 82 mAh·g^(-1)capacity at 1 A·g^(-1) and maintains 91%of the original capacitance after 10000 cycling.It is superior to the other assembled zinc-ion devices in single-component electrolytes.The finding demonstrates that the double-ion storage mechanism enables the superior rate performance and long cycle lifetime of ZHCs.