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We estimate the source parameters of small-magnitude earthquakes that occurred during 2008-2020 in the Irpinia faults area (southern Italy).
We apply a spectral decomposition approach to isolate the source contribution from propagation and site effects for similar to 3000 earthquakes in the local magnitude range between M-L 0 and 4.2.
We develop our analyses in three steps. First, we fit the Brune (1970) model to the nonparametric source spectra to estimate corner frequency and seismic moment, and we map the spatial distribution of stress drop across the Irpinia area.
We found stress drops in the range 0.4-8.1 MPa, with earthquakes deeper than 7 km characterized by higher average stress drop (i.e., 3.2 MPa).
Second, assuming a simple stress-release model (kanamori and Heaton, 2000), we derive fracture energy and critical slip-weakening distance. The spatial variability of stress drop and fracture energy allows us to image the present stress conditions of fault segments activated during the 23 November 1980 M-s 6.9 earthquake.
The variability of the source parameters shows clear patterns of the fault mechanical properties, suggesting that the Irpinia fault system can be divided into three main sectors, with the northern and southern ones showing different properties from the central one.
Our results agree with previous studies indicating the presence of fluids with different composition in the different sectors of the Irpinia fault system. In the third step, we compare the time evolution of source parameters with a time series of geodetic displacement recorded near the fault system.
Temporal trends in the correlation between geodetic displacement and different source parameters indicate that the poroelastic deformation perturbation generated by the karst aquifer recharge is modulating not only the occurrence rate of micro-seismicity ( D' Agostino et al., 2018) but may lead to rupture asperities with different sizes and characteristics.
On 21 April 2021, the European Commission presented its long-awaited proposal for a Regulation “laying down harmonized rules on Artificial Intelligence”, the so-called “Artificial Intelligence Act” (AIA). This article takes a critical look at the proposed regulation. After an introduction (1), the paper analyzes the unclear preemptive effect of the AIA and EU competences (2), the scope of application (3), the prohibited uses of Artificial Intelligence (AI) (4), the provisions on high-risk AI systems (5), the obligations of providers and users (6), the requirements for AI systems with limited risks (7), the enforcement system (8), the relationship of the AIA with the existing legal framework (9), and the regulatory gaps (10). The last section draws some final conclusions (11).
Electrochemical methods offer great promise in meeting the demand for user-friendly on-site devices for monitoring important parameters. The food industry often runs own lab procedures, for example, for mycotoxin analysis, but it is a major goal to simplify analysis, linking analytical methods with smart technologies. Enzyme-linked immunosorbent assays, with photometric detection of 3,3',5,5'-tetramethylbenzidine (TMB), form a good basis for sensitive detection. To provide a straightforward approach for the miniaturization of the detection step, we have studied the pitfalls of the electrochemical TMB detection. By cyclic voltammetry it was found that the TMB electrochemistry is strongly dependent on the pH and the electrode material. A stable electrode response to TMB could be achieved at pH 1 on gold electrodes. We created a smartphone-based, electrochemical, immunomagnetic assay for the detection of ochratoxin A in real samples, providing a solid basis for sensing of further analytes.
Solar filaments often erupt partially. Although how they split remains elusive, the splitting process has the potential of revealing the filament structure and eruption mechanism. Here we investigate the pre-eruption splitting of an apparently single filament and its subsequent partial eruption on 2012 September 27. The evolution is characterized by three stages with distinct dynamics. During the quasi-static stage, the splitting proceeds gradually for about 1.5 hr, with the upper branch rising at a few kilometers per second and displaying swirling motions about its axis. During the precursor stage that lasts for about 10 minutes, the upper branch rises at tens of kilometers per second, with a pair of conjugated dimming regions starting to develop at its footpoints; with the swirling motions turning chaotic, the axis of the upper branch whips southward, which drives an arc-shaped extreme-ultraviolet front propagating in a similar direction. During the eruption stage, the upper branch erupts with the onset of a C3.7-class two-ribbon flare, while the lower branch remains stable. Judging from the well-separated footpoints of the upper branch from those of the lower one, we suggest that the pre-eruption filament processes a double-decker structure composed of two distinct flux bundles, whose formation is associated with gradual magnetic flux cancellations and converging photospheric flows around the polarity inversion line.
Non-local muscle fatigue effects on muscle strength, power, and endurance in healthy individuals
(2021)
Background
The fatigue of a muscle or muscle group can produce global responses to a variety of systems (i.e., cardiovascular, endocrine, and others). There are also reported strength and endurance impairments of non-exercised muscles following the fatigue of another muscle; however, the literature is inconsistent.
Objective
To examine whether non-local muscle fatigue (NLMF) occurs following the performance of a fatiguing bout of exercise of a different muscle(s).
Design
Systematic review and meta-analysis.
Search and Inclusion
A systematic literature search using a Boolean search strategy was conducted with PubMed, SPORTDiscus, Web of Science, and Google Scholar in April 2020, and was supplemented with additional 'snowballing' searches up to September 2020. To be included in our analysis, studies had to include at least one intentional performance measure (i.e., strength, endurance, or power), which if reduced could be considered evidence of muscle fatigue, and also had to include the implementation of a fatiguing protocol to a location (i.e., limb or limbs) that differed to those for which performance was measured. We excluded studies that measured only mechanistic variables such as electromyographic activity, or spinal/supraspinal excitability. After search and screening, 52 studies were eligible for inclusion including 57 groups of participants (median sample = 11) and a total of 303 participants.
Results
The main multilevel meta-analysis model including all effects sizes (278 across 50 clusters [median = 4, range = 1 to 18 effects per cluster) revealed a trivial point estimate with high precision for the interval estimate [- 0.02 (95% CIs = - 0.14 to 0.09)], yet with substantial heterogeneity (Q((277)) = 642.3, p < 0.01), I-2 = 67.4%). Subgroup and meta-regression analyses showed that NLMF effects were not moderated by study design (between vs. within-participant), homologous vs. heterologous effects, upper or lower body effects, participant training status, sex, age, the time of post-fatigue protocol measurement, or the severity of the fatigue protocol. However, there did appear to be an effect of type of outcome measure where both strength [0.11 (95% CIs = 0.01-0.21)] and power outcomes had trivial effects [- 0.01 (95% CIs = - 0.24 to 0.22)], whereas endurance outcomes showed moderate albeit imprecise effects [- 0.54 (95% CIs = - 0.95 to - 0.14)].
Conclusions
Overall, the findings do not support the existence of a general NLMF effect; however, when examining specific types of performance outcomes, there may be an effect specifically upon endurance-based outcomes (i.e., time to task failure). However, there are relatively fewer studies that have examined endurance effects or mechanisms explaining this possible effect, in addition to fewer studies including women or younger and older participants, and considering causal effects of prior training history through the use of longitudinal intervention study designs. Thus, it seems pertinent that future research on NLMF effects should be redirected towards these still relatively unexplored areas.
Involvement in sport and exercise not only provides participants with health benefits but can be an important aspect of living a meaningful life. The COVID-19 pandemic and the temporary cessation of public life in March/April/May 2020 came with restrictions, which probably also made it difficult, if not impossible, to participate in certain types of sport or exercise. Following the philosophical position that different types of sport and exercise offer different ways of "relating to the world," this study explored (dis)continuity in the type of sport and exercise people practiced during the pandemic-related lockdown, and possible effects on mood. Data from a survey of 601 adult exercisers, collected shortly after the COVID-19 outbreak in Finland, were analyzed. Approximately one third (35%) of the participants changed their "worldmaking" and shifted to "I-Nature"-type activities. We observed worse mood during the pandemic in those who shifted from "I-Me," compared to those who had preferred the "I-Nature" relation already before the pandemic and thus experienced continuity. The clouded mood of those experiencing discontinuity may be the result of a temporary loss of "feeling at home" in their new exercise life-world. However, further empirical investigation must follow, because the observed effect sizes were small.
Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a competitive inhibitory regulatory interaction with an enzyme. First, we show that CIRI outperforms the naive approach based on a structural similarity threshold for a putative competitive inhibitor and the substrates of a metabolic reaction. We also validate the performance of CIRI on several unseen data sets and databases of metabolite-protein interactions not used in the training, and demonstrate that the classifier can be effectively used to predict competitive inhibitory interactions. Finally, we show that CIRI can be employed to refine predictions about metabolite-protein interactions from a recently proposed PROMIS approach that employs metabolomics and proteomics profiles from size exclusion chromatography in E. coli to predict metaboliteprotein interactions. Altogether, CIRI fills a gap in cataloguing metabolite-protein interactions and can be used in directing future machine learning efforts to categorize the regulatory type of these interactions.
Clusty is a new open source toolbox dedicated to earthquake clustering based on waveforms recorded across a network of seismic stations. Its main application is the study of active faults and the detection and characterization of faults and fault networks. By using a density-based clustering approach, earthquakes pertaining to a common fault can be recognized even over long fault segments, and the first-order geometry and extent of active faults can be inferred. Clusty implements multiple techniques to compute a waveform based network similarity from maximum cross-correlation coefficients at multiple stations. The clustering procedure is designed to be transparent and parameters can be easily tuned. It is supported by a number of analysis visualization tools which help to assess the homogeneity within each cluster and the differences among distinct clusters. The toolbox returns graphical representations of the results. A list of representative events and stacked waveforms facilitate further analyses like moment tensor inversion. Results obtained in various frequency bands can be combined to account for large magnitude ranges. Thanks to the simple configuration, the toolbox is easily adaptable to new data sets and to large magnitude ranges. To show the potential of our new toolbox, we apply Clusty to the aftershock sequence of the M-w 6.9 25 October 2018 Zakynthos (Greece) Earthquake. Thanks to the complex tectonic setting at the western termination of the Hellenic Subduction System where multiple faults and faulting styles operate simultaneously, the Zakynthos data set provides an ideal case-study for our clustering analysis toolbox. Our results support the activation of several faults and provide insight into the geometry of faults or fault segments. We identify two large thrust faulting clusters in the vicinity of the main shock and multiple strike-slip clusters to the east, west and south of these clusters. Despite its location within the largest thrust cluster, the main shock does not show a high waveform similarity to any of the clusters. This is consistent with the results of other studies suggesting a complex failure mechanism for the main shock. We propose the existence of conjugated strike-slip faults in the south of the study area. Our waveform similarity based clustering toolbox is able to reveal distinct event clusters which cannot be discriminated based on locations and/or timing only. Additionally, the clustering results allows distinction between fault and auxiliary planes of focal mechanisms and to associate them to known active faults.
Scanning manufacturing parameters determining the residual stress state in LPBF IN718 small parts
(2021)
The influence of scan strategy on the residual stress (RS) state of an as-built IN718 alloy produced by means of laser powder bed fusion (LPBF) is investigated. Two scan vector rotations (90 degrees-alternation and 67 degrees-rotation), each produced following two different scan vector lengths (long and short), are used to manufacture four rectangular prisms. Neutron diffraction (ND) and laboratory X-ray diffraction (XRD) techniques are used to map the bulk and surface RS state, respectively. The distortion induced upon removal from the baseplate is measured via profilometry. XRD measurements show that the two long scan vector strategies lead to higher RS when compared with the equivalent short scan vector strategies. Also, the 67 degrees-rotation strategies generate lower RS than their 90 degrees-alternation counterparts. Due to the lack of reliable stress-free d0 references, the ND results are analyzed using von Mises stress. In general, ND results show significant RS spatial non-uniformity. A comparison between ND and distortion results indicates that the RS component parallel to the building direction (Z-axis) has a predominant role in the Z-displacement. The use of a stress balance scheme allows to discuss the d0 variability along the length of the specimens, as well as examine the absolute RS state.
Objective:
Little attention has been given to the relationship between cyber polyvictimization and academic outcomes (e.g., classroom misconduct, school readiness, academic performance, absenteeism, school behavioral problems), and the factors, such as parent social support, that buffer against the negative outcomes associated with experiencing multiple forms of victimization. Addressing gaps in the literature by including a longitudinal design and objective assessments of academic outcomes, the present study examined the moderating effect of parent social support in the association between cyber polyvictimization and academic outcomes over one and a half years later.
Method:
Participants were 371 8th graders (50% female) from middle schools in the United States, who completed questionnaires on offline and cyber polyvictimization and parent social support during the 7th grade. Teachers completed questionnaires on students' classroom misconduct and school readiness during 7th and 8th grade. School records were used to determine absenteeism, academic performance, and school behavioral problems (i.e., referrals, in-school suspension, out-of-school suspension) during 7th and 8th grade.
Results:
Findings revealed that 7th grade cyber polyvictimization was related positively to 8th grade classroom misconduct, absenteeism, and school behavioral problems, while it was negatively associated with 8th grade academic performance and school readiness. Parent social support moderated the associations between cyber polyvictimization and school readiness, academic performance, and absenteeism. Conclusion: The results highlight the importance of intervening in adolescents' experience of cyber polyvictimization to reduce negative academic outcomes.