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Dynamic-Window DAC Switching in SAR ADCs and its Mismatch Modelling for High Peak-to-Average Ratio Input Signals
Get PDFThis paper presents a novel Dynamic-Window (DW) Digital-to-Analog Converter (DAC) switching technique to enhance the power efficiency of the Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC). DW is achieved by dynamically adjusting the reference voltage used by the DAC when the input signal amplitude falls within a certain pre-specified window. The proposed DW is implemented by choosing combinations of DAC slices to effectively create an attenuation of the reference voltage thus dynamically adjusting the SAR ADC’s successive approximation window. This method monitors the input signal amplitude using a coarse front-end (FE) circuit that can be easily implemented with negligibly small energy overhead. DW is particularly energy-efficient for real-world signals with a high Peak-to-Average Ratio (PAR) such as audio and OFDM signals in wireless communication. The effects of DW on a 12-bit 1MS/s SAR ADC are simulated in MATLAB® for a Gaussian distributed input signal with a 6σ, 8σ input full-scale (FS) range, demonstrating its ability to reduce DAC energy consumption respectively by 35%, 41% with 2-level DW and 45%, 54% with 4-level DW. The simulations also explore the effects of DW in the ADC performance metrics such as Signal-to-Noise and Distortion Ratio (SNDR), Differential Non-Linearity (DNL), and Integral Non-Linearity (INL) to show DW can be implemented without major degradation in ADC performance for DAC capacitor mismatch as high as 2.5% per fF
Physics-based deep kernel learning for parameter estimation in high dimensional PDEs
Get PDFInferring parameters of high-dimensional partial differential equations (PDEs) poses significant computational and inferential challenges, primarily due to the curse of dimensionality and the inherent limitations of traditional numerical methods. This paper introduces a novel two-stage Bayesian framework that synergistically integrates training, physics-based deep kernel learning (DKL) with Hamiltonian Monte Carlo (HMC) to robustly infer unknown PDE parameters and quantify their uncertainties from sparse, exact observations. The first stage leverages physics-based DKL to train a surrogate model, which jointly yields an optimized neural network feature extractor and robust initial estimates for the PDE parameters. In the second stage, with the neural network weights fixed, HMC is employed within a full Bayesian framework to efficiently sample the joint posterior distribution of the kernel hyperparameters and the PDE parameters. Numerical experiments on canonical and high-dimensional inverse PDE problems demonstrate that our framework accurately estimates parameters, provides reliable uncertainty estimates, and effectively addresses challenges of data sparsity and model complexity, offering a robust and scalable tool for diverse scientific and engineering applications
Who Needs Real Data Anyway? Exploring the Use of Synthetic Data in Economic Evaluations of Health Interventions
Get PDFObjectives: Data needed for economic evaluations in healthcare are often subject to privacy regulations and confidentiality, limiting accessibility. This poses challenges for conducting, reviewing, and validating health economic evaluations. The use of “synthetic data” may solve this problem. Methods: An economic evaluation compared “shamectomy” with “usual care” for the prevention of a fictitious disease called shame. A data set (Dorg) was created, consisting of 1000 patients in the base case. Next, synthetic data (Dsyn) were created from Dorg. Dorg and Dsyn were used, separately, to inform a model-based economic evaluation, and the similarity of the results was assessed for various scenarios: different sizes of Dorg, order of synthetization, method of synthetization, number of synthesized data sets, and missing data. Results: With standard settings, incremental cost-effectiveness ratio (ICER)-results for shamectomy were €25 848/quality-adjusted life-year in Dorg and on average €25 857 in 500 Dsyns, 95% CI (€16 776; €60 021). In the base case, 15% of the generated Dsyns resulted in an ICER leading to a positive reimbursement decision, as opposed to a negative decision when using Dorg. With smaller Dorg data sets (n = 50 and n = 500), ICER ranges increased to 95% CI (negative; €151 542) and 95% CI (negative; €669 717), respectively. Conclusions: Outcomes and conclusions of economic analyses based on synthetic data may deviate from those obtained by using the original data. For data sets < 1000 patients, which are common, deviations may be substantial and lead to suboptimal policy decisions. Based on our results, we propose a stepwise approach to using synthetic data for model-based health economic evaluations, using a large number of synthetic data sets (ie, >100) with the same size as the original data.</p
Primary care patients’ perspectives on CT coronary calcium scoring and exercise electrocardiography
Get PDFBackground: Computed tomography coronary calcium scoring (CT-CCS) shows higher sensitivity for obstructive coronary artery disease (OCAD) detection than exercise electrocardiography (x-ECG), but its role as initial diagnostic test in primary care remains unclear. Objective(s): This study assessed patients’ perspectives on CT-CCS or x-ECG testing and diagnostic results. Methods: In this one-year pilot study, 38 general practitioner practices were included. After cluster randomisation, 19 practices were assigned to refer patients with atypical angina pectoris or non-specific thoracic complaints for CT-CCS and 19 practices were assigned to request x-ECG. Patients’ management remained at the discretion of the GPs. Patients’ perspectives on the diagnostic test were assessed through a questionnaire, and clinical data were collected using electronic patient records. Outcome measures included patients’ perspectives, OCAD diagnosis and initiation of cardiovascular risk management (CVRM). Results: 101 patients (25 x-ECG; 76 CT-CCS) were included. Overall, CT-CCS patients were more satisfied with the test compared to x-ECG patients (p < 0.001), found the test easier to undergo (p < 0.001), had a higher willingness to retest (p = 0.01) and better perception of the information received from the GP (p = 0.03). Four of 17 CT-CCS patients (24%) with CT-CCS ≥100 were diagnosed with OCAD, and 14 (82%) started CVRM. The only patient with a positive x-ECG out of 25 (4%) was included in CVRM but not diagnosed with OCAD. Conclusion: CT-CCS patients were overall more satisfied with their test than x-ECG patients. Coronary calcium scoring is a promising diagnostic tool for detecting OCAD in primary care.</p
Randomized Controlled Trial of Group-Blended and Individual-Unguided Online Mindfulness-Based Cognitive Therapy to Reduce Psychological Distress in People With Cancer
Get PDFObjective: Online mindfulness-based cognitive therapy (eMBCT) can reduce psychological distress in people with cancer, but adherence and scalability could be improved. Through co-creation, we developed two eMBCT formats: group-blended and individual-unguided. This trial compared the effects of the two eMBCT to care as usual (CAU) on psychological distress and other mental health outcomes in people with cancer. Methods: In this parallel, three-armed randomized controlled trial, people with cancer were randomly allocated to group-blended eMBCT, individual-unguided eMBCT, or CAU. Participants completed baseline, mid-treatment, post-treatment, and 3 months follow-up assessments. The primary outcome analyzed in the intention-to-treat (ITT) population was psychological distress (Hospital Anxiety and Depression Scale) at post-treatment. Results: In total, 186 participants were randomized to group-blended eMBCT (N = 57), individual-unguided eMBCT (N = 75), or CAU (N = 54). Most participants were female (81%) with breast cancer (49%), and treated with curative intent (76%). In ITT analyses, group-blended eMBCT participants reported significantly lower levels of psychological distress at post-treatment (Cohen's d = 0.38) and follow-up (Cohen's d = 0.64) than those receiving CAU, while individual-unguided eMBCT participants only had significantly less psychological distress at follow-up (Cohen's d = 0.48). Additionally, participants in group-blended eMBCT had less rumination and greater mindfulness, decentering, and self-compassion than those in CAU at follow-up. Participants in individual-unguided eMBCT had greater decentering than those in CAU at post-treatment and follow-up, and less rumination and greater mindfulness skills than CAU at follow-up. Conclusions: Compared to CAU, both eMBCT conditions were effective in reducing psychological distress and could be an accessible, and potentially low-cost intervention to reduce distress in people with cancer. Trial registration: Dutch Registry CCMO, NL73117.091.20; clinicaltrials.gov, NCT05336916.</p
Exploring labor market dynamics in digital transformations:The perspective of Dutch SMEs
No full textThis study investigates the impact of digital transformation on workforce dynamics in small and medium-sized enterprises (SMEs) and explores how these impacts vary across sectors such as agriculture, manufacturing, and services. A thematic analysis was conducted on semistructured interviews with 31 participants, including chief executive officers, employees responsible for digital transformation, digital consultants, and technology experts, from 17 SMEs operating in the Netherlands. The findings reveal that digital technologies are adopted differently across sectors and that their impacts on the workforce vary according to the specific needs of each sector. Moreover, the success of digital transformation is shown to depend not only on technological investments but also on employee skills, managerial capacity, and sector-specific infrastructure. By developing a model that explains the relationship between digital transformation and the workforce, this study highlights that digitalization in SMEs is a multidimensional and human-centered process.</p
Measuring Availability and Production of Primary Students' Self-Regulated Learning Strategies During Inquiry-Based Science Education
Get PDFSupporting students' self-regulated learning (SRL) is essential in education, yet most interventions adopt a one-size-fits-all approach, overlooking individual differences in students' ability to engage in SRL. Tailored instruction requires reliable assessment tools that distinguish between students' availability (knowledge) and production (actual use) of SRL strategies—an important conceptual distinction that has not yet been operationalized. This study developed and validated the SRL-IBSL, a 17-item questionnaire assessing the availability and production of SRL strategies in the context of inquiry-based science learning among primary students. The instrument covers four key SRL strategies: Time and Effort Planning, Activation of Prior Knowledge, Monitoring of Behavior, and Monitoring of Learning. Validation results support the questionnaire's internal structure and reliability, with partial evidence for its convergent and discriminant validity. Moderate correlations with teacher appraisals and small group differences based on science competency levels suggest the questionnaire's potential for group-level assessment and instructional use. The SRL-IBSL provides a practical tool for researchers and educators to identify students' SRL profiles and adapt instruction accordingly, offering a promising step toward more personalized support for SRL in primary science education.</p
Convolutional neural networks decode finger movements in motor sequence learning from MEG data
Get PDFObjective: Non-invasive Brain–Computer Interfaces provide accurate classification of hand movement lateralization. However, distinguishing activation patterns of individual fingers within the same hand remains challenging due to their overlapping representations in the motor cortex. Here, we validated a compact convolutional neural network for fast and reliable decoding of finger movements from non-invasive magnetoencephalographic (MEG) recordings. Approach: We recorded healthy participants in MEG performing a serial reaction time task (SRTT), with buttons pressed by left and right index and middle fingers. We devised classifiers to identify left vs. right hand movements and among four finger movements using a recently proposed decoding approach, Linear Finite Impulse Response Convolutional Neural Network (LF-CNN). We also compared LF-CNN to existing deep learning architectures such as EEGNet, FBCSP-ShallowNet, and VGG19. Results: Sequence learning was reflected by a decrease in reaction times during SRTT performance. Movement laterality was decoded with an accuracy superior to 95% by all approaches, while for individual finger movement, decoding was in the 80–85% range. LF-CNN stood out for (1) its low computational time and (2) its interpretability in both spatial and spectral domains, allowing to examine neurophysiological patterns reflecting task-related motor cortex activity. Significance: We demonstrated the feasibility of finger movement decoding with a tailored Convolutional Neural Network. The performance of our approach was comparable to complex deep learning architectures, while providing faster and interpretable outcome. This algorithmic strategy holds high potential for the investigation of the mechanisms underlying non-invasive neurophysiological recordings in cognitive neuroscience.</p
Improving the quality of classroom allocation in higher education:Towards efficient and effective use of space
No full textThis research addresses the complex and often inefficient process of academic planning and scheduling, particularly concerning the allocation of rooms for educational activities. The current system, which relies on forecasted student numbers, is found to result in disturbingly low realised efficiency.Key Findings and Problems with Current PracticesThe academic scheduling process involves many stakeholders (students, lecturers, administration, management), but current practices often fail to fully consider the interests of all stakeholders.Interviews revealed that stakeholders care about factors beyond just room-fitting capacity, such as walking distances, a sense of community, and having suitable classrooms for the education at hand.The effectiveness of current room allocations is not measured and often unknown, and existing practices are inefficient.Proposed Solutions and Research ContributionsThe study proposes and designs new methods to optimize room allocation, focusing on both efficiency and effectiveness (i.e. quality), and presents a new governance model.1. Efficiency Improvement (Dynamic Allocation):The research introduces the concept of dynamic room allocation, which uses feedback on actual student attendance (measured occupancy and utilisation) to rearrange timetables. The theoretical model shows this could improve efficiency by up to 30%.2. Effectiveness/Quality Improvement: The research designs methods to allocate rooms based on factors important to stakeholders:- Minimizing walking distances for students to contribute to a sense of student community.- Allocating the most suitable rooms for the type of lecture, using a new model to measure room suitability.3. Governance Model:The combination of measuring efficiency and improving quality requires a new approach. The research proposes a new governance model and operational framework for educational institutions to adapt their support organisation to these changing needs.In essence, the research advocates for moving beyond simply fitting students into rooms and instead adopting a more dynamic, data-driven, and quality-focused approach that accounts for the real-world needs and preferences of all academic stakeholders