A non-linear discrete stochastic optimization model for Advanced Access patient appointment scheduling

Access to healthcare is a major problem in which patients are deprived of receiving timely admission to healthcare. Poor access has resulted in significant but avoidable healthcare cost, poor quality of healthcare, and deterioration in the general public health. Advanced Access is a simple and direct approach to appointment scheduling in which the majority of a clinic's appointments slots are kept open in order to provide access for immediate or same day healthcare needs and therefore, alleviate the problem of poor access the healthcare. This research formulates a non-linear discrete stochastic mathematical model of the Advanced Access appointment scheduling policy. The model objective is to maximize the expected profit of the clinic subject to constraints on minimum access to healthcare provided. Patient behavior is characterized with probabilities for no-show, balking, and related patient choices. Structural properties of the model are analyzed to determine whether Advanced Access patient scheduling is feasible. To solve the complex combinatorial optimization problem, a heuristic that combines greedy construction algorithm and neighborhood improvement search was developed. The model and the heuristic were used to evaluate the Advanced Access patient appointment policy compared to existing policies. Trade-off between profit and access to healthcare are established, and parameter analysis of input parameters was performed. The trade-off curve is a characteristic curve and was observed to be concave. This implies that there exists an access level at which at which the clinic can be operated at optimal profit that can be realized. The results also show that, in many scenarios by switching from existing scheduling policy to Advanced Access policy clinics can improve access without any decrease in profit. Further, the success of Advanced Access policy in providing improved access and/or profit depends on the expected value of demand, variation in demand, and the ratio of demand for same day and advanced appointments. The contributions of the dissertation are a model of Advanced Access patient scheduling, a heuristic to solve the model, and the use of the model to understand the scheduling policy trade-offs which healthcare clinic managers must make. ^

Simultaneous optimisation of structural topology and material grading using level set method

Peer reviewed

Model updating of a full-scale bridge structure using particle swarm optimization

Peer reviewed

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Structural and Functional Characterization of the Human Tribbles Homologue 2 Pseudokinase

The Tribbles Homologues are a family of three eukaryotic pseudokinases (Trb1, Trb2, Trb3) that act as allosteric inhibitors and regulatory scaffold sites in pathways governing adipogenesis, cell proliferation and insulin signaling. The Tribbles Homologues have the same overall tertiary structure of the eukaryotic protein kinase domain, but lack multiple residues necessary to catalysis in the nucleotide-binding P-loop and the Mg2+-coordinating DFG motif. Trb1 has been shown conclusively to be incapable of binding ATP, whereas a recent study presents evidence that Trb2 autophosphorylates independently of Mg2+ in vitro. This finding is surprising given the high degree of sequence similarity between the two proteins (71%), and suggests unique nucleotide binding and phosphotransfer mechanisms. The goal of this project was to investigate whether Trb2 possesses kinase activity or not and determine its structural basis. A method for the high-yield recombinant expression and purification of stable Trb2 was developed. Trb2 nucleotide binding and autophosphorylation could not be detected across multiple experimental approaches, including thermal shift assays, MANT-ATP fluorescence, radiolabeled phosphate incorporation, and nonspecific ATPase activity assays. Further characterization also revealed that Trb2 forms homomultimers with possible functional consequences, and extensive crystallization screening has yielded multiple promising conditions that could produce diffraction-quality crystals with further optimization. This project explores the difficulties in functionally characterizing putatively active pseudokinases, and proposes a structural basis for the conserved pseudokinase features of the Tribbles homologues.

Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models

Thesis (Ph.D.)--University of Washington, 2016-08

Probabilistic design optimization of horizontal axis wind turbine rotors

Considerable interest in renewable energy has increased in recent years due to the concerns raised over the environmental impact of conventional energy sources and their price volatility. In particular, wind power has enjoyed a dramatic global growth in installed capacity over the past few decades. Nowadays, the advancement of wind turbine industry represents a challenge for several engineering areas, including materials science, computer science, aerodynamics, analytical design and analysis methods, testing and monitoring, and power electronics. In particular, the technological improvement of wind turbines is currently tied to the use of advanced design methodologies, allowing the designers to develop new and more efficient design concepts. Integrating mathematical optimization techniques into the multidisciplinary design of wind turbines constitutes a promising way to enhance the profitability of these devices. In the literature, wind turbine design optimization is typically performed deterministically. Deterministic optimizations do not consider any degree of randomness affecting the inputs of the system under consideration, and result, therefore, in an unique set of outputs. However, given the stochastic nature of the wind and the uncertainties associated, for instance, with wind turbine operating conditions or geometric tolerances, deterministically optimized designs may be inefficient. Therefore, one of the ways to further improve the design of modern wind turbines is to take into account the aforementioned sources of uncertainty in the optimization process, achieving robust configurations with minimal performance sensitivity to factors causing variability. The research work presented in this thesis deals with the development of a novel integrated multidisciplinary design framework for the robust aeroservoelastic design optimization of multi-megawatt horizontal axis wind turbine (HAWT) rotors, accounting for the stochastic variability related to the input variables. The design system is based on a multidisciplinary analysis module integrating several simulations tools needed to characterize the aeroservoelastic behavior of wind turbines, and determine their economical performance by means of the levelized cost of energy (LCOE). The reported design framework is portable and modular in that any of its analysis modules can be replaced with counterparts of user-selected fidelity. The presented technology is applied to the design of a 5-MW HAWT rotor to be used at sites of wind power density class from 3 to 7, where the mean wind speed at 50 m above the ground ranges from 6.4 to 11.9 m/s. Assuming the mean wind speed to vary stochastically in such range, the rotor design is optimized by minimizing the mean and standard deviation of the LCOE. Airfoil shapes, spanwise distributions of blade chord and twist, internal structural layup and rotor speed are optimized concurrently, subject to an extensive set of structural and aeroelastic constraints. The effectiveness of the multidisciplinary and robust design framework is demonstrated by showing that the probabilistically designed turbine achieves more favorable probabilistic performance than those of the initial baseline turbine and a turbine designed deterministically.

Multi-objective optimization of material model parameters of an adhesive layer by using SPEA2

The usage of multi material structures in industry, especially in the automotive industry are increasing. To overcome the difficulties in joining these structures, adhesives have several benefits over traditional joining methods. Therefore, accurate simulations of the entire process of fracture including the adhesive layer is crucial. In this paper, material parameters of a previously developed meso mechanical finite element (FE) model of a thin adhesive layer are optimized using the Strength Pareto Evolutionary Algorithm (SPEA2). Objective functions are defined as the error between experimental data and simulation data. The experimental data is provided by previously performed experiments where an adhesive layer was loaded in monotonically increasing peel and shear. Two objective functions are dependent on 9 model parameters (decision variables) in total and are evaluated by running two FEsimulations, one is loading the adhesive layer in peel and the other in shear. The original study converted the two objective functions into one function that resulted in one optimal solution. In this study, however, a Pareto frontis obtained by employing the SPEA2 algorithm. Thus, more insight into the material model, objective functions, optimal solutions and decision space is acquired using the Pareto front. We compare the results and show good agreement with the experimental data.

Intelligent Sensor Systems for Structural Health Monitoring Applications

The convergence between the recent developments in sensing technologies, data science, signal processing and advanced modelling has fostered a new paradigm to the Structural Health Monitoring (SHM) of engineered structures, which is the one based on intelligent sensors, i.e., embedded devices capable of stream processing data and/or performing structural inference in a self-contained and near-sensor manner. To efficiently exploit these intelligent sensor units for full-scale structural assessment, a joint effort is required to deal with instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing. In this framework, the main goal of this Thesis is to tackle the multi-faceted nature of the monitoring process, via a full-scale optimization of the hardware and software resources involved by the {SHM} system. The pursuit of this objective has required the investigation of both: i) transversal aspects common to multiple application domains at different abstraction levels (such as knowledge distillation, networking solutions, microsystem {HW} architectures), and ii) the specificities of the monitoring methodologies (vibrations, guided waves, acoustic emission monitoring). The key tools adopted in the proposed monitoring frameworks belong to the embedded signal processing field: namely, graph signal processing, compressed sensing, ARMA System Identification, digital data communication and TinyML.

Structural health monitoring of storage tanks

In this thesis, Ph.D candidate presents a compact sensor node (SN) designed for long-term and real-time acoustic emission (AE) monitoring of above ground storage tanks (ASTs). Each SN exploits up to three inexpensive low-frequency sensors based on piezoelectric diaphragms for effective leakage detection, and it is capable by means of built-in Digital Signal Processing functionalities to process the acquired time waveforms extracting the AE features usually required by testing protocols. Alternatively, capability to plug three high frequency AE sensors to a SN for corrosion simulated phenomena detection is envisaged and demonstrated. Another innovative aspect that the Ph.D candidate presents in this work is an alternative mathematical model of corrosion location on the bottom of the AST. This approach implies considering the three-dimensional localization model versus the two-dimensional commonly used according to the literature. This approach is aimed at significant optimization in the number of sensors in relation to the standard approach for solving localization problems as well as to allow filtering the false AE events related to the condensate droplets from AST ceiling. The technological implementation of this concept required the solution of a number of technical problems, such as the precise time of arrival (ToA) signal estimation, vertical localization of the AE source and multilaration solution that were discussed in detail in this work. To validate the developed prototype, several experimental campaigns were organized that included the simulation of target phenomena both in laboratory conditions and on a real water storage tank. The presented test results demonstrate the successful application of the developed AE system both for simulated leaks and for corrosion processes on the tank bottom. Mathematical and technological algorithms for localization and characterization of AE signals implemented during the development of the prototype are also confirmed by the test results.

Optimization of an innovative T6 heat treatment for the AlSi10Mg alloy processed by Laser-based Powder Bed Fusion: effect on microstructure, mechanical properties, and tribological behavior

Laser-based Powder Bed Fusion (L-PBF) technology is one of the most commonly used metal Additive Manufacturing (AM) techniques to produce highly customized and value-added parts. The AlSi10Mg alloy has received more attention in the L-PBF process due to its good printability, high strength/weight ratio, corrosion resistance, and relatively low cost. However, a deep understanding of the effect of heat treatments on this alloy's metastable microstructure is still required for developing tailored heat treatments for the L-PBF AlSi10Mg alloy to overcome the limits of the as-built condition. Several authors have already investigated the effects of conventional heat treatment on the microstructure and mechanical behavior of the L-PBF AlSi10Mg alloy but often overlooked the peculiarities of the starting supersatured and ultrafine microstructure induced by rapid solidification. For this reason, the effects of innovative T6 heat treatment (T6R) on the microstructure and mechanical behavior of the L-PBF AlSi10Mg alloy were assessed. The short solution soaking time (10 min) and the relatively low temperature (510 °C) reduced the typical porosity growth at high temperatures and led to a homogeneous distribution of fine globular Si particles in the Al matrix. In addition, it increased the amount of Mg and Si in the solid solution available for precipitation hardening during the aging step. The mechanical (at room temperature and 200 °C) and tribological properties of the T6R alloy were evaluated and compared with other solutions, especially with an optimized direct-aged alloy (T5 alloy). Results showed that the innovative T6R alloy exhibits the best mechanical trade-off between strength and ductility, the highest fatigue strength among the analyzed conditions, and interesting tribological behavior. Furthermore, the high-temperature mechanical performances of the heat-treated L-PBF AlSi10Mg alloy make it suitable for structural components operating in mild service conditions at 200 °C.

Structural steel design with BIM technology - interventions on a three buildings block in Deruta (PG)

The aim of this thesis is to use the developments, advantages and applications of "Building Information Modelling" (BIM) with emphasis on the discipline of structural design for steel building located in Perugia. BIM was mainly considered as a new way of planning, constructing and operating buildings or infrastructures. It has been found to offer greater opportunities for increased efficiency, optimization of resources and generally better management throughout the life cycle of a facility. BIM increases the digitalization of processes and offers integrated and collaborative technologies for design, construction and operation. To understand BIM and its benefits, one must consider all phases of a project. Higher initial design costs often lead to lower construction and operation costs. Creating data-rich digital models helps to better predict and coordinate the construction phases and operation of a building. One of the main limitations identified in the implementation of BIM is the lack of knowledge and qualified professionals. Certain disciplines such as structural and mechanical design depend on whether the main contractor, owner, general contractor or architect need to use or apply BIM to their projects. The existence of a supporting or mandatory BIM guideline may then eventually lead to its adoption. To test the potential of the BIM adoption in the steel design process, some models were developed taking advantage of a largely diffuse authoring software (Autodesk Revit), to produce construction drawings and also material schedule that were needed in order to estimate quantities and features of a real steel building. Once the model has been built the whole process has been analyzed and then compared with the traditional design process of steel structure. Many relevant aspect in term of clearness and also in time spent were shown and lead to final conclusions about the benefits from BIM methodology.

Lung Age Is Related To Carotid Structural Alterations In Hypertensive Subjects.

Hypertensive patients exhibit higher cardiovascular risk and reduced lung function compared with the general population. Whether this association stems from the coexistence of two highly prevalent diseases or from direct or indirect links of pathophysiological mechanisms is presently unclear. This study investigated the association between lung function and carotid features in non-smoking hypertensive subjects with supposed normal lung function. Hypertensive patients (n = 67) were cross-sectionally evaluated by clinical, hemodynamic, laboratory, and carotid ultrasound analysis. Forced vital capacity, forced expired volume in 1 second and in 6 seconds, and lung age were estimated by spirometry. Subjects with ventilatory abnormalities according to current guidelines were excluded. Regression analysis adjusted for age and prior smoking history showed that lung age and the percentage of predicted spirometric parameters associated with common carotid intima-media thickness, diameter, and stiffness. Further analyses, adjusted for additional potential confounders, revealed that lung age was the spirometric parameter exhibiting the most significant regression coefficients with carotid features. Conversely, plasma C-reactive protein and matrix-metalloproteinases-2/9 levels did not influence this relationship. The present findings point toward lung age as a potential marker of vascular remodeling and indicate that lung and vascular remodeling might share common pathophysiological mechanisms in hypertensive subjects.

Structural Connectivity Of The Default Mode Network And Cognition In Alzheimer׳s Disease.

Disconnectivity between the Default Mode Network (DMN) nodes can cause clinical symptoms and cognitive deficits in Alzheimer׳s disease (AD). We aimed to examine the structural connectivity between DMN nodes, to verify the extent in which white matter disconnection affects cognitive performance. MRI data of 76 subjects (25 mild AD, 21 amnestic Mild Cognitive Impairment subjects and 30 controls) were acquired on a 3.0T scanner. ExploreDTI software (fractional Anisotropy threshold=0.25 and the angular threshold=60°) calculated axial, radial, and mean diffusivities, fractional anisotropy and streamline count. AD patients showed lower fractional anisotropy (P=0.01) and streamline count (P=0.029), and higher radial diffusivity (P=0.014) than controls in the cingulum. After correction for white matter atrophy, only fractional anisotropy and radial diffusivity remained significantly lower in AD compared to controls (P=0.003 and P=0.05). In the parahippocampal bundle, AD patients had lower mean and radial diffusivities (P=0.048 and P=0.013) compared to controls, from which only radial diffusivity survived for white matter adjustment (P=0.05). Regression models revealed that cognitive performance is also accounted for by white matter microstructural values. Structural connectivity within the DMN is important to the execution of high-complexity tasks, probably due to its relevant role in the integration of the network.

Solid Lipid Nanoparticles For Hydrophilic Biotech Drugs: Optimization And Cell Viability Studies (caco-2 & Hepg-2 Cell Lines).

Insulin was used as model protein to developed innovative Solid Lipid Nanoparticles (SLNs) for the delivery of hydrophilic biotech drugs, with potential use in medicinal chemistry. SLNs were prepared by double emulsion with the purpose of promoting stability and enhancing the protein bioavailability. Softisan(®)100 was selected as solid lipid matrix. The surfactants (Tween(®)80, Span(®)80 and Lipoid(®)S75) and insulin were chosen applying a 2(2) factorial design with triplicate of central point, evaluating the influence of dependents variables as polydispersity index (PI), mean particle size (z-AVE), zeta potential (ZP) and encapsulation efficiency (EE) by factorial design using the ANOVA test. Therefore, thermodynamic stability, polymorphism and matrix crystallinity were checked by Differential Scanning Calorimetry (DSC) and Wide Angle X-ray Diffraction (WAXD), whereas the effect of toxicity of SLNs was check in HepG2 and Caco-2 cells. Results showed a mean particle size (z-AVE) width between 294.6 nm and 627.0 nm, a PI in the range of 0.425-0.750, ZP about -3 mV, and the EE between 38.39% and 81.20%. After tempering the bulk lipid (mimicking the end process of production), the lipid showed amorphous characteristics, with a melting point of ca. 30 °C. The toxicity of SLNs was evaluated in two distinct cell lines (HEPG-2 and Caco-2), showing to be dependent on the concentration of particles in HEPG-2 cells, while no toxicity in was reported in Caco-2 cells. SLNs were stable for 24 h in in vitro human serum albumin (HSA) solution. The resulting SLNs fabricated by double emulsion may provide a promising approach for administration of protein therapeutics and antigens.