Games in Energy Markets

We study energy markets in game theoretic framework. The energy markets consist of two types of energy producers: exhaustible producer and renewable producer. An exhaustible producer produces energy with exhaustible resources, such as oil. The resource reserves of each exhaustible producer diminish due to production, and also get replenished with costly effort to explore for new resources. This exploration activity is modeled through a controlled point process that leads to stochastic increments to reserves level. A renewable producer uses renewable resources, such as solar power, to produce energy. The renewable resources are infinite, but costly in production. Each producer chooses optimal controls of production quantity and exploration effort (exhaustible producers only), in order to maximize individual profit that equals his quantity of production multiplied by market price, minus costs of production and exploration. The producers interact with each other through the energy price that is a function of aggregate production, as one's profit does not only depend on his own production quantity, but also depends on the total quantity of all other producers. We aim to study the equilibrium total production and price. In Chapter 2 we study the game between an exhaustible producer and a renewable producer under stochastic demand that switches between different regimes. We study how the regime changes and the relative cost of production, which is a proxy for market competitiveness, affect game equilibria, and compare with the case of deterministic demand. A novel feature driven by stochasticity of demand is that production may shut down during low demand to conserve reserves. In Chapter 3 we study game with a continuum of homogeneous exhaustible producers. Mean field game approach is employed to solve for an approximate Markov Nash equilibrium of the game. We develop numerical schemes to solve the resulting system of partial differential equations: a backward Hamilton-Jacobi-Bellman (HJB) equation for the game value function of a representative producer and a forward transport equation for the distribution of the reserves levels among all producers.In Chapter 4 we study a time-stationary mean field game model, in which the reserves level remains invariant due to the counteracting effects of production and exploration. We also study the impact of uncertainty in the regime that the exploration process becomes asymptotically deterministic, so that discovery of new resources happens at high frequency with small amount of each discovery

Deep Kalman Filters Can Filter

Deep Kalman filters (DKFs) are a class of neural network models that generate Gaussian probability measures from sequential data. Though DKFs are inspired by the Kalman filter, they lack concrete theoretical ties to the stochastic filtering problem, thus limiting their applicability to areas where traditional model-based filters have been used, e.g.\ model calibration for bond and option prices in mathematical finance. We address this issue in the mathematical foundations of deep learning by exhibiting a class of continuous-time DKFs which can approximately implement the conditional law of a broad class of non-Markovian and conditionally Gaussian signal processes given noisy continuous-times measurements. Our approximation results hold uniformly over sufficiently regular compact subsets of paths, where the approximation error is quantified by the worst-case 2-Wasserstein distance computed uniformly over the given compact set of paths

Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing

We propose an optimal iterative scheme for federated transfer learning, where a central planner has access to datasets ${\cal D}_1,\dots,{\cal D}_N$ for the same learning model $f_{\theta}$. Our objective is to minimize the cumulative deviation of the generated parameters $\{\theta_i(t)\}_{t=0}^T$ across all $T$ iterations from the specialized parameters $\theta^\star_{1},\ldots,\theta^\star_N$ obtained for each dataset, while respecting the loss function for the model $f_{\theta(T)}$ produced by the algorithm upon halting. We only allow for continual communication between each of the specialized models (nodes/agents) and the central planner (server), at each iteration (round). For the case where the model $f_{\theta}$ is a finite-rank kernel regression, we derive explicit updates for the regret-optimal algorithm. By leveraging symmetries within the regret-optimal algorithm, we further develop a nearly regret-optimal heuristic that runs with $\mathcal{O}(Np^2)$ fewer elementary operations, where $p$ is the dimension of the parameter space. Additionally, we investigate the adversarial robustness of the regret-optimal algorithm showing that an adversary which perturbs $q$ training pairs by at-most $\varepsilon>0$, across all training sets, cannot reduce the regret-optimal algorithm's regret by more than $\mathcal{O}(\varepsilon q \bar{N}^{1/2})$, where $\bar{N}$ is the aggregate number of training pairs. To validate our theoretical findings, we conduct numerical experiments in the context of American option pricing, utilizing a randomly generated finite-rank kernel.Comment: 54 pages, 3 figure

Profile of Sheared Cable Bolts Strand Wires

For the past several decades cable bolt technology has been used for ground reinforcement in civil, mining and other construction projects. The strength properties of these cables, used as cable bolts, have been evaluated mainly by their ultimate tensile strength as this kind of test could be carried out in the field as well as in the laboratory. Only recently there has been a growing interest in cable bolt failures in shear because of documented field failure evidence. A series of single and double shear tests were carried out to study the extent shear failure of cable bolts in concrete blocks. Tests were made using both single and double shear rigs at the University of Wollongong. Various types of marketed cable bolts were tested using both types of shearing equipment. Various pertinent parameters were examined with direct influence on the failure characteristics of cable bolts were examined. This paper illustrates the strand wires failure profiles in both test methods and with particular focus being directed to the shear failures of both plain and indented cable bolts currently used in Australian mines. The nature of cable failure and the extent of sheared cable displacement affecting the profile of broken strands wires are reported to indicate the way the cable bolt has failed and its failure load

Two new flavonoids from Mosla soochouensis Matsuda

1078-108

Exogenous Fe2+ alleviated the toxicity of CuO nanoparticles on Pseudomonas tolaasii Y-11 under different nitrogen sources

Extensive use of CuO nanoparticles (CuO-NPs ) inevitably leads to their accumulation in wastewater and toxicity to microorganisms that effectively treat nitrogen pollution. Due to the effects of different mediums, the sources of CuO-NPs-induced toxicity to microorganisms and methods to mitigating the toxicity are still unclear. In this study, CuO-NPs were found to impact the nitrate reduction of Pseudomonas tolaasii Y-11 mainly through the action of NPs themselves while inhibiting the ammonium transformation of strain Y-11 through releasing Cu2+. As the content of CuO-NPs increased from 0 to 20 mg/L, the removal efficiency of NO3− and NH4+ decreased from 42.29% and 29.83% to 2.05% and 2.33%, respectively. Exogenous Fe2+ significantly promoted the aggregation of CuO-NPs, reduced the possibility of contact with bacteria, and slowed down the damage of CuO-NPs to strain Y-11. When 0.01 mol/L Fe2+ was added to 0, 1, 5, 10 and 20 mg/L CuO-NPs treatment, the removal efficiencies of NO3- were 69.77%, 88.93%, 80.51%, 36.17% and 2.47%, respectively; the removal efficiencies of NH4+ were 55.95%, 96.71%, 38.11%, 20.71% and 7.43%, respectively. This study provides a method for mitigating the toxicity of CuO-NPs on functional microorganisms

Single shear testing of various cable bolts used in Australian mines

Sixteen single shear tests were carried out on eight geometric cable variations provided for testing from Australian suppliers – Jennmar, Megabolt and Minova. Each test was subjected to varying pre-tension values of zero and 15 tonnes, exploring the effect of plain, spiral, bulbed, indented and a combination of plain and indented wire strands. The results obtained demonstrated that the shear strength of plain strand cable was higher than the spiral and/or indented profiled cables with direct correlation to the strands ultimate tensile strength. All the plain profiled cables experienced an element of partial debonding suggesting that their application at embedment length less than 1.8 m each anchor side may not be adequate. The spiral and indented profile strands provided greater bond strength at the cable-grout interface due to the surface roughness of the wires imposing an interlocking effect, leading to reduced shear displacement. The data suggests that the spiral profile was superior to the indented profile due possibly to the compromised integrity of the strand from the impact of stress raisers when creating the indented profile. No study was carried on the button indented profile cable bolts. This report is the first validation that type of apparatus selected to test the shearing capacity of a cable strand will not affect results

Double shear testing of cable bolts with no concrete face contacts

A new series of double shear tests were carried out using a newly modified double shear apparatus which prevented contacts between concrete block surfaces during shearing. 13 double shear tests were carried out using 21 mm diameter 19 (9 × 9 × 1) seal construction wire strand cable (also called Superstrand cable), Plain SUMO, Indented SUMO, Spiral MW9 and Plain MW10 cable bolts. These cables were tested subjected to different pretension loads. Concrete blocks with Uniaxial Compressive Strength (UCS) of 40 MPa and Stratabinder grout were used for all the tests to maintain test consistency. The results show that the peak shear load and the corresponding shear displacement decrease by increasing the pretension load of the tested cable. The Ultimate tensile strength, lay length, number of wires and cable bolt surface profile type (plain and spiral/indented) are important factors in total shear strength of the cable bolt

Screening ANLN and ASPM as bladder urothelial carcinoma-related biomarkers based on weighted gene co-expression network analysis

Introduction: Bladder cancer (BLCA) is one of the most common malignancies in the urinary system with a poor prognosis and high treatment costs. Identifying potential prognostic biomarkers is significant for exploring new therapeutic and predictive targets of BLCA.Methods: In this study, we screened differentially expressed genes using the GSE37815 dataset. We then performed a weighted gene co‐expression network analysis (WGCNA) to identify the genes correlated with the histologic grade and T stage of BLCA using the GSE32548 dataset. Subsequently, Kaplan Meier survival analysis and Cox regression were used to further identify prognosis‐related hub genes using the datasets GSE13507 and TCGA‐BLCA. Moreover, we detected the expression of the hub genes in 35 paired samples, including BLCA and paracancerous tissue, from the Shantou Central Hospital by qRT‐polymerase chain reaction.Results: This study showed that Anillin (ANLN) and Abnormal spindle-like microcephaly-associated gene (ASPM) were prognostic biomarkers for BLCA. High expression of ANLN and ASPM was associated with poor overall survival.The qRT‐PCR results revealed that ANLN and ASPM genes were upregulated in BLCA, and there was a correlation between the expression of ANLN and ASPM in cancer tissues and paracancerous tissue. Additionally, the increasing multiples in the ANLN gene was obvious in high-grade BLCA.Discussion: In summary, this preliminary exploration indicated a correlation between ANLN and ASPM expression. These two genes, serving as the risk factors for BLCA progression, might be promising targets to improve the occurrence and progression of BLCA

Investigation of the Charge-Transfer Between Ga-Doped ZnO Nanoparticles and Molecules Using Surface-Enhanced Raman Scattering: Doping Induced Band-Gap Shrinkage

Semiconductor nanomaterial is a kind of important enhancement substrate in surface-enhanced Raman scattering (SERS), and the charge-transfer (CT) process contributes dominantly when they are used as the enhancement substrate for SERS. Doping has significant effect on the CT process of semiconductor nanomaterials. Yet till now, none attempts have been made to explore how doping affects the CT process between the semiconductor and probe molecules. For the first time, this paper investigates the effect of gallium (Ga) doping on the CT process between ZnO nanoparticles and 4-mercaptobenzoic acid (4-MBA) monolayer. In this paper, a series of Ga-doped ZnO nanoparticles (NPs) with various ratio of Ga and Zn are synthesized and their SERS performances are studied. The study shows that the doped Ga can cause the band gap shrinkage of ZnO NPs and then affect the CT resonance process form the valence band (VB) of ZnO NPs to the LUMO of 4-MBA molecules. The band gap of Ga-doped ZnO NPs is gradually narrowed with the increasing doping concentration, and a minimum value (3.16 eV) is reached with the Ga and Zn ratio of 3.8%, resulting in the maximum degree of CT. This work investigates the effects of doping induced band gap shrinkage on CT using SERS and provides a new insight on improving the SERS performance of semiconductor NPs