Search Results (31)

Accurate prediction of shallow groundwater levels is crucial for water resource management in over-exploited regions like the North China Plain, where intensive pumping has created non-steady flow fields with strong spatial hydraulic interactions. Traditional approaches—whether physical models constrained by parameter equifinality or machine learning methods assuming spatial independence—fail to explicitly characterize aquifer hydraulic connectivity and effectively integrate multi-source monitoring data. This study proposes a Multi-source Fusion Spatiotemporal Graph Convolutional Network (MF-STGCN) that represents the monitoring well network as a hydraulic connectivity graph, employing graph convolutions to capture spatial water level propagation patterns while integrating temporal dynamics through LSTM modules. An adaptive fusion mechanism quantifies contributions of natural drivers (precipitation, evaporation) and anthropogenic extraction to water level responses. Validation using 518 monitoring stations (2018–2022) demonstrates that MF-STGCN reduces RMSE compared to traditional time series models, with improvement primarily attributed to explicit modeling of spatial hydraulic dependencies. Interpretability analysis identifies Hebi and Shijiazhuang as severe over-exploitation zones and reveals significant response lag effects in the Handan-Xingtai corridor. This study demonstrates that spatial propagation patterns, rather than single-point temporal features, are key to improving prediction accuracy in over-exploited aquifers, providing a new data-driven paradigm for regional groundwater dynamics assessment and targeted management strategies. Full article

This study investigates the critical macro-level conditions that determine the success of crowdfunding platforms, a pivotal segment of the FinTech landscape. We propose a novel configurational theory to decipher how combinations of institutional, economic, and social factors drive platform performance across diverse European economies. Utilizing fuzzy-Set Qualitative Comparative Analysis (fsQCA), we move beyond linear models to reveal that high platform success is not a product of any single factor but emerges from specific, equifinal configurations. Our findings demonstrate that robust crowdfunding ecosystems can thrive even in contexts with less advanced technological infrastructure, provided there is a synergistic interplay of platform governance, institutional trust, regulatory quality, and economic competitiveness. This research contributes to the FinTech literature by reframing crowdfunding success as a complex, context-dependent phenomenon, offering valuable insights for platform developers, regulators, and investors seeking to foster vibrant digital financing environments. Full article

Mass-balanced compartmental systems defy classical deterministic entropy measures since both metric and topological entropy vanish in dissipative dynamics. By interpreting open compartmental systems as absorbing continuous-time Markov chains that describe the random journey of a single representative particle, we allow established information-theoretic principles to be applied to this particular type of deterministic dynamical system. In particular, path entropy quantifies the uncertainty of complete trajectories, while entropy rates measure the average uncertainty of instantaneous transitions. Using Shannon’s information entropy, we derive closed-form expressions for these quantities in equilibrium and extend the maximum entropy principle (MaxEnt) to the problem of model selection in compartmental dynamics. This information-theoretic framework not only provides a systematic way to address equifinality but also reveals hidden structural properties of complex systems such as the global carbon cycle. Full article

Natural silk textiles are regarded as integral components of cultural heritage, historically embedded within centuries of sericulture, natural dyeing, and communal weaving. The preservation of intangible cultural heritage in northeast Thailand is investigated through natural sericulture, plant-dyed silk—frequently produced with Indigofera tinctoria—and recent dyeing innovations. A qualitative methodology was employed, guided by the trajectory equifinality model (TEM). Interviews were undertaken with fifteen women weavers from Maha Sarakham Province. Through TEM analysis, four thematic domains were identified: natural sericulture, plant-dyed silk, dyeing innovations, and intangible cultural heritage (ICH). Plant-dyed silk textile production, sustained by ecological sericulture and dyeing practices, was found to support the safeguarding and promotion of intangible cultural heritage. Heritage preservation in the region was demonstrated to be reinforced by sustainable sericulture and innovative plant-dyeing. Full article

Digital transformation, organizational resilience, and agility are now becoming key to meeting the competitive challenges of modern organizations. It is no surprise that digital transformation and digital technologies have also begun to significantly impact the energy industry, moving towards improving the sector’s profitability and efficiency. However, to move the difficult process of digital transformation in today’s dynamically changing environment, organizations, including those in the energy sector, need to build organizational resilience. Nevertheless, the relationship between digital transformation and organizational resilience has not yet been explained in a satisfactory and sufficient manner. Focusing on the level of digital transformation, and more precisely within the two dimensions of digital maturity, i.e., digital intensity and transformation management intensity, as well as based on the perspective of dynamic capabilities, this study developed a configurational framework and proposed a theoretical model to study the equifinal paths through which digital transformation and dynamic capabilities influence organizational resilience in energy sector companies. Based on a fuzzy set qualitative comparative analysis (fs/QCA) conducted on selected companies in the energy sector, i.e., Polish CHP plants, the relationship among digital transformation, dynamic capabilities, and organizational resilience was investigated. The results show that a high level of organizational resilience is possible to achieve through two main paths based on the dominance of dynamic capabilities and the dominance of digital maturity. The results show that a high level of organizational resilience is possible to achieve through two main paths based on the dominance of dynamic capabilities and the dominance of digital maturity. The study found that digital maturity can significantly influence CHP resilience. Moreover, the transformation management intensity is strongly related to high organizational resilience. The paper concludes by describing theoretical and practical implications, as well as research limitations and prospects for future research. Full article

Recently, the Agricultural Policy Extender (APEX) model was enhanced with a grazing module, and the modified grazing database, APEXgraze, recommends sustainable livestock farming practices. This study developed a combinatorial deterministic approach to calibrate runoff-related parameters, assuming a normal probability distribution for each parameter. Using the calibrated APEXgraze model, the impact of grazing operations on native prairie and cropland planted with winter wheat and oats in central Oklahoma was assessed. The existing performance criteria produced four solutions with very close values for calibrating runoff at the farm outlet, exhibiting equifinality. The calibrated results showed that runoff representations had coefficients of determination and Nash–Sutcliffe efficiencies >0.6 in both watersheds, irrespective of grazing operations. Because of non-unique solutions, the key parameter settings revealed different metrics yielding different response variables. Based on the least objective function value, the behavior of watersheds under different management and grazing intensities was compared. Model simulations indicated significantly reduced water yield, deep percolation, sediment yield, phosphorus and nitrogen loadings, and plant temperature stress after imposing grazing, particularly in native prairies, as compared to croplands. Differences in response variables were attributed to the intensity of tillage and grazing activities. As expected, grazing reduced forage yields in native prairies and increased crop grain yields in cropland. The use of a combinatorial deterministic approach to calibrating parameters offers several new research benefits when developing farm management models and quantifying sensitive parameters and uncertainties that recommend optimal farm management strategies under different climate and management conditions. Full article

This study explores low-carbon behavior (LCB), considering a number of psychological predictors deemed important according to the theory of planned behavior and the norm-activation model. Four distinct clusters were identified by conducting a cluster analysis of data collected from an online survey of young people in Croatia in 2022, revealing both consistent and inconsistent patterns of LCB. The study highlights the complexity of factors influencing LCB and utilizes a fuzzy-set qualitative comparative analysis to identify specific configurations of psychological variables that contribute to high and not-high levels of LCB within each cluster. The results validate the significance of established psychological determinants in explaining variations in low-carbon intentions and behaviors among young people, challenging the assumption of intention as the single best determinant of LCB and underscoring the presence of multiple causal complexities and equifinalities. Furthermore, the study demonstrates the asymmetric effects of different psychological conditions on high and not-high levels of LCB, suggesting that consistent and inconsistent LCBs cannot simply be viewed as opposite poles of the same continuum and that a variety of pathways can be explored to enhance carbon reduction activities. Full article

DRAINMOD is a process-based hydrologic model used to analyze the effectiveness of various drainage systems and management strategies. In this study, a sensitivity analysis of DRAINMOD hydrologic parameters for two different field settings located at Champaign, Illinois, was performed to determine the most sensitive parameters that affect the subsurface flow and relative productivity of corn. Latin-Hypercube One-Factor-at-a-Time (LH-OAT) was used to determine the sensitivity index of 17 parameters for six objective functions for daily flow, water balance, and relative yield for the productivity of corn. The results indicated that flow and yield were highly sensitive to drainage design parameters such as drainage depth and spacing. Winter flow and the water balance were sensitive to soil thermal conductivity parameters; however, they had no impact on the relative corn yield. The significant difference in sensitivity of the two fields was observed in the hydraulic conductivity of soil layers due to varying thicknesses for different soil types. This study highlights the need for more careful calibration of these sensitive parameters to reduce equifinality and model output uncertainty and appropriate drainage design for optimizing crop productivity and drainage outflow. Full article

This study aimed to clarify the longitudinal goal negotiation and collaboration process of achieving activity- and participation-level goals. We conducted a qualitative study using the trajectory equifinality model. Nine occupational therapists with experience in setting and achieving activity- and participation-level goals were recruited and interviewed about their clients. We identified two phases and four pathways in the setting and attainment process for activity- and participation-level goals. Throughout the longitudinal goal-setting process, when the occupational therapist and client had difficulty discussing activity- and participation-level goals, the therapist respected the client’s expectations, explained the purpose of occupational therapy in detail, and conducted individual face-to-face interviews. When it was difficult to provide work-based interventions, the occupational therapist made flexible use of functional training, elemental movement training, occupation-based practice, and environmental modifications. The results of this study may assist in supporting clients to improve their activity and participation in home rehabilitation. Full article

Vine phenology modelling is increasingly important for winegrowers and viticulturists. Model calibration is often required before practical applications. However, when multiple models and optimization methods are applied for different varieties, it is rarely known which factor tends to mostly affect the calibration results. We mainly aim to investigate the main source of the variability in the modelling errors for the flowering timings of two important varieties of vine in the Douro Demarcated Region (DDR) of Portugal; this is based on five phenology model simulations that use optimal parameters and that are estimated by three optimization algorithms (MLE, SA and SCE-UA). Our results indicate that the main source of the variability in calibration can be affected by the initially assumed parameter boundary. Restricting the initial parameter distribution to a narrow range impedes the algorithm from exploring the full parameter space and searching for optimal parameters. This can lead to the largest variation in different models. At an identified appropriate boundary, the difference between the two varieties represents the largest source of uncertainty, while the choice of algorithm for calibration contributes least to the overall uncertainty. The smaller variability among different models or algorithms (tools for analysis) compared to between different varieties could indicate the overall reliability of the calibration. All optimization algorithms show similar results in terms of the obtained goodness-of-fit: the RMSE (MAE) is 5–6 (4–5) days with a negligible mean bias and moderately good R² (0.5–0.6) for the ensemble median predictor. Nevertheless, a similar predictive performance can result from differently estimated parameter values, due to the equifinality or multi-modal issue in which different parameter combinations give similar results. This mainly occurs for models with a non-linear structure compared to those with a near-linear one. Yet, the former models are found to outperform the latter ones in predicting the flowering timing of the two varieties in the DDR. Overall, our findings highlight the importance of carefully defining the initial parameter boundary and decomposing the total variance of prediction errors. This study is expected to bring new insights that will help to better inform users about the importance of choice when these factors are involved in calibration. Nonetheless, the importance of each factor can change depending on the specific situation. Details of how the optimization methods are applied and of the continuous model improvement are important. Full article

In this study, learning pathways are modelled by networks constructed from the log data of student–LMS interactions. These networks capture the sequence of reviewing the learning materials by the students enrolled in a given course. In previous research, the networks of successful students showed a fractal property; meanwhile, the networks of students who failed showed an exponential pattern. This research aims to provide empirical evidence that students’ learning pathways have the properties of emergence and non-additivity from a macro level; meanwhile, equifinality (same end of learning process but different learning pathways) is presented at a micro level. Furthermore, the learning pathways of 422 students enrolled in a blended course are classified according to learning performance. These individual learning pathways are modelled by networks from which the relevant learning activities (nodes) are extracted in a sequence by a fractal-based method. The fractal method reduces the number of nodes to be considered relevant. A deep learning network classifies these sequences of each student into passed or failed. The results show that the accuracy of the prediction of the learning performance was 94%, the area under the receiver operating characteristic curve was 97%, and the Matthews correlation was 88%, showing that deep learning networks can model equifinality in complex systems. Full article

Extreme rainfall is a common triggering factor of landslide disasters, for infiltration and pore water pressure propagation can reduce suction stress and shear strength at the slip surface. The subsurface hydrological model is an essential component in the early-warning system of rainfall-triggered landslides, whereas soil moisture and pore water pressure simulated by the Darcy–Richards equation could be significantly affected by uncertainties in soil hydraulic parameters. This study conducted an inverse analysis of in situ measured soil moisture in an earthquake-induced landslide deposit, and the soil hydraulic parameters were optimized with the Differential Evolution Markov chain Monte Carlo method (DE-MC). The DE-MC approach was initially validated with a synthetic numerical experiment to demonstrate its effectiveness in finding the true soil hydraulic parameters. Besides, the soil water characteristic curve (SWCC) and hydraulic conductivity function (HCF) described with optimized soil hydraulic parameter sets had similar shapes despite the fact that soil hydraulic parameters may be different. Such equifinality phenomenon in inversely estimated soil hydraulic parameters, however, did not affect the performance of simulated soil moisture dynamics in the synthetic numerical experiment. The application of DE-MC to a real case study of a landslide deposit also indicated satisfying model performance in terms of accurate match between the in situ measured soil moisture content and ensemble of simulations. In conclusion, based on the satisfying performance of simulated soil moisture and the posterior probability density function (PDF) of parameter sets, the DE-MC approach can significantly reduce uncertainties in specified prior soil hydraulic parameters. This study suggested the integration of the DE-MC approach with the Darcy–Richards equation for an accurate quantification of unsaturated soil hydrology, which can be an essential modeling strategy to support the early-warning of rainfall-triggered landslides. Full article

The aging transformation of digital health services faces issues of how to distinguish influencing factors, redesign services, and effectively promote measures and policies. In this study, in-depth interviews were conducted, and grounded theory applied to open coding, main axis coding, and selective coding to form concepts and categories. Trajectory equifinality modeling clarified the evolution logic of digital transformation. Based on the theory of service ecology, a digital health service aging model was constructed from the “macro–medium–micro” stages and includes governance, service, and technology transformation paths. The macro stage relies on organizational elements to promote the institutionalization of management and guide the transformation of governance for value realization, including the construction of three categories: mechanism, indemnification, and decision-making. The meso stage relies on service elements to promote service design and realize service transformation that is suitable for aging design, including the construction of three categories: organization, resources, and processes. The micro stage relies on technical elements to practice experiencing humanization, including the construction of three categories: target, methods, and evaluation. These results deepen the understanding of the main behaviors and roles of macro-organizational, meso-service, and micro-technical elements in digital transformation practice and have positive significance for health administrative agencies to implement action strategies. Full article

This commentary features a review of two recently reformulated models of the development of child and adolescent: (1) social withdrawal by Rubin and Chronis-Tuscano 2021, and (2) social anxiety by Spence and Rapee 2016. The articles that present these reformulated models now cover advances made during the prior 12 to 18 years of research, including increased knowledge of genetic vulnerability to anxiety and longitudinal patterns of development, and acknowledgement of multiple pathways towards and away from the development of social withdrawal or social anxiety (i.e., equifinality, multifinality). However, these reformulated models also contain several blind spots. The model of social withdrawal development would be improved by explicitly referring to peer treatment (not only attitudinal peer rejection), especially peer exclusion; and incorporating the potential development of clinically significant anxiety in childhood (not only adolescence) and delays in developmental milestones in adulthood. The model of social anxiety development would be improved by featuring social withdrawal as a proximal affective-behavioral profile (rather than a temperament) and drawing upon the literature on social withdrawal and its links to peer relations. Overall, there is a continuing lack of integration between developmental and clinical research and models of the development of social withdrawal and social anxiety. Full article

The prediction of hydrological phenomena using simpler hydrological models requires less computing power and input data compared to the more complex models. Ordinarily, a more complex, white-box model would be expected to have better predictive capabilities than a simple grey box or black-box model. But complexity may not necessarily translate to better prediction accuracy or might be unfeasible in data scarce areas or when computer power is limited. Therefore, the shift of hydrological science towards the more process-based models needs to be justified. To answer this, the paper compares 2 hydrological models: (a) the simpler tank model; and (b) the more complex TOPMODEL. More precisely, the difference in performance between tank model as a lumped model and the TOPMODEL concept as a semi-distributed model in Atari River catchment, in Eastern Uganda was conducted. The objectives were: (1) To calibrate tank model and TOPMODEL; (2) To validate tank model and TOPMODEL; and (3) To compare the performance of tank model and TOPMODEL. During calibration, both models exhibited equifinality, with many parameter sets equally likely to make acceptable hydrological simulations. In calibration, the tank model and TOPMODEL performances were close in terms of ‘Nash-Sutcliffe efficiency’ and ‘RMSE-observations standard deviation ratio’ indices. However, during the validation period, TOPMODEL performed much better than tank model. Owing to TOPMODEL’s better performance during model validation, it was judged to be better suited for making runoff forecasts in Atari River catchment. Full article