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Human trafficking is a serious crime and violation of human rights that results in numerous harms. Although the phenomenon is not new, scholarship on the issue has grown substantially since the first legal framework was passed in 2000. However, the existing literature has been criticized for its skewed focus on victims, among other things. The dearth of information on traffickers and their operations limits our ability to reduce or prevent perpetration. The current study presents a comprehensive and critical review of the existing literature focused on traffickers to synthesize what is already known and highlight the key gaps. Twenty-nine articles met the inclusion criteria of (1) focusing on traffickers and their operations and (2) relying on data either directly from traffickers or sources that contained detailed information about criminal cases against traffickers. We used an iterative process to identify relevant studies, which included collecting articles of which we were already familiar or were identified in existing reviews, searching their reference lists, and conducting cited-by searches until saturation was reached. Topics found in the extant literature included: characteristics of traffickers, relationships between traffickers and victims, organizational characteristics and networks, operations, connections with other crimes, motivations, perceptions of behavior, and risks associated with trafficking. It concludes with recommendations for future research and a discussion of how bridging gaps in the literature could support more rigorous mathematical modeling that is needed to identify and assess promising perpetration prevention and intervention strategies. [ABSTRACT FROM AUTHOR]

Abstract. Classic vehicle routing models usually treat fuel cost as input data, but fuel consumption heavily depends on the travel speed, which leads to the study of optimizing speeds over [...]

In this study, we present a semi-analytical method to simulate the propagation of seismic waves in horizontally stratified double-porosity media. We solve the governing equations in the frequency–wavenumber domain and then compute the time–space domain solutions by Hankel and fast Fourier transforms. We conduct numerical simulations to study the properties of the seismic waves in the double-porosity media. The results show the existence of three P waves, one fast P wave and two slow P waves. The two slow P waves are highly attenuated and can be observed by assuming a low fluid viscosity. By comparing the fast P wave in a single-porosity medium and that in a double-porosity medium, we find that the double-porosity model predicts much higher attenuation of the fast P wave due to the local fluid flow between the background medium and the inclusions. We also study the parameters affecting the attenuation, such as the radius of the spherical inclusions, the viscosity of the pore fluid, the permeability of the background medium and the porosity of the inclusions. Finally, we present a cross-well survey model to investigate the seismic responses in the double-porosity media and compare them with the responses in the single-porosity media. We find that both the amplitude and velocity of the fast P wave decrease with the increase of the volume ratio of the inclusions. [ABSTRACT FROM AUTHOR]

Abstract. We present a computational study of several strategies to solve two-stage stochastic linear programs by integrating the adaptive partition-based approach with level decomposition. A partition-based formulation is a relaxation [...]

Analytic solutions are derived for the problem of scattering of obliquely incident plane waves from a rectangular crack in a fluid-saturated porous solid. The propagation direction and polarization direction of the incident waves can be arbitrary. The solutions are based on expanding the crack opening displacements in terms of Chebyshev functions. Explicit expressions for the frequency-dependent stress intensity factors and scattering far fields are obtained. The poroelastic effects play a key role in determining the crack opening mode by changing the effective normal stress applied on the crack surfaces. Consequently, the dynamic responses of the crack can be quite different from those of conventional dry crack in an elastic solid. The effects of scattered slow-P waves as well as wave-induced crack fluid pressure on the dynamic stress intensity factors are clarified, and a corner effect is observed in the scattering patterns for moderately high frequencies. The comparisons of synthetic seismograms with the elastic model show the slow-P waves can cause significant energy loss and waveform change. This study can be used for fracture analysis of porous media and nondestructive testing for individual hydraulic fractures in reservoirs. [ABSTRACT FROM AUTHOR]

Capacitor voltage transformer (hereinafter referred to as CVT) with the growth of the capacitance of the operation period of aging, the phenomenon of breakdown, resulting in measurement, automation, protection and other equipment abnormalities. Under the influence of preventive test procedures, lack of accuracy, insufficient capacity, low test voltage, the preventive test project can not detect the initial failure of the capacitor and the breakdown of a few capacitor elements. The operating voltage as reference voltage, estimate feasibility analysis of CVT capacitor element of the state by the state of the secondary voltage, and through the field, find out more abnormal CVT can achieve CVT capacitor element of online monitoring function is proposed.

We study a network interdiction problem involving two agents: a defender and an evader. The evader seeks to traverse a path from a source node to a terminus node in a directed network without being detected. The game takes place in two stages. In the first stage, the defender removes a set of arcs in the network. In the second stage, the defender and evader play a simultaneous game. The defender monitors a set of arcs, thus increasing the probability that the evader will be detected on that arc (if the evader uses the arc). The evader selects a source‐terminus path. Because the second stage is played simultaneously, both agents use mixed‐strategy solutions. We approach the solution of the second‐stage problem by proposing a constraint‐and‐column generation algorithm. We show that both the constraint‐generation and column‐generation problems are NP‐hard. Accordingly, we prescribe approximate versions of these problems that can be solved more efficiently. Our algorithm relies on solving the approximate versions until it is necessary to obtain an exact solution of the constraint‐generation and column‐generation problems. Then, to link the first‐ and second‐stage problems, we model the original problem using an epigraph reformulation, which we solve using a Benders‐decomposition based approach. The efficacy of our approach is demonstrated on a set of randomly generated test instances. [ABSTRACT FROM AUTHOR]

We consider a Stackelberg game in a network where a leader minimizes the cost of interdicting arcs and a follower seeks the shortest distance between given origin and destination nodes [...]

Healthcare workers' (HCWs) safety and availability to care for patients are critical during a pandemic such as the one caused by severe acute respiratory syndrome coronavirus 2. Among providers of different specialities, it is critical to protect those working in hospital settings with a high risk of infection. Using an agent-based simulation model, various staffing policies were developed and simulated for 90 days using data from the largest health systems in South Carolina. The model considers staffing policies that include geographic segregation, interpersonal contact limits, and a combination of factors, including the patient census, transmission rates, vaccination status of providers, hospital capacity, incubation time, quarantine period, and interactions between patients and providers. Comparing the existing practices to various risk-adjusted staffing policies, model predictions show that restricted teaming and rotating schedules significantly (p -value <0.01) reduced weekly HCW unavailability and the number of infected HCWs by 22% and 38%, respectively, when the vaccination rates among HCWs were lower (<75%). However, as the vaccination rate increases, the benefits of risk-adjusted policies diminish; and when 90% of HCWs were vaccinated, there were no significant (p -value = 0.09) benefits. Although these simulated outcomes are specific to one health system, our findings can be generalised to other health systems with multiple locations. [ABSTRACT FROM AUTHOR]

We consider a class of packing problems with uncertain data, which we refer to as the chance-constrained binary packing problem. In this problem, a subset of items is selected that [...]

In the multi‐vehicle covering tour problem with speed optimization, we aim to construct a set of maximal coverage routes for a fleet of vehicles that serve (observe) a set of secondary sites, given a fixed time schedule and coverage requirements. We develop an exact solution approach using a branch‐and‐price framework with a label‐correcting algorithm and a set of innovative dominance rules to solve the resulting pricing problem. We also develop a two‐stage heuristic capable of finding effective initial solutions. In addition, we consider practical extensions to the model by incorporating risk thresholds, energy capacities, and time windows. To validate our proposed solution approaches, we perform an extensive set of numerical experiments. Numerical results show the computational advantage of our proposed solution approaches compared with a state‐of‐the‐art commercial solver. [ABSTRACT FROM AUTHOR]

In this work, we study the optimal design of two-armed clinical trials to maximize the accuracy of parameter estimation in a statistical model, where the interaction between patient covariates and treatment are explicitly incorporated to enable precision medication decisions. Such a modeling extension leads to significant complexities for the produced optimization problems because they include optimization over design and covariates concurrently. We take a min-max optimization model and minimize (over design) the maximum (over population) variance of the estimated interaction effect between treatment and patient covariates. This results in a min-max bilevel mixed integer nonlinear programming problem, which is notably challenging to solve. To address this challenge, we introduce a surrogate optimization model by approximating the objective function, for which we propose two solution approaches. The first approach provides an exact solution based on reformulation and decomposition techniques. In the second approach, we provide a lower bound for the inner optimization problem and solve the outer optimization problem over the lower bound. We test our proposed algorithms with synthetic and real-world data sets and compare them with standard (re)randomization methods. Our numerical analysis suggests that the proposed approaches provide higher-quality solutions in terms of the variance of estimators and probability of correct selection. We also show the value of covariate information in precision medicine clinical trials by comparing our proposed approaches to an alternative optimal design approach that does not consider the interaction terms between covariates and treatment. Summary of Contribution: Precision medicine is the future of healthcare where treatment is prescribed based on each patient information. Designing precision medicine clinical trials, which are the cornerstone of precision medicine, is extremely challenging because sample size is limited and patient information may be multidimensional. This work proposes a novel approach to optimally estimate the treatment effect for each patient type in a two-armed clinical trial by reducing the largest variance of personalized treatment effect. We use several statistical and optimization techniques to produce efficient solution methodologies. Results have the potential to save countless lives by transforming the design and implementation of future clinical trials to ensure the right treatments for the right patients. Doing so will reduce patient risks and reduce costs in the healthcare system. [ABSTRACT FROM AUTHOR]

Background: Hand spasticity after stroke influences the rehabilitation of hand function. Immediate and effective relief of spasticity potentially creates conditions for later rehabilitation training, which has far-reaching significance in the smooth transition of patients to the recovery period. Objective: To evaluate the immediate effect of dry needling (DN) at myofascial trigger point on hand spasticity in stroke patients. Methods: This was a prospective, evaluator blind, multicenter, randomized controlled study. A total of 210 participants were randomly divided into DN group (DN, N = 70), sham dry needling group (SDN, N = 70), and control group (N = 70). Participants in the DN group were treated with DN at myofascial trigger point five times (30 min each time) every week for 4 weeks. Subjects in the SDN group were manipulated the same way as in the DN group, except that the acupuncture site was located in the area adjacent to the myofascial trigger point, which constituted a SDN. Routine rehabilitation treatment was performed for participants in the two groups and in the control group. The primary evaluation index was the immediate effect of hand spasticity relief. Secondary evaluation indicators included the cumulative effect of hand spasticity relief from baseline to week 4, and the changes in flexion angles of the wrist, thumb, and fingers 2–5 in the rest position before, immediately after, and 4 weeks after intervention. Results: The immediate effective rate of spasticity relief (thumb, fingers 2–5, and wrist) of patients with different degrees of spasticity in the DN group was higher than that in the control and SDN groups (thumb, χ2 = 55.833, P < 0.001; fingers 2–5, χ2 = 68.096, P < 0.001; wrist, χ2 = 49.180, P < 0.001) (P < 0.05). The effective rate of spasticity relief from baseline to 4 weeks in the DN group exceeded that in the control group and SDN groups (thumb, χ2 = 8.806, P = 0.012; fingers 2–5, χ2 = 8.087, P = 0.018; and wrist, χ2 = 8.653, P = 0.013) (P < 0.05). No difference in immediate and cumulative effect was found between the control group and SDN group. The change of joints flexion angles in resting position before and after each treatment in the DN group was higher than that in the control and SDN groups (P < 0.05), but it was not significantly different between the control group and SDN group. At 4 weeks, although the change in the DN group was higher than that in the control group and SDN group, this difference was not statistically significant (P > 0.05). Conclusion: Dry needling can relieve varying degrees of hand spasticity instantly in post-stroke. Trial Registration: www.chictr.org.cn, ChiCTR1900022379. [ABSTRACT FROM AUTHOR]

The fire plume height (smoke injection height) is an important parameter for calculating the transport and lifetime of smoke particles, which can significantly affect regional and global air quality and atmospheric radiation budget. To develop an observation‐based global fire plume‐rise data set, a modified one‐dimensional plume‐rise model was used with observation‐based fire size and Maximum Fire Radiative Power (MFRP) data, which are derived from satellite fire hotspot measurements. The resulting data set captured well the observed plume height distribution derived from the Multiangle Imaging SpectroRadiometer (MISR) measurements. The fraction of fire plumes penetrating above the boundary layer is relatively low at 20% at the time of MISR observation (10:30 am LT) but increases to an average of ∼55% in the late afternoon, implying that the MISR observation data sampled in late morning underestimate the average daytime fire plume heights and plume mixing into the free troposphere. Therefore, adjustments are required through dynamic modeling or parameterization of fire plume height as a function of meteorological and fire conditions when the MISR data set is applied in climate model simulations. We conducted sensitivity simulations using the Community Atmospheric Models version 5 (CAM5). Model results show that the incorporation of fire plume rise in the model tends to significantly increase fire aerosol impacted regions. We applied the offline plume‐rise data to develop an online fire plume height parameterization, allowing for simulating the feedbacks of climate/weather on fire plume rise in climate models. Plain Language Summary: The wildfire smoke injection height is important to study the lifetime and transport capacity of smoke particles. This study developed a long‐term global wildfire injection height data set, which has been well evaluated and applied to global climate model simulation. For fully coupled atmosphere‐land simulation purposes, implementation has been developed for the climate model to generate injection height at each time step. The fire plume height (smoke injection height) is important for calculating the transport and lifetime of smoke particles. A modified one‐dimensional plume‐rise model was used with observation‐based fire size and Maximum Fire Radiative Power (MFRP) data. The resulting data set captured well the observed plume height distribution. The simulated plume penetration rate suggests higher fire emission mixed in the free troposphere in the late afternoon. We conducted sensitivity simulations using the Community Atmospheric Models version 5 (CAM5). In downwind regions, model results show that the incorporation of fire plume rise tends to increase fire aerosol transport. Additionally, we develop an online fire plume height parameterization, allowing for simulating the feedbacks of climate/weather on fire plume rise in climate models. Key Points: An observation‐based fire plume height data set is developed for 2002–2010. It is in general agreement with global Multiangle Imaging SpectroRadiomete observationsLate morning satellite observations tend to underestimate the fraction of fire plumes reaching the free troposphere during the dayModeling using online fire plume‐rise parameterizations based on this data set shows extensive free‐tropospheric transport of fire aerosols [ABSTRACT FROM AUTHOR]

Food insecurity affects approximately 8% of older adults in the U.S. and may be connected to social isolation. This study aimed to understand how loneliness and social support associated with food insecurity among older adults in Tampa Bay, Florida. We conducted surveys among 236 older adults in three healthcare clinics. Surveys included demographic questions, the Six-Item Short Form U.S. Household Food Security Survey, the de Jong Gierveld 11-item Loneliness scale, and the Multidimensional Scale of Perceived Social Support. Unadjusted logistic regression indicated that loneliness (p < 0.001, OR = 0.738) and lack of social support (p = 0.001, OR = 1.754) were significantly associated with higher odds of being food insecure. In the adjusted logistic regression model, loneliness (p = 0.005, OR = 1.356) and being divorced (p = 0.008, OR = 0.208) significantly predicted increased odds of food insecurity. As expected, food insecurity also associated with participation in the Supplemental Nutrition Assistance Program (p = 0.003, OR = 4.765). These results show food insecurity involves many underlying determinants including psychosocial factors. [ABSTRACT FROM AUTHOR]

Within substations installed with flexible ac transmission systems, the magnetic flux leakage (MFL) generated by air core reactors in static var compensator (SVC) can be very high. To reduce the influence of the high magnetic field, an optimal suppression method is selected based on fuzzy comprehensive evaluation. First, a series of onsite measurements are performed around the reactors in the SVC. From the measurement results, the low-voltage cables, dc system, grounding grid, and magnetic flux density around the air core reactors are seriously affected by the MFL generated by the air core reactors with currents of a few kiloamperes. Then, according to the actual parameters of the air core reactors, a three-dimensional simulation model is constructed using the finite-element method (FEM) to calculate the magnetic flux density and some parameters related to the magnetic field. Moreover, four different suppression methods are proposed to suppress the MFL influence. Due to the multiple aspects affected by MFL, it is always difficult to make a quantitative comparison for the suppression methods based on previous research. For this reason, a fuzzy comprehensive evaluation method based on a linear evaluation model is proposed to quantitatively compare the suppression methods by using the suppression effect value δ. In a comparison of different suppression methods, the aluminum plate shielding with proper parameters has prove to be an optimal method for achieving a comprehensive suppression effect. The modeling and evaluation methods presented in this article are both valuable to solve the complex MFL influence problem in different SVC-based substations. [ABSTRACT FROM AUTHOR]

Despite many existing research on data forwarding in low-duty-cycle wireless sensor networks (WSNs), relatively little work has been done on energy-efficient data forwarding with probabilistic delay bounds. Probabilistic delay guarantees (i.e., delay bounded data delivery with reliability constraints) are of increasing importance for many delay-constrained applications, since deterministic delay bounds are prohibitively expensive to guarantee in WSNs. However, radio duty-cycling and unreliable wireless links pose challenges for achieving the probabilistic delay guarantee in WSNs. In this paper, we propose EEAF, a novel energy-efficient adaptive forwarding technique tailored for low-duty-cycle WSNs with unreliable wireless links. We show the existence of path diversity in low-duty-cycle WSNs, where delay-optimal routing and energy-optimal routing are likely following different paths. The key idea of EEAF is to exploit the intrinsic path diversity to provide probabilistic delay guarantees while minimizing transmission cost. In EEAF, an early arriving packet will be adaptively switched to the energy-optimal path for energy conservation. Delay quantiles are derived at each node in a distributed manner and are used as the guidelines in the adaptive forwarding decision making. Extensive testbed experiment and large-scale simulation show that EEAF effectively reduces the transmission cost by 12%~25% with probabilistic delay guarantees under various network settings. In addition, we extend the EEAF technique with data aggregation for event-based traffic scenarios. Evaluation using publicly available WSN event traffic traces yields very encouraging results with up to 40% energy saving in probabilistic delay bounded data delivery. [ABSTRACT FROM AUTHOR]