To evaluate the ability of woven fabrics to drape in a more accurate way, a three-dimensional point cloud of a draped woven fabric was captured via an in-house drape-scanner. A new indicator, total drape angle (TDA), was proposed based on the three-dimensional fabric drape to characterize the ability of a woven fabric to drape. The relationship between TDA and the drape coefficient (DC) was analyzed to validate the performance of TDA. The result indicated that TDA is more stable and representative than the traditional DC in characterizing the ability of a woven fabric to drape. In addition, the drape angle distribution function (DADF) of the triangular mesh was employed to describe fabric drape, as well as to bridge the gap between drape configuration and the warp bending rigidity of woven fabric. The results showed that the correlation coefficient between the real warp bending rigidity value and what was predicted warp based on DADF and fabric weight was 0.952.

Reef islands are some of the most highly sensitive landforms to the impacts of future environmental change. Previous assessments of island morphodynamics primarily relied on historical aerial and satellite imagery. These approaches limit analysis to two-dimensional parameters, with no ability to assess long-term changes to island volume or elevation. Here, we use high-resolution airborne LiDAR data to assess three-dimensional reef island features for 22 islands along the north-western coast of Australia. Our primary objective was to utilize two regional LiDAR datasets to identify characteristics indicative of island sensitivity and future vulnerability. Results show reef platform area to be an accurate predictor of island area and volume suggesting larger island volumes may reflect (1) increased carbonate production and supply from the reef platform and/or (2) enhanced shoreline protection by larger reef platforms. Locations of foredune scarping (an erosional signature) and island orientations were aligned to the regional wind and wave climate. Reef island characteristics (island area, volume, elevation, scarping, and platform area) were used to rank islands according to sensitivity, using a new Island Sensitivity Characteristics Index (ISCi) where low ISCi indicates stable islands (large areas and volumes, high elevations, and fewer scarped areas) and high ISCi indicates unstable islands (small areas and volumes, low elevations, and more scarped areas). Comparison of two LiDAR surveys from 2016 and 2018 validates the use of 3D morphometrics as important (direct) measurements of island landform change, and can complement the use of 2D parameters (e.g., area) moving forward. Results demonstrate that ongoing use of airborne LiDAR and other 3D technology for monitoring coral reef islands at regional scales will enable more accurate quantification of their sensitivity to future impacts of global environmental change.

Botrytis cinerea causes grey mould, a major disease occurring in vineyards worldwide, resulting in loss of grape production and wine quality. Predictive models of favorability of Botrytis cinerea were used. Therefore, a series of meteorological data from 2010 to 2019 was used. The results showed that the frequency of years with low risk of Botrytis cinerea was 10%, medium risk 10%, high risk 80%. The disease can drastically reduce both yield and wine quality (Ribereau Gayon et al., 1980). The harvest years 2010, 2011, 2012, 2013 and 2014, favored the manifestation of an attack degree of 62.9% (2012) and 34.2% (2013). Positive correlations were observed in the case of Broome index and Bacchus index with the duration of sunlight (r2 = 0.935), respectively (r2 = 0.944) and the sum of the hours of moisture on the leaves (r2 = 0.833, r2 = 0.848). Based on the results a model for prediction of Botrytis cinerea risk will be developed.

Botrytis cinerea causes grey mould, a major disease occurring in vineyards worldwide, resulting in loss of grape production and wine quality. Predictive models of favorability of Botrytis cinerea were used. Therefore, a series of meteorological data from 2010 to 2019 was used. The results showed that the frequency of years with low risk of Botrytis cinerea was 10%, medium risk 10%, high risk 80%. The disease can drastically reduce both yield and wine quality (Ribereau Gayon et al., 1980). The harvest years 2010, 2011, 2012, 2013 and 2014, favored the manifestation of an attack degree of 62.9% (2012) and 34.2% (2013). Positive correlations were observed in the case of Broome index and Bacchus index with the duration of sunlight (r2 = 0.935), respectively (r2 = 0.944) and the sum of the hours of moisture on the leaves (r2 = 0.833, r2 = 0.848). Based on the results a model for prediction of Botrytis cinerea risk will be developed.

The service life of wood in outdoor use under humid conditions is mainly determined by its material resistance and the exposure situation. Different standards such as EN 350 (2016) point on the relevance of wood’s resistance against moisture for its expected service life. Recently, different standardized but also numerous nonstandardized methods were suggested to test the water permeability of wooden materials. In the context of this study, different European-grown soft- and hardwoods, tropical hardwoods, modified wood and wood treated with water- and oil-borne preservatives were subjected to floating and submersion tests according to CEN/TS 16818 (2018) and different short-term water uptake and release tests. Moisture performance data from field tests with the same materials were analyzed and used to assess the predictive power of different laboratory moisture indicators. The moisture characteristics suggested by CEN/TS 16818 (2018)—rm₁₆₈ (residual moisture content after water uptake and release processes) and res₃₁₂ (residue as a percentage of the absorbed moisture)—showed the little potential to predict the outdoor moisture performance of the tested materials. In contrast, the mean moisture content during absorption and desorption (MCₘₑₐₙ) predicted well the outdoor moisture performance of the materials under test. Short-term water uptake and release of small specimens also showed high predictive power.

ABSTRACT Meat productivity and quality of beef are determined by a number of factors, including pedigree traits of animals. Meat productivity is closely related to the biological patterns of their growth and development. Considering the patterns that affect meat productivity enables effective growing and fattening of livestock and obtaining commercially viable beef. To predict economically useful traits in beef cattle breeding, interior indicators can be used, as they reflect the metabolic picture of the animal’s body. The research studies in physiology and biochemistry of livestock aimed at revealing the persistent mechanisms of a growing animal organism make them relevant. The article identifies a correlation between the interior indicators and the fattening indicators of three experimental groups of steers. The main forecasting factors of meat productivity indicators have been substantiated. Regression coefficients have been found and show how much the live weight varies depending on the determining factors. Meat productivity predicting procedures have been modeled with respect to the protein content in blood serum.

Herein, the environmental impact of commercial microalgae-based products was assessed. The whole dried biomass production by Chlorella vulgaris and Arthrospira platensis were evaluated under different cultivation configurations. Besides, pigments such as β-carotene, astaxanthin, and phycocyanin, by Dunaliella salina, Haematococcus pluvialis, and Arthrospira platensis, respectively, were also subjected to environmental analysis, as well as docosahexaenoic (DHA) and eicosapentaenoic (EPA) acids by Crypthecodinium cohnii, Nitzschia laevis, and Nannochloropsis oculata. These products were estimated by midpoint indicators through the nine impact categories and subsequently submitted to the normalization stage. Also, environmental critical points of the process were identified and discussed, focusing on the combined operations of dehydration and drying of biomass, as well as the central role of the energy footprint and the challenges of the transition from fossil energy to renewable matrices. Among the results found for whole dried biomass, the parameters that most influence the burdens are directly related to the energy requirements of the downstream processes. In intracellular microalgal products, the extraction stage and yield of the compounds are factors that most affect environmental performance. Additionally, this work provides quantitative numbers of sustainability indicators for microalgae projects considering the current stage of the technology. Therefore, the results found in this study are the first step found in the literature to identify environmental improvements in the consolidated biotechnological routes of microalgae.

The performance of screening methods, as for any qualitative analytical method, is characterised by four parameters which define the success (sensitivity and specificity) and the prediction (predictive values) method capabilities regarding the objects/samples belonging to each of the classes or categories. But establishing a priori the critical values of these parameters to be used as validation requirements is not an easy task for stakeholders. In this tutorial, a new overall approach to carry out this task is described considering three application scenarios (conformity assessment, quality-oriented trade/marketing and profit-oriented trade/marketing). In addition, for greater ease, four applicability indicators (error index, saving index, penalty index and loss index) are proposed whose values can be intuitively assigned in order to determine the critical validation requirement of the method-performance parameters.

For the years of 2017 and 2018, the Sustainable Development Goals index (SDG index) includes around 80 global indicators for more than 150 parties (countries) to the United Nations, which makes it a very comprehensive multidimensional composite index for assessing their sustainable development (SD) achievement rates. As would be observed in the present study, when the comparison between countries on their progresses in well-being and economy is desirable, gathering indicators of both environmental and development pillars of the SD as performed in the SDG index construction can be misleading. Based on the SDG index and with the aim of better monitoring and assessing countries’ SD levels, the current study introduces a composite index called DEVI which merely consists of the “development goals indicators.” The DEVI showed a high correlation with the human development index. The lack of separation between the developed and developing countries in the SDGs is a disadvantage especially when various interactions with different countries are considered according to their different development levels. In this regard, by innovating a combined statistics-based algorithm, compatible with the available conventional lists for developed countries, countries are classified based on the similarities in their DEVI scores into the developed and developing countries. Applying this algorithm, it determined 43 and 40 countries as developed countries in 2017 and 2018, respectively.