We present a new derivation of the Milky Way’s current star formation rate (SFR) based on the data of the Herschel InfraRed Galactic Plane Survey (Hi-GAL). We estimate the distribution of the SFR across the Galactic plane from the star-forming clumps identified in the Hi-GAL survey and calculate the total SFR from the sum of their contributions. The estimate of the global SFR amounts to 2.0 ± 0.7 M _⊙ yr⁻¹, of which 1.7 ± 0.6 M _⊙ yr⁻¹ coming from clumps with reliable heliocentric distance assignment. This value is in general agreement with estimates found in the literature of last decades. The profile of SFR density averaged in Galactocentric rings is found to be qualitatively similar to others previously computed, with a peak corresponding to the Central Molecular Zone and another one around Galactocentric radius R _gal ∼ 5 kpc, followed by an exponential decrease as log ( Σ SFR / [ M ⊙ yr − 1 kpc − 2 ] ) = a R gal / [ kpc ] + b , with a = −0.28 ± 0.01. In this regard, the fraction of SFR produced within and outside the solar circle is 84% and 16%, respectively; the fraction corresponding to the far outer Galaxy (R _gal > 13.5 kpc) is only 1%. We also find that, for R _gal > 3 kpc, our data follow a power law as a function of density, similarly to the Kennicutt–Schmidt relation. Finally, we compare the distribution of the SFR density across the face-on Galactic plane and those of median parameters, such as temperature, luminosity/mass ratio, and bolometric temperature, describing the evolutionary stage of Hi-GAL clumps. We found no clear correlation between the SFR and the clump evolutionary stage.

The physical mechanisms behind the fragmentation of high-mass dense clumps into compact star-forming cores and the properties of these cores are fundamental topics that are heavily investigated in current astrophysical research. The ALMAGAL survey provides the opportunity to study this process at an unprecedented level of detail and statistical significance, featuring high-angular resolution 1.38 mm ALMA observations of 1013 massive dense clumps at various Galactic locations. These clumps cover a wide range of distances (~2–8 kpc), masses (~10²–10⁴ M_⊙), surface densities (0.1–10 g cm⁻²), and evolutionary stages (luminosity over mass ratio indicator of ~0.05 < L/M < 450L_⊙/M_⊙). Here, we present the catalog of compact sources obtained with the CuTEx algorithm from continuum images of the full ALMAGAL clump sample combining ACA-7 m and 12 m ALMA arrays, reaching a uniform high median spatial resolution of ~1400 au (down to ~800 au). We characterize and discuss the revealed fragmentation properties and the photometric and estimated physical parameters of the core population. The ALMAGAL compact source catalog includes 6348 cores detected in 844 clumps (83% of the total), with a number of cores per clump between 1 and 49 (median of 5). The estimated core diameters are mostly within ~800–3000 au (median of 1700 au). We assigned core temperatures based on the L/M of the hosting clump, and obtained core masses from 0.002 to 345 M_⊙ (complete above 0.23 M_⊙), exhibiting a good correlation with the core radii (M ∝ R^2.6). We evaluated the variation in the core mass function (CMF) with evolution as traced by the clump L/M, finding a clear, robust shift and change in slope among CMFs within subsamples at different stages. This finding suggests that the CMF shape is not constant throughout the star formation process, but rather it builds (and flattens) with evolution, with higher core masses reached at later stages. We found that all cores within a clump grow in mass on average with evolution, while a population of possibly newly formed lower-mass cores is present throughout. The number of cores increases with the core masses, at least until the most massive core reaches ~10M_⊙. More generally, our results favor a clump-fed scenario for high-mass star formation, in which cores form as low-mass seeds, and then gain mass while further fragmentation occurs in the clump.

Context. Investigating the flow of material along filamentary structures towards the central core can help provide insights into high-mass star formation and evolution. Aims. Our main motivation is to answer the question of what the properties of accretion flows are in star-forming clusters. We used data from the ALMA Evolutionary Study of High Mass Protocluster Formation in the Galaxy (ALMAGAL) survey to study 100 ALMAGAL regions at a ∼1″ resolution, located between ∼2 and 6 kpc. Methods. Making use of the ALMAGAL ∼1.3 mm line and continuum data, we estimated flow rates onto individual cores. We focus specifically on flow rates along filamentary structures associated with these cores. Our primary analysis is centered around position velocity cuts in H₂CO (3_0, 3–2_0, 2), which allow us to measure the velocity fields surrounding these cores. Combining this work with column density estimates, we were able to derive the flow rates along the extended filamentary structures associated with cores in these regions. Results. We selected a sample of 100 ALMAGAL regions, covering four evolutionary stages from quiescent to protostellar, young stellar objects (YSOs), and HII regions (25 each). Using a dendrogram and line analysis, we identify a final sample of 182 cores in 87 regions. In this paper, we present 728 flow rates for our sample (4 per core), analysed in the context of evolutionary stage, distance from the core, and core mass. On average, for the whole sample, we derived flow rates on the order of ∼10⁻⁴ M_⊙ yr⁻¹ with estimated uncertainties of ±50%. We see increasing differences in the values among evolutionary stages, most notably between the less evolved (quiescent and protostellar) and more evolved (YSO and HII region) sources and we also see an increasing trend as we move further away from the centre of these cores. We also find a clear relationship between the calculated flow rates and core masses ∼M^2/3, which is in line with the result expected from the tidal-lobe accretion mechanism. The significance of these relationships is tested with Kolmogorov–Smirnov and Mann-Whitney U tests. Conclusions. Overall, we see an increasing trend in the relationships between the flow rate and the three investigated parameters, namely: evolutionary stage, distance from the core, and core mass.

This paper presents a methodological framework aiming to support the creation of a smart-tourism destination. Specifically, the study is realised in the frame of NEST, a European Union Interreg project aiming to create a smart-tourism destination in the Adriatic–Ionian area. Therefore, the study focuses on the southeastern Adriatic–Ionian area, as a portion of the European macro-region including the Italian regions of Molise and Apulia and the Balkan countries of Albania and Montenegro. This area presents a clear vocation for tourism, with a distinctive cultural heritage, attractive natural landscapes, and old culinary traditions offering a variety of eno-gastronomic and folk craft products. In the frame of the EU neighbour policies and in coherence with the objectives of the EU smart specialization strategy, several initiatives have been promoted for innovating the tourism offering in this area. Despite this, the full achievement of the creation of an Adriatic–Ionian smart destination calls for the adoption of a multidimensional strategy able to leverage knowledge-intensive dynamics of collaboration. This paper, therefore, aims to highlight the opportunities of adoptions and implications of this methodological framework for the cross-border marketing and management of the Adriatic–Ionian smart destination.

Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effectiveness across diverse fraud scenarios. We analyze various fraud types, including credit card fraud, financial statement fraud, insurance fraud, and money laundering, along with their specific detection challenges. The review outlines supervised, unsupervised, and hybrid learning approaches, discussing their applications and performance in different fraud detection contexts. We examine commonly used datasets in fraud detection research and evaluate performance metrics for assessing these systems. The review is further grounded by two case studies applying supervised models to real-world banking data, illustrating the practical challenges of implementing fraud detection systems in operational environments. Through our analysis of the recent literature, we identify persistent challenges, including data imbalance, concept drift, and privacy concerns, while highlighting the emerging trends in deep learning and ensemble methods. This review provides valuable insights for researchers, financial institutions, and practitioners working to develop more effective, adaptive, and interpretable fraud detection systems capable of operating within real-world financial environments.