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The AGEMERA project (Agile Exploration and Geo-Modelling for European Critical Raw Materials) advances the exploration of critical raw materials in the EU by deploying innovative, non-invasive geophysical technologies. Funded by the Horizon Europe programme, it aligns with the European Critical Raw Materials Act to enhance resource security and sustainability. Utilising passive seismic methods, drone-based electromagnetic sensing, and muography, the project maps subsurface characteristics across multiple countries in Europe and Zambia. Outcomes are integrated into a dynamic web-based platform for enhanced co-visualisation of different data sets.

Aiming to offer a diagnostic tool to aid examiners with correct hip typing and decision-making about patient management, members of the Board of the International Interdisciplinary Consensus Committee on DDH Evaluation (ICODE – https://www.icode.expert), introduced the cross-platform mobile application (Android and iOS) called “ICODE Hip US Calculator”.

Prolonged and excessive use of chemical fertilizers has resulted in serious harm to soil health and ecosystems. This study aimed to reduce the cultivation costs for apricot trees, nearly 1/3rd of which are spent on fertilizers. The research was conducted on fully grown apricot trees of the cultivar “New Castle,” in the Solan district of Himachal Pradesh, India. The experiment consisted of fourteen treatment combinations evaluated in triplicate and statistically analyzed using a randomized block design (RBD). Results revealed that treatment T12 [50% Nitrogen (Calcium Nitrate) + 50% Nitrogen (Urea) + Azotobacter + Phosphate Solubilizing Bacteria + Vermicompost] resulted in the highest percent increase in tree trunk girth (6.82%), highest leaf chlorophyll content (3.00 mg g−1 fresh weight), leaf area (58.29 cm), fruit set (61.00%) and total yield (61.9 kg tree−1). In terms of nutrient status, T12 had the highest leaf N (2.95%), leaf K (2.60%), soil N (386.33 kg ha−1), soil P (51.00 kg ha−1) and soil organic carbon (1.81%). The highest net return and profit over recommended dose of fertilizers (RDF) was also recorded in treatment T12. The results of this study show that judicious fertilizer use along with integrated organic manure and bio-fertilizers can reduce cultivation costs, improve soil health, and increase fruit production with minimum ecosystem damage.

The present work describes original experimental results obtained on the melting and solidification behaviour of mixed PuO2 – Fe3O4 samples. The samples were laser-heated into the liquid phase in a pressurised autoclave under a controlled atmosphere of argon or air, whereby the sample temperature was measured in-situ with the help of fast pyrometers. In addition, the oxygen release in the argon experiments was measured by an O2 probe. By these means, solidus and liquidus temperatures in the Fe3O4-PuO2 system were derived as a function of the PuO2 mole fraction at two oxygen partial pressures (pO2 = 1e-6 and pO2 = 0.2 bar). It was shown that the system exhibits a eutectic reaction, and that the observed liquidus and eutectic temperatures are higher for higher oxygen contents. This is due to the stabilisation of stoichiometric oxides in an oxidising atmosphere, as opposed to the appearance of oxygen-defective, lower-melting phases in an inert/reducing environment. The occurrence of the eutectic in the system was confirmed by electron microscopy analysis. It occurs at a composition between 22 mol % and 23 mol % of PuO2 in the Fe3O4-PuO2 pseudo-binary system. Outside the Fe3O4 – PuO2 plane, the eutectic temperature was observed to depend upon the oxygen potential, varying from approximately 1800 K in air to approximately 1700 K in inert (Ar) gas. The experimental solidus/liquidus data were used to derive a thermodynamic model on the system. Thermodynamic calculations supported the oxygen release of the samples in both argon and air and the interpretation of the quenched microstructure of the samples.

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ∼ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 sequential observations (>6 hours). However, we found that diel patterns in fish detection strongly affected the abundance estimates due to vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.

The study of suicidal risk has increasingly emphasized the importance of assessing specific suicidal motivations. Motivations express an elaboration of the condition of psychache, representing an effective perspective on the management of suicidal risk in psychotherapy. This study explores suicidal motivations and personality pathology in a clinical sample of adolescents with suicidal ideation or a history of suicide attempts. We aim to investigate how specific motivational factors and personality disorders (PDs) contribute to the foreseeability of suicidal outcomes, such as the occurrence, number, and lethality of suicide attempts and their interaction with the impact of personality disorders. A sample of 134 adolescents aged 12-18, with active suicidal ideation or recent suicide attempts, was assessed using a combination of self-report measures and structured clinical interviews. Binomial logistic regressions and linear regressions were conducted to explore the predictive value of PDs and motivational factors on suicidal behaviors. The results indicate that specific suicidal motivations, such as interpersonal influence, escape fantasy, and absence of fear, provide an additional increase in the foreseeability value beyond personality disorder criteria alone. These findings suggest that assessing suicidal motivations can significantly enhance risk evaluation and inform more effective therapeutic interventions. Beyond identifying certain risk factors, the therapist’s ability to diss and process specific suicidal motivations in the context of the therapeutic relationship can be a decisive factor in monitoring and directly intervening on the risk.

Phytophthora meadii causes fruit rot disease (FRD) in arecanut palm trees in the Western Ghats of Southern India. The control of this disease is challenging due to intrinsic factors like continuous rainfall, skilled climbers being required and the emergence of virulent biotypes. Farmers have been relying on the fungicides for the FRD control, however efficacy of these fungicides is largely dependent on application strategy, schedule and timings. The main aim of this investigation was to evaluate commonly used and newly developed 12 oomycete-specific fungicidal products with different application strategies in 2018 and 2019 at Malnad regions. Fungicides viz., Bordeaux mixture, Mandipropamid, Metalaxyl + Mancozeb and Fosetyl-Al were the most effective in reducing FRD and efficiently controlling (70–80%) the disease with a statistically significant difference compared to untreated control (p ≤ 0.05). However, the efficacy of the evaluated fungicides greatly varied over the years. In terms of application strategies, the pre-monsoon application proved to be more effective in controlling FRD under field conditions. Further, this study suggests that a calendar-based approach to fungicidal application is recommended for effective management of FRD.

High energy states in the self-conjugate 16O nucleus were investigated by analyzing 13C(3He,α)12C reactions in the bombarding energy range 1.4–2.2 MeV. Thanks to a low-threshold and high-resolution array, we succeed in measuring cross sections of the decay channel where an α particle is emitted in coincidence with a 12C nucleus in the Hoyle state, leading to 4α particles in the final channel. The analysis of angular distributions indicates the occurrence of two low-spin resonant states, respectively with Jπ=2+ and 3−, at energies of ≈ 24.1 and 24.5 MeV. In this energy window, the observed branching ratios for the transition involving the emission of the Hoyle state are well larger than calculations based on barrier penetration, signaling the possible occurrence of high-energy states in 16O with a large cluster component.

Abstract Chondrichthyans (sharks, rays, skates, and chimaeras) make up one of the oldest and most ecologically diverse vertebrate groups, yet they face severe threats from fishing, necessitating improved management strategies. To effectively manage these species, we need to understand their spatial interactions with fisheries. However, this understanding is often challenged by limited data on chondrichthyan catches and species identification. In such cases, assessing potential risks from fishing activities can provide valuable insights into these spatial interactions. Here, we propose a method combining geostatistical models fitted to a fishery‐independent dataset with vessel monitoring system (VMS) data to estimate the spatial overlap between chondrichthyans and fishing. Our case study focuses on the western Adriatic Sea in the Mediterranean, examining the overlap between bottom trawling (including otter bottom trawling and beam trawling) and demersal chondrichthyans. We find that the northwestern part of the basin is a hotspot where threatened chondrichthyans (classified as Vulnerable, Endangered, or Critically Endangered by the International Union for Conservation of Nature Red List) greatly overlap with bottom trawling activities. Moreover, some areas, such as the northernmost part of the Adriatic and the “area dei fondi sporchi” in the north‐central offshore part, exhibit minimal overlap between threatened chondrichthyans and bottom trawling, potentially serving as refuges. We recommend prioritizing the management of otter bottom trawling in the northwestern basin to protect these threatened species, while also paying attention to the possible impacts of beam trawling on skates and chondrichthyan habitats. Despite certain limitations, our findings demonstrate that combining geostatistical models of species distributions with VMS data is a promising method for identifying areas of concern for species vulnerable to fishing. This approach can inform targeted management measures and cost‐effective onboard monitoring programs.

ABSTRACT Knowledge about sex‐specific difference in life‐history traits—like growth, mortality, or behavior—is of key importance for management and conservation as these parameters are essential for predictive modeling of population sustainability. We applied a newly developed molecular sex identification method, in combination with a SNP (single nucleotide polymorphism) panel for inferring the population of origin, for more than 300 large Atlantic bluefin tuna (ABFT) collected over several years from newly reclaimed feeding grounds in the Northeast Atlantic. The vast majority (95%) of individuals were genetically assigned to the eastern Atlantic population, which migrates between spawning grounds in the Mediterranean and feeding grounds in the Northeast Atlantic. We found a consistent pattern of a male bias among the eastern Atlantic individuals, with a 4‐year mean of 63% males (59%–65%). Males were most prominent within the smallest ( 250 cm) length classes, while the sex ratio was close to 1:1 for intermediate sizes (230–250 cm). The results from this new, widely applicable, and noninvasive approach suggests differential occupancy or migration timing of ABFT males and females, which cannot be explained alone by sex‐specific differences in growth. Our findings are corroborated by previous traditional studies of sex ratios in dead ABFT from the Atlantic, the Mediterranean, and the Gulf of Mexico. In concert with observed differences in growth and mortality rates between the sexes, these findings should be recognized in order to sustainably manage the resource, maintain productivity, and conserve diversity within the species.