Modeling and Validation of Fatigue and Recovery of Muscles for Manual Demolition Tasks

Manual demolition tasks are heavy, physically demanding tasks that could cause muscle fatigue accumulation and lead to work-related musculoskeletal disorders (WMSDs). Fatigue and recovery models of muscles are essential in understanding the accumulation and the reduction in muscle fatigue for forceful exertion tasks. This study aims to explore the onset of muscle fatigue under different work/rest arrangements during manual demolition tasks and the offset of fatigue over time after the tasks were performed. An experiment, including a muscle fatigue test and a muscle fatigue recovery test, was performed. Seventeen male adults without experience in demolition hammer operation were recruited as human participants. Two demolition hammers (large and small) were adopted. The push force was either 20 or 40 N. The posture mimicked that of a demolition task on a wall. In the muscle fatigue test, the muscle strength (MS) before and after the demolition task, maximum endurance time (MET), and the Borg category-ratio-10 (CR-10) ratings of perceived exertion after the demolition task were measured. In the muscle fatigue recovery test, MS and CR-10 at times 1, 2, 3, 4, 5, and 6 min were recorded. Statistical analyses were performed to explore the influence of push force and the weight of the tool on MS, MET, and CR-10. Both muscle fatigue models and muscle fatigue recovery models were established and validated. The results showed that push force affected MET significantly (p < 0.05). The weight of the tool was significant (p < 0.05) only on the CR-10 rating after the first pull. During the muscle fatigue recovery test, the MS increase and the CR-10 decrease were both significant (p < 0.05) after one or more breaks. Models of MET and MS prediction were established to assess muscle fatigue recovery, respectively. The absolute (AD) and relative (RD) deviations of the MET model were 1.83 (±1.94) min and 34.80 (±31.48)%, respectively. The AD and RD of the MS model were 1.39 (±0.81) N and 1.9 (±1.2)%, respectively. These models are capable of predicting the progress and recovery of muscle fatigue, respectively, and may be adopted in work/rest arrangements for novice workers performing demolition tasks.

Why Do People with Severe Mental Illness Have Poor Cardiovascular Health?—The Need for Implementing a Recovery-Based Self-Management Approach

People with severe mental illness (SMI) die significantly earlier than their well counterparts, mainly due to preventable chronic conditions such as cardiovascular disease (CVD). Based on the existing research, this perspective paper summarises the key contributors to CVD in people with SMI to better target the areas that require more attention to reduce, and ultimately resolve this health inequity. We discuss five broad factors that, according to current international evidence, are believed to be implicated in the development and maintenance of CVD in people with SMI: (1) bio-psychological and lifestyle-related factors; (2) socio-environmental factors; (3) health system-related factors; (4) service culture and practice-related factors; and (5) research-related gaps on how to improve the cardiovascular health of those with SMI. This perspective paper identifies that CVD in people with SMI is a multi-faceted problem involving a range of risk factors. Furthermore, existing chronic care or clinical recovery models alone are insufficient to address this complex problem, and none of these models have identified the significant roles that family caregivers play in improving a person’s self-management behaviours. A new framework is proposed to resolve this complex health issue that warrants a collaborative approach within and between different health and social care sectors.

A large invasive consumer reduces coastal ecosystem resilience by disabling positive species interactions

Invasive consumers can cause extensive ecological damage to native communities but effects on ecosystem resilience are less understood. Here, we use drone surveys, manipulative experiments, and mathematical models to show how feral hogs reduce resilience in southeastern US salt marshes by dismantling an essential marsh cordgrass-ribbed mussel mutualism. Mussels usually double plant growth and enhance marsh resilience to extreme drought but, when hogs invade, switch from being essential for plant survival to a liability; hogs selectively forage in mussel-rich areas leading to a 50% reduction in plant biomass and slower post-drought recovery rate. Hogs increase habitat fragmentation across landscapes by maintaining large, disturbed areas through trampling of cordgrass during targeted mussel consumption. Experiments and climate-disturbance recovery models show trampling alone slows marsh recovery by 3x while focused mussel predation creates marshes that may never recover from large-scale disturbances without hog eradication. Our work highlights that an invasive consumer can reshape ecosystems not just via competition and predation, but by disrupting key, positive species interactions that underlie resilience to climatic disturbances.

The seasonality of three childhood infections in a pre-industrial society without schools

Background: The burden of many infectious diseases varies seasonally and a better understanding of the drivers of infectious disease seasonality would help to improve public health interventions. For directly transmitted highly-immunizing childhood infections, the leading hypothesis is that seasonality is strongly driven by social gatherings imposed by schools, with maxima and minima during school terms and holidays respectively. However, we currently have a poor understanding of the seasonality of childhood infections in societies without schools and whether these are driven by human social gatherings. Here, we used unique nationwide data consisting of >40 epidemics over 100 years in 18th and 19th century Finland, an agricultural pre-health care society without schools, to (i) quantify the seasonality of three easily identifiable childhood infections, smallpox, pertussis and measles and (ii) test the extent to which seasonality of these diseases is driven by seasonal social gatherings. Methods: We quantified the seasonality of transmission using time series Suscpetibel-Infected-Recovery models, wavelet analyses and general additive mixed models.Results: We found that all three infections were seasonal and the seasonality patterns differed from those in industrialized societies with schools. Smallpox and measles showed high transmission in the first half of the year, but we could not associate this with seasonal human gatherings events. For pertussis, however, transmission was higher during social gathering events such as New Year and Easter.Conclusions: Our results show that the seasonality of childhood infections is more variable than previously described in other populations and indicate a pathogen-specific role of human social aggregation in driving the infectious disease dynamics.Funding: Academy of Finland (278751, 292368), Nordforsk (104910), the Ehrnrooth Foundation, the Finnish Cultural Foundation, the University of Turku Foundation and the Doctoral Programme in Biology, Geography and Geology, University of Turku.

The Trauma Recovery Actions Checklist: Applying Mixed Methods to a Holistic Gender-Based Violence Recovery Actions Measure

Gender-Based Violence (GBV) trauma recovery models have evolved in such a way that survivors are viewed as actively engaging in a multitude of strategies. In addition to seeking help and coping, survivors engage in diverse lifestyle, social, spiritual, and practical strategies to promote their health and wellbeing. This exploratory sequential mixed-methods study develops an instrument to measure the holistic recovery actions used by GBV survivors. The qualitative phase combined recovery action codes from interviews with 50 GBV survivors in three different survivor samples to create an initial six-concept 41-item Trauma Recovery Actions Checklist (TRAC). The quantitative psychometrics phase used data from 289 American GBV survivors. Results revealed a five-factor 35-item final version (sharing/connecting; building positive emotions; reflecting and creating healing spaces; establishing security; and planning the future). There were positive significant correlations between sharing/connecting and depression scores, and between sharing/connecting, reflecting, and building security with PTSD scores. No correlations were found between any recovery action type and the barriers to help-seeking subscales of Problem Management Beliefs, Discrimination, or Unavailability. However, there were significant negative correlations between Shame and Financial barriers and Sharing/Connecting, and between Feeling Frozen, Constraints, and Establishing Security. Implications for research, clinical practice and ways of understanding survivorship recovery are suggested.

Inverse and forward uncertainty quantification of models for foam–assisted enhanced oil recovery

Like many other engineering applications, oil recovery and enhanced oil recovery are sensitive to the correct administration of economic resources. Pilot tests and core flood experiments are crucial elements to design an enhanced oil recovery (EOR) project. In this direction, numerical simulators are accessible alternatives for evaluating different engineering configurations at many diverse scales (pore, laboratory, and field scales). Despite the advantages that numerical simulators possess over laboratory experiences, they are not fully protected against uncertainties. In this thesis, we show advances in analyzing uncertainties in two-–phase reservoir simulations, focusing on foam–based EOR. The methods employed in this thesis analyze how experimental uncertainties affect reservoir simulator’s responses. Our framework for model calibration and uncertainty quantification uses the Markov Chain Monte Carlo method. The parametric uncertainty is tested against identifiability studies revealing situations where posterior density distributions with high variability are related to high uncertainties and practical non–identifiability issues. The model’s reliability was evaluated by adopting surrogate models based on polynomial chaos expansion when the computational cost was an issue for the analysis. Once we quantified the model’s output variability, we performed a global sensitivity analysis to map the model’s uncertainty to the input parameters distributions. Main and total Sobol indices were used to investigate the model’s uncertainty and highlight how key parameters and their interactions influence the simulation’s output. As a consequence of the results presented in this thesis, we show a technique for parameter and uncertainty estimation that can be explored to reduce the uncertainty in foam–assisted oil recovery models, which in turn can provide reliable computational simulations. Such conclusions are of utmost interest and relevance for the design of adequate techniques for enhanced oil recovery.

Modelling the Creep of Nickel

Many metals and alloys have a stress exponent for the creep rate that is considerably higher than the value of 3 that is typically predicted by creep recovery models. One example is pure Ni. Creep data from Norman and Duran that are analyzed in the paper give a stress exponent of about 7 in the temperature range 0.3 to 0.55 of the melting point. It has recently been shown that the high creep exponent of Al and Cu in the power-law break down regime can be explained by the presence of strain-induced vacancies. By applying a creep recovery model that does not involve adjustable parameters, it is shown that strain induced vacancies can also explain the high-stress exponent of pure nickel.

The Role of Climate Change in the Assessment of the Seismic Resilience of Infrastructures

Climate change is modifying scientific attitudes toward pre- and post-event assessments of natural hazards. Unprecedented levels of destruction need renewed focus on addressing and protecting communities forcing the decision makers to change their attention to vulnerability and risk assessment. In particular, society and economy rely heavily on infrastructures, as fundamental links for movement of goods and people, and are extremely vulnerable to multiple hazards (such as droughts, floods, storms, and coastal hazards). In this regard, resilience quantifies the recovery time and procedures to facilitate and enhance pre-hazard and post-hazard event mitigation and emergency response strategies of systems and entire communities. Resilience calculation depends on two important contributions: loss and recovery models that need to consider the effects of climate change. This paper aims to propose a methodology that implements the most recent approaches to assess climate change inside the traditional framework of resilience. The proposed framework is then applied to a case study of a bridge.