**7. Glaucomatous versus non-glaucomatous damage**

Optic disc cupping is not pathognomonic for the glaucomatous optic neuropathy only [58]. Other diseases such as arteritic ischemic optic neuropathy (AION), optic neuritis, optic disc pit, colobomas, tilted disc, traumatic optic neuropathy, methanol toxicity, compressive lesions of the anterior visual pathways [59], disc drusen, long standing papilledema [26]. However nonglaucomatous disc damage produces optic disc rim pallor while glaucomatous damage produces focal or diffuse obliteration of the neuroretinal rim [60]. Glaucoma damage tends to produce deeper cups than the nonglaucomatous type [61]. In this study open angle glaucoma eyes had larger and deeper cups and smaller neuretinal rims compared to eyes with nonar‐ teritic and arteritic AION. Contrary to glaucoma PPCA does not increase in nonglaucomatous damage [62].

#### **Summary box**

**Figure 13.** Large PPCA in advanced glaucoma with small alpha zone temporally (arrow) and a large beta zone (arrow‐

**Figure 14.** Optic disc haemorrhage in advanced glaucoma. Note the presence of focal arteriolar narrowing (arrow).

head). Bayoneting of the arterioles (white arrowhead)

308 Glaucoma - Basic and Clinical Aspects

There is great variability among healthy subjects and people from different races in the morphology of the optic disc which makes the diagnosis of glaucoma very complicated. The clinician should take into consideration various aspects of the anatomy of the optic nerve head and the RNFL before deciding whether a patient has glaucoma or not


**4. Conclusion**

By means of a multivariate analysis of the results, we point out the differences in the influence of the investigated parameters depending on the individual degrees of study - all years from the bachelor's degree to the master's degree. The influence of the current year of study on the perception of digital education was manifested in the parameters assessing the fun and speed of the education process, the quality of communication, the ability to work with text, time management, and the adequacy of software equipment. Almost half of the student sample has a negative perception of social communication and internet connection in the context of distance digital education.

In the study, we identified three clusters, where the first one consists of all the studied skills, the second one is connected to the teacher's activities, and the third one is related to the student's activities. Based on the results, it can be concluded that in

digital education, just as in the case of the classical model of education, the teacher is the primary activating source of the flow of information. But its role is expanded by activating the student in the digital environment so that the student develops not only his skills and knowledge in the given field of study but also develops his digital skills. Thus, the role of the teacher is not only to educate but also to manage the entire teaching process in a digital environment, using information flows within a structured multimedia educational system.

On the basis of the above, it is necessary to expand the education of teachers to include digital skills and work management (didactics in a digital environment). By managing the quality of digital education, we aim to increase student interaction and also improve the digital skills of all stakeholders.

On the based on the above results, we recommend:


3.2.4. SMS Ore Production

As SMS deposits are polymetallic, deep ocean mining is not specifically targeted toward copper recovery and will result in multiple minerals being recovered—in fact, in many cases copper is more likely to be a coproduct. For example, the ore body considered here consists of copper, lead, zinc, gold and silver. The ore grade has been estimated at 0.4% (copper), 1.4% (lead) and 5.8% (zinc) [22]. To estimate environmental impact factors of SMS ore production, allocation by weight is usually adopted. Previous research revealed that multiple mineral production from SMS ore reduces energy consumption, CO2 emissions and waste disposal amounts (due to their allocations to each material extracted) when compared to the production of copper ore alone [10]. This study applies the assumption that deep ocean mining will produce various minerals in addition to the target metal of copper for the estimation of environmental impacts.

According to current estimates, SMS reserves of ore are not su fficient to cover Japan's production [12], however there are large uncertainties as to the available resources, so this study assumes for the purpose of analysis that there is no limitation on this resource and that SMS ore can meet the demand.

### 3.2.5. Energy Consumption and CO2 Emissions

The recycling of end-of-life products is considered to reduce energy consumption and CO2 emissions when compared with processing ore, since recycling does not require an oxidization process. Additionally, since it is possible to utilize waste plastics as a reducing agent and energy source, we estimate that utilizing end-of-life products will assist in reducing CO2.

Deep ocean mining is also able to reduce energy consumption and CO2 emissions due to shorter material transportation distances. Mining, concentrating and transportation are invisible processes for resource importing countries since these processes are conducted only in resource exporting nations. Domestic mineral production by Japan may also have the flow on e ffect of reducing CO2 emissions in resource exporting countries, although this is not considered in this study. To estimate Japanese CO2 emission impacts, existing LCA data are used [26].


*2.1. Study Area*

The West African (shown in Figure 1) region comprises of 15 countries namely Benin, Burkina Faso, Gambia, Ghana, Guinea Bissau, Guinea, Ivory Coast, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo. It is geographically located at latitude 4–20 ◦N and 16 ◦W–20 ◦E and has rainfed agriculture as its mainstay economy. The region can be divided into three Food and Agriculture Organization (FAO) agro ecological zones (AEZs) namely, Guinea (4–8 ◦N), Savanna (8–12 ◦N) and the Sahel (12–20 ◦N) [24,25]. The region also has some localized highlands (Cameroon Mountains, Jos Plateau, and Guinea Highlands) which influence its climate. The climate of the region is mainly controlled by the West African Monsoon (WAM) which accounts for about 70% of the annual rainfall [24,26]. WAM is an important and dynamic characteristic of the West African climate during the summer month [27].WAM is produced from the reversal of the land and ocean differential heating and dictates the seasonal pattern of rainfall over West Africa between latitudes 9◦ and 20 ◦N. It is characterized by winds that blow south-westerly during warmer months (June–September) and north-easterly during cooler months (January–March) of the year [25,27]. It is the major system that influences the onset, variability and pattern of rainfall over West Africa [28], [29]. It alternates between wet (April–October) and dry seasons (November–March) as the rainfall belt follows the migration of Inter-Tropical Discontinuity (ITD) [30] and thus affects the rainfall producing systems with an impact on the rainfed agriculture and influences crops suitability and food production in the region.

**Figure 1.** The study area, showing the West African topography and the three Food and Agriculture Organization (FAO) agro-ecological zones, designated as Guinea, Savannah and Sahel, respectively [24,25].

Different crops are grown in various parts of West Africa. Some of the major crops grown in the region are cassava, groundnut, millet, maize, sorghum, yam, plantain, cocoa, rice, wheat [8,26,31,32]. Millet and sorghum account for 64% of the cereal production over the regions in the year 2000 thus making them among the important staple crops in West Africa [26,33]. Cassava is one of the most important staple food crops in terms of production in sub-Saharan West Africa owing to high resilience to drought in the region [26,32,34] This also applies to the Yam production, which account for about

91% of the world's production [26,34,35]. Cereal, maize provides about 20% of the calorie intake in West Africa and is adjudged the most important staple food in the sub-Saharan Africa [26,33]. Other crops such as cocoa and plantain to mention a few contribute significantly to the economy of the region.


**5. Conclusions**

Because of its simple structure and analytical inverse, the PI model are used widely in hysteretic nonlinear modeling and compensation. However, the PI model can only describe symmetric hysteresis. In this paper, based on PI model, we provide a novel PMPI model to describe and compensate asymmetric hysteresis of PEAs. First, the PMPI model is introduced, which includes the M-Play operator and memoryless polynomial. Then, the congruency property of PMPI model is analyzed and verified. The minor loop mathematical model is also established. Next, the correctness of the PMPI model is proved by simulation. It should be noted that when considering the initial loading curve, the PMPI model can accurately characterize asymmetric hysteresis. Compared with the PI model and the Gu-PI model, the error of PMPI model is reduced by 83.3%. In the end, based on the PMPI model, the I-M compensator is designed for hysteresis compensation. The stability of I-M compensator is analyzed. The experimental results show that the I-M controller has superior tracking performance.

Although the PMPI model has satisfactory results for asymmetric hysteresis, it is not suitable for rate-dependent and load-dependent hysteresis. In the future, further research is to expand the PMPI model to rate-dependent and load-dependent applications.

**Author Contributions:** Conceptualization, W.W. and J.W.; methodology, J.W.; software, R.W.; validation, K.L. and R.W.; formal analysis, Z.S.; investigation, K.L.; resources, W.W.; data curation, Z.C.; writing—original draft preparation, W.W. and J.W.; writing—review and editing, Z.C. and Z.S.; visualization, J.W.; supervision, W.W.; project administration, W.W.; funding acquisition, W.W. and B.J.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ16E050001, and the National Natural Science Foundation of China under Grant No. U1709206, No.51275465.

**Conflicts of Interest:** The authors declare no conflict of interest.


**4. Providing estimates: robustness of SRT method and weakness of IDW method**

The SRT method was tested against the Inverse Distance Weighted (IDW) method to deter‐ mine the representativeness of estimates obtained (29). The SRT method outperformed the IDW method in complex terrain and complex microclimates. To illustrate this we have taken the data from a national cooperative observer site at Silver Lake Brighton, UT.The elevation at Silver Lake Brighton is 8740 ft. The nearest neighboring station is located at Soldier Sum‐ mit at an elevation of 7486 ft. This data is for the year 2002. Daily estimates for maximum and minimum temperature were obtained for each day by temporarily removing the obser‐ vation from that day and applying both the IDW (eq. 1) and the SRT (eq.2) methodsagainst 15 neighboring stations. The estimations for the SRT method were derived by applying the method (deriving the un-biased estimates) every 24 data.

**Figure 1.** The results of estimating maximum temperature at Silver Lake Brighton, UT for both the IDW and the SRT methods.

Fig. 1 shows the result for maximum temperature at Silver Lake Brighton, Utah. The IDW approach results in a large bias. The best fit line for IDW indicates the estimates are system‐ atically high by over 8 F (8.27); the slope is also greater than one (1.0684). When the best fit line for IDW estimates was forced through zero, the slope was 1.2152. On the other hand the estimates from the SRT indicate almost no bias as evidenced by the best-fit slope (0.9922).

**5.1. Relationship between interval of measurement and QA failures**

Network uses a midnight to midnight observation period.

**Figure 3.** Example time intervals for observations at Mitchell, NE (after 28).

to two observation periods.

Analyses were conducted to prepare artificial max and min temperature records (not the measurements, but the values identified as the max and min from the hourly time series) for different times-of-observation from available hourly time series of measurements. The ob‐ servation time for coop weather stations varies from site-to-site. Here we define the AM sta‐ tion, PM station, and nighttime station according to the time of observation (i.e. morning, afternoon-evening, and midnight respectively). The cooperative network has a higher num‐ ber of PM stations but AM measurements are also common; the Automated Weather Data

Toward a Better Quality Control of Weather Data

http://dx.doi.org/10.5772/51632

11

The daily precipitation accumulates the precipitation for the past 24 hours ending at the time of observation. The precipitation during the time interval may not match the precipi‐ tation from nearby neighboring stations due to event slicing, i.e. precipitation may occur both before and after a station's time of observation. Thus, a single storm can be sliced in‐

The measurements of the maximum and the minimum temperature are the result of making discrete intervals on a continuous variable. The maximum or minimum temperature takes the maximum value or the minimum value of temperature during the specific time interval. Thus the maximum temperature or the minimum temperature is not necessarily the maxi‐ mum or minimum value of a diurnal cycle. Examples of the differences were obtained from three time intervals (see Fig 3) after28)). The hourly measurements of air temperature were retrieved from 1:00 March 11 to 17:00 March 13, 2002 at Mitchell, NE. The times of observa‐ tion are marked. Point *A* shows the minimum air temperature obtained for March 11 for AM stations, and *B* is the maximum temperature obtained for March 13 at the PM stations. The minimum temperature may carry over to the following interval for AM stations and the

For the minimum temperature estimates a similar result was found (Fig. 2). The slope of the best-fit line for the SRT indicates an unbiased (0.9931) while the slope for the IDW estimates indicates a large bias on the order of 20% (slope = 1.1933). The reader should note the SRT unbiased estimators are derived every 24 days (see ) and that applying the SRT only once for the entire period will degrade the results shown (7).

**Figure 2.** The results of estimating minimum temperature at Silver Lake Brighton, UT for both the IDW and the SRT methods.