Search

It has become evident that additional metrics along the particle mass concentration, together with dense air quality monitoring networks within cities, are needed to understand the most efficient ways to tackle the health burden of particulate pollution. Particle lung-deposited surface area (LDSAal) is a metric to estimate particle exposure in the lung alveoli, and it has gained interest as a parameter for air quality monitoring as it is relatively easy and cost-efficient to measure with electrical particle sensors. Also, various studies have indicated its potential as a health-relevant metric. In addition to the electrical particle sensors, the LDSAal can be measured with various size distribution methods. However, different LDSAal measurement methods have fundamental differences in their operation principles, e.g., related to the measurement size ranges, size classification or conversion from the originally measured quantity into the LDSAal. It is not well understood how these differences affect the accuracy of the measurement in ambient conditions, where especially the particle effective density and hygroscopicity can considerably change the particle lung deposition efficiencies. In this study, the electrical particle sensor measurement (Partector) and two size distribution approaches (ELPI+ and DMPS/SMPS) were compared in road traffic environments with different environmental conditions in Helsinki and Prague. The results were compared by utilising the general assumptions of the LDSAal measurement (spherical hydrophobic particles with the standard density) and by evaluating the effects of the particle effective density and hygroscopicity. Additionally, the Partector and ELPI+ approaches were compared in various urban environments near road traffic, airports, river traffic and residential wood combustion. The results show that the comparison of different LDSAal measurement methods can be complicated in ambient measurements. The challenges were especially related to the accumulation mode particles roughly larger than 200–400 nm for which the dominant deposition mechanism in the lung changes from diffusion to impaction and the particle effective density and hygroscopicity tend to increase. On the other hand, the results suggest that the differences between the methods are reasonably low when considering only ultrafine and soot particles, which have an effective density closer to the standard (1.0 g cm−3) and are more hydrophobic, highlighting the suitability of the LDSAal as a monitored metric when estimating the spatial differences in the particulate pollution within cities.

Among various Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) charged from the grid are seen as the most advanced ones, as they can drive dozens of kilometers using only the electric engine and thus producing less tailpipe greenhouse gas emissions than vehicles with internal combustion engines or other HEVs. The proportion of PHEVs among all vehicles is still relatively low but increasing rapidly in many countries. However, the real-world emissions from these novel hybrid technologies are not straightforward to estimate. This study investigates multiple properties of the particle emissions of a PHEV, with gasoline direct injection, GDI, compared to two conventional gasoline vehicles, one with port fuel injection, PFI and one with GDI. Distance-based emission factors (EFs) for each vehicle in various driving modes, including battery-hold and battery-charge driving modes for the PHEV, were analyzed. The results showed that the PHEV produced smaller particles in size, resulting that particle mass (PM) and black carbon (BC) were lower by factor of ten in comparison to EFs from the vehicles with PFI and GDI engines. The PHEV consistently emitted lower distance-based EFs than the PFI and GDI vehicles in all driving modes, though EF for particle number (PN) in battery-charge mode was close to the EFs from the other two vehicles. The study also found that the vehicle cold start effect was present in the case of the shorter driving route but not as significant in the longer one. Overall, the study demonstrated that PHEVs could produce lower particle and BC emissions compared to traditional gasoline-powered vehicles. The vehicle cold start and systematic combustion engine restart effects still can have significant impacts on particle emissions, especially in shorter trips.

Abstract Background Patients with fibromyalgia (FM) exhibit low peak oxygen uptake ( $$\dot{\text{V}}$$ V ˙ O2peak). We aimed to detect the contribution of cardiac output to ( $$\dot{\text{Q}}$$ Q ˙ ) and arteriovenous oxygen difference $$[\text{C}(\text{a-v})\text{O}_{2}]$$ [ C ( a-v ) O 2 ] to $$\dot{\text{V}}\text{O}_{2}$$ V ˙ O 2 from rest to peak exercise in patients with FM. Methods Thirty-five women with FM, aged 23 to 65 years, and 23 healthy controls performed a step incremental cycle ergometer test until volitional fatigue. Alveolar gas exchange and pulmonary ventilation were measured breath-by-breath and adjusted for fat-free body mass (FFM) where appropriate. $$\dot{\text{Q}}$$ Q ˙ (impedance cardiography) was monitored. $$\text{C}(\text{a-v})\text{O}_{2}$$ C ( a-v ) O 2 was calculated using Fick’s equation. Linear regression slopes for oxygen cost (∆ $$\dot{\text{V}}$$ V ˙ O2/∆work rate) and $$\dot{\text{Q}}$$ Q ˙ to $$\text{V}$$ V O2 (∆ $$\dot{\text{Q}}$$ Q ˙ /∆ $$\dot{\text{V}}$$ V ˙ O2) were calculated. Normally distributed data were reported as mean ± SD and non-normal data as median [interquartile range]. Results $$\dot{\text{V}}$$ V ˙ O2peak was lower in FM patients than in controls (22.2 ± 5.1 vs. 31.1 ± 7.9 mL∙min−1∙kg−1, P

Abstract Fibromyalgia patients vary in clinical phenotype and treatment can be challenging. The pathophysiology of fibromyalgia is incompletely understood but appears to involve metabolic changes at rest or in response to stress. We enrolled 54 fibromyalgia patients and 31 healthy controls to this prospective study. Symptoms were assessed using the Fibromyalgia Impact Questionnaire (FIQ) and blood samples were collected for metabolomics analysis at baseline and after an oral glucose tolerance test and a cardiopulmonary exercise test. We identified key symptoms of fibromyalgia and related them to changes in metabolic pathways with supervised and unsupervised machine learning methods. Algorithms trained with the FIQ information assigned the fibromyalgia diagnosis in new data with balanced accuracy of 88% while fatigue alone already provided the diagnosis with 86% accuracy. Supervised analyses reduced the metabolomic information from 77 to 13 key markers. With these metabolites, fibromyalgia could be identified in new cases with 79% accuracy. In addition, 5‐hydroxyindole‐3‐acetic acid and glutamine levels correlated with the severity of fatigue. Patients differed from controls at baseline in tyrosine and purine pathways, and in the pyrimidine pathway after the stress challenges. Several key markers are involved in glutaminergic neurotransmission. This data‐driven analysis highlights fatigue as a key symptom of fibromyalgia. Fibromyalgia is associated with metabolic changes which also reflect the degree of fatigue. Responses to metabolic and physical stresses result in a metabolic pattern that allows discrimination of fibromyalgia patients from controls and narrows the focus on key pathophysiological processes in fibromyalgia as treatment targets.

Recent studies indicate that monitoring only fine particulate matter (PM2.5) may not be enough to understand and tackle the health risk caused by particulate pollution. Health effects per unit PM2.5 seem to increase in countries with low PM2.5, but also near local pollution sources (e.g., traffic) within cities. The aim of this study is to understand the differences in the characteristics of lung-depositing particles in different geographical regions and urban environments. Particle lung deposited surface area (LDSAal) concentrations and size distributions, along with PM2.5, were compared with ambient measurement data from Finland, Germany, Czechia, Chile, and India, covering traffic sites, residential areas, airports, shipping, and industrial sites. In Finland (low PM2.5), LDSAal size distributions depended significantly on the urban environment and were mainly attributable to ultrafine particles (

Present-day Software Engineering working environment is highly international. Teams are commonly formed of people from different nationalities and cultural backgrounds. The team’s productivity and efficiency depend significantly on its members’ international communication and collaboration competences. Therefore, understanding the required competences is essential. The software organizations hiring new software engineers need to be able to define and describe the competences, and the junior software engineers applying for their first jobs should know what competences they need to possess and demonstrate. In this study, to increase understanding of necessary communication and collaboration competences in the international Software Engineering working environment, competences were, first, analyzed from the job advertisements applicable to junior software engineers and, second, identified by a literature review. The results were compared to identify what competences junior software engineers should learn and demonstrate, to be competent in the international software engineer job markets. The job advertisement findings show that the international operational environment expects extensive competence in collaboration, high competence in English, and considerable competence in local language and communication. Intercultural competence and other languages are hardly expected. The literature review emphasizes inter-related communication, collaboration, intercultural, and language competences at various levels. Eventually, junior software engineers should demonstrate a tolerant and adaptable attitude, cooperativeness, independence, openness, courage to influence, oral and written social interaction skills, fluent professional English and local language communication skills as well as field-specific and general collaboration methods. The findings benefit juniors and Software Engineering education through which also employers obtain more competent jobseekers.

Recent developments in vehicle automation require simulations of human-robot interactions in the road traffic context, which can be achieved by computational models of human behavior such as game theory. Game theory provides a good insight into road user behavior by considering agents’ interdependencies. However, it is still unclear whether conventional game theory is suitable for modeling vehicle-pedestrian interactions at unsignalized locations or if more complex models like behavioral game theory are needed. Hence, we compared four game-theoretic models based on two different payoff formulations and two solving algorithms, to answer this question. Unlike the most previous studies that employed naturalistic datasets to test and validate such models, this study utilized a distributed simulation dataset to test and compare the models. The study was conducted by connecting a CAVE-based pedestrian simulator to a motion-based driving simulator to replicate the traffic scenarios for 32 pedestrian-driver pairs. The findings demonstrated that there is a high variability between participant pairs’ behaviors. Our proposed behavioral game-theoretic model outperformed other models in predicting the interaction outcome. This translates to a decrease by 70% and 67% in the root mean squared error (RMSE) when compared to the baseline model, for marked and unmarked crossings, respectively. The model can also predict which interaction will take the longest time to resolve. According to our results, road users cannot be expected to behave in line with the Nash equilibrium of conventional game theory that underscores the complexity of human behavior with implications for the testing and development of automated vehicles.

Abstract Fibromyalgia (FM) is associated with sympathetically dominant dysautonomia, but the connection between dysautonomia and FM symptoms is unclear. Dysautonomia can be analysed with heart rate variability (HRV) and it has been proposed that FM patients comprise subgroups with differing profiles of symptom severity. In our study, 51 female FM patients aged 18 to 65 years and 31 age-matched healthy female controls followed a 20-min protocol of alternating relaxation and cognitive stress (mental arithmetic). Heart rates and electrocardiograms were registered. The HRV measures of heart rate (HR), mean interval between heart beats (RRmean), root mean squared interval differences of successive beats (RMSSD), and the standard deviation of intervals between normal heart beats (SDNN) were analysed with generalized linear modelling. Features in HRV reactivity which differed between FM patients and controls were used to cluster the FM patients and cluster characteristics were analysed. FM patients had higher baseline HR (72.3 [SD 12.7] vs 64.5 [7.80], p

We studied ice-nucleating abilities of particulate emissions from a modern heavy-duty diesel engine using three different types of fuel. The polydisperse particle emissions were sampled during engine operation and introduced to a continuous-flow diffusion chamber (CFDC) instrument at a constant relative humidity RHwater=110 %, while the temperature was ramped between −43 and −32 ∘C (T scan). The tested fuels were EN 590 compliant low-sulfur fossil diesel, hydrotreated vegetable oil (HVO), and rapeseed methyl ester (RME); all were tested without blending. Sampling was carried out at different stages in the engine exhaust aftertreatment system, with and without simulated atmospheric processing using an oxidation flow reactor. In addition to ice nucleation experiments, we used supportive instrumentation to characterize the emitted particles for their physicochemical properties and presented six parameters. We found that the studied emissions contained no significant concentrations of ice-nucleating particles likely to be of atmospheric relevance. The substitution of fossil diesel with renewable fuels, using different emission aftertreatment systems such as a diesel oxidation catalyst, and photochemical aging of total exhaust had only minor effect on their ice-nucleating abilities.

Many models account for the traffic flow of road users, but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations. Building on an existing model of human sensorimotor control, we develop an agent-based modeling framework applying the principles of utility maximization, motor primitives, and intermittent action decisions to account for the details of interactive behaviors among road users. The framework connects the three principles to the decision theory and is tested to determine whether such an approach can reproduce the following four phenomena. Firstly, when two pedestrians travel on crossing paths, their interaction is sensitive to initial kinematic asymmetries, and secondly, based on the asymmetries, the two pedestrians rapidly resolve collision conflict by adapting their behaviors. Thirdly, when a pedestrian crosses a road while facing an approaching car, the pedestrian adapts his or her crossing behavior according to the time-to-arrival of the car, and fourthly, either the pedestrian or the driver of the car may yield to the other to resolve their conflict. We show that these phenomena emerge naturally from our modeling framework. We believe the proposed behavior model and phenomenon-centered approach of analysis offer promising tools to examine road user interactions. We conclude with a discussion on how the model can be generalized to safety-critical situations and to include other variables affecting road-user interactions.

Introduction: This study was performed to assess the prevalence and circulating genotypes of rotavirus, norovirus, and sapovirus in children. The results were compared to those of previous surveillance studies covering the years 2006–2008, 2009–2011, and 2012–2014 with similar methodology and setting, encompassing the start of universal vaccination with RotaTeq in 2009. Methods: Stool samples were collected from children aged

Abstract Background Fibromyalgia (FM) patients are likely to differ from healthy controls in muscle activity and in reactivity to experimental stress. Methods We compared psychophysiological reactivity to cognitive stress between 51 female FM patients aged 18 to 65 years and 31 age- and sex-matched healthy controls. They underwent a 20-minute protocol consisting of three phases of relaxation and two phases of cognitive stress. We recorded surface electromyography normalized to maximum voluntary muscle contraction (%EMG), the percentage of time with no muscle activity (EMG rest time), and subjective pain and stress intensities. We compared group reactivity using linear modelling and adjusted for psychological and life-style factors. Results The FM patients had a significantly higher mean %EMG (2.2 % vs. 1.0 %, p

Several time synchronous ad hoc networks may operate in the same geographical area. Although network time synchronization within an individual network has been widely discussed, internetwork time synchronization relying on time sources the networks carry has not been addressed. Therefore, this paper proposes an internetwork time synchronization procedure that allows to use one network as a time source and share its time, like a global navigation satellite system time, to other networks. The internetwork time synchronization procedure runs in gateway nodes that could be a member in several networks, and it collaborates with the individual network time synchronization algorithms that operate at the network level. Consequently, the paper proposes a generic, robust hybrid network time synchronization protocol and analyses its performance using a sensible metric called a mutual consensus. It is shown that the common time is achieved with a limited variance in the networks that operate in the master-slave, distributed or hybrid mode. Provided numerical examples illustrate the behavior of the algorithm and confirm the theoretical findings.

Abstract Automated vehicles (AVs) will change the role of the driver, from actively controlling the vehicle to primarily monitoring it. Removing the driver from the control loop could fundamentally change the way that drivers sample visual information from the scene, and in particular, alter the gaze patterns generated when under AV control. To better understand how automation affects gaze patterns this experiment used tightly controlled experimental conditions with a series of transitions from ‘Manual’ control to ‘Automated’ vehicle control. Automated trials were produced using either a ‘Replay’ of the driver’s own steering trajectories or standard ‘Stock’ trials that were identical for all participants. Gaze patterns produced during Manual and Automated conditions were recorded and compared. Overall the gaze patterns across conditions were very similar, but detailed analysis shows that drivers looked slightly further ahead (increased gaze time headway) during Automation with only small differences between Stock and Replay trials. A novel mixture modelling method decomposed gaze patterns into two distinct categories and revealed that the gaze time headway increased during Automation. Further analyses revealed that while there was a general shift to look further ahead (and fixate the bend entry earlier) when under automated vehicle control, similar waypoint-tracking gaze patterns were produced during Manual driving and Automation. The consistency of gaze patterns across driving modes suggests that active-gaze models (developed for manual driving) might be useful for monitoring driver engagement during Automated driving, with deviations in gaze behaviour from what would be expected during manual control potentially indicating that a driver is not closely monitoring the automated system.