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Primary Production and Nutrient Content in Two Salt Marsh
Seasonal variation patterns of aboveground and belowground biomass, net primary production, and nutrient
accumulation were assessed in Atriplex portulacoides L. and Limoniastrum monopetalum (L.) Boiss. in Castro Marim salt marsh,
Portugal. Sampling was conducted for five periods during 2001–2002 (autumn, winter, spring, summer, and autumn). This
study indicates that both species have a clear seasonal variation pattern for both aboveground and belowground biomass.
Mean live biomass was 2516 g m22 yr21 for L. monopetalum and 598 g m22 yr21 for A. portulacoides. Peak living biomass, in
spring for both species, was three times greater in the former, 3502 g m22 yr21, than in the latter, 1077 g m22 yr21. For both
the Smalley (Groenendijk 1984) and Weigert and Evans (1964) methods, productivity of L. monopetalum (2917 and
3635 g m22 yr21, respectively) was greater than that of A. portulacoides (1002 and 1615 g m22 yr21, respectively). Belowground
biomass of L. monopetalum was 1.7 times greater than that of A. portulacoides. In spite of this, the root:shoot ratio for A.
portulacoides was greater throughout the year. This shows that A. portulacoides allocates more biomass to roots and L.
monopetalum to aerial components. Leaf area index was similar for both species, but specific leaf area of A. portulacoides was
twice that of L. monopetalum. The greatest nutrient contents were found in leaves. Leaf nitrogen content was maximum in
summer for both species (14.6 mg g21 for A. portulacoides and 15.5 mg g21 for L. monopetalum). Leaf phosphorus
concentration was minimum in summer (1.1 mg g21 in A. portulacoides and 1.2 mg g21 in L. monopetalum). Leaf potassium
contents in A. portulacoides were around three times greater than in L. monopetalum. Leaf calcium contents in L. monopetalum
were three times greater than in A. portulacoides. There was a pronounced seasonal variation of calcium content in the former,
while in the latter no clear variation was registered. Both species exhibited a decrease in magnesium leaf contents in the
summer period. Manganese content in L. monopetalum leaves was tenfold that in A. portulacoides. Seasonal patterns of nutrient
contents in A. portulacoides and L. monopetalum suggest that availability of these elements was not a limiting factor to biomass production
The Spatial Distribution of Absolute Skeletal Muscle Deoxygenation During Ramp-Incremental Exercise Is Not Influenced by Hypoxia.
Time-resolved near-infrared spectroscopy (TRS-NIRS) allows absolute quantitation of deoxygenated haemoglobin and myoglobin concentration ([HHb]) in skeletal muscle. We recently showed that the spatial distribution of peak [HHb] within the quadriceps during moderate-intensity cycling is reduced with progressive hypoxia and this is associated with impaired aerobic energy provision. We therefore aimed to determine whether reduced spatial distribution of skeletal muscle [HHb] was associated with impaired aerobic energy transfer during exhaustive ramp-incremental exercise in hypoxia. Seven healthy men performed ramp-incremental cycle exercise (20 W/min) to exhaustion at 3 fractional inspired O2 concentrations (FIO2): 0.21, 0.16, 0.12. Pulmonary O2 uptake (VO₂) was measured using a flow meter and gas analyser system. Lactate threshold (LT) was estimated non-invasively. Absolute muscle deoxygenation was quantified by multichannel TRS-NIRS from the rectus femoris and vastus lateralis (proximal and distal regions). VO₂peak and LT were progressively reduced (p < 0.05) with hypoxia. There was a significant effect (p < 0.05) of FIO2 on [HHb] at baseline, LT, and peak. However the spatial variance of [HHb] was not different between FIO2 conditions. Peak total Hb ([Hbtot]) was significantly reduced between FIO2 conditions (p < 0.001). There was no association between reductions in the spatial distribution of skeletal muscle [HHb] and indices of aerobic energy transfer during ramp-incremental exercise in hypoxia. While regional [HHb] quantified by TRS-NIRS at exhaustion was greater in hypoxia, the spatial distribution of [HHb] was unaffected. Interestingly, peak [Hbtot] was reduced at the tolerable limit in hypoxia implying a vasodilatory reserve may exist in conditions with reduced FIO2
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Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions
Data availability:
Data will be made available on request.Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes
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