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Impact of MAPbI3 Phase Transitions on Solar Cell Performance: Everything you need to know about ab-initio methods in device performance
No full textThis paper presents a first step toward a pragmatic phenomenological multiscale approach to evaluate perovskite solar cell performance which determines material properties at the atomistic scale with first-principles calculations, and applies them in macro-scale device models. This work focuses on the MAPbI3 (MA = CH3NH3) perovskite and how its phase transitions impact on its optical, electronic, and structural properties which are investigated at the first-principles level. The obtained data are coupled to a numerical drift-diffusion device model enabling evaluation of the performance of corresponding single junction devices. The first-principles simulation applies a hybrid exchange-correlation functional adapted to the studied family of compounds. Validation by available experimental data is presented from materials properties to device performance, justifying the use of the approach for predictive evaluation of existing and novel perovskites. The coupling between atomistic and device models is described in terms of a framework for exchange of optical and electronic parameters between the two scales. The obtained results are systematically discussed in terms of first-principles levels of approximation performances
Middle Eocene hyperthermal seasonality from Paris Basin marine mollusks
No full textInternational audienceThe Earth has experienced hyperthermal events in the past, characterized by maximum durations of hundreds thousand years, significant magnitude, global extent, and drivers associated with increases in greenhouse gas concentrations, therefore making them potential analogues for current climate change. The Middle Eocene Climatic Optimum (MECO) that occurred 40 Ma ago, is marked by a CO2-driven global warming of +4 to +6° C, affecting global temperatures. Here, we present a detailed reconstruction of seasonal fluctuations in seawater temperatures during this warming event in littoral environment, based on geochemical analyses (δ18O and Δ47) of shallow-marine mollusks from the Paris Basin. Our data show a stability in mean winter temperatures compared to pre-MECO conditions, but a marked warming of +10°C in maximum estuarine water temperatures, with a seasonal temperature range increasing from 12°C before the MECO to 22°C at the climax of the event. We demonstrate that at mid-latitudes, annual maximum shallow-water temperatures increased from 30 ± 2°C before the event to a maximum of 41 ± 4°C at the warming peak. This pattern is associated with a seasonal regime characterized by dry summers and wet winters, implying that the Paris Basin experienced a super-hot summer Mediterranean climate during the MECO
Benchmark for two-dimensional large scale coherent structures in partially magnetized E×B plasmas -Community collaboration & lessons learned
No full textLow-temperature plasmas are essential to both fundamental scientific research and critical industrial applications. As in many areas of science, numerical simulations have become a vital tool for uncovering new physical phenomena and guiding technological development. Code benchmarking remains crucial for verifying implementations and evaluating performance. This work continues the Landmark benchmark initiative, a series specifically designed to support the verification of low-temperature plasma codes. In this study, seventeen simulation codes from a collaborative community of nineteen international institutions modeled a partially magnetized E×B Penning discharge. The emergence of large scale coherent structures, or rotating plasma spokes, endows this configuration with an enormous range of time scales, making it particularly challenging to simulate. The codes showed excellent agreement on the rotation frequency of the spoke as well as key plasma properties, including time-averaged ion density, plasma potential, and electron temperature profiles. Achieving this level of agreement came with challenges, and we share lessons learned on how to conduct future benchmarking campaigns. Comparing code implementations, computational hardware, and simulation runtimes also revealed interesting trends, which are summarized with the aim of guiding future plasma simulation software development.</div
Career Change Into Farming: The Scope and Limits of a Farming Degree for New Entrants in Agriculture
No full textInternational audienceABSTRACT Generational renewal in agriculture has become a pressing issue in most post‐industrial countries, even as some people from other careers seek to enter farming. Such people had little formal agricultural training in the past, but today their careers may be facilitated by public centres for agricultural training. This article presents findings from a mixed methods study heavily reliant on a relational approach in a public training centre, using the concept of educational capital to study a farming degree as a condition of success for new entrants in agriculture. Findings highlight that educational capital is now a resource for entering agriculture mid‐career. Aspiring farmers’ initial educational certifications and skills were found to be determinant in access to formal training. The study shows that although the value of such degrees is still uncertain with actors in conventional farming, it has become important to local governments and organic farming networks. Last, it demonstrates that formal training is an ‘institution of change’ offering a bridge into farming from other paths
When coral dies but fish remain: limits of local restoration under climate pressure (Bora Bora, French Polynesia)
No full textInternational audienceWhile global change presents significant threats to all environments, local scale conservation efforts such as coral restoration may help mitigate reef degradation. Our study investigates the combined effects of local coral restoration versus a coral bleaching event on coral health and fish assemblages. Between January and April 2024, approximately 60-70 marine heatwave days occurred across the Bora Bora region (French Polynesia). Two experiments were conducted on the fringing reef of Bora Bora: the first surveyed coral and reef fish communities in two biotopes (shallow fringing reefs and a deeper "reef drop") before and after the 2024 bleaching event; the second assessed fish communities at restoration sites managed by high school children (citizen science) at three time points: (i) before restoration, (ii) after restoration, and (iii) after bleaching. Our results show a significant decline in live coral on the shallow fringing reef (with or without reef restoration) after the 2024 bleaching event, whereas the deeper reef drop exhibited minimal change, highlighting a depth-related difference in reef vulnerability to thermal stress. Despite these changes in coral cover, fish abundance and richness remained relatively stable across time (before and after the bleaching event). Overall, our findings highlight the lack of resilience of coral reefs under heatwaves and provide useful insights into the limitations of small-scale restoration when faced with large-scale thermal stress. This underscores the urgent need to design local-scale restoration strategies that are better aligned with addressing global change, particularly in regions where healthy reefs are vital for the survival of local human communities
QUBIC: an algorithm for detecting cosmic rays
No full textInternational audience: QUBIC (the Q & U Bolometric Interferometer for Cosmology) is an international ground-based experiment designed to observe the polarization of the cosmic microwave background. It has been installed at a high-altitude site in Alto Chorrillos, Argentina (4,869 m above sea level). At this altitude, the cosmic ray flux is high, thus requiring an advanced algorithm for their detection and removal from raw data. Cosmic rays can leave two types of traces in the data: above and below the noise level. This article describes an algorithm for detecting the above-noise traces. : An algorithm was developed for detecting cosmic ray events in the time-ordered data (TOD) of transition-edge sensor detectors (TES bolometers). Raw signals were pre-processed to obtain de-noised data. Events were searched by applying a threshold to isolate segments showing a rapid increase and subsequent exponential decay. The final goal is to fit each segment to extract the time scale of the candidate and verify the fit quality statistically. : The cosmic ray detection algorithm was applied to datasets acquired in Salta (Argentina) in 2022, during a testing campaign. So far, no candidates have been found after exploring different thresholds for initiating the cosmic ray search, along with various combinations of minimum points required for the sudden increase and exponential decay expected in the signal. : We select only high signal-to-noise regions to find the most energetic cosmic ray candidates matching the filters proposed in the method. The null result is not surprising since, for the energy range of cosmic muons of interest here (approximately [1,100] Gev), the expected energy deposited in our very thin bolometer membranes is small and produces a small signal with respect to the measured noise. However, this methodology could be applied to future longer campaigns to estimate, from the largest (and rare) cosmic ray energy depositions, the TES time constants
Search for displaced decays of long-lived particles in events with missing transverse momentum in TeV collisions with the ATLAS detector
No full textInternational audienceA search for long-lived particles in events with significant missing transverse momentum and at least one displaced vertex is presented. This analysis is performed using 137 of collision data collected between 2016--2018 during Run 2 of the Large Hadron Collider by the ATLAS detector. Displaced vertices are identified using two different secondary vertexing algorithms, including a novel ``fuzzy'' vertexing algorithm optimized for identifying displaced decays of heavy quarks. Separate searches are performed using each algorithm, and the expected Standard Model background is independently estimated for each search using a data-driven procedure. No significant excess is observed over the background in either case. The results are used to set 95% confidence-level limits on potential beyond-the-Standard Model physics that could produce this final state. Results are interpreted in the context of four models: long-lived gluinos that form -hadrons before decaying, neutralinos decaying via Higgs-mediated channels in the Bino-Wino coannihilation model, long-lived Higgsinos decaying to axinos, and an exotic Higgs portal model predicting displaced decays of light pseudoscalars
Optical kernel machine with programmable nonlinearity
No full textOptical kernel machines offer high throughput and low latency. A nonlinear optical kernel can handle complex nonlinear data, but power consumption is typically high with the conventional nonlinear optical approach. To overcome this issue, we present an optical kernel with structural nonlinearity that can be continuously tuned at low power. It is implemented in a linear optical scattering cavity with a reconfigurable micro-mirror array. By tuning the degree of nonlinearity with multiple scattering, we vary the kernel sensitivity and information capacity. We further optimize the kernel nonlinearity to best approximate the parity functions from first order to fifth order for binary inputs. Our scheme offers potential applicability across photonic platforms, providing programmable kernels with high performance and low power consumption.10 pages, 6 figure
A Random Matrix Theory of Masked Self-Supervised Regression
No full textIn the era of transformer models, masked self-supervised learning (SSL) has become a foundational training paradigm. A defining feature of masked SSL is that training aggregates predictions across many masking patterns, giving rise to a joint, matrix-valued predictor rather than a single vector-valued estimator. This object encodes how coordinates condition on one another and poses new analytical challenges. We develop a precise high-dimensional analysis of masked modeling objectives in the proportional regime where the number of samples scales with the ambient dimension. Our results provide explicit expressions for the generalization error and characterize the spectral structure of the learned predictor, revealing how masked modeling extracts structure from data. For spiked covariance models, we show that the joint predictor undergoes a Baik--Ben Arous--Péché (BBP)-type phase transition, identifying when masked SSL begins to recover latent signals. Finally, we identify structured regimes in which masked self-supervised learning provably outperforms PCA, highlighting potential advantages of SSL objectives over classical unsupervised method