Electron impact excitation-ionization of molecules

In the last few decades, the study of atomic collisions by electron-impact has made significant advances. The most difficult case to study is electron impact ionization of molecules for which many approximations have to be made and the validity of these approximations can only be checked by comparing with experiment. In this thesis, I have examined the Molecular three-body distorted wave (M3DW) or Molecular four-body distorted wave (M4DW) approximations for electron-impact ionization. These models use a fully quantum mechanical approach where all particles are treated quantum mechanically and the post collision interaction (PCI) is treated to all orders of perturbation. These electron impact ionization collisions play central roles in the physics and chemistry of upper atmosphere, biofuel, the operation of discharges and lasers, radiation induced damage in biological material like damage to DNA by secondary electrons, and plasma etching processes. For the M3DW model, I will present results for electron impact single ionization of small molecules such as Water, Ethane, and Carbon Dioxide and the much larger molecules Tetrahydrofuran, phenol, furfural, 1-4 Benzoquinone. I will also present results for the four-body problem in which there are two target electrons involved in the collision. M4DW results will be presented for dissociative excitation-ionization of orientated D2. I will show that M4DW calculations using a variational wave function for the ground state that included s- and p- orbital states give better agreement to the experimental measurements than a ground state approximated as a product of two 1s-type Dyson orbitals --Abstract, page iv

Study of Environmental Pollution Rustling from Balhaf Liquid Natural Gas Station Using Spectroscopy Analysis, Shabwah Governorate – Yemen

The present work aimed to determine the contaminants in soil resulting due to emissions that come from natural gas in the station of Balhaf which is it LNG project in Shabowah Gov, Republic of Yemen. Total 20 samples soil samples were collected at depth of approximately (0-10 cm) from different locations from the Balhaf natural gas station. These samples were analyzed by Laser Breakdown Plasma Spectroscopy (LIBS) to determine the samples toxic elements. The components of the toxic elements present in the soil samples were further confirmed by spectroscopic technique induced coupled plasma (ICP) and Fourier Transform Infrared spectroscopy (FTIR) techniques. The LIBS spectra showed the presence of different amounts of elements, including radioactive and toxic elements such as (Ac, Pu, Th, U) and (Al, Ba, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Tl, Zn), in all of the soil samples. The concentrations of the elements were calculated by ICP spectroscopy. The average concentrations of elements in the samples appeared to be 410 ppb for Al, 1.9 ppb for Ba, 2.6 ppb for Cd,2.7 ppb for Co, 2.3 ppb for Cr, 257 ppb for Cu, 30 ppb for Mn, 6,8 ppb for Ni, 63 ppb for Th, 56 ppb for Tl and 6.1 ppb for Zn, but these values did not exceed normal levels for these elements in earth crust according to reference values such as ATSDR 2020, IAEA, 200. The presence of a high concentration of toxic elements in the soil around the natural gas facility, that indicates the presence of emissions from this facility that leads to the accumulation of these elements in the soil

Game-theoretic decentralized model predictive control of thermal appliances in discrete-event systems framework

This paper presents a decentralized model predictive control (MPC) scheme for thermal appliances coordination control in smart buildings. The general system structure consists of a set of local MPC controllers and a game-theoretic supervisory control constructed in the framework of discrete-event systems (DES). In this hierarchical control scheme, a set of local controllers work independently to maintain the thermal comfort level in different zones, and a centralized supervisory control is used to coordinate the local controllers according to the power capacity and the current performance. Global optimality is ensured by satisfying the Nash equilibrium at the coordination layer. The validity of the proposed method is assessed by a simulation experiment including two case studies. The results show that the developed control scheme can achieve a significant reduction of the peak power consumption while providing an adequate temperature regulation performance if the system is P-observable

Discrete-Event Systems-Based Power Admission Control of Thermal Appliances in Smart Buildings

This paper addresses the admission control of thermal appliances in the context of smart buildings. The scheduling of thermal devices operation is formulated in the framework of discrete-event systems, which allows for the modeling and design of admission control to be carried out in a systematic manner and ensuring the existence of the feasible scheduling prior to exploring control solutions. Two algorithms are developed for the purpose of peak demand reduction. While the first algorithm validates the schedulability for the control of thermal appliances, the second algorithm may achieve a more efficient use of available capacity by exploring the concept of max-min fairness. Simulation studies are carried out in MATLAB/Simulink platform and the results show a noticeable improvement on peak power reduction

Refined Continuous Control of DDPG Actors via Parametrised Activation

Continuous action spaces impose a serious challenge for reinforcement learning agents. While several off-policy reinforcement learning algorithms provide a universal solution to continuous control problems, the real challenge lies in the fact that different actuators feature different response functions due to wear and tear (in mechanical systems) and fatigue (in biomechanical systems). In this paper, we propose enhancing the actor-critic reinforcement learning agents by parameterising the final layer in the actor network. This layer produces the actions to accommodate the behaviour discrepancy of different actuators under different load conditions during interaction with the environment. To achieve this, the actor is trained to learn the tuning parameter controlling the activation layer (e.g., Tanh and Sigmoid). The learned parameters are then used to create tailored activation functions for each actuator. We ran experiments on three OpenAI Gym environments, i.e., Pendulum-v0, LunarLanderContinuous-v2, and BipedalWalker-v2. Results showed an average of 23.15% and 33.80% increase in total episode reward of the LunarLanderContinuous-v2 and BipedalWalker-v2 environments, respectively. There was no apparent improvement in Pendulum-v0 environment but the proposed method produces a more stable actuation signal compared to the state-of-the-art method. The proposed method allows the reinforcement learning actor to produce more robust actions that accommodate the discrepancy in the actuators’ response functions. This is particularly useful for real life scenarios where actuators exhibit different response functions depending on the load and the interaction with the environment. This also simplifies the transfer learning problem by fine-tuning the parameterised activation layers instead of retraining the entire policy every time an actuator is replaced. Finally, the proposed method would allow better accommodation to biological actuators (e.g., muscles) in biomechanical systems

Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images

Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (ResUnet) architecture that can segment oropharyngeal tumors with high performance as demonstrated through internal and external validation of large-scale datasets (training size = 224 patients, testing size = 101 patients) as part of the 2021 HECKTOR Challenge. Specifically, we leverage ResUNet models with either 256 or 512 bottleneck layer channels that demonstrate internal validation (10-fold cross-validation) mean Dice similarity coefficient (DSC) up to 0.771 and median 95% Hausdorff distance (95% HD) as low as 2.919 mm. We employ label fusion ensemble approaches, including Simultaneous Truth and Performance Level Estimation (STAPLE) and a voxel-level threshold approach based on majority voting (AVERAGE), to generate consensus segmentations on the test data by combining the segmentations produced through different trained cross-validation models. We demonstrate that our best performing ensembling approach (256 channels AVERAGE) achieves a mean DSC of 0.770 and median 95% HD of 3.143 mm through independent external validation on the test set. Our DSC and 95% HD test results are within 0.01 and 0.06 mm of the top ranked model in the competition, respectively. Concordance of internal and external validation results suggests our models are robust and can generalize well to unseen PET/CT data. We advocate that ResUNet models coupled to label fusion ensembling approaches are promising candidates for PET/CT oropharyngeal primary tumors auto-segmentation. Future investigations should target the ideal combination of channel combinations and label fusion strategies to maximize segmentation performance.</p

Prospective Validation of Diffusion-Weighted MRI as a Biomarker of Tumor Response and Oncologic Outcomes in Head and Neck Cancer: Results From an Observational Biomarker Pre-Qualification Study

PURPOSE: To determine DWI parameters associated with tumor response and oncologic outcomes in head and neck (HNC) patients treated with radiotherapy (RT). METHODS: HNC patients in a prospective study were included. Patients had MRIs pre-, mid-, and post-RT completion. We used T2-weighted sequences for tumor segmentation which were co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Treatment response was assessed at mid- and post-RT and was defined as: complete response (CR) vs. non-complete response (non-CR). The Mann-Whitney U test was used to compare ADC between CR and non-CR. Recursive partitioning analysis (RPA) was performed to identify ADC threshold associated with relapse. Cox proportional hazards models were done for clinical vs. clinical and imaging parameters and internal validation was done using bootstrapping technique. RESULTS: Eighty-one patients were included. Median follow-up was 31 months. For patients with post-RT CR, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10-3 mm2/s vs. (1.37 ± 0.22) × 10-3 mm2/s, p \u3c 0.0001), while patients with non-CR had no significant increase (p \u3e 0.05). RPA identified GTV-P delta (Δ)ADCmean \u3c 7% at mid-RT as the most significant parameter associated with worse LC and RFS (p = 0.01). Uni- and multi-variable analysis showed that GTV-P ΔADCmean at mid-RT ≥ 7% was significantly associated with better LC and RFS. The addition of ΔADCmean significantly improved the c-indices of LC and RFS models compared with standard clinical variables (0.85 vs. 0.77 and 0.74 vs. 0.68 for LC and RFS, respectively, p \u3c 0.0001 for both). CONCLUSION: ΔADCmean at mid-RT is a strong predictor of oncologic outcomes in HNC. Patients with no significant increase of primary tumor ADC at mid-RT are at high risk of disease relapse

Influence of the gut microbiome on IgE and non-IgE-mediated food allergies

Congress of the European-Academy-of-Allergy-and-Clinical-Immunology (EAACI) -- MAY 26-30, 2018 -- Munich, GERMANYWOS: 000441690400204Background: The prevalence of food allergy (FA) in children has been increasing in last decade. Recent studies show changes in gut microbiome with FA. However, whether gut microbiome may differ between IgE and non‐IgE‐mediated FA is not defined. The aim of this study is to examine the intestinal microbiome composition in infants with IgE and non‐IgE‐mediated FA and healthy infants. Method: Infants younger than 1‐year‐old, breastfed and diagnosed with FA by a physician were included in the study. DNA was isolated from stool samples of infants with non‐IgE‐mediated FA (n = 25) and IgE‐mediated FA (n = 11) and healthy infants (n = 7). Whole genome shotgun sequencing was applied to identify the composition of microbial DNA (an average depth of 3.1 ± 0.8 million paired end reads and 0.9 ± 0.2 gigabase pairs). Results: There were compositional differences among 3 different groups. Shannon index was significantly higher in IgE‐mediated FA compared to non‐IgE‐mediated FA group (Kruskal‐Wallis test, P = 0.034). Even though β‐diversity was similar, the Sparse Partial Least Square Discriminant Analysis (sPLS‐DA) demonstrated that there were taxa‐level differences among three groups. In species level, Veillonella parvula was in a significantly higher density in healthy infants compared to IgE and non‐IgE‐mediated FA groups. Rahnella aquatilis and Lactobacillus salivarius were significantly lower and Treponema succinifaciens significantly higher in IgE‐mediated FA group compared to other groups. Additionally, Prevotella sp. oral taxon 299 was significantly lower in non‐IgE‐mediated FA group compared to others. Prevotella sp oral taxon 299 was related to mucus in stool whereas urticaria related species were Olsenall uli, Bactreoides thetaiotaomicron, Klebsiella variiocola, Rahnella aquatilis, Treponema succinfaciens, Ethanoligenens harbinenese. Conclusion: Analysis of microbiome differences in FA patients may aid in the understanding of the disease process. The present data suggest that there are compositional variations mostly in species‐ level among infants with FA and healthy ones. Our results suggest that the gut microbiome has a stronger relationship to IgE‐mediated than non‐IgE‐mediated FA. Further functional analysis of the microbiome may help better understand the changes seen in the gut microbiome in FAs and improve our knowledge in the disease etiopathology.European Academy of Allergy and Clinical Immunolog

Quality Assurance Assessment of Intra-Acquisition Diffusion-Weighted and T2-Weighted Magnetic Resonance Imaging Registration and Contour Propagation for Head and Neck Cancer Radiotherapy

BACKGROUND/PURPOSE: Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images. MATERIALS/METHODS: We compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same imaging session in twenty immobilized HNC patients. In addition, we used the non-registered images (None) as a control comparator. Ground-truth segmentations of radiotherapy structures (tumour and organs at risk) were generated by a physician expert on both image sequences. For each registration approach, structures were propagated from T2W to DWI images. These propagated structures were then compared with ground-truth DWI structures using the Dice similarity coefficient and mean surface distance. RESULTS: 19 left submandibular glands, 18 right submandibular glands, 20 left parotid glands, 20 right parotid glands, 20 spinal cords, and 12 tumours were delineated. Most DIR methods tookcase, with the exception of Elastix 23 which took ∼458 s to execute per case. ADMIRE and Elastix 23 demonstrated improved performance over None for all metrics and structures (Bonferroni-corrected p \u3c 0.05), while the other methods did not. Moreover, ADMIRE and Elastix 23 significantly improved performance in individual and pooled analysis compared to all other methods. CONCLUSIONS: The ADMIRE DIR method offers improved geometric performance with reasonable execution time so should be favoured for registering T2W and DWI images acquired during the same scan session in HNC patients. These results are important to ensure the appropriate selection of registration strategies for MR-guided radiotherapy