Antihypertensive effects of whey protein hydrolysate involve reshaping the gut microbiome in spontaneously hypertension rats

Novel angiotensin-converting enzyme(ACE)inhibitory peptides were identified from whey protein hydrolysates(WPH)in vitro in our previous study and the antihypertensive abilities of WPH in vivo were further investigated in the current study.Results indicated that WPH significantly inhibited the development of high blood pressure and tissue injuries caused by hypertension.WPH inhibited ACE activity(20.81%,P<0.01),and reduced renin concentration(P<0.05),thereby reducing systolic blood pressure(SBP)(12.63%,P<0.05)in spontaneously hypertensive rats.The increased Akkermansia,Bacteroides,and Lactobacillus abundance promoted high short chain fatty acid content in feces after WPH intervention.These changes jointly contributed to low blood pressure.The heart weight and cardiomyocyte injuries(hypertrophy and degeneration)were alleviated by WPH.The proteomic results revealed that 19 protein expressions in the heart mainly associated with the wingless/integrated(Wnt)signaling pathway and Apelin signaling pathway were altered after WPH supplementation.Notably,WPH alleviated serum oxidative stress,indicated by the decreased malondialdehyde content(P<0.01),enhanced total antioxidant capacity(P<0.01)and superoxide dismutase activity(P<0.01).The current study suggests that WPH exhibit promising antihypertensive abilities in vivo and could be a potential alternative for antihypertensive dietary supplements.

True-temperature inversion algorithm for a multi-wavelength pyrometer based on fractional-order particle-swarm optimization

Herein,a method of true-temperature inversion for a multi-wavelength pyrometer based on fractional-order particle-swarm optimization is proposed for difficult inversion problems with unknown emissivity.Fractional-order calculus has the inherent advantage of easily jumping out of local extreme values;here,it is introduced into the particle-swarm algorithm to invert the true temperature.An improved adaptive-adjustment mechanism is applied to automatically adjust the current velocity order of the particles and update their velocity and position values,increasing the accuracy of the true temperature values.The results of simulations using the proposed algorithm were compared with three algorithms using typical emissivity models:the internal penalty function algorithm,the optimization function(fmincon)algorithm,and the conventional particle-swarm optimization algorithm.The results show that the proposed algorithm has good accuracy for true-temperature inversion.Actual experimental results from a rocket-motor plume were used to demonstrate that the true-temperature inversion results of this algorithm are in good agreement with the theoretical true-temperature values.

Complete Coverage Path Planning Based on Improved Area Division

It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments. .

Effective Improvement of the Photovoltaic Performance of Carbon-Based Perovskite Solar Cells by Additional Solvents

A solvent-assisted methodology has been developed to synthesize CH_3NH_3 PbI_3perovskite absorber layers.It involved the use of a mixed solvent of CH_3NH_3 I,PbI_2,c-butyrolactone,and dimethyl sulfoxide(DMSO) followed by the addition of chlorobenzene(CB).The method produced ultra-flat and dense perovskite capping layers atop mesoporous TiO_2 films,enabling a remarkable improvement in the performance of free hole transport material(HTM) carbon electrode-based perovskite solar cells(PSCs).Toluene(TO) was also studied as an additional solvent for comparison.At the annealing temperature of 100 °C,the fabricated HTM-free PSCs based on drop-casting CB demonstrated power conversion efficiency(PCE) of 9.73 %,which is 36 and 71 % higher than those fabricated from the perovskite films using TO or without adding an extra solvent,respectively.The interaction between the PbI_2–DMSO–CH_3NH_3I intermediate phase and the additional solvent was discussed.Furthermore,the influence of the annealing temperature on the absorber film formation,morphology,and crystalline structure was investigated and correlated with the photovoltaic performance.Highly efficient,simple,and stable HTM-free solar cells with a PCE of 11.44 % were prepared utilizing the optimum perovskite absorbers annealed at 120 °C.

Dye-sensitized Solar Cells with Higher J_(sc) by Using Polyvinylidene Fluoride Membrane Counter Electrodes

A flexible counter electrode(CE) for dye-sensitized solar cells(DSCs) has been fabricated using a micro-porous polyvinylidene fluoride membrane as support media and sputtered Pt as the catalytic material.Non-conventional structure DSCs have been developed by the fabricated CEs. The Pt metal was sputtered onto one surface of the membrane as the catalytic material. DSCs were assembled by attaching the Ti O2 electrode to the membrane surface without Pt coating. The membrane was with cylindrical pore geometry. It served not only as a substrate for the CE but also as a spacer for the DSC. The fabricated DSC with the flexible membrane CE showed higher photocurrent density than the conventional sandwich devices based on chemically deposited Pt/FTO glass, achieving a photovoltaic conversion efficiency of 4.43%. The results provides useful information in investigation and development of stable, low-cost, simple-design, flexible and lightweight DSCs.

Efficiency Enhancement of Inverted Polymer Solar Cells Using Ionic Liquid-functionalized Carbon Nanoparticles-modified ZnO as Electron Selective Layer

ZnO thin film was fabricated on tin-doped indium oxide electrode as an electron selective layer of inverted polymer solar cells using magnetron sputtering deposition. Ionic liquid-functionalized carbon nanoparticles(ILCNs) film was further deposited onto ZnO surfaces by drop-casting ILCNs solution to improve interface properties. The power conversion efficiency(PCE) of inverted polymer solar cells(PSCs)with only ZnO layer was quickly decreased from 2.7% to 2.2% when the thickness of ZnO layer was increased from 15 nm to 60 nm. However, the average PCE of inverted PSCs with ZnO layer modified with ILCNs only decreased from 3.5% to 3.4%, which is comparable to that of traditional PSCs with poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) anode buffer layer. The results suggested that the contact barrier between ZnO layer and poly(3-hexylthiophene) and phenyl-C61-butyric acid methylester(P3HT:PCBM)blended film compared to ZnO bulk resistance can more significantly influence the performance of inverted PSCs with sputtered ZnO layer. The vanishment of negative capacitive behavior of inverted PSCs with ILCNs modified ZnO layer indicated ILCNs can greatly decrease the contact barrier of ZnO/P3HT:PCBM interface.

Fabrication and Evaluation of Low-cost Cu_2ZnSn(S,Se)_4 Counter Electrodes for Dyesensitized Solar Cells

We explore a simple and eco-friendly approach for preparing CZTS powders and a screen-printing process for Cu_2ZnSn(S,Se)_4(CZTSSe) counter electrodes(CEs) in dye-sensitized solar cells(DSCs). Cu_2ZnSnS_4(CZTS) nanoparticles have been synthesized via a hydrazine-free solvothermal approach without the assistance of organic ligands. CZTS has been prepared by directly drop-casting the CZTS ink on the cleaned FTO glass, while CZTSSe CEs have been fabricated by screen-printing CZTS pastes, followed by post selenization using Se vapor obtained from elemental Se pellets. The crystal structure, composition and morphology of the as-deposited CZTS nanoparticles and CZTSSe electrodes are characterized by X-ray diffractometer, energy dispersive spectrometer, field emission scanning electron microscopy and transmission electron microscopy.The electrochemical properties of CZTS, CZTSSe and Pt CE based DSCs are examined and analyzed by electrochemical impedance spectroscopy. The prepared CZTS and CZTSSe CEs exhibit a cellular structure with high porosity. DSCs fabricated with CZTSSe CEs achieve a power conversion efficiency of 5.75% under AM 1.5 G illumination with an intensity of 100 m W/cm^2, which is higher than that(3.22%) of the cell using the CZTS CE. The results demonstrate that the CZTSSe CE possesses good electrocatalytic activity for the reduction of charge carriers in electrolyte. The comprehensive CZTSSe CE process is cheap and scalable. It can make large-scale electro-catalytic film fabrication cost competitive for both energy harvesting and storage applications.

Water Control Practice and Characteristics of Historical Water Control Celebrity Jiang Shidu

Firstly,Jiang Shidu and his historical background were introduced.His water control practice and characteristics were analyzed in detail,such as drawing water to irrigate farmland,opening up waterways for grain transportation by ship,guaranteeing the supply of fresh water to salt ponds,transforming drainage channels,establishing urban water supply system,and constructing military defense projects.

智能无人机集群协同感知计算研究综述

随着无人机智能化技术快速发展,无人机在智慧农业、灾后救援和战场侦察等领域具有广阔的应用前景。但是,感知计算能力有限的单无人机难以独立地完成实际应用中的复杂任务。因此,无人机集群协同感知计算技术被提出并成为未来无人机领域的主要研究方向。无人机集群协同感知计算技术是利用无线网络连接,多架无人机能够共享信息并协同地完成复杂的感知计算任务。从集群感知数据收集和协同感知策2方面,对无人机集群协同感知研究现状进行了深入调研分析。同时,详细归纳了无人机集群协同计算的最新研究进展,包括计算任务调度、算力资源分配以及数据存储策略。最后,探讨了智能无人机集群在协同感知计算方面的一些潜在研究问题和可行解决方法,如协同感知计算方法的可扩展性、多任务适应性以及开放无人机集群系统的协同感知计算方法等,为研究者对无人机集群协同感知计算的后续研究提供一定参考。

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos

Background:In vitro fertilization(IVF)has emerged as a transformative solution for infertility.However,achieving favorable live-birth outcomes remains challenging.Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods,including static images and temporal videos.However,traditional embryo selection methods,primarily reliant on visual inspection of morphology,exhibit variability and are contingent on the experience of practitioners.Therefore,an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable.Methods:We employed artificial intelligence(AI)for embryo morphological grading,blastocyst embryo selection,aneuploidy prediction,and final live-birth outcome prediction.We developed and validated the AI models using multitask learning for embryo morphological assessment,including pronucleus type on day 1 and the number of blastomeres,asymmetry,and fragmentation of blastomeres on day 3,using 19,201 embryo photographs from 8271 patients.A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5,and predict live-birth outcomes.Additionally,a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing(PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.Results:These two approaches enabled us to automatically assess the implantation potential.By combining embryo and maternal metrics in an ensemble AI model,we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists(46.1%vs.30.7%on day 3,55.0%vs.40.7%on day 5).Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians(area under the curve:0.769,95%confidence interval:0.709-0.820).These findings could potentially provide a noninvasive,high-throughput,and low-cost screening tool to facilitate embryo selection and achieve better outcomes.Conclusions:Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction,highlighting its potential as a noninvasive,efficient,and cost-effective tool for improved embryo selection and enhanced IVF outcomes.The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Machine Learning-Based Temperature and Wind Forecasts in the Zhangjiakou Competition Zone during the Beijing 2022 Winter Olympic Games

Weather forecasting for the Zhangjiakou competition zone of the Beijing 2022 Winter Olympic Games is a challenging task due to its complex terrain.Numerical weather prediction models generally perform poorly for cold air pools and winds over complex terrains,due to their low spatiotemporal resolution and limitations in the description of dynamics,thermodynamics,and microphysics in mountainous areas.This study proposes an ensemble-learning model,named ENSL,for surface temperature and wind forecasts at the venues of the Zhangjiakou competition zone,by integrating five individual models—linear regression,random forest,gradient boosting decision tree,support vector machine,and artificial neural network(ANN),with a ridge regression as meta model.The ENSL employs predictors from the high-resolution ECMWF model forecast(ECMWF-HRES) data and topography data,and targets from automatic weather station observations.Four categories of predictors(synoptic-pattern related fields,surface element fields,terrain,and temporal features) are fed into ENSL.The results demonstrate that ENSL achieves better performance and generalization than individual models.The root-mean-square error(RMSE) for the temperature and wind speed predictions is reduced by 48.2% and 28.5%,respectively,relative to ECMWF-HRES.For the gust speed,the performance of ENSL is consistent with ANN(best individual model) in the whole dataset,whereas ENSL outperforms on extreme gust samples(42.7% compared with 38.7% obtained by ECMWF-HRES in terms of RMSE reduction).Sensitivity analysis of predictors in the four categories shows that ENSL fits their feature importance rankings and physical explanations effectively.

Accurate prediction of myopic progression and high myopia by machine learning

Background Myopia is a leading cause of visual impairment in Asia and worldwide.However,accurately predicting the progression of myopia and the high risk of myopia remains a challenge.This study aims to develop a predictive model for the development of myopia.Methods We first retrospectively gathered 612530 medical records from five independent cohorts,encompassing 227543 patients ranging from infants to young adults.Subsequently,we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia.Result The model to predict the progression of myopia achieved an R^(2) value of 0.964 vs a mean absolute error(MAE)of 0.119D[95%confidence interval(CI):0.119,1.146]in the internal validation set.It demonstrated strong generalizability,maintaining consistent performance across external validation sets:R^(2)=0.950 vs MAE=0.119D(95%CI:0.119,1.136)in validation study 1,R^(2)=0.950 vs MAE=0.121D(95%CI:0.121,1.144)in validation study 2,and R^(2)=0.806 vs MAE=−0.066D(95%CI:−0.066,0.569)in the Shanghai Children Myopia Study.In the Beijing Children Eye Study,the model achieved an R^(2) of 0.749 vs a MAE of 0.178D(95%CI:0.178,1.557).The model to predict the risk of high myopia achieved an area under the curve(AUC)of 0.99 in the internal validation set and consistently high area under the curve values of 0.99,0.99,0.96 and 0.99 in the respective external validation sets.Conclusion Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.

CHD4-induced up-regulation of ERα activity contributes to breast cancer progression

The estrogen signaling system is a crucial regulator of metabolicandphysiologicalprocesses.However,abnormal activation of estrogen signaling may play a role in breast cancer initiation and progression.Crucial to this pathway is the interaction between estrogen receptor alpha(ERa)and various co-transcription activators.1 Although numerous studies have investigated ER coregulators,the protein-protein interaction networks of ERa are not fully understood.Recent research has shown that high chromodomain helicase DNA-binding 4(CHD4)expression is linked to poor prognosis in various cancers.2,?In this study,we demonstrated that both CHD4 and ERαcontribute to breast cancer progression while providing evidence of the regulatory processes and functional interplay between these two proteins.

Multi model forecast biases of the diurnal variations of intense rainfall in the Beijing-Tianjin-Hebei region

Forecasts of the intense rainfall events are important for the disaster prevention and reduction in the Beijing-TianjinHebei region(BTHR). What are the common biases in the forecasts of intense rainfall in the current operational numerical models? What are the possible causes of model bias? In this study, intense rainfall events in the BTHR were categorized into two types: those mainly due to strong synoptic forcings(SSF) and those with weak synoptic forcings(WSF). The results showed that,the numerical forecasts tend to overestimate the frequency of intense rainfall events but underestimate the rainfall intensity. Of these, the overestimation of precipitation frequency mainly appeared in the mountainous areas in the afternoon. Compared with global models, high-resolution mesoscale models showed a notable improvement in forecasting the afternoon intense rainfall,while they all have an obvious bias in forecasting the nighttime rainfall. For the WSF type, both global model and mesoscale model have a low forecast skill, with large biases in subdaily propagation feature. The possible causes are related to a poor performance of the model in reproducing the local thermodynamical circulations and the dynamical processes in the planetary boundary layer. So, the biases in forecasting the WSF type intense rainfall showed notable features of nonlinearity, which made it really challenging to understand their physical processes and to improve the associated forecasts.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅱ-Voyage report data,AIS data and meteorological data

When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources.These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models.Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments.For each ship,nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested.Experimental results revealed the benefits of fusing voyage report data,AIS data,and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate.Over the best datasets,the performances of several decision tree-based models are promising,including Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG).With the best datasets,their R^(2) values over the training sets are all above 0.96 and mostly reach the level of 0.99-1.00,while their R^(2) values over the test sets are in the range from 0.75 to 0.90.Fit errors of ET,AB,GB,and XG on daily bunker fuel consumption,measured by RMSE and MAE,are usually between 0.8 and 4.5 ton/day.These results are slightly better than our previous study,which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data,compared with the estimated geographical positions derived from the great circle route,in retrieving weather and sea conditions the ship sails through.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅰ-Voyage report data and meteorological data

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimization,weather routing,and the virtual arrival policy.The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed,displacement/draft,trim,weather conditions,and sea conditions.Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection.To overcome this issue,this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution.Eleven widelyadopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company.The best datasets found reveal the benefits of fusing voyage report data and meteorological data,as well as the practically acceptable quality of voyage report data.Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG)present the best fit and generalization performances.Their R^(2) values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set,and 0.74 to 0.90 for the test set.Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day.These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

Sonochemical synthesis of silver nanoparticles coated copper wire for low-temperature solid state bonding on silicon substrate

Silver nanoparticles (AgNPs) are directly grown on surface of ~25 μm copper wire by ultrasound-assisted chemical reduction. Silver nitrate is used as precursors, when polyvinylpyrrolidone (PVP) is added as a controller of the dimension of AgNPs. Influence of growth parameters such as precursor's concentration, ratio proportion of PVP and ultra-sonication on the growth of AgNPs coating are determined. The best morphology, size of the AgNPs are observed on copper wire. The results show that the copper wire coated with AgNPs of^100 nm diameter exhibits good antioxidation and ohmic contact after sinter on Si substrate at a temperature as low as 320℃, is especially suitable as a substitute for silver paste electrode used in silicon solar cells.

The Effect of Instant Messaging Social Media Platform Characteristics on Consumers’ Purchase Intention: An Empirical Study of WeChat

With the rapid development of mobile communication equipment,the significant role of social media platforms is realized in social media marketing.To determine the effect of instant messaging social media platform characteristics on consumers’purchase intention,we collected WeChat user data and designed an empirical model based on the technology acceptance theory.Analysis of 388 qualified surveys revealed significant positive effects of instant messaging social media platform characteristics,such as social presence,media richness,immediacy of communication,privacy protection,and entertainment on customers’purchase intention.This study aims to extend the scope of technology acceptance theory,providing practical ideas for firms and highlighting the prominent role of instant messaging social media platforms in marketing activities.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅲ-Sensor data and meteorological data

Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.