Assessing individual differences and variability in animal movement patterns is essential to improve our understanding of the evolution and ontogeny of migratory strategies.In long-distance migratory species,fledged j...Assessing individual differences and variability in animal movement patterns is essential to improve our understanding of the evolution and ontogeny of migratory strategies.In long-distance migratory species,fledged juveniles often rely on an extremely restricted time span to learn the essential skills for survival and to prepare for migration,possibly the most risky phase of their lives.Collecting detailed information on the dynamics of the movements during the crucial pre-migratory phase is hence essential to understand the solutions developed by migratory species in different environmental contexts.Here,we used high-resolution GPS/GSM transmitters to collect information on the movement ecology of seven juvenile Montagu's Harriers(Circus pygargus)born in central Italy,investigating their early life stages,namely the post-fledging dependence period(PFDP)and the pre-migratory phase(PMP),until autumn migration.After fledging,individuals showed high variability(both in space and time)in home range size,daily distances covered(6.88±11.44 km/day),distance from the nest(1.45±2.8 km)and PFDP length(23.3±5.3 days).Residence time at the natal site significantly decreased,while time interval between revists in the natal area significantly increased,as the PFDP progressed.During the PMP,explored areas and distance from the nest(max value up to 320.8 km)varied among individuals,despite daily distances covered(27±40 km/day)and time allocation between traveling(60.7%)and foraging(39.3%)were similar across individuals.The PMP lasted 38±14 days.Land cover composition of foraging locations was mostly represented by agricultural lands(~78.2%),though habitat use differed among individuals.More than 76%of such locations were outside protected areas.This individual-based tracking study represents a novel approach that improves previous knowledge based on field studies on the early life stages of the Montagu's Harrier.High inter-individual variability in movement patterns,broad-range exploratory movements and foraging locations outside the protected area network make the application of standard conservation measures difficult,raising concerns about the long-term preservation of this vulnerable migratory species in Italy.展开更多
The increasing conversion of agricultural land to organic farming requires the development of specifically adapted cultivars.So far,in tomato there is lack of research for selection of germplasm suitable for sustainab...The increasing conversion of agricultural land to organic farming requires the development of specifically adapted cultivars.So far,in tomato there is lack of research for selection of germplasm suitable for sustainable agroecosystems.In this study,we investigated the genotypic and environmental factors affecting the variation of plant,fruits,and root traits in 39 tomato genotypes grown under organic farming conditions.Four independent experiments were conducted in Italy and Spain across two consecutive seasons in 2019 and 2020.For all traits,the factorial linear regression model to estimate the main effects of genotype(G),location(L),year of cultivation(Y)and their interactions,revealed highly significant(P<0.001)variations,with the G factor being largely predominant for most traits.The implementation of the“which-won-where”,“mean performance versus stability”and“discriminative vs representativeness”patterns in the GGE(Genotype plus Genotype by Environment interaction)analysis,allowed the identification of superior cultivars with high stability across the testing environments.Genomic characterization with 30890 high quality SNPs from dd RADseq genotyping analysis,revealed that a specific cluster of cherry tomato accessions were low performing in terms of yield and fruit weight,on the contrary,showed a high content of soluble solids,which in agreement with GGE analysis.Results of this study provide a framework for the potential use of this locally adapted tomato germplasm to address the needs of more sustainable agriculture.展开更多
This study systematically investigated the microstructural evolution of binary Ni-Cu alloys(Cu55Ni45,Cu60Ni40,and Ni65Cu35)under deep undercooling conditions.The controlled rapid solidification experiments combined wi...This study systematically investigated the microstructural evolution of binary Ni-Cu alloys(Cu55Ni45,Cu60Ni40,and Ni65Cu35)under deep undercooling conditions.The controlled rapid solidification experiments combined with optical microscopy and electron backscatter diffraction(EBSD)analysis demonstrate that increasing undercooling(ΔT)can induce a consistent sequence of microstructural transitions:coarse dendrites,fine equiaxed grains(first refinement),oriented fine dendrites,and fine equiaxed grains(second refinement).Two distinct grain refinement events are identified,with critical undercooling thresholds(ΔT)dependent on composition:increasing Cu content increases the critical undercoolingΔT*required for the second refinement(Cu55Ni45:227 K;Cu60Ni40:217 K;Ni65Cu35:200 K).The BCT(Bridgman Crystal Growth)model quantitatively elucidates this behavior,revealing a shift from solute-diffusion-dominated growth at low undercooling to thermally dominated diffusion at high undercooling(ΔT).Crucially,refined grains at high undercooling exhibit smaller sizes(10μm)and higher uniformity than those at low undercooling(20μm).These findings provide fundamental insights into non-equilibrium solidification mechanisms and establish a foundation for designing high-performance Ni-Cu alloys via deep undercooling processing.展开更多
The integration of academic research methodologies into design thinking processes presents a transformative approach to addressing complex challenges in group housing,fostering inclusive,sustainable,and user-centered ...The integration of academic research methodologies into design thinking processes presents a transformative approach to addressing complex challenges in group housing,fostering inclusive,sustainable,and user-centered solutions.This research explores how methodologies such as Participatory Action Research,post-occupancy evaluations,and Research through Design can be systematically embedded within design thinking to bridge the gap between academic rigor and empathy-driven,iterative design practices.By synthesizing these paradigms,the study proposes a framework for group housing design that prioritizes co-design processes,empathy-based data collection,and participatory evaluation,while emphasizing adaptability through sociocultural insights and user feedback.Case studies analysis demonstrate the effectiveness of flexible,community-driven design,while emerging technologies like IoT-enabled cohousing signal new opportunities for innovation.Challenges,including scalability,long-term validation,and reconciling user autonomy with professional expertise,are critically analyzed.Ultimately,this research advances a hybrid methodology to redefine the conceptualization,implementation,and assessment of group housing,offering actionable pathways to achieve affordable,inclusive,and context-sensitive housing solutions.展开更多
A brain tumor is a disease in which abnormal cells form a tumor in the brain.They are rare and can take many forms,making them difficult to treat,and the survival rate of affected patients is low.Magnetic resonance im...A brain tumor is a disease in which abnormal cells form a tumor in the brain.They are rare and can take many forms,making them difficult to treat,and the survival rate of affected patients is low.Magnetic resonance imaging(MRI)is a crucial tool for diagnosing and localizing brain tumors.However,themanual interpretation of MRI images is tedious and prone to error.As artificial intelligence advances rapidly,DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors.In this study,we introduce a deep convolutional neural network(DCNN)framework for brain tumor classification that uses EfficientNet-B6 as the backbone architecture and adds additional layers.The model achieved an accuracy of 99.10%on the public Brain Tumor MRI datasets,and we performed an ablation study to determine the optimal batch size,optimizer,loss function,and learning rate to maximize the accuracy and robustness of the model,followed by K-Fold cross-validation and testing the model on an independent dataset,and tuning Hyperparameters with Bayesian Optimization to further enhance the performance.When comparing our model to other deep learning(DL)models such as VGG19,MobileNetv2,ResNet50,InceptionV3,and DenseNet201,aswell as variants of the EfficientNetmodel(B1–B7),the results showthat our proposedmodel outperforms all othermodels.Our investigational results demonstrate superiority in terms of precision,recall/sensitivity,accuracy,specificity,and F1-score.Such innovations can potentially enhance clinical decision-making and patient treatment in neurooncological settings.展开更多
Different habitat types exert particular challenges to ecological performance,ultimately having a strong influence on the evolution of morphology.Although it is well known that external morphology can evolve under the...Different habitat types exert particular challenges to ecological performance,ultimately having a strong influence on the evolution of morphology.Although it is well known that external morphology can evolve under the selective pressure of habitat structure,the evolutionary response of internal morphological traits remains vastly unexplored.Here,we test for morphological divergence between arenicolous and nonarenicolous species in a clade of tropidurid lizards,considering external morphological proportions and limb muscle dimensions.We found that arenicolous species seem to have evolved internal and external morphological adaptations that separate them from other habitat specialists.Moreover,comparative analyses suggested that the traits that differed the most between arenicolous and nonarenicolous lizards might have evolved divergently towards different optima.Additionally,the axis of higher morphological divergence between arenicolous and nonarenicolous species represented an important proportion of the morphological diversity within our sample,indicating that the hypothetical adaptive divergence of internal and external traits has contributed significantly to phenotypic diversity.Our results show that evolutionary associations between morphology and habitat use can be detected on both external body proportions and muscle morphology.Moreover,they highlight the emergent importance of internal anatomical traits in ecomorphological studies,especially when such traits are directly involved in determining functional performance.展开更多
Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.Th...Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold.However,the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR.Ramanujan’s sum(s)(RS)is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals.Hence,we propose a novel DL method with threshold estimation based on RS to improve the output SNR.In this work,we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising.We analyze the results of the proposed work on synthetically generated and field data sets.We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error.The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD,SURE-based,and adaptive soft-thresholding techniques.展开更多
The management of agricultural wastes is essential for resource conservation and environmental sustainability.Due to escalating worries regarding plastic pollution and the surging expenses linked to petroleum-based pl...The management of agricultural wastes is essential for resource conservation and environmental sustainability.Due to escalating worries regarding plastic pollution and the surging expenses linked to petroleum-based plastics,there has been a notable transition towards the creation of biodegradable alternatives sourced from natural materials.Biofibres and bioplastics,especially those derived from agricultural waste,have garnered significant attention for their prospective uses in food packaging,biomedical sciences,and sustainable manufacturing.This study examines the viability of employing banana peel as a natural and environmentally sustainable raw material for the production of biodegradable bioplastic sheets.Due to its abundant polysaccharides and lignocellulosic fibers,banana peel presents advantageous structural and mechanical characteristics for bioplastic manufacturing.Experimental findings demonstrate that bioplastic derived from banana peels has enhanced biodegradability and environmental compatibility relative to traditional synthetic plastics,positioning it as a feasible alternative to mitigate the worldwide plastic waste epidemic.An optimal formulation was constructed using Design Expert software,comprising 55.38 g of banana peel,27.63 g of fish scales,and 20 g of chitosan powder.This formulation improves the film’s tensile strength,flexibility,and degradation rate,ensuring its efficacy in industrial applications including food packaging and molding.The study’s results highlight the promise of bioplastics made from banana peels as an economical and sustainable alternative,decreasing dependence on petroleum-based plastics and alleviating environmental pollution.展开更多
Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(D...Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive analytics.CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains.This survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation capabilities.The layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless integration.The study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these obstacles.Applications in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT integration.In addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT systems.By synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and practitioners.The findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.展开更多
Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate...Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate the daily mean(CFR)and diurnal variation(CDV)of cloud fraction across high-,middle-,low-level,and total clouds in the FGOALS-f3-L general circulation model.The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels.Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime.The simulation biases of low clouds,especially their CDV biases,dominate the bias of total CDV.Compensation effects exist among the bias decompositions,where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds.Meanwhile,the bias contributions have notable land–ocean differences and region-dependent characteristics,consistent with the model biases in these variables.Additionally,the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects.It reveals that the impacts of CDV biases can reach half of those from CFR biases,highlighting the importance of accurate CDV representation in climate models.展开更多
Nerve guidance conduits(NGCs)effectively support and guide the regeneration of injured nerves.However,traditional NGCs often lack essential growth factors and fail to create a biomimetic microenvironment conducive to ...Nerve guidance conduits(NGCs)effectively support and guide the regeneration of injured nerves.However,traditional NGCs often lack essential growth factors and fail to create a biomimetic microenvironment conducive to nerve regrowth.This study develops a highly bionic nerve guidance conduit(HB-NGC)using hybrid high-voltage electrotechnologies that integrate electrospinning with electrohydrodynamic(EHD)printing.The outer layer consists of electrospun polycaprolactone fibers loaded with carboxyl-multi-walled carbon nanotubes,while the inner layer is composed of highly aligned polycaprolactone fibers created by EHD printing.The tubular core of the HB-NGC is filled with hyaluronic acid methacryloyl(HAMA)hydrogel encapsulating bone marrow mesenchymal stem cells(BMSCs).This highly biomimetic NGC is conductive,capable of guiding axon growth,and sustainably releases growth factors,effectively mimicking the structure,function,and characteristics of natural peripheral nerves.Its distinctive architectural layers provide an exceptional bionic microenvironment by restoring physical pathways,facilitating electrical signal conduction,and supplying an extracellular matrix(ECM)environment enriched with essential growth factors.Additionally,the HB-NGC’s morphology,along with its physicochemical and mechanical properties,effectively bridges the gap between severed nerve ends.In vivo animal studies validate the HB-NGC’s effectiveness,highlighting its significant potential to enhance peripheral nerve regeneration.展开更多
Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things(IoMT).Multi-head convolution with channel attention can facilit...Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things(IoMT).Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer.Therefore,this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention(ResMHCNN)blocks to classify medical images.We introduced three novel lightweight deep learning models(BT-Net,LCC-Net,and BC-Net)utilizing the ResMHCNN block as their backbone.These models were crossvalidated and tested on three publicly available medical image datasets:a brain tumor dataset from Figshare consisting of T1-weighted magnetic resonance imaging slices of meningioma,glioma,and pituitary tumors;the LC25000 dataset,which includes microscopic images of lung and colon cancers;and the BreaKHis dataset,containing benign and malignant breast microscopic images.The lightweight models achieved accuracies of 96.9%for 3-class brain tumor classification using BT-Net,and 99.7%for 5-class lung and colon cancer classification using LCC-Net.For 2-class breast cancer classification,BC-Net achieved an accuracy of 96.7%.The parameter counts for the proposed lightweight models—LCC-Net,BC-Net,and BT-Net—are 0.528,0.226,and 1.154 million,respectively.The presented lightweight models,featuring ResMHCNN blocks,may be effectively employed for accurate medical image classification.In the future,these models might be tested for viability in resource-constrained systems such as mobile devices and IoMT platforms.展开更多
The intensifying global issues of freshwater scarcity and antibiotic contamination,especially in coastal environments,present interconnected threats to both ecosystems and public health.Addressing these issues demands...The intensifying global issues of freshwater scarcity and antibiotic contamination,especially in coastal environments,present interconnected threats to both ecosystems and public health.Addressing these issues demands innovative solutions that synergistically enhance energy efficiency,promote sustainability,and deliver multifunctional benefits.In this study,we present a solar-driven photothermalphotocatalytic synergistic platform(SPSP)constructed from a PF/Co_(3)O_(4)/CNTs@O-ANF composite(PCCO),engineered to achieve simultaneous seawater desalination and antibiotic degradation.The strategically designed 3D hierarchical architecture combines broadband solar absorption,interfacial hydrophobic regulation,and catalytic heterojunction engineering,enabling an elevated water evaporation rate of 1.75 kg·m^(-2)·h^(–1) and efficient degradation of over 98%of tetracycline(TC)across three operational cycles.Outdoor field tests confirmed the system’s operational robustness,producing 6.82 kg·m^(–2)·day^(–1) of purified water.Comprehensive water quality analyses further verified the removal of more than 99%of dissolved salts and organic contaminants,with the collected water exhibiting a neutral pH and complete absence of residual antibiotic activity.More importantly,the purified water facilitated robust growth of Brassica rapa,resulting in a 210% increase in biomass relative to plants irrigated with contaminated water,thereby demonstrating both ecological safety and agricultural applicability.Collectively,this SPSP technology represents a substantial advancement in sustainable water treatment,offering an integrated,energy-efficient solution for producing clean water and effectively remedying antibiotics.展开更多
Sessile oak(Quercus petraea(Matt.)Liebl.)is widely distributed across most of Europe particularly the hills and lower mountain ranges,so is considered“the oak of the mountains”.This species grows on a wide variety o...Sessile oak(Quercus petraea(Matt.)Liebl.)is widely distributed across most of Europe particularly the hills and lower mountain ranges,so is considered“the oak of the mountains”.This species grows on a wide variety of soils and at altitudes ranging from sea level to 2200 m,especially in Atlantic and sub-Mediterranean climates,and it is sensitive to low winter temperatures,early and late frosts,as well as high summer temperatures.Sessile oak forms both pure and mixed stands especially with broadleaves such as European beech,European hornbeam,small-leaved lime and Acer spp.These form the understorey of sessile oak stands,promoting the natural shedding of lower branches of the oak and protecting the trunk against epicormic branches.Sessile oak is a long-lived,light-demanding and wind-firm species,owing to its taproot and heart-shaped root system.Its timber,one of the most valuable in Europe,is important for fur-niture-making(both solid wood and veneer),construction,barrels,railway sleepers,and is also used as fuelwood.It is one of the few major tree species in Europe that is regener-ated by seed(naturally or artificially)and by stump shoots in high forest,coppice-with-standards and coppice forests.Sessile oak forests are treated in both regular and irregular systems involving silvicultural techniques such as uniform shelterwood,group shelterwood,irregular shelterwood,irregular high forest,coppice-with-standards and simple coppice.Young naturally regenerated stands are managed by weeding,release cutting and cleaning-respacing,keeping the stands quite dense for good natural pruning.Plantations are based on(1)2-4-year old bare-root or container-grown seedlings produced in nurseries using seeds from genetic resources,seed stands and seed orchards.The density of sessile oak plantations(mostly in rows,but also in clusters)is usually between 4000 and 6000 ind.ha^(−1).Sessile oak silviculture of mature stands includes crown thinning,focus-ing on final crop trees(usually a maximum of 100 ind.ha^(−1))and targeting the production of large-diameter and high quality trees at long rotation ages(mostly over 120 years,sometimes 250-300 years).In different parts of Europe,conversion of simple coppices and coppice-with-standards to high forests is continuing.Even though manage-ment of sessile oak forests is very intensive and expensive,requiring active human intervention,the importance of this species in future European forests will increase in the con-text of climate change due to its high resistance to distur-bance,superior drought tolerance and heat stress resistance.展开更多
文摘Assessing individual differences and variability in animal movement patterns is essential to improve our understanding of the evolution and ontogeny of migratory strategies.In long-distance migratory species,fledged juveniles often rely on an extremely restricted time span to learn the essential skills for survival and to prepare for migration,possibly the most risky phase of their lives.Collecting detailed information on the dynamics of the movements during the crucial pre-migratory phase is hence essential to understand the solutions developed by migratory species in different environmental contexts.Here,we used high-resolution GPS/GSM transmitters to collect information on the movement ecology of seven juvenile Montagu's Harriers(Circus pygargus)born in central Italy,investigating their early life stages,namely the post-fledging dependence period(PFDP)and the pre-migratory phase(PMP),until autumn migration.After fledging,individuals showed high variability(both in space and time)in home range size,daily distances covered(6.88±11.44 km/day),distance from the nest(1.45±2.8 km)and PFDP length(23.3±5.3 days).Residence time at the natal site significantly decreased,while time interval between revists in the natal area significantly increased,as the PFDP progressed.During the PMP,explored areas and distance from the nest(max value up to 320.8 km)varied among individuals,despite daily distances covered(27±40 km/day)and time allocation between traveling(60.7%)and foraging(39.3%)were similar across individuals.The PMP lasted 38±14 days.Land cover composition of foraging locations was mostly represented by agricultural lands(~78.2%),though habitat use differed among individuals.More than 76%of such locations were outside protected areas.This individual-based tracking study represents a novel approach that improves previous knowledge based on field studies on the early life stages of the Montagu's Harrier.High inter-individual variability in movement patterns,broad-range exploratory movements and foraging locations outside the protected area network make the application of standard conservation measures difficult,raising concerns about the long-term preservation of this vulnerable migratory species in Italy.
基金supported by the European Union's Horizon 2020 Research and Innovation Programme under Grant No.774244(Breeding for resilient,efficient and sustainable organic vegetable productionBRESOV)by‘RGV-FAO'project funded by the Italian Ministry of Agriculture,Food Sovereignty and Forests。
文摘The increasing conversion of agricultural land to organic farming requires the development of specifically adapted cultivars.So far,in tomato there is lack of research for selection of germplasm suitable for sustainable agroecosystems.In this study,we investigated the genotypic and environmental factors affecting the variation of plant,fruits,and root traits in 39 tomato genotypes grown under organic farming conditions.Four independent experiments were conducted in Italy and Spain across two consecutive seasons in 2019 and 2020.For all traits,the factorial linear regression model to estimate the main effects of genotype(G),location(L),year of cultivation(Y)and their interactions,revealed highly significant(P<0.001)variations,with the G factor being largely predominant for most traits.The implementation of the“which-won-where”,“mean performance versus stability”and“discriminative vs representativeness”patterns in the GGE(Genotype plus Genotype by Environment interaction)analysis,allowed the identification of superior cultivars with high stability across the testing environments.Genomic characterization with 30890 high quality SNPs from dd RADseq genotyping analysis,revealed that a specific cluster of cherry tomato accessions were low performing in terms of yield and fruit weight,on the contrary,showed a high content of soluble solids,which in agreement with GGE analysis.Results of this study provide a framework for the potential use of this locally adapted tomato germplasm to address the needs of more sustainable agriculture.
基金Funded by the Central Government-Guided Local Development Fund Project(No.YDZJSX2025D042)the Key R&D Program of Shanxi Province(No.202202150401018)+1 种基金the Basic Research Program of Shanxi Province(No.20210302124220)the State Key Laboratory of CAD/CG of Zhejiang University(No.A2325)。
文摘This study systematically investigated the microstructural evolution of binary Ni-Cu alloys(Cu55Ni45,Cu60Ni40,and Ni65Cu35)under deep undercooling conditions.The controlled rapid solidification experiments combined with optical microscopy and electron backscatter diffraction(EBSD)analysis demonstrate that increasing undercooling(ΔT)can induce a consistent sequence of microstructural transitions:coarse dendrites,fine equiaxed grains(first refinement),oriented fine dendrites,and fine equiaxed grains(second refinement).Two distinct grain refinement events are identified,with critical undercooling thresholds(ΔT)dependent on composition:increasing Cu content increases the critical undercoolingΔT*required for the second refinement(Cu55Ni45:227 K;Cu60Ni40:217 K;Ni65Cu35:200 K).The BCT(Bridgman Crystal Growth)model quantitatively elucidates this behavior,revealing a shift from solute-diffusion-dominated growth at low undercooling to thermally dominated diffusion at high undercooling(ΔT).Crucially,refined grains at high undercooling exhibit smaller sizes(10μm)and higher uniformity than those at low undercooling(20μm).These findings provide fundamental insights into non-equilibrium solidification mechanisms and establish a foundation for designing high-performance Ni-Cu alloys via deep undercooling processing.
文摘The integration of academic research methodologies into design thinking processes presents a transformative approach to addressing complex challenges in group housing,fostering inclusive,sustainable,and user-centered solutions.This research explores how methodologies such as Participatory Action Research,post-occupancy evaluations,and Research through Design can be systematically embedded within design thinking to bridge the gap between academic rigor and empathy-driven,iterative design practices.By synthesizing these paradigms,the study proposes a framework for group housing design that prioritizes co-design processes,empathy-based data collection,and participatory evaluation,while emphasizing adaptability through sociocultural insights and user feedback.Case studies analysis demonstrate the effectiveness of flexible,community-driven design,while emerging technologies like IoT-enabled cohousing signal new opportunities for innovation.Challenges,including scalability,long-term validation,and reconciling user autonomy with professional expertise,are critically analyzed.Ultimately,this research advances a hybrid methodology to redefine the conceptualization,implementation,and assessment of group housing,offering actionable pathways to achieve affordable,inclusive,and context-sensitive housing solutions.
基金funded by the King Saud University,Riyadh,Saudi Arabia,for funding this work through the Researchers Supporting Research Funding program,(ORF-2025-1268).
文摘A brain tumor is a disease in which abnormal cells form a tumor in the brain.They are rare and can take many forms,making them difficult to treat,and the survival rate of affected patients is low.Magnetic resonance imaging(MRI)is a crucial tool for diagnosing and localizing brain tumors.However,themanual interpretation of MRI images is tedious and prone to error.As artificial intelligence advances rapidly,DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors.In this study,we introduce a deep convolutional neural network(DCNN)framework for brain tumor classification that uses EfficientNet-B6 as the backbone architecture and adds additional layers.The model achieved an accuracy of 99.10%on the public Brain Tumor MRI datasets,and we performed an ablation study to determine the optimal batch size,optimizer,loss function,and learning rate to maximize the accuracy and robustness of the model,followed by K-Fold cross-validation and testing the model on an independent dataset,and tuning Hyperparameters with Bayesian Optimization to further enhance the performance.When comparing our model to other deep learning(DL)models such as VGG19,MobileNetv2,ResNet50,InceptionV3,and DenseNet201,aswell as variants of the EfficientNetmodel(B1–B7),the results showthat our proposedmodel outperforms all othermodels.Our investigational results demonstrate superiority in terms of precision,recall/sensitivity,accuracy,specificity,and F1-score.Such innovations can potentially enhance clinical decision-making and patient treatment in neurooncological settings.
文摘Different habitat types exert particular challenges to ecological performance,ultimately having a strong influence on the evolution of morphology.Although it is well known that external morphology can evolve under the selective pressure of habitat structure,the evolutionary response of internal morphological traits remains vastly unexplored.Here,we test for morphological divergence between arenicolous and nonarenicolous species in a clade of tropidurid lizards,considering external morphological proportions and limb muscle dimensions.We found that arenicolous species seem to have evolved internal and external morphological adaptations that separate them from other habitat specialists.Moreover,comparative analyses suggested that the traits that differed the most between arenicolous and nonarenicolous lizards might have evolved divergently towards different optima.Additionally,the axis of higher morphological divergence between arenicolous and nonarenicolous species represented an important proportion of the morphological diversity within our sample,indicating that the hypothetical adaptive divergence of internal and external traits has contributed significantly to phenotypic diversity.Our results show that evolutionary associations between morphology and habitat use can be detected on both external body proportions and muscle morphology.Moreover,they highlight the emergent importance of internal anatomical traits in ecomorphological studies,especially when such traits are directly involved in determining functional performance.
文摘Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold.However,the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR.Ramanujan’s sum(s)(RS)is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals.Hence,we propose a novel DL method with threshold estimation based on RS to improve the output SNR.In this work,we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising.We analyze the results of the proposed work on synthetically generated and field data sets.We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error.The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD,SURE-based,and adaptive soft-thresholding techniques.
文摘The management of agricultural wastes is essential for resource conservation and environmental sustainability.Due to escalating worries regarding plastic pollution and the surging expenses linked to petroleum-based plastics,there has been a notable transition towards the creation of biodegradable alternatives sourced from natural materials.Biofibres and bioplastics,especially those derived from agricultural waste,have garnered significant attention for their prospective uses in food packaging,biomedical sciences,and sustainable manufacturing.This study examines the viability of employing banana peel as a natural and environmentally sustainable raw material for the production of biodegradable bioplastic sheets.Due to its abundant polysaccharides and lignocellulosic fibers,banana peel presents advantageous structural and mechanical characteristics for bioplastic manufacturing.Experimental findings demonstrate that bioplastic derived from banana peels has enhanced biodegradability and environmental compatibility relative to traditional synthetic plastics,positioning it as a feasible alternative to mitigate the worldwide plastic waste epidemic.An optimal formulation was constructed using Design Expert software,comprising 55.38 g of banana peel,27.63 g of fish scales,and 20 g of chitosan powder.This formulation improves the film’s tensile strength,flexibility,and degradation rate,ensuring its efficacy in industrial applications including food packaging and molding.The study’s results highlight the promise of bioplastics made from banana peels as an economical and sustainable alternative,decreasing dependence on petroleum-based plastics and alleviating environmental pollution.
文摘Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive analytics.CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains.This survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation capabilities.The layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless integration.The study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these obstacles.Applications in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT integration.In addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT systems.By synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and practitioners.The findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.
基金supported by the National Natural Science Foundation of China[grant number 42275074].
文摘Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate the daily mean(CFR)and diurnal variation(CDV)of cloud fraction across high-,middle-,low-level,and total clouds in the FGOALS-f3-L general circulation model.The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels.Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime.The simulation biases of low clouds,especially their CDV biases,dominate the bias of total CDV.Compensation effects exist among the bias decompositions,where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds.Meanwhile,the bias contributions have notable land–ocean differences and region-dependent characteristics,consistent with the model biases in these variables.Additionally,the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects.It reveals that the impacts of CDV biases can reach half of those from CFR biases,highlighting the importance of accurate CDV representation in climate models.
基金supported by the Natural Science Foundation of Hebei Province of China(Nos.H2020202002 and H2023202001)the Natural Science Foundation of Tianjin City of China(No.24JCQNJC01180)Science Research Project of Hebei Educational Department(No.BJK2023034).
文摘Nerve guidance conduits(NGCs)effectively support and guide the regeneration of injured nerves.However,traditional NGCs often lack essential growth factors and fail to create a biomimetic microenvironment conducive to nerve regrowth.This study develops a highly bionic nerve guidance conduit(HB-NGC)using hybrid high-voltage electrotechnologies that integrate electrospinning with electrohydrodynamic(EHD)printing.The outer layer consists of electrospun polycaprolactone fibers loaded with carboxyl-multi-walled carbon nanotubes,while the inner layer is composed of highly aligned polycaprolactone fibers created by EHD printing.The tubular core of the HB-NGC is filled with hyaluronic acid methacryloyl(HAMA)hydrogel encapsulating bone marrow mesenchymal stem cells(BMSCs).This highly biomimetic NGC is conductive,capable of guiding axon growth,and sustainably releases growth factors,effectively mimicking the structure,function,and characteristics of natural peripheral nerves.Its distinctive architectural layers provide an exceptional bionic microenvironment by restoring physical pathways,facilitating electrical signal conduction,and supplying an extracellular matrix(ECM)environment enriched with essential growth factors.Additionally,the HB-NGC’s morphology,along with its physicochemical and mechanical properties,effectively bridges the gap between severed nerve ends.In vivo animal studies validate the HB-NGC’s effectiveness,highlighting its significant potential to enhance peripheral nerve regeneration.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2023-00259678)by INHA UNIVERSITY Research Grant.
文摘Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things(IoMT).Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer.Therefore,this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention(ResMHCNN)blocks to classify medical images.We introduced three novel lightweight deep learning models(BT-Net,LCC-Net,and BC-Net)utilizing the ResMHCNN block as their backbone.These models were crossvalidated and tested on three publicly available medical image datasets:a brain tumor dataset from Figshare consisting of T1-weighted magnetic resonance imaging slices of meningioma,glioma,and pituitary tumors;the LC25000 dataset,which includes microscopic images of lung and colon cancers;and the BreaKHis dataset,containing benign and malignant breast microscopic images.The lightweight models achieved accuracies of 96.9%for 3-class brain tumor classification using BT-Net,and 99.7%for 5-class lung and colon cancer classification using LCC-Net.For 2-class breast cancer classification,BC-Net achieved an accuracy of 96.7%.The parameter counts for the proposed lightweight models—LCC-Net,BC-Net,and BT-Net—are 0.528,0.226,and 1.154 million,respectively.The presented lightweight models,featuring ResMHCNN blocks,may be effectively employed for accurate medical image classification.In the future,these models might be tested for viability in resource-constrained systems such as mobile devices and IoMT platforms.
基金supported by the National Natural Science Foundation of China(No.52502267)the Hebei Province’s Funding Project for Introducing Overseas Scholars(No.C20230119)+1 种基金the Hebei Innovation and Entrepreneurship Education Teaching Reform Project(No.2023cxcy145)the Science Research Project of Hebei Education Department(No.BJK2023019).
文摘The intensifying global issues of freshwater scarcity and antibiotic contamination,especially in coastal environments,present interconnected threats to both ecosystems and public health.Addressing these issues demands innovative solutions that synergistically enhance energy efficiency,promote sustainability,and deliver multifunctional benefits.In this study,we present a solar-driven photothermalphotocatalytic synergistic platform(SPSP)constructed from a PF/Co_(3)O_(4)/CNTs@O-ANF composite(PCCO),engineered to achieve simultaneous seawater desalination and antibiotic degradation.The strategically designed 3D hierarchical architecture combines broadband solar absorption,interfacial hydrophobic regulation,and catalytic heterojunction engineering,enabling an elevated water evaporation rate of 1.75 kg·m^(-2)·h^(–1) and efficient degradation of over 98%of tetracycline(TC)across three operational cycles.Outdoor field tests confirmed the system’s operational robustness,producing 6.82 kg·m^(–2)·day^(–1) of purified water.Comprehensive water quality analyses further verified the removal of more than 99%of dissolved salts and organic contaminants,with the collected water exhibiting a neutral pH and complete absence of residual antibiotic activity.More importantly,the purified water facilitated robust growth of Brassica rapa,resulting in a 210% increase in biomass relative to plants irrigated with contaminated water,thereby demonstrating both ecological safety and agricultural applicability.Collectively,this SPSP technology represents a substantial advancement in sustainable water treatment,offering an integrated,energy-efficient solution for producing clean water and effectively remedying antibiotics.
文摘Sessile oak(Quercus petraea(Matt.)Liebl.)is widely distributed across most of Europe particularly the hills and lower mountain ranges,so is considered“the oak of the mountains”.This species grows on a wide variety of soils and at altitudes ranging from sea level to 2200 m,especially in Atlantic and sub-Mediterranean climates,and it is sensitive to low winter temperatures,early and late frosts,as well as high summer temperatures.Sessile oak forms both pure and mixed stands especially with broadleaves such as European beech,European hornbeam,small-leaved lime and Acer spp.These form the understorey of sessile oak stands,promoting the natural shedding of lower branches of the oak and protecting the trunk against epicormic branches.Sessile oak is a long-lived,light-demanding and wind-firm species,owing to its taproot and heart-shaped root system.Its timber,one of the most valuable in Europe,is important for fur-niture-making(both solid wood and veneer),construction,barrels,railway sleepers,and is also used as fuelwood.It is one of the few major tree species in Europe that is regener-ated by seed(naturally or artificially)and by stump shoots in high forest,coppice-with-standards and coppice forests.Sessile oak forests are treated in both regular and irregular systems involving silvicultural techniques such as uniform shelterwood,group shelterwood,irregular shelterwood,irregular high forest,coppice-with-standards and simple coppice.Young naturally regenerated stands are managed by weeding,release cutting and cleaning-respacing,keeping the stands quite dense for good natural pruning.Plantations are based on(1)2-4-year old bare-root or container-grown seedlings produced in nurseries using seeds from genetic resources,seed stands and seed orchards.The density of sessile oak plantations(mostly in rows,but also in clusters)is usually between 4000 and 6000 ind.ha^(−1).Sessile oak silviculture of mature stands includes crown thinning,focus-ing on final crop trees(usually a maximum of 100 ind.ha^(−1))and targeting the production of large-diameter and high quality trees at long rotation ages(mostly over 120 years,sometimes 250-300 years).In different parts of Europe,conversion of simple coppices and coppice-with-standards to high forests is continuing.Even though manage-ment of sessile oak forests is very intensive and expensive,requiring active human intervention,the importance of this species in future European forests will increase in the con-text of climate change due to its high resistance to distur-bance,superior drought tolerance and heat stress resistance.
