**6. Conclusion**

100 Wireless Mesh Networks – Efficient Link Scheduling, Channel Assignment and Network Planning Strategies

algorithm LACA with the same number of radio interfaces reached value 9.47%.

**Figure 24.** Values of packet loss for various radio interfaces and different CA schemes

2468 **Number of Radio Interfaces**

**Average Jitter**

**Figure 25.** Average values of jitter for various radio interfaces and different CA schemes

2468 **Number of Radio Interfaces**

27 ms).

**Packet Loss (%)**

**Jitter (ms)**

0.00 100.00 200.00 300.00 400.00 500.00 600.00 700.00 800.00 900.00 1000.00

As we can see from figure 24 the highest value of packet loss for all number of interfaces was reached in WMN with CCA approach, with the best value reached for 6 radio interfaces (63.56 %). The best result (5.86 %) reached FRCA algorithm for 4 radio interfaces, whereas

Figure 25 shows average values of average jitter. The best values of average jitter were again reached with FRCA algorithm for 4 radio interfaces (124.8 ms). CCA algorithm reached the best value for 6 radio interfaces (601.25 ms) and LACA approach for 4 radio interfaces (167.

**Packet Loss**

CCA LACA FRCA

CCA LACA FRCA In this chapter, the study of optimal number of radio interfaces and new channel assignment approach was presented (FRCA). The study of optimal number of radio interfaces was created for two different topologies (grid and random), different number of data flows and different number of nodes. The study was based on increasing number of radio interfaces (1 to 8) for each mesh nodes. The results show that by increasing the number of interfaces it is possible to increase network capacity by enhancing of QoS parameters. For all simulations of WMN with common channel assignment method CCA, the number of six radio interfaces appears as an optimum solution, because the further increasing of the number of interfaces improved the capacity of WMN only slightly and using more than seven radio interfaces decreased the network performance.

For further increasing of network performances more sophisticated channel assignment algorithms were used. The new channel assignment approach called First random channel assignment (FRCA) was compared with existing channel assignment algorithms (CCA, LACA). The results show that by using the suitable CA algorithm it is possible to further increase the network capacity. From all results it can be concluded that the multi interface approach with suitable CA algorithm can dramatically increase the whole network performance. In that case, if it is used the simplest CA approach (CCA), we need to assign for each node up to 6 radio interfaces to maximize network performance, but by using suitable dynamic CA algorithm (e.g. FRCA or LACA), the network performance may be maximized with only 4 radio interfaces.


**5.1. The results of MCDA fault indicators for broken bars at different IM load**

The complex stator current analysis using simultaneous amplitude and phase demodulation of the two-pole IM is depicted in Fig. 6 – individual windows from above: stator current autospectrum i.e. MCS, AM fault current extracted from the stator current by the amplitude demodulation (9), PM fault current extracted from the stator current by the phase demodulation (10). Corresponding spectra are depicted in the 4th and 5th windows. The time courses of AM and also PM clearly show fault currents with 2 dominating frequencies *fsp* and *fr* which are better seen in the corresponding spectra. It is clearly seen that the phases of AM and PM at *fsp* are exactly opposite, compare 2nd window to 3rd window from the top.

MCDA fault indicators are depicted in 4th window from the top.

**Figure 6.** Complex demodulation analysis of IM current, 2-poles 1.1.kW motor, 75% of full load

Demodulation methods suppress large *Il* and therefore the relatively accurate linear scale for spectrum can be used, but for the observation of higher harmonics of *fsp* a logarithmic scale also can be used.

Equation (5) which pays only under steady conditions and low inertia, was verified. The full agreement with the theory of *aAPL* and *aAPH* equality was found in the range of possible *aAPL, aAPH* variations, which can be in the range of ± 2.5dB.

There are two reasons for the *aAPL* and *aAPH* variation:

534 Induction Motors – Modelling and Control

Spectrum averaging, which lowers stator current non-stationary errors, should be always used for the error minimization. FFT computation time is substantially shorter than the acquisition time, so the start of a new acquisition and a new averaging can start earlier than the end of the previous acquisition time. This process is called overlapping. It is expressed in percent of the acquisition time in the range of 0% - no overlapping- to max, when the new acquisition starts immediately at the end of previous FFT. The overlapping implementation

Spectrum of demodulated current does not contain any sidebands components, it is transparent and easy readable and only one simple spectral peak is the fault indicator.

The fault indicator *Ispa* [A] for broken bars is the amplitude of the amplitude modulating current on fault frequency *fsp* so the spectral magnitude of amplitude demodulated IM current on *fsp*, see Fig.6, 4rd window from the top. The fault indicator *Ira* [A] for dynamic eccentricity is the amplitude of the amplitude modulating current on fault frequency *fr* so the

Fault indicators clearly show the rotor faults but do not show the real fault severity. *Ispa* and

Fault severity dimensionless coefficients *ksp, kr* [%] are fault indicators normalized by a

/ *r ra nom kII* (14)

In order to keep the independence of fault severity coefficients of the different load, the normalizing value must be a constant. Therefore the coefficients *ksp, kr* as the basic evaluating

Five various experiments, presented in paragraphs 5.1 to 5.5, covering different IM states,

**5.1. The results of MCDA fault indicators for broken bars at different IM load**

The complex stator current analysis using simultaneous amplitude and phase demodulation of the two-pole IM is depicted in Fig. 6 – individual windows from above: stator current autospectrum i.e. MCS, AM fault current extracted from the stator current by the amplitude demodulation (9), PM fault current extracted from the stator current by the phase demodulation (10). Corresponding spectra are depicted in the 4th and 5th windows. The time courses of AM and also PM clearly show fault currents with 2 dominating frequencies *fsp* and *fr* which are better seen in the corresponding spectra. It is clearly seen that the phases of AM and PM at *fsp* are exactly opposite, compare 2nd window to 3rd window from the top.

tool for the assessment of fault severity and for the state of rotor bars was suggested.

time varying load and IM energized from inverters were performed.

MCDA fault indicators are depicted in 4th window from the top.

/ *sp spa nom kII* (13)

spectral magnitude of amplitude demodulated IM current on *fr*, Fig.6, 4rd window.

(programming) is easy. Overlapping more than 50% is recommended.

*Ira* amplitudes considerably differ with the IM power.

where fault indicators *Ispa*,*Ira* are expressed in RMS.

constant value - motor rated current *Inom*

The shortened experimental results are briefly summarized in Table 2 (suffix*H* for the demodulation using Hilbert transform, suffix*P* for the demodulation using space transform) for 2-pole motors and Table 3 for 4-pole motors.

Rotor Cage Fault Detection in Induction Motors by Motor Current Demodulation Analysis 537

At no load and at low load below 20 % of full load *Ispa* decreases- see Table 5. Therefore for the industrial diagnostics the recommended load range is from 20% of load to the full load. For more accurate diagnostics, the range from 25% of load to the full load is recommended.

Fault severity coefficient *ksp* for 2 broken bars at 4-poles motor are little smaller in

Healthy motor shows some residual modulation, due to irregularities in rotor bars layout,

The acceptable limits for *ksp,* should be experimentally stated for various motor types because they can differ. Namely the sizes of *ksp* for large IM can have different acceptable

The values of fault severity coefficient *kr* for dynamic eccentricity slightly decrease with increasing load. Over the 85% of full load the decrease is slightly greater. The values of *Ira* at a factory balanced rotor are still sufficient for a sensorless rotor electromagnetic field speed and speed irregularities measurement (Jaksch & Zalud, 2010). PM for rotor eccentricity

Two introduced demodulation methods for dynamic rotor fault detection - Hilbert and space transforms - give the same results and both can be used for rotor fault diagnostic. Both measurement systems - Bruel&Kjaer PULSE and NI 4472B give the same fault indicators.

The second experiment examined the spectral magnitudes of amplitude and phase demodulated IM current on *fsp* at different loads, together with the comparison of the MCSA low sideband *aAPL* on *fl-fsp*. The results are depicted in Fig.7 -9 and summarized in Table 4.

AM - MCDA fault indicator *Ispa* [mA] 70 77 78 78

The experiment proved the theory that PM substantially increases with increasing load

PM increases 2.2 times within the interval between 25 - 85%. The increase of PM spectral magnitude *aP* is caused both by *Il* increase at increasing load and also by *Ispp* increase, (3). It is the real cause of MCS fault indicators *aAPL* and *aAPH* increasing with increasing load, Fig.3

**Table 4.** AM and PM, 2 broken bars, at different IM load in comparison to low sideband *aAPL*

(2nd row in Table 4 and peaks in circles on *fsp* in lower window in Fig.7 to Fig.9).

25% load

PM [mrad] 24 33 44 49

Low sideband *aAPL* [mA] 36 44 46 48

50% load 75% load

85% load

comparison with 2-poles motor owing to a greater number of rotor bars *nrb* - 26 on 23.

but *ksp* were not at all measurements greater than 0.3 %.

slightly decreases with increasing load unlike PM for broken bars.

**5.2. The comparison of AM and PM at different loads**

2-poles IM, 1.1 kW 2 broken bars

limits.

and (5).

**Table 2.** Fault Indicators and fault severity coefficients for broken bars and dynamic eccentricity, Hilbert and Space Transforms, 2- poles IM

**Table 3.** Fault Indicators and fault severity coefficients for broken bars and dynamic eccentricity, Hilbert and Space Transforms, 4- poles IM

## *5.1.1. The experimental results discussion*

The experiments proved the correctness of JAPM theory and the correctness of the used demodulation techniques.

The experiments show that broken bar AM is almost insensitive to the motor load. The *Ispa* changes are in the range of 11% within the interval between 25 – 85% and in the range of 7 % in the interval 50 – 85%. The same holds for the fault severity coefficient *ksp* because the denominator in (13) is a constant value. The values of fault indicator for 2 continuous rotor bars are almost twice greater then indicators for 1 broken bar.

At no load and at low load below 20 % of full load *Ispa* decreases- see Table 5. Therefore for the industrial diagnostics the recommended load range is from 20% of load to the full load. For more accurate diagnostics, the range from 25% of load to the full load is recommended.

Fault severity coefficient *ksp* for 2 broken bars at 4-poles motor are little smaller in comparison with 2-poles motor owing to a greater number of rotor bars *nrb* - 26 on 23.

Healthy motor shows some residual modulation, due to irregularities in rotor bars layout, but *ksp* were not at all measurements greater than 0.3 %.

The acceptable limits for *ksp,* should be experimentally stated for various motor types because they can differ. Namely the sizes of *ksp* for large IM can have different acceptable limits.

The values of fault severity coefficient *kr* for dynamic eccentricity slightly decrease with increasing load. Over the 85% of full load the decrease is slightly greater. The values of *Ira* at a factory balanced rotor are still sufficient for a sensorless rotor electromagnetic field speed and speed irregularities measurement (Jaksch & Zalud, 2010). PM for rotor eccentricity slightly decreases with increasing load unlike PM for broken bars.

Two introduced demodulation methods for dynamic rotor fault detection - Hilbert and space transforms - give the same results and both can be used for rotor fault diagnostic. Both measurement systems - Bruel&Kjaer PULSE and NI 4472B give the same fault indicators.

## **5.2. The comparison of AM and PM at different loads**

536 Induction Motors – Modelling and Control

Hilbert and Space Transforms, 2- poles IM

Hilbert and Space Transforms, 4- poles IM

demodulation techniques.

*5.1.1. The experimental results discussion*

bars are almost twice greater then indicators for 1 broken bar.

 *IspaP* [mA] 69 78 79 77 interrupted *IspaH* [mA] 67 75 77 76 rotor bars *ksp*[%] 1.93 1.98 1.95 2.01 Dynamic ecc. *IraP*[mA] 3.9 3.9 3.9 3.5 Balanced *IraH*[mA] 3.8 3.9 4.1 3.3 rotor *kr*[%] 0.15 0.15 0.16 0.13

**Table 2.** Fault Indicators and fault severity coefficients for broken bars and dynamic eccentricity,

Motor state 25% load 50% load 75% load 85% load 4- poles *fr* [Hz] 24.56 24.41 24.21 24.12 0.75 kW *fsp* [Hz] 1.73 2.3 3.1 3.45 Health *IspaP* [mA] 3.3 3.4 3.4 3.3 motor *IspaH* [mA] 3.4 3.3 3.4 3.2

2 contin. *IspaP* [mA] 37 41 43 42 interrupted *IspaH* [mA] 38 42 43 41 Rotor bars *ksp*[%] 1.46 1.68 1.70 1.68 Balanced *IraP*[mA] 5.1 5.4 5.3 4.9 rotor *IraH*[mA] 5.2 5.5 5.4 4.9

**Table 3.** Fault Indicators and fault severity coefficients for broken bars and dynamic eccentricity,

The experiments proved the correctness of JAPM theory and the correctness of the used

The experiments show that broken bar AM is almost insensitive to the motor load. The *Ispa* changes are in the range of 11% within the interval between 25 – 85% and in the range of 7 % in the interval 50 – 85%. The same holds for the fault severity coefficient *ksp* because the denominator in (13) is a constant value. The values of fault indicator for 2 continuous rotor

*ksp* [%] 0.28 0.29 0.28 0.25

*ksp* [%] 0.18 0.18 0.18 0.17

*kr*[%] 0.28 0.28 0.27 0.26

The second experiment examined the spectral magnitudes of amplitude and phase demodulated IM current on *fsp* at different loads, together with the comparison of the MCSA low sideband *aAPL* on *fl-fsp*. The results are depicted in Fig.7 -9 and summarized in Table 4.

**Table 4.** AM and PM, 2 broken bars, at different IM load in comparison to low sideband *aAPL*

The experiment proved the theory that PM substantially increases with increasing load (2nd row in Table 4 and peaks in circles on *fsp* in lower window in Fig.7 to Fig.9).

PM increases 2.2 times within the interval between 25 - 85%. The increase of PM spectral magnitude *aP* is caused both by *Il* increase at increasing load and also by *Ispp* increase, (3). It is the real cause of MCS fault indicators *aAPL* and *aAPH* increasing with increasing load, Fig.3 and (5).

Rotor Cage Fault Detection in Induction Motors by Motor Current Demodulation Analysis 539

**5.3. The analysis of indicator** *Ispa* **at very low load from no load to 20 % of full load**

The 3rd experiment analyses the changes of *Ispa* in the range of no load to 20 % of full load. Table 5 shows the MCDA broken bars fault indicator *Ispa* decline under 20% of full load. For very low load at *s*=0.44%, *fsp*=0.44 Hz the *Ispa* for 2 broken bars decreases to *Ispa*=31mA, which is approximately the half of its nominal value and it corresponds to *Ispa* for 1 broken bar

> s[%] 0.21 0.37 0.44 0.69 0.94 1.16 1.37 fsp [Hz] 0.21 0.37 0.44 0.69 0.94 1.16 1.37 IspaH [mA] 19 25 31 37 47 56 69

The decrease of *Ispp* representing PM, and therefore the decrease of MCSA fault indicators *aAPL, aAPH* under 20% are substantially faster than the decrease of MCDA fault indicator *Ispa*

The aim of 4th experiment was to verify presented theory of the time varying load and its influence on MCDA fault indicator *Ispa*. The time varying load frequency *fload* = 4 Hz was

*aAPL aAPH*

*fl-fload*

*fl+fload*

*fr*

**Figure 10.** Windows from above: MCS, time course of amplitude demodulated current, and its MCDA

(Table 2), so great confusion in broken bar diagnostics may come.

**Table 5.** *Ispa* changes from no load to 20% of full load, 2 broken bars, 1.1.kW IM

chosen very near to the broken bars fault indicator frequency *fsp* =1.9 Hz.

**5.4. IM rotor fault diagnostics at time varying load**

(Fig.7–Fig.9, PM in circles).

spectrum, 25% of full load.

*Iload*

*Ispa*

*fsp fload*

**Figure 7.** IM spectrum, spectrum of amplitude and phase demodulated current, 25% of full load

**Figure 8.** IM spectrum, spectrum of amplitude and phase demodulated current, 50% of full load

**Figure 9.** IM spectrum, spectrum of amplitude and phase demodulated current, 85% of full load


**4. Conclusion**

**Sources Morphological parts Comments References**

*Arachishypogaea* Plant parts, aqueous extract Hypolipidemic activity at 175

"Ata-Ofa' (polyherbal tea) Leaves, methanolic extract Hypolipidemic activity at 50

*Xylopiaaethiopica* Seed, methanolic extract Hypolipidemic activity at 250

*Parinaripolyandra* Fruit, ethanolic extract Hypolipidemic activity at

*Spondiamombia* Aqueous leave extract Lipid lowering effect at the

*Bauhiniathoningii* Aqueous crude extract Hypoglycemic and lipidemic

*Cajanuscajan* Methanolic leaf extract Antioxidant and

*Melantherascandens* Ethanolic leaf extract Antidiabetic and

*Jatrophatanjorensis* Methanolic leaf extract Serum lipid profile and

*Ricinuscommunis* Aqueous root extract Hypoglycaemic potential, lipid

**Table 3.** Medicinal plants investigated in Nigeria for use as Antihypolipidemic agent

methanolic extract

rhizomes

Telfairia occidentalis Plant parts,

248 Antioxidant-Antidiabetic Agents and Human Health

*Curcuma* longa methanol extract of the

*Momordicacharantia* Linn Methanolic extract of the fruits

*Morindamorindoides* Root bark, methanolic extract Hypolipidemic activity Olukunle *et al*., 2012

mg/kg

mg/kg

mg/kg

mg/kg

mg/kg

mg/kg

effecte

ranges

500mg/kg

These plants have been identified, authenticated and investigated from Nigeria flora against hyperlipidemia, using pharmacological validated animal models. They all have levels and with some levels of increase in LDL, TC, TG and decrease in HDL. Furthermore, there has been recent interest on the research towards hyperlipidemia due to its obvious relationship with

*Crotonzambesicus* Ethanolic leaf extract Lipid lowering effect Ofusori *et al*, 2012.

50-250 mg/kg

hypoglycemic and hypolipidemic acitivity 100

Anti-Diabetic and Hypolipidemic Effects at the doses of 200, 400 and 600

doses of 250, 500and 750

hypolipidemic activity at the dose of 200 mg/kg

phytochemical composition at 100, 200 and 500mg/kg dose

hypolipidemic activities at the doses of 37, 74 &111 mg/kg

profile effects At a dose of

Bilbis *et al*., 2002

Atawodi, 2001

Nwozo *et al*., 2011

Abolaji *et al*., 2007

Nwozo *et al*, 2009.

Igwe *et al*, 2008

2012.

Kolawole and Ayankunle,

Ojezele and Abatan, 2011.

Akinloye and Solanke 2011.

Oluwole *et al,* 2011.

Akpan *et al*, 2012.

Matthew *et al*, 2012.

Hypolipidemic activity Adaramoye *et al*.*,* 2007

The use of herbal or natural medicines for the treatment of various disorders has a long and extensive history. The reported plants have the potential to act as lipid-lowering agents with minimal side effects (advantage over currently synthetic drugs) and thus could find their way onto the world market as alternatives to prescribed drugs currently available to treat hyper‐ lipidermia. Most of the studies were carried out with crude extract and administered orally. The principal families in which such activity has been reported are Acanthaceae, Apiaceae, Asteraceae, Azoaceae, Combretaceae, Cucurbitaceae, Euphorbiaceae, Fabaceae, Lamiaceae, Liliaceae, Malvaceae, Myrtaceae, Rubiaceae, Rutaceae and Zingiberaceae, Finally, all the plant species appear to be promising as hypolipidemic agents with activity mediated through various mechanisms.


**4. Conclusions**

**Supplementary Materials:** Supplementary materials are available online at http://www.mdpi.com/2079-6412/9/ 8/508/s1, Table S1: Parameters related to the titration, Figure S1: Variations of a portion of 1H NMR spectrum (400 MHz) of MPT 2.8 mM during its titration with β-CD 12 mM in D2O, the molar fractions of host are reported in Table S1, Figure S2: Stacked plot and computational analysis of the DOSY experiment of a sample containing MPT alone. MPT signal decays according to the gradients (G) together with the corresponding graphical analysis of the data. The di ffusion coe fficients are indicated with the letter F, Figure S3. Stacked plot and computational analysis of the DOSY experiment of a sample containing MPT + β-CD. MPT signal decays according to the gradients (G) together with the corresponding graphical analysis of the data. The di ffusion coe fficients are indicated with the letter F, Figure S4: Stacked plot and computational analysis of the DOSY experiment of a sample containing MPT + β-CD. β-CD signal decays according to the gradients (G) together with the corresponding graphical analysis of the data.

**Author Contributions:** Conceptualization and Methodology, C.M., G.F., and A.B.; Formal analysis C.M., G.F., and A.B.; Investigation G.F., G.D.C., C.M., F.Z., and A.B.; Writing—Review and Editing C.M., G.F., C.M., F.Z., and A.B.

**Funding:** This research was carried out in the scope of the B-IMPACT project (project ID 1149) of the M-ERA.NET 2013 call, supported by the national funding organizations of the partners Slovenia (MIZS), Italy (MIUR), and France (RMP).

**Acknowledgments:** The authors also gratefully acknowledge the University of Ferrara for financial support (FAR funds). Thanks are also given to P. Formaglio for NMR spectroscopic experiments and to T. Bernardi for mass spectrometric experiments.

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


**Author details**

112 Advances in Computational Stability Analysis

following expression:

**5. Conclusion**

nonuniform members.

(Salmon et al., 2009). By applying a similar modification to the analytical results obtained in this study for *ideal* three-segment compression members, a *more realistic* analytical curve is drawn. This curve is plotted in Fig. 12 with a label '0.877 *Pcr,analy*'. From Fig. 12, it is seen that the "modified" analytical curve *almost* "averages" most of the test results. The larger discrepancies observed in stiffened specimens with *s*=0.3333 and *s*=0.5 are believed to be resulted from the residual stresses locked in the specimens during welding of the steel stiffening plates, which highly depends on quality of workmanship. For this reason, while calculating the buckling load of a multi-segment compression member formed by welding, not only the initial out-ofstraightness of the member, but also the effects of welding have to be taken into account. Considering that stiffened columns will always have more initial imperfections than uniform columns, it is suggested that a smaller modification factor be used in the design of multisegment columns. Based on the limited test data obtained in the experimental phase of this study, the following modification factor is proposed to be used in the design of three-segment

symmetric steel compression members formed by welding steel stiffening plates:

experimental part of this study and needs being verified by further studies.

where *s* is the stiffened length ratio of the compression member, which equals to the weld length in the stiffened members. Thus, the proposed buckling load (*Pcr,proposed*) for such a member can be computed by modifying the analytical buckling load (*Pcr,analy*) as in the

The proposed buckling loads for the multi-segment columns tested in the experimental part of this study are computed using Eq. (26) with Eq. (25) and plotted in Fig. 12 with a label '*Pcr,proposed*'. For easier comparison, a linear trend line fitted to the experimental data is also plotted in the same figure. Fig. 12 shows perfect match of design values of buckling loads with the trend line. While using Eq. (25), it should be kept in mind that the modification factor proposed in this paper is derived based on the limited test data obtained in the

In an attempt to design economic and aesthetic structures, many engineers nowadays prefer to use nonuniform members in their designs. Strengthening a steel braced structure which have insufficient lateral resistant by stiffening the braces through welding additional steel plates or wrapping fiber reinforced polymers in partial length is, for example, a special application of use of multi-segment nonuniform members in earthquake resistant structural engineering. The stability analysis of multi-segment (stepped) members is usually very complicated, however, due to the complex differential equations to be solved. In fact, most of the design formulae/charts given in design specifications are developed for uniform members. For this reason, there is a need for a practical tool to analyze buckling behavior of

*MF* 0.877 0.2*s* (25)

*cr proposed* , , *cr analy P MF P* (26)

Seval Pinarbasi Cuhadaroglu, Erkan Akpinar, Fuad Okay, Hilal Meydanli Atalay and Sevket Ozden *Kocaeli University, Turkey*


**Preface to "Image and Video Processing and Recognition Based on Artificial Intelligence"**

Recent developments have led to the powerful application of artificial intelligence (AI) techniques to image and video processing and recognition. While the state-of-the-art technology has matured, its performance is still affected by various environmental conditions and heterogeneous databases. The purpose of this Special Issue was to invite high-quality and state-of-the-art academic papers on challenging issues in the field of AI-based image and video processing and recognition. We solicited original papers of unpublished and completed research that were not under review by any other conference, magazine, or journal. Topics of interest included but were not limited to the following:

> **Kang Ryoung Park, Sangyoun Lee, Euntai Kim** *Editors*


*3.3. The E*ff*ect Of Year Of Slaughter On Carcass Characteristics*

The year of slaughter affected the mean carcass weight and mean age of slaughter significantly (*p* ≤ 0.001) as it is shown in Table 2. Regarding the distribution of muscle conformation and fat deposit classes of carcasses during the seven years (2011–2017) as indicated by the SEUROP classification system the findings are presented in Figure 2, given as explanatory spider web charts.

**Table 2.** Number of carcasses, means ± std. error for the age of slaughter (days, d) and carcass weight (kg) during 2011 to 2017.

Means within the same column followed by different superscript for each variable (a, b, c) among years differ significantly (*p* ≤ 0.05).

**Figure 2.** Proportion of muscle conformation (**a**) and fat deposit (**b**) classes for the carcasses during 2011–2017.

Over the course of seven years, fluctuations in both mean carcass weight and age of slaughter regardless of the gender were observed. The total number of cattle slaughtered was decreased over the years. In fact, the highest decrease was observed in 2016 and 2017, and reached the reduction rate of approximately 31.4% and 29.2%, respectively, compared to 2011, where it reached the highest value.

In Figure 2 the majority of carcasses were classified according to muscle conformation in classes R, O and U, while for the fat deposit in classes 2 and 3.

#### *3.4. The E*ff*ect of Geographical Region of Farms on Carcass Characteristics*

The largest number of cattle farms in the country was located in Northern Greece and specifically in the regions of Eastern Macedonia and Thrace and Central Macedonia and the lowest number was located in the region of Attica [33]. Table 3 shows that from the records of 322,609 cattle slaughtered in the 13 regions, the lightest carcass weight was found for carcasses derived from the region of Epirus (247.2 ± 0.8 kg). This observation is in accordance with the relatively small number of cattle farms in this area that cover only 7.5% of the total number of farms in the country. Taking into account

the geographical criteria, the region of Epirus is a mountainous area in the northwestern part of the country where traditionally bred sheep and goats. Furthermore, the largest percentage of carcasses (32.2%) in the region of Epirus originated from Holstein breed, while 21.6% of them belonged to Greek breeds (Table S1) that showed low mean carcass weight as mentioned in Table 1.

**Table 3.** Number of cattle farms and beef carcasses (N), means ± std. error for the age at slaughter (days) and carcass weight (kg) distributed in the 13 geographical regions.

Means within the same column followed by different superscript for each variable (a, b, c, d, e, f, g, h, i, j) among geographical regions differ significantly (*p* ≤ 0.05).

Regarding the mean age of slaughter, it is interesting to note that the lowest slaughter age observed in the region of Thessaly, could be attributed to the number of cattle raised for fattening in this region. The total percentage reached 16.74% of the total number of carcasses, as well as the fact that 39% of these carcasses originated from Limousin breed and 38.4% from the local breed Greek Red (Table S1).

Furthermore, in Table 3, we observed that only in three regions (Western Greece, Ionian Islands and North Aegean) occurred the highest value of the mean age of slaughter (over 600 d) and the mean carcass weight (over 300 kg). In addition, the cattle breed that slaughtered in these regions was mainly Limousin, a pure meat breed (data in Supplementary Table S1).

Figure 3 given as explanatory spider web charts depicts the proportion of muscle conformation (a) and fat deposit (b) classes in the 13 regions of the country.

As it shown in Figure 3, the major proposition of muscle conformation classes of the Greek carcasses in all over the 13 regions was R. In the region of Grete, a wide proportion of carcasses is classified in conformation class U and in the Eastern Macedonia & Thrace, a large proportion of carcasses classified in class O. The class E appeared mainly in carcasses slaughtered in the North Aegean. According to fat deposit the class 2 appeared in all 13 regions and only in regions of Epirus and Grete there was a large proportion that classified their carcasses in classes 3 and 1, respectively.

**Figure 3.** Proportions of muscle conformation (**a**) and fat deposit (**b**) classes distributed across 13 regions.

#### *3.5. The E*ff*ect of Month of Slaughter on Carcass Characteristics*

Regarding the total number of carcasses had significant differences between the first and the second half of the year (Table 4). It is worth noting that a remarkable number of carcasses (over 30,000) were slaughtered in the second half of the year especially on the 7th, 8th, 11th and 12th month. On the contrary the lowest number of carcasses showed on the 1st month of the year (n = 8997).

Comparisons among the months of slaughter showed that both the mean carcass weight and the mean age of slaughter differentiated significantly (*p* ≤ 0.001). The lowest value for the mean carcass weight was in the 3rd month of the year (293.2 ± 0.7 kg) while the highest value was in the 11th month (301.4 ± 0.6 kg). The difference between the two values of the mean carcass weight was 8.2 kg.

As for the age of slaughter the lower value appeared in 5th and 6th month of the year (555.3 ± 0.9 d and 555.4 ± 0.9 d respectively) and the highest in the 1st month (568.1 ± 1.6 d).

In Figure 4 given as explanatory spider web charts, the distribution of conformation classes is homogeneous across the months and the highest proportion of muscle conformation in class R is widespread all over in the 12 months.

**Table 4.** Number of beef carcasses per month, means ± std. error for the age at slaughter (days, d) and carcass weight (kg).

Means within the same column followed by different superscript for each variable (a, b, c, d, e,f) among month of slaughter differ significantly (*p* ≤ 0.05).

**Figure 4.** Proportions of muscle conformation (**a**) and fat deposit (**b**) classes distributed across 12 months.

#### *3.6. Evaluation of Carcass Quality Characteristics Based on the SEUROP Classification System*

Regarding the classification of carcasses into 6 categories (A,B,D,E,Z,V) based on gender and age at slaughter (Table 5), the heaviest mean carcass weight was recorded in the male carcasses of category B (329.2 ± 0.6 kg) and A (320.7 ± 0.2 kg).As for the female carcasses (categories D and E) the mean carcass weight ranged from 239.1 ± 0.8 kg and 219.4 ± 0.4 kg, respectively. Finally, in categories Z and V, it was appeared a lower mean carcass weight (218.1 ± 0.5 kg and 187.4 ± 1.6 kg, respectively) due to their younger age at slaughter.

**Table 5.** Number of beef carcasses (N), means ± std. error for the age at slaughter (days, d) and the carcass weight (kg) according to SEUROP classification scale 1 (category, muscle conformation and fat deposit).

1 each class/category includes subclasses (+)&(−); \*A: carcasses of uncastrated male animals aged from 12 months to less than 24 months; B: carcasses of uncastrated male animals aged from 24 months; D: carcasses of female animals that have calved; E: carcasses of other female animals aged from 12 months; Z: carcasses of animals aged from 8 months to less than 12 months; V: carcasses of animals aged less than 8 months; \*\* S: superior; E: excellent; U: very good; R: good; O: fair; P: poor; \*\*\* class 1 = low; class 2 = slight; class 3 = average; class 4 = high; class 5 = very high; means within the same column followed by different superscript for each variable (a,b,c,d,e,f) among; classes/categories differ significantly (*p* ≤ 0.05).

According to Table 5 the largest percentage of carcasses (67.7%) belonged to category A (n = 218,732). The main reason was that the cattle imported to the country with the main purpose of breeding was fattening, they were preferred to be male, due to higher growth rate, better carcass performance and muscle conformation. Category V (animal carcasses under eight months old) had the lowest number of carcasses (n = 2,535) depicting the great need of Greek beef farmers to buy calves for breeding and fattening from abroad, as long as the demand was not met by the local market.

Regarding the classification of carcasses by category of muscle conformation according to SEUROP (Table 5), it is observed that the largest percentage of them were classified in class R (n = 130,302) as "good", followed by the other classes (O, U, E, P & S). These findings are explained by the fact that the majority of carcasses were male animals that were mainly classified in category R. Additionally, in category P where the number of carcasses was relatively low (n = 6515), they were classified carcasses of poor muscle conformation. In addition, mean carcass weight followed by a normal distribution with the highest value appearing in category S (446.7 ± 1.8 kg) and the lowest in category P (209.4 ± 1.0 kg). The mean carcass weight of category R was 286.7 ± 0.2 kg.

As for the mean age at slaughter, it is noticed that in category P there was a relatively extended number of days (600.6 ± 2.5 d) which could be explained by the fact that in this category mainly female carcasses were classified. Female cattle from this category removed from the livestock farm to the slaughterhouse due to low milk yields or possible accidents in the farm.

Regarding to the fattening state, most carcasses (n = 199,138) classified to class 2 followed by classes 3, 1, 4 and final class 5.