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Wavefront Shaping with varying degrees of freedom
No full textOptical WaveFront Shaping (WFS) uses the physical feature that whereas light scattering is complex, it is a linear process, thus deterministic. The incident wavefront is controlled to focus light through a scattering sample, by spatially dividing an incoming wavefront and modulating the resulting segments with Spatial Light Modulators (SLMs) or Digital Micromirror Devices (DMDs) paired with a holography system.The main criterion for such a process is the enhancement of the intensity at the target, defined as the ratio of the optimized intensity at the target, and the average intensity at the target for many realizations of the scattering sample. We focus on the effect of restricting the degrees of freedom of the phase modulating devices on the optimization performance. By turning off certain segments, which contribute very little to the optimization, it is possible to greatly shorten optimizations without a significant loss in enhancement. By shrinking the active area of segments, issues with holography systems occur, as small segments and phase transitions negatively affect performance.Our results lead to better choices regarding the areas of interest and limits of such optimizations to improve speed and efficiency, which are relevant for WFS applications
Economic and environmental assessment of water-based cathode and silicon anode vis-à-vis solvent-free electrodes in LIB production
Get PDFNew material chemistries and process technologies are receiving high considerations in the effort to reduce cost and carbon footprint of lithium-ion batteries (LIB). As a result of product-process interdependencies, changes in the cell design imply changes in the process. This paper investigated the impact of introducing the combination of water-based cathode and silicon anode vis-à-vis the introduction of solvent-free electrodes through dry coating technology in the battery manufacturing process chain. A value stream map (VSM) based modelling approach was used to assess the process chain under uncertainties. This provides more detailed and holistic insights than the common high level calculation models that use deterministic values. The study demonstrated the importance of assessing the impact in the manufacturing process chain when considering changes in the material composition of electrodes. The results of the assessment showed that the introduction of water-based cathode and silicon anode in the same factory was the most promising technology with potential cost savings ranging between 5-8%. The introduction of solvent-free electrodes led to a potential cost savings ranging between 2-4%. However, solvent-free electrode also showed slight potential for ∼2% cost increase when running at current pessimistic operating conditions. Both technologies showed comparable kgCO2-eq profiles, with water-based cathode and silicon anode offering the best benefits when mapped on a cost-carbon footprint matrix, due to economic advantages. Considering that both technologies are not yet matured, when current hurdles are overcome, these technologies will shape the next generation of batteries for both economic and environmental stewardship benefits
Vapor–Liquid Equilibrium for the Separation of the Close-Boiling Mixture Methylcyclohexane–Toluene with Dimethyl Isosorbide as a Biobased Entrainer and 1-Butylpyrrolidin-2-one as a Greener Entrainer
Get PDFIn this work, dimethyl isosorbide (DMI) and 1-butylpyrrolidin-2-one (NBP), as biobased and greener organic solvents, were used for the first time as entrainers in extractive distillation to separate a close-boiling mixture of methylcyclohexane and toluene. Vapor–liquid equilibrium (VLE) data were collected for pseudoternary mixtures consisting of methylcyclohexane and toluene in the presence of DMI and NBP at various entrainer-to-feed ratios (E/F) and pressures. The VLE measurements were conducted by using a Fischer Labodest VLE602 ebulliometer, and the thermodynamic consistency of the data was verified by using the Van Ness test. Both DMI and NBP were found to increase the relative volatility of methylcyclohexane to toluene, successfully eliminating close-boiling behavior. Compared to benchmark entrainers, both outperformed 1-methylpyrrolidin-2-one (NMP) and sulfolane under certain conditions. In comparison with other green entrainers, DMI and NBP showed similar performance to gamma-valerolactone (GVL) and Cyrene under specific conditions. The VLE data were accurately correlated by using the nonrandom two-liquid (NRTL) model.</p
Modified Benders-DES algorithm for real-world flow shop and job shop scheduling problems
Get PDFReal-world scheduling problems are often extremely challenging to solve, and DES is commonly used to find feasible production schedules without even considering optimization. However, DES can be integrated with MILP using Benders decomposition, which leads to an efficient Benders-DES algorithm (BDES). This work shows that BDES can be developed further to solve complex, real-world scheduling problems illustrated by 2 case studies. First, cut randomization, robustification, and re-optimization are proposed to enhance the solution speed and quality for a hybrid flow shop problem from agrochemical production involving lot-sizing decisions and secondary resources. BDES performs similarly well to a monolithic-sequential MILP-DES approach, a genetic algorithm, and an integer-linear programming approach, while offering practical advantages of solution robustness and re-optimization. Second, BDES is applied to a flexible paint production job shop with makespan and overproduction minimization objective. The performance of BDES is superior to that of the GA originally suggested, resulting in faster solution improvement with fewer DES model evaluations. This work concludes that BDES is a promising algorithmic approach for real-world scheduling problems and an alternative to established optimization methods.</p
From Promise to Practice: Evaluating the Health and Economic Impact of AI in Breast Cancer Imaging Surveillance
Get PDFMedical imaging systems generate vast amounts of data, demanding advanced methods for efficient analysis and interpretation. Artificial intelligence (AI) has demonstrated remarkable potential in this domain, enabling more accurate diagnostics, earlier disease detection, and personalized treatment planning. As AI technologies continue to transform healthcare, their integration into clinical workflows and evaluation by health technology assessment (HTA) agencies require rigorous evidence of clinical validity, reliability, and cost-effectiveness.The introduction of AI-driven tools into healthcare presents new methodological and practical challenges for HTA bodies, particularly in assessing their broader economic and clinical impact. Traditional health economic modelling approaches may not fully capture the dynamic and adaptive nature of AI systems, necessitating innovative frameworks that can evaluate both their short- and long-term value. Oncology provides an ideal setting to explore these challenges, given its complex treatment pathways and rapid technological innovation. Within oncology, breast cancer remains a major focus due to its global prevalence and the potential for AI-enhanced imaging to improve outcomes through more tailored surveillance strategies.This thesis examines how AI can add value to medical imaging and how its health and economic impact can be systematically assessed. A review of recent health economic evaluations of AI applications revealed that most studies have focused narrowly on short-term cost savings rather than broader health outcomes, with limited transparency and insufficient assessment of uncertainty. These findings underscore the need for more comprehensive and methodologically robust evaluations to guide the responsible adoption of AI in healthcare.Using extensive real-world data from Dutch national cancer registries and hospital records, this research explored variations in breast cancer surveillance practices and their adherence to clinical guidelines. The analyses revealed substantial inconsistencies in follow-up intensity, with some low-risk patients receiving more imaging than necessary. These patterns highlight the potential of AI-based risk prediction tools to support more personalized, risk-adapted surveillance strategies, optimizing resource use while maintaining quality of care.To further investigate AI’s potential, a dynamic discrete event simulation (DES) model was developed to assess how AI-enhanced mammography sensitivity and alternative surveillance intervals could influence patient outcomes and healthcare costs. The model, calibrated with empirical data and expert input, demonstrated that AI could reduce missed recurrences and improve early detection. However, threshold analyses indicated that, under current assumptions, AI would be cost-effective only if its incremental health benefits were sufficiently large or its implementation costs relatively low.Overall, the findings highlight both the promise and the limitations of AI in breast cancer imaging. While AI has the potential to enhance diagnostic accuracy and support more efficient, individualized care, current economic evaluations often fail to capture its full value, particularly non-traditional benefits such as improved patient autonomy, decision-making, and well-being. Future research should expand health economic frameworks to include these dimensions, ensuring that AI technologies are assessed not only for their efficiency but also for their contribution to more equitable, patient-centered healthcare
CPSLint:A Domain-Specific Language Providing Data Validation and Sanitisation for Industrial Cyber-Physical Systems
Get PDFRaw datasets are often too large and unstructured to work with directly, and require a data preparation process. The domain of industrial Cyber-Physical Systems (CPS) is no exception, as raw data typically consists of large amounts of time-series data logging the system's status in regular time intervals. Such data has to be sanity checked and preprocessed to be consumable by data-centric workflows. We introduce CPSLint, a Domain-Specific Language designed to provide data preparation for industrial CPS. We build up on the fact that many raw data collections in the CPS domain require similar actions to render them suitable for Machine-Learning (ML) solutions, e.g., Fault Detection and Identification (FDI) workflows, yet still vary enough to hope for one universally applicable solution.CPSLint's main features include type checking and enforcing constraints through validation and remediation for data columns, such as imputing missing data from surrounding rows. More advanced features cover inference of extra CPS-specific data structures, both column-wise and row-wise. For instance, as row-wise structures, descriptive execution phases are an effective method of data compartmentalisation are extracted and prepared for ML-assisted FDI workflows. We demonstrate CPSLint's features through a proof of concept implementation
Monitoring and mapping the biodiversity of bacteria, fungi and microarthropods in soils using multi-source remote sensing and environmental DNA metabarcoding profiles
Get PDFSustainable innovations as discontinuation targets in the context of anti-sustainability trends
No full textUntangling the fragmented landscape of extreme heat services and warning systems
Get PDFExtreme heat warning systems are expanding globally, yet remain conceptually fragmented and operationally diverse. With a myriad of heat indices in use and limited guidance on their purpose or performance, countries risk adopting ineffective systems misaligned with local risks and decision-making needs. This Perspective traces the roots of this fragmentation across disciplinary, operational, and institutional lines, showing how differing approaches from health, meteorology, and climate science have led to incompatible definitions and thresholds. We then propose a clear typology of heat indices, aligned with WMO guidance: (1) temperature indicators, (2) thermal indices, and (3) heatwave intensity indices. The typology clarifies what each type measures, where it performs best, and the trade-offs involved, helping systems move toward greater transparency, coherence, and fit-for-purpose. Each type offers distinct strengths, and many countries will benefit from layered approaches that combine them. Moving toward intensity-based approaches represents a conceptual shift, from identifying hot days to quantifying the severity of heatwaves. By aligning early warning systems with this understanding, countries can improve coordination, reduce health and societal impacts, and accelerate progress under global frameworks such as the UN’s Early Warnings for All initiative