Cascade Model Research Articles

Serial Cascade Effects of Relative Deprivation and Anger Rumination on the Development of Social Aggression Over 2.5 Years in Emerging Adults.

Although the mechanisms of development of aggression have been focused on day by day, the complicated effects of distal and proximal factors on the development of social aggression in emerging adults have not been uncovered. A serial cascade model of aggression was proposed to address this issue. A longitudinal investigation over 2.5 years was conducted to test this model by exploring the serial cascade effects of relative deprivation (a representative of distal factors) and anger rumination (a representative of proximal factors) on the development of social aggression. A total of 1113 Chinese university students (Mage = 18.95 ± 0.96, 63.10% female) from six universities in five areas participated in this study. The results suggest that developmental trajectories and longitudinal changes in anger rumination mediate the relationship between developmental trajectories and longitudinal changes in relative deprivation and social aggression, and developmental trajectories and changes in relative deprivation mediate the longitudinal relationship between anger rumination and social aggression. These findings support the serial cascade effects of distal and proximal factors on the development of aggression and expand upon the general aggression model (GAM).

Generalized hop‐based approaches for identifying influential nodes in social networks

AbstractLocating a set of influential users within a social network, known as the Influence Maximization (IM) problem, can have significant implications for boosting the spread of positive information/news and curbing the spread of negative elements such as misinformation and disease. However, the traditional simulation‐based spread computations under conventional diffusion models render existing algorithms inefficient in finding optimal solutions. In recent years, hop and path‐based approaches have gained popularity, particularly under the cascade models to address the scalability issue. Nevertheless, these existing functions vary based on the considered hop‐distance and provide no guidance on capturing spread sizes beyond two‐hops. In this paper, we introduce Hop‐based Expected Influence Maximization (HEIM), an approach utilizing generalized functions to compute influence spread across varying hop‐distances in conventional diffusion models. We extend our investigation to the Linear Threshold (LT) model, in addition to the Independent Cascade (IC) and Weighted Cascade (WC) models, filling a gap in current literature. Our theoretical analysis shows that the proposed functions preserve both monotonicity and submodularity, and the proposed HEIM algorithm can achieve an approximation ratio of under a limited hop‐measures, whereas a multiplicative ‐approximation under global measures. Furthermore, we show that expected spread methods can serve as a better benchmark approach than existing simulation‐based methods. The performance of the HEIM algorithm is evaluated through experiments on three real‐world networks, and is compared to six other existing algorithms. Results demonstrate that the three‐hop based HEIM algorithm achieves superior solution quality, ranking first in statistical tests, and is notably faster than existing benchmark approaches. Conversely, the one‐hop‐based HEIM offers faster computation while still delivering competitive solutions, providing decision‐makers with flexibility based on application needs.

Eigenvalue spectra of finely structured random matrices.

Random matrix theory allows for the deduction of stability criteria for complex systems using only a summary knowledge of the statistics of the interactions between components. As such, results like the well-known elliptical law are applicable in a myriad of different contexts. However, it is often assumed that all components of the complex system in question are statistically equivalent, which is unrealistic in many applications. Here we introduce the concept of a finely structured random matrix. These are random matrices with element-specific statistics, which can be used to model systems in which the individual components are statistically distinct. By supposing that the degree of "fine structure" in the matrix is small, we arrive at a succinct "modified" elliptical law. We demonstrate the direct applicability of our results to the niche and cascade models in theoretical ecology, as well as a model of a neural network, and a directed network with arbitrary degree distribution. The simple closed form of our central results allow us to draw broad qualitative conclusions about the effect of fine structure on stability.

Robustness analysis of interdependent network accounting for failure probability and coupling patterns.

The robustness of interdependent networks against perturbations is an important problem for network design and operation. This paper focuses on establishing a cascading failure dynamics model and analyzing the robustness for interdependent networks, in which the states of the nodes follow certain failure probability and various connectivity patterns. First, to describe the removal mechanism of an overloaded node, the failure probability associated with the load distribution of components was proposed. Then, we present the node capacity cost and the average capacity cost of the network to investigate the propagation of cascading failures. Finally, to discuss the impact of the configuration parameters on robustness, some numerical examples are conducted, where the robustness was analyzed based on the proposed method and different interdependence types. Our results show that, the larger the overload parameter, the more robust the network is, but this also increases the network cost. Furthermore, we find that allocating more protection resources to the nodes with higher degree can enhance the robustness of the interdependent network. The robustness of multiple-to-multiple interdependent networks outperforms that of one-to-one interdependent networks under the same coupling pattern. In addition, our results unveil that the impact of coupling strategies on the robustness of multiple-to-multiple interdependent networks is smaller than that of one-to-one interdependent networks.

An Interprofessional Graduate Student and Family Coaching Program in Naturalistic Communication Techniques.

Researchers implemented a short-term cascading coaching model focusing on naturalistic developmental behavioral intervention with three participant triads. Triads consisted of a graduate student clinician, a minimally verbal child with autism spectrum disorder, and the child's parent. Coaching and intervention occurred during an interprofessional summer clinic that included graduate student clinicians from special education and speech and hearing sciences departments. The efficacy of short-term instruction, researcher coaching for student clinicians, and student clinician coaching of parents was evaluated using a multiple baseline across participants' design. The dependent variables were student clinician's and parent's use of elicitation techniques (creating communication temptations, waiting, and prompting) and response techniques (naturally reinforcing children's communication and providing spoken language models). Following coaching, parents and student clinicians from all triads increased their use of elicitation and response techniques, with very large effect sizes across all variables. Visual analysis findings suggest individualized differences and variability across triads. Implications for graduate education and parent coaching programs are discussed.

Cascade computational model for prediction impact of transient depth change on combustion parameters of certain timber species under continuous heating rate

The purpose of this research is to examine the effects of depth variation and exposure duration on the combustion characterization of the Maple wood species (Acer platanoides L.) and Walnut (Juglans regia L.) according to ISO 5660–1 in case of exposing the specimens to a constant heating rate 50 kWm−2. The delivered experimental data by cone calorimeter apparatus is used to train, test, and validate the optimized cascade forward artificial neural network with topology 2–8–8–12–2. This structure is simulated to predict the target parameters: the heat release rate (HRR) and the mass burning rate (MBR). With real systems, it is possible to achieve a reasonable performance and the closest approximations for the cascade model. The experimental combustion parameters: time of flame out, average specific mass burning rate, total heat release, maximum average rate of heat emission, and fire growth index are considered to make a systematic analysis and assessment with change depth. Three regions are considered for investigation of output target curves, thermally thin (d ≤ 1.1 cm), thermally medium (1.1 cm < d< 1.5 cm), thermally thick (d≥ 1.5cm). The findings demonstrate that independent of exposure period, an increase in depth change causes a sizable reduction in the HRR and MBR. Beyond 1.5 cm of depth in thermally thick regions, there is no discernible effect of time instant on changes in target parameters. In contrast, HRR and MBR gradients across depth in thermally thin regions are striking.

Scientific and methodological grounds for iterative prediction of risk and harm to human health under chemical environmental exposures: From protein targets to systemic metabolic disorders

Increasing the predictive potential of early diagnostics and correction of negative outcomes is becoming especially relevant for preventing and reducing personalized and population health risks, including those caused by environmental exposures. The purpose of the study was to develop scientific and methodological grounds for iterative numerical prediction of risk and harm to human health under chemical environmental exposures. The study design was based on the consistent implementation of an algorithm for system analysis of the development of negative effects under a chemical environmental exposure, from protein targets to systemic metabolic disorders. The in-depth examination covered more than 1 million people living under real long-term combined inhalation exposure at doses up to 5–10 RfC. About 350 digital multifactor models, including about 5.5 thousand parameters, were evaluated. Structural bioinformation matrices were constructed to identify the sequence of response events at the molecular-cellular level. These events are initiated by the transformation of the protein-peptide profile of human blood plasma, which determine the metabolome. The study clarifies the elements of involvement of 20 target proteins in the pathogenesis of metabolic disorders associated with hypertension, dyslipidemia, obesity, hepatosis, and cognitive dysfunction associated with chemical combined exposure. The criteria for the safe content of 10 contaminants were substantiated, taking into account their combinations in human biological media. Predictive assessments of pathogenetic pathways were confirmed by the facts of their implementation at the cellular-tissue, organ and body level as metabolic disorders and existing diseases of the cardiovascular, nervous systems, lipoprotein metabolism, etc., proven to be associated with effects produced by airborne pollutants, including combined ones. The study expands the existing methodological approaches to assessing combined effects of chemicals taking into account parameterized cause-effect relations of biomarkers of exposure and effects and quantitative assessment of additional cases of risk occurrence. Assessment of the developed digital models revealed that combined chemical exposures were predominantly synergic and emergent (up to 70 % cases). We developed conceptual grounds and architecture of iterative risk prediction and the development of risk-associated diseases, including real harm to health upon elevated expression of protein targets. Thus, the digitized version of the forecast (digital platform), as a multi-level cascade model, is a tool for scientific analysis of a hygienic situation with the parameterization of expected negative outcomes. It determines methods for their correction and prevention, which increases the reliability of hygienic assessments and the validity of management decisions.

Adaptive Knowledge Distillation for High-Quality Unsupervised MRI Reconstruction With Model-Driven Priors.

Magnetic Resonance Imaging (MRI) reconstruction has made significant progress with the introduction of Deep Learning (DL) technology combined with Compressed Sensing (CS). However, most existing methods require large fully sampled training datasets to supervise the training process, which may be unavailable in many applications. Current unsupervised models also show limitations in performance or speed and may face unaligned distributions during testing. This paper proposes an unsupervised method to train competitive reconstruction models that can generate high-quality samples in an end-to-end style. Firstly teacher models are trained by filling the re-undersampled images and compared with the undersampled images in a self-supervised manner. The teacher models are then distilled to train another cascade model that can leverage the entire undersampled k-space during its training and testing. Additionally, we propose an adaptive distillation method to re-weight the samples based on the variance of teachers, which represents the confidence of the reconstruction results, to improve the quality of distillation. Experimental results on multiple datasets demonstrate that our method significantly accelerates the inference process while preserving or even improving the performance compared to the teacher model. In our tests, the distilled models show 5%-10% improvements in PSNR and SSIM compared with no distillation and are 10 times faster than the teacher.

Научно-методические основы итерационного прогноза риска и вреда здоровью человека при воздействии химических факторов среды обитания: От белковых мишеней до системных метаболических нарушений

Increasing the predictive potential of early diagnostics and correction of negative outcomes is becoming especially relevant for preventing and reducing personalized and population health risks, including those caused by environmental exposures. The purpose of the study was to develop scientific and methodological grounds for iterative numerical prediction of risk and harm to human health under chemical environmental exposures. The study design was based on the consistent implementation of an algorithm for system analysis of the development of negative effects under a chemical environmental exposure, from protein targets to systemic metabolic disorders. The in-depth examination covered more than 1 million people living under real long-term combined inhalation exposure at doses up to 5–10 RfC. About 350 digital multifactor models, including about 5.5 thousand parameters, were evaluated. Structural bioinformation matrices were constructed to identify the sequence of response events at the molecular-cellular level. These events are initiated by the transformation of the protein-peptide profile of human blood plasma, which determine the metabolome. The study clarifies the elements of involvement of 20 target proteins in the pathogenesis of metabolic disorders associated with hypertension, dyslipidemia, obesity, hepatosis, and cognitive dysfunction associated with chemical combined exposure. The criteria for the safe content of 10 contaminants were substantiated, taking into account their combinations in human biological media. Predictive assessments of pathogenetic pathways were confirmed by the facts of their implementation at the cellular-tissue, organ and body level as metabolic disorders and existing diseases of the cardiovascular, nervous systems, lipoprotein metabolism, etc., proven to be associated with effects produced by airborne pollutants, including combined ones. The study expands the existing methodological approaches to assessing combined effects of chemicals taking into account parameterized cause-effect relations of biomarkers of exposure and effects and quantitative assessment of additional cases of risk occurrence. Assessment of the developed digital models revealed that combined chemical exposures were predominantly synergic and emergent (up to 70 % cases). We developed conceptual grounds and architecture of iterative risk prediction and the development of risk-associated diseases, including real harm to health upon elevated expression of protein targets. Thus, the digitized version of the forecast (digital platform), as a multi-level cascade model, is a tool for scientific analysis of a hygienic situation with the parameterization of expected negative outcomes. It determines methods for their correction and prevention, which increases the reliability of hygienic assessments and the validity of management decisions.

A process model of parental executive functioning as a spillover mechanism linking interparental conflict and parenting difficulties across parenting domains.

There is a well-documented interdependency between destructive interparental conflict (IPC) and parenting difficulties (i.e., spillover effect), yet little is known about the mechanisms that "carry" spillover between IPC and parenting. Guided by a cascade model framework, the current study used a longitudinal, multimethod, multi-informant design to examine a process model of spillover that tested whether parental executive functioning (working memory, cognitive flexibility, inhibitory control) served as a mediator of the prospective associations between IPC and subsequent changes in parenting over a 2-year period. Mothers and fathers were separated into differentiated models and multiple domains of parenting were examined (i.e., authoritarian discipline and scaffolding behavior). Participants included 231 families (both mothers and fathers of preschoolers). Race was reported as White (62%), Black (21%), Mixed (8%), Asian (3%), or Other (6%) and 14% considered their ethnicity to be Hispanic/Latino. Median household income was $65,000. Results indicated that for fathers, IPC indirectly predicted domain-general parenting difficulties (increased authoritarian parenting and decreased scaffolding) via deficits in paternal cognitive flexibility (but not inhibitory control or working memory). In mothers, IPC directly predicted domain-specific parenting difficulties (decreased scaffolding only) that did not operate via maternal executive functions. Notably, these effects occurred over and above the influence of parental socioeconomic status. This study constitutes a first step toward documenting parental executive functioning as a mechanism underlying the spillover of IPC to the parent-child relationship. Family interventions intended to interrupt IPC spillover should emphasize father involvement and consider targeting parental executive functions as change mechanisms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Innovating in the Detection of Depression in Young Adults: Development of a Mobile Application Based on the Enhanced Cascade Model

Depression is a disorder that usually begins in early adulthood, having a duration according to the World Health Organization (WHO) from a couple of months to a couple of years, however, in approximately 20% of cases there is a chronic evolution that results in a high mortality rate by suicide due to lack of adequate treatment. Therefore, the objective of this project is to facilitate the tools for an adequate care of depression, mainly in young adults through the development of a mobile application that monitors patients. To carry out the project, the enhanced cascade model was implemented, because the feedback between the different phases of the system development life cycle is important to make the appropriate modifications. The application of interviews and the use of brainstorming were the selected way in which all the system requirements were designed and the goals to be met by the system were adequately defined.

Family cumulative risk, life satisfaction, and anxiety and depression in adolescents: A developmental cascades model.

Family cumulative risk (FCR) is predominantly regarded as an antecedent for adolescent mental health, as the prevailing perspective continues to emphasize the influential role of parents, despite recognizing the child's influence. To identify the interplay between family adversity (FCR, process-related FCR, and sociodemographic-related FCR), life satisfaction (LS), and anxiety and depression (AD), this study examined the cascade effects among these constructs. Participants (N = 707; 52.9% male; grades 10 and 11) from four high schools in Wuhan, China, were recruited to participate, and they completed the measures in October 2018, April 2019, and November 2019. Family sociodemographic risk (e.g., single parenthood) and family process risk (e.g., low family cohesion) were simulated in the models for FCR, sociodemographic-related FCR, and process-related FCR. The random intercept cross-lagged panel models (RI-CLPMs) revealed a lagged effect from LS to FCR; lagged effects from LS and AD to process-related FCR at the within-person level; and significant associations between LS, AD, and family adversity at the between-person level. The lagged effects provide evidence for the influential child perspective and suggest that FCR and family process risk are sensitive to adolescent well-being and psychopathological symptoms. School mental health prevention and intervention programs that take a complete mental health approach to enhance children's well-being and alleviate symptoms would help prevent increases in family risk.

A modified two temperature molecular dynamics (2T-MD) model for cascades

Two-Temperature molecular dynamics (2T-MD) is a common approach for describing how electrons contribute to the evolution of a damage cascade by addressing their role in the redistribution of energy in the system. However, inaccuracies in 2T-MD’s treatment of the high-energy particles have limited its utilisation. Here, we propose a reformulation of the traditional 2T-MD scheme to overcome this limitation by addressing the spurious double-interaction of high-energy atoms with electrons. We conduct a series of radiation damage cascades for 30, 50, and 100 keV primary knock-on atoms in increasingly large cubic W cells. In the simulations, we employ our modified 2T-MD scheme along with other treatments of electron–phonon coupling to explore their impact on the cascade evolution and the number of remnant defects. The results suggest that with the proposed modification, 2T-MD simulations account for the temperature time evolution during the ballistic phase and remove arbitrary choices, thus providing a better description of the underlying physics of the damage process.

MODELS OF PROFESSIONAL DEVELOPMENT FOR BIOLOGY TEACHERS IN THE CONTEXT OF SOCIAL CHANGES

This article aims to explore the impact of social changes on the professional development of biology teachers. The purpose of the study is the analysis of modern challenges in the field of education and the development of a model of professional growth of a biology teacher. The following methods are employed in the study: analysis and synthesis of literature on the research topic to determine the current state of the investigated problem, systematic analysis, and synthesis of obtained information to identify the main models of professional development for biology teachers. The regulatory framework ensuring conditions for the professional development of teachers has been analyzed. Modern economic and technological changes, socio-cultural and political transformations, dynamics of interethnic and cultural relations in Ukraine, high population mobility, and migration processes affect the education system and the professional development of teachers. In the scientific discourse, among effective models of professional development for teachers, scholars distinguish: concepts of teaching and development of teachers' professional abilities based on knowledge (knowledge for practice, knowledge in practice, personal knowledge); models of professional development for educators (training model, award-bearing model, deficit model, cascading model, standards-based model, coaching/mentoring model, community of practice model, action research model, transformational model); competency, cognitive, human-centered models; models of applied science, craftsmanship, reflective, apprentice-expert model, rationalist model, case studies model, cascading. The author proposes an original model of professional development, which involves six stages: analysis of contemporary challenges, professional training based on teachers' needs, creation of an individual development plan, integration of innovative technologies into professional activities, self-evaluation, and the establishment and support of professional communities. The main research results indicate the need to reconsider traditional approaches to teaching biology and emphasize the use of modern technologies in the professional development of teachers for successful implementation of changes in the educational environment.

Cascading failure modelling in global container shipping network using mass vessel trajectory data

Port plays a key role in maintaining traffic flows and the effectiveness of global maritime logistics. However, the vulnerability of the Global Container Shipping Network (GCSN) is likely to increase when a single port interruption entails failures in cascading when ports encounter situations like congestions, labor strikes or natural disasters. Such situations require the deployment of port protection measures and adjustments of shipping schedules. This paper introduces a cascading model, which employs extensive and worldwide vessel trajectory data to comprehensively analyze the occurrence of cascading failures within a GCSN. The principles behind the cascading failure model are that port failures are simulated and the maritime traffic is redistributed and equilibrated to other routes and ports. A Motter-Lai overload model is applied, complemented by a three-level balanced redistribution of the traffic flows according to the specific roles of the disrupted ports. Overall, this favors the analysis of the GCSN's vulnerability, reliability, potential risks, and possible impacts. It enables maritime authorities and decision-makers to optimize service routes and mitigate the GCSN's vulnerability.

MADESANT: malware detection and severity analysis in industrial environments

Malware remains a persistent threat to industrial operations, causing disruptions and financial losses. Traditional malware detection approaches struggle with the increasing complexity of false positives and negatives. However, existing Intrusion Detection Systems (IDSs) often lack the capability to assess the severity of detected malware, crucial for effective threat mitigation. This paper presents a novel model, MAlware DEtection and Severity Analysis for eNcrypted Traffic (MADESANT), designed to detect and analyze malware severity in encrypted traffic data. MADESANT combines Deep Learning (DL)-based intrusion detection with Machine Learning (ML)-based severity analysis, specifically customized for the minutiae of IoT systems and assets. Notably, MADESANT introduces a cascading model integrating a Cascading Forward Back Propagation Neural Network (CFBPNN) with the J48 tree to systematically assess risk factors in network traffic. Our assessment, conducted on diverse encrypted datasets including UNSW-NB15, IoT23, and XIIoTID, highlights the remarkable efficacy of MADESANT. Impressively, it achieves a flawless 0% false positive rate in detecting binary attack instances, surpassing benchmarks set by conventional models. Additionally, MADESANT excels in accurately estimate malware severity, providing invaluable insights into the factors contributing to the risk. To further validate its efficiency, we compared MADESANT against prevalent Neural Network models like FeedForward and Recurrent Neural Networks, with MADESANT emerging as the superior choice. The experimentation encompasses both the entire dataset and subsets generated through meticulous risk factor analysis. These results underscore MADESANT’s prowess in not only identifying malware but also in evaluating its potential impact, signifying a significant leap forward in industrial cybersecurity.

A Blocking Method for Overload-Dominant Cascading Failures in Power Grid Based on Source and Load Collaborative Regulation

Adjusting generator output and cutting off load can effectively solve the problem of continuous overload and disconnection of power lines caused by power flow transfer. To suppress large-scale power outages caused by overload-dominant cascading failures, a blocking method for overload-dominant cascading failures in the power grid based on source and load collaborative regulation is proposed. The shortest path algorithm was used to identify loads and generators with shorter electrical distances from overloaded lines. Under the premise of meeting the static safety constraints of the power grid, considering the regulation of generator output and the removal of interruptible loads, a multiobjective cascading failure blocking model is established with the minimum overall control cost of the system and the minimum probability coefficient of line failure. Use the NSGA-II algorithm to solve the model and obtain the optimal plan for adjusting generator output and cutting off load. Through example verification, the proposed method can effectively alleviate the phenomenon of line overload, thereby blocking the continuation of overload-dominant cascading failures.

Adap-BDCM: Adaptive Bilinear Dynamic Cascade Model for Classification Tasks on CNV Datasets.

Copy number variation (CNV) is an essential genetic driving factor of cancer formation and progression, making intelligent classification based on CNV feasible. However, there are a few challenges in the current machine learning and deep learning methods, such as the design of base classifier combination schemes in ensemble methods and the selection of layers of neural networks, which often result in low accuracy. Therefore, an adaptive bilinear dynamic cascade model (Adap-BDCM) is developed to further enhance the accuracy and applicability of these methods for intelligent classification on CNV datasets. In this model, a feature selection module is introduced to mitigate the interference of redundant information, and a bilinear model based on the gated attention mechanism is proposed to extract more beneficial deep fusion features. Furthermore, an adaptive base classifier selection scheme is designed to overcome the difficulty of manually designing base classifier combinations and enhance the applicability of the model. Lastly, a novel feature fusion scheme with an attribute recall submoduleis constructed, effectively avoiding getting stuck in local solutions and missing some valuable information. Numerous experiments have demonstrated that our Adap-BDCM model exhibits optimal performance in cancer classification, stage prediction, and recurrence on CNV datasets. This study can assist physicians in making diagnoses faster and better.

Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer

Influence maximization (IM) has drawn significant attention in recent years. Most existing IM methods primarily focus on homogeneous networks, and do not take into account the heterogeneity and the attributes of different types of nodes in heterogeneous networks. However, heterogeneous networks are ubiquitous in real world, encompassing rich semantics and complex structural information. Additionally, the clustering characteristics inherent in a network have a critical and substantial impact on the process of information diffusion, which is often overlooked in IM models designed for heterogeneous networks. To address the challenges posed by the heterogeneity and clustering structure in heterogeneous networks, we propose a novel deep learning framework based on a self-supervised clustered heterogeneous graph transformer for IM in heterogeneous networks, which we have named SCHGT-IM. SCHGT-IM aggregates the heterogeneity and clustering information in heterogeneous networks and incorporates a clustered cascade (CC) model as an information diffusion model to enhance the realism of simulations. We evaluate the performance of SCHGT-IM in comparison with that of state-of-the-art IM models using three academic heterogeneous networks extracted from the DBLP dataset. The experimental results on influence spread demonstrate that SCHGT-IM is superior to fourteen state-of-the-art algorithms and is highly effective in selecting influential seed nodes of different types from heterogeneous networks.

Mapping ecosystem service temporal trends: a case study of European wood potential, supply and demand between 2008 and 2018

Wood is one of the key forest Ecosystem Services (ES) of growing ecological, social and economic importance; therefore, we need more precise information about its long-term usage. To achieve this, it is necessary to examine the spatio-temporal aspects of wood ES potential, supply and demand. In this study, we analyse spatio-temporal patterns of wood ES supply and demand at continental, national and regional scales to identify areas of increasing and decreasing supply and demand levels in Europe. In addition, we present background information about the potential of European forests to provide wood ES and its relationship to supply and demand. Our results showed that the overall European wood supply and demand as well as the wood ES potential were characterised by increasing trends. Furthermore, this increase was also regional, particularly in central and northern Europe. This study demonstrates not only the significance of spatio-temporal data in ES mapping, but also the importance of considering a broader range of components of the ES cascade model when assessing change. Our research has shown that potential, supply and demand can all increase in the same area, but also that low supply and demand do not guarantee wood potential growth. In addition, we found that a broad scale assessment helps to identify more general patterns and trends, but analysing data at a more accurate scale provides more comprehensive insights for identifying areas that may require targeted action for sustainable forest management.