**9. Project executives lacked domain expertise**

told by the external review? It is not immediately obvious why this situation was allowed to unfold in the manner in which it did. The project appeared to comply with all the appropriate governance structures and reporting requirements, yet an historical or retrospective view would allow that the project was never managed effectively. Indeed, the findings of the Commission of Inquiry [28] state that 'Its (Queensland Health payroll) failure, attended by enormous cost, damage to government and impact on workforce, may be the most spectacular example of all the unsuccessful attempts to impose a uniform solution on a highly complicated and individualised agency'. The Commissions conclusion was that there were two primary causes for the failure of the payroll project (1) 'unwarranted urgency' and (2) a 'lack of diligence on behalf of State officials' [28]. The Commissions report elaborated further on lack of diligence, describing it as 'poor decisions made in scoping the Interim Solution, in their Governance of the project, and in failing to hold IBM to account' [28]. The Commissioner further reported that 'the problems are systemic to government and to the natural commercial self-interest of vendors' [28] which supports the observation that Normalisation of Deviance was at play throughout the conduct of this project. However, these findings by the Commission do not explain what motivated senior management to ignore the lessons learned from immediately preceding projects, to ignore the warnings and advice of their own personnel. It is unclear, from the Commissions report, what specific steps a subsequent project might

These are the clear and obvious failures of the project: project management failed, there was a lack of requirements definition, management was in conflict. All of the issues which appear

Of potential significance is that the evidence provided by witness statements mapped to the project chronology showed that issues related to the identified themes were raised by staff and consultants throughout the project phases, and yet they still they remained as issues that were not resolved nor remediated at the time they were raised. The evidence is that management was made aware of these failures. So it was not a lack of awareness of the failure risks, and therefore highlighting these as the contributory factors of project failure lacks explanatory completeness.

As was evident from the analysis of the witness statements - management was regularly informed of what was going on with their project by both staff and external consultants [37]. Management knew that the project was facing problems (or at least should have known). The reports on the 2005 Whole-of-Government initiative [38], the KPMG Report [30], the KJ Ross report on testing [39], the IBM and CorpTech report to 'reconstruct' the business requirements [31] and the 2009 Queensland Audit Office report [40] all provided clear statements identifying where the project was failing and what needed to be done to remedy the situation. Yet the problems persisted until the total project costs had blown out to beyond A\$1 billion. Faced with the clear and certain statement that the project was performing badly, and with specific statements of where the project was failing, successive managements failed to act appropriately to stem the problems. The only conclusion that can be drawn from this failure to act

implement to ensure that they too did not all into these traps.

in the literature on failed projects—nothing new or unexpected!

**8. The big question … WHY?**

36 Dark Sides of Organizational Behavior and Leadership

'Organisational artefacts such as mission statements, goals and objectives, strategic plans and the like function as tools to reduce choice, not to guide it' [42]. In the same manner, the specification of requirements, the business case, the architecture and solution design of the project are all intended to constrain choice to deliver 'order'. In the QH project 'order' should have been represented by a defined scope of work, a defined project plan which sets out not only what work will be done, but also what work will not be done, and by an agreed contract. None of these things existed on the QH payroll project, and any efforts to enforce them were resisted by the vendor with the support (tacit or otherwise) of Departmental executives.

The issue of transparent flows of information between parties, of experts being able to make informed decisions utilising tacit information compared to less experienced people needing to 'follow the script' [43], of actors controlling the release of information, and of stakeholders presenting different versions of themselves across multiple stages becomes critical when one considers both the makeup of the governance and management of the QH project and the individuals involved. 'The involvement of non-IT stakeholders can actually work detrimentally and confound and confuse proceedings, even causing error' [15]. Non-IT experienced management, placed in a position of authority 'may be influenced by some suppliers or colleagues to whose IT knowledge they had access, and insist on a certain course of action' [15] which may result in confusion, delay or inappropriate decision-making, and contribute to the risk of IT project failure.

An appropriate lens through which to view this performance construct is referred to as the Dunning-Kruger Effect [44]. This effect is where the less competent an individual is with respect to a particular domain then the more they are likely to overstate their perceived knowledge and ability. This may be referred to as a 'confidence/competence dissonance'. Individuals that lack competence in a particular domain (incompetent) but are not self-aware of their lack of competence, generally perceive their performance to be not significantly inferior to those who possess significant competence, training and ability (the experts).

representation at board or executive level of organisational governing management. Only 15% of organisations had board members with any IT-related qualifications, and in their United Kingdom (UK) sample, no organisation exhibited board level oversight of organisational IT through qualified representation directly as a board member. A focus on general business competence over specific IT competence continues at the CIO level where less than 50% of CIOs in the United States of America (US) public sector had primary qualifications from technical or engineering backgrounds [48]. Management and leadership is devoid of the

Situational Incompetence: An Investigation into the Causes of Failure of a Large-Scale IT Project

http://dx.doi.org/10.5772/intechopen.76791

39

Narcissism, in modern terms has been defined as 'a person who possesses an extreme love of the self, a grandiose sense of self-importance, and a powerful sense of entitlement' [49], and while generally applied to individuals, the concept of narcissistic personalities has also been applied to groups and organisations [50]. Of significance in this research is that 'the narcissistic personality is characterised by the denial of a difference between the ideal and the actual self' [50] which segues directly into the studies of competence versus confidence by Kruger and Dunning [44] and Ryvkin, Krajc and Ortmann [45]. The narcissistic leader that holds 'very inflated self-views and (is) preoccupied with having those self-views continuously reinforced [51], was a behaviour which was evident on the Queensland Health payroll project, where the evidence suggested that the project was in trouble this was discounted or ignored because it did not fit the 'self-image' of the project leader that everything was under control. Narcissistic leaders in organisations are more likely to engage in behaviour which might lead to failing standards and reduced ethical and moral behaviour [52] which could be seen to be an antecedent for the 'normalisation of deviance'. As standards fall, decision by decision, what is considered normal behaviour slowly erodes until a 'new normal' gradually and

Narcissism is growing and becoming more prevalent and we can expect to see an increase in organisational narcissism as a direct consequence. Twenge and Foster [53] found that 'there has been a 30% tilt towards narcissistic attitudes in US students since 1979', and that 'The Narcissism Epidemic' [54] breeds 'the idea that being highly self-confident is the key to success'. Twenge and Campbell [55] were at pains to point out that there is no correlation between confidence and successful outcomes. Kremer [54] reported that 'over 15,000 journal articles have examined the links between high self-esteem and measurable outcomes in real life, such as educational achievement, job opportunities, popularity, health, happiness and adherence to laws and social codes' and found no correlation or causation. Highly confident, narcissistic project leaders are likely to exhibit behaviours that would put projects at risk. They over-estimate their own abilities, and are incapable of observing competence in others and learning by observing others. Narcissistic project leaders will be 'blind' to evidence that does not support their distorted view of their own abilities and of the status of the project for

'Over the last 30 years confidence has replaced competence' [54]. Positive thinking has replaced knowledge. An increase in narcissism correlates with the unskilled and unaware

skills needed to understand or lead complex information technology projects.

**10. Narcissism and leadership competence**

almost imperceptibly emerges.

which they are accountable.

This phenomena has also been described as the Unskilled and Unaware Problem (UUP) [45]. Essentially UUP argues that individuals that are unskilled in a particular domain overestimate their own competence in both absolute terms and relative terms. Top performers underestimate their absolute and relative performance. Kruger and Dunning [44] found that an unskilled person was more likely to dramatically misstate their absolute and relative competence. Ehrlinger et al. [46] have argued that UUP is a persistent feature of decision-making. Furthermore, and potentially much more concerning for complex IT projects, Kruger and Dunning [44] determined that the skills necessary to do the job, are the same skills necessary to identify competence in others. This facet of the UUP research is particularly important when an unskilled individual is placed in a position of decision-making authority, in this case with respect to an IT Project. Where an unskilled individual possesses neither the skills necessary to do the job, nor the skills necessary to identify competence in others they are not in a position to make informed decisions on complex issues. The application of this principle to the Queensland Health Payroll project would suggest that the Executive Director, the Department Secretary, and the governance boards lacked the skills needed to identify competence in others, and to comprehend informed advice when it was provided, preferring instead to rely upon those with similar personality attributes as themselves.

Engelbrecht et al. [15] aimed to 'identify whether a causal relationship exists between the various components of business managers' IT competence and IT success'. What they found was that a 'business managers' IT competence can, and does, exert a substantial influence on project success'. They reported a 'surprising' finding where a lack of knowledge or competence was likely to have a negative impact on project outcomes, 'although one would have expected a positive relationship and a positive impact, it has been reported that the involvement of non-IT stakeholders can actually work detrimentally and confound and confuse proceedings, even causing errors'.

Engelbrecht et al. [15] also found that 'business managers may be influenced by some suppliers or colleagues to whose IT knowledge they had access, and insist on a certain course of action. If that business manager is particularly influential in an organisation, then there could be similar confusions, delays, and even inappropriate decisions'. This finding is reflective of the behaviours referred to in the Witness Statements. The senior executives of Queensland Health deferred to the advice of the vendor, rather than their own staff. The researcher in this instance has neither the data nor the training to consider the role of amoral actors in this project, and has elected instead to make the assumption that the entire collective management must have been acting with the best intent for the Department (even if individual actors may have been compromised). This leads the researcher to conclude that it is a lack of knowledge of information technology projects, and the executives inability to parse the information being presented that lays the foundations of a theory to explain how the Queensland Health payroll project became so dysfunctional and ended in failure.

Given the importance of information technologies to business success, and their presence in almost every endeavour, one would expect to see an increase in technically literate, skilled or experienced managements to provide effective oversight and governance. Coertze and von-Solms [47] found that 10% of organisations had Chief Information Officer (CIO) or equivalent representation at board or executive level of organisational governing management. Only 15% of organisations had board members with any IT-related qualifications, and in their United Kingdom (UK) sample, no organisation exhibited board level oversight of organisational IT through qualified representation directly as a board member. A focus on general business competence over specific IT competence continues at the CIO level where less than 50% of CIOs in the United States of America (US) public sector had primary qualifications from technical or engineering backgrounds [48]. Management and leadership is devoid of the skills needed to understand or lead complex information technology projects.


*3.1. Inside–Out Method*

The Inside–Out method divides the mathematical model into an inside and outer loop for iterative calculation, and uses two sets of thermodynamic models, a strict thermodynamic model and an approximate thermodynamic model ( *K* value model and enthalpy value model). The simple approximate thermodynamic model was used for frequent inside loop calculations to reduce the time it takes to calculate the thermodynamic properties. The strict thermodynamic model was used for the outer loop calculations, and the parameters of the approximate thermodynamic model were corrected at the outer loop.

Defining the inside loop variables—relative volatility α*i,j*, stripping factors *Sb,j* of the reference components, liquid phase stripping factors *RL,j*, and the vapor phase stripping factors *RV,j*.

$$\mathbf{a}\_{i,j} = \mathbb{K}\_{i,j} / \mathbb{K}\_{b,j} \; \mathbb{S}\_{b,j} = \mathbb{K}\_{b,j} V\_j / L\_{j},\tag{6}$$

$$R\_{L,j} = 1 + lL\_j/L\_j \ R\_{V,j} = 1 + \mathcal{W}\_j/V\_j. \tag{7}$$

The inside loop uses the vapor and liquid phase flow rates of each component instead of the composition, for the calculations. Their relationship with the vapor and liquid phase composition and flow is as follows:

$$\mathbf{y}\_{i,\mathbf{j}} = \upsilon\_{i,\mathbf{j}} / V\_{\mathbf{j}} \ \mathbf{x}\_{i,\mathbf{j}} = \mathbf{l}\_{i,\mathbf{j}} / L\_{\mathbf{j}} \tag{8}$$

$$V\_j = \sum\_{i=1}^{c} v\_{i,j} \ L\_j = \sum\_{i=1}^{c} l\_{i,j} \tag{9}$$

According to the inside loop variables defined by the mathematical model of the Inside–Out method, the material balance Equation (M) and the phase equilibrium Equation (E) can be rewritten into the following relationship.

Material Balance:

$$(R\_{i,j-1} - (R\_{L,j} + a\_{i,j} S\_{b,j} R\_{V,j}) l\_{i,j} + (a\_{i,j+1} S\_{b,j+1}) l\_{i,j+1} = -f\_{i,j} + \sum\_{r} \upsilon\_{r,i} R\_{r,j}.\tag{10}$$

Phase balance:

$$w\_{i,j} = a\_{i,j} \mathbf{S}\_{b,j} l\_{i,j}.\tag{11}$$


*2.2.1 Aortic regurgitation*

Significant aortic valve insufficiency (AI) can create a short circulation loop in LVAD patients whereby a substantial fraction of the blood pumped by the LVAD to the aorta returns directly to the LV (**Figure 7**). This reduces effective perfusion, causes LV distention and elevates left heart filling pressures, ultimately causing HF. Consequently, moderate to severe AI is a contraindication to LVAD unless valve intervention is planned at the time of surgery [68].

Among patients in the STS-INTERMACS database implanted between 2016 and 2020, only 0.7% had severe AI at the time of LVAD surgery [8]. Moderate or severe AI was present at implant in 4.5% of over 16,000 patients in the IMACS database [30]. By contrast, mild AI prior to LVAD is relatively common, being found in 29.7% of patients in INTERMACS [69] and 31.2% in IMACS [70].

#### **Figure 7.**

*Aortic valve insufficiency (AI) with an LVAD. Significant AI creates a short circulation loop whereby a substantial portion of LVAD flow regurgitates back into the LV. This reduces functional cardiac output and increases left heart filling pressures, ultimately causing heart failure.*

#### *Recurrent Heart Failure after Left Ventricular Assist Device Placement DOI: http://dx.doi.org/10.5772/intechopen.107022*

Importantly, AI can progress or develop de novo after LVAD implant; this is clearly a result of LVAD support rather than disease progression (**Figure 8A**) [71]. Two mechanisms drive the development of AI on LVAD support. First, full LVAD support substantially reduces or completely eliminates aortic valve (AV) opening during systole. This promotes fusion of the commissures between valve leaflets; the resulting fibrosis causes retraction of the leaflets and AI with a central regurgitant jet. Second, decompression of the LV and the high volume delivered to the proximal ascending aorta generates a substantial and continuous pressure gradient across the valve that favors flow into the LV.

The prevalence of AI increases with the duration of LVAD support (**Figure 8B**); in INTERMACS, 13.2% developed moderate/severe AI over a mean follow-up of 13.4 months [69]. Of those with no AI prior to LVAD, 10.7% developed moderate or severe AI, while 18.9% with mild AI prior to LVAD developed moderate or severe AI. By 6-months post-LVAD, 55% had developed at least mild AI.

Key factors that increase the risk of significant AI across multiple studies are (1) older age at LVAD implant; (2) female sex; (3) low body surface area; (4) longer duration of LVAD support; (5) baseline mild (vs. no) AI; (6) no AV opening; and (7) continuous flow (vs. pulsatile) pump [69, 71–73]. The presence of moderate or severe AI is associated with a modest but significant increase in hospitalizations [69]. Moderate/severe AI is also associated with greater severity of MR, but surprisingly, is not associated with significantly worse QOL or reduced 6-minute walk distance. Finally, the presence of moderate or severe AI (vs. no or mild AI) is associated with lower survival (49.1% vs. 35.6% at 5-years, p < 0.001) [69].

#### *2.2.2 Mitral regurgitation*

Hemodynamically significant mitral regurgitation (MR) is the commonest valve lesion at the time of LVAD implant. It is almost always functional MR secondary to LV dilation. Among >26,000 patients in STS-INTERMACS, 22.8% had severe MR

#### **Figure 8.**

*Natural history of aortic insufficiency (AI) after LVAD implant. (A) Proportion of patients with progression of AI from baseline up to 4-years in those receiving an LVAD as compared to patients with end-stage HF who did not undergo LVAD surgery (NS-ESHF). Reprinted with permission [71]. (B) Progression and severity of AI in LVAD patients from the INTERMACS database, with number of patients assessed listed above each bar. Reprinted with permission [69].*

at baseline [8]. Similarly, at implant, 57% had moderate/severe MR in IMACS [30], and 46% had moderate/severe MR, or underwent concomitant MV surgery in the MOMENTUM 3 trial [74].

With offloading of the LV, MR improves in the majority of LVAD patients [74]. When moderate to severe MR persists, the impact on outcomes remains uncertain. In the MOMENTUM 3 trial, persistent MR was uncommon (~6.5% at 1-year, n = 619), and was not associated with survival, adverse events including right HF, or functional capacity [74]. However, in INTERMACS (n = 8364), persistent MR was ~3-fold more common (18.8% at a median of 15 months), and was associated with increased rates of right HF and renal failure, and a modest 16% increase in mortality of borderline significance (p = 0.07) [75]. Single center studies also show mixed results, with frequent right HF but little to no impact on survival.

#### *2.2.3 Tricuspid regurgitation*

TR is also common in advanced HF. In the STS-INTERMACS database, 11.5% had severe TR at baseline [8], and 41% had moderate or severe TR at implant in the IMACS database [30]. Improvements in PVR and afterload with LVAD support often leads to a reduction in TR [23, 76]. Outcomes data on post-LVAD TR are very limited, but large single center studies as well as data from the EUROMACS registry suggest that moderate or severe TR after LVAD is associated with a small but significant increase in mortality [76, 77].

#### **2.3 Device malfunction**

Right HF can be caused by device malfunction, cannula obstruction or inappropriate LVAD speed. LVAD pump thrombosis is discussed elsewhere in this textbook. Data on device malfunction and outcomes are scant. Further, malfunction caused by manufacturing problems often leads to a recall, which abruptly changes incidence [78]. LVAD failure as a cause of death occurs in ~2% [8, 30], with events declining in recent years [8]. In a large single-center study, device malfunction occurred at a rate of 3.06 events per 1000 patient-days [79]. Notably, device malfunction is pumptype specific, with more events in the HMII. The only data for the HM3 comes from the European ELEVATE registry, which showed device malfunction in 3.9% [80]. However, almost 90% of these events were due to outflow graft twisting, a problem since corrected by the manufacturer.

Device malfunction can be grouped by the component that failed: (1) controller; (2) pump/driveline; and (3) peripheral components (e.g., cables, batteries, monitor). Importantly, not all malfunction results in right HF, with pump or driveline failure the most likely to result in a clinically significant event (i.e., HF or death). Controller malfunction was commonest (~30%), while pump or driveline malfunction constituted 13% of malfunction events [79].

Inflow cannula obstruction is a rare event, typically associated with thrombus and/or cannula malposition [81]. Abnormal inflow cannula position is associated with increased left heart filling pressures and a > 2-fold increased risk of recurrent HF [82]. By contrast, outflow cannula obstruction is more common and likely underappreciated; dozens of case reports and case series exist, with an event rate of 0.03 EPPY in the largest study [83]. The commonest pathology seems to be external compression from buildup of an acellular fibrinous material between the outflow graft and the protective GoreTex wrap that is frequently placed around the graft at implant. Notably, ~80% of patients with clinically significant outflow cannula obstruction will have recurrent HF [83].


*2.3. Methodology*

#### 2.3.1. Temporal Pattern

To explore the temporal pattern of flash floods in Yunnan, we analyzed the characteristics of year and month in this study, wherein we calculated the characteristic of year by the package "changepoint" in R software (R Core Development Team, R Foundation for Statistical Computing, Vienna, Austria), which implements various mainstream and specialized changepoint methods to find single and multiple changepoints within data [38]. The month characteristic was analyzed (from January to December) by the year characteristic.

### 2.3.2. Spatial Pattern

### (1) Kernel density estimation

In this paper, kernel density estimation techniques were used for establishing representations for the flash floods, since the kernel function can regard this estimate as averaging the impact of a kernel function centered at the flash flood point and evaluated at each point. We calculated spatial intensity of the flash floods using a kernel density estimation:

$$\lambda\_h(P) = \frac{1}{nh} \sum\_{i=1}^{n} k \left( \frac{P - P\_i}{h} \right) \tag{1}$$

where λ*h*(*P*) is the estimated spatial intensity of the flash floods, *P*1, · · · , *Pn* are the locations of *n* observed flash floods, *k*(.) is the kernel, a meristic but not necessarily positive function that unites to one, and *h* is the bandwidth and determines the radius of a circle centered on *P* that the flash flood points *Pi* will contribute to the intensity λ*h*(*P*). We calculated the spatial density of the flash floods to conduct the kernel density estimation in ArcGIS 10.2 and determined the search radius (bandwidth) based on the mean random distance (RD mean) calculations [39].

#### (2) Spatial mismatch analysis

Spatial mismatch analysis has a profound impact in the economy to measure misbalance between two spatially distributed factors, like unemployment and job distribution etc. In this study, the spatial distribution of number of flash floods in different time periods was taken as the input factor. We used spatial mismatch analysis to measure the imbalance between the number of flash floods in two different time periods. The formula is given as follows [40]:

$$SMI\_i = \left(\frac{B\_i}{\sum\_{i=1}^n B\_i} - \frac{F\_i}{\sum\_{i=1}^n F\_i}\right) \times 100\tag{2}$$

where *SMIi* expresses the spatial balance of city *i* between the number of the flash floods in two different time periods, *Fi* and *Bi* refer to the number of flash floods during start and end time periods. To judge whether the spatial relationship of the number of flash floods in two time periods was balanced, we selected a standard value to assist us by Jenks Natural Breaks Classification, which has a minimum variance sum for each class and indicates that features in the same class are more consistent. We divided the value of *SMIi* into six classes: the middle two classes were balanced, and the absolute value of the two classes was less than 1.5. If the absolute value of *SMIi* was more than 1.5, it showed that the relationship between the number of flash floods in two different time periods was unbalanced in city *i* [41].

#### (3) Standard deviational ellipse

The standard deviational ellipse (SDE) has long served as a general geographic information system tool used to measure bivariate distributed characteristics. The tool typically is used to delineate the geographic distribution trend of features by summarizing both their orientation and dispersion. Since our flash flood data is presented in the form of points, this method can be used to determine the direction and trend. In ArcGIS10.2, the SDE is primarily determined by three elements: average location, dispersion (or concentration), and orientation. In addition, the SDE can be described as one, two, or three standard deviations, which can contain approximately 68%, 95%, or 99% of centroids of all input features, respectively. In this study, we selected one standard deviation [42].

#### (4) Spatial gravity center model

The spatial gravity center model has the advantage to research the spatial-temporal migrations of factors by analyzing the trajectory of their gravity center. We used the spatial gravity center model visually and accurately to reveal the distribution and evolution characteristics of factors in two-dimensional space [40]. To analyze the change trajectories of flash floods, precipitation, and human activities, we calculated the gravity center coordinates of the flash floods, annual mean precipitation, and population density [43]. The time interval of annual mean precipitation and population data was from 2001 to 2015, and we analyzed flash floods consistent with this interval. The gravity center coordinate of the flash floods is given as follows:

$$\mathcal{G}\_f(\mathbf{x}) = \frac{\sum\_{i=1}^n f\_i(\mathbf{x})}{n}, \mathcal{G}\_f(y) = \frac{\sum\_{i=1}^n f\_i(y)}{n} \tag{3}$$

where *Gf* (*x*) and *Gf* (*y*) show the gravity center coordinate of annual flash floods, *n* is the annual number of flash floods, and *fi* (*x*) and *fi* (*y*) are the geometric coordinates of *i*-th flash floods (*i* = 1, 2,· · · *n*) [25]. Moreover, the gravity center coordinate of the annual mean precipitation and population density are given as follows:

$$\mathbf{G}(\mathbf{x}) = \frac{\sum\_{i=1}^{n} \mathbf{x}\_{i} \times k\_{i}}{\sum\_{i=1}^{n} k\_{i}}, \mathbf{G}(\mathbf{y}) = \frac{\sum\_{i=1}^{n} y\_{i} \times k\_{i}}{\sum\_{i=1}^{n} k\_{i}} \tag{4}$$

where *G*(*x*) and *G*(*y*) express the yearly gravity center coordinate of the factor (annual mean precipitation or population density), (*xi* , *yi* ) is the geometric coordinate of *i*-th meteorological station of annual mean precipitation (or municipal administrative unit of population density), and *ki* refers to the attribute value of the *i*-th meteorological station (or municipal administrative unit) [40].

#### 2.3.3. Driving Force Analysis

In this study, we explored the relationship between flash floods and driving factors in subregions of the landcover. These subregions include grassland, settlement, farmland, and forest, as almost all flash floods occurred in these subregions. We generated 1000 random points in each subregion and extracted the values of the driving factors and the results of kernel density estimation for the flash floods at each random point on the spatiotemporal scale. On this basis, correlation and interaction were analyzed.

#### (1) Pearson correlation coefficient

The Pearson correlation coefficient is a classic mathematical method used to show the correlation between two factors *x* and *y* where the value of it ranges between (−1) and (+1). The mathematical formula is given by the following:

$$\tau = \frac{\sum\_{i=1}^{n} \mathbf{x}\_i y\_i - \frac{\sum\_{i=1}^{n} \mathbf{x}\_i \sum\_{i=1}^{n} y\_i}{n}}{\sqrt{\left(\sum\_{i=1}^{n} \mathbf{x}\_i^2 - \frac{\left(\sum\_{i=1}^{n} \mathbf{x}\_i\right)^2}{n}\right) \left(\sum\_{i=1}^{n} y\_i^2 - \frac{\left(\sum\_{i=1}^{n} y\_i\right)^2}{n}\right)}}\tag{5}$$

where *r* is the Pearson correlation coefficient between values *x* and *y*, and *n* refers to the numbers of values *x* and *y*. The *r* values and corresponding correlation levels are shown in Table 2 [44].

**Table 2.** Pearson correlation coefficient and corresponding correlation levels.

#### (2) Driving factors interaction on the flash floods

Different factors have different effects on the flash floods, and the same factors have different influence on the flash floods in the different subregions of the landcover. In this paper, many factors were selected to explore the driving force, there may be a correlation between the factors. To avoid the multicollinearity of driving factors and explore the interaction of driving factors on flash floods and understand which factors were dominant in the interaction, we selected principal component analysis and multiple linear regression, which enabled us to summarize and visualize the information in the driving factors.

Principal component analysis is an oldest multivariate technique and it is employed by almost all scientific disciplines. Its goal is to reflect the important information from the original data. Meanwhile, it can transform the multicollinearity of driving factors into a set of linearly independent principal

components [45]. Detailed information about the principal component analysis were described by previous researches. We determined the number of principal components by the total variance, which was more than 85% [46]. In addition, we measured the contribution of the variable on the principal component by using the "FactoMineR" R package.

Multiple linear regression model is a statistical tool that can be considered as the course of fitting models to data. It can summarize data as well as investigate the relationship between input variables. In this study, the input variables were the principal components of the output of the principal component analysis. Multivariate linear regression is described in detail in reference to previously published studies [47].

#### 2.3.4. Sensitivity analysis

To assess response of flash floods on economic development, we employed a sensitivity assessment model to calculate the flash floods' sensitivity to GDP. This method is a quantitative analysis that researches the impacts of changing GDP on the flash floods. The formula is given as follows:

$$S = \frac{\left[\left(F\_{t\_2} - F\_{t\_1}\right) / F\_{t\_1}\right]}{\left[\left(G\_{t\_2} - G\_{t\_1}\right) / G\_{t\_1}\right]}\tag{6}$$

where *S* represents the value of sensitivity; *Ft*1 and *Ft*2 refer to, respectively, the flash floods at the start and end of the time period; and *Gt*1 and *Gt*2 refer to, respectively, GDP at the start and end of the time period. We divided sensitivity into four levels according to the published studies (Table 3) [40,48].

**Table 3.** *S* value and corresponding sensitivity level.

#### **3. Results**

#### *3.1. Temporal Pattern of the Flash Floods*

Figure 2 shows the characteristics of the temporal variation of flash floods in Yunnan. The annual flash floods gradually increased from 1949 to 2015 (Figure 2b, Table 4). On the basis of the descriptions in Section 2.3.1, annual flash floods were divided into four time periods, the number of flash floods was 104, 331, 704, and 2027 during 1949–1962, 1963–1981, 1982–1995, and 1996–2015, respectively (Figure 2a). For the four time periods (1949–1962, 1963–1981, 1982–1995, and 1996–2015), a high level of monthly flash floods occurred in June (30, 53, 133, and 297 flash floods, respectively), July (31, 99, 213, and 643 flash floods, respectively), and August (34, 94, 131, and 552 flash floods, respectively), and no flash floods occurred in February (Figure 2c).

respectively), July (31, 99, 213, and 643 flash floods, respectively), and August (34, 94, 131, and 552

**Table 4.** The number of flash floods on different time scales. **Year Interval Year June July August**  1949–1962 104 30 31 34 1963–1981 331 53 99 94 1982–1995 704 133<PHONE_NUMBER>–2015 2027 297 643 552

flash floods, respectively), and no flash floods occurred in February (Figure 2c).

**Figure 2.** The characteristics of temporal variation of the flash floods: (**a**) a histogram of the number of the flash floods per year and the year interval of the flash floods based on changepoint analysis (the green dotted line is 1949–1962, the blue dotted line is 1963–1981, the purple dotted line is 1982– 1995, and the red dotted line is 1996–2015); (**b**) variation trends of annual flash floods. The red line is the fitting line and the dark green line is the change line of the annual number of flash floods; and(**c**) **Figure 2.** The characteristics of temporal variation of the flash floods: (**a**) a histogram of the number of the flash floods per year and the year interval of the flash floods based on changepoint analysis (the green dotted line is 1949–1962, the blue dotted line is 1963–1981, the purple dotted line is 1982–1995, and the red dotted line is 1996–2015); (**b**) variation trends of annual flash floods. The red line is the fitting line and the dark green line is the change line of the annual number of flash floods; and (**c**) characteristics of monthly flash floods in different time periods.

characteristics of monthly flash floods in different time periods. **Table 4.** The number of flash floods on different time scales.

#### Baoshan (10 flash floods), Yuxi (10 flash floods), and Zhaotong (10 flash floods), and the lowest level occurred in Lijiang in 1949–1962 (Figure 3a). In the 1963–1981 period, the most frequent occurrence *3.2. Spatial Pattern of the Flash Floods*

#### of flash floods was in Baoshan (47 flash floods); other high-incidence areas included Kunming (32 flash floods), Dali (37 flash floods), and Yuxi (32 flash floods) (Figure 3b). Furthermore, the 3.2.1. The Result of Kernel Density Estimation

frequency of the flash floods has increased significantly since the 1982–1995 period. A high frequency of flash floods occurred in Qujing (113 flash floods), Dali (88 flash floods), Chuxiong (74 flash floods), and Baoshan (76 flash floods) in 1982–1995; a low frequency was concentrated in Nujiang (7 flash floods) and Xishuangbanna (6 flash floods) (Figure 3c). In general, the number of the flash floods in Yunnan reached its maximum in the 1996–2015 period. The areas with more than 200 flash floods included Qujing (275 flash floods) and Yuxi (201 flash floods), and the lowest number occurred in Xishuangbanna (30 flash floods) (Figure 3d). The results of kernel density estimations of flash floods are shown in Figure 3. A relatively high number of flash floods was concentrated in Kunming (18 flash floods), Chuxiong (14 flash floods), Baoshan (10 flash floods), Yuxi (10 flash floods), and Zhaotong (10 flash floods), and the lowest leveloccurred in Lijiang in 1949–1962 (Figure 3a). In the 1963–1981 period, the most frequent occurrence of flash floods was in Baoshan (47 flash floods); other high-incidence areas included Kunming (32 flash floods), Dali (37 flash floods), and Yuxi (32 flash floods) (Figure 3b). Furthermore, the frequency of the flash floods has increased significantly since the 1982–1995 period. A high frequency of flash floods occurred in Qujing (113 flash floods), Dali (88 flash floods), Chuxiong (74 flash floods), and Baoshan (76 flash floods) in 1982–1995; a low frequency was concentrated in Nujiang (7 flash floods) and Xishuangbanna (6 flash floods) (Figure 3c). In general, the number of the flash floods in Yunnan reached its maximum in the 1996–2015 period. The areas with more than 200 flash floods included Qujing (275 flash floods) and Yuxi (201 flash floods), and the lowest number occurred in Xishuangbanna (30 flash floods) (Figure 3d).

**Figure 3.** Kernel density estimation of the flash floods for the four time periods: (**a**) 1949–1962, (**b**) **Figure 3.** Kernel density estimation of the flash floods for the four time periods: (**a**) 1949–1962, (**b**) 1963–1981, (**c**) 1982–1995, and (**d**) 1996–2015.

#### 1963–1981, (**c**) 1982–1995, and (**d**) 1996–2015. 3.2.2. Results of Spatial Mismatch Analysis and SDE

3.2.2. Results of Spatial Mismatch Analysis and SDE The regions with spatial unbalance were distributed mainly in Zhaotong, Qujing, Kunming, Chuxiong, Puer, Dali, Diqing, and Baoshan from the 1949–1962 to the 1963–1981 time period (Figure 4a). Similarly, standard deviational ellipses showed that the orientation and trends were located in these regions. From the 1963–1981 to the 1982–1995 time periods, the regions with an unbalanced frequency of flash floods had extended to the southern areas, which included Lincang and Xishuangbanna (Figure 4b). Additionally, the orientation and trend of standard deviational The regions with spatial unbalance were distributed mainly in Zhaotong, Qujing, Kunming, Chuxiong, Puer, Dali, Diqing, and Baoshan from the 1949–1962 to the 1963–1981 time period (Figure 4a). Similarly, standard deviational ellipses showed that the orientation and trends were located in these regions. From the 1963–1981 to the 1982–1995 time periods, the regions with an unbalanced frequency of flash floods had extended to the southern areas, which included Lincang and Xishuangbanna (Figure 4b). Additionally, the orientation and trend of standard deviational ellipses also were located in these areas. Fewer unbalanced areas, however, were evident from the 1982–1995 to the 1996–2015 time periods (Figure 4c), and the flash floods mainly occurred in these unbalanced areas (Zhaotong, Qujing, Kunming, Yuxi, Dali, Nujiang, and Baoshan). Overall, the orientation and trend of standard deviational ellipses were consistent with the unbalanced areas.

ellipses also were located in these areas. Fewer unbalanced areas, however, were evident from the 1982–1995 to the 1996–2015 time periods (Figure 4c), and the flash floods mainly occurred in these unbalanced areas (Zhaotong, Qujing, Kunming, Yuxi, Dali, Nujiang, and Baoshan). Overall, the orientation and trend of standard deviational ellipses were consistent with the unbalanced areas.

orientation and trend of standard deviational ellipses were consistent with the unbalanced areas.

**Figure 3.** Kernel density estimation of the flash floods for the four time periods: (**a**) 1949–1962, (**b**)

The regions with spatial unbalance were distributed mainly in Zhaotong, Qujing, Kunming, Chuxiong, Puer, Dali, Diqing, and Baoshan from the 1949–1962 to the 1963–1981 time period (Figure 4a). Similarly, standard deviational ellipses showed that the orientation and trends were located in these regions. From the 1963–1981 to the 1982–1995 time periods, the regions with an unbalanced frequency of flash floods had extended to the southern areas, which included Lincang and Xishuangbanna (Figure 4b). Additionally, the orientation and trend of standard deviational ellipses also were located in these areas. Fewer unbalanced areas, however, were evident from the 1982–1995 to the 1996–2015 time periods (Figure 4c), and the flash floods mainly occurred in these

**Figure 4.** The spatial pattern of spatial mismatch index and standard deviational ellipse at the start and end time periods: (**a**) from the 1949–1962 to the 1963–1981 time period, (**b**) from the 1963–1981 to the 1982–1995 time period, and (**c**) from the 1982–1995 to the 1996–2015 time period. Green dots and standard deviational ellipses indicate the start time period, and red indicates the end time period. *Sustainability* **2019**, *11*, x FOR PEER REVIEW 10 of 18 **Figure 4.** The spatial pattern of spatial mismatch index and standard deviational ellipse at the start and end time periods: (**a**) from the 1949–1962 to the 1963–1981 time period, (**b**) from the 1963–1981 to the 1982–1995 time period, and (**c**) from the 1982–1995 to the 1996–2015 time period. Green dots and

#### 3.2.3. Results of the Spatial Gravity Center Model standard deviational ellipses indicate the start time period, and red indicates the end time period.

gradually along the southwest region from 2001 to 2015.

1963–1981, (**c**) 1982–1995, and (**d**) 1996–2015.

3.2.2. Results of Spatial Mismatch Analysis and SDE

As shown in Figure 5, the gravity center of the flash floods was located mainly in Chuxiong, and its change trajectories were random. The gravity center of annual mean precipitation was focused on the border between Chuxiong and Puer, and its change trajectories were also random, but they had some similarity to that of the flash floods. The gravity center of the population moved gradually along the southwest region from 2001 to 2015. 3.2.3. Results of the Spatial Gravity Center Model As shown in Figure 5, the gravity center of the flash floods was located mainly in Chuxiong, and its change trajectories were random. The gravity center of annual mean precipitation was focused on the border between Chuxiong and Puer, and its change trajectories were also random, but they had some similarity to that of the flash floods. The gravity center of the population moved

**Figure 5.** Change trajectories of gravity centers of flash floods, annual mean precipitation, and population from 2001 to 2015. **Figure 5.** Change trajectories of gravity centers of flash floods, annual mean precipitation, and population from 2001 to 2015.

#### *3.3. Driving Factors for the Spatial Distribution of the Flash Floods 3.3. Driving Factors for the Spatial Distribution of the Flash Floods*

We performed a correlation analysis between the flash floods and driving factors in the different subregions of the landcover, as shown in Table 5. Additionally, we analyzed the interaction of driving factors on the flash floods; the results are presented in Figure 6. In the grassland, the highest *r* value was the H24\_20 (*r* = 0.501, *P* < 0.001). Other higher *r* values included M10\_20 (*r* = 0.487, *P* < 0.001), H6\_20 (*r* = 0.449, *P* < 0.001), ELE (*r* = 0.431, *P* < 0.001), and M60\_20 (*r* = 0.335, *P* = 0.001). The highest R2 value of multiple linear regression was 0.213, which was based on the principal component (Dim.1, Dim.2, Dim.3, and Dim.4). Driving factors that had a greater We performed a correlation analysis between the flash floods and driving factors in the different subregions of the landcover, as shown in Table 5. Additionally, we analyzed the interaction of driving factors on the flash floods; the results are presented in Figure 6. In the grassland, the highest *r* value was the H24\_20 (*r* = 0.501, *P* < 0.001). Other higher *r* values included M10\_20 (*r* = 0.487, *P* < 0.001), H6\_20 (*r* = 0.449, *P* < 0.001), ELE (*r* = 0.431, *P* < 0.001), and M60\_20 (*r* = 0.335, *P* = 0.001). The highest R 2 value of multiple linear regression was 0.213, which was based on the principal component (Dim.1,

(16.84%), H6\_20 (15.21%), ELE (12.73%), and PD (10.49%). Regarding the farmland, M10\_20, H6\_20, ELE, H24\_20, and M60\_20 were the most important driving factors, with *r* values of 0.445 (*P* < 0.001), 0.445 (*P* < 0.001), 0.402 (*P* < 0.001), 0.381 (*P* < 0.001), and 0.348 (*P* = 0.001), respectively. The highest R2 value occurred in Dim.1, and the main contributing factors were M10\_20 (23.01%), M60\_20 (21.81%), H6\_20 (21.07%), H24\_20 (21.06%), and ELE (12.28%). The characteristics of forest were

contribution included M10\_20 (17.91%), M60\_20 (17.39%), H24\_20 (16.99%), and H6\_20 (16.93%). Regarding the settlement, the higher *r* values of driving factors exhibited the following ranking: Dim.2, Dim.3, and Dim.4). Driving factors that had a greater contribution included M10\_20 (17.91%), M60\_20 (17.39%), H24\_20 (16.99%), and H6\_20 (16.93%). Regarding the settlement, the higher *r* values of driving factors exhibited the following ranking: M10\_20 (*r* = 0.675, *P* < 0.001) > ELE (*r* = 0.674, *P* < 0.001) > M60\_20 (*r* = 0.593, *P* < 0.001) > H24\_20 (*r* = 0.519, *P* < 0.001) > H6\_20 (*r* = 0.395, *P* < 0.001). The interaction of Dim.1 and Dim.4 contributed the highest R2 value, which were controlled mainly by the M10\_20 (17.95%), M60\_20 (17.79%), H24\_20 (16.84%), H6\_20 (15.21%), ELE (12.73%), and PD (10.49%). Regarding the farmland, M10\_20, H6\_20, ELE, H24\_20, and M60\_20 were the most important driving factors, with *r* values of 0.445 (*P* < 0.001), 0.445 (*P* < 0.001), 0.402 (*P* < 0.001), 0.381 (*P* < 0.001), and 0.348 (*P* = 0.001), respectively. The highest R2 value occurred in Dim.1, and the main contributing factors were M10\_20 (23.01%), M60\_20 (21.81%), H6\_20 (21.07%), H24\_20 (21.06%), and ELE (12.28%). The characteristics of forest were similar to those of the other three subregions. The higher *r* values included H6\_20 (*r* = 0.492, *P* < 0.001), H24\_20 (*r* = 0.485, *P* < 0.001), M10\_20 (*r* = 0.466, *P* < 0.001), and M60\_20 (*r* = 0.326, *P* = 0.001). In addition, the highest R2 was determined by the interaction of Dim.1 and Dim.3, and H6\_20 (17.14%), M10\_20 (16.83%), M60\_20 (16.19%), H24\_20 (15.72%), and ELE (10.48%) were dominant.

**Table 5.** Pearson correlation coefficient (*r*) between the flash floods and driving factors in the different subregions of the landcover based on Formula (5).

**Figure 6.** Driving factors interaction based on multiple linear regression and principal component analysis for flash floods, and the contribution of driving factors to the principal components (Dim.1, Dim.2, Dim.3, and Dim.4) in the different subregions of the landcover: (**a**) R2 value of multiple linear regression of principal components to the flash floods in grassland; (**b**) the contribution of driving factors to Dim.1 and Dim.3 in grassland; (**c**) R2 value of multiple linear regression of principal components to the flash floods in settlement; (**d**) the contribution of driving factors to Dim.1 and Dim.4 in settlement; (**e**) R2 value of multiple linear regression of principal components to the flash floods in farmland; (**f**) the contribution of driving factors to Dim.1 in farmland; (**g**) R2 value of multiple linear regression of principal components to the flash floods in forest; and (**h**) the contribution of driving factors to Dim.1 and Dim.3 in forest. The red number is the highest R2 value. The contribution of driving factors to the principal components greater than the red dotted line **Figure 6.** Driving factors interaction based on multiple linear regression and principal component analysis for flash floods, and the contribution of driving factors to the principal components (Dim.1, Dim.2, Dim.3, and Dim.4) in the different subregions of the landcover: (**a**) R2 value of multiple linear regression of principal components to the flash floods in grassland; (**b**) the contribution of driving factors to Dim.1 and Dim.3 in grassland; (**c**) R2 value of multiple linear regression of principal components to the flash floods in settlement; (**d**) the contribution of driving factors to Dim.1 and Dim.4 in settlement; (**e**) R2 value of multiple linear regression of principal components to the flash floods in farmland; (**f**) the contribution of driving factors to Dim.1 in farmland; (**g**) R2 value of multiple linear regression of principal components to the flash floods in forest; and (**h**) the contribution of driving factors to Dim.1 and Dim.3 in forest. The red number is the highest R2 value. The contribution of driving factors to the principal components greater than the red dotted line indicates that they are dominant.

**Factors Grassland Settlement Farmland Forest**

*3.4. Sensitivity Analysis between the Flash Floods and Economy Development*

M10\_20 0.487 <0.001 0.675 <0.001 0.445 <0.001 0.466 <0.001 M60\_20 0.335 0.001 0.593 <0.001 0.348 0.001 0.326 0.001 H6\_20 0.449 <0.001 0.395 <0.001 0.445 <0.001 0.492 <0.001 H24\_20 0.501 <0.001 0.519 <0.001 0.381 <0.001 0.485 <0.001 ELE 0.431 <0.001 0.674 <0.001 0.402 <0.001 0.257 0.01 TR −0.077 0.446 −0.146 0.172 0.219 0.032 −0.014 0.888 SLP −0.05 0.619 −0.094 0.382 −0.176 0.085 −0.081 0.424 NDVI −0.241 0.016 0.061 0.571 −0.222 0.029 −0.234 0.02 PD 0.082 0.42 −0.211 0.048 0.108 0.297 −0.098 0.335 GDP 0.098 0.332 0.042 0.697 0.226 0.027 0.233 0.02

By combining the data of the flash floods and GDP and using Formula (6), we calculated the value of sensitivity (*S*) of the flash floods to the economic development of each city in Yunnan Province, as shown in Figure 7. From an overall perspective, fewer regions experienced medium and high sensitivity during the study period, and the quantity of regions without sensitivity first decreased and then remained stable. During 1995–2000, all regions were non-sensitive. In comparison, the quantity of low-sensitivity regions during 2000–2005 and 2005–2010 increased to

*r P r P r P r P*

indicates that they are dominant.


**5. Conclusion**

Laparoscopy, as a less aggressive surgical technique, offers an alternative approach to the treatment of congenital duodenal obstruction, allowing for minimally invasive access and early recovery after the surgery. Since the first report of a successful laparoscopic anastomosis in a patient with duodenal atresia, laparoscopic reconstruction was expected to become a golden standard of congenital duodenal obstruction treatment. However, its complexity and time consumption prevented this technique from being widely used among pediatric surgical centers compared to an open repair. Laparoscopic reconstruction remains the prerogative only for advanced pediatric surgery centers specializing in minimally invasive surgery in neonates. Transoral techniques based on flexible endoscopy have a special place in minimally invasive treatment of partial duodenal obstruction and allow for expansion of the narrow site using a balloon or a sphincterotome with no incision required.