**2.2.3 B parameter measurement system**

We used the following equation to calculate the B parameter [16-17] of Dill based the resist transmission factor at the time overexposure completely breaks down the PAG.

$$B = -\frac{1}{d} \ln \left( T\_{\ll} \right) \tag{1}$$

We used a development analyzer to measure development parameters. When homogeneous light is irradiated onto a resist film during development, the light waves reflected from the resist surface and light waves reflected from the wafer surface interfere, generating unique waveforms. Analyzing the waveforms of the reflected light allows us to obtain resist development rates. By varying exposure values and measuring resist development rates at different exposures, we can calculate the development parameter, among the simulation parameters [15]. This measurement has been performed before using a monitor wavelength of 470 nm. However, thin films do not generate the interference needed, and a monitor wavelength of 470 nm limits us to resist film thicknesses exceeding 100 nm. Since the film thickness of EUV resists ranges from approximately 50 to 100 nm, we developed a measurement system for our experiments based on a monitor wavelength of 265 nm

**2.2.2 Development parameter measurement system**

Fig. 11. Resist development analyzer RDA-800EUV

We used the following equation to calculate the B parameter [16-17] of Dill based the resist

( ) 1 *B T* = − ln ∞ *d* (1)

transmission factor at the time overexposure completely breaks down the PAG.

**2.2.3 B parameter measurement system**

(Fig.11).

Here, *d* is resist film thickness and T∞ the resist transmission factor at the time overexposure completely breaks down the PAG. We developed a system for measuring the resin transmission factor using EUV light. Incorporating a LPP light manufactured by Toyota Macs as its light source and using a solid Cu target, this system irradiates EUV light onto a Si/Mo multilayer reflecting mirror to measure reflection intensity, while mirror angles are varied. To calculate the spectral transmission factor, we used the difference in reflectance between the case in which resist is applied to the multilayer mirror and the case in which no resist is applied.

Fig.12 illustrates the measurement system and gives a chart of the results of spectral transmission factor calculations for the MET resist.

Fig. 12. B parameter measurement system using EUV exposure


**2. Methods and Tools for GPR Survey**

#### *2.1. Experimental Setup*

Birhane et al. [11] pointed out that fouled ballast shows more signal scattering, especially at the boundary of the ballast and reinforced subgrade. In this study, to assess the fouling level of the ballast meticulously, further GPR surveys were conducted on lab-built and actual ballast tracks.

In the lab, the ballast tracks were simulated using four ballast boxes with distnict fouling levels. The four ballast boxes represent the stages of fouling on a ballast track: (1) clean (Box 1); (2) partially fouled (Box 2); (3) fully fouled (Box 3); and (4) Box 4, which has increasing fouling material content according to the depth, as illustrated in Table 1. The fouling material is added to the ballast aggregate in all cases at predetermined ratios and mixed before the boxes are filled.

**Table 1.** Test Track Ballast Boxes with Various Fouling Levels.

\* Bottom to upper layer (1st layer = bottom, 5th layer = surface layer), () = ratio of fouling material to clean ballast.

The ballast tracks consisted of 2 m × 2 m × 0.6 m wooden frames. The front side wall was composed of a transparent material to facilitate visual inspections. Figure 1 illustrates the construction procedure of the ballast boxes. Using an excavator, the gravel and fouling materials were mixed. This mixed material was then poured into the boxes, as shown in Figure 1.

**Figure 1.** Construction of ballast boxes.

Figure 2 shows the gradation of the ballast aggregates and the fouled materials. Only 0.2% of the ballast aggregates can pass through 4.75 mm and 0.075 mm sieves. In contrast, in the fouling silt material, 100% and 1.9% of the material can pass through the 4.75 mm and 0.075 mm sieves, respectively. The fouling index can be calculated by Equation (1), suggested by Selig and Waters [22]:

$$FI = P\_{\%}^4 + P\_{\%}^{200} \tag{1}$$

where *P*4 % is the percentage by mass of the sampled ballast material finer than the 4.75 mm (No. 4) sieve, and *P*200 % is the percentage by mass finer than the 0.075 mm (No. 200) sieve.

The corresponding fouling indices of each of the ballast boxes were 0.4, 34.23, 51.15, and 30.08 for Boxes 1 to 4, respectively, as shown in Table 1.

**Figure 2.** Grain size distribution of the ballast gravel and fouling silt.


**6.2 Nutrition**

There is an imbalance between the increased protein requirement and the inadequate dietary food intake caused by HD, which results in skeletal muscle loss in CKD patients. Increased protein synthesis and anabolism from regular PE may help to slow the rate at which people lose lean body mass as they age. On non-HD days, however, As found in aged adults, and HD patients have a lower muscle protein synthesis response to diet. Furthermore, combining physical exercise with a high-protein diet has been shown to help decrease or even stop muscle loss. When used in conjunction with RE, intradialytic nutritional supplements improve both body composition and muscle mass. With PE, nutritional supplementation has significantly stronger protein anabolic effects when taken orally. Increased phosphorylation of mRNA translational signalling proteins due to RE and whey protein intake leads to enhanced protein synthesis in untrained individuals. In addition, ingestion of whey protein after RE activates the mTOR signalling pathway in a dose-dependent manner [63]. Supplementing PE with proper energy sources such as carbohydrates, protein, vitamins and iron will help keep muscle protein breakdown under control. Due to the energy loss and decreased digestive function

associated with dialysis, patients are advised to consume more protein (1.2 times) than the average person.


*Strengths and Weaknesses of the Study*

The first step in arriving at a correct diagnosis of mental health problems is to distinguish depression from other psychiatric syndromes that can cause confusion, such as delirium, dementia, apathy, or anxiety. Evaluating different mental problems concurrently is also important to distinguish between the right diagnoses, given the overlap between them. Careful and broad evaluation of mental health problems at the hospital is a strong side of PROPOLIS.

Prior psychiatric illness can influence mental status post-stroke, i.e., represents either recurrence or continuation of a preexisting psychiatric illness. Therefore, in PROPOLIS, we carefully screened for neuro-psychiatric conditions including depression, dementia, delirium, anxiety, and apathy pre-stroke.

This study had prospective design and included a large number of patients at the baseline, which helped to sustain a reasonably large number of patients during all follow-ups. Patients that were lost in the follow-up didn't differ significantly from those followed-up.

A variety of raters; neurologist and psychologist assessed patients at baseline and during follow-up visits. This is considered as the strength of this study, because follow-up raters were blind for the patients' previous performance and behavior. On the other hand, patients who are more familiar to assessors are more willing to ask for help if they have problems with understanding the questions from the questionnaire and therefore provide more adequate answers. Therefore, a variety of raters can be also considered as a weakness of the study.

Some limitations of our study and bias inducers should also be addressed. Firstly, the PROPOLIS was designed to determine frequency, predictors, and clinical consequences of post-stroke delirium. Depressive symptoms were considered as a secondary endpoint of the study. Secondly, we used questionnaires to describe symptoms of depression, since using interviews with mental health professional was not feasible. Thirdly, the first evaluation for depressive disorders took place before the 14th day after stroke, which may have overestimated the prevalence of depression in the acute phase of stroke. Fourthly, during the follow-up visits, we observed, most depressed patients did not have formal diagnosis of depression and were not treated, but data on the treatment with antidepressants were not collected during the follow-ups. Because treatment with antidepressants might influence the study

outcome, this is considered as a limitation. Fifth, as this was a single center study, the generalizability of our results may be limited.