The University of Molise (Italian: Università degli Studi del Molise), also known as UNIMOL, is an Italian public university located in Campobasso, Italy. It was founded in 1982 by the Law n.590 according to the plan for development and institution of new universities; the campuses of this university are all set in the region of Molise and they are: in Campobasso (Headquarters), Isernia, Pesche and Termoli.
Despite increasing use of waste-derived polymers in extrusion-based 3D printing (3DP), formulation-process relationships governing 3DP fidelity for food waste protein-based inks remain poorly understood. Addressing this gap, we aimed to study the development of 3DP ink using sodium caseinate recovered from expired pasteurized milk for sustainable packaging applications. A formulation-driven approach was adopted by varying sodium caseinate (11-13%), hydroxypropyl methylcellulose (10-12%) and glycerol (15-20%). The inks were described for stability, steady-shear flow at 50 degrees C, syringe extrudability, mechanical properties at 50 and 25 degrees C, dimensional accuracy of printed constructs, and deposition cooling dynamics. All formulations exhibited shearthinning behavior, with concentration-dependent differences in viscosity and shear stress. 3DP fidelity was highest for formulations containing 12% hydroxypropyl methylcellulose, 13% sodium caseinate, and 15% glycerol, which produced constructs with dimensional deviation below 10%. In contrast, formulations with 20% glycerol and 10% hydroxypropyl methylcellulose content showed dimensional deviation exceeding 60%. The extrudability results showed that formulations exhibiting a stable force plateau enabled continuous material deposition, whereas formulations characterized by pronounced force peaks showed uneven extrusion and poor printing accuracy. Mechanical properties at printing temperature (50 degrees C) indicated that inks exhibiting intermediate hardness (153-224 g) retained their shape after deposition. Infrared thermography revealed deposited filaments exhibiting initial temperatures above 42 degrees C and cooling toward 30 degrees C within a layer. In contrast, delayed extrusion was characterized by lower initial layer temperatures, coinciding with reduced interlayer adhesion. This study provides a systematic demonstration of caseins from expired milk as ink for sustainable 3D printed materials.
High-intensity resistance and impact training (HiRIT) has emerged as a promising strategy to counteract bone loss and functional decline in peri- and postmenopausal women. However, heterogeneity in intervention design and inadequate reporting limit reproducibility and clinical applicability. The Consensus on Exercise Reporting Template (CERT) provides a standardized framework for evaluating the completeness of exercise intervention reporting. Therefore, this review aimed to assess the reporting quality of HiRIT interventions using the CERT and to describe the key characteristics and training content of the implemented protocols. A systematic review was conducted following PRISMA guidelines and registered in PROSPERO. Six electronic databases were searched up to June 2025. Original studies investigating HiRIT interventions in peri- or postmenopausal women were included. Reporting quality was assessed using the 19-item CERT checklist by two independent reviewers. Thirty-nine studies involving 3349 women (mean age 57.6 years) met the inclusion criteria. The mean CERT score was 14.3 out of 19, indicating moderate to good reporting quality. Exercise description, supervision, intervention structure, and equipment were consistently reported, whereas motivation strategies, home-based components, and detailed individualization were frequently omitted. Four progression-related CERT items were adequately reported in approximately two-thirds of the studies. Commonly prescribed exercises included the overhead press, squat, leg press, deadlift, and high-impact jumping tasks. HiRIT trials in peri- and postmenopausal women generally report core exercise components adequately, but important gaps remain in the reporting of progression, individualization, and behavioral support strategies. Improving the completeness and transparency of exercise reporting through consistent use of frameworks such as the CERT is essential to enhance reproducibility, facilitate clinical implementation, and optimize exercise prescription for bone health in this population.
Constructed soils (CSs) are important for rehabilitation of degraded lands, carbon (C) sequestration, and urban agriculture, but little is known about the development and dynamics of organic matter pools in these soils. In this study, we tracked these pools over 21 months in CSs (1/3 compost and 2/3 fine glacio-fluvial sediments) planted with eight different vegetation treatments with densimetric and enzymatic approaches. Time was the main driver influencing soil parameters, followed by vegetation. A high diversity "All" treatment had a significant impact on soil development, notably enriching mineral/sand-associated organic carbon (MSOC) and nitrogen (MSON) by 15% and 12%, respectively, compared to a non-vegetated "Bare" treatment. The presence of sunflower (Helianthus annuus L.) in the vegetation treatment was associated with low values of nitrate-N, particulate organic N (PON), hot-water extractable carbon (HWEC), and beta-glucosidase. These CSs appear to be undergoing rapid soil development driven by organic matter accumulation processes that facilitate nutrient cycling and accumulation of stable MSOC.
Autonomous remote-sensing technologies are increasingly contributing to biodiversity monitoring by enabling scalable, repeatable, and minimally invasive data collection. We present a ground-based robotic remote-sensing framework that integrates artificial intelligence and standardized quality assurance to support the derivation of decision-ready ecological indicators. Using European coastal dunes as a case study, we deployed an AI-enabled quadruped robot equipped with near-ground imaging sensors to monitor the host-herbivore interaction between Pancratium maritimum and Brithys crini. In this citizen-to-robot pipeline, expert-verified citizen-science imagery was used to train lightweight detection models for on-board inference and higher-capacity models for offline auditing, ensuring reproducibility and transparency across missions. Field trials demonstrated that the system achieved consistent image quality, accurate detections, and low-disturbance operation under natural conditions, capturing spatially explicit evidence of herbivory and host condition. By coupling standardized protocols with robotic autonomy, this approach implements a proximal remote-sensing layer that complements aerial and satellite observations. The workflow is designed to support transferable quantification of species interactions and habitat condition across sites and seasons, contributing to the integration of robotics and ecological remote sensing for biodiversity assessment and conservation management.
Background: Soft tissue sarcomas are rare and highly heterogeneous malignant tumors, often asymptomatic in the early stages. Accurate diagnosis and reliable assessment of the risk of metastasis, classified as low, intermediate, or high, are therefore essential for effective clinical decision-making. However, the application of Artificial Intelligence (AI) approaches to these diseases is often limited by the small size and quality of available datasets, which can compromise model robustness and reliability. Methods: The use of formal methods, based on mathematical modeling and logical verification, can be an alternative to AI techniques. When integrated with radiomics, formal methods provide a structured and interpretable approach to support disease diagnosis. Results: The proposed methodology yielded encouraging results, in line with those reported in the literature. A process was conducted to extract several first- and second-order radiomic classes, which were then selected based on their significance. The resulting models were evaluated using standard performance metrics and obtained 80% accuracy, 83% precision, and 83% recall. Conclusion: The transparency of formal methods improves the interpretability of models and radiomic features, allowing new links with clinical practice to be discovered. The proposed approach is presented as a feasibility and proof-of-concept framework aimed at improving interpretability. Given the very small cohort size, performance metrics should be considered preliminary and descriptive, as they require validation on larger external datasets before any clinical applicability can be claimed.
