Tampere University

This letter proposes a methodology for phase-normalization of the complex-valued I/Q inputs of a real-valued time delay neural network (RVTDNN). The normalization enables modeling of the nonlinear behavior of a radio frequency (RF) power amplifier (PA) in a more efficient way, by complying with the physical characteristics of the distortions at RF. The presented digital predistortion (DPD) linearization experiments with a Doherty GaN PA at 3.5 GHz show a 4-dB improvement in the output linearity compared to state-of-the-art neural network (NN) and polynomial-based DPD models, allowing linearization to below $-$ 50 dBc adjacent channel leakage ratio (ACLR) levels with feasible processing complexity.

Second Harmonic Generation Microscopy (SHG) is widely acknowledged as a valuable non-linear optical imaging tool, its contrast mechanism providing the premises to non-invasively identify, characterize, and monitor changes in the collagen architecture of tissues. However, the interpretation of SHG data can pose difficulties even for experts histopathologists, which represents a bottleneck for the translation of SHG-based diagnostic frameworks to clinical settings. The use of artificial intelligence methods for automated SHG analysis is still in an early stage, with only few studies having been reported to date, none addressing ocular tissues yet. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We observe that Deep Learning models building on different architectures provide complementary results for the classification of cornea SHG images and demonstrate an AU-ROC = 0.98 for their joint use. These results have potential to be extrapolated to other diagnostics scenarios, such as automated extraction of hydration level of cornea, or identification of corneal edema causes, and thus pave the way for novel methods for precision diagnostics of the cornea with Deep-Learning assisted SHG imaging.

This article presents a direct model predictive con-trol (MPC) scheme for drive systems consisting of a three-phase three-level neutral-point-clamped (3L-NPC) inverter and an induction machine (IM). Even though the discussed MPC algorithm is a direct control strategy, it operates the inverter at a fixed switching frequency, while the output harmonic spectrum of the stator current is discrete, with harmonics at non-triplen, odd integer multiples of the fundamental frequency. As a result, the proposed method achieves similar or superior steady-state behavior than that of modulator-based control schemes. Moreover, thanks to its direct control nature, it exhibits the fast transient responses that characterize direct controllers due to the absence of an explicit modulator. Furthermore, the multiple control objectives of the system, i.e., stator current control and neutral point (NP) potential balancing, are addressed in one computational stage, thus avoiding any additional control loops in a cascaded or parallel structure. This favorable control structure is facilitated by the adopted modeling approach, according to which the system behavior is described by the gradient of the system output. In doing so, not only a simple, versatile system model is derived, but also the direct MPC can be formulated as a constrained quadratic program (QP), which can be easily solved in real time with an in-house solver. The effectiveness of the proposed control scheme is experimentally verified on a 4-kW drive system.

This paper focuses on indirect model predictive control (MPC) for variable speed drives, such as induction and synchronous machine drives. The optimization problem underlying indirect MPC is typically written as a standard constrained quadratic programming (QP) problem, which requires a QP solver to find the optimal solution. Although many mature QP solvers exist, solving the QP problems in industrial real-time embedded systems in a matter of a few tens of microseconds remains challenging. Instead of using the complex general-purpose QP solvers, this paper proposes a geometrical method for isotropic machine drives and an analytical method for anisotropic machine drives to find the optimal output voltage. This is done by examining and subsequently exploiting the geometry of the associated optimization problems. Both methods are simple, and easy to implement on industrial control platforms. The effectiveness of the proposed geometrical and analytical methods is demonstrated by experimental results for an induction machine drive and an interior permanent-magnet synchronous machine drive, respectively. Index Terms-Model predictive control (MPC), quadratic programming (QP), induction machine (IM), interior permanent-magnet synchronous machine (IPMSM).

Haptic upper limb exoskeletons are robots that assist human operators during task execution while having the ability to render virtual or remote environments. Therefore, ensuring the stability of such robots in physical human-robot-environment interaction (pHREI) is crucial. Having a wide range of Z-width, which indicates the region of passively renderable impedance by a haptic display, is also important for rendering a broad range of virtual environments. To address these issues, this study designs subsystem-based adaptive impedance control to achieve a stable pHREI for 7 degrees of freedom haptic exoskeleton. The presented controller decomposes the entire system into subsystems and designs the controller at the subsystem level. The stability of the controller in the presence of contact with a virtual environment and human arm force is proven by employing the concept of virtual stability. Additionally, the Z-width of the 7-DoF haptic exoskeleton is illustrated using experimental data and improved by exploiting varying virtual mass element. Experimental results are provided to demonstrate the performance of the controller. The control results are also compared to state-of-the-art control methods, highlighting the excellence of the designed controller.

Metacognitive awareness is knowing about learners’ own thinking and learning, facilitated by introspection and self-evaluation. Although metacognitive functions are personal, they cannot be explained simply by individual conceptions, especially in a collaborative group learning context. This study considers metacognitive awareness on multiple levels. It investigates how metacognitive awareness at the individual, social, and environmental levels are associated with collaborative problem solving (CPS). Seventy-seven higher education students collaborated in triads on a computer-based simulation about running a fictional company for 12 simulated months. The individual level of metacognitive awareness was measured using the Metacognitive Awareness Inventory. The social level of metacognitive awareness was measured multiple times during CPS through situated self-reports, that is, metacognitive judgements and task difficulty. The environmental level of metacognitive awareness was measured via a complex CPS process so that group members’ interactions were video recorded and facial expression data were created by post-processing video-recorded data. Perceived individual and group performance were measured with self-reports at the end of the CPS task. In the analysis, structural equation modelling was conducted to observe the relationships between multiple levels of metacognitive awareness and CPS task performance. Three-level multilevel modelling was also used to understand the effect of environmental-level metacognitive awareness. The results reveal that facial expression recognition makes metacognitive awareness visible in a collaborative context. This study contributes to research on metacognition by displaying both the relatively static and dynamic aspects of metacognitive awareness in CPS.

Collaborative engagement between international and local nongovernmental organizations (NGOs) has recently been promoted as an effective strategy to enhance internal process strengths but less as a strategy to localize humanitarian aid programs; a grand strategy that aims to strengthen local capacity, develop local capabilities, and boost regional humanitarian project performance. While stakeholders deem to play an important role in leveraging the efficiencies of such collaborative engagements between international and local actors, there is limited empirical knowledge about how stakeholder pressure affects the association between the collaboration–performance association within international and local NGOs. Drawing on stakeholder theory, we propose a model to examine the role of donors, media, and governments, three major stakeholders noteworthy because of their power and legitimacy to moderate the collaboration–performance association in this NGO context. We test our hypotheses across a series of samples collected at both international and local NGOs in 2015 and 2020. From a practical perspective, we discuss how the traditional role of NGOs as implementers of aid programs is shifting toward a new role as conveners and capability builders.

Whilst China has become home to the second largest doctoral education system in the world, with over 20% of its doctoral graduates taking up postdoctoral researcher positions inside and outside of China, a lack of information regarding the expectations of these doctoral graduates in pursuing postdocs has resulted in a failure to meet their expectations, leading to insufficient institutional support for their career development. In order to improve this situation and provide more tailored institutional support for Chinese postdocs, we conducted interviews with 30 doctoral graduates from elite Chinese universities from February 2020 to December 2021 to understand their expectations for and experiences of postdocs. The data identified four expected‐to‐accumulated capitals during postdoc experiences: personal scientific capital, discipline‐related social capital, institution‐related social capital and family‐related social capital. Among these, the primary consideration for engaging in postdocs is to enhance personal scientific capital in both qualitative and quantitative aspects. Chinese doctoral graduates who choose domestic postdocs have higher expectations for increasing institution‐based social capital, while those who go abroad expect to develop discipline‐related social capital within the international academic community. Understanding these expectations will be instrumental in developing optimal approaches to providing institutional support for the career development of Chinese postdocs.

LGBTQ people and the Evangelical Lutheran Church have a long history of tension in Finland. Christian queer activists have fought this tension since the late 1960s. This article asks how Christian queer activism was born and personally experienced in Finland from the late 1960s to the early 2000s. Theoretically, this article builds on queer history and affect theory. My data contains autobiographical texts and oral history interviews of the activists and their contemporaries, as well as statements by the Church, newspaper articles and a TV debate that help to contextualise the personal activist narratives. Using the method of close reading, I pay attention to affective circulation and moments in which activism emerged or started to decline. I argue that a wide circulation of negative affects attached to homosexuality in Finland in this era created an atmosphere that both inspired Christian queer activists to act, but as time went on, also caught them up in political despair when nothing seemed to change, making them reorient their activist hope.

Objective and Method Electronic gambling machines are a prominent cause of significant gambling harms globally. We use simulations of a simplified video poker game to show how changes in game volatility, defined primarily by the size of the main prize, affect patterns of wins and losses as well as winning streaks. Results We found that in low- and medium volatility games the proportion of winning players quickly drops to zero after about 30 h of play, while in the high volatility game 5% of players are still winning after playing for 100 h. However, the proportion of winning streaks was significantly higher in the low- and medium volatility games compared with high volatility: the simulated players were on a winning streak about 26.3, 25.6 and 18% of the time in the low-, medium- and high volatility games, respectively. Conclusions Fast-paced video poker with varying volatility levels but identical return-to-player rates and win frequencies can yield highly different result patterns across individuals. These patterns may be counter-intuitive for players and difficult to realize without simulations and visualizations. We argue that the findings have relevance for responsible gambling communication and for building a better understanding of how cognitive biases influence gambling behaviour.

Comprehensive studies comparing impacts of building and street levels interventions on air temperature at metropolitan scales are still lacking despite increased urban heat-related mortality and morbidity. We therefore model the impact of 9 interventions on air temperatures at 2 m during 2 hot days from the summer 2018 in the Greater London Authority area using the WRF BEP-BEM climate model. We find that on average cool roofs most effectively reduce temperatures (~ -1.2°C), outperforming green roofs (~ 0°C), solar panels (~ -0.3°C) and street level vegetation (~ -0.3°C). Application of air conditioning across London increase air temperatures by ~ +0.15°C but related energetic consumption could be covered by energy production from solar panels. Current realistic deployments of green roofs and solar panels are ineffective at large scale reduction of temperatures. We provide a detailed decomposition of the surface energy balance to explain changes in air temperature and guide future decision-making.

We investigate explainability via short Boolean formulas in the data model based on unary relations. As an explanation of length k , we take a Boolean formula of length k that minimizes the error with respect to the target attribute to be explained. We first provide novel quantitative bounds for the expected error in this scenario. We then also demonstrate how the setting works in practice by studying three concrete data sets. In each case, we calculate explanation formulas of different lengths using an encoding in Answer Set Programming. The most accurate formulas we obtain achieve errors similar to other methods on the same data sets. However, due to overfitting, these formulas are not necessarily ideal explanations, so we use cross validation to identify a suitable length for explanations. By limiting to shorter formulas, we obtain explanations that avoid overfitting but are still reasonably accurate and also, importantly, human interpretable.

Better understanding of the early events in the development of type 1 diabetes is needed to improve prediction and monitoring of the disease progression during the substantially heterogeneous presymptomatic period of the beta cell damaging process. To address this concern, we used mass spectrometry-based proteomics to analyse longitudinal pre-onset plasma sample series from children positive for multiple islet autoantibodies who had rapidly progressed to type 1 diabetes before 4 years of age (n = 10) and compared these with similar measurements from matched children who were either positive for a single autoantibody (n = 10) or autoantibody negative (n = 10). Following statistical analysis of the longitudinal data, targeted serum proteomics was used to verify 11 proteins putatively associated with the disease development in a similar yet independent and larger cohort of children who progressed to the disease within 5 years of age (n = 31) and matched autoantibody negative children (n = 31). These data reiterated extensive age-related trends for protein levels in young children. Further, these analyses demonstrated that the serum levels of two peptides unique for apolipoprotein C1 (APOC1) were decreased after the appearance of the first islet autoantibody and remained relatively less abundant in children who progressed to type 1 diabetes, in comparison to autoantibody negative children.

This paper reports findings from a study focusing on user experience of image search tool utilizing content-based image retrieval methods. Previous studies have indicated challenges in textual image search especially in the historical domain. As a part of the project, a prototype tool was created for searching digitized historical images based on their visual contents to provide support for user needs identified in earlier studies. The tool was tested by 15 participants who evaluated their user experience with User Experience Scale and by verbal feedback. Our results indicate that participants derived benefits from the search capabilities provided by the tool, which went beyond relying on textual image descriptions. However, problems occurred, for example, in evaluating the search results and in user skills. Results also emphasize the value of intellectually produced metadata for image searching and use. Therefore, future developments should focus on creating hybrid systems supporting both textual and visual image searching.

Photoisomerization of azobenzenes from their stable E isomer to the metastable Z state is the basis of numerous applications of these molecules. However, this reaction typically requires ultraviolet light, which limits applicability. In this study, we introduce disequilibration by sensitization under confinement (DESC), a supramolecular approach to induce the E -to- Z isomerization by using light of a desired color, including red. DESC relies on a combination of a macrocyclic host and a photosensitizer, which act together to selectively bind and sensitize E -azobenzenes for isomerization. The Z isomer lacks strong affinity for and is expelled from the host, which can then convert additional E- azobenzenes to the Z state. In this way, the host–photosensitizer complex converts photon energy into chemical energy in the form of out-of-equilibrium photostationary states, including ones that cannot be accessed through direct photoexcitation.