The INFLUENCE 3.0 model: updated predictions of locoregional recurrence and contralateral breast cancer, now also suitable for patients treated with neoadjuvant systemic therapy

Background: Individual risk prediction of 5-year locoregional recurrence (LRR) and contralateral breast cancer (CBC) supports decisions regarding personalised surveillance. The previously developed INFLUENCE tool was rebuild, including a recent population and patients who received neoadjuvant systemic therapy (NST). Methods: Women, surgically treated for nonmetastatic breast cancer, diagnosed between 2012 and 2016, were selected from the Netherlands Cancer Registry. Cox regression with restricted cubic splines was compared to Random Survival Forest (RSF) to predict five-year LRR and CBC risks. Separate models were developed for NST patients. Discrimination and calibration were assessed by 100x bootstrap resampling. Results: In the non-NST and NST group, 49,631 and 10,154 patients were included, respectively. Age, mode of detection, histology, sublocalisation, grade, pT, pN, hormonal receptor status ± endocrine treatment, HER2 status ± targeted treatment, surgery ± immediate reconstruction ± radiation therapy, and chemotherapy were significant predictors for LRR and/or CBC in non-NST patients. For NST patients this was similar, but excluding (y)pT and (y)pN status, and including presence of ductal carcinoma in situ, axillary lymph node dissection and pathologic complete response. For non-NST patients, the Cox and RSF models were integrated in the online tool with 5-year AUCs of 0.77 (95%CI:0.77–0.77) and 0.68 (95%CI:0.67–0.68)] for LRR and CBC prediction, respectively. For NST patients, the RSF model performed best (AUCs 0.77 (95%CI:0.76–0.78) and 0.73 (95%CI:0.69–0.76) for LRR and CBC, respectively). Regarding calibration, observed-predicted differences were all &lt;1 %. Conclusion: This INFLUENCE 3.0 models showed moderate performance in LRR and CBC prediction. The models have been made available as online tool to enable clinical decision support regarding personalised follow-up.</p

Temperature-driven wetting transition and localized oil entrapment in stimuli-responsive Poly-octadecylmethacrylate brushes

Temperature-driven wetting transition and localized oil entrapment in stimuliresponsive Poly-octadecylmethacrylate brushesSmart polymer coatings with switchable wettability gained considerable attention due to their potentialin a wide range of industrial applications such as microfluidics, self – cleaning surfaces and controlleddrug delivery. Surface wettability is governed by surface chemistry and morphology and can bemodified by external stimuli such as temperature, light, and pH.In this study, we explore the switchable wettability of n-alkanes on stimulus-responsive polyoctadecylmethacrylate (P18MA) brushes and introduce a method for reversible oil entrapment usinglaser-induced heating with micrometer-scale precision.Our results reveal a two-stage temperature-driven wetting transition of n-alkanes on these brushes.Initially, a moderate temperature increase causes the brush layer to swell while maintaining a finitecontact angle of the oil. As the temperature rises further, the oil spreads across the already swollen brushlayer. Combining macroscopic wetting experiments, Atomic Force Microscopy (AFM) adhesionmeasurements, and non-linear optical Sum Frequency Generation (SFG) spectroscopy, we show thatthis two-stage wetting transition is linked to the initial melting transition of the bulk polymer and asubsequent, surface-specific melting transition that remains in an ordered state up to a few degreesabove the bulk melting temperature

Statistical theory for image classification using deep convolutional neural network with cross-entropy loss under the hierarchical max-pooling model

Convolutional neural networks (CNNs) trained with cross-entropy loss have proven to be extremely successful in classifying images. In recent years, much work has been done to also improve the theoretical understanding of neural networks. Nevertheless, it seems limited when these networks are trained with cross-entropy loss, mainly because of the unboundedness of the target function. In this paper, we aim to fill this gap by analysing the rate of the excess risk of a CNN classifier trained by cross-entropy loss. Under suitable assumptions on the smoothness and structure of the a posteriori probability, it is shown that these classifiers achieve a rate of convergence which is independent of the dimension of the image. These rates are in line with the practical observations about CNNs.</p

Design of an Underactuated, Flexure-Based Gripper, Actuated Through a Push–Pull Flexure

The design of grippers for the agro-industry is challenging. To be cost-effective, the picked object should be moved around fast requiring a firm grip on the fruit of different hardnesses, shapes, and sizes without causing damage. This article presents a self-adaptive flexure-based gripper design optimized for high acceleration loads. A main novelty is that it is actuated through a push–pull flexure that is loaded in tension when the gripper closes, allowing it to handle high actuation forces without the risk of buckling. To create a robust gripper that can handle relatively high loads, flexures are used that are reinforced and have a thickness variation over the length. The optimal thickness distribution of these flexures is derived analytically to facilitate the design process. The derived principles are generally applicable to flexure hinges. The resulting advanced cartwheel flexure joint, as used in this gripper, has a 2.5 times higher support stiffness and a 1.5 times higher buckling load when compared toa conventional cartwheel joint of the same size and actuation stiffness. The PP-gripper is numerically optimized for a high pull-out force, using analytical design insights as a starting point. The gripper can grip circular objects with radii between 30 and 40 mm. The pull-out force is 21.4 N, with a maximum actuation force of 100 N. Good correspondence is found between the geometric design approach, the numerically optimized design, and the results of the experimental validatio

A systematic review of predictive, optimization, and smart control strategies for hydrogen-based building heating systems

The use of energy in the built environment contributes to over one-third of the world's carbon emissions. To reduce that effect, two primary solutions can be adopted, i.e. (i) renovation of old buildings and (ii) increasing the renewable energy penetration. This review paper focuses on the latter. Renewable energy sources typically have an intermittent nature. In other words, it is not guaranteed that these sources can be harnessed on demand. Thus, complement solutions should be considered to use renewable energy sources efficiently. Hydrogen is recognized as a potential solution. It can be used to store excess energy or be directly exploited to generate thermal energy. Throughout this review, various research papers focusing on hydrogen-based heating systems were reviewed, analyzed, and classified from different perspectives. Subsequently, articles related to machine learning models, optimization algorithms, and smart control systems, along with their applications in building energy management were reviewed to outline their potential contributions to reducing energy use, lowering carbon emissions, and improving thermal comfort for occupants. Furthermore, research gaps in the use of these smart strategies in residential hydrogen heating systems were thoroughly identified and discussed. The presented findings indicate that the semi-decentralized hydrogen-based heating systems hold significant potential. First, these systems can control the thermal demand of neighboring homes through local substations; second, they can reduce reliance on power and gas grids. Furthermore, the model predictive control and reinforcement learning approaches outperform other control systems ensuring energy comfort and cost-effective energy bills for residential buildings.<br/

Bridging silos through governance innovations: the role of the EU cities mission

Cities and local governments are increasingly under pressure to accelerate transformative change in energy and climate transitions. To help cities in their climate actions, the European Commission (EC) has established the EU Cities Mission, which aims for climate neutrality by 2030 for participating cities. The literature argues that one of the main obstacles to accelerating decarbonization lies in organizational divisions and other forms of structural silos. One of the possible ways to address these challenges and accelerate transformation is through governance innovations. The EU Cities Mission is a governance innovation that aims to incentivize and support climate and energy transitions in cities. In this paper, we critically assess the EU Cities Mission’s framework and implementation plan in terms of its potential and possible gaps in addressing different types of silos. To do so, we develop an analytical framework based on academic literature that outlines types of silos and strategies for addressing them. Our results show that key EU Cities Mission documents include several strategies to bridge silos, but that some silos are less frequently addressed. This is particularly the case for silos that rely on political leadership. The paper concludes by drawing out the implications of our findings for the scholarly literature and practice

Embedding (Semi-) automatic cadastral boundary extraction into fit-for-purpose land administration in peri-urban Ethiopia

The creation and upkeep of cadastral data is essential for maintaining land and resources and advancing sustainable development. However, developing nations face land use challenges in peri-urban areas due to the fast-paced increase in population and rapid urbanization. Moreover, conventional cadastral surveying methods are neither time nor cost-effective. Automatic feature extraction (AFE) is an emerging alternative to conventional field surveying methods, which can help land sector professionals adhere to the principles of fit-for-purpose land administration (FFPLA). The study aims to test and subsequently affirm the potential of the AFE approach using open-source tools for cadastral mapping in peri-urban areas. It adopts a generalized pre-exiting AFE workflow, utilizing free and open-source tools for the complete solution, including image segmentation, boundary classification, interactive delineation, and validation. High-resolution satellite images and a reference cadastral dataset are used for the pilot. The case location is a peri-urban area in Dukem, with source material obtained from the United States Agency for International Development (USAID) Land Governance Activity (LGA) Ethiopia branch. The LGA experts actively participated in the pilot testing through demonstrations, hands-on practice, focus group discussions, and questionnaire data collection. The pilot testing demonstrate that interactively delineated cadastral parcel boundaries delivered 66 % correctness for buffer widths of 0.5 and 0.4 m for the reference and interactively extracted boundary lines, respectively. The implemented AFE approach was further evaluated against the FFPLA elements and found to meet the affordability, attainability, flexibility, and upgradeability requirements. The strengths, weaknesses, opportunities, and threats (SWOT) analysis indicated favorable strengths and opportunities with manageable weaknesses and threats. The approach is supposed to be applicable for cadastral mapping and updating in peri-urban areas and newly emerging towns across the country due to rapid urbanization. Nonetheless, comparisons against conventional non-AFE methods, such as GNSS or total station surveys, in terms of time and cost implications are still needed. Moreover, further enhancement and testing with different land administration settings are recommended to apply the approach to real-world scenarios such as the LGA cadastral mapping project

No impact of story context and avatar power on performance in a stop-signal game

This study investigates the impact of gamification on response inhibition in a Stop-Signal Task (SST) and examines participants' gamification experience. The findings reveal that, after accounting for approach- and avoidance-motivation as well as impulsiveness, higher immersion is associated with impaired response inhibition. This effect could be attributed to a substantial decline in immersion between the first and second SST sessions. Despite intrinsic motivation and avatar identification not significantly predicting performance, both factors exhibited a decline across sessions, suggesting an overall diminished gaming experience in the second session. Alternatively, motivational variables as immersion and avatar identification might be detrimental to response inhibition, by shifting attention away from relevant task elements. Contrary to expectations, approach and avoidance narratives did not influence outcome variables or participant experience, while different avatars led to altered avatar identification, particularly favouring strong avatars. The declining motivation over time might stem from a lack of tangible goals within the gamified task, where narrative elements alone failed to induce sufficient goal-oriented motivation. These findings underscore the nuanced interplay between gamification elements, task complexity, and participants' expectations, emphasizing the need for carefully tailored gamification strategies in experimental designs.</p

Mathematical analysis of dynamic high power laser beam shaping using Galvanometer Scanners or Deformable Mirrors

In high power laser material processing technologies, such as laser welding, laser cladding or laser surface treatment, tailoring the spatial intensity profile of the laser beam, popularly known as beam shaping, is used to optimize the processing results in terms of processing quality and/or production rate. To allow for dynamic beam shaping — i.e. tailoring the intensity profile during processing — dynamic optics in the optical setup are required. Current dynamic optical devices suitable to shape a single high power laser beam are Galvanometer Scanners and Deformable Mirrors. However, an objective comparison of the beam shaping capabilities, such as resolution and shaping performance, of these beam shaping devices is lacking. This work proposes a novel mathematical framework to analyze and compare the two beam shaping concepts. This framework is used to quantify beam shaping capabilities as function of relevant laser setup parameters. Next, using the mathematical framework, the performance of the Galvanometer Scanner and Deformable Mirror is simulated when targeting a splitted laser beam, creating a horseshoe shaped intensity profile and creating a square uniform profile. Results show that, in practice, both devices will be able to create these three desired laser intensity profiles in the focal plane, with similar small average errors when compared to the desired beam shape. However, the error distributions show differences which are characteristic for the physical limitations of each individual beam shaping device

Pore formation and pore inter-connectivity in plasma electrolytic oxidation coatings on aluminium alloy

The porosity and microstructure of plasma electrolytic oxidation (PEO) coatings are key factors in determining their properties and applications. Despite advances in understanding the PEO process, the mechanisms driving pore formation and their correlation with process parameters remain unclear due to the complex interplay between these variables. This study investigates the effects of treatment time and duty cycle on the microstructure of PEO coatings produced on an aluminium alloy in an alkaline electrolyte, with a particular focus on pore formation. Our findings reveal that longer treatment durations lead to the significant development of sub-surface pores at the interface between the inner and outer layers. Additionally, a lower duty cycle leads to an increase in sub-surface pores, while a higher duty cycle favours the formation of surface pores. Morphological, 3D microstructural mapping, and chemical analyses reveal that pore formation is driven by the micro-discharges, gas generation, and the preferred gas escape path within the micro-melt pools formed during the PEO formation process. The preferred gas escape path is closely linked to the characteristics and lifetime of local micro-melt pools, elucidating the mechanisms behind pore formation