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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
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
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
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
Towards a non-equilibrium thermodynamic theory of ecosystem assembly and development
Non-equilibrium thermodynamics has had a significant historic influence on the development
of theoretical ecology, even informing the very concept of an ecosystem. Much of this influence
has manifested as proposed extremal principles. These principles hold that systems will tend
to maximise certain thermodynamic quantities, subject to the other constraints they operate
under. A particularly notable extremal principle is the maximum entropy production principle
(MaxEPP); that systems maximise their rate of entropy production. However, these principles
are not robustly based in physical theory, and suffer from treating complex ecosystems in
an extremely coarse manner. To address this gap, this thesis derives a limited but physically
justified extremal principle, as well as carrying out a detailed investigation of the impact of
non-equilibrium thermodynamic constraints on the assembly of microbial communities. The extremal
principle we obtain pertains to the switching between states in simple bistable systems,
with switching paths that generate more entropy being favoured. Our detailed investigation
into microbial communities involved developing a novel thermodynamic microbial community
model, using which we found the rate of ecosystem development to be set by the availability
of free-energy. Further investigation was carried out using this model, demonstrating the way
that trade-offs emerging from fundamental thermodynamic constraints impact the dynamics of
assembling microbial communities. Taken together our results demonstrate that theory can be
developed from non-equilibrium thermodynamics, that is both ecologically relevant and physically
well grounded. We find that broad extremal principles are unlikely to be obtained, absent
significant advances in the field of stochastic thermodynamics, limiting their applicability to
ecology. However, we find that detailed consideration of the non-equilibrium thermodynamic
mechanisms that impact microbial communities can broaden our understanding of their assembly
and functioning.Open Acces
Improving the Logarithmic Accuracy of the Angular-Ordered Parton Shower
Monte Carlo event generators are a key tool for making theoretical predictions that can be compared with the results of collider experiments, our most
accurate probes of fundamental particle physics. New developments in the way parton shower accuracy is assessed have led us to re-examine the accuracy of the angular-ordered parton shower in the Herwig 7 event generator, focussing on the way recoil is handled after successive emissions. We first discuss how the evolution variable is defined in the Herwig angular-ordered shower and how the choice of this definition determines the recoil scheme. We then show how the recoil scheme can affect the logarithmic accuracy of final-state radiation produced by the algorithm. As part of this investigation we consider a new interpretation of the evolution variable intended to mitigate problems with previous iterations of the shower. To test this, simulated events for each scheme are compared with experimental data from both LEP and the LHC. Next we extend our analysis to initial-state radiation and perform the same process of assessing the logarithmic accuracy of different interpretations of the evolution variable. This time, we compare simulated events for each scheme with LHC data for the vector boson production. Additionally, we consider the impact that the choice of NLO matching scheme has on the accuracy of these simulations, with reference to the same LHC data
A Cross-cultural Comparative Study of Dark Triad and Five-Factor Personality Models in Relation to Prejudice and Aggression
When examining socially malevolent outcomes in the form of prejudice and aggression, previous research on the Dark Triad and five-factor personality models has failed to consider potential cross-cultural differences. A deeper understanding of cross-cultural variations is necessary because these factors represent important social problems and risks. Prior investigation has so far only established preliminary relationships between the Dark Triad and the Big Five model and these outlined associations influence prejudice and aggression. Accordingly, this thesis consisted of two phases. The first examined interrelationships between Dark Triad traits (psychopathy, narcissism, and Machiavellianism) and Big Five personality dimensions (extraversion, neuroticism, agreeableness, openness, conscientiousness) in UK and Russian samples. The second used the results from the initial phase to inform the baseline of a predictive model, which was extended. Both phases used cross-sectional designs, correlation-based methods of analysis (e.g., confirmatory factor analysis, structural equation modelling with mediation, path analysis and invariance analysis), and large samples, comprising a range of backgrounds and ages. The analysis identified the strongest and weakest relationships between personality traits and prejudice and aggression. This research made an original contribution to existing literature by identifying novel relationships
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