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Analysis and Design of Highly Linear Capacitive Stacking Mixer-First Receivers
Get PDFA radio receiver ideally combines flexibility to serve various wireless standards with high linearity to handle interference. Although acoustic wave filters are widely used to add selectivity and thus relax the linearity demands on the receiver, their inflexibility obstructs further CMOS integration. This thesis presents a highly flexible and highly linear, wideband capacitive stacking mixer-first receiver (MF-RX) architecture that does not rely on acoustic wave filters for higher order filtering at the mixer baseband (BB) node. Now, the traditional capacitive stacking MF-RX designs combine voltage gain through capacitor stacking to relax noise demands with good linearity due to their passive nature. However, they suffer from a limited maximum radio frequency (RF) range of operation due to parasitic loading at the RF input as the RF input capacitors define the bandwidth (BW), constraining their value and typically resulting in a large parasitic capacitance.This thesis covers an alternative low-loss capacitive stacking N-path filter/mixer (CSNPFM) architecture in which the capacitors at BB define the BW, allowing for smaller RF capacitors, extending the RF input range. A full characterization of this architecture through intuitive explanations, simulations, and analyses shows major benefits over the traditional design aside from the extended RF range, such as a 4x smaller total capacitor area and elimination of the switch resistance selectivity bottleneck. However, this characterization also reveals that the low-loss CSNPFM exhibits a stronger BB response when excited with interferers around even harmonics of the RF fundamental. This thesis shows that this is due to a second system response in the low-loss architecture that not only limits rejection around even harmonics, but also complicates its analysis. To address this, a novel analysis methodology based on the adjoint network is presented. Using a discrete-time state-space model and eigendecomposition, the effects of the individual system responses can be analysed in isolation, increasing circuit insight. The resulting closed-form expression for the transfer function enables derivation of simple design equations for use of the circuit in the mixing region. Measurements on a 22 nm FDSOI CMOS prototype show support for a decade of RF input range, up to 10 GHz. With excellent far out-of-band linearity across the entire RF range and a reasonable NF while consuming only very limited dynamic power, the low-loss CSNPFM shows great promise in serving as a flexible wideband receiver building block. To further boost linearity, this thesis presents a state-of-the-art BB network that enhances the CSNPFM’s filtering slope, covering its design procedure and circuit implementation. This BB network uses a higher order capacitive feedback loop to realize near-third-order closed-loop filtering at the mixer BB node. Measurements on a 22 nm FDSOI CMOS prototype show that the loop indeed improves the filtering slope at this node to -15 decibel per octave, boosting near out-of-band linearity without having to rely on the use of inflexible acoustic wave filters
Sustainable Rubber Solutions:A Study on Bio-Based Oil and Resin Blends
Get PDFOne of the most important challenges the tire industry faces is becoming carbon-neutral and using 100% sustainable materials by 2050. Utilizing materials from renewable sources and recycled substances is a key aspect of achieving this goal. Petroleum-based oils, such as Treated Distillate Aromatic Extract (TDAE), are frequently used in rubber compounds, and a promising strategy to enhance sustainability is to use bio-based plasticizer alternatives. However, research has shown that the replacement of TDAE oil with bio-based oils or resins can significantly alter the glass transition temperature (Tg) of the final compound, influencing the tire properties. In this study, the theory was proposed that using a plasticizer blend, comprising oil and resin, in a rubber compound would result in similar Tg values as the reference compound containing TDAE. To test this, the cycloaliphatic di-ester oil Hexamoll DINCH, which can be made out of bio-based feedstock by the BioMass Balance approach, was selected and blended with the cycloaliphatic hydrocarbon resin Escorez 5300. Various oil-to-resin ratios were investigated, and a linear increase in the Tg of the vulcanizate was obtained when increasing the resin content and decreasing the oil content. Additionally, a 50/50 blend, consisting of 18.75 phr Hexamoll DINCH and 18.75 phr Escorez 5300, resulted in the same Tg of −19 °C as a compound containing 37.5 phr TDAE. Furthermore, this blend resulted in similar curing characteristics and cured Payne effect as the reference with TDAE. Moreover, a similar rolling resistance indicator (tan δ at 60 °C = 0.115), a slight deterioration in wear resistance (ARI = 83%), but an improvement in the stress–strain behavior (M300 = 9.18 ± 0.20 MPa and Ts = 16.3 ± 0.6 MPa) and wet grip indicator (tan δ at 0 °C = 0.427) were observed. The results in this work show the potential of finding a balance between optimal performance and sustainability by using plasticizer blends.</p
Response Options Related to Health Benefits of Gardening in Times of Crisis
No full textIn this chapter, we discuss response options aimed at optimising the use of gardens and gardening in times of crisis. We discuss three options related to access and ownership of garden spaces, the design of gardens for restoration and stress relief, and finally, options related to governance and healthcare. These insights are used to formulate recommendations and solutions for health professionals, policymakers and other stakeholders who may be interested in using gardening as a tool to address current and future crises. The chapter takes a broad perspective encompassing response options to optimise the health benefits of gardens and gardening in acute crisis situations as well as response options for vulnerable populations in both low- and high-income countries experiencing more persistent challenges in their daily living circumstances
Pupillary responses to masked and gaze-averted faces
Get PDFFace masks, a common practice during COVID-19, remain important in various cultural and medical contexts. Studies have shown how face masks affect our ability to recognize emotions, highlighting the role of facial features. Gaze direction plays a key role in modulating the identification of emotions, particularly in the presence of masks. So far, little is known about how gaze and masks influence emotion processing via physiological measures like pupil size. Here, we used pupillometry with 40 participants to investigate how emotion recognition (anger, fear, neutral) is affected by both gaze direction (direct, averted) and face mask conditions (mask, no mask). Behaviorally, our findings align with previous research, showing that the eye region plays a key role in identifying anger and neutral expressions more effectively than fear. Similarly, direct gaze improves accuracy for anger and neutral, while averted gaze enhances fear recognition. Pupillometry results revealed condition-specific changes in pupil size that partially mirrored the behavioral patterns, although no strong correlation with accuracy was found. However, pupil size was even more strongly modulated by recognition errors, with significantly greater dilation during incorrect trials across all emotions, especially for masked fearful faces, suggesting increased cognitive effort and ambiguity. The data also indicate compensatory processing mechanisms, when masks obscured parts of the face, participants appeared to rely more heavily on gaze direction and visible emotional cues. We propose that pupil dilation may reflect the cognitive load of emotion identification, providing important input for adaptive support applications in HCI and VR to improve user experiences.</p
A novel user-centric decentralized multi-objective energy management system for energy communities
Get PDFThis paper presents a decentralized energy management approach based on a Multi-Objective Energy Management System called DMOEMS, designed for Energy Communities (ECs), aiming to create resilient and sustainable energy systems. DMOEMS integrates a multi-objective optimization framework that aggregates conflicting goals–minimizing electricity cost and CO2, reducing Photovoltaic (PV) curtailment, and maximizing self-consumption–by converting them into a single objective using user-defined weight factors. Each local controller optimizes the operation of distributed assets based on localized constraints and user preferences, while an EC controller coordinates aggregated power profiles through an iterative feedback mechanism. This coordination dynamically adjusts weight factors and curtailment strategies to resolve grid congestion without compromising individual privacy. Simulation studies on the realistic Aardehuizen EC demonstrate that DMOEMS effectively mitigates overloading scenarios across diverse operating conditions (high EV charging, normal demand, and excess PV generation), enhances user satisfaction, reduces operational costs, and lowers CO2 emissions. The proposed framework highlights the potential of a democratic, decentralized approach to energy management in modern ECs. The numerical results for asset management using DMOEMS indicate improvements in different aspects such as reduction of 20 % in CO2 emissions, improvement of 4 % in electricity cost savings, and a 30 % reduction in PV curtailment relative to baseline scenarios. Furthermore, the proposed mechanism in the DMOEMS shows improvement in computational cost by converging faster to resolve grid congestion compared to conventional approaches.</p
Exploring the Effects of the Guanidinium:Methylammonium Ratio on the Photophysical Dynamics of ⟨n⟩ = 5 ACI Perovskites
No full textQuasi-two-dimensional (quasi-2D) lead halide perovskites with alternating cations in the interlayer (ACI) space represent a promising type of material for optoelectronics. Similar to the Ruddlesden–Popper and Dion–Jacobson types of perovskites, domains with different thicknesses (n) and bandgaps are formed within a single film. This work focuses on ⟨n⟩ = 5 ACI perovskites based on guanidinium (GA+) and methylammonium (MA+) cations and investigates the influence of the GA:MA ratio in the interlayer space on the photophysical processes after photoexcitation. Using a combination of time-resolved photoluminescence (TRPL) and femtosecond transient absorption (TA) spectroscopy, hot carrier cooling, the occurrence and directionality of energy or charge transfer between the different domains, and the exciton and charge carrier dynamics are studied and modeled using target analysis. After the thermalization of hot carriers and excitons, exciton transfer from low-n to high-low-n domains occurs within 10 ps, after which they dissociate into free charges. From there, charge transfer into the intermediate-n domains occurs in about 22–54 ps. In the layers with excess GA, this process possibly occurs in an undesirable competition with self-trapped exciton (STE) formation. From the intermediate-n domains, charges are transferred into the high-n domains in 95–159 ps, which process occurs the fastest in the GA-MA layer. Finally, charge carriers decay intrinsically on the nanosecond scale with the longest lifetimes for the GA-MA and GA-2MA systems, which is beneficial for PV applications.</p
Does GLUT4 Queue?:A Mechanistic Mathematical Model for Insulin Response in Adipocytes
Get PDFMammalian cells regulate their glucose levels by redistributing glucose transporter proteins within the cell. Glucose Transporter 4 (GLUT4) is the main insulin-regulated glucose transporter in mammalian cells. Insulin signals the redistribution of GLUT4 from intracellular compartments to the cell surface. The mechanisms of the release of GLUT4 and subsequent transport to the plasma membrane remain an open question. Here, a biologically plausible model of GLUT4 translocation is presented. Using a stochastic queuing model, we find that changing only the number of fusion sites available for GLUT4-containing vesicles as a function of insulin is sufficient to explain experimental observations. Thus, the activity of the fusion sites could be the primary determinant of the dynamics of GLUT4.</p
Integrating WENSLO, Rough OPA, and ALWAS Methods for Strategic 5G Deployment:A Case Study from Turkey
No full textBy supporting transformative technologies such as the Internet of Things (IoT), Radio Frequency Identification (RFID), and blockchain, fifth-generation wireless technology (5G) has become a cornerstone of economic growth and digital transformation. These advancements position 5G at the forefront of mobile communications research, unlocking innovation across industries. However, deploying 5G networks poses challenges, particularly in prioritizing service points. While developed countries have established mature 5G networks, the transition process in developing countries like Turkey remains in its early stages. Limited resources, shifting demographics, and infrastructure constraints demand robust strategies for resource allocation and site prioritization. This study presents an integrated decision-making framework using Weights by ENvelope and SLOpe (WENSLO), Rough Ordinal Priority Approach (OPA), and Aczel-Alsina-Weighted Assessment (ALWAS) methods to prioritize service points in Istanbul for a telecommunications company operating in Turkey during its 5G migration process. While Rough OPA calculates criteria weights based on expert judgments, WENSLO reduces subjectivity in weight assignments by adopting a data-driven approach. Validation from four R and D experts and a Spearman correlation test (coefficient of 0.85) between proposed and expert rankings confirms the framework's robustness and effectiveness for strategic 5G deployment planning in resource-constrained environments.</p
Mining exceptional social behavior on attributed interaction networks
Get PDFSocial interactions are prevalent in our lives. These can be observed, e. g., online using social media, however, also offline specifically using sensors. In such contexts, typically time-stamped interactions are recorded, which can also be inferred from real-time location of humans. Such interaction data can then be modeled as so-called social interaction networks. For their analysis, a variety of different approaches can be applied. A prominent research direction is then the detection of patterns describing specific subgroups with exceptional behavioral characteristics, given some measure of interest. In the standard case of plain graphs modeling the interaction networks, methods for identifying such subgroups mainly focus on structural characteristics of the network and/or the induced subgraph. For attributed social networks, then additional attributive information can be exploited. This paper proposes to focus on the dyadic structure of the attributed social interaction networks, thus enabling a compositional perspective for identifying interesting subgroup patterns. Specifically, we can then analyze spatio-temporal data modeled as attributed social interaction networks for identifying exceptional social behavior. The presented approach adapts local pattern mining using subgroup discovery to the dyadic setting, exploiting attribute information of the spatio-temporal attributed interaction networks. With this, specific characteristics of social interactions are considered, i. e., duration and frequency, for identifying subgroups capturing social behavior that deviates from the norm. For subgroup discovery, we propose according interestingness measures in the form of seven novel quality functions and discuss their properties. In our experimentation, we perform an evaluation demonstrating the efficacy of the presented approach using four real-world datasets on face-to-face interactions in academic conferencing as well as school playground contexts. Our results indicate that the proposed method returns interesting, meaningful, and valid findings and results.</p
Modelling alcohol consumption patterns to enable policy impact assessment
No full textObjective To prevent harmful effects of alcohol use, various countries implement policies preventing excessive and heavy episodic drinking. To enable the evaluation of the impact of such policies on (future) drinking behaviour, we aimed to develop a model that predicts alcohol consumption patterns. Methods The model predicts alcohol use in three stages. First, a logistic submodel predicts probabilities of drinking any alcohol. Second, for drinkers, a submodel predicts the weekly consumption through a negative binomial distribution for the number of beverages. Finally, based on the predicted weekly consumption, a logistic submodel predicts probabilities of heavy episodic drinking. The distribution for the weekly consumption was calibrated, targeted to predict the prevalence of excessive and heavy episodic drinking accurately. Model parameters were estimated using Dutch individual-level cross-sectional survey data covering the years 2008–2022. The characteristics age, sex, education, calendar time and their interactions were used as predictors and the model accounts for trend breaks in the data. Model performance was assessed by comparing population-level predictions with observed data on which the model was calibrated (2014–2022). Results A comparison between predictions of the calibrated model and observed data shows that the prevalences of excessive (error <0.2 percent point (pp)) and heavy episodic drinking (error <0.1 pp) align, averaged over the years 2014–2022. Visual inspection using qq-plots and within-sample validation over time further indicates that the model fits well for predicting excessive and heavy episodic drinking, based on the predicted distribution for the weekly consumption. Conclusions We developed a model for alcohol consumption patterns based on Dutch data. This model enables evaluation of the impact of interventions on the (future) prevalence of excessive and heavy episodic drinking.</p