An early Little Ice Age brackish water invasion along the south coast of the Caspian Sea (sediment of Langarud wetland) and its wider impacts on environment and people

Caspian Sea level has undergone significant changes through time with major impacts not only on the surrounding coasts, but also offshore. This study reports a brackish water invasion on the southern coast of the Caspian Sea constructed from a multi-proxy analysis of sediment retrieved from the Langarud wetland. The ground surface level of wetland is >6 m higher than the current Caspian Sea level (at -27.41 m in 2014) and located >11 km far from the coast. A sequence covering the last millennium was dated by three radiocarbon dates. The results from this new study suggest that Caspian Sea level rose up to at least -21.44 m (i.e. >6 m above the present water level) during the early Little Ice Age. Although previous studies in the southern coast of the Caspian Sea have detected a high-stand during the Little Ice Age period, this study presents the first evidence that this high-stand reached so far inland and at such a high altitude. Moreover, it confirms one of the very few earlier estimates of a high-stand at -21 m for the second half of the 14th century. The effects of this large-scale brackish water invasion on soil properties would have caused severe disruption to regional agriculture, thereby destabilizing local dynasties and facilitating a rapid Turko-Mongol expansion of Tamerlane’s armies from the east.N Ghasemi (INIOAS), V Jahani (Gilan Province Cultural Heritage and Tourism Organisation) and A Naqinezhad (University of Mazandaran), INQUA QuickLakeH project (no. 1227) and to the European project Marie Curie, CLIMSEAS-PIRSES-GA-2009-24751

A Systemic Analysis of Impacts of Individual and Shared Automated Mobility in Austria

Rationale: Increasing digitalization and automation is expected to significantly change the transport system, mobility and settlement structures. A decade ago automated, self-driving vehicles were nothing more than an unrealistic (boyhood) dream. But today the concept of highly and fully automated vehicles is rapidly becoming a reality, with a series of real-world trial applications underway. Government plans and industry predictions expect automation to be introduced from the early 2020s onwards. Nevertheless, there is still a high level of uncertainty in which form and to what extent automated vehicles will enter the market. Furthermore, there are ongoing discussions concerning net effects of positive and negative aspects of automation. Background: The authors have been involved in several research projects analyzing potential impacts of automated driving. The EU funded project CityMobil (Towards Advanced Road Transport for the Urban Environment) was one of first to address automated driving on a large scale. As part of this project the System Dynamics based model MARS (Metropolitan Activity Relocation Simulator) was adapted to assess scenarios of automated driving in four European cities. Simulations demonstrated that automated vehicles integrated into public transport have a potential to reduce car kilometers travelled and improve carbon footprint. On the contrary, privately owned automated vehicles lead to an increase in car kilometers travelled and carbon footprint, unless propulsion technology is changed. While the focus of CityMobil was on the urban scale, the nationally funded Austrian project Shared Autonomy (Potential Effects of the Take-up of Automated Vehicles in Rural Areas – own translation) focused on rural areas. The findings of Shared Autonomy show potential contributions of automated cars to improve the environmental situation and social inclusion in rural areas. Finally, the nationally funded Austrian project SAFiP (System Scenarios Automated Driving in Personal Mobility) takes a look at the national territory of Austria. Method: The relationship between vehicle automation, travel demand and environmental effects consists of a multitude of complex cause-effect-chains. The toolbox of System Dynamics offers appropriate methods to tackle such complexities. Causal Loop Diagrams are used to analyze and discuss relevant cause-effect-chains and are used to adapt an existing Stock-Flow-Model of the Austrian land use and transport demand system. The modified Stock-Flow-Model is used for a quantitative impact assessment. Sensitivity analysis in form of Monte-Carlo-Simulations is employed to tackle the high level of uncertainty concerning key factors. Findings, results: The key factors, influencing mode choice and travel demand, are generalized costs of travel time, weighted costs of use and availability. The automation of driving, expressed as the share of highly and fully automated vehicles in the fleet, is influencing all three key factors via different cause-effect-chains and feedback loops. In SAFiP we identified four key impact sources: automated and remote parking, road capacity and travel speed, value of in-vehicle time and widening the range of users. Sensitivity tests for each of the impact sources have been carried out. Widening the range of users has the highest impact on a national level, potentially increasing car kilometers by about 17 percent in 2050. Remote parking increases car kilometers by about 5 percent in total, ranging from about 1 percent in peripheral districts to about 17 percent in Vienna

Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning

To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three published neural network-based shower separation models were applied to simulation and experimental data to measure the performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL) technological prototype in distinguishing the energy deposited by a single charged and single neutral hadron for Particle Flow. The performance of models trained using only standard spatial and energy and charged track position information from an event was compared to models trained using timing information available from AHCAL, which is expected to improve sensitivity to shower development and, therefore, aid in clustering. Both simulation and experimental data were used to train and test the models and their performances were compared. The best-performing neural network achieved significantly superior event reconstruction when timing information was utilised in training for the case where the charged hadron had more energy than the neutral one, motivating temporally sensitive calorimeters. All models under test were observed to tend to allocate energy deposited by the more energetic of the two showers to the less energetic one. Similar shower reconstruction performance was observed for a model trained on simulation and applied to data and a model trained and applied to data

Software Compensation for Highly Granular Calorimeters using Machine Learning

A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied

Software compensation for highly granular calorimeters using machine learning

A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied

Design, construction and commissioning of a technological prototype of a highly granular SiPM-on-tile scintillator-steel hadronic calorimeter

The CALICE collaboration is developing highly granular electromagnetic and hadronic calorimeters for detectors at future energy frontier electron-positron colliders. After successful tests of a physics prototype, a technological prototype of the Analog Hadron Calorimeter has been built, based on a design and construction techniques scalable to a collider detector. The prototype consists of a steel absorber structure and active layers of small scintillator tiles that are individually read out by directly coupled SiPMs. Each layer has an active area of 72 × 72 cm^2 and a tile size of 3 × 3 cm^2. With 38 active layers, the prototype has nearly 22,000 readout channels, and its total thickness amounts to 4.4 nuclear interaction lengths. The dedicated readout electronics provide time stamping of each hit with an expected resolution of about 1 ns. The prototype was constructed in 2017 and commissioned in beam tests at DESY. It recorded muons, hadron showers and electron showers at different energies in test beams at CERN in 2018. In this paper, the design of the prototype, its construction and commissioning are described. The methods used to calibrate the detector are detailed, and the performance achieved in terms of uniformity and stability is presented

Design, construction and commissioning of a technological prototype of a highly granular SiPM-on-tile scintillator-steel hadronic calorimeter

The CALICE collaboration is developing highly granular electromagnetic and hadronic calorimeters for detectors at future energy frontier electron-positron colliders. After successful tests of a physics prototype, a technological prototype of the Analog Hadron Calorimeter has been built, based on a design and construction techniques scalable to a collider detector. The prototype consists of a steel absorber structure and active layers of small scintillator tiles that are individually read out by directly coupled SiPMs. Each layer has an active area of 72 × 72 cm^2 and a tile size of 3 × 3 cm^2. With 38 active layers, the prototype has nearly 22,000 readout channels, and its total thickness amounts to 4.4 nuclear interaction lengths. The dedicated readout electronics provide time stamping of each hit with an expected resolution of about 1 ns. The prototype was constructed in 2017 and commissioned in beam tests at DESY. It recorded muons, hadron showers and electron showers at different energies in test beams at CERN in 2018. In this paper, the design of the prototype, its construction and commissioning are described. The methods used to calibrate the detector are detailed, and the performance achieved in terms of uniformity and stability is presented