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Pain and post-traumatic stress disorder (PTSD) are frequently co-morbid in the aftermath of a traumatic event. Although several models attempt to explain the relationship between these two disorders, the mechanisms underlying the relationship remain unclear. The aim of this study was to investigate the relationship between each PTSD symptom cluster and pain over the course of post-traumatic adjustment. In a longitudinal study, injury patients (n=824) were assessed within 1 week post-injury, and then at 3 and 12 months. Pain was measured using a 100-mm Visual Analogue Scale (VAS). PTSD symptoms were assessed using the Clinician-Administered PTSD Scale (CAPS). Structural equation modelling (SEM) was used to identify causal relationships between pain and PTSD. In a saturated model we found that the relationship between acute pain and 12-month pain was mediated by arousal symptoms at 3 months. We also found that the relationship between baseline arousal and re-experiencing symptoms, and later 12-month arousal and re-experiencing symptoms, was mediated by 3-month pain levels. The final model showed a good fit [chi2=16.97, df=12, p>0.05, Comparative Fit Index (CFI)=0.999, root mean square error of approximation (RMSEA)=0.022]. These findings provide evidence of mutual maintenance between pain and PTSD.

Background: The mutual maintenance model proposes that post-traumatic stress disorder (PTSD) symptoms and chronic physical symptoms have a bi-directional temporal relationship. Despite widespread support for this model, there are relatively few empirical tests of the model and these have primarily examined patients with a traumatic physical injury. Objective: To extend the assessment of this model, we examined the temporal relationship between PTSD and physical symptoms for military personnel deployed to combat (i.e., facing the risk of death) who were not evacuated for traumatic injury. Methods: The current study used a prospective, longitudinal design to understand the cross-lagged relationships between PTSD and physical symptoms before, immediately after, 3 months after, and 1 year after combat deployment. Results: The cross-lagged results showed physical symptoms at every time point were consistently related to greater PTSD symptoms at the subsequent time point. PTSD symptoms were related to subsequent physical symptoms, but only at one time-point with immediate post-deployment PTSD symptoms related to physical symptoms at three months after deployment. Conclusion: The findings extend prior work by providing evidence that PTSD and physical symptoms may be mutually maintaining even when there is not a severe traumatic physical injury. * We followed soldiers from before to after combat and found a high comorbidity of PTSD and physical symptoms.* PTSD and physical symptoms were mutually maintaining among soldiers who did not experience a traumatic injury resulting in hospitalization.

Abstract Objective Pain and post-traumatic stress disorder (PTSD) symptoms are strongly correlated in veteran populations. Arguments for which one condition predicts or worsens the other condition have gone in both directions. However, research addressing this issue has been primarily limited to cross-sectional studies rather than examinations of a potential bidirectional relationship between pain interference and PTSD symptoms over time. In addition, no studies have examined deployment injury status as potentially moderating this bidirectional effect in veterans. To address these gaps in the literature, the present longitudinal study examined whether there is a bidirectional relationship between pain interference and PTSD symptoms in a sample of male and female veterans returning from Operation Iraqi Freedom, Operation Enduring Freedom, or Operation New Dawn (N = 729) and whether deployment injury status moderates this relationship. Methods Participants completed phone interviews regarding pain interference and PTSD symptoms at three time points, each three months apart. Results Pain interference at Time 1 predicted worse PTSD symptoms at Time 2 for the subset of veterans who sustained injuries during deployment (n = 381) but not for veterans with pain interference who did not sustain injuries (n = 338). From Time 1 to Time 3, elevations in PTSD symptoms were mediated by pain interference for injured veterans; in contrast, PTSD symptoms did not appear to drive changes in pain interference in either group. Conclusions These results indicate that physical symptom management should be a crucial target of psychological intervention for returning veterans with PTSD symptoms and deployment-related injuries.

Chronic pain and chronic PTSD are often comorbid sequelae in patients who have experienced life-threatening experiences such as combat, assaults, or motor vehicle accidents, presenting lifelong challenges for patients and for medical management in all settings. This article briefly reviews four models for exploring the interrelationships of chronic pain and chronic PTSD. The article presents a longitudinal case study, documented over 10 years, of a patient with chronic back pain, and delayed-onset chronic PTSD related to sexual trauma experienced as a young adult. Data from the case study are examined for evidence in support of the chronic pain/chronic PTSD models. There is evidence to support all four models, with considerable evidence supporting the Mutual Maintenance Model (Sharp & Harvey, in Clinical Psychology Review 21(6): 857–77, 2001). Data show significant recovery over time from both conditions with improvements in function, work, and relationships, in response to Psychodynamic Therapy (PDT), Cognitive Behavioral Therapy (CBT), and hypnotic interventions, physical therapy, and pilates-based exercise. Notably, both chronic conditions were addressed simultaneously, with providers working collaboratively and sharing information through the patient. Emphasis is on non-pharmaceutical rehabilitative trauma-informed and patient-centered approaches to care.

This article describes F-IVM, a unified approach for maintaining analytics over changing relational data. We exemplify its versatility in four disciplines: processing queries with group-by aggregates and joins; learning linear regression models using the covariance matrix of the input features; building Chow-Liu trees using pairwise mutual information of the input features; and matrix chain multiplication. F-IVM has three main ingredients: higher-order incremental view maintenance; factorized computation; and ring abstraction. F-IVM reduces the maintenance of a task to that of a hierarchy of simple views. Such views are functions mapping keys, which are tuples of input values, to payloads, which are elements from a ring. F-IVM supports efficient factorized computation over keys, payloads, and updates. It treats uniformly seemingly disparate tasks: While in the key space, all tasks require general joins and variable marginalization, in the payload space, tasks differ in the definition of the sum and product ring operations. We implemented F-IVM on top of DBToaster and show that it can outperform classical first-order and fully recursive higher-order incremental view maintenance by orders of magnitude while using less memory.

The previous research on distributed production scheduling focuses on supply side, ignoring the interconnection of supply side and demand side: the delivery time of spare parts from the supply side will influence the maintenance scheduling of distributed equipment of demand side, while the maintenance scheduling of distributed equipment will affect the scheduling decision of supply of spare parts. In addition, in practice, inventory is an important link between manufacturers and customers. Therefore, we firstly propose an optimal scheduling problem of integrated production, inventory and imperfect maintenance with mutual feedback of supply side and demand side, shortened to PIM-DCSP. In PIM-DCSP, production resources and inventory work together to provide spare parts for demand side, while demand side makes imperfect maintenance scheduling for its distributed equipment to postpone deterioration and finally extend the operating time of equipment. The goal of PIM-DCSP is to make an optimal scheduling that jointly optimizes the scheduling of both sides, that is, reasonably arrange production resources, inventory and workers to realize the minimization of the total cost of supplier and the total cost of demander respectively. A mathematical model is established to describe the presented problem and an improved adaptive cooperative algorithm (IACA) is designed. Effective operators including two heuristic initialization methods, six problem-oriented and two random local search structures are developed to strengthen population diversity and search capability. The comparison experiment of IACA and three other outstanding algorithms is carried out on 96 instances, and the superiority of IACA in solving PIM-DCSP is certificated thoroughly.

Nowadays, the energy sector is experiencing a profound transition. Among all renewable energy sources, wind energy is the most developed technology across the world. To ensure the profitability of wind turbines, it is essential to develop predictive maintenance strategies that will optimize energy production while preventing unexpected downtimes. With the huge amount of data collected every day, machine learning is seen as a key enabling approach for predictive maintenance of wind turbines. However, most of the effort is put into the optimization of the model architectures and its parameters, whereas data-related aspects are often neglected. The goal of this paper is to contribute to a better understanding of wind turbines through a data-centric machine learning methodology. In particular, we focus on the optimization of data preprocessing and feature selection steps of the machine learning pipeline. The proposed methodology is used to detect failures affecting five components on a wind farm composed of five turbines. Despite the simplicity of the used machine learning model (a decision tree), the methodology outperformed model-centric approach by improving the prediction of the remaining useful life of the wind farm, making it more reliable and contributing to the global efforts towards tackling climate change.

Induction motors are very robust, with low operating and maintenance costs, and are therefore widely used in industry. They are, however, not fault-free, with bearings and rotor bars accounting for about 50% of the total failures. This work presents a two-stage approach for three-phase induction motors diagnosis based on mutual information measures of the current signals, principal component analysis, and intelligent systems. In a first stage, the fault is identified, and, in a second stage, the severity of the defect is diagnosed. A case study is presented where different severities of bearing wear and bar breakage are analyzed. To test the robustness of the proposed method, voltage imbalances and load torque variations are considered. The results reveal the promising performance of the proposal with overall accuracies above 90% in all cases, and in many scenarios 100% of the cases are correctly classified. This work also evaluates different strategies for extracting the signals, showing the possibility of reducing the amount of information needed. Results show a satisfactory relation between efficiency and computational cost, with decreases in accuracy of less than 4% but reducing the amount of data by more than 90%, facilitating the efficient use of this method in embedded systems.

Corporations have a huge influence on the life of every citizen--this book offers a visionary but practical plan to give every citizen a say in how corporations are run while also gaining some supplemental income.It lays out a clear approach that uses the mechanisms of the private market to hold corporations accountable to the public.

This paper investigated an electromagnetic torque ripple level of BLDC drives with multiple three-phase (TP) permanent magnet (PM) motors for electric vehicles. For this purpose, mathematical models of PM machines of different armature winding sets-single (STP), dual (DTP), triple (TTP), and quadruple (QTP) ones of asymmetrical configuration and optimal angular displacement between winding sets were developed and corresponding computer models in the Matlab/Simulink environment were created. In conducted simulation, the influence of various factors on the electromagnetic torque ripple of the multiple-TP BLDC drives was investigated—degree of modularity, magnetic coupling between armature winding sets, and drive operation in open and closed-loop control systems. Studies have shown an increase of the electromagnetic torque ripple generated by one module in the multiple TP BLDC drives with magnetically coupled winding sets, due to additional current pulsations caused by magnetic interactions between the machine modules. However, the total electromagnetic torque ripples are much lower than in similar drives with magnetically insulated winding sets. Compared with the STP BLDC drive, the multiple TP BLDC drives with the same output parameters showed a reduction of the electromagnetic torque ripple by 27.6% for the DTP, 32.3% for the TTP, and 34.0% for the QTP BLDC drive.