**6. Conclusion**

Given that integration of water allocation and land reform is very important but at the same time also a problem in South Africa, not only in terms of policy development but also in terms of implementation, what recommendations can be given for more effective integration of these two processes in future? In addition, what lessons does this chapter provide to other countries in terms of the impacts of non-integration and the challenges to successfully implementing integrated reform programmes?

With the Departments for Water Affairs and Rural Development and Land Reform (DWA and DRDLR) jointly acknowledging the importance of joint water allocation and land reform (Kleinbooi, 2009), there is a renewed onus on the South African government to achieve higher levels of integration between these two processes. The question now remains how this can best be achieved.

Integrated water allocation and land reform needs to go beyond quick-fix attempts to try and merge different existing programmes, and instead has to focus on identifying the root causes of why existing programmes are not working and how these causes can best be addressed. It is of course also important to ground any water and land reform integration programmes in the context of the South African legislative framework to ensure that the ethos of the country's progressive legislation is adhered to. Noticeably, existing transsectoral coordination efforts seem to have focused mostly on collaboration between the departments (DAFF, DRDLR and DWA). Other government departments that might also have an important role to play, such as the Department of Environmental Affairs (DEA), in terms of the environmental sustainability of agricultural practices, and the Department of Cooperative Governance and Traditional Affairs (DCGTA), perhaps need to be more involved.

In addition, three important premises can be identified to achieve more effective integration between the water allocation and land reform processes in South Africa? Firstly, it is important to acknowledge the multiplicity of the actor landscape and the presence of different stakeholder perspectives, linkages and interdependencies with other resources and sectors as a starting point. This may involve bringing on board other key stakeholders such as macro- and micro- lending institutions (for example, the Land Bank), commercial farmers who function as "strategic partners", irrigation boards, water user associations, land reform beneficiaries, and members of civil society, to try and find more innovative and inclusive solutions to address the need for integration. By determining the needs of stakeholders on the ground, it may be easier to establish how coordination between different parties may function more effectively. What should be key for government departments when involving a range of stakeholders is knowing when to solicit whose inputs and doing so strategically to prevent themselves from being overwhelmed by too many inputs all at once as this could be counter-productive.

Secondly, different government structures need to stop working in silos and need to start cooperating in terms of budget allocation and promoting integration.

Thirdly, there needs to be an acknowledgement that increased levels of integration and communication can take a long time, which necessitates patience, endurance and a long term vision on behalf of those who are seeking to improve integration.

Other countries can also benefit from this analysis by taking note of some of the impacts of non-integration of water and land management related programmes and the challenges to successfully implementing integrated reform programmes. The impacts of non-integration include governments having to deal with the effects of failed programmes and stakeholder collaboration, and the simultaneous manifestation of a disjuncture between policy and practice. Another impact is that failed integration efforts cause promising paradigms such as IWRM to lose credibility, both at the national and international level. It seems so difficult to implement integration focused water and land management programmes because of the bureaucratic culture of managing projects and programmes in silos. Implementation is furthermore impeded by government structures traditionally being hierarchical and compartmentalised and making it difficult for information to flow freely and easily between different units within government. Therefore there is a need in South Africa and elsewhere, when promoting greater levels of integration between water and land management related programmes, to try to ensure that different government structures work together both horizontally across sectors, and hierarchically from the national to the local level. In addition, a multi-stakeholder and multi-sectoral effort is required at all levels, from the local to national if integration is to be operational and implementable.


**3. System description**

### **3.1 The model for machine tool**

Manufacturing systems organize machine tools, material handling equipment, inspection equipment, and other manufacturing assets in a variety of layouts. With the advent of Industry 4.0 technologies, these manufacturing resources can be networked, and cyber-physical manufacturing systems can be implemented that integrate hardware and software with mechanical and electrical systems designed for a specific purpose. The model of **Figure 2** shows a representation of the main components of a generic industrial machine from the point of view of data collection and is sufficiently general to be applied to many industrial machines that can be part of process, cellular and line layouts.

The generic machine Mi receives from the network a command message known as part program. It is a set of detailed step-by-step instructions executed by the CONTROL system (CNC, PLC) that direct the actions implemented by the actuators. The actuators act as transducers changing a physical quantity, typically electric current, into another type of physical quantity such as rotational speed of an electric motor. During the execution of a part program, the machine tool operates and the CONTROL system produces a log containing data about the executed instructions as well as control messages that indicate some particular machine state. In the meantime, execution data captured by the sensors are sent via the communication

must be interpreted, choosing only the pertinent ones; finally, the data must be

*Text Mining for Industrial Machine Predictive Maintenance with Multiple Data Sources*

In the next step of the pre-processing phase, the system associates an alert level, based on a chromatic scale, to each of the messages coming from the previous step.

**Yellow** Level (warning): There have been sporadic, but not serious, anomalies

**Orange** Level (serious): some serious anomalies, or some cluster of anomalies,

**Black** Level (immediate stop): the system runs the immediate risk of irreparable

In the third step of the pre-processing phase, to be carried out only if there are several data sources associated with the machine tool, composite messages are created, obtained by composing the messages with at least an yellow alert level, a composite message. The order in which messages are juxtaposed is predefined and must be the same as it will appear at runtime. At this point, a general alert level is

Finally, a digital data structure is built containing all composite messages: a text dictionary, in which each line contains a composite message and the relative general

Steps 1 and 2 must be performed for each of the data source entities associated

In the runtime phase, the real data coming from a machine tool are collected and normalized according to the specifications identified in the pre-processing phase. Then, all messages with a compatible timestamp are aggregated into a line of text to

with a specific machine tool. Identifying alert levels requires the help of the machine tool expert, thus steps 2 and 3 need the help of a machine tool expert.

The whole data design process is reported in **Figure 3**.

**Red** Level (very serious): Very serious anomalies were found in the system,

semantically normalized, to adapt it to a common data semantics.

have been found in the system, which could affect the future of work.

capable of affecting, in the very near future, the production activity.

and the system is able to continue without problems.

The levels are as follows:

**White** Level (no problem).

*DOI: http://dx.doi.org/10.5772/intechopen.96575*

equipment or product failures.

alert level.

**Figure 3.** *Data design process.*

**5**

**3.4 Runtime phase**

associated with each composite message.

#### **Figure 2.**

*A simple model for machine tool data collection.*

network to the factory control system. Here, the feedback loop is closed, and feedback actions can eventually be taken.

The generic sensor is represented by its two constituent parts: the transducer (Ti) and the control electronics (CEi); this distinction is useful because even if the sensor is a unified whole, the transducer and the control electronics can be placed into different area of the machine tool. For example, the accelerometer transducer can be placed on the spindle while its control electronics is usually placed within the cabinet together with other control subsystems.

### **3.2 System functionality**

The event-based dynamic cyber-physical system proposed here integrates physical devices with advanced Text Mining analytical technologies and is very general, therefore adaptable to any production domain. It needs the ontology of the messages emitted by the data sources of a machine tool during its normal operation and is able to intercept its (even slightly) anomalous behaviors, which allow to evaluate whether it is moving towards a failure state such as to require the activation of safeguard procedures.

The system consists of a design pre-processing phase to create the main message ontology and which is performed only once for each data source (for example a sensor), and an algorithmic runtime phase. Each message correspond to an event of the data source, has the form of a text messages and has associated an adequate alert level.

#### **3.3 Pre-processing design phase**

The pre-processing phase is performed only once for each data source and allows you to create the initial ontology of the messages. It consists of four main steps.

In the first step of the pre-processing phase, for each data source the set of all messages that it can emits is identified and normalized. In particular, for each industrial machine and for each data source associated with that machine, the possible types of messages that can be issued must be identified; then, these data


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**RECENT ADVANCES IN**

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Edited by **Seloame Tatu Nyaku**

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**3. Discussion**

Shiga toxin (Stx) is central to the development of hemolytic uremic syndrome (HUS). The supportive treatment is the current default procedure for STEC-infected patients, also, the administration of some antibiotic classes could increase the Stx production or release, which could lead to a risk of catastrophic consequences with HUS development, making this treatment option highly controversial [6,10]. Therefore, it is mandatory to develop either an effective treatment or a prevention method for the deleterious effects of Stx intoxication [19]. The STEC prevention is focused on individual and industry levels, such as hygiene procedures, meat processing protocols, and slaughterhouse maintenance, for example. Regarding therapy, it is focused mainly on inpatient supportive care, even though some strategies are in development aiming at different stages of infection, such as bacterial growth control without increasing Stx secretion, toxin trafficking interference and cellular response to the toxin. Moreover, it is worth mentioning the challenges regarding a therapeutic approach against Stx-induced symptoms, especially for clinical trials, such as the low incidence of STEC infections and HUS, the lack of highly specific diagnostic screening, and the narrow therapeutic window (onset of disease 3 days after infection, HUS development one week after the first symptom), which is also hypothetical [29]. Thus, despite great achievements towards a therapeutic tool against Stx, a specific treatment remains elusive.

Specific antibodies against Stx as a tool either to prevent or treat the HUS disease process is a promising approach [20]. Indeed, some other recombinant antibodies have also shown neutralizing ability against Stx2 in vitro or in vivo. Such as, the family Stx2Bbinding VHHs which were constructed with one anti-Stx2B VHH, and two copies were fused to one anti-human albumin VHH, neutralizing Stx2 in vitro [24]. Another VHH also protected mice against Stx2 intoxication, but it was not humanized [30]. Concerning scFv, the one described by Maa et al. [31] and Luz et al. [25], neutralizes the cytotoxic ability of Stx2 in vivo and in vitro, respectively, however, none of them is a human antibody, produced in a bacterial system, which impairs their use as therapeutic agents and costs of production.

The recombinant human Fab and F(ab')2 fragments characterized by Akiyoshi et al. [32] showed neutralizing capacity, however, the production was dependable on mammalian cells (CHO), having a high cost for obtaining as with hybridoma technology. Therefore, the library F [27] was employed to select specific Fab high binders against Stx toxins. Two phage clones showed high affinity and binding ability against Stx2. The FabC11:Stx2 was the first to be characterized and showed cross-reactivity with Stx1 besides being able to prevent Stx2 toxicity to human kidney cells and in mice [21,22].

Herein, the other Fab selected against Stx2 (FabF8:Stx2) was characterized and employed in different cell assays. The variety of toxin subtypes that could be expressed by a diverse set of STEC serotypes able to express one or more toxin types at the same time is a major challenge for antibody-based Shiga toxin neutralizers, once to be universally effective, should neutralize multiple Stx1 and Stx2 subtypes [14]. In the present study, using the gold standard Vero cell neutralization assay, we observed that FabF8:Stx2 neutralized the cytotoxicity of 23 of 27 supernatants from Stx2 or Stx1/Stx2-producing STEC strains. These strains belong to different serotypes and present diverse *stx* subtypes, it is worth mentioning that no differences were found with neutralization percentage and its *stx* subtype, even though the FabF8:Stx2 was generated against a Stx2a toxin, some strains harboring *stx2a* gene were not neutralized whereas two non-Stx2a producing were neutralized. This kind of investigation is not commonly employed, usually, most neutralization assays are tested against one type and/or one subtype of the toxin, therefore in this work, for the first time, we showed how one recombinant monoclonal antibody neutralizes different Stx combinations obtained from the STEC isolates culture.

The human microvascular endothelial cells are an excellent cell model for in vitro therapeutic studies once it can express 50-fold higher Gb3 levels compared to the endothelial cells of large vessels [33]. In this sense, previously, we developed human glomerular endothelial cells (HGEC) primary cultures and demonstrated the decrease of cell viability by apoptosis and endothelial injury like that documented in kidney biopsies of HUS patients after incubation with Stx2 [28]. In this work, additionally, we assayed FabF8:Stx2 antibodies on HGEC exposed to Stx2 and we were able to corroborate their great effectiveness on the protection of Stx2 cytotoxicity on HGEC, in about 80–90% at the pre-incubation condition. These results were coincident with the high capacity of these antibodies to prevent HGEC apoptosis in about 75–90% under pre-incubation and co-incubation conditions. Furthermore, at pre-incubation, cell detachment was avoided in approximately 60–65% and swelling, in about 90–95%.

Previously, we demonstrated that by using 10 μg/mL FabC11:Stx1/Stx2 we observed lower protection of the HGEC viability (54.0% and 52.0%, for pre-incubation and coincubation, respectively) compared to the same concentration of FabF8:Stx2 when cells were exposed to a 1 CD50 of Stx2, therefore, preventing Stx2 toxicity on human kidney cells [26]. Additionally, FabC11:Stx1/Stx2 cell detachment protection was also lower than FabF8:Stx2, which showed protection levels of 43.5% under pre-incubation and 36% under co-incubation conditions. With respect to swelling, although we demonstrated good prevention, pre-incubation: 97.0% and co-incubation: 81.0%, it is noteworthy that this protection had been obtained with a higher concentration of FabC11:Stx1/Stx2 (10 μg/mL) compared with FabF8:Stx2 (1 μg/mL). Our results conclusively demonstrate the efficacy of

FabF8:Stx2 antibodies to avoid the cytotoxic effects of Stx2 on human renal microvascular endothelial cells, one of the principal target cells for this toxin, raising the possibility of the development of a new therapeutic molecule against Stx2 toxicity.

#### **4. Conclusions**

The present work showed encouraging results about the effectiveness of FabF8:Stx2 antibodies to neutralize the cytotoxic effects of both purified Stx2 and Stx2 or Stx1/Stx2 produced by STEC strains. Thus, they could be a promising therapeutic strategy to prevent kidney damage and the subsequent development of HUS. Future studies will be focused on analyzing the efficacy of FabF8:Stx2 in in vivo models.


**1. Anatomy and development of root**

#### **1.1 Root system architecture**

In different plant species, root system architecture (RSA) has diverse morphologies. There are basically two types of RSA, the taproot system (or allorhizic system) in gymnosperms and dicotyledons, like *Arabidopsis thaliana* (*Arabidopsis*), tomato (*Solanum lycopersicum* L.), carrot (*Daucus carota*), and poplar (*Populus* spp.) and the fibrous root system (or homorhizic system) in monocotyledons such as maize (*Zea mays* L.), rice (*Oryza sativa* L.), onion (*Allium cepa*), garlic (*Allium sativum*), and tulip (*Tulipa* spp.) [1]. The taproot system consists of a single thick central primary root (PR) with thin or no lateral roots (LRs); the fibrous root system has small and short-lived primary and adventitious roots (ARs) derived from shoots, stems, or leaves [1].

### **1.2 Intrinsic developmental signals and environmental conditions modify root system architecture**

*Arabidopsis* as a eudicot has a taproot system, which consists of an embryonic radicle-derived PR and postembryonic-developed LRs and ARs. Root regeneration exists throughout the plants' lifetime; it is a distinctive feature of plants and contributes to their robustness in adverse conditions.

In *Arabidopsis*, LRs initiate from pairs of pericycle cells that possess developmental potential as plant stem cells. These pericycle cells are selected and directed to become LR founder cells and form LRs by both intrinsic and environmental signals [2–5]. The primary LR is initiated from the basal meristem of the PR, where root cap-derived auxin influences the amplitude of oscillatory gene expression in the basal meristem and the elongation zone of the root, which leads to the prepatterning of LR initiation sites [6, 7]. The pre-patterning process is marked by the expression of a series of genes, like *GATA23*, *MEMBRANE-ASSOCIATED KINASE REGULATOR 4* (*MAKR4*), and *IAA19* [7]. In the basal meristem and elongation zone, *DR5::Luciferase* expression was observed to rhythmically pulse with a period of ~6 h, which matched with the period of LR pre-branch site production [6]. It is recently reported that the source of auxin is provided by the cyclic programmed cell death of root cap cells [8, 9].

It is noteworthy that not all of the pre-branch sites emerge to be LRs [6]. The dormant pre-branch sites may present a selective mechanism for LR formation under certain growth conditions, such as water availability, nutrient levels, physical obstacles, or damage [5, 10–13]. It is interesting that many of the external signals converge on phytohormones to regulate root development. Among these phytohormones, auxin functions as a central mediator.

Mechanical forces are important regulators for plant morphogenesis. LRs always emerge from the convex side of PR bending, resulting in a left-right alternation of LRs. Bending caused by gravitropic curvature led to the initiation of LRs, where a subcellular relocalization of PIN1 was observed [11]. Release the pericycle cells from the restraints of adjacent endodermis by targeted single cell ablation of endodermal cells triggered the pericycle to reenter the cell cycle and induced auxindependent LR initiation [14]. Excision of the *Arabidopsis* PR leads to the promotion of LR formation, which is mediated by activated auxin biosynthesis and auxin transport [15].

## **2. Roles of phytohormones on root formation**

#### **2.1 Auxin**

The phytohormone auxin which plays fundamental roles in many aspects of plant growth and development is also a well-documented key regulator of LR development [16, 17]. The natural auxin, indole-3-acetic acid (IAA), is mainly synthesized in a two-step pathway from tryptophan. First, tryptophan is converted to indole-3-pyruvate (IPA) by the TAA1/SAV3 family of aminotransferases; IPA is then converted to IAA by the YUCCA (YUC) family of flavin monooxygenases [18–23]. Auxin biosynthesis has been shown to play an essential role on both programed and wound-induced LR and AR developments [15, 24, 25].

Polar auxin transport (PAT), mediated by auxin influx (AUX1 and LAXs) and efflux carriers (PINs and MDR/PGPs) [26–29], generates auxin gradients and maintains an auxin maximum to regulate LR formation and positioning [17, 30–33].

Auxin signaling is known to be an integrator of endogenous and exogenous signals for root branching [17, 30, 34, 35]. It begins with the degradation of a class of AUXIN/INDOLE-3-ACETIC ACID (Aux/IAA) through TRANSPORT INHIBITOR RESPONSE 1 (TIR1) auxin receptor [36, 37], resulting in the activation of the AUXIN RESPONSE FACTOR (ARF) [38, 39]. ARF7 and ARF19 transcription factors further induce the expression of downstream target genes like *LATERAL*

**23**

*Phytohormone-Mediated Homeostasis of Root System Architecture*

*ORGAN BOUNDARIES DOMAIN/ASYMMETRIC LEAVES2-LIKE* (*LBD/ASL*) family genes *LBD16/ASL18* and *LBD29/ASL16*, promoting LR initiation at the

Cytokinin is also a main player in root development. In higher plants, isopentenyladenine (iP), trans-zeatin (tZ), and dihydrozeatin (dZ) are the predominant cytokinins [44]. Cytokinin level and patterning in plant are controlled by a fine equilibrium between cytokinin synthesis and catabolism [44, 45]. Cytokinin biosynthesis is dependent on the activity of *ATP/ADP-isopentenyltransferase* (*IPT*) and *LONELY GUY* (*LOG*) gene family [46–48], and the metabolism is mainly through the *CYTOKININ OXIDASE/DEHYDROGENASE* (*CKX*) genes [44, 45]. Cytokinin can also be inactivated through conjugation to glucose [49]. The spatial and temporal distribution of cytokinin is in part due to the specific expression of cytokinin

In *Arabidopsis*, cytokinin signaling starts with the perception by the transmembrane cytokinin receptors ARABIDOPSIS HISTIDINE KINASE (AHK), AHK2, AHK3, and AHK4/WOL1/CRE1 [53–55], which target the histidine phosphotransfer protein AHPs to activate the type-A and type-B ARABIDOPSIS RESPONSE REGULATORS (ARRs) that negatively and positively regulate cytokinin signaling, respectively [55–61]. Two type-A ARRs, ARR7, and ARR15 were induced by both cytokine and auxin and are essential for embryonic root development [62].

Although some evidences showed that cytokinins act as both local and longdistance signals [51, 63–65], and some transporter proteins have been shown to be involved in cytokine transport [66–70], the molecular mechanisms of cytokinin

Postembryonic root development is regulated by the root apical meristem (RAM), where cytokinin is known to act antagonistically with auxin to control the balance of cell division in the division zone and cell differentiation in the transition zone, which is essential for the maintenance of the RAM and affects the growth and patterning of the root [64, 71]. Application of cytokinin reduces the number of meristem cells and the size of RAM and promotes cell differentiation in the transition zone; cytokinin biosynthesis and signaling mutants as well as *CKX* overexpression mutants have a larger RAM with more meristem cells [45, 64, 72]. Conversely, auxin treatment increases meristem size and promotes cell division in the proximal meristem, and auxin transporter *PIN* mutants display a smaller RAM [64, 73]. The cross-talk of cytokine and auxin in regulating RAM activity was shown to converge on the auxin-inducible *AUX/IAA* family gene *SHORT HYPOCOTYL 2/ INDOLE-3-ACETIC ACID 3* (*SHY2/IAA3*) in the transition zone, where cytokinin activates *SHY2* via the *AHK3/ARR1* two-component signaling pathway to suppress *PIN3* and *PIN7* expression and promote cell differentiation, while auxin suppressed SHY2 protein, leading to the activation of PINs and promotion of cell division [71]. Furthermore, Moubayidin et al. [74] revealed that in transition zone, SCR, a member of the GRAS family of transcription factors, directly represses the expression of *ARR1*, which controls auxin production via the *ASB1* gene and sustains stem cell activity, to simultaneously control stem cell division and differentiation and ensure coherent root growth. Cytokinin affects the expression of multiple *PINs* differentially in a tissue-specific manner to regulate auxin distribution [75, 76]. Auxin-cytokinin interactions lead the generation of distinct hormonal response

zones, thus controlling the development of root vascular tissue.

On contrary to auxin, which is a positive regulator of LR development, cytokinin

acts as a negative regulator of LR formation. Cytokinin suppresses LR initiation

*DOI: http://dx.doi.org/10.5772/intechopen.82866*

protoxylem-pole pericycle cells [40–43].

synthesis and catabolism genes [45, 47, 48, 50–52].

transport are still not well characterized.

**2.2 Cytokinin**

*ORGAN BOUNDARIES DOMAIN/ASYMMETRIC LEAVES2-LIKE* (*LBD/ASL*) family genes *LBD16/ASL18* and *LBD29/ASL16*, promoting LR initiation at the protoxylem-pole pericycle cells [40–43].


*8.1. Israelite Ethos of Simplicity and Egalitarianism*

Many studies, on the basis of various considerations, textual and material alike, concluded that an ethos of egalitarianism and simplicity existed in Israelite society. Before focusing on the material evidence for this ethos, which is our main concern here, I should note that that many biblical scholars and historians, on the basis of various texts, claimed that Israelite society had such an ideology (e.g., Lods 1932; Wolf 1947; Albright 1961, p. 119; Mendenhall 1962; Kelso 1968, p. 48; Speiser 1971; Gordis 1971, pp. 45–60; Humphrey 1978; Gottwald 1979; Lenski 1980; Cross 1988; Berman 2008; Shapira 2009).19 While the texts are problematic sources, and each interpretation is met with counter-interpretations, the plethora of evidence for this ethos in so many biblical genres cannot be easily dismissed. Moreover, the ancient Israelites' material culture, which seems more straightforward, clearly support the existence of such an ethos. In the section below I would therefore like to present some of the material patterns that expose this ethos (for a more extensive discussion, see Faust forthcoming).

Before outlining the evidence, however, it must be stressed that we are not discussing an egalitarian society—such societies do not exist—but a society that has an ethos of egalitarianism. In such a society, many overt signs of hierarchy are not accepted, but others might be found, along with evidence for real stratification (see (Faust 2004, 2011, 2013), and many references; cf., (Parker Pearson 1982). Even pre-monarchic Israel had various form of leadership (like the elders), and this is clearly true for the stratified Iron II society, which had a full spectrum of hierarchy, from kings, though administrators, to the local elders (e.g., Reviv 1989; Faust 2012, and references). As we will see, the discussed ethos coexisted with actual stratification, and while not negating it, it did limited some expressions of hierarchy (while allowing others, cf., Stein 1994; Blanton 1998; Faust forthcoming).


**4. Conclusion**

Although the contribution of apoptosis to prion-induced death of central neurons including cerebellar ones is strongly supported, our studies of scrapie-infected PCs show that although caspase-3 is activated, the pro-apoptotic BAX/BCL-2-dependent mitochondrial pathway is not involved in the prion-induced death of these neurons. This is also the case for BSE-induced death of hippocampal and thalamic neurons [119], suggesting that prions exert neurotoxicity through BAX-independent activation of caspase-3. Ultrastructural evidence of ER stress and robust autophagy in the scrapieinfected cerebellar neurons both *in vivo* and *ex vivo* implicate them in these BAXindependent neurotoxic mechanisms. Furthermore, the autophagic blockade resulting from prion protein-deficiency in ZH-I and Ngsk *Prnp*0/0 mice may contribute to neuronal death in infectious prion-diseased cerebellar neurons. In Ngsk *Prnp*0/0 cerebellar neurons, Dpl neurotoxicity and PrP-deficiency contribute to neuronal death probably through an interplay between autophagic blockade and BAX-dependent apoptosis.

**31**

**Author details**

Audrey Ragagnin1

Nicolas Vitale1

France

Anne-Sophie Wilding1

provided the original work is properly cited.

, Qili Wang1

, Stéphane Gasman1

*Prion Proteins and Neuronal Death in the Cerebellum DOI: http://dx.doi.org/10.5772/intechopen.80701*

© 2019 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,

, Aurélie Guillemain1

, Nancy Grant1

1 Intracellular Membrane Trafficking in the Nervous and Neuroendocrine System,

2 In Vitro Imaging Platform, CNRS UPS 3156, University of Strasbourg, Strasbourg,

INCI, CNRS UPR3212, University of Strasbourg, Strasbourg, France

\*Address all correspondence to<EMAIL_ADDRESS>
, Siaka Dole1

and Yannick Bailly1

, Valérie Demais1,2, Cathy Royer1,2, Anne-Marie Haeberlé1

,

\*

,

*Prion Proteins and Neuronal Death in the Cerebellum DOI: http://dx.doi.org/10.5772/intechopen.80701*

*Prions - Some Physiological and Pathophysiological Aspects*

**30**

**4. Conclusion**

**Figure 18.**

Although the contribution of apoptosis to prion-induced death of central neurons including cerebellar ones is strongly supported, our studies of scrapie-infected PCs show that although caspase-3 is activated, the pro-apoptotic BAX/BCL-2-dependent mitochondrial pathway is not involved in the prion-induced death of these neurons. This is also the case for BSE-induced death of hippocampal and thalamic neurons [119], suggesting that prions exert neurotoxicity through BAX-independent activation of caspase-3. Ultrastructural evidence of ER stress and robust autophagy in the scrapieinfected cerebellar neurons both *in vivo* and *ex vivo* implicate them in these BAXindependent neurotoxic mechanisms. Furthermore, the autophagic blockade resulting from prion protein-deficiency in ZH-I and Ngsk *Prnp*0/0 mice may contribute to neuronal death in infectious prion-diseased cerebellar neurons. In Ngsk *Prnp*0/0 cerebellar neurons, Dpl neurotoxicity and PrP-deficiency contribute to neuronal death probably through an interplay between autophagic blockade and BAX-dependent apoptosis.

*Western blot of autophagic markers p62, beclin-1 and LAMP-1.* **A***. p62 and* **B***. Beclin-1. The markers were weakly expressed in WT COCS, but increased in DIV3 and DIV5 COCS from PrP-deficient mice.* **C***,* **D***. LAMP-1 did not vary in WT and ZH-1 COCS from DIV3 to DIV7, but increased in DIV7 Ngsk Prnp0/0 COCS*

*indicating increased lysosomal activity (p < 0.05; n = 3 mice/genotype and DIV).*


4.3.2. Environmental Savings

Comparative LCA results of heat pumps and gas boilers are calculated for the UK in a previous study [44]. In this study, Orkney results show that there is a significant reduction in most categories when it is compared with the results for the UK (Figure 10). The main reason for that is the change in the use phase. In UK results, the use phase was dominating the remaining categories. However, the amount of electricity used for the heat pump throughout the lifetime (20 years) is reduced because of higher efficiencies (2.8 SPF used for the UK study and the average optimized SPF modelled for archetypes in Orkney is 4.5). Moreover, electricity is produced mainly from wind energy. Therefore, the negative consequences are decreased.

The highest reduction occurs in the Ionising Radiation (IR) category with a nearly 99% decrease. The main contributor to this category is electricity from nuclear. Therefore, renewable electricity helps to reduce this impact. Other high reductions occur in agricultural land occupation (ALO), terrestrial ecotoxicity (TE) and national land transformation (NLT) categories, with 98%, 98%, and 96%, respectively. The reduction in ALO and TE categories is relevant to electricity produced from biomass which exists in the UK electricity mix but not in Orkney. NLT category is relevant to the fossil fuels that exist in the UK electricity mix.

**Figure 10.** Lifecycle environmental impacts of heat pump uptake scenarios by house archetypes.

The lowest changes occur in freshwater ecotoxicity (FE) and marine ecotoxicity (ME) categories with a 9% and 14% reduction. The main processes that contribute to these categories are the manufacturing and disposal of scrap metals so as there are no changes in these phases the results remain similar and only the use phase creates these differences. urban land occupation (ULO) and metal depletion (MD) categories also have 19% and 34% lower results mainly because of the differences in the electricity mix.

The climate change (CC) category results decreased from 44,320 kgCO2e to 5621 kgCO2e on average. Even though the average value is very low when compared with the UK figure, results vary based on the archetype and building specification. It can reach 6284 kgCO2e if the building is an unrefurbished detached house or decrease to 5295 kgCO2e if it is a new semi-detached house. The new buildings category shows 14%, 5%, 7%, and 9% lower results for detached, semi-detached, end-terraced, and mid-terraced archetypes, respectively.

The highest changes in one impact category exist in FE, ME, and ULO categories. The differences between an unrefurbished detached house and a new building end-terrace house could be as high as 78% in FE, 73% in ME, and 49% in ULO category. These results emphasize that not only the environmental impacts of different space heating technologies are important, but also the house archetypes and specifications. A refurbished house and a new building category have 5% and 10% lower results than an unrefurbished one in the CC category. The highest change occurs in the FE category with a 16% and 30% reduction in refurbished and new building categories. ME category shows similar reductions with 15% and 29% for the same building specifications. ULO category also shows reductions of around 11% and 22% with EEI.

Figure 11 illustrates the breakdown of GHG emissions for the baseline scenario and total emissions for future scenarios. Oil is responsible for 77% of total emissions (20,552 tCO2e), followed by coal with 16% (4248 tCO2e) and electricity with 6% (1612 tCO2e) in the baseline scenario. Wood and LPG account for only 1% (269 tCO2e) of total emissions. RE and CE scenarios reduce total emissions by 79% and 98% respectively. RE scenario expects an 81% reduction in fossil fuel emissions and a 44% reduction in electricity emissions. CE scenario replaces all heating technologies with heat pumps, so the emissions are 659 tCO2e coming from electricity, which is very low when compared with the RE scenario. Even though the demand is higher in the future with around 2000 more new dwellings, ambitious EEI targets also help to reduce both demand and emissions. The UK's net zero target is an ambitious target and requires ambitious steps, including not only a shift in the heating system, but also a shift in EEI.

**Figure 11.** Comparison of current and heat pump uptake scenario GHG for house archetypes contributed by fuel type.


*Edited by Guozhao Ji and Jingliang Dong*

Computational fluid dynamics (CFD) is a powerful tool that enables engineers and scientists to simulate fluid flows in a variety of applications, including thermal engineering, biomedical engineering, and environmental modeling. This book provides a comprehensive introduction to CFD, encompassing fundamental theory, mathematical and numerical techniques, and practical applications. The book begins by systematically introducing the basic concepts and terminology of CFD, such as the continuity equation, Navier-Stokes equations, energy equation, source/sink terms, and types of grids. The mathematical and numerical methods utilized to solve the CFD governing equations, including the finite difference method and the finite volume method, are then described in a beginner-friendly manner, accompanied by vivid and straightforward graphical illustrations. In addition to covering the foundation of CFD theory, the book presents several practical applications of CFD in diverse fields such as biomedical modeling, renewable energy, and thermal engineering. To extract useful information, the simulated CFD results need to be analyzed and visualized. Therefore, the book demonstrates common post-processing and visualization techniques, such as contour plots, streamlines, vectors, and charts. Overall, this book provides a comprehensive introduction to CFD, encompassing the essential theory, methods, and applications, making it an ideal choice as a textbook for graduate and post-graduate students or a reference for researchers and engineers working on CFD simulations.

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Computational Fluid Dynamics - Recent Advances, New Perspectives and Applications

Computational Fluid

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Recent Advances, New Perspectives

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*Edited by Guozhao Ji and Jingliang Dong*


**3. Results**

Demographic characteristics: Forty-two percent of the sample were women. Descriptive statistics for workers stratified by gender and trust are presented in Table 1. For both women and men, the largest percent of respondents (72.0% and 72.2%, respectively) were White, Non-Hispanic with a mean age of about 42. More than half of the respondents were married and had either technical training or some college.

Mistrustful environment: Approximately 22% of women and 20.3% of men indicated that their supervisor did not always create an open and trusting environment (Table 1). As shown in Table 1, for both women and men, the highest prevalence of mistrust reported was among workers ages 45–64, (women = 24.4%, men = 23.0%) followed by workers ages 30–44 (women = 22.3%, men = 20.5%). Black women (23.2%), followed by White women (22.4%) had the highest prevalence of reporting that their supervisor does not create an open and trusting environment, compared with women of other races/ethnicities (Asian, Hispanic, Other). White men (21.1%) followed by Black men (20.6%) reported higher prevalence of a mistrustful environment. Prevalence of a mistrustful environment was higher for women with increasing levels of education (highest for those with college or post-graduate education, 25.2%). Men with technical training or some college/associate degree had a slightly higher prevalence (20.9%) of a mistrustful environment. Divorced women (26.8%) and men (24.7%) had the highest prevalence of reporting a mistrustful work environment. Prevalence by income was similar for both women and men, with slightly higher prevalence of a mistrustful environment for those earning \$3000–\$4999 per month among women (23.0%) and for those earning \$5000–\$7499 among men (21.7%).

**Table 1.** Weighted prevalence (%) and standard errors (SE) of open and trusting and open work environment factors by sociodemographic characteristics and gender (Gallup-Sharecare Well-Being Index, 2010–2012).

\* *Z*-test for difference between women and men *p* < 0.05.

Table 2 shows the weighted prevalence of LS7 risk factors by trust. The prevalence of all LS7 risk factors were higher for both women and men who reported working in a mistrustful environment, compared to those whose working environment was not mistrustful.

**Table 2.** Weighted prevalence (%) and standard errors (SE) for LS7 CVD risk factors by work environment characteristics and gender (Gallup-Sharecare Well-Being

*Z*-test for difference between women and men significant *p*

Multivariate results for trust with LS7 CVD risk factors, after adjustment for demographic factors and having any health insurance, are shown in Table 3. Confidence intervals excluded 1.0, indicating statistical significance at *p* < 0.05 for each outcome. Trust was associated with the LS7 CVD risk factors in both men and women after adjustment for covariates. Due to the large sample size, we report effect sizes with a focus on those ≥10%. We found that workers who did not work in an open and trusting environment had greater odds of having high blood pressure (women = 15%, men = 20%), high cholesterol (women = 18%, men = 22%), and diabetes (women = 15%, men = 18%) compared to those who reported having an open and trusting environment with their supervisor. Both women and men workers had greater odds of being a current smoker (both 15%), having a poor diet (women = 10%, men = 11%), and being obese (both women and men = 18%). Women reporting a mistrustful environment also had greater odds of having a low physical activity level (10%). Odds ratios for having four or more LS7 CVD risk factors were elevated for those working in a mistrustful environment (women = 22%, men = 29%).

**Table 3.** Multivariate associations between LS7 CVD risk factors and open and trusting work environment stratified by gender (Gallup-Sharecare Well-Being Index, 2010–2012) a.

a Models are adjusted for age, race/ethnicity, education, marital status, family income, and any health insurance.

#### **4. Discussion**

The findings of this study suggest that lower workplace social capital, as measured by the WBI, is associated with higher odds of having one or more of the LS7 CVD risk factors.

Our findings are consistent with others who have found associations between social capital and health [39,46,48,65,81–83]. Previous research reported that working in a negative environment and having low social support could lead to stress and psychosocial distress [84]. Workplace stress can directly increase CVD risk through biological pathways (e.g., inflammation) or CVD risk factors [12,13]. Studies have found that workers experiencing job stress were more likely to have diabetes [85]. Associations between work environment and high blood pressure and high blood cholesterol are mixed, with some studies reporting results similar to our findings [86,87] and others reporting no association [85].

Furthermore, workplace stress can indirectly affect CVD risk through at-risk health behaviors [85,87]. These behaviors include poor diet, insufficient physical activity [85,86], smoking [85], high alcohol consumption, and lack of sleep [88]. Workers who reported a lack of support from supervisors were more likely to be heavy smokers [89]. In addition, men with low workplace social support were more likely to be obese [90].

Although we adjusted for potential demographic confounders in our models, we examined each of the LS7 CVD risk factors separately. Health behaviors are often interrelated and can affect the presence or absence of other health behaviors. For example, smoking was found to increase caloric intake [91], while a healthy lifestyle (diet and exercise) was negatively associated with smoking [92]. Because of the potential co-occurrence, we conducted sensitivity analyses to see whether the odds ratios increased if we selected having four or more of the LS7 risk factors instead of only one. We used

this as the dependent variable in our regression models. Both women (22%) and men (29%) showed an increase in odds for having four or more LS7 risk factors if they indicated that their supervisor did not create an open and trusting environment.

Analyses were presented separately by gender, not only due to the differences in CVD risk between women and men, but also due to the importance of gender in the social capital and managerial psychology literature [93]. Odds ratios were similar for both genders when the LS7 factors were looked at individually, and slightly higher among men when the dependent variable was having four or more LS7 factors.

Improvements to the work environment are needed to reduce CVD risk among workers. Social modification to the work environment, such as adjusting managerial style to create an open and trusting environment, can decrease work stress. Considering managerial trust from a Total Worker Health® framework meets the goals of illness prevention to advance worker well-being. Efforts can also be made to target the health behaviors themselves. There are a range of possible strategies for addressing the LS7 risk factors in the workplace. For example, physical modification to the work environment, such as installing sit/stand desk stations and walking workstations, can reduce sedentary behavior and may increase physical activity. Additionally, increased access to nutritious food in the workplace may improve diet. Supervisors who support workplace wellness may help in reducing CVD risk factor in workers [94].


*Article* **The Impact of Urbanization on Income Inequality: A Study in Vietnam**

**Nguyen Minh Ha 1,\*, Nguyen Dang Le 2 and Pham Trung-Kien 3**

Received: 28 June 2019; Accepted: 19 August 2019; Published: 10 September 2019

**Abstract:** This paper explores the impact of urbanization on income inequality in Vietnam, using the regression estimation method with panel data including Driscoll and Kraay, and Pooled Mean Group. The research data cover 63 provinces in Vietnam from 2006 to 2016. The results show that in the long term, urbanization has an impact on reducing income inequality. In the short term, urbanization has a negligible impact on income inequality. The hypothesis of an inverted-U-shaped relationship between urbanization and income inequality is confirmed. The high school enrollment rate and the proportion of agriculture have an effect on reducing income inequality.

**Keywords:** urbanization; income inequality; Driscoll and Kraay; PMG

#### **1. Introduction**

Many economists argue that increasing inequality is the root cause of economic crises (Stiglitz 2009). Meanwhile, the world faces serious income inequality. The gap between the rich and other groups has increased significantly. In 2015, the richest 1% of the population owned more assets than the other 99%. In Asia, the income of the poorest 70% has decreased, while that of the richest 10% has increased significantly (Hardoon et al. 2016).

The relationship between income inequality and development has long been a topic of particular interest to researchers in developed as well as developing countries. Many studies have tried to determine whether countries must make a trade-off between income inequality and growth. If so, what is the specific model of the relationship and why?

Kuznets (1955) was the first to introduce the idea of a link between inequality and development, pointing out that development involves a shift in population from traditional activities to modern activities. "An invariable accompaniment of growth in developed countries is the shift away from agriculture, a process usually referred to as industrialization and urbanization", he wrote (Kuznets 1955, p. 7). Therefore, in a simple model, income distribution among the entire population can be viewed as a combination of income distribution among those in rural and urban areas. We observe that income per capita is often lower in rural areas than in urban areas, and inequality in income distribution is lower than in rural areas in urban areas. What conclusions can we draw from these observations? First, under the same conditions, increasing the share of the urban population does not necessarily reduce economic growth: in fact, some evidence indicates that growth may be higher because urban per capita productivity increased faster in agriculture. If this is true, inequality in income distribution increases. This ideal was highlighted and clarified by Bourguignon and Morrisson (1998), Piketty (2006).

Industrialization and urbanization are closely related to economic development. The process of industrialization and urbanization affects income distribution, causing income inequality. In the short term, urbanization can increase income inequality because wages are higher for urban jobs than rural work. However, in the long term, when urbanization is highly developed, the difference in income distribution in the two regions may decrease, and income inequality will decrease.

In Vietnam today, urbanization and industrialization have progressed considerably. Vietnam's urbanization level is still low compared to the global average but, in recent years, higher than the average in other developing countries as well as countries in Southeast Asia. In terms of Gini data, income inequality in Vietnam increased until 2008 and then decreased, and in 2014 began to increase again. So, does urbanization increase income inequality? This is the question that we address in this study.

#### **2. Literature Review**

Income inequality refers to an unequal distribution of income among individuals or households. To calculate the degree of income inequality, scholars often rely on the percentage of income held by different shares of the population. Income inequality is often associated with unfairness, such as when rich people hold a significantly larger share of national income relative to their proportion of the population (Todaro 1989).

Income inequality can be measured by various methods, such as using quintiles to measure income gaps between the poorest and the richest; using World Bank Standard 40, measured with the Lorenz curve (Lorenz 1905), the Gini coefficient (Gini 1913, 1921), and the Theil index (Akita et al. 1999). In this research, we use the Gini coefficient to represent income inequality.

Urbanization concerns the physical, human, and economic development of cities. This term also includes the concentration of people and social activities on the settlement model characterized by the development of land with high population density. The result of urbanization is partly due to the increase in population, natural and by migration, as well as economic, social, and technological changes that motivate people to migrate to urban areas, which have many jobs and opportunities. Market rules and government policies promote urbanization and create related changes in people's livelihoods, land use, health, and natural resource management. Job placement decisions, rural-urban transformation and production systems, and government development and distribution policies often create urban immigration and focus on economic activities in cities (Gotham 2012).

According to Bloom et al. (2010), a basic concept commonly used to determine the level of urbanization is the proportion of the population living in urban areas, represented by Urbant and defined using the following equation:

$$Lrhm\_t = \frac{PLI\_t}{PLI\_t + PR\_ft}$$

in which *PUt* and *PRt* represent urban and rural population respectively.

#### *2.1. How Does Urbanization A*ff*ect Income Inequality?*

An economic model including urban and rural areas has four main elements of income inequality: (1) the level of urbanization; (2) the urban-rural income gap; (3) urban income inequality; and (4) rural income inequality. By keeping urban and rural disparities and rural and urban inequalities constant, Kuznets (1955) outlines an inverted-U-shaped relationship between inequality and urbanization. In this study, urbanization is viewed as an important factor affecting income inequality, and we examine Kuznets's inverted-U-shape hypothesis based on Vietnam's provinces.

A great deal of evidence supports the idea that urbanization promotes economic growth, at least in the early stages of development, implying that a balance exists between economic growth and equal income distribution, at least geographically. Brülhart and Sbergami (2009) argue that poor countries face an awkward choice between inequality reduction and higher economic growth. In fact, the relationship between development and income inequality described by Kuznets is highly relevant to urbanization.

The classic dual economic model examining structural change shows that inequality is an inevitable result of urbanization that is characteristic of economic development (Harris and Todaro 1970; Lewis 1954; Rauch 1993). Similarly, the New Economic Geography helps explain how economic development is associated with increased urbanization and inequality in its early stages (Krugman 1991). Both models show an increasing profit from industrial activities. In fact, many good workers are concentrated in urban areas with higher industrial wages. Economic growth is facilitated by structural changes in the economy, allowing it to enjoy the benefits of increasing profits and the economics of urbanization.

The process of urbanization brings about changes in economic structure, with people and resources being reallocated from agricultural activities to industrial activities. This process is associated with increased inequality, with higher incomes in urban areas than in rural areas. In this sense, both higher inequality and greater urbanization can enhance the concentration of production factors necessary for growth, at least in the early stages of development. And this focus further strengthens the reallocation of labor from rural to urban areas (Ross 2000). Therefore, both inequality and geographic concentration indicate, to some extent, capital accumulation (both physical and human). However, in the later stages of development, especially urban growth and the growth of a large concentration—urban accumulation—is linked to increasing inequality (Behrens and Robert-Nicoud 2014).

#### *2.2. Previous Related Studies*

Kanbur and Zhuang (2013) studied the impact of the change of two sectional economic structure and urbanization that affect inequality in Asia. Based on the Kuznets model and the inequality measurement method using the Theil index, this study analyzes four specific countries: China, India, Indonesia, and the Philippines. Their results show the following.

First, the effect of urbanization and the economic structure change on income inequality depends on the specific country. Urbanization contributes about 300% to the increase in inequality in the Philippines, more than 50% in Indonesia, and nearly 15% in India. However, it reduces inequality in China. Meanwhile, the change in the urban-rural income gap reduces inequality in Indonesia and the Philippines, but it increases inequality in India and China. Moreover, in China, the increase in income inequality is mainly due to the raise of 43% rural inequality. This is contrary to the popular point of view that increasing income inequality is the result of the expansion in the urban–rural income gap and the rising urban inequality.

Second, how urbanization will affect inequality in the future depends on the status urbanization of the country. If a country passes its "turning point", urbanization will reduce inequality in, for example, China. In contrast, urbanization will increase inequality in countries such as India, Indonesia, and the Philippines.

Third, there are four factors driving inequality in the Asian country including the degree of urbanization, urban–rural income gap, and urban and rural inequality. Among them, the urban-rural income gap is expected to have the largest marginal impact on inequality.

Sagala et al. (2014) studied the relationship between inequality in expenditure and urbanization in Indonesia. A panel data regression analysis was carried out to test the Kuznets inverted-U-shaped hypothesis based on a dataset of 33 provinces in 2000–2009. The results of the study supported the inverted-U-shaped hypothesis, regardless whether the Gini coefficient or Theil index is used as a measure of inequality. Inequality in expenditure is expected to peak at an urbanization rate of about 46–50%. Because the urbanization rate in Indonesia in 2010 was 50%, this indicates that inequality in spending has reached its highest value. Therefore, further urbanization will reduce inequality in expenditure, with other factors constant.

Oyvat (2016) studied the impact of agricultural structure and urbanization on income inequality. The author investigated the empirical relationship between inequality in land holding, urbanization,

and income inequality using cross-data sets. The estimated results indicated that the inequality of land holding has a significant impact on urbanization and urban income and inequality. Moreover, the analysis found that excessive urbanization increases income inequality. The results of the study showed that policy makers need to have a broader view of the importance of agricultural policies. An issue of progressive land reform and subsidies to protect small farmers rogressive land reform and subsidies to protect small farmers could also reduce urban income inequality and poverty in the long term.

Wu and Rao (2017) studied inequality in China, focusing on identifying the main causes of inequality. The main objective of the study was to examine the relationship between urbanization and income inequality using provincial data. Panel data in 20 provinces was collected from the China Statistical Yearbook for five years include 1998, 2000, 2002, 2005, and 2010. The empirical analysis was based on ordinary least squares estimator and fixed and random effects models, showing a strong inverted-U-shaped relationship between inequality and urbanization. An urbanization rate of 0.53 has been determined, with the implication that provinces with higher levels of urbanization can reduce income inequality.

However, Angeles (2010) used urban population density to represent the urbanization rate, and its square as an explanatory variable in the regression analysis of panel data on income inequality. With panel data on 226 countries and regions in 1960–2005, a U-shaped relationship was found, not an inverted-U-shaped relationship. Although it is not statistically significant, this result does not support Kuznets's hypothesis.

#### **3. Methodology and Data**

#### *3.1. Empirical Framework*

Based on the theory related to urbanization and income inequality, the impact of urbanization on income equality, and the research model of Sagala et al. (2014), Wu and Rao (2017), and related studies, we present a quantitative model assessing the impact of urbanization on income inequality as follows:

$$\begin{aligned} \text{INEQ}\_{\text{it}} &= \alpha + \beta\_1 \text{LIRB}\_{\text{it}} + \beta\_2 \text{LIRB\\_sq}\_{\text{it}} + \beta\_3 \text{GRDPpc}\_{\text{it}} + \beta\_4 \text{GRDPpc\\_sq}\_{\text{it}} + \\ \beta\_5 \text{initial} \text{INEQ}\_{\text{G}} &\text{GRDP}\_{\text{it}} + \beta\_6 r \text{EXP}\_{\text{it}} + \beta\_7 r \text{GOV}\_{\text{it}} + \beta\_8 r \text{EDI}\_{\text{it}} + \beta\_9 r \text{AGR}\_{\text{it}} + \varepsilon\_{\text{it}} \end{aligned} \tag{1}$$

Definition of variables in research model are summarized in Table 1.

**Table 1.** Definition of variables in research model.

#### *3.2. Data*

Due to limited access to Vietnam's statistical data and availability of the dataset Vietnam Household Living Standard Survey (VHLSS), this study uses panel data on 63 provinces in Vietnam for every two years from 2006 to 2016. The total number of observations is 378 (6 years × 63 provinces).

The data used in our quantitative analysis include the Gini coefficient, representing income inequality, which was calculated by the author from VHLSS, and secondary data was collected from reports and surveys conducted by the General Statistics Office of Vietnam every two years.

The research uses urban population as a share of total population to proxy for urbanization. This variable is commonly used in previous studies (Nguyen and Nguyen 2018).

Data for independent and control variables used in quantitative models include urbanization, gross regional domestic product (GRDP) per capita, export volume as a share of the province's GRDP, public expenditure as a share of the province's GRDP, students entering high school as a share of the average population of the province, and agricultural value as a share of the province's GRDP, which were collected from the annual statistical yearbook of the General Statistics Office and the Provincial Statistics Office in Vietnam.

The study uses regression methods to estimate static panel data with OLS, FE, RE, and regression correction techniques proposed by Driscoll and Kraay (1998) and pooled mean group (PMG) estimation (Pesaran et al. 1999; Pesaran and Smith 1995).

#### **4. Empirical Results**

#### *4.1. Descriptive Statistics of Variables in the Research Model*

Table 2 summarizes the descriptive statistics of all the variables used in the model. The mean value of *INEQ* is 0.3794, its standard deviation is 0.0537, its minimum is 0.2498, and its maximum is 0.5883. Thus, our sample does not have much difference in the income inequality index. For *URB*, the mean is 0.2597, the standard deviation is 0.1640, the minimum is 0.0736, and the maximum is 0.8746—this shows a significant disparity in the level of urbanization among provinces during the study period. Like *URB*, *GRDPpc*, *rEXP*, *rGOV*, and *rAGR* also have large differences among provinces. For *rEDU*, the average is 0.0313, and the standard deviation is 0.0082, indicating a relatively small difference.

**Table 2.** Descriptive Statistics.

#### *4.2. Correlation Matrix and Multicollinearity*

The correlation between variables in the regression model indicates the presence of multicollinearity that can affect the accuracy of the regression results. The results of the correlation analysis between variables in Table 3 show that the pairs of independent variables are not significantly correlated. However, to ensure accurate estimation results, we conduct a test on the multicollinearity phenomenon between variables in Table 4.

**Table 3.** Correlation matrix.

In Table 4, the results show that the coefficient VIF of the variables is quite small (<2), so the model has no multicollinearity.

**Table 4.** Checking multicollinearity.

#### *4.3. Assessing the Impact of Urbanization on Income Inequality*

First, the implementation of static panel data regression estimation methods follows that in model (1). To assess the impact of urbanization (*URB*) on income inequality (*INEQ*), without considering the relevant factors, we revised this model by excluding *URB\_sq, GRDPpc\_sq,* and *initialINEQGRDPpc* in model (2):

$$IINQ\_{it} = \alpha + \beta\_1 LRB\_{it} + \beta\_3 GRDPpC\_{it} + \beta\_6 \tau EXP\_{it} + \beta\_7 \tau GOV\_{it} + \beta\_8 \tau EDLI\_{it} + \beta\_9 \tau AGR\_{it} + \varepsilon\_{it} \tag{2}$$

To test the inverted-U-shaped hypothesis between income inequality and urbanization, we added *URB\_sq* to model (2), revised as model (3):

$$\text{INEQ}\_{\text{ll}} = \alpha + \beta\_{\text{I}} \text{I} \\ \text{IRB}\_{\text{ll}} + \beta\_{\text{Z}} \text{I} \\ \text{IRB}\_{\text{m}} \text{sq}\_{\text{ll}} + \beta\_{\text{S}} \text{G} \\ \text{RDPp} \\ \text{sq}\_{\text{ll}} + \beta\_{\text{S}} r \text{EXP}\_{\text{ll}} + \beta\_{\text{T}} r \text{G} \\ \text{O} \\ \text{I}\_{\text{ll}} + \beta\_{\text{S}} r \text{A} \\ \text{GR}\_{\text{ll}} + \varepsilon\_{\text{II}} \quad \text{(5)}$$

To test the inverted-U-shaped hypothesis between income inequality and economic growth, we added *GRDPpc\_sq* to model (2), revised as model (4):

$$INEQ\_{ll} = \alpha + \beta \eta IIRB\_{ll} + \beta \eta GRAPpc\_{ll} + \beta \eta GDPPpc\_{sm} \\ \text{sq}\_{ll} + \beta \eta FXP\_{ll} + \beta \eta rGOV\_{ll} + \beta \eta rEDL\_{ll} + \beta \eta rAGR\_{ll} + \varepsilon\_{ll} \tag{4}$$

To analyze the effects of initial inequality on the impact of economic growth on income inequality, we added *initialINEQGRDPpc* to model (2), revised as model (5):

$$\begin{aligned} IREQ\_{it} &= \alpha + \beta\_1 IRB\_{it} + \beta\_3 GRBPpc\_{it} + \beta\_5 \text{initialINEQGRDPpc\_{it}} + \beta\_6 rEXP\_{it} + \beta\_7 rGOV\_{it} + \varepsilon\_{it} \\ &+ \beta\_8 rEDL\_{it} + \beta\_9 rAGB\_{it} + \varepsilon\_{it} \end{aligned} \tag{5}$$

Finally, to analyze the impact of *URB* on *INEQ* under the simultaneous influence of all factors, we constructed model (6):

$$\begin{aligned} \text{INEQ}\_{it} &= \alpha + \beta\_1 \text{LIRB}\_{it} + \beta\_2 \text{LIRB}\_{-} \text{sq}\_{it} + \beta\_3 \text{GRDPpc}\_{it} + \beta\_4 \text{GRDPpc\\_sq}\_{-} + \\ \beta\_5 &\text{initialINEQGRDPpc}\_{it} + \beta\_6 r \text{EXP}\_{it} + \beta\_7 r \text{GOV}\_{it} + \beta\_8 r \text{EDLI}\_{it} + \beta\_9 r \text{AGR}\_{it} + \varepsilon\_{it} \end{aligned} \tag{6}$$

To select an estimation method that is consistent with the dataset, we chose between pooled OLS and FE methods through F-testing with the hypothesis "H0: Pooled OLS method is appropriate (ui = 0)". The regression result of the FE method in Table 5 shows that "F-test ui = 0" has a *p*-value = 0.000 < 0.01 in all models, which indicates that the FE method is more appropriate than pooled OLS. The results in Table 5 also show that the F-test has a *p*-value < 0.01 in all models, indicating that at the 1% significance level, the models are statistically significant. Then, to choose between FE and RE, the study uses the Hausman test with the hypothesis "H0: RE model is appropriate". The test results show (Prob > Chi2) = 0.0000 < 0.01 in all models, which demonstrates sufficient statistical evidence to reject H0, indicating that the more efficient FE model should be selected.

However, the results of post-regression tests with the FE model have the following violations: aWald test with the hypothesis "H0: constant variance" gives the result Prob > Chi2 = 0.0000 < 0.01 in all empirical models, rejecting H0 at a significance level of 1%, which means that the existing models of variance change. The Wooldridge autocorrelation test with the hypothesis "H0: no autocorrelation" gives the statistical result Prob > F = 0.0000 < 0.05 in all models, rejecting H0 at a significance level of 5%, which means that the model has autocorrelation. The result from testing Pesaran cross-sectional dependence with the hypothesis "H0: Cross-sectional independence" is (Prob > z) <0.01 in all models, rejecting the H0 hypothesis at a significance level of 1%, which means that cross-sectional dependence exists in models. The study also uses the Durbin-Wu-Hausman test for endogeneity of explanatory variables with the hypothesis "H0: explanatory variables are exogenous," i.e., not correlated with residuals. The results show that *rEXP* and *rGOV* are endogenous in the model.1

Standard errors in parentheses: \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.

1 The results of estimating OLS and RE regression, Hausman test, and post-regression tests are detailed in the Appendix A.

The models have an endogenous element caused by *rEXP* and *rGOV*, but this is not the main research focus, so we ignore this problem.

A regression model can be used for analysis of the estimation results, and statistical inference techniques should be used for appropriate revisions. Technical studies revise regressions following Driscoll and Kraay (1998), whose method resolves violations in the FE model, such as changes in the variance, autocorrelation, and cross-dependence. The results are in Table 6.

Finally, the impact of urbanization on income inequality in the short and long term is assessed. The technical econometric dynamic panel regression estimatation method used in this study is PMG (Pesaran et al. 1999; Pesaran and Smith 1995). Because the number of years of observation is not long enough, the study assesses only the impact of urbanization on income inequality unaffected by other factors. The results in Table 7 show that urbanization affects income inequality in the long term but not in the short term.

**Table 6.** Results of Driscoll and Kraay regression.

Standard errors in parentheses: \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.

**Table 7.** Results of PMG regression.

Standard errors in parentheses: \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.

#### **5. Discussion of Results**

The results of the econometric models lead to the following findings.

*URB* has a negative regression coefficient with a statistical significance of 1% in model (2), indicating the negative impact of urbanization on income inequality in Vietnamese provinces. This means that higher urbanization contributes to reducing income inequality, which is consistent with the impact of urbanization on income inequality in models (3), (4), and (5). This result is consistent with Johansson and Wang (2014) and contrary to Beladi et al. (2017). In fact, in Vietnam, urbanization is associated with the formation of industrial zones and clusters. People who have little or no land in rural areas migrate to cities to work in factories with higher wages than previous jobs in rural areas, which raises their income. Therefore, urbanization contributes to reducing income inequality in Vietnam. This impact is consistent with the PMG method in the long term.

*URB\_sq*, the impact of urbanization on income inequality, may be nonlinear, in other words, this effect may be in accordance with the inverted-U-shaped hypothesis. *URB\_sq* is included in models (3) and (6) to test this hypothesis, and the estimation results show that its impact on *INEQ* is negatively significant at the 5% level. Model (6), which includes with regression coefficients for *URB* and *URB\_sq,* shows the nonlinear relationship between urbanization and income inequality. Urbanization increases income inequality in the early stages until it reaches a certain threshold, after which it reduces income inequality. This result is consistent with Sagala et al. (2014) in Indonesia and Wu and Rao (2017) in China.

*GRDPpc*, which has a negative regression coefficient with a statistical significance of 5% in model (2), shows the negative impact of per capita income on income inequality in Vietnam provinces, which means that higher per capita income contributes to reducing income inequality. This result is consistent with the impact of per capita income on income inequality in models (3) and (4), consistent with Jin (2009) who studies China in 1990–2006. In addition, the interaction variable between initial economic growth and inequality (*initialINEQGRDPpc*) has no significant regression coefficients. The results of model (5) show that the effect of economic growth on income inequality in Vietnam in 2006–2016 is not affected by the level of initial inequality. The results are contrary to those of Johansson and Wang (2014).

The regression coefficients of *GRDPpc\_sq*, which are not statistically significant in model (4), shows that the inverted-U-shape hypothesis between economic growth and income inequality in Vietnam is not confirmed from 2006 to 2016. The results are contrary to those of Sagala et al. (2014), who found evidence supporting the inverted-U-shape hypothesis in Indonesia between 2000 and 2009.

*rEXP*, which is not statistically significant, shows that trade, which represents the share of exports in GDP, has a negligible effect on income inequality in Vietnam. This result is similar to that of Johansson and Wang (2014).

*rGOV*, which is not statistically significant, shows that the government spending in the provinces has a negligible effect on income inequality in Vietnam. This result is similar to that of Johansson and Wang (2014) and Beladi et al. (2017).

*rEDU*, which represents human capital, has a negative regression coefficient, showing the negative impact of education on income inequality. Although the level of statistical significance is not consistent in all empirical models, the results show that education plays a role in decrease income inequality in Vietnam. This result contrasts with Jin (2009), showing the education increased income inequality in China between 1990 and 2006, with Johansson and Wang (2014), which found no impact of education on income inequality in 90 countries in 1981–2005.

*rAGR*, which has a negative regression coefficient and is statistically significant at the 1% significance level in models (2) and (3) and 5% in models (4) and (5) shows the negative effect of the proportion of agricultural value to income inequality. This result is consistent with that of Wu and Rao (2017).

#### **6. Conclusions and Policy Implications**

This study analyzed and evaluated the impact of urbanization on income inequality in Vietnam in the period 2006–2016. We used the following econometric techniques and methods: the estimation methods with static panel data regression used are OLS, FE, RE, and Driscoll and Kraay. The method used for estimating dynamic panel data regression is PMG, enabling us to consider the impact of urbanization on income inequality in the short and long term.

Our research results lead us to draw the following conclusions. Urbanization helps reduce income inequality in Vietnam, including in the long term. We confirm the inverted-U-shaped relationship between urbanization and income inequality in Vietnam, where economic growth reduces income inequality. The impact of economic growth on income inequality is not affected by the initial level of inequality. The hypothesis on an inverted-U-shaped relationship between economic growth and income inequality is not confirmed during the study period. The share of exports has a negligible effect on income inequality in Vietnam. Public spending does not significantly affect income inequality in Vietnam. The high school enrollment rate and the proportion of agriculture influence reductions in income inequality.

Because urbanization has the effect of reducing income inequality, even in the long term, Vietnam needs to continue to promote urbanization. Economic growth has the effect of reducing income inequality, therefore policies to increase economic growth and social welfare should be maintained to reduce the gap between rich and poor. As improving intellectual standards will help reduce inequality, more supportive policies are needed to improve education. Because high value agricultural development will contribute to reducing inequality, appropriate policies are needed to support and develop agriculture, farmers, and rural areas.

**Author Contributions:** All authors contributed equally and reviewed the final manuscript.

**Funding:** This research was funded by a research grant number E2018.10.2 from Ho Chi Minh City Open University, Vietnam. The authors declare no conflict of interest.

**Acknowledgments:** We are grateful to the three anonymous referees for their constructive comments. We also thank the participants at the 3rd Vietnam's Business and Economics Research Conference VBER2019 (Ho Chi Minh City Open University, Vietnam, 18–20 July 2019) for their helpful suggestions. The authors wish to acknowledge financial supports from Ho Chi Minh City Open University. The authors are solely responsible for any remaining errors or shortcomings.

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

#### **Appendix A**

**Table A1.** Results of pooled OLS regression.

Standard errors in parentheses: \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.

Standard errors in parentheses: \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.

**Table A3.** Hausman test results.

**Table A4.** Wald test of constant variance.

**Table A5.** Wooldridge test inspection of autocorrelation.

**Table A6.** Pesaran test inspection dependence between units.

**Table A7.** Durbin-Wu-Hausman test on the endogenousness of explanatory variables.

#### **References**

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Wu, Dongjie, and Prasada Rao. 2017. Urbanization and Income Inequality in China: An Empirical Investigation at Provincial Level. *Social Indicators Research* 131: 189–214. [CrossRef]

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