Clinical overbooking is intended to reduce the negative impact of patient no-shows on clinic operations and performance. In this paper, we study the clinical scheduling problem with overbooking for ...heterogeneous patients, i.e. patients who have different no-show probabilities. We consider the objective of maximizing expected profit, which includes revenue from patients and costs associated with patient waiting times and physician overtime. We show that the objective function with homogeneous patients, i.e. patients with the same no-show probability, is multimodular. We also show that this property does not hold when patients are heterogeneous. We identify properties of an optimal schedule with heterogeneous patients and propose a local search algorithm to find local optimal schedules. Then, we extend our results to sequential scheduling and propose two sequential scheduling procedures. Finally, we perform a set of numerical experiments and provide managerial insights for health care practitioners.
Microbes that use the single-carbon substrates methanol and methane offer great promise to bioindustry along with substantial environmental benefits. Methanotrophs and other methylotrophs can be ...engineered and optimized to produce a wide range of products, from biopolymers to biofuels and beyond. While significant limitations remain, including delivery of single-carbon feedstock to bioreactors, efficient growth, and scale-up, these challenges are being addressed and notable improvements have been rapid. Development of expression chassis, use of genome-scale and regulatory models based on omics data, improvements in bioreactor design and operation, and development of green product recovery schemes are enabling the rapid development of single-carbon bioconversion in the industrial space.
The biopolymer poly(3-hydroxybutyrate) (PHB) is an excellent candidate to replace many petroleum-sourced polymers for a wide range of applications. Improving PHB recovery and processing methods ...remains an important step toward expanding its implementation and economic viability. Switchable solvents are a class of molecules that can be toggled between hydrophobic and hydrophilic forms through the addition of carbon dioxide and water, which makes them promising candidates as recyclable solvents for the recovery of bioproducts such as PHB. Here, we used the Hansen solubility parameters and the Stefanis–Panayiotou group contribution method to select candidate switchable solvents for processing PHB. We evaluated their ability to dissolve PHB over a range of temperatures and found that the theoretical methods accurately predicted interactions between PHB and the solvents below 100 °C. Above 100 °C, PHB was dissolved in all candidate solvents and kinetic factors became significant in determining the extent of PHB dissolution during fixed-time experiments, with N,N-dimethylcyclohexylamine dissolving as much as 25.86 g/L PHB in 25 h. These results show that the solubility parameter model is valid for switchable solvents and that these solvents exhibit a reversible interaction with PHB.
The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and ...public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.
Maintenance of mechanical and rotational equipment often includes bearing inspection and/or replacement. Thus, it is important to identify current as well as future conditions of bearings to avoid ...unexpected failure. Most published research in this area is focused on diagnosing bearing faults. In contrast, this paper develops neural-network-based models for predicting bearing failures. An experimental setup is developed to perform accelerated bearing tests where vibration information is collected from a number of bearings that are run until failure. This information is then used to train neural network models on predicting bearing operating times. Vibration data from a set of validation bearings are then applied to these network models. Resulting predictions are then used to estimate the bearing failure time. These predictions are then compared with the actual lives of the validation bearings and errors are computed to evaluate the effectiveness of each model. For the best model, we find that 64% of predictions are within 10% of actual bearing life, while 92% of predictions are within 20% of the actual life.
This paper considers the problem of establishing live resource allocation in workflows with synchronization stages. Establishing live resource allocation in this class of systems is challenging since ...deciding whether a given level of resource capacities is sufficient to complete a single process is NP-complete. In this paper, we develop two necessary conditions and one sufficient condition that provide quickly computable tests for the existence of process completing sequences. The necessary conditions are based on the sequence of completions of n subprocesses that merge together at a synchronization. Although the worst case complexity is O(2n), we expect the number of subprocesses combined at any synchronization will be sufficiently small so that total computation time remains manageable. The sufficient condition uses a reduction scheme that computes a sufficient capacity level of each resource type to complete and merge all n subprocesses. The worst case complexity is O(n·m), where m is the number of synchronizations. Finally, the paper develops capacity bounds and polynomial methods for generating feasible resource allocation sequences for merging systems with single unit allocation. This method is based on single step look-ahead for deadly marked siphons and is O(2n). Throughout the paper, we use a class of Petri nets called Generalized Augmented Marked Graphs to represent our resource allocation systems.
The AMPATH program is a leading initiative in rural Kenya providing healthcare services to combat HIV. Malnutrition and food insecurity are common among AMPATH patients and the Nutritional ...Information System (NIS) was designed, with cross-functional collaboration between engineering and medical communities, as a comprehensive electronic system to record and assist in effective food distribution in a region with poor infrastructure.
The NIS was designed modularly to support the urgent need of a system for the growing food distribution program. The system manages the ordering, storage, packing, shipping, and distribution of fresh produce from AMPATH farms and dry food supplements from the World Food Programme (WFP) and U.S. Agency for International Development (USAID) based on nutritionists' prescriptions for food supplements. Additionally, the system also records details of food distributed to support future studies.
Patients fed weekly, patient visits per month.
With inception of the NIS, the AMPATH food distribution program was able to support 30,000 persons fed weekly, up from 2,000 persons. Patient visits per month also saw a marked increase.
The NIS' modular design and frequent, effective interactions between developers and users has positively affected the design, implementation, support, and modifications of the NIS. It demonstrates the success of collaboration between engineering and medical communities, and more importantly the feasibility for technology readily available in a modern country to contribute to healthcare delivery in developing countries like Kenya and other parts of sub-Saharan Africa.
The integration of condition monitoring with queueing systems to support decision making is not well explored. This paper addresses the impact of condition monitoring of the server on the ...system-level performance experienced by entities in a queueing system. The system consists of a queue with a single-server subject to Markovian degradation. The model assumes a Poisson arrival process with service times and repair times according to general distributions. We develop stability conditions and perform steady-state analysis to obtain performance measures (average queue length, average degradation, and so on). We propose minimizing an objective function involving four types of costs: repair, catastrophic failure, quality, and holding. The queue performance measures derived from steady-state analysis are benchmarked and compared to those from a discrete event simulation model. After verifying the queuing model, a sensitivity analysis is performed to determine the relationships between system performance and model parameters. Results indicate that the total cost function is convex and, thus, subject to an optimal repair policy. The model is sensitive to service time, quality costs, and failure costs for late-stage policy repairs decisions and sensitive to expected repair times and repair costs for early stage policy repair decisions.
Chemotherapy operations planning and scheduling in oncology clinics is a complex problem due to several factors such as the cyclic nature of chemotherapy treatment plans, the high variability in ...resource requirements (treatment time, nurse time, pharmacy time) and the multiple clinic resources involved. Treatment plans are made by oncologists for each patient according to existing chemotherapy protocols or clinical trials. It is important to strictly adhere to the patient's optimal treatment plan to achieve the best health outcomes. However, it is typically difficult to attain strict adherence for every patient due to side effects of chemotherapy drugs and limited resources in the clinics. In this study, our aim is to develop operations planning and scheduling methods for chemotherapy patients with the objective of minimizing the deviation from optimal treatment plans due to limited availability of clinic resources (beds/chairs, nurses, pharmacists). Mathematical programming models are developed to solve the chemotherapy operations planning and scheduling problems. A two-stage rolling horizon approach is used to solve these problems sequentially. Real-size problems are solved to demonstrate the effectiveness of the proposed algorithms in terms of solution quality and computational times.
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