**3.1 Definition of occupational health**

*Occupational Wellbeing*

leishmaniasis, ascariasis, and hookworm.

(iii) Tools and farm equipment: Injuries, cuts, and hearing impairment;

(iv) Physical labour: musculoskeletal disorders, e.g. pain and fatigue; (v) Pesticides: poisonings, neurotoxicity, reproductive effects, and cancer; (vi) Dusts, fumes, gases, particulates: Irritation, respiratory tract, allergic reactions, respiratory diseases such as asthma, chronic obstructive pulmonary disease, and hypersensitivity pneumonitis, and (vii) Biological agents and vectors of disease: Skin diseases, fungal infections, allergic reactions, malaria, schistosomiasis, sleeping sickness,

Work has both social and economic importance because work is done in any society primarily to produce and distribute goods and services that are needed by people in the society [5]. WHO [5] further explains that work plays a psychological role in the formation of self-esteem, a sense of order, and in the shaping of a person's self-identity. Work and sound health are highly and positively correlated in the sense that the more healthy a worker is, the more likely he is to work both effectively and efficiently while an ill-health per positively correlated in the sense that the more healthy a worker is, the more likely he is to work both effectively and efficiently while an ill-health person may likely work effectively and efficiently. Egbetokun [6] reported that a 1% improvement in farmers' health condition will lead to a 21 percent increase in work efficiency. On the other hand, poor farmers' health will reduce income, efficiency and productivity [7]. Hence, for one to work optimally, it is important to keep and maintain sound health especially at the workplace. Like every other work or means of survival and livelihood, the fisheries sector is not without some work-related hazards and risks. In fact, like other agriculture-related works, fisheries have been recognized as one of the riskiest work in the world [8, 9]. Injuries and illnesses are among the health-related factors limiting the productivity from fisheries. According to the Global Burden Disease [10], approximately 2.78 million deaths that occur annually are associated with work-related hazards. The World Health Organization also noted that between 20 and 50 per cents of workers; especially in developing countries like Nigeria are suffering from occupational risks [5]. In Nigeria, fisheries supply and its products come from two broad sources viz. – importation of fish and fisheries products, and local fish production. With the nation's increasing human population, most of whom are youths within the economically active age groups, the nation's expenditure on fish importation is too high enough to put tangible developmental projects to the nation. This is despite the natural resources which favour fisheries production from both aquaculture and wild capture sources. For the purpose of this chapter, the authors are interested in the domestic fish sources which come from aquaculture, artisanal fishing and industrial fishing. All these sources are with their unique work-related hazards which affect the health and general wellbeing of those who take part in them. As hazards can occur during the different stages of the fisheries value chain (production, processing, handling, storage and transportation), occupational hazards is treated in this chapter in totality as it affects all the value chain actors in the Nigerian fisher-

The remaining of this chapter is structured under the preceding headings: overview of occupational hazards, classification of occupational hazards, fisheries in Nigeria, occupational hazards in fisheries, and empirical evidence of occupational

Information used in this chapter was obtained through the desk review of published journals, conference proceedings and books as well as personal experiences

**16**

ies sector.

**2. Methods**

hazards in fisheries in Nigeria.

Health is defined as a condition of complete physical, mental and social wellbeing which does not mean the mere absence of disease and infirmity. Health is associated to the physical conditions of both mind and body, of all people at the workplace including the workers, contractors and visitors, and their protection from harm in the form of injury or disease [11]. Occupational health is a branch of medicine concerned with the prevention and treatment/control of job-related injuries and illnesses. It is closely linked to public health and health systems development. According to WHO [12], occupational health is a multidisciplinary field of healthcare which deals with all aspects of health and safety, in order to enable a worker to discharge their occupational responsibilities, in a way that causes least harm to their health. This is achieved through the prevention of hazards.

According to the definition jointly provided by International Labour Organization and World Health Organization [13], the aims of occupational health include promoting and maintaining the highest level of physical, mental and social well-being of workers in all occupations; preventing amongst workers of departures from health caused by their working conditions; protecting the workers in their employment from risks resulting from factors adverse to health; the placing and maintenance of the worker in an occupational environment adapted to his physiological and psychological capabilities; and, above all, the adaptation of work to man and of each man to his job.


**3. Results**

## *3.1. Experimental Validity*

In all the bioassays conducted, control M24 was <10% and at M72 was <13%. BFS was ≥50% in both the human arm and the rabbit controls and was <23% in the membrane control (Table 2).

**Table 2.** Impact of bait on mortality and blood-feeding adjusted for the net condition. The difference in the odds of mosquito mortality at 24-h (M24) or 72-h (M72) and blood feeding success (BFS) for 100 pyrethroid-resistant *Anopheles arabiensis* exposed to Interceptor® and Interceptor® G2 with either a rabbit, human arm or membrane feeder as bait \*.

\* Mosquitoes were exposed for 12 h. Data presented are mean proportion (%) with 95% confidence interval (95% CI) and odds ratios (OR) derived from regression analysis with 95% CI adjusted for net type and condition.

#### *3.2. Experiment 1: The Impact of Baits*

The bait used affected both the feeding and mortality endpoints measured. The membrane measured a similar mortality and a lower blood feeding success than the rabbit. The human arm measured a lower mortality and higher blood feeding success than the rabbit.

M24 in the intervention arms was not significantly different between the rabbit and membrane (OR: 0.90, 95% CI: 0.79–1.02, *p* = 0.086) and was significantly lower using the human arm (OR: 0.42, 95% CI: 0.37–0.48, *p* < 0.001) compared to the rabbit (Table 2). M72 in the intervention arms was not significantly different between the rabbit and membrane (OR: 1.07, 95% CI: 0.93–1.22, *p* = 0.352) and was significantly lower using the human arm (OR: 0.31, 95% CI: 0.27–0.35, *p* < 0.001) compared to the rabbit (Table 2). Control M24 was higher in the membrane and human arms; but control M72 was higher in the human arm (OR: 1.83, 95% CI: 1.22–2.75, *p* = 0.004) and was not different between rabbit and membrane (OR: 1.16, 95% CI: 0.84–1.59, *p* = 0.366). In the treatment arm, BFS was significantly lower using a membrane (OR: 0.34, 95% CI: 0.28–0.48, *p* < 0.001) and was significantly higher using a human arm (OR: 9.81, 95% CI: 8.25–11.67, *p* < 0.001) compared to the rabbit (Table 2). The same trend was observed in the control arm.

The same trends in mortality and blood feeding inhibition (BFI) were observed for both Interceptor® ITN and Interceptor® G2 (Figure 4). Higher blood feeding resulted in lower mortality (Figure S1), which will explain the lower mortality measured with the human arm, which also had substantially higher BFS. Therefore, the human arm could not be considered for further evaluation. Between the membrane and the rabbit with 100 mosquitoes per replicate, no systematic difference was observed for agreement by Bland and Altman methods (Figure S2). The mean difference was 6% (−10.81–23.01) for BFS and −1.09% (−72.91–70.73) for M72.

**Figure 4.** Mean percentage mortality and 95% Confidence Interval (CI) for mortality at (**A**) 24-h (M24), (**B**) 72-h (M72) post exposure and (**C**) blood feeding inhibition (BFI) for Interceptor® and Interceptor® G2 nets with 100 pyrethroid-resistant *Anopheles arabiensis* mosquitoes using rabbit, Hemotek® membrane feeders and human arm as bait in the WHO tunnel bioassay. Red dashed line depicts the WHO minimum bioefficacy criteria of ≥80% M24 and ≥95% BFI.

#### *3.3. Experiment 2: Impact of Exposure Time on Mortality and Blood Feeding*

Increasing the time that mosquitoes are left in the tunnel from 1-h to 12-h increased mortality with either the human arm or the membrane (Table 3). With the membrane bait, longer exposure significantly increased both the odds of M72 (OR: 2.30, 95% CI: 2.02–2.62, *p* = 0.001) and the odds of BFS (OR: 1.55, 95% CI: 1.08–2.22, *p* = 0.017). Similarly, in the human arm, the longer exposure significantly increased the odds of M72 (OR: 1.66, 95% CI: 1.45–1.90, *p* = 0.001), while the effect of exposure time on BFS could not be measured since the human arm was only available for one hour (Figure 5). The time that mosquitoes are left in the tunnel overnight is a significant factor in mosquito mortality and should always be recorded and reported.

**Table 3.** Impact of exposure time on mortality and blood-feeding adjusted for the net condition; The difference in the odds of mosquito mortality at 24-h (M24) or 72-h (M72) and blood-feeding success (BFS)) for 100 pyrethroid-resistant *Anopheles arabiensis* exposed to Interceptor® and Interceptor® G2 with either a human arm or a membrane feeder as bait \*.

\* Mosquitoes were exposed for either 1 h before being removed from the tunnel and placed in holding cups with access to sugar or left overnight in the tunnel for 12 h. Data presented are a mean proportion (%) with a 95% confidence interval (95% CI) and odds ratios (OR) derived from regression analysis with 95% CI adjusted for net conditions.

**Figure 5.** Mean and 95% Confidence Interval (CI) for (**A**) 24-h (M24); (**B**) 72-h (M72); and (**C**) blood feeding success (BFS) with 100 pyrethroid-resistant *Anopheles arabiensis* mosquitoes with 12 h or 1 h exposure time in the WHO tunnel bioassay using Hemotek® membrane or human arm as bait.

#### *3.4. Experiment 3: Effects of Mosquito Density on Tunnel Test Endpoints*

M24, M72, and BFS were very similar and were not statistically different when either 50 or 100 mosquitoes were used in the tunnel bioassay with rabbit bait for either the susceptible or resistant strains (Table 4). This was consistent for both Interceptor® and Interceptor® G2, unwashed and washed 20 times (Figure 6). No systematic difference in agreement between methods was observed by Bland and Altman methods (Figure S3). The mean difference was −4.54% (−31.62–22.54) in BFS and 1.71% (−28.71–32.12) in M72. Furthermore, when tested using the pyrethroid-resistant strain, the 50-rabbit bioassay predicted the superiority of Interceptor® G2 to Interceptor®, as did the 100-rabbit (Table 5).

**Table 4.** Effects of mosquito density on mortality and blood-feeding. The difference in the odds of mosquito mortality at 24 h (M24) or 72 h (M72) and blood feeding success (BFS) for resistant *Anopheles arabiensis* exposed to Interceptor® G2 or susceptible *Anopheles gambiae* to Interceptor® in the gold standard rabbit-100 and 50-rabbit mosquitoes \*.

\* Mosquitoes were exposed for 12 h in the tunnel. Data presented are a mean proportion (%) with 95% confidence interval (95% CI) and odds ratios (OR) derived from regression analysis with 95% CI adjusted for net type and condition.

**Figure 6.** Mean and 95% confidence Interval (CI) for (**A**) 24 h mortality (M24), (**B**) blood feeding inhibition (BFI) of Interceptor® ITN with 100 vs. 50 pyrethroid susceptible *Anopheles gambiae*; (**C**) 72-h mortality (M72); and (**D**) BFI of Interceptor® G2 ITN with 100 vs. 50 pyrethroid-resistant *Anopheles arabiensis* in the WHO tunnel test. Red dashed line depicts WHO minimum bioefficacy thresholds of ≥80% M24 and ≥95% BFI.

**Table 5. Superiority of Interceptor® G2 over Interceptor® using 100 versus 50 resistant mosquitoes:** The difference in the odds of mosquito at 24 h (M24) and 72 h (M72) and blood feeding success (BFS) for pyrethroid-resistant *Anopheles arabiensis* exposed to Interceptor® G2 and Interceptor® in the gold standard rabbit-100 and 50-rabbit mosquitoes \*.

\* Mosquitoes were exposed for 12 h in the tunnel. Data presented are a mean proportion (%) with a 95% confidence interval (95% CI) as well as odds ratios (OR) derived from regression analysis with 95% CI, adjusted for net conditions.

However, when considering the superiority of Interceptor® and Interceptor® G2, the lower mosquito density (50) resulted in a higher BFS in the Interceptor® G2 arm (Table 5). This indicates that mosquitoes at a high density are either interacting with each other to disturb each other from feeding, or discomfort from high biting rates is making the host more defensive. This increased blood feeding success is likely translating into the lower odds of mortality observed for washed Interceptor® G2 relative to Interceptor® using 50 mosquitoes (OR: 1.07, 95% CI: 0.85–1.34, *p* = 0.579) compared to 100 mosquitoes (OR: 1.31, 95% CI: 1.12–1.54, *p* = 0.001) (Table 5). This observation underlines the importance of consistent control blood feeding success on mortality estimates from the WHO tunnel test and this should always be recorded and reported.

#### *3.5. Experiment 4: Possibility to Replace Standard Bait with the Membrane Feeding*

The membrane assay with 50 mosquitoes (membrane-50) did not measure statistically different M24 or M72 compared to the rabbit with 100 mosquitoes (rabbit-100) (Table 6) when testing pyrethroid only Interceptor® or Interceptor® G2 against pyrethroid-resistant *An. arabiensis.* Again, BFS was different, with a far higher BFS in the rabbit-100 assay than in the membrane 50-assay.

**Table 6.** Comparison of the membrane assay to the gold standard with rabbit assay. The difference in the odds of mosquito mortality at 24-h (M24) and 72-h (M72) and blood feeding success (BFS) for resistant *Anopheles arabiensis* was measured between the gold standard rabbit assay with 100 mosquitoes and the membrane assay with 50 mosquitoes \*.

\* Mosquitoes were exposed for 12 h in the tunnel. Data presented are a mean proportion (%) with a 95% confidence interval (95% CI) as well as odds ratios (OR) derived from regression analysis with a 95% CI adjusted for net type.

However, when used for predicting the difference in bio-efficacy between Interceptor® and Interceptor® G2, both assays were measured in the same way (Figure 7) and both predicted superior odds of M72 for Interceptor® G2 (100-rabbit OR: 1.23 (95% CI: 1.10–1.38), *p* < 0.0001; 50-membrane 1.79 (95% CI: 1.50–2.14) *p* < 0.0001) and inferior reduction in blood feeding (100-rabbit OR: 1.76 (95% CI: 1.47–2.10), *p* < 0.0001; 50-membrane 1.87 (95% CI: 1.05–3.33) *p* = 0.033) with Interceptor® G2 relative to Interceptor® (Table 7). No systematic difference was observed in agreement for membrane-50 and rabbit-100 by Bland and Altman methods, with a mean difference (and limits of agreement) of 9.06 % (−11.42–29.54) on BFS and −5.43 % (−50.3–39.45) on M72 (Figure S4).

**Figure 7.** Mean percentage mortality and 95% confidence interval (CI) for (**A**) 24-h (M24); (**B**) 72-h (M72); and (**C**) blood feeding inhibition (BFI) for Interceptor® and Interceptor® G2 nets against pyrethroid-resistant *Anopheles arabiensis* with 100-rabbit (rabbit bait and density of 100 mosquitoes) and 50-membrane (Hemotek® membrane bait and density of 50 mosquitoes) in the WHO tunnel test. The red dashed line depicts the WHO minimum bio-efficacy thresholds of ≥80% M24 and ≥95% BFI.

**Table 7.** Superiority of Interceptor® G2 over Interceptor® was estimated by comparing the membrane assay to the gold standard assay with pyrethroid-resistant mosquitoes. The difference in the odds of mosquito at 72-h (M72) and blood feeding success (BFS) for resistant *Anopheles arabiensis* measuring the superiority of Interceptor® G2 and Interceptor® with the gold standard with 100-rabbit compared to 50-membrane bioassays \*.

\* For the gold standard, 100-mosquitoes with rabbit and 50-mosquito with 2 Hemotek® membrane feeders augmented with worn socks were used in the WHO tunnel bioassay, adjusted for net type and condition.


**4.1.2 Current velocity**

8 Will-be-set-by-IN-TECH

Fig. 5. A sketch of triangular grid (upper) and locally zoomed in mesh near Ganpu station

(lower) for modeling astronomical tide

It is clearly seen from Figures 7 and 8 that the maximum tidal ranges occur at the Ganpu station (T4). Thus, it is expected that the maximum tidal current may occur near this region. The tidal currents were measured at four locations H1-H4 across the estuary near Ganpu. These measurements are used to verify the numerical model. Figures 9 and 10 are the comparison between simulated and measured depth-averaged velocity magnitude and direction for the spring and neap tidal currents, respectively. It is seen that the flood tidal velocity is clearly greater than the ebb flow velocity for both the spring and neap tides. The maximum flood velocity occurs at H2 with the value of about 3.8 m/s, while the maximum ebb flow velocity is about 3.1 m/s during the spring tide. During the neap tide, the maximum velocities of both the flood and ebb are much less than those in the spring tide with the value of 1.5 m/s for flood and 1.2 m/s for ebb observed at H2. The maximum relative error for the ebb flow is about 17%, occurring at H2 during the spring tide. For the flood flow the maximal relative error occurs at H3 and H4 for both the spring and neap tides with values being about 20%. In general, the depth-averaged simulated velocity magnitude and current direction agree well with the measurements, and the maximal error percentage in tidal current is similar as that encountered in modeling the Mahakam Estuary (Mandang & Yanagi, 2008).

Fig. 8. Comparison of the computed and measured neap tidal elevations at stations T2-T6. −:

Astronomical Tide and Typhoon-Induced Storm Surge in Hangzhou Bay, China 189

computed; ◦: measured

Fig. 7. Comparison of the computed and measured spring tidal elevations at stations T2-T6. −: computed; ◦: measured

10 Will-be-set-by-IN-TECH

Fig. 7. Comparison of the computed and measured spring tidal elevations at stations T2-T6.

−: computed; ◦: measured

Fig. 8. Comparison of the computed and measured neap tidal elevations at stations T2-T6. −: computed; ◦: measured

Fig. 10. Comparison of the computed and measured depth-averaged neap current velocities

Astronomical Tide and Typhoon-Induced Storm Surge in Hangzhou Bay, China 191

at stations H1-H4. −: computed; ◦: measured

Fig. 9. Comparison of the computed and measured depth-averaged spring current velocities at stations H1-H4. −: computed; ◦: measured

12 Will-be-set-by-IN-TECH

Fig. 9. Comparison of the computed and measured depth-averaged spring current velocities

at stations H1-H4. −: computed; ◦: measured

Fig. 10. Comparison of the computed and measured depth-averaged neap current velocities at stations H1-H4. −: computed; ◦: measured

Fig. 11. Comparison of the computed and measured spring current velocities at different

Astronomical Tide and Typhoon-Induced Storm Surge in Hangzhou Bay, China 193

depths at station H1. −: computed; ◦: measured

The vertical distributions of current velocities during spring tide are also compared at stations H1 and H4. The measured and simulated flow velocities in different depths (sea surface, 0.2D, 0.4D, 0.6D and 0.8D, where D is water depth) at these two stations are shown in Figures 11 and 12. It is noted that the current magnitude obviously decreases with a deeper depth (from sea surface to 0.8D), while the flow direction remains the same. The numerical model generally provides accurate current velocity along vertical direction, except that the simulated current magnitude is not as high as that of measured during the flood tide. The maximum relative error in velocity magnitude during spring tide is about 32% at H4 station. Analysis suggests that the errors in the tidal currents estimation are mainly due to the calculation of bottom shear stress. Although the advanced formulation accounts for the impacts of flow acceleration and non-constant stress distribution on the calculation of bottom shear stress, it can not accurately describe the changeable bed roughness that depends on the bed material and topography.

#### **4.2 Typhoon-induced storm surge**

#### **4.2.1 Wind field**

Figures 13 and 14 show the comparisons of calculated and measured wind fields at Daji station and Tanxu station during Typhoon Agnes, in which the starting times of x-coordinate are both at 18:00 of 29/08/1981 (Beijing Mean Time). In general, the predicted wind directions agree fairly well with the available measurement. However, it can be seen that calculated wind speeds at these two stations are obviously smaller than observations in the early stage of cyclonic development and then slightly higher than observations in later development. The averaged differences between calculated and observed wind speeds are 2.6 m/s at Daji station and 2.1 m/s at Tanxu station during Typhoon Agnes. This discrepancy in wind speed is due to that the symmetrical cyclonic model applied does not reflect the asymmetrical shape of near-shore typhoon.

#### **4.2.2 Storm surge**

Figure 15 displays the comparison of simulated and measured tidal elevations at Daji station and Tanxu station, in which the starting times of x-coordinate are both at 18:00 on 29/08/1981 (Beijing Mean Time). It can be seen from Figure 15 that simulated tidal elevation of high tide is slightly smaller than measurement, which can be directly related to the discrepancy of calculated wind field (shown in Figures 13 and 14). A series of time-dependent surge setup, the difference of tidal elevations in the storm surge modeling and those in purely astronomical tide simulation, are used to represent the impact of typhoon-generated storm. Figure 16 having a same starting time in x-coordinate displays simulated surge setup in Daji station and Tanxu station. There is a similar trend in surge setup development at these two stations. The surge setup steadily increases in the early stage (0-50 hour) of typhoon development, and then it reaches a peak (about 1.0 m higher than astronomical tide) on 52nd hour (at 22:00 on 31/08/1981). The surge setup quickly decreases when the wind direction changes from north-east to north-west after 54 hour. In general, the north-east wind pushing water into the Hangzhou Bay significantly leads to higher tidal elevation, and the north-west wind dragging water out of the Hangzhou Bay clearly results in lower tidal elevation. The results indicate that the typhoon-induced external forcing, especially wind stress, has a significant impact on the local hydrodynamics.

14 Will-be-set-by-IN-TECH

The vertical distributions of current velocities during spring tide are also compared at stations H1 and H4. The measured and simulated flow velocities in different depths (sea surface, 0.2D, 0.4D, 0.6D and 0.8D, where D is water depth) at these two stations are shown in Figures 11 and 12. It is noted that the current magnitude obviously decreases with a deeper depth (from sea surface to 0.8D), while the flow direction remains the same. The numerical model generally provides accurate current velocity along vertical direction, except that the simulated current magnitude is not as high as that of measured during the flood tide. The maximum relative error in velocity magnitude during spring tide is about 32% at H4 station. Analysis suggests that the errors in the tidal currents estimation are mainly due to the calculation of bottom shear stress. Although the advanced formulation accounts for the impacts of flow acceleration and non-constant stress distribution on the calculation of bottom shear stress, it can not accurately describe the changeable bed roughness that depends on the bed material and topography.

Figures 13 and 14 show the comparisons of calculated and measured wind fields at Daji station and Tanxu station during Typhoon Agnes, in which the starting times of x-coordinate are both at 18:00 of 29/08/1981 (Beijing Mean Time). In general, the predicted wind directions agree fairly well with the available measurement. However, it can be seen that calculated wind speeds at these two stations are obviously smaller than observations in the early stage of cyclonic development and then slightly higher than observations in later development. The averaged differences between calculated and observed wind speeds are 2.6 m/s at Daji station and 2.1 m/s at Tanxu station during Typhoon Agnes. This discrepancy in wind speed is due to that the symmetrical cyclonic model applied does not reflect the asymmetrical shape of

Figure 15 displays the comparison of simulated and measured tidal elevations at Daji station and Tanxu station, in which the starting times of x-coordinate are both at 18:00 on 29/08/1981 (Beijing Mean Time). It can be seen from Figure 15 that simulated tidal elevation of high tide is slightly smaller than measurement, which can be directly related to the discrepancy of calculated wind field (shown in Figures 13 and 14). A series of time-dependent surge setup, the difference of tidal elevations in the storm surge modeling and those in purely astronomical tide simulation, are used to represent the impact of typhoon-generated storm. Figure 16 having a same starting time in x-coordinate displays simulated surge setup in Daji station and Tanxu station. There is a similar trend in surge setup development at these two stations. The surge setup steadily increases in the early stage (0-50 hour) of typhoon development, and then it reaches a peak (about 1.0 m higher than astronomical tide) on 52nd hour (at 22:00 on 31/08/1981). The surge setup quickly decreases when the wind direction changes from north-east to north-west after 54 hour. In general, the north-east wind pushing water into the Hangzhou Bay significantly leads to higher tidal elevation, and the north-west wind dragging water out of the Hangzhou Bay clearly results in lower tidal elevation. The results indicate that the typhoon-induced external forcing, especially wind stress, has a significant impact on

**4.2 Typhoon-induced storm surge**

**4.2.1 Wind field**

near-shore typhoon.

**4.2.2 Storm surge**

the local hydrodynamics.

Fig. 11. Comparison of the computed and measured spring current velocities at different depths at station H1. −: computed; ◦: measured

Fig. 13. Comparison of calculated and measured wind fields at Daji station during Typhoon Agnes. (a): wind speed; (b): wind direction. Starting time 0 is at 18:00 of 29/08/1981

Astronomical Tide and Typhoon-Induced Storm Surge in Hangzhou Bay, China 195

Fig. 14. Comparison of calculated and measured wind fields at Tanxu station during Typhoon

Agnes. (a): wind speed; (b): wind direction. Starting time 0 is at 18:00 of 29/08/1981

Fig. 12. Comparison of the computed and measured spring current velocities at different depths at station H4. −: computed; ◦: measured

16 Will-be-set-by-IN-TECH

Fig. 12. Comparison of the computed and measured spring current velocities at different

depths at station H4. −: computed; ◦: measured

Fig. 13. Comparison of calculated and measured wind fields at Daji station during Typhoon Agnes. (a): wind speed; (b): wind direction. Starting time 0 is at 18:00 of 29/08/1981

Fig. 14. Comparison of calculated and measured wind fields at Tanxu station during Typhoon Agnes. (a): wind speed; (b): wind direction. Starting time 0 is at 18:00 of 29/08/1981

**5. Conclusions**

estuary.

**6. References**

2001.

530-536, 2009.

In this study, the results from field observation and 3D numerical simulation are used to investigate the characteristics of astronomical tide and typhoon-induced storm surge in the

Astronomical Tide and Typhoon-Induced Storm Surge in Hangzhou Bay, China 197

1. Tidal hydrodynamics in the Hangzhou Bay is significantly affected by the irregular geometrical shape and shallow depth and is mainly controlled by the M2 harmonic constituent. The presence of tropical typhoon makes the tidal hydrodynamics in the

2. The tidal range increases significantly as it travels from the lower estuary towards the middle estuary, mainly due to rapid narrowing of the estuary. The tidal range reaches the maximum at Ganpu station (T4) and decreases as it continues traveling towards the upper

3. The flood tidal velocity is clearly greater than the ebb flow velocity for both the spring and neap tides. The maximum flood velocity occurs at H2 with the value of about 3.8 m/s, while the maximum ebb flow velocity is about 3.1 m/s during the spring tide. During the neap tide, the maximum velocities of both the flood and ebb are much less than those in the spring tide with the value of 1.5 m/s for flood and 1.2 m/s for ebb observed at H2. 4. The vertical distributions of current velocity at stations H1 and H4 show that the current magnitude obviously decreases with a deeper depth (from sea surface to 0.8D), while the

5. Tropical cyclone, in terms of wind stress and pressure gradient, has a significant impact on its induced storm surge. In general, the north-east wind pushing water into the Hangzhou Bay significantly leads to higher tidal elevation, and the north-west wind dragging water

Cao, Y. & Zhu, J. "Numerical simulation of effects on storm-induced water level after

Chang, H. & Pon, Y. "Extreme statistics for minimum central pressure and maximal wind

Chen, C., Liu, H. & Beardsley, R. "An unstructured, finite-volume, three-dimensional,

Guo, Y., Zhang, J., Zhang, L. & Shen, Y. "Computational investigation of typhoon-induced

Hu, K., Ding, P., Zhu, S. & Cao, Z. "2-D current field numerical simulation integrating Yangtze Estuary with Hangzhou Bay," *China Ocean Engineering*, vol. 14(1), pp. 89-102, 2000. Hu, K., Ding, P. & Ge, J. "Modeling of storm surge in the coastal water of Yangtze Estuary and Hangzhou Bay, China," *Journal of Coastal Research*, vol. 51, pp. 961-965, 2007.

*of Atmospheric and Oceanic Technology*, vol. 20, pp. 159-186, 2003.

contraction in Qiantang estuary," *Journal of Hangzhou Institute of Applied Engineering*,

velocity of typhoons passing around Taiwan," *Ocean Engineering*, vol. 1, pp. 55-70,

primitive equation ocean model: application to coastal ocean and estuaries," *Journal*

storm surge in Hangzhou Bay, China," *Estuarine, Coastal and Shelf Science*, vol. 85, pp.

out of the Hangzhou Bay clearly results in lower tidal elevation.

Hangzhou Bay. Some conclusions can be drawn as below:

Hangzhou Bay further complicated.

flow direction remains the same.

vol. 12, pp. 24-29, 2000.

Fig. 15. Comparison of calculated and measured water elevations during Typhoon Agnes. (a): Daji station; (b): Tanxu station. Starting time 0 is at 18:00 of 29/08/1981

Fig. 16. The simulated surge setup at two stations during Typhoon Agnes. (a) Daji station; (b) Tanxu station. Starting time 0 is at 18:00 of 29/08/1981


*3.1. Experimental Setup*

We used the CelebFaces Attributes (CelebA) dataset (Figure 4) [20], which consists of 202,599 facial images of celebrities cropped to 178 × 218. We cropped each image further to 170 × 170, and then resized them to 128 × 128. For progressive training, we downsampled the images to 32 × 32 and used them in the first stage. The height and width of each training image were doubled every five training epochs. In our experiments, the coefficients of the objective functions in Equations (3) and (6) were set to γ = 0.5 and α = 0.1. We used the ADAM [21] solver with β1 = 0.5 and β2 = 0.999, and the learning rate was set initially to 0.0005. *L*1 loss was adopted as the loss function for the auto-encoder. All other parameters were the same as in BEGAN. We used Tensorflow with cuDNN as the deep-learning framework and an NVIDIA GTX 1080Ti graphics card.

**Figure 4.** CelebFaces Attributes (CelebA) dataset.

#### *3.2. Qualitative Results*

We conducted qualitative analysis by comparing the output of our model with those of two other auto-encoder-based GAN models, BEGAN [12] and BEGAN-CS [18]. BEGAN-CS adds a latent constraint to BEGAN. The results are shown in Figure 5. The columns (a) to (c) represent 5, 10, and 15 epochs, respectively, while each row represents the qualitative results from the compared methods. The output images are produced by the generator and the input vectors are sampled randomly from a Gaussian distribution. It should be noted that the output of our model in (a) (5 epochs) has a lower resolution than the other models because of the progressive learning strategy it employs. The visual quality of the images improves as training progresses in all three models. However, the results from BEGAN contain some artifacts, such as checkerboard patterns, while BEGAN-CS produces blurred and unstructured facial images (Figure 5d). Once the size of the training images is increased, our model produces similar visual quality in Figure 5b and clearer images than the other models after 15 epochs (Figure 5c,d).

**Figure 5.** Qualitative results for facial image generation. The rows from top to bottom present the results of BEGAN, BEGAN-CS, and the proposed model, respectively. Each column represents five epochs; i.e., (**a**) epoch 5, (**b**) epoch 10, and (**c**) epoch 15. (**d**) An enlargement of the red box in (c).

### *3.3. Quantitative Results*

It is difficult to verify the diversity of output images using several images. Therefore, we conducted quantitative experiments using the Fréchet inception distance (FID) [22]. The FID score can be used to measure the quality and diversity of images. The FID score is calculated using Equation (11):

$$\text{FID} = \|\mu\_x - \mu\_y\|\_2^2 + Tr\left(\Sigma\_x + \Sigma\_y - 2\left(\Sigma\_x \Sigma\_y\right)^{\frac{1}{2}}\right),\tag{11}$$

where *x* and *y* denote the image sets *x* and y.  Because the FID score considers the mean (μ) and variance (Σ) of the images, it can represent the visual quality and diversity of the images. If the two sets of images have a similar probability distribution, the FID score is low (Equation (11)). Therefore, lower FID scores are better

when comparing GAN models. We measured the FID score based on 5000 real samples and 5000 fake samples in epoch 15 for each model. The results are summarized in Table 1.

**Table 1.** Visual quality in terms of the Fréchet inception distance (FID) score, where a lower score is better.

Our model produces the best results. As a result, our model can be seen as superior in terms of image quality and diversity.

#### *3.4. Facial Synthesis Results*

We test the synthesis of facial images using our model as described in Section 3.4. In Figure 6, the right-most image in each row represents the synthesis output of the two left-side images. The front module of our decoder takes a latent vector encoded from the left image, and the back module takes a latent vector from the right image. The output image has the characteristics of both images but different scale-specific features. In other words, the output has the coarse-scale characteristics of the first image (e.g., the overall structures or locations of facial attributes) and the fine-scale features of the second image (e.g., the eyes or the skin color). Note that facial synthesis is achieved without requiring additional information, such as binary attribute labels for each image.

**Figure 6.** Qualitative results of facial synthesis with the proposed model. The right-most image is the synthesis of the two left-side images.

#### **4. Conclusions**

In this paper, we proposed an enhanced GAN model for unsupervised facial image generation and synthesis. To overcome the limitations of GAN models (particularly auto-encoder-based models), we first introduced an enhanced generator and discriminator structure. Our generator and decoder utilize two input vectors, and every block reflects the information from the input vectors with adaptive instance normalization layers. Each layer plays a role in producing scale-specific components of the facial image. We also applied a progressive learning method to the proposed auto-encoder-based model, in which the training process was divided into several stages depending on the size of the training image. Consequently, our model can both generate and synthesize facial images via an auto-encoder

structure. Our model can generate arbitrary images because it also takes noise as input and synthesizes two existing images using an encoder and decoder within the discriminator. Therefore, it does not require additional training to encode existing images or a pre-trained network. We demonstrated that the visual quality and diversity of the output images were higher than those of the baseline models using both qualitative and quantitative analysis. Additionally, we presented a method for synthesizing two existing images by exploiting the auto-encoder structure of the discriminator. Our model did not need to train a subnetwork that could encode the images for mixing. All of the networks in our model were trained in an end-to-end manner without the labeling of the images. In future research, we will further investigate this novel method from a variety of perspectives to enhance the visual quality of the output images and to ensure stable training for large-scale image generation. Furthermore, we will extend our model for use in not only unsupervised generation tasks but also conditional image generation or synthesis tasks, such as image-to-image translation.

**Author Contributions:** Conceptualization: J.g.K.; methodology: J.g.K.; software: J.g.K.; investigation: J.g.K.; writing—original draft preparation: J.g.K.; writing—review and editing: H.K.; supervision: H.K. All authors have read and agreed to the published version of the manuscript.

**Acknowledgments:** This research was supported by a National Research Foundation (NRF) grant funded by the MSIP of Korea (number 2019R1A2C2009480).

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


**4. Genaral DCT algorithm**

The DCT for real signal *xn* gives independent spectral coefficients for k = 0,1,...,N-1, changing *fk* also from zero to *fsampl* 2 but with *fsampl* 2*N* grid. DCT vs. DFT gives twice better resolution. Splitting the sum (1) and redefine the indices we get:

$$\bar{X}\_k = a\_k \left\{ \sum\_{n=0}^{\frac{N}{2}-1} \left( x\_n \cos \left[ \frac{\pi}{N} \left( n + \frac{1}{2} \right) k \right] - x\_{N-1-n} \cos \left[ \frac{\pi}{N} \left( N - \left( n + \frac{1}{2} \right) \right) k \right] \right) \right\} \tag{10}$$

Due to symmetry of the cosine function

$$\bar{X}\_k = a\_k \sum\_{n=0}^{\frac{N}{2}-1} \cos\left[\frac{\pi}{N}\left(n + \frac{1}{2}\right)k\right] \times \left(\mathbf{x}\_n + (-1)^k \mathbf{x}\_{N-1-n}\right) \tag{11}$$

We can introduce the new set of variables:

$$A\_{\rm ll} = \begin{cases} \mathbf{x}\_{\rm n} + \mathbf{x}\_{N-1-n} & (n = 0, \dots, \frac{N}{2} - 1) \\ \mathbf{x}\_{N-1-n} - \mathbf{x}\_{\rm n} & (n = \frac{N}{2}, \dots, N-1) \end{cases} \tag{12}$$

DCT coefficients can be separated for even and odd indices respectively:

$$\mathcal{R}\_{\binom{\text{even}}{\text{odd}}} = \mathfrak{a}\_k \sum\_{n=\binom{\text{0}}{N-1}}^{\frac{N}{2}-\binom{1}{0}} A\_{\mathcal{U}} \cos\left[\frac{\pi}{N}\left(n+\frac{1}{2}\right)k\right] \tag{13}$$

Let us notice that (13) for even indices has the same structure as (1) with only shorter range of indices. Recurrently we can introduce new sets of variables for the set of indices k = 2p, where

Experiments 9

387 An Optimization of 16-Point Discrete Cosine Transform Implemented into a FPGA as a Design for a Spectral First Level Surface Detector Trigger in Extensive Air Shower Experiments

> 101110 1 0 −1 1 −1 0 1 −1 0 −101 110 −1 0 −1

A direct approach from the classical definition requires: a single multiplication for even indices (20) and 5 multiplications for odd indices (22). The scaled coefficients *S*1,7,3,5*X*¯ 1,7,3,5 in (22) can be expressed in an equivalent way introduced by Arai, Agui, Nakajima (AAN,

> 1110 −1 1 1 −101 1 −1 0 −1 1 1 −101 −1

*B*7 *S*4*B*5 (*S*2 + *S*6)*B*6 (*S*2 − *S*6)*B*4 *S*6(*B*6 − *B*4)

*B*7 *S*2*B*4 *S*6*B*4 *S*4*B*5 *S*2*B*6 *S*6*B*6

(22)

(23)

For odd indices with a support of (15) we get:

4

4

  *S*1*X*¯ 1 *S*7*X*¯ 7 *S*3*X*¯ 3 *S*5*X*¯ 5  =

  *S*1*X*¯ 1 *S*7*X*¯ 7 *S*3*X*¯ 3 *S*5*X*¯ 5  =

1988)., which allows reducing an amount of multiplications from 5 to 4 only.

Fig. 4. A fast DCT algorithm developed in 1988 by Arai, Agui and Nakajima

A minimization of multiplications amounts is one of a fundamental goal in long-term numerical calculations. Reduction of product terms significantly speed up sophisticated calculations, because a single multiplication requires several clock cycles of processor. Multiplications in powerful FPGA chips can be however performed in very fast dedicated DSP blocks in a single clock cycle. Signals processed in parallel threads in a hardware implementation of a pipeline design have to be synchronized to each other. Pipeline approach requires additional shift registers for synchronization also for signal currently not being processed. However, such synchronization needs additional resources. Fig. 5 shows the part of pipeline chain corresponding to odd indices of DCT coefficients (lower part in Fig. 4).

p is integer, till k < N. In order to use symmetry of trigonometric functions in a maximal way, N should be a power of 2, similarly to Radix-2 approach used in FFT algorithm. If N = 2q, recurrent minimization is possible till p = q. The twiddle factors for successive minimization steps m equal to *cos* 2*π* 2*q* 2*p*+*m* 2 = −1 , because the sum of step index m and range factor p is constant and equals to q. For the rest of indices twiddle factor depends on fractional angle *α* = *π*2*q*−*m*−1 *N* .

After the 1st step of minimization, the terms of the sum (13) for odd indices depends only on the odd multiplicity of the fractional angle

$$\bar{X}\_k = \alpha\_k \sum\_{n=N-1}^{\frac{N}{2}-1} A\_n \cos \left[ \frac{\pi}{2N} \left( 2n+1 \right) k \right] \tag{14}$$

Using a following trigonometric identity

$$\cos \mathfrak{a} = \frac{1}{2 \cos \mathfrak{\mathfrak{z}}} (\cos(\mathfrak{a} + \mathfrak{z}) + \cos(\mathfrak{a} - \mathfrak{z})) \tag{15}$$

the fractional angles can be increased by the factor of 2 for *β* = *kπ* 2*N* . Thus:

$$\bar{X}\_k = \frac{a\_k}{2\cos\left(\frac{k\pi}{2N}\right)} \sum\_{n=N-1}^{N/2} A\_n \times \left[ \cos\left(\frac{k\pi}{N}(n+1)\right) + \cos\left(\frac{k\pi}{N}n\right) \right] \tag{16}$$

Let us notice that:

1). *cos*(*kπ*)=(−1)*k*, for n = N-1, hence pure *An* coefficient survives,

2). *cos*( *kπ* 2 ) = 0, for *n* = *N* 2 because of odd k,

3). the rest of indices appear in cosine terms twice in *An*+1 and *An* coefficients, which allows introducing the new set of variables

$$B\_{N-1} = A\_{N-1}$$

$$B\_{N-1-n} = A\_{N-n} + A\_{N-1-n} \tag{17}$$

The range of *Bn* indices is continuous and can be split again on even and odd parts. The above procedure can be repeated in recurrence.

#### **5. 8-point DCT algorithm**

For N = 8 according to formulae (12) and (17) we get :

$$A\_{0,1,2,3} = \mathbf{x}\_{0,1,2,3} + \mathbf{x}\_{7,6,5,4} \qquad \qquad A\_{7,6,5,4} = \mathbf{x}\_{0,1,2,3} - \mathbf{x}\_{7,6,5,4} \tag{18}$$

$$B\_{0,1} = A\_{0,1} + A\_{3,2} \qquad B\_{2,3} = A\_{0,1} - A\_{3,2} \qquad B\_{6,5,4} = A\_{7,6,5} + A\_{6,5,4} \qquad B\_7 = A\_7 \tag{19}$$

For even indices the DCT coefficients are expressed as follows:

$$2\sqrt{2}\begin{bmatrix} \check{X}\_0\\ \check{X}\_4 \end{bmatrix} = \begin{bmatrix} 1 & 1\\ 1 & -1 \end{bmatrix} \begin{bmatrix} B\_0\\ B\_1 \end{bmatrix} \qquad\qquad 4\begin{bmatrix} S\_2\check{X}\_2\\ S\_6\check{X}\_6 \end{bmatrix} = B\_3 + S\_4 \begin{bmatrix} 1 & 1\\ -1 & -1 \end{bmatrix} \begin{bmatrix} B\_3\\ B\_2 \end{bmatrix} \tag{20}$$

where

$$S\_k = \cos\left(\frac{k\pi}{16}\right) \tag{21}$$

For odd indices with a support of (15) we get:

4

8 Will-be-set-by-IN-TECH

p is integer, till k < N. In order to use symmetry of trigonometric functions in a maximal way, N should be a power of 2, similarly to Radix-2 approach used in FFT algorithm. If N = 2q, recurrent minimization is possible till p = q. The twiddle factors for successive minimization

is constant and equals to q. For the rest of indices twiddle factor depends on fractional angle

After the 1st step of minimization, the terms of the sum (13) for odd indices depends only on

*Ancos*

*π*

2*N* (2*n* + 1) *k*

*N* (*n* + 1)

*A*0,1,2,3 = *x*0,1,2,3 + *x*7,6,5,4 *A*7,6,5,4 = *x*0,1,2,3 − *x*7,6,5,4 (18)

= *B*3 + *S*4

 1 1 −1 −1  *B*3 *B*2

(20)

(21)

= −1 , because the sum of step index m and range factor p

2*cosβ* (*cos*(*α* + *β*) + *cos*(*α* − *β*)) (15)

 + *cos*

*BN*−1−*n* = *AN*−*n* + *AN*−1−*n* (17)

2*N* . Thus:

 *kπ N n*  (14)

(16)

steps m equal to *cos*

Let us notice that:

2). *cos*( *kπ*

*α* = *π*2*q*−*m*−1 *N* . 2*π* 2*q* 2*p*+*m* 2

the odd multiplicity of the fractional angle

Using a following trigonometric identity

*X*¯ *k* = *αk* 2*cos kπ* 2*N*

2 ) = 0, for *n* = *N*

introducing the new set of variables

procedure can be repeated in recurrence.

For N = 8 according to formulae (12) and (17) we get :

For even indices the DCT coefficients are expressed as follows:

 *B*0 *B*1

**5. 8-point DCT algorithm**

2 √ 2 *X*¯ 0 *X*¯ 4 = 1 1 1 −1

where

*X*¯ *k* = *αk*

*cosα* = 1

the fractional angles can be increased by the factor of 2 for *β* = *kπ*

*N*/2 ∑ *n*=*N*−1

1). *cos*(*kπ*)=(−1)*k*, for n = N-1, hence pure *An* coefficient survives,

2 because of odd k,

*An* × *cos kπ*

*BN*−1 = *AN*−1

3). the rest of indices appear in cosine terms twice in *An*+1 and *An* coefficients, which allows

The range of *Bn* indices is continuous and can be split again on even and odd parts. The above

*B*0,1 = *A*0,1 + *A*3,2 *B*2,3 = *A*0,1 − *A*3,2 *B*6,5,4 = *A*7,6,5 + *A*6,5,4 *B*7 = *A*7 (19)

4 *S*2*X*¯ 2 *S*6*X*¯ 6

 *kπ* 16

*Sk* = *cos*

*N* 2 −1 ∑ *n*=*N*−1

$$
\begin{bmatrix} S\_1 \bar{X}\_1 \\ S\_7 \bar{X}\_7 \\ S\_3 \bar{X}\_3 \\ S\_5 \bar{X}\_5 \end{bmatrix} = \begin{bmatrix} 1 & 0 & 1 & 1 & 1 & 0 \\ 1 & 0 & -1 & 1 & -1 & 0 \\ 1 & -1 & 0 & -1 & 0 & 1 \\ 1 & 1 & 0 & -1 & 0 & -1 \end{bmatrix} \begin{bmatrix} B\_7 \\ S\_2 B\_4 \\ S\_6 B\_4 \\ S\_4 B\_5 \\ S\_2 B\_6 \\ S\_6 B\_6 \end{bmatrix} \tag{22}
$$

A direct approach from the classical definition requires: a single multiplication for even indices (20) and 5 multiplications for odd indices (22). The scaled coefficients *S*1,7,3,5*X*¯ 1,7,3,5 in (22) can be expressed in an equivalent way introduced by Arai, Agui, Nakajima (AAN, 1988)., which allows reducing an amount of multiplications from 5 to 4 only.

$$
\mathbf{4} \begin{bmatrix} S\_1 \bar{X}\_1 \\ S\_7 \bar{X}\_7 \\ S\_3 \bar{X}\_3 \\ S\_5 \bar{X}\_5 \end{bmatrix} = \begin{bmatrix} 1 & 1 & 1 & 0 & -1 \\ 1 & 1 & -1 & 0 & 1 \\ 1 & -1 & 0 & -1 & 1 \\ 1 & -1 & 0 & 1 & -1 \end{bmatrix} \begin{bmatrix} B\_7 \\ S\_4 B\_5 \\ (S\_2 + S\_6) B\_6 \\ (S\_2 - S\_6) B\_4 \\ S\_6 (B\_6 - B\_4) \end{bmatrix} \tag{23}
$$

Fig. 4. A fast DCT algorithm developed in 1988 by Arai, Agui and Nakajima

A minimization of multiplications amounts is one of a fundamental goal in long-term numerical calculations. Reduction of product terms significantly speed up sophisticated calculations, because a single multiplication requires several clock cycles of processor. Multiplications in powerful FPGA chips can be however performed in very fast dedicated DSP blocks in a single clock cycle. Signals processed in parallel threads in a hardware implementation of a pipeline design have to be synchronized to each other. Pipeline approach requires additional shift registers for synchronization also for signal currently not being processed. However, such synchronization needs additional resources. Fig. 5 shows the part of pipeline chain corresponding to odd indices of DCT coefficients (lower part in Fig. 4).

Experiments 11

389 An Optimization of 16-Point Discrete Cosine Transform Implemented into a FPGA as a Design for a Spectral First Level Surface Detector Trigger in Extensive Air Shower Experiments

Routines E and F from Fig. 5 have been merged into single routine E (Fig. 6) to short an

D45p64=C45+C64 S3X3= D7p66-D45p24

D7m26=C7-C26

D7m66=C7-C66

D45m64=C45-C64 D7p66=C7+C66

D45m64=C45-C24

D45p24=C45+C24

S1X1= D7p26+ D45p64

S7X7= D7m26+D45m64

S5X5= D7m66-D45m64

32 gives:

(26)

(27)

B6=A6+A7 D7p26=C7+C26

Fig. 7. Optimized, shorter pipeline chain based on the classical approach. The reduction of

A classical approach reduces a length of the chain from 6 to 5 stages only, at the cost of one additional multipliers. An abridgement of the pipeline chain and in a consequence a reduction of the shift registers needed for synchronization allows saving significant amount of logic blocks, especially for wide data bus. In order to reduce an approximation errors, the data bus

The 16-point DCT algorithm will be implemented according to the classical approach with an optimization of the number of pipeline stages at the cost of an utilization of embedded multipliers (Szadkowski, 2009). The 1st and the 2nd pipeline stages utilize the set of variables (12) and (17) respectively. For N = 16 the fractional angle of the twiddle factor in the 1st step of minimization equals to *β* = *π* . The same fractional angle corresponds to the 2nd step of

The scaling procedure used for odd indices of *X*¯ *k* with the fractional angles *β* = *kπ*

Coefficients *X*¯ *k* for even indices can be expressed by variables (24) and scaling factor (21)

*S*4 *S*4 *S*4 *S*4 *S*4 −*S*4 −*S*4 *S*4 *S*2 *S*6 −*S*6 −*S*2 *S*6 −*S*2 *S*2 −*S*6

*S*7 *S*5 *S*3 *S*1 −*S*1 *S*3 −*S*5 *S*7 −*S*5 −*S*1 −*S*7 *S*3 *S*3 *S*7 −*S*1 *S*5

*B*0,1,2,3 = *A*0,1,2,3 + *A*7,6,5,4 *B*4,5,6,7 = *A*3,2,1,0 − *A*4,5,6,7 (24)

*B*15 = *A*15 *B*14,...,8 = *A*15,...,9 + *A*14,...,8 (25)

*B*4 *B*5 *B*6 *B*7

*B*0 *B*1 *B*2 *B*3

C7 = B7

C66=S6B6 C26=S2B6

C45=S4B5

C64=S6B4 C24=S2B4

amount of pipeline stages and remove unnecessary shift registers.

B7 = A7

in the intermediate stages is enlarged.

minimization for even indices corresponded to *An*.

*X*¯ 0 *X*¯ 8 *X*¯ 4 *X*¯ 12

*X*¯ 2 *X*¯ 14 *X*¯ 6 *X*¯ 10  = 1 2 √2

 = 1 2 √2

**6. 16-point DCT algorithm**

the length of the chain at the cost of an additional multiplier.

A7=x0-x7

A6=x1-x6

A5=x2-x5

A4=x3-x4

B5=A5+A6

B4=A4+A5

Fig. 5. The AAN algorithm limited to indices4-7 only with a time-oriented structure. Adders, sub-tractors, multipliers and shift registers are marked by the following colours: blue, gray, black and green, respectively. Red colour corresponds to routines requiring a cascade processes.

A direct implementation of the pure AAN algorithm requires 7 pipeline stages, which utilize additional resources of shift registers for synchronization for operations like: X(t+1) = X(t). In a numerical calculation in processors data are simply waiting for a next performance cycle. The *D*64 block contains a cascade of the sum and the multiplication. An implementation of the cascade in a single clock FPGA logic block significantly reduce a speed. Additionally, the *lpm\_add\_sub* mega-function from the Altera® library of parameterized modules (LPM) does not support an inversion of a sum i.e. *B*4 = −(*A*4 + *A*5) or *E*4 = −(*D*64 + *D*4). These operations would have to be performed in a cascade way by an adder and a sign inversion. Cascade operations performed in the same clock cycle significantly slow down a global registered performance.

Fig. 6. Optimized AAN algorithm for indices 4 - 7. A redefinition and splitting of variables allowed a reduction of the chain length.

A simple redefinition of nodes removes difficulties mentioned above. The *B*4 node defined as the sum of *A*4,5 nodes requires a simple *lpm\_add\_sub* mega-function. The *D*4 node with currently inverted sign allows using *lpm\_add\_sub* in *E*4 performing a subtraction. The *D*64 node from Fig. 5 can be split into the subtraction *C*64 and the multiplication *D*64 in the next clock cycle (Fig. 6).

Routines E and F from Fig. 5 have been merged into single routine E (Fig. 6) to short an amount of pipeline stages and remove unnecessary shift registers.

Fig. 7. Optimized, shorter pipeline chain based on the classical approach. The reduction of the length of the chain at the cost of an additional multiplier.

A classical approach reduces a length of the chain from 6 to 5 stages only, at the cost of one additional multipliers. An abridgement of the pipeline chain and in a consequence a reduction of the shift registers needed for synchronization allows saving significant amount of logic blocks, especially for wide data bus. In order to reduce an approximation errors, the data bus in the intermediate stages is enlarged.


**Author details**

Fernando Cassinda Quissanga1,2

1 Iberoamerica International University, Mexico

2 European University of the Atlantic, Spain

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Comparative Study of Information Security in Mobile Operating Systems: Android and Apple iOS DOI: http://dx.doi.org/10.5772/intechopen.109652*


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**27**

*1,2Albania 3Germany*

Ana Mane Kapaj1 and Ilir Kapaj2,3

*Department of Economy and Agrarian Policy Tirana,*

*Department of Agribusiness Management Tirana,*

*Computer Applications in Agriculture, Stuttgart,*

**Olive Oil Sector in Albania and Its Perspective**

Albania, situated on the eastern shore of the Adriatic Sea, may be divided into two major regions: a mountainous highland region (north, east, and south) constituting 70% of the land area, and a western coastal lowland region that contains nearly all of the country's agricultural lands and is the most densely populated part of Albania. Due to the mountains landscape and especially because of its many divisions, the climate varies from region to region. It is warmer in the western part of the country which is affected by the warm air masses from the sea (the Adriatic costal region has a typical Mediterranean climate). This

The transition of Albanian economy from a centrally planned to a market economy is associated with the implementation of a considerable number of structural and institutional reforms necessary for a sustainable market economy. Trade liberalization policies were implemented associated with elimination of price controls as the economy was

Despite the progress made, especially in terms of macroeconomic and financial stability, Albania continues to have one of the lowest levels of income per capita in. In addition, there is a big income gap between rural and urban areas, since the agricultural sector comprise about 58% of total labour force and count for 25% of Albania Gross Domestic Product (INSTAT, 2010). Albania's economic growth can be achieved primarily through strengthening the agricultural sector. The current macroeconomic situation along with the climatic, geographic, and cultural advantages as comparable to neighbouring countries provide the opportunity for a fast and sustainable growth of the agricultural sector. Even though the olive production does not take a large share in the total agricultural production, it is an industry with huge potentials that has been steadily growing during the years.

Like many of the other agricultural products, the major supply of oil (vegetable and olive) in Albania comes from imports. This is because of the inconsistent and unreliable supply of

climate makes Albania an important producer of olives and olive oil for the region.

decentralized to balance the supply and demand of goods and services.

**1. Introduction**

*1Agriculture University of Tirana, Faculty of Economy and Agribusiness,*

*2Agriculture University of Tirana, Faculty of Economy and Agribusiness,*

*3Hohenheim University, Institute of Agribusiness Management and*


**1. Introduction**

Tracheal intubation is one of the cornerstones of anesthesiology. To the lay person, and even our surgical colleagues, it should not be that difficult …just put a narrow tube in the trachea; no big deal. Indeed, we as a profession make it look easy, and 95% of the time it is. The incidence of difficult direct laryngoscopy these days is only 4.9% [1] with difficult or failed intubation much lower than this at 0.33% and 0.01% respectively [2]. This book will look at four different aspects of advanced tracheal intubation including videolaryngoscopy, ultrasound use for airway management, the video-assisted intubating stylet technique, and airway management in obstetrics.

Without question, videolaryngoscopy (VL) has changed the practice of endotracheal tube (ETT) placement and the practice of anesthesiology. Much of the time, VL makes ETT placement look easy (to the lay person). First introduced in 2001 [3], it has grown ubiquitous in ETT placement. In fact, aside from "practice" or the need to "keep our skills up," do many/any of us, who grew up in the days of direct laryngoscopy (DL) or early days of VL, DL anymore? Can anyone imagine how we intubated patients with "anterior" cords atraumatically? Given all the advantages of VL including successful ETT placement in difficult airways, multiple clinicians' ability to view the airway, and documentation of airway findings, it is no wonder that VL has gained a prominent place in the American Society of Anesthesiologists Practice Guidelines of Difficult Airway Management [4]. There are still disadvantages with VL, namely the dreaded "blind zone" where injuries can occur, a bulkier design if a channeled blade is used, and increased cost compared with DL, but it has forever changed the practice of ETT placement.

VL is one of the indispensable tools in our arsenal, and ultrasound may be the next. It is commonly used by anesthesiologists for invasive line placements, nerve blocks, and point-of-care ultrasound. Not surprisingly, given its success in these realms, it is starting to be used for airway management. There is work being done using the US to predict difficult mask ventilation or intubation by measuring tongue thickness or soft tissue thickness at the vocal cords, predict proper ETT size in pediatric patients or patients with obesity, identification of airway nerves for awake fiberoptic blocks, identification of upper airway inflammation including epiglottitis and sinusitis (useful for nasal ETT placement), guidance of the ETT to the correct depth in the trachea, confirmation of ETT and supraglottic airway placement, lung ultrasound as an indirect sign of correct ETT placement, surgical airway guidance via visualization of the cricothyroid membrane and trachea, and evaluation of other pulmonary pathology including pneumothorax.

#### *Advances in Tracheal Intubation*

When faced with a difficult airway, anesthesiologists have multiple tools from which to choose. A lesser-known tool is a video-assisted intubating stylet (VS) which is simple, portable, and easy to use. It can be used for the Shikani technique for intubation. The Shikani optical stylet is a bendable stylet with an endoscope at the distal tip that is used to visually guide the ETT. The Shikani method of intubation uses this stylet for guidance while grasping the mandible to lift the jaw anteriorly and advances the ETT and stylet combination into the larynx with direct visualization from the stylet. This technique negates: the need for a laryngoscope, increased forces on dentition, or cervical spine extension. An endoscopic stylet was first introduced in 1979 and now multiple similar video-assisted stylets are on the market. They can be used for various ETTs including regular ETTS, laser, and double-lumen tubes. Advantages of using VS include both the ease of obtaining a superior glottic view and placing the ETT through the viewed glottis. Potential drawbacks are lens fogging and secretions obscuring the view (though not exclusive to VS). It can be combined with DL and VL which is particularly useful in difficult airways as it uses the strengths of both modalities in that DL/VL is used to open the airway and lift the epiglottis allowing the VS to sneak under the epiglottis and obtain an optimized glottic view. Due to its many advantages, VS can not only be in the anesthesiologist's arsenal of difficult airway tools, but also for every day and every airway use in both adults and pediatric patients.

Despite the many advances to manage airways that our specialty has seen over the last several decades, airways in pregnancy (particularly in parturients) still elude us. Indeed, the incidence of failed intubation in pregnancy has not changed in 40 years. Airway complications are the leading causes of anesthesia-related maternal mortality with an incidence of failed intubation at 1 in 390 [5]. Multiple physiologic and anatomical changes put obstetric patients at risk for airway complications including airway changes due to edema particularly during labor and delivery, reduced functional residual capacity to closing capacity and increased oxygen demand resulting in faster onset of hypoxemia at induction, increased gastric pressure and relative incompetence of the lower esophageal sphincter, prolonged gastric emptying time during labor, pregnancy-related weight gain, and breast enlargement.

We must mitigate those changes with what we have until technology can be developed to magically (or at least competently) intubate during pregnancy 100% of the time. At this time in our arsenal, we can: do a preanesthetic airway assessment (sometimes under the duress of time), have an induction plan and multiple backup plans, be familiar with algorithms for difficult airways in pregnancy, premedicate to reduce pH and reduce gastric volume, perform rapid sequence inductions or positive pressure ventilation with cricoid pressure likely with VL and a relatively smaller, cuffed endotracheal tube with an introducer, and lastly, consider a second generation supraglottic airway and proceed with surgery if unable to intubate. In the case of "can't intubate, can't oxygenate," laryngospasm should be quickly ruled out, and then proceed with either a tracheotomy or cricothyrotomy.

All four of the following chapters highlight the advances, successes, and continued challenges that we face with tracheal intubation. The hope that we have with this book is that with widespread education and new technology eventually we can make ETT placement look easy 100% of the time.

*Introductory Chapter: Advances in Tracheal Intubation DOI: http://dx.doi.org/10.5772/intechopen.110146*


**Chapter 6**

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