Spatio-temporal patterns in the coral reef communities of the Spermonde Archipelago, 2012–2014, II: Fish assemblages display structured variation related to benthic condition

The Spermonde Archipelago is a complex of ~70 mostly populated islands off Southwest Sulawesi, Indonesia, in the center of the Coral Triangle. The reefs in this area are exposed to a high level of anthropogenic disturbances. Previous studies have shown that variation in the benthos is strongly linked to water quality and distance from the mainland. However, little is known about the fish assemblages of the region and if their community structure also follows a relationship with benthic structure and distance from shore. In this study, we used eight islands of the archipelago, varying in distance from 1 to 55 km relative to the mainland, and 3 years of surveys, to describe benthic and fish assemblages and to examine the spatial and temporal influence of benthic composition on the structure of the fish assemblages. Cluster analysis indicated that distinct groups of fish were associated with distance, while few species were present across the entire range of sites. Relating fish communities to benthic composition using a multivariate generalized linear model confirmed that fish groups relate to structural complexity (rugosity) or differing benthic groups; either algae, reef builders (coral and crustose coralline algae) or invertebrates and rubble. From these relationships we can identify sets of fish species that may be lost given continued degradation of the Spermonde reefs. Lastly, the incorporation of water quality, benthic and fish indices indicates that local coral reefs responded positively after an acute disturbance in 2013 with increases in reef builders and fish diversity over relatively short (1 year) time frames. This study contributes an important, missing component (fish community structure) to the growing literature on the Spermonde Archipelago, a system that features environmental pressures common in the greater Southeast Asian region

Spatio-temporal patterns in coral reef communities of the Spermonde Archipelago, 2012-2014, I: Comprehensive reef monitoring of water and benthic indicators reflect changes in reef health

Pollution, fishing, and outbreaks of predators can heavily impact coastal coral reef ecosystems, leading to decreased water quality and benthic community shifts. To determine the main environmental drivers of coral reef status in the Spermonde Archipelago, Indonesia, we monitored environmental variables and coral reef benthic community structure along an on-to-offshore gradient annually from 2012 to 2014. Findings revealed that concentrations of phosphate, chlorophyll a-like fluorescence, suspended particulate matter, and light attenuation significantly decreased from on-to-offshore, while concentrations of dissolved O2 and values of water pH significantly increased on-to-offshore. Nitrogen stable isotope signatures of sediment and an exemplary common brown alga were significantly enriched nearshore, identifying wastewater input from the city of Makassar as primary N source. In contrast to the high temporal variability in water quality, coral reef benthic community cover did not show strong temporal, but rather, spatial patterns. Turf algae was the dominant group next to live coral, and was negatively correlated to live coral, crustose coralline algae (CCA), rubble and hard substrate. Variation in benthic cover along the gradient was explained by water quality variables linked to trophic status and physico-chemical variables. As an integrated measure of reef status and structural complexity, the benthic index, based on the ratio of relative cover of live coral and CCA to other coral reef organisms, and reef rugosity were determined. The benthic index was consistently low nearshore and increased offshore, with high variability in the midshelf sites across years. Reef rugosity was also lowest nearshore and increased further offshore. Both indices dropped in 2013, increasing again in 2014, indicating a period of acute disturbance and recovery within the study and suggesting that the mid-shelf reefs are more resilient to disturbance than nearshore reefs. We thus recommend using these two indices with a selected number of environmental variables as an integral part of future reef monitoring

Concept Study of a fast VTOL-UAV Technology-Demonstrator for MUM-T

The Manned Unmanned Teaming (MUM-T) of rotorcraft offers the potential to increase the effectivity and survivability of the combined tactical unit. Currently, commercially available Unmanned Aerial Vehicles (UAVs) with Vertical Take-Off and Landing (VTOL) capability are not specifically designed for fast forward flight and would slow down the entire tactical unit. This study presents the first results of the development of a technology-demonstrator with a maximum airspeed of at least 180 kt. The investigation of different VTOL-UAV concepts, the selection of a thrust-compound configuration and the first details of the predesign are described. Furthermore, the flight performance is analyzed with focus on maximum airspeed, power, endurance and range. The results show, that the proposed design of the VTOL-UAV is expected to fulfill the requirements

Structural Sizing of a Rotorcraft Fuselage Using an Integrated Design Approach

For many years, the primary design objective of new helicopters was the design of the main rotor(s). Within the last couple of years, this approach has changed into an assessment of all helicopter components as an overall system, thus turning rotorcraft design into a highly interdisciplinary process. For instance, aerodynamics, flight mechanics, and the structural evaluation strongly affect each other, and these mutual influences are taken into account from the early phase of the conceptual design. Weight prediction in early design stages represents an essential part of the design process as it determines the basic properties of the rotorcraft. Owing to its function to carry crew and payload but also to serve as the central mounting for all components, the fuselage represents a major part of the rotorcraft. Therefore, the structural design of the fuselage airframe constitutes a significant factor of the rotorcraft design at the preliminary level.&lt;br/&gt; In this paper, an approach to include a higher fidelity method using finite elements for the structural analysis of rotorcraft fuselages within an integrated design environment is presented. Model generation and static analysis are conducted automatically. The helicopter is described using a common parametric data model during the complete design process, therefore providing a fast analysis of model changes. The generic finite element model presented in this paper was generated and structurally sized in about 2.5 min using a standard office computer, thus offering the integration of higher fidelity methods into early design sizing loops.</jats:p

Understanding and controlling the friction of human hair

Pleasant sensory perception when touching, brushing, and combing hair is largely determined by hair friction. As hair ages and weathers, its friction increases, mainly due to the progressive loss of the protective 18-methyleicosanoic acid (18-MEA) monolayer on its surface. Hair also displays anisotropic friction due to the protruding edges of the cuticles, which can interlock when sliding towards the root of hair. Moreover, certain chemical (e.g. bleaching and colouring), thermal (e.g. straightening and curling), and mechanical (e.g. brushing and combing) processes can dramatically accelerate 18-MEA loss, leading to much higher friction and unsatisfactory sensory perception. Hair care products, and in particular conditioners, have been developed to temporarily repair this damage through the deposition of various chemicals on the surface of the hair. These formulations can reduce friction to levels similar to that measured for virgin hair. Other external factors can also affect hair friction, such as humidity and cleanliness, as well as biological characteristics, such as ethnicity and age. Here, we provide a perspective on the advances made in the field of hair tribology, meaning the friction, lubrication and wear of hair. Historic and state-of-the-art experimental, theoretic and computational techniques for measuring hair friction are reviewed. We discuss different hair friction mechanisms across the scales and review the roles of surface chemistry and surface roughness on hair tribology. The influence of hair care products on hair friction is further discussed. Finally, we highlight open challenges and opportunities for future hair tribology experiments and models

Coarse-grained molecular models of the surface of hair

We present a coarse-grained molecular model of the surface of human hair, which consists of a supported lipid monolayer, in the MARTINI framework. Using coarse-grained molecular dynamics (MD) simulations, we identify a lipid grafting distance that yields a monolayer thickness consistent with both atomistic MD simulations and experimental measurements of the hair surface. Coarse-grained models for fully-functionalised, partially damaged, and fully damaged hair surfaces are created by randomly replacing neutral thioesters with anionic sulfonate groups. This mimics the progressive removal of fatty acids from the hair surface by bleaching and leads to chemically heterogeneous surfaces. Using molecular dynamics (MD) simulations, we study the island structures formed by the lipid monolayers at different degrees of damage in vacuum and in the presence of polar (water) and non-polar (n-hexadecane) solvents. We also use MD simulations to compare the wetting behaviour of water and n-hexadecane droplets on the model surfaces through contact angle measurements, which are compared to experiments using virgin and bleached hair. The model surfaces capture the experimentally-observed transition of the hair surface from hydrophobic (and oleophilic) to hydrophilic (and oleophobic) as the level of bleaching damage increases. By selecting surfaces with specific damage ratios, we obtain contact angles from the MD simulations that are in good agreement with experiments for both solvents on virgin and bleached human hairs. To negate the possible effects of microscale curvature and roughness of real hairs on wetting, we also conduct additional experiments using biomimetic surfaces that are co-functionalised with fatty acids and sulfonate groups. In both the MD simulations and experiments, the cosine of the water contact angle increases linearly with the sulfonate group surface coverage with a similar slope. We expect that the proposed systems will be useful for future molecular dynamics simulations of the adsorption and tribological behaviour of hair, as well as other chemically heterogeneous surfaces

Predicting infection and sepsis; what predictors have been used to train machine learning algorithms? A systematic review

Abstract Introduction Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients who may be at risk of developing infection and subsequent sepsis and assist clinicians with their care management. Aim To identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis and inform clinical decision making. Methods This systematic review was registered in PROSPERO database (CRD42020158685). We searched 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase, using appropriate search terms. We included quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adult population (&amp;gt;18 years) in all care settings, which included data on predictors to develop machine learning algorithms. The timeframe of the search was 1st January 2000 till the 25th November 2019. Data extraction was performed using a data extraction sheet, and a narrative synthesis of eligible studies was undertaken. Narrative analysis was used to arrange the data into key areas, and compare and contrast between the content of included studies. Quality assessment was performed using Newcastle-Ottawa Quality Assessment scale, which was used to evaluate the quality of non-randomized studies. Bias was not assessed due to the non-randomised nature of the included studies. Results Fifteen articles met our inclusion criteria (Figure 1). We identified 194 predictors that were used to train machine learning algorithms to predict infection and subsequent sepsis, with 13 predictors used on average across all included studies. The most significant predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR&amp;lt;60ml/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit &amp;lt; 30%. These predictors were used for the development of all the algorithms in the fifteen articles. All included studies used artificial intelligence techniques to predict the likelihood of sepsis, with average sensitivity 77.5±19.27, and average specificity 69.45±21.25. Conclusion The type of predictors used were found to influence the predictive power and predictive timeframe of the developed machine learning algorithm. Two strengths of our review were that we included studies published since the first definition of sepsis was published in 2001, and identified factors that can improve the predictive ability of algorithms. However, we note that the included studies had some limitations, with three studies not validating the models that they developed, and many tools limited by either their reduced specificity or sensitivity or both. This work has important implications for practice, as predicting the likelihood of sepsis can help inform the management of patients and concentrate finite resources to those patients who are most at risk. Producing a set of predictors can also guide future studies in developing more sensitive and specific algorithms with increased predictive time window to allow for preventive clinical measures. </jats:sec