4653 research outputs found
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The likelihood of requiring a diagnostic test: Classifying emergency department patients with logistic regression
No full textBackground: Emergency departments (EDs) play an important role in health systems since they are the front line for patients with emergency medical conditions who frequently require diagnostic tests and timely treatment. Objective: To improve decision-makiHealth Policy & Services; Medical InformaticsHealth Care Sciences & Services; Medical Informatic
Evaluating resilience in food supply chains during COVID-19
Get PDFThe COVID-19 outbreak has revealed weaknesses in the supply chains (SCs) and how easily it can be influenced by these disruptions. Food supply chains (FSCs) is one of the most affected SCs, and it needs to be more resilient against SC disruptions because their vulnerable structure such as having perishable products. Therefore, this article aims to uncover the need for resilience in FSCs during the COVID-19 outbreak. For this purpose, the enablers of resilience on FSCs are determined after a detailed examination of the current literature. Then, the graph theory matrix approach has been used to reveal the relationships between these enablers and investigate importance of enablers of resilience in FSCs during COVID-19 outbreak. It is significant to determine preference of enablers and rank of importance to take actions effectively. Depending on the results, the rank orders of the enablers are classified as readiness, collaboration with stakeholders, IT alignment, risk aware, responsiveness, flexibility, appearance and sustainability, respectively. Suggested implications can be provided benefits for policymakers and managers in FSCs
Predicting waiting and treatment times in emergency departments using ordinal logistic regression models
Get PDFBackground: Since providing timely care is the primary concern of emergency departments (EDs), long waiting times increase patient dissatisfaction and adverse outcomes. Especially in overcrowded ED environments, emergency care quality can be significantly improved by developing predictive models of patients' waiting and treatment times to use in ED operations planning.
Methods: Retrospective data on 37,711 patients arriving at the ED of a large urban hospital were examined. Ordinal logistic regression models were proposed to identify factors causing increased waiting and treatment times and classify patients with longer waiting and treatment times.
Results: According to the proposed ordinal logistic regression model for waiting time prediction, age, arrival mode, and ICD-10 encoded diagnoses are all significant predictors. The model had 52.247% accuracy. The model for treatment time showed that in addition to age, arrival mode, and diagnosis, triage level was also a significant predictor. The model had 66.365% accuracy. The model coefficients had negative signs in the corresponding models, indicating that waiting times are negatively related to treatment times.
Conclusion: By predicting patients' waiting and treatment times, ED workloads can be assessed instantly. This enables ED personnel to be scheduled to better manage demand supply deficiencies, increase patient satisfaction by informing patients and relatives about expected waiting times, and evaluate performances to improve ED operations and emergency care quality
Joint forecasting-scheduling for the internet of things via subspace-based application-specific error metric emulation
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Dentification of the functional food potency of çalkama: A traditional recipe with edible Mediterranean wild greens from Turkish cuisine
No full textACKGROUND: The phytochemical contents of traditional foods are necessary to further elucidate the impacts of the Mediterranean diet on health. Çalkama is traditional food prepared by using wild green plants including chard (Beta vulgaris var.cicl), wild fennel (Foeniculum vulgare spp), common mellow (Malva sylvestris L), common poppy (Papaver rhoeas L), dock (Rumex spp), common nettle (Urtica dioica L), sow-thistle (Sonchus asper (L) Hill), common stork's bill (Erodium circutarium (L)L Hér) and wild leek (Allium ampeloprasum L). OBJECTIVE: In this research, the antioxidant activity and the flavonoid and phenolic acid composition were separately analyzed for each plant and çalkama. METHODS: Total phenolic content, total flavonoid content and total antioxidant capacity of each plant and çalkama were measured spectrophotometric assays. Ultra-fast liquid chromatography (UFLC) was performed to detect specific flavonoid groups. RESULTS: It was detected that one portion of çalkama (100g) contained approximately 250mg of flavonoids. Particularly, quercetin and apigenin contributed to the main flavonoid source and chlorogenic acid was the major phenolic acid in çalkama. CONCLUSIONS: According to our findings, this food can be considered as a good phenolic and flavonoid source which protects its high antioxidant capacity through preparation and cooking processes
Comparative performance and thermoeconomic analyses of high temperature polymer electrolyte membrane based two hybrid systems
Get PDFThe main objective of this study is to compare the two systems in terms of the thermoeconomic and the performance. The first one is called hybrid I and consists of high temperature polymer electrolyte membrane and thermocapacitive cycle. The second one is named hybrid II, which is composed of high temperature polymer electrolyte membrane and thermoelectric generator. Thermocapacitive cycle and thermoelectric generator have various advantages, such as generally lower cost and higher power density. So, they have good potential to utilize waste heat. The performance parameters of the considered hybrid systems include power density, energy efficiency, exergy efficiency and exergy destruction rate. The results have shown that hybrid I is more advantageous than hybrid II. The maximum power density values for hybrid I and hybrid II are obtained to be 2536.91W and 2049.62W while their energy efficiencies are 77.4% and 76.8%, respectively
Speech Noise Reduction with Wavelet Transform Domain Adaptive Filters
No full textAdaptive filters are one of the most promising solutions to several signal enhancement problems in a non-stationary environment. However, depending on the characteristics of the signals and noise, the processing complexity and convergence speed for adaptive filters vary. Therefore, it is often preferred to apply adaptive filters in the transform domain to reduce complexity and increase convergence speed. In this paper, the application of the LMS (Least Mean Square) algorithm, which is the most preferred algorithm of adaptive filters in the field of speech noise cancellation, in the wavelet transform domain was studied. For this purpose, improving speech signals with different Signal to Noise Ratio (SNR) using Wavelet Transform Domain LMS (WTD-LMS) algorithm in the proposed method was applied. The results obtained were evaluated with measures that are frequently used in speech enhancement applications. It is observed that the success of the proposed method outperforms adaptive and traditional methods for two sensor measurements are available
A new model for minimizing the electric vehicle battery capacity in electric travelling salesman problem with time windows
Get PDFThe growing pollution in the environment and the negative shift in the global climate compel authorities to take action to protect the environment and human health. Transportation is one of the major contributors to this environmental decay. The harmful gases released to the air by the vehicles using petroleum fuel increase each day. One of the solutions is to make a gradual transition to electric vehicles. A major part of manufacturing an electric vehicle is to produce an efficient electric motor and battery for it. Reducing the manufacturing and operating costs of these components will result in reducing the overall costs of electric vehicles. In this study, a new variant of the electric travelling salesman problem with time windows (E-TSPTW) was proposed. The objective function of the problem is to minimize the required initial battery capacity of the electric vehicle. For this goal, a new energy consumption model considering the load of the vehicle was proposed with three scenarios. The proposed model was solved with a hybrid simulated annealing algorithm for all these scenarios. The performance of the proposed method was compared to the solutions found by a mixed integer linear programming model. The experimental results on the benchmark instances show that up to a 35% reduction in initial battery capacity, hence reduction in its cost is possible