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Obtaining the total wavefunction evolution of interacting quantum systems provides access to important properties, such as entanglement, shedding light on fundamental aspects, e.g., quantum energetics and thermodynamics, and guiding towards possible application in the fields of quantum computation and communication. We consider a two-level atom (qubit) coupled to the continuum of travelling modes of a field confined in a one-dimensional chiral waveguide. Originally, we treated the light-matter ensemble as a closed, isolated system. We solve its dynamics using a collision model where individual temporal modes of the field locally interact with the qubit in a sequential fashion. This approach allows us to obtain the total wavefunction of the qubit-field system, at any time, when the field starts in a coherent or a single-photon state. Our method is general and can be applied to other initial field states.

Existing tests for factorial designs in the non-parametric case are based on hypotheses formulated in terms of distribution functions. Typical null hypotheses, however, are formulated in terms of some parameters or effect measures, particularly in heteroscedastic settings. Here this idea is extended to non-parametric models by introducing a novel non-parametric analysis-of-variance type of statistic based on ranks or pseudoranks which is suitable for testing hypotheses formulated in meaningful non-parametric treatment effects in general factorial designs. This is achieved by a careful detailed study of the common distribution of rank-based estimators for the treatment effects. Since the statistic is asymptotically not a pivotal quantity we propose three different approximation techniques, discuss their theoretic properties and compare them in extensive simulations together with two additional Wald-type tests. An extension of the presented idea to general repeated measures designs is briefly outlined. The rankand pseudorank-based procedures proposed maintain the preassigned type I error rate quite accurately, also in unbalanced and heteroscedastic models.

The repeated interaction model provides a framework for emulating and analyzing the dynamics of open quantum systems. We explore here the dynamics generated by this protocol in a system that is simultaneously coupled to two baths through noncommuting system operators. One bath is made to couple to nondiagonal elements of the system, thus it induces dissipative dynamics, while the other couples to diagonal elements, and by itself it generates pure dephasing. By solving the model analytically exactly, we show that when both baths act concurrently, a strong system-bath coupling gives rise to nonadditive effects in the dynamics. A prominent signature of this nonadditivity is the characteristic slowing down of population relaxation, driven by the influence of the dephasing bath. Beyond dynamics, we investigate the thermodynamic behavior of the model. Previous studies, using quantum master equations, showed that strong system-bath coupling created bath-cooperativity in this model, allowing heat exchange to the dephasing (diagonally coupled) bath. We find instead that, under the repeated interaction (RI) scheme, heat flows exclusively to the dissipative bath (coupled through nondiagonal elements). Our results highlight the need for a deeper understanding of the types of open quantum system (OQS) dynamics and steady-state phenomena that emerge within the RI framework and the relation of this protocol to other common OQS techniques.

Many researchers favor repeated measures designs because they allow the detection of within-person change over time and typically have higher statistical power than cross-sectional designs. However, the plethora of inputs needed for repeated measures designs can make sample size selection, a critical step in designing a successful study, difficult. Using a dental pain study as a driving example, we provide guidance for selecting an appropriate sample size for testing a time by treatment interaction for studies with repeated measures. We describe how to (1) gather the required inputs for the sample size calculation, (2) choose appropriate software to perform the calculation, and (3) address practical considerations such as missing data, multiple aims, and continuous covariates.

In the usual difference in differences (DD), there is a control group that is never treated and a treatment group that is treated at some time point. However, there are DD cases where the control group is always treated (instead of always untreated), which we call ‘DD in reverse (DDR)’. This paper examines how the usual DD identification and estimation procedures change for DDR. As it turns out, DDR estimation can be performed in the same way as DD estimation. In contrast, the identification procedure is quite different, because DDR essentially identifies pre-treatment-period effects, whereas DD identifies post-treatment-period effects. An empirical illustration of the effects of a work-hour limit law on actual work hours and wages is provided, where the law is applied to large firms first and then small firms 1 year later in South Korea so that in the second year, the large firms constitute the always-treated control group and the small firms constitute the treatment group. We find that the law raised South Korean workers’ well-being, as their work hours decreased while their real weekly wage increased.

Background Depression is a prevalent mental disorder, and prolonged exposure to social defeat is a major contributing factor in the onset of depression. Repeated social defeat stress (RSDS) is a commonly used animal model for depression, significantly impacting on the pathogenesis of depression-related to social disorders. The basolateral amygdala (BLA) and the ventral hippocampus (vHPC) are critical brain regions involved in RSDS-induced social behavioral disorders, but the specific neural oscillations occurring in these regions following social defeat remain unclear. Methods Using simultaneous multi-electrode recordings, we captured local field potentials (LFPs) from BLA and vHPC while the stressed mice underwent a social interaction test. Power spectral analysis and Amplitude transform entropy were respectively applied to assess social defeat–induced alterations in neural oscillatory activity and directional inter-regional communication. Results Our study demonstrated that repeated social defeat induces social avoidance and depression-like behaviors. Notably, the power spectral analysis within the BLA and vHPC revealed statistically differences in the theta band (4–12 Hz) between control and RSDS groups, particularly during the With CD1 phase in the 0–3 s stage, when mice entered the social interaction zone, compared to the − 3 –0 s stage prior to enter the zone. Moreover, machine learning analysis successfully classified control and RSDS groups based on neural oscillatory activity in the BLA and vHPC. Finally, ketamine treatment was found to reduce social avoidance and depressive-like behaviors, as well as enhance theta oscillation in the BLA and vHPC. Conclusion These results suggest that social defeat alters theta oscillations in the BLA and vHPC, highlighting potential therapeutic avenues for addressing depression-related social dysfunction.

This study investigates the application of random regression models for analyzing multi-harvest data in cacao breeding. The aim was to understand the genetic dynamics over ten harvest years and select high-performing genotypes. The trial was conducted in Ouro Preto D’Oeste, Rondônia, Brazilian Amazon. Twenty biparental cacao crosses were evaluated over ten years using random regression models. Models with different polynomial degrees and covariance structures for the residual effects were compared, and the best model was determined using Akaike Information Criterion. We also compared the genetic gains after selecting using three criteria: breeding values, persistence, and area under genotypic trajectories. The best random regression models differed between traits. Genotype-by-harvest interactions were observed, emphasizing the temporal variability in genotype performance. Genetic correlations across harvests illustrated the dynamic nature of genetic expression. Accuracy and heritability fluctuated over successive harvests, emphasizing the complexity of genotype performance prediction. Non-linear genotypic trajectories revealed the presence of unique genetic attributes associated with each trait, with number of healthy fruits showing a tendency towards standardization and dry bean weight displaying a more complex pattern. Consistency in selecting genotypes based on number of healthy fruits highlights reliable selection. Conversely, the variability in choosing top genotypes for dry bean weight underscores the need for cautious selection strategies, as it is a more complex trait to optimize. Despite these insights, future research should consider specific environmental conditions, management practices, and the integration of genomic information for a more comprehensive understanding of genetic dynamics in cacao breeding.

Background: Existing evidence suggest that those who are socially isolated are at risk for taking up or continuing smoking. This study investigated country-based differences in social isolation and smoking status.Methods: We performed a repeated cross-sectional study using two waves of data from two ongoing aging studies: the English Longitudinal Study of Ageing and the Japan Gerontological Evaluation Study. Participants from both studies aged ≥65 years were included. We applied a multilevel Poisson regression model to examine the association between social isolation and smoking status and adjusted for individual sociodemographic characteristics. We used the social isolation index which comprises the following domains: marital status; frequency of contact with friends, family, and children; and participation in social activities. Interaction terms between each country and social isolation were also entered into the mode.Results: After exclusion of never smokers, we analyzed 75,905 participants (7,092 for ELSA and 68,813 for JAGES, respectively). Taking ex-smokers as the reference, social isolation was significantly associated with current smoking; the prevalence ratios (PRs) were 1.06 (95% credible interval [CrI], 1.05–1.08) for men and 1.08 (95% CrI, 1.04–1.11) for women. Taking Japan as a reference, the interaction term between country and social isolation was significant for both sexes, with increased PRs of 1.32 (95% CrI, 1.14–1.50) for men and 1.30 (95% CrI, 1.11–1.49) for women in England.Conclusions: Older people who were less socially isolated were more likely to quit smoking in England than in Japan, possibly explained by the strict tobacco control policies in England.

What makes money essential for the functioning of modern society? Through an experiment, we present evidence for the existence of a relevant behavioral dimension in addition to the standard theoretical arguments. Subjects faced repeated opportunities to help an anonymous counterpart who changed over time. Cooperation required trusting that help given to a stranger today would be returned by a stranger in the future. Cooperation levels declined when going from small to large groups of strangers, even if monitoring and payoffs from cooperation were invariant to group size. We then introduced intrinsically worthless tokens. Tokens endogenously became money: subjects took to reward help with a token and to demand a token in exchange for help. Subjects trusted that strangers would return help for a token. Cooperation levels remained stable as the groups grew larger. In all conditions, full cooperation was possible through a social norm of decentralized enforcement, without using tokens. This turned out to be especially demanding in large groups. Lack of trust among strangers thus made money behaviorally essential. To explain these results, we developed an evolutionary model. When behavior in society is heterogeneous, cooperation collapses without tokens. In contrast, the use of tokens makes cooperation evolutionarily stable.