**2. Motor cortex hyperexcitability in ALS**

Although the term 'hyperexcitability' is widely used, it still does not have a commonly accepted definition. According to Bae et al. (2013), it 'means an increased or exaggerated response to a stimulus, which may usually have been expected to evoke a normal response' [7]. We deem that hyperexcitability is discussed in modern TMS studies more broadly: as the ability to respond to stimuli that normally do not evoke any response and, speaking more generally, as the predominance of excitation over inhibition.

Hyperexcitability in ALS patients can be determined using various methods at different levels of the motor system. It can be detected at the level of individual neurons and ion channels in cell cultures in transgenic animals, clinically (presenting as the well-known phenomena, such as fasciculation or cramps) or using the modern neurophysiological techniques such as electromyography (EMG) and TMS [7].

Motor cortex excitability is a complex integral parameter that depends on numerous factors. Hence, the hyperexcitability phenomenon is also a complex and multifactorial process that depends on glutamate synthesis and release, its reuptake and degradation, expression and functional status of several types of glutamate receptors, excitable properties of neuronal membranes, and the status of inhibitory GABAergic neurotransmission. A number of experi‐ mental studies demonstrated that all the aforementioned processes are disturbed in ALS [8, 9]. **Table 1** summarizes the results of some key studies.

A significant advance in understanding the pathophysiological mechanisms of the develop‐ ment of the neurodegenerative process of ALS has been made over the past years after novel neurophysiological and neuroimaging techniques were introduced into the research and clinical practice [2–4]. Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that is used to evaluate the functional status of the upper motor neuron in ALS patients. It has been shown in the recent studies that different TMS parameters are altered in ALS patients [4]. TMS is currently viewed as a valuable research and diagnostic tool

Development of hyperexcitability of the primary motor cortex and the entire motor system is a well-studied phenomenon in ALS. Motor cortex hyperexcitability can be determined using TMS as reduced resting motor threshold and increased motor evoked potential (MEP) amplitude, decreased silent period, reduced effectiveness of short-interval intracortical inhibition (SICI), and increased intracortical facilitation (ICF) (see review [4]). Most authors attribute motor cortex hyperexcitability in ALS patients to enhanced glutamatergic neuro‐ transmission in the neocortex and reduced gamma-aminobutyric acid (GABA)ergic inhibitory neurotransmission, thus suggesting that hyperexcitability provokes degeneration of motor neurons [5–7]. However, no direct evidence has been obtained yet that would unambiguously demonstrate the relationship between motor cortex hyperexcitability in ALS patients and its degeneration. There is an alternative opinion that hyperexcitability can potentially be related to neuroplasticity processes taking place in the motor cortex and compensation for the lost

We would like to discuss the potential relationship of motor cortex hyperexcitability with motor neuron degeneration and neuroplasticity in ALS patients, as well as the possible methods to solve this problem using modern neurophysiological and neuroimaging methods.

Although the term 'hyperexcitability' is widely used, it still does not have a commonly accepted definition. According to Bae et al. (2013), it 'means an increased or exaggerated response to a stimulus, which may usually have been expected to evoke a normal response' [7]. We deem that hyperexcitability is discussed in modern TMS studies more broadly: as the ability to respond to stimuli that normally do not evoke any response and, speaking more

Hyperexcitability in ALS patients can be determined using various methods at different levels of the motor system. It can be detected at the level of individual neurons and ion channels in cell cultures in transgenic animals, clinically (presenting as the well-known phenomena, such as fasciculation or cramps) or using the modern neurophysiological techniques such as

Motor cortex excitability is a complex integral parameter that depends on numerous factors. Hence, the hyperexcitability phenomenon is also a complex and multifactorial process that

**2. Motor cortex hyperexcitability in ALS**

electromyography (EMG) and TMS [7].

generally, as the predominance of excitation over inhibition.

in ALS.

48 Update on Amyotrophic Lateral Sclerosis

function.

**Table 1.** Potential mechanisms of development of motor neuron hyperexcitability in ALS patients.

Today, TMS is the key and actually the only method for clinical investigation of motor cortex excitability. The use of the entire range of TMS parameters allows one to perform a relatively differentiated study of various factors contributing to motor cortex excitation [26]. TMS can be employed for assessing the functional status of the corticospinal tract due to the ability to excite motor cortex neurons by induced electrical current followed by propagation of excitation to alpha motor neurons of spinal cord. This causes contraction of muscle fibers within a certain motor unit, which can be recorded by cutaneous electrodes as a MEP [27]. TMS assesses the functional status of neuronal contours of the motor cortex [28].

Motor threshold is the key parameter used to assess motor cortex excitability. The motor threshold represents the density of corticospinal projections and can be regarded as a bio‐ marker of neuronal membrane excitability [26]. Decreased motor threshold is considered to be one of the key signs of motor cortex hyperexcitability.

According to most studies, the motor threshold in ALS patients is increased, probably being indicative of degeneration of cortical motor neurons [29–35]. Meanwhile, a paradoxical decrease in the motor threshold when examining patients at the onset of the disease was shown in some studies [36–39]. The motor threshold is likely to decrease at the onset of ALS, probably until clinical signs appear, and subsequently increases as motor neurons die. A statistically significant direct correlation between the motor threshold and disease duration was demon‐ strated in some studies [29, 40].

Physiologically, the motor threshold is primarily determined by rapid AMPA receptor (AMPAR)-mediated glutamatergic neurotransmission in the neocortex and excitability of motor neuron membranes that depends on voltage-gated sodium channels [41]. In ALS, AMPA receptor-mediated glutamatergic neurotransmission increases and properties of sodium channels change (presenting as increased conductance) [23, 24, 42, 43]. Alteration in functional properties of potential-gated sodium channels has been revealed: more rapid recovery after inactivation, increased permeability to sodium, and increased density of ion channels [21]. Pieri et al. demonstrated a decrease in the action potential threshold, an increase in pulse frequency, and an increase in persistent sodium current in cortical motor neurons isolated in G93A mutant mice [22].

MEP amplitude is determined by the number of reduced motor units and the number of activated alpha motor neurons in the spinal cord. Increased stimulation intensity enhances MEP amplitude due to superimposition of late I-waves and I1-wave [28, 44]. Like the motor threshold, MEP amplitude is determined by the density of corticospinal projections. Mean‐ while, MEP amplitude to a greater extent represents the function of neurons with lower excitability or those located farther from the stimulation site [4]. GABAergic drugs reduce MEP amplitude, which results from the scheme of generation of late I-waves modulated by inserted inhibitory GABAergic interneurons [41]. Noteworthy, the motor threshold and MEP ampli‐ tude are modulated by drugs belonging to different pharmacological classes, thus emphasiz‐ ing the difference between the mechanisms of their formation.

Identically to the motor threshold, MEP amplitude in ALS patients changes in opposite directions depending on stage of the disease. MEP amplitude decreases in most cases, being accompanied by increased motor threshold and representing a decrease in motor neuron number and reduction of density of corticospinal projections [4]. On the contrary, some patients with the reduced motor threshold may have increased MEP amplitude and the ratio between MEP amplitude and M-response amplitude [45, 46]. The increased slope ratio of the amplitude vs intensity curve is additional evidence to motor cortex hyperexcitability in ALS patients as it demonstrates a more pronounced amplitude increment with increasing stimu‐ lation intensity compared to the norm. The increase in MEP amplitude and the slope ratio of the amplitude vs intensity curve in ALS patients is probably related to both enhanced glutamatergic and reduced inhibitory GABAergic neurotransmission in the neocortex [4].

The *cortical silent period* (cSP) represents inhibition of voluntary muscular activity during a certain period after a magnetic stimulus was applied [47]. It has been demonstrated that the first one-third of cSP is mostly controlled by inhibitory mechanisms at the spinal cord level, while the remaining two thirds are of cortical origin and are related to inhibitory neurotrans‐ mission through GABA(B) receptors [27].

Motor threshold is the key parameter used to assess motor cortex excitability. The motor threshold represents the density of corticospinal projections and can be regarded as a bio‐ marker of neuronal membrane excitability [26]. Decreased motor threshold is considered to

According to most studies, the motor threshold in ALS patients is increased, probably being indicative of degeneration of cortical motor neurons [29–35]. Meanwhile, a paradoxical decrease in the motor threshold when examining patients at the onset of the disease was shown in some studies [36–39]. The motor threshold is likely to decrease at the onset of ALS, probably until clinical signs appear, and subsequently increases as motor neurons die. A statistically significant direct correlation between the motor threshold and disease duration was demon‐

Physiologically, the motor threshold is primarily determined by rapid AMPA receptor (AMPAR)-mediated glutamatergic neurotransmission in the neocortex and excitability of motor neuron membranes that depends on voltage-gated sodium channels [41]. In ALS, AMPA receptor-mediated glutamatergic neurotransmission increases and properties of sodium channels change (presenting as increased conductance) [23, 24, 42, 43]. Alteration in functional properties of potential-gated sodium channels has been revealed: more rapid recovery after inactivation, increased permeability to sodium, and increased density of ion channels [21]. Pieri et al. demonstrated a decrease in the action potential threshold, an increase in pulse frequency, and an increase in persistent sodium current in cortical motor neurons isolated in

MEP amplitude is determined by the number of reduced motor units and the number of activated alpha motor neurons in the spinal cord. Increased stimulation intensity enhances MEP amplitude due to superimposition of late I-waves and I1-wave [28, 44]. Like the motor threshold, MEP amplitude is determined by the density of corticospinal projections. Mean‐ while, MEP amplitude to a greater extent represents the function of neurons with lower excitability or those located farther from the stimulation site [4]. GABAergic drugs reduce MEP amplitude, which results from the scheme of generation of late I-waves modulated by inserted inhibitory GABAergic interneurons [41]. Noteworthy, the motor threshold and MEP ampli‐ tude are modulated by drugs belonging to different pharmacological classes, thus emphasiz‐

Identically to the motor threshold, MEP amplitude in ALS patients changes in opposite directions depending on stage of the disease. MEP amplitude decreases in most cases, being accompanied by increased motor threshold and representing a decrease in motor neuron number and reduction of density of corticospinal projections [4]. On the contrary, some patients with the reduced motor threshold may have increased MEP amplitude and the ratio between MEP amplitude and M-response amplitude [45, 46]. The increased slope ratio of the amplitude vs intensity curve is additional evidence to motor cortex hyperexcitability in ALS patients as it demonstrates a more pronounced amplitude increment with increasing stimu‐ lation intensity compared to the norm. The increase in MEP amplitude and the slope ratio of the amplitude vs intensity curve in ALS patients is probably related to both enhanced glutamatergic and reduced inhibitory GABAergic neurotransmission in the neocortex [4].

be one of the key signs of motor cortex hyperexcitability.

ing the difference between the mechanisms of their formation.

strated in some studies [29, 40].

50 Update on Amyotrophic Lateral Sclerosis

G93A mutant mice [22].

It was demonstrated in most studies that patients with sporadic and familial ALS had either decreased cSP or no cSP at all, which is regarded as significant evidence attesting to intracort‐ ical inhibitory dysfunction in ALS patients [48–50].

*Paired pulse stimulation* is used to assess intracortical inhibition and excitation processes. The method involves sequential generation of two pulses: the conditioning (S1) and testing (S2) pulses. The physiological effects of paired pulse stimulation are determined by the intensity of S1 and the interval between S1 and S2 pulses. Application of the sub-threshold S1 pulse 1– 6 ms before the supra-threshold S2 pulse reduces MEP amplitude compared to isolated application of a supra-threshold stimulus [51]. This phenomenon is known as SICI. Contrari‐ wise, an increase of the interstimulus interval to 8–20 ms rises the amplitude of MEP to the testing pulse (ICF). Several protocols have been proposed where the conditioning pulse has the supra- and sub-threshold intensity. MEP amplitude increases at the long interstimulus interval (long-interval intracortical inhibition, LICI) and decreases at the short interval (shortinterval intracortical facilitation, SICF) [27].

The neurophysiological mechanisms of formation of these phenomena remain insufficiently studied; however, it has been demonstrated rather convincingly that they have the predomi‐ nantly intracortical origin. Thus, SICI and LICI protocols cause suppression of amplitude and the number of late descending waves [51, 52]. Intracortical inhibition processes are assumed to be caused by activation of neocortical inhibitory interneurons under the action of the conditioning pulse; SICI being mediated by inhibition through GABA(A) receptors and LICI, through GABA(B) receptors. The ICF phenomenon is presumably caused by activation of glutamatergic neurotransmission through NMDA glutamate receptors [26].

Disruption of intracortical inhibition and facilitation under paired-pulse stimulation is currently believed to be the most convincing evidence that motor cortex hyperexcitability develops in ALS patients and has been detected in numerous studies [6, 53, 54, 55]. It should be mentioned that a decrease in efficiency of SICI is revealed at the earliest stages of the disease, including asymptomatic SOD1 mutation carriers, and correlates with axonal degeneration of peripheral motor neurons [45]. Assessment of the disruptions of intracortical inhibition using the new threshold tracking technique is considered to be a potential diagnostic tool in ALS demonstrating high sensitivity and specificity [56, 57].

The role of inhibitory interneurons dysfunction in pathogenesis of ALS has been actively studied over the past years [58–60]. Degeneration of inhibitory interneurons both in the spinal cord and in the motor cortex was demonstrated in several pathomorphological studies carried out in the 1990s. Nihei et al. (1993) reported a reduced amount of parvalbumin-positive interneurons in the motor cortex in ALS patients [61]. In addition, Petri et al. (2003) demon‐ strated that the level of GABA(A) receptor alpha-1 subunit mRNA decreases in ALS patients [20]. Dysfunction of inhibitory neurotransmission in the neocortex has also been confirmed in neuroimaging studies presenting as a decrease in GABA amount according to magnetic resonance spectroscopy [18] and decreased 11C-flumazenil binding as shown by PET [19].

The TMS data additionally confirm that inhibitory neurotransmission in the neocortex is disrupted in ALS patients. It should be mentioned that TMS has been used to show the disturbance of inhibition in the motor cortex both mediated by GABA(A) (decreased SICI) and by GABA(B) receptors (decreased LICI and sCP), being indicative of degeneration of inter‐ neurons or GABA metabolism disruption rather than dysfunction of receptors of this mediator in the postsynaptic membrane.

Other factors that cause SICI disruption in ALS patients are being discussed. Thus, riluzole was reported to induce partial recovery of SICI in ALS patients, which gives grounds for suggesting that enhancement of glutamatergic neurotransmission plays a role in disturbance of intracortical inhibition in ALS patients. NMDA receptor antagonists, memantine and amantadine, were shown to increase SICI and decrease ICF in several pharmaco-TMS studies [41]. These data suggest that decreased intracortical inhibition in ALS patients can be related not only to reduction of inhibitory GABAergic neurotransmission but also to enhancement of glutamatergic neurotransmission through NMDAR.

Increased intracortical facilitation is another evidence for involvement of NMDAR in patho‐ genesis of ALS. This phenomenon is currently predominantly attributed to glutamatergic neurotransmission through NMDAR, which is confirmed, in particular, by its reduction due to antagonists of these receptors [41].

Hence, a large body of data attesting to the development of motor cortex hyperexcitability in ALS patients can currently be obtained by TMS. Signs attesting to degeneration of the upper motor neuron are detected simultaneously (**Table 2**). Importantly, some TMS parameters (MEP threshold and amplitude) can change in opposite directions and attest to motor cortex hyperexcitability at onset of the disease and degeneration as the disease progresses. Mean‐ while, such signs of hyperexcitability as decreased cSP and efficiency of SICI are detected even in patients with pronounced degeneration of motor neurons. This phenomenon in ALS patients was referred to as 'dying but overactive' [7]; it can cause difficulties for interpretation of TMS results in a specific patient.

**Table 2.** Motor cortex hyperexcitability and degeneration in ALS: TMS findings.

**Table 3** summarizes the TMS data on the potential mechanisms responsible for the signs of motor cortex hyperexcitability in ALS patients. It should be mentioned that the variety of TMS procedures makes it possible not only to reveal the dysfunction of individual mediator systems but also to perform differentiated evaluation of neurotransmission through different types of glutamate and GABA receptors in ALS patients.

**Table 3.** Potential mechanisms of formation of TMS signs of motor cortex hyperexcitability in ALS.

Based on TMS findings, it is rather promising and reasonable to suggest that motor cortex hyperexcitability and the underlying molecular events (e.g., excitotoxicity) cause degeneration of motor neurons in ALS patients. We attempt to discuss the specific potential mechanisms underlying motor cortex hyperexcitability in ALS and present the evidence for its pathogenetic relationship with degeneration of motor neuron.


**5. Conclusion**

Moyamoya disease (MMD) is a unique, slowly progressive cerebral vasculopathy with an incidence of 0.09–10/100,000 individuals. It is highly prevalent in East Asia. It has an unknown etiology and classically presents with symptoms of headaches, transient ischemic attacks such as dysarthria, hemiparesis, cognitive decline, aphasia, and seizures. Early diagnosis and early treatment are effective in preventing further deterioration. Medical treatment is aimed at managing symptoms and typically includes anti-seizure, antihypertensive, and antiplatelet therapy. Surgical revascularization is the cornerstone in treating MMD patients to reduce further ischemic insults and neurological deterioration. Its unique pathophysiology requires anesthesiologists to strategize an individualized anesthetic plan and closein line communications with the neurosurgical team. Premedication is essential in pediatric patients undergoing revascularization surgery as the goal is to avoid crying and hyperventilation which provokes cerebral vasoconstriction. Perioperative management of moyamoya disease has a direct influence on the outcomes of the surgery. The key goals of perioperative management are maintenance of normotension, normovolaemia, normocapnia and normothermia, and adequate analgesia. Postoperatively, patients are admitted to the intensive care unit for close hemodynamic assessment and timely neurological assessment. Effective analgesia and fluid resuscitation must be employed postoperatively. Antiepileptic drugs are continued perioperatively, and antiplatelet agents are started postoperatively.


**4. Synchronization with external signals**

Once the movement is learned we can change its frequency. The new frequency can be determined from an external signal using the canonical dynamical system. This allows easy synchronization to external measured signals, such as drumming, already presented in Section 3.3. In this section we show how we applied the system to a rope turning task, which is task that requires continuous cooperation of a human and a robot. We also show how we can synchronize to an EMG signal, which is inherently very noisy.

## **4.1 Robotic rope turning**

We performed the rope-turning experiment on a Mitsubishi PA-10 robot with a JR-3 force/torque sensor attached to the top of the robot to measure the torques and forces in the string. Additionally, an optical system (Optotrak Certus) was used for validation, i.e. for measuring the motion of a human hand. The two-layered control system was implemented in Matlab/Simulink. The imitation system provides a pre-defined desired circular trajectory for the robot. The motion of a robot is constrained to up-down and left-right motion using inverse kinematics. Figure 14 shows the experimental setup.

Determining the frequency is done using the canonical dynamical system. Fig. 15 left shows the results of frequency extraction (top plot) from the measured torque signal (second plot). The frequency of the imitated motion quickly adapts to the measured periodic signal. When the rotation of the rope is stable, the human stops swinging the rope and maintains the hand in a fixed position. The movement of the human hand is shown in the third plot. In the last plot we show the movement of the robot. By comparing the last two plots in Fig. 15 left, we can see that after 3 s the energy transition to the rope is done only by the motion of the robot.

extraction (third plot) from the envelope (second plot) of the measured EMG signal (top plot). The bottom plots show the power spectrums of the input signal from 0 s to 30 s, from 30 s to 60 s and from 60 s to 90 s, respectively. The power spectrums were determined off-line.

23 Performing Periodic Tasks: On-Line Learning,

EMG HOAP-3

Fig. 16. Proposed two-layered structure of the control system for synchronizing the robotic

*r*

0 0.2 0.4 0.6 y

> 0 0.2 0.4 0.6

y

Fig. 17. *Left*:Raw EMG signal in the top plot and envelope of the EMG signal, which is the input into the proposed system, in the second plot. The third plot shows the extracted frequency Ω. The bottom plots show the power spectrum of the signal at different times (determined off-line). *Right*: Extracted frequency in the top plot. Comparison between the

frequency-extraction system, and the generated output trajectory for the robot arm (*x*) is

Fig. 17 right show the comparison between the envelope of the measured and rectified EMG signal (*y*), and the generated movement signal (*x*). As we can see the proposed system

Ω [rad] *x*

0 30 60 90 0

0 30 60 90 −0.2

x y

30 35 40 45 −0.2

t [s]

*ydemo* CDS ODS *wi*

motion to the EMG signal.

yraw

y

5

0 5 10 15

Ω [rad]

Ω [rad]

> P ow.

0 30 60 90

Adaptation and Synchronization with External Signals

0 30 60 90

0 30 60 90 0

t [s]

0 5 10 15 Ω [rad]

shown in the middle and bottom plots.

0 5 10 15 Ω [rad]

envelope of the rectified EMG signal (*y*), which is used as the input into the

matches the desired movement of the robot with the measured movement.

AF AF l

The frequency of the task depends on the parameters of the rope, i.e. weight, length, flexibility etc., and the energy which is transmitted in to the rope. The rotating frequency of the rope can be influenced by the amplitude of the motion, i.e. how much energy is transmitted to the rope. The amplitude can be easily modified with the amplitude parameter *r*.

Fig. 15 right shows the behavior of the system, when the distance between the human hand and the top end of the robot (second plot) is changing, while the length of the rope remains the same, consequently the rotation frequency changes. The frequency adaptation is shown in the top plot. As we can see, the robot was able to rotate the rope and maintain synchronized even if disturbances like changing the distance between the human hand and the robot occur. This shows that the system is adaptable and robust.

#### **4.2 EMG based human-robot synchronization**

In this section we show the results of synchronizing robot movement to an EMG signal measured from the human biceps muscle. More details can be found at (Petriˇc et al., 2011). The purpose of this experiment is to show frequency extraction from a signal with a low signal-to-noise ratio. This type of applications can be used for control of periodic movements of limb prosthesis (Castellini & Smagt, 2009) or exoskeletons.

In our experimental setup we attached an array of 3 electrodes (Motion Control Inc.) over the biceps muscle of a subject and asked the subject to flex his arm when he hears a beep. The frequency of beeping was 1 Hz from the start of the experiment, then changed to 0.5 Hz after 30 s, and then back to 1 Hz after additional 30 s. Fig. 16 left shows the results of frequency

Fig. 15. *Left*: the initial frequency adaptation process (top plot) of the cooperative human-robot rope turning. The second plot shows the measured torque signal. The third plot shows the movement of a human hand, and the bottom plot shows the movement of a robot. *Right*: the behavior of our proposed system when human changes the distance between human hand and top end of the robot. Frequency adaptation is shown in the top plot, and the second plot shows the measured torque signal. The third plot shows the movement of a human hand, and the bottom plot shows the movement of a robot.

20 Will-be-set-by-IN-TECH

The frequency of the task depends on the parameters of the rope, i.e. weight, length, flexibility etc., and the energy which is transmitted in to the rope. The rotating frequency of the rope can be influenced by the amplitude of the motion, i.e. how much energy is transmitted to the

Fig. 15 right shows the behavior of the system, when the distance between the human hand and the top end of the robot (second plot) is changing, while the length of the rope remains the same, consequently the rotation frequency changes. The frequency adaptation is shown in the top plot. As we can see, the robot was able to rotate the rope and maintain synchronized even if disturbances like changing the distance between the human hand and the robot occur.

In this section we show the results of synchronizing robot movement to an EMG signal measured from the human biceps muscle. More details can be found at (Petriˇc et al., 2011). The purpose of this experiment is to show frequency extraction from a signal with a low signal-to-noise ratio. This type of applications can be used for control of periodic movements

In our experimental setup we attached an array of 3 electrodes (Motion Control Inc.) over the biceps muscle of a subject and asked the subject to flex his arm when he hears a beep. The frequency of beeping was 1 Hz from the start of the experiment, then changed to 0.5 Hz after 30 s, and then back to 1 Hz after additional 30 s. Fig. 16 left shows the results of frequency

0.8 1 1.2 1.4 1.6

−0.2 0 0.2

−0.1 0 0.1

0 10 20 30 40

t [s]

Ω [rad/s]

hz [m]

hx,y [m]

xx,y [m]

Fig. 15. *Left*: the initial frequency adaptation process (top plot) of the cooperative

human-robot rope turning. The second plot shows the measured torque signal. The third plot shows the movement of a human hand, and the bottom plot shows the movement of a robot. *Right*: the behavior of our proposed system when human changes the distance between human hand and top end of the robot. Frequency adaptation is shown in the top plot, and the second plot shows the measured torque signal. The third plot shows the movement of a human hand, and the bottom plot shows the movement of a robot.

rope. The amplitude can be easily modified with the amplitude parameter *r*.

This shows that the system is adaptable and robust.

of limb prosthesis (Castellini & Smagt, 2009) or exoskeletons.

0 1 2 3 4 5 6 7

t [s]

**4.2 EMG based human-robot synchronization**

−1 0 1

−0.2 0 0.2

−0.1 0 0.1

hx,y [m]

xx,y [m]

yx,y [Nm]

Ω [rad/s] extraction (third plot) from the envelope (second plot) of the measured EMG signal (top plot). The bottom plots show the power spectrums of the input signal from 0 s to 30 s, from 30 s to 60 s and from 60 s to 90 s, respectively. The power spectrums were determined off-line.

Fig. 16. Proposed two-layered structure of the control system for synchronizing the robotic motion to the EMG signal.

Fig. 17. *Left*:Raw EMG signal in the top plot and envelope of the EMG signal, which is the input into the proposed system, in the second plot. The third plot shows the extracted frequency Ω. The bottom plots show the power spectrum of the signal at different times (determined off-line). *Right*: Extracted frequency in the top plot. Comparison between the envelope of the rectified EMG signal (*y*), which is used as the input into the frequency-extraction system, and the generated output trajectory for the robot arm (*x*) is shown in the middle and bottom plots.

Fig. 17 right show the comparison between the envelope of the measured and rectified EMG signal (*y*), and the generated movement signal (*x*). As we can see the proposed system matches the desired movement of the robot with the measured movement.

Buchli, J., Righetti, L. & Ijspeert, A. J. (2008). Frequency Analysis with coupled nonlinear

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**Abstract**

Cardiac amyloidosis is a protein-folding disorder mostly caused by abnormal deposition of either transthyretin proteins or light chain (AL) proteins, into one or more organs, including the heart. The main cardiac manifestations are right ventricular heart failure and arrhythmias. Extracardiac symptoms usually precede cardiac symptoms and are evident several years before the development of symptomatic cardiac problems. The prognosis is poor without appropriate management. Non-invasive evaluation with multi-imaging modalities has allowed earlier diagnosis, particularly when used in combination with monoclonal gammopathy evaluation. Management will vary depending on the subtype of amyloidosis. It consists of supportive treatment of cardiac-related symptoms, pharmacological treatment that targets amyloid fibrils formation and deposition, thus attacking the underlying disease, and addressing the management of extracardiac symptoms to improve the patients' quality of life.

**Keywords:** cardiac amyloidosis (CA), transthyretin cardiomyopathy (ATTR-CM), wild type-ATTR (wtATTR), hereditary (variant, mutant) transthyretin cardiomyopathy (hATTR, vATTR), systemic (AL) cardiac amyloidosis (AL-CM), atrial fibrillation (AF), heart failure (HF)

### **1. Introduction**

Amyloidosis is an infiltrative disorder primarily caused by extracellular tissue deposition of amyloid fibrils. It occurs when the misfolded protein assembles with similar proteins to form oligomers, which circulate in the blood and deposit as highly ordered fibrils, in the interstitial space of target organs [1]. These deposits are comprised of insoluble low molecular weight protein subunits ranging from 5 to 25 kD leading to the outlined systemic disease [2]. Up to date there are at least 25 different human and 8 different animal amyloidogenic proteins identified [1]. Tissue infiltration occurs in many organs such as kidney, liver, autonomic nervous system, and heart. However, >95% of cardiac amyloidosis is secondary to immunoglobulin light chain amyloidosis (AL) and transthyretin amyloidosis (ATTR) [3].

It was previously understood that cardiac amyloidosis was a rare and fatal disease. Nevertheless, with the emergence of advanced cardiac imaging studies as well as the availability of new pharmacological treatment for transthyretin amyloidosis and increased awareness of the disease among physicians, and better guidance for accurate diagnosis there has been a marked increase in diagnosis of this previously underrecognized medical condition. Nevertheless, despite increased awareness of the disease, sociodemographic disparities in diagnosis and management of cardiac

amyloidosis exist, thus leading to a delay in treatment and increased disease severity at time of diagnosis within certain sociodemographic groups [4, 5].

In this chapter, we will discuss in detail the clinical presentation, identification process, and implications for early detection of cardiac amyloidosis to improve patient outcomes. Additionally, we will discuss new emerging therapeutic approaches and the importance of socioeconomic disparities within the disease.


**3. Discussion**

Films of PPY loaded with an anionic drug (either DMP or MER, see Figure 1) were deposited onto the surface of ITO-coated glass electrodes via electropolymerization, yielding films where the positive charges on the backbone of the polypyrrole were counterbalanced by the presence of the anionic dopants from the electrolyte during the electropolymerization reaction (in this case, one of the anionic drugs, DMP or MER). PPY films prepared via electropolymerization on flat electrodes typically display cauliflower-like morphologies [16,46].

Clearly, surface morphologies and surface-area-to-volume ratios of materials used for drug delivery play an important role in the rate of release of the payloads, and it was observed that mass transport from PPY films with low surface-area-to-volume ratios (e.g., the cauliflower-like morphology) released drugs more slowly than materials with high surface-area-to-volume ratios (e.g., nanowire-like PPY) [16,46,59].

The films were observed to be somewhat imperfect with cracks and inhomogeneities observable (visually or via SEM), and they occasionally delaminated from the underlying ITO electrode. Such problems (i.e., cracks/inhomogeneities and delamination) may be solved using alternative materials for the underlying electrode (e.g., gold, glassy carbon, etc.); through the use of composites, wherein the polymeric dopant forms an interpenetrating network with the conducting polymer binding them together [60–63], or through the development of heat/solution processable electroactive block copolymers, in which one block is electroactive and the other block is heat/solvent responsive [64,65]. Analysis via spectroscopy (EDX, FTIR, and UV-vis), XRD, and electrochemical techniques (CV and EIS) confirmed the presence of the drugs in the films, and subtle differences in the packing of the PPY chains in the films in the presence of each drug. Interestingly, measurements of the total mass of the drug in the films revealed differences in loading, with DMP-doped PPY films containing 12 wt % of DMP, and MER-doped PPY films containing 4 wt % of MER. The physical descriptors for DMP and MER (Table 1) offered an explanation as to why this was observed: the dipole of DMP was lower than that of MER (1.7033 vs. 9.2305, respectively), as was the number of hydrogen-bond acceptors (8 vs. 9, respectively) and hydrogen-bond donors (5 vs. 7, respectively). Furthermore, the LogP of DMP was higher than that of MER (1.2640 vs. −0.5960, respectively). Consequently, the more hydrophobic drug (DMP) was more readily loaded into the hydrophobic PPY matrix than the hydrophilic MER.

For both DMP-doped and MER-doped films, the drugs were observed to passively diffuse from the films, as is the norm for drug-loaded polymers. The passive diffusion of DMP from the films was ca. 10–15% of the total DMP content of the films over the course of the experiment, whereas the passive diffusion of MER from the films was ca. 10–30% of the total MER content of the films over the course of the experiment.

The application of a reducing potential of 0.6 V (for 30 s) to the drug-loaded PPY films resulted in the proactive release of the anionic drug from the films, and rest periods (10.5 min) between the applications of electrical potential offered opportunities for re-doping by other anions (either the anionic drug, or anions from the PBS buffer: H2PO4− and HPO42−, and Cl−). We observed the amount of drug released from the electrically stimulated films (including the rest period) to be higher than that for the passive control samples at each time point measured (every 11 min). For the DMP-doped films (Figure 9B), there was an increase of ca. 10–15% in the amount of drug released at various time points; whereas for the MER-doped films (Figure 9C), there was an increase of ca. 15–30% in the amount of drug released at various time points after electrical stimulation. The differences in the in vitro release behavior observed for DMP and MER, despite their close molecular weights, could be attributed to differences in their physical descriptors (such as the dipole moment, total number of hydrogen-bond donors and acceptors present, molecular flexibility, LogP, and molecular globularity), which confirmed MER to be more hydrophilic and polar than DMP, rendering MER easier to release via diffusion from the PPY matrix, and therefore, more responsive to electrical stimuli (Table 1) [66].

The clinically translated examples of electronic interfaces for the body are all currently metal-based. The mechanical properties of these metals are markedly different from the soft tissues in which they are implanted. This mechanical mismatch leads to inflammatory responses and the formation of scar tissue around the electronic interface. A topic of intense current research interest is the development of soft conductive OEM-based coatings for the surface of the metal electrodes, and it is attractive to be able to deliver bioactive substances from the electrode coating (e.g., anti-inflammatories such as DMP). It is also noteworthy that the surgical procedures necessary to implant such devices are complex, and problems associated with microbial infections in the proximity of these devices can potentially be addressed through the delivery of antimicrobials (e.g., MER) from the surface coatings. The release of the clinically relevant drugs (DMP or MER) loaded into the PPY films was observed to be enhanced by the application of an electrochemical stimulus, thereby demonstrating proof of concept that such materials may form a useful conformal coating on the surface of implantable medical devices, potentially diminishing adverse reactions to their implantation in vivo [16,24,60].

### **4. Materials and Methods**


**5.2 Older adult drivers**

*Security and Privacy From a Legal, Ethical, and Technical Perspective*

tions (i.e. income and wealth, job and earnings and housing) [29].

depressive symptoms in late life [30].

social and productive activities [32].

to monitor vital signs [34].

**driving**

**5.1 Driving task**

**4. Successful ageing**

According to the 'Six-factor Model of Psychological Well-being', six factors contribute to an individual's psychological well-being, contentment, and happiness [28]. This includes positive relationships with others, personal mastery, autonomy, a feeling of purpose and meaning in life, and personal growth and development [28]. Quality of life is inextricably connected to well-being. As defined by the OECD, well-being can be defined/measured in relation to (1) quality of life (i.e. health status, personal security, social connection and participation/activity, work/life balance, subjective well-being, environmental quality, etc.), and (2) material condi-

Self-efficacy is defined as a person's belief in his or her own ability to accomplishing a task or succeeding in specific situations. One's sense of self-efficacy can play a major role in how one approaches goals, tasks, and challenges. The promotion of self-efficacy is a key element for success in interventions designed to reduce

The beginning of old age is between the age of 60 or 65 [31]. Definitions of old age are multi-dimensional and include a combination of chronological, functional and social definitions [31].  Often, older adults are segmented based on factors such as ageing phases, levels of fitness, severity of physical limitations, mobility patterns and social activities. According to Rowe and Kahn, successful ageing is multidimensional, encompassing the avoidance of disease and disability, the maintenance of high physical and cognitive function, and sustained engagement in

The prevalence of mental health issues is high in older adults as compared with the general population [30]. Older adults are at risk for developing anxiety and depression, given increased frailty, medical illnesses and medication and the potential for loss, reduced social connection and trauma (arising from injuries/accidents such as falls). On the other hand, younger older people are generally happier with a strong happiness increase around the age of 60 followed by a major decline after 75 [33]. Growth in ageing populations is a global trend. In Japan, Taiwan and Singapore, governments are defining smart ageing strategies to ensure that the growing ageing population ages well. This includes the promotion of multi-generational living, awareness of Dementia and other age-related health conditions and smart devices

**5. Driving task, older adult drivers and health conditions impacting on**

The driving is not a task isolated from everyday life. It occurs for a purpose (to get to somewhere, to see the scenery, etc.) and is often undertaken in parallel with other activities (for example, talking, listening to the radio, singing, planning-ahead and eating). The driving task involves a complex and rapidly repeating cycle that requires a level of skill and the ability to interact with both the vehicle and the external environment at the same time [5]. Information about the road environment is obtained via the visual and auditory senses. The information is operated on by many cognitive and behavioural processes including short and long-term memory and judgement,

**22**

It is estimated that by 2030, a quarter of all drivers will be older than 65 [36]. Further, by 2030, more than 90% of men over 70 will be driving [37]. Research indicates a general increase in both car access and licensing rates in the older population [38]. This increase is mainly attributable to significant increases in the number of older female drivers [38].

A number of studies have sought to categorise older adults in terms of their physical abilities [39] their economic, geographic/spatial and activity patterns [40], use of cars as a transportation mode [41], and lifestyles and associated requirements in relation to transport services [42]. The most nuanced categorisation is that of the GOAL project which proposes five distinctive profiles or segments of older people [43]. The segments take demographics, physical and mental health characteristics, social life, living environment, mobility-related aspects and transition points into account. The five profiles differ significantly according to age and level of activity/ mobility and health [43]. They include.


**4.2 Characteristics of education**

The student-teacher relations remained the same as it was in ancient and medieval, but students did not live in the teacher's house. As technology is increasing day by day, the education sector is also following the trend of technology by teaching the students through online lectures and Massive Open Online Course (MOOC). In Aviation and the medical sector, more emphasis is on practical knowledge as compared to other sectors. Women's education is giving more importance, and the Government has launched many programs to encourage women's education. In the modern era electronics gadgets like projectors, Light Emitting Diode (LED), and computers are used to teach the students. The Government has established many programs and there are many organizations that promote education in India.


*Article* **Evaluation of Communal Waste in Slovakia from the View of Chosen Economic Indicators**

**Beáta Stehlíková 1 , Katarína Culkov ˇ á 2,\* , Marcela Taušová 2 , L'ubomír Štrba 3 and Eva Mihaliková 4**

**Abstract:** Waste treatment consists of activities required to make sure that waste has the least practical impact on the environment. In Slovakia, more than 50% of waste is in storage. Waste development depends on the economic situation of the state. In Slovakia, there is economic intolerance of waste treatment due to the weaker economic situation of the inhabitants. The goal of this contribution is to study the development of waste production in Slovakia in regard to economic indexes of households with the aim of improving waste management. The goal is achieved by searching for a relation between economic indexes and households by a correlation matrix and by verification of polynomial dependence. According to the results of the statistical importance, we found similarity of the regions in chosen indexes by using of cluster analysis. By this method a sustainable economy and healthy environment is guaranteed and waste is used to produce energy.

**Keywords:** waste economy; productivity of energy sources; circular economy; waste treatment; Slovakia

#### **1. Introduction**

Everyone produces waste; some less, some more. The richer the country, the more waste produced by its inhabitants. The more developed the country, the higher the rate of recycled waste [1]. In northern and western European countries waste stocks almost do not exist any more. However, in countries of eastern and southern Europe more than half of the waste is in storage [2]. Slovakia is no exception. The amount of municipal waste produced increases every year [3]. The most recent data of the Statistical Office of the Slovak Republic for 2019 show up to 434.63 kg of municipal waste per capita and a high percentage of this waste in landfill (50.45%). The annual reduction of land filling has been positive; for example, in 2017 up to 61.43% of municipal waste was in landfill in Slovakia.

The European Union wants to increase waste recycling by one half of the total waste. This means we need to deal with the waste, since waste treatment allows it to be further processed into reusable materials and energy, thereby maximizing the savings of primary resources and energy, as well as reducing environmental burdens [4]. This process is referred to as recycling, a key part in the modern waste reduction hierarchy. Waste treatment must be evaluated from the view of environmental, technical, as well as economic points of view to be useful for decision-making in the case of pollution impairing living environment [5].

The basis of recycling is a properly set up waste sorting system. Thorough sorting of waste at the source provides the "cleanest" waste for recycling [6]. Activities, related to

**Citation:** Stehlíková, B.; Culková, K.; ˇ Taušová, M.; Štrba, L'.; Mihaliková, E. Evaluation of Communal Waste in Slovakia from the View of Chosen Economic Indicators. *Energies* **2021**, *14*, 5052. https://doi.org/10.3390/ en14165052

Academic Editors: Michal Cehlar and Sergey Zhironkin

Received: 16 June 2021 Accepted: 11 August 2021 Published: 17 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

municipal waste management, including sorting, in Slovakia are provided by the local government. Its interest should be to increase communities' discipline concerning generation and sorting of municipal waste, as the level of sorting also depends on the amount of the fee paid by the municipality for depositing the waste in a landfill. This, in turn, has an impact on setting the level of the municipal waste fee for the population. Sorting can thus be considered an important step towards waste recovery. The classification process can be influenced by several factors, from building the environmental awareness of the population to socio-economic factors related to the size of the household, the level of education and also the household income [7].

The EU goal is to live in 2050 in balance with the ecologic limits of the planet and to have waste-to-energy plants that burn waste to produce energy (steam in the boiler, etc.) that can be used to generate electricity [8–10].

The present contribution is orientated to communal waste production in individual regions of Slovakia with the goal to find out the development of waste production in Slovakia in regard to economic indexes of households following a consideration of obstacles and possibilities for improving waste management. The goal of the contribution results from the actual situation: In the field of waste economy, Slovakia is significantly behind many EU countries due to a low level of recycling and a high level of municipal waste storage. According to the European Commission, the problem is in the system of collection and waste sorting, as well as worse waste treatment in socially weaker regions. The legislation on waste in Slovakia should be stricter to move from landfill to recycling and waste reduction across the whole country.

#### **2. Present State of Problem Solving**

Various authors research waste treatment and waste economy. Waste recycling can be done by composting when producing fertilizing products for the market of EU [11]. By this method, composting could mitigate the environmental risk of waste creation. Paul et al. [12] studied waste attributes, such as pollution index, ecological risk index and geoaccumulation index and also revealed adverse impacts of the deeper landfill layers on the surrounding environment and found the industrial value of waste through economic evaluation of waste treatment technologies.

Deterioration of soil quality presents consequences of open waste dumping which have resulted in growing public concern [13]. Warguła et al. [14] conducted research and development works on reducing the environmental impact of machines used to process branches in urbanized areas. They show that it is possible to reduce fuel consumption [14,15] and the emission of harmful exhaust gases [14,16,17] generated during these processes by introducing innovative fuel supply systems [14,15] or by using alternative fuels to drive machines [16–18]. The process of processing branches on the premises of households is often a requirement for collecting green waste or supporting the process of transport, storage and processing, e.g., by burning or composting thanks to reducing the volume [19]. Dangi et al. [20] studied waste creation from restaurants, hotels, schools and 72 streets, and found a greater potential for recovery of organic wastes via composting and recycling.

Roychoudhury et al. [21] incorporated a symbiotic waste reduction system with the efficient functionalism of micro and macro-conversion agents which is attributed to the synthesis of waste-derived co-products from the same platform. Their study presents a novel approach, which is capable of addressing a wide fraction of solid waste and domestic sewage, and is capable of strengthening the sustainable waste management strategy [22,23].

Worldwide, vast development of rural areas has taken place, but due to the limited economic development level, some villages lack facilities for waste treatment [24], while it is easy to find practical and mature technology to treat one kind of waste in rural area, but there is a lack of overall consideration for recycling of produced and organic waste in the village.

Moreover, electrical and electronic equipment waste can be an important source of recycling [25]. However, according to D'Adamo et al. [26], the management of electronic components still presents many challenges. In order to raise awareness about the situation in electronic and electrical waste, Pérez-Beliz et al. [27] analyzed and found an extensive number of articles have been published around the world.

To make sure of the success of any policies aiming to decrease living environment pollution, it is also necessary to resolve the growing crisis of water pollution necessary. Water treatment surveys show vast differences in different regions and cities, as water is treated more thoroughly in larger cities [28]. High and fast economic increase and urbanization worldwide have influenced water consumption; the trend has increased in recent years. In this connection, Bian et al. [29] performed an analysis of regional water use and treatment in China with the aim of finding efficient waste water systems, as well as finding inefficiencies. According to Dong et al. [30] the issue could be solved by considering plant size, capacity, climate type and environmental impact. Warguła et al. [31] indicate that the increase in nutrients for aquatic plants resulting from inefficient wastewater treatment systems or too intense fertilization of fields is a serious problem for the tourism industry, due to contamination of beaches and water reservoirs, e.g., with seaweed. Wastewater and liquid waste present an important service problem in municipalities. In this area, Vialkova et al. [32] evaluated and confirmed an energy-efficient economy for microwave irradiation, using municipal wastewater sediments [32,33].

In the area of waste tire dumps also threaten human health and living environments [34]. Worldwide, waste from tires amounts to 1.3–1.5 billion tons/year and by the end of 2025 is expected to amount to more than 2.5 billion tones. In the area of waste, tire dumps also threaten human health and living environments. In 2013, the EU countries reached 3.6 million tons of used tires. The cheapest treatment method at present is landfill and is considered to be a major threat for the environment and public health.

Therefore, the problems of waste tire recycling must be solved as well. In this connection Karagoz et al. [35] found that tire pyrolytic oil production from waste tires is important from the viewpoint of both waste management and protection of fossil fuel resource depletion. Moreover, Symeonides et al. [36] evaluated the tire waste management system in Cyprus to strengthen the circular economy [36–38].

It is profitable to study waste production in individual countries. For example, with the extensive application of decentralized sewage treatment systems in China, a study must show the environmental economic value of resources and sustainability of systems to improve the efficiency of environmental resource use and decrease or avoid waste [39]. In this area, Yang et al. [40] evaluated the environmental economic value and the sustainability of a decentralized sewage treatment plant and found a remarkable economic benefit. The reuse of treated water can bring environmental benefits that are consistent with the requirements of sustainable development; the method has a very wide application prospect in developing countries [41,42].

The presented literature review shows there are no detailed studies addressing economic indexes of households influencing waste treatment development, such as average disposable equivalent incomes of households, waste costs, net money expenditure of households, people below the poverty line per one household, measure of poverty risk, etc. This creates the space for the presented contribution research, offering an evaluation of the waste treatment from the view of productivity in communal and separated waste production and productivity of waste sorting, as well as an evaluation of the waste cost development in individual regions, which could bring attention to possible regional developments, contributing to the sustainability of the regions.

#### **3. Materials and Methods**

The goal of the contribution is to evaluate the development of communal waste creation and to evaluate the waste sorting of individual elements of communal waste. The evaluation was done in accord with households and indexes, connected incomes

and expenses of households, available from the Statistical Office [43] and other economic indexes related to inhabitants' standards of living [44,45]. The evaluation is realized both for Slovakia as a whole, as well as according to the regional structure. Regions are presented as the eight following counties: Bratislavský, Trnavský, Trenˇciansky, Nitriansky, Žilinský, Banskobystrický, Prešovský and Košický. The indexes are used per one household and summary indexes are used for the development of waste creation as well. The contribution also verifies the statistical importance of the factors (region and year) to the value of waste creation per household and economic indexes relating to the inhabitants' standard of living concerning the household [46,47]. We searched for the existence of a relation between indexes with a correlation matrix and verified for chosen indexes the existence of polynomial dependence. According to the results of the statistical importance we searched for the similarity of the regions in chosen indexes using a cluster analysis. The evaluation for Slovakia was carried out by region and according to development over time (years).

## *3.1. Indexes of the Analysis*

The values of the indexes were obtained from the Statistical Office of Slovakia. Some rate indexes, used in the analysis, were calculated from the data available from the Statistical Office.

Number of inhabitants—value obtained from the website of the Statistical Office of Slovakia. Due to the fact that the number of inhabitants, as registered in the data, is the same in several different time periods, for the evaluation and comparison we chose three periods: 2006, 2012 and 2018. The difference between individual periods is the same, 6 years. In the case of other time periods, the results may be slightly different. Discussion of the exactness and reliability of the results will be mentioned in the conclusion of the paper.

Amount of communal waste in tons per year—index obtained from the website of the Statistical Office of Slovakia. From the index the volume of communal waste in tons per one household is determined; this value is also used as the productivity of communal waste.

Volume of separated waste in tons per year—index obtained from website of the Statistical Office of Slovakia. From the index the volume of separated waste in tons per one household is determined; the value is further used as the productivity of separated waste.

Productivity of waste sorting presents the index, calculated as the volume of separated waste in tons per one household/volume of communal waste in tons per one household.

Net incomes of household in euros—the index was obtained from the website of the Statistical Office of Slovakia.

Net money expenditure of households in euros—calculated from the expenses of households in euros per person per month times the average number of persons in the household times 12.

Waste cost in euros per kilogram and year—calculated from the waste cost index in euros per year/waste in kilogram: Waste costs = net expenses in euros/communal waste in kg per 1 household.

Average disposable equivalent income of households (euros/month) was obtained from the website of Statistical Office of Slovakia.

People below the poverty line per 1 household—calculated from the index of people under level of poverty/number of households, obtained from the website of the Statistical Office of Slovakia.

Measure of poverty risk: 60% median (%)—measure obtained from the Statistical Office of Slovakia [48].

The development of waste productivity was evaluated with the summary index of Laspeyers, Paasche and Fisher by comparing three periods: 2006, 2012 and 2018. In a graphical illustration of the indexes for each region there is value of the index for Slovakia due to the possibility to compare the situation in each region.

Analysis of development of waste creation was carried out by summary index. The index of communal waste volume in tons per one household is recorded as production of communal waste. Volume of produced waste in a time period and territorial unit will be marked with the sign:

*wu*,*p*

where *u* is territorial unit and *p* is analyzed period.

For types of waste shortage, we will use the abbreviations CW (communal waste) and SW (separated waste); shortages and types of waste will be explained with reference to their place of use.

Number of households in a time period in the frame of a territorial unit will be marked as:

$$
abla\_{\mathfrak{u},p} \tag{1}$$

where *u* is territorial unit and *p* is analyzed period.

Waste production in a time period in the frame of a territorial unit will be:

$$wp\_{\mathfrak{u},p} = \frac{w\_{\mathfrak{u},p}}{nh\_{\mathfrak{u},p}} \tag{2}$$

Summary index of waste productivity with weights from the basis period is:

$$I\_V^{(L)} = \frac{\sum\_{u=1}^{\eta} \frac{w\_{u,0}}{w p\_{u,0}}}{\sum\_{u=1}^{\eta} \frac{w\_{u,0}}{w p\_{u,1}}} \tag{3}$$

Summary index of waste productivity with weights from the common period is:

$$I\_V^{(P)} = \frac{\sum\_{u=1}^{\mathcal{U}} \frac{w\_{u,1}}{w p\_{u,0}}}{\sum\_{u=1}^{\mathcal{U}} \frac{w\_{u,1}}{w p\_{u,1}}} \tag{4}$$

*3.2. Analysis of Economic and Waste Indexes from the View of Verification of Statistical Importance of Regions and Year to the Development of Index Values*

The economic and waste indexes used in the analysis are as follows:

The analysis consisted of two steps. The first step was the ANOVA analysis, testing whether factors of the territorial unit (region) and periods are statistically important. The second step was the choice of indexes that would be used in a cluster analysis. We chose the indexes that would be statistically important for a region. According to the results for the factor ´year´ we chose to cluster analyze two periods for which the difference between various indexes would be statistically important. ANOVA test of the statistical hypothesis of the importance of the difference between the medium values during the process of choosing according to the region and period was as follows:

$$\text{H0}: \,\mu\_1 = \,\mu\_2 = \,\dots = \,\mu\_n\tag{5}$$

$$\text{HA} \colon \begin{aligned} \mu\_i \neq \mu\_j \end{aligned} \tag{6}$$

ANOVA test can be used only in the case of homogeneity of variances and normality of residuals.

#### **4. Results**

#### *4.1. Evaluation of Communal Waste Productivity*

As for the evaluation of communal waste productivity, Figure 1 illustrates calculated values of communal waste productivity in regions in the three analyzed periods. Regions are ranked by comparison with the country.

**Figure 1.** Productivity of communal waste in Slovakian regions.

In 2012 there is obvious smooth growth and in 2018 rapid growth of the index against the earlier period. The greatest volume of communal waste per household in 2018 was registered in the Trnavský region, the lowest in the Košický region. To evaluate the development of waste productivity, we used volume extensive indexes. The results of the indexes are given in Table 1.

**Table 1.** Indexes of communal waste production according to region.

Waste production between periods developed as follows:

When using summary index of waste productivity with weights from the basis period (Laspeyres extensive index), in comparing 2006 and 2018 we registered a vivid difference against the average Fisher index. In the common period 2018 the index grew against the basis period 2012 by 22.81%. Moreover, using the Paasche index, we registered a difference for the same basic and common period. In the common period 2018 the index grew against the basis period 2012 by 37.11%. The growth of waste productivity between 2012 and 2018 was caused by an increase in awareness of waste treatment in the society. Communal waste of production in time development is given in Figure 2.

**Figure 2.** Development of communal waste per household.

#### *4.2. Evaluation of Separated Waste Productivity*

Figure 3 illustrates the calculated value of separated waste productivity for regions in three analyzed periods. In 2012 there is obvious smooth growth and in 2018 rapid growth of the index against the earlier period.

To evaluate the development of waste production we uses extensive indexes (see Table 2).

**Table 2.** Indexes of communal waste production.

Production of waste sorting between periods developed as follows:

Production of waste sorting in time development is given in Figure 4.

**Figure 4.** Development of separated waste per household.

#### *4.3. Evaluation of Waste Sorting Productivity*

Productivity of waste sorting was registered as sep/kom per household and year as percentage expression.

Figure 5 shows data of waste sorting productivity in time development according to the regions. Productivity of waste sorting between analyzed periods developed as shown in Figure 6 as follows: Support of waste sorting is visible from the increase of sorted waste by communal waste volume. According to data in Figures 5 and 6 we can state the following: In 2006 the rate of separated waste by communal waste changed by around 5%; the highest rate was in the region Banskobystrický—9%; the lowest in the region Košický— 2%. In 2012 the value of the median changed over 10%; the highest separation was in the region Banskobystrický—13%; waste was sorted the least in the region Nitriansky—6%. In 2018 the medium value changed by over 27%; maximum 31% of the sorting belonged to region the Banskobystrický; waste was sorted the least in the regions Bratislavský and Nitriansky—23%.

**Figure 5.** Development of waste sorting productivity according to the regions.

**Figure 6.** Development of waste sorting productivity.

#### *4.4. Evaluation of Waste Cost*

The index was created with aim of comparing the volume of communal waste in monetary terms. It presents how many euros of net income from households is produced per one kilogram of waste.

Figures 7 and 8 address the following: While in 2006 and 2012 the value of the waste cost moved in regions by between 11 and 14 €, in 2018 it was around 8–11 €. Due to the volume of communal waste per household net incomes also increased; the index development addresses the gradually decreasing production of waste over time.

**Figure 7.** Development of waste cost.

**Figure 8.** Development of waste cost in time (in €/kg).

The total value of waste cost changed due to the change of costs by weight in relation to the basis period—the Laspeyers index—due to the influence of waste volume change. The total value of waste cost changed also due to the influence of cost change with weight in relation to the common period—the Paasche index—due to the influence of price change.

Table 3 mentions evaluated fictitious sales of the waste, the value of the waste in prices and the volume according to the selected periods for the Paasche and Laspeyers indexes. The indexes speak of the increased waste value due to the changes in price (on decreasing the kilograms of the waste). Due to the influence of the change in waste volume, the value of the waste increased by 44% between 2006 and 2018, and by 37% between 2012 and 2018.

**Table 3.** Indexes of waste cost development in analyzed periods.

*4.5. Analysis of Economic and Waste Indexes from the View of Verification of Statistical Importance of Regions and Year to the Development of Indexes Value*

Table 4 mentions the result of the ANOVA test for chosen economic and waste indexes. We mention the value *p*, the probability of zero hypothesis rejection, in case it is correct. The periods and regions are the factors of the choice. Except as mentioned, the table mentions results of the *p* value of the Bartlett test of homogeneity of dispersion and the Shapiro Wilk test of residue normality, which means verification of whether assumptions are achieved and ANOVA test can be used.

**Table 4.** Results, *p* values of ANOVA test and verification of assumptions tests.

According to the results, as shown in the table, for the region and indexes—komnadom, prodsep, waste cost, pdedom, cpvd, ospodch, mieraregch—a valid alternative hypothesis confirmed that there was at least one pair of regions for which the medium values were statistically different. According to the results, as shown in the table, for the region and indexes—komnadom, prodsep, waste cost, pdedom, cpvd, ospodch—a valid alternative hypothesis confirmed that there was at least one pair of regions for which the average values were statistically different. The results of the *p* value Bartlett test of dispersion homogeneity and the Shapiro–Wilk test of residual normality are as follows: All *p* values were higher than the determined level of importance, which means the hypothesis of dispersion homogeneity cannot be rejected and the hypothesis of residual normality cannot be rejected either. The assumptions for ANOVA test were realized.

For the index sepnadom not all the assumptions of dispersion homogeneity were realized, nor for residual normality; therefore, the ANOVA test cannot be used. The results of the index evaluation are outlined in the following. To find out the differences between indexes, we carried out a pair comparison for period and region. In the following tables, we outline the results of the comparison of *p*-values, the probability of rejection of zero hypotheses—concerning whether the consistency between the medium values of the two populations is correct. Evaluation and rejection were realized at the level of importance 0.05. The results of pair comparison for the period and indexes, when at least one pair of periods is statistically important, are as follows (see Table 5):

**Table 5.** Pair comparison of indexes and period.

According to the results, presented in the table, it is obvious that three indexes have statistically important differences between 2006 and 2012. Between 2006 and 2018 and between 2012 and 2018 five indexes have statistically important differences. In accordance with the results, we carried out a cluster analysis for 2018 and 2012. Results of the pair comparison of the indexes for the region were obtained when for at least one pair of regions there was a statistically important difference. The results are given by Table 6.

According to the results, presented in the table, we can say that for the region pairs Žilinský–Trenˇciansky, Žilinský–Nitriansky, Trenˇciansky–Nitriansky and Košický– Banskobystrický there is no significant difference in the values of any index. Index prodsep registered significant difference only for one pair of regions Nitriansky–Banskobystrický; therefore, it was not used in the cluster analysis. The results, presented in the table, offer an assumption for cluster analysis use; there are regions that could be more similar from the view of the chosen indexes.

**Table 6.** Results of pair comparison of indexes for regions.

Note: For sepnadom the assumption was not met; therefore, the ANOVA test was not used to verify the factors' importance for the region and the year nonparametric Friedman rank test was used with the result *p* value = 0.04052. For pair comparison for the region Nemenyi's test of multiple comparisons for independent samples (tukey) was used successfully when any pair of regions confirmed a statistically important difference. Pair comparison of Nemenyi's test of multiple comparisons for independent samples (tukey) for the factor 'period' in the table is for the factor 'years'.

> The following indexes were used in the cluster analysis: Komnadom, nakladovost, pedom, cpvd, ospodch and mierareg chudoby. These factors, which were originally considered, were not included: Sepnadom and prodsep. A cluster analysis was carried out for 2018 and 2012 with a subsequent comparison of results.

> Analysis of development in years and of which region is the weakest and which is strongest can be done according to the mentioned graphs and the following tables. As can be seen in Figure 9: The first four graphs present indicators of waste creation, sorting and costing. All indicators were described in detail in an earlier analysis, from which results the following:

> In 2018 compared to 2012 there was increase of communal waste per household, which means increased production of waste. Waste sorting, volume of separated waste per one household and the rate of separated waste by communal waste increased. Costs of waste decreased, which means kilogram production of waste in 2018 cost less to a household.

**Figure 9.** Comparison of the indicators for 2012 and 2018. Note: (**a**) Separated waste in t per 1 household; (**b**) communal waste in t per 1 household; (**c**) waste cost in €; (**d**) ratio of separated/communal waste; (**e**) average household disposable income in €; (**f**) net money expenditure of households in €; (**g**) at risk of poverty percentage; (**h**) people below the poverty line per 1 household.

Indicators concerning the financial situation of the households in regions and at risk of poverty are presented in the second group of four graphs.

Average disposal incomes in 2012 changed in each region by between 555 and 778 €. In 2018 the lower level changed by over 596 € and the upper level by over 782 €. In both cases, the minimal value was recorded in the region Prešovský and the maximal value was recorded in the region Bratislavský—registered by outliers. The second highest value was in 2012—674 € for the region Trenˇciansky; and in 2018 for the region Trnavský—682 €.

Net money expenditure in 2012 was between 9919 and 12,663 €; the highest value is the outlier, belonging to the region Bratislavský; distribution is asymmetric (skew = 0.55).

In 2018 the levels moved upwards from 11,476 € to 14,432 €. Distribution is more symmetric (skew −0.22).

The next two indicators, presenting the state of poverty—at risk of poverty rate and people below the poverty line per one household—developed similarly.

At risk of poverty rate: In 2018 the minimum decreased from 6.30 to 4.30; the maximum decreased as well from 19.90 to 18.40. The box width in the graph shows that the differences between the most and the least threatened regions decreased. In spite of there being a registered decrease in the average value and the levels of the indicator, the value increased in the regions Banskobystrický and Košický.

The indicator "people below the poverty line per one household" in 2018 did not decrease compared to 2012 in the regions Žilinský, Banskobystrický and Košický. The results are provided in Tables 7–10.

**Table 7.** Input data in 2012.

#### **Table 8.** Description characteristics in 2012.

**Table 9.** Input data in 2018.

#### **Table 10.** Description characteristics in 2018.

According to the calculated description characteristics we can find out how the regions changed between 2012 and 2018.

Figure 10 shows that some regions are, from the perspective of pair indexes (productivity of waste and cpvd—net incomes of households), closer than others. We calculated the Euclidean distance of all region pairs from the values of the indexes, chosen for the cluster analysis. Values of mutual distances are presented in Table 11. Euclidean distance is calculated from the standardized variables, which have medium value 0 and standard deviation 1. Individual regions present points of multivariate space, created by chosen indexes. In 2012 the closest regions were Trenˇciansky and Žilinský. The next closest pair was regions Košický and Banskobystrický. Region Prešovský was close to region Košický and Banskobytrický. Region Bratislavský was not close to any of the regions.

**Figure 10.** Communal waste development according to the net expenses of household in 2012.

**Table 11.** Values of mutual distances 2012.

As for the Euclidean distance of regions, in the cluster analysis for 2018 we see that some regions are close; their Euclidean distance is around 1; other regions are remote (see Table 12).

**Table 12.** Euclidean distance of regions in cluster analysis 2018.

According to the results we constructed a dendogram. Clusters show a graphical presentation of a double graph: Waste productivity and net incomes of households. Cluster dendogram in 2018 (see Figure 11) and Cluster dendogram in 2012 (see Figure 12) provided information for Figures 13 and 14. Figure 13 is for the cluster dendogram Figure 11, and Figure 14 is for the cluster dendogram Figure 12.

**Figure 11.** Cluster dendogram in 2018.

**Figure 12.** Cluster dendogram in 2012.

**Figure 13.** Comparison of indexes after clustering for 2018: (**a**) Communal waste per household in t per year according to net expenses of household in (€); (**b**) measure of poverty risk—60% of median at percentage (%) according to net expenses of household in €; (**c**) communal waste per household in t per year according people below the poverty line per 1 household; (**d**) communal waste per household in t per year according to waste costs in € with relation to kg.

**Figure 14.** Comparison of indexes after clustering 2012: (**a**) Communal waste per household in t per year according net expenses of household in €; (**b**) measure of poverty risk—60% of median at percentage (%) according to net expenses of household in €; (**c**) communal waste per household in t per year according to people below the poverty line per 1 household; (**d**) communal waste per household in t per year according to waste costs in € with relation to kg.

Figures 13 and 14 illustrate pairs of indexes for individual regions. The same colors of points at Figures 13 and 14 mean individual clusters according to cluster dendrograms. As for the regions' names, for convenience, the following abbreviations are used: Bratislavský as Ba, Trnavský as Tna, Trenˇciansky as Tre, Nitriansky as Ni, Žilinský as Zi, Banskobystrický as Bb, Prešovský as Po, Košický as Ke. Similar colors of points mean regions with the same cluster in the dendogram.

In 2012 the closest regions were Banskobystrický and Prešovský, Trnavský and Trenˇciansky, Košický and Žilinský. Every region was close to Bratislavský.

Figure 13 shows a comparison of indexes after clustering.

When comparing dendograms in 2012 and 2018 we see a slight regrouping of region groups. While in 2012 the region Bratislavský was in any group, in 2018 it presents a pair with region Trenˇciansky.

Figure 14 illustrates how the groups, created by clustering, are structured for the following pairs of indexes:

#### **5. Discussion**

The most dominant fraction in communal waste is biological decomposted waste [49]. The organic fraction in the waste amounts to around 48–69% [50]. Therefore, great attention is given to the sorting of biological decomposted waste and its evaluation. A pilot project, carries out in Ghane, showed that sorting into proper baskets would be effective in many areas, since it achieved on average more than 80% in the case of "biologically removable elements, except paper waste" and more than 75% in "other waste". High efficiency of waste sorting meant that using a one-way sorting system was proper for the households that participated. Treatment of biological waste separated from the source is still an important task due to the high rate of contamination [51].

The system of waste sorting in Slovakia is not unified. In Slovakia there are over 2800 autonomous regions and it is not possible to create a unified document for waste treatment. In some autonomies, every waste element must be separated, in other autonomies the costs of sorting and waste treatment are saved and waste is collected in common. It depends on the capacity of the autonomy to deal with waste sorting companies. On the other hand all autonomies have a goal to achieve in the area of waste economy—to be sorting 65% of waste before 2025. Presently waste sorting is at only 36%.

The Slovak Republic is aware of the importance and significance of sorting and is constantly improving the relevant systems. From the beginning of 2021, a ban was issued on the disposal of waste by landfill, unless it has undergone treatment. At the same time, waste sorting at the source is also considered waste treatment.

Awareness of the population about the need and methods of sorting plays a big role in this. For example it is necessary to teach households to sort and to recognize the recycling labels on the packaging in order to know what can be separated and how. If households are not used to separating waste, they must gradually learn new habits such as sorting biodegradable waste. This follows from the obligation of municipalities from 2021 to make sure of separate collection of biodegradable municipal waste.

Another role of municipalities in increasing the rate of waste sorting is to have a proper set up of the system [52]. It also turns out, also on the basis of experience in developed countries, that the quality of waste sorting is increased by the application of a "door to door" system, which is based on bag collection in which anonymity is eliminated. Another motivating element in the sorting process is waste charges, which should be set fairly according to the "pay for what you throw away" pattern. Residents would thus be motivated to decrease unsorted waste.

A good "mirror" for the public is also information about the production of municipal waste, sorting and management. These should show as realistically as possible that this is possible only by precise waste records, which should be provided in Slovakia by a fully functional waste management information system from 2022.

Living standard in Slovakia in 2018 increased to 78% of the EU average. Incomes and consumption of households is increasing. Most expenses are orientated towards the household and food. Consumption of food is also increasing, causing communal waste. There is a dependence between living standard and production and sorting of communal waste, which is confirmed by the presented results of individual regions of Slovakia.

On the other hand, the more economically developed the region, the more production of communal waste; this is illustrated for example in the regions Bratislavský, Trnavský and Trenˇciansky. Those regions belong to the western part of Slovakia, in which there is higher living standard for inhabitants, developed industry, better infrastructure and considerably higher inflow of foreign investment [53,54]. This approach can also be linked to the possibilities of effective use of GIS data [55] that are commonly used in such research. It has been shown that the amount of waste is growing with increasing household incomes, from which the increase in their expenditures also stems. In addition to the inhabitants and household motivation to sort waste, employees of enterprises must also be motivated to properly treat waste [56].

This paper defined new possibilities for waste evaluation through economic variables. The fact that the presented contribution deals only with economic indexes of households creates the space for the next research project: To find out the behavior of households when throwing away and separating communal waste. However, problems of household waste should also be considered, such as, wood-based waste, which means, for example, furniture [57].

#### **6. Conclusions**

The use and consumption of goods are natural and necessary parts of human societies. The more a society and a country are developed, the higher the consumption of goods, and consequently the market produces a higher volume of products. In this connection, the waste economy belongs to the one of the most important areas: Sustainable development. The hierarchy of the waste economy results from the need to minimize negative impacts to the living environment. This brings a decrease in storing and an increase in waste recycling. Various researchers are dealing with the possibilities of recycling. Proper sorting is the foundation of good recycling. Metals, paper, glass and plastics are the most common materials that are separated and have high recycling potential.

The presented paper dealt not only with the production and sorting of municipal waste in Slovakia but also with economic factors that may affect it.

In Slovakia there are rather considerable differences between individual regions, which is reflected in the research indicators, including measure of poverty risk. Results of the analysis show there is long-term poverty risk measure in the regions Prešovský, Košický and Banskobystrický. This could be caused by a lower living standard, higher unemployment, higher marginalized groups of inhabitants or a higher number of families with many children and not full families. The regions are typical due to the lower incomes, as well as lower volume of waste per household.

The results of the contribution can be used not only for evaluation of waste development over time in Slovakia, but also for evaluation of the position of Slovakia in the frame of the EU, when the situation shows the waste treatment in Slovakia is weak in comparison with the EU. This demands a change in legislative decrees and an increase in waste treatment awareness. This should result in verified systems that run effectively in the EU countries, when the systems are effective from the view of financial investment in relation to the achieved results of waste recycling.

Since the volume of mixed communal waste is not changing considerably over time, but the volume of communal waste is growing, the volume of other elements in communal waste must also be growing. Such a situation demands future research from the perspective of trends of communal waste growth, as well as from the perspective of assumed development of GDP, according to which there is the aim to find a growing trend of communal waste creation in a future period.

**Author Contributions:** Conceptualization, B.S., E.M.; methodology, B.S., K.C.; software, B.S., M.T.; ˇ validation, E.M.; formal analysis, B.S., K.C.; resources, B.S.; data curation, E.M.; writing—original ˇ draft preparation, M.T., and K.C.; writing—review and editing K. ˇ C.; visualization, M.T.; supervision, ˇ L'.Š.; project administration, K.C.; funding acquisition, M.T. All authors have read and agreed to the ˇ published version of the manuscript.

**Funding:** This research was funded by grant project VEGA, grant number 1/0264/21 and grant project VEGA, grant number 1/0797/20.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Contribution presents partial result of grant project VEGA 1/0515/18, VEGA 1/0797/20 and VEGA 1/0264/21.

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

#### **References**


**2.5 The anticancer effect of** *Ginkgo Folium* **extract**

The anticancer effect of *Ginkgo Folium* was studied extensively in recent years. The tumor inhibition effect of *Ginkgo Folium* was demonstrated in mouse S180 mouse sarcoma, which might be due to the elevation on free radical scavenger

enzymes [58]. The main reported death way induced by *Ginkgo Folium* is caspase dependent apoptosis that was observed in several cancer types, including cervical cancer [59], colon cancer [60], gastric cancer [61], melanoma [62]. The mechanism responsible for *Ginkgo Folium* induced apoptosis is the disruption on the balance of antiapoptotic protein Bcl-2 and proapoptotic protein BAX, characterized by the decrease on Bcl-2 and the increase on BAX.

Despite of apoptosis, *Ginkgo Folium* can also arrest cell cycle and inhibit cancer cell migration and invasion. The cell cycle arrest induced by *Ginkgo Folium* was due to the suppression on G0/G1 phase in gastric cancer [61, 63]. The suppression on cancer cell migration and invasion were observed in several cancer types through multiple ways. First, upregulation on E-cadherin via lincRNA-p21 mediated suppression on E-cadherin degradation in colon cancer [64]. Second, downregulation on ERK/NF-kB signaling in gastric cancer [65]. Third, inhibition on heat-shock protein 27 (HSP27) mediated by AKT and p38 MAPK pathways in NSCLC [66].

The extracts of *Ginkgo Folium* can combine with herbal formulation to exhibit synergistic effect on anticancer. For instance, it was reported that a new formulation consisted of *Ginkgo Follium* and an herbal mixture Yu Ping Feng San can sensitize cisplatin resistant lung cancer cells through WT1/MVP mediated stabilization on mTOR/AKT pathway [67]. There are few clinical studies on anticancer effect of *Ginkgo.* Considering *Ginkgo Folium* extract has the standard commercial product EGb761, thus, it will be alliable to conduct clinical research with EGb761 to observe whether *Ginkgo* could serve as an adjuvant therapy on anticancer.