We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuromodulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g. Laplace\'s equation) are solved for the spatial distribution of the field, which is separated from the field\'s temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

UNASSIGNED: To assess leukemia risk in occupational populations exposed to low levels of benzene.
UNASSIGNED: Leukemia incidence data from the Chinese Benzene Cohort Study were fitted using the Linearized multistage (LMS) model. Individual benzene exposure levels, urinary S-phenylmercapturic acid (S-PMA) and trans, trans-muconic acid (t, t-MA) were measured among 98 benzene-exposed workers from factories in China. Subjects were categorized into four groups by rounding the quartiles of cumulative benzene concentrations (< 3, 3-5, 5-12, ≥12 mg/m3·year, respectively). The risk of benzene-induced leukemia was assessed using the LMS model, and the results were validated using the EPA model and the Singapore semi-quantitative risk assessment model.
UNASSIGNED: The leukemia risks showed a positive correlation with increasing cumulative concentration in the four exposure groups (excess leukemia risks were 4.34, 4.37, 4.44 and 5.52 × 10-4, respectively; Ptrend < 0.0001) indicated by the LMS model. We also found that the estimated leukemia risk using urinary t, t-MA in the LMS model was more similar to those estimated by airborne benzene compared to S-PMA. The leukemia risk estimated by the LMS model was consistent with both the Singapore semi-quantitative risk assessment model at all concentrations and the EPA model at high concentrations (5-12, ≥12 mg/m3·year), while exceeding the EPA model at low concentrations (< 3 and 3-5 mg/m3·year). However, in all four benzene-exposed groups, the leukemia risks estimated by these three models exceeded the lowest acceptable limit for carcinogenic risk set by the EPA at 1 × 10-6.
UNASSIGNED: This study demonstrates the utility of the LMS model derived from the Chinese benzene cohort in assessing leukemia risk associated with low-level benzene exposure, and suggests that leukemia risk may occur at cumulative concentrations below 3 mg/m3·year.

We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuro-modulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g., Laplace\'s equation) are solved for the spatial distribution of the field, which is separated from the field\'s temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

Collecting and sharing information about affected areas is an important activity for optimal decision-making in relief processes. Defects such as over-sending some items to affected areas and mistakes in transferring injured people to medical centers in accidents are due to improper management of this information. Because cloud computing as a processing and storage platform for big data is independent of the device and location and can also perform high-speed processing, its use in disasters has been highly regarded by researchers. In this environment, a three-stage dynamic procedure for evacuation operations and logistics issues is presented. The first stage of the proposed model is image processing and tweet mining in a cloud center in order to determine the disaster parameters. In stage II, a mixed-integer multi-commodity model is presented for the relief commodity delivery, wounded people transportation with capacity constraints, and locating of the possible on-site clinics and local distribution centers near disaster areas. In stage III, by using a system of equations, detailed vehicle load/unload instructions are obtained. Finally, the effectiveness of the proposed model on the data of an earthquake disaster in Iran is investigated. The results of comparing the proposed approach with a two-stage algorithm show that the total number of unsatisfied demand for all types of commodities in the proposed approach was better than the other. Also, the number of survivors in the three-stage model is significantly higher than in the two-stage one. The better performance of the proposed algorithm is due to the fact that online data is continuously available and that decisions such as sending relief items and dispatching are made more effectively.