Evaluation Function Research Articles

Multi-objective optimization of full depth reclamation with Portland cement using NSGA-II for sustainable pavement rehabilitation

Full-depth reclamation with Portland cement (FDR-PC) is a promising technology in modern pavement engineering due to its capability of achieving deep-level treatment of pavement base layer distresses. This study aimed to optimize the material performance of FDR-PC materials while considering their environmental impact, developing a multi-objective optimization model to comprehensively evaluate and optimize these aspects. Laboratory tests were first conducted to investigate the effects of reclaimed asphalt pavement (RAP) content and cement content on 7-day unconfined compressive strength (UCS), indirect tensile strength (ITS), and relative compressive strength (RCS) after freeze-thaw cycles. A comprehensive performance evaluation function was established based on these key indicators. Subsequently, carbon emission and energy consumption models for FDR-PC were developed using life cycle assessment (LCA), which together formed an environmental impact function. The non-dominated sorting genetic algorithm II (NSGA-II) was employed to perform multi-objective optimization of the FDR-PC mix design and obtain the Pareto front. The technique for order of preference by similarity to ideal solution (TOPSIS) was then used to identify optimal parameter combinations under various objective weighting scenarios. Results revealed a significant negative correlation between material performance and environmental impact. The parameter combinations corresponding to the non-dominated solutions were mainly concentrated in cement content ranging from 4.8% to 6.0% and RAP content from 20% to 34%. Parameter combinations corresponding to high material performance were found in regions with RAP content below 20%, which also corresponded to high environmental impact. According to the TOPSIS analysis, the optimal mix under a performance-priority strategy consists of 6.0% cement and 5% RAP; the environmentally preferred mix recommends 4.6% cement and 32% RAP; and a balanced compromise suggests 5.2% cement and 27% RAP.

Optimum multistage deep drawing process design using artificial neural network-based forming quality evaluation function

Optimum multistage deep drawing process design using artificial neural network-based forming quality evaluation function

Seismic risk and loss estimation models for building clusters considering the effects of the earthquake intensity and temperature field.

Earthquake disasters and extreme temperature fields directly affect the vulnerability and economic losses of building clusters. Seismic intensity measures and structural capacity indicators are widely used to evaluate the seismic fragility and economic losses of buildings. However, the dual effects of low-temperature fields and seismic intensity on the estimation of building economic damage have been considered less. This article proposes a structural seismic hazard model that considers the coupling effect of the seismic intensity and temperature field, utilizing the theory of seismic hazard and risk assessment. An innovative structural seismic vulnerability and loss evaluation function based on the influence of the temperature field was proposed. Four types of building damage samples (170,388,204sq.m) and temperature data were statistically classified from 12 typical earthquakes (6 in low-temperature fields and 6 in normal-temperature fields) in Xinjiang, China, between 1995 and 2024. An economic loss assessment model for regional building clusters considering the temperature field and seismic intensity measures was developed using probabilistic vulnerability and risk analysis methods. A function for evaluating the actual seismic losses and vulnerability of structures under different temperature fields using nonlinear regression and cumulative economic loss functions is proposed. A structural economic loss evaluation curve is generated considering the effects of temperature fields and intensity measures. A novel calculation method for evaluating the number of people who have lost their homes after an earthquake has been proposed, which combines the dual effects of the temperature field and seismic intensity measures on structural vulnerability.

Investigation of Driving Safety on Desert Highways Under Crosswind Direction Disturbances

Desert highways, with open terrain and minimal wind barriers, expose high-speed vehicles to significant stability risks from combined crosswinds and sand accumulation. This study uses numerical simulation to assess the effects of varying wind direction angles and sand thicknesses on vehicle stability across different models. Five dynamic indicators—lateral displacement, yaw angle, aerodynamic sideslip angle, lateral acceleration, and roll angle—are analyzed. The results show that a 120° wind angle causes the most pronounced parameter changes, while stability is lowest at 150°, where critical thresholds are reached within 0.75 s and danger thresholds by 2.25 s. Rapid wind speed variations further degrade stability. Compared to small SUVs, mid-size SUVs perform worse under identical conditions. A comprehensive stability evaluation function is proposed to quantify the combined impact of wind angle and surface friction, providing a new approach for safety assessment on sand-covered desert roads.

Role of Transesophageal Echocardiography in Guiding and Evaluating Left Atrial Appendage Occlusion in Patients With Non‐Organic Heart Disease

ABSTRACTObjectiveTo assess the guiding role of transesophageal echocardiography (TEE) intraoperatively and its evaluative function postoperatively during left atrial appendage occlusion (LAAO) in patients with non‐organic heart disease (NOHD).MethodsIn this retrospective observational study, a total of 48 patients with NOHD who underwent LAAO in the Department of Cardiology at The First People's Hospital of Lanzhou City from April 2020 to September 2022 were recruited. TEE findings during and after the procedure, cardiac chamber size, and cardiac function parameters at different surgical stages, postoperative occlusion efficacy, and complications were recorded. The application value of TEE in LAAO for patients with NOHD was evaluated.ResultsComparative analysis TEE‐measured the maximum diameter of the LAAO (22.37 ± 3.86 mm) was significantly smaller than X‐ray angiographic measurement (23.45 ± 4.22 mm; p < 0.05). One month after radiofrequency ablation, TTE revealed a statistically significant reduction in left atrial diameter (p < 0.05). Four cases (8%) exhibited minor peri‐device leak (< 3 mm), and no major complications occurred.ConclusionTEE shows significant application value for monitoring anatomical changes, guiding device sizing, and detecting peri‐device leaks during and after LAAO for patients with NOHD.

Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning

In mobile robot path planning, the traditional A* algorithm suffers from high path redundancy and poor smoothness, while the Dynamic Window Approach (DWA) tends to deviate from the global optimal path and has low efficiency in avoiding dynamic obstacles when integrated with global path planning. To address these issues, a smoothing optimised A*-guided DWA fusion algorithm (SOA-DWA) is proposed in this paper. Firstly, the A* algorithm was improved by introducing a path smoothing strategy and path pruning mechanism, generating a globally optimal path that complied with the vehicle kinematic constraints. Secondly, three sub-functions were introduced into the evaluation function of the DWA algorithm: the distance evaluation between the reference trajectory and the global path, the path direction evaluation, and the dynamic obstacle avoidance evaluation, to enhance the real-time performance of dynamic obstacle avoidance and the consistency of the global path. The SOA-DWA algorithm ensured that the mobile robot could effectively avoid obstacles in complex environments without deviating from the global optimal path. Thirdly, experimental results show that in a static environment, the path length and turning angle of the SOA-DWA algorithm are reduced by an average of 13.3% and 16.25%, respectively, compared with the traditional algorithm. In a dynamic environment, the path length and turning angle are reduced by an average of 10.5% and 14.5% compared to the traditional DWA algorithm, respectively, significantly improving the smoothness of the path and driving safety. Compared to the existing fusion algorithm, the SOA-DWA algorithm reduces the path length by an average of 10.1%, improves planning efficiency by an average of 42%, and effectively enhances obstacle avoidance efficiency. Finally, the effectiveness of the improved algorithm proposed in this paper was further verified by mobile robot experiments.

Self-Adjusting Look-Ahead Distance of Precision Path Tracking for High-Clearance Sprayers in Field Navigation

As a core component of agricultural machinery autonomous navigation, path tracking control holds significant research value. The pure pursuit algorithm has become a prevalent method for agricultural vehicle navigation due to its effectiveness at low speeds, yet its performance critically depends on the selection of the look-ahead distance. The conventional approaches require extensive parameter tuning due to the complex influencing factors, while fixed look-ahead distances struggle to balance the tracking accuracy and adaptability. Considerable effort is required to fine-tune the system to achieve optimal performance, which directly affects the accuracy of the path tracking and the results in the cumbersome task of selecting an appropriate goal point for the tracking path. To address these challenges, this paper introduces a pure pursuit algorithm for high-clearance sprayers in agricultural machinery, utilizing a self-adjusting look-ahead distance. By developing a kinematic model of the pure pursuit algorithm for agricultural machinery, an evaluation function is then employed to estimate the pose of the machinery and identify the corresponding optimal look-ahead distance within the designated area. This is done based on the principle of minimizing the overall error, enabling the dynamic and adaptive optimization of the look-ahead distance within the pure pursuit algorithm. Finally, this algorithm was verified in simulations and bumpy field tests under various different conditions, with the average value of the lateral error reduced by more than 0.06 m and the tuning steps also significantly reduced compared to the fixed look-ahead distance in field tests. The tracking accuracy has been improved and the applicability of the algorithm for rapid deployment has been enhanced.

A versatile framework for attitude tuning of beamlines at light source facilities.

Aside from regular beamline experiments at light sources, the preparation steps before these experiments are also worthy of systematic consideration in terms of automation; a representative category in these steps is attitude tuning, which typically appears in contexts like beam focusing, sample alignment etc. With the goal of saving time and human effort in both writing and using such code, a Mamba-based attitude-tuning framework is created. It supports flexible input/output ports, easy integration of diverse evaluation functions and free selection of optimization algorithms. With the help of Mamba's infrastructure, machine learning (ML) and artificial intelligence (AI) technologies can also be readily integrated. The tuning of a polycapillary lens and of an X-ray emission spectrometer are given as examples for the general use of this framework, featuring powerful command-line interfaces (CLIs) and friendly graphical user interfaces (GUIs) that allow comfortable human-in-the-loop control. The tuning of a Raman spectrometer demonstrates more specialized use of the framework with customized optimization algorithms. With similar applications in mind, this framework is estimated to be capable of fulfilling most attitude-tuning needs. Also reported is a virtual-beamline mechanism based on easily customisable simulated detectors and motors, which facilitates both testing for developers and training for users, as well as the encapsulation of digital twins.

Gradient-based optimization of core-shell particles with discrete materials for directional scattering

Designing nanophotonic structures traditionally grapples with the complexities of discrete parameters, such as real materials, often resorting to costly global optimization methods. This paper introduces an approach that leverages generative deep learning to map discrete parameter sets into a continuous latent space, enabling direct gradient-based optimization. For scenarios with non-differentiable physics evaluation functions, a neural network is employed as a differentiable surrogate model. The efficacy of this methodology is demonstrated by optimizing the directional scattering properties of core-shell nanoparticles composed of a selection of realistic materials. We derive suggestions for core-shell geometries with strong forward scattering and minimized backscattering. Our findings reveal significant improvements in computational efficiency and performance when compared to global optimization techniques. Beyond nanophotonics design problems, this framework holds promise for broad applications across all types of inverse problems constrained by discrete variables.

The Positive Interaction Between Online Public Opinion and Judicial Processes from a Comparative Law Perspective

In the era of social media, the proliferation of online actors, the diversity of content, and the rapid dissemination of information have created conditions for widespread public participation in the judiciary, enhancing transparency while simultaneously posing challenges to judicial rationality and independence. Taking the cases of Jiang Ge and the Kunshan self-defense case as examples, this paper illustrates how differing judicial cultures, institutional frameworks, and penal execution practices result in varied responses to significant criminal cases in China and Japan. It is essential to fully leverage the moral evaluative function of criminal law, appropriately respond to public sentiment, and explore connections between public opinion and the judiciary that align with the construction of a networked rule of law with Chinese characteristics. This approach is fundamental to achieving good law and governance.

Force-position-workspace hybrid-based stability optimization of reconfigurable cable-driven parallel robot

Abstract Cable-driven parallel robots (CDPRs) have been widely used as motion executers for their large workspace and lower inertia. However, there are few studies on structural optimization design considering its stability. This paper proposes a stability optimization method based on force-position workspace for a reconfigurable cable-driven parallel robot (RCDPR). First, the structural optimization analysis of RCDPR is carried out. Then, the forces distribution algorithm based on the feasibility of real-time control is determined, and the boundary contour algorithm (BCA) of the RCDPR force feasible workspace (FFW) on the central plane is proposed. Second, the stiffness and cables driving force space (CFS) models of RCDPR are established. Subsequently, the stability evaluation function is established to optimize the structure of RCDPR, which uses FFW and main task feasible workspace (MFW) as carriers and stiffness and CFS as weights. Finally, an experimental prototype of the developed robot is constructed, and motion performance and workspace verification experiments are conducted. The results demonstrate that the developed RCDPR has good motion accuracy and stable workspace, and the results also verify the feasibility of the stability evaluation function and BCA.

Development and validation of a multi-feature immune scoring prognostic model for stage II and III rectal cancer patients.

e15636 Background: Stage II and III rectal cancer patients undergoing direct surgery after clinical diagnosis remain at risk of postoperative recurrence, with significant variability in their prognoses. Immune scoring, based on the assessment of immune cells within the tumor microenvironment, offers a promising approach to predict outcomes and guide clinical decision-making. Methods: Multiplex immunohistochemistry (mIHC) was performed to simultaneously stain CD3, CD8, and PANCK on a single slide. Besides, an algorithm was developed for image segmentation, region identification, and cell recognition, resulting in the extraction of 429 features. The Lasso-Cox regression model was applied for feature selection and training, leading to the development of a multi-feature immune scoring prognostic model. Results: The training set for modeling included 384 samples, while the single-center validation set comprised 165 samples, and the multi-center test set contained 157 samples. For the disease-free survival (DFS) model, the Lasso-Cox regression model with 10-fold cross-validation was applied to the training set for feature selection, using partial likelihood deviance as the evaluation function. Features corresponding to the lambda value with the minimum partial likelihood deviance were selected, resulting in 18 features being incorporated. The multi-feature ensemble model’s prediction signatures were identified as significant prognostic factors for DFS in rectal cancer patients who underwent surgery (p &lt; 0.001, HR 0.42, 95% CI 0.29–0.60). For the overall survival (OS) model, the Lasso-Cox regression model with 10-fold cross-validation was used for feature selection in the training set, with the C-index as the evaluation metric. Features corresponding to the lambda value with the maximum C-index were selected, incorporating 19 features. The prediction signatures of the multi-feature ensemble model were significant prognostic factors for OS in rectal cancer patients who underwent surgery (p &lt; 0.001, HR 0.37, 95% CI 0.24–0.58). Notably, the multi-feature model outperformed patient stratification based on the Lancet Immunoscore. Specifically, the hazard ratio (HR) for the 360-μm margin dichotomy was 0.49 for DFS and 0.53 for OS, while the HR for the 500-μm margin dichotomy was 0.49 for DFS and 0.52 for OS. Additionally, the C-index for the multi-feature model was 0.67 for DFS and 0.74 for OS, demonstrating its superior prognostic performance. Conclusions: The DFS-based model enables the assessment of postoperative recurrence risk in patients, while the OS-based model provides predictions of patient prognosis. Together, the multi-feature immune scoring prognostic model demonstrates significant potential for clinical application.

Actor critic with experience replay‐based automatic treatment planning for prostate cancer intensity modulated radiotherapy

Abstract BackgroundAchieving highly efficient treatment planning in intensity‐modulated radiotherapy (IMRT) is challenging due to the complex interactions between radiation beams and the human body. The introduction of artificial intelligence (AI) has automated treatment planning, significantly improving efficiency. However, existing automatic treatment planning agents often rely on supervised or unsupervised AI models that require large datasets of high‐quality patient data for training. Additionally, these networks are generally not universally applicable across patient cases from different institutions and can be vulnerable to adversarial attacks. Deep reinforcement learning (DRL), which mimics the trial‐and‐error process used by human planners, offers a promising new approach to address these challenges. PurposeThis work aims to develop a stochastic policy‐based DRL agent for automatic treatment planning that facilitates effective training with limited datasets, universal applicability across diverse patient datasets, and robust performance under adversarial attacks.MethodsWe employ an actor–critic with experience replay (ACER) architecture to develop the automatic treatment planning agent. This agent operates the treatment planning system (TPS) for inverse treatment planning by automatically tuning treatment planning parameters (TPPs). We use prostate cancer IMRT patient cases as our testbed, which includes one target and two organs at risk (OARs), along with 18 discrete TPP tuning actions. The network takes dose–volume histograms (DVHs) as input and outputs a policy for effective TPP tuning, accompanied by an evaluation function for that policy. Training utilizes DVHs from treatment plans generated by an in‐house TPS under randomized TPPs for a single patient case, with validation conducted on two other independent cases. Both online asynchronous learning and offline, sample‐efficient experience replay methods are employed to update the network parameters. After training, six groups, comprising more than 300 initial treatment plans drawn from three datasets, were used for testing. These groups have beam and anatomical configurations distinct from those of the training case. The ProKnow scoring system for prostate cancer IMRT, with a maximum score of 9, is used to evaluate plan quality. The robustness of the network is further assessed through adversarial attacks using the fast gradient sign method (FGSM).ResultsDespite being trained on treatment plans from a single patient case, the network converges efficiently when validated on two independent cases. For testing performance, the mean standard deviation of the plan scores across all test cases before ACER‐based treatment planning is . After implementing ACER‐based treatment planning, of the cases achieve a perfect score of 9, with only scoring between 8 and 9, and no cases being below 7. The corresponding mean standard deviation is . This performance highlights the ACER agent's high generality across patient data from various sources. Further analysis indicates that the ACER agent effectively prioritizes leading reasonable TPP tuning actions over obviously unsuitable ones by several orders of magnitude, showing its efficacy. Additionally, results from FGSM attacks demonstrate that the ACER‐based agent remains comparatively robust against various levels of perturbation.ConclusionsWe successfully trained a DRL agent using the ACER technique for high‐quality treatment planning in prostate cancer IMRT. It achieves high generality across diverse patient datasets and exhibits high robustness against adversarial attacks.

Gene Selection Algorithms in a Single-Cell Gene Decision Space Based on Self-Information

A critical step for gene selection algorithms using rough set theory is the establishment of a gene evaluation function to assess the classification ability of candidate gene subsets. The concept of dependency in a classic neighborhood rough set model plays the role of this evaluation function. This criterion only notes the information provided by the lower approximation and omits the upper approximation, which may result in the loss of some important information. This paper proposes gene selection algorithms within a single-cell gene decision space by employing self-information, taking into account both lower and upper approximations. Initially, the distance between gene expression values within each subspace is defined to establish the tolerance relation on the cell set. Subsequently, self-information is introduced through the lens of tolerance classes. The relationship between these measures and their respective properties is then examined in detail. For gene expression data, the proposed self-information metric demonstrates superiority over other measures by accounting for both lower and upper approximations, thereby facilitating the selection of optimal gene subsets. Finally, gene selection algorithms within a single-cell gene decision space are developed based on the proposed self-information metric, and experiments conducted on 10 publicly available single-cell datasets indicate that the classification performance of the proposed algorithms can be enhanced through the selection of genes pertinent to classification. The results demonstrate that Fi−SI achieves an average classification accuracy of 93.7% (KNN) while selecting 48.3% fewer genes than Fisher’s score.

Catchment-based water resources modelling and planning of the transboundary Inkisi River in the Congo Basin

ABSTRACT Large river basins exhibit many local-scale management concerns for which a catchment-based approach is required. This study addresses the need for hydrological and water resources management information at the scale of decision making, the catchment scale. A hydrology and water resources planning modelling approach is applied to a transboundary Inkisi River in the Congo basin, based on six catchments that were partitioned following the catchment classification framework for Congo basin. The model is established for the reference period, spanning from 1948 to 2021, to generate the necessary information on water availability, and is used to assess future scenarios of climate change and water demands for the horizon 2100. The model is calibrated using on 20 years of available streamflow data, which shows a good performance based on the objective functions of hydrological model evaluation. Assessment of the impact of climate change, based on two emissions scenarios, RCP4.5 and RCP 8.5, shows a decreasing trend in water availability, highlighting the need for adaptive water management strategies.

An Exploration of Process Evaluation in Senior High School English Teaching from the Perspective of Smart Education

Education informationization 4.0 brings convenience to education evaluation, especially in the process evaluation of senior high school English teaching under smart education. The process evaluation under smart education has significant advantages, which are reflected in the evaluation content, objectives, functions and subjects. In order to implement effective process evaluation, this study puts forward the strategy of using information technology for real-time evaluation, the design and application of diversified evaluation means, the implementation and optimization of refined evaluation methods, and the construction of interactive evaluation between teachers and students. The integration of process evaluation and new technology cannot be separated from technical support. At the same time, we should also pay attention to the protection of students’ privacy and avoid increasing the burden of teachers and students. Only by constantly exploring and improving the strategies and methods of smart education and process evaluation can we better improve the effect and quality of senior high school English teaching and cultivate more excellent talents.

Routing and Scheduling in Time-Sensitive Networking by Evolutionary Algorithms.

Routing and scheduling in Time-Sensitive Networking (TSN) is an NP-hard problem. In this paper, we propose a novel routing and scheduling approach for TSN based on evolutionary algorithm. Specifically, we introduce a flow grouping method that leverages the greatest common divisor to optimize flow aggregation. On this basis, we develop a flow routing strategy that employs a genetic algorithm, where the evaluation function considers not only flow combinability but also path length and network load. By exploiting the non-combinable properties of flows, we effectively reduce the search space for the genetic algorithm. Furthermore, we design a scheduling method based on differential evolution algorithms tailored to TSN's requirements of zero jitter and no frame loss. We propose a gene coding method and rigorously prove its correctness, which significantly reduces the search space of the differential evolution algorithm. The experimental results demonstrate that our approach enables more flows to traverse along the shortest path compared to both k-shortest path methods and integer linear programming approaches, while achieving a faster execution time in large-scale scheduling scenarios.

Multilevel assessment of the impact of stopping point capacity reserve on the service-resource efficiency of urban passenger transport

Abstract. Problem. The efficiency of urban public passenger transport is proposed to be considered from the perspective of assessing the impact of the stopping point capacity reserve on the level of service-resource efficiency of route operations and accounting for conflict situations in route flow interactions. Methodology. Based on the consideration of conditions for stabilizing the operation of urban public passenger transport elements and the principles of sustainable urban development, the structure of the feedback loop influencing technological solutions to ensure the efficiency of route flow interactions at stopping points in transport hubs has been identified. The functional objective is to ensure the level of interaction organization aimed at maximizing the service-resource potential of routes and reducing conflict situations that lead to negative consequences for bus dwell time at stopping points. Originality. The proposed relationship is based on a multilevel description of the consequences of reserving stopping point capacity on the service-resource potential of the route and the level of conflict in route flow interactions. A key feature of the proposed approach is its focus on assessing changes in passenger service quality parameters along the route and ensuring the necessary reserve of its carrying capacity. This approach enables the evaluation of urban passenger transport route efficiency, considering the impact of service conflicts in the inbound route flow at stopping points. Results. Based on experimental studies, a characteristic dependency of the impact of stopping point capacity reserve on the efficiency of urban public passenger transport routes has been established. The obtained efficiency evaluation function reflects trends described by an inverse exponential function. It has been determined that there is a transition threshold from a stable state to conditions of conflict interaction. The analysis of conflict situation distribution clusters allows for identifying this transition boundary at a stopping point capacity reserve level of 0.3. In the absence of precise coordination of route movement through the stopping point, the capacity reserve level should be at least 0.4–0.6. At this level, the proportion of conflict situations at the stopping point will not exceed 6.6–22%, and the reduction in service-resource efficiency of the routes will remain within 9.1–22.3%, which corresponds to acceptable passenger service quality conditions. Practical value. The obtained dependencies allow for the establishment of rational values for the reserve parameters of stopping points at transport hubs. Based on the presented methodology, it is possible to assess the effectiveness of implementing optimization management measures aimed at organizing bus arrivals at stopping points.

Obstacle Avoidance Path Planning for UAV Applied to Photovoltaic Stations Based on Improved Dynamic Window Method

In order to solve the problem of obstacle avoidance and low endurance of UAV autonomous flight in column obstacle scenes such as transmission towers, this paper improves and integrates the path planning algorithm to achieve efficient and accurate path planning in unstructured and unknown dynamic environments in column scenes. An improved DWA algorithm is proposed to improve the obstacle avoidance effect by adaptively improving the evaluation function and increasing the relevant evaluation function. At the same time, a hybrid path planning method in the obstacle scene of the UAV column is proposed. Firstly, the improved A* algorithm is used for global optimal planning. The UAV moves along the global optimal path, performs local dynamic perception at the same time, and calls the improved DWA algorithm to complete real-time obstacle avoidance and local replanning. Finally, the effectiveness and applicability of the improved DWA algorithm are verified by designing simulation experiments in different scenarios. The improved DWA algorithm is superior to the traditional DWA algorithm in terms of path length, running time, minimum safe distance, and number of iterations. The path length is reduced by 23.92%, the running time is reduced by 33.23%, and the number of iterations is reduced by 22.57% in the complex environment. At the same time, the simulation experiment of unknown obstacle avoidance for a photovoltaic scene in the area of transmission tower erection and the simulation experiment of path planning for UAV column obstacle scene in ROS environment are completed, which verifies that the hybrid algorithm has good robustness and environmental adaptability.

The organisation of evaluations: the influence of the ministry of finance on evaluation systems.

Despite increasing scholarly interest in the organisation of evaluations within different countries' political-administrative landscapes, not much attention has hitherto been paid to the consequences of a specific institutional set-up for the function of evaluations within government. This article investigates how the organisational anchorage of policy evaluations within central administration shapes the function those evaluations primarily serve. We focus on the role of ministries of finance for coordinating countries' evaluation systems, and study its influence in Denmark and the Netherlands through a combination of document analysis and interviews with centrally placed civil servants. Our analysis shows how the ministries of finance come to influence the evaluation activities of the whole central administration by constituting a specific economic outlook on evaluation, which (1) narrows down the applied evaluation methods and criteria; (2) inserts the ministry of finance as primary evaluation user; and hereby (3) furthers accountability rather than learning as the main function of evaluations within central administration. In both countries, the result is that the ministry of finance's main role in the evaluation systems favours somewhat defensive qualities, where evaluations are primarily used for control and piecemeal changes in policies, rather than fundamental revisions or reflections on the appropriateness of specific policies. Our findings indicate that the influence of evaluation systems is not only dependent on the degree of institutional anchorage of evaluation activities, but also very much a matter of whom the evaluation systems is centred around.