Ecohydrological Modeling in Agroecosystems: Examples and Challenges

Human societies are increasingly altering the water and biogeochemical cycles to both improve ecosystem productivity and reduce risks associated with the unpredictable variability of climatic drivers. These alterations, however, often cause large negative environmental consequences, raising the question as to how societies can ensure a sustainable use of natural resources for the future. Here we discuss how ecohydrological modeling may address these broad questions with special attention to agroecosystems. The challenges related to modeling the two‐way interaction between society and environment are illustrated by means of a dynamical model in which soil and water quality supports the growth of human society but is also degraded by excessive pressure, leading to critical transitions and sustained societal growth‐collapse cycles. We then focus on the coupled dynamics of soil water and solutes (nutrients or contaminants), emphasizing the modeling challenges, presented by the strong nonlinearities in the soil and plant system and the unpredictable hydroclimatic forcing, that need to be overcome to quantitatively analyze problems of soil water sustainability in both natural and agricultural ecosystems. We discuss applications of this framework to problems of irrigation, soil salinization, and fertilization and emphasize how optimal solutions for large‐scale, long‐term planning of soil and water resources in agroecosystems under uncertainty could be provided by methods from stochastic control, informed by physically and mathematically sound descriptions of ecohydrological and biogeochemical interactions

Quantifying uncertainty in an industrial approach : an emerging consensus in an old epistemological debate

Uncertainty is ubiquitous in modern decision-making supported by quantitative modeling. While uncertainty treatment has been initially largely developed in risk or environmental assessment, it is gaining large-spread interest in many industrial fields generating knowledge and practices going beyond the classical risk versus uncertainty or epistemic versus aleatory debates. On the basis of years of applied research in different sectors at the European scale, this paper discusses the emergence of a methodological consensus throughout a number of fields of engineering and applied science such as metrology, safety and reliability, protection against natural risk, manufacturing statistics, numerical design and scientific computing etc. In relation with the applicable regula-tion and standards and a relevant quantity of interest for decision-making, this approach involves in particular the proper identification of key steps such as : the quantification (or modeling) of the sources of uncertainty, possibly involving an inverse approach ; their propagation through a pre-existing physical-industrial model; the ranking of importance or sensitivity analysis and sometimes a subsequent optimisation step. It aims at giving a consistent and industrially-realistic framework for practical mathematical modeling, assumingly restricted to quantitative and quantifiable uncertainty, and illustrated on three typical examples. Axes of further research proving critical for the environmental or industrial issues are outlined: the information challenges posed by uncertainty modeling in the context of data scarcity, and the corresponding calibration and inverse probabilistic techniques, bound to be developed to best value industrial or environmental monitoring and data acquisition systems under uncertainty; the numerical challenges entailing considerable development of high-performance computing in the field; the acceptability challenges in the context of the precautionary principle

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Low case numbers enable long-term stable pandemic control without lockdowns

The traditional long-term solutions for epidemic control involve eradication or population immunity. Here, we analytically derive the existence of a third viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events, and only moderate restrictions remain necessary. In this equilibrium, daily cases stabilize around ten new infections per million people or less. However, stability is endangered if restrictions are relaxed or case numbers grow too high. The latter destabilization marks a tipping point beyond which the spread self-accelerates. We show that a lockdown can reestablish control and that recurring lockdowns are not necessary given sustained, moderate contact reduction. We illustrate how this strategy profits from vaccination and helps mitigate variants of concern. This strategy reduces cumulative cases (and fatalities) 4x more than strategies that only avoid hospital collapse. In the long term, immunization, large-scale testing, and international coordination will further facilitate control.Comment: Final versio

Multi-Scale Hierarchical Conditional Random Field for Railway Electrification Scene Classification Using Mobile Laser Scanning Data

With the recent rapid development of high-speed railway in many countries, precise inspection for railway electrification systems has become more significant to ensure safe railway operation. However, this time-consuming manual inspection is not satisfactory for the high-demanding inspection task, thus a safe, fast and automatic inspection method is required. With LiDAR (Light Detection and Ranging) data becoming more available, the accurate railway electrification scene understanding using LiDAR data becomes feasible towards automatic 3D precise inspection. This thesis presents a supervised learning method to classify railway electrification objects from Mobile Laser Scanning (MLS) data. First, a multi-range Conditional Random Field (CRF), which characterizes not only labeling homogeneity at a short range, but also the layout compatibility between different objects at a middle range in the probabilistic graphical model is implemented and tested. Then, this multi-range CRF model will be extended and improved into a hierarchical CRF model to consider multi-scale layout compatibility at full range. The proposed method is evaluated on a dataset collected in Korea with complex railway electrification systems environment. The experiment shows the effectiveness of proposed model

Measurements, Models, Systems and Design

531 s.

Strange oscillating beauty-mesons

In dieser Ar­beit wird eine Messung der Bs-BsBar Oszillationsfrequenz DeltaMs vor­ge­stellt. Diese Frequenz ermöglicht die Bestimmung einiger Parameter der CKM-Matrix und dient als wich­ti­ger externer Parameter für Messungen von CP-Verletzung mit Bs-Mesonen. Die Analyse wird mit ei­nem Datensatz von 378 700 Zerfällen von Bs → DsPi durchgeführt, was einer integrierten Luminosität von 6/fb von Proton-Proton Kol­li­si­o­nen entspricht. Der Datensatz wurde zwischen 2015 und 2018 bei einer Schwerpunktsenergie von 13 TeV mit dem LHCb-Ex­peri­ment auf­ge­nom­men. Die Oszillationsfrequenz wird mit Hilfe eines ungebinnten und gewichteten Maximum-Likelihood-Fits zu DeltaMs = (17.7683 ± 0.0051 ± 0.0032)/ps bestimmt. Das Ergebnis stellt eine der bisher präzisesten Messungen der LHCb-Kollaboration dar. Es ist im Einklang mit aktuellen Voraussagen des Standard Modells

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen