Achievements are presented for truss models of RC structures developed in previous years: 1. Two constitutive models, biaxial and triaxial, are based on regular trusses, with bars obeying nonlinear uniaxial σ-ε laws...Achievements are presented for truss models of RC structures developed in previous years: 1. Two constitutive models, biaxial and triaxial, are based on regular trusses, with bars obeying nonlinear uniaxial σ-ε laws of material under simulation;both models have been compared with test results and show a dependence of Poisson ratio on curvature of σ-ε law. 2. A truss finite element has been used in the nonlinear static and dynamic analysis of plane RC frames;it has been compared with test results and describes, in a simple way, the formation of plastic hinges. 3. Thanks to the very simple geometry of a truss, the equilibrium equations can be easily written and the stiffness matrix can be easily updated, both with respect to the deformed truss, within each step of a static incremental loading or within each time step of a dynamic analysis, so that to take into account geometric nonlinearities. So the confinement of a RC column is interpreted as a structural stability effect of concrete. And a significant role of the transverse reinforcement is revealed, that of preventing, by its close spacing and sufficient amount, the buckling of inner longitudinal concrete struts, which would lead to a global instability of the RC column. 4. The proposed truss model is statically indeterminate, so it exhibits some features, which are not met by the “strut-and-tie” model.展开更多
The rat high-impact free weight drop model mimics the diffuse axonal injury caused by severe traumatic brain injury in humans,while severe controlled cortical impact can produce a severe traumatic brain injury model u...The rat high-impact free weight drop model mimics the diffuse axonal injury caused by severe traumatic brain injury in humans,while severe controlled cortical impact can produce a severe traumatic brain injury model using precise strike parameters.In this study,we compare the pathological mechanisms and pathological changes between two rat severe brain injury models to identify the similarities and differences.The severe controlled cortical impact model was produced by an electronic controlled cortical impact device,while the severe free weight drop model was produced by dropping a 500 g free weight from a height of 1.8 m through a plastic tube.Body temperature and mortality were recorded,and neurological deficits were assessed with the modified neurological severity score.Brain edema and bloodbrain barrier damage were evaluated by assessing brain water content and Evans blue extravasation.In addition,a cytokine array kit was used to detect inflammatory cytokines.Neuronal apoptosis in the brain and brainstem was quantified by immunofluorescence staining.Both the severe controlled cortical impact and severe free weight drop models exhibited significant neurological impairments and body temperature fluctuations.More severe motor dysfunction was observed in the severe controlled cortical impact model,while more severe cognitive dysfunction was observed in the severe free weight drop model.Brain edema,inflammatory cytokine changes and cortical neuronal apoptosis were more substantial and blood-brain barrier damage was more focal in the severe controlled cortical impact group compared with the severe free weight drop group.The severe free weight drop model presented with more significant apoptosis in the brainstem and diffused blood-brain barrier damage,with higher mortality and lower repeatability compared with the severe controlled cortical impact group.Severe brainstem damage was not found in the severe controlled cortical impact model.These results indicate that the severe controlled cortical impact model is relatively more stable,more reproducible,and shows obvious cerebral pathological changes at an earlier stage.Therefore,the severe controlled cortical impact model is likely more suitable for studies on severe focal traumatic brain injury,while the severe free weight drop model may be more apt for studies on diffuse axonal injury.All experimental procedures were approved by the Ethics Committee of Animal Experiments of Tianjin Medical University,China(approval No.IRB2012-028-02)in Febru ary 2012.展开更多
This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing...This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing patient queries about disease and lifestyle behaviors.The models selected were ChatGPT-4o,Gemini 2.0 Pro,Claude 3.5 Sonnet,and DeepSeek V3,with 12 questions chosen by two HCV experts from the domains of prevention,diagnosis,and treatment.展开更多
Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerativ...Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerative diseases but cannot cure them.Inflammation is involved in the development and progression of neurodegenerative diseases,and oxidative stress is implicated in neurodegeneration associated with cognitive decline and age-related cognitive impairment.Polyphenols such as curcumin,quercetin,and resveratrol possess potent anti-inflammatory and antioxidant properties.Nanoformulations of curcumin and quercetin can optimize their pharmacological effects in the treatment of neurodegenerative diseases.Nanocarriers play a crucial role in delivering drugs across the blood-brain barrier,thereby lowering the risk of peripheral side effects.Various nanoforms have been developed to induce bioavailability and solubility of curcumin and quercetin,including nanoparticles and nanoemulsions.The studies reviewed included 17 using curcumin nanoformulations and seven with quercetin nanoformulations and were tested in widely used animal models of Alzheimer’s disease,Parkinson’s disease,Huntington’s disease,and multiple sclerosis.Many of the curcumin and quercetin nanoformulations brought about improvements in learning and memory in behavioral tests of Alzheimer’s disease models and were effective in reducing oxidative stress in the brain.Both nanocurcumin and nanoquercetin decreased the levels of inflammatory markers in the brain.Nanocurcumin formulations improved motor behavior,gait,and memory in Parkinson’s disease models and increased dopaminergic neurons in the striatum and substantia nigra.Furthermore,nanocurcumin improved locomotor activity,memory,and learning,and the number of dendrites of medium spiny neurons in Huntington’s disease models.Nanocurcumin formulations decreased oxidative stress and inflammation in a model of demyelination.Several important limitations were identified in the studies reviewed and these need to be considered in future studies.Also,clinical trials could be performed using the currently available nanoforms of curcumin and quercetin.展开更多
The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in S...The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources.In this paper,we compare two predominant AI-based approaches for the forensic detection of malicious hate speech:(1)finetuning encoder-only models that have been trained in Spanish and(2)In-Context Learning techniques(Zero-and Few-Shot Learning)with large-scale language models.Our approach goes beyond binary classification,proposing a comprehensive,multidimensional evaluation that labels each text by:(1)type of speech,(2)recipient,(3)level of intensity(ordinal)and(4)targeted group(multi-label).Performance is evaluated using an annotated Spanish corpus,standard metrics such as precision,recall and F1-score and stability-oriented metrics to evaluate the stability of the transition from zero-shot to few-shot prompting(Zero-to-Few Shot Retention and Zero-to-Few Shot Gain)are applied.The results indicate that fine-tuned encoder-only models(notably MarIA and BETO variants)consistently deliver the strongest and most reliable performance:in our experiments their macro F1-scores lie roughly in the range of approximately 46%–66%depending on the task.Zero-shot approaches are much less stable and typically yield substantially lower performance(observed F1-scores range approximately 0%–39%),often producing invalid outputs in practice.Few-shot prompting(e.g.,Qwen 38B,Mistral 7B)generally improves stability and recall relative to pure zero-shot,bringing F1-scores into a moderate range of approximately 20%–51%but still falling short of fully fine-tuned models.These findings highlight the importance of supervised adaptation and discuss the potential of both paradigms as components in AI-powered cybersecurity and malware forensics systems designed to identify and mitigate coordinated online hate campaigns.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
Traumatic brain injury(TBI) is a major contributor of long-term disability and a leading cause of death worldwide. A series of secondary injury cascades can contribute to cell death, tissue loss, and ultimately to the...Traumatic brain injury(TBI) is a major contributor of long-term disability and a leading cause of death worldwide. A series of secondary injury cascades can contribute to cell death, tissue loss, and ultimately to the development of functional impairments. However, there are currently no effective therapeutic interventions that improve brain outcomes following TBI. As a result, a number of experimental TBI models have been developed to recapitulate TBI injury mechanisms and to test the efficacy of potential therapeutics. The pig model has recently come to the forefront as the pig brain is closer in size, structure, and composition to the human brain compared to traditional rodent models, making it an ideal large animal model to study TBI pathophysiology and functional outcomes. This review will focus on the shared characteristics between humans and pigs that make them ideal for modeling TBI and will review the three most common pig TBI models–the diffuse axonal injury, the controlled cortical impact, and the fluid percussion models. It will also review current advances in functional outcome assessment measures and other non-invasive, translational TBI detection and measurement tools like biomarker analysis and magnetic resonance imaging. The use of pigs as TBI models and the continued development and improvement of translational assessment modalities have made significant contributions to unraveling the complex cascade of TBI sequela and provide an important means to study potential clinically relevant therapeutic interventions.展开更多
Damped wave diffusion effects during oxygen transport in islets of Langerhans is studied. Simultaneous reaction and diffusion models were developed. The asymptotic limits of first and zeroth order in Michaelis and Men...Damped wave diffusion effects during oxygen transport in islets of Langerhans is studied. Simultaneous reaction and diffusion models were developed. The asymptotic limits of first and zeroth order in Michaelis and Menten kinetics was used in the study. Parabolic Fick diffusion and hyperbolic damped wave diffusion were studied separately. Method of relativistic transformation was used in order to obtain the solution for the hyperbolic model. Model solutions was used to obtain mass inertial times. Convective boundary condition was used. Sharma number (mass) may be used in evaluating the importance of the damped wave diffusion process in relation to other processes such as convection, Fick steady diffusion in the given application. Four regimes can be identified in the solution of hyperbolic damped wave diffusion model. These are;1) Zero Transfer Inertial Regime, 0 0≤τ≤τinertia;2) Rising Regime during times greater than inertial regime and less than at the wave front, Xp > τ, 3) at Wave front , τ = Xp;4) Falling Regime in open Interval, of times greater than at the wave front, τ > Xp. Method of superposition of steady state concentration and transient concentration used in both solutions of parabolic and hyperbolic models. Expression for steady state concentration developed. Closed form analytic model solutions developed in asymptotic limits of Michaelis and Menten kinetic at zeroth order and first order. Expression for Penetration Length Derived-Hypoxia Explained. Expression for Inertial Lag Time Derived. Solution was obtained by the method of separation of variables for transient for parabolic model and by the method of relativistic transformation for hyperbolic models. The concentration profile was expressed as a sum of steadty state and transient parts.展开更多
With the increasing demand for understanding skin physiology and advancing regenerative medicine,in vitro three-dimensional(3D)functional skin tissue models have become vital tools in dermatological research.These mod...With the increasing demand for understanding skin physiology and advancing regenerative medicine,in vitro three-dimensional(3D)functional skin tissue models have become vital tools in dermatological research.These models effectively mimic the complex structure and functions of human skin.This review comprehensively discusses the latest advancements in construction techniques,material selection,and applications of 3D skin models.It highlights the advantages and challenges associated with cutting-edge technologies such as layer-by-layer cell coating,3D bioprinting,bio-spray technology,and photolithographic microfabrication in creating highly realistic skin models.Moreover,it examines the wide-ranging applications of 3D skin models,includingelucidation of skin disease mechanisms,investigation of skin barrier functions,studies on skin aging and repair,hair regeneration,efficacy screening of therapeutic agents,cosmetic safety assessment,and personalized medicine.Finally,this review anticipates future trends in developing 3D skin models with greater structural and functional complexity,enhanced multifunctionality,and improved clinical translation.展开更多
Advances in Alzheimer's disease(AD)research have deepened our understanding,yet the mechanisms driving its progression remain unclear.Although a range of in vivo biomarkers is now available(e.g.,measurements of am...Advances in Alzheimer's disease(AD)research have deepened our understanding,yet the mechanisms driving its progression remain unclear.Although a range of in vivo biomarkers is now available(e.g.,measurements of amyloidbeta(Aβ)and ta u accumulation-the molecular hallmarks of AD-structural magnetic resonance imaging(MRI),assessments of brain metabolism,and,more recently,blood-based markers),a definitive diagnosis of AD continues to be challenging.For example,Frisoni et al.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate unt...Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.展开更多
The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-super...The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.展开更多
Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensiv...Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensive occupational databases such as O∗NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories,thereby facilitating standardization,cross-system interoperability,and access to metadata for each occupation(e.g.,tasks,knowledge,skills,and abilities).In this work,we explore the effectiveness of fine-tuning existing language models(LMs)to classify job offers with occupational descriptors from O∗NET.This enables a more precise assessment of candidate suitability by identifying the specific knowledge and skills required for each position,and helps automate recruitment processes by mitigating human bias and subjectivity in candidate selection.We evaluate three representative BERT-like models:BERT,RoBERTa,and DeBERTa.BERT serves as the baseline encoder-only architecture;RoBERTa incorporates advances in pretraining objectives and data scale;and DeBERTa introduces architectural improvements through disentangled attention mechanisms.The best performance was achieved with the DeBERTa model,although the other models also produced strong results,and no statistically significant differences were observed acrossmodels.We also find that these models typically reach optimal performance after only a few training epochs,and that training with smaller,balanced datasets is effective.Consequently,comparable results can be obtained with models that require fewer computational resources and less training time,facilitating deployment and practical use.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral f...This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral frequencies feature to a two-factor parameterization corresponding to speaker identity and phonetic information, the so-called style and content factors. This decomposition offers a flexible representation suitable for voice conversion and facilitates the use of efficient training algorithms based on singular value decomposition. In a contextual approach (bilinear) models are trained on subsets of the training data selected on the fly at conversion time depending on the characteristics of the feature vector to be converted. The performance of bilinear models and context modeling is evaluated in objective and perceptual tests by comparison with the popular GMM-based voice conversion method for several sizes and different types of training data.展开更多
Based on approaches deduced from previous research findings and empirical observations from density control experiments, genetic worth effect response models were developed for black spruce (Picea mariana (Mill) BSP.)...Based on approaches deduced from previous research findings and empirical observations from density control experiments, genetic worth effect response models were developed for black spruce (Picea mariana (Mill) BSP.) and jack pine (Pinus banksiana Lamb.) plantations. The models accounted for the increased rate of stand development arising from the planting of genetically-improved stock through temporal adjustments to the species-specific site-based mean dominant height-age functions. The models utilized a relative height growth modifier based on known estimates of genetic gain. The models also incorporated a phenotypic juvenile age-mature age correlation function in order to account for the intrinsic temporal decline in the magnitude of genetic worth effects throughout the rotation. Integrating the functions into algorithmic variants of structural stand density management models produced stand development patterns that were consistent with axioms of even-aged stand dynamics.展开更多
In this paper,the vector equation of a generaliseddoubly wound helix was derived.Treloar’s pliedyarn geometry could be obtained as a special case ofthe generalised doubly wound helix.The shortest fi-bre length around...In this paper,the vector equation of a generaliseddoubly wound helix was derived.Treloar’s pliedyarn geometry could be obtained as a special case ofthe generalised doubly wound helix.The shortest fi-bre length around the surface of a helical tube(formed by fibre helices)was determined by apply-ing variational principles.The fibre length as calcu-lated by using Treloar’s geometry was compared in-directly with the shortest possible fibre length at dif-ferent levels of yarn deformation when some of Tre-loar’s rigid geometrical constraints were relaxed.Anew idea based on non-concentric circles was intro-duced to approximate the fibre helix movement in atwo-ply yarn.A torsional model of two-ply yarnwas developed and the theoretical predictions werecompared with some preliminary experimental re-sults.展开更多
The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streaml...The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs.展开更多
Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of ...Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of this species,it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects,especially for permanent carbon forests.Future projections of any natural resource systems rely on modeling;however,the acceleration of climate change makes future projections of yield less certain.These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement.Using a large national-scale set of contemporary ground-measured data(2013–2023),this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers:1)the Pumice Plateau Model 1988(PPM88)and 2)the 300-index(including a model variant of regional drift).Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time.Basal area(BA,m^(2)⋅ha^(-1))and volume(m^(3)⋅ha^(-1))were simulated,and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years.Model residuals were then separated and analysed for the main plantation growing regions.The models overpredicted observed growth by between 6.8%and 16.2%,but model predictions and errors varied significantly between regions.The results of this study provided clear evidence of divergence between the outputs of both models and the measured data.Finally,this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.展开更多
文摘Achievements are presented for truss models of RC structures developed in previous years: 1. Two constitutive models, biaxial and triaxial, are based on regular trusses, with bars obeying nonlinear uniaxial σ-ε laws of material under simulation;both models have been compared with test results and show a dependence of Poisson ratio on curvature of σ-ε law. 2. A truss finite element has been used in the nonlinear static and dynamic analysis of plane RC frames;it has been compared with test results and describes, in a simple way, the formation of plastic hinges. 3. Thanks to the very simple geometry of a truss, the equilibrium equations can be easily written and the stiffness matrix can be easily updated, both with respect to the deformed truss, within each step of a static incremental loading or within each time step of a dynamic analysis, so that to take into account geometric nonlinearities. So the confinement of a RC column is interpreted as a structural stability effect of concrete. And a significant role of the transverse reinforcement is revealed, that of preventing, by its close spacing and sufficient amount, the buckling of inner longitudinal concrete struts, which would lead to a global instability of the RC column. 4. The proposed truss model is statically indeterminate, so it exhibits some features, which are not met by the “strut-and-tie” model.
基金supported by the National Natural Science Foundation of China,No.81671221(to RCJ)
文摘The rat high-impact free weight drop model mimics the diffuse axonal injury caused by severe traumatic brain injury in humans,while severe controlled cortical impact can produce a severe traumatic brain injury model using precise strike parameters.In this study,we compare the pathological mechanisms and pathological changes between two rat severe brain injury models to identify the similarities and differences.The severe controlled cortical impact model was produced by an electronic controlled cortical impact device,while the severe free weight drop model was produced by dropping a 500 g free weight from a height of 1.8 m through a plastic tube.Body temperature and mortality were recorded,and neurological deficits were assessed with the modified neurological severity score.Brain edema and bloodbrain barrier damage were evaluated by assessing brain water content and Evans blue extravasation.In addition,a cytokine array kit was used to detect inflammatory cytokines.Neuronal apoptosis in the brain and brainstem was quantified by immunofluorescence staining.Both the severe controlled cortical impact and severe free weight drop models exhibited significant neurological impairments and body temperature fluctuations.More severe motor dysfunction was observed in the severe controlled cortical impact model,while more severe cognitive dysfunction was observed in the severe free weight drop model.Brain edema,inflammatory cytokine changes and cortical neuronal apoptosis were more substantial and blood-brain barrier damage was more focal in the severe controlled cortical impact group compared with the severe free weight drop group.The severe free weight drop model presented with more significant apoptosis in the brainstem and diffused blood-brain barrier damage,with higher mortality and lower repeatability compared with the severe controlled cortical impact group.Severe brainstem damage was not found in the severe controlled cortical impact model.These results indicate that the severe controlled cortical impact model is relatively more stable,more reproducible,and shows obvious cerebral pathological changes at an earlier stage.Therefore,the severe controlled cortical impact model is likely more suitable for studies on severe focal traumatic brain injury,while the severe free weight drop model may be more apt for studies on diffuse axonal injury.All experimental procedures were approved by the Ethics Committee of Animal Experiments of Tianjin Medical University,China(approval No.IRB2012-028-02)in Febru ary 2012.
基金funded by the National Key Research and Development Program of China(No.2021YFA1100500)the National Natural Science Foundation of China(No.82370662)the Key Research&Development Plan of Zhejiang Province(No.2024C03051).
文摘This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing patient queries about disease and lifestyle behaviors.The models selected were ChatGPT-4o,Gemini 2.0 Pro,Claude 3.5 Sonnet,and DeepSeek V3,with 12 questions chosen by two HCV experts from the domains of prevention,diagnosis,and treatment.
文摘Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerative diseases but cannot cure them.Inflammation is involved in the development and progression of neurodegenerative diseases,and oxidative stress is implicated in neurodegeneration associated with cognitive decline and age-related cognitive impairment.Polyphenols such as curcumin,quercetin,and resveratrol possess potent anti-inflammatory and antioxidant properties.Nanoformulations of curcumin and quercetin can optimize their pharmacological effects in the treatment of neurodegenerative diseases.Nanocarriers play a crucial role in delivering drugs across the blood-brain barrier,thereby lowering the risk of peripheral side effects.Various nanoforms have been developed to induce bioavailability and solubility of curcumin and quercetin,including nanoparticles and nanoemulsions.The studies reviewed included 17 using curcumin nanoformulations and seven with quercetin nanoformulations and were tested in widely used animal models of Alzheimer’s disease,Parkinson’s disease,Huntington’s disease,and multiple sclerosis.Many of the curcumin and quercetin nanoformulations brought about improvements in learning and memory in behavioral tests of Alzheimer’s disease models and were effective in reducing oxidative stress in the brain.Both nanocurcumin and nanoquercetin decreased the levels of inflammatory markers in the brain.Nanocurcumin formulations improved motor behavior,gait,and memory in Parkinson’s disease models and increased dopaminergic neurons in the striatum and substantia nigra.Furthermore,nanocurcumin improved locomotor activity,memory,and learning,and the number of dendrites of medium spiny neurons in Huntington’s disease models.Nanocurcumin formulations decreased oxidative stress and inflammation in a model of demyelination.Several important limitations were identified in the studies reviewed and these need to be considered in future studies.Also,clinical trials could be performed using the currently available nanoforms of curcumin and quercetin.
基金the research project LaTe4PoliticES(PID2022-138099OB-I00)funded by MCIN/AEI/10.13039/501100011033 and the European Fund for Regional Development(ERDF)-a way to make Europe.Tomás Bernal-Beltrán is supported by University of Murcia through the predoctoral programme.
文摘The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources.In this paper,we compare two predominant AI-based approaches for the forensic detection of malicious hate speech:(1)finetuning encoder-only models that have been trained in Spanish and(2)In-Context Learning techniques(Zero-and Few-Shot Learning)with large-scale language models.Our approach goes beyond binary classification,proposing a comprehensive,multidimensional evaluation that labels each text by:(1)type of speech,(2)recipient,(3)level of intensity(ordinal)and(4)targeted group(multi-label).Performance is evaluated using an annotated Spanish corpus,standard metrics such as precision,recall and F1-score and stability-oriented metrics to evaluate the stability of the transition from zero-shot to few-shot prompting(Zero-to-Few Shot Retention and Zero-to-Few Shot Gain)are applied.The results indicate that fine-tuned encoder-only models(notably MarIA and BETO variants)consistently deliver the strongest and most reliable performance:in our experiments their macro F1-scores lie roughly in the range of approximately 46%–66%depending on the task.Zero-shot approaches are much less stable and typically yield substantially lower performance(observed F1-scores range approximately 0%–39%),often producing invalid outputs in practice.Few-shot prompting(e.g.,Qwen 38B,Mistral 7B)generally improves stability and recall relative to pure zero-shot,bringing F1-scores into a moderate range of approximately 20%–51%but still falling short of fully fine-tuned models.These findings highlight the importance of supervised adaptation and discuss the potential of both paradigms as components in AI-powered cybersecurity and malware forensics systems designed to identify and mitigate coordinated online hate campaigns.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
文摘Traumatic brain injury(TBI) is a major contributor of long-term disability and a leading cause of death worldwide. A series of secondary injury cascades can contribute to cell death, tissue loss, and ultimately to the development of functional impairments. However, there are currently no effective therapeutic interventions that improve brain outcomes following TBI. As a result, a number of experimental TBI models have been developed to recapitulate TBI injury mechanisms and to test the efficacy of potential therapeutics. The pig model has recently come to the forefront as the pig brain is closer in size, structure, and composition to the human brain compared to traditional rodent models, making it an ideal large animal model to study TBI pathophysiology and functional outcomes. This review will focus on the shared characteristics between humans and pigs that make them ideal for modeling TBI and will review the three most common pig TBI models–the diffuse axonal injury, the controlled cortical impact, and the fluid percussion models. It will also review current advances in functional outcome assessment measures and other non-invasive, translational TBI detection and measurement tools like biomarker analysis and magnetic resonance imaging. The use of pigs as TBI models and the continued development and improvement of translational assessment modalities have made significant contributions to unraveling the complex cascade of TBI sequela and provide an important means to study potential clinically relevant therapeutic interventions.
文摘Damped wave diffusion effects during oxygen transport in islets of Langerhans is studied. Simultaneous reaction and diffusion models were developed. The asymptotic limits of first and zeroth order in Michaelis and Menten kinetics was used in the study. Parabolic Fick diffusion and hyperbolic damped wave diffusion were studied separately. Method of relativistic transformation was used in order to obtain the solution for the hyperbolic model. Model solutions was used to obtain mass inertial times. Convective boundary condition was used. Sharma number (mass) may be used in evaluating the importance of the damped wave diffusion process in relation to other processes such as convection, Fick steady diffusion in the given application. Four regimes can be identified in the solution of hyperbolic damped wave diffusion model. These are;1) Zero Transfer Inertial Regime, 0 0≤τ≤τinertia;2) Rising Regime during times greater than inertial regime and less than at the wave front, Xp > τ, 3) at Wave front , τ = Xp;4) Falling Regime in open Interval, of times greater than at the wave front, τ > Xp. Method of superposition of steady state concentration and transient concentration used in both solutions of parabolic and hyperbolic models. Expression for steady state concentration developed. Closed form analytic model solutions developed in asymptotic limits of Michaelis and Menten kinetic at zeroth order and first order. Expression for Penetration Length Derived-Hypoxia Explained. Expression for Inertial Lag Time Derived. Solution was obtained by the method of separation of variables for transient for parabolic model and by the method of relativistic transformation for hyperbolic models. The concentration profile was expressed as a sum of steadty state and transient parts.
文摘With the increasing demand for understanding skin physiology and advancing regenerative medicine,in vitro three-dimensional(3D)functional skin tissue models have become vital tools in dermatological research.These models effectively mimic the complex structure and functions of human skin.This review comprehensively discusses the latest advancements in construction techniques,material selection,and applications of 3D skin models.It highlights the advantages and challenges associated with cutting-edge technologies such as layer-by-layer cell coating,3D bioprinting,bio-spray technology,and photolithographic microfabrication in creating highly realistic skin models.Moreover,it examines the wide-ranging applications of 3D skin models,includingelucidation of skin disease mechanisms,investigation of skin barrier functions,studies on skin aging and repair,hair regeneration,efficacy screening of therapeutic agents,cosmetic safety assessment,and personalized medicine.Finally,this review anticipates future trends in developing 3D skin models with greater structural and functional complexity,enhanced multifunctionality,and improved clinical translation.
文摘Advances in Alzheimer's disease(AD)research have deepened our understanding,yet the mechanisms driving its progression remain unclear.Although a range of in vivo biomarkers is now available(e.g.,measurements of amyloidbeta(Aβ)and ta u accumulation-the molecular hallmarks of AD-structural magnetic resonance imaging(MRI),assessments of brain metabolism,and,more recently,blood-based markers),a definitive diagnosis of AD continues to be challenging.For example,Frisoni et al.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金This publication is part of the TrustBoost project,that has received funding from MICIU/AEI/10.13039/501100011033,from FEDER,UEIt is a coordinated project by a multidisciplinary team from the Universidad Politécnica de Madrid(UPM)and University of Granada(UGR),with two subprojects that address TrustBoost’s objectives:“Enhancing Trustworthiness in Conversational AI through Multimodal Affective Awareness”(Trust Boost-UPM,ref.PID2023-150584OB-C21)“Breaking the Duality of Conversational AI:Going beyond Guided Conversations While Ensuring Compliance with Domain Rules and Constraints”(Trust Boost-UGR,ref.PID2023-150584OB-C22).
文摘Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.
基金supported by the National Natural Science Foundation of China(32471964)。
文摘The collection and annotation of lar ge-scale bird datasets are resource-intensive and time-consuming processes that significantly limit the scalability and accuracy of biodiversity monitoring systems.While self-supervised learning(SSL)has emerged as a promising approach for leveraging unannotated data,current SSL methods face two critical challenges in bird species recognition:(1)long-tailed data distributions that result in poor performance on underrepresented species;and(2)domain shift issues caused by data augmentation strategies designed to mitigate class imbalance.Here we present SDNet,a novel SSL-based bird recognition framework that integrates diffusion models with large language models(LLMs)to overcome these limitations.SDNet employs LLMs to generate semantically rich textual descriptions for tail-class species by prompting the models with species taxonomy,morphological attributes,and habitat information,producing detailed natural language priors that capture fine-grained visual characteristics(e.g.,plumage patterns,body proportions,and distinctive markings).These textual descriptions are subsequently used by a conditional diffusion model to synthesize new bird image samples through cross-attention mechanisms that fuse textual embeddings with intermediate visual feature representations during the denoising process,ensuring generated images preserve species-specific morphological details while maintaining photorealistic quality.Additionally,we incorporate a Swin Transformer as the feature extraction backbone whose hierarchical window-based attention mechanism and shifted windowing scheme enable multi-scale local feature extraction that proves particularly effective at capturing finegrained discriminative patterns(such as beak shape and feather texture)while mitigating domain shift between synthetic and original images through consistent feature representations across both data sources.SDNet is validated on both a self-constructed dataset(Bird_BXS)an d a publicly available benchmark(Birds_25),demonstrating substantial improvements over conventional SSL approaches.Our results indicate that the synergistic integration of LLMs,diffusion models,and the Swin Transformer architecture contributes significantly to recognition accuracy,particularly for rare and morphologically similar species.These findings highlight the potential of SDNet for addressing fundamental limitations of existing SSL methods in avian recognition tasks and establishing a new paradigm for efficient self-supervised learning in large-scale ornithological vision applications.
文摘Classifying job offers into occupational categories is a fundamental task in human resource information systems,as it improves and streamlines indexing,search,and matching between openings and job seekers.Comprehensive occupational databases such as O∗NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories,thereby facilitating standardization,cross-system interoperability,and access to metadata for each occupation(e.g.,tasks,knowledge,skills,and abilities).In this work,we explore the effectiveness of fine-tuning existing language models(LMs)to classify job offers with occupational descriptors from O∗NET.This enables a more precise assessment of candidate suitability by identifying the specific knowledge and skills required for each position,and helps automate recruitment processes by mitigating human bias and subjectivity in candidate selection.We evaluate three representative BERT-like models:BERT,RoBERTa,and DeBERTa.BERT serves as the baseline encoder-only architecture;RoBERTa incorporates advances in pretraining objectives and data scale;and DeBERTa introduces architectural improvements through disentangled attention mechanisms.The best performance was achieved with the DeBERTa model,although the other models also produced strong results,and no statistically significant differences were observed acrossmodels.We also find that these models typically reach optimal performance after only a few training epochs,and that training with smaller,balanced datasets is effective.Consequently,comparable results can be obtained with models that require fewer computational resources and less training time,facilitating deployment and practical use.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral frequencies feature to a two-factor parameterization corresponding to speaker identity and phonetic information, the so-called style and content factors. This decomposition offers a flexible representation suitable for voice conversion and facilitates the use of efficient training algorithms based on singular value decomposition. In a contextual approach (bilinear) models are trained on subsets of the training data selected on the fly at conversion time depending on the characteristics of the feature vector to be converted. The performance of bilinear models and context modeling is evaluated in objective and perceptual tests by comparison with the popular GMM-based voice conversion method for several sizes and different types of training data.
文摘Based on approaches deduced from previous research findings and empirical observations from density control experiments, genetic worth effect response models were developed for black spruce (Picea mariana (Mill) BSP.) and jack pine (Pinus banksiana Lamb.) plantations. The models accounted for the increased rate of stand development arising from the planting of genetically-improved stock through temporal adjustments to the species-specific site-based mean dominant height-age functions. The models utilized a relative height growth modifier based on known estimates of genetic gain. The models also incorporated a phenotypic juvenile age-mature age correlation function in order to account for the intrinsic temporal decline in the magnitude of genetic worth effects throughout the rotation. Integrating the functions into algorithmic variants of structural stand density management models produced stand development patterns that were consistent with axioms of even-aged stand dynamics.
文摘In this paper,the vector equation of a generaliseddoubly wound helix was derived.Treloar’s pliedyarn geometry could be obtained as a special case ofthe generalised doubly wound helix.The shortest fi-bre length around the surface of a helical tube(formed by fibre helices)was determined by apply-ing variational principles.The fibre length as calcu-lated by using Treloar’s geometry was compared in-directly with the shortest possible fibre length at dif-ferent levels of yarn deformation when some of Tre-loar’s rigid geometrical constraints were relaxed.Anew idea based on non-concentric circles was intro-duced to approximate the fibre helix movement in atwo-ply yarn.A torsional model of two-ply yarnwas developed and the theoretical predictions werecompared with some preliminary experimental re-sults.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under grant number 114-2221-E-182-041-MY3by Chang Gung University and Chang Gung Memorial Hospital under project number NERPD4Q0021.
文摘The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs.
基金funded by Scion's Strategic Science Investment Fund(SSIF)the Forest Growers Levy Trust(FGLT)through the Resilient Forests Programme(Task No.A89220)。
文摘Pinus radiata(D.Don)dominates New Zealand's forestry industry,constituting 91%of plantations,and is among the world's most important plantation species.Given the socio-economic and environmental importance of this species,it is important to have accurate and precise projections over time to make efficient decisions for forest management and greenfield investments in afforestation projects,especially for permanent carbon forests.Future projections of any natural resource systems rely on modeling;however,the acceleration of climate change makes future projections of yield less certain.These challenges also impact national expectations of the contribution planted forests will provide to address climate change and meet international commitments under the Paris Agreement.Using a large national-scale set of contemporary ground-measured data(2013–2023),this study investigates the performance of two growth models developed over 30 years ago that are widely used by NZ plantation growers:1)the Pumice Plateau Model 1988(PPM88)and 2)the 300-index(including a model variant of regional drift).Model simulations were made using the FORECASTER modeling suite with geographic boundaries to adjust for drift in space and time.Basal area(BA,m^(2)⋅ha^(-1))and volume(m^(3)⋅ha^(-1))were simulated,and standard errors and goodness-of-fit metrics calculated up to a typical rotation age of 30 years.Model residuals were then separated and analysed for the main plantation growing regions.The models overpredicted observed growth by between 6.8%and 16.2%,but model predictions and errors varied significantly between regions.The results of this study provided clear evidence of divergence between the outputs of both models and the measured data.Finally,this study suggests future measures to address challenges posed by these discrepancies that will provide better information for forest management and investment decisions in a changing climate.
