Biochemical Methane Potential (BMP) tests are fundamental for assessing the methane production capacity of organic substrates used for anaerobic digestion (AD). However, reproducibility of such tests still represents a significant challenge due to variations in experimental setups, the measurement techniques for biogas volume, as well as inoculum characteristics. To address these issues, standard protocols are used to ensure the reproducibility of results, but such protocols still have some gaps that can impact the test. This research aims to evaluate the impact of headspace pressure in pressurized and non-pressurized bottles, using manometric and automatic volumetric BMP systems to generate those different conditions, varying the filling volume of the bottles (from 30% to 70%) and the inoculum-to-substrate ratio (ISR) (from 2 to 6), to highlight its influence on methane yield. The results indicate that high headspace pressure, caused by a high filling volume and a low ISR, correlates with reduced methane production. The headspace composition leads to the assumption that CO2 dissolves into the liquid phase at high-pressure, leading to pH alterations that could inhibit the process. Our findings demonstrate that headspace pressure must be carefully controlled in BMP tests, especially when using manometric systems, to avoid underestimation of methane potential.
QuTiP, the Quantum Toolbox in Python, has been at the forefront of open-source quantum software for the last ten years. It is used as a research, teaching, and industrial tool, and has been downloaded millions of times by users around the world. Here we introduce the latest developments in QuTiP v5, which are set to have a large impact on the future of QuTiP and enable it to be a modern, continuously developed and popular tool for another decade and more. We summarize the code design and fundamental data layer changes as well as efficiency improvements, new solvers, applications to quantum circuits with QuTiP-QIP, and new quantum control tools with QuTiP-QOC. Additional flexibility in the data layer underlying all "quantum objects" in QuTiP allows us to harness the power of state-of-the-art data formats and packages like JAX, CuPy, and more. We explain these new features with a series of both well-known and new examples. The code for these examples is available in a static form on GitHub and will be available also in a continuously updated and documented notebook form in the qutip-tutorials package.
Children housed in Child Protection Centres (CPC) have typically been exposed to maltreatment and disrupted caregiving environments, contributing to an accumulation of personal and familial risk factors that may compromise their mental well-being. Despite this heightened vulnerability, few studies have examined their characteristics in terms of attachment, mentalizing capacity (MC), and emerging borderline personality features (EBPF). The present cross-sectional study aimed to assess group differences in attachment style, MC, and EBPF between CPC children and a community group (CG). The study includes 33 school-age CPC children (Mage = 9.91, SD = 1.55) and 36 CG children (Mage = 8.28, SD = 1.49). Participants completed a semi-structured interview to assess attachment style, security and organization, and MC. They also answered a self-report survey to assess EBPF. Chi-square and analyses of variance were used to investigate group differences. CPC children had significantly less secure (CPC: -2.3 standard deviation of residuals (SDr), CG: 2.1 SDr) and more insecure-disorganized (CPC: 2.6 SDr, CG: -2.4 SDr) attachment (χ2 = 21.95, p = .001), lower levels of MC (F(1,67) = 15.42, p = .001) and higher levels of EBPF (F(1,64) = 5.295, p = .025) compared to CG ones. Findings support the clinical validity of EBPF in CPC children and offer modifiable targets regarding attachment security and MC for early prevention and intervention.
The combination of unoccupied aerial vehicles and deep learning offers a promising approach for mapping invasive alien plant species (IAPS), though its effectiveness in early detection case remains uncertain. In this study, we evaluated the suitability of this approach based on a convolutional neural network for mapping the location of common reed (Phragmites australis subsp. australis) within Parc national des & Icirc;les-de-Boucherville located in southern Qu & eacute;bec, Canada. We collected data on six distinct dates (July-October 2022) during the growing season, covering environments with different levels of reed invasion (Dense, Establishing and Post-treatment). Overall, model performance was high for the different dates and zones, especially for recall (mean of 0.89). The results showed an increase in performance, reaching a peak following the appearance of the inflorescence in September (highest F1-score at 0.98). Despite challenges associated with common reed mapping in a post-treatment monitoring context with poorer detection, this approach has the potential to serve as an effective tool for speeding up the work of biologists in the field and ensuring better management of IAPS. To this end, we provide a comprehensive dataset of high-resolution imagery and deep learning models enabling the detection of common reed across its phenological cycle. Combiner les drones et l'apprentissage profond offre une approche prometteuse pour cartographier les esp & egrave;ces v & eacute;g & eacute;tales exotiques envahissantes (EVEE), bien que leur efficacit & eacute; pour d & eacute;tecter les repousses & agrave; un stade pr & eacute;coce reste & agrave; d & eacute;montrer. Dans cette & eacute;tude, nous avons & eacute;valu & eacute; la pertinence de cette approche bas & eacute;e sur l'utilisation d'un r & eacute;seau neuronal convolutif pour cartographier l'emplacement du roseau commun (Phragmites australis subsp. australis) & agrave; l'int & eacute;rieur du Parc national des & Icirc;les-de-Boucherville situ & eacute; dans le sud du Qu & eacute;bec, Canada. Nous avons collect & eacute; des donn & eacute;es & agrave; six dates distinctes (juillet-octobre 2022) durant la saison de croissance, couvrant des environnements pr & eacute;sentant diff & eacute;rents niveaux d'envahissement par le roseau (dense, en voie d'& eacute;tablissement et post-traitement). De fa & ccedil;on g & eacute;n & eacute;rale, la performance du mod & egrave;le & eacute;tait & eacute;lev & eacute;e pour les diff & eacute;rentes dates et zones, surtout au niveau du rappel (moyenne globale de 0.89). Les r & eacute;sultats ont montr & eacute; une augmentation de la performance pour atteindre un sommet & agrave; la suite de l'apparition de l'inflorescence en septembre (F1-score le plus haut & agrave; 0.98). Malgr & eacute; des d & eacute;fis associ & eacute;s & agrave; la cartographie du roseau commun dans un contexte de gestion post-traitement avec un taux de d & eacute;tection plus faible, cette approche pourrait & ecirc;tre un outil efficace pour acc & eacute;l & eacute;rer le travail des biologistes sur le terrain et assurer une meilleure gestion des EVEE. & Agrave; cette fin, nous fournissons un jeu de donn & eacute;es complet comprenant des images haute r & eacute;solution et des mod & egrave;les d'apprentissage profond permettant la d & eacute;tection du roseau commun tout au long de son cycle ph & eacute;nologique.
Many individuals with work disability due to musculoskeletal disorders struggle to achieve a sustainable return to work. Although self-management interventions offer promising avenues to support work participation, their content, delivery characteristics and evaluation approaches remain unclear. This scoping review aimed to map the self-management interventions evaluated in relation to work participation among individuals with work disability due to musculoskeletal disorders, including their components, delivery methods and evaluation approaches. A scoping review was conducted in 10 databases (Academic Search Complete, AMED, SPORTDiscus, Medline, PsycINFO, CINAHL, Embase, Scopus, Cochrane Library, Physiotherapy Evidence Database) from inception to July 2025. Primary studies involving working-age individuals with musculoskeletal disorders, a self-management intervention and an assessment of work participation were included. Out of 8310 records, 31 studies representing 23 self-management interventions were included with a median of 10 components (range 3–16). Most studies included non-specific musculoskeletal disorders (n = 13, 41.9
