**3. Results**

#### *3.1. Fatigue Protocol*

After the fatigue protocol, the mean values of HR, BL, and RPE were greater than 185 beats/min, 14 mmol/L, and a score of 18, respectively. Results of repeated measurement indicated that HR, BL, and RPE were all affected by the fatigue protocol (partial η2: 0.99, 0.876, and 0.994 respectively), and the *p* values were all less than 0.001 (Table 1).

Figure 1 illustrates the HR–time curve (mean) of four participants throughout the fatigue protocol. To divide the fatigue protocol into stages, time was normalized, the slope of the HR–time curve was calculated, and four stages were distinguished, namely, P1 (0–10% duration of fatiguing process (D)), P2 (10–30%D), P3 (30–60%D) and P4 (60–100%D).

**Figure 1.** Heart rate (HR) of four participants during the fatiguing process (mean). According to the slope of the HR–time curve, the overall duration of the fatiguing process was divided into four stages, that is, P1, P2, P3, and P4. Duration of fatiguing process (%) vs. heart rate (beats/min).

#### *3.2. Kinematics of Lower Extremity Joints*

In the sagittal plane, Table 2 shows that the hip, knee, and ankle initial contact angle (CAH, CAK, CAA, respectively), peak angle (PAH, PAK, PAA, respectively), time to peak angle, and range of motion (RoM) all decreased during the process of the fatigue protocol (from P1 to P4).

**Table 2.** Kinematics variables in the four stages of fatiguing process (P1–P4) from pre- to post-fatigue protocol in the sagittal plane (mean ± SD).

Notes: CAH, CAK, and CAA: initial contact angle of hip, knee, and ankle joint, respectively; PAH, PAK, and PAA: peak joint angle of hip, knee, and ankle joint, respectively. P1–P4: the 1st, 2nd, 3rd, and 4th stages of the fatiguing process, respectively. *a*, *a\**: significant differences between P1 and P2 at 0.05 and 0.01 levels, respectively; *b*: significant differences between P1 and P3 at 0.05 level; *c*: significant differences between P1 and P4 at 0.05 level; *d\**: significant differences between P2 and P3 at 0.01 level; *e\**: significant differences between P2 and P4 at 0.01 level; *f*: significant differences between P3 and P4 at 0.05 level.

At initial contact, a significant decrease was found for hip flexion (P1 vs. P4, *p* = 0.044; P2 vs. P3, *p* = 0.009; P2 vs. P4, *p* = 0.003) and knee flexion (P1 vs. P2, *p* = 0.028; P1 vs. P3, *p* = 0.037; P1 vs. P4, *p* = 0.004). Hip, knee, and ankle joints were all flexed (plantar-flexed) to the peak joint angle in less time. There were statistically significant differences in the time to peak value for the knee joint (P1 vs. P4, *p* = 0.009; P3 vs. P4, *p* = 0.012) and the ankle joint (P1 vs. P2, *p* = 0.036; P1 vs. P3, *p* = 0.021; P1 vs. P4, *p* = 0.001). The significance values (*p*) of the knee joint RoM were 0.013 (P1 vs. P2) and 0.048 (P1 vs. P4).

#### *3.3. Five Sub-Phases of the Stance Phase*

Table 3 shows that significant differences existed in the duration of the sub-stance phases II, III, IV, and V during the fatiguing process: durations were shorter for II (P1 vs. P3: *p* = 0.012), III (P1 vs. P4: *p* = 0.041; P3 vs. P4: *p* = 0.005), and V (P1 vs. P3: *p* = 0.021; P1 vs. P4: *p* = 0.005), and longer for IV (P1 vs. P2: *p* = 0.022; P1 vs. P3: *p* = 0.024; P1 vs. P4: *p* = 0.002; P2 vs. P4: *p* = 0.002; P3 vs. P4: *p* = 0.036).

#### *3.4. Kinetics of Lower Extremity Joints*

For the mean moments and power at the hip, knee, and ankle joints in the sagittal plane. Significant differences were found for moments at the hip (the 1st peak, P1 vs. P4: *p* = 0.03; the 2nd peak, P1 vs. P4: *p* = 0.01) and the knee (the 1st peak, P1 vs. P4: *p* < 0.001; the 2nd peak, P1 vs. P4: *p* = 0.01); power generation at the hip (P1 vs. P3: *p* = 0.025; P1 vs. P4: *p* = 0.014) and knee (P1 vs. P2, P1 vs. P3, P1 vs. P4: *p* < 0.001); and power consumption at the knee (P1 vs. P2: *p* = 0.029; P1 vs. P3: *p* = 0.009 and P1 vs. P4: *p* < 0.001) (details in Figure 4a).

#### *3.5. SPM (1D) of Kinematics of Lower Extremity Joints*

For the four stages of the fatiguing process (P1–P4), Figure 2a illustrates the joint angle–time curves in the sagittal plane (mean ± SD). SPM (1D) analysis results with significant differences are illustrated in Figure 2b. At the hip, significant differences exist in the initial contact (I), impact loading (II), weight acceptance (III), and pre-drive-off (IV) phases (P2 vs. P3 (I and II: *p* = 0.01; III and IV: *p* < 0.001); P2 vs. P4 (I–IV: *p* < 0.001)). At the knee, significant differences were found mainly in the weight acceptance and pre-drive-off phases (P1 vs. P2 and P1 vs. P4: *p* < 0.001).

**Table 3.** Duration of five sub-phases (%) (mean ± SD).

Notes: PF1: initial impact peak (the 1st peak vertical ground reaction force, vGRF); PF2: secondary impact peak (the 2nd peak vGRF); PF3: third impact peak (the 3rd peak vGRF); PAK: peak knee joint flexion angle. FO: foot off. The stance phase was divided into five sub-phases (I–V) according to impact peak and PAK. P1–P4: the 1st, 2nd, 3rd, and 4th stages of the fatiguing process, respectively. *a*, *a\**: significant differences between P1 and P2 at 0.05 and 0.01 levels, respectively; *b*: significant differences between P1 and P3 at 0.05 level; *c*, *c\**: significant differences between P1 and P4 at 0.05 and 0.01 levels, respectively; *e\**: significant differences between P2 and P4 at 0.01 level; *f*, *f\**: significant differences between P3 and P4 at 0.05 and 0.01 levels, respectively.

**Figure 2.** (**a**) Summary of the joint angles (mean ± SD) of lower limbs in the sagittal plane. (**b**) Results with significant differences of one-dimensional statistical parametric mapping (SPM (1D)) for hip and knee joint angles. Positive angles represent hip and knee flexion and ankle dorsi-flexion. P1–P4: the 1st, 2nd, 3rd, and 4th stages of the fatiguing process, respectively.

#### *3.6. SPM (1D) of Kinetics of Lower Extremity Joints*

Figure 3 illustrates the vGRF waveform data of P1, P2, P3, and P4, and the results of SPM (1D) analysis with significant differences, which are shown mainly in the pre-drive-off phases between P1 and P4 (*p* < 0.001).

Figure 4a illustrates the waveform data of the hip, knee, and ankle joint moments and power in the sagittal plane (mean ± SD) in P1, P2, P3, and P4. SPM (1D) analysis results of moments and power with significant differences are illustrated in Figure 4b,c, respectively. For hip and knee moments, significant differences exist in the partial period of the pre-drive-off phase (IV) between P1 and P3 (*p* < 0.001), and in the IV phase between P1 and P4 (*p* < 0.001). Moreover, Figure 4c illustrates that significant differences exist in the partial IV and drive-off (V) phases for hip power (P1 vs. P4: *p* < 0.05) and knee power (P1 vs. P3: *p* < 0.001; P1 vs. P4: *p* < 0.001).

**Figure 3.** Vertical ground reaction force (vGRF) and results with significant differences of one-dimensional statistical parametric mapping (SPM (1D)). P1–P4: the 1st, 2nd, 3rd, and 4th stages of the fatiguing process, respectively.

**Figure 4.** *Cont.*

**Figure 4.** (**a**) Summary of joint moments and power (mean ± SD) of lower extremities in the sagittal plane during the lunge. (**b**) Results with significant differences of one-dimensional statistical parametric mapping (SPM (1D)) for hip and knee joint moments. (**c**) Results with significant differences of SPM (1D) for hip and knee joint power. Positive moments represent hip and knee extensor and ankle plantar flexor moments, and positive joint power indicates periods of power generation. P1–P4: the 1st, 2nd, 3rd, and 4th stages of the fatiguing process, respectively. *a*, *a\**: significant differences between P1 and P2 at 0.05 and 0.01 levels, respectively; *b*: significant differences between P1 and P3 at 0.05 level; *c*, *c\**: significant differences between P1 and P4 at 0.05 and 0.01 levels, respectively.

## **4. Discussion**

An increasing number of people now play badminton. Both athletes and recreational players attempt to optimize their performance, thus increasing the risk of injury. Prevention of sports-related injuries is an important challenge. Fatigue is a major factor causing injury. Consequently, this study investigated the fatigue effects on a specific movement in badminton, i.e., the footwork associated with the lunge, which is one of the most used and integral movements [10,11].

Considering the task dependency of fatigue, a repeated forehand forward lunge, until reaching exhaustion, was proposed as the fatigue protocol, which was subdivided into four stages (P1, P2, P3, and P4) according to the slope of the heart rate (HR)–time curve. After the protocol, the mean values of HR, blood lactate (BL), and rating of perceived exertion (RPE) increased significantly at the significance level of 0.001; in particular, the mean HR was greater than 185 beats/min, the mean BL value was greater than 14 mmol/L, and the mean RPE score was greater than 18, indicating that all participants were fatigued. At P1, the first stage of the fatigue protocol, all participants were in a pre-fatigue state and, at P4, the final stage of the fatigue protocol, they were fatigued. In addition, according to previous studies [10,20,21], the first, second, and third impact peak (PF1, PF2, and PF3) and knee flexion peak angle (PAK) were used to subdivide the lunge stance phase into initial contact (I: 0-PF1)), impact loading (II: PF1-PF2), weight acceptance (III: PF2-PAK), pre drive-o ff (IV: PAK-PF3), and drive-o ff (V: PF3-end) periods. Then, statistical analyses were undertaken for both discrete and waveform kinematic and kinetic data in the sagittal plane, in which the largest movements occurred, comparing not only the pre- and post-fatigue states, but also the four stages of the fatiguing process.

The results supported the hypothesis that the biomechanical characteristics of the lunge change significantly during the fatiguing process. At the initial contact time, participants exhibited a more "erect" posture for the lunge, which is usually observed by coaches, and was shown as less hip and knee flexion, and ankle dorsi-flexion. Less dorsi-flexion at the foot strike has been found for recreational players with a relative lack of muscle power compared to national-level badminton athletes [19]. In the lateral jump performed in badminton, Herbaut et al. [27] found a decreased plantar-flexion angle at the foot strike post-fatigue. These changes may be induced by muscle fatigue caused by repeated stretch-shortening. Furthermore, the range of motion (RoM) of hip, knee, and ankle joints was decreased due to the fatiguing process.

Smaller peak joint angles were found at hip, knee, and ankle joints in the sagittal plane, particularly at the knee joint (with significant di fferences between P1 and the other three stages of the fatiguing process). After fatigue, a decrease in the knee peak angle was also found by Valldecabres et al. [16], however, no significant di fference was found. The discrepancy may be due to the participant's sports level [24]. The decrease in the peak angle could be explained by the decrease in joint moments (details in Figure 4). There were significant di fferences in peak joint moments at the hip and knee extensor (P1 vs. P4: hip (*p* = 0.03), knee (*p* < 0.001)). A similar relationship was illustrated by Fu et al. [19] between professional and amateur badminton players; that is, professional players with greater muscle strength and better performance showed higher knee and ankle joint moments. Additionally, a shorter time was taken to flex to the peak angle, with significant decreases in time shown at the knee and ankle. This may be caused by impaired control due to fatigue. This can also be explained in this study by the decrease in joint moments and power.

In addition to the angles, the durations of the four sub-stance phases (II–V) changed during the process of the fatigue protocol. The shorter durations of II and III indicate that less time was taken to reach PF2 and PAK, respectively; that is, due to the fatiguing process, the participant placed his foot flat and flexed his knee more quickly. This is consistent with the increased ankle plantar flexor and knee flexor moments in the present study (Figure 4a), and with the opinion that fatigue reduces the capacity of muscles to generate force. The most significant increase was found in the IV phase (pre-drive-o ff), increasing from 27.25 ± 10.69% stance to 43.38 ± 15.24% stance. Significantly decreased hip and knee peak power in this phase provided su fficient support for this change. Less power was generated for players for the drive-o ff. Kuntz et al. [10] indicated that a hop style lunge generates higher peak vertical force during loading. In this study, three participants used the hop style at the final stage of the fatigue protocol. The change of lunge style may be a strategy to generate more power for driving-o ff and returning to the starting position [14].

Furthermore, considering the time continuity of biomechanical variables of the lunge motion, one-dimensional statistical parametric mapping SPM (1D) was used to analyze the biomechanical waveform data in the sagittal plane. Most of the joint angle, moment, and power waveforms decreased consistently during the process of the fatigue protocol (among P1, P2, P3, and P4). Significant di fferences were found in the hip and knee joint angles, mainly in the pre-drive-o ff phase (IV) (most *p* values were less than 0.001). The results support the hypothesis that the fatigue e ffects were di fferent in the five

periods of the lunge. Moreover, these results support the view that lunge characteristics change due to fatigue.

Taking into account the joint moments, both the hip and knee joints showed a significant response to the fatigue protocol. Significant di fferences were found between P1 and P3 in part of the period of IV, and between P1 and P4 in IV. The hip moments had a larger e ffective scope. However, there were only significant di fferences in the hip power between P1 and P4. For knee power, the significant di fferences were seen between P1 and P3, and also between P1 and P4, with a larger e ffective scope of the period. This indicates that the significant decrease in joint power occurred earlier and was mostly evident at the knee; that is, the fatigue responses manifested mostly at the knee joint. This result is consistent with a previous epidemiology study that reviewed musculoskeletal injuries among Malaysian badminton players, which reported that the majority of injuries sustained by players were due to overuse, primarily of the knee [7]. Another study [28] suggested that the rapidly changing eccentric/concentric work of the quadriceps in the varying degree of knee flexion was probably associated with patellar tendon injury. This may be why patellar tendinopathy is the most common injury of lower limbs among badminton players, and this result also supports our third hypothesis. Coaches and players should pay more attention to the training of the knee, and particularly the knee extensors.

Additionally, a comparison indicates that the discrete and waveform results are consistent. There are significant di fferences in CAH (P2 vs. P3), CAK (P2 vs. P4), PAK (P1 vs. P2; P1 vs. P4), knee peak moment (P1 vs. P4), hip peak power (P1 vs. P4), and knee peak power (P1 vs. P3; P1 vs. P4). In addition, significant di fferences were shown in these periods of stance phase in the discrete data. The results of SPM (1D) analysis clearly provided more information, indicating the period rather than a point of time in the fatigue response. This is also helpful in identifying the key body segments a ffected by fatigue. Thus, it is important for coaches and players to design a corresponding training program to improve the technique and muscle strength. Moreover, the results of SPM (1D) can provide support for monitoring of training.

Considering the key findings of this study, a few limitations should be noted when interpreting the results. First, the participants were male badminton players with at least 8 years of special badminton training. Results may di ffer for players of di fferent levels, ages, and gender, thus studies of players with a range of abilities and ages, including female players, should be considered. Second, all tests were conducted on a simulated badminton court. Third, the sample size was limited. Fourth, the changing slope of the HR–time curve was used to subdivide the fatiguing process. However, it is not su fficient to explain the status of fatigue. Electromyography (EMG) data might be more suitable and could be used in future work. Finally, although joint moments and power allow further assessment of the functional contribution of the joints, EMG and musculoskeletal system simulations would help understand movement changes during the fatiguing process.


**1. Introduction**

The acoustic nonlinearity parameter (β) is widely used for diagnosing and inferring material damage and it can be measured using displacement amplitudes of fundamental and second-order harmonics waves [1–11]. The exact value of an acoustic nonlinearity parameter is called the absolute acoustic nonlinearity parameter. To measure the absolute acoustic nonlinearity parameter, it is necessary to measure the extremely small displacement amplitudes of the second harmonic frequency component [12]. However, such experimental measurement methods are complicated and practically difficult to apply in the field. Hence, the relative acoustic nonlinearity parameter available for relative comparison is frequently measured using the voltage amplitude of the device, measuring the detected ultrasonic wave. Nevertheless, measurement of the absolute acoustic nonlinearity parameter is indispensable for quantitative characterization of materials.

There are two ways to obtain the absolute acoustic nonlinearity parameter: a direct method and an indirect method. The direct method is used to measure the displacement amplitude of the ultrasonic wave directly or to use a calibration to convert the detected voltage signal amplitude into the displacement amplitude [12]. The indirect method is used to estimate the absolute acoustic nonlinearity parameter of a test material by measuring the ratio of relative acoustic nonlinearity parameters between

the test material and the reference material, where the absolute acoustic nonlinearity parameter of the reference material is known [13]. This indirect method, introduced in the aforementioned paper, is simpler than the direct method, but requires the assumption that the voltage-displacement proportionality coefficients (VDCs) of the test and reference materials are the same. The VDC indicates the proportionality coefficient between the detected voltage signal amplitude of ultrasonic wave and the displacement amplitude of that wave, which is dependent on the material properties and the sensitivity of the receiving transducer. If the experimental conditions are kept constant for two similar materials, an indirect method can be applied to them because the VDCs of the two materials are almost the same. On the other hand, if the test and reference materials are dissimilar, an indirect method cannot be applied even if the experimental conditions are kept consistent because the VDCs of the two materials are different. In particular, the material dependency of the VDC is critical in the case of contact detection. For example, in the case of contact reception of ultrasonic waves using piezoelectric transducers, energy loss due to impedance mismatching can occur when ultrasonic energy is converted into electrical energy, so that the VDC varies if the material's acoustic impedance is different. Therefore, to overcome the limitation of the previously proposed indirect method, a non-contact detection method using a laser interferometer has been proposed for longitudinal waves [14]. Since the interferometer obtains an output directly proportional to the ultrasonic displacement, its proportionality is independent of the material.

Meanwhile, many studies have investigated the acoustic nonlinearity parameter of surface acoustic waves [3,4,15–19]. In this regard, an indirect method to measure the acoustic nonlinearity parameter of surface acoustic waves has also been studied for the case of similar test and reference materials [3]. Unfortunately, however, when a surface acoustic wave is transmitted and received using wedges as a contact technique, this method cannot be applied for dissimilar materials because of the constraints mentioned above.

This paper proposes a fully non-contact surface acoustic wave technique using lasers for the indirect measurement of the acoustic nonlinearity parameter. This technique allows the application of the indirect method even when the test and reference materials are dissimilar. Here, a pulsed Nd:YAG laser was used to generate surface acoustic waves, and a laser beam was irradiated onto the specimens through a line-arrayed slit mask to generate tone burst waves. In the contact method, the initial second harmonic frequency coming from the system, i.e., electronic devices and transducers, can pose a problem. However, in the proposed technique the initial second harmonic frequency can be easily suppressed by adjusting the duty ratio of the line-arrayed slit mask [17]. This characteristic is an advantage that may be difficult to obtain in the case of using other non-contact excitations, for example EMAT (electromagnetic acoustic transducer) or ACT (air-coupled transducer). For surface acoustic wave reception, a laser interferometer produced an output signal directly proportional to the out-of-plane displacement of the surface waves and maintained a constant VDC regardless of the used material. Furthermore, we established a measurement principle that applies the proposed non-contact technique to the indirect measurement of the absolute acoustic nonlinearity parameter using surface acoustic waves. This study differs from previous studies on longitudinal waves [14] in that additional compensation is required for the difference in wavenumbers in the test and reference materials.

For experimental verification, aluminum and copper specimens were used as reference and test materials, respectively, and the relative acoustic nonlinearity parameters of the two materials were measured from the surface acoustic waves generated and received using lasers. The absolute acoustic nonlinearity parameter of copper was estimated from the ratio of relative acoustic nonlinearity parameters with the compensation of wavenumbers. Additionally, the absolute parameters of aluminum and copper were measured by longitudinal wave using a conventional direct measurement method, with the former being used as a reference value and the latter being used for comparison with the estimation result.

#### **2. Principles**

The acoustic nonlinearity parameter of a surface acoustic wave (β) can be derived in terms of the out-of-plane displacement amplitude for the fundamental and second-order harmonic frequency components of the surface acoustic wave as follows [1,6,18,19],

$$\beta = \frac{8}{k\_L^2 x} \frac{A\_2}{A\_1^2} \frac{k\_T^2 \sqrt{k\_S^2 - k\_L^2}}{k\_S (2k\_S^2 - k\_T^2)}\tag{1}$$

Here, *A*1 and *A*2 are the out-of-plane displacement amplitudes of the fundamental and second-order harmonic components of surface acoustic waves, respectively; *x* is the wave propagation distance; and *kL*, *kT*, and *kS* are the wavenumbers of longitudinal, transverse, and surface acoustic waves, respectively. If the term consisting only of wavenumbers in Equation (1) is defined as a parameter *F* as in Equation (2),

$$F = \frac{k\_T^2 \sqrt{k\_S^2 - k\_L^2}}{k\_S(2k\_S^2 - k\_T^2)}\tag{2}$$

Equation (1) can then be expressed as Equation (3).

$$
\beta = \frac{8}{k\_L^2} \frac{A\_2}{A\_1^2} F
\tag{3}
$$

In contrast, the relative acoustic nonlinearity parameter of surface acoustic wave β*'SAW* is defined by the voltage amplitudes as follows.

$$
\beta\_{SAW}' = \frac{A\_2'}{A\_1'^2} \tag{4}
$$

where *A*1 and *A*2 are the detected signal amplitudes of the fundamental and second-order harmonic components of the surface acoustic wave, respectively.

In a previous study, it has been proved that when a laser interferometer is used as a receiver, the detected signal amplitude is proportional to the displacement amplitude regardless of the difference in materials. Thus, the VDC is not dependent on the material, and the displacement amplitudes of the fundamental and second-order harmonic components can be expressed as follows [14].

$$\begin{aligned} A\_{1,t} &= \alpha\_1 \cdot A'\_{1,t} \\ A\_{1,r} &= \alpha\_1 \cdot A'\_{1,r} \\ A\_{2,t} &= \alpha\_2 \cdot A'\_{2,t} \\ A\_{2,r} &= \alpha\_2 \cdot A'\_{2,r} \end{aligned} \tag{5}$$

Here, the subscripts *t* and *r* refer to the test and reference materials, respectively. α1 and α2 are the VDCs of the fundamental and second-order harmonic frequencies, respectively. They are only related to the sensitivity of the laser detector, which is dependent on the frequency but not on the material.

Now, in order to apply the indirect method, we consider the relationship between the ratio of the absolute acoustic nonlinearity parameters of the two materials and the ratio of their relative acoustic nonlinearity parameters, as shown in the following equation.

$$\frac{\beta\_t}{\beta\_r} = \frac{\frac{8}{k\_{t,L}^2} \frac{\alpha\_2 A\_{2,t}'}{a\_1^2 A\_{1,t}'^2} F\_t}{\frac{8}{k\_{r,L}^2} \frac{\alpha\_2 A\_{2,r}'}{a\_1^2 A\_{1,r}'^2} F\_r} = \frac{\frac{F\_t}{k\_{t,L}^2} \frac{A\_{2,t}'}{A\_{1,t}'^2}}{\frac{F\_r}{k\_{r,L}^2} \frac{A\_{2,r}'}{A\_{1,r}'^2}} = k\_r' \frac{\beta\_{SAW,t}'}{\beta\_{SAW,r}'} \tag{6}$$

The second term of Equation (6) was obtained by substituting Equation (5) into Equation (3) for the test and reference materials. This can be simplified to the third term when the propagation distance *x* is fixed. The VDCs of the two materials are canceled out. The fourth term is a rearrangement of the third term using Equation (4), where *k* is a factor representing the ratio of *F* values in the two materials and is defined in Equation (7).

$$k' = \frac{F\_t \cdot k\_{r,L}^2}{F\_r \cdot k\_{t,L}^2} \tag{7}$$

Finally, the absolute acoustic nonlinearity parameter of the test material can be determined from the relationship in Equation (6).

$$
\beta\_t = k' \frac{\beta\_{SAW,t}'}{\beta\_{SAW,r}'} \beta\_r
\tag{8}
$$

Equation (8) indicates that the absolute acoustic nonlinearity parameter of the test material can be estimated from the ratio of the relative acoustic nonlinearity parameters of the test and reference materials. The absolute acoustic nonlinearity parameter of the reference material as well as the wavenumber-dependent factor *k* (referred to as the wavenumber compensation factor in this paper) should be known in advance. If the test material and the reference material are similar, *k* is almost one and thus it can be ignored. However, if they are different, the wavenumbers in the two materials are different and they should be taken into account.

#### **3. Specimens**

To verify the proposed method, two kinds of specimens with different materials, pure copper and aluminum (Al2024), were prepared as shown in Figure 1. The dimensions of both the specimens were same at 120 mm × 40 mm × 20 mm. Aluminum was used as the reference material and copper was used as the test material.

**Figure 1.** Specimens.

#### **4. Experiments**

#### *4.1. Experimental Setup*

The experimental scheme for the relative acoustic nonlinearity measurements using the fully non-contact surface acoustic wave method is shown in Figure 2. A pulsed Nd:YAG laser beam of 1064 nm (SL280, Spectron Laser Systems, Warwickshire, UK) was used to excite surface acoustic waves. The pulse duration of the pulsed laser is 10 ns, the repetition rate is 10 Hz, and the maximum energy is 300 mJ. A line-arrayed slit mask was designed to create surface acoustic waves with a wavelength of 2.92 mm, which corresponds to the fundamental frequency of 1 MHz in aluminum and 0.75 MHz in copper. The duty ratio of the slit mask was 50%, which theoretically does not generate a second harmonic component [12].

**Figure 2.** Experimental setup to generate and receive surface acoustic waves using the fully non-contact laser-ultrasonic technique.

The wave propagated on the specimens was received at the other point on the same side of the specimen by a photorefractive interferometer (TWM, Tecnar, Saint-Bruno-de-Montarville, QC, Canada). In general, when detecting the displacement of the surface wave, the diameter of the measuring laser beam should be smaller than the wavelength to avoid a decrease in signal sensitivity. The beam diameter of the interferometer used in the experiment was 0.8 mm, which is sufficiently small compared to the wavelength of the excited surface acoustic wave. The propagation distance was 65 mm. If the propagation distance is too long, the effects of attenuation and diffraction cannot be ignored. The received surface acoustic wave signal was captured by a digital oscilloscope (Lecroy HDO4034A, Teledyne LeCroy, Chestnut Ridge, NY, USA).

The Hanning window was applied to minimize the effect of the side lobe, and the fast Fourier transform was used to obtain the amplitudes of the fundamental frequency component *A*1 and the second harmonic frequency component *A*2 . The Hanning window size is the same as the data length. Sampling frequency was 10 GHz, and the number of data points was 10,000. As a result, the frequency resolution in FFT (Fast fourier transform) spectrum is 0.001 MHz. In order to obtain the magnitudes at the fundamental and second harmonic frequencies, we searched for peaks within the ±0.1 MHz range at each frequency. The frequency of the peak detected in the experimental results was within the range of ±0.02 MHz from the predicted frequency. The measurements were repeated by increasing the laser energy from 95 to 145 mJ in seven steps. The relative nonlinearity parameter β *SAW* was determined from the linearity slope of between *A*1 2 and *A*2 .

#### *4.2. Measurement of Relative Nonlinearity Parameter*

The received surface acoustic wave signals, their frequency spectra, and the linear fitting plots of *A*1 2 and *A*2 are shown in Figure 3 for aluminum and copper specimens. Figure 3a–c shows the results of the aluminum specimen and Figure 3d–f shows the results of the copper specimen. The surface acoustic wave signals shown in Figure 3a–d were obtained at the highest laser intensity, and Figure 3b–e shows their frequency spectra. The magnitudes of the fundamental and second-order harmonic frequency components are indicated by red dots. The fundamental frequency in aluminum was detected at 1.0 MHz and the second harmonic frequency was detected at 2.0 MHz, as intended. In copper, the fundamental frequency and second harmonic frequency components were detected at 0.74 MHz and 1.47 MHz, as expected.

**Figure 3.** Experimental results: (**a**–**c**) represent Al2024, and (**d**–**f**) represent copper. (**a**,**d**): Raw signals, (**b**,**e**): frequency spectra of raw signals, (**c**,**f**): linear fitting of *A*1 2 and *A*2 .

Note that a relatively large third harmonic is generated, because the surface acoustic wave close to the square wave form is generated by the line-arrayed slit mask. In this case, even-order harmonics are suppressed; however, the occurrence of odd-order harmonics is unavoidable [20]. When the third harmonic frequency is put in together with the fundamental frequency, the magnitude of the second harmonic frequency may change owing to the mixing effect. Nevertheless, as the magnitude of odd-order harmonics depends on the fundamental frequency, only the constant '8' in Equation (1) will vary. However, because this constant will be canceled in the calculation of the relative ratio in Equation (6), Equation (8) is valid as it is and does not affect the proposed measurement technique based on the relative ratio.

The linear relationship between *A*2 and *A*1 2 is shown in Figure 3c,f. In both specimens, the R-squared values are approximately 0.98, which confirms the linearity between *A*2 and *A*1 2. The measurements were repeated four times at each laser power, the deviation was so small that it cannot be seen in the figure.

Table 1 shows the relative nonlinearity parameter measurement results of each specimen and their ratio.

**Table 1.** Measurement values for the relative nonlinearity parameter of Al2024 and copper.

#### *4.3. Measurement of Wavenumber Compensation Factor k*

The ultrasonic velocity was measured for each specimen to determine the wavenumber of the longitudinal, transverse, and surface acoustic waves required to calculate the wavenumber compensation factor. The velocities of the longitudinal and transverse waves were obtained by measuring the time-of-flight (TOF) between the back-wall echo signals.

Pulser-receiver (Olympus 5077PR, Tokyo, Japan) and PZT transducers with main-resonance frequencies of 5.0 MHz for longitudinal waves and 2.25 MHz for transverse waves were used in the experiment. Figure 4 shows the experimental setup for wave velocity measurement. The TOF between the echo signals was measured using the auto-correlation of the received signal [21]. By using the measured TOF and thickness of the specimen, the velocities of the longitudinal and transverse waves were calculated for each specimen.

**Figure 4.** Experimental setup for measuring the velocities of longitudinal and transverse waves.

The surface wave velocity was measured using the same apparatus shown in Figure 2. Velocity of the surface acoustic wave is measured by non-contact method using a laser. The velocity was obtained by multiplying the fundamental frequency of the received signal by the wavelength given in the line array interval [17].

The measured longitudinal, transverse, and surface acoustic wave velocities are shown in Table 2. The *F* values of each material were calculated using Equation (2), and the wavenumber compensation factor *k* was obtained using Equation (7).

**Table 2.** Measurement results for wave velocities and wavenumber compensation factor.

#### *4.4. Estimation of Absolute Nonlinearity Parameter*

In order to verify the validity of the proposed method, the absolute nonlinearity parameters of the two materials were measured first using the conventional calibration method [12], which uses a pre-measured calibration function converting the electrical output of the receiving transducer into the displacement amplitude. The results are shown in Table 3. The absolute parameter of aluminum was used as a reference value and the absolute parameter of copper was used for comparison with the result estimated by the proposed technique.

**Table 3.** Absolute acoustic nonlinearity parameters of Al2024 and copper measured by the direct method.

Next, the absolute nonlinearity parameter of copper was estimated by substituting the measured wavenumber compensation factor *k* and the relative nonlinearity parameters of Al2024 and copper in Equation (8), in which the absolute nonlinearity parameter of Al2024 shown in Table 3 was used as the reference. The result is shown in Table 4. The absolute nonlinearity parameter measured using the direct method is shown for comparison. Considering the deviation, the estimated value agrees well with the direct measurement result.

**Table 4.** Absolute nonlinearity parameter of copper measured by the proposed method and the direct measurement method.

The results verify that the proposed indirect method using surface acoustic waves with a fully non-contact laser-ultrasonic technique is effective for estimating acoustic nonlinearity parameters.

#### **5. Conclusions**

This paper proposed a novel indirect method to measure the absolute acoustic nonlinearity parameter using surface acoustic waves with a fully non-contact laser-ultrasonic technique. The relationship between the ratio of relative nonlinearity parameters of two different materials (a test material and a reference material) measured using the proposed method and the ratio of absolute nonlinearity parameters of these two materials was theoretically derived. Using this relationship, when the absolute nonlinearity parameter of the reference material is known, the absolute nonlinearity parameter of the test material can be obtained from the ratio of the measured relative parameters of the two materials. The effectiveness of the proposed method was verified using the experimental results; the absolute nonlinearity parameter of copper measured by the proposed method was in good agreement with that obtained from direct measurement, in which aluminum (Al2024) was used as the reference material. The proposed technique avoids the inconvenience of direct measurement, maintains the advantage of surface waves, and can be applied even when the test material and the reference material are dissimilar, which is difficult to investigate using conventional contact techniques.

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

**Funding:** This research was supported by the Nuclear Power Research and Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2013M2A2A9043241).

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

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**The Role of Biopolymers in Obtaining Environmentally Friendly Materials The Role of Biopolymers in Obtaining Environmentally Friendly Materials**

Rodolfo Rendón‐Villalobos, Amanda Ortíz‐Sánchez, Efraín Tovar‐Sánchez and Emmanuel Flores‐Huicochea Rodolfo Rendón‐Villalobos, Amanda Ortíz‐Sánchez, Efraín Tovar‐Sánchez and Emmanuel Flores‐Huicochea

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/65265

#### **Abstract**

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nucleating effect. Macromolecules. 2016;49(2782‐2790).

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Polymeric materials have had a boom in the global industry over the past two decades, because of its adaptability, durability, and price so much so that now we cannot imagine a product that does not contain it. However, many synthetic polymers that have been developed are mainly derived from petroleum and coal as raw material, which make them incompatible with the environment, since they cannot be included in what is a natural recycling system. Aware of the environmental impacts that produce synthetic polymers, a solution could be the mixtures with different types and sources of biological materials, called biopolymers, such as starch, cellulose, chitosan, zein, gelatin among others and that gradually replace synthetic polymers to address and resolve these problems. The development of new applications, such as composite materials by incorporation of alternative materials, found in nature that has similar properties to oil‐ based polymers, but its main feature is its biodegradability and offering competitive to current material costs. In this sense, various investigations are aimed at decreasing the amounts of plastic waste and to manufacture products with less aggressive environment since the synthetic plastics are difficult to recycle and can remain in nature for over a century.

**Keywords:** biopolymers, biodegradability, friendly materials

© 2016 The Author(s). Licensee InTech. 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. © 2016 The Author(s). Licensee InTech. 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.


**Monitoring of Honey Bee Colony Losses**

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*Edited by Dalyse Toledo Castanheira*

*Coffee – Production and Research* presents a diversity of important issues related to coffee, with an emphasis on the science of coffee growing. Coffee is one of the highest value commodities traded worldwide. Cultivated and consumed widely, it generates progress for both the economy and society. Divided into six sections, this book examines two coffee species of commercial importance, *Coffea arabica* L. and *Coffea canephora* Pierre ex. A. Froehner. Chapters cover such topics as biotechnology, growing, harvesting, post-harvest handling, quality, chemistry, commercialization, and byproducts of coffee.

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Coffee - Production and Research

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*Article* **CNN Training Using 3D Virtual Models for Assisted Assembly with Mixed Reality and Collaborative Robots**

**Kamil Židek, Ján Pitel' \*, Michal Balog, Alexander Hošovský, Vratislav Hladký, Peter Lazorík, Angelina Iakovets and Jakub Demˇcák**

> Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, Bayerova 1, 08001 Presov, Slovakia<EMAIL_ADDRESS>(K.Ž<EMAIL_ADDRESS>(M.B<EMAIL_ADDRESS>(A.H<EMAIL_ADDRESS>(V.H<EMAIL_ADDRESS>(P.L<EMAIL_ADDRESS>(A.I<EMAIL_ADDRESS>(J.D.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +421-90-524-1605 or +421-55-602-6455

**Abstract:** The assisted assembly of customized products supported by collaborative robots combined with mixed reality devices is the current trend in the Industry 4.0 concept. This article introduces an experimental work cell with the implementation of the assisted assembly process for customized cam switches as a case study. The research is aimed to design a methodology for this complex task with full digitalization and transformation data to digital twin models from all vision systems. Recognition of position and orientation of assembled parts during manual assembly are marked and checked by convolutional neural network (CNN) model. Training of CNN was based on a new approach using virtual training samples with single shot detection and instance segmentation. The trained CNN model was transferred to an embedded artificial processing unit with a high-resolution camera sensor. The embedded device redistributes data with parts detected position and orientation into mixed reality devices and collaborative robot. This approach to assisted assembly using mixed reality, collaborative robot, vision systems, and CNN models can significantly decrease assembly and training time in real production.

**Keywords:** assisted assembly; mixed reality; collaborative robot; digital twin; convolutional neural networks


**7.2 Hygiene practice**

Hygiene practice is an important aspect of animals' health management and this entails keeping the farm environment free of any anything or condition that could induce pest infestation and disease infections in the animals. This includes

**Figure 4.** *A lamb with lame foot as result of infection. Source: Photo by author.*

ensuring that farmhouses and animal beddings are dry and regularly cleaned, and as well as the equipment and all facilities used for movement and care of farm animals. Cleaning may include scraping, sweeping, washing, possibly with the use of disinfectants. This action is underscored by the understanding that pathogenic micro-organisms and eggs of external and internal parasites could survive under any condition, particularly in a wet and filthy environment. With hygiene practices, no favourable condition is created for the micro-organisms to be active or survive to cause infections in the animals. Disinfection may though be essential, most disinfectants are not particularly effective in combating viruses *thereby* emphasising the need for physical cleaning and burning of bedding [81]. Droppings or faeces by animals, feathers and dead animals need to be promptly removed pens and ranches as these are sources of pathogens [82]. Also, too many animals on-site and overcrowding within building and cages should be avoided to prevent the rapid spread of emerging diseases of stock [16].


**Spatial Changes in Invertebrate Structures as a Factor of Strong Human Activity in the Bed and Catchment Area of a Small Urban Stream**

**Robert Czerniawski 1,2,\*, Łukasz Sługocki 1,2, Tomasz Krepski 1,2, Anna Wilczak 1 and Katarzyna Pietrzak 1**

Received: 3 February 2020; Accepted: 19 March 2020; Published: 24 March 2020

**Abstract:** The threats to small urban streams lead to a decrease in their water quality and dysregulate their ecological balance, thereby affecting the biodiversity and causing degradation of indicators that determine the ecological potential. The aim of our study was to determine the impact of abiotic conditions induced by intensive human activity on the community structures of invertebrates (zooplankton and macroinvertebrates) in the small urban stream Bukówka in the Szczecin agglomeration (NW Poland). This stream exhibits the same characteristics as a large river, in which the mass of live organic matter increases with their length. The composition of invertebrates (zooplankton and macroinvertebrates) was strongly influenced by the changes caused by humans in the stream bed. The construction of small reservoirs and bed regulation in this small urban streams had a similar effect on the quality of the water and ecological potential as in large rivers, but at a lower scale.

**Keywords:** macroinvertebrates; zooplankton; water quality; anthropogenic impact; stream ecology; riverbed regulation

#### **1. Introduction**

Small urban watercourses are a common and desirable component of the landscape of agglomerations [1]. However, urban running waters are intensively affected by humans, most often due to the pollution caused by them and their constant interference in the shape and continuity of the riverbed [1]. These threats lead to a decrease in the water quality of streams and rivers and dysregulate the ecological balance, thereby affecting the biodiversity and causing a reduction in the value of indicators that determine the ecological potential [2]. In addition, these exert harmful effects on other basins. Urban streams flow into larger rivers or lakes, often affecting the abiotic and biotic conditions of the water bodies [3]. These recipients can constitute sites of permanent existence of resident fauna or act as a transitional shelter to native or migratory fauna [3,4]. They might also function as a refuge for fauna that colonize the whole bed from the headwaters to the mouth.

Micro(zoo)plankton and macroinvertebrates are good indicators of the physical and chemical influence on the running water bed [2,5–7]. Any spatial change in the invertebrate composition of running waters reflects the physical and chemical effects induced by dam impoundments and bed regulation. Regulated, dammed, and polluted streams or ditches are characterized by smaller numbers and lower diversity indexes of macroinvertebrate taxa compared to forest streams [2,8,9]. On the other hand, zooplankton almost always respond positively to dam impoundments and new conditions that

are conducive to their development and reproduction [10]. Among the studies on the presence of micro- and macroinvertebrates in small streams, most have focused on a common topic—the impact of environmental factors (current velocity, water retention time, inorganic nutrients concentration, and other physico-chemical compounds) on their taxonomy and abundance [11–15]. Small urban streams undergo very rapid environmental changes, as these are under the strong influence of local factors that disrupt their functioning, such as human activity in the catchment and in the bed or rapid water runoff from the urban surface.

The urban impact on the spatial composition of invertebrates can be studied via small streams, such as Bukówka located in the Szczecin agglomeration (NW Poland). This watercourse is subject to intensive human activity over its entire length, such as regulation, bed damming, and pollution. The water of the stream is characterized by a high concentration of conductivity, total dissolved solids (TDS), and chlorides; these being very common parameters for indicating human activity. In addition, the lower section of the Bukówka stream is exposed to water inlet from the recipient, the Oder river, within the estuary.

The aim of our study was to determine the impact of abiotic conditions induced by intensive human activity on the structures of invertebrates in this small human-mediated urban stream.

#### **2. Methods**

#### *2.1. Study Area*

This study was performed in the Bukówka stream (and drainage of the lower stretch of the Oder river, NW Poland) (Figure 1). Bukówka is a regulated and non-natural stream flowing in the Szczecin agglomeration. It is the left tributary of the estuary of the Oder river. Bukówka is 9-km long, has a catchment area of 69 km2, and is characterized by a mean slope of 0.075%. The stream provides water to the recreational, strongly eutrophicated reservoir "Słoneczne lake", also called a pond; therefore, a dam and its impoundments are located along the stream. The total area of this reservoir is 7 ha, and its maximum depth is 1.5 m.

Each section of the stream was regulated and channelized. It means that the bed of the stream was extremely changed by humans. The bed of the upper section (Site 1—U1; Site 2—U2) was covered by sand and dead organic matter. The sediments of the reservoir—"Słoneczne Lake" (POND)—were marshy and also contained dead organic matter. The bottom of the reservoir was densely covered by emerging macrophytes (dominants: *Glyceria maxima*, *Phragmites australis*, and *Sparganium erectum*). Due to the abundance of phytoplankton in the reservoir and low visibility, there was no occurrence of submerged macrophytes. The dominant components of the sediments in the middle section (Site 3—M3; Site 4—M4) were sand and gravel, together with a small amount of organic matter. No macrophytes were observed in this section. The lower downstream sections (Site 5—D5; Site 6—D6) were characterized by a high amount of dead organic matter at the bottom, without the presence of minerals. The riparian zone of the bed in the lower section was covered by emerging macrophytes (dominants: P. communis, G. maxima, and S. erectum). Moreover, this section contained many small floodplains and slack water areas.

The percentage of land use in the catchment was calculated using the Corine Land Cover inventory. The land use in the catchment was separately estimated in the total catchment area and the buffer zone, which was located within 100 m of the stream shoreline. For our calculations, we used the QGIS Wien software [2,7,8] (QGIS Development Team). The land use of the Bukówka total catchment is occupied by agricultural areas (52%), artificial surfaces (44%), seminatural areas (3%), and water bodies (<1) (GIS data). In the 100 m buffer zone the catchment is mostly covered by artificial surfaces (94%) and definitely less by seminatural areas (5%) and water bodies (<1). Along the entire stream there are a numerous rainwater runoffs.

No field sampling permits with regard to the site locations were needed.

**Figure 1.** Study area. Numbers on the Bukówka stream indicate the sampling sites.

#### *2.2. Sampling Methods*

#### 2.2.1. Environmental Factors

Environmental factors were measured monthly from March to October in 2016 (*n* = 8) in the same day when the invertebrate samples were collected. Temperature, dissolved oxygen, pH, and conductivity, as well as the content of chlorophyll *a*, total dissolved solids (TDS), and chloride were measured in situ using the Hydrolab DS5 multiparameter probe (USA). In addition, water velocity, width, and depth were measured at each site using an OTT electromagnetic water flow sensor (Germany). The values of the measured environmental parameters are reported in Table 1.

**1.**Mean±SDofenvironmentalvariablesattheexaminedsitesintheBukówkastream.Temp—temperature;Cond—conductivity.

#### 2.2.2. Zooplankton

Zooplankton were collected monthly from March to October in 2016 (*n* = 8). At each site a 50 L water sample was collected from the surface of the river current using a 10-L bucket. The water was filtered through a plankton net having a mesh of 30 μm. The samples were concentrated to 150 mL and fixed in a solution of 4%–5% formalin. Then, they were divided into ten subsamples measuring 3 mL each, and the contents of each were counted using a Sedgewick–Rafter counting chamber. A Nikon Eclipse 50i microscope was used for the identification of organisms. Species were identified using the keys described by Radwan [16] and Rybak and Bł ˛edzki [17]. Rotifers were divided into two categories according to their habitat preference—pelagic species (plankton) and benthic, epiphytic, and epilithic species attached to the substratum or otherwise known altogether as benthic species [16].

The sites for zooplankton sampling were selected in the lotic, free-flowing sections of Bukówka. Two sites were located in the upper section (U1, U2) before the reservoir, two in the middle section (M3, M4) below the reservoir, and two in the lower-downstream section (D5, D6) of the stream. In addition, a control site was chosen in the open water zone of the reservoir (POND). In the lower section, there was the inlet of waters from the recipient, the Oder river.

The Lagrangian scheme was followed in the Bukówka stream, according to which the cross sections were sampled in a downstream sequence with the zooplankton sampling interval approximating the time of travel between the sites. The sampling sites of Bukówka were chosen taking into account the following: (1) influence of the reservoir, (2) influence of the water inlet from the Oder river, (3) differences in environmental conditions, and (4) ease of access.

#### 2.2.3. Macrozoobenthos—Macroinvertebrates

Macroinvertebrates were collected twice in 2016—in April and October. Sampling was conducted using a hydrobiological net, by scraping a fragment sized 0.5 m2 from the bottom. The material was preserved in 96% ethyl alcohol. In the laboratory, the organisms were separated and identified to the family level before they were counted, excluding Oligochaeta, which was counted at the same level. A specialist key was used for the identification of the organisms, and their abundance was expressed in ind·m2.

The following indexes and metrics were used to estimate the quality of water: BMWP-PL (Biomonitoring Working Party for Polish Standards), Margalef biodiversity index, ASPT (Average Score Per Taxon), Shannon–Weaver biodiversity index, TBI (Trent Biotic Index), GOLD (% of Gastropoda, Oligochaeta, and Diptera in the total abundance), and %DIPTERA (% of Diptera in the total abundance) [18]. The Polish BMWP-PL (Biological Monitoring Working Party) index is based on the British BMWP, where the list of indicator taxa (families) has been adapted to Polish standards. Each family from the BMWP-PL list is assigned. Each family on the list is assigned a score, indicating the sensitivity of the taxon. The higher the score, the more sensitive the taxon is to pollution. The BMWP-PL score is obtained by summing the number of taxa points found in the sample. Value >100 = high water quality, 70–99 = good water quality, 40–69 = moderate water quality, 10–39 = poor water quality, and <10 = bad water quality. The Margalef biodiversity index is an index calculated from the formula: D = S/logN where S—the number of taxa in the family rank, and N—total number of individuals from all species. Values >5.5 indicated a high water quality, 4.0–5.49 = good water quality, 2.50–3.99 = moderate water quality, 1.0–2.49 = poor water quality, and <1.0 = bad water quality.

#### 2.2.4. Statistical Analyses

Taxonomical similarity of zooplankton and macroinvertebrates between the sites was determined by calculating the Jaccard's index. The Mann–Whitney *U* test (*p* < 0.05) was used for verifying the significance of differences in the number of species of total zooplankton and the abundance of each zooplankton group between the sites (*p* < 0.05). The same test was also used to identify significant differences in the taxa number and density of macroinvertebrates between the examined sections (*p* < 0.05). To illustrate the ordination of the zooplankton groups and macroinvertebrates groups in terms of their abundance with regard to environmental factors, a canonical correspondence analysis (CCA) was used.

It was expected that, as a basin of stagnant water, a reservoir has a large positive effect on the reproduction and development of zooplankton. Hence, when making the CCA between the zooplankton abundance and the environmental factors of the stream as running water, the environmental conditions of the reservoir were not taken into consideration, as they do not characterize the running but standing water and could affect the final results.

#### **3. Results**

#### *3.1. Zooplankton*

From the upper section of the stream to the lower downstream section, the species number of zooplankton was found to increase (Figure 2). A significantly lower species number was observed at the sites selected in the upper section (*p* < 0.05).

**Figure 2.** Spatial changes of mean + SD zooplankton species numbers at two sites in the upper section (U1, U2), in POND, at two sites in the middle section (M3, M4), and at two sites in the downstream section (D5, D6) of the Bukówka stream. Different letters on the columns show significant differences between sites (*p* < 0.05).

However, the Jaccard taxonomical similarity coefficient showed that the upper, middle, and lower downstream sections were clearly divided between themselves (Figure 3). This indicated that the taxonomical composition of each section was made up of different species, as described in Table 2.

**Figure 3.** Jaccard taxonomical similarity between sites in the Bukówka stream.

**Table 2.** *Cont.*

**Table 2.** *Cont.*

All the zooplankton groups contributed to the increase in the abundance of zooplankton in the stream below the reservoir and the slight decrease of abundance in the last site in the middle section (Figure 4). This influence was especially noticeable in the case of pelagic rotifers, the abundance of which was significantly lower in the upper section than the other sections (*p* < 0.05). Further from the reservoir, the abundance of pelagic rotifers was lower. Although not very clear, a similar pattern was observed in the abundance of benthic rotifers.

A different pattern of abundance was observed in the case of crustaceans (at adult and larval stages). Their stream abundance was found to be increased below the reservoir, and then decreased, while at the last site in the downstream section, the abundance increased again (Figure 4). Cladocerans (*p* < 0.05) and adult copepods were characterized by a much higher abundance in the lowest site compared to the other sites. At both sites in the upper section, benthic rotifers were found to be dominant (Figure 5). On the other hand, from the pond to the last site in the lower downstream section, pelagic rotifers were found to be dominant. However, their abundance decreased in the lower downstream sections. In the last site of the stream, the abundance of adult crustaceans, especially cladocerans, was found to increase.

**Figure 4.** Spatial changes of the mean + SD zooplankton group abundance at two sites in upper section (U1, U2), in POND, at two sites in the middle section (M3, M4), and at two sites in the downstream section (D5, D6) of the Bukówka stream. The different letter on the column show significant differences between sites (*p* < 0.05).

**Figure 5.** Spatial changes of percentage abundance of zooplankton groups at two sites in the upper section (U1, U2), in POND, at two sites in the middle section (M3, M4), and at two sites in the downstream section (D5, D6) of the Bukówka stream.

The first CCA axis explained 41% of the total variability in zooplankton group abundance with regard to the environmental factors (Figure 6). The second axis explained 9% of that variability. The morphological factors of stream (depth and width) correlated best with the first axis. The best correlation with the second axis was found for chlorophyll a and current velocity. Cladocerans and copepods (that the most correlated with the firs axis) correlated positively with depth and width and negatively with current velocity.

**Figure 6.** Zooplankton taxa abundance and species number of total zooplankton among environmental factors in the Bukówka stream. Canonical correspondence analysis (CCA) constrained ordination of taxa from sites with different environmental conditions. Environmental variables: chla—chlorophyll a, oxy—dissolved oxygen, temp—temperature, Cl—chloride, cond—conductivity, TDS—total dissolved solids, vel—current velocity, wid—width of bed, dep—depth of bed; Zooplankton taxa: RoP—pelagic rotifers, RoB—benthic rotifers, Rot—total rotifers, NaC—nauplii of copepods, Cla—cladocerans, Cop—copepodid stages and adult copepods, SpeN—species number of total zooplankton.

#### *3.2. Macrozoobenthos—Macroinvertebrates*

During the entire study period, 24 taxa of macroinvertebrates were identified at all sites. No significant differences were found in the number of taxa between the three sections of the Bukówka stream.

According to Jaccard's index, the most similar sites in terms of taxa were U1 and U2, which were in the upper section of the stream, and M3 and M4, which were in the middle section (Table 3). In addition, sites M4 and D5 were similar in taxa composition. The largest differences in the composition of taxa were observed between the site M3 and the POND.

**Table 3.** Taxonomic similarity of macroinvertebrates between sites (Jaccard's index).

The Mann–Whitney *U* test used to test for differences in family abundances in different sections of the stream showed that the number of snails belonging to the Hydrobiidae family was significantly higher in the upper section of the stream than in the middle section (*p* < 0.05) (Figure 7). In the upper section, a higher number of Chironomidae larva and Oligochaeta were observed than the lower downstream section (*p* < 0.05). In the middle section, the Erpobdellidae family was more abundant than in the upper one (*p* < 0.05). No significant differences were found in the abundance of taxa between the middle and the lower downstream section (*p* > 0.05).

**Figure 7.** The significant differences between stream sections in abundance of Hydrobiidae, Oligochaeta, Erpobdellidae, and Chironomidae. The different letters on the columns show significant differences between sections (*p* < 0.05).

The first CCA axis explained 27% of the total variability in macroinvertebrates group abundance with regard to the environmental factors (Figure 8). The second axis explained 23% of that variability. The morphological factors of stream (depth and width) correlated best with the first axis. The best correlation with the second axis was found for temperature, chlorophyll a, and current velocity. Viviparidae, Platycnemidae, Notonectidae (that correlated the most with the first axis), and, to a lesser extent Coenagrionidae, correlated positively with depth and width and negatively with current velocity.

**Figure 8.** Macroinvertebrates taxa abundance among environmental factors in the Bukówka stream. Canonical correspondence analysis (CCA) constrained ordination of taxa from sites with different environmental conditions. Environmental variables: chla—chlorophyll *a*, oxy—dissolved oxygen, temp—temperature, Cl—chloride, cond—conductivity, TDS—total dissolved solids, vel—current velocity, wid—width of bed, dep—depth of bed; Macroinvertebrates taxa: Sph—Sphaeriidae, Bit—Bithyniidae, Lym—Lymnaeidae, Hyd—Hydrobiidae, Pla—Planorbidae, Phy—Physidae, Val—Valvatidae, Viv—Viviparidae, Oli—Oligochaeta, Erp—Erpobdellidae, Glo—Glossiphoniidae, Ase—Asellidae, Bae—Baetidae, Coe—Coenagrionidae, Plt—Platycnemidae, Not—Notonectidae, Hyp—Hydropsychidae, Odo—Odontoceridae, Hal—Haliplidae, Cer—Ceratopogonidae, Chi—Chironomidae, Lim—Limonidae, Tip—Tipulidae, Cra—Crambidae.

The values of the calculated indexes and metrics were characteristic of poor-quality water (Table 4). Both the BMWP-PL index and TBI pointed to class IV water quality for most of the sites, the biodiversity indexes (Margalef and Shannon–Weaver) were relatively low, and the GOLD and %DIPTERA metrics indicated a relatively high number of ubiquitous species in the studied stream.

**Table 4.** The values of the calculated indexes and metrics at the examined sites. Orange color stands for poor quality of water (IV class), yellow for moderate water quality (III class), green for good quality of water (II class), and blue for very good quality of water (I class).

#### **4. Discussion**

#### *4.1. Zooplankton*

The spatial pattern of the richness and abundance of zooplankton in the Bukówka stream showed that the value of these parameters increased with an increase in the distance from the river source. It was identified that these parameters were dependent on many environmental conditions that favored the growth of the zooplankton population. Thus, the abundance of zooplankton and their species number spatially changed in a similar manner as other running waters, including large rivers that were found to be affected by human activity [19,20]. The greatest impact on the development of typical planktonic organisms (pelagic rotifers, cladocerans, and some copepods) in Bukówka was posed by the reservoir as a basin due to its low current velocity and long water retention time. Hence, even a small dam that created a very shallow reservoir caused a significant difference in the richness and abundance of zooplankton between the upstream and the downstream sections. A similar pattern of the distribution of the zooplankton community was reported in small forest streams and rivers impounded by human-made dams or beaver dams in some studies [7,10,14,21]. We observed that in the impoundment (POND)—the "Słoneczne lake" of Bukówka—the richness and abundance of zooplankton were high, which was comparable to or even exceeded those in the typical temperate limnetic basins (e.g., eutrophic lakes or reservoirs) [22,23]. Our earlier studies on the increase of the zooplankton abundance in streams or rivers caused by damming did not show such high densities as observed in the Bukówka reservoir. It can be concluded that the reservoir was characterized by desirable trophic conditions for filter-feeding plankters because of the high concentration of chlorophyll *a*. Phytoplankton and other primary producers can reach high densities in Bukówka due to the exposure of the stream and reservoir to pollution in the form of excessive nutrient enrichment. This issue is often encountered in urban lakes in which zooplankton are found at very high densities [24]. Our present study shows that the abundance of zooplankton can also attain high values in urban reservoirs and streams.

A very common observation is that the abundance of zooplankton decreases along the section stretching from the dam (reservoir) or lake outlet to the lotic sections downstream to the mouth [20,23]. Probably this pattern of decreasing abundance is an effect of fish predation on zooplankton, grazing by suspension-feeding, filter-feeding, or net-spinning of macrozoobenthos, and settling of sediments [6,25,26]. However, in the lower downstream section of Bukówka, the abundance of some zooplankton groups (cladocerans and copepods) was higher than the sites in the middle section. This spatial pattern is much different from that observed in running waters and could result from the

following: (1) low current velocity in the most downstream site, (2) occurrence of slack waters densely covered by macrophytes in the last section, and (3) water influx from the recipient.

It is generally known that zooplankton disperse passively from the outlet of stagnant basins to downstream and can colonize new habitats [27]. Drifting zooplankton can colonize the river if its bed offer zones with low current velocity or with stagnant water (e.g., slack waters, impoundments, and floodplains) [7,10]. The main variables that contribute to the increase in the richness and abundance of zooplankton in the Bukówka stream at the more downstream sites are as follows: a decrease in the current velocity, a longer water retention time or greater areas of open-water zones, higher number of slack water areas, covering of floodplains by macrophytes, and adjacent water bodies [14,28,29]. The majority of microfauna, including freshwater zooplankton, are unable to persist if the current velocity goes beyond 0.1 m s−1 [28,30]. The low values of current velocity noted at the last site of Bukówka (D6) were favorable for the occurrence of zooplankton and the increase in their populations. Moreover, crustaceans and other plankters could have migrated or been washed out from the riparian zones or slack waters connected with the stream water [12]. The highest width and depth of the stream bed at the last site might have also favored this phenomenon.

However, we hypothesize that the greatest impact on the higher densities of crustaceans at the most downstream site was posed by the influx of crustacean assemblages in the inlet waters from the recipient Oder river into the stream. This might have occurred during the high-water period. The reason for arriving at this conclusion is the occurrence of *Ceriodaphnia* sp., *Moina micrura*, *Polyphemus pediculus*, *Eucyclops macrurus*, *Thermocyclops oithonoides*, and *Eurytemora velox*, which were found only in the lower downstream sections of Bukówka. Most of these crustaceans are not typical of small-stream fauna. Crustaceans that could came from the Oder river could have migrated up the stream mainly in the last site (D6) because of low current velocities, which were too high at the D5 site for most of the crustaceans. Thus, it can be concluded that the highest impact on the zooplankton-shaping structures in the last section of the stream was posed by the inlet water from the Oder river; however, it is a hypothesis.

#### *4.2. Macroinvertebrates*

A relatively low number of taxa were found in the studied stream, regardless of the section considered. One of the reasons for this observation was the regulation of the stream. Sudduth and Meyer [31] reported a high number of pollution-tolerant taxa related to urban stream banks in their study. Such a pattern is caused by the regulation of stream banks, leading to the homogeneity of the environment [32,33]. Only an appropriate bioengineering mechanism could slightly increase the abundance of sensitive species [31,34]; however, a lot of other factors also decrease the diversity of urban streams. The stream examined in the present study flows through a part of the city that has no natural or forest area and does not have many trees adjacent to the stream. Couceiro et al. [35] showed that deforestation in the urban regions decreases the taxa richness of macroinvertebrates. In addition, the authors explained that water pollution (by nitrogen and phosphorus compounds) affects the composition of macroinvertebrates similar to deforestation. Similar pattern was observed by Gołdyn et al. [36] in small urban river in Pozna ´n agglomeration (Poland), where stormwater runoff into this river was a source of significant contamination with heavy metals, organic matter and other pollutants, causing a strong transformation in the communities of benthic macroinvertebrates. Bukówka is a stream in the close neighborhood of allotments, from where a large amount of fertilizers is released. Moreover, the presence of a pond in the middle of the stream can be associated with the high values of TDS and conductivity and release of a large amount of total nitrogen and phosphorus, mostly from anthropogenic activities, such as the use of baits in fishing ground or feeding of aquatic birds. Many authors claim that biogenic compounds found in excess in urban streams negatively affect the macroinvertebrate composition [37–39]. Moreover, a channelization and regulation of Bukówka bed caused the low values of current velocity that affected the higher densities of taxa typical for ponds, lakes and large river, what was observed in lower section of stream. Therefore, low taxa richness and

relatively high abundance in Bukówka are typical of a small, degraded stream, which is under the influence of high anthropogenic pressure.

All the biological indexes of macrozoobenthos are dependent on taxa compositions, and most of them take abundance into account. The values of TBI and BMWP-PL and its modification—ASPT index—were rather low for the examined stream, pointing at its poor water condition, and it is similar for many other Polish streams that are under anthropogenic pressure [40]. However, these indexes are basically calculated based on the taxa composition (the examined site has a lot of sensitive stenobiotic taxa). In addition, both Margalef and Shannon–Weaver biodiversity indexes were lower in Bukówka compared to other streams in Poland, and also correlated with abundance and taxa richness [41]. The last two metrics—GOLD and %DIPTERA—can shed some light on the whole pattern: these are based on ubiquitous taxa of macroinvertebrates, and thus, the higher their value, the poorer the conditions of the stream. In the case of Bukówka, the values of both metrics were high, which shows a high proportion of pollution-tolerant taxa in the abundance of macrozoobenthos. Taking all the indexes into account—and the fact that the statistical analysis of the basic environmental parameters did not reveal any significant biological correlations—we can state that streams like Bukówka represent a different type of running water, which are strongly affected by human activity. Therefore, urban streams should be given more attention, constantly monitored, and treated with special measures.

The macroinvertebrates of Bukówka were mainly represented by ubiquitous species, which are typical of streams flowing through urban agglomerations. Bukówka is a regulated stream flowing along the entire section through highly urbanized areas, carrying a large impact of the Oder River in its final section. Furthermore, the examined watercourse is not characterized by too many microhabitats for macroinvertebrates, and the riverbed is homogeneous what is non-natural for running waters. According to many authors, such a pattern leads to the degradation of the richness of bottom fauna, and consequently, to a decrease in the value of the biological indexes. So, it would be very important to pay attention to the need to rehabilitate the Bukówka stream to reach better values of ecological potential and biodiversity.

As Karr [42] claims, ecological integrity is the sum of the elements of biological diversity and processes occurring in an environment. Zooplankton and benthic macroinvertebrate taxonomic composition and abundances, macroinvertebrates metrics, and physicochemical parameters used to evaluate the state of the Bukówka stream can be considered together as biological and abiotic elements. It seems our results constitutes good ecosystem integrity in terms of the observed water quality and aquatic biology. Substantially, all these elements show that the worst conditions in the stream occurred in the last section in which pollution accumulated from the entire basin and where the riverbed was most transformed. However, all parameters react similarly on the changes that occurred in the stream.

#### **5. Conclusions**

In small urban streams the amount of live organic matter increases with their length. An additional and important factor that affects the increase of this matter is the intensive human activity in the catchment area and in the bed. The construction of reservoirs and bed regulation also have a similar effect on the ecological potential in small urban streams as in large rivers, but of course at a lower scale. The composition of invertebrates is also influenced by the influx of water from the recipient river (especially water with a different chemical composition). It seems that the structures of invertebrate assemblages in Bukówka are significantly governed by stream-scale variables, similar to the findings of Knott et al. [43].

**Author Contributions:** Conceptualization, R.C., Ł.S., and T.K.; methodology, R.C., Ł.S., and T.K.; software, R.C., Ł.S., and T.K.; validation, X.X., Y.Y. and Z.Z.; formal analysis, R.C., Ł.S., and T.K.; investigation, R.C., Ł.S., and T.K.; resources, R.C., Ł.S., T.K., A.W. and K.P.; data curation, R.C., Ł.S., and T.K.; writing—original draft preparation, R.C., Ł.S., and T.K.; writing—review and editing, R.C., Ł.S., and T.K.; visualization, R.C., Ł.S., and T.K.; supervision, R.C., Ł.S., and T.K.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

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

#### **References**

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