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The process by which Palaeolithic Europe was transformed from a Neanderthal-dominated region to one occupied exclusively by Homo sapiens has proven challenging to diagnose. A blurred chronology has made it difficult to determine when Neanderthals disappeared and whether modern humans overlapped with them. Italy is a crucial region because here we can identify not only Late Mousterian industries, assumed to be associated with Neanderthals, but also early Upper Palaeolithic industries linked with the appearance of early H. sapiens, such as the Uluzzian and the Aurignacian. Here, we present a chronometric dataset of 105 new determinations (74 radiocarbon and 31 luminescence ages) from four key southern Italian sites: Cavallo, Castelcivita, Cala, and Oscurusciuto. We built Bayesian-based chronometric models incorporating these results alongside the relative stratigraphic sequences at each site. The results suggest; 1) that the disappearance of Neanderthals probably pre-dated the appearance of early modern humans in the region and; 2) that there was a partial overlap in the chronology of the Uluzzian and Protoaurignacian, suggesting that these industries may have been produced by different human groups in Europe. Here, the authors present 74 radiocarbon and 31 luminescence age determinations from four sites in southern Italy. They use these data to suggest that Neanderthal disappearance in the region predated the appearance of early modern humans, a previously unclear chronology [ABSTRACT FROM AUTHOR]

The precise detection of volatile organic compounds plays a pivotal role in addressing environmental concerns, industrial safety, and medical diagnostics. The accurate identification and quantification of these compounds because of their ubiquity and potential health hazards has fueled the development of advanced sensing technologies. This work presents a sensing system in the realm of long-wavelength infrared spectroscopy for achieving enhanced selectivity and sensitivity of benzene, toluene, and propane detection through quartz-enhanced photoacoustic spectroscopy. High-resolution gas spectroscopy is made possible by the use of specially designed InAs/AlSb-based quantum cascade lasers, emitting in the wavelength range 13–15 μm, and quartz tuning forks. The sensor system, characterized by its robustness and precision, demonstrates exceptional capabilities in benzene, toluene, and propane detection. The system's capacity for practical applications in environmental monitoring and medical diagnostics is demonstrated by its ability to distinguish these volatile organic compounds with a minimum detection limit of 113 ppb, 3 ppb, and 3 ppm for toluene, benzene, and propane at an integration time of 10 s, even in complex gas matrices. This work advances gas sensing technology while also offering insightful information on spectral interferences, a persistent problem in the field. The results usher in a new era of sophisticated and reliable gas sensing techniques meeting the growing demand for precise volatile organic compounds detectors for environmental monitoring purposes. [ABSTRACT FROM AUTHOR]

In this paper, an end-to-end methane gas detection algorithm based on transformer and multi-layer perceptron (MLP) for tunable diode laser absorption spectroscopy (TDLAS) is presented. It consists of a Transformer-based U-shaped Neural Network (TUNN) filtering algorithm and a concentration prediction network (CPN) based on MLP. This algorithm employs an end-to-end architectural design to extract information from noisy transmission spectra of methane and derive the CH 4 concentrations from denoised spectra, without intermediate steps. The results demonstrate the superiority of the proposed TUNN filtering algorithm over other typically employed digital filters. For concentration prediction, the determination coefficient (R 2 ) reached 99.7%. Even at low concentrations, R 2 remained notably high, reaching up to 89%. The proposed algorithm results in a more efficient, convenient, and accurate spectral data processing for TDLAS-based gas sensors.