Contents

#### **Preface XIII**

#### **Part 1 Basic Science 1**

#### **Part 2 Clinical Aspects 103**

Contents VII

Chapter 19 **Hospital at Home for Elderly**

Chapter 22 **A Multi-Targeted Antisense**

Chapter 23 **Cell Therapy in Chronic**

**Patients with Acute Exacerbation of**

Chapter 20 **Chest Mobilization Techniques for Improving**

**Oligonucleotide-Based Therapy**

Rosanne Seguin and Nicolay Ferrari

**Obstructive Pulmonary Disease: State of the Art and Perspectives 455** João Tadeu Ribeiro-Paes, Talita Stessuk and Rodrigo de las Heras Kozma

Donrawee Leelarungrayub

**Chronic Obstructive Pulmonary Disease 375** Aimonino Ricauda Nicoletta, Tibaldi Vittoria, Bertone Paola and Isaia Giovanni Carlo

Chapter 21 **Antipneumococcal Vaccination in COPD Patients 423**  Angel Vila-Corcoles and Olga Ochoa-Gondar

**Ventilation and Gas Exchange in Chronic Lung Disease 399**

**Directed at Phosphodiesterases 4 and 7 for COPD 435**

#### **Part 3 Treatment 273**

VI Contents

Chapter 8 **Psychosocial Dimensions of**

Chapter 9 **Alpha-1 Antitrypsin Deficiency –**

and Mohammad Towhidi

Chapter 12 **Chronic Obstructive Pulmonary**

Chapter 13 **Evaluation of Dyspnea and**

Chapter 14 **Adherence to Therapy in Chronic**

**Part 3 Treatment 273**

Cenk Kirakli

Chapter 17 **Noninvasive Positive-Pressure**

Zeynep Zeren Ucar

Chapter 18 **Types of Physical Exercise**

Janice Gullick

**COPD for the Patient and Family 153**

**A Genetic Risk Factor for COPD 179** Tomás P. Carroll, Catherine A. O'Connor, Emer P. Reeves and Noel G. McElvaney

Chapter 10 **The Six-Minute Walk Test on the Treadmill 217**

Katarzyna R. Świda, Zbigniew W. Szczurek, Jerzy Gałecka and Agnieszka Winiarska

Chapter 11 **COPD Due to Sulfur Mustard (Mustard Lung) 231**  Shahrzad M. Lari, Davood Attaran

> **Disease and Diabetes** *Mellitus* **239**  Elisabet Martinez-Ceron, Beatriz Barquiel, Luis Felipe Pallardo and Rodolfo Alvarez-Sala

**Fatigue Among the COPD Patients 257**  Hatice Tel, Zeynep Bilgiç and Zübeyde Zorlu

**Obstructive Pulmonary Disease 275** Tamas Agh and Agnes Meszaros

Chapter 15 **Management of Acute Exacerbations 291**

Chapter 16 **Novel Concept in Pulmonary Delivery 299**  Maria Carafa, Carlotta Marianecci, Paolino Donatella, Luisa Di Marzio,

> **Training for COPD Patients 351** R. Martín-Valero, A. I. Cuesta-Vargas and M. T. Labajos-Manzanares

Christian Celia, Massimo Fresta and Franco Alhaique

**Ventilation Therapy in Patients with COPD 333**

Fryderyk Prochaczek, Jacek S. Brandt, Witold Żmuda,

Preface

observation.

readers and the patients they are helping.

It is indeed heartening to note the ardent interest in Chronic Obstructive Pulmonary Disease (COPD) and the progress that has been achieved in the management of this disorder in recent years. A decade or so ago, many clinicians were described as having an unnecessarily 'nihilistic' view of COPD. This has certainly changed over the years, and the contributions that we have received from numerous distinguished sources as well as the keen anticipation for the publication of this book are testament to this

The 'open-access' format of this book provides a platform for scientists and clinicians from around the world to present their knowledge of the disease and up-to-date scientific findings, and avails the reader to a multitude of topics: from recent discoveries in the basic sciences to state-of-the-art interventions on COPD. This clearly reflects the wide-ranging academic interest in this disease. Indeed, those of us privileged to have a part in the management of patients with COPD will have known that this disease challenges the whole gamut of Respiratory Medicine – necessarily pushing frontiers in pulmonary function (and exercise) testing, radiologic imaging, pharmaceuticals, chest physiotherapy, intensive care with respiratory therapy, bronchology and thoracic surgery. In addition, multi-disciplinary inputs from other specialty fields such as cardiology, neuro-psychiatry, geriatric medicine and palliative care are often necessary for the comprehensive management of COPD. The recent progress and a multi-disciplinary approach in dealing with COPD certainly bode well for the future. Nonetheless, the final goal and ultimate outcome is in improving the health status and survival of our patients. With that in mind, I sincerely hope that this assemblage of subject reviews and novel insights on COPD will be of benefit for our

**Dr Kian-Chung Ong**

Singapore

MBBS, MRCP (UK), FRCP (Edin), FCCP (USA)

Specialist - Respiratory Medicine, Mt Elizabeth Medical Centre

President, Chronic Obstructive Pulmonary Disease Association

Global Initiative for Chronic Obstructive Lung Disease (GOLD) National Leader


*2.2. Classification of Face Recognition Systems*

Compared with other biometric systems such as the eye, iris, or fingerprint recognition systems, the face recognition system is not the most efficient and reliable [5]. Moreover, this biometric system has many constraints resulting from many challenges, despite all the above advantages. The recognition under the controlled environments has been saturated. Nevertheless, in uncontrolled environments, the problem remains open owing to large variations in lighting conditions, facial expressions, age, dynamic background, and so on. In this paper survey, we review the most advanced face recognition techniques proposed in controlled/uncontrolled environments using different databases.

Several systems are implemented to identify a human face in 2D or 3D images. In this review paper, we will classify these systems into three approaches based on their detection and recognition method (Figure 2): (1) local, (2) holistic (subspace), and (3) hybrid approaches. The first approach is classified according to certain facial features, not considering the whole face. The second approach employs the entire face as input data and then projects into a small subspace or in correlation plane. The third approach uses local and global features in order to improve face recognition accuracy.

**Figure 2.** Face recognition methods. SIFT, scale-invariant feature transform; SURF, scale-invariant feature transform; BRIEF, binary robust independent elementary features; LBP, local binary pattern; HOG, histogram of oriented gradients; LPQ, local phase quantization; PCA, principal component analysis; LDA, linear discriminant analysis; KPCA, kernel PCA; CNN, convolutional neural network; SVM, support vector machine.


2.2.1. Land-Use Data

Focusing on the cultivation of rice during the monsoon season, the district-wise data on land use was collected from Directorate of Economics and Statistics, Ministry of Agriculture and Family Welfare, Government of India from the year 2000 to 2014. From this, data on the area under paddy sown during the *Kharif* season and the net area sown under all crops was collected. Net area sown, defined as the total area which is sowed at least once in the same year, was used for the analysis. The dataset provides details on the area sown with rice crop by seasons, which are broadly categorised as autumn, winter and summer. The main rice growing season in India is the *Kharif* season and roughly 84% of the rice is grown during this season. The sowing time of winter (*Kharif*) rice is in June–July which is when monsoon arrives and it is harvested between the months of November–January). Given that the focus of this study is on the *Kharif* season, when most of the flooding happens in India, the land-use data on area during *Kharif* season was used.

**Figure 3.** (**a**,**b**) MODIS TERRA satellite data taken on 22 September 2010; (**c**,**d**) are the flood inundation extent covering the states of Uttar Pradesh and Bihar, India. Source: IWMI.

Furthermore, the study considers only 17 states for analysis, which are Andhra Pradesh, Assam, Bihar, Chhattisgarh, Haryana, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Meghalaya, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttarakhand and West Bengal. These 17 states represent approximately 89% of the rice-growing area across the Indian states. The states of Maharashtra and Gujarat are considerably big, both in terms of their economic capacity and agricultural share; however, rice is not the primary grown crop in these regions owing to their agro-climatic conditions which are better suited for growing cotton, sugar cane, millet and so on. Overall, the rice growing area is 5% for both the states combined. The remaining states that were not included in the study were excluded either because district level land use data was not available, for states such as Gujarat, Maharashtra, Manipur and Telangana; or because average area used for growing rice was negligible, such as for the states of Arunachal Pradesh, Himachal Pradesh, Jammu and Kashmir.


**Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters**

**Sigfredo Fuentes 1,\*, Claudia Gonzalez Viejo 1, Brendan Cullen 2, Eden Tongson 1, Surinder S. Chauhan 2 and Frank R. Dunshea 2**

Received: 27 April 2020; Accepted: 22 May 2020; Published: 24 May 2020

**Abstract:** Increased global temperatures and climatic anomalies, such as heatwaves, as a product of climate change, are impacting the heat stress levels of farm animals. These impacts could have detrimental effects on the milk quality and productivity of dairy cows. This research used four years of data from a robotic dairy farm from 36 cows with similar heat tolerance (Model 1), and all 312 cows from the farm (Model 2). These data consisted of programmed concentrate feed and weight combined with weather parameters to develop supervised machine learning fitting models to predict milk yield, fat and protein content, and actual cow concentrate feed intake. Results showed highly accurate models, which were developed for cows with a similar genetic heat tolerance (Model 1: n = 116, 456; R = 0.87; slope = 0.76) and for all cows (Model 2: n = 665, 836; R = 0.86; slope = 0.74). Furthermore, an artificial intelligence (AI) system was proposed to increase or maintain a targeted level of milk quality by reducing heat stress that could be applied to a conventional dairy farm with minimal technology addition.

**Keywords:** machine learning; heat stress; animal welfare; climate change; automation

## **1. Introduction**

Robotic dairy farms or Automated Milking Systems (AMS) are the result of the implementation of state of the art technology related to robotics to increase milk yield through increased efficiency and automation [1,2]. These technologies are developed in response to the increasing market opportunities for the dairy industry globally, which is projected to grow by 35% by 2030 [3]. However, global demands will also be accompanied by 14 million traditional dairy farms shutting down production due to increased competitiveness and requirements for guaranteed milk quality and animal welfare [4]. The latter is considered a growing concern for consumers, which is achieved by AMS since it is based on the "milking when they like" system increasing wellbeing and welfare of cows [5]. Further potential advances to AMS technologies have been researched in recent years through the implementation of biometrics monitoring of animals to assess physiological changes in production systems [6]. Some of these technologies are noninvasive using visible (RGB) imagery/video, and infrared thermal imagery for heart rate, respiration rate, and body temperature assessments. These technologies could result in improvements in the monitoring of heat stress in farm animals.

Modeling heat stress in AMS has concentrated recently on the rumination and milking performance [7], identifying specific thresholds with production factors [8] and thermal comfort

indices [9], mainly through the calculation of the temperature-humidity index (THI) using several models [10]. According to a study by Nascimento et al. [11], who compared nine different models to calculate THI, the equation from Berman et al. [12] (Equation (10) below) was significantly correlated with physiological data of cows such as respiration rate, heart rate, rectal, and skin temperatures. However, all previous methods use deterministic mathematical equations with minimal animal information in the analysis, and the noncontact biometric analysis could be cost-prohibitive for the near-future application to conventional dairy farms.

Artificial intelligence (AI) applied to Digital Agriculture deals with the implementation and integration of digital data, sensors, and tools on agricultural applications from the farm to consumers [13]. These technologies can include big data, sensor technology, sensor networks, remote sensing, robotics, and unmanned aerial vehicles (UAV). Data processing is performed using new and emerging technologies, such as computer vision, machine learning, and AI, among others. The implementation of AI not only should benefit high technological systems, such as AMS, but also conventional dairy farms to increase their competitiveness in the future.

This research was based on machine learning modeling using ubiquitous environmental data obtained from automatic meteorological stations and cow information available by all dairy farms as inputs. Target information related to important parameters related to milk productivity, milk quality, and actual feed of dairy cows was obtained from an AMS belonging to The University of Melbourne, Australia. High accurate machine learning (ML) models that can be applied to any dairy farm from AMS to conventional were obtained. Furthermore, this paper proposes an AI system model to be implemented in any dairy farm to automatically assess and ameliorate heat stress by implementing ML models developed with an automated sorting and gate system.

## **2. Materials and Methods**

## *2.1. Site, Robotic Dairy Farm, and Data Acquisition*

The study was conducted in a dairy farm located at The University of Melbourne Dookie College, Victoria, Australia (36◦22'48" S, 145◦42'36" E). This region had an average annual rainfall of 537 mm (monthly extremes: 30.5–57.6 mm) and mean daily solar exposure of 17 MJ m2 −1 (extremes: 7.3–27.3 m2 −1) from 1991–2019; data obtained from the Bureau of Meteorology (BoM) Dookie Agricultural College station 081013. The farm consists of 43 ha of irrigated pastures based on perennial ryegrass (Lolium perenne) and annual ryegrass (Lolium multiflorum). The herd in this site consists of Holstein-Friesian cows. The farm contains three Lely Astronaut robotic milking machines (Lely Holding S.à.r.l., Maassluis, The Netherlands), with a capacity of 60 cows per machine (maximum capacity of 180 cows) that move voluntarily for milking. As described by Dunshea et al. [14], cows wear an identification transponder neck collar (Lely Holding S.à.r.l., Maassluis, The Netherlands), which records the cows' activity. The robotic milking system can automatically record parameters such as lactation days counted from day 0 at calving up to the time of next calving including the dry cow period, lactation number, milking frequency per day, milk yield (kg day−1), milk protein (%), milk fat (%) and somatic cells, programmed concentrate feed (kg day−1), concentrate feed intake (kg day−1), and liveweight (kg). Records of these data from June 2016 to March 2019 were used for this study.

Weather data were obtained from the meteorological station (Adcon Telemetry GmbH, Klosterneuburg, Austria), located at the Dookie Agricultural College, which provides data every 15 min for each day of the year. Parameters obtained were (i) temperature (*T*; ◦C), (ii) relative humidity (RH; %), (iii) rainfall as daily running total (mm), (iv) wind speed (km h−1), and (v) wind direction ( ◦). Based on these data, other variables such as dewpoint temperature (*Tdp*; ◦C; Equation (1); [15]), wet bulb temperature (*Twet*; ◦C), and THI were calculated. The latter was calculated using the following nine different equations (Equations (2)–(10); [11]):

$$T\_{dp} = \frac{243.5\left(\frac{17.67 \times T}{243.5 + T} + \ln\frac{RH}{100}\right)}{17.67 - \left(\frac{17.67 \times T}{243.5 + T} + \ln\frac{RH}{100}\right)}\tag{1}$$

$$THI\_1 = 0.4 \times (T + T\_{\text{net}}) \times 1.8 + 32 + 15\tag{2}$$

$$THI\_2 = (0.15 \times T + 0.85 \times T\_{\text{net}}) \times 1.8 + 32\tag{3}$$

$$THI\_3 = (T \times 0.35 + T\_{\text{wet}} \times 0.65) \times 1.8 + 32\tag{4}$$

$$THI\_4 = 0.72 \times (T + T\_{\text{net}}) + 40.6\tag{5}$$

$$THI\_5 = (1.8 \times T + 32) - \left[ (0.55 - 0.0055 \times RH) \times (1.8 + T - 26) \right] \tag{6}$$

$$THI\_6 = \left(0.55 \times T + 0.2 \times T\_{dp}\right) \times 1.8 + 32 + 17.5\tag{7}$$

$$THI\_7 = T + \left(0.36 \times T\_{dp}\right) + 41.2\tag{8}$$

$$THl\\$ = \left(0.8 \times T\right) + \left(\frac{RH}{100}\right) \times \left(T - 14.4\right) + 46.4\tag{9}$$

$$THI\rho = 3.43 + 1.058 \times T - 0.293 \times RH + 0.0164 \times T \times RH + 35.7 \tag{10}$$

where *Twet* was calculated in batch using a customized code written in MATLAB®R2020a (Mathworks Inc., Natick, MA, USA; [16]), calculations were based on *T*, *Tdp*, and surface pressure and the bisection search method.

#### *2.2. Statistical Data and Machine Learning Modeling*

Mean values of *THI* calculated with Equation (10) along with milk yield, milk protein, and fat content, and concentrate feed intake were obtained and plotted to visualize the effects of the different seasons on each parameter. Statistical data obtained from the inputs and targets consisted of minimum, maximum, and mean values of each parameter.

Two ML models were developed based on artificial neural networks (ANN) using the Bayesian Regularization training algorithm. The latter was chosen as it showed the best accuracy and performance as well as no over or underfitting [17] after testing 17 different algorithms using a customized code written in MATLAB®R2020a. The inputs for the models (Figure 1) were based on the maximum values per day of the weather data (i) *T*, (ii) *RH*, (iii) rainfall, (iv) wind speed, (v) wind direction, (vi) *Tdp*, (vii) *Twet*, (viii–xvi) *THI* calculated with the nine equations, and some data obtained from the robotic milking system, (xvii) programmed concentrate feed, (xviii) lactation days, (xix) lactation number, (xx) milking frequency, and (xxi) liveweight. The targets were also obtained from the robotic milking system. They consisted of (i) milk yield, (ii) milk protein, (iii) milk fat, and (iv) concentrate feed intake (i.e., cereal grain-based pellets fed to cows during milking, making up approximately 40% of cows diet). All data were normalized from −1 to 1. Model 1 was constructed using the data of cows with a similar heat tolerance (N = 36; heat tolerance range: 93–112) determined by estimation of Australian genomic breeding values for heat tolerance [18] following genotyping of each cow using hair follicle samples as per the commercial procedure (CLARIFIDE for dairy, Zoetis Australia Pty Ltd, Banyo, QLD, Australia). The genotyping experiment was approved by the University of Melbourne Faculty of Veterinary and Agricultural Science (FVAS) Animal Ethics Committee (AEC ID 1814645.1). In general, for heat stress, cows with Australian breeding values < 100 are less tolerant to hot, humid conditions than the average, while the cows with values > 100 are more tolerant than the average. Specifically, cows with breeding values of 93 will be 7% less heat tolerant than an average cow, and a cow with heat tolerance breeding values of 110 would be 10% more heat tolerant as compared to an average cow. In contrast, Model 2 was developed using data from all cows (N = 312) independent of their heat

tolerance to create a general model. Samples were divided randomly as 70% for training and 30% for testing using a default derivative function. Ten neurons were chosen as the best number giving the highest accuracy and best performance based on the means squared error (MSE).

**Figure 1.** Diagram of the two-layer feedforward regression models with a tan-sigmoid function in the hidden layer and linear transfer function in the output layer. Abbreviations: THI: Temperature-humidity index; W: Weights; b: Bias.

## **3. Results**

Figure 2 shows the mean values per season of each year for *THI*9 and the four parameters used as targets in the ML models to represent the effect of different weather patterns on those variables. As expected, the highest *THI* were obtained in the summer seasons of all years (77.5–79.7) and the lowest in winter of all years (47.6–49.1). The highest average milk yield per cow was observed in winter (33.4 kg day−1) and spring 2017 (33.5 kg day−1) with the lowest yield in summer 2018–2019 (23.4 kg day−1). The latter season also presented the lowest protein content in milk (3.1%) and concentrate feed intake (4.3 kg day−1).

**Figure 2.** Mean values per season of each year for temperature-humidity index (*THI*9) and the four parameters used as targets in the machine learning (ML) models to represent the effect of different weather patterns on potential heat stress, milk productivity, and quality.

Table 1 shows the minimum, maximum, and mean values per year of each parameter used as inputs to construct the ML models. The lowest mean temperature (19.3 ◦C) was observed during 2016, which, at the same time, presented the lowest mean *THI*1–*THI*9 (58.1–72.0), highest mean *RH* (95.6%), and daily rainfall (3.9 mm). On the contrary, 2019 had the highest maximum temperature (44.9 ◦C) and, until March, the lowest mean *RH* (69.2%), and daily rainfall (0.3 mm), as well as the highest mean *THI*1–*THI*9 (68.6–82.8). Data for lactation days = 0 are the day the calf was born, and milk production commenced. Due to the voluntary milking system on the farm, there are some days when cows are not milked (i.e., milking frequency = 0). Furthermore, there were cows on the farm with extended lactations (>600 days). These were 'carryover' cows that were in an extended lactation because they failed to get pregnant in a timely manner.

Table 2 shows the minimum, maximum, and mean values of the parameters used as targets for the ML models. It can be observed that 2017 presented the highest milk yield per cow on average (30.7 kg day−1), although 2016 had the highest maximum milk yield per cow (65.4 kg day−1). Likewise, for milk protein, 2017 had the highest maximum value (6.1%), while 2018 presented the highest mean value (3.4%). Regarding milk fat content, 2019 had the highest maximum and mean values (10.9% and 4.3%, respectively). In 2019, the lowest average concentrate feed intake (4.0 kg day−1) was observed, while 2017 presented the highest mean (7.4 kg day−1) and the highest maximum value (24.3 kg day−1).

**Table 2.** Minimum, maximum, and mean values of the parameters used as targets to develop the machine learning models.

\* Values from 2016 cover from June to December and 2019 cover from January to March. Abbreviations: Min: Minimum; Max: Maximum.

Table 3 shows the statistical results of both models to predict milk yield, milk fat, and protein content, and concentrate feed intake. It can be observed that both models presented similar results with high overall correlation coefficients (Model 1: R = 0.87; Model 2: 0.86; Figure 3). None of the models showed any signs of overfitting as the correlation coefficient of all stages was the same, and the performance of training (Model 1: MSE = 0.0186; Model 2: MSE = 0.0154) was lower than the testing stage (Model 1: MSE = 0.0189; Model 2: MSE = 0.0157). According to the 95% confidence bounds, Model 1 presented 3.88% outliers (4513 out of 116,456) and Model 2 presented 3.60% (23,998 out of 665,836).

**Table 3.** Statistical results of each stage of the machine learning models.

Abbreviations: R: Correlation coefficient; b: Slope; MSE: Means squared error.

**Figure 3.** Overall regression graphs of (**a**) Model 1: Using the 36 cows with similar heat tolerance (93–112), and (**b**) Model 2: Using data from 312 cows.

## **4. Discussion**

## *4.1. Seasonality and Milk Yield*

During the four years included in this study (2016–2019), there was a clear variation within seasons reflected by environmental parameters (*THI*) and milk productivity parameters (Figure 2). Higher heat stress risks for cows were observed in the summer of 2018–2019. Even though the *THI* parameter had a higher tendency, it was not significantly greater compared to the *THI* of summers belonging to 2017 and 2016 (*THI* = 79.7 compared to 78.7 and 77.5, respectively). However, milk yield and quality parameters were lower for 2018 compared with previous years. The high variability among all parameters shown through the years considered for this study can be considered as an advantage for ML modeling. These differences can be further supported by the data presented in Tables 1 and 2 with more specific data per year. Prolonged periods of high temperature and relative humidity have shown to be detrimental to dairy cows performance due to heat stress [19]. This makes more critical the development of cost-effective methodologies to measure and alleviate heat stress during these periods of high *THI* [20].

#### *4.2. Machine Learning Models*

By investigating thermotolerance in cows from a genetic point of view, it could help to decrease economic losses associated with lower milk productivity, quality, and animal welfare [21,22]. Other methods have been based on the physical modification of the environment, such as shade and shelters, and dietary interventions to reduce heat stress effects, such as grape residue [23], açai [24], betaine [14,25], slowly fermentable grains [26], and other types of feed [27,28].

The ML models developed in this research (Model 1 and Model 2) do not differ much when considering 36 genetically similar cows for heat tolerance compared to a total of 320 cows. There is a slight difference in the slope for the general model considering all cows (Model 2; slope = 0.74) compared to Model 1 (slope = 0.76). Considering highly heat stress-tolerant cows helps to decrease underestimations made by Model 1 compared to Model 2. However, it can be considered that these differences are minimal when considering the number of cows deemed for Model 1 (n = 36) compared to Model 2 (n = 312). Furthermore, Model 1 presented a slightly higher percentage of outliers, considering them as outside the 95% confidence bounds, with 3.88% compared to 3.60% for Model 2 (Figure 3), this difference is minimal and small for both models considering the number of observations in each model (Table 3).

## *4.3. Artificial Intelligence to Manage Heat Stress and Milk Productivity*

Physical modification of the environment to reduce ambient temperature or increase heat loss from the animal body, such as shading and fans, have been previously applied for lactating buffaloes with positive results [29], and in dairy cows using mixed-flow fans [30]. However, one of the most effective methods found is spraying water over animals using sprinkler systems [31–34]. This paper proposed the implementation of Model 2 with an automated system based on an individual cow assessment combined with environmental factors obtained from an automatic meteorological station (AME) (Figure 4). The AME can be easily connected to a processing unit (microprocessor or smartphone App) that can read the RFID from cows that are going to be milked to obtain cow information required by the model (Figure 1). The model outputs can be automatically set to specific thresholds for volume and milk quality that is desired by the dairy farm. The system can then automatically control gates to direct individual cows either to a cooling system with water sprinklers, the cows to reduce heat stress or to normal milking sections. The heat-stressed cows will be assessed the next day again, if they continue to be heat stressed, they will go to the sprinkler system and get milked to avoid mastitis.

**Figure 4.** Proposed artificial intelligence (AI) application based on the automated processing of meteorological station and radio frequency identification system (RFID) for specific cow data input and machine learning (ML) processing. This system activates the gate system to draft cows to a cooling system or normal milking.

The technical advantages of the proposed system (Figure 4) are: (i) ML modeling is based on readily available environmental data by most of the dairy farms and from government services with meteorological stations close to the farms; (ii) the environmental data can be automatically extracted from government services, such as the Bureau of Meteorology (BoM, Australia) [35] or by direct connectivity of a nearby automatic meteorological station to the RFID & ML Processing Unit (Figure 4); (iii) the digital database per cow can be implemented as part of the system to incorporate data such as programmed concentrate feed, lactation days and number, milking frequency, and liveweight. This information will need to be updated by the dairy farm personnel; (iv) cows can be identified by the system with normal RFID systems to extract cow data automatically from databases, and (v) the system requires an automated gate system to draft cows to the heat stress sprinkler system or the normal milking facilities.

The managerial advantages that could be obtained by implementing the system proposed are: (i) milk volume and quality information available in real-time, per cow, and according to daily environmental conditions; (ii) prediction of actual concentrate feed intake per cow for feed monitoring management compared to programmed concentrate feed; (iii) real-time information to manage heat stress in a per cow basis to increase efficiency and maintain milk volumes and quality set as objectives, and (iv) data recorded from specific dairy farms can be incorporated in the model to increase the accuracy of target predictions.

With these considerations, an AI system for dairy farms can be implemented with reasonable investment affordable to small and medium dairy farmers. An alternative or complementary approach to an engineering solution may be to introduce dietary interventions such as betaine or antioxidants to cows likely to experience heat stress [14,28]. However, the time lag before the tissue concentrations of these nutrients are optimized could reduce the immediacy of this approach.

It should be noted that individual pasture intake could not be included in the model as the cows grazed as a single herd, so it was not measured. While this could no doubt add precision to the model, individual pasture intake cannot be measured under commercial grazing systems, and inclusion in the model would reduce its commercial utility.

## **5. Conclusions**

The machine learning models developed in this research may be applied to assess automatically animal welfare, milk productivity, and quality. Based on the inputs of the models, this machine learning modeling technique can be applied to any dairy farm. Implementation of Artificial Intelligence in dairy farms and the ML models developed here will require minimal technological additions, automated gate, and cooling systems. This paper has shown a practical application of AI using detailed information from a robotic dairy farm for the benefit of small and medium dairy farms to increase competitiveness in an increasingly demanding international market.

**Author Contributions:** Conceptualization, S.F. and C.G.V.; data curation, B.C.; formal analysis, S.F. and C.G.V.; investigation, S.F., C.G.V., B.C., S.S.C., and F.R.D.; methodology, S.F. and C.G.V.; project administration, B.C. and F.R.D.; resources, B.C. and F.R.D.; software, S.F.; supervision, B.C. and F.R.D.; validation, S.F., C.G.V., B.C., S.S.C., and F.R.D.; visualization, S.F., C.G.V., and E.T.; writing—original draft, S.F. and C.G.V.; writing—review and editing, B.C., S.S.C., and F.R.D. All authors have read and agreed to the published version of the manuscript.

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

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

## **References**

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


*2.3. Results and Discussion*

#### 2.3.1. Validation of the Three Scales

The results of item analysis, CFA, and tests of reliability and convergent validity are shown in Table 2. Item-sum correlations were computed to examine item functioning. The PPCS and PPUS yielded higher coefficients, and both these scales also yielded good fit indices (i.e., CFA) and stronger reliability coefficients. PPCS, PPUS, and s-IAT-sex significantly positively related with SCS, PCQ, OSAs and usage time severally, and PPCS demonstrated stronger convergent validity.

**Table 2.** Reliability and validity of the three scales.

1 PPCS = Problematic Pornography Consumption Scale, 2 PPUS = Problematic Pornography Use Scale, 3 s-IAT-sex = Short Internet Addiction Test Adapted to Online Sexual Activities, 4 CFI = comparative fit index, 5 TLI = Tucker-Lewis index, 6 RMSEA = root mean square error of approximation, 7 CI = confidence interval, 8 SCS = Sexual Compulsivity Scale, 9 PCQ = Pornography Craving Questionnaire, 10 OSAs = online sexual activities, 11 UT = usage time. \*\*\* *p* < 0.001.

#### 2.3.2. LPA

The results of LPA are shown in Table 3. For PPCS, the Lo-Mendell-Rubin adjusted likelihood ratio test (LMRT) results were significant when the number of classes was 4, and the entropy value was lower. Thus, the classification accuracy was not as high as that of the three-class solution; accordingly, the three-class solution was selected. For PPUS, when the model consisted of three classes, the LMRT results were significant; furthermore, the entropy value was evidently higher than that of the four-class solution. With regard to the s-IAT-sex, the nonsignificant *p*-value that emerged for the LMRT results suggested that the three- and four-class solutions should be rejected in favor of the two-class solution.

**Table 3.** Fit indices for latent profile analysis of the three scales assessing problematic internet pornography use.

1 PPCS = Problematic Pornography Consumption Scale, 2 PPUS = Problematic Pornography Use Scale, 3 s-IAT-sex = Short Internet Addiction Test Adapted to Online Sexual Activities, 4 classes = number of latent classes, 5 AIC = Akaike information criterion, 6 BIC = Bayesian information criterion, 7 SSABIC = sample-size-adjusted Bayesian information criterion, 8 LMRT = Lo-Mendell-Rubin adjusted likelihood ratio test, *p* = *p*-value associated with the LMRT results. Bold text is the finally selected models. \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001.

With regard to the three groups that emerged for the PPCS and PPUS, the first class obtained the lowest averages across all the scale dimensions; thus, this group was referred to as nonproblematic consumption. The second class obtained moderate scores on all the scale dimensions; therefore, these group members were referred to as low-risk pornography users. The third class obtained the highest scores on all the scale dimensions; thus, this group was referred to as at-risk users. As shown in Table 4, with regard to the two classes that emerged for the s-IAT-sex, class 1 obtained lower scores than class 2 on both the scale dimensions; therefore, they were referred to as the nonproblematic and at-risk groups, respectively (group differences in scores on the specific dimensions are shown in Appendix A).

**Table 4.** Comparisons of the accuracy of the three scales.

1 PPCS = Problematic Pornography Consumption Scale, 2 PPUS = Problematic Pornography Use Scale, 3 s-IAT-sex = Short Internet Addiction Test Adapted to Online Sexual Activities, 4 Sensitivity = the proportion of persons with positive symptoms and members of the at-risk group that was identified through LPA, 5 Specificity = the proportion of persons with negative symptoms and the nonproblematic group.

#### 2.3.3. Sensitivity and Specificity Analysis

The results showed that the sensitivity of the PPCS was 89.66%, which is higher than the values that emerged for the PPUS (i.e., 81.25%) and the s-IAT-sex (i.e., 71.72%). There were differences in the specificity of the three scales, and the values ranged from 85.86% to 94.95%. The PPCS demonstrated greater sensitivity (89.66%), and its specificity was 85.86%. This indicates that approximately 10% of problematic users had been classified as nonproblematic users and that approximately 14% of nonproblematic users had not been identified. In general, the PPCS and PPUS performed better than the s-IAT-sex. Since this study aimed to identify the scale with greater sensitivity in detecting problematic IPU, the PPCS was investigated in greater detail.

### **3. The Qualitative Part: Identification of the Most Accurate Scale**

#### *3.1. Methods*

#### 3.1.1. Sample

We interviewed 22 (20 men; mean age = 27.2) problematic IPU service volunteers (who provide online services on the following website: http://www.ryeboy.org/; average service time = 3.3 years) and 11 therapists (who have worked with individuals with problematic IPU and had more than 3 years of clinical experience).

#### 3.1.2. The Interview Outline

Since the used scales were easy to administer and consisted of close-ended questions, interviews were conducted to examine participants' perspectives more deeply and comprehensively. The interview guide primarily sought to explore interviewees' understanding of problematic IPU/addiction and their evaluations of the dimensions of the selected scale. The interviewees were required to rate the importance of the dimensions on a scale that ranged from 1 (not at all important) to 7 (very important).

#### 3.1.3. Procedure

In this study, we primarily explored their understanding of the concept of problematic IPU and the dimensions of the recommended scale. Two psychology graduate students served as the interviewers. At the beginning of the interview, the interviewees were informed about the purpose and significance of the interview and assured of the anonymity and strict confidentiality of their interview data; the interviews were recorded with their permission.

#### *3.2. Analysis*

The interview recordings were transcribed into verbatim scripts, and participants' identifying information was concealed. Next, we undertook thematic analysis of the text; in other words, we collated different interviewees' responses to the same question to create new text. Tree Nodes were established based on the dimensions of the selected scale, and interviewees' original statements were identified and summarized as a named code. Through this process, NVivo automatically generated statistics for all the references of the texts.

#### *3.3. Results*

With regard to the characteristics of problematic IPU, we generated a total of 20 codes by analyzing the interview data. Among these features, preoccupation with IPU (22 mentions), IPU to escape or avoid a negative emotional state (21 mentions), interpersonal conflict (22 mentions), and physiological and psychological symptoms (45 mentions) were most commonly mentioned. Furthermore, the 20 codes were summarized into the six dimensions of the PPCS (see Figure 1).

**Figure 1.** Volunteers and therapists' frequency of mentioning the dimensions of the Problematic Pornography Consumption Scale, features, and importance ratings for the six dimensions (average scores across 33 interviewees). Note: the numbers in the color blocks represent the frequency of mentions, whereas the polyline represents importance ratings for the six dimensions (range = 1–7).

Instance of the interview:

Interviewer: According to your service experience, what do you think is problematic internet pornography use? In other words, what are the expressions/symptoms of problematic internet pornography use?

Interviewee (service volunteer): They (problematic users) show difficulty controlling the craving for internet pornography (code: pornography carving), they are unable to control their own behavior, for instance, browsing pornographic websites, masturbating while watching porn frequently (code: difficulties in control).  If they are not exposed to internet pornography, they will feel uncomfortable, or feel that their heart is empty (code: depression resulting from unsuccessful withdrawal).

After presenting interviewees with the definitions of the six components of problematic IPU and further clarifying their meaning using examples, we presented them with questions "Based on your service experience, do you endorse this structure? Which dimension or dimensions do you think are particularly central to IPU?" Most (>95%) participants endorsed the six dimensions. It also can be inferred from Figure 1 that both volunteers and therapists emphasized the centrality of conflict, relapse and withdrawal in IPU (basing the frequency of mentions); at the same time, they weighted the mood modification, relapse and withdrawal as more important features in the problematic use (basing the important rating).

#### **4. General Discussion**

Problematic IPU is still a controversial issue; notably, it appears that no real consensus exists regarding the conceptualization and screening tool of problematic IPU. Several scales are available; thus, the assessment of problematic IPU is inconsistent, indicating that findings in this area are not readily comparable. The present study aimed to selected a more sensitive scale to screen problematic IPU, because higher sensitivity implies lower rate of missed diagnosis (i.e., problematic users who have been incorrectly screened as nonproblematic users). Basing on a systematic literature review, three scales were retained. Considering that research with mixed methods combining quantitative and qualitative analyses can enrich and improve our understanding of complicated phenomena [38,39], a quantitative method was used to identify a "more accurate" analysis from the three retained scales. Results of CFA showed that all three scales have good applicability in the wide range of adult groups (age in this case ranged from 18 to 45 years) in three highly homogeneous samples; compared to the other two scales, the PPCS demonstrated greater sensitivity and comparative specificity among samples drawn from the general population (results of the QUAN). Considering that the expression of questionnaire survey is brief and closed, and that the interview can understand the participants' undefined views more deeply and comprehensively, subsequently, results of QUAL showed that symptoms of problematic IPU proposed by the servers (volunteers and therapists) can be grouped into the six dimensions of PPCS and most of the servers supported the six-factor structure of PPCS.

Among the three scales, the PPCS score was most robustly related to the duration of usage, frequency of engagement in OSAs, and pornography cravings. Problematic IPU can appear under the umbrella of hypersexuality similarly to frequent engaging in various forms of cybersex, intense craving for pornography, and compulsive sexual behaviors [40], insofar that the robust relationship not only demonstrated a higher criterion validity, but also implied that co-screening instruments (i.e., pornography craving, frequency and duration of use, compulsive use) are expected to work as auxiliary screening indicators. Recent studies have revealed that for some people, pornographic use gave rise to their feeling of discord and shame contributing to their conflict of actual sexual materials consumption and their belief; in turn, these feelings of distress and shame may drive a morbid self-perception that they are addicted, but this may not be a real behavioral disorder [41,42]. In order to avoid misjudgment due to the self-perceived problematic use, it is more advisable to combine other supporting scales, and the combination diagnosis indexes of the diversity were selected to screen the prevalence of problematic IPU. In this study, with the higher correlation of PPCS with frequency of OSAs, the PCQ showed that combined with other indicators, it can better screen out problematic use and is more likely to avoid the misjudgment caused by subjective self-perceived addiction.

The more robust psychometric properties and higher recognition accuracy of the PPCS may be attributable to the fact that it has been developed in accordance with Griffiths's six-component structural theory of addiction (i.e., in contrast to the PPUS and s-IAT-sex). The PPCS has a very strong theoretical framework, and it assesses more components of addiction [11]. In particular, tolerance and withdrawal are the important dimensions of problematic IPU that are not assessed by the PPUS and s-IAT-sex; PPCS is the only instrument that explicitly assesses the "tolerance" component [11,14]. According to the "two-phased" internet pornography addiction model, in which the first step is characterized by an excessive use to internet pornography, and the second functions as a marker by repeated failures to break free from excessive use, despite negative consequences [43]. Items related to information about salience, carving, and tolerance reflect the engagement in internet pornography, corresponding to the first step, whereas items related to withdrawal, relapse, and conflict measure addiction more, corresponding to the second step. Obviously, components of PPCS includes both engagement in pornography and addiction of IPU, which has an intact theoretical framework of addiction.

The PPCS appears to be a more valid instrument for assessing problematic pornography use, has potential application in detecting prevalence concerning problematic IPU or cybersex addiction, and may be useful in assessing treatment outcomes.  Thus, it appears important for clinicians to be aware of problematic pornography use and its related associations such as pornography craving, compulsive use. Moreover, it is important to note that the scale PPCS is recommended as a screening instruments to identify problematic users in the public and assess the prevalence rather than a diagnostic tool; future studies should further research its validity and cutoff in clinical sample; we also encourage individuals to visit a clinical therapist after being identified with problematic IPU by the use of PPCS.

This study has several limitations. First, data were collected using self-report measures; therefore, the reliability of the results depends on the respondents' honesty and accuracy of their comprehension of the scale items. Second, the study sample was recruited through an online survey company; therefore, the participants of this study may have been more educated and affluent than the average Chinese person. Furthermore, the study participants primarily lived in the capital/provincial capital, cities, and towns. Third, because the sample consisted of only a small number of non-heterosexual subjects, it was not possible to examine whether the factor structure and meaning of the contents of the PPCS differed across individuals with different sexual orientations.

#### **5. Conclusions**

The present study showed that the PPUS, PPCS, and s-IAT-sex are promising measures of problematic IPU. However, when sensitivity and specificity were simultaneously examined, the PPCS emerged as a more suitable measure of problematic IPU. The qualitative findings further confirmed that service providers endorsed the underlying structure of the PPCS.

**Author Contributions:** Conceptualization, L.C.; Data curation, L.C.; Formal analysis, X.J.; Funding acquisition, L.C.; Investigation, X.J.; Methodology, L.C.; Project administration, L.C.; Resources, L.C.; Supervision, L.C.; Visualization, X.J.; Writing—original draft, L.C.; Writing—review and editing, L.C. and X.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Social Science Foundation of China (Grant No. CEA150173 and 19BSH117) and the education reform project of Fujian province (FBJG20170038). The funding agencies did not have input into the content of the manuscript and the views described in the manuscript reflect those of the authors and not necessarily those of the funding agencies.

**Acknowledgments:** We would like to acknowledge Bin Wu and Yan Zhao (the founders of the "*Reyboys*", a non-governmental organization focusing on helping the problematic internet pornography users) for their help to recruit volunteers who serviced the addicts in the qualitative step, and pay tribute to them for their effort in helping problematic users.

**Conflicts of Interest:** The authors report no conflict of interest with respect to the content of this manuscript.

#### **Appendix A**

**Figure A1.** The average scores of the three latent classes based on the dimensions of PPCS. Note: PPCS = Problematic Pornography Consumption Scale, range = 1–7; \*\*\* *p* < 0.001 indicate that the score of at-risk group was significantly higher than that of low-risk group; *p* < 0.001 indicate that the score of low-risk group was significantly higher than that of non-problematic group; -- *p* < 0.001 indicate that the score of at-risk group was significantly higher than that of non-problematic group. The same below.

**Figure A2.** The average scores of the three latent classes based on the dimensions of PPUS. Note: PPUS = Problematic Pornography Use Scale, range = 0–5.

**Figure A3.** The average scores of the tow latent classes based on the dimensions of s-IAT-sex. Note: s-IAT-sex = short version of the Internet Addiction Test adapted to online sexual activities, range = 1–5.

#### **References**

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

International Journal of *Environmental Research and Public Health*


Use of Humic Substances from Vermicompost in Poultry

*Jesús A. Maguey-González, Sergio Gómez-Rosales, María de Lourdes Ángeles and Guillermo Téllez-Isaías*

## **Abstract**

In recent years, there has been a surge in antibiotic resistance in both humans and animals, as well as increased public concern over medication residues in animal products. As a result, the use of antibiotics as growth promoters in chicken has been banned in the European Union, and consumer pressure is likely to lead to their removal in other countries. More recently, the United States of America adopted the same restriction in 2017. Different alternatives to antibiotics have been proposed as a measure to eliminate pathogens or to improve growth and feed conversion in poultry, such as probiotics, enzymes, bacteriophages and antimicrobial peptides, herbal compounds and organic acids. These substances exert their effects on the gastrointestinal biota and digestion processes, directly or indirectly. Humic substances (HS) in animal applications have shown improved live-weight, growth rates and feed intakes by improving immune functions and gut health. In poultry nutrition as an alternative to growth-promoting has been proven with promising results on the growth and health of birds. Additional research suggests that HS can increase gut integrity and performance when combined with good nutrition, management, and biosecurity policies. Therefore, recent results of HS extracted from vermicompost in poultry will be described in this chapter.

**Keywords:** humic substances, vermicompost, gut health, poultry

## **1. Introduction**

During the previous decade, the poultry business has been the most active and fastest-growing sector in the worldwide market for the production of meat directed to human consumption. The world's population is forecasted to double by 2050, and agricultural production is expected to double as well [1]. The supply of protein of animal origin in countries like Mexico is insufficient to fulfill the population's demand; nevertheless, the OECD-FAO Agricultural Outlook 2016–2025 projects that chicken meat output will expand at a pace of 2.9 per cent per year on average between average 2016 and 2025. As a result, national output and consumption will continue to rise [2].

Increased livestock output must be done with the most efficient use of resources as possible, without compromising environmental integrity or animal or human health. For achieving this, one of the most significant obstacles is the restriction to

the use of growth-promoting antibiotics (GPA) as feed additives due to concerns about pathogenic bacteria developing antibiotic resistance, which represents a threat to human and animal health [3]. As a result, non-pharmacological growth promoters have been studied for decades in an attempt to replace or alternate the application of conventional promoters [4].

Humic substances (HS) are one of the alternative additives that has been studied for several years as a means to promote animal health. HS has been employed in agriculture for many years to enrich the soils, but because of their many qualities, they have been recently resurrected in environmental sciences and the human biomedical sector [5, 6]. In veterinary medicine, HS have been used as antidiarrheal, analgesic, immunostimulatory, and antibacterial agents [7]. When HS has been combined with the correct ration, husbandry, and biosecurity procedures, has been shown to enhance intestinal integrity and performance in chickens [8–11].

The majority of HS used in these studies were commercial products generated from mineral sources such as lignites and leonardite Vermicompost, from which compost and leachates are produced, is one of the first HS sources employed by Gomez and Angeles [11, 12], they have been reported to have promising advantages in poultry production as growth promoters.

## **2. Humic substances**

HS are organic macromolecules that play an important role in biochemistry; they are a fraction of soil organic matter and have the highest density in soil and composts. They are produced by the biodegradation of organic matter, which involves physical, chemical, and microbiological processes [5], in which eukaryotes (worms and fungi) and prokaryotes (aerobic bacteria) further decompose organic matter [11]. HS are a natural component of streams, lakes, and oceans, containing the majority of the nutrients in the soil, accounting for approximately 80% of the carbon in soils and 60% of the dissolved carbon in the aquatic environment [13].

Based on their solubility, HS are classified as humic acids (HA), fulvic acids (FA) and humin. It has been stated that the isolating and characterizing of the organic components of soils is challenging due to the breakdown products of organic matter associated with other minerals [5]. For the first time, Senn and Kingman [14] characterized the molecular structure of HS, determining that the oxidized sites provide the molecule with a negative charge, allowing it to attach to mineral ions.

HA molecules have a wide range of weight and size, ranging from hundreds to thousands of atomic mass units, and are constituted of aromatic units, units linked by oxygen and nitrogen, functional groups mostly linked to carboxylic acids, phenols, and hydroxyl alcohol, ketone and quinone groups [15]. These chemical properties provide HA with the capacity to serve as a surfactant, binding to a variety of substances and generating hydrophobic and hydrophilic chemical complexes [16]. HS are excellent at transporting and binding organic and inorganic agents in the environment because of this function, which is combined with their colloidal characteristics [17].

The great electron transport capability of HS in oxidation–reduction processes is their principal attraction [6, 18]. HS also can develop bindings with ions such as Mg2+, Ca2+, Fe2+, and Fe3+ due to the presence of carboxylic groups and phenolates. Creating chelate compounds with one or more of these ions and controlling metal ion bioavailability [19].

### **2.1 Use of humic substances in animals**

HS have been used as an antidiarrheal, analgesic, immunostimulatory and antimicrobial agent in veterinary practices in Europe [7]. Furthermore, they have been shown to have a strong affinity for binding to heavy metals, mutagens, minerals, bacteria, and aflatoxins [20]. HS can be used orally in horses, ruminants, pigs and poultry for the treatment of diarrhoeas, dyspepsia and acute intoxications following the recommendations of the Committee for Veterinary Medicinal Products of the European Agency for the Evaluation of Medicinal Products [7].

*In vitro* studies of the antioxidant properties of HS in doses of 0.1 per cent in rat liver, the organ with the highest metabolic function and responsible for the metabolism of pharmacological compounds, found that HS aids in the elimination of free radicals and superoxide radicals, as well as maintaining the balance in the oxidation– reduction reactions of the mitochondria [21]. In rats with 2/3 of their liver removed, the administration of HS resulted in liver regeneration [18].

When rats were administered HS in their drinking water, Yasar [22] discovered a considerable rise in weight, as well as an increase in epithelial surface, intestinal villi length, and crypt depth.

#### **2.2 Use of humic substances in poultry**

The effects of different concentrations of HS in the diet on live weight, feed consumption, carcass characteristics, and gastrointestinal characteristics in broilers have been extensively studied [8, 23–26]. The addition of HS in the drinking water or feed improves most of the productive parameters, such as daily weight gain, in addition to enhancing the carcass yield of broilers.

According to Jin [27], the feed conversion does not change at 21 days but improves at 42 days, implying that the increase in weight gain and feed conversion efficiency might be due to the stimulating impact of HS in the digestive system and the nutrient utilization in metabolic processes. Adding HS to laying and broiler chickens can boost profitability by improving the production performance, reducing mortality, lowering feed conversion, and increasing egg output [28].

HS have been used to reduce mycotoxicosis in chicken due to its adsorbent capability [29–31]. Several studies have found that HS reduces ammonia emissions to the environment [9, 32, 33]. Similarly, in farms with a high density of chicken population, HS have been found to have a considerable anti-stress impact, reducing the negative effects of chronic stress in laying hens in production [34].

The mechanism through which HS affects poultry performance remains unclear. According to Shermer [35], HS might affect poultry performance by modifying the microbiota in the gastrointestinal tract, particularly in *Escherichia coli* populations, by altering the pH and promoting a greater activity of intestinal enzymes and feed digestibility. Several trace elements in HS can act as co-factors, increasing the activity of numerous enzymes involved in digestion and metabolic processes [36].

## **3. Humic substances derived from vermicompost**

Vermicomposting is a bio-oxidative process that involves the breakdown of organic materials by litter-dwelling detritivorous earthworm species. Earthworms are important because they fragment organic materials and increase surface area, but microscopic species in the earthworm stomach and their castings are responsible for the actual decomposition [37, 38]. These gut and cast-related processes are considered to have a significant impact on the properties of vermicompost. *Eisenia fetida*, *Eisenia andrei*, and *Dendrobaena veneta* are the earthworm species used in vermicomposting. *E. fetida* is one of the most frequently utilized earthworm species due to its high rate of organic matter digestion, tolerance to environmental conditions, fast reproductive rate and short life cycle, and tolerance to handling [39].

Two phases can be distinguished in vermicomposting: 1) an active phase in which earthworms process the waste through physical comminution, ingestion, and microbial decomposition, and 2) a maturation-like phase in which earthworms move to fresher layers of undigested waste and microbes provide additional decomposition [40].

Bacteria and fungi are the two primary kinds of microorganisms involved in the composting process. Bacteria degrade sugars and other easily accessible organic materials faster than fungus. They are, therefore, crucial in the early phases of composting, when the feedstocks contain large quantities of carbohydrates. More complex compounds, such as hemicellulose, starch, and even lignin, can be degraded by actinomycetes. They are more common in the later stages of composting, after the majority of easily degradable substrates have been used [41].

The fresh organic matter (animal manure, food wastes, green wastes, agricultural leftovers, etc.) is transformed into more stable humus-like substances, nutrients are recycled, and energy is created throughout the composting process [42]. The process should be aerobic, with a portion of it taking place under thermophilic circumstances. It minimizes phytotoxicity, removes pathogens, and stabilizes the material in terms of nitrogen and oxygen demand, avoiding N immobilization by soil biota, which competes with the plant for limited nitrogen resources [43].

#### **3.1 Establishment of vermicompost**

The establishment of our vermicompost took place in the greenhouse area, which has concrete and stone walls and a white polyethylene roof (**Figure 1**). The temperature inside the greenhouse is maintained throughout the year, with maximum temperatures of 35°C and minimum temperatures of 15°C, as well as relative humidity of 70–80%. *Eisenia Foetida* is used in vermicompost; and it is composed of 50% pastures from the area/50% sheep manure; with an internal temperature of 15–25°C, a pH of 6.5 to 7.5 and relative humidity of 80–90%.

Vermicompost is made by enclosing a 2 × 5 m rectangular space with bricks and covering it with polypropylene (**Figure 2**). To reduce the bacterial content, the organic material and manure are first pre-composted for a month. The components are then combined and placed in the compost's lower side; worms are then sewn on top, and the compost is watered. Finally, a part of the organic material is coated over the compost to protect it from light rays, covered with black polyethylene (**Figure 3**).

The compost is moistened once a week, vegetable matter is added once a week, and all parameters are checked regularly for upkeep. To collect the compost, the vermicompost must grow for four months. The worm harvest is conducted first, and then the compost is sifted to eliminate any unprocessed organic and inorganic elements. The compost was preserved after being oven-dried for 24 hours at 60°C.

**Figure 1.** *Greenhouse area.*

**Figure 2.** *Delimitation of the area with bricks and covered with polypropylene.*

## **3.2 Humic substances extraction/isolation**

HA, FA, and humin are the three main types of humic compounds found in soils and sediments [5, 13]. A strongly basic aqueous solution of sodium hydroxide or potassium hydroxide is used to extract HS from humus and other solid phases. The HS precipitate in this solution when the pH is adjusted to 1 with hydrochloric or sulfuric acid, leaving the FA in the solution (upper section) [19, 44]. This is the most important distinction: FA are insoluble in alkaline media, while HA are insoluble in acid media, and humin are insoluble at any pH (**Figure 4**).

#### **Figure 3.**

*Vermicompost is covered with organic matter and polyethylene.*

**Figure 4.** *Fractions of humic substances.*

In addition to alkaline solvents, chelating agents, organic solvents, and aqueous saline solutions have been proposed for the extraction of HS. Alkaline solvents are the most efficient and commonly utilized of them [44]. The extraction of HA with NaOH is a standard method for isolating HA, with an extraction efficiency of more than 80% of samples from soils (**Table 1**).

The isolation and extraction of HS from the earthworm compost were carried out using an alkaline extraction process. Sodium hydroxide (0.1 M NaOH) was used in a ratio of 5:1 parts of compost (mL/g); it was left to rest for 24 h at room temperature, and then filtered through a 125 μm mesh; sulfuric acid was added (H2SO4, 10%),

#### **Table 1.**

*Agents used for the extraction of humic substances.*

#### **Table 2.**

*Estimated chemical properties of the humic and fulvic acid molecules, extracted from vermicompost.*

#### **Figure 5.**

*The flat structure of a humic acid molecule with aromaticity (a) and flat structure of a fulvic acid molecule with aromaticity (b).*

rectifying a pH of 2. The solids and liquids were separated by decantation. The precipitate (HA) was washed two times with distilled water to remove sulfuric acid residues and between each washing it was centrifuged for 20 min at 5000 rpm. Then, to remove the remnants of sulfuric acid, in a rotary evaporator, the sample was dried at 60°C until it had a gel consistency. Finally, it was dried in an oven at 60°C. The result was a yellowish-brown powder with a pH of 10.

For the identification of functional groups, the extracted HS were analyzed using an infrared spectrophotometer with Fourier transformation with attenuated total reflectance (FTIR-ATR); the elemental analysis was carried out using energy dispersive X-Ray spectroscopy (EDS); and the crystal types were detected with X-ray diffraction (XRD). These results were used for the calculation of aromaticity and were published in a previous paper [45]. Additionally, the chemical properties (**Table 2**) and the flat structures of the HS molecules with aromaticity (**Figure 5**) using the chemistry software ACD Lab v.12 (Advanced Chemistry Development, Toronto, Canada) were estimated [46].

## **4. Use of humic substances from vermicompost in poultry**

Various *in vitro* and *in vivo* models for evaluating the behavior of different chemicals in chicken supplements have recently been established in our lab. The *in vitro* digestion model replicates broiler body temperature, peristaltic motions, enzymatic and pH conditions in each simulated compartment (crop, proventriculus, and small intestine) [47, 48]. In addition, *in vivo* models of intestinal inflammation in birds have been employed. Non-starch polysaccharide-rich diets [49, 50]; dexamethasone [51]; dextran sodium sulfate (DSS) [52, 53]; and 24 hours of feed restriction [54, 55]. With the help of these research models, we have been able to elucidate some effects of HA.

Firstly, two experiments were conducted to evaluate the effects of HA on recovery of *Salmonella* Enteritidis (SE) [56]; there were no effects of HA on SE recovery in an *in vitro* digestive system, or in Salmonella intestinal colonization, bacterial numbers in ceca, intestinal IgA, or serum FITC-d in neonatal broiler chicks.

*a Data expressed as Mean ± SE.*

*b Intestinal viscosities evaluated in Log10 (in centipoise, cP = 1/100 dyne sec/cm2 ), n = 5 chickens/group.*

*c Serum (FITC-d) was evaluated in 20 chickens/group.*

*d Liver bacterial translocation was evaluated in 12 chickens/group.*

*e Data expressed as positive/total chickens (%).*

*f,gSuperscripts within columns indicate significant difference at P < 0.05.\* P < 0.001.*

#### **Table 3.**

*Evaluation of intestinal viscosity, serum FITC-d, and bacterial liver translocation in chickens consuming a corn-based diet with or without the inclusion of 0.2% of humic acids following 24 hours of feed restriction (FR) in broiler chickensa .*

#### *Use of Humic Substances from Vermicompost in Poultry DOI: http://dx.doi.org/10.5772/intechopen.102939*

Additionally, a second study was carried out [57], to evaluate direct or indirect impacts of HA on intestinal integrity because of their physical and chemical features. Using a 24-hour feed restriction model, the aim was to investigate how HA affected intestinal viscosity, leaky gut, and ammonia excretion in broiler chicks. The experimental group was given 0.2% HA had increased intestinal viscosity and showed lower levels of FITC-d, bacterial liver translocation, and ammonia in the excreta. It was confirming its advantages, enhancing the viscosity and the integrity of the intestine (**Table 3**).

The previous researches were the first to address the effects of HA from vermicompost on bacterial challenges with SE both *in vitro* and *in vivo* in chicks; furthermore, the mechanism of action of HA on the maintenance of intestinal integrity in poultry was demonstrated for the first time. In line with this finding, Mudronová [58] reported that HS positively regulates MUC-2 gene expression.

In further research in broilers fed an extract of HS from vermicompost, increased carcass yield and lactic acid bacteria (LAB) and reduced coccidian oocysts excretion were observed; but increased *Clostridium perfringens* (CP) counts were also seen compared to broilers fed diets supplemented with GPA (**Figure 6**) [45].

In a recent report, broilers kept in floor pens from 1 to 42 days of age and fed increasing levels of HS from vermicompost, showed lower feed intake (FI) and overall mortality, besides, a better feed conversion rate (FCR) compared to negative control birds not supplemented with HS and positive control birds added with antibiotics (**Table 4**) [59]. The greatest benefits of adding HS were observed in the last period of the trial, from 29 to 42; these findings closely resemble the observations in previous research. For the authors, it is unknown whether improved FCR from 29 to 42 days was dependent on, or independent of, the addition of HS from 1 to 14 and 15–28 days. This topic deserves further clarification in future research.

Although, HS have been proven to have prospective benefits in chicken production as growth promoters, data on their antimicrobial properties are inconsistent. Using an *in vitro* chicken digestive system, it was essential to examine the effect of HA isolated from vermicompost on the recovery of *Salmonella Enteritidis* (SE), *E. coli* (EC), *C. perfringens* (CP), *Bacillus subtilis* (BS), and *Lactobacillus salivarius* (LS). In general,

#### **Figure 6.**

*Counts of lactic acid bacteria (LAB) and Clostridium perfringens (CP), and the number of coccidian oocysts excreted in broilers fed with humic substances.*

*a SEM = estandard error of the mean.*

*b–dMeans with a different superscript in the same row differ significantly (P < 0.05).*

#### **Table 4.**

*Growth performance of broilers from 1 to 42 days of age fed increasing levels of humic substances extracted from vermicompost.*

*\* The initial inoculum of SE, EC and CP in the feed was 108 CFU/g.*

*\*\*Data are expressed as log10 CFU. \*\*\*Standard error of the mean.*

*a–fValues in columns with different letters differ significantly (P ≤ 0.0001).*

#### **Table 5.**

*Effect of humic acids on the counts of Salmonella Enteritidis, Escherichia coli and Clostridium perfringens\* under an in vitro poultry digestive model\*\*.*

the number of microorganisms counted increased in the final simulation, remarkably when the HA inclusion concentration increased (**Tables 5** and **6**).

HS may be utilized as substrates by bacteria since they are organic sources of carbon, nitrogen, phosphorus, and other nutrients. In this way, they can be used as prebiotics to enhance nutrient digestion.

As previously stated, HS have the potential to chelate minerals; when added to drinking water at concentrations of 345.0, 322.5 and 347.8 g/L, improvements in bone mineralization in broilers at 21 (**Table 7**) and 42d (**Table 8**) of age have been observed [46].

Subsequently, 28-year-old broilers were challenged to a radical change in diet, in addition to inducing immune stress when vaccinated against Newcastle, Infectious bronchitis and Salmonella, with the intention of causing damage to the intestinal mucosa. The results of this research [60] show that HS does not prevent intestinal mucosal atrophy but increase the number of goblet cells, and probably the mucus layer, compared to chickens that received the treatment without GPA.

On the other hand, when compared to a commercial zeolite, the adsorption capacity of HA against AFB1 when added to the feed in a 1% concentration exhibited the maximum effectivity of capture (**Figure 7**).

*Use of Humic Substances from Vermicompost in Poultry DOI: http://dx.doi.org/10.5772/intechopen.102939*

*\* The initial inoculum of BS and LS in the feed was 108 CFU/g.*

*\*\*Data are expressed as log10 CFU. \*\*\*Standard error of the mean.*

*a−e Values in columns with different letters differ significantly (P ≤ 0.0001).*

#### **Table 6.**

*Effect of humic acids on the counts of Bacillus subtilis and Lactobacillus salivarius\* under an in vitro poultry digestive model\*\*.*

*a n = 12, using six tibias per replicate..*

*b SEM = standard error of the mean.*

*d,eMeans with a different superscript in the same row differ significantly (P < 0.05).*

*f,gMeans with a different superscript in the same row differ significantly (P < 0.01).*

#### **Table 7.**

*Dry matter, ashes, calcium and phosphorus content of the tibia of 21 days old broiler chickens added with increasing levels of humic substances in the drinking watera .*

*a n = 12, using six tibias per replicate.*

*b SEM = estandard error of the mean.*

*d,e Means with different superscript in the same row differ significantly (P < 0.05).*

*f,g Means with different superscript in the same row differ significantly (P < 0.01).*

#### **Table 8.**

*Dry matter, ashes, calcium and phosphorus content of the tibia of 42 days old broiler chickens added with increasing levels of humic substances in the drinking watera .*

#### **Figure 7.**

*Adsorption capability of humic acids (HA), humic acids purified (HAP) and zeolite (ZEO) against AFB1 using an in vitro poultry digestive model.*

Finally, the addition of HS extracted from vermicompost in piglet feed at weaning [61] improved the weight gain and feed efficiency during 1–42 days post-weaning. The final body weight at 42 days, weight gain and feed efficiency were higher with the inclusion level of 0.50% HS. Most of the productive responses showed an increasing linear pattern as the addition of HS increased.

## **5. Conclusions**

Although, the antibacterial activity of HS/HA is unclear, they might be regarded as viable alternatives to replace or alternate the use of antibiotics in poultry. As was documented, HS extracted from vermicompost improve performance, increase intestinal viscosity and intestinal integrity in poultry. These benefits have been attributed to the macro colloidal structure of HS, which ensures effective protection on gastric and intestinal mucous membranes, as well as the promotion of mucin synthesis in the gut. However, their applications depend on the source and the way they are extracted. HS stimulates the growth of microorganisms, suggesting that they can be used as prebiotics.

*Use of Humic Substances from Vermicompost in Poultry DOI: http://dx.doi.org/10.5772/intechopen.102939*

In addition, multiple doses were investigated, which might have contributed to the differences in response variables among our investigations. It can also be argued that whether in a more challenging environment, the effects of HS may be better. Finally, our working team is focusing on elucidating the mechanism by which HS exert their effects on the intestinal mucosa and the intestinal microbiota.

## **Acknowledgements**

This research was supported by the Arkansas Biosciences Institute under the project: Development of an avian model for evaluation early enteric microbial colonization on the gastrointestinal tract and immune function.

CONACYT provided funding to carry out several experiments described in this chapter through the project "Efficacy of an extract of humic substances as a growth promoter, reducing infectious diseases and increasing the profitable production of meat of birds and pork" (PDCPN 2017/4777).

## **Conflict of interest**

The authors declare no conflict of interest.

## **Author details**

Jesús A. Maguey-González1 \*, Sergio Gómez-Rosales2 , María de Lourdes Ángeles2 and Guillermo Téllez-Isaías3

1 National Autonomous University of Mexico (UNAM), Postgraduate Unit, University City, Coyoacán, Mexico City, Mexico

2 National Center of Disciplinary Research in Animal Physiology and Genetics, National Institute of Research in Forestry, Agriculture and Livestock, Ajuchitlan, Queretaro, Mexico

3 Department of Poultry Science, University of Arkansas, Fayetteville, AR, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## **Chapter 8**


**5. Limitations**

This study presented several limitations. Firstly, ultrasound M-mode was not used, which could have been convenient for the evaluation of muscle and tissue features. Secondly, a pressure platform was not employed which may have been useful to contemplate load variables, such as plantar pressures.


**5. Conclusions**

In this review of 3D printed acetabular implants, we compared and contrasted 3D printing with conventional manufacturing. We defined the clinical and engineering rationale of 3D printed acetabular cups, summarized the key variables involved in the manufacturing process, and created a classification of the applications. Whilst early clinical outcomes related to 3D printed cups have been promising, in-depth robust investigations are needed.

3D printing has already revolutionized the way customized orthopaedic implants are produced and the same may happen for o ff-the-shelf implants. Complex porous structures to achieve enhanced fixation with bone and the printing of personalized shapes are the two main advantages of 3D printing over conventional manufacture for customized implants. Enhanced porous structures and design flexibility is the main rationale for the use of o ff-the-shelf implants.

However, the importance of standardization of all the steps involved in the production process before implantation in the human body is clear. Both the International Organization for Standardization (ISO) and the American Society for Testing and Material (ASTM) have released guidelines related to terminology, design, process, materials and test methods [25,31,110], but significant gaps remain with respect to the interplay of various parameters in the properties of the final part and how to investigate the potential presence of defects.

In-depth analysis of the properties of these implants is highly suggested in order to avoid unexpected wide-scale failures as occurred with metal-on-metal implants [10,11]. A better understanding of the link between the microstructure, processing and properties of 3D printed components is necessary, considering the complex thermal cycles and the distinctive layer-over-layer building experienced with this technology, which are issues uniquely associated with the use of 3D printer technology [6].

Although it is still unclear if this new manufacturing technology is completely suitable for orthopaedic implants, further analysis of both pristine and retrieved components, together with long-term clinical outcomes and surveillance of new implants placed on the market, will help the transition to 3D printing to be managed safely.

**Author Contributions:** Conceptualization, L.D., H.H., A.D.L., J.H., A.H.; writing—original draft preparation, L.D.; writing—review and editing, H.H., A.D.L., J.H., A.H.

**Acknowledgments:** This review was supported by The Maurice Hatter Foundation, the RNOH Charity, the Rosetrees Trust and the Stoneygate Trust and the National Institute for Health Research University College London Hospitals Biomedical Research Centre.

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


**5. Conclusions**

This paper investigated an AOTF-based 10 nm spectral resolution HSL, and for which the consecutive spectral bands were tunable covering 500–1000 nm. An AOTF was installed after the super-continuum laser source and consecutively selecting the wavelength of the passing laser beam. 51-channel spectral information was acquired. Then, four different plants with four green leaves and two yellow leaves were measured by the AOTF-HSL and with corresponding referenced spectral profiles collected by an SVC spectrometer. For green leaves, the "Red Edge"-related parameters extracted using the first-order reflectance slope (FRS) from the AOTF-HSL and SVC spectrometer were trivial, and this demonstrated the AOTF-HSL was capable of measuring vegetation "Red Edge"-related parameters. Moreover, the observed shift behaviours were consistent with previous research results, which also supported the fact that measurement results from the AOTF-HSL are reliable. In addition, another two methods, LFPIT and LET, were employed for calculating the REP for further comparing the results between HSL and SVC. The results were similar to those from the FRS method, which further demonstrated the effectiveness of the HSL in this vegetation index detection and measurement.

**Author Contributions:** C.J. wrote this paper and processed the data; Y.C. proposed the idea, revised the paper designed this HSL and offered the funding; W.L. set up the HSL, the idea, collected the data, revised the paper and offered the funding; H.Z. discussed the idea and revised the paper; H.J. offer the funding, revised the paper and the guided the HSL design. Y.B., H.S., S.S. and E.P. reviewed the paper and provided valuable advice for paper writing and revision.

**Funding:** This research was financially supported by the Academy of Finland projects "Centre of Excellence in Laser Scanning Research (CoE-LaSR) (307362)" and "New laser and spectral field methods for in situ mining and raw material investigations (project 292648). Additionally, the Chinese Academy of Science (181811KYSB20160113), by the Chinese Ministry of Science and Technology (2015DFA70930) and Shanghai Science and Technology Foundations<PHONE_NUMBER>0) are acknowledged.

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


**Author details**

Ladislav Župčán1 \* and Martina Župčánová2

1 Faculty of Arts, Department of Historical Sciences and Central European Studies, University of St. Cyril and Methodius in Trnava, Trnava, Slovakia

2 ZSS Viničky, Nitra, Slovakia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


3.1.17. Other Service Activities

Only three studies are presented in this category as well (Table 19).

Repair of Computers, and Personal and Household Goods

An ergonomic assessment was carried out on bicycle repair workers. The RULA, REBA and OCRA methods were used, along with other parameter measurement. The tasks assessed were shown to be at a high-risk level, and that immediate improvement actions were required [231].

Other Personal Service Activities

Some authors focused on spa workers, with the aim of preventing them from developing MSD during three of their tasks. An assessment was carried out with RULA and QEC. The results showed that ergonomic information and training would improve the postures adopted [232]. A similar study, using only RULA and a survey, was conducted on tattooists. It was concluded that 71% of the postures adopted had a high level of risk and therefore urgent corrective measures were needed [230].

3.1.18. Activities of Households as Employers; Undifferentiated Goods- and Services-Producing Activities of Households for Own Use

Table 20 summarises the publications in this category.

**Table 20.** Activities of households as employers; undifferentiated goods-and services-producing activities of households for own use.

Domestic workers are also exposed to the onset of musculoskeletal disorders, although, according to Apostoli et al. [234], at a medium or low level. This was deduced by research in which RULA and other assessment methods or tools were applied. Another similar study was carried out using the RULA and MAPFRE methods, but in this case the workers were in wheelchairs. It was inferred that there were risks caused by the postures that had to be adopted in the chair in order to carry out the tasks [233].