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e) f) c) d) Plasma adsorption measurements confirmed a reduction of the adsorption on PEEK-WC-20 and PEEK-WC-60 membranes.
Data were normalised with respect to the value of CA membrane.
Interpolation of experimental data is reported as line.
Data were normalised with respect to the value of CA membrane at plasmatic concentration.
Plasma adsorption followed the same trend of albumin, fibrinogen and IgG adsorption, although some scattering of data was observed (Fig. 10).

The phase diagram has been obtained from temperature dependent magnetization, resistivity, and x-ray diffraction measurements to investigate the intermartensitic transitions (TIM), the spin glass region (SSG) is highlighted (data provided by M.
Data provided by M.
Data provided by P.
This alone is sufficient to obtain the reduction of the martensitic transformation temperature MS with increasing field.
Gruner, unpublished data (2016)

In practical applications, a reasonable scale of intersection group is very important for traffic analysis. 1) Intersections contained in the group will affect not only the adjacent intersections but also the intersections non-adjacent. 2) The larger the scale of intersection group, the more work and resources have to put into it to get enough information and transportation data to do traffic analysis. 3) If the group contains too many intersections, the random of traffic in the network will increase and the reliability will decline.
Robertson and Hunt[5] used reduction in the queue (QR), which was calculated from travel time and volume, to determine coordination desirability.
Let G is the set of intersection geometric design data including number of lanes, types and directions of lanes, etc., P is the set of signal timing characteristics.
Use present geometric and signal data of intersection r Gr0,Pr0 to do traffic assignment to get the traffic volume of intersections that are out of and adjacent to the boundary X0=x01,x02,⋯,x0k.
Set the counter factor n=1; Step 2: Generate geometric design and signal timing data Grn,Prn randomly and set to intersections r; Step 3: Traffic assignment using models described above and get Xn=xn1,xn2,⋯,xnk; Step 4: Calculate the degree of traffic volume changes at intersections that out of and adjacent to the boundary Tn=tn1,tn2,⋯,tnk: tni=xni-x0ix0i (10) If tni≥ϵ, then ϕni=1; otherwise, ϕni=0; where, ϵ is the allowed value of traffic volume changes; Step 5: Let n=n+1, if n>N, turn to Step 6; otherwise, turn to Step 2; where, N is the times that planned to change the condition of intersection r; Step 6: Calculate the ϕi as follows: ϕi=nϕniN (11) Step 7: Calculate traffic flow influence index Y=k-iϕik, and put out.

The peak of the O-H group on the durian rind cellulose backbone has been substituted by the polyacrylamide chain, as evidenced by the change in the output of the O-H peak in the FTIR data of the grafted polymer.
The following data shows the swelling capacity results in Table 3 and Fig. 3.
Table 4 Grafting data of irradiation time variation.
The reduction in grafting percentage after reaching the optimal time is attributed to the decomposition of the grafted polymer.
This is in line with the data trend from grafting percentage results (Table 4).

Published Data and Discussion Surface Energy.
During annealing the Cu and Al films deposited on amorphous coated substrates are not likely to have any directional preference in the energy reduction of the film-substrate interface.
There are no grain boundary energy and mobility data on the Fe-Ni alloys.
These values are based on the room temperature data.
Since a ratio of diffracted intensities (I200/I111) from {200} and {111} planes of about 0.46 is expected for random orientation, the data in Fig.11 indicate that the as-deposited Cu is strongly oriented with the {111} planes parallel to the deposit surface, the {111} orientation.

This process, commonly associated with frailty, is characterized by a reduction in physiological reserves, leading to greater vulnerability to adverse health events such as falls, disability, and premature death [5].
The rationale for using computer vision is based on the need for an accessible, sensor free system that enables older adults to exercise safely at home while providing clinicians with actionable performance data.
It is structured to display True Positives (TP), False Positives (FP), and False Negatives (FN) for each exercise, offering a comprehensive view of the model's successes and errors.Table 2: Performance metrics per exercise Exercise TP FP FN Precision (%) Recall (%) F1-score (%) Walking 700 0 0 100 100 100 Squeezing a ball 492 3 12 99.4 97.6 98.5 Raising arms w/ bottle 459 7 45 98.5 91.1 94.7 Leg extension 592 11 38 98.2 93.9 96.0 Chair stands 505 1 1 99.8 99.8 99.8 Walking in line 644 13 12 98.0 98.2 98.1 Arm stretching 126 0 0 100 100 100 From the data of the 14 participants, the system demonstrated notable performance in various exercises.
Addressing this requires the collection of more diverse training data and adaptive thresholding strategies to account for individual biomechanics.Finally, our platform's average processing speed ( 28 FPS) supports real time feedback, a critical feature for home based exercise assistance.
Also, we aim to develop a database for storing exercise performance data to enable longitudinal tracking and clinician review, incorporate training plan customization to adapt exercises based on user performance, and implement repetition cycle monitoring to assess fatigue and prevent injury.

Feng et al. [28] used variance analysis and signal-to-noise ratio for data analysis.
Normalizing the original data sequence and generating a data sequence is necessary before conducting a grey relational analysis.
According to equation (2), normalization was performed to obtain the data shown in Table 5.
The experimental data is shown in Table 8.
Fit the data to a polynomial model using analysis of variance (ANOVA).

The data and information used in this paper mostly come from municipal statistic year books and published papers.
In order to ensure the credibility of this study, we take key-informant interviews as the second source to access to the precious data.
To achieve a better understanding of the current situation of MSWM in Urumqi, it is necessary to present data on waste generation and its characteristic.
Table 1 The change of MSW composition of Urumqi in 2007, 2008 and 2011 Composition 2007 2008 2011 Metal 0.65 0.75 1.00 Glass 1.20 2.41 1.00 Plastic 9.03 5.37 3.00 Paper 13.38 2.41 3.00 Fabric 0.83 2.59 - Grass and wood 1.99 9.13 - Tile and brick 17.03 4.12 - Dust and ash - 2.27 - Kitchen waste 55.06 62.11 57.00 Mixed 0.83 8.84 35.00 Note: [18] data of year 2007; [19] data of year 2008; data of 2011 was accessed during the interview in Urumqi Dapugou Solid Waste Comprehensive Disposal Site; ‘-’ means the date goes into other catalogues MSW collection and transportation.
During the field search period in Urumqi, authors find out that precise data on MSWM, including data related to government report, development planning, waste generation, waste collection and waste final disposal, is difficult to access, and the reasons are various, including but not restricted to special political background, lack of valuable data statics and limited fundamental research.

Here, some parameters such as temperature, strain, strain rate and interpass time are used as input data to predict the pass-by-pass microstructural changes taking place during hot rolling.
Heightening this temperature will lead to grain coarsening and slight reduction in retained strain due to relatively complete softening.

The glass transition temperature (Tg) data were obtained by cooling and heating the samples.
However, ionic liquids containing active hydroxyl groups could be more easily adsorbed on the surface of friction contact pairs to form an effective boundary film than non-functionalized ionic liquids, which mainly contribute to improve the friction-reduction and antiwear performance during the sliding process.