Does Kinesiophobia Mediate the Relationship between Pain Intensity and Disability in Individuals with Chronic Low-Back Pain and Obesity?

Individuals suffering from chronic low-back pain and obesity face severe physical and functional limitations. According to the fear-avoidance model, kinesiophobia might play a crucial role in the relationship between pain intensity and disability. Thus, the purpose of this study was to verify the role of kinesiophobia as a mediator in the association between pain intensity and disability in individuals with both chronic low-back pain and obesity. A total of 213 individuals with chronic low-back pain and obesity were included in the study. The level of kinesiophobia, pain intensity and disability were all assessed using self-reported questionnaires. We verified through a simple mediation analysis that kinesiophobia partially mediated the association between pain intensity and disability in our sample. According to our findings, we emphasize the crucial role of kinesiophobia as a psychological factor that should be addressed in chronic low-back pain rehabilitative protocols to reduce disability in individuals with obesity

The Role of Pain Catastrophizing and Pain Acceptance in Performance-Based and Self-Reported Physical Functioning in Individuals with Fibromyalgia and Obesity

Impaired physical functioning is one of the most critical consequences associated with fibromyalgia, especially when there is comorbid obesity. Psychological factors are known to contribute to perceived (i.e., subjective) physical functioning. However, physical function is a multidimensional concept encompassing both subjective and objective functioning. The contribution of psychological factors to performance-based (i.e., objective) functioning is unclear. This study aims to investigate the contribution of pain catastrophizing and pain acceptance to both self-reported and performance-based physical functioning. In this cross-sectional study, 160 participants completed self-report measures of pain catastrophizing, pain acceptance, and pain severity. A self-report measure and a performance-based test were used to assess physical functioning. Higher pain catastrophizing and lower pain acceptance were associated with poorer physical functioning at both self-reported and performance-based levels. Our results are consistent with previous evidence on the association between pain catastrophizing and pain acceptance with self-reported physical functioning. This study contributes to the current literature by providing novel insights into the role of psychological factors in performance-based physical functioning. Multidisciplinary interventions that address pain catastrophizing and pain acceptance are recommended and might be effective to improve both perceived and performance-based functioning in women with FM and obesity

Water Contaminants Detection Using Sensor Placement Approach in Smart Water Networks

Incidents of water pollution or contamination have occurred repeatedly in recent years, causing significant disasters and negative health impacts. Water quality sensors need to be installed in the water distribution system (WDS) to allow real-time water contamination detection to reduce the risk of water contamination. Deploying sensors in WDS is essential to monitor and detect any pollution incident at the appropriate time. However, it is impossible to place sensors on all nodes of the network due to the relatively large structure of WDS and the high cost of water quality sensors. For that, it is necessary to reduce the cost of deployment and guarantee the reliability of the sensing, such as detection time and coverage of the whole water network. In this paper, a dynamic approach of sensor placement that uses an Evolutionary Algorithm (EA) is proposed and implemented. The proposed method generates a multiple set of water contamination scenarios in several locations selected randomly in the WDS. Each contamination scenario spreads in the water networks for several hours, and then the proposed approach simulates the various effect of each contamination scenario on the water networks. On the other hand, the multiple objectives of the sensor placement optimization problem, which aim to find the optimal locations of the deployed sensors, have been formulated. The sensor placement optimization solver, which uses the EA, is operated to find the optimal sensor placements. The effectiveness of the proposed method has been evaluated using two different case studies on the example of water networks: Battle of the Water Sensor Network (BWSN) and another real case study from Madrid (Spain). The results have shown the capability of the proposed method to adapt the location of the sensors based on the numbers and the locations of contaminant sources. Moreover, the results also have demonstrated the ability of the proposed approach for maximising the coverage of deployed sensors and reducing the time to detect all the water contaminants using a few numbers of water quality sensor

Removal of benzene and toluene from a refinery waste air stream by water sorption and biotrickling filtration|Remoção de benzeno e tolueno de um efluente de refinaria por absorção e filtração “biotrickling”

The paper presents the results of an analysis of a two-stage pilot plant for the removal of toluene and benzene from the exhaust air of an industrial wastewater treatment plant (WWTP). The two-stage air process combines a water scrubber and a biotrickling filter (BTF) in sequence, and treats air stripped from the liquid phase compartments of the WWTP. During the experimental period, the pilot plant treated an airflow of 600 Nm3h-1. Average concentrations of the waste air stream entering the water scrubber were 10.61 mg Nm-3 benzene and 9.26 mg Nm-3 toluene. The water scrubber obtained medium-high removal efficiencies (averages 51% and 60%, for benzene and toluene, respectively). Subsequent passage through the BTF allowed a further reduction of average concentrations, which decreased to 2.10 mg Nm-3 benzene and to 0.84 mg Nm-3 toluene, thereby allowing overall average removal efficiencies (REs) of 80% and 91% for benzene and toluene, respectively. Results prove the benefits obtained from a combination of different removal technologies: water scrubbers to remove peak concentrations and soluble compounds, and BTFs to remove compounds with lower solubility, due to the biodegradation performed by microorganisms