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Keywords = Wiener path integral

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22 pages, 9432 KiB  
Article
Conservation Management Practices for Biodiversity Preservation in Urban Informal Green Spaces: Lessons from Central European City
by Piotr Archiciński, Arkadiusz Przybysz, Daria Sikorska, Marzena Wińska-Krysiak, Anderson Rodrigo Da Silva and Piotr Sikorski
Land 2024, 13(6), 764; https://doi.org/10.3390/land13060764 - 29 May 2024
Viewed by 713
Abstract
Urban informal green spaces (IGS) represent valuable reservoirs of biodiversity within urban areas and are increasingly recognized as integral components of green infrastructure. They are perceived as temporary ecosystems, and the management of their vegetation is relatively understudied. The development time of spontaneous [...] Read more.
Urban informal green spaces (IGS) represent valuable reservoirs of biodiversity within urban areas and are increasingly recognized as integral components of green infrastructure. They are perceived as temporary ecosystems, and the management of their vegetation is relatively understudied. The development time of spontaneous vegetation on transformed lands is considered to be in the range of decades, which makes it even more necessary to provide managers with better guidelines for such a long period. Two suggested management approaches for these areas involve: (1) retaining vegetation at various stages of succession (non-forest IGS) and (2) protecting advanced developmental stages (forest IGS), with options for balanced intervention or complete non-intervention. However, the differences in biodiversity between these two types in cities across Central Europe remain unknown, as well as whether the predictors of biodiversity at both local and landscape scales are consistent for non-forest and forest IGS. We examined factors such as habitat continuity, landscape structure, soil quality, and human impact to shed light on pathways for enhancing urban floristic diversity. Conducting extensive botanical surveys in existing informal green spaces (IGS) in Warsaw, we derived various parameters, including the total number of species, Shannon-Wiener biodiversity index, hemeroby, urbanity, share of species from distinct ecological groups, and the number of rare and ancient forest plant species. Tracing habitat continuity from the early 20th century using digitized aerial imagery provided a unique long-term perspective on IGS development. We revealed that no management is pivotal for the conservation of select rare and ancient forest species. On the other hand, partial abandonment with occasional maintenance may enrich species diversity across different successional phases. We uncovered the significant influence of landscape structure and human activity on vegetation species composition within IGS. Notably, IGS proximate to extensive forest landscapes displayed a marked abundance of forest species, alongside a greater prevalence of rare species. However, the presence of other vegetation types in the vicinity did not yield similar effects. Our findings indicate that IGS, when left untouched for decades near forested areas, are valuable for urban biodiversity. As cities across the globe seek sustainable paths, this research underscores the importance of properly understanding and integrating IGS into urban ecological planning. Full article
(This article belongs to the Section Land, Biodiversity, and Human Wellbeing)
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Figure 1
<p>The investigated types of IGS: (<b>A</b>) non-forest IGS, permanent open habitats resulting from the management practices of selective tree and shrub removal; (<b>B</b>) forest IGS, habitats resulting from total abandoned management, allowing unhampered development of forest plant communities. (<b>C</b>) Map showing formal and informal green spaces across Warsaw, with study plots in non-forest (white dots) and forest (black dots) IGS categories.</p>
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<p>The share of species from different ecological groups in IGS.</p>
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20 pages, 4288 KiB  
Article
A Physics-Based Tweedie Exponential Dispersion Process Model for Metal Fatigue Crack Propagation and Prognostics
by Lin Yang, Zirong Wang, Zhen Chen and Ershun Pan
Processes 2024, 12(5), 849; https://doi.org/10.3390/pr12050849 - 23 Apr 2024
Viewed by 656
Abstract
Most structural faults in metal parts can be attributed to fatigue crack propagation. The analysis and prognostics of fatigue crack propagation play essential roles in the health management of mechanical systems. Due to the impacts of different uncertainty factors, the crack propagation process [...] Read more.
Most structural faults in metal parts can be attributed to fatigue crack propagation. The analysis and prognostics of fatigue crack propagation play essential roles in the health management of mechanical systems. Due to the impacts of different uncertainty factors, the crack propagation process exhibits significant randomness, which causes difficulties in fatigue life prediction. To improve prognostic accuracy, a physics-based Tweedie exponential dispersion process (TEDP) model is proposed via integrating Paris Law and the stochastic process. This TEDP model can capture both the crack growth mechanism and uncertainty. Compared with other existing models, the TEDP taking Wiener process, Gamma process, and inverse process as special cases is more general and flexible in modeling complex degradation paths. The probability density function of the model is derived based on saddle-joint approximation. The unknown parameters are calculated via maximum likelihood estimation. Then, the analytic expressions of the distributions of lifetime and product reliability are presented. Significant findings include that the proposed TEDP model substantially enhances predictive accuracy in lifetime estimations of mechanical systems under varying operational conditions, as demonstrated in a practical case study on fatigue crack data. This model not only provides highly accurate lifetime predictions, but also offers deep insights into the reliability assessments of mechanically stressed components. Full article
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Figure 1

Figure 1
<p>Fatigue crack growth rate curve. Note: The corresponding point at log((1 – <span class="html-italic">R</span>)<span class="html-italic">K<sub>c</sub></span>) is defined as the failure point.</p>
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<p>The parameter estimation process for the physics-based TEDP model.</p>
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<p>Test specimen and Virkler dataset. (<b>a</b>) Test specimen; (<b>b</b>) 68 crack growth curves observed by Virkler et al. [<a href="#B43-processes-12-00849" class="html-bibr">43</a>]</p>
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<p>Parameter iteration processes. (<b>a</b>) Iteration of parameter <span class="html-italic">λ</span>; (<b>b</b>) Iteration of parameter <span class="html-italic">ρ</span>; (<b>c</b>) Iteration of parameter <span class="html-italic">ε</span>; (<b>d</b>) Iteration of parameter <span class="html-italic">m</span>; (<b>e</b>) Iteration of parameter <span class="html-italic">d</span>; (<b>f</b>) Iteration of parameter <span class="html-italic">q</span>; (<b>g</b>) Iteration of parameter <span class="html-italic">b</span> (Represents the reciprocal of <span class="html-italic">C</span>).</p>
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<p>Parameter iteration processes. (<b>a</b>) Iteration of parameter <span class="html-italic">λ</span>; (<b>b</b>) Iteration of parameter <span class="html-italic">ρ</span>; (<b>c</b>) Iteration of parameter <span class="html-italic">ε</span>; (<b>d</b>) Iteration of parameter <span class="html-italic">m</span>; (<b>e</b>) Iteration of parameter <span class="html-italic">d</span>; (<b>f</b>) Iteration of parameter <span class="html-italic">q</span>; (<b>g</b>) Iteration of parameter <span class="html-italic">b</span> (Represents the reciprocal of <span class="html-italic">C</span>).</p>
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<p>Comparisons of the estimated PDFs of the lifetime based on different models.</p>
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<p>Comparisons of the estimated CDFs of the lifetime based on different models.</p>
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<p>Prediction results of the proposed model. (<b>a</b>) Predictions of cycle number <span class="html-italic">N</span>(<span class="html-italic">a</span>); (<b>b</b>) The residual errors of predictions.</p>
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<p>Prediction p-percentile lifetime of the proposed model. (<b>a</b>) Empirical p-percentile lifetime; (<b>b</b>) Prediction p-percentile lifetime of the proposed model.</p>
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<p>The prediction results of Model-1 and Model 2. (<b>a</b>) Estimation of crack length a by Model-1; (<b>b</b>) Prediction of cycle number <span class="html-italic">N</span>(<span class="html-italic">a</span>) by Model-2.</p>
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20 pages, 3048 KiB  
Article
Reliability-Based Preventive Maintenance Strategy for Subsea Control System
by Yuxin Wen, Yuanlong Yue, Xin Zuo and Xiaoguang Li
Processes 2024, 12(4), 761; https://doi.org/10.3390/pr12040761 - 9 Apr 2024
Cited by 1 | Viewed by 962
Abstract
The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore [...] Read more.
The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore oil and gas extraction underscores the necessity for advanced reliability modeling and effective maintenance strategies for subsea control systems. Given the enhanced reliability of subsea equipment due to technological advancements, resulting in scarce failure data, traditional reliability modeling methods reliant on historical failure data are becoming inadequate. This paper proposes an innovative reliability modeling technique for subsea control systems that integrates a Wiener degradation model affected by random shocks and utilizes the Copula function to compute the joint reliability of components and their backups. This approach considers the unique challenges of the subsea environment and the complex interplay between components under variable loads, improving model accuracy. This study also examines the effects of imperfect maintenance on degradation paths and introduces a holistic lifecycle cost model for preventive maintenance (PM), optimized against reliability and economic considerations. Numerical simulations on a Subsea Control Module demonstrate the effectiveness of the developed models. Full article
(This article belongs to the Section Process Control and Monitoring)
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<p>Subsea control system structure.</p>
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<p>Degradation process with random shocks.</p>
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<p>The degradation process of imperfect PM.</p>
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<p>Preventive maintenance under reliability threshold constraints.</p>
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<p>Preventive maintenance strategy map of subsea control system.</p>
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<p>Subsea control system structure model.</p>
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<p>Simulation of SCM degradation process.</p>
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<p>Reliability of SCM.</p>
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<p>Reliability of redundancy system.</p>
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<p>Different reliability curves of SCM at various maintenance frequencies.</p>
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<p>PM cost rate for the entire lifecycle of SCM.</p>
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57 pages, 732 KiB  
Article
Simultaneous Measurements of Noncommuting Observables: Positive Transformations and Instrumental Lie Groups
by Christopher S. Jackson and Carlton M. Caves
Entropy 2023, 25(9), 1254; https://doi.org/10.3390/e25091254 - 23 Aug 2023
Cited by 2 | Viewed by 1390
Abstract
We formulate a general program for describing and analyzing continuous, differential weak, simultaneous measurements of noncommuting observables, which focuses on describing the measuring instrument autonomously, without states. The Kraus operators of such measuring processes are time-ordered products of fundamental differential positive transformations [...] Read more.
We formulate a general program for describing and analyzing continuous, differential weak, simultaneous measurements of noncommuting observables, which focuses on describing the measuring instrument autonomously, without states. The Kraus operators of such measuring processes are time-ordered products of fundamental differential positive transformations, which generate nonunitary transformation groups that we call instrumental Lie groups. The temporal evolution of the instrument is equivalent to the diffusion of a Kraus-operator distribution function, defined relative to the invariant measure of the instrumental Lie group. This diffusion can be analyzed using Wiener path integration, stochastic differential equations, or a Fokker-Planck-Kolmogorov equation. This way of considering instrument evolution we call the Instrument Manifold Program. We relate the Instrument Manifold Program to state-based stochastic master equations. We then explain how the Instrument Manifold Program can be used to describe instrument evolution in terms of a universal cover that we call the universal instrumental Lie group, which is independent not just of states, but also of Hilbert space. The universal instrument is generically infinite dimensional, in which case the instrument’s evolution is chaotic. Special simultaneous measurements have a finite-dimensional universal instrument, in which case the instrument is considered principal, and it can be analyzed within the differential geometry of the universal instrumental Lie group. Principal instruments belong at the foundation of quantum mechanics. We consider the three most fundamental examples: measurement of a single observable, position and momentum, and the three components of angular momentum. As these measurements are performed continuously, they converge to strong simultaneous measurements. For a single observable, this results in the standard decay of coherence between inequivalent irreducible representations. For the latter two cases, it leads to a collapse within each irreducible representation onto the classical or spherical phase space, with the phase space located at the boundary of these instrumental Lie groups. Full article
(This article belongs to the Special Issue Quantum Probability and Randomness IV)
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Figure 1
<p>Schematic of a sequence of indirect, differential weak measurements; full understanding comes after reading <xref ref-type="sec" rid="sec2dot1-entropy-25-01254">Section 2.1</xref> and <xref ref-type="sec" rid="sec2dot2-entropy-25-01254">Section 2.2</xref>. A system in a state <inline-formula><mml:math id="mm927"><mml:semantics><mml:mrow><mml:mo>|</mml:mo><mml:mi>ψ</mml:mi><mml:mo>〉</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula> is indirectly measured by a sequence of weak interactions <inline-formula><mml:math id="mm928"><mml:semantics><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>i</mml:mi><mml:mi>H</mml:mi><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msup></mml:semantics></mml:math></inline-formula>, where each set of meters is observed after its interaction; that is, the system is continuously monitored. The incremental Kraus operator for the measurement at time <italic>t</italic>, given outcomes <inline-formula><mml:math id="mm929"><mml:semantics><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>, is <inline-formula><mml:math id="mm930"><mml:semantics><mml:mrow><mml:msqrt><mml:mrow><mml:msup><mml:mi>d</mml:mi><mml:mi>n</mml:mi></mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mi>t</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:msqrt><mml:mfenced separators="" open="〈" close="〉"><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mi>t</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>i</mml:mi><mml:mi>H</mml:mi><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msup><mml:mo>|</mml:mo><mml:mover accent="true"><mml:mn>0</mml:mn><mml:mo>→</mml:mo></mml:mover><mml:mspace width="0.166667em"/></mml:mfenced></mml:mrow></mml:semantics></mml:math></inline-formula>. Under the conditions outlined in <xref ref-type="sec" rid="sec2dot1-entropy-25-01254">Section 2.1</xref>, this Kraus operator is the differential positive transformation of Equation (<xref ref-type="disp-formula" rid="FD1-entropy-25-01254">1</xref>), that is, <inline-formula><mml:math id="mm931"><mml:semantics><mml:mrow><mml:msqrt><mml:mrow><mml:mi>d</mml:mi><mml:mi>μ</mml:mi><mml:mo>(</mml:mo><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mi>t</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:msqrt><mml:mspace width="0.166667em"/><mml:msub><mml:mi>L</mml:mi><mml:mover accent="true"><mml:mi>X</mml:mi><mml:mo>→</mml:mo></mml:mover></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mi>t</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula>, with <inline-formula><mml:math id="mm932"><mml:semantics><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mover accent="true"><mml:mi>X</mml:mi><mml:mo>→</mml:mo></mml:mover></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mi>t</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:msup><mml:mover accent="true"><mml:mi>X</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mn>2</mml:mn></mml:msup><mml:mi>κ</mml:mi><mml:mspace width="0.166667em"/><mml:mi>d</mml:mi><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mover accent="true"><mml:mi>X</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mo>·</mml:mo><mml:msqrt><mml:mi>κ</mml:mi></mml:msqrt><mml:mspace width="0.166667em"/><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:msup></mml:mrow></mml:semantics></mml:math></inline-formula>. The incremental Kraus operators “pile up” to become, at time <italic>T</italic>, the overall Kraus operator <inline-formula><mml:math id="mm933"><mml:semantics><mml:mrow><mml:msqrt><mml:mrow><mml:mi mathvariant="script">D</mml:mi><mml:mi>μ</mml:mi><mml:mo>[</mml:mo><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mrow><mml:mo>[</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mi>T</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub><mml:mo>]</mml:mo></mml:mrow></mml:msqrt><mml:mspace width="0.166667em"/><mml:mi>L</mml:mi><mml:mrow><mml:mo>[</mml:mo><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mrow><mml:mo>[</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mi>T</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></inline-formula>, which is written as a time-ordered exponential in Equation (<xref ref-type="disp-formula" rid="FD2-entropy-25-01254">2</xref>). The overall Kraus operator gives the unnormalized final state at time <italic>T</italic>, as shown in the figure. The collection of Kraus operators at time <italic>T</italic>, for all Wiener outcome paths <inline-formula><mml:math id="mm934"><mml:semantics><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mover accent="true"><mml:mi>W</mml:mi><mml:mo>→</mml:mo></mml:mover><mml:mrow><mml:mo>[</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mi>T</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>, defines an <italic>instrument</italic>, which can be analyzed on its own, independent of system states—simply omit <inline-formula><mml:math id="mm935"><mml:semantics><mml:mrow><mml:mo>|</mml:mo><mml:mi>ψ</mml:mi><mml:mo>〉</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula> from the figure—a style of analysis we call <italic>instrument autonomy</italic>. The Kraus operators move across the manifold of an <italic>instrumental Lie group</italic>, which is generated by the measured observables. Placing the instrument within its instrumental Lie group and analyzing its evolution there is what we call the <italic>Instrument Manifold Program</italic>.</p>
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57 pages, 592 KiB  
Article
Simultaneous Momentum and Position Measurement and the Instrumental Weyl-Heisenberg Group
by Christopher S. Jackson and Carlton M. Caves
Entropy 2023, 25(8), 1221; https://doi.org/10.3390/e25081221 - 16 Aug 2023
Cited by 2 | Viewed by 1135
Abstract
The canonical commutation relation, [Q,P]=i, stands at the foundation of quantum theory and the original Hilbert space. The interpretation of P and Q as observables has always relied on the analogies that exist between the [...] Read more.
The canonical commutation relation, [Q,P]=i, stands at the foundation of quantum theory and the original Hilbert space. The interpretation of P and Q as observables has always relied on the analogies that exist between the unitary transformations of Hilbert space and the canonical (also known as contact) transformations of classical phase space. Now that the theory of quantum measurement is essentially complete (this took a while), it is possible to revisit the canonical commutation relation in a way that sets the foundation of quantum theory not on unitary transformations but on positive transformations. This paper shows how the concept of simultaneous measurement leads to a fundamental differential geometric problem whose solution shows us the following. The simultaneous P and Q measurement (SPQM) defines a universal measuring instrument, which takes the shape of a seven-dimensional manifold, a universal covering group we call the instrumental Weyl-Heisenberg (IWH) group. The group IWH connects the identity to classical phase space in unexpected ways that are significant enough that the positive-operator-valued measure (POVM) offers a complete alternative to energy quantization. Five of the dimensions define processes that can be easily recognized and understood. The other two dimensions, the normalization and phase in the center of the IWH group, are less familiar. The normalization, in particular, requires special handling in order to describe and understand the SPQM instrument. Full article
(This article belongs to the Special Issue Quantum Probability and Randomness IV)
401 KiB  
Article
Chern-Simons Path Integrals in S2 × S1
by Adrian P. C. Lim
Mathematics 2015, 3(3), 843-879; https://doi.org/10.3390/math3030843 - 21 Aug 2015
Cited by 3 | Viewed by 4012
Abstract
Using torus gauge fixing, Hahn in 2008 wrote down an expression for a Chern-Simons path integral to compute the Wilson Loop observable, using the Chern-Simons action \(S_{CS}^\kappa\), \(\kappa\) is some parameter. Instead of making sense of the path integral over the space of [...] Read more.
Using torus gauge fixing, Hahn in 2008 wrote down an expression for a Chern-Simons path integral to compute the Wilson Loop observable, using the Chern-Simons action \(S_{CS}^\kappa\), \(\kappa\) is some parameter. Instead of making sense of the path integral over the space of \(\mathfrak{g}\)-valued smooth 1-forms on \(S^2 \times S^1\), we use the Segal Bargmann transform to define the path integral over \(B_i\), the space of \(\mathfrak{g}\)-valued holomorphic functions over \(\mathbb{C}^2 \times \mathbb{C}^{i-1}\). This approach was first used by us in 2011. The main tool used is Abstract Wiener measure and applying analytic continuation to the Wiener integral. Using the above approach, we will show that the Chern-Simons path integral can be written as a linear functional defined on \(C(B_1^{\times^4} \times B_2^{\times^2}, \mathbb{C})\) and this linear functional is similar to the Chern-Simons linear functional defined by us in 2011, for the Chern-Simons path integral in the case of \(\mathbb{R}^3\). We will define the Wilson Loop observable using this linear functional and explicitly compute it, and the expression is dependent on the parameter \(\kappa\). The second half of the article concentrates on taking \(\kappa\) goes to infinity for the Wilson Loop observable, to obtain link invariants. As an application, we will compute the Wilson Loop observable in the case of \(SU(N)\) and \(SO(N)\). In these cases, the Wilson Loop observable reduces to a state model. We will show that the state models satisfy a Jones type skein relation in the case of \(SU(N)\) and a Conway type skein relation in the case of \(SO(N)\). By imposing quantization condition on the charge of the link \(L\), we will show that the state models are invariant under the Reidemeister Moves and hence the Wilson Loop observables indeed define a framed link invariant. This approach follows that used in an article written by us in 2012, for the case of \(\mathbb{R}^3\). Full article
(This article belongs to the Special Issue Mathematical physics)
1023 KiB  
Article
Bondonic Effects in Group-IV Honeycomb Nanoribbons with Stone-Wales Topological Defects
by Mihai V. Putz and Ottorino Ori
Molecules 2014, 19(4), 4157-4188; https://doi.org/10.3390/molecules19044157 - 3 Apr 2014
Cited by 31 | Viewed by 7339
Abstract
This work advances the modeling of bondonic effects on graphenic and honeycomb structures, with an original two-fold generalization: (i) by employing the fourth order path integral bondonic formalism in considering the high order derivatives of the Wiener topological potential of those 1D systems; [...] Read more.
This work advances the modeling of bondonic effects on graphenic and honeycomb structures, with an original two-fold generalization: (i) by employing the fourth order path integral bondonic formalism in considering the high order derivatives of the Wiener topological potential of those 1D systems; and (ii) by modeling a class of honeycomb defective structures starting from graphene, the carbon-based reference case, and then generalizing the treatment to Si (silicene), Ge (germanene), Sn (stannene) by using the fermionic two-degenerate statistical states function in terms of electronegativity. The honeycomb nanostructures present η-sized Stone-Wales topological defects, the isomeric dislocation dipoles originally called by authors Stone-Wales wave or SWw. For these defective nanoribbons the bondonic formalism foresees a specific phase-transition whose critical behavior shows typical bondonic fast critical time and bonding energies. The quantum transition of the ideal-to-defect structural transformations is fully described by computing the caloric capacities for nanostructures triggered by η-sized topological isomerisations. Present model may be easily applied to hetero-combinations of Group-IV elements like C-Si, C-Ge, C-Sn, Si-Ge, Si-Sn, Ge-Sn. Full article
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) The honeycomb mesh characterizing the 1D nanoribbons with the two independent atoms and the two unit cell vectors which, by translation, cover the entire structure; (<b>b</b>) side view of the lattice and (<b>c</b>) of the buckling structural parameter δ spacing silicene hexagonal sublattices A and B; the Si-Si bond distance is also depicted; in silicene typical distortion parameter is δ=0.44 Ȧ with <span class="html-italic">d</span>(Si-Si)=2.25 Ȧ; (<b>d</b>) The 5|7|7|5 Stone-Wales rotation seen in the direct and dual representation in the nanoribbon honeycomb mesh; (<b>e</b>) view of the SW defect in the mesh (<b>a</b>), pentagonal (heptagonal) rings are in red (green).</p>
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<p>Propagation of the Stone-Wales wave-like defect along the zig-zag direction caused by the insertion of pairs of hexagons at <span class="html-italic">η</span> = 1, corresponding to the SW defect generations step; the size of this dislocation dipole ranges from <span class="html-italic">η</span> = 0 (pristine lattice) to <span class="html-italic">η</span> = 5; pristine (rearranged) hexagons are in blue (and orange); pentagons and heptagons are in red and green, respectively.</p>
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<p>The Wiener based topological potentials of Equations (27)–(34): from top to bottom in successive orders and from left to right for the forming (“0”) SW and for propagating (“D”) of the SW defects on the medium (<span class="html-italic">η</span> = 0–7) and long range (<span class="html-italic">η</span> = 0–10) range, respectively.</p>
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<p>The critical (left column), forming (middle column) and transforming (right column) of SWw in <a href="#molecules-19-04157-f002" class="html-fig">Figure 2</a>, as based on Equation (16) on the short (first upper row) and long range (second upper row), and of their respective differences.</p>
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<p>The bondonic “length” for SWw in forming (left column), defective propagation (middle column) along their differences (right column) behavior: on short (upper row) and long (lower row) ranges, upon considering critical information of Equation (16) into Equation (10).</p>
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<p>Side-by-side canonical internal energies of bondons in honey-comb supercells of <a href="#molecules-19-04157-f002" class="html-fig">Figure 2</a>, as computed with the fourth order formulation of Equation (13) side by side with the former second order formulation of reference [<a href="#B16-molecules-19-04157" class="html-bibr">16</a>], for pristine “0” (upper row), defective “D” (middle row) and their differences (lower row), respectively.</p>
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<p>The same type of representations as in <a href="#molecules-19-04157-f006" class="html-fig">Figure 6</a>, here for caloric capacity of Equation (14) and of former formulation of reference [<a href="#B16-molecules-19-04157" class="html-bibr">16</a>], in the fourth and second order path integral of bondonic movement, respectively.</p>
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