Piezoelectric Energy Harvesting via Frequency Up-conversion Technology

Ambient energy harvesting has attracted significant attention over the last years for applications such as wireless sensors, implantable devices, health monitoring systems, and wearable devices. The methods of vibration-to-electric energy conversion can be included in the following categories: electromagnetic, electrostatic, and piezoelectric. Among various techniques of vibration-based energy harvesting, piezoelectric transduction method has received the most attention due to the large power density of the piezoelectric material and its simple architectures. In contrast to electromagnetic energy harvesting, the output voltage of a piezoelectric energy harvester is high, which can charge a storage component such as a battery. Compared to electrostatic energy harvester, the piezoelectric energy harvester does not require an external voltage supply. Also, piezoelectric harvesters can be manufactured in micro-scale, where they show better performance compared to other energy harvesters, owing to the well-established thick-film and thin-film fabrication techniques. The main drawback of the linear piezoelectric harvesters is that they only retrieve energy efficiently when they are excited at their resonance frequencies, which are usually high, while they are less efficient when the excitation frequency is distributed over a broad spectrum or is dominant at low frequencies. High-frequency vibrations can be found in machinery and vehicles could be used as the energy source but, most of the vibration energy harvesters are targeting at low-frequency vibration sources which are more achievable in the natural environment. One way to overcome this limitation is by using the frequency-up-conversion technology via impacts, where the source of the impacts can be one or two stoppers or more massive beams. The impact makes the piezoelectric beam oscillate in its resonance frequency and brings nonlinear behavior into the system

TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING

The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses

Operating Room Planning under Surgery Type and Priority Constraints

AbstractOperating room (OR) planning is critical in healthcare systems to reduce cost and improve the efficiency of OR scheduling. The OR planning problem is complicated, involving many conflicting factors, such as overtime and idle time, both of which affect OR utilization and consequently affect cost to a hospital. Allocating different types of surgeries into OR blocks affects the setup cost, whereas priorities of surgeries affect OR block scheduling. Surgery durations affect both OR utilization and OR block scheduling. Traditionally, one important method for OR block scheduling is the bin packing model, and the longest processing time (LPT) rule is the most commonly used method to generate the initial sequence for bin packing. In this study. We propose a multistep approach and a priority-type-duration (PTD) rule to generate the initial sequence for bin packing. The results of our case studies show that our PTD rule outperforms the LPT rule based on the cost to OR scheduling

Stochastic BI-Level Optimization Models for Efficient Operating Room Planning

Within a hospital, the operating room (OR) department has the largest cost and revenue. Because of the aging population, the demand for surgical services has been increasing sharply in recent years. At the other hand, the rate of OR capacity expansion is lower than the rate of increasing demand. As a result, OR managers must leverage their resources by efficient OR planning. OR planning is challenging because of multiple competing\conflicting objectives such cost minimization and throughput maximization. Inherent uncertainty in the surgical procedures and patients arrivals complicate the decision making process. This increases the risk of non-realization of the system objectives. In this paper, stochastic bi-level optimization models were formulated to optimize total cost and throughput of ORs under the presence of uncertainties in patient arrivals and case times. Newsvendor model and chance-constrained optimization method were used to optimize multiple objectives under the presence of uncertainties. Using historical data, a simulation model was established to validate the results of optimization models. Using statistical process control (SPC) stability of each model was investigated. Using bi-level optimization, we addressed managerial preferences over total cost and throughput. Optimizing one objective may lead to compromise on the optimality of the other objective, which generates trade-offs. Using a trade-off balancing model, we found solutions that minimize the sum of deviations from the best solutions for both total cost and throughput. Trade-off balancing optimization models may lead to better solutions, compared to traditional multi-objective optimization models. The results of this paper are applicable to manufacturing systems, where managers face multiple objectives and uncertainties in the system

An Optimization Model for Operating Room Scheduling to Reduce Blocking Across the Perioperative Process

Operating room (OR) scheduling is important. Because of increasing demand for surgical services, hospitals must provide high quality care more efficiently with limited resources. When constructing the OR schedule, it is necessary to consider the availability of downstream resources, such as intensive care unit (ICU) and post anaesthesia care unit (PACU). The unavailability of downstream resources causes blockings between every two consecutive stages. In this paper we address the master surgical schedule (MSS) problem in order to minimize blockings between two consecutive stages. First, we present a blocking minimization (BM) model for the MSS by using integer programming, based on deterministic data. The BM model determines the OR block schedule for the next day by considering the current stage occupancy (number of patients) in order to minimize the number of blockings between intraop and postop stages. Second, we test the effectiveness of our model under variations in case times and patient arrivals, by using simulation. The simulation results show that our BM model can significantly reduce the number of blockings by 94% improvement over the base model. Scheduling patient flow across the 3-stage periop process can be applied to work flow scheduling for the s-stage flow shop shop production in manufacutirng, and also Smoothing patient flow in periop process can be applied to no-wait flow shop production

Maximizing Operating Room Performance Using Portfolio Selection

The operating room (OR) is responsible for most hospital admissions and is one of the most cost and work intensive areas in the hospital. From recent trends, we observe an ironic parallel increase among expenditure and waiting time. Therefore, improving OR scheduling has become obligatory, particularly in terms of patient flow and benefit. Most of the hospitals rely on average patient arrivals and processing times in OR planning. But in practice, variations in arrivals and processing times causes high instability in OR performance. Our model of optimization provides OR schedules maximizing patient flow and benefit at a fixed level of risk using portfolio selection. The simulation results show that the performance of the OR has a direct relationship with the risk

Sustainable Production Through Balancing Trade-Offs Among Three Metrics in Flow Shop Scheduling

In sustainable manufacturing, inconsistencies exist among objectives defined in triple-bottom-lines (TBL) of economy, society, and environment. Analogously, inconsistencies exist in flow shop scheduling among three objectives of minimizing total completion time (TCT), maximum completion time (MCT), and completion time variance (CTV), respectively. For continuous functions, the probability is zero to achieve the objectives at their optimal values, so is it at their worst values. Therefore, with inconsistencies among individual objectives of discrete functions, it is more meaningful and feasible to seek a solution with high probabilities that system performance varies within the control limits. We propose a trade-off balancing scheme for sustainable production in flow shop scheduling as the guidance of decision making. We model trade-offs (TO) as a function of TCT, MCT, and CTV, based on which we achieve stable performance on min(TO). Minimizing trade-offs provides a meaningful compromise among inconsistent objectives, by driving the system performance towards a point with minimum deviations from the ideal but infeasible optima. Statistical process control (SPC) analyses show that trade-off balancing provides a better control over individual objectives in terms of average, standard deviation, Cp and Cpk compared to those of single objective optimizations. Moreover, results of case studies show that trade-off balancing not only provides a stable control over individual objectives, but also leads to the highest probability for outputs within the specification limits. We also propose a flow shop scheduling sustainability index (F S S I). The results show that trade-off balancing provides the most sustainable solutions compared to those of the single objective optimizations

GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets

In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose an interpretable AI approach to diagnose patients with neurofibromatosis using blood tests and pathogenic variables. We evaluated the proposed method using a dataset from the AACR GENIE project and compared its performance with modern approaches. Our proposed approach outperformed existing models with 99.86% accuracy. We also conducted NF1 and interpretable AI tests to validate our approach. Our work provides an explainable approach model using logistic regression and explanatory stimulus as well as a black-box model. The explainable models help to explain the predictions of black-box models while the glass-box models provide information about the best-fit features. Overall, our study presents an interpretable AI approach for diagnosing patients with neurofibromatosis and demonstrates the potential of AI in the medical field.Comment: The authors would like to acknowledge the American Association for Cancer Research and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing. Interpretations are the responsibility of study author

Rosmarinic Acid and Its Methyl Ester as Antimicrobial Components of the Hydromethanolic Extract of Hyptis atrorubens

Primary biological examination of four extracts of the leaves and stems of Hyptis atrorubens Poit. (Lamiaceae), a plant species used as an antimicrobial agent in Guadeloupe, allowed us to select the hydromethanolic extract of the stems for further studies. It was tested against 46 microorganisms in vitro. It was active against 29 microorganisms. The best antibacterial activity was found against bacteria, mostly Gram-positive ones. Bioautography enabled the isolation and identification of four antibacterial compounds from this plant: rosmarinic acid, methyl rosmarinate, isoquercetin, and hyperoside. The MIC and MBC values of these compounds and their combinations were determined against eight pathogenic bacteria. The best inhibitory and bactericidal activity was found for methyl rosmarinate (0.3 mg/mL). Nevertheless, the bactericidal power of rosmarinic acid was much faster in the time kill study. Synergistic effects were found when combining the active compounds. Finally, the inhibitory effects of the compounds were evaluated on the bacterial growth phases at two different temperatures. Our study demonstrated for the first time antimicrobial activity of Hyptis atrorubens with identification of the active compounds. It supports its traditional use in French West Indies. Although its active compounds need to be further evaluated in vivo, this work emphasizes plants as potent sources of new antimicrobial agents when resistance to antibiotics increases dramatically

Fabrication of aluminum matrix composites reinforced with Al2ZrO5 nano particulates synthesized by sol-gel auto-combustion method

Nanocrystalline Al2ZrO5 with particle size of about 38 nm was directly synthesized by combination of sol−gel auto-combustion and ultrasonic irradiation techniques from metal nitrates and glycine as precursors. The overall process involves formation of homogeneous sol, formation of dried gel and combustion process of the dried gel. Aluminum alloy matrix composites reinforced with 0.75%, 1.5% and 2.5% Al2ZrO5 nanoparticles were fabricated via stir casting method and the fabrication was performed at various casting temperatures. The resulting composites were tested for their nanostructure and present phases by SEM and XRD analysis. Optimum amount of reinforcement and casting temperature were determined by evaluating the density, hardness and compression strength of the composites. Al matrix alloy reinforced by Al2ZrO5 nanoparticles improves the hardness and compressive strength of the alloy to maximum values of BHN 61 and 900 MPa, respectively. The most improved mechanical properties are obtained with the specimen including 1.5% Al2ZrO5 produced at 850 °C