Real Patterns Research Articles

РАСЧЕТНАЯ ОЦЕНКА ВЛИЯНИЯ ДВИЖЕНИЯ РЕШЕТ НА АЭРОДИНАМИЧЕСКИЕ ПОТОКИ В СИСТЕМЕ ОЧИСТКИ ЗЕРНОУБОРОЧНОГО КОМБАЙНА

The paper examines aerodynamic modeling of air flows in the cleaning system of a combine harvester. A methodology has been developed for modeling the cleaning system, taking into account the movement of its elements, such as screens and boards, using the Simcenter Amesim and Ansys Fluent software packages. The main stages of the process of aerodynamic modeling of the cleaning system are considered using the example of the combine harvester produced by OJSC “Gomselmash”. A comparative analysis of air flows in a cleaning system with moving elements and when they are fixed in the middle position in a 2D formulation was performed. The results of numerical modeling of the aerodynamics of air flows are presented in the form of numerical values at control points, and the velocity fields and streamlines of the air flow in the flow area are illustrated. The results of the comparative analysis showed that the discrepancy between the speeds and directions of the air flow at the control points is 50 % and 4.5°, respectively. It is concluded that the movements of the cleaning elements during its aerodynamic modeling must be taken into account in order to obtain a real pattern of the air flows in the flow area. The results of modeling with fixed cleaning elements can act as a preliminary calculation to check the adequacy of the model based on the results of experimental measurements in cleaning also with fixed elements. The developed methodology for modeling the aerodynamics of the cleaning flow area will be used in further studies of the cleaning system of the combine harvester to analyze the combined behavior of air flows and heaps.

Identification of the degree of greenness of the proposed skills with the requirements of green jobs based on fuzzy logic

The deepening climate crisis development have prompted most countries to recognize the importance of transition to a green economy. This causes a rapid growth of “green” jobs in various spheres of human activity and, accordingly, the need for “green” skills necessary to use new technologies. New professions and specialties requiring the acquisition of green skills create a discrepancy between the demand for human resources of the required profile and their supply. In this context, it was clarified what the terms green jobs and green skills mean. Considering the content of these two concepts, studies dedicated to determining the relationship between the green transition and relevant skills were reviewed. In this regard, the selection of personnel with the necessary skills determines the relevance of developing methods for assessing the supply and demand for specialists, providing an opportunity to assess the compliance of the proposed skills with the requirements of green jobs. This article proposes a methodological approach to intelligent management of supply and demand in the green segment of the labor market. It also develops decision support methods to identify of compliance the greenness degree of the proposed skills with the level of the requirements of green jobs. Decision making methods were based on fuzzy situational analysis and pattern recognition: reference situations are determined (fuzzy reference pattern), real situations are determined (fuzzy real pattern); degree of green similarity of the reference situation to each real situation is calculated in accordance with the chosen measure of assessment of similarity between two fuzzy situations

Rethinking AI Acceptance in Corporate: A Human-Centric Extension of UTAUT

In the business corporate world, artificial intelligence (AI) is becoming a disruptive force. This study explores the intricacies of adopting AI in corporate environments, emphasizing factors that affect both behavioral intentions and real usage patterns. This study, which drew on the Unified Theory of Acceptance and Use of Technology (UTAUT), identified the distinctive features of AI and added new determinants, including perceived humanness, bias, job threat, functionality, transparency, and privacy and security issues. These determinants cover technological, human-centric, and situational aspects which can either catalyze or hinder AI acceptance. Our quantitative research, involving 223 professionals across diverse sectors in Saudi Arabia, expanded the UTAUT model by revealing critical factors driving AI acceptance, including ethics and privacy considerations. Intriguingly, certain latent factors were identified to inversely affect AI application. This research addresses important ethical, security, and operational issues related to AI deployment, while also expanding the theoretical understanding of AI's role in business. Such insights are paramount for decision-makers, practitioners, and academics alike, ensuring the sustainable and responsible incorporation of AI in the business realm.

Real-world exploration of LDL-cholesterol management in patients with atherosclerotic cardiovascular disease

BackgroundAlthough guidelines recommend low-density lipoprotein cholesterol (LDL-C) to be <70mg/dL in patients with atherosclerotic cardiovascular disease (ASCVD), the rate of achieving this goal remains suboptimal. We sought to understand real world contemporary practice patterns of LDL-C management in patients with ASCVD, and whether LDL-C testing influenced management across US health systems. MethodsA retrospective cohort study utilizing electronic medical record data from five health systems participating in the CardioHealth Alliance was performed on patients with an LDL-C measurement in 2021 and prior ASCVD. Multivariable regression modeling was used to determine the relationship of clinical factors with achievement of guideline directed LDL-C target. Changes in lipid lowering therapy (LLT) after LDL-C testing were also described. ResultsAmong 216,074 patients with ASCVD, 129,886 (60.1%) had uncontrolled LDL-C (i.e. ≥70 mg/dL). Compared with participants with controlled LDL-C (<70mg/dL), those with uncontrolled LDL-C were more frequently female (50.9% vs 35.1%), or Black (13.7% vs. 10.3%), and less commonly had coronary artery disease as the form of vascular disease (73.0% vs. 83.5% %), heart failure (21.3% vs. 29.1% %), diabetes (34.1% vs. 48.2%), atrial fibrillation (19.3% vs. 26.1%), or chronic kidney disease (25.1% vs. 32.2%). In multivariable analyses, the factors most strongly associated with failure to achieve LDL-C control were female sex (RR 1.13 [95% CI 1.12-1.14] p <0.001) and Black race (1.15 [1.14-1.17] p <0.001). Among the 53,957 (41.5%) of those with uncontrolled LDL-C ≥70 mg/dL not on lipid lowering therapy (LLT) at baseline, only 21% were initiated on any LLT within 6 months of the uncontrolled LDL-C value. ConclusionsWithin five diverse large health systems in the CardioHealth Alliance, more than half of the patients with ASCVD had uncontrolled LDL-C with significant disparities based on sex and race at baseline. The vast majority were not initiated on any lipid lowering therapy within 6 months of an elevated test result indicating persistent gaps in care that will likely worsen health inequities in outcomes. This highlights the urgent need for implementation efforts to improve equitable care.

Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds.

Why do individuals animals spontaneously change their preferences over time? While stable idiosyncratic behavioral preferences have been proposed to help species survive unpredictable environments as part of a bet-hedging strategy, the role of intraindividual shifts in preferences is unclear. Using Drosophila melanogaster , we show the stability of individual preferences is influenced by genetic background and neuromodulation, and is therefore a regulated phenomenon. We use theoretical modeling to show that shifts in preferences may be adaptive to environments that change within an individual's lifespan, including many real world patterns of environmental fluctuations. Together, this work suggests that the stability of individual preferences may affect the survival of species in unpredictable worlds - understanding that may be increasingly important in the face of anthropogenic change.

Whole-brain mechanism of neurofeedback therapy: predictive modeling of neurofeedback outcomes on repetitive negative thinking in depression

Real-time fMRI neurofeedback (rtfMRI-NF) has emerged as a promising intervention for psychiatric disorders, yet its clinical efficacy remains underexplored due to an incomplete mechanistic understanding. This study aimed to delineate the whole-brain mechanisms underpinning the effects of rtfMRI-NF on repetitive negative thinking in depression. In a double-blind randomized controlled trial, forty-three depressed individuals underwent NF training targeting the functional connectivity (FC) between the posterior cingulate cortex and the right temporoparietal junction, linked to rumination severity. Participants were randomly assigned to active or sham groups, with the sham group receiving synthesized feedback mimicking real NF signal patterns. The active group demonstrated a significant reduction in brooding rumination scores (d = −1.52, p < 0.001), whereas the sham group did not (d = −0.23, p = 0.503). While the target FC did not show discernible training effects or group differences, connectome-based predictive modeling (CPM) analysis revealed that the interaction between brain activity during regulation and brain response to the feedback signal was the critical factor in explaining treatment outcomes. The model incorporating this interaction successfully predicted rumination changes across both groups. The FCs significantly contributing to the prediction were distributed across brain regions, notably the frontal control, salience network, and subcortical reward processing areas. These results underscore the importance of considering the interplay between brain regulation activities and brain response to the feedback signal in understanding the therapeutic mechanisms of rtfMRI-NF. The study affirms rtfMRI-NF’s potential as a therapeutic intervention for repetitive negative thinking and highlights the need for a nuanced understanding of the whole-brain mechanisms contributing to its efficacy.

A spring-mass-damper model based on separated phase flow mode for pulsating heat pipe with adjustive-structured channels

Pulsating heat pipe (PHP) is a kind of efficient passive phase-change cooling device. The pulsating behaviors of the two-phase flow inside PHP significantly affect the heat transfer performance for PHP, the investigation of which will greatly contribute to the optimal design of PHP for electronic heat dissipation in small space. In the present work, the heat transfer performance of PHP is optimized via structure analysis and modeling calculation. A new “spring-mass-damper” model in terms of separated phase flow mode is established, where the frictional pressure loss of the real two-phase flow pattern − slug flow in PHP is considered. Besides, a prototype of PHP with adjustive-structured channel (ASCPHP) is proposed. The heat transfer performance of ASCPHP is evaluated with the newly established model. With theoretical computation method, the frequency of ASCPHP the superiority of ASPHP is also confirmed by comparison with other types of PHPs.

Generating realistic training images from synthetic data for excavator pose estimation

Computer vision-based 3D pose estimation for automated excavator operation monitoring requires numerous training images annotated with 3D pose labels. Owing to challenges in collecting such datasets in a field setting, using synthetic images from virtual environments has emerged recently. However, synthetic images lack the realism inherent in onsite images, potentially impacting pose estimation performance on real images. This paper thus proposes a generative model for generating realistic training excavator images with multiple backgrounds. The evaluation was conducted by comparing estimation models trained on synthetic images (Model #1), generated excavator images with single background (Model #2), and generated excavator images with multiple backgrounds (Model #3). Model #3 exhibited the lowest mean angular error of 5.96° on real data, implying its superiority in generalizing real patterns. The proposed model facilitates data acquisition for improving pose estimation without manual annotation, providing rich information on excavator movements for proactive safety and productivity management.

Market demands vs. scientific realities: A comparative analysis in the context of BIM-based and user-centred lighting control

Real utilisation patterns often only become apparent when a building is put into operation. Since planning is therefore based on several assumptions, the potential of building technology is often limited. Post-occupancy evaluations offer the potential to adapt operation to real usage patterns and draw conclusions for future planning, thereby improving performance. Although solution approaches are available in the literature, this potential is currently hardly being utilised. A survey in the construction industry is therefore used to compare market requirements with existing solution concepts. The results emphasise the relevance of auto-commissioning, contractual agreements on performance evaluation and the coupling of digital twins with post-occupancy evaluations. A stronger anchoring of the control industry in BIM is proving to be necessary for better cross-disciplinary collaboration between all parties involved. Even though the focus of this study is on lighting, the most important findings are in principle transferable to other trades.

Phase quantification using deep neural network processing of XRD patterns.

Mineral identification and quantification are key to the understanding and, hence, the capacity to predict material properties. The method of choice for mineral quantification is powder X-ray diffraction (XRD), generally using a Rietveld refinement approach. However, a successful Rietveld refinement requires preliminary identification of the phases that make up the sample. This is generally carried out manually, and this task becomes extremely long or virtually impossible in the case of very large datasets such as those from synchrotron X-ray diffraction computed tomography. To circumvent this issue, this article proposes a novel neural network (NN) method for automating phase identification and quantification. An XRD pattern calculation code was used to generate large datasets of synthetic data that are used to train the NN. This approach offers significant advantages, including the ability to construct databases with a substantial number of XRD patterns and the introduction of extensive variability into these patterns. To enhance the performance of the NN, a specifically designed loss function for proportion inference was employed during the training process, offering improved efficiency and stability compared with traditional functions. The NN, trained exclusively with synthetic data, proved its ability to identify and quantify mineral phases on synthetic and real XRD patterns. Trained NN errors were equal to 0.5% for phase quantification on the synthetic test set, and 6% on the experimental data, in a system containing four phases of contrasting crystal structures (calcite, gibbsite, dolomite and hematite). The proposed method is freely available on GitHub and allows for major advances since it can be applied to any dataset, regardless of the mineral phases present.

Erosion Induced Flow Changes in Pelton Bucket: A Numerical Approach

Abstract The hydro-turbines working in sediment-laden conditions often experience some material loss from their surface, which modifies the surface design. This material loss alters the mechanical strength of the turbine component as well as the corresponding flow phenomenon. These changes in the flow pattern can lead to efficiency losses, but they can also be exploited to determine the actual condition of the component in real time. In fact, if detected, they can be useful for developing maintenance strategies to predict the turbine conditions in real time. To compare the flow parameters, a numerical simulation has been performed for a 2D stationary Pelton bucket under both eroded and uneroded scenarios considering clean water as the working fluid. The 2D middle section of a reference Pelton bucket has been considered in two configurations: the uneroded bucket case and eroded, whose uneven surface patterns were created taking inspiration from real erosion patterns inside Pelton buckets observed in sediment-laden power plants. The CFD simulations have been carried out using OpenFOAM multiphase solver interFoam. The two cases were then compared based on several flow parameters such as water film thickness, velocity, and exit flow angle. The result analysis confirmed flow disturbances due to erosion causes the decrement in the overall flow velocity and the increment in water film thickness. The velocity reduction effect, as well as the water film thickness increment effect, strengthens approaching the outlet of the bucket. The results also showed a significant alteration of exit flow angle due to erosion. Specifically, for the erosion pattern considered in this study, the exit flow angle was observed to be larger compared to the uneroded bucket case.

Towards intrusion detection in fog environments using generative adversarial network and long short-term memory network

Recently, fog computing has been developed to complement cloud computing, which can provide cloud services at the edge of the network with real-time processing. However, the computational power of fog nodes is limited and this leads to security issues. On the other hand, cyber-attacks have become common with the exponential growth of Internet of Things (IoT) connected devices. This fact necessitates the development of Intrusion Detection Systems (IDSs) in fog environments with the aim of detecting attacks. In this paper, we develop an IDS named GAN-LSTM for fog environments that uses Generative Adversarial Networks (GANs) and Long Short-Term Memory Networks (LSTMs). GAN-LSTM is used to identify anomalies in network traffic to specific types of attacks or non-attacks. In general, GAN-LSTM consists of three components: data preprocessing, generation of real traffic patterns, and sequence analysis of real traffic data. Data preprocessing ensures data quality by removing noise and irrelevant features. The pre-processed data is fed to the GAN to generate real traffic as a baseline for normal behavior. Finally, the LSTM component is applied to detect anomalous anomalies in fog computing. The proposed algorithm was evaluated on public databases and experimental results showed that GAN-LSTM improves the accuracy of attack detection compared to equivalent approaches.

Party priorities in different pre-election New Zealand policy statements, 1984-2023

ABSTRACT Pre-election policy statements are important because they reveal information about parties’ policies and intended post-election priorities. Content analysis data from election manifestos, webpages, television broadcasts, and leaders’ speeches reveal clear left-right differences between the dominant Labour and National parties at elections between 1984 and 2023, with the exception of 1990. Between 1993 and 1999 and since 2011 broadcasts and speeches by National have been more right-wing on a left-right summary dimension than National’s manifestos and detailed webpages. The reverse was usually true for the right-wing Act Party, while New Zealand First’s position varied more in broadcasts and speeches than in its print programme. No real pattern for left-right topics was evident for the other parties. However, references to the environment, indigenous rights, and foreign policy were higher in long print documents than in television broadcasts and speeches. In contrast, political authority references were relatively low in print documents. When using equivalent documents, the Green and Māori parties, followed by Labour, were usually most supportive of environmental protection and socially progressive policies. The results confirm the importance of using similar documents, insofar as is possible, when making comparisons. In addition, shorter documents may be more useful for understanding parties’ main priorities.

An agent-based method to estimate 3D cell migration trajectories from 2D measurements: Quantifying and comparing T vs CAR-T 3D cell migration

Background and objective:Immune cell migration is one of the key features that enable immune cells to find invading pathogens, control tissue damage, and eliminate primary developing tumors. Chimeric antigen receptor (CAR) T-cell therapy is a novel strategy in the battle against various cancers. It has been successful in treating hematological tumors, yet it still faces many challenges in the case of solid tumors. In this work, we evaluate the three-dimensional (3D) migration capacity of T and CAR-T cells within dense collagen-based hydrogels. Quantifying three-dimensional (3D) cell migration requires microscopy techniques that may not be readily accessible. Thus, we introduce a straightforward mathematical model designed to infer 3D trajectories of cells from two-dimensional (2D) cell trajectories. Methods:We develop a 3D agent-based model (ABM) that simulates the temporal changes in the direction of migration with an inverse transform sampling method. Then, we propose an optimization procedure to accurately orient cell migration over time to reproduce cell migration from 2D experimental cell trajectories. With this model, we simulate cell migration assays of T and CAR-T cells in microfluidic devices conducted under hydrogels with different concentrations of type I collagen and validate our 3D cell migration predictions with light-sheet microscopy. Results:Our findings indicate that CAR-T cell migration is more sensitive to collagen concentration increases than T cells, resulting in a more pronounced reduction in their invasiveness. Moreover, our computational model reveals significant differences in 3D movement patterns between T and CAR-T cells. T cells exhibit migratory behavior in 3D whereas that CAR-T cells predominantly move within the XY plane, with limited movement in the Z direction. However, upon the introduction of a CXCL12 chemical gradient, CAR-T cells present migration patterns that closely resemble those of T cells. Conclusions:This framework demonstrates that 2D projections of 3D trajectories may not accurately represent real migration patterns. Moreover, it offers a tool to estimate 3D migration patterns from 2D experimental data, which can be easily obtained with automatic quantification algorithms. This approach helps reduce the need for sophisticated and expensive microscopy equipment required in laboratories, as well as the computational burden involved in producing and analyzing 3D experimental data.

The metaphysics of mechanisms: an ontic structural realist perspective

Existing metaphysical accounts of mechanisms commit to the existence of objects or entities posited in scientific theories, and thus fall within the category of maximal metaphysics. In this paper, I demonstrate the incompatibility of object-based metaphysics of mechanisms with the prevailing trend in the philosophy of physics by discussing the so-called bottoming-out problem. In response, I propose and flesh out a structuralist metaphysics of mechanisms based on Ontic Structural Realism (OSR), which is a kind of minimal metaphysics. I argue that the metaphysical underpinnings of mechanisms are structures, whose metaphysical nature is elaborated through comparison with existing metaphysical theories of mechanisms. After that, I address the concern of whether objects in mechanisms can be accommodated in my account by invoking existing metaphysical theories of objects in special science by structuralists, such as Ladyman, J., & Ross, D. (2007). Every Thing Must Go: Metaphysics Naturalized. Oxford University Press.)’s real pattern account, and suggesting a potential alignment between OSR and processual ontology. Finally, I demonstrate how my view can naturally serve as the metaphysics for Mechanism 2.0 and be applied to systems biology.

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19.

Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.

Graph Neural Networks for Pressure Estimation in Water Distribution Systems

AbstractPressure and flow estimation in water distribution networks (WDNs) allows water management companies to optimize their control operations. For many years, mathematical simulation tools have been the most common approach to reconstructing an estimate of the WDNs hydraulics. However, pure physics‐based simulations involve several challenges, for example, partially observable data, high uncertainty, and extensive manual calibration. Thus, data‐driven approaches have gained traction to overcome such limitations. In this work, we combine physics‐based modeling and graph neural networks (GNN), a data‐driven approach, to address the pressure estimation problem. Our work has two main contributions. First, a training strategy that relies on random sensor placement making our GNN‐based estimation model robust to unexpected sensor location changes. Second, a realistic evaluation protocol that considers real temporal patterns and noise injection to mimic the uncertainties intrinsic to real‐world scenarios. As a result, a new state‐of‐the‐art model, GAT with Residual Connections, for pressure estimation is available. Our model surpasses the performance of previous studies on several WDNs benchmarks, showing a reduction of absolute error of ≈40% on average.

The Concept of Sustainable Development, Government Regulation, and Consumer Society

In the article, the author substantiates the possibility and necessity of government influence on the economy within the framework of implementing the concept of sustainable development. At the beginning of the article, the behavioral models of producers and consumers in the era of the «green» economy, declared and welcomed at the official level by the state and society, and their antagonistic contradictions with actual trends in the production of most consumer goods and services are described. The causes and possible long-term consequences of these contradictions for the development of the modern economy and modern consumer society are analyzed. Particular attention is devoted to the role of the state in solving global socio-economic problems. At the end of the article, several promising directions for improving production within the concept of sustainable development are given, which do not contradict the real patterns of development of the economy and society.

Dynamic stress analysis of rock joints under railway loading

The proper estimation of stresses generated by the passage of a train is fundamentally important to the serviceability and longevity of railways, and yet very limited knowledge is available where the track substructure is built on a jointed rock mass. The present study introduces an analytical solution for estimating the ground stresses arising from moving wheel loads, causing a change in the three-dimensional stress state in the track formation, in relation to the stress variation with depth and along the longitudinal track section – that is, the direction of train passage. Based on 21 case histories, an array of field measurements and numerical simulations covering a wide range of freight tonnage, train speeds and different formation conditions were considered to validate the proposed analytical solution. The proposed methodology (analytical solution) was then applied to a jointed rock subgrade to determine the normal and shear stresses acting along a specific discontinuity plane. The main analytical outcome demonstrates that the orthogonal vertical and shear stresses present different and phase-shifted history plots for homogeneous ground conditions with principal stresses rotation. However, conversely for a jointed subgrade, the normal and shear stresses along the discontinuity have the same history plot pattern and are in phase. As a practical guide, the results from this study would help to define which cyclic loads should be applied in laboratory tests to simulate realistic traffic patterns of trains travelling over a jointed rock subgrade.

Amended Vegetation Filters as Nature-Based Solutions for the Treatment of Pharmaceuticals: Infiltration Experiments Coupled to Reactive Transport Modelling.

In small populations and scattered communities, wastewater treatment through vegetation filters (VFs), a nature-based solution, has proved to be feasible, especially for nutrient and organic matter removal. However, the presence of pharmaceuticals in wastewater and their potential to infiltrate through the vadose zone and reach groundwater is a drawback in the evaluation of VF performances. Soil amended with readily labile carbon sources, such as woodchips, enhances microbial activity and sorption processes, which could improve pharmaceutical attenuation in VFs. The present study aims to assess if woodchip amendments to a VF's soil are able to abate concentrations of selected pharmaceuticals in the infiltrating water by quantitatively describing the occurring processes through reactive transport modelling. Thus, a column experiment using soil collected from an operating VF and poplar woodchips was conducted, alongside a column containing only soil used as reference. The pharmaceuticals acetaminophen, naproxen, atenolol, caffeine, carbamazepine, ketoprofen and sulfamethoxazole were applied daily to the column inlet, mimicking a real irrigation pattern and periodically measured in the effluent. Ketoprofen was the only injected pharmaceutical that reached the column outlet of both systems within the experimental timeframe. The absence of acetaminophen, atenolol, caffeine, carbamazepine, naproxen and sulfamethoxazole in both column outlets indicates that they were attenuated even without woodchips. However, the presence of 10,11-epoxy carbamazepine and atenolol acid as transformation products (TPs) suggests that incomplete degradation also occurs and that the effect of the amendment on the infiltration of TPs is compound-specific. Modelling allowed us to generate breakthrough curves of ketoprofen in both columns and to obtain transport parameters during infiltration. Woodchip-amended columns exhibited Kd and μw values from one to two orders of magnitude higher compared to soil column. This augmentation of sorption and biodegradation processes significantly enhanced the removal of ketoprofen to over 96%.