Advancing Open Science

How can the interaction between the seismological community and society contribute to the exploitation and usage of renewable energy resources? We try to provide an answer by describing the seismic experiment realized in March–April 2025 in the hydrothermal area close to Contursi Terme municipality (Southern Italy). We deployed a 29-station seismic array thanks to the availability of local citizens, civic administrations, schools, and accommodation facilities, which provided hosting and power for six-component seismological instruments over a one-month period. By computing the Probabilistic Power Spectral Densities (PPSD) and spectrograms, we assessed the noise level and the quality of the dataset. The seismic recordings were also used for studying the local seismic response of the area by the HVSR method and detecting small magnitude (1.4–4.2) local and regional earthquakes. We thus described some solutions to tackle the challenges of a possible geothermal exploitation project in the area: (a) to map the energy resource through a tomography on good-quality ambient-noise data; (b) to manage the seismic risk related to the resource exploitation by installing a proper local seismic network; (c) to increase the acceptance by the population through a citizen-science action for instituting a fruitful alliance between different actors of civil society.

Background: Standard single-cell RNA sequencing (scRNA-seq) analysis workflows face significant limitations, particularly the rigidity of clustering-dependent methods that can obscure subtle cellular heterogeneity and the potential loss of biologically meaningful cells during stringent quality control (QC) filtering. This study aims to develop scSelector (v1.0), an interactive software toolkit designed to empower researchers to flexibly select and analyze cell populations directly from low-dimensional embeddings, guided by their expert biological knowledge. Methods: scSelector was developed using Python, relying on core dependencies such as Scanpy (v1.9.0), Matplotlib (v3.4.0), and NumPy (v1.20.0). It integrates an intuitive lasso selection tool with backend analytical modules for differential expression and functional enrichment analysis. Furthermore, it incorporates Large Language Model (LLM) assistance via API integration (DeepSeek/Gemini) to provide automated, contextually informed cell-type and state prediction reports. Results: Validation across multiple public datasets demonstrated that scSelector effectively resolves functional heterogeneity within broader cell types, such as identifying distinct alpha-cell subpopulations with unique remodeling capabilities in pancreatic tissue. It successfully characterized rare populations, including platelets in PBMCs and extremely low-abundance endothelial cells in liver tissue (as few as 53 cells). Additionally, scSelector revealed that cells discarded by standard QC can represent biologically functional subpopulations, and it accurately dissected the states of outlier cells, such as proliferative NK cells. Conclusions: scSelector provides a flexible, researcher-centric platform that moves beyond the constraints of automated pipelines. By combining interactive selection with AI-assisted interpretation, it enhances the precision of scRNA-seq analysis and facilitates the discovery of novel cell types and complex cellular behaviors.

This article presents an experimental and statistical investigation of how optical magnification influences calibration constants, measurement results, and uncertainty in a digital optical microscope. Measurements were performed on reference gauge blocks with nominal lengths from 1.0 mm to 1.5 mm at five magnification levels (1×–5×) to quantify the effect of magnification on dimensional accuracy. A combined statistical methodology integrating non-parametric hypothesis testing and bootstrap-based uncertainty analysis was developed to evaluate data distributions and validate the use of a normal coverage factor (k = 2) for expanded uncertainty. The results showed that magnification has a statistically significant effect on the measured lengths for most standards, with the smallest combined standard uncertainty achieved at approximately 4× magnification. The uncertainty budget analysis revealed that the dominant component arises from the microscope’s declared Maximum Permissible Error (MPE), while type A and reference-standard components contribute only marginally. All expanded uncertainties remained within the declared MPE limits, confirming the reliability and traceability of the measurement process. Practical recommendations were proposed for selecting optimal magnification and for implementing calibration verification procedures at each zoom level. The presented methodology provides a validated framework for minimizing uncertainty in image-based dimensional measurements using digital optical microscopes.

Combined application of organic (M) and chemical fertilizer (C) is a significant measure to enhance soil fertility and ensure food security. In 2023 and 2024, we established six treatments: T1 (no fertilization), T2 (100% C), T3 (75% C + 25% M), T4 (50% C + 50% M), T5 (25% C +75% M), and T6 (100% M), with three replicates for each treatment. The total amount of nitrogen applied to the soil for T2–T6 was the same, and the organic fertilizer was compost sourced from cow dung. The aims of this study were to explore the effects of organic fertilizer combined with chemical fertilizer on soil fertility, and apparent nutrient balance, to investigate its possible economic benefits. We also analyzed the influence of the combined application of organic and chemical fertilizers on the degree of coupling and coordination (D) between soil fertility and economic benefits. The total phosphorus, total potassium, available phosphorus, available potassium, and organic matter in the soil all showed an increasing trend with an increase in the proportion of organic fertilizer applied. T2 reduced the soil pH by 7.41–8.94% compared with T1, while applying organic fertilizers (T3–T6) increased the soil pH by 0.72–8.62% compared with T2. T4 is conducive to the balance of income and expenditure of nitrogen, phosphorus, and potassium elements. The corn yield, net income, and input–output ratio all showed an initial increase followed by a decrease with an increase in the proportion of organic fertilizer applied, and their values all reached the maximum under T4. Based on the CRITIC-TOPSIS method and the coupling coordination degree model, it was determined that the fertilization strategy with the highest comprehensive score and D under the conditions of this experiment was 50% C +50% M (T4), which not only improved soil fertility but also achieved the highest economic benefit. The research results were of great significance for promoting sustainable agricultural development.

Creating trust in society for new technologies, such as a new types of powertrains, and making them marketable requires transparent, neutral, and independent technical verification. This is crucial for the acceptance and success of electrified vehicles in the used car markets. A key component of electric vehicles is the traction battery, whose current and future condition, particularly regarding aging, determines its residual value and safe operation. This review aims to identify and evaluate methods for predicting the lifetime of onboard traction batteries, focusing on their applicability in technical inspections. A systematic literature and patent review was conducted using targeted keywords, yielding 22 patents and 633 publications. From these, 150 distinct lifetime prediction methods were extracted and categorized into a four-level mind map. These methods are summarized, cited, and structured in detailed tables. The relationships between approaches are explained to clarify the current research landscape. Long Short-Term Memory, Convolutional Neural Networks, and Particle Filters were identified as the most frequently used techniques. However, no methods were found suitable for predicting the lifetime of traction batteries during technical vehicle inspections, which operate under short test durations, limited data access, and diverse real-world operating conditions. Most studies focused on cell-level testing and did not address complete battery systems in operational vehicles. This gap highlights the need for applied research and the development of practical methods to support battery assessment in real-world conditions. Advancing this field is essential to foster confidence in battery systems and enable a sustainable transition to electromobility.

To address flight safety risks from rotor defects in rotorcraft drones operating in complex low-altitude environments, this study proposes a high-precision diagnostic model based on the Multimodal Data Input and Spatio-Temporal Feature Fusion Network (MDI-STFFNet). The model uses a dual-modality coupling mechanism that integrates vibration and air pressure signals, forming a “single-path temporal, dual-path representational” framework. The one-dimensional vibration signal and the five-channel pressure array are mapped into a texture space via phase space reconstruction and color-coded recurrence plots, followed by extraction of transient spatial features using a pre-trained ResNet-18 model. Parallel LSTM networks capture long-term temporal dependencies, while a parameter-free 1D max-pooling layer compresses redundant pressure data, reducing LSTM parameter growth. The CSW-FM module enables adaptive fusion across modal scales via shared-weight mapping and learnable query vectors that dynamically assign spatiotemporal weights. Experiments on a self-built dataset with seven defect types show that the model achieves 99.01% accuracy, improving by 4.46% and 1.98% over single-modality vibration and pressure inputs. Ablation studies confirm the benefits of spatiotemporal fusion and soft weighting in accuracy and robustness. The model provides a scalable, lightweight solution for UAV power system fault diagnosis under high-noise and varying conditions.

Paediatric formulations are pharmaceutical dosage forms specifically designed to meet the physiological, developmental, pharmacokinetic, and practical needs of patients from birth to adolescence. Developing safe, effective, and age-appropriate medicines for children remains a significant challenge due to their age-dependent variability in physiological development, pharmacokinetic profiles, and therapeutic needs. These differences, combined with practical barriers such as poor palatability, limited swallowability, inappropriate dosage form size, and instability, often lead to the modification of adult medicines—practices that can cause dosing inaccuracies, contamination risks, and reduced therapeutic efficacy. Three-dimensional printing has emerged as a promising solution to address these limitations by creating personalised paediatric dosage forms with adjustable strengths, multilayer structures for controlled release, and child-friendly shapes that may improve acceptability and adherence. This review offers an overview of the physiological, technological, and regulatory factors involved in developing 3D-printed paediatric medicines. The Critical Quality and Performance Attributes relevant to this field—including dose accuracy and flexibility, release kinetics, palatability, product dimensions, material choice, safety, stability, cost-effectiveness, production time, scalability, and reproducibility—are discussed in the article. Additionally, the review discusses the evolving Good Manufacturing Practice and regulatory landscape necessary to ensure the quality, safety, and consistency of 3D-printed medicinal products. Overall, these insights underline the transformative potential of 3D printing as a pathway towards safer, more effective, and truly personalised pharmacotherapy for paediatric patients.

Introduction: Promoting healthy habits in childhood is fundamental for fostering long-term well-being. This study aimed to develop and psychometrically validate an app-integrated instrument to assess knowledge, habits, and attitudes related to health in children aged 8–11, within the context of the MHealth intervention Healthy Jeart. Methods: A quantitative, cross-sectional design was used. An initial item pool underwent expert content validation before being administered to a sample of 623 children from primary education centers in Andalusia, Spain. Construct validity was examined through exploratory and confirmatory factor analyses. Results: The analyses supported a coherent four-factor structure comprising 21 items: (1) Use of technologies, (2) diet and growth, (3) psychological well-being, and (4) physical activity and well-being. The instrument demonstrated satisfactory model fit and internal consistency, providing a multidimensional assessment of children’s health-related behaviors. The sample was recruited from primary schools in Andalusia (Spain), which may limit the generalizability of the findings to other regions and cultural contexts. Conclusions: The validated instrument offers a reliable and efficient means of evaluating healthy habits in children aged 8–11, particularly when embedded within digital interventions such as Healthy Jeart. It represents a valuable tool for educators and pediatric nursing professionals working in school settings, enabling early identification of gaps in health literacy and supporting targeted interventions that promote holistic child well-being.