This study explores Dutch homeowners' intentions to adopt shallow geothermal solutions for the energy transition in existing buildings, using the Theory of Planned Behavior (TPB) as a theoretical framework. Through a mixed-methods approach combining qualitative elicitation interviews with 20 homeowners and a quantitative survey of 800 representative Dutch households, the study identifies key psychological and socio-demographic factors influencing adoption intentions. The findings indicate that approximately 33% of surveyed homeowners express intention to adopt geothermal technology within the next five years. Structural Equation Modeling reveals that attitudes toward geothermal technology and subjective norms significantly influence adoption intentions, while perceived behavioral control has no significant impact on intention. Economic benefits and environmental protection emerge as the strongest attitudinal drivers, with uncertainty about investment payback periods acting as the primary barrier. Normative influences from environmental advocates, suppliers, and community members also strongly shape adoption intentions. Among socio-demographic factors, higher energy cost-to-income ratios, higher income levels, and homeowners’ association membership positively influence adoption intentions, while age shows a negative correlation. These insights provide evidence-based guidance for policymakers to develop targeted interventions addressing specific psychological barriers experienced by different homeowner segments, potentially accelerating the transition to renewable heating systems in the Netherlands' existing housing stock.

Given a public transportation network, which and how many passenger routes can potentially be shortest paths, when all possible timetables are taken into account? This question leads to shortest path problems on graphs with interval costs on their arcs and is closely linked to multi-objective optimisation. We introduce a Dijkstra algorithm based on polynomials over the tropical semiring that computes complete or minimal sets of efficient paths. We demonstrate that this approach is computationally feasible by employing it on the public transport network of the city of Wuppertal and instances of the benchmarking set TimPassLib, and we evaluate the resulting sets of passenger routes.

Coarse-grained (CG) molecular dynamics simulations extend the length and time scales of atomistic simulations by replacing groups of correlated atoms with CG beads. Machine-learned coarse-graining (MLCG) has recently emerged as a promising approach to construct highly accurate force fields for CG molecular dynamics. However, the calibration of MLCG force fields typically hinges on force matching, which demands extensive reference atomistic trajectories with corresponding force labels. In practice, atomistic forces are often not recorded, making traditional force matching infeasible on pre-existing datasets. Recently, noise-based kernels have been introduced to adapt force matching to the low-data regime, including situations in which reference atomistic forces are not present. While this approach produces force fields that recapitulate slow collective motion, it introduces significant local distortions due to the corrupting effects of the noise-based kernel. In this work, we introduce more general kernels based on normalizing flows that substantially reduce these local distortions while preserving global conformational accuracy. We demonstrate our method on small proteins, showing that flow-based kernels can generate high-quality CG forces solely from configurational samples.

Cross-platform verification is the task of comparing the output states produced by different physical platforms using solely local quantum operations and classical communication. While protocols have previously been suggested for this task, their exponential sample complexity renders them impractical even for intermediate-scale quantum systems. In this work, we propose a novel protocol for this task based on Pauli sampling, a subroutine that generates Paulis distributed according to their weight in the expansion of a quantum state in the Pauli basis. We show that our protocols for both Pauli sampling and cross-platform verification are efficient for pure states with low magic and entanglement [i.e., of the order 𝑂⁡(log𝑛) ]. Conversely, we show superpolynomial lower bounds on the complexity of both tasks for states with 𝜔⁡(log(𝑛)) magic and entanglement. Interestingly, when considering states with real amplitudes, the requirements of our protocol for cross-platform verification can be significantly weakened.

Lassa fever, caused by the Lassa virus (LASV), is a deadly disease characterized by hemorrhages. Annually, it affects approximately 300,000 people in West Africa and causes about 5,000 deaths. It currently has no approved vaccine and is categorized as a top-priority disease. Apart from its endemicity to West Africa, there have been exported cases in almost all continents, including several European countries. Distinct Lassa virus lineages circulate in specific regions, and have been reported to show varying immunological behaviors and may contribute to differing disease outcomes. It is therefore important to rapidly identify which lineage caused an outbreak or an exported case. We present CLASV, a machine learning-based lineage assignment tool built using a Random Forest classifier. CLASV processes raw nucleotide sequences and assigns them to the dominant circulating lineages (II, III, and IV/V) rapidly and accurately. CLASV is implemented in Python for ease of integration into existing workflows and is freely available for public use.

Distractor-induced deafness (DID) refers to the impaired detection of an auditory target when preceded by a rapid sequence of auditory distractors sharing the target’s features. We examined whether the neural signatures underlying DID resemble those found in visual distractor-induced blindness (DIB). Participants completed a rapid serial auditory presentation (RSAP) task in which distractor number and cue-target onset asynchrony were systematically manipulated. Behaviorally, target detection declined with increasing distractor number, closely replicating the behavioral DIB effect. Event-related brain potentials (ERPs) evoked by the distractors, however, revealed modality-specific differences: In contrast to earlier DIB findings, the amplitude of a fronto-central negativity decreased with increasing distractor load in the auditory task. Whereas the ERP responses in the visual DIB setup supported the notion of a cumulative inhibitory mechanism triggered by distractors, the current DID findings are rather in line with temporal expectation accounts.

Background

In small animals, serum total thyroxine concentration (TT4) is an important parameter used to assess thyroid function in patients. In rabbits, no information is available on the prevalence of thyroid disease and its diagnosis by laboratory methods. The aim of this study was to establish a device- and method-specific reference interval for TT4 in clinically healthy rabbits.

Objectives and Methods

TT4 concentrations were determined by chemiluminescence assay (CLIA) in 120 clinically healthy pet rabbits (median age 36 months); the study population was divided into age groups (Group 1: 7–24 months, Group 2: 25–59 months, Group 3: > 60 months). The pet rabbits were kept indoors, outdoors, and indoor/outdoor. The reference interval (95 percentile) was calculated using SPSS (IBM, version 28.0) according to the standard of the ASVCP.

Results

A median TT4 concentration of 0.8 μg/dL (10.28 nmol/L) and a reference interval of 0.6–1.98 μg/dL (7.71–25.44 nmol/L) were determined. No statistically significant differences were found for sex (p = 0.889), age (p = 0.176), and between animals in indoor and outdoor housing (p = 0.967). Highly significant differences were observed between indoor and indoor/outdoor housing (p = 0.002) and significant differences between outdoor and indoor/outdoor housing (p = 0.006).

Conclusions

Concentrations of TT4 in pet rabbits measured by chemiluminescence appear to be independent of sex and age. Laboratory and device-specific reference intervals for pet rabbits should be used to avoid the influence of different methods, devices, calculation methods, and sample size.

Uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) 2-epimerase (GNE)/N-acetylmannosamine kinase is the rate-limiting enzyme in sialic acid biosynthesis and a promising therapeutic target. We applied mass photometry (MP) to investigate GNE oligomerization and its modulation by three small-molecule inhibitors (C5, C13, and C15). Substrate-binding (UDP-GlcNAc) stabilized tetramer formation by increasing dimer–dimer affinity 98-fold. All inhibitors destabilized tetramers in a concentration-dependent manner, with IC50 values in the low micromolar range. Using a modified Cheng–Prusoff equation, IC50 values were converted into KB,app values. Schild analysis and Operational Model of Allosterically Modulated Agonism were applied to estimate an apparent KB,app value and assess cooperative inhibition effects. Molecular docking confirmed competitive binding for all inhibitors and helped rationalize observed potency trends. While MP has previously been used to study protein assembly, our work demonstrates its applicability for the label-free, quantitative characterization of small-molecule inhibitors affecting protein oligomerization. These findings provide a foundation for further mechanistic studies and underscore the potential of MP in drug-target interaction profiling.

Higher-order structures are essential for the function of biological macromolecules. Tuning the conformational space of peptides by stapling improves their pharmacological properties. The three-dimensional (3D) structures of glycans are much less well understood than those of peptides and oligonucleotides, and willful modulation of oligosaccharide structures to improve binding to proteins has not been described to date. Herein, we describe stapling of β-(1,3)-glucans to tune their conformation, aiming to mimick the naturally occurring triple helix. The stapled glycans are prepared by automated glycan assembly, followed by linker construction and ring-closure assisted by solid-phase peptide synthesis. Thereby, staples of different lengths, polarities, and topologies can be readily introduced. Molecular dynamics simulations served to evaluate the effect of stapling on the conformational space. Glycan microarray experiments revealed that stapled glycans bound significantly more tightly to monoclonal mouse and rabbit antibodies than did linear glycans. Controlling the conformational space of short oligosaccharides creates opportunities for synthetic glycans in drug and vaccine development.

Memristive devices, which emulate the synaptic behavior of biological systems, are at the forefront of next-generation memory and neuromorphic computing technologies. Here, we investigate bipolar resistive switching in Pt/polycrystalline ErMnO3/Ti/Au memristive devices and show how mixed orthorhombic and hexagonal ErMnO3 polymorph films can be engineered to optimize the device performance. The two crystalline phases are evidenced by a combination of correlative microscopies (scanning electron microscopy, optical microscopy and conductive atomic force microscopy) and Raman spectroscopy. The devices exhibit high ROFF/RON ratios (~105) and ultra-low RON resistances (~10 Ω). The resistive switching is the result from the formation and rupture of an oxygen-vacancy-based conductive filament, which likely occurs either in the orthorhombic phase or at the boundary between the two polymorphs. An increased fraction of orthorhombic phase strongly reduces the operating voltage (down to VSet ~ −2.07 V) and its variability. The presence of the hexagonal phase, which is much less conductive than the orthorhombic one, reduces leakage currents in the devices, that otherwise would not exhibit switching behavior.

We propose a novel wave function partitioning method that integrates deep-learning variational Monte Carlo with ansätze based on generalized product functions. This approach effectively separates electronic wave functions (WFs) into multiple partial WFs representing, for example, the core and valence domains or different electronic shells. Although our ansätze do not explicitly include correlations between individual electron groups, we show that they accurately reproduce the underlying physics and chemical properties, such as dissociation curve, dipole moment, reaction energy, ionization energy, or atomic sizes. We identify the optimal number of core electrons and define physical core sizes for Li to Mg atoms. Our results demonstrate that core electrons can be effectively decoupled from valence electrons. We show that the core part of the WF remains nearly constant across different molecules and their geometries, enabling the transfer and reuse of the core part in WFs of more complex systems. This work provides a general framework for WF decomposition, offering potential advantages in computing and studying larger systems, and possibly paving the way for ab initio development of effective core potentials. Although currently limited to small molecules due to scaling, we highlight several directions for extending our method it to larger systems.

Free-fermionic states, also known as fermionic Gaussian states, represent an important class of quantum states that are ubiquitous in physics. They are uniquely and efficiently described by their correlation matrix. However, in practical experiments, the correlation matrix can only be estimated with finite accuracy. This raises the question: How does the error in estimating the correlation matrix affect the trace-distance error of the state? We show that if the correlation matrix is known with an error 𝜀 , the trace-distance error also scales as 𝜀 (and vice versa). Specifically, we provide distance bounds between (both pure and mixed) free-fermionic states in relation to their correlation-matrix distance. Our analysis also extends to cases in which one state may not be free-fermionic. Importantly, we leverage our preceding results to derive significant advancements in property testing and tomography of free-fermionic states. Property testing involves determining whether an unknown state is close to or far from being a free-fermionic state. We first demonstrate that any algorithm capable of testing arbitrary (possibly mixed) free-fermionic states would inevitably be inefficient, implying that there is no efficient strategy to estimate the non-Gaussianity of a state. Then, we present an efficient algorithm for testing low-rank free-fermionic states. For free-fermionic state tomography, we provide improved bounds on the sample complexity in the pure-state scenario, substantially improving over previous literature, and we generalize the efficient algorithm to mixed states, discussing its noise robustness.

Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases thus constructed rely on the generalized gradient approximation (GGA) for electron exchange and correlation. This limits the reliability of these databases, as well as that of the artificial intelligence (AI) models trained on them, in particular for materials and properties which are not accurately described by GGA. Here, we describe a database of 7,024 inorganic materials presenting diverse structures and compositions. Crucially, the database was generated using hybrid functional calculations,efficiently implemented in the all-electron code FHI-aims. The database is used to evaluate the thermodynamic and electrochemical stability of oxides relevant to catalysis and energy related applications. We illustrate how the database can be used to train AI models for material properties using the sure-independence screening and sparsifying operator (SISSO) approach.

In conditional reasoning tests (CRTs), participants are asked to choose a response that follows logically from premises presented in the item stem. CRTs rest on the rationale that individuals with a high standing on an undesirable trait will draw inferences from the set of premises that provide a justification for their trait standing. In this preregistered study, we presented the CRT for aggression either with or without premises (N = 778). In line with CRT theory, results revealed that CRT-A scores were affected by the omission of premises. On the item level, for six of 22 items, the frequency of choosing illogical responses did not differ when the premises were removed. Correlations with self-reported aggression were similar for both versions of the CRT-A. Thus, findings speak to the theorizing behind CRTs, but also point to possible improvements on the item level.

In this essay, I explore the relationship between film form, academic star personas, and the concept of resentment through a discussion of Jürgen Schaflechner’s film ‘The Toxic Reigns of Resentment’ (2020). I will argue that through its form, ‘Toxic Reigns’ allows us to consider different meanings of ‘faces’ – from individualising star personas, the circulation of resentment to a sense of affect that goes beyond individuation. By doing so, I argue that the film enables academic viewers to rethink how we often overly personalise the concepts we use in our everyday academic life. Furthermore, I argue that this also pertains to academic star-personas we come to see as the bearer of the concepts and who are often invested with resentment. In the course of my discussion, I will show how the film’s form leads us beyond the deadlocks of identitarian and personalised resentment that are tangled up with such academic star-personas.

This article examines arguments for and against women’s suffrage that circulated in Chile, Uruguay, and Argentina between 1860 and 1890. It analyses constitutional law books, press articles, university textbooks, and dissertations, that discussed women’s political rights within the context of a broader debate about the performance of representative systems in the region. Rather than assess the impact of these writings on the later attainment of women’s suffrage in the twentieth century, the focus of the article is in establishing the channels of circulation of these arguments and their connection to the discussion on electoral reforms at the time.

This study investigates long-term impacts of empires on local socio-ecosystems in western Anatolia (modern western Türkiye) over the past four millennia. We focus on Buldan Yayla Lake, located in a small mountain basin north of the Büyük Menderes (Great Meander) River valley. By examining palynological data alongside historical and archaeological records, we show how four major empires—Hittite, Roman, Byzantine, and Ottoman—shaped land use, vegetation, and resource management practices in a specific locality within a wider regional and transregional economic network. The ebb and flow of empire resulted in cycles of land use intensification and rewilding, resembling broader patterns of regional integration and fragmentation. The different administrative and economic structures of each empire, however, left distinct ecological imprints, with evidence of shifts from extensive pastoralism to specialised crop cultivation, or from olive-focused agriculture to one dominated by mixed agriculture. These shifts underscore both the variation and the adaptability of local socio-ecological systems within broader imperial networks and highlight the interplay of transregional and local factors in landscape transformation.

To what extent was the summer of 1931 a financial crisis in Britain? Previous research has shown how London merchant banks were affected, but it remains unclear to what extent the largest commercial banks of London – the clearing banks – were under threat. Monthly balance sheets reported by clearing banks do not fully reflect the day-to-day liquidity in financial crisis, possibly overstating available liquidity. This article does not rely on what clearing banks reported, but what the largest five London clearing banks actually did on a daily basis during the summer of 1931. I find that the London clearing banks were not seriously under threat. This is because large-scale asset purchases by the Bank of England stabilized the liquidity of the London clearing banks. This intervention is consistent with previous crisis situations during the nineteenth century and at the outset of the First World War.

The origin of microseisms—whether from deep‐ocean sources or coastal reflections—has been debated for decades. In this study, we use Distributed Acoustic Sensing (DAS) and Ocean Bottom Seismometer data collected offshore Oregon to investigate microseisms sources across a range of frequency bands. Our results reveal a clear frequency dependence: high‐frequency (0.35–1.5 Hz) microseisms primarily originates near the coastline due to wind ocean waves, with minimal contribution from the deep ocean. In short‐period double frequency (SPDF, 0.2–0.35 Hz) microseisms, the source regions extend farther offshore and are increasingly influenced by deep‐ocean sources. Long‐period double frequency (LPDF, 0.1–0.2 Hz) microseisms are predominantly generated in the deep ocean. Furthermore, we find that microseisms generated by coastal reflections do not propagate into the deep ocean.