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Institution

California State University, Long Beach

EducationLong Beach, California, United States
About: California State University, Long Beach is a education organization based out in Long Beach, California, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 10036 authors who have published 13933 publications receiving 377394 citations. The organization is also known as: Cal State Long Beach & Long Beach State.


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Journal ArticleDOI
TL;DR: It is postulate that inadequate strength and resistance training background in women is the cause for the apparent increased susceptibility of women to these skeletal injuries.
Abstract: Stress fractures of the lower ribs related to sports are rare. Fractures of the ribs related to rowing sports have not been reported. We have documented seven cases of stress fractures of the lower ribs in female athletes; four were elite rowers and three were engaged in tennis, golf, and gymnastics. Each athlete was initially seen and treated elsewhere for muscular strain. All athletes presented with pain in the posterolateral thorax in and around the scapula. The time from onset of symptoms to diagnosis ranged from 2 to 6 months. Bone scans were used to document the fractures when roentgenograms were equivocal. Each athlete responded to a 4 to 8 week period of rest or training modification. Biomechanical analysis of the forces across the ribs demonstrates that these fractures tend to occur along the posterolateral segment where the bending stresses on the rib are greatest. The predominant muscle forces are generated by the forced couple of scapular retraction and protraction acting through the serratus anterior. We postulate that inadequate strength and resistance training background in women is the cause for the apparent increased susceptibility of women to these skeletal injuries.

87 citations

Journal ArticleDOI
TL;DR: In this article, a multidisciplinary design optimization (MDO) process that can simultaneously account for the impact of selected geometric variables on all vehicle subsystems is described, and the development and implementation of an MDO design system that combines propulsion and external aerodynamic forces.
Abstract: Airbreathing hypersonic aircraft and missiles are characterized by a high degree of interdependence between airframe and engine For nonaxisymmetric vehicles the propulsion system exerts a major ine uence on vehicle lift and pitching moment; this in turn ine uences vehicle stability, control, and overall mission performance Because of strong interactions between the airframe and engine, conceptual design of this class of vehicle requires a multidisciplinary design optimization (MDO) process that can simultaneously account for the impact of selected geometric variables on all vehicle subsystems This paper describes the development and implementation of an MDO design system that combines propulsion and external aerodynamic forces, mass properties and internal volumetric modeling, and performs geometric optimization of a hypersonic cruise missile to maximize overall mission range The result is a cone guration with range 46% greater than the initial baseline Such a dramatic performance increase is indicative not only of the power of optimization, but of the dife culty in cone guring hypersonic vehicles to synergize the interaction of all vehicle components without MDO methods

87 citations

Journal ArticleDOI
TL;DR: In this article, the authors assessed the independent and combined effects of different levels of monetary incentives and a theory-based educational intervention on return for tuberculosis (TB) skin test reading in a sample of active injection drug and crack cocaine users.
Abstract: OBJECTIVES: This study assessed the independent and combined effects of different levels of monetary incentives and a theory-based educational intervention on return for tuberculosis (TB) skin test reading in a sample of active injection drug and crack cocaine users. Prevalence of TB infection in this sample was also determined. METHODS: Active or recent drug users (n = 1004), recruited via street outreach techniques, were skin tested for TB. They were randomly assigned to 1 of 2 levels of monetary incentive ($5 and $10) provided at return for skin test reading, alone or in combination with a brief motivational education session. RESULTS: More than 90% of those who received $10 returned for skin test reading, in comparison with 85% of those who received $5 and 33% of those who received no monetary incentive. The education session had no impact on return for skin test reading. The prevalence of a positive tuberculin test was 18.3%. CONCLUSIONS: Monetary incentives dramatically increase the return rate for ...

87 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a reinforcement on federated learning (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem of joint decision of device selection and computing and spectrum resource allocation in distributed industrial IoT networks.
Abstract: In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a “ Reinforcement on Federated ” (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.

87 citations

Journal ArticleDOI
TL;DR: A large number of patients have been found to demonstrate evidence of intraosseous trabecular disruption, or edema and hemorrhage of medullary bone, or stress type injuries, all of which are radiographically occult.
Abstract: During the course of MRI examinations of the knee for possible internal derangement, the hip for avascular necrosis, and the shoulder for rotator cuff tears, we have encountered many examples of unsuspected fractures of the tibial plateau, femoral condyles, pelvis, hip and proximal humerus. These fractures were either radiographically inapparent or demonstrated very subtle abnormalities that were missed on prospective interpretation. In addition, a large number of patients have been found to demonstrate evidence of intraosseous trabecular disruption, or edema and hemorrhage of medullary bone, or stress type injuries, all of which are radiographically occult. The clinical significance of these osseous abnormalities varies and is dependent upon the degree of injury. It is believed that an awareness of these osseous abnormalities will improve the accuracy of MRI interpretation, will heighten an appreciation of the subtle radiographic abnormalities that may be present, and will improve patient evaluation and ...

87 citations


Authors

Showing all 10093 results

NameH-indexPapersCitations
David A. Weitz1781038114182
Menachem Elimelech15754795285
Josh Moss139101989255
Ron D. Hays13578182285
Matthew J. Budoff125144968115
Harinder Singh Bawa12079866120
Kamyar Kalantar-Zadeh118102556187
Dionysios D. Dionysiou11667548449
Kathryn Grimm11061847814
Richard B. Kaner10655766862
William Oh10086748760
Nosratola D. Vaziri9870834586
Jagat Narula9897847745
Qichun Zhang9454028367
Muhammad Shahbaz92100134170
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202324
202260
2021663
2020638
2019578
2018536