The gelation of crude oil with high wax and asphaltene content at low temperatures often results in the block of transportation pipeline in Africa. In recent years, it was reported that surface hydrophobicmodified nan...The gelation of crude oil with high wax and asphaltene content at low temperatures often results in the block of transportation pipeline in Africa. In recent years, it was reported that surface hydrophobicmodified nanoparticles have important applications in crude oil flow modification. In this work, four kinds of core-shell hybride nanoparticles by grafting poly(octadecyl, docosyl acrylate) and poly(acrylate-α-olefin) onto the surface of nano-sized SiO_(2) were synthesized by grafting polymerization method.The chemical structure of nanoparticles was analyzed by Fourier transform infrared spectroscopy(FT-IR),scanning electron microscopy(SEM) and thermogravimetric analysis(TGA). The rheological behaviors of crude oil and precipitation of asphaltenes in the presence of nanoparticles were studied by measuring the viscose-temperature relationship curve, the cumulative wax precipitation amount, and morphology of waxes and asphaltenes. The results indicate that the docosyl polyacrylate@SiO_(2) nanoparticle(PDA@SiO_(2)) can reduce the cumulative wax precipitation amount of crude oil by 72.8%, decline the viscosity of crude oil by 85.6% at 20℃, reduce the average size of wax crystals by 89.7%, and inhibit the agglomeration of asphaltene by 74.8%. Therefore, the nanoparticles not only adjust the crystalline behaviors of waxes, but also inhibit the agglomeration of asphaltenes. Apparently, core-shell hybride nanoparticles provides more heterogeneous nucleation sites for the crystallization of wax molecules,thus inhibiting the formation of three-dimensional network structure. The core-shell polymer@SiO_(2) hybride nanoparticles are one of promising additives for inhibiting crystallization of waxes and agglomeration of asphaltenes in crude oil.展开更多
The article presents the results of studies on the resistance of hybrid cotton lines to a new virulent isolate (strain) of the fungus <i><span style="font-family:Verdana;">Fusarium verticillioide...The article presents the results of studies on the resistance of hybrid cotton lines to a new virulent isolate (strain) of the fungus <i><span style="font-family:Verdana;">Fusarium verticillioides</span></i><span style="font-family:Verdana;"> upon inoculation of the host plant. Based on the studies, it was found that the complex genotypic resistance of the studied lines</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span></span></span></span></span><span><span><span><span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> when the host plants are inoculated with isolates of -100</span><i><span style="font-family:Verdana;"> V. dahliae</span></i></span></span></span></span></span></span></span><span><span><span><span><span><span><i><span style="font-family:;" "=""> </span></i></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><i><span style="font-family:Verdana;">Kleb</span></i></span></span></span></span></span></span><span><span><span><span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> fungus and 103 </span><i><span style="font-family:Verdana;">Fusarium verticillioides</span></i><span style="font-family:Verdana;"> fungi</span></span></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> depend</span></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> on the degree of resistance of the parental forms and their combination ability.</span></span></span></span></span></span></span>展开更多
The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input mult...The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input multi-output,nonlinearity,and strong coupling presents significant challenges.The substantial internal force generated during the adjustment process can potentially damage the LAC and degrade the assembly quality.Hence,a workspace-based hybrid force position control scheme was developed to achieve high quality assembly with high-precision and lower internal force.Firstly,an offline workspace analysis with inherent geometric characteristics to form time-varying posture error constraint.Then,the posture error is integrated into the online position axis control to ensure tracking the ideal posture,while the force control axis compensates for posture deviation by minimizing internal force,thereby achieving high precision and low internal force.Finally,the effectiveness was demonstrated through experiments.The root mean square errors of orientation and position are 104 rad and 0.1 mm,respectively.A reduction in internal force can range from 10.96%to 57.4%compared to the traditional method.Key points'max position error is decreased from 0.32 mm to 0.18 mm,satisfying the 0.5 mm tolerance.Therefore,the proposed method will help promote the development of high-performance manufacturing.展开更多
We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptiona...We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptional mechanical performance,including an ideal compressive strength of 343 GPa at a pressure of 300 GPa,~33% higher than that of diamond.This ultrahigh strength arises from the synergistic interplay between its three-dimensional covalent framework and hybridized bonding topology,which enables isotropic stress accommodation and dynamic electronic rearrangement.These results establish cage-N as a promising non-carbon ultrahard material and provide a bonding-driven route toward designing superhard frameworks under extreme conditions.展开更多
This paper proposes a tamper detection technique for semi-fragile watermarking using Quantizationbased Discrete Cosine Transform(DCT)for tamper localization.In this study,the proposed embedding strategy is investigate...This paper proposes a tamper detection technique for semi-fragile watermarking using Quantizationbased Discrete Cosine Transform(DCT)for tamper localization.In this study,the proposed embedding strategy is investigated by experimental tests over the diagonal order of the DCT coefficients.The cover image is divided into non-overlapping blocks of size 8×8 pixels.The DCT is applied to each block,and the coefficients are arranged using a zig-zag pattern within the block.In this study,the low-frequency coefficients are selected to examine the impact of the imperceptibility score and tamper detection accuracy.High accuracy of tamper detection can be achieved by checking the surrounding blocks to determine whether the corresponding block has been tampered with.The proposed tamper detection is tested under various malicious,incidental,and hybrid attacks(both incidental and malicious attacks).The experimental results demonstrate that the proposed technique achieves a Peak-Signal-to-Noise Ratio(PSNR)value of 41.2318 dB,an average Structural Similarity Index Measure(SSIM)value of 0.9768.The proposed scheme is also evaluated against malicious attacks such as copy-move,object deletion,object manipulation,and collage attacks.The proposed scheme can detect the malicious attack localization under various tampering rates.In addition,the proposed scheme can still detect tampered pixels under a hybrid attack,such as a combination ofmalicious and incidental attacks,with an average accuracy of 96.44%.展开更多
The world’s first hybrid commutated converter(HCC)—a next-generation high-voltage direct current(HVDC)transmission valve based on integrated gate commutated thyristor(IGCT)technology—officially commenced commercial...The world’s first hybrid commutated converter(HCC)—a next-generation high-voltage direct current(HVDC)transmission valve based on integrated gate commutated thyristor(IGCT)technology—officially commenced commercial operation at the Lingbao Converter Station in Henan Province,China,on December 28,2025,as shown in Figure 1.This milestone signifies the resolution of the“commutation failure”challenge that has plagued global HVDC transmission systems for over half a century.展开更多
Penduline tits(genus Remiz)are small passerines distributed across Europe,Central and East Asia,and North Africa,renowned for their elaborate nests and unusually diverse mating systems.However,the taxonomy and evoluti...Penduline tits(genus Remiz)are small passerines distributed across Europe,Central and East Asia,and North Africa,renowned for their elaborate nests and unusually diverse mating systems.However,the taxonomy and evolutionary relationships within this genus have remained contentious due to overlapping breeding distributions and extensive hybridization.Using broad-range geographic sampling and whole-genome sequencing,here we report the phylogenetic relationships within this genus.Our results from maximum likelihood trees,species trees,population structure,and PCA analyses consistently identify four distinct,well-supported monophyletic clades.Based on these robust results,we support dividing Remiz into four species:the Eurasian Penduline Tit(R.pendulinus),Black-headed Penduline Tit(R.macronyx),White-crowned Penduline Tit(R.coronatus),and Chinese Penduline Tit(R.consobrinus).Among these species,R.consobrinus diverged earlier from other species,followed by R.coronatus,and then,R.pendulinus and R.macronyx.R.pendulinus and R.macronyx showed shallow genetic differentiation with recent divergence(~87,000 years ago)and ongoing gene flow.Our findings demonstrate the effectiveness of phylogenomic approaches in resolving taxonomic ambiguities and provide a robust evolutionary framework for tracing the diversification of life history traits,particularly nest structures and mating systems,across the genus.展开更多
Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers u...D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers using the distinct d-π hybridization monomers R_(1) and R_(2).R_(1)(Ru^(Ⅱ)-(tpz)Cl_(2))features tetradentate ligands(tpz,6,6'-di(1H-pyrazol-1-yl)-2,2'-bipyridine)enforcing quasi-octahedral geometry;R_(2)(Ru^(Ⅱ)-(bpp)_(2))incorporates tridentate ligands(bpp,2,6-di(1H-pyrazol-1-yl)pyridine)inducing pronounced geometric distortion.The planar ligand(tpz)in R_(1) facilitates ordered molecular assembly through high conformational rigidity and extensive π-π stacking,resulting in increased molecular densities and enhanced morphological uniformity compared to R_(2) metallopolymers.Due to pyrazole’s weaker π-acceptance and strongerσ-donation compared to pyridine,R_(1) exhibits a 119 nm red-shift in metal-to-ligand charge transfer(MLCT)band and a 30 mV anodic shift in Ru^(+2/+3)redox potential relative to R_(2).Coupled with a reduced HOMO-LUMO gap,the uniform and ordered structure leads to a lower conductance decay constant in R_(1).Additionally,R_(2) metallopolymers exhibit superior memristive performance(characterized by lower switching voltage and higher switching ratio)via redox-induced aromatic transitions in axial ligands enhancing electronic delocalization.This work compares two metallopolymers with different ligand geometries,revealing how this difference leads to distinct charge transport and memristive behaviors.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven...Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.展开更多
The design and fabrication of ordered epitaxial MOF-on-MOF heterostructures as highly efficient electrocatalysts for water splitting is crucial but still challenging.In this study,a simple coordination-driven self-ass...The design and fabrication of ordered epitaxial MOF-on-MOF heterostructures as highly efficient electrocatalysts for water splitting is crucial but still challenging.In this study,a simple coordination-driven self-assembly method is used to fabricate controllable MOF-on-MOF multiscale heterostructures,where triangular host MOF(ZIF-67)nanosheets undergo in situ epitaxial growth to form uniform orthogonal vip MOF(CoFe PBA)nanosheets.Phosphorus(P)is further introduced in situ to fabricate CoP and Fe_(2)P heterostructured nanosheets(CoFe-P-NS),which exhibit excellent bifunctional electrocatalytic performance due to the enhancement of intrinsic electrocatalytic activity by p-d orbital hybridization.Specifically,the CoFe-P-NS requires low overpotential of 259 and 307 mV to reach 500 mA cm−2 for HER and OER,respectively.Remarkably,the assembled electrolysis cell maintained a large current density of 300 mA cm−2 for over 360 h with negligible voltage increase during alkaline seawater electrolysis.Experiments and theoretical calculations show that the synergistic catalytic activity of bimetallic phosphides arises from p-d orbital hybridization,where the CoP-P sites enhance HER by optimizing H*adsorption in the Volmer-Heyrovsky steps,while the Fe_(2)P-Fe sites accelerate OER by lowering the energy barrier of the rate-determining step from O*to OOH*.This study provides valuable insights into the design of a controllable MOF-on-MOF-based electrocatalyst toward alkaline seawater splitting.展开更多
Zinc-ion hybrid supercapacitors(ZIHCs)are compelling candidates for next-generation energy storage owing to their intrinsic safety,low cost,and high power density.However,their practical implementation remains hindere...Zinc-ion hybrid supercapacitors(ZIHCs)are compelling candidates for next-generation energy storage owing to their intrinsic safety,low cost,and high power density.However,their practical implementation remains hindered by the limited energy density of traditional carbon-based cathodes.Here,we rationally design porous carbon nanofibers embedded with atomically dispersed Zn and Fe dual-metal sites(ZnFe/PCNFs),synthesized via electrospinning followed by controlled carbonization.The introduction of Fe modulates the local electronic structure of Zn centers,thereby facilitating enhanced d-orbital hybridization and stronger ion adsorption through the formation of ZnFeN_(6) coordination motifs.Coupled with high surface area and hierarchical porosity,these atomic-level interactions facilitate exceptional ion accessibility and rapid charge-transfer kinetics.As a cathode for ZIHCs,ZnFe/PCNFs deliver a specific capacity of 213 mAh g^(-1),exceptional high-rate capability,and longterm cycling stability over 20000 cycles.This work elucidates mechanisms of dual-metal atomic coordination and provides a robust design strategy for high-performance,durable aqueous energy storage systems.展开更多
Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been propo...Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been proposed across classical image processing,machine learning(ML),deep learning(DL),and hybrid/ensemble models.This study conducts a systematic literature review using the PRISMA methodology,analyzing 57 selected articles to explore how these methods have evolved and been applied.The review highlights the strengths and limitations of each approach,identifies commonly used public datasets,and observes emerging trends in model integration and clinical relevance.By synthesizing current findings,this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection.Overall,our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources,while DL models are preferable when pixel-level annotations and resources are available,and hybrid pipelines are most appropriate when fine-grained clinical precision is required.展开更多
In an academic environment increasingly shaped by metrics and the imperatives of“publish or perish”,it is rare to encounter a leading scientist willing to interweave personal narrative with conceptual reflection.The...In an academic environment increasingly shaped by metrics and the imperatives of“publish or perish”,it is rare to encounter a leading scientist willing to interweave personal narrative with conceptual reflection.The Soul of Geography by Fu(2025)achieves precisely this.The book resists simple categorisation:it is neither a conventional monograph nor a memoir,but rather a hybrid text that integrates autobiography,disciplinary reflection,and scientific arguments.In doing so,Fu articulates not only the trajectory of his own career but also a vision of geography as a discipline of theoretical depth and practical relevance.展开更多
Decarbonising the building sector,particularly residential heating,represents a critical challenge for achieving carbon-neutral energy systems.Efficient solutions must integrate both technological performance and rene...Decarbonising the building sector,particularly residential heating,represents a critical challenge for achieving carbon-neutral energy systems.Efficient solutions must integrate both technological performance and renewable energy sources while considering operational constraints of existing systems.This study investigates a hybrid heating system combining a natural gas boiler(NGB)with an air-to-water heat pump(AWHP),evaluated through a combination of laboratory experiments and dynamic modelling.A prototype developed in the Electrical and Energy Engineering Laboratory enabled the characterization of both heat generators,the collection of experimental data,and the calibration of a MATLAB/Simulink model,including emissions and exhaust analyses.Sensitivity analyses were performed to identify optimal configurations for energy efficiency and system control,accounting for interactions between subsystems.Results highlight that hybridisation significantly improves primary energy efficiency and reduces fuel consumption compared to conventional NGB-only systems.Environmental performance,assessed through CO_(2) and NOx emissions and renewable energy integration,demonstrates the benefits of partial electrification in the residential sector.Economic assessment further quantifies decarbonization costs and fuel savings,illustrating tradeoffs between low-capital,moderate-performance systems and high-efficiency,high-renewable solutions requiring larger investments.The analysis shows that strategic decisions for residential decarbonisation cannot be separated from system-wide considerations,including control strategies,component integration,and economic feasibility.The study underlines the importance of hybrid and renewable-based solutions as pivotal pathways for energy transition in the residential building sector.展开更多
This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and s...This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.展开更多
基金Financial support by project of China Petroleum Engineering Corp., Ltd (CPEC), Study on microstructural regulation of polyacrylate and applying as pour-point depressant for crude oil (2021ZYGC-01-01)。
文摘The gelation of crude oil with high wax and asphaltene content at low temperatures often results in the block of transportation pipeline in Africa. In recent years, it was reported that surface hydrophobicmodified nanoparticles have important applications in crude oil flow modification. In this work, four kinds of core-shell hybride nanoparticles by grafting poly(octadecyl, docosyl acrylate) and poly(acrylate-α-olefin) onto the surface of nano-sized SiO_(2) were synthesized by grafting polymerization method.The chemical structure of nanoparticles was analyzed by Fourier transform infrared spectroscopy(FT-IR),scanning electron microscopy(SEM) and thermogravimetric analysis(TGA). The rheological behaviors of crude oil and precipitation of asphaltenes in the presence of nanoparticles were studied by measuring the viscose-temperature relationship curve, the cumulative wax precipitation amount, and morphology of waxes and asphaltenes. The results indicate that the docosyl polyacrylate@SiO_(2) nanoparticle(PDA@SiO_(2)) can reduce the cumulative wax precipitation amount of crude oil by 72.8%, decline the viscosity of crude oil by 85.6% at 20℃, reduce the average size of wax crystals by 89.7%, and inhibit the agglomeration of asphaltene by 74.8%. Therefore, the nanoparticles not only adjust the crystalline behaviors of waxes, but also inhibit the agglomeration of asphaltenes. Apparently, core-shell hybride nanoparticles provides more heterogeneous nucleation sites for the crystallization of wax molecules,thus inhibiting the formation of three-dimensional network structure. The core-shell polymer@SiO_(2) hybride nanoparticles are one of promising additives for inhibiting crystallization of waxes and agglomeration of asphaltenes in crude oil.
文摘The article presents the results of studies on the resistance of hybrid cotton lines to a new virulent isolate (strain) of the fungus <i><span style="font-family:Verdana;">Fusarium verticillioides</span></i><span style="font-family:Verdana;"> upon inoculation of the host plant. Based on the studies, it was found that the complex genotypic resistance of the studied lines</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span></span></span></span></span><span><span><span><span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> when the host plants are inoculated with isolates of -100</span><i><span style="font-family:Verdana;"> V. dahliae</span></i></span></span></span></span></span></span></span><span><span><span><span><span><span><i><span style="font-family:;" "=""> </span></i></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><i><span style="font-family:Verdana;">Kleb</span></i></span></span></span></span></span></span><span><span><span><span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> fungus and 103 </span><i><span style="font-family:Verdana;">Fusarium verticillioides</span></i><span style="font-family:Verdana;"> fungi</span></span></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> depend</span></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span></span></span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> on the degree of resistance of the parental forms and their combination ability.</span></span></span></span></span></span></span>
基金co-supported by the National Natural Science Foundation of China(No.52125504)the Liaoning Revitalization Talents Program(No.XLYC2202017)Dalian Support Policy Project for Innovation of Technological Talents(No.2023RG001)。
文摘The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input multi-output,nonlinearity,and strong coupling presents significant challenges.The substantial internal force generated during the adjustment process can potentially damage the LAC and degrade the assembly quality.Hence,a workspace-based hybrid force position control scheme was developed to achieve high quality assembly with high-precision and lower internal force.Firstly,an offline workspace analysis with inherent geometric characteristics to form time-varying posture error constraint.Then,the posture error is integrated into the online position axis control to ensure tracking the ideal posture,while the force control axis compensates for posture deviation by minimizing internal force,thereby achieving high precision and low internal force.Finally,the effectiveness was demonstrated through experiments.The root mean square errors of orientation and position are 104 rad and 0.1 mm,respectively.A reduction in internal force can range from 10.96%to 57.4%compared to the traditional method.Key points'max position error is decreased from 0.32 mm to 0.18 mm,satisfying the 0.5 mm tolerance.Therefore,the proposed method will help promote the development of high-performance manufacturing.
基金supported by the Natural Science Foundation of China(Grant Nos.T2325013,52288102,52090024,12034009,12474004,and 12304036)the National Key R&D Program of China Grant No.2023YFA1610000+1 种基金the Fundamental Research Funds for the Central Universitiesthe Program for Jilin University and Sun Yat-sen University.
文摘We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptional mechanical performance,including an ideal compressive strength of 343 GPa at a pressure of 300 GPa,~33% higher than that of diamond.This ultrahigh strength arises from the synergistic interplay between its three-dimensional covalent framework and hybridized bonding topology,which enables isotropic stress accommodation and dynamic electronic rearrangement.These results establish cage-N as a promising non-carbon ultrahard material and provide a bonding-driven route toward designing superhard frameworks under extreme conditions.
基金funded by Ministry of Higher Education Malaysia through Universiti Malaysia Pahang Al-Sultan Abdullah under Internal Research Grant(RDU233003).
文摘This paper proposes a tamper detection technique for semi-fragile watermarking using Quantizationbased Discrete Cosine Transform(DCT)for tamper localization.In this study,the proposed embedding strategy is investigated by experimental tests over the diagonal order of the DCT coefficients.The cover image is divided into non-overlapping blocks of size 8×8 pixels.The DCT is applied to each block,and the coefficients are arranged using a zig-zag pattern within the block.In this study,the low-frequency coefficients are selected to examine the impact of the imperceptibility score and tamper detection accuracy.High accuracy of tamper detection can be achieved by checking the surrounding blocks to determine whether the corresponding block has been tampered with.The proposed tamper detection is tested under various malicious,incidental,and hybrid attacks(both incidental and malicious attacks).The experimental results demonstrate that the proposed technique achieves a Peak-Signal-to-Noise Ratio(PSNR)value of 41.2318 dB,an average Structural Similarity Index Measure(SSIM)value of 0.9768.The proposed scheme is also evaluated against malicious attacks such as copy-move,object deletion,object manipulation,and collage attacks.The proposed scheme can detect the malicious attack localization under various tampering rates.In addition,the proposed scheme can still detect tampered pixels under a hybrid attack,such as a combination ofmalicious and incidental attacks,with an average accuracy of 96.44%.
文摘The world’s first hybrid commutated converter(HCC)—a next-generation high-voltage direct current(HVDC)transmission valve based on integrated gate commutated thyristor(IGCT)technology—officially commenced commercial operation at the Lingbao Converter Station in Henan Province,China,on December 28,2025,as shown in Figure 1.This milestone signifies the resolution of the“commutation failure”challenge that has plagued global HVDC transmission systems for over half a century.
基金supported by the National Natural Science Foundation of China(32161143024,32500368,31970405)the Third Xinjiang Scientific Expedition Program(2022xjkk0200)+2 种基金INSF-NSFC Joint Research Project(No.4002006)HUN-REN-Debrecen University Reproductive Strategies Research Group grant(Ref 1102207)supported by the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240121。
文摘Penduline tits(genus Remiz)are small passerines distributed across Europe,Central and East Asia,and North Africa,renowned for their elaborate nests and unusually diverse mating systems.However,the taxonomy and evolutionary relationships within this genus have remained contentious due to overlapping breeding distributions and extensive hybridization.Using broad-range geographic sampling and whole-genome sequencing,here we report the phylogenetic relationships within this genus.Our results from maximum likelihood trees,species trees,population structure,and PCA analyses consistently identify four distinct,well-supported monophyletic clades.Based on these robust results,we support dividing Remiz into four species:the Eurasian Penduline Tit(R.pendulinus),Black-headed Penduline Tit(R.macronyx),White-crowned Penduline Tit(R.coronatus),and Chinese Penduline Tit(R.consobrinus).Among these species,R.consobrinus diverged earlier from other species,followed by R.coronatus,and then,R.pendulinus and R.macronyx.R.pendulinus and R.macronyx showed shallow genetic differentiation with recent divergence(~87,000 years ago)and ongoing gene flow.Our findings demonstrate the effectiveness of phylogenomic approaches in resolving taxonomic ambiguities and provide a robust evolutionary framework for tracing the diversification of life history traits,particularly nest structures and mating systems,across the genus.
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
文摘D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers using the distinct d-π hybridization monomers R_(1) and R_(2).R_(1)(Ru^(Ⅱ)-(tpz)Cl_(2))features tetradentate ligands(tpz,6,6'-di(1H-pyrazol-1-yl)-2,2'-bipyridine)enforcing quasi-octahedral geometry;R_(2)(Ru^(Ⅱ)-(bpp)_(2))incorporates tridentate ligands(bpp,2,6-di(1H-pyrazol-1-yl)pyridine)inducing pronounced geometric distortion.The planar ligand(tpz)in R_(1) facilitates ordered molecular assembly through high conformational rigidity and extensive π-π stacking,resulting in increased molecular densities and enhanced morphological uniformity compared to R_(2) metallopolymers.Due to pyrazole’s weaker π-acceptance and strongerσ-donation compared to pyridine,R_(1) exhibits a 119 nm red-shift in metal-to-ligand charge transfer(MLCT)band and a 30 mV anodic shift in Ru^(+2/+3)redox potential relative to R_(2).Coupled with a reduced HOMO-LUMO gap,the uniform and ordered structure leads to a lower conductance decay constant in R_(1).Additionally,R_(2) metallopolymers exhibit superior memristive performance(characterized by lower switching voltage and higher switching ratio)via redox-induced aromatic transitions in axial ligands enhancing electronic delocalization.This work compares two metallopolymers with different ligand geometries,revealing how this difference leads to distinct charge transport and memristive behaviors.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金support from the Ministry of Science and Technology of Taiwan(Contract Nos.113-2221-E-011-130-MY2 and 113-2218-E-011-002)the support from Intelligent Manufactur-ing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan.
文摘Accurate photovoltaic(PV)power generation forecasting is essential for the efficient integration of renewable energy into power grids.However,the nonlinear and non-stationary characteristics of PV power signals,driven by fluctuating weather conditions,pose significant challenges for reliable prediction.This study proposes a DOEP(Decomposition–Optimization–Error Correction–Prediction)framework,a hybrid forecasting approach that integrates adaptive signal decomposition,machine learning,metaheuristic optimization,and error correction.The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features.Subsequently,the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy.The main novelty of the proposed DOEP framework lies in the incorporation of Segment-based EBQPSO as a structured optimization mechanism that balances elite exploitation and population diversity during LSSVM tuning within the CEEMDAN-based forecasting pipeline.This strategy effectively mitigates convergence instability and sensitivity to initialization,which are common limitations in existing hybrid PV forecasting models.Each IMF is then predicted individually and aggregated to generate an initial forecast.In the error-correction stage,the residual error series is modeled using LSTM,and the final prediction is obtained by combining the initial forecast with the predicted error component.The proposed framework is evaluated using two PV power plant datasets with different levels of complexity.The results demonstrate that DOEP consistently outperforms benchmark models across multiple error-based and goodness-of-fit metrics,achieving MSE reductions of approximately 15%–60%on the ResPV-BDG dataset and 37%–92%on the NREL dataset.Analyses of predicted vs.observed values and residual distributions further confirm the superior calibration and robustness of the proposed approach.Although the DOEP framework entails higher computational costs than single model methods,it delivers significantly improved accuracy and stability for PV power forecasting under complex operating conditions.
基金financial support of the National Natural Science Foundation of China (21875247,21072221, 21172252)the Project of Talent Cultivation for Carbon Peak and Carbon Neutrality of the University of Chinese of Academy of Science
文摘The design and fabrication of ordered epitaxial MOF-on-MOF heterostructures as highly efficient electrocatalysts for water splitting is crucial but still challenging.In this study,a simple coordination-driven self-assembly method is used to fabricate controllable MOF-on-MOF multiscale heterostructures,where triangular host MOF(ZIF-67)nanosheets undergo in situ epitaxial growth to form uniform orthogonal vip MOF(CoFe PBA)nanosheets.Phosphorus(P)is further introduced in situ to fabricate CoP and Fe_(2)P heterostructured nanosheets(CoFe-P-NS),which exhibit excellent bifunctional electrocatalytic performance due to the enhancement of intrinsic electrocatalytic activity by p-d orbital hybridization.Specifically,the CoFe-P-NS requires low overpotential of 259 and 307 mV to reach 500 mA cm−2 for HER and OER,respectively.Remarkably,the assembled electrolysis cell maintained a large current density of 300 mA cm−2 for over 360 h with negligible voltage increase during alkaline seawater electrolysis.Experiments and theoretical calculations show that the synergistic catalytic activity of bimetallic phosphides arises from p-d orbital hybridization,where the CoP-P sites enhance HER by optimizing H*adsorption in the Volmer-Heyrovsky steps,while the Fe_(2)P-Fe sites accelerate OER by lowering the energy barrier of the rate-determining step from O*to OOH*.This study provides valuable insights into the design of a controllable MOF-on-MOF-based electrocatalyst toward alkaline seawater splitting.
基金supported by the Major Basic Research Projects of Shandong Natural Science Foundation(ZR2024ZD37)the Taishan Scholar Program of Shandong Province,China(No.tsqn202211048)+3 种基金the National Natural Science Foundation of China(No.22179123,22579155)the National Science Fund for Distinguished Young Scholars(52125305)the Science and Technology Key Project of Wuhan(No.2023010302020030)and the Science and Technology Major Project of Xinjiang Autonomous Region(No.2022A03009).
文摘Zinc-ion hybrid supercapacitors(ZIHCs)are compelling candidates for next-generation energy storage owing to their intrinsic safety,low cost,and high power density.However,their practical implementation remains hindered by the limited energy density of traditional carbon-based cathodes.Here,we rationally design porous carbon nanofibers embedded with atomically dispersed Zn and Fe dual-metal sites(ZnFe/PCNFs),synthesized via electrospinning followed by controlled carbonization.The introduction of Fe modulates the local electronic structure of Zn centers,thereby facilitating enhanced d-orbital hybridization and stronger ion adsorption through the formation of ZnFeN_(6) coordination motifs.Coupled with high surface area and hierarchical porosity,these atomic-level interactions facilitate exceptional ion accessibility and rapid charge-transfer kinetics.As a cathode for ZIHCs,ZnFe/PCNFs deliver a specific capacity of 213 mAh g^(-1),exceptional high-rate capability,and longterm cycling stability over 20000 cycles.This work elucidates mechanisms of dual-metal atomic coordination and provides a robust design strategy for high-performance,durable aqueous energy storage systems.
基金funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.:5199990914048).
文摘Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been proposed across classical image processing,machine learning(ML),deep learning(DL),and hybrid/ensemble models.This study conducts a systematic literature review using the PRISMA methodology,analyzing 57 selected articles to explore how these methods have evolved and been applied.The review highlights the strengths and limitations of each approach,identifies commonly used public datasets,and observes emerging trends in model integration and clinical relevance.By synthesizing current findings,this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection.Overall,our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources,while DL models are preferable when pixel-level annotations and resources are available,and hybrid pipelines are most appropriate when fine-grained clinical precision is required.
文摘In an academic environment increasingly shaped by metrics and the imperatives of“publish or perish”,it is rare to encounter a leading scientist willing to interweave personal narrative with conceptual reflection.The Soul of Geography by Fu(2025)achieves precisely this.The book resists simple categorisation:it is neither a conventional monograph nor a memoir,but rather a hybrid text that integrates autobiography,disciplinary reflection,and scientific arguments.In doing so,Fu articulates not only the trajectory of his own career but also a vision of geography as a discipline of theoretical depth and practical relevance.
基金supported by European Commission and is a part of the HORIZON2020 project RES Heatfunding from the European Union’s Horizon 2020 program in the field of research and innovation on the basis of grant agreement No.956255.
文摘Decarbonising the building sector,particularly residential heating,represents a critical challenge for achieving carbon-neutral energy systems.Efficient solutions must integrate both technological performance and renewable energy sources while considering operational constraints of existing systems.This study investigates a hybrid heating system combining a natural gas boiler(NGB)with an air-to-water heat pump(AWHP),evaluated through a combination of laboratory experiments and dynamic modelling.A prototype developed in the Electrical and Energy Engineering Laboratory enabled the characterization of both heat generators,the collection of experimental data,and the calibration of a MATLAB/Simulink model,including emissions and exhaust analyses.Sensitivity analyses were performed to identify optimal configurations for energy efficiency and system control,accounting for interactions between subsystems.Results highlight that hybridisation significantly improves primary energy efficiency and reduces fuel consumption compared to conventional NGB-only systems.Environmental performance,assessed through CO_(2) and NOx emissions and renewable energy integration,demonstrates the benefits of partial electrification in the residential sector.Economic assessment further quantifies decarbonization costs and fuel savings,illustrating tradeoffs between low-capital,moderate-performance systems and high-efficiency,high-renewable solutions requiring larger investments.The analysis shows that strategic decisions for residential decarbonisation cannot be separated from system-wide considerations,including control strategies,component integration,and economic feasibility.The study underlines the importance of hybrid and renewable-based solutions as pivotal pathways for energy transition in the residential building sector.
文摘This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.
