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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Quantum Mechanics Lecture Notes. Selected Chapters
These are extended lecture notes of the quantum mechanics course which I am
teaching in the Weizmann Institute of Science graduate physics program. They
cover the topics listed below. The first four chapter are posted here. Their
content is detailed on the next page. The other chapters are planned to be
added in the coming months.
1. Motion in External Electromagnetic Field. Gauge Fields in Quantum
Mechanics.
2. Quantum Mechanics of Electromagnetic Field
3. Photon-Matter Interactions
4. Quantization of the Schr\"odinger Field (The Second Quantization)
5. Open Systems. Density Matrix
6. Adiabatic Theory. The Berry Phase. The Born-Oppenheimer Approximation
7. Mean Field Approaches for Many Body Systems -- Fermions and Boson
Dimension-8 SMEFT Analysis of Minimal Scalar Field Extensions of the Standard Model
We analyze the constraints obtainable from present data using the Standard
Model Effective Field Theory (SMEFT) on extensions of the Standard Model with
additional electroweak singlet or triplet scalar fields. We compare results
obtained using only contributions that are linear in dimension-6 operator
coefficients with those obtained including terms quadratic in these
coefficients as well as contributions that are linear in dimension-8 operator
coefficients. We also implement theoretical constraints arising from the
stability of the electroweak vacuum and perturbative unitarity. Analyzing the
models at the dimension-8 level constrains scalar couplings that are not
bounded at the dimension-6 level. The strongest experimental constraints on the
singlet model are provided by Higgs coupling measurements, whereas electroweak
precision observables provide the strongest constraints on the triplet model.
In the singlet model the present di-Higgs constraints already play a
significant role. We find that the current constraints on model parameters are
already competitive with those anticipated from future di- and tri-Higgs
measurements. We compare our results with calculations in the full model,
exhibiting the improvements when higher-order SMEFT terms are included. We also
identify regions in parameter space where the SMEFT approximation appears to
break down. We find that the combination of current constraints with the
theoretical bounds still admits regions where the SMEFT approach is not valid,
particularly for lower scalar boson masses.Comment: 66 Pages, 14 Figures, 4 Table
Pollution-induced community tolerance in freshwater biofilms – from molecular mechanisms to loss of community functions
Exposure to herbicides poses a threat to aquatic biofilms by affecting their community structure, physiology and function. These changes render biofilms to become more tolerant, but on the downside community tolerance has ecologic costs. A concept that addresses induced community tolerance to a pollutant (PICT) was introduced by Blanck and Wängberg (1988). The basic principle of the concept is that microbial communities undergo pollution-induced succession when exposed to a pollutant over a long period of time, which changes communities structurally and functionally and enhancing tolerance to the pollutant exposure. However, the mechanisms of tolerance and the ecologic consequences were hardly studied up to date. This thesis addresses the structural and functional changes in biofilm communities and applies modern molecular methods to unravel molecular tolerance mechanisms.
Two different freshwater biofilm communities were cultivated for a period of five weeks, with one of the communities being contaminated with 4 μg L-1 diuron. Subsequently, the communities were characterized for structural and functional differences, especially focusing on their crucial role of photosynthesis. The community structure of the autotrophs was assessed using HPLC-based pigment analysis and their functional alterations were investigated using Imaging-PAM fluorometry to study photosynthesis and community oxygen profiling to determine net primary production. Then, the molecular fingerprints of the communities were measured with meta-transcriptomics (RNA-Seq) and GC-based community metabolomics approaches and analyzed with respect to changes in their molecular functions. The communities were acute exposed to diuron for one hour in a dose-response design, to reveal a potential PICT and uncover related adaptation to diuron exposure. The combination of apical and molecular methods in a dose-response design enabled the linkage of functional effects of diuron exposure and underlying molecular mechanisms based on a sensitivity analysis.
Chronic exposure to diuron impaired freshwater biofilms in their biomass accrual. The contaminated communities particularly lost autotrophic biomass, reflected by the decrease in specific chlorophyll a content. This loss was associated with a change in the molecular fingerprint of the communities, which substantiates structural and physiological changes. The decline in autotrophic biomass could be due to a primary loss of sensitive autotrophic organisms caused by the selection of better adapted species in the course of chronic exposure. Related to this hypothesis, an increase in diuron tolerance has been detected in the contaminated communities and molecular mechanisms facilitating tolerance have been found. It was shown that genes of the photosystem, reductive-pentose phosphate cycle and arginine metabolism were differentially expressed among the communities and that an increased amount of potential antioxidant degradation products was found in the contaminated communities. This led to the hypothesis that contaminated communities may have adapted to oxidative stress, making them less sensitive to diuron exposure. Moreover, the photosynthetic light harvesting complex was altered and the photoprotective xanthophyll cycle was increased in the contaminated communities. Despite these adaptation strategies, the loss of autotrophic biomass has been shown to impair primary production. This impairment persisted even under repeated short-term exposure, so that the tolerance mechanisms cannot safeguard primary production as a key function in aquatic systems.:1. The effect of chemicals on organisms and their functions .............................. 1
1.1 Welcome to the anthropocene .......................................................................... 1
1.2 From cellular stress responses to ecosystem resilience ................................... 3
1.2.1 The individual pursuit for homeostasis ....................................................... 3
1.2.2 Stability from diversity ................................................................................. 5
1.3 Community ecotoxicology - a step forward in monitoring the effects of chemical
pollution? ................................................................................................................. 6
1.4 Functional ecotoxicological assessment of microbial communities ................... 9
1.5 Molecular tools – the key to a mechanistic understanding of stressor effects
from a functional perspective in microbial communities? ...................................... 12
2. Aims and Hypothesis ......................................................................................... 14
2.1 Research question .......................................................................................... 14
2.2 Hypothesis and outline .................................................................................... 15
2.3 Experimental approach & concept .................................................................. 16
2.3.1 Aquatic freshwater biofilms as model community ..................................... 16
2.3.2 Diuron as model herbicide ........................................................................ 17
2.3.3 Experimental design ................................................................................. 18
3. Structural and physiological changes in microbial communities after chronic
exposure - PICT and altered functional capacity ................................................. 21
3.1 Introduction ..................................................................................................... 21
3.2 Methods .......................................................................................................... 23
3.2.1 Biofilm cultivation ...................................................................................... 23
3.2.2 Dry weight and autotrophic index ............................................................. 23
3.2.4 Pigment analysis of periphyton ................................................................. 23
3.2.4.1 In-vivo pigment analysis for community characterization ....................... 24
3.2.4.2 In-vivo pigment analysis based on Imaging-PAM fluorometry ............... 24
3.2.4.3 In-vivo pigment fluorescence for tolerance detection ............................. 26
3.2.4.4 Ex-vivo pigment analysis by high-pressure liquid-chromatography ....... 27
3.2.5 Community oxygen metabolism measurements ....................................... 28
3.3 Results and discussion ................................................................................... 29
3.3.1 Comparison of the structural community parameters ............................... 29
3.3.2 Photosynthetic activity and primary production of the communities after
selection phase ................................................................................................. 33
3.3.3 Acquisition of photosynthetic tolerance .................................................... 34
3.3.4 Primary production at exposure conditions ............................................... 36
3.3.5 Tolerance detection in primary production ................................................ 37
3.4 Summary and Conclusion ........................................................................... 40
4. Community gene expression analysis by meta-transcriptomics ................... 41
4.1 Introduction to meta-transcriptomics ............................................................... 41
4.2. Methods ......................................................................................................... 43
4.2.1 Sampling and RNA extraction................................................................... 43
4.2.2 RNA sequencing analysis ......................................................................... 44
4.2.3 Data assembly and processing................................................................. 45
4.2.4 Prioritization of contigs and annotation ..................................................... 47
4.2.5 Sensitivity analysis of biological processes .............................................. 48
4.3 Results and discussion ................................................................................... 48
4.3.1 Characterization of the meta-transcriptomic fingerprints .......................... 49
4.3.2 Insights into community stress response mechanisms using trend analysis
(DRomic’s) ......................................................................................................... 51
4.3.3 Response pattern in the isoform PS genes .............................................. 63
4.5 Summary and conclusion ................................................................................ 65
5. Community metabolome analysis ..................................................................... 66
5.1 Introduction to community metabolomics ........................................................ 66
5.2 Methods .......................................................................................................... 68
5.2.1 Sampling, metabolite extraction and derivatisation................................... 68
5.2.2 GC-TOF-MS analysis ............................................................................... 69
5.2.3 Data processing and statistical analysis ................................................... 69
5.3 Results and discussion ................................................................................... 70
5.3.1 Characterization of the metabolic fingerprints .......................................... 70
5.3.2 Difference in the metabolic fingerprints .................................................... 71
5.3.3 Differential metabolic responses of the communities to short-term exposure
of diuron ............................................................................................................ 73
5.4 Summary and conclusion ................................................................................ 78
6. Synthesis ............................................................................................................. 79
6.1 Approaches and challenges for linking molecular data to functional
measurements ...................................................................................................... 79
6.2 Methods .......................................................................................................... 83
6.2.1 Summary on the data ............................................................................... 83
6.2.2 Aggregation of molecular data to index values (TELI and MELI) .............. 83
6.2.3 Functional annotation of contigs and metabolites using KEGG ................ 83
6.3 Results and discussion ................................................................................... 85
6.3.1 Results of aggregation techniques ........................................................... 85
6.3.2 Sensitivity analysis of the different molecular approaches and endpoints 86
6.3.3 Mechanistic view of the molecular stress responses based on KEGG
functions ............................................................................................................ 89
6.4 Consolidation of the results – holistic interpretation and discussion ............... 93
6.4.1 Adaptation to chronic diuron exposure - from molecular changes to
community effects.............................................................................................. 93
6.4.2 Assessment of the ecological costs of Pollution-induced community
tolerance based on primary production ............................................................. 94
6.5 Outlook ............................................................................................................ 9
Four Lectures on the Random Field Ising Model, Parisi-Sourlas Supersymmetry, and Dimensional Reduction
Numerical evidence suggests that the Random Field Ising Model loses
Parisi-Sourlas SUSY and the dimensional reduction property somewhere between 4
and 5 dimensions, while a related model of branched polymers retains these
features in any . These notes give a leisurely introduction to a recent
theory, developed jointly with A. Kaviraj and E. Trevisani, which aims to
explain these facts. Based on the lectures given in Cortona and at the IHES in
2022.Comment: 55 pages, 11 figures; v2 - minor changes, mentioned forthcoming work
by Fytas et a
Model Diagnostics meets Forecast Evaluation: Goodness-of-Fit, Calibration, and Related Topics
Principled forecast evaluation and model diagnostics are vital in fitting probabilistic models and forecasting outcomes of interest. A common principle is that fitted or predicted distributions ought to be calibrated, ideally in the sense that the outcome is indistinguishable from a random draw from the posited distribution. Much of this thesis is centered on calibration properties of various types of forecasts.
In the first part of the thesis, a simple algorithm for exact multinomial goodness-of-fit tests is proposed. The algorithm computes exact -values based on various test statistics, such as the log-likelihood ratio and Pearson\u27s chi-square. A thorough analysis shows improvement on extant methods. However, the runtime of the algorithm grows exponentially in the number of categories and hence its use is limited.
In the second part, a framework rooted in probability theory is developed, which gives rise to hierarchies of calibration, and applies to both predictive distributions and stand-alone point forecasts. Based on a general notion of conditional T-calibration, the thesis introduces population versions of T-reliability diagrams and revisits a score decomposition into measures of miscalibration, discrimination, and uncertainty. Stable and efficient estimators of T-reliability diagrams and score components arise via nonparametric isotonic regression and the pool-adjacent-violators algorithm. For in-sample model diagnostics, a universal coefficient of determination is introduced that nests and reinterprets the classical in least squares regression.
In the third part, probabilistic top lists are proposed as a novel type of prediction in classification, which bridges the gap between single-class predictions and predictive distributions. The probabilistic top list functional is elicited by strictly consistent evaluation metrics, based on symmetric proper scoring rules, which admit comparison of various types of predictions
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
Annals [...].
Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin
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