Single-path Model Research Articles

Childhood Unpredictability is Associated With Religious Coping Through Attachment to God and Divine Forgiveness

Because a majority of the world’s population is religious and believes in some higher power, it is important to understand what may facilitate or hinder religious psychological processes that give rise to well-being. The current work therefore threads together behavioral ecological, attachment, and forgiveness theoretical perspectives to assess candidate correlates of divine forgiveness and religious coping. Study 1 ( N = 441) showed, via a single path model, that childhood unpredictability was positively associated with avoidant attachment to God, which was in turn negatively associated with divine forgiveness, such that avoidant attachment to God fully mediated the association between childhood unpredictability and divine forgiveness. Study 2 ( N = 417) extended the investigation to religious coping, again analyzing data via a single path model. Childhood unpredictability was associated with positive (but not negative) coping through a mediation sequence of insecure attachment (avoidant and anxious) and, in turn, divine forgiveness. For avoidance, unpredictability was positively associated with avoidant attachment, which was negatively associated with divine forgiveness, which was positively associated with positive religious coping. For anxious attachment, unpredictability was positively associated with anxious attachment, which was positively associated with divine forgiveness, which was positively associated with positive religious coping. Notably, avoidant attachment was also curvilinearly associated with anxious attachment and coping, such that people high and low (vs. average) in avoidance are high in anxious attachment as well as positive and negative religious coping. Thus, the work provides insights important to both the study of early childhood environments and the study of religious psychological processes.

Problem orientation as a moderator of the relation between discrimination and passive suicidal ideation

BackgroundPassive suicidal ideation (i.e., the desire to be dead) may be an important, yet understudied, risk factor for suicidal behavior. Experiences of discrimination have been linked to greater severity of suicidal ideation though less work has examined factors that influence this relation. Though not explicitly tested, prior work suggests that problem orientation, defined as an individual's general beliefs about problems in daily life, may be an important moderator of this relation. In the present study, positive and negative problem orientation were hypothesized to moderate the relation between everyday discrimination and past month frequency of passive suicidal ideation. MethodThe study consisted of a diverse (62.4 % non-White; 21.4 % Latino/a/x; 27 % sexual minority) adult sample (N = 392; Mage = 20.24, SD = 2.49, 67.3 % female) recruited from a U.S. Mid-Atlantic college campus. Discrimination, problem orientation, past-month depressive symptoms, and past-month frequency of passive suicidal ideation were assessed using self-report measures. A single path model with Bayesian estimation was used to evaluate concurrent relations among study variables. ResultsAfter controlling for depressive symptoms, sex, sexual identity, and age, positive problem orientation buffered the impact of lifetime discrimination on past month passive suicidal ideation (b = -0.01, 95 % credibility interval [CI]: [-0.008, -0.001]). Negative problem orientation did not moderate this relation (b = 0.00, 95 % CI: [-0.002, 0.003]). LimitationsStudy design was cross-sectional in nature. ConclusionsResults suggest that bolstering positive problem orientation in treatment with young adults who have experienced discrimination may help reduce suicide risk.

A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.

Adaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient-specific data was integrateda into a registration-guided multi-channel multi-path (Rg-MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation. This study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg-MCMP segmentation framework, the first-course CT images (CT1) were registered to second-course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U-Net consisting of a channel-based multi-path feature extraction network. The performance of the Rg-MCMP segmentation framework was evaluated and compared with the single-channel single-path model (SCSP), the standalone registration methods, and the registration-guided multi-channel single-path (Rg-MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics. The average DSC of CTV for the deformable image DIR-MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR-MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR-MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration-guided multi-channel single-path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p<0.05). The proposed Rg-MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

Single-Path Bit Sharing for Automatic Loss-Aware Model Compression.

Network pruning and quantization are proven to be effective ways for deep model compression. To obtain a highly compact model, most methods first perform network pruning and then conduct quantization based on the pruned model. However, this strategy may ignore that the pruning and quantization would affect each other and thus performing them separately may lead to sub-optimal performance. To address this, performing pruning and quantization jointly is essential. Nevertheless, how to make a trade-off between pruning and quantization is non-trivial. Moreover, existing compression methods often rely on some pre-defined compression configurations (i.e., pruning rates or bitwidths). Some attempts have been made to search for optimal configurations, which however may take unbearable optimization cost. To address these issues, we devise a simple yet effective method named Single-path Bit Sharing (SBS) for automatic loss-aware model compression. To this end, we consider the network pruning as a special case of quantization and provide a unified view for model pruning and quantization. We then introduce a single-path model to encode all candidate compression configurations, where a high bitwidth value will be decomposed into the sum of a lowest bitwidth value and a series of re-assignment offsets. Relying on the single-path model, we introduce learnable binary gates to encode the choice of configurations and learn the binary gates and model parameters jointly. More importantly, the configuration search problem can be transformed into a subset selection problem, which helps to significantly reduce the optimization difficulty and computation cost. In this way, the compression configurations of each layer and the trade-off between pruning and quantization can be automatically determined. Extensive experiments on CIFAR-100 and ImageNet show that SBS significantly reduces computation cost while achieving promising performance. For example, our SBS compressed MobileNetV2 achieves 22.6× Bit-Operation (BOP) reduction with only 0.1% drop in the Top-1 accuracy.

Predicting particle deposition using a simplified 8-path in silico human lung prototype

Understanding particle deposition in the human lung is crucial for the assessment of environmental pollutants and the design of new drug delivery systems. Traditionally, research has been carried out by experimental analysis, but this generally requires expensive equipment and exposure of volunteers to radiation, resulting in limited data. To overcome these drawbacks, there is an emphasis on the development of numerical models capable of accurate predictive analysis. The most advanced of these computer simulations are based on three-dimensional computational fluid dynamics. Solving the flow equations in a complete, fully resolved lung airway model is currently not feasible due to the computational resources required. In the present work, a simplified lung model is presented and validated for accurate prediction of particle deposition. Simulations are performed for an 8-path approximation to a full lung airway model. A novel boundary condition method is used to ensure accurate results in truncated flow branches. Simulations are performed at a steady inhalation flow rate of 18 l min−1, corresponding to a low activity breathing rate, while the effects of particle size and density are investigated. Comparison of the simulation results with available experimental data shows that reasonably accurate results can be obtained at a small fraction of the cost of a full airway model. The simulations clearly evaluate the effect of both particle size and particle density. Most importantly, the results show an improvement over a previously documented single-path model, both in terms of accuracy and the ability to obtain regional deposition rates. The present model represents an improvement over previously used simplified models, including single-path models. The multi-path reduced airway approach described can be used by researchers for general and patient-specific analyses of particle deposition and for the design of effective drug delivery systems.

Relationship Between Abuse History and Gastrointestinal and Extraintestinal Symptom Severity in Irritable Bowel Syndrome.

This study aimed to investigate the associations between the different abuse types, and gastrointestinal (GI) and extraintestinal symptom severity in irritable bowel syndrome (IBS), and possible mediators of these relationships. We assessed sexual and physical abuse in childhood and adulthood with the Drossman and Leserman abuse questionnaire, whereas GI and extraintestinal symptoms were assessed with the Gastrointestinal Symptom Rating Scale and the Symptom Check List-90 Revised. General linear models with bootstrapping tested the mediating role of depressive symptoms, anxiety symptoms, and GI-specific anxiety and rectal pain threshold. A path model analysis testing all relationships simultaneously was also performed. Among our 186 patients with IBS, an overall history of abuse (i.e., at least one type) was found in 37%. The effects of child and adult sexual abuse on GI symptom severity were fully mediated by GI-specific anxiety and rectal pain threshold (F = 21.540, R2 = 0.43, and F = 22.330, R2 = 0.44, respectively; p < .001 for both). The effect of adult sexual abuse and child physical abuse on extraintestinal symptom severity was fully mediated by GI-specific anxiety, depressive symptoms, and rectal pain threshold, whereas the effect of child sexual abuse was partially mediated (F = 14.992, R2 = 0.28; F = 15.065, R2 = 0.30; and F = 18.037, R2 = 0.32, respectively; p < .001 for all). When analyzed in a single path model, child sexual abuse and adult physical abuse only had a direct effect on extraintestinal symptom severity, whereas child physical abuse had an indirect effect through depressive symptoms. Abuse is associated with increased GI and extraintestinal symptom severity in IBS. These associations are mediated by levels of GI-specific anxiety, depressive symptoms, and rectal sensitivity.

Perceived Stress Mediates the Relationship Between Trait Mindfulness and Physical Symptoms of Stress: a Replication Study Using Structural Equation Modeling.

ObjectivesThe mindfulness stress buffering account posits mindfulness may benefit physical health by reducing stress. Previous research supports this account and suggests the non-judging facet of mindfulness may be most strongly associated with physical symptoms of stress, via lower perceived stress. The current replication study used structural equation modeling to analyze relationships between multiple facets of mindfulness, perceived stress, and physical symptoms of stress.MethodsUndergraduate students (n = 534, 68% White, 65% female) completed surveys measuring trait mindfulness (Five Facet Mindfulness Questionnaire—Short Form), perceived stress (Perceived Stress Scale), and physical symptoms of stress (Cohen-Hoberman Inventory of Physical Symptoms).ResultsAs hypothesized, results showed the negative relationship between four facets of mindfulness (describing, non-judging, non-reactivity, and acting with awareness) and physical symptoms of stress was partially mediated by lower perceived stress. Observing, however, was associated with more physical symptoms of stress.ConclusionsThe current findings successfully replicated the results of two previous studies in an independent sample, using a more parsimonious analytic strategy that included all variables in a single path model. Results confirm the stress-buffering effect of trait mindfulness, particularly non-judging. Future research may test whether changes in trait mindfulness, particularly non-judging, explain individual differences in objective measures of stress and physical health.

Automatic segmentation of high-risk clinical target volume for tandem-and-ovoids brachytherapy patients using an asymmetric dual-path convolutional neural network.

Preimplant diagnostic magnetic resonance imaging is the gold standard for image-guided tandem-and-ovoids (T&O) brachytherapy for cervical cancer. However, high dose rate brachytherapy planning is typically done on postimplant CT-based high-risk clinical target volume (HR-CTVCT ) because the transfer of preimplant Magnetic resonance (MR)-based HR-CTV (HR-CTVMR ) to the postimplant planning CT is difficult due to anatomical changes caused by applicator insertion, vaginal packing, and the filling status of the bladder and rectum. This study aims to train a dual-path convolutional neural network (CNN) for automatic segmentation of HR-CTVCT on postimplant planning CT with guidance from preimplant diagnostic MR. Preimplant T2-weighted MR and postimplant CT images for 65 (48 for training, eight for validation, and nine for testing) patients were retrospectively solicited from our institutional database. MR was aligned to the corresponding CT using rigid registration. HR-CTVCT and HR-CTVMR were manually contoured on CT and MR by an experienced radiation oncologist. All images were then resampled to a spatial resolution of 0.5× 0.5 × 1.25mm. A dual-path 3D asymmetric CNN architecture with two encoding paths was built to extract CT and MR image features. The MR was masked by HR-CTVMR contour while the entire CT volume was included. The network put an asymmetric weighting of 18:6 for CT: MR. Voxel-based dice similarity coefficient (DSCV ), sensitivity, precision, and 95% Hausdorff distance (95-HD) were used to evaluate model performance. Cross-validation was performed to assess model stability. The study cohort was divided into a small tumor group(<20 cc), medium tumor group (20-40cc), and large tumor group(>40cc) based on the HR-CTVCT for model evaluation. Single-path CNN models were trained with the same parameters as those in dual-path models. For this patient cohort, the dual-path CNN model improved each of our objective findings, including DSCV , sensitivity, and precision, with an average improvement of 8%, 7%, and 12%, respectively. The 95-HD was improved by an average of 1.65mm compared to the single-path model with only CT images as input. In addition, the area under the curve for different networks was 0.86 (dual-path with CT and MR) and 0.80 (single-path with CT), respectively. The dual-path CNN model with asymmetric weighting achieved the best performance with DSCV of 0.65 ± 0.03 (0.61-0.70), 0.79 ± 0.02 (0.74-0.85), and 0.75 ± 0.04 (0.68-0.79) for small, medium, and large group. 95-HD were 7.34 (5.35-10.45) mm, 5.48 (3.21-8.43) mm, and 6.21 (5.34-9.32) mm for the three size groups, respectively. An asymmetric CNN model with two encoding paths from preimplant MR (masked by HR-CTVMR ) and postimplant CT images was successfully developed for automatic segmentation of HR-CTVCT for T&O brachytherapy patients.

Data-driven super-resolution reconstruction of supersonic flow field by convolutional neural networks

The pursuit of high-resolution flow fields is meaningful for the development of hypersonic technology. Flow field super-resolution (SR) based on deep learning is a novel and effective method to provide HR flow fields in a scramjet isolator. Single-path and multiple-path network models based on convolutional neural networks (CNNs) have been developed to augment the spatial resolution of the experimental supersonic flow field. The single-path model uses a simple convolutional layer and fully connected layer serial architecture, and the multiple-path model increases the branch path by adding pooling layers to achieve a fusion structure architecture. Ground experiments of flow in a supersonic isolator at various working conditions are conducted to establish an experimental dataset. The trained single-path and multiple-path CNNs are compared with the traditional interpolation method on the flow field SR reconstruction accuracy. The results demonstrated that single-path CNNs have certain learning ability, but the SR accuracy is not satisfactory; multiple-path CNNs significantly improve the accuracy of flow field SR, and the multiple-path CNN with one branch path achieves the best SR performance.

Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

The effect of alveolar mixing on particle retention and deposition investigated by a dynamic single-path model

A dynamic, single-path model is developed for dry powder transport in the lungs. The model differentiates between particle behavior in the respiratory bronchioles and alveolar ducts on one hand and...

Modeling public responses to soft-target transportation terror

Transportation systems are one of the most frequent and high-profile targets for terrorist attacks, and such attacks can cause reduced or altered public travel behavior that can have severe economic consequences. Thus, understanding the relationship between transportation terror and public response is critical. We recruited n = 430 participants to read one of three hypothetical transportation-based terror attacks, and use Partial Least Squares path modeling to identify the interrelationships between the affective, cognitive, and (intended) behavioral facets of their reactions. The three terror scenarios were structured to allow for comparisons between a cyber and non-cyber attack on ground transportation, and an aviation versus ground explosives attack, which allowed us to test the robustness of the path model across situational features. Results indicated that the attack features did not moderate any of the interrelationships between reaction variables, but had medium-sized effects on self-reported risk perception. Collapsing data from all three scenarios into a single path model confirmed previously reported findings regarding the opposing roles of fear and anger in risk perception, suggested differing roles for terrorism risk attitudes and general risk attitudes, and found a surprisingly negligible role for self-reported trust in government. Implications for further research on soft-target and transportation-based terrorism risk perception are discussed.

Capacity Analysis of NOMA With mmWave Massive MIMO Systems

Non-orthogonal multiple access (NOMA), millimeter wave (mmWave), and massive multiple-input-multiple-output (MIMO) have been emerging as key technologies for fifth generation mobile communications. However, less studies have been done on combining the three technologies into the converged systems. In addition, how many capacity improvements can be achieved via this combination remains unclear. In this paper, we provide an in-depth capacity analysis for the integrated NOMA-mmWave-massive-MIMO systems. First, a simplified mmWave channel model is introduced by extending the uniform random single-path model with angle of arrival. Afterward, we divide the capacity analysis into the low signal to noise ratio (SNR) and high-SNR regimes based on the dominant factors of signal to interference plus noise ratio. In the noise-dominated low-SNR regime, the capacity analysis is derived by the deterministic equivalent method with the Stieltjes–Shannon transform. In contrast, the statistic and eigenvalue distribution tools are invoked for the capacity analysis in the interference-dominated high-SNR regime. The exact capacity expression and the low-complexity asymptotic capacity expression are derived based on the probability distribution function of the channel eigenvalue. Finally, simulation results validate the theoretical analysis and demonstrate that significant capacity improvements can be achieved by the integrated NOMA-mmWave-massive-MIMO systems.

The Roles of Anxious Rearing, Negative Affect, and Effortful Control in a Model of Risk for Child Perfectionism

Perfectionism has been linked with psychopathology and other difficulties in children. As such, research has begun to focus on those factors that predict perfectionism in children. Separate lines of research have identified temperament and parenting as two specific areas that are associated with perfectionism in children. Yet, few studies have investigated these two areas within a single study and none have examined whether effortful control reduces perfectionism. The current study sought to examine the roles of negative affect, effortful control, and parental anxious rearing on child perfectionism using a single path model. Participants were 59 parent-child dyads recruited from the community. Children were between the ages of 7.5 and 13 years (55% female; 75% Caucasian; 16.7% African American). The model fit the data well. Results indicated that anxious rearing predicted increased perfectionism, whereas effortful control predicted decreased perfectionism. Additionally, there was a significant indirect effect of anxious rearing on the association between negative affect and perfectionism. However, contrary to expectations, negative affect did not significantly predict increased perfectionism, and effortful control did not moderate these effects. Results suggest that negative affect may be indirectly related to child perfectionism, through anxious rearing, and that effortful control may help protect children from developing high levels of perfectionism. Additionally, the results are consistent with theoretical models of perfectionism development and suggest multiple domains contribute to perfectionism.

Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo.

We propose a computational model for shape, illumination and albedo inference in a pulsed time-of-flight (TOF) camera. In contrast to TOF cameras based on phase modulation, our camera enables general exposure profiles. This results in added flexibility and requires novel computational approaches. To address this challenge we propose a generative probabilistic model that accurately relates latent imaging conditions to observed camera responses. While principled, realtime inference in the model turns out to be infeasible, and we propose to employ efficient non-parametric regression trees to approximate the model outputs. As a result we are able to provide, for each pixel, at video frame rate, estimates and uncertainty for depth, effective albedo, and ambient light intensity . These results we present are state-of-the-art in depth imaging. The flexibility of our approach allows us to easily enrich our generative model. We demonstrate this by extending the original single-path model to a two-path model, capable of describing some multipath effects. The new model is seamlessly integrated in the system at no additional computational cost. Our work also addresses the important question of optimal exposure design in pulsed TOF systems. Finally, for benchmark purposes and to obtain realistic empirical priors of multipath and insights into this phenomena, we propose a physically accurate simulation of multipath phenomena.

Quantum model for a periodically driven selectivity filter in a K+ ion channel

In this work, we present a quantum transport model for the selectivity filter in the KcsA potassium ion channel. This model is fully consistent with the fact that two conduction pathways are involved in the translocation of ions through the filter, and we show that the presence of a second path may actually bring advantages for the filter as a result of quantum interference. To highlight interferences and resonances in the model, we consider the selectivity filter to be driven by a controlled time-dependent external field, which changes the free-energy scenario and consequently the conduction of the ions. In particular, we demonstrate that the two-pathway conduction mechanism is more advantageous for the filter when dephasing in the transient configurations is lower than in the main configurations. As a matter of fact, K+ ions in the main configurations are highly coordinated by oxygen atoms of the filter backbone, and this increases noise. Moreover, we also show that for a wide range of dephasing rates and driving frequencies, the two-pathway conduction used by the filter leads to higher ionic currents than the single–path model.

Performance Analysis of Cooperative Time Hopping UWB Systems with Multi-User Interference

In this letter, we propose an analytical model to study the performance of cooperative links for time hopping ultra wideband (UWB) systems under two relay models: amplify-and-forward (AF) and decode-and-forward (DF). The effects of multi-user interference (MUI) are considered when deriving closed-form expressions for outage probability and average bit error probability (BEP) over single-path model. Simulation results show that the presented analytical model can accurately predict the link performance.

Analysis and modeling of resistive switching statistics

The resistive random access memory (RRAM), based on the reversible switching between different resistance states, is a promising candidate for next-generation nonvolatile memories. One of the most important challenges to foster the practical application of RRAM is the control of the statistical variation of switching parameters to gain low variability and high reliability. In this work, starting from the well-known percolation model of dielectric breakdown (BD), we establish a framework of analysis and modeling of the resistive switching statistics in RRAM devices, which are based on the formation and disconnection of a conducting filament (CF). One key aspect of our proposal is the relation between the CF resistance and the switching statistics. Hence, establishing the correlation between SET and RESET switching variables and the initial resistance of the device in the OFF and ON states, respectively, is a fundamental issue. Our modeling approach to the switching statistics is fully analytical and contains two main elements: (i) a geometrical cell-based description of the CF and (ii) a deterministic model for the switching dynamics. Both ingredients might be slightly different for the SET and RESET processes, for the type of switching (bipolar or unipolar), and for the kind of considered resistive structure (oxide-based, conductive bridge, etc.). However, the basic structure of our approach is thought to be useful for all the cases and should provide a framework for the physics-based understanding of the switching mechanisms and the associated statistics, for the trustful estimation of RRAM performance, and for the successful forecast of reliability. As a first application example, we start by considering the case of the RESET statistics of NiO-based RRAM structures. In particular, we statistically analyze the RESET transitions of a statistically significant number of switching cycles of Pt/NiO/W devices. In the RESET transition, the ON-state resistance (RON) is a key parameter to describe the initial state of the CF. Hence, we subdivide the statistical samples (obtained mainly in a single device) in several RON ranges so as to study the switching statistics as a function of RON. In this regard, we have found that the experimental data can be nicely fit to a Weibull model in all the resistance ranges. Moreover, the distributions significantly change with RON. This change might be even more significant than the device-to-device related variations and, hence, mostly determine the overall statistics of switching parameters. In particular, we have found that the Weibull slopes of both VRESET, IRESET, and PRESET cumulative distributions increase linearly with 1/RON, i.e., they increase with the area of the CF. On the other hand, while the scale factor of the VRESET distribution (V63%) is roughly independent of RON, the scale factor of the distribution of IRESET and PRESET (I63% and P63%) linearly increases with 1/RON. Upon a direct analogy with the cell-based analytical percolation model of oxide BD, two simple geometrical cell-based models, the single path model with variable width and the multiple parallel path model, are proposed to address the RESET statistics. In the limit where these two geometrical models coincide, we incorporate a deterministic model for the RESET switching dynamics based on the self-accelerated thermal dissolution of the CF. With these two ingredients, the complete physics-based model for the RESET statistics is constructed. This analytical model is shown to nicely account for the experimental results with remarkable agreement.

Multipath multihop routing analysis in mobile ad hoc networks

This paper proposes an analytical modeling framework to investigate multipath routing in multihop mobile ad hoc networks. In this paper, a more generalized system has been considered and mathematically analyzed to observe some of the related performance measures of the ad hoc network. Each node in the network is assumed to have finite buffer. The single-path model is approximated to be a multi-node M/M/1/B tandem network, and the multi-path model as a set of multiple parallel paths. This proposed model allows us to investigate issues such as end-to-end delivery delay, throughput and routing reliability in mobile ad hoc networks. Theoretical results have been verified by numerical results. An optimal path selection strategy has been proposed to select a minimized delay path among the available multiple paths between source-destination pair.

Comparison of Axisymmetric and Three-Dimensional Models for Gas Uptake in a Single Bifurcation During Steady Expiration

Reactive gas uptake is predicted and compared in a single bifurcation at steady expiratory flow in terms of Sherwood number using an axisymmetric single-path model (ASPM) and a three-dimensional computational fluid dynamics model (CFDM). ASPM is validated in a two-generation geometry by comparing the average gas-phase mass transfer coefficients with the experimental values. ASPM predicted mass transfer coefficients within 20% of the experimental values. The flow and concentration variables in the ASPM were solved using Galerkin finite element method and in the CFDM using commercial finite element software FIDAP. The simulations were performed for reactive gas flowing at Reynolds numbers ranging from 60 to 350 in both symmetric bifurcation for three bifurcation angles, 30 deg, 70 deg, and 90 deg, and in an asymmetric bifurcation. The numerical models compared with each other qualitatively but quantitatively they were within 0.4-8% due to nonfully developed flow in the parent branch predicted by the CFDM. The radially averaged concentration variation along the axial location matched qualitatively between the CFDM and ASPM but quantitatively they were within 32% due to differences in the flow field. ASPM predictions compared well with the CFDM predictions for an asymmetric bifurcation. These results validate the simplified ASPM and the complex CFDM. ASPM predicts higher Sherwood number with a flat velocity inlet profile compared to a parabolic inlet velocity profile. Sherwood number increases with the inlet average velocity, wall mass transfer coefficient, and bifurcation angle since the boundary layer grows slower in the parent and daughter branches.