Photothermal catalytic C-C coupling to ethylene from CO_(2) with high efficiency by synergistic cooperation of oxygen vacancy and half-metallic WTe_(2)

Photothermal catalytic CO_(2) conversion provides an effective solution targeting carbon neutrality by synergistic utilization of photon and heat.However,the C-C coupling initiated by photothermal catalysis is still a big challenge.Herein,a three-dimensional(3D)hierarchical W_(18)O_(49)/WTe_(2) hollow nanosphere is constructed through in-situ embodying of oxygen vacancy and tellurium on the scaffold of WO_(3).The light absorption towards near-infrared spectral region and CO_(2) adsorption are enhanced by the formation of half-metal WTe_(2) and the unique hierarchical hollow architecture.Combining with the generation of oxygen vacancy with strengthened CO_(2) capture,the photothermal effect on the samples can be sufficiently exploited for activating the CO_(2) molecules.In particular,the close contact between W_(18)O_(49)and WTe_(2) largely promotes the photoinduced charge separation and mass transfer,and thus the~*CHO intermediate formation and fixedness are facilitated.As a result,the C-C coupling can be evoked between tungsten and tellurium atoms on WTe_(2).The ethylene production by optimized W_(18)O_(49)/WTe_(2) reaches 147.6μmol g^(-1)with the selectivity of 80%.The in-situ diffuse reflectance infrared Fourier transform spectroscopy(DRIFTS)and density functional theory(DFT)calculations are performed to unveil the presence and significance of aldehyde intermediate groups in C-C coupling.The half-metallic WTe_(2) cocatalyst proposes a new approach for efficient CO_(2) conversion with solar energy,and may especially create a new platform for the generation of multi-carbon products.

Enhancing milk quality and modulating rectal microbiota of dairy goats in starch‑rich diet:the role of bile acid supplementation

Background Diets rich in starch have been shown to increase a risk of reducing milk fat content in dairy goats.While bile acids(BAs)have been used as a lipid emulsifier in monogastric and aquatic animals,their effect on ruminants is not well understood.This study aimed to investigate the impact of BAs supplementation on various aspects of dairy goat physiology,including milk composition,rumen fermentation,gut microbiota,and BA metabolism.Results We randomly divided eighteen healthy primiparity lactating dairy goats(days in milk=100±6 d)into two groups and supplemented them with 0 or 4 g/d of BAs undergoing 5 weeks of feeding on a starch-rich diet.The results showed that BAs supplementation positively influenced milk yield and improved the quality of fatty acids in goat milk.BAs supplementation led to a reduction in saturated fatty acids(C16:0)and an increase in monounsaturated fatty acids(cis-9 C18:1),resulting in a healthier milk fatty acid profile.We observed a significant increase in plasma total bile acid concentration while the proportion of rumen short-chain fatty acids was not affected.Furthermore,BAs supplementation induced significant changes in the composition of the gut microbiota,favoring the enrichment of specific bacterial groups and altering the balance of microbial populations.Correlation analysis revealed associations between specific bacterial groups(Bacillus and Christensenellaceae R-7 group)and BA types,suggesting a role for the gut microbiota in BA metabolism.Functional prediction analysis revealed notable changes in pathways associated with lipid metabolism,suggesting that BAs supplementation has the potential to modulate lipid-related processes.Conclusion These findings highlight the potential benefits of BAs supplementation in enhancing milk production,improving milk quality,and influencing metabolic pathways in dairy goats.Further research is warranted to elucidate the underlying mechanisms and explore the broader implications of these findings.

Corrosion techniques and strategies for used fuel containers with copper corrosion barriers under deep geological disposal conditions:A review

Safe emplacement of high-level nuclear waste(HLNW)arising from the utilization of nuclear power is a frequently en-countered and considerably challenging issue.The widely accepted and feasible approach for the permanent disposal of HLNW involves housing it in a corrosion-resistant container and subsequently burying it deep in a geologic repository.The focus lies on ensuring the dur-ability and integrity of the container in this process.This review introduces various techniques and strategies employed in controlling the corrosion of used fuel containers(UFCs)using copper(Cu)as corrosion barrier in the context of deep geological disposal.Overall,these corrosion prevention techniques and methods have been effectively implemented and employed to successfully mitigate the corrosion challenges encountered during the permanent disposal of Cu containers(e.g.,corrosion mechanisms and corrosion parameters)in deep geologic repositories.The primary objective of this review is to provide an extensive examination of the alteration in the corrosion envir-onment encountered by the UFCs when subjected to deep geologic repository conditions and focusing on addressing the potential corro-sion scenarios.

Regulatory mechanisms of iron homeostasis in maize mediated by ZmFIT

Regulation of iron homeostasis in maize remains unclear,despite the known roles of FER-Like Fe deficiency-induced transcription factor(FIT)in Arabidopsis and rice.ZmFIT,like At FIT and Os FIT,interacts with iron-related transcription factors 2(ZmIRO2).Here,we investigate the involvement of ZmFIT in iron homeostasis.Mutant ZmFIT lines exhibiting symptoms of Fe deficiency had reduced shoot iron content.Transcriptome analysis revealed downregulation of Fe deficiency-responsive genes in the roots of a Zmfit mutant.ZmFIT facilitates the nuclear translocation of ZmIRO2 to activate transcription of downstream genes under Fe-deficient conditions.Our findings suggest that ZmFIT,by interaction with ZmIRO2,mediates iron homeostasis in maize.Notably,the binding and activation mechanisms of ZmFIT resemble those in Arabidopsis but differ from those in rice,whereas downstream genes regulated by ZmFIT show similarities to rice but differences from Arabidopsis.In brief,ZmFIT,orthgologs of Os FIT and At FIT in rice and maize,respectively,regulates iron uptake and homeostasis in maize,but with variations.

InGaZnO-based photoelectric synaptic devices for neuromorphic computing

Photoelectric synaptic devices could emulate synaptic behaviors utilizing photoelectric effects and offer promising prospects with their high-speed operation and low crosstalk. In this study, we introduced a novel InGaZnO-based photoelectric memristor. Under both electrical and optical stimulation, the device successfully emulated synaptic characteristics including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation (LTP), and long-term depression (LTD). Furthermore, we demonstrated the practical application of our synaptic devices through the recognition of handwritten digits. The devices have successfully shown their ability to modulate synaptic weights effectively through light pulse stimulation, resulting in a recognition accuracy of up to 93.4%. The results illustrated the potential of IGZO-based memristors in neuromorphic computing, particularly their ability to simulate synaptic functionalities and contribute to image recognition tasks.

MWCNT Doped Reverse-Mode Polymer Network Liquid Crystals with Frequency Response Property

Polymer-liquid crystals(PLCs)are common materials for smart windows.However,PLC smart windows usually require high driving voltage to maintain transparency.We synthesized a novel PLC smart film by doping multi-wall carbon nanotubes(MWCNTs)into a reverse-mode polymer network liquid crystal(R-PNLC).

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation management.Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced data.However,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update module.This paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system.Here,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance.Computation of user reputation scores relies on their behavioral history,actively discouraging undesirable conduct.Additionally,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and reputation.Our scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior.This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains.

具有高效激子解离和明确反应位点的Tröger碱基三维多孔芳基有机骨架及其光催化近单一选择性CO_(2)转化

植物光合作用是吸收光能,把CO_(2)和水转化成富能有机物,同时释放氧气的过程.受此启发,利用太阳光将CO_(2)转化为碳氢燃料的人工碳中和技术引起了广泛关注.人工光合作用能否成功实施取决于光催化剂的设计制备.无机半导体已被广泛研究用于CO_(2)光还原反应(CO_(2)PRR),但其存在金属氧化物的带隙较宽且难以调节、导致光吸收较差和金属硫化物的光腐蚀问题严重等明显的缺点.此外,高载流子复合率和低比表面积会影响光催化效率,从而限制光子利用.因此,基于有机聚合物的无金属催化剂因其突出的可设计调控性而被提出,其中,具有超高比表面积的材料—多孔芳香骨架(PAFs)聚合物是研究热点之一,但是传统PAFs材料多为二维平面结构,用于光催化的无金属三维PAFs报道较少.此外,具有孤对电子的杂原子(N,B,F)修饰的材料可以与CO_(2)分子产生特定的偶极-四极相互作用,提高材料对CO_(2)的吸附和活化能力,是提升有机聚合物光催化剂性能的有效策略.本文采用Sonogashira-Hagihara偶联将具有不同共轭程度的芳香炔烃(2,2’,7,7‘-四乙炔基-9,9’-螺二芴,SPF-T;四(4-乙炔基苯基)甲烷,TEPM-T;1,1,2,2-四(4-乙炔基苯基)乙烯,TEPE-T)与含有N杂原子的Tröger碱聚合制备了具有三维结构的多孔芳香骨架聚合物X-TB-PAFs(X=TEPE,TEPM,SPF).通过X-射线衍射、红外光谱、13C核磁共振(NMR)以及1H NMR等表征手段验证了目标聚合物的成功合成.通过紫外-可见光谱和Mott-Schottky曲线测试研究了聚合物具体的能带结构,发现三种PAFs聚合物材料在热力学上同时满足光催化CO_(2)-CO的还原反应条件(Eθ=-0.51 Vvs.NHE,pH=7)和光催化H2O-O_(2)的氧化条件(Eθ=0.82 V vs.NHE,pH=7).V形骨架结构的Tröger碱(TB)单元和芳炔的结合赋予了聚合物刚性稳定的孔隙率以及较高的比表面积,材料中的多孔结构可以使其暴露更多的活性位点,三维框架结构为反应物接近活性位点提供了丰富的开放式空腔,这些都有利于材料对CO_(2)的捕获,增强催化剂对CO_(2)的吸附/活化能力.此外,炔基充当连接通道还可以增强体系的载流子迁移率,提升材料的光催化性能.密度泛函理论计算和光电性能测试结果表明,TB官能团引入带来的分子内极化和电子陷阱位点的优势,其与三维共轭网络结构一起协同调节了光生载流子的分离和反应位点分布.三种三维PAFs中,基于全共轭结构TEPE-T的TEPE-TB-PAF表现出最高效的光生载流子传输与分离效率,在没有助催化剂和牺牲剂的情况下表现出较好的光催化CO产率(194.50μmolg^(-1)h^(-1))和近乎单一的选择性(99.74%).全共轭TEPE-T的引入和分子内极化的存在可以促进框架内载流子的分离和迁移.材料中的电偶极矩(从负电荷到正电荷)指向TB中含有叔氮官能团的桥接位点,使其成为明确的催化反应位点.光电流和阻抗测试结果表明,TEPE-TB-PAF具有更好的电子-空穴分离能力和更小的电荷迁移位阻.三维框架构建产生的多重散射截面可以促进材料中的光子吸收,从而提高其光催化性能.理论计算和原位漫反射傅立叶变换红外光谱结果表明,材料中CO解吸的低能垒和*CHO形成的高能垒是TEPE-TB-PAF高CO产率和选择性的根本机制.综上,本文为多功能高效有机聚合物光催化剂的合成提供了有效途径,并为同时改善光催化剂的转化率和选择性提供了借鉴.

Resilience mediates the association between alexithymia and stress in Chinese medical students during the COVID-19 pandemic

Background Evidence indicates that medical students have had high rates of mental health problems,especially during the COVID-19 pandemic,which could be affected by alexithymia—a marked dysfunction in emotional awareness,social attachment and interpersonal relationships—and stress.However,psychological resilience might relieve alexithymia and stress levels.Aims This study aimed to investigate the role of resilience in alexithymia and stress in medical students.Methods A total of 470 medical students completed online and offline surveys,including the Toronto Alexithymia Scale-20(TAS-20),the Connor-Davidson Resilience Scale(CD-RISC)and the College Student Stress Questionnaire(CSSQ).The data of five participants were excluded because of a lack of integrity.Mann-Whitney U test or Kruskal-Wallis test was used to compare group differences in the CD-RISC scores among categorical variables.Spearman correlation analysis was employed to evaluate the associations between resilience and alexithymia and between resilience and stress.Mediation analysis was used to test the mediating effect of resilience between alexithymia and stress.Results Of the medical students considered in the analysis,382(81.28%)were female and 88(18.72%)were male.There was a significant negative correlation between the TAS-20 scores and the total and subtotal CD-RISC scores(p<0.001).The CSSQ scores also significantly negatively correlated with the total and subtotal CD-RISC scores(p<0.001).Resilience mediated the relationship between alexithymia and stress(total effect=1.0447,p<0.001).The indirect effect of alexithymia significantly impacted stress through resilience(effect=0.1670,95%CI:0.069 to 0.281).Conclusions Our findings suggest that resilience might effectively reduce alexithymia and stress.They also contributed to a better understanding of the mediating effects of resilience on alexithymia and stress during the COVID-19 pandemic.The evidence from these results encourages universities to focus on improving students’resilience.

Effects of multiple dynamic processes on chlorophyll variation in the Luzon Strait in summer 2019 based on glider observation

Luzon Strait is the main channel connecting the South China Sea(SCS)and the western Pacific,with complex atmospheric and oceanic dynamic processes.Based on 44 days of glider measurements and satellite observations,we investigated the temporal and vertical variations of chlorophyll-a(Chl-a)concentration in the Luzon Strait from July 25 to September 6,2019.The Chl a was mainly distributed above 200 m and concentrated in the subsurface chlorophyll maximum(SCM)layer.The depth of SCM ranged between 50 m and 110 m,and the magnitude of SCM varied from 0.42 mg/m3 to 1.12 mg/m3.The variation of Chl a was identified with three stages responding to different dynamic processes.Under the influence of Kuroshio intrusion,the SCM depth sharply deepened,and its magnitude decreased in Stage 1.Afterward,a prominent Chl-a bloom was observed in the SCM layer from August 6 to August 16.The Chl-a bloom in Stage 2 was related to the influence of a cyclonic eddy,which uplifted of the thermocline and thus the deep nutrients.During Stage 3,prolonged heavy rainfall in the northeastern SCS resulted in a significant salinity decrease in the upper ocean.The convergence of upper water deepened the thermocline and the mixed layer.Thus,the Chl a decreased in the SCM layer but increased in the surface layer.In particular,a typhoon passed through the Luzon Strait on August 24,which induced the Chl a increase in the upper 50 m.However,there was little change in the depth-integrated Chl a(0-200 m),indicating that the Chl a increase in the surface layer was likely associated with physical entrainment of SCM caused by strong mixing,rather than the phytoplankton bloom in the upper water column.Underwater gliders provide frequent autonomous observations that help us understand the regional ocean’s complex dynamic processes and biological responses.

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment,photographing habits,camera resolution and other factors,resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits.Developing an anticounterfeiting code authentication algorithm based on mobile phones is of great commercial value.Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes,the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy.To address the small differences in texture features,low response speed and excessively large deep learning models used in mobile phone anti-counterfeiting and identification scenarios,we propose a feature-guided double pool attention network(FG-DPANet)to solve the reprinting forgery problem of printing anti-counterfeiting codes.To address the slight differences in texture features in high-quality reprinted anti-counterfeiting codes,we propose a feature guidance algorithm that creatively combines the texture features and the inherent noise feature of the scanner and printer introduced in the reprinting process to identify anti-counterfeiting code authenticity.The introduction of noise features effectively makes up for the small texture difference of high-quality anti-counterfeiting codes.The double pool attention network(DPANet)is a lightweight double pool attention residual network.Under the condition of ensuring detection accuracy,DPANet can simplify the network structure as much as possible,improve the network reasoning speed,and run better on mobile devices with low computing power.We conducted a series of experiments to evaluate the FG-DPANet proposed in this paper.Experimental results show that the proposed FG-DPANet can resist highquality and small-size anti-counterfeiting code reprint forgery.By comparing with the existing algorithm based on texture,it is shown that the proposed method has a higher authentication accuracy.Last but not least,the proposed scheme has been evaluated in the anti-counterfeiting code blurring scene,and the results show that our proposed method can well resist slight blurring of anti-counterfeiting images.

A Novel Deep Learning Representation for Industrial Control System Data

Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and actuators.Hence,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this paper.Although traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS data.In addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL.Training the denoising autoencoder allows the model to better mitigate the sensor noise problem.In this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more effectively.We tested our model with absolute and relative accuracy experi-ments on two large-scale ICS datasets.Compared with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and F1-score.This study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.

Effect of Human Umbilical Cord Mesenchymal Stem Cells on GRP78/ATF4 Pathway in Alzheimer s Disease Model Mice

[Objectives]To study the effect of human umbilical cord mesenchymal stem cells(hUC-MSCs)on GRP78/ATF4 pathway in APP/PS1 mice.[Methods]Twelve 6-month-old female APP/PS1 mice were randomly divided into model group(MOD,n=6)and human umbilical cord mesenchymal stem cell treatment group(MSC,n=6);six 6-month-old C57BL/6N mice were used as control group(CON,n=6).The mice in each group were treated with the fourth generation of human umbilical cord mesenchymal stem cells through tail vein.Four weeks later,the mice in each group were killed.The expression of GFP78 and ATF4 in the cortex of mice in each group was detected by Western blotting and real-time fluorescence quantitative PCR.[Results]The results of immunoblotting and real-time fluorescence quantitative PCR showed that the expression of GRP78 in MOD group was lower than that in CON group and the expression of ATF4 increased.The expression of GRP78 protein in MSC group was higher than that in MOD group,but the expression of ATF4 protein was lower.The results of real-time fluorescence quantitative PCR showed that the mRNA level of GRP78 decreased and the mRNA level of ATF4 increased in MOD group compared with CON group.The mRNA level of GRP78 in MSC group was higher than that in MOD group,while the mRNA level of ATF4 in MSC group was lower than that in MOD group.[Conclusions]Human umbilical cord mesenchymal stem cells can regulate the expression of GRP78/ATF4 pathway in APP/PSI mice,which may be related to the stress level of endoplasmic reticulum in the brain of APP/PS1 mice mediated by human umbilical cord mesenchymal stem cells.

Effect of Nitrogen Application Rates on the Nitrogen Utilization, Yield and Quality of Rice

Research on the effect of the nitrogen application rate on the balance of the nitrogen utilization, yield and quality of rice is common in South China but is relatively lacking in Northeast China, especially in the Liaohe Delta. In this study, Yanfeng 47 rice was planted in Panjin city, China, to explore the effect of six nitrogen rates (0, 160, 210, 260, 315 and 420 kg N/ha) on the nitrogen use efficiency, rice quality and grain yield of rice plants. The results showed that the application of an appropriate nitrogen rate (210-260 kg N/ha) remarkably increased the nitrogen use efficiency of rice plants, grain yield, rice milling quality and nutritional quality and resulted in a moderate rice eating quality. Although low nitrogen rates (160 kg N/ha) maintained a high rice eating quality, they decreased grain yield and other rice qualities, and excessive nitrogen (315 kg N/ha) increased rice appearance quality but significantly reduced the nitrogen use efficiency (p < 0.05), yield and eating quality of rice. Therefore, to produce rice in the Liaohe Delta by an environmentally friendly method and guarantee rice with high quality and yield, the recommended nitrogen application rate is 210 kg N/ha.

Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks

During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.

In-situ growth of ZnS/FeS heterojunctions on biomass-derived porous carbon for efficient oxygen reduction reaction

It is an urgent task to develop highly efficient non-noble metal electrocatalysts in the direction of ORR,but still a huge and long-term challenge.Herein,an efficient one-step pyrolysis of Sichuan pepper powder,2,2-bipyridine,FeCl3,Na SCN,and ZnCl2 at 900℃ provides the FeS/ZnS@N,S-C-900 hybrid catalyst.Transmission electron microscopy(TEM)images and Mott-Schottky curves clearly reveal the in-situ constructed abundant FeS/ZnS-based p-n junctions dispersed on the biomass-derived porous carbon surface of FeS/ZnS@N,S-C-900.The as-prepared FeS/ZnS@N,S-C-900 hybrid exhibits superior ORR performance in comparison with Pt/C in 0.1 M KOH with high onset(Eonset)and half-wave potentials(E1/2)of 1.00 and 0.880 V vs.RHE,large limiting current density(JL)of 5.60 mA cm-2,and robust durability and methanol tolerance.Impressively,upon the light irradiation,FeS/ZnS@N,S-C-900 produces a photocurrent as high as ca.0.3μA cm-2,resulting in further improvement over Eonset,E1/2,and JLof FeS/ZnS@N,S-C-900 to1.10 V vs.RHE,0.885 V vs.RHE,and 6.02 mA cm-2.Experiment in combination with theoretical calculations demonstrate the significant effect of FeS/ZnS heterojunction on the enhanced ORR catalytic activity of FeS/ZnS@N,S-C-900.This work is useful for the development of advanced heterojunction-based ORR catalysts for various energy conversion devices.

3D hierarchical architecture collaborating with 2D/2D interface interaction in Ni Al-LDH/Ti_(3)C_(2) nanocomposite for efficient and selective photoconversion of CO_(2)

Photocatalytic conversion of CO_(2)into a special chemical fuel with high yield and selectivity is still a major challenge.Herein,a 3 D hierarchical Ni Al-LDH/Ti_(3)C_(2)MXene(LDH/TC)nanocomposite is constructed through in situ loading of Ti_(3)C_(2)nanosheets on the Ni Al-LDH scaffold during the hydrothermal process.The formation of a uniform and well-defined 2 D/2 D heterogeneous interface can be realized by optimizing the ratio of Ti_(3)C_(2)and the precursors for Ni Al-LDH.The 3 D hierarchical scaffold with high specific surface area contributes to the favourable photon adsorption and utilization.The intimate contact between Ti_(3)C_(2)and Ni Al-LDH with numerous interfaces effectively promotes the separation of the photoinduced electron-hole pairs in Ni Al-LDH.Together with the highly exposed oxidation-reduction active sites and the enhanced CO_(2)capture and activation.The maximum photocatalytic CO production rate on Ni AlLDH/Ti_(3)C_(2)reaches 11.82 lmol g^(-1)h^(-1)with 92%selectivity and superior stability.This work provides an effective approach for the development of an ideal photocatalyst by collaborative utilization of materials with different dimensionalities.

A Deep Learning Based Approach for Response Prediction of Beam-like Structures

Beam-like structures arde a class of common but important structures in engineering.Over the past few centuries,extensive research has been carried out to obtain the static and dynamic response of beam-like structures.Although build-ing the finite element model to predict the response of these structures has proven to be effective,it is not always suitable in all the application cases because of high computational time or lack of accuracy.This paper proposes a novel approach to predict the deflection response of beam-like structures based on a deep neural net-work and the governing differential equation of Euler-Bernoulli beam.The Prandtl-Ishlinskii model is introduced as an element of prediction model to simu-late the plasticity of this beam structure.Finally the application of the proposed approach is demonstrated through four numerical examples including linear elastic/ideal plastic beam under concentrated/sinusoidal load and elastic/plastic continues beam under seismic load to demonstrate a proof of concept for the effectiveness of this AI-based approach.

Toward intelligent wireless communications:Deep learning-based physical layer technologies

Advanced technologies are required in future mobile wireless networks to support services with highly diverse requirements in terms of high data rate and reliability,low latency,and massive access.Deep Learning(DL),one of the most exciting developments in machine learning and big data,has recently shown great potential in the study of wireless communications.In this article,we provide a literature review on the applications of DL in the physical layer.First,we analyze the limitations of existing signal processing techniques in terms of model accuracy,global optimality,and computational scalability.Next,we provide a brief review of classical DL frameworks.Subsequently,we discuss recent DL-based physical layer technologies,including both DL-based signal processing modules and end-to-end systems.Deep neural networks are used to replace a single or several conventional functional modules,whereas the objective of the latter is to replace the entire transceiver structure.Lastly,we discuss the open issues and research directions of the DL-based physical layer in terms of model complexity,data quality,data representation,and algorithm reliability.

Mobility-driven user-centric AP clustering in mobile edge computing-based ultra-dense networks

ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile users.In order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,respectively.However,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP clustering.In this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent.In addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area.Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.