Field Study on Wood Accumulation at a Bridge Pier

Transported large wood (LW) in rivers may block at river infrastructures such as bridge piers and pose an additional flood hazard. An improved process understanding of LW accumulations at bridge piers is essential for a flood risk assessment. Therefore, we conducted a field study at the River Glatt in Zurich (Switzerland) to analyze the LW accumulation process of single logs at a circular bridge pier and to evaluate the results of previous flume experiments with respect to potential scale effects. The field test demonstrated that the LW accumulation process can be described by an impact, rotation, and separation phase. The LW accumulation was described by combining two simplified equilibria of acting forces and moments, which are mainly a function of the pier diameter, pier roughness, and flow properties. We applied the resulting analytic criterion to the field data and demonstrated that the criterion can explain the behavior of 82% of the logs. In general, the field observations confirmed previous results on the LW accumulation probability in the laboratory, which supports the applicability of laboratory studies to investigate LW–structure interactions.

Australia’s Black Summer pyrocumulonimbus super outbreak reveals potential for increasingly extreme stratospheric smoke events

The Black Summer fire season of 2019–2020 in southeastern Australia contributed to an intense ‘super outbreak’ of fire-induced and smoke-infused thunderstorms, known as pyrocumulonimbus (pyroCb). More than half of the 38 observed pyroCbs injected smoke particles directly into the stratosphere, producing two of the three largest smoke plumes observed at such altitudes to date. Over the course of 3 months, these plumes encircled a large swath of the Southern Hemisphere while continuing to rise, in a manner consistent with existing nuclear winter theory. We connect cause and effect of this event by quantifying the fire characteristics, fuel consumption, and meteorology contributing to the pyroCb spatiotemporal evolution. Emphasis is placed on the unusually long duration of sustained pyroCb activity and anomalous persistence during nighttime hours. The ensuing stratospheric smoke plumes are compared with plumes injected by significant volcanic eruptions over the last decade. As the second record-setting stratospheric pyroCb event in the last 4 years, the Australian super outbreak offers new clues on the potential scale and intensity of this increasingly extreme fire-weather phenomenon in a warming climate.

DEVELOPMENT AND VALIDATION OF AN INDUSTRY 4.0 ADAPTATION POTENTIAL SCALE (4IRAPS)

The aim of this study was to develop a scale that can measure the potential of adapting to Industry 4.0, which refers to the fourth industrial revolution described as a combination of the innovation of various digital technologies rapidly developed in recent years. In addition, the reliability and validity of the Industry 4.0 Adaptation Potential (4IRAPS) is demonstrated. This research was conducted in two stages of a pilot and a main study. The data was collected from 174 participants enrolled in technical and management departments at the graduate and associate degree levels of two different universities. A 50-item questionnaire concerning Industry 4.0 prepared by experts experienced in this field was applied to the participants. As a result of a factor analysis, 30 items and 11 subscales with low a factor load and reliability level were removed from the questionnaire. The reliability and validity of 4IRAPS were verified by” the analyses via PLS-SEM. Finally, the remaining four sub-dimensions referring to Industry 4.0 were labelled as interested, effort for adaptation, readiness, and pessimism. This study developed the first scale of the industry 4.0 adaptation potential. The scale consists of four sub-dimensions and 17 items. It was determined that this scale was statistically reliable and valid.

Predicting mammalian hosts in which novel coronaviruses can be generated

Novel pathogenic coronaviruses – such as SARS-CoV and probably SARS-CoV-2 – arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-host interactions remain unknown. Here, by deploying a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), we predict which mammals are hosts of multiple coronaviruses. We predict that there are 11.5-fold more coronavirus-host associations, over 30-fold more potential SARS-CoV-2 recombination hosts, and over 40-fold more host species with four or more different subgenera of coronaviruses than have been observed to date at >0.5 mean probability cut-off (2.4-, 4.25- and 9-fold, respectively, at >0.9821). Our results demonstrate the large underappreciation of the potential scale of novel coronavirus generation in wild and domesticated animals. We identify high-risk species for coronavirus surveillance.

The Role of Making Voluntary Function in Prognosticating and Preventing from Addiction Potential: A Survey Study Among Some Members of the Iranian Red Crescent Society

Background: Considering Voluntary Function (VF), the purpose of the present study was to prognosticate the addiction potential (AP) among some members of the Iranian Red Crescent Society (IRCS) in Iran. To get the research done, we made use of both correlative and descriptive methods as research methods. Methods: The statistical population of the current study included all youths who have been members of the IRCS. We employed multistage cluster sampling as the sampling method. We recruited 620 active volunteers of the IRCS from 31 province and 175 cities of Iran (48.7% female and 50.1 Male mean age 23.27±3.32, range 14–31 years). Iranian Addiction Potential Scale (IAPS) and Voluntary Function Inventory (VFI) have assisted us in collecting research data. Results: Findings proved that there was a negative significant correlation between the AP and all measurements of VF such as protective enhancement, understanding, career, values, and motives; meaning that the more time youth spent on participating in voluntary activities, the less likely they sought to resort to misusing AP. Findings of the simultaneous-entry multiple regression has proved that volunteerism could prognosticate 15% of changes in the AP as a criterion variable. Conclusions: Due to its positive features, volunteerism played a key role in prognosticating and preventing the AP. Thus, it is incumbent to draw particular attention to this worthy factor.

NIEPEWNOŚĆ NAUKI A DZIAŁANIE, CZYLI – PROBLEMY Z EVIDENCE – BASED POLICY

The purpose of this article is primarily to introduce the topic of scientific uncertainty to the wider context of economics and management. Scientific uncertainty is one of the manifestations of irreducible uncertainty and reflection on it should enable better decision making. An entity that bases its operation on current scientific research, which depreciates over time and ultimately leads to erroneous decisions, is referred to as the “loser”. The text indicates estimation of potential scale of this problem supplemented by an outline of sociological difficulties identified in the analysis of the process of building scientific statements. The article ends with a sketch of the answer to the question “how to act in the context of scientific uncertainty?”.

A New Look at Geyser Formation in Sewer Systems

Previous analysis of pressure and velocity data collected at a site experiencing stormwater geysers suggested a mechanism for the occurrence. Laboratory experiments attempting to reproduce the geysers were promising but inconclusive due to potential scale effects in the small scale experiments. Subsequent researchers have generally used this early work as the starting point for subsequent laboratory experiments. Over the years, a number of videos of geysers in actual systems have been obtained and a review of important features from these videos calls into question certain aspects of the original hypothesis. An alternative explanation for the phenomenon has been developed and an experimental system has been identified that may help resolve some questions. However, laboratory experiments may also be impacted by scale effects and several preliminary studies must be conducted to determine whether there is a fruitful path forward in advancing our knowledge of this phenomenon.

Digging roots is easier with AI

The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76) and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.